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  • Published: 14 February 2023

Fighting wheat rusts in China: a look back and into the future

  • Jie Zhao   ORCID: orcid.org/0000-0001-5932-6708 1 &
  • Zhensheng Kang 1  

Phytopathology Research volume  5 , Article number:  6 ( 2023 ) Cite this article

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Wheat rusts, including stripe, leaf, and stem rusts, are severe wheat diseases and cause huge yield loss in China annually. Benefiting from utilizing the genetic resistance wheat varieties, wheat stem rust has been effectively controlled since the 1970s; however, the wheat stripe and leaf rusts are still threating the wheat production in China due to lack of effective agricultural regulations. This review summarizes the research advances on wheat rust physiology, epidemiology, and fungicide resistance in China. In addition, the corresponding field management strategies for the integrated control of rust diseases are also discussed.

Wheat is one of the four staple crops in China. Stripe, leaf, and stem rusts are the three dominant rust diseases on wheat, which are caused by three Puccinia species in phylum Basidiomycota. Historically, the three wheat rust diseases caused severe epidemic incidents and significant wheat yield loss in China. Currently, stripe rust is the most devastating disease on wheat among the three in China. Several excellent reviews have summarized the occurrence and management of wheat stripe and leaf rusts in China (Shen and Wang 1962 ; Wang et al. 1988 ; Wu and Niu 2000 ; Li and Zeng 2002 ; Zeng and Luo 2006 ; Wan et al. 2007 ; Song et al. 2010 ; Wang et al. 2010 ; Chen et al. 2013 ; Kang et al. 2015 ; Ma 2018 ; Zhao et al. 2016a ,  2018 ; Zeng et al. 2022 ), but the research advances of wheat stem rust in China have not been comprehensively reviewed yet. Recently, many exciting progresses related to the wheat rust disease controls have been achieved in China. Here, we reviewed the history of the wheat rust in China and proposed the future perspective for the disease control from following aspects: the economic importance, epidemiology, fungicide resistance, and integrated managements.

Historical and current status of wheat rusts

Common wheat ( Triticum aestivum L.) is one of the most important staple cereal crops, the rice, corn, wheat, and potato. China is the largest wheat-producing and consuming country, which produces an annual yield of over 128 million metric tons, accounting for approximately 17.5% of the global wheat production based on the 10-year’s data from 2011 to 2020 (FAOSTAT 2020 ). In 2021, the total planting area of wheat is 23.6 million hectares, which produces approximately 137 million tons of wheat ( http://www.stats.gov.cn/ ). Therefore, wheat is of extremely and economically important crop in China. Currently, the major wheat-planting regions are distributed in five provinces, Shandong, Hebei, Henan, Jiangsu, and Anhui, which is also known as ‘the Huang-Huai-Hai winter wheat areas’ (Wan et al. 2007 ).

Wheat rusts, including stripe rust (or yellow rust) (Fig.  1 ), leaf rust (or brown rust) (Fig.  2 ), and stem rust (or black rust) (Fig.  3 ), are the dominant wheat fungal diseases. These wheat diseases significantly limit the yield reduction. Wheat rust is an ancient disease. The recorded occurrence of wheat rusts in China can be tracked back to as early as 4000 years ago, the time of the introduction of wheat into Hexi Corridor in Gansu during the Shang Dynasty (Li and Zeng 2002 ; Yang et al. 2016 ; Wei 2021 ). It was first documented in detail in a Chinese ancient agricultural book, Qimingyaoshu 《齐民要术》 by the author Sixie Jia during AD 533 to 544 in the Beiwei Dynasty. In this book, it was documented that wheat was vulnerable to ‘jaundice’ disease (actually stripe rust) and the disease was figuratively described as ‘jaundice’ because it resembled the color of newly born infants. Currently, wheat stripe rust is the most destructive disease among the three wheat rust diseases in China. It mainly prevails in the northwest and southwest China. Because of the severe epidemics, the stripe rust disease is listed in first class crop diseases management in the 333th bulletin by Ministry of Agriculture and Rural Affairs of the People’s Republic of China on September 15, 2020 ( http://www.moa.gov.cn/govpublic/ZZYGLS/202112/t20211224_6385489.htm ).

figure 1

Single stripe signs of uredia between leaf veins of wheat stripe rust ( a ) and a nursery field showing severe stripe rust infection on wheat plants at elongation stage in Mianyang, Sichuan on March 18, 2011 ( b )

figure 2

Symptoms of leaf rust on wheat leaves. a A few uredia at early stage of the development. b Numerous uredia produced on a leaf at late stage of the development

figure 3

Symptoms of stem rust in wheat fields. a Uredia on a diseased stem. b Uredia on awns and glumes of a wheat head

Wheat leaf rust usually takes place in the North China Plain, the middle-lower reaches of the Yangtz River, southwestern and northeastern regions of China (Liu and Chen 2012 ). Wheat leaf rust has been well controlled in China in the last decades, but the epidemic of the disease has often occurred in many wheat-growing provinces, especially in ‘Huang-Huai-Hai regions’ recently (Zhao et al. 2008 ; Zhang et al. 2018 , 2020b ; Wang et al. 2022b ). The increasing incident of wheat leaf rust has potentially threatened the wheat production in these regions, and is a major rust disease after stripe rust in China.

Wheat stem rust primarily occurred in the northeastern spring wheat-growing region of China (Zeng et al. 1963 ; Li and Zeng 2002 ). This disease has been problematic in China prior to the 1970s. However, the disease rarely occurs in China nowadays (Han et al. 2010 ; Li et al. 2017 ), which benefits from extensive application of wheat cultivars that carry the stem rust-resistant gene Sr31 since the 1970s (Li and Zeng 2002 ). Although Ug99 (race TTKSK) and its variants that successfully overcome the resistance of Sr31 and have widely spread from the origin of Uganda to many other African and Asian countries ( https://rusttracker.cimmyt.org/?page_id=260 ), Ug99 has not been detected in China yet (Cao et al. 2007 ). However, most of tested Chinese native wheat cultivars (98.3% out of 118 varieties) are highly susceptible to Ug99. Ug99 also overcomes Sr21 and Sr38 that are two key resistance genes to stem rust in China. Therefore, invasion of Ug99 lineage races to China is of significantly potential risk. Regulations to prevent the invasion of Ug99 races are necessary.

Severe impacts recorded in the last 70 years

Prior to 1949, several severe epidemic incidents of wheat stripe rust were reported in Sichuan and Fujian provinces in 1939–1940, which resulted in a yield reduction up to 15% and 60%, respectively. In the 1940s, the disease severely occurred in the middle regions (Guanzhong plain) of Shaanxi Province, especially in the years of 1942, 1946, 1948, and 1949 (Li and Zeng 2002 ). Since 1950, China has encountered five nationwide severe wheat stripe rust epidemics, which took place in 1950, 1964, 1990, 2002, and 2017, respectively. These epidemics resulted in the wheat rust outbreak in a total of 550 million hectares, leading to the yield loss up to 13.8 million metric tons (Li and Zeng 2002 ; Ma 2018 ). The most sever epidemics occurred in 1950 and 1964, which affected the growing area over 13.33 million hectares, with a yield loss of 6 million and 3.2 million metric tons, respectively (Li and Zeng 2002 ). From 1972 to 1983, several severe stripe rust epidemic events occurred in the key oversummering (Qinghai, Gansu) and overwintering regions, which are distributed in Sichuan, Shaanxi, Henan, and Hubei provinces. Each incident resulted in the infection of wheat areas approximately 1.33 million to 2.0 million hectares (Li and Zeng 2002 ). In addition, large-scale epidemics caused by the disease occurred in 1975, 1983, and 1985, resulting in an estimated crop yield reduction up to 0.865 million, 1.074 million, and 0.85 million metric tons, respectively. In 1991, an extremely severe nationwide epidemic took place in Gansu, Ningxia, Shaanxi, Henan, Hubei, and Shandong provinces, which destroyed approximately 6.53 million hectares of wheat and caused a conspicuous crop yield loss of 0.434 million metric tons, although the fungicides were timely applied (Li and Zeng 2002 ). Based on the data acquired from 2006 to 2015, the average yield reduction caused by wheat stripe rust is approximately 0.159 million metric tons annually (Liu et al. 2016 ). The most recent large scale of stripe rust epidemic occurred in 2019, which is believed infecting two million hectares of wheat. Notably, no significant yield loss was observed due to the application of fungicides.

Serious epidemics caused by wheat leaf rust have been reported in winter wheat-growing areas of northern China and spring wheat-growing areas of northeastern China. This disease has led to a disastrous decrease in yield during the 1950s–1980s (Hu and Roelfs 1985 ; Li and Zeng 2002 ; Zhou et al. 2013 ; Peng et al. 2016 ). In this period, four moderate epidemics of leaf rust occurred in the north winter wheat-planting areas in 1969, 1973, 1975, and 1979, respectively, which also resulted in a huge yield reduction (Li and Zeng 2002 ). Since the late 1990s, five severe leaf rust epidemics in China have been documented in the year of 2008, 2009, 2012, 2012, and 2015 (Zhou et al. 2013 ; Zhang et al. 2015 , 2020b , 2020c ; Wu et al. 2019 ), and the most severe epidemic of leaf rust occurred in Anhui, Gansu, Henan, Sichuan, and Shanxi provinces in 2012. It damaged more than 15 million hectares of wheat growing area and caused a yield reduction near 3 million metric tons (Zhou et al. 2013 ; Wu et al. 2019 ).

Wheat stem rust is known as a serious issue in wheat-growing regions before the 1970s, especially in spring wheat growing regions of northeastern China, where nine severe epidemics were reported from 1923 to 1964 (Li and Zeng 2002 ). Two most severe wheat stem rust epidemics occurred in 1923 and 1948, which caused a massive yield reduction of 7.4 million and 5.6 million metric tons, respectively (Wu et al. 2020b ). In 1956, 1958, and 1964, the moderate and severe large-scale epidemics occurred in ‘Jiang-Huai region’ (also known as ‘Yangtz-Huaihe region’), from 1949 to 1966. Each epidemic caused a massive yield loss. For instance, in 1956, the epidemic in Jiangsu and Anhui provinces caused a noteworthy yield loss up to 1.0 million metric tons (Li and Zeng 2002 ). Since the 1970s, wheat stem rust has not been a notable issue and the pathogen is considered as opportunistic pathogen and cannot cause a serious threat to wheat production. Therefore, wheat stripe rust is the most destructive rust disease and more attentions should be paid to control this disease.

The causal agents

Stripe rust, leaf rust, and stem rust on wheat are caused by different Puccinia species in Pucciniaceae family of phylum Basidiomycota. Wheat stripe rust is caused by Puccinia striiformis Westendorp f. sp. tritici Eriksson ( Pst ) [syn. P. glumarum (Schumacher) Erichsen et Hennings] (Fig.  4 a, b). Traditionally, P. striiformis f. sp. tritici is one of five different formae speciales (f. sp., pl.) of P. striiformis (Eriksson 1894 ; Stubbs 1985 ). Whereas, based on morphological and genomic data, P. striiformis (stripe rust agents of wheat, Aegilops , Elymus, and barley) were clustered into the same clade. Therefore, they were all re-designated as P. striiformis (Liu and Hambleton 2010 ). Wheat leaf rust is caused by P. triticina Eriksson ( Pt ) (syn. P. recondite Roberge ex Desmaz f. sp. tritici Eriksson et Hennings) (Mains 1932 ) (Fig.  4 c, d); while wheat stem rust is caused by P. graminis f. sp. tritici Eriksson et Hennings ( Pgt ) (Fig.  4 e, f). The differences are also reflected by the distinct uredia and urediospores of three rust species, where they exhibit differences in color, spore size and cause different symptoms on the hosts.

figure 4

Urediospores and teliospores of the wheat stripe, leaf, and stem rust fungus. a , b Urediospores ( a ) and teliospores ( b ) of Puccinia striiformis f. sp. tritici . c , d Urediospores ( c ) and teliospores ( d ) of Puccinia triticina . e , f Urediospores ( e ) and teliospores ( f ) of Puccinia graminis f. sp. tritici

Primary hosts and alternate hosts

Primary hosts (uredial host).

The wheat rusts Pst , Pt , and Pgt are obligate parasites. These pathogens primarily infect wheat, other cereal crops, and grasses. By infecting these hosts, they go through the uredial or telial stages (Stubbs 1985 ). The monocot plants Triticum , Aegilops , Agropyron , Bromus , Elymus , Hordeum , and Secale are all vulnerable to Pst (Stubbs 1985 ). In fact, many approaches have been made to determine the susceptibility of grass plants to Pst , Pt , and Pgt (Ling 1945 ; Lu et al. 1958 ; Peng and Chen 1987 ; Wang et al. 1987 ; Niu et al. 1991a , 1991b ; Yuan et al. 1994 ; Wei et al. 2021 ; Qin et al. 2022 ; Li and Zeng 2002 ). Currently, 88 grass species (including varieties) from 16 genera in the family Poaceae could serve as uredinial hosts or accessory hosts for Pst (Li and Zeng 2002 ). However, Pt isolated from leaf rust of six grass species, Agrimonia Pilosa , Bromus inermis , Elymus dahuricus , E. sibiricus , Roegneria penduline , and R. ciliaris , could infect wheat (Wang et al. 1987 ), suggesting that these plants assist the wheat leaf rust prevailing in field.

Alternate host (aecial host)

The pathogens Pgt , Pt , and Pst are known to be heteroecious and macrocyclic. The have to infect alternate hosts to complete the sexual reproduction. For Pgt and Pt , their alternate hosts were discovered over a century ago (de Bary 1866 ; Jackson and Mains 1921 ); however, the alternate hosts for Pst remained to be mysterious till 2010 (Jin et al. 2010 ). Now it is known that Berberis and Mahonia are the alternate common hosts for Pgt and Pst (Roelfs 1985 ; Jin et al. 2010 ; Zhao et al. 2013 ; Cheng et al. 2022 ). Notably, there are some differences for Berberis and Mahonia species or subspecies when they serve as alternate hosts for Pgt and Pst . For example, Berberis circumserrata could be an alternate host for Pst but not for Pgt (Roelfs 1985 ; Zhao et al. 2013 ). There are 215 endemic Berberis and 36 endemic Mahonia species in China, while there are 500 Berberis and 60 Mahonia species around the world (Ying and Chen 2001 ). So far, more than forty Chinese Berberis species and four Mahonia species/subspecies have been reported to serve as alternate hosts for Pst (Zhao et al. 2013 , 2016b , 2018 ; Du et al. 2019 ; Zhuang et al. 2019 ; Cheng et al. 2022 ). However, only one endemic Berberis species, the B. amurensis Rupr., was identified as an alternate host for Pgt in China (Zeng et al. 1963 ). Under field conditions, Pgt infects five Berberis species, B. aggregata , B. brachypoda , B. potaninii , B. shensiana , and B. soulieana , and sexual reproduction of this rust is completed during the infection of these hosts (Zhao et al. 2015 ). These observations clearly indicated that the above mentioned Berberis species are alternate hosts for Pgt .

Although many Thalictrum , Isopyrum , and Clematis species in Ranunculaceae family, and a few Anchusa and Echium species in the Boraginaceae family have been identified as alternate hosts for Pt (Chester 1946 ; Sibilia 1960 ; d’Oliveira and Samborski 1966 ), only four meadow rue ( Thalictrum ) species are the native alternate hosts in China. These species were identified as T. minus L., T. petaloideum L., T. minus var. hypoleucum , and T. baicalense recently (Zhao et al. 1994 , 2021 ).

Life cycle of the rusts

Pst , Pgt , and Pt are the heteroecious, macrocyclic rust fungi. They complete their life cycle with five different types of spores on two unrelated hosts (Fig.  5 ). Their full life cycle includes asexual and sexual stages. Under favorable conditions, basidiospores generate from teliospores. After germination, it can infect an alternate host to produce pycnia and pycniospores, as well as receptive hyphae (trichogyne) and paraphyses. With these mating type and receptive hyphae, they complete their sexual life cycle and consequently produce aecial clusters from abaxial leaves, where the aeciospores generate inside of the aecial clusters. Once the aecial clusters broke, aeciospores are released from aecial clusters and spread by wind to infect primary hosts, wheat and grasses. Urediospores are produced after aeciospores infect the primary hosts. However, teliospores are primarily formed in wheat host tissues at a late wheat growth stage.

figure 5

Life cycle of Puccinia striiformis f. sp. tritici

Wheat rust epidemiology

The wheat stripe rust epidemic in China can be divided into different epidemiological regions. In fact, the epidemiological regions of the disease are consistent (Li and Zeng 2002 ) till 1995, when Zeng and the colleagues proposed that the Chinese epidemiological region of wheat stripe rust can be divided into three regions (Zeng and Sun 1995 ). Based on a combined method of large-scale and long-term field surveillances, geographic information system (GIS) system and molecular data, they divided the epidemiological regions into oversummering region (for the autumn spores), winter Pst -reproducing region that for the spring spores, and spring epidemic region (Chen et al. 2013 ). Later, Zeng and Luo ( 2006 ) proposed to subdivide China’s main stripe rust epidemiological region into 15 epidemiological zone according to the geographic features, crop cultivation modes, the regularity for pathogen oversummering and overwintering, and the frequency of stripe rust epidemics. It is worth to mention that because of the unique geography, the Yunnan epidemiological region is relatively independent because the pathogen can complete the disease cycle and over-summering and over-wintering without traveling to other regions (Li and Zeng 2002 ). As a result, this epidemiological region is almost isolated from other regions. However, recent studies revealed that the Yunnan epidemiological region and other southwestern epidemiological regions are also involved in wheat stripe rust epidemics in China (Awais et al. 2022 ; Huang et al. 2022 ; Ju et al. 2022 ; Zhan et al. 2022a ). In addition to Yunnan region, Tibet and Xinjiang also developed to be the independent stripe rust epidemiological regions (Li and Zeng 2002 ; Hu et al. 2017 ; Awais et al. 2022 ). Importantly, Xinjiang and other Chinese provincial Pst populations are all isolated from that of Pakistan due to extremely high genetic divergence (Awais et al. 2023 ). Chen et al. ( 2013 ) considered that Chinese stripe rust epidemiological regions include oversummering regions, winter- Pst reproductive regions, and spring epidemic regions. The oversummering regions include Gansu (area of Longnan, Tianshui, Dingxi, Linxia, Pingliang, Qingyang, and Gannan), Ningxia (Guyuan), Qinghai (Haidong), Shaanxi (Baoji), and Sichuan (Ganzi, Aba, and Liangshan area). However, the winter- Pst reproductive regions include low mountain, valley, mountain dam, and plain areas in Sichuan, South Shaanxi, Northwest Hubei, Yunnan, Guizhou, and Chongqing. While, the spring epidemic regions are the most of winter wheat-growing regions, including the ‘Huang-Huai-Hai Plain’, the regions of Beijing, Tianjin, Hebei, Henan, Shandong, Jiangsu, Anhui, the Guanzhong Plain of Shaanxi, and mid-lower reaches of Yangtz River.

For wheat stem rust, spring wheat-growing regions of Northeast China and Inner Mongolia, Northwest China, and wheat-growing regions of South Yunnan Province (Dehong, Honghe, Wenshan, and Simao regions) are important epidemiological areas. Weak winter wheat-growing regions of middle and lower reaches of Yangtz River and Sichuan (Ganzi) are regular epidemiological areas. Wheat-growing regions of Fujian Province and Southeastern coastal regions (Guangdong and Guangxi provinces) are also epidemiological areas (Wu and Huang 1987 ; Cao and Chen 2009 ).

Wheat leaf rust frequently occurs in wheat-growing regions of Southwest China and the mid-lower reaches of Yangtz River, but many of them appear in Yangtz River reaches, ‘Huang-Huai-Hai Plain’, and southwestern China. Occasionally, severe epidemics can occur in wheat-planting regions of North and Northeast China, and even Northwest China (Jin et al. 2017 ; Zhou et al. 2013 ).

The disease cycle of Puccinia species

Puccinia species are obligate pathogens when they infect wheat, where they fully depend on this living host to complete disease cycle. Temperature is the key factor for their disease cycle, and the rust diseases require different temperature for their growth. Wheat stem rust prefers higher temperature (opt. 30°C) than wheat leaf rust (opt. 25°C) and wheat stripe rust (opt. 12–15°C) (Roelfs et al. 1992 ). Cool and humid weather favor the development of wheat stripe rust. By contrast, high temperature inhibits the disease development (Rapilly 1979 ). In China, wheat stripe rust complete disease cycle by windborne urediospore infection, including oversummering and overwintering spores and the spores of infected autumn-sown wheat (Li and Zeng 2002 ). In susceptible hosts, the temperature for Pst oversummering period cannot exceed 23°C (max. aver. Temp. of a 10-day duration) in July and August, the two hottest months (Li and Zeng 2002 ; Zeng and Luo 2006 ). In general, the lowest altitude for Pst oversummering is over 1600 m above sea level, where the highest average temperature is typically below 23°C. Urediospores in oversummering areas are spread to autumn-sown wheat seedlings by wind in the local and overwintering areas, where the pathogen infects the plants and develops the stripe rust. In addition, the lowest temperature for Pst overwintering period is −6°C to −7°C from December to next January which are the coldest months. However, as long as the wheat seedlings are covered by snow, these pathogens can safely overwinter even if temperature drops to −10°C (Fig.  6 ) (Li and Zeng 2002 ). Pst usually overwinters in infected wheat tissues in the form of hyphae. In these regions, wheat grows slowly in autumn and winter, which is usually warmer than other regions. Under such circumstances, Pst continuously grow on infected wheat plants during winter and subsequently develop as the Pst -reproducing regions. South Henan, North Hubei, Longnan of South Gansu, South Shaanxi, and Sichuan Basin are the primary overwintering regions (Li and Zeng 2002 ; Chen et al. 2013 ). In spring, the pathogens in overwintering regions are transmitted to wide wheat-growing regions in East China and other regions to cause inter-regional epidemics.

figure 6

Autumn-sown winter wheat seedling leaves showing developing stripe rust infection underneath snow patches in Baoji, western of Shaanxi Province in China, based on field observations on December 14, 2015

Importantly, Chinese researchers found that in spring of the Northwest oversummering areas, such as Qinghai, Gansu, Western Shaanxi, and Tibet, Pst basidiospores can infect susceptible barberry to complete their sexual cycle (Zhao et al. 2013 , 2022 ; Wang et al. 2016 ; Chen et al. 2021a ; Liu et al. 2021 ; Du et al. 2022 ). In some regions, such as Qinghai, Shaanxi, and Gansu, susceptible barberry released basidiospores are the major source of infection, and often cause the epidemics of wheat stripe rust (Chen et al. 2021a ; Liu et al. 2021 ; Zhao et al. 2022 ). Nevertheless, it was found recently that in Tibet, Pst can infect susceptible barberry to complete their sexual reproduction in autumn (Du et al. 2022 ), but whether susceptible barberry is related to stripe rust infection on wheat is unknown.

Unlike other rusts, wheat leaf rust has wider oversummering and overwintering areas in China. In particular, in some places, Pt urediospores can continuously infect the young wheat stumps after harvesting and are preserved in the local for oversummering. In autumn, the pathogens further infect the winter wheat seedlings that are sown in autumn season to cause leaf rust and overwinter in the infected wheat tissues in the form of hyphae. Generally, the frequency of overwintering in warmer regions is higher than that in cold regions, and the frequency of overwintering is positively correlated to the level of wheat leaf rust epidemic in the coming spring. The epidemic is mainly attributed to the continuous re-infection via the windborne urediospores.

In contrast, wheat stem rust has narrow overwintering areas due to the nature of urediospores that are sensitive to cold. As such, the pathogens overwinter in southeastern regions (i.e. Fujian, Guangdong) and South Yunnan rather than in north wheat-producing regions. The rust can parasitize wheat plants in overwintering areas in which the average minimum temperature in December to next January is above 10°C (Huang et al. 1993 ). Pgt can attack autumn-sown wheat seedlings in Shandong Peninsula and ‘Xuhuai regions’ of Jiangsu Province. However, they cannot survive the cold winter in most of these regions. Although few pathogens may successfully overwinter in these regions, they contribute inconsiderably to wheat stem rust epidemics (Huang et al. 1993 ). In spring and summer, spores in overwintering areas spread from south to north and west via the Yangtz River reaches, the North China Plain and reach spring wheat-producing regions in Northeastern and Northwestern China as well as Inner Mongolia. Thus, the dispersal of the pathogen causes vast wheat stem rust epidemics. Pgt urediospores mostly oversummer on late-maturing spring wheat and wheat stump in Northwestern and Southwestern China, and also on volunteer winter wheat in the plains of Jiaodong of Shandong Province and Huaibei of Jiangsu Province (Huang et al. 1993 ).

The race evolution of the pathogens

In nature, pathogens can rapidly evolve. The rule is also seen in the three wheat rust pathogens. Indeed, wheat rust pathogens can evolve to new virulent races with a high frequency in the field. New races often overcome the resistance of wheat varieties and cause disease. Some of the races dominate epidemics in the field by overcoming a certain resistance gene and evolve as the emerging new races.

The Pst races

Race identification of Pst in China was commenced in the 1940s by Fang ( 1944 ), who identified nine races from isolates in southwestern of China. These pathogen races are mostly from Yunnan Province (Fang 1944 ). Later, Lu et al. ( 1956 ) identified 10 isolates that were collected from seven provinces in 1951 as 5 races which were used by Gassner and Straib on wheat cultivars Carsten V, Michigan Amber, Spaldings Prolific, Blé rouge Décosse, and Heines Kolbens (Gassner and Straib 1930 ). Based on the maximum scores of reaction on the differentials including Early Premium, Nongda 3, Bima 1, Bima 4, Liying 3, and Yupi, fifty Pst isolates collected in 1953–1955 were determined as 16 races, and 8 of which were recovered from Elymus sibiricus , E. chinense , and Agropyron spp. (Lu et al. 1956 ). Since 1957, CY (CY = Chinese yellow) series were assigned to Chinese Pst races, and later CYR (CYR = Chinese yellow rust) series designation for races of this rust has been used till nowadays. In addition, pathotypes that are virulent to a certain genotype of Chinese differential sets, such as Hybrid 46 (Hy46) pathotype, Suwon 11 (Su11) pathotype, Lovrin 10/13 (Lv10/13) pathotype, Guinong 22 (G22) pathotype, and Jubilejina 2 (Ju2) pathotype, were also determined as Pst races that were not designated to CYR series. Since the designation of physiological CYR series races (CYR1 to CYR34) (Table 1 ), it has been designated according to the race with an outbreak frequency higher than 10% and a continuous prevalence in China (Lu et al. 1963 ; Liu et al. 2017 ). During 1957–1961, 10 CYR races (CYR1–CYR10) were identified from 325 Pst isolates. Among the races, three of which, the CRY1, CYR8, and CYR3 were widespread (Lu et al. 1963 ). During 1963–1966, CYR1 and CYR10 were the dominant races, while CYR10 displayed the highest outbreak frequency in 1964, but CYR8 decreased to rare race. CYR13 was firstly found in Lintao County of Gansu Province in 1962 and it exhibited an increasing frequency from 6.4% to 16.3% during 1964–1966. By contrast, during 1971–1979, CYR1, CYR8, CYR10, and CYR13 displayed a decreasing frequency and were not detected any more after 1975. Meanwhile, CYR17, CYR18 (virulent to Abbondanza), and CYR19 rapidly developed into major races. CYR17 caused an epidemic in Shaanxi Province in 1965. It gradually developed to be dominant race during 1974–1976 in North and East China; whereas, CYR18 that was founded for the first time in Gangu County in Gansu Province exhibit an extremely low frequency of outbreak. At the same time, in Sichuan Province, CYR18 was prevalent, but CYR17 was not. In Gansu Province, however, CYR18 was conspicuous and CYR17 had a high frequency of epidemic. Both races showed high outbreak frequency in Shaanxi Province (Wang et al. 1986 ). Since 1975, the outbreak frequency of CYR17 and CYR18 remarkably decreased, but CYR19 rapidly increased as a major race, where the outbreak frequency reached the highest of 81.1% in all the countrywide races in 1977 after the first appearance in Qingsheng County of Sichuan Province in 1972 (Wang et al. 1986 ). During 1980–1985, the CYR19 was proved as a complex of races, which was further separately designated as CYR23 (previously 19-1); however, CYR24 (previously 19-3), CYR25 (previously 19-4), CYR26 (previously 19-2), CYR23, CYR25, and CYR26 were prevalent races, and CYR25 was predominant race (Wang et al. 1986 ). CYR20 was first found virulent to the wheat variety Fengchan 3 in Shaanxi Province in 1971 (SXIPP 1976 ). CYR21 was initially detected in the Pingliang of Gansu Province in 1975. However, both CYR20 and CYR21 were not developed to be the dominant races (Wang et al. 1986 ). In 1982, Su11 pathotypes that are virulent to wheat genotype Suwon 11 were first detected in an experimental field at Qinghai Academy of Agricultural Sciences (Li 1983 ; Wang et al. 1986 ). CYR22 was first detected in Tianshui of Gansu Province in 1975, which then developed to be the dominant race in Gansu and Shaanxi provinces with the outbreak frequency of 25.5% and 22.7% in 1983, respectively. Lv10 and Lv13 pathotypes that are virulent to wheat genotypes Lovrin 10/13 ( Yr9 ) were initially detected in Longnan of Gansu Province in 1975 and 1979, respectively (Kang and Li 1984 ; Kang et al. 1987 ). CYR27, also known as pathotype 82-1, was first detected in Xihe County of Gansu Province in 1980. Later, it reached a high outbreak frequency in 1983. The trend was promptly decreased in 1984 in the provinces Gansu, Shaanxi, Sichuan, Yunnan as well as eastern regions of China, including Shanxi, Hebei, Shandong, Henan, Jiangsu, Anhui, Hebei, Hunan, and Inner Mongolia (CNWRCG 1985 ; Wang et al. 1986 ). CYR28 is a Lv10 pathotype complex and CYR29 is also known as Lv13-1. Both races are the members of Lv10/13 pathotypes. They were first detected in 1983 and 1985, respectively (CNWRCG 1987 ). During 1986–1990, CYR29 rapidly became the top outbreak frequency race over others in 1988, and reached the maximum frequency of 40.3% in 1989. The frequency remained the highest in the following 2 years (Wu et al. 1993 ). Meanwhile, Lv10/13 pathotypes rapidly developed into a prevalent pathotype. Due to rapid development of CYR29 and Lv10/13 pathotypes, the susceptible wheat which was planted about 8.8 million hectares in 1990 and accounted for 62.7% of the total planted areas in that year, was suffered with severe wheat stripe rust epidemic (CNWRCG 1991 ). In contrast, CYR28 remained low outbreak frequencies consistently. During the same period, the outbreak frequency of CYR23, CYR25, and CYR26 races were rapidly decreased (CNWRCG 1991 ). In 1991–1996, CYR29 kept prevailing and emerged as the most dominant race till 1995, but it became inconsiderable to the rust epidemic since 1996. Although CYR25 ever developed as a second dominant race during 1991–1992, its outbreak frequency was low. CYR30, previously names as race 91-1, is virulent to the genotypes Hybrid 46 ( Yr3b , Yr4b , and YrH46 ). Similarly, CYR31 previously named as race 93-1, is virulent to genotypes Hybrid 46 and Suwon 11 ( YrSu ). CYR30 and CYR31 were first detected in Sichuan in 1991 and in Gansu in 1993, respectively; both pathogens have a broad virulence spectrum than CYR28 and CYR29. As a result, CYR 30 and CYR31 rapidly became third and second prevalent races during 1993–1995. In particular, CYR31 ever emerged as the top prominent race in 1996 (Wang et al. 1996 ; Wan et al. 1999 ). Notably, Hy46 and Su11 became the major pathotypes during 1994–1996, and they were further classified to 9 and 12 sub-pathotypes, respectively, based on their virulence differentiation (Wan et al. 1999 ). CYR32, a previous name Hy-3, is designated in 2002 and is more virulent than CYR30 and CYR31. This race was first detected in the wheat cultivar Red Abbondanza in Huangzhong of Qinghai Province in 1994 (Wan et al. 2003 ). The outbreak frequency of this race is comparable to that of CYR31, which are about 11.7% in 2000. However, in 2001, the outbreak frequency reached incredibly to 28.8% (Wan et al. 2003 ). CYR33 (also known as Su11-14 previously) is virulent on Suwon 11, which was designated in 2008. This race was detected in 1997 with an outbreak frequency less than 1%, but the frequency unbelievably jumped to 26.72% in 2007 (Chen et al. 2009 ). Since 2000, CYR32 and CYR33 become the dominant races (Wan et al. 2003 ; Liu and Chen 2012 ; Wang et al. 2014 ; Li et al. 2016b ; Wang et al. 2017 ; Jia et al. 2018a , 2021 ); with an exception of CYR33 that exhibited a remarkably low frequency (< 5%) in Gansu in 2018 (Jia et al. 2021 ). Based on the annual reports from 2010 to 2011, CYR32 and CYR33 were mostly detected in Gansu, Shaanxi, and Sichuan provinces (Liu et al. 2012 ). There are 133 races and pathotypes were identified from 1014 isolates that are collected from 14 provinces. Thirteen of which are CYR races, including CYR17, CYR20, CYR21, CYR23, CYR25, CYR26, CYR27, CYR28, CYR29, CYR30, CYR31, CYR32, and CYR33. The remaining 115 isolates were known pathogens (Liu et al. 2012 ), which increases 35 pathotypes than that of identified before 2004 (Wan et al. 2004 ). Su11 pathotypes include 586 isolates (57.8% of the total) and is followed by Hy46 pathotypes that consist 273 isolates (26.9% of the total). G22 pathotypes, known to be virulent on genotype Guinong 22 which harbors resistance gene Yr26 , Yr24 , and Yr10 , are spreading since the first detection of the sub-pathotype 9 (G22-9) in Pi County of Sichuan Province in 2009 (Liu et al. 2010 ). Due to rapid spreading, the outbreak frequency of G22-9 increased from 0.11% in 2009 to 10.56% in 2015. As the result, the sub-pathotype G22-9 promptly developed to be the dominant pathotype and therefore was designated as CYR34 in 2016 (Liu et al. 2017 ). Currently, CYR34, CYR33, CYR32, and G22 pathotypes are dominate races/pathotypes (Han et al. 2016 ; Li et al. 2016b ; Jia et al. 2018a , 2021 ). Meanwhile, more attention should be paid on monitoring the emerging pathotypes. For instance, the ZS pathotype, which is virulent to wheat genotype Zhong 4 (ZS). This pathotype was first detected from wheat cultivar Baomai in Taibai County of Shaanxi Province in 2003. It exhibits a similar virulence spectrum on 19 Chinese wheat varieties in addition to Zhong 4. (Li et al. 2016b ). However, a pathotype ZS-1 suddenly caused epidemics in Gansu Province during 2017–2018 (Jia et al. 2021 ). A rising concern is the high virulence pathotypes that have broken the fence of the resistance gene Yr-5 that exists in the genotype Triticum aestivum subsp. spelta var. album . This pathotype displays similar virulence to the widely distributed CYR32 and CYR34 races and currently has evolved to generate the same lineage pathotypes (Zhang et al. 2020a , 2022 ). Recently, more Pst races have been sporadically identified in a few provinces since 2012. Race identification of Pst is very important to understand temporal dynamics, and can guide Yr gene deployment in the epidemiological regions. It is equally important for managing wheat stripe rust and race-targeted wheat breeding program.

The Pt races

Pt race identification in China started from 1940 by professor Huanru Wang who temporarily worked at the Institute of Agricultural Research, Tsinghua University in Kunming, Yunnan Province (Wang 1947 ). He identified three races, the race 1, 63, and 123 from Yunnan isolates that were collected in 1940–1942 using the same international differential hosts applied by Mains and Jackson ( 1926 ). Later, Kening Wang identified 417 Pt isolates collected from 1949 to 1951 using the international differential hosts set; however, the differentials was not suitable for identifying Pt races of China (Wang 1961 ). Until the early 1970s, the uniform differential hosts set, including eight wheat cultivars, viz. Lovrin 10, 6068, INR66-331, Redman, Dongfanghong3, Fengchan 3, Baiyoubao, and Taishan 4, were used as Chinese differentials to differentiate Pt races. Meanwhile, local wheat cultivars were added to the differential hosts. Therefore, the non-uniform names of Pt races, such as Zhi, Chun, Yu, and Lu series, were used in designating isolates in different regions of China (Wang et al. 1982 ). By 1977, a uniform nomenclature was determined to designate Chinese Pt races with CL (CL = China leaf rust) plus a hyphen and a number (also Chinese Yezhong) series. Using this nomenclature rule, 1237 Pt isolates were identified annually from 18 provinces during 1974–1979, and finally were identified as 11 races by the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, the College of Plant Protection, Hebei Agricultural University, and the Institute of Plant Protection, Heilongjiang Provincial Institute of Agricultural Sciences. Three out of 11 prevalent races were renamed as CL-1 (Yezhong 1), CL-2 (Yezhong 2), and CL-3 (Yezhong 3). The remaining 8 races, including Zhi 2 to Zhi 7, Zhi 13, and Shandong A, were not uniformly determined (Wang et al. 1982 ). In 1981, a combination of original standard differentials and additional eight wheat cultivars (Taishan 1, Zhong 5, Rulofen, Lovrin 12, Predgornaia 2, Avrora, Kavkez, and Kangyin 655) was used to differentiate Pt races. In 1986, 16 races, CL-4, CL-38, CL-34, CL-7, CL-2, CL-44, CL-3, CL-19, CL-29, CL-12, and CL-17, and 5 unnamed races with additional virulence patterns were identified from 113 isolates using eight Chinese differentials. Among them, CL-4 was the most prevalent race in China, with a highest 29% outbreak frequency (Hu and Roelfs 1989 ). It is worth to mention that Hu and Roelfs ( 1989 ) used 16 Thatcher near-isogenic lines to identify Pt races in China. They detected a virulence frequency of 84% to 95% on Lr2c , Lr14a , Lr14b , Lr21 , Lr17 , and Lr3 . Unified designated race 13, with virulence on Lr1 , Lr2a , Lr2c , and Lr3 , was prevalent across China. From 1996, 48 races, viz. CL-1 to CL-48 (also Yezhong 1 to 48), were identified in China. During 1976–1996, the dominant races, CL-1, CL-2, CL-3, CL-29, CL-38 (Yezhong series), and Lovrin 10 pathotypes (virulent to wheat cv. Lovrin 10, including CL-4, 34, 46, 19, and 45) have been detected annually (Yuan et al. 1983 , 1991 ; Yuan 1984 ; Chen et al. 1994 ; Yuan and Zhu 1995 ). In 1997, PHT (virulent to Lr1 , Lr2c , Lr3 , Lr3ka , Lr11 , Lr16 , Lr17 , Lr26 , and Lr30 ), PCR (virulent to Lr1 , Lr2c , Lr3 , Lr3ka , Lr11 , Lr26 , and Lr30 ), and THT (virulent to Lr1 , Lr2a , Lr2c , Lr3 , Lr3ka , Lr11 , Lr16 , Lr17 , Lr26 , and Lr30 ) were dominant races among 41 races identified from 110 isolates collected from nine provinces (Qin et al. 1998 ). During 1998–2000, four out of 162 races, the FHB, PHT, FHG, and THT, were identified from 479 Pt isolates and exhibit an outbreak frequency much higher than other races. Races TTJ, TRT, THD, FCJ, FCD, FHD, KHD, THB, and PHB in Shanghai, FHB in Hebei, PHT in Shandong, FHJ, FHG, FRG, and KRB in Shaanxi, and FHT, NHJ, PHJ, and THT in Yunnan were dominant races in the corresponding regions. In contrast, although the outbreak frequency of races FHB, PRF, and TMG were higher than 36 other races identified from 43 isolates in Jiangsu Province, only three isolates were detected among all isolates, and there were no dominant races in this region (Yang et al. 2002 ). Isolates with virulence to Lr2c , Lr3 , Lr2b , Lr16 , Lr26 , Lr10 , Lr37 , and Lr14b exhibited an outbreak frequency over 80%, and isolates displaying virulent to Lr1 , Lr2a , Lr9 , Lr11 , Lr14a , Lr29 , Lr18 , Lr14ab , Lr17 , and Lr28 showed a frequency around 50%. Notably, increasing number of isolates show virulence to Lr2a , Lr2b , Lr3 , Lr9 , Lr19 , Lr24 , Lr28 , and Lr29 , but there are also decreasing numbers with virulence to Lr1 , Lr3ka , Lr15 , Lr14B , Lr6 , Lr7 , and Lr30 in the 3 years (Yang et al. 2002 ). In addition, 79 races were identified from 613 Pt isolates during 2000–2006. The races PHT, THT, PHJ, and THJ were prominent in the field and were virulent to Lr1 , Lr2c , Lr3 , Lr11 , Lr16 , Lr17 , and Lr26 . An increasing virulence diversity has been seen in those years, although no pathotypes showed virulence to Lr9 and Lr24 (Liu and Chen 2012 ). In 2007, 96 isolates from Shaanxi, Hebei, and Sichuan provinces were discovered that, PHST and FHST in Shaanxi, THQT, THQS, THQR, THQN, and PHSP in Hubei, and THTT in Sichuan were dominant races, respectively. The outbreak frequency of isolates with virulence to Lr2c , Lr3 , Lr3bg , LrB , Lr11 , Lr14a , Lr14b , Lr16 , Lr25 , Lr26 , and Lr33 accounted for over 70% in the three provinces, and those isolates displayed avirulence to Lr9 , Lr24 , and Lr38 were null (Wu et al. 2009 ). To make a conveniently comparison for Pt races between countries, a set of wheat Thatcher genetic background-based near-isogenic lines with Lr1 , Lr2a , Lr2c , Lr3 , Lr9 , Lr16 , Lr24 , Lr26 , Lr3ka , Lr11 , Lr17 , and Lr30 was suggested to use (Jin et al. 2008 ). During 2009–2010, three races, the FCBQQ, PCGLN, and PCGLL, which are the three out of 48 races identified from 155 Pt isolates collected from seven provinces were determined to be prevalent races. Almost all isolates, except for four isolates, showed virulence to Lr26 , and none of them was virulent to Lr18 and Lr24 (Kolmer 2015 ). During 2011–2015, 158 Pt races were identified from isolates collected from 18 provinces. Six races, the THTT, THTS, PHTT, THJS, and THJT are the most prominent. In particular, THTT and THTS were widely spread (Zhang et al. 2020b , 2020c ). Over 90% of the isolates (2296) collected from 18 provinces in 2011–2013 were virulent to Lr1 , Lr2c , Lr3 , Lr3bg , Lr10 , Lr14a , Lr14b , Lr16 , Lr17 , Lr26 , Lr33 , Lr37 , Lr50 , and LrB (Zhang et al. 2020c ). More than 80% of the isolates (1143) from 15 provinces in 2014–2015 showed virulence to Lr1 , Lr2a , Lr2b , Lr2c , Lr3 , Lr3bg , Lr10 , Lr11 , Lr14a , Lr14b , Lr16 , Lr17 , Lr26 , Lr32 , Lr33 , Lr50 , and LrB (Zhang et al. 2020b ). In 2017, 52 races were identified from 1407 Pt isolates collected from nine provinces using 16 Thatcher near-isogenic lines ( Lr1 , Lr2a , Lr2c , Lr3 , Lr9 , Lr16 , Lr24 , Lr26 , Lr3ka , Lr11 , Lr17 , Lr30 , LrB , Lr10 , Lr14a , and Lr18 ), where THTT, THTS, PHTT, THKT, PHTS, THKS, and THJT were the dominant races. THTT, PHTT, and THTS in Sichuan, THTT, THKT, and THJT in Shandong, THTT and PHTT in Hebei, THTT and THTS in Hubei, Henan, and Gansu, and THTS and THTT in Anhui and Jiangsu provinces were the prevalent races, respectively (Jia et al. 2018b ).

The Pgt races

In China, Tu ( 1934 ) first identified six Pgt races in Guangdong Province in 1934, and subsequently, Yin ( 1947 ) identified fifteen races from Pgt isolates collected from twelve provinces in 1947. Later, the race 1 was detected in 15 sampling sites in Northeast China and race 2 was identified in Jiangsu, Hebei, and Shandong provinces (Wang et al. 1950 ). Wu and Huang ( 1987 ) summarized that, during 1959–1965 and 1973–1985, sixteen races, including 17, 19, 21, 21C1, 21C2, 21C3, 34, 34C1, 34C2, 34C3, 34C4, 40, 116, 194, 207, and Ketai 1 were detected from 10068 Pgt isolates in China. Of these races, race 21 and 34, and their race group (C series) were dominant. These races were virulent to Sr resistance genes Sr7a , Sr7b , Sr8 , Sr9a , Sr12 , Sr14 , Sr17 , Sr23 , and Sr29 , but avirulent to Sr11 , Sr15 , Sr21 , Sr22 , Sr24 , Sr26 , Sr27 , SrTmp , and SrTt-2 (Wu and Huang 1987 ). During 1956–1961, six races, the race 1 to race 6, were identified on a set of differential hosts that consisted of 12 wheat cultivars, viz. Hezuo 6, Songhuajiang 1, Songhuajiang 2, Gansu 96, Mailiduo, Tubuqi, Manggou 335A-531, Khapli, Fule, Einkorn, Reliance, and Kehua (Zeng et al. 1963 ). Three of which, race1, 2 and 3, were identified from 1700 Pgt isolates. However, race 1 was dominant race, and race 3 was rarely discovered (Zeng et al. 1963 ). In addition, race 4 and 5 were recovered from aecia produced on B. amurensis via artificial inoculation (Zeng et al. 1963 ). Based on rust tests using standard (international) differentials that are comprised of the wheat varieties Little Club, Marguis, Reliance, Kota, Arnautka, Mindum, Spelmar, Kubanka, Acme, Einkorn, Uernal, and Khapli, with additional wheat cultivars (Mianzi 52/Mianzi49) as accessory differentials hosts. As a result, 26 and 334 isolates, collected in Liaoning Province in 1960 and 1961 respectively, were determined as six races including 17, 21, 34, 40, 21C1 (C = Chinese), and 34C1 (Wu et al. 1964 ). The race 116 was first detected during 1952–1957 by the team of Institute of Northeast Agricultural Sciences and Institute of Applied Fungi, Chinese Academy of Sciences, but not recorded pathogenicity on wheat genotypes. This race was detected and identified from samples collected on wheat cultivar Mentana in Huaihua County in Hunan Province in 1982. This race was detected late than the race 40. However, both races were highly virulent to the wheat cultivar Vernal (Huang et al. 1984b ). Race 34C3, detected on the wheat cultivar Orofen that was introduced into China in 1970s and used as resistance germplasm against wheat stem rust, was avirulent to the wheat cultivar Rulofen that was introduced as a resistance germplasm to wheat leaf rust (Huang et al. 1984a ). By 1977, race 34C4 (provisionally 34CR), virulent to Orofen and Rulofen, was detected based on reactions on a set of differential hosts including Reliance, Mianzi 52, M2761, Huadong 6, Rulofen and Orofen (Huang et al. 1984a ). During 1990 to 1994, 19 races (pathotypes) that are 21C3CKH, 21C3CKR, 21C3CTR, 21C3CTH, 21C3CPH, 21C3CPR, 21C3CFH, 21C3CFR, 34C2MKH, 34C2MKR, 34C2MKK, 34C2MFK, 34C2MFR, 34MKG, 34MFG, 34MFK, 34C1MKH, 34C1MKR, and 34C1MFH were identified among 1224 Pgt isolates from 18 provinces (Yunnan, Fujian, Sichuan, Guizhou, Hunan, Hubei, Zhejiang, Shanghai, Jiangsu, Shaanxi, Henan, Hebei, Gansu, Inner Mongolia, Jilin, Liaoning, Heilongjiang, and Qinghai) of China (Yao et al. 1997 ). Among those races, race 21C3 and race 34C2 were dominant ones (Yao et al. 1997 ). The new race (or pathotype) 21C3CTR that is virulent to Sr11 was first detected in Emeishan of Sichuan Province in 1993, and later it reached an outbreak frequency as high as 31.0% by widely spreading in Sichuan, Yunnan, Hubei, Henan, Hebei, and Gansu provinces (Yao et al. 1996 ). During 2007–2008, four races 21C3CTH, 21C3CFH, 21C3CPH, and 34MKG were identified from 59 Pgt isolates in Heilongjiang, Sichuan, and Yunnan provinces. Of which, 21C3CTH was prevalent with high outbreak frequency of 72.9% (Han et al. 2010 ). During 2012–2013, 13 races (pathotypes), 21C3CTHTM, 21C3CTQSM, 21C3CTTSC, 21C3HTTTM, 34MKGQM, 34MRGQM, 34MRGSM, 34MTGSM, 34Oroll-MTGSM, 34Oroll-MRGQM, 34C3RTGQM, 34C3RKGQM, and 34C3RKGSM, were identified from 23 Pgt isolates collected from wheat plants and 30 from Berberis species. Two of which, 34C3RTGQM and 34Oroll-MRGQM, were prominent races. Six of these races, 34MRGQM, 34MRGSM, 34MTGSM, 34Oroll-MTGSM, 34Oroll-MRGQM, and 34C3RTGQM, emerged recently and were first detected with a combined virulence to Sr5  +  Sr11 (Cao et al. 2016 ). Over the past decade, many dominant races have decreased in the field. However, there is an exception that 21C3 and 34C2 have remained prominent with a consistently high outbreak frequency so far (Wu et al. 1964 ; Yao et al. 1993 ; Han et al. 2010 ; Cao et al. 2016 ).

Variable oversummering and overwintering regions for the pathogens

Variable oversummering regions of pst.

Intriguingly, many studies showed that most of the new Pst races in China were originally uncovered in northwestern regions and some of southern regions, especially in Longnan of Gansu Province and northwestern of Sichuan Province, such as CYR13, CYR17, CYR18, CYR19, CYR21, CYR22, CYR27, CYR28, CYR29, CYR30, CYR31, CYR32, and CYR34 (Wang et al. 1986 , 1996 ; Wan et al. 2003 ; Liu and Hambleton 2010 ; Liu et al. 2017 ). Due to the emergence of new Pst races, the resistance of wheat cultivars was often overcome in these regions. Molecular studies revealed that the Pst population in Gansu, especially in the Longnan region, had a high level of genetic diversity (Shan et al. 1998 ; Zheng et al. 2005 ; Duan et al. 2010 ; Lu et al. 2012 ). Therefore, the regions mentioned above are considered as the most important Pst genetic variation regions, and are also the origins of new Pst races. These races in turn provide vast Pst inoculum to the wheat plants grown in eastern regions. The formation of Pst genetically variable region is not known until recently. So far, more than 40 barberries ( Berberis spp.) and four Mahonia spp. that are native in China have been identified as alternate hosts for Pst and more than 10 Berberis species and at least two Mahonia spp. are widely distributed in Pst oversummering regions (Zhao et al. 2013 , 2016b ; Zhuang et al. 2019 ; Du et al. 2019 ; Li et al. 2021 ; Cheng et al. 2022 ) (Fig.  7 ). Importantly, it has been demonstrated that under natural conditions, sexual cycle of Pst in China occurs more frequently based on known and new races of Pst isolates that were recovered from naturally-rusted barberry (Zhao et al. 2013 ; Li et al. 2016a ; Wang et al. 2016 ), and Mahonia (Cheng et al. 2022 ). Accordingly, wide distributed Berberis spp. and frequent occurrence of sexual cycle of Pst resulted in the latently genetic recombination and the continual generation of new races, which represents the formation of oversummering Pst genetically variable regions in China.

figure 7

The map showing extensive distribution of most of Chinese Berberis spp. and Mahonia spp. serving as alternate hosts for Puccinia striiformis f. sp. tritici in North-western area of oversummering areas, and a few Berberis spp. for P. graminis f. sp. tritici in Gansu, Shaanxi and Tibet in China based on data collections of field investigations during 2010–2020 (Zhao et al. 2013 , 2015 , 2016b ; Wang et al. 2016 ; Du et al. 2019 ; Li et al. 2021 ; Zhuang et al. 2019 ; Cheng et al. 2022 ). Geographic outline of oversummering areas were redraw according to a review by Wan et al. ( 2007 ) and Tibet of oversummering area was added. 1 North-western area. 2 South-western area. 3 Xinjiang area. 4 Northern area. 5 Tibet area. Map resource: http://bzdt.ch.mnr.gov.cn/ . Data resource: Information on barberry data in Northeast China from Yuanyin Cao’s laboratory at Shenyang Agricultural University, Shenyang, Liaoning Province

Pgt genetically variable regions

Based on the studies of Pgt isolates in 1963–1967 and 1973–1992, Yunnan, Sichuan, and, Guizhou provinces are known as Pgt genetically variable regions. In these regions, new virulent races emerge and accumulate more rapidly than other regions. One of the reasons is that the pathogens can oversummer and overwinter to complete their disease cycle locally (Huang et al. 1993 ).

Pt genetically variable regions

Although many Pt genetically variable regions in China have not been designated due to the lack of evidence, an increasing number of high genetic and virulence diversity have been found in the pathogen population habitats distributed in Hebei, Henan, Shandong, Sichuan, Yunnan, Gansu, and Shaanxi provinces (Xu et al. 2013 ; Ge et al. 2015 ; Kolmer 2015 ; Ma et al. 2020 ). However, these regions are considered unlikely the potentially variable regions for Pt in China due to low clonal population (Kolmer 2015 ).

High temperature-tolerant isolates

Temperature is a key factor that affects wheat rust fungi growth and development. Relatively, Pst requires the lowest high temperatures, which is lower than Pt and Pgt (Roelfs et al. 1992 ), as high temperature restricts the development of Pst . When the average 10-day temperatures are above 23°C in July and August, which is the two hottest months, can halt the development of the disease (Roelfs et al. 1992 ; Li and Zeng 2002 ). The data of global land–ocean temperature index indicate that the annual average temperature has arisen 0.85°C in 2021 ( https://climate.nasa.gov/vital-signs/global-temperature/ ). In China, especially the Central and East regions, it has increased 0.97°C (CMA 2021 ). Recently, studies on high-temperature tolerance have been investigated using a Chinese Pst population consisting of 126 isolates from 12 provinces. Results showed that the Chinese Pst population had a remarkable adaptation to high temperature and the average ET 50 values, a temperature that is required to obtain 50% of the maximum effect, were 24.1°C with a range of 18.46–27.01°C, which has passed the highest temperature limitation of 23°C (Zhang et al. 2013 ). Moreover, genetic diversity of Pst population had a nicely negative correlation with average ET 50 values as well as a significantly positive correlation with the coefficient of ET 50 variation, but there was no correlation with genetic diversity (Lian et al. 2016 ). Field investigations revealed that Pst can oversummer during 23–25°C in Pingliang of Gansu Province (Wang 2009 ), and that Pst can overwinter in high altitude with higher temperature and oversummer in lower altitude with lower temperature. The over-wintering altitudes can be seen in Tianshui of Gansu Province from 1800 m up to 2080 m, and oversummering altitude can be the place of 1650 m down to 1450 m. While, in Yunnan Province which is at a higher altitude, the oversummering altitude for Pst ranges from 2300 to 1950 m (Pan et al. 2011 ). Therefore, under high temperature conditions (> 23°C), high temperature-tolerant Pst isolates have greater potential to complete the disease cycle than high temperature-sensitive ones. The potential influence of high temperature-tolerant Pst isolates on wheat stripe rust occurrence should be under consideration. Recently, in the eastern coastal epidemiological regions of Zhejiang and Jiangsu provinces, wheat stripe rust is usually an ignorable issue because it normally develops slowly and sometimes stops infection in early April; however, it is not a severe issue until early May in 2019 (Ju et al. 2022 ). The outbreak is possibly due to the warmer weather where the high temperature-tolerant isolates prevailed. Following the global warming, the race dynamics of high temperature-tolerant Pst isolates should be paid more attention and taken necessary measures to manage wheat stripe rust in China.

Fungicide resistance of Puccinia species

There are a variety of fungicides used to control Puccinia species pathogen infection. One of the key fungicides triazole plays an important role in preventing wheat from rust disease infection. In China, fungicide application for wheat rust control can be tracked back to the 1950s (Ou and Meng 1958 ; Lu et al. 1962 ). Now, more than ten chemicals are used as fungicides to control this disease, such as sodium sulfanilate and fluorides; however, wheat often suffers from yield lose when severe epidemic hits (Wang et al. 1988 ). Nevertheless, those chemicals had been extensively applied to control wheat rust diseases in the 1960s–1970s, and made a considerable success (Wang et al. 1988 ). Notably, the fungicide triadimefon was introduced into China in 1976 and was locally synthesized by Institute of Elemental Organic Chemistry of Nankai University (Wang et al. 1988 ). This fungicide effectively controlled wheat rust infection by seed treatment and foliar spray inoculation (Wang et al. 1988 ). Additionally, other triazole fungicides, such as tebuconazole and hexaconazole, have been developed to control wheat rusts. Triazole type of fungicides has maintained high efficiency in controlling the wheat rusts for 5 decades. A worrisome situation is that following the long duration use of triazole type of fungicides, especially triadimefon, the insensitive and anti-fungicide isolates have been found in Chinese wheat rust populations (Cook et al. 2021 ; Zhan et al 2022b ). A recent study by Zhan et al. ( 2022b ) showed that there are about 7% of Pst isolates in total of 446 isolates collected from winter-producing regions and northwest oversummering regions exhibiting triadimefon resistance and cross-resistance to triadimefon, tebuconazole, and hexaconazole. However, the majority of the resistance isolates are from southwestern of China. The isolates in Xinjiang and Tibet epidemic regions are still high sensitive to triadimefon. Compared with the Pst isolates from Europe, United States, Ethiopia, and Chile, Chinese Pst isolates have a high percentage of fungicide-resistant mutants (Cook et al. 2021 ). Genetic analyses revealed that single-site mutation by Y134F substitution in the target gene of demethylase inhibitor (DMI; Cyp51 ) resulted in fungicide resistance in Chinese Pst population (Cook et al. 2021 ; Zhan et al. 2022b ).

Notably, fungicide-resistance has also been detected in Chinese Pgt population recently. A study by Wu et al. ( 2020a ) reported that low to moderate triadimefon-resistance had been detected in 29 Pgt isolates accounting for ~ 32.6% in the tested 89 Pgt isolates that were sampled from wheat and barberry in Heilongjiang, Liaoning, Sichuan, and Shaanxi provinces during 2013–2015. Chinese Pgt population had a positive correlation between resistance to triadimefon and carbendazim, and no cross-resistance to triadimefon, thiophanate-methyl, and kresoxim-methyl. In addition, triazole type of fungicides have been consistently used to control wheat leaf rust in China since the late 1970s. While, isolates of Pst and Pgt with the resistance to triazole fungicides have emerged in China. Although no evidence to demonstrate Pt isolates are resistant to fungicides, it is plausible to propose that the risk of anti-fungicide of Pt against triazole type of fungicide may need to be investigated.

Emergence of new rust races

Although wheat cultivars carrying resistance genes have been effectively used to control the three rusts, new races often overcome the resistance of these wheat cultivars and cause disease. As a result, many of which developed to be the prevalent races and cause huge yield reduction annually. The emergence and rapid accumulation of new pathogenic rust races are usually accompanied with the high level of threatening to wheat production. Due to the emergence of new pathogenic races, the resistance of many cultivated varieties are facing danger than ever before, where they turn to be vulnerable to the new emerged races. It has been observed that a few new strip rust races quickly diffused to other wheat-growing regions that are far away from their origin sites. These new races caused a severe interregional wheat stripe rust epidemic. So far, eight main cultivated wheat cultivars across China have been displaced (Li and Zeng 2002 ; Wan et al. 2007 ; Han et al. 2016 ). Recently, a newly-emergence race, named TSA-6 which is virulence to Yr5, has been identified in Shaanxi Province (Zhang et al. 2020a ). Later, it was detected in Qinghai Province (unpublished data). The Yr5 -virulent race and its mutant TSA-9 possess similar pathogenicity with dominant Pst races CYR34 and CYR 32, which are pathogenic to most of the 165 tested Chinese wheat cultivars (Zhang et al. 2022 ). Historically, in China, a new race could develop into a prevalent race within 6–9 years, and sometimes within the frame of 3–5 years after initial emergence (Lu et al. 1963 ; Wang et al. 1986 ; Wu et al. 1993 ; Jiang et al. 1996 ; Wan et al. 2003 , 2004 ; Li et al. 2016b ; Liu et al. 2017 ). Thus, the enhanced surveillance on the dynamics of new emerging Pst isolates should be taken in consideration.

New races of Chinese Pgt population have been intensively reported for nearly 5 decades since the 1970s. Dominant 21C3 and 34C0 race families have been existed for many years. However, during 2009–2015, three new races, 21C3CTTTM and 34C0MRGSM identified from wheat, and 34C3MTGQM identified from Berberis species become the dominant races in China (Zhao et al. 2013 , 2015 ; Li et al. 2018 ; Cao et al. 2019 ).

Recent studies have reported that although new races of China Pt population emerged over the past years, occurrence frequencies of new races were extremely low and new races were somewhat different from surveillance years (Zhang et al. 2020b , 2020c ). Generally, leaf rust epidemics are thought to be closely related to the appearance of new races, but the outbreaks of wheat leaf rust in China during 2011–2015 were considered as a result of climatic and host conditions instead of new races (Zhang et al. 2020b , 2020c ). Since 2011, no case with regard to new races developing to be prevalent races to cause wheat leaf rust epidemics in China during these years have been reported.

Invasion risk of alien races

Wheat rusts are air borne diseases where the fungal spores can spread with a long distance. Theoretically, the wind can help the spores travel across regions and even continents. In particular, human activities accelerate the spread by the travel between continents. In fact, the inter-continental spread of wheat rusts have become a major disease propagation means. Over the past 30 years, stripe rust has spread to Australia in 1979 (O’Brien et al. 1980 ; Wellings et al. 2003 ; Wellings 2007 ), New Zealand in 1980 (Beresford 1982 ), and South Africa in 1996 (Pretorius et al. 1997 ). A recent well known case is the spread of the Pgt race Ug99 (TTKSK) lineage that traveled from Uganda in 1999, and finally landed in Iran in 2019, demonstrating the incredible long-distance travel of wheat rusts (Fig.  8 ; relabeled based on data information from https://rusttracker.cimmyt.org/?page_id=22 ).

figure 8

The re-labelled map sketch illustrating origin (Uganda indicated by red-dotted circle), evolution and dispersal of the Puccinia graminis f. sp. tritici race TTKSK (Ug99) lineage and potential invasion risk to China. Data resource: CIMMYT, September 2021 at https://rusttracker.cimmyt.org/?page_id=22

In China, since 1970s, wheat stem rust has been effectively controlled for 5 decades because of the cultivation of stem rust-resistant wheat cultivars. Notably, Pgt races have been found to mutate at a low frequency in the field, and two race groups, 21C3 and 34C, finally become dominant for nearly 5 decades since the 1970s (Wu and Huang 1987 ; Yao et al. 1998 ; Cao et al. 2016 ). However, the new Pgt race TTKSK (previously TTKS, also known as Ug99) breaks the resistance of Sr31 , a resistance gene that maintains a long-lasting protection from wheat stem rust infection for over a half century and introduced to most of the wheat variety worldwide. This race was first detected in Uganda in 1998 (Pretorius et al. 2000 ). Currently, Ug99 has developed to 15 Ug99 lineage variants through somatic recombination (Li et al. 2019b ), and each has a combined virulence not only to Sr31 but also to some of the eight important Sr genes, which are Sr 21, Sr24 , Sr30 , Sr36 , Sr38 , Sr9h , SrTmp , and Sr8155B1 . Since Ug99 race group has invaded Iran, much attention should be paid as they are geographically not far from China, although Ug99 and its variants have not been detected in China yet. In fact, only two (~ 1.7%) wheat varieties out of 118 in the tested Chinese wheat varieties are moderately resistant or fully resistant to the Ug99 race (Singh et al. 2006 ). Therefore, wind-borne spores of Ug99 lineage have a strong potential to be spread to China.

Discovery of the sexual cycle of the rusts

The sexual stage of Pgt has been known for a long time. The finding of susceptible barberry serving as alternate host has greatly pushed our understanding of Pst sexual cycle forward. It was recognized that susceptible barberry plays an important role in providing rust spores that cause primary stem rust infection of wheat in United States (Roelfs 1982 ). In China, although attempts were made to verify the role of barberry relating to occurrence of wheat stem rust under field conditions over the past decades (Wang 1955 ; Zhang et al. 1957 ; Wang et al. 1958 ; Zeng and Xue 1963 ), they all failed. Until recently, the existence of sexual cycle of Pgt in the fields has been discovered in China (Zhao et al. 2015 ). However, the role of susceptible barberry in a wheat stem rust epidemic is still not fully understood. Further work should be focused on this issue in China.

Since many Chinese barberry ( Berberis spp.) and Mahonia spp. were identified as alternate hosts for Pst , the occurrence of Pst sexual cycle has been intensively investigated under field conditions. Chinese researchers demonstrated that Pst could infect susceptible Berberis and Mahonia spp. which are native in China to complete the sexual cycle in spring (Zhao et al. 2013 , 2022 ; Wang et al. 2016 ; Liu et al. 2021 ; Chen et al. 2021a ; Cheng et al. 2022 ), and that Pst could infect endemic Berberis to achieve sexual reproduction in autumn in Tibet (Du et al. 2022 ). In regions such as Qinghai and Shaanxi provinces, where susceptible Berberis spp. and wheat grow adjacently, under this situation, barberry provides aeciospores as inoculum to cause stripe rust infection on wheat (Chen et al. 2021a ; Zhao et al. 2022 ). In addition, whether susceptible Mahonia spp. is involved in providing aeciospores as inoculum to trigger stripe rust outbreak on wheat also needs further investigation.

Attempts have also been made to demonstrate the relationship between Thalictrum spp. as alternate hosts of Pt and leaf rust on wheat and grasses, but the relationship remains obscure. In 1960s, Guichao Huang at Institute of Agricultural Sciences in Jiamusi, testified that rusts on Thalictrum spp. were related to leaf rust on Agropyron instead of leaf rust on wheat (Wang et al. 1987 ). In 1980s, Wang et al. ( 1987 ) reported that, in the Baishitougou village of Inner Mongolia, leaf rust on Agrostis spp. can complete sexual cycle on T. petaloideum ; however the aeciospores from T. minus , T. minus var. stipellatum , T. minus var. hypoleucum , and/or T. petaloideum failed to cause wheat leaf rust by artificial inoculation. Although a few Chinese Thalictrum spp. have been identified as alternate hosts for Pt , the role of Thalictrum spp. in the occurrence of leaf rust on wheat under natural conditions remains unknown. Sequence alignment of internal transcribed space (ITS) indicated that more than 20 aeciospores from susceptible T. baicalense plants had 95–96% of sequence similarity with P. triticina (Zhao et al. 2021 ). However, inoculation experiment of aeciospores on susceptible wheat cultivars were not conducted to justify the potential infection by Pt urediospores in fields.

Sources of Pst teliospores for alternate host infection

Teliospores are essential for infecting alternate hosts ( Berberis and Mahonia ) to invoke sexual cycle. Under favorable conditions, basidiospores, which germinate from teliospores, infect alternate hosts to initiate sexual reproduction in the three wheat rusts. Therefore, vigorous teliospore sources are associated with sexual stage of the three rust pathogens. Field investigations and laboratory experiments demonstrate that Pst teliospores can be produced at all growth stages and possess germination capacity in field. However, the teliospore production and germination rate are dependent of the fungi growth stage, weather condition, and locations (Chen et al. 2021b ). In addition, wheat straw stacks of diseased tissues are the harbor of Pst teliospores in oversummering regions, such as Gansu and Qinghai provinces (Chen et al. 2021b ). A study by Qin et al. ( 2022 ) reported that grass residues can harbor the overwintering Pst for the primary infection in the coming spring. Survival of Pst teliospores on grasses after overwintering can also serve as the potential source to infect alternate hosts of Pst .

Wheat stripe rust management

Planting rust-resistant wheat cultivars has been considered as an effective, economical, and green strategy to control wheat rust diseases. In China, comprehensive application of wheat cultivars carrying Sr resistance genes has been successful for long-term disease control. By deploying an integrated management strategy for wheat stripe rust, the disease has been effectively controlled in most wheat-producing areas since 2004 in China (Chen et al. 2013 ). After the year 2010, wheat stripe rust has led to the infection around 2.67 million hectares perennially, and caused annual yield loss around 0.17 million metric tons (Huang et al. 2018 ). One of the key reasons is that the resistance genes in wheat cultivars were frequently overcome by the emerging new races, resulting in the wheat cultivars to be vulnerable within a short period after released in the fields. Therefore, an integrated strategy should be considered to slow down the new rust race emergence.

Mining novel resistance genes for durable control

At present, 83 wheat stripe rust (yellow rust) resistance genes ( Yr ), viz. Yr81 - Yr83 , have been designated (McIntosh et al. 2017 ; Li et al. 2020 ). Of the 83 Yr genes, only Yr15 , Yr45 , and Yr61 possess effective resistance to prevalent Chinese Pst races (Zhang et al. 2020a ; Feng et al. 2022 ). Moreover, unnamed new Yr genes from current wheat varieties or other Triticum species, such as YrElm , YrElm1-4 , YrElm4 , YrLm2 , YrM97 , and YrM852 from Elymus mollis (Yang et al. 2009b , 2010 ; He et al. 2010 ; Xu et al. 2012 ; Bai et al. 2013 ; Zhang et al. 2014 ), YrHua , YrHy, YrH122, YrH9014 , YrH9020a , YrHua9020 , and YrHu from Psathyrostachys huashanica (Cao et al. 2005 ; Liu et  al. 2008 ; Yao et al. 2010 ; Tian et al. 2011 ; Ma et al. 2013 , 2015a , b , 2016 ; Liu et al. 2014 ), YrV1 , YrHV , YrWV , and YrV3 from Haynaldia villosa (Zhou et al. 2008 ; Hou et al. 2009 , 2013 ; Wang et al. 2011a ), YrCH5383 , YrL693 , and YrCH5026 from Thinopyrum intermedium (Hou et al. 2015 ; Huang et al. 2014 ; Zhan et al. 2014b ), and YrM8003 from rye (Xu et al. 2010 ), have been identified. In addition, 12 meta-quantitative trait loci (MQTL), including both quantitative resistance loci (QRL) and major resistance genes, were discovered from 194 QRL that have been identified previously (Cheng et al. 2019 ), which can be used for breeding stripe rust-resistant wheat cultivars by marker-assisted selection (MAS).

So far, 63 wheat stem rust resistance genes ( Sr ) have been identified worldwide (Mago et al. 2022 ). In China, eight Sr genes, including Sr9e , Sr26 , Sr31 , Sr33 , Sr37 , Sr38 , Sr47 , and SrTt3 , are still resistant to local Pgt races. Nevertheless, much attention should be paid to those races with combined virulence to the resistances Sr5 and Sr11 (Cao et al. 2016 ). The stem rust resistance genes have been confirmed to be effective against the dominant races 34MKGQM, such as Sr9e , Sr10 , Sr11 , Sr13 , Sr14 , Sr17 , Sr18 , Sr19 , Sr20 , Sr21 , Sr23 , Sr25 , Sr26 , Sr30 , Sr31 , Sr32 , Sr33 , Sr34 , Sr35 , Sr36 , Sr37 , Sr38 , Sr47 , Srdp-2 , SrTmp , SrTt3 , and SrWld-1 . The resistant genes against the dominant race 21C3CTHSM include Sr5 , Sr9e , Sr19 , Sr20 , Sr21 , Sr22 , Sr23 , Sr25 , Sr26 , Sr27 , Sr30 , Sr31 , Sr32 , Sr33 , Sr36 , Sr37 , Sr38 , Sr47 , and SrTmp (Han et al. 2018 ). Fifteen Sr genes, viz. Sr9e , Sr19 , Sr20 , Sr21 , Sr23 , Sr25 , Sr26 , Sr30 , Sr31 , Sr32 , Sr36 , Sr37 , Sr38 , Sr47 and SrTmp exhibited resistance to both predominant races. Li et al. ( 2019a ) reported that 83 Heilongjiang wheat cultivars, carrying Sr2 , Sr24 , Sr25 , Sr26 , Sr31 , and Sr38 based on molecular detection, were resistance to three prevalent races 21C3CTHQM, 34MKGQM, and 34C3RTGQM, respectively. Field adult-plant resistance to all three prevalent Pgt races 21C3CTH, 21C3CFH, and 34MKG were identified in 56 out of 78 (71.79%) alien Ug99-resistance wheat varieties (lines) that were introduced from International Maize and Wheat Improvement Center (CIMMYT), and 72 out of 142 (50.7%) domestic wheat varieties from 15 provinces of China (Han et al. 2013 ). Wu et al. ( 2020b ) identified the wheat lines from CIMMYT carrying Sr9e , Sr21 , Sr26 , Sr33 , Sr35 , Sr37 , Sr38 , Sr47 , and SrTt3 resistance genes against Ug99, and the lines possessing resistance genes against the prevalent Pgt races 21C3CTTTM, 34C0MRGSM, and 34C3MTGQM in China. Those Sr genes are important resistance germplasm resources for wheat breeding.

Currently, over 100 wheat leaf rust resistance genes ( Lr ) have been identified worldwide, and 80 of which have been officially named (McIntosh et al. 2017 ; Kumar et al. 2021 ). Wu et al. ( 2020a ) reported that, based on resistance of 100 Chinese cultivars that challenged with 20 prevailing Pt isolates, nine Lr genes, viz. Lr9 , Lr18 , Lr19 , Lr24 , Lr28 , Lr29 , Lr47 , Lr51 , and Lr53 , exhibited a broad resistance spectrum to all tested isolates. It is worth to mention that the Lr genes can be utilized for leaf rust-resistant wheat breeding, but Lr genes, including Lr2c , Lr3 , Lr16 , Lr17 , LrB , Lr3bg , Lr14b , Lr23 , and Lr39 , should be avoided since they are high susceptible to the 20 prevailing Pt isolates in the fields (Wu et al. 2020a ). In addition, six Lr genes, Lr1 , Lr33 , Lr34 , Lr45, and Lr46 , were identified in 37 Chinese wheat cultivars. Of which, 29 cultivars carrying Lr34 and Lr46 , and exhibit adult-plant resistance to leaf rust (Wu et al. 2020a ). Chinese cultivar Shanghai 7 displays high resistance to Ug99, but it is difficult to identify the Ug99-resistance gene in this cultivar due to the unknown genetic background of this wheat variety (Singh et al. 2006 ). Currently, over 70 quantitative trait loci (QTL) against wheat leaf rust have been identified, and 11 of which possess pleiotropic resistance to the disease (Zhang et al. 2016 ; Liu and Li 2019 ; Yan et al. 2022 ).

Pyramiding multi-gene resistance to wheat rusts

Pyramiding rust-resistant genes is an important strategy to breed wheat resistance cultivars. Previously, 1BL/IRS translocation lines that carry the stem rust gene Sr31 , and the stripe rust gene Yr9 were widely used in stem rust-, and stripe rust-resistant wheat breeding. Chinese wheat cultivars carrying both genes play an important role in controlling stripe and stem rust. Wheat cultivars with multi-resistance genes exhibit a broader resistance spectrum. Multi-gene pyramiding strategy therefore has been verified to be practicable for durable control of wheat rusts. By pyramiding Yr15 and Yr64 to the resistance wheat line RIL- Yr64 / Yr15 , a wider spectrum and durable resistance wheat variety was obtained (Qie et al. 2019 ). Zhang and Zhang ( 2016 ) introduced both YrSM139-1B and YrSM139-2D into the wheat cultivar Shaanmai 139, which increased the reception wheat with a broad resistance to wheat rusts remarkably. Zeng et al. ( 2015 ) reported that wheat cultivars carrying multi- Yr genes displayed stripe rust resistance in adult plant. However, pyramiding multi- Lr or  Sr  genes to a wheat cultivar has not been reported in China yet. Notably, the wheat variety carrying tandem resistance genes, such as Sr24 - Lr24 and Lr37-Yr17-Sr38 , can simultaneously resist the three wheat rusts, which is a good donor germplasm for wheat breeding.

Deployment of wheat cultivars carrying rust resistance genes

The deployment of wheat varieties carrying resistance genes in epidemiological regions can theoretically control disease outbreak. Wheat varieties with whole growth stage resistance have been grown in epidemiological regions now. In 1965, wheat varieties Abbondanza and Fengchan 3 were widely grown in South Shaanxi and central Shaanxi Province to control wheat stripe rust for 9 years (SXIPP 1976 ). In the 1970s, breeding and application of stem rust-resistant wheat cultivars, especially those carrying Sr31 , play a significant role in controlling the rust disease outbreak in China. Since then, wheat stem rust has been a sporadic-occurring disease in China (Cao et al. 1994 ; Wang et al. 2010 ). One of the suggestions regarding the deployment of resistance genes is to cultivate the wheat varieties carrying multi-resistance genes but not a single resistance gene at a large scale or in epidemiological region.

Regulation of alternate hosts

Alternate hosts and vigorous teliospores are required for wheat rusts to complete the sexual stage. Sexual genetic recombination of wheat rusts can conceive high virulence progenies of the pathogen. Some techniques have been employed to reduce possibility of the new race generation by controlling the pathogen’s sexual reproduction on alternate hosts, which largely reduced the potential emergence of new races generated in the habitat of barberry species. Some useful tips are recommended: (1) triazole fungicides (i.e. triadimefon) should be frequently used on alternate host plants; (2) eradicating alternate host plants close to wheat fields; (3) reducing overwintering teliospore levels by removing wheat straw.

Use of fungicides

Chemical fungicides, such as Flutriafol, hexaconazole, diniconazole, propiconazole, tebuconazole, and triadimefon, have been registered and applied in China. However, long-term and intensive application of triazole fungicides has led to the emergence of anti-fungicide Pst and Pgt races in China (Wu et al. 2020a ; Zhan et al. 2022b ). The trouble is that the fungicide-resistant isolates are continuously emerging. Therefore, exploring new fungicides or alternative utilization of fungicides is an issue on table.

Biocontrol of the rust disease

Mycoparasitism mechanism is common in rust fungi, especially in the genus of Puccinia , which can be a useful and environmental-friendly method to control the rust diseases in addition to the fungicides. To date, approximately 30 genera of fungi are able to hyper-parasitize rust fungi. However, only five fungal species, Lecanicillium lecanii , Typhula idahoensis , Microdochium nivale (Littlefield 1981 ), Cladosporium cladosporioides (Zhan et al. 2014a ), and Alternaria alternata (Zheng et al. 2017 ), have been reported to infect and kill Pst urediospores. Likewise, hyper-parasitism of two Verticillium spp., V. psalliotae and V. tenuipes , on P. triticina (syn. P. recondita ), and Aphanocladium album on P. graminis have been reported (Koc et al. 1981 ; Leinhos and Buchenauer 1992 ). In addition, the biocontrol agent Pseudomonas aurantiaca was reported to have a potential control effect on wheat leaf rust (Wang et al. 2011b ). However, effects of hyper-parasitic mycoparasites and biocontrol agents on three wheat rusts were observed under laboratory conditions. Application of hyper-parasites and biocontrol agents in fields to control wheat rusts is on the way.

Monitoring and forecasting wheat rust epidemics

Monitoring and forecasting dynamics of crop disease can help to manage crop diseases. These field managements include pathogen spore volume, the planting area of susceptible host plants, and environmental conditions. By monitoring race dynamics, virulence variation, and pathogen population structure, we can obtain valuable information of the pathogen dynamics which will determine how and why to deploy the agricultural regulations. A classical case is that in 1958, a monitoring and forecasting method was employed to control wheat stripe rust. Based on the pathogen volume in winter and the coming early spring, the susceptible wheat cultivars planted, and the climatic factors, it predicted the epidemics of wheat stripe rust in 1964, 1973, and 1977. By 1977, more than 30 monitoring and forecasting stations were established national wide. This prediction method was proved to be reliable and it still is adopted nowadays. For instance, monitoring and forecasting wheat stripe rust was carried out in 14 individual years during 1960–1979, 8 epidemics were successfully predicted (Wang et al. 1988 ). Later, the computer-based models to predict the mid/long term epidemics of wheat stripe and leaf rusts were established and successfully applied (Yucheng Plant Protection Station 1979 ; Zeng et al. 1981 ; Xiao et al. 1983 ; Dong et al. 1987 ; Wu et al. 1991 ; Cao et al. 1995 ; Jiang et al. 1996 ; Pu et al. 2012 ). For the short-term prediction, overwintering inoculum and weather conditions during/after overwintering are predicted to be associated with the occurrence of wheat rust epidemics. In addition, high virulence frequency of a single dominant race and a few other races, the virulence spectrum, parasite fitness, and susceptible wheat cultivars planted can be used to predict epidemics of wheat rusts. For instance, the 1990s severe nationwide epidemic of wheat stripe rust was predicted in advance based on the high virulence frequency of the race CYR29 (up to 40.3%) and 6.7 million planting areas of susceptible wheat cultivars in 1989 (Wu et al. 1991 ). Currently, a series of internet-based devices or technologies, such as inoculum trapping, remote sensing, geographic information system (GIS), Global positioning system (GPS), atmospheric circulation modelling, and Internal of Things (IoT), have been developed and applied to manage crop diseases including wheat rusts (Hu et al. 2022 ). The modern agricultural technologies will undoubtedly enable us to precisely monitor and predict the development of wheat rusts and other crop diseases, and as a result to control the wheat rusts.

Planting wheat variety mixtures

Monoculture often fosters compatible pathogen accumulation. Growing a mixture of different wheat varieties can effectively control epidemics in fields. Many studies indicated that planting multi-wheat variety mixtures is an effective approach to reduce wheat stripe and leaf rusts outbreak in field. The low density susceptible wheat plants, such as 3:1 (resistant: susceptible) ratio, will decline disease development in wheat variety mixtures (Cao and Zeng 1994 ; Shen et al. 2008 ; Lü et al. 2014 ; Wang et al. 2022a ). However, it is not determined if increasing of wheat variety can further reduce the occurrence of wheat rusts. Nevertheless, the mixing planting of distinct wheat varieties to reduce rust infection is worth of further filed practicing.

Intercropping

Intercropping of wheat and other crops can also decrease wheat rust occurrence. For example, intercropping of rust-resistant wheat cultivars with faba bean can reduce wheat stripe rust infection by 22–100% according to 1-year field trial (Xiao et al. 2005 ). Likewise, Yang et al. ( 2009a ) reported that, based on 6 years trials, intercropping of wheat and faba bean can decrease 30.4–63.55% wheat stripe rust occurrences with an increase of 0.28–0.63 metric tons per hectare of crop yields. In addition, intercropping of wheat and faba bean, namely the Yumai 1(wheat)/Yuxi (local bean variety) and Qiekuina (wheat)/Yuxi (local bean variety), achieve 38.7–39.6% of control to wheat leaf rust (Yang et al. 2003 ).

The outlook to the future

Due to the emerging new rust pathogens, there is a potential risk that the new rust pathogens would overcome the resistance of currently-growing wheat cultivars and cause a large scale of epidemics. Therefore, the work that monitoring and analyzing the emerging rust races in field should be strengthened to avoid wheat rust outbreaks. On the other hand, monitoring the effectiveness of wheat rust resistance genes will help to guide the rust managements, such as the deployment and introducing of new resistance genes. Mining of new wheat rust resistance genes would always promote our capability to fight against these devastating pathogens.

New technologies, especially the novel biotechnology, will assist to defend wheat rusts. The techniques, such as the molecular-assisted selection, and gene-editing technology have been applied to help breed disease resistant wheat cultivars, including wheat stripe rust (Li et al. 2022 ; Wang et al. 2022b ). MAS breeding is not only shortening the breeding procedure but also can rapidly locate the resistance genes for further pyramiding multi-resistance genes in a given variety. Multi-resistance gene wheat cultivars possess the merit of broad disease resistance spectrum, which can be generated by introducing the all-stage resistance genes.

In addition, management of alternate hosts is important for reducing the generation of new wheat rust races. Eradication of barberry bushes has been confirmed as an effective long-term control of wheat stem rust in the United States (Roelfs 1982 ). In China, the barberry species are abundant and widespread and are often observed in spring, even autumn-wheat planting regions (Du et al. 2022 ). Therefore, controlling the barberry rust infection by applying fungicides timely prior to the early stage of pycnial development has been successful in interrupting sexual cycle of rust pathogens.

Investigation of the avirulence genes in rust pathogens is essential for understanding the pathogenesis variation of the wheat rusts and for the targeted wheat breeding. Although some avirulence genes have been cloned in Pgt , such as AvrSr27 , AvrSr35 , and AvrSr50 (Chen et al. 2017 ; Salcedo et al. 2017 ; Upadhyaya et al. 2021 ), none of the avirulence genes in Pst and Pt has been cloned so far. Therefore, identification of avirulence genes of Pst and Pt and more avirulence genes of Pgt should be taken into consideration.

Conclusions

Wheat stripe, leaf, and stem rusts are destructive fungal diseases on wheat in China. Their spores can travel a long distance by wind. Severe epidemics of the three wheat rust diseases frequently occurred and have resulted in huge yield and economic losses. Strategies for the management of the wheat rusts have been made, which has achieved the effective control on wheat rusts in China, especially the wheat stem rust. Recently, new research progresses have been achieved on the control of wheat rusts. Herein, we summarized the rust epidemics, fungicide-resistance and the agricultural managements in China. With the aids of new bio-technologies, we are confident to fully control the wheat rust epidemics in China in the near future.

Availability of data and materials

Not applicable.

Abbreviations

Puccinia graminis f. sp. tritici

Puccinia striiformis f. sp. tritici

Puccinia triticina

Chinese yellow rust

Formae specialis

Yellow rust

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This study was financially supported by the National Key Research and Development Program of China (2021YFD1401000), the National Natural Science Foundation of China (32072358, 31871918, 32272507, and 31071641), the Earmarked Fund for CARS-03, the Natural Science Basic Research Plan in Shaanxi Province of China (2020JZ-15), and National ‘111 Plan’ (BP0719026).

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Zhao, J., Kang, Z. Fighting wheat rusts in China: a look back and into the future. Phytopathol Res 5 , 6 (2023). https://doi.org/10.1186/s42483-023-00159-z

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Insights into Diversity, Distribution, and Systematics of Rust Genus Puccinia

Shubhi avasthi.

1 School of Studies in Botany, Jiwaji University, Gwalior 474011, India; [email protected]

Ajay Kumar Gautam

2 School of Agriculture, Abhilashi University, Mandi 175028, India

3 Patanjali Herbal Research Department, Patanjali Research Institute, Haridwar 249405, India; [email protected]

Mekala Niranjan

4 Department of Botany, Rajiv Gandhi University, Rono Hills, Doimukh, Itanagar 791112, India; moc.liamg@634ureen

5 Fungal Biotechnology Lab, Department of Biotechnology, School of Life Sciences, Pondicherry University, Kalapet 605014, India

Rajnish Kumar Verma

6 Department of Plant Pathology, Punjab Agricultural University, Ludhiana 141004, India; moc.liamg@5891hsinjaramrev

Samantha C. Karunarathna

7 Center for Yunnan Plateau Biological Resources Protection and Utilization, College of Biological Resource and Food Engineering, Qujing Normal University, Qujing 655011, China; moc.liamg@anhtaranurakahtnamas

8 National Institute of Fundamental Studies (NIFS), Hantana Road, Kandy 20000, Sri Lanka

Ashwani Kumar

Nakarin suwannarach.

9 Research Center of Microbial Diversity and Sustainable Utilization, Chiang Mai University, Chiang Mai 50200, Thailand

10 Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

Associated Data

Not applicable.

Puccinia , which comprises 4000 species, is the largest genus of rust fungi and one of the destructive plant pathogenic rust genera that are reported to infect both agricultural and nonagricultural plants with severe illnesses. The presence of bi-celled teliospores is one of the major features of these rust fungi that differentiated them from Uromyces , which is another largest genus of rust fungi. In the present study, an overview of the current knowledge on the general taxonomy and ecology of the rust genus Puccinia is presented. The status of the molecular identification of this genus along with updated species numbers and their current statuses in the 21st century are also presented, in addition to their threats to both agricultural and nonagricultural plants. Furthermore, a phylogenetic analysis based on ITS and LSU DNA sequence data available in GenBank and the published literature was performed to examine the intergeneric relationships of Puccinia . The obtained results revealed the worldwide distribution of Puccinia . Compared with other nations, a reasonable increase in research publications over the current century was demonstrated in Asian countries. The plant families Asteraceae and Poaceae were observed as the most infected in the 21st century. The phylogenetic studies of the LSU and ITS sequence data revealed the polyphyletic nature of Puccinia. In addition, the presences of too short, too lengthy, and incomplete sequences in the NCBI database demonstrate the need for extensive DNA-based analyses for a better understanding of the taxonomic placement of Puccinia .

1. Introduction

Puccinia Pers. is an obligatory plant pathogenic genus of rust fungi that belongs to the family Pucciniaceae of the order Pucciniales ( Basidiomycota ). It is the largest genus of rust fungi, containing about 4000 species [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], and it has a worldwide distribution. The recently published manuscript “The Outline of Fungi” provides a total of 3300 accepted species of Puccinia [ 8 ]. This genus of rust fungi is reported to infect a variety of plant hosts found in all land areas except the polar regions. Although various species of Puccinia parasitize large groups of vascular plants, the members of the plant families Asteraceae , Cyperaceae , Fabaceae , Liliaceae , Malvaceae , and Poaceae serve as hosts for a large number of them. A rust genus is a group of plant pathogenic fungi that are responsible for serious diseases in both agricultural (e.g., wheat, barley, and oats) and nonagricultural crops (e.g., Cynodon sp., Fagopyrum sp., Grewia sp., Parthenium sp., and Rubia sp.) [ 1 ]. Because of the recognition and importance of the species of Puccinia as global rust pathogens, this rust genus has a well-defined history. These rust pathogens have been reported to cause several globally important plant diseases, such as black stem rust and leaf brown rust of small grains and other grasses, stripe or yellow rust of wheat and other grasses, crown rusts of oats and other grasses and rust of common corn, sugarcane, sunflower, safflower, cotton, asparagus, mint, snapdragon, hollyhock, and many more. Due to the involvement of humans everywhere, their activities, along with other physical and biological agents, may promote the global spread of many rust fungi from unknown centers of origin.

The species of Puccinia often cause severe losses especially in cereals and gramineous crops across the globe. These are obligate parasites that spread through spores and infect the aerial parts of the host. This spread and further infection are sometimes complete on a single host, or another host is required to complete the life cycle of the rust fungus. Therefore, in nature, some species of Puccinia are autoecious (the life cycle is completed on a single species of the host), while others are heteroecious (two host plant species are required to complete the life cycle). The life cycle of Puccinia spp. is more complex, compared with those of other plant pathogenic fungi because they have different spore stages (up to five) infecting single or two taxonomically different plant hosts. A maximum of five spore types can be produced by these fungi, depending on the species, environment, and season. The fungi start infection with the formation of asexual urediniospores on the main host (primary infection), which further infect the neighboring plants (secondary infection) of the same host plant. The infection by these urediniospores generally occurs during summer, while sexual teliospores are normally produced near the end of the season and overwinter on plant debris. The teliospores then germinate in the spring season and produce basidiospores, which ultimately disseminate all over again and start infecting the secondary or alternate host (for heteroecious rusts), or start the infection of the same host (for autoecious rusts). Here, the spore types, namely, pycniospores, are produced within pycnia, and, later, aeciospores are generally produced and complete the life cycles of these fungi. However, the produced aeciospores are dispersed by different dispersal agents and infect the primary host once again. Some species of Puccinia form all five spore types, which are known as macrocyclic species, while species that lack urediniospores are demicyclic, species that lack pycniospores and aeciospores are hemicyclic, and species that lack pycniospores, aeciospores, and urediniospores are microcyclic [ 1 , 9 , 10 ].

Puccinia species infect nearly every category of plants; however, the species that cause rusts on cereals are the most economically important. Many serious diseases are caused by the species of Puccinia (e.g., Puccinia coronata infects mainly oats; P. graminis infects mainly wheat, barley, and oats; P. helianthi infects sunflower; P. hordei infects barley; P. purpurea infects sorghum; P. melanocephala infects sugarcane; P. recondita infects mainly rye; P. sorghi infects maize; P. striiformis infects mainly wheat and barley; P. triticina infects wheat; and P. malvacearum infects hollyhock). Of all the wheat rust diseases, Puccinia graminis , P. triticina , and P. striiformis cause the rust of wheat, barley, and rye stem, leaves, and grains, primarily occurring in most wheat-growing areas all over the world. They cause severe seasonal diseases in India, and they generate serious outbreaks in North America, Mexico, and South America. Puccinia graminis , the original species of Puccinia , was examined as a biological warfare agent during the Cold War in addition to being studied as a plant pathogen [ 11 ]. The present overview sheds light on the current status of the genus Puccinia , with a special reference to up-to-date information on the numbers of species, trends in the last decade, asexual and sexual states, and molecular studies. Other aspects based on diversity and distribution, are also discussed to provide an understanding of the complete distributional range of these fungi.

2. The Genus Puccinia : General Taxonomy and Ecology

  • Kingdom: Fungi
  • Division: Basidiomycota
  • Class: Pucciniomycetes
  • Order: Pucciniales
  • Family: Pucciniaceae
  • Genus: Puccinia Pers. (1801)
  • Type species: Puccinia graminis Pers. (1794)

Rusts are obligate parasites that show phenotypic and genetic plasticity because of their complete dependency on the presence of living host plants to complete their life cycles [ 1 , 12 ]. These fungi show unique systematic characteristics by producing interesting morphologically and cytologically distinct spore-producing structures, which have attracted the interest of mycologists for centuries. The species of Puccinia may produce up to five morphologically and cytologically distinct spore-producing structures. The production of these distinctive structures differentiates these rust fungi from other fungal groups. These structures are produced by the species of Puccinia in the infection process of the host-pathogen interaction. These diverse structures are generally the successive stages of reproduction produced by rust fungi, and they may vary from species to species. These fungi commonly appear as yellow-orange or brown pustules on healthy and vigorously growing plant parts, such as leaves, petioles, tender shoots, stems, and fruits. The infection pustules are often associated with chlorotic lesions, which may cause the premature wilt and senesce of infected leaves in cases of severe infection. The spore-producing pustules are present as solitary, scattered, or aggregated in groups, arranged linearly, concentrically, or irregularly, and are often erumpent. The spore-producing structures of the species of Puccinia are the basic spore states, which are generally recognized as spermogonium, aecium, uredinium, telium, and basidium. These states are generally assigned Roman numerals (0, I, II, III, and IV, respectively) during the taxonomic description of rust fungi, including Puccinia spp. Only a few species of Puccinia , such as P. vexans Farl., produce six morphologically and functionally different spore stages. Besides the production of regular pale-colored urediniospores, this species produces thick-walled dark-pigmented urediniospores called amphispores. Two different systems are generally suggested, morphology and ontogeny, and they have been applied in the definition and terminology of the spore states of rust. It is important to discuss this aspect here because the terminology used to describe the biology of Puccinia spp., including the morphology and life cycle, are also the same as that used for all rust fungi. The spore morphology is generally considered the basis for defining the spore states [ 13 , 14 ]. The spore definitions based on this system are as follows:

Aeciospores: defined as being produced in chains and with ornamentation that is traditionally known as verrucose;

Urediniospores: defined as being always unicellular and borne singly on pedicels, and usually with ornamentation that is traditionally known as echinulate [ 15 ].

In the ontogenic system, the position of the spore states in the life cycle rather than the morphology is utilized for defining the spore terminology [ 16 , 17 , 18 , 19 ]. The general descriptions of these diverse spores as they are produced by rust fungi (including Puccinia spp.) are as follows:

Teliospores: spores that produce basidia (probasidia and hypobasidia);

Basidiospores: spores produced on basidia and are haploid and frequently binucleate, but that are not dikaryotic spores;

Spermatia: dikaryotizing elements;

Aeciospores: dikaryotic nonrepeating spores that are produced in sori typically associated with spermogonia, and that give birth to dikaryotic vegetative mycelia;

Urediniospores: repeated dikaryotic mycelia that typically give rise to dikaryotic mycelia on the same host, and that are sometimes referred to as uredospores or urediospores.

The introduction of a taxonomic grouping as Forma specialis (plural formae speciales ) is allowed by the International Code of Botanical Nomenclature (ICBN). In the case of fungi, it is applied a taxonomic grouping. It is generally adapted when authors do not feel a subspecies or variety name is appropriate. For example, Puccinia striiformis Westend. consists of several formae speciales based on host specialization, including P. striiformis f.sp. tritici , P. striiformis f.sp. hordei , P. striiformis f.sp. elymi , P. striiformis f.sp. agropyri , and P. striiformis f.sp. secalis . Among the five forms of P. striiformis , the sexual stage was confirmed only for the wheat form of the rust P. striiformis f.sp. tritici , but not known for the other four forms. Based on the host specificity, the numbers of Forma specialis of Puccinia graminis such as P. graminis f.sp. avenae , P. graminis f.sp. secalis , and P. graminis f.sp. tritici ,. are available, which were proved to be helpful in the taxonomy of rust fungi including the genus Puccinia [ 1 , 2 , 3 ].

It is believed that the rust pustules of uredinia that are present on the stem and leaf sheath tissues often survive for a longer duration in comparison with those that are present on the leaves. In the case of spore production, the number of spores produced by leaf pustules is generally higher. Under continuous conditions, stem rust urediniospores show more resistance to atmospheric conditions than leaf rust spores [ 20 , 21 ]. The species of Puccinia are responsible for causing all possible types of rust disease symptoms by producing all five basic spore states, which are generally recognized as spermogonium, aecium, uredinium, telium, and basidium. There is great variation in the production of the spore states by the different species of this rust genus. While some species produce all the spore states, others may produce less than five. The teliosori and teliospores of different Puccinia species are presented in Figure 1 and Figure 2 , respectively.

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Occurrences of teliosori of different Puccinia species with some host plants: ( a ) Puccinia abrupta var. partheniicola on Parthenium sp.; ( b ) Puccinia cynodontis on Cynodon dactylon ; ( c ) Puccinia tiliaefolia on Grewia tiliifolia ; ( d ) Puccinia himachalensis on Clematis sp.; ( e ) Puccinia clematidis on Clematis sp.; ( f ) Puccinia colletiana on Rubia sp.; and ( g ) Puccinia fagopyri on Fagopyrum esculentum . Scale bar = 1 mm. (Photo taken by Dr. Ajay Kumar Gautam).

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Teliospores of different Puccinia species: ( a ) Puccinia colletiana from Rubia sp.; ( b ) Puccinia cynodontis from Cynodon dactylon ; ( c ) Puccinia abrupta var. partheniicola from Parthenium sp.; ( d ) Puccinia clematidis from Clematis sp.; ( e ) Puccinia himachalensis from Clematis sp.; ( f ) Puccinia tiliaefolia from Grewia tiliifolia ; ( g ) Puccinia himachalensis from Clematis grata ; and ( h ) Puccinia fagopyri from Fagopyrum esculentum . Scale bar = 10 µm. (Photo taken by Dr. Ajay Kumar Gautam).

3. Data Collection and Compilation

This paper was compiled based on the information retrieved from an extensive search of peer-reviewed publications, field guides, monographs, books, conference proceedings, project reports, dissertations, theses, and other offline and online resources. Information based on taxonomic studies, checklists, and new reports, as well as reports on the new taxa, was generally considered in the compilation of this study. The scientific names of the hosts and fungi were then cross-verified for scientific entities. The Plant List ( http://www.theplantlist.org , accessed on 20 April 2022) was consulted for the verification of the host plant names, and the fungal databases MycoBank ( www.mycobank.org ; accessed on 20 April 2022), Species Fungorum ( www.speciesfungorum.org ; accessed on 20 April 2022), and IndexFungorum ( www.indexufngorum.org ; accessed on 20 April 2022) were consulted to gather information on the current fungal names, numbers, and distributions. Fungal Databases, US National Fungus Collections, ARS, and USDA, which are important online sources of plant pathogens and their hosts, were also used during the compilation. To understand the general trend of the outline and a higher-rank classification of Puccinia , publications such as [ 1 , 4 , 5 , 6 , 8 , 22 , 23 ] were consulted. An attempt was made to summarize all the collected information in the form of the current statuses of the species numbers, their distributions with respect to hosts, and the trends of the published literature in the last century and decade. The publication indices of Puccinia spp. in terms of year, decade, and century were also analyzed and are presented in this paper. In addition, the references in other languages were translated into English so that the scientific community could easily understand them. In addition, the role of Puccinia as a threat to biodiversity is also discussed on a global scale in the present paper. A short discussion on the limitations to the current knowledge of Puccina and future recommendations is also presented here.

4. Phylogenetic Analyses

We worked on the Puccinia species phylogeny, and the NCBI search showed 292,000 Puccinia hits, of which most were repeated and whole-genome sequences. An NCBI search with “ Puccinia and type” showed 52,446 sequence results, of which 13 sequences were found to be type sequences. We chose Index Fungorum to search for species deposited more than two decades between 2000 and 2022 (till July), for which a total of 228 species were found. From the above two sources, we collected the ITS (69), LSU (65), SSU (15), cytochrome oxidase COX (9), TUB (8), RPB2 (1), and TEF1 sequences. Because most regions have a small number of sequences, we selected the ITS and LSU sequences to construct the multigene phylogeny. The DNA sequence data of the Puccinia species from the LSU and ITS rDNA were downloaded from GenBank and earlier published literature. Individual nucleotide sequences of the LSU and ITS were distinctly aligned using the MAFFT v7.450 online server ( https://mafft.cbrc.jp/alignment/server/ ; accessed on 20 April 2022) and exported to aligned sequence data [ 24 ], and they were then manually checked and edited where necessary in BioEdit v.7.0.9 [ 25 ]. The sequences of taxa containing weakly aligned portions, incomplete data, missing sequence data, and gaps were removed. The separate aligned gene regions of the LSU and ITS were combined in BioEdit. The combined multigene sequence alignment was converted to the PHYLIP format (.phy) using the alignment transformation environment ( http://sing.ei.uvigo.es/ALTER/ ; accessed on 20 April 2022) for randomized accelerated maximum likelihood (RAxML) analysis. The aligned LSU and ITS single-gene datasets and a concatenated dataset of LSU and ITS genes were analyzed with maximum likelihood using the RAxML-HPC2 on XSEDE (8.2.8) [ 26 , 27 ] on the CIPRES Science Gateway platform [ 28 ] using the GTR + I + G model of evolution. Maximum likelihood bootstrap values equal to or greater than 70% were given above each node. Phylogenetic trees were visualized with the FigTree v.1.4.0 program [ 29 ] and reorganized in Microsoft PowerPoint. A checklist of molecular studies on Puccinia spp., along with the names of the isolates, was also prepared and is presented in Table 1 .

GenBank and voucher/culture collection accession numbers of Puccinia species included in phylogenetic analyses.

Most of the Puccinia species were identified based on the morphology and microscopic characteristics of the uredia and telia, or based on other successive stages observed on the collected samples. The identification of this rust genus based on molecular parameters is not up to the mark and still requires extensive studies. In the phylogenetic results, the Puccinia species were separated into two complexes in both the ITS and LSU sequence data. Both complexes of the ITS and LSU share many similar sequences. The incomplete sequences were mostly found in the Puccinia sequence dataset (e.g., ITS1 and 5.8S or ITS1, 5.8S complete, and ITS partial or 28S partial). Approximately, 50% of the sequences had up to 300 nucleotides, while the remaining sequences had up to 800 nucleotides. Incomplete sequences can result in two complexes in a single genus. Therefore, complete gene sequences from the ITS and LSU are needed to analyze these complex clades. The phylogenetic analyses exposed the polyphyletic nature of this genus, which requires further DNA-based analyses of the rust disease caused by Puccinia to develop a better understanding of its taxonomic placement. The genus Uromyces also showed a polyphyletic nature during a study carried out by the authors of [ 30 ]. A study carried out by the authors of [ 6 , 31 , 32 ] also confirmed the polyphyletic nature of the rust genus Puccinia ( Figure 3 ).

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Multigene phylogeny of Puccinia species. Maximum likelihood tree alignment of 134 reference ITS and LSU sequences along with outgroup. Alignment was performed with MAFFT v.7.450 online server ( https://mafft.cbrc.jp/alignment/server/ ; accessed on 20 April 2022) exported to aligned sequence data. Sequences were edited in Change the FASTA to PHYLIP format ( http://www.sing-group.org/ALTER/ ; accessed on 20 April 2022). The RAxML-HPC2 in XSEDE (version 8.2.8) [ 27 ] on the CIPRES Science Gateway platform [ 28 ] was used with the GTR + I + G evolution model. The phylograms were generated by FigTree v.1.4.0 [ 29 ] and were reorganized in Microsoft PowerPoint. Tree nodes represent ≥70% bootstrap values. Scale bar represents number of substitutions expected per site. The tree is rooted with Helicobasidium purpureum CBS 163.24. GenBank accession numbers are listed in Table 1 . RAxML analysis yielded a minimum scoring tree with a final ML optimization likelihood value of −12,532.792474. The matrix had 834 distinct alignment patterns, with 47.11% indeterminate characters or gaps. The estimated base frequencies were as follows: A = 0.315347; C = 0.157275; G = 0.229122; T = 0.298256; substitution rate AC = 1.494199; AG = 2.855095; AT = 1.851749; CG = 0.513805; CT = 4.845757; and GT = 1.000000. Proportion of invariable sites: I = 0.127613; and gamma distribution shape parameter: α = 0.463021.

5. Current Status of Numbers of Species

The occurrence of the rust genus Puccinia is considered cosmopolitan. All the continents, except Antarctica, show the presence of many species of the rust genus Puccinia . The genus is one of the broadly studied rust genera, and the fungi of this genus also possess broad host ranges and distributions. Nearly all categories of plants belonging to approximately all the families have been found to be infected with these fungi. Similar to the trends of the occurrences of different rust fungi on their hosts, the occurrence of Puccinia rust has also been reported to be the most profuse on plant hosts that belong to the families Asteraceae , Poaceae , and Ranunculaceae . When we came across the number of research papers previously published by several researchers, a similar trend of the occurrence of Puccinia rust was observed. Similarly, plant families such as Apiaceae , Polygonaceae , Rubiaceae , Cyperaceae , Acanthaceae , Berberidaceae , Lamiaceae , and Saxifragaceae are among the most infected plant families with Puccinia rust; however, the infection and host range of Puccinia rust is not only limited to these plant families. A tentative distribution of Puccinia rust in the major plant families is summarized and presented in Figure 4 . When we talk about the species boundaries of Puccinia rust, a total of 5450 epithets are available on Index Fungorum ( www.indexfungorum.com ; accessed on 20 April 2022). However, a total of 3300 species of Puccinia have been reported all over the world on a variety of hosts [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 22 , 33 ].

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General distribution of Puccinia rust on plant host families (based on literature).

6. Trends in Published Literature

In this section, the research on Puccinia rust reported and published in various journals is presented. A total of 988 papers were published from the year 1794 to 2020, and they were included in the present study to understand the general publication trend. The data from different online platforms, as well as offline resources, were retrieved to compile the information on these rust fungi. To understand the decadal trend, the numbers of publications were counted per ten years and are presented according to century and some more criteria. Publications on the new records, reports, and taxa (genus/species) were generally included in this analysis and are presented in Table 2 .

Decadal and centurial trends of published literature on Puccinia .

The results revealed that the research on Puccinia rust was quite encouraging during the 19th and 20th centuries. A total of 277 papers were published during the 19th century, while 627 were published during the 20th century. The trend of research publications for the current century (up to 2020) has also been high in terms of both qualitative and quantitative aspects. Similarly, in the decadal analysis of the published research on Puccinia rust, the highest numbers of papers were published at the end of the 19th century and the beginning of the 20th century (121 and 144, respectively). A further trend in the decadal analysis of the number of publications on Puccinia rust during the 19th century was observed in the following order: 1891–1900 (121), 1871–1880 (61), and 1881–1890 (50). There were less than ten publications in each of the other decades. Similarly, in the 20th century, the trend of publication was as follows: 1901–1910 (144); 1911–1920 (85); 1951–1960 (78); 1931–1940 (71); 1941–1950 (58); 1931–1940 (51); 1971–1980 (44); 1981–1990 (39); 1991–2000 (29); and 1961–1970 (28).

Morphotaxonomy alone is not enough to describe a new taxon. The molecular aspects play an important role in resolving the correct taxonomic placement of all fungi, including rust fungi. This might explain the variations in the number of publications during the current century. Not all researchers can afford a good laboratory and access to resources. Nowadays, insufficient funding is also a major constraint in the performance of basic taxonomic research.

7. Threat to Biodiversity

Rust fungi are considered one of the most serious threats to both agricultural (e.g., wheat, soybean, or coffee), and non-agricultural crops and tree species as well. Puccinia is one of the harmful biotrophic fungal genera that seriously harm major cereal crops (except rice) and nonagricultural plants all over the world. The type species of Puccinia ( Puccinia graminis ) is one of the destructive rust fungi reported to cause the mass destruction of wheat and barley stem rust (black rust). Similarly, Puccinia striiformis f. sp. tritici , wheat stripe rust (yellow rust), and P. triticina , wheat leaf rust (brown rust), are also destructive rusts that are distributed all around the globe. To understand why the species of Puccinia are a threat to biodiversity, some examples of rust diseases caused by them are presented in this section.

Puccinia graminis Pers., Neues Mag. Bot. 1: 119 (1794)

This is a macrocyclic heteroecious rust that has devastated wheat for many decades, and it is one of the most studied rust fungi. It causes the black stem rust of wheat and poses a serious threat to food security. It may cause crop losses of up to 70%. The Berberis , Berberis , Mahoberberis , and Mahonia serve as alternate hosts for this fungus. This fungus occurs in all major wheat-growing areas around the world. Puccinia graminis has also been studied in detail and has long been used as a model for studying the cytology, physiology, biochemistry, and molecular aspects of rust fungus biology [ 34 , 35 , 36 ]. Another extremely contagious race of Puccinia graminis , TTKSK (Ug99), was discovered in Uganda. Because it does not recognize any national borders and can infect fields anywhere, it poses a serious threat to wheat growers all over the world. This strain is aggressive against numerous resistance genes that have previously shielded wheat against stem rust. It can cause losses of the victim crop of up to 100%. Although there are Ug99-resistant wheat variants available, their cultivation range is not broad [ 37 , 38 , 39 , 40 ].

Puccinia striiformis Westend., Bull. Acad. R. Sci. Belg. , Cl. Sci. 21 (no. 2): 235 (1854)

This is a biotrophic and heteroecious rust pathogen that has been reported to cause yellow (stripe) rust. At least two lineages are exclusive to grasses, while one lineage primarily infects cereals. The pathogen has the widest host range within the tribe Triticeae (in the plant genera Aegilops , Elymus , Hordeum , and Triticum ) and Berberis spp. as the alternate host or sexual host. It is reported to be one of the most damaging cereal rusts compared with other rusts. This fungus reduces the photosynthetic area and the production of sugars for the plant. As the fungus mainly infects the leaf, it also causes substantial water loss while erupting the epidermis. Based on the disease severity and susceptibility of the variety, this rust can cause mild to very high losses; however, a 30% loss is common in susceptible varieties [ 41 ].

Puccinia coronata Corda, Icon. Fung. (Prague) 1: 6 (1837)

This rust is reported to cause crown rust disease in cultivated and wild oat ( Avena spp.). It infects two hosts to complete its life cycle: oat (asexual phase) and Rhamnus spp. (sexual phase) as the primary and alternate hosts, respectively. Oat crop cultivation areas with warm temperatures (20–25 °C) and high humidity are more prone to this rust epidemic. Infection by the pathogen leads to plant lodging and shriveled grains of poor quality. This rust pathogen can infect 290 species of grass hosts [ 42 , 43 , 44 ].

Puccinia psidii G. Winter, Hedwigia 23: 171 (1884)

The rust Puccinia psidii is a pathogen with a broad host range in the myrtle family ( Myrtaceae ). However, the common guava ( Psidium guajava ) and Eucalyptus spp. are at more risk, as it causes severe infection in these plants [ 45 , 46 , 47 ]. A severe infection of P. psidii was reported in Brazil, which caused damage to various members of the family Myrtaceae [ 46 , 48 , 49 ]. Similarly, this fungus causes eucalyptus rust in Australia and poses a threat to the biodiversity in this country, as well as to the eucalyptus forest industry worldwide [ 45 ]. In 2017, based on a DNA-based molecular analysis of rust samples, the names were synonymized by Beenken in a new genus as Austropuccinia psidii [ 50 ]. Apart from the abovementioned diseases caused by the rust genus Puccinia , these fungi are reported to cause several diseases on other plants. Several research and review papers are available on different online and offline platforms that describe the diversities, distributions, and host ranges of rust fungi, including Puccinia [ 31 , 32 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. A list of the species of Puccinia that cause destructive diseases in agricultural and nonagricultural crops is presented in Table 3 .

Species of Puccinia that cause destructive diseases in agricultural and nonagricultural crops.

8. Puccinia in the Present Century

To understand the status of the rust genus Puccinia in the current century, the published literature on new genera and species and new geographical records were taken into consideration, and the data obtained were compiled concerning the host, host family, yearly publication details, and distribution throughout the regions, countries, and continents around the globe. Data based on other aspects of Puccinia rust, such as physiology and biochemistry, were not considered in this study. The publication details of the last two decades of the current century reveal that a total of 82 papers on Puccinia rust were published, 42 of which were published during 2001–2010, and 40 of which were published from 2010 to the present date ( Figure 5 ).

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Trends of published literature (by year) on Puccinia during the present century.

After analyzing the yearly data, it was observed that the publication record numbers reached up to seven in many years, while the lowest number (one) was also observed for many years. Further, a total of 126 records in the form of new geographical records from 62 regions belonging to 37 countries were recorded during the last two decades. All seven continents showed distributions of the species of Puccinia . If we compare the continental distribution of these rust fungi during the current century, then the highest number of records is found in Asia (42 records from 10 countries), followed by South America (27 records in four countries), Africa (23 records in three countries), Europe (17 records in seven countries), North America (13 records in 11 countries), Oceania (six records in two countries), and Australia (with three records) ( Figure 6 ).

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Trends of published literature (by continent) on Puccinia during the present century.

The rich biodiversity and variable climatic conditions of Asia might be responsible for the occurrence of rust fungi ( Puccinia spp.) in high numbers. The same explanation is applicable to America and Africa, while the lowest reports from Oceania and Australia directly correlate with the agroclimatic conditions of these two continents. When we analyze all 126 records, only 34 species of Puccinia have been identified at the molecular level using multigene analysis. From the data obtained on the host distribution, a total of 124 plant species belonging to 90 plant genera of 34 plant families have been found to be infected with different species of Puccinia . As observed in the previous section, Asteraceae and Poaceae have been found to be highly infected with different species of Puccinia . The data on Puccinia rust during the present century is summarized and graphically presented in Figure 7 and Figure 8 .

An external file that holds a picture, illustration, etc.
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Trends of published literature (by family) on Puccinia during the present century.

An external file that holds a picture, illustration, etc.
Object name is jof-09-00639-g008.jpg

Map showing the occurrence of Puccinia rust in the 21st century. Color indicators: green color indicates minimum values (0–5); yellow and orange medium values (10–15); and red color maximum values (15–20).

9. Limitations of Current Knowledge

The current research on rust fungi is mainly based on morpho-taxonomy (the morphologies of the shapes and sizes of certain spore stages), while, the inclusion of recent technologies, and specifically DNA-based techniques, brings a new turn to the taxonomy of rust fungi. When it is not possible to differentiate two similar fungal species based on their morphological characteristics, molecular techniques can be used to successfully differentiate them, even at the difference of one base pair of nucleotides. However, few studies are based on the use of modern instruments and molecular-based techniques for fungal taxonomy, and specifically DNA-based techniques. The fundamental reasons for the slow adoption of molecular techniques in taxonomic studies on the rust fungi of Puccinia are the same as those that apply to all other rust fungi. When studying the species of Puccinia , mycologists encounter several problems/limitations, including the lack of necessary databases, resources, and funding, the reduced interest of budding researchers, and the pricey services offered by many agencies. To understand these limitations, a detailed explanation is given below:

  • Apart from the availability of 5000 species of Puccinia , only a few species are known to have DNA sequence data. The difficulty in culturing rust fungi is one of the possible reasons behind the reduced availability of molecular data. Further, DNA isolation directly from rust fungi present on a natural host and further processing are not easy tasks. In addition, the available sequences are not up to the mark. While others are too long, some sequences are too short, and some are incomplete. While some sequences are up to 300 nucleotides long, others are up to 800 nucleotides long, reflecting the intricacy of their taxonomic evaluation. Incomplete sequences in the Puccinia sequence dataset can result in two complexes in a single genus. Therefore, to investigate these complicated clades, entire gene sequences are required. The phylogenetic studies revealed the polyphyletic nature of the species of Puccinia , which require more DNA-based analyses for a better understanding of their taxonomic placement. The nonavailability of molecular data for all collections of Puccinia all over the globe is another limitation that highlights the requirement for fresh collections of Puccinia species and their molecular characterizations to generate molecular data so that their phylogenetic relationships can be explained more precisely. Although country-wide databases of rust fungi are available on various online platforms, the lack of a universal platform exclusively for global rust fungi is also a major limitation in the research on rust fungi, including Puccinia .
  • When we talk about the day-by-day decreased interest of budding scientists/researchers in the field of the basic taxonomy of fungi, the reasons behind this are complex, such as insufficient funds, expensive outsourced mycological services, and, overall, the difficulty in publishing taxonomies in high-impact journals without modern techniques. These issues are leading to the decreased interest of mycologists in fungal taxonomy, which is ultimately decreasing the number of fungal taxonomists [ 32 ].
  • Publication in high-impact journals has now become a criterion to assess the quality of research and the performance of researchers/scientists/academicians, or to appraise whether they should be promoted. However, taxonomy based on DNA-based molecular techniques has now become a minimum criterion to process any submitted manuscript, even for initial review. Luckily, few journals are still focusing on the novelty of the research, and most are now considering fungal manuscripts that are purely based on morpho-taxonomy.
  • Despite being less expensive to support than applied research, basic fungus research is no longer prioritized for funding. This scenario is common in developing countries. The fundamental inventorying and identification of fungi is not everyone’s cup of tea, similar to obtaining funding for applied research (ideally in biotechnology). Due to a lack of sufficient funding, laboratories working on taxonomic studies of fungi continue to lack current equipment (e.g., that which is used in DNA isolation, amplification (PCR), and sequencing), and they are gradually turning their attention to the practical elements of the field. Additionally, not every mycologist can afford the service fees for the molecular techniques offered by many agencies/institutions of national and worldwide reputation, and particularly researchers working on a self-finance basis.

10. Conclusions

In conclusion, it was observed that Puccinia is the largest genus of rust fungi that infect a wide variety of host plants of both agricultural and nonagricultural importance. The genus shows variation in its diversity and distribution worldwide. Based on compiled data, a total of 5450 epithets (3300 species) are available [ 70 ]. In the evaluation of the host distribution, plant families such as Asteraceae and Poaceae were found to be highly infected with different species of Puccinia . The analyses of the trends in the published literature on rust showed that researchers from Asian countries are among those who have published the highest numbers of papers on all the continents. The NCBI search showed 292,000 hits of repeated and whole-genome sequences. While some sequences are too short, others are too lengthy, and some are incomplete. It was also observed that the taxonomic statuses of a number of Puccinia spp. are still unclear, as only morpho-taxonomic traits have been used to identify the majority of them. Moreover, molecular data on most Puccinia spp. are not available so far, and their taxonomic placement is still doubtful; hence, they are classified as incertae sedis . This generates a potential area of research interest for both current and future mycologists. Similarly, the generic names of many Puccinia spp. have been changed or transferred to different genera; however, the incorporation of this revision is still required in their original collections (types or records). Furthermore, the phylogenetic analyses exposed the polyphyletic nature of this genus, which requires further DNA-based analyses of the rust disease caused by Puccinia to develop a better understanding of its taxonomic placement. A study carried out by the authors of [ 6 , 31 , 32 ] also confirmed the polyphyletic nature of the rust genus Puccinia . Therefore, fresh collections of Puccinia species and their molecular characterizations to generate molecular data are highly recommended so that their phylogenetic relationships can be explained more precisely. All these limitations generate excellent opportunities for mycologists to explore this rust genus based on morpho-taxonomy and molecular data to determine and confirm the taxonomic positions of its species. Additionally, the development of a universal digital platform exclusively for global rust fungi is also recommended in the present study so that researchers who are working on this specific group of fungi can take advantage of this information in one place.

Acknowledgments

The authors thank their respective organizations for providing the necessary laboratory facilities and valuable support during the study.

Funding Statement

The authors gratefully acknowledge the financial support provided by Chiang Mai University, Thailand. Samantha C. Karunarathna thanks the National Natural Science Foundation of China (NSFC 32260004).

Author Contributions

Conceptualization, S.A. and A.K.G.; methodology, A.K.G., S.A. and M.N.; software, R.K.V., A.K.G., A.K. and M.N.; validation, A.K.G., R.K.V., N.S. and S.C.K.; formal analysis, A.K.G., M.N. and N.S.; investigation, A.K.G., R.K.V., N.S. and S.C.K.; resources, A.K.G., R.K.V. and N.S.; data curation, A.K.G., R.K.V., M.N. and N.S.; writing—original draft preparation, A.K.G., R.K.V., N.S., S.A. and S.C.K.; writing—review and editing, A.K.G., N.S., S.C.K. and A.K.G.; visualization, N.S., R.K.V. and A.K.G.; supervision, A.K.G. and N.S.; project administration, A.K.G., R.K.V., M.N. and N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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  • Published: 11 March 2024

Effects of dietary intervention on human diseases: molecular mechanisms and therapeutic potential

  • Yu-Ling Xiao   ORCID: orcid.org/0000-0002-3684-0816 1 , 2   na1 ,
  • Yue Gong 1 , 2   na1 ,
  • Ying-Jia Qi   ORCID: orcid.org/0009-0006-9878-4019 1 , 2   na1 ,
  • Zhi-Ming Shao 1 , 2 &
  • Yi-Zhou Jiang 1 , 2  

Signal Transduction and Targeted Therapy volume  9 , Article number:  59 ( 2024 ) Cite this article

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  • Cancer imaging
  • Cancer metabolism

Immunotherapy

Diet, serving as a vital source of nutrients, exerts a profound influence on human health and disease progression. Recently, dietary interventions have emerged as promising adjunctive treatment strategies not only for cancer but also for neurodegenerative diseases, autoimmune diseases, cardiovascular diseases, and metabolic disorders. These interventions have demonstrated substantial potential in modulating metabolism, disease trajectory, and therapeutic responses. Metabolic reprogramming is a hallmark of malignant progression, and a deeper understanding of this phenomenon in tumors and its effects on immune regulation is a significant challenge that impedes cancer eradication. Dietary intake, as a key environmental factor, can influence tumor metabolism. Emerging evidence indicates that dietary interventions might affect the nutrient availability in tumors, thereby increasing the efficacy of cancer treatments. However, the intricate interplay between dietary interventions and the pathogenesis of cancer and other diseases is complex. Despite encouraging results, the mechanisms underlying diet-based therapeutic strategies remain largely unexplored, often resulting in underutilization in disease management. In this review, we aim to illuminate the potential effects of various dietary interventions, including calorie restriction, fasting-mimicking diet, ketogenic diet, protein restriction diet, high-salt diet, high-fat diet, and high-fiber diet, on cancer and the aforementioned diseases. We explore the multifaceted impacts of these dietary interventions, encompassing their immunomodulatory effects, other biological impacts, and underlying molecular mechanisms. This review offers valuable insights into the potential application of these dietary interventions as adjunctive therapies in disease management.

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Introduction

Nutrients play a crucial role in regulating various physiological processes. 1 The main source of nutrients is usually considered to be diet. The quantity, quality, and composition of the food consumed, as well as the timing of meals, directly impact human health by influencing the availability of nutrients. 2 Although there have been advancements in understanding the link between diet and disease in recent years, there is still much to learn about how specific dietary components affect disease risk and prevention. 3

Epidemiological studies have linked various dietary patterns to cancer and other diseases. 4 For instance, diets high in saturated fats and sugars have been associated with an increased risk of cardiovascular diseases (CVD) and type 2 diabetes. 5 Conversely, diets rich in fiber, fruits, and vegetables are associated with a lower risk of these conditions. 6 Similarly, conditions such as osteoporosis and certain neurological disorders have also shown links to dietary patterns, highlighting the broad influence of diet on overall health. 7 , 8 In the context of cancer, increased consumption of alcohol and red or processed meat is associated with a heightened risk of cancer, whereas adherence to a Mediterranean dietary pattern—characterized by high intake of fruits, vegetables, whole grains, legumes, fish, and olive oil, along with moderate consumption of dairy products such as yogurt—may confer protective effects against carcinogenesis. 9 , 10 Similarly, a strong adherence to the plant-based Paleolithic diet and a Paleolithic-like lifestyle has been found to significantly reduce the risk of colorectal cancer (CRC), especially in individuals with a body mass index (BMI) less than 30. 11 Although many cancer patients are interested in using dietary intervention to improve cancer therapy outcomes or even using it as a key component of the therapeutic process, 12 there is currently no solid evidence showing that any nutrition-related regimen can be a primary treatment for cancer. 13 However, preclinical studies suggest that calorie and energy restrictions can hinder tumor growth and progression and increase the efficacy of chemotherapy and radiotherapy. 14 , 15 A rising number of clinical trials are exploring the impact of dietary interventions or nutritional supplements in conjunction with standard antitumor therapies, with some showing clinical benefits. 16 , 17

Diet is a crucial source of nutrients for tumors and has emerged as a key component in determining whole-body metabolism. 18 The nutrients in the tumor microenvironment (TME) largely regulate tumor cell and immune cell metabolism. 19 Recent evidence suggests that metabolic reprogramming, a crucial hallmark of cancer, involves several metabolic adaptations by tumor cells to sustain proliferation and metastasis in the TME. 19 , 20 , 21 The TME constitutes a multifaceted and dynamic ecosystem comprising an assortment of cell types, including tumor cells, immune cells, and stromal cells, in addition to components of the extracellular matrix. The interplay among these constituents, along with the challenging environmental conditions, exerts a significant influence on the growth trajectory and progression of tumors. 22 For example, oxygen levels within the TME can vary due to increased metabolic demand from rapidly proliferating tumor cells, resulting in low oxygen tension, known as hypoxia, in tissues. In addition, nutrient availability, including the availability of glucose, fatty acids, and amino acids, can vary within the TME, impacting metabolic processes and energy production. The accumulation of metabolic waste products and alterations in pH can further contribute to a hostile TME, which can impair immune function and promote tumor progression. 23 These factors, along with dynamic interactions within the TME, play crucial roles in influencing tumor proliferation and the effectiveness of antitumor immune responses. 24

As our understanding of the complex relationships between diet, metabolic reprogramming, and various diseases continues to evolve, it becomes increasingly evident that dietary components and patterns significantly influence disease risk, prevention, and progression. This review delves into the unique metabolic characteristics and nutrient availability of tumors. Furthermore, we investigate recent evidence and emerging trends concerning the effects of dietary interventions on both cancer and other diseases, underscoring the potential therapeutic benefits these dietary strategies may offer to a wide range of patients (Fig. 1 ).

figure 1

Overview of the relationship between dietary interventions and diseases. The cellular microenvironment, including the tumor microenvironment (TME), plays a crucial role in disease biology, and diet serves as a vital source of nutrients that can influence these microenvironments. Metabolic reprogramming, a prominent feature associated with disease progression, can affect cell metabolism and immune function. Dietary interventions, such as caloric restriction (CR), fasting-mimicking diet (FMD), and ketogenic diet (KD), can modulate the progression and treatment sensitivity of various diseases, including cancer. Additionally, dietary interventions can alter the composition and functional capacity of the gut microbiome, thereby indirectly influencing the progression and treatment of diseases. These direct and indirect effects of dietary interventions can influence metabolic reprogramming, modulate immune responses, and potentially enhance the clinical efficacy of treatments for various diseases. This figure was created with BioRender.com

Metabolic characteristics and nutrient availability in the tumor

Cellular metabolism encompasses a complex array of biochemical reactions that utilize specific nutrients, including carbohydrates, fatty acids, and amino acids. These nutrients are the primary sources for maintaining energy homeostasis and synthesizing macromolecules. 25 Our focus here is on cancer metabolism, which differs from that in corresponding healthy tissues in terms of nutrient levels and metabolic demands. 26 Within the TME, cancer cells can establish an immunosuppressive metabolic microenvironment by depriving immune cells of vital metabolites such as glucose and oxygen while also elevating the levels of mediators such as lactate and adenosine that limit the function of immune cells. 27 Therefore, different subsets of immune cells undergo metabolic reprogramming in tumors, and specific nutrients are required for these metabolic programs. 28 , 29 Generally, the metabolic programs that play vital roles in immune cells include glycolysis, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), the pentose phosphate pathway (PPP), fatty acid oxidation (FAO), fatty acid synthesis (FAS) and the amino acid metabolic pathway 30 (Fig. 2 ).

figure 2

Major metabolic pathways associated with different immune cell subtypes within the tumor microenvironment (TME). Summary of the main metabolic pathways of immune cells, highlighting the distinctive metabolic characteristics and requirements of different subsets of immune cells. This figure was created with BioRender.com

Glucose metabolism

Glucose serves as a vital energy source, facilitating the functioning of immune cells. Once transported across the plasma membrane, glucose is metabolically processed via three distinct pathways: glycolysis, the PPP, and the TCA cycle. Glycolysis, which occurs in the cytosol, transforms glucose into pyruvate and lactate, simultaneously generating adenosine triphosphate (ATP). Under aerobic conditions, pyruvate is channeled into the TCA cycle, where OXPHOS occurs, yielding additional ATP. Moreover, glucose-6-phosphate, a derivative of glycolysis, fuels the PPP, culminating in the production of ribose-5-phosphate and nicotinamide adenine dinucleotide phosphate (NADPH). Recent research has indicated a marked disparity in energy consumption between immune cells in resting and activated states. 18 Although glycolysis does not generate as many ATP molecules as OXPHOS, glycolysis produces ATP more rapidly, which is important to metabolically active immune cells.

Cancer cells are characterized by their rapid proliferation, primarily fueled by the consumption of glucose as an energy source. Intriguingly, these cells continue to rely on glycolysis for energy production even in the presence of ample oxygen, a phenomenon referred to as the “Warburg effect”. 31 This unique phenomenon leads to glucose depletion and lactic acid (LA) accumulation in the microenvironment, ultimately inhibiting antitumor responses. 32 High glycolytic rates in triple-negative breast cancer cells promote the infiltration of myeloid-derived suppressor cells (MDSCs) and suppress T-cell function, while suppressing glycolysis inhibits tumor colony-stimulating factor (CSF) expression and MDSC development. 33 Cancer cells produce LA through glycolysis, which reduces the antitumor activity of CD8 + T cells and natural killer (NK) cells. However, the activation of LA metabolism pathways in regulatory T cells (Tregs) is increased, and these cells adapt to high-LA conditions. 34 , 35 Furthermore, cancer cells can take advantage of immune cells by utilizing their metabolic byproducts. LA can shift tumor-associated macrophages (TAMs) from a proinflammatory (M1-like) to an anti-inflammatory (M2-like) phenotype in the TME. Notably, lactate-activated TAMs enhance cancer cell adhesion, migration, invasion in vitro, and promote metastasis in vivo. 36

T cells play crucial roles in the TME. Upon activation, these cells undergo metabolic reprogramming, which subsequently yields diverse functional outcomes. Naïve T cells, which are metabolically quiescent, exhibit basic nutrient intake rates and low glycolysis rates. They primarily generate ATP through TCA cycle-fueled OXPHOS. 37 The activation of specific membrane receptors triggers the differentiation of naïve T cells into effector T cells, also known as T eff cells. This process is accompanied by a pronounced increase in both energy demand and biosynthetic activity within T eff cells. In T eff cells, the metabolic state is changed to increasingly rely on glycolysis, as these cells upregulate GLUT1, increase glucose intake. 38 , 39 , 40 , 41 Simultaneously, this metabolic alteration benefits T eff cells by reducing their reliance on oxygen for energy production, which enables them to maintain cytokine production and cytolytic activity even when they migrate into microenvironments within solid tumors that have low oxygen levels. 42 In contrast to naïve and T eff cells, memory T cells undergo a metabolic rewiring process that leads them to enter a quiescent state characterized by elevated OXPHOS rates compared to the glycolysis rate. 43 Tregs, known for their suppressive function, exhibit decreased glycolysis rates and primarily rely on OXPHOS to support their function, while glycolysis is crucial for their migration. 44 It has been reported that the Treg-specific transcription factor FOXP3 reprograms Treg metabolism by suppressing Myc expression and glycolysis while promoting OXPHOS and NAD(H) oxidation. This adaptation enables Tregs to be more adaptable to low-glucose and/or lactate-rich microenvironments. 45

There are several other types of cells within the TME that exhibit distinct metabolic functions. In the case of NK cells, glycolysis and OXPHOS play important roles in maintaining their cytotoxicity, as indicated by the inhibition of these processes leading to diminished expression of IFNγ and Fas ligands. 46 Researchers have shown that transcription factor-controlled glucose metabolism, specifically by sterol regulatory element-binding proteins (SREBPs), which conventionally control lipid synthesis, is essential for metabolic reprogramming in activated NK cells. 47 Dendritic cells (DCs), on the other hand, rely on glycolysis and the PPP for energy production to sustain their function, including cytokine production, antigen processing and presentation, and the stimulation of T cells. 48 Furthermore, different subsets of macrophages present distinct metabolic functions. M1-like macrophages predominantly utilize anabolic metabolism, specifically glycolysis and the PPP, to generate energy and synthesize cellular components, whereas M2-like macrophages are more reliant on OXPHOS, particularly through the enhancement of FAO. 49

Lipid metabolism

Lipids, such as fatty acids, triglycerides, cholesterol, phospholipids, and sphingolipids, play crucial roles as precursors to many important biological molecules. 50 Lipids, including substances such as cholesterol and fatty acids that are widely distributed in organelles, are key components of internal cellular membranes. Moreover, lipids are essential biological molecules that provide energy during nutrient deficiency, participate in the synthesis of complex fat-containing substances, and aid in cellular signal transmission as second messengers. 51 Lipids within the microenvironment profoundly influence the proliferation of cancer cells and regulate the functional activity of immune cells.

Cancer cells undergo metabolic reprogramming of lipids in the tumor niche. The activation of adipocytes triggers the lipolysis of stored triglycerides and secretion of fatty acids. Cancer cells can then take up these fatty acids to fulfill their lipid requirements for rapid growth. 52 Research has also demonstrated that ovarian cancer cells stimulate membrane cholesterol efflux from TAMs, fostering an environment that promotes tumor growth by enhancing interleukin (IL)-4-mediated reprogramming and suppressing IFNγ-induced gene expression. The deletion of ABC transporters, responsible for cholesterol efflux, reversed the tumor-promoting functions of TAMs, leading to reduced tumor progression. 53

Furthermore, elevated cholesterol levels in the microenvironment stimulate the expression of immune checkpoints, including PD-1, 2B4, TIM-3, and LAG-3, in T cells, driving T-cell exhaustion via the activation of the endoplasmic reticulum stress response. 54 In contrast to the negative effects of reprogramming T-cell lipid metabolism on antitumor immunity, the inhibition of ACAT1, a pivotal enzyme responsible for cholesterol esterification in CD8 + T cells, results in elevated cholesterol levels in the plasma membrane. This increase subsequently amplifies TCR signaling and promotes antitumor activity. These findings highlight the complex mechanisms through which cholesterol regulates T-cell function. 55

For efficient tumor antigen processing and presentation to T cells, activated DCs need high rates of cell surface or secretory protein biosynthesis, which is partly regulated by FAS-induced increases in cytokine production. 56 T eff cells depend mainly on FAS to support inflammatory cytokine secretion and proliferation, while naïve T cells and memory T cells maintain their basic functions by increasing the FAO rate. 57 , 58 , 59 Although T eff cells rely mainly on glycolysis for energy, CD8 + T cells that undergo enhanced FAO exhibit stable antitumor functions even under conditions of low glucose and oxygen levels. By promoting fatty acid catabolism, CD8 + T cells exhibit increased functionality, and the efficacy of immunotherapy in patients with melanoma can thus increase. 60

While these studies indicate a positive influence of lipids on the functionality and metabolism of CD8 + T cells in the TME, it is important to note that alterations to T-cell lipid metabolism might attenuate their antitumoral effects. In obesity-related breast cancer murine models, the activation of STAT3 triggered an increase in FAO in CD8 + T cells, which suppressed glycolysis and weakened their tumor-suppressing ability. 61 Moreover, enhanced lipid uptake and peroxidation can result in high oxidative stress, which leads to CD8 + T cell dysfunction. CD36, a fatty acid scavenger receptor, facilitates the incorporation of arachidonic acid into CD8 + T cells. This process subsequently triggers lipid peroxidation and ferroptosis, events that cumulatively attenuate the antitumor immune response and reduce the efficacy of immunotherapy. 62 , 63 , 64

Lipid metabolism also plays an active role in regulating Treg function. Fatty acid synthase (FASN)-mediated FAS contributes to the proliferation and maturation of Tregs, and FAO provides the energy crucial for Treg infiltration into the TME. 65 Research has shown that OX40 plays a role in modifying the lipid composition of Tregs, leading to the proliferation of OX40 + Tregs in the TME. This effect is achieved through increased FAS expression and glycolysis rate in Tregs. 66 CD36, via the peroxisome proliferator-activated receptor-β (PPAR) signaling pathway, maintains the mitochondrial fitness of Tregs, promoting Treg viability and inhibitory functions. 67 SREBPs have been found to show increased activity in Tregs that infiltrate tumors. Inhibiting FAS and metabolic signaling by targeting SREBPs has been shown to effectively activate the antitumor immune response without causing autoimmune toxicity. When the SREBP-SCAP axis was inhibited, in addition to tumor growth attenuation, immunotherapy effectiveness was boosted. These findings suggest that SREBPs may be promising targets for cancer therapy. 68

High expression of FASN in TAMs promotes the accumulation of fatty acids, leading to enhanced tumor immune tolerance via the FAO pathway. 69 Notably, lipid metabolism differs between M1-like and M2-like macrophages. M1-like macrophages prevalently engage the FAS pathway, while M2-like macrophages predominantly utilize the mitochondrial FAO pathway for their bioenergetic demands. 70 , 71 Receptor-interacting protein kinase 3 (RIPK3), which is crucial for necroptosis, is found to be diminished in hepatocellular carcinoma (HCC)-associated macrophages, leading to inhibited caspase1-mediated cleavage of PPAR, a process vital for enhancing fatty acid metabolism, including FAO. This metabolic shift results in increased accumulation and polarization of M2-like macrophages in the TME, contributing to accelerated HCC growth. 72

MDSCs also exert a substantial influence in suppressing antitumor immunity in the microenvironment, and they can be categorized into monocytic MDSCs (M-MDSCs) and granulocytic MDSCs (PMN-MDSCs). 73 Tumor-infiltrating MDSCs increase fatty acid uptake and induce FAO. 74 The accumulation of lipids in MDSCs increases oxidative metabolism, resulting in MDSC acquisition of an immunosuppressive and anti-inflammatory phenotype. 75

Amino acid metabolism

Amino acids are the primary substrates for protein biosynthesis, and recent evidence emphasizes the critical role of amino acid availability and metabolism in the regulation of antitumor immunity.

Glutamine is the most abundant amino acid and a crucial energy substrate, as well as an important nitrogen and carbon donor for various biosynthetic precursors. 76 T eff cells require higher levels of glutamine than naïve T cells due to their rapid proliferation and demand for sufficient raw materials for macromolecule synthesis and cytokine secretion. 77 Cancer cells have been shown to exhibit the highest glutamine uptake capacity and consume most of the glutamine in the microenvironment. 76 In turn, elevated glutamine consumption by cancer cells diminishes the glutamine supply necessary for T cells, consequently impeding the antitumor immune response. 78 In the microenvironment, cancer cells consume glutamine to synthesize γ-aminobutyric acid (GABA) via glutamate decarboxylase 1 (GAD1). By activating the GABA B receptor, GABA inhibits GSK-3β activity, which enhances β-catenin signaling, promoting cancer cell proliferation while suppressing intratumoral infiltration of CD8 + T cells. 79 Furthermore, elimination of glutaminase, a vital enzyme for glutamine metabolism, within tumor cells stimulates T-cell activation and augments the efficacy of antitumor immune responses. The compound V-9302, an inhibitor of the glutamine transporter, selectively impedes glutamine uptake in cancer cells while simultaneously enhancing both glutamine assimilation and glutathione synthesis in T eff cells, ultimately enhancing their function. 80

Tryptophan is another essential amino acid. Following its entry into eukaryotic cells via the transport proteins SLC1A5 or SLC7A5, tryptophan is primarily subjected to three primary metabolic pathways: incorporation into protein synthesis, metabolism via the kynurenine (Kyn) pathway, or conversion through the serotonin pathway. 81 Notably, a substantial fraction of tryptophan is directed through the Kyn pathway, culminating in the production of a suite of metabolites with significant physiological implications. 82 Tryptophan plays a crucial role in determining the strength and effectiveness of the T cell response by affecting its availability in the microenvironment. 83 However, within the tumor niche, cancer cells, MDSCs, TAMs, suppressive DCs, and cancer-associated fibroblasts, among other cell types, exhibit upregulated expression of indoleamine 2,3-dioxygenase (IDO), which metabolizes tryptophan into suppressive kynurenine to promote Tregs and suppress CD8 + T cell function. 84 , 85 , 86 Most cancer cells overexpress IDO, and the level of kynurenine in the microenvironment is associated with poor prognosis in multiple solid and hematological malignancies. 87 Kynurenine has been found to bind to the aryl hydrocarbon receptor (AHR) in naïve CD4 +  T cells, which promotes Treg differentiation. 87

An additional metabolite generated through the Kyn pathway is the essential redox cofactor nicotinamide adenine dinucleotide (NAD+), a molecule of fundamental importance for the maintenance of cellular homeostasis. 88 In particular, cancer cells heavily depend on NAD+ to promote metabolic reprogramming and meet higher demands for ATP. Elevated NAD+ levels have been demonstrated to promote the proliferation of cancer cells. 89 Although the majority of studies suggest that an increase in NAD+ drives cellular proliferation, prior investigations have proposed that a decrease in NAD+ levels can lead to genomic instability, subsequently instigating liver tumorigenesis. 90 Moreover, tryptophan metabolism mediated by IDO affects not only the Kyn pathway but also other pathways, such as the purine, nicotinamide, and pyrimidine metabolism pathways, ultimately leading to decreased T-cell function. 91 In addition to IDO, another enzyme, tryptophan 2,3-dioxygenase (TDO), is involved in tryptophan catabolism. High TDO expression has been shown to impair T-cell antitumor immunity and to be correlated with poor clinical prognosis. Suppressing TDO expression can increase the antitumor efficacy of immune checkpoint inhibitors (ICIs). 92

In addition to the aforementioned amino acids, other amino acids play crucial roles in regulating tumor metabolism. T-cell proliferation relies heavily on arginine consumption. L-arginine supplementation has been shown to facilitate the metabolic shift from glycolysis to OXPHOS, enhancing T-cell survival and boosting antitumor responses of CD8 + tumor infiltrating lymphocytes (TILs). 93 Notably, the functional differences resulting from TAM polarization partially depend on arginine metabolism. In macrophages with the M1-like phenotype, arginine is converted into nitric oxide (NO) and citrulline via inducible nitric oxide synthase (iNOS), and this anabolic pathway is closely associated with macrophage cytotoxicity and antitumor effects. Conversely, in macrophages with the M2-like phenotype, arginine is hydrolyzed to yield ornithine and urea through arginase 1 (Arg1). 94 This metabolic shift affects arginine availability, which in turn impacts the activation and proliferation of T cells and NK cells, leading to immune suppression within the microenvironment. Notably, Arg1 expression in MDSCs contributes to arginine depletion in the microenvironment, further inhibiting T-cell antitumor function and reducing their survival. 95 , 96 In addition, depletion of cystine and cysteine is also linked to the immunosuppressive effect of MDSCs. T cells are unable to synthesize the essential amino acid cysteine from substances such as cystine or methionine, necessitating its import from external sources for their functionality. 97 MDSCs import cystine but do not release cysteine, thus the levels of cysteine in the microenvironment are regulated, inhibiting T-cell activation. 98 Asparagine is another amino acid that significantly boosts CD8 + T-cell activation and antitumor responses. Restricting dietary asparagine or inhibiting its uptake impaired T-cell activation and differentiation into memory-like cells. 99 Cancer cells consume higher levels of methionine due to increased expression of its transporter (SLC43A2), which inhibits methionine metabolism and function in CD8 + T cells by altering histone methylation patterns. 100

Organ-specific metabolic profiles

Understanding the metabolic differences between various organs is critical for developing targeted therapeutic strategies in cancer treatment. Each organ has unique metabolic demands and pathways that can be dysregulated in cancer, leading to distinct metabolic profiles for different types of tumors. 101 This organ-specific metabolic reprogramming plays a key role in cancer progression and survival, and its understanding could be leveraged for therapeutic benefits.

Consider primary brain tumors as an example. These tumors, often found nestled within the intricate neural networks of the brain, exhibit a remarkable metabolic flexibility. 102 They are known to express elevated levels or alternative isoforms of glycolytic enzymes, a trait that points towards a potential therapeutic opportunity. 103 Specifically, the therapeutic strategy of glucose deprivation could selectively starve brain tumor cells while sparing healthy neurons, which are capable of surviving on alternative fuels such as ketone bodies. 104 Similarly, HCC cells undergo a significant metabolic shift from glucose production (a state known as gluconeogenesis) to glucose usage. 105 HCC cells also exhibit a marked increase in amino acid metabolism, particularly in the metabolism of glutamine. 106 Additionally, studies have shown that HCC cells often exhibit abnormal lipid accumulation, increased FAS, and enhanced cholesterol metabolism. These changes contribute to the aggressive and metastatic behaviors of HCC. 107

Moreover, hormone-sensitive tissues such as the breast, endometrium, and prostate also exhibit significant metabolic fluctuations in response to hormone levels. 101 Hyperactivation of the PI3K pathway, a lipid kinase that promotes proliferation and nutrient uptake in response to growth signals, has been implicated in breast and endometrial cancers, providing a possible mechanism for hormonal therapy evasion. 107 This pathway could be a potential target for therapeutic interventions, particularly in hormone therapy-resistant cancers.

In summary, understanding organ-specific metabolic profiles and their dysregulation in cancer can open up new avenues for targeted cancer therapy. By exploiting these unique metabolic dependencies of tumors, more effective and personalized treatment strategies can be developed.

Targeted dietary interventions and mechanistic insights into their impact on cancer

Understanding the metabolic pathways of glucose, lipids, and amino acids lays a crucial foundation for exploring the effects of various dietary restrictions. Macronutrients, including carbohydrates, fats, and proteins, are the primary sources of energy for our bodies, and they each follow distinct metabolic pathways. By manipulating the relative intake of these macronutrients, we can influence the metabolic pathways they utilize and thereby exert control over our systemic metabolism. This concept forms the basis for various dietary restrictions and special diets, such as caloric restriction (CR), fasting or fasting-mimicking diet (FMD), ketogenic diet (KD), high-fat diet (HFD), or amino acid-defined diet. Moreover, high-salt diet (HSD), although not directly involving macronutrients, is noteworthy due to its potential impact on tumor biology. Therefore, an in-depth discussion on the role of HSD in cancer research and treatment is included in our exploration.

The connections between various dietary patterns and cancer risk are likely rooted in several biological mechanisms, such as inflammation and immune function; specific factors, such as the gut microbiota and their metabolites; unfavorable events, such as certain epigenetic changes and metabolic or hormonal disruptions; and stress, such as oxidative stress. 108 Alterations in dietary composition impact not only the availability of nutrients within tumor cells but also the surrounding microenvironment, thereby offering potential opportunities to impede tumor growth 109 (Table 1 ).

Calorie restriction

Effective CR is a dietary intervention that reduces energy intake by approximately 15–30% while maintaining a balanced proportion of macronutrients and preventing malnutrition. 110 CR has been shown to prolong life and reduce age-related diseases, including cancer, in experimental models. 111

Although the antitumor effect of CR has been confirmed, the underlying mechanism remains unclear. Nonetheless, it is believed that the tumor-inhibiting effect is partially mediated by several biological changes, such as increased apoptosis rates in cancer cells, decreased circulating blood glucose levels, inhibited insulin-like growth factor 1 (IGF-1) signaling, reduced insulin levels, and mediators that regulate metabolic pathway activation and inhibit angiogenesis. 112 In particular, controlling IGF-1 signal transduction is a critical component underlying the antitumor effects of CR. The IGF-1 signaling pathway is frequently activated in cancer cells, and it shifts metabolic resources toward growth and proliferation. Therefore, the reduction in IGF-1 levels in response to CR leads to attenuated tumor growth and progression. 113 The impact of CR on cancer is also interconnected with mutations and oncogenic pathways. A study showed that CR results in a reduction of insulin levels, thereby diminishing tumor PI3K signaling. 114 CR has also been found to suppress xenograft tumor growth by upregulating the aldolase A (ALDOA)/DNA-PK/p53 pathway, with ALDOA acting as a potential oncogene that can also activate the tumor suppressor p53. 115 Moreover, CR has been shown to modify the cancer stem cell (CSC) phenotype, reducing their carcinogenic and metastatic potential. Notably, in MMTV-ErbB2 transgenic mice, the CSC subpopulation was most affected by CR, as shown by a reduction of luminal cells (CD24 high /CD49f low ), putative mammary reconstituting unit subpopulations (CD24 high /CD49f high ) and luminal progenitor cells (CD61 high /CD49f high ). These effects were largely attributed to the concurrent inhibition of estrogen receptor and ErbB2 signaling. 116

CR has been shown to shape the TME in several ways, including through the specific reduction in the number of TAMs, increase in the formation of CD8 + cytotoxic T cells and memory T cells, and negative modulation of immunosuppressive Treg cell activity and immunosuppressive cytokine levels. 117 Additionally, CR promotes favorable changes in the immune signature, providing enhanced protection against tumor growth and metastasis, possibly in part by remodeling the TME. In mice, no impact of a CR diet was observed on the number of CD4 + or CD8 + cells in the TME; however, the cytotoxic killing potential of these cells was elevated. Notably, higher expression of CD103 + , a marker of crucial tissue-resident memory T cells that possess enhanced cytotoxic capacity and can contribute to tissue protection against tumor cell invasion, was found. Additionally, a downward trend in the frequency of Tregs was observed, and a significant reduction in the total number of MDSCs was detected. 118 Hence, it was concluded that CR not only inhibits cancer cell proliferation but also helps maintain antitumor immunity.

Furthermore, research has shown that fasting, CR, and caloric restriction mimetics (CRMs) can promote T-cell-mediated tumor cytotoxicity, alter NK cell function, and potentially trigger immunogenic cell death, thereby stimulating cancer immunosurveillance pathways. 119 CRMs are pharmacological agents or natural compounds that imitate the biochemical effects of CR by reducing the lysine acetylation rates of cellular proteins. 120 Examples of CRMs include hydroxycitrate (an inhibitor of ATP citrate lyase), spermidine (an inhibitor of EP300 acetyl transferase activity), and resveratrol (an activator of sirtuin-1 deacetylase activity). 121 Treatment with CRMs has been found to decrease the concentration of free IGF-1, promote autophagy in cancer cells, and improve the antitumor immune response, resulting in a reduction in tumor growth when combined with immunogenic chemotherapeutics. 119 CRM hydroxycitrate has been found to stimulate autophagy in U2OS osteosarcoma cells in vitro, thereby increasing antitumor immunosurveillance and reducing tumor mass in mice with autophagy-competent mutant KRAS-induced lung cancers. 122 Moreover, in vitro treatment with resveratrol inhibits mitochondrial respiration in breast cancer cell lines through a SIRT1-dependent mechanism, diminishes the expression of markers associated with breast CSCs, and promotes their differentiation. 123 Collectively, these findings suggest that CRMs may enhance antitumor immunosurveillance in preclinical models.

Moderate physical activity, energy restriction, and their combination can also affect tumor growth. In fact, the combined effects of moderate physical activity and 10% energy restriction (PA + ER) have been shown to significantly delay primary tumor growth, reduce spontaneous metastases, and prolong survival. These effects on tumor progression and survival are accompanied by beneficial changes in immune cell infiltrates within the microenvironment. Specifically, the PA + ER combination leads to an increase in the percentage of CD8 + T cells and a decrease in the percentage of total MDSCs and MDSC subsets within tumors. 124

Nevertheless, it is crucial to emphasize that there are established nutritional recommendations for cancer care, and the weight loss or reduction in protein intake often associated with CR may conflict with these guidelines. 125 These dietary practices could exacerbate the risk of malnutrition, sarcopenia, fatigue, delayed wound healing, and impaired immunity, particularly in cancer patients who are already at an increased age-associated risk for these conditions. 126 Therefore, while exploring dietary interventions for cancer treatment, the potential adverse effects on overall patient health and nutritional status must be carefully considered.

Fasting or fasting-mimicking diet

In addition to CR, alternative approaches such as intermittent fasting (IF), including short-term fasting (STF), intake of an FMD, and time-restricted feeding (TRF), which limits food consumption to a specific time window each day, are being condisered. 127 , 128 The term “fasting” has a broad definition, encompassing a range of eating patterns, including complete and voluntary deprivation of food with no restriction on drinking water. 129 An FMD is based on a regimen of low-calorie and low-protein foods that mimics the effects of fasting but induces fewer side effects. This approach retains the benefits of traditional fasting methods while minimizing their potential drawbacks. 130

Fasting or intake of an FMD can cause various metabolic changes, including alterations in the systemic levels of hormones and growth factors such as insulin, glucagon, growth hormone, IGF-1, glucocorticoids or adrenaline. 131 In response to these changes, normal cells activate protective mechanisms against stress and toxic insults, thereby reducing their metabolic requirements and cell division rate. On the other hand, because fasting or FMDs reduce tumor growth-promoting nutrients and factors, cancer cells struggle to manage metabolite deprivation and thus develop greater sensitivity to cancer therapies. 132 In obesity-driven postmenopausal cancer mouse models, TRF was shown to delay the onset of tumors and reduce lung metastasis. Moreover, TRF was found to increase systemic insulin sensitivity and decrease hyperinsulinemia. Importantly, TRF could also restore the circadian rhythm of gene expression within tumors while attenuating both tumor growth and insulin signal transduction. 133 Fasting can cause an “anti-Warburg effect” by reducing aerobic glycolysis and glutaminolysis while increasing OXPHOS uncoupled from ATP synthesis. 134 In cancer cells, OXPHOS increases reactive oxygen species (ROS) production and leads to oxidative stress, activation of p53 signaling and DNA damage, particularly when combined with chemotherapy or other cancer therapies. 135 Therefore, the unique metabolic vulnerabilities of cancer cells, which differ from those of normal cells, can be strategically targeted to develop novel and effective therapeutic interventions. According to a recent study, the combination of chemical treatment with an FMD reduces the expression of heme oxygenase-1 (HO-1), which is a stress-responsive enzyme that protects cancer cells against oxidative damage and apoptosis in vivo. Interestingly, this combination treatment resulted in upregulated HO-1 expression in normal cells. The downregulation of HO-1 production in cancer cells, in part, facilitated FMD-induced chemosensitization of cancer cells by boosting CD8 + TIL-dependent cytotoxicity, which was possibly facilitated by decreased Tregs. 136 A separate study conducted with mouse models of colon cancer indicated that alternate day fasting for 2 weeks triggered autophagy in cancer cells, which in turn downregulated CD73 expression. As a result, the production of immunosuppressive adenosine in cancer cells was reduced, ultimately preventing macrophages from acquiring an M2 immunosuppressive phenotype. 137

Clinical experiments have suggested that intake of an FMD can induce metabolic changes and increase antitumor immunity in cancer patients. In fact, the final outcomes of an FMD-treated clinical trial (NCT03340935) demonstrated that a severely calorie-restricted, five-day FMD regimen was well tolerated and resulted in substantial systemic metabolic changes in patients with different tumor types who were concurrently receiving antitumor therapies. 138 , 139 In another clinical trial called DigesT (NCT03454282), a five-day FMD regimen was found to broadly reshape intratumor immunity in breast cancer patients. Specifically, the FMD was shown to promote the infiltration of activated and cytotoxic immune cell populations, including total and activated intratumoral CD8 + T cells, M1-like macrophages, aDCs, and NK cells. These changes were paralleled by an increase in immune signatures associated with improved clinical outcomes in cancer patients. 138

Ketogenic diet

A KD comprises a high-fat component, very low carbohydrate levels, and low to moderate protein levels, as explained in a recent study. 140 A traditional KD is typically formulated at a 4:1 ratio of fat:carbohydrate plus protein. 141 In this classical formulation, 80–85% of calories are derived from fat, 10–15% from protein, and less than 5% from carbohydrates. 142 A KD is known to be effective at treating epilepsy, lowering glucose levels, and producing ketone bodies in vivo. 143 There is increasing evidence to support the use of KD as a potential tumor treatment or prevention method, either as a standalone approach or in combination with other medicines. 144

The Warburg effect indicates that lower intratumoral glucose levels can impede tumor growth, which can be achieved through pharmacological intervention and dietary changes such as a KD. Cancer cells, unable to utilize ketone bodies produced by KD for energy due to their aberrant mitochondrial function and diminished enzyme activity, can essentially be “starved” of glucose. Hence, KD emerges as a potentially promising strategy for cancer prevention. 145 One of the primary ways in which a KD potentially promotes potential anticancer effects is by increasing the levels of β-hydroxybutyrate (β-HB), which is the most abundant ketone body. 146 For instance, β-HB has been proven to inhibit CRC by activating the transcriptional regulator Hopx through the surface receptor Hcar2, thereby reducing the proliferation of colonic crypt cells and suppressing tumor growth. 147 Another antitumoral effect of KD is upregulating the expression of the circadian clock gene Per (Period) by activating AMPK and upregulating SIRT1 (Sirtuin1), resulting in enhanced apoptosis and growth delay in tumor cells. 148 KD also decreases insulin-regulated PI3K-Akt-mTOR signaling, which is overactivated in pancreatic neuroendocrine tumors (PanNETs), resulting in decreased blood glucose levels and a suppressive effect on the development and progression of PanNETs. 149

Emerging evidence suggests that a KD may be a valuable clinical tool to enhance T-cell-mediated antitumor immune responses. In vitro and in vivo studies have shown that KD intake markedly increased the specific responses of human T cells, resulting in enhanced CD4 + , CD8 + , and Treg capacity, as well as augmented T memory cell formation. Under conditions of KD intake, CD8 + T cells undergo metabolic reprogramming to rely on OXPHOS in response to increased ketone bodies, leading to enhanced cellular energy and respiratory reserve, potentially improving their functionality. 150 In addition, KD intake prevented the progression of colon tumors by inducing tumor cell oxidative stress, inhibiting MMP-9 expression, and promoting M2 to M1 TAM polarization. 151 In a mouse model of malignant glioma, KD feeding led to significantly enhanced innate and adaptive tumor-specific immune responses. Mice fed a KD showed increased cytokine production (IFNγ, TNF, and IL-2) and greater tumor-reactive CD8 + T-cell cytotoxicity. Moreover, the mice maintained on a KD presented with a higher number of immune cells and a higher ratio of CD4 + T cells to Tregs, while the functionality of the Tregs was weakened. Feeding mice with the KD resulted in a noteworthy decrease in the expression of immune inhibitory receptors (PD-1 and CTLA-4) on CD8 + TILs, as well as a reduction in the expression of inhibitory ligands (CD86 and PD-L1) on cancer cells. 152 These findings suggest that a KD has the potential to attenuate tumor-induced T-cell suppression by decreasing the population of cells susceptible to the inhibitory PD-1 pathway.

Although KD has shown various potential benefits to tumor patients with its promising effects of inhibiting tumor cell growth and activating immune response, there is still limitation in its clinical application owing to its inevitable side effects. 153 It should be considered that KD also presents some risks, as they are typically high in saturated fats and may lack a substantial amount of nutrients, specifically carbohydrates and dietary fiber, as well as micronutrients such as calcium, magnesium, potassium and vitamins A, B and B6. 154 , 155 According to a recent research, KD delayed tumor growth but meanwhile accelerated cachexia onset, therefore shortening survival in a mouse model of IL-6-producing cancer. Excitingly, the same research group found that applying dexamethasone during KD treatment might delay cachexia onset without affecting the inhibition of tumor growth, providing fundamental insight into reversing the limitations of the clinical application of KD. 156

Protein restriction diet

The prevailing notion suggests that high protein intake, particularly among individuals under the age of 65, potentially escalates the risk of overall and cancer-related mortality. 157 To establish a protein restriction diet, either dietary protein intake or the number of amino acids can be reduced. 140 Recent research has demonstrated that dietary protein restriction is linked with a reduced incidence of tumor occurrence and a decreased risk of mortality. 158

Dietary restriction of protein and certain amino acids, including serine, methionine, and branched-chain amino acids (BCAAs) such as leucine, isoleucine, and valine, has been shown to impede tumor growth. 159 One mechanism through which protein restriction may inhibit tumor growth is via the IGF-1 signaling pathway. In melanoma and breast cancer mouse models, it has been observed that mice fed a low-protein diet (4% kcal protein) exhibit reduced IGF-1 levels and slower tumor progression compared to those fed a high-protein diet (18% kcal protein). A low-protein diet has been associated with reduced IGF-1 levels in patients aged 50–65 years, subsequently decreasing their risk of death from cancer. Conversely, a low-protein diet has been linked with an increased mortality rate in older patients (aged 65 and above), suggesting that a life-stage-specific approach to protein intake could optimize healthspan and longevity. 157 Other potential mechanisms for cancer prevention that are mediated by protein restriction could involve mTOR signaling, amino acid metabolic programming, FGF21, and autophagy. 158 In addition to these general effects, specific dietary restrictions on certain amino acids, such as serine and glycine, have been associated with prolonged survival in mouse models of various tumor types. The mechanisms underlying this observed survival benefit could include the correction of abnormal cellular nucleotide, protein, and lipid synthesis; improved mitochondrial function; and changes in epigenetic modifications. 160 , 161

The antitumoral effect of a low-protein diet also hinges on promoting immunosurveillance against cancer, while the dietary restriction of amino acids may adversely affect the metabolic reprogramming of the TME in various ways. In multiple mouse models, reducing dietary methionine inhibited tumor growth and boosted antitumor immunity by increasing the quantity and cytotoxicity of tumor-infiltrating CD8 + T cells. 162 Moreover, restricted intake of dietary protein or methionine/cystine has been shown to modify the infiltration and tumoricidal capacity of TAMs, leading to a significant increase in tumor-infiltrating CD8 + T cells and a decrease in the number of infiltrating MDSCs. Mechanistically, a protein-restricted diet inhibited mTOR pathway activation and increased macrophage acquisition of an antitumor phenotype by increasing the number of macrophages undergoing polarization to the M1 type. 163 Macrophages might sense diet-derived cytosolic amino acids via the GTPase Rag, which subsequently regulates the expression of TFEB, TFE3 and mTORC1 when activated. 164 Furthermore, an isocaloric diet that moderately reduced protein intake (by 25%) was shown to trigger an unfolded protein response (UPR) that depended on IRE1α in cancer cells. The increase in UPR activation, in turn, led to an increase in the recruitment of CD8 + T cells and enhanced antitumor immunosurveillance. Notably, intake of a low-carbohydrate diet did not exert the same effect. 165 Although a low-protein isocaloric diet has been proven to reduce the concentration of amino acids in tumor tissues, it remains uncertain whether this reduction is limited to certain amino acids. Thus, further research is needed to explore the correlation between a low-protein isocaloric diet and the decrease in the levels of specific amino acids in tumors.

Interestingly, several studies have shown that high-protein diets may also benefit the restriction of tumor growth or clinical outcoming of cancer patients, which seem contradictory to the findings of the protein restriction diet discussed above. However, the underlying mechanisms are totally different. A high-protein diet increased the production of urinary urea in a tumor protein 53 (TP53)-mutated orthotopic bladder tumor mouse model, leading to the cascade modulation of ammonia in tumor cells, which induces tumor apoptosis. 166 These findings challenge the former hypothesis that high urinary urea concentrations caused by a high-protein diet might serve as a potential carcinogenic factor in the bladder, suggesting the urgent need for further investigation. 167 Applying a high-protein diet may improve the overall survival of older outpatients with advanced gastrointestinal cancer, which may improve the nutritional state of these patients with poor digestive system function. 168

Moreover, there have been efforts to develop a series of drugs that mimic amino acid restriction. One focus of researchers in the cancer therapy field has been on glutamine metabolism, as cancer cells rely heavily on glutamine. Glutaminase inhibitors, for instance, have been shown to decrease tumor burden. 169 , 170 The use of 6-diazo-5-L-oxo-norleucine (DON) promoted antitumor immunity by greatly favoring OXPHOS over glycolysis in CD8 + T cells while disrupting the metabolism of cancer cells. 171 Notably, DON showed the ability to significantly inhibit the generation and recruitment of MDSCs and to reprogram M2-like TAMs into proinflammatory TAMs, which increased tumor antigen cross-presentation to T cells and enhanced the efficacy of immune checkpoint blockade (ICB). 172 In addition, CB-839, which is considered the most effective glutaminase inhibitor, can be utilized alone or in combination with PD-1 inhibitors to treat solid or hematological malignancies. 173 , 174 , 175 As previously mentioned, IDO and TDO are tryptophan catabolism enzymes, and inhibitors of these enzymes have been developed and evaluated in various clinical trials. 176 For example, epacadostat is a novel compound that serves as an IDO1 inhibitor, suppressing systemic tryptophan catabolism. 177 Both in vitro and in vivo studies have demonstrated that epacadostat can reduce tumor growth and promote the proliferation of T cells and NK cells. 178 Furthermore, cyst(e)inase, a glutathione inhibitor that degrades cysteine and cystine, reduces tumor progression by elevating ROS levels and inducing tumor cell-selective ferroptosis. 179 , 180

High-salt diet

HSD has long been considered as a risk factor and trigger of malignancies. However, recent studies have provided new insights into the effect of sodium intake. As research continues, it is becoming increasingly clear that salt can accumulate in the interstitium and modulate immune cell differentiation, activation, and function through the effects of extracellular hypersalinity. 181 In addition, consumption of a HSD can lead to elevated tissue sodium concentrations and affect immune responses within microenvironments, ultimately impacting the development of immune-regulated diseases such as infections and cancer. 182

HSD, comprising 4% sodium chloride (NaCl), is recognized as a robust immunomodulator that is capable of eliciting a substantial inflammatory response. 183 Indeed, research has shown that high salt conditions can inhibit tumor growth by enhancing antitumor immunity, particularly through the modulation of MDSC functions. 184 According to a recent study, an HSD reduced the production of cytokines essential for the expansion of MDSCs and thus attenuated the accumulation of MDSCs within the tumor niche. As a result, the two primary types of MDSCs acquired different phenotypes: M-MDSCs differentiated into antitumor macrophages, and PMN-MDSCs adopted a proinflammatory phenotype, which led to the reactivation of T-cell antitumor functions. 185 Furthermore, a high salt level has been found to induce the transformation of anti-inflammatory Tregs into proinflammatory Th1 cells, which led to the secretion of the inflammatory cytokine IFNγ. 186 In another study, salt functioned as an adjuvant that enhanced the effectiveness of anti-PD-1 immunotherapy in tumor regression. Specifically, an HSD induces NK cell-mediated tumor immunity by suppressing PD-1 expression while increasing IFNγ levels and the serum hippurate concentration. Notably, hippurate is a microbial benzoate metabolism product that has been identified as a metabolic marker of effective PD-1 immunotherapy in responsive patients. 183 Although the major antitumoural effect of HSD is modulating immune cell function, mechanisms other than immunomodulation have also been discovered. For instance, HSD suppressed tumor growth and lung metastasis in a murine model of breast cancer, possibly by inducing hyperosmotic stress or through mimicking CR. 187

Nevertheless, despite the potential benefits of salt intake on cancer treatment effectiveness, high salt intake can also lead to the development of a proinflammatory state, which can negatively impact cancer outcomes. 188 High salt intake is a risk factor for various types of cancer in humans, including lung, testicular, bladder, renal cell, pancreatic, esophageal, and gastric cancer. 182 HSD has been shown to induce chronic inflammation, which may in turn incite continuous cell proliferation, DNA damage, or cancer transformation. However, whether there is a connection remains uncertain. 188 IL-17, specifically IL-17A, plays an important role in the mechanism of action of HSD. Evidence suggests that high salt intake can induce the differentiation of Th17 cells, a prominent source of IL-17A. 189 The overproduction of IL-17A can lead to inflammation and other immune responses that contribute to various pathologies. Furthermore, in the case of breast cancer, an HSD has been found to promote tumor progression and lung metastasis, increase the proportion of Th17 cells, and activate the MAPK/ERK signaling pathway in breast cancer cells through the secretion of IL-17F. The increase in the secreted IL-17F level results in the unregulated expression of protumor genes and the induced inflammatory responses, ultimately accelerating the proliferation, migration and invasion of breast tumors. 190 In addition, the combination of high NaCl concentrations with subeffective IL-17 has been proven to reduce reactive nitrogen and oxygen species (RNS/ROS) levels and enhance the growth of breast cancer cells. 191 , 192 Recent research has also demonstrated that intake of an HSD can disrupt the development and function of NK cells in mice. 193 Therefore, it can be concluded that dietary salt may exert dual effects on tumorigenesis, and the contradictory results obtained may be due to variations in the effects of high salt concentrations on tumors in different tissues and during different phases of tumor development.

Obesity and high-fat diet

Obesity, a serious health issue characterized by excessive body fat, is a known risk factor for multiple types of cancer. It can be induced or exacerbated by HFD, characterized by the consumption of foods rich in saturated fats and cholesterol. 194 Obesity can induce systemic metabolic disruptions within the body, leading to dyslipidemia, hypercholesterolemia, insulin resistance, alterations in hormone levels, and changes in the baseline inflammation status. 195 Conversely, a low-fat diet, typically associated with reduced total fat intake, can potentially lower the risk of certain types of cancer. 196 , 197 Given that both HFD and obesity are major factors influencing cancer risk, the forthcoming discussion will primarily focus on these aspects. By diving deeper into the mechanisms by which HFD and obesity affect cancer development and progression, we aim to provide a more comprehensive understanding of this intricate relationship.

Dietary obesity is associated with multiple factors related to cancer occurrence and exacerbation of immune suppression in tumor niches. 198 In the context of obesity, increased hepatic expression of the unconventional prefoldin RPB5 interactor (URI) has been shown to couple nutrient surplus with inflammation, leading to nonalcoholic steatohepatitis (NASH) and consequent HCC. This process involves URI-induced DNA damage in hepatocytes triggering Th17 lymphocyte-mediated inflammation, and subsequent IL-17A-induced adipose tissue neutrophil infiltration, which promotes insulin resistance and hepatic fat accumulation, thereby inducing NASH and HCC. 199 Notably, obesity also accelerates Helicobacter felis -induced gastric carcinogenesis by enhancing the trafficking of immature myeloid cells and the Th17 response. This exacerbates proinflammatory immune responses, characterized by cross-talk between inflamed gastric and adipose tissues, thereby contributing to a protumorigenic gastric microenvironment. 200

Diet-induced obesity has been shown to elevate nitric oxide (NO) production, which enhances tumor growth. This is primarily due to the recruitment of macrophages and the overexpression of inducible NO synthase as a result of HFD. 201 Additionally, in response to HFD intake, IL-6-mediated inflammation has been shown to accelerate prostate cancer tumor growth and increase the fraction of MDSCs and the M2/M1 macrophage ratio. 202 The effects of diet-induced obesity extend to the microenvironment of colitis-associated CRC. Here, diet-induced obesity has been shown to increase IL-6 expression and promote the polarization of macrophages into M2-like macrophages, enhancing the production of CC-chemokine-ligand (CCL) 20. CCL20 recruits CC-chemokine receptor 6 (CCR6)-expressing B cells and γδ T cells, ultimately leading to colitis-associated CRC progression. 203 In animal models of HFD-induced obesity, the infiltration rate of TAMs and the expression of cytokines in M2-like macrophages were increased, enhancing tumor growth and metastasis. However, ablation of VEGFR-1 signaling can reverse the abnormal TME associated with obesity and reprogram TAMs to promote their acquisition of the M1 phenotype. 204

The intake of an HFD has been shown to significantly increase the incidence of oral squamous cell carcinoma (OSCC) by expanding MDSCs within the local immune microenvironment. 205 Obesity induced by diet can also trigger the accumulation of PMN-MDSCs, leading to Fas/FasL-mediated apoptosis of tumor-infiltrating CD8 + T cells and causing resistance to immunotherapy in breast cancer treatment. 206 Obesity has been shown to suppress the infiltration and function of CD8 + T cells, which was linked to decreased chemokine production, reduced fatty acid availability, and alterations in amino acid metabolism. 207 , 208 Moreover, based on findings from mouse models, obesity reduced the number and function of CD4 + T cells in the TME of CRC, leading to a compromised antitumor response of both CD4 + and CD8 + T cells and ultimately accelerating disease progression. 209 Furthermore, considerable evidence shows that obesity-associated adipocytes in pancreatic ductal adenocarcinoma can secrete IL-1β to attract tumor-associated neutrophils (TANs), which subsequently activate pancreatic stellate cells and contribute to tumor growth. 210

HFD or diet-induced obesity may induce tumor metastasis. HFD has been proven to increase palmitate secretion from alveolar type 2 cells and nuclear factor-kappaB subunit p65 acetylation in the lung to prepare a premetastatic niche. 211 HFD-induced fatty liver may promote liver metastasis by facilitating the secretion of hepatocyte-derived extracellular vesicles (EVs), which transfer Yes-associated protein (YAP) signaling-regulating microRNAs, hence elevating nuclear YAP expression, CYR61 expression, and M2-like macrophage infiltration. 212 Another mechanism of HFD-induced liver metastasis is the upregulation of NOD-like receptor C4 (NLRC4), which further induces M2-like macrophage activation and IL-1β processing. An alteration from an indolent to a metastatic state may be stimulated by HFD-induced lipid accumulation in prostate tumors, the mechanism of which may be related to the sterol regulatory element-binding protein (SREBP)-related prometastatic lipogenic program. 213 In addition, it is widely acknowledged that the fatty acid receptor CD36 plays an important role in HFD-related metastasis promotion by enhancing the metastatic potential of CD36 + metastasis-initiating cells. 214 However, a recent study revealed that CD36 may prevent palmitate-induced lipotoxicity rather than facilitating HFD-driven metastasis, suggesting that further investigations of the dual effects of CD36 are needed. 215

An elevated cholesterol level is an obesity comorbidity, and studies suggest that the effects of obesity on cancer may be partly mediated by increased cholesterol levels. 216 In fact, a high-cholesterol diet (HCD) alone has been shown to promote macrophage infiltration and significantly enhance the growth of CRC tumors. 217 One mechanism by which HCD promotes CRC progression is through the inhibition of the CD8 + T-cell response. Specifically, macrophages with infiltration driven by HCD can secrete CCL5, which obstructs the activation of CD8 + T cells, thereby facilitating the evasion of immune system surveillance by CRC cells. 218 27-Hydroxycholesterol (27-HC) is a crucial mediator of the effects of dietary cholesterol on cancer metastasis. This oxysterol is synthesized through the action of the CYP27A1 enzyme and is present at high levels in the circulatory system. 219 Oxysterol has been shown to modulate the TME by recruiting immunosuppressive neutrophils to the metastatic niche, facilitating cancer progression. 220 However, some studies have reported conflicting findings regarding the effects of high serum cholesterol levels on cancer progression. For instance, one study showed that high serum levels of cholesterol attributed to HCD intake increased the accumulation of NK cells and promoted their effector functions to reduce the growth of liver tumors in mice. 221 However, further studies are needed to understand these conflicting findings.

In expanding on the relationship between HFD and tumor promotion, it is worth noting that the tumor-promoting effect of HFD is not universal and depends largely on the subtype of fatty acids involved. Mouse models of breast cancer developed comparable obesity levels from an HFD of either cocoa butter or fish oil. However, the consumption of the cocoa butter HFD, which is high in saturated fatty acids, led to faster mammary tumor growth and increased protumor macrophages and IL-10 expression while reducing B-cell and CD8 + T-cell infiltration. On the other hand, the fish oil HFD, which is rich in omega-3 fatty acids, disrupted the typical obesity-tumor growth link and reduced the number of protumor macrophages. 222 This effect of dietary omega-3 fatty acids is mediated by host GPR120 and has also been shown to inhibit prostate cancer. 223 Moreover, oleic acid (OA) and linoleic acid (LA) are the most common unsaturated fatty acids in dietary oils. While both an HFD rich in OA and an HFD rich in LA can similarly induce obesity in mice, a diet high in LA specifically encourages the growth of mammary tumors. Furthermore, an LA-rich HFD can impair antitumor T-cell responses via the induction of mitochondrial dysfunction. 224 Based on these findings, it appears that modulating dietary oil composition may constitute a promising strategy for enhancing immune function in both the prevention and treatment of obesity-associated cancers. By carefully selecting and balancing the types of fatty acids in HFDs, it may be possible to reduce the tumor-promoting effects of obesity while simultaneously increasing immune responses against tumors. Further research in this area may help to identify more precise dietary interventions that can ultimately improve outcomes for individuals at risk of developing obesity-associated cancers.

Potential role of dietary factors in cancer treatment

Recent studies have highlighted the pivotal influence of the TME on the efficacy of immunotherapy in cancer treatment. 225 Immunotherapy, recognized as a substantial advance in cancer treatment, has revolutionized the field of oncology by augmenting the body’s innate defenses to effectively target and eliminate malignant cells. 226 Various forms of cancer immunotherapy have been developed, including oncolytic virus therapies, cancer vaccines, cytokine therapies, adoptive cell transfer, and ICIs, all of which have shown promise in clinical practice. 227 Among these therapies, ICIs are perhaps the most important, as they are antibody-based drugs that can eliminate the influence of tumor-specific CD8 + T cells. 228 In particular, ICIs targeting PD-1 or its ligand PD-L1 have demonstrated notable clinical efficacy in the treatment of various advanced cancers. 229

Extensive research has been conducted to identify the effects of various dietary substances and patterns on tumor growth, metastasis and TME reprogramming, which has led to the consideration of nutritional intervention as a possible strategy for increasing the efficacy of tumor treatment 230 , 231 (Tables 2 , 3 ). The decline in T-cell functionality with aging, a widely documented phenomenon, is linked to a reduced efficacy of anti-OX40 immunotherapy in murine models. 232 CR not only preserves T-cell function but also improves the response of aged CD4 + T-cell populations to anti-OX40 therapy. 233 When used in combination with immunogenic cell death (ICD)-inducing chemotherapy and immunotherapy, CRMs potentially enhance the efficacy of cancer treatments through synergistic effects. 234 Preclinical studies have shown that STF, which serves as an adjunct to various cancer treatments, may bolster antitumor immunity by attenuating immunosuppressive conditions and amplifying CD8 + T-cell cytotoxicity. 235 For example, an experimental study of non-small cell lung cancer demonstrated that STF sensitized cancer cells to anti-PD-1 therapy. The antitumor efficacy of combination therapy was achieved by inhibiting IGF-1-IGF-1R signaling in cancer cells, boosting the intratumoral CD8 cell: Treg ratio in the TME. 132 Furthermore, intake of an FMD has been shown to enhance the effectiveness of immunotherapy against triple-negative breast cancer with low immunogenicity by affecting the TME. Specifically, intake of an FMD has been shown to reactivate T eff cells that underwent early exhaustion, shift cancer metabolism from glycolytic to OXPHOS, and reduce the collagen deposition rate. 236 These effects led to the increased efficacy of anti-PD-L1 and anti-OX40 immunotherapy. These results suggest that combining immunotherapy with dietary restriction may lead to profound synergistic effects.

KD also enhances the antitumor effects of PD-1 blockade alone or in combination with anti-CTLA-4 antibodies. Mechanistically, the principal ketone body 3-hydroxybutyrate (3HB) in a KD prevented the ICB-mediated upregulation of PD-L1 on myeloid cells while simultaneously promoting the expansion of CXCR3 + T cells. 237 Similarly, KD enhanced the effectiveness of anti-CTLA-4 immunotherapy by reducing PD-L1 protein levels and augmenting the expression of interferons and antigen presentation-related genes. When combined with immunotherapy, the intake of a KD can reshape the TME by increasing the population of CD8 + TILs, macrophages and CD86 + DCs. Mechanistically, the activation of AMPK via KD intake is the key molecular event that promotes immunotherapy efficacy. This activated AMPK phosphorylates PD-L1 on Ser283, which interrupts its association with CMTM4 and results in PD-L1 degradation. Furthermore, AMPK phosphorylates EZH2, which impedes polycomb repressive complex 2 (PRC2), leading to an increase in interferons and antigen-presenting gene expression. 238

Combining a protein-restricted diet with a vaccine or anti-PD-1 therapy has been shown to significantly inhibit tumor growth and prolong survival. 239 Notably, treatment with a methionine-/cystine-restricted diet significantly increased the number of tumor-infiltrating CD8 + T cells and cytotoxic granzyme B + CD8 + T cells, which was further enhanced when combined with immunotherapy. 163 Another study confirmed the inhibitory effect of dietary methionine restriction on tumor growth and its ability to synergize with PD-1 blockers to increase tumor control. Mechanistically, this dietary approach reduced the number of metabolites, such as S-adenosylmethionine (SAM), which controls N6-methyladenosine (m6A) methylation reactions, in cancer cells. A reduction in the SAM level altered the m6A modification rate and decreased the expression of PD-L1 and V-domain Ig suppressor of T-cell activation (VISTA) in cancer cells. 162 Moreover, the enzyme cyst(e)inase breaks down cystine and cysteine, thereby bolstering T-cell-mediated antitumor immunity and inducing ferroptosis in tumor cells when combined with PD-L1 blockade. 240 IDO1 is a critical enzyme in the tryptophan–kynurenine pathway and has been identified as a promising immunomodulatory target. 241 A phase 1/2 (ECHO-202/KEYNOTE-037) trial evaluating the effectiveness of the IDO1 inhibitor epacadostat combined with pembrolizumab on advanced solid tumors showed a high objective response rate (ORR) of 40.3% overall and 61.9% in malignant melanoma patients, demonstrating promising antitumor efficacy. 242 Unfortunately, phase 3 trials failed to confirm these benefits. The ECHO-301/KEYNOTE-252 trial showed that combining epacadostat with pembrolizumab failed to prolong progression-free survival (PFS) or overall survival (OS) compared to pembrolizumab alone in patients with advanced melanoma. 243

Despite being linked to T-cell dysfunction and poor cancer prognosis, obesity has paradoxically been shown to enhance the response to anti-PD-1/PD-L1 immunotherapy. 244 Recent research suggests that immunotherapy yielded superior outcomes in obese patients, evidenced by an improved response rate and extended PFS and OS, in comparison to lean patients. 245 However, obesity also promoted tumor growth and T-cell exhaustion, leading to increased PD-1 expression and dysfunction, partly due to high leptin levels. Despite this outcome, PD-1-mediated T-cell dysfunction in individuals with obesity was found to significantly enhance tumor responsiveness to PD-1/PD-L1 inhibitors, as confirmed by preclinical and clinical data. 246 Therefore, obesity seems to be a double-edged sword for cancer immunotherapy, and the underlying mechanisms remain unclear and require further investigation.

Chemotherapy

Chemotherapy, a cornerstone of traditional cancer treatment, employs drugs to destroy rapidly dividing cells, a defining characteristic of cancer. 247 Despite its widespread use and undeniable efficacy in many cases, chemotherapy often has substantial side effects due to its impact on healthy cells. 248 Additionally, individual responses to chemotherapy can vary greatly and are influenced by a multitude of factors, including genetics, tumor characteristics, and, intriguingly, diet. 15 A growing body of research now highlights the role of dietary interventions in modulating the effectiveness of chemotherapy, emphasizing the need to further understand these interactions for improved therapeutic outcomes.

Due to their expression of oncogenes, cancer cells are more susceptible to the effects of fasting and CR than are normal cells, an effect termed ‘differential stress resistance’. 14 , 249 , 250 Based on this characteristic, CRM hydroxycitrate has been shown to increase sensitivity to chemotherapy by eliciting an adaptive cellular immune response, resulting in a decrease in the number of tumor-infiltrating Tregs into the tumor niche in various tumor models. 122

Emerging research also suggests a profound influence of fasting or FMD on the efficacy of chemotherapy. In vitro studies indicate that fasting cycles not only retard tumor growth but also sensitize a wide array of cancer cell types to chemotherapy. 14 This heightened sensitivity has been observed in various contexts, including the enhancement of gemcitabine efficacy in mice with prostate cancer xenografts and the increased efficacy of chemotherapy in triple-negative breast cancer via the upregulation of ROS. 251 , 252 FMD combined with vitamin C can potentially increase the effectiveness of chemotherapy for treating KRAS-mutant cancer cells by reversing the vitamin C-induced upregulation of HO-1 and ferritin. 253 Furthermore, when combined with a ferroptosis inducer, FMD can effectively eliminate slow-cycling, chemotherapy-resistant cells, suggesting a potential strategy for enhancing the sensitivity of certain difficult-to-treat cancers to chemotherapy through dietary interventions. 254 Interestingly, fasting can also counteract certain adverse effects of chemotherapy. For instance, it has been demonstrated to enhance self-renewal in hematopoietic stem cells and mitigate the immunosuppression induced by cyclophosphamide chemotherapy in mice. 255 In tumor-bearing mice, both prolonged fasting and FMDs can induce specific stress resistance responses, enhancing chemotoxicity in cancer cells while protecting normal cells. 256 This dual action is partly mediated by the reduction in IGF-1 and glucose levels, thus shielding normal cells and organs from chemical toxicity. 250 The potential of FMD in clinical settings has been supported by the ‘DIRECT’ study involving HER2-negative stage II/III breast cancer patients. This study revealed that treatment with FMD, administered three days prior to and during neoadjuvant chemotherapy, enhanced therapeutic efficacy without increasing toxicity or reducing chemotherapy-induced DNA damage in T cells. 257 Collectively, these findings highlight the potential of fasting and FMD as adjuncts to chemotherapy, warranting further exploration and clinical testing.

In addition to slowing tumor growth, KD also sensitizes tumor cells to classic chemotherapy. For example, the combination of KD with metronomic cyclophosphamide significantly enhances antitumor effects, resulting in the regression of neuroblastoma tumors. 258 , 259 Similarly, in pancreatic cancer, cotreatment with KD and cytotoxic chemotherapy substantially elevates tumor NADH levels, synergistically suppressing tumor growth and tripling survival benefits compared to chemotherapy alone. 260

Radiotherapy

Dietary interventions have emerged as promising strategies for enhancing the efficacy of radiotherapy in cancer treatment. For instance, CR combined with radiotherapy, has been shown to modulate the TME in a triple-negative breast cancer model by decreasing the number of intratumoral Tregs, increasing the CD8 + cell: Treg ratio, and upregulating PD-1 expression on CD8 + T cells. Furthermore, compared with patients who received radiotherapy alone, breast cancer patients who underwent CR concurrently with radiotherapy exhibited a significant reduction in the serum levels of immunosuppressive cytokines, suggesting potential benefits of CR in mitigating radiation-induced immunosuppression. 261

When combined with radiation or radiochemotherapy, KD slows tumor growth in lung cancer xenografts, potentially through a mechanism involving increased oxidative stress. 262 Additionally, KD was shown to enhance radiation sensitivity in a pancreatic cancer xenograft model, suggesting potential improvements in therapeutic outcomes. However, phase 1 clinical trials in patients with locally advanced non-small cell lung cancer and pancreatic cancer showed suboptimal compliance with the diet, indicating challenges in practical application. 263

Moreover, other dietary restrictions, such as methionine deprivation, have shown promising results in enhancing the efficacy of radiation and antimetabolite chemotherapy. In patient-derived xenograft and autochthonous tumor mouse models, methionine restriction sensitized tumor cells to these treatments, possibly via alterations in one-carbon metabolism. 264

Other therapies

In hormone receptor-positive breast cancer mouse models, periodic fasting or an FMD can enhance the therapeutic effects of endocrine agents such as tamoxifen and fulvestrant. This enhancement is believed to occur through a reduction in circulating IGF1, insulin, and leptin levels and suppression of AKT-mTOR signaling. Concurrent administration of these dietary strategies with a therapeutic regimen of fulvestrant and palbociclib has been associated with prolonged tumor regression and reversal of treatment resistance. Analogous metabolic alterations found in patients on an FMD during estrogen therapy suggest the potential of diet as an adjuvant in treating hormone receptor-positive breast cancer. 265

In addition to their effects on hormone-driven cancers, fasting or FMD has also been shown to enhance the efficacy of tyrosine kinase inhibitors (TKIs) across different cancer cell lines. Mechanistically, these effects are attributed to the increased ability of TKIs to block cancer cell growth and inhibit the MAPK signaling pathway under starvation conditions. 266 Another study reported that in HCC cells, xenografts, and patient-derived organoids, fasting improved the therapeutic response to sorafenib through the regulation of glucose transporters and proapoptotic protein expression by p53. 267

KD has also shown promise in supporting the effectiveness of phosphatidylinositol 3 kinase (PI3K) inhibitors and overcoming drug resistance in various mouse cancer models, including pancreatic, bladder, endometrial, and breast cancer models, as well as acute myeloid leukemia. 145 KD appears to enhance this effectiveness by decreasing hyperglycemia and reducing insulin secretion, actions correlated with a decrease in mTORC1 signaling within the tumor. 268

Finally, the combination of serine deprivation and biguanide treatment, such as phenformin and metformin, can lead to metabolic stress in cancer cells. This stress arises from the forced upregulation of glycolysis due to the biguanide-induced reduction in OXPHOS. Under conditions of serine deficiency, this stress may exceed the metabolic flexibility of cancer cells, leading to their potential death and, consequently, enhanced anticancer effects. 269

In summary, these findings underscore the potential of dietary interventions to modulate the therapeutic landscape of cancer treatment, enhancing the effectiveness of drugs and potentially overcoming resistance mechanisms. However, it should be viewed with cautious optimism. The biological plausibility of diet modifying treatment efficacy and resistance is compelling; however, the translation of this concept into clinical practice requires rigorous validation. It is critical to remain grounded in evidence-based medicine, recognizing that dietary strategies are adjuncts, not replacements, for established therapeutic regimens. Further exploration and clinical validation are necessary to fully understand these interactions and to integrate dietary strategies into standard cancer care effectively and safely.

Diet changes the gut microbiome in conjunction with antitumor effects and cancer treatment

The gut microbiome encompasses the genetic makeup of all species within the gut, such as bacteria, viruses, yeasts, protozoans, fungi, and archaea, and can be affected by a range of internal and external factors. 270 The gut microbiota plays a significant role in influencing the health and disease status of the host. The constituents of the gut microbiome and their interactions with the host immune system can impact the development of tumors and carcinogenesis. 271 Various dietary patterns have been found to significantly influence the composition and functionality of the gut microbiome. 272 , 273 It is through these changes in the gut microbiome that dietary patterns can indirectly influence the outcomes of cancer patients. 274

In recent early studies, several interventional strategies, ranging from dietary interventions to fecal microbiome transplant (FMT) and prebiotic, probiotic and antibiotic treatments, have shown promise in altering the composition or functional capacity of the gut microbiome. 275 Two prospective cohort studies have suggested that diet-related inflammation can alter the gut microbiome, leading to the development of CRC by suppressing adaptive antitumor immune responses. 276 , 277 Other prospective cohort studies have revealed the associations between prudent diets (rich in whole grains and dietary fiber) and Western diets (rich in red and processed meat, refined grains, and desserts) with CRC risk and indicated that the effect of these diets may differ based on the presence of Fusobacterium nucleatum in tumor tissue. 278 , 279 Specifically, these studies showed that, compared with a Western diet, adhering to a long-term prudent diet is associated with a reduced risk of F. nucleatum -positive CRC; however, it does not appear to mitigate the risk of F. nucleatum -negative CRC. 278 A recent study investigated the impact of the gut microbiota and dietary patterns on the response to ICIs in patients with melanoma. The present study revealed that patients with microbiomes dominated by the Ruminococcaceae family had greater response rates than did those with microbiomes dominated by the Bacteroidaceae family. Furthermore, another finding revealed that a poor response was associated with decreased intake of fiber and omega-3 fatty acids. 280 These results suggest that dietary interventions may be promising for improving cancer treatment outcomes.

Accumulating data suggest that alterations in the gut microbiome primarily contribute to the progression, prognosis, and treatment of cancer, primarily through interactions with the immune system. Metabolites produced by the microbiota play important roles in modulating antitumor immunity. 281 , 282 Microbiota-derived metabolites have been demonstrated to influence the efficacy of tumor immunotherapy. Short-chain fatty acids (SCFAs) are produced primarily by the fermentation of nondigestible carbohydrates, such as dietary fiber, by the microbiota. The main SCFAs include acetate, propionate, and butyrate. 283 , 284 The gut microbiota, which is mediated by SCFAs, can potentiate the antitumor activity of CD8 + T cells, thereby influencing the efficacy of tumor immunotherapy both in vitro and in vivo. 285 Metabolic and epigenetic reprogramming enables pentanoate and butyrate to enhance the effectiveness of cancer immunotherapy by boosting the antitumor activity of antigen-specific cytotoxic T lymphocytes and ROR1-targeting chimeric antigen receptor (CAR)-T cells. 286 Inosine is another important metabolite produced by the microbiome and is closely associated with immunotherapy. Intestinal Bifidobacterium pseudolongum promoted Th1 cell transcriptional differentiation and antitumor activity to increase the efficacy of immunotherapy, mainly through the action of inosine. 287 Inosine is instrumental in enhancing antitumor therapy by serving as a carbon source for CD8 + T cells in glucose-restricted microenvironments, facilitating their growth and optimal functioning. 288 Moreover, engineered bacteria can modify the concentration of metabolites in the microenvironment, thereby altering the composition of the TME. For instance, the genetically engineered probiotic strain Escherichia coli Nissle 1917 colonizes tumor sites and continuously converts ammonia metabolites into L-arginine. When injected into the tumor, this strain has been shown to increase the concentration of L-arginine within the microenvironment, leading to increased infiltration of tumor-infiltrating T cells, sustained effector T-cell functions, increased tumor-specific T-cell memory formation, and enhanced efficacy of PD-L1-blocking antibodies. 289

Recent research has highlighted the role of the gut microbiota in the antitumor effects of dietary intervention (Fig. 3 ). Specifically, enrichment of Bifidobacterium bifidum after CR increases acetate levels, which in turn elevates IFNγ + CD8 + T cells in the TME. In contrast, the antitumor effect of IF was not mediated by the gut microbiome, as it was not abrogated after the microbiota was depleted. 290 Similarly, recent studies have revealed that KD significantly influences the gut microbiota, inducing a shift from a population dominated by tolerogenic bacteria ( Lactobacilli spp., Clostridium asparagiforme ) toward a population dominated by an increase in immunogenic bacteria (such as Akkermansia muciniphila ). 237 It has been reported that a shift in the gut microbiota is partially attributable to the host’s production of ketone bodies due to the intake of a KD. Among these ketone bodies, β-HB selectively suppresses the proliferation of Bifidobacterium . This suppression subsequently leads to a reduction in intestinal Th17 immune cells. 291 Dietary methionine/cystine restriction has been shown to alter the gut microbiota and potentially contribute to immune system alterations. Specifically, this type of diet restriction promoted a significant decrease in the relative abundance of multiple Ruminococcaceae and Prevotellaceae families while increasing the presence of members of the Lactobacillaceae family. 163 Consumption of an HSD promotes an increase in the abundance of Bifidobacterium , which, due to enhanced gut permeability, infiltrates tumors, subsequently augmenting the functionality of NK cells and ultimately contributing to tumor regression. These results suggest that HSD intake modulates the gut microbiome, which may stimulate NK cell-dependent tumor immunity, thereby providing potential implications for the development of novel therapeutic interventions. 183 The intake of HSD has also been shown to inhibit enterotoxigenic Bacteroides fragilis (ETBF)-promoted colon carcinogenesis by decreasing the expression of IL-17A and iNOS, thereby inhibiting inflammation. 292 However, intake of an HSD can exacerbate Helicobacter pylori infection, contributing to gastric carcinogenesis. 293 In a mouse model of Barrett’s esophagus, feeding an HFD was observed to promote dysplasia and carcinogenesis by modulating the esophageal microenvironment and gut microbiome, thereby inducing inflammation and promoting stem cell proliferation. 294 The bile salt hydrolase (BSH) enzyme expressed by Bacteroides was also found to play a crucial role in CRC progression in overweight patients and in model mice with HFD-induced CRC. High BSH activity activates the β-catenin/CCL28 axis, resulting in an increase in immunosuppressive Tregs and accelerated CRC progression. 295 Moreover, HFD feeding can reduce the level of SCFA-producing bacteria and the rate of SCFA production, leading to decreased levels of SCFAs that can activate the MCP-1/CCR2 axis. This effect promotes M2 TAM recruitment and polarization, ultimately contributing to CRC progression. 296

figure 3

Mechanisms by which diet modulates antitumor effects and cancer treatment via modulation of the gut microbiome. a Calorie restriction (CR) elevates IFNγ + CD8 + T cells in the tumor microenvironment (TME) by enriching Bifidobacterium bifidum and increasing acetate levels. b Ketogenic diet (KD) induces a shift from tolerogenic ( Lactobacilli spp., Clostridium asparagiforme ) toward immunogenic bacteria (such as Akkermansia muciniphila ) driven by host production of ketone bodies, of which β-HB selectively inhibits the growth of bifidobacteria, resulting in KD-associated decreases in intestinal Th17 cell levels. c High-salt diet (HSD) increases the abundance of Bifidobacterium and leads to intratumoral localization of Bifidobacterium , further enhancing NK cell functions and tumor regression. HSD decreases the expression of IL-17A and iNOS and inhibits inflammation, which reduces enterotoxigenic Bacteroides fragilis (ETBF)-promoted colon carcinogenesis. HSD exacerbates Helicobacter pylori infection and promotes gastric carcinogenesis. d High-fat diet (HFD), through augmentation of queuosine-producing gut bacteria, can incite chemotherapy resistance in pancreatic cancer patients. HFD reduces SCFA-producing bacteria and SCFA production, leading to decreased levels of short-chain fatty acids (SCFAs) that activate the MCP-1/CCR2 axis, which promotes M2 TAM recruitment and polarization, ultimately contributing to colorectal cancer (CRC) progression. High bile salt hydrolase (BSH) enzyme activity in an HFD mouse model activates the β-catenin/CCL28 axis, further inducing immunosuppressive Tregs and accelerating CRC progression. e Dietary intake rich in tryptophan stimulates certain Bacteroides to produce the metabolite indole-3-acetic acid (3-IAA). Increased levels of 3-IAA enhance the efficacy of chemotherapy treatment. Dietary intake rich in tryptophan, through the action of the probiotic Lactobacillus reuteri (Lr), leads to the production of the metabolite indole-3-aldehyde (I3A). This metabolite promotes the production of IFNγ from CD8 + T cells, thereby enhancing antitumor immunity and the efficacy of immune checkpoint inhibitors (ICIs). f High-fiber diet enriches Akkermansia muciniphila which produces the microbiota-derived STING agonist c-di-AMP, inducing type I interferon (IFN-I) production by intratumoural monocytes, resulting in various TME modulation pathways, including reprogramming of mononuclear phagocytes into immunostimulatory monocytes and DCs, promoting macrophage polarization toward an antitumor phenotype and stimulating crosstalk between NK cells and DCs, further enhancing the therapeutic effect of immunotherapy. Dietary fiber inulin can enhance the effectiveness of anti-PD-1 therapy by increasing the abundance of beneficial commensal microbes (e.g., Akkermansia , Lactobacillus and Roseburia ) and SCFAs, further increasing the number of stem-like T-cell factor-1 (Tcf1) + PD-1 + CD8 + T cells numbers. Dietary fiber pectin can improve the effectiveness of anti-PD-1 therapy by increasing the abundance of butyrate-producing bacteria, further promoting T-cell infiltration and activation in the TME. This figure was created with BioRender.com

Studies suggest that the gut microbiota plays a crucial role in modulating the therapeutic response to immunotherapy. 297 , 298 In fact, specific gut microbial signatures have been shown to differentiate responders from nonresponders across various epithelial tumor types in cohorts treated with ICB. 299 Considering the profound impact of the gut microbiota on the immune system, research investigating the modulation of the gut microbiota via dietary interventions to optimize cancer treatment efficacy has been predominantly centered around immunotherapy. A high-fiber dietary intervention has been associated with significantly prolonged PFS in melanoma patients receiving ICB treatment. 300 Microbiota-derived STING agonists, specifically c-di-AMP, induce the production of type I interferon (IFN-I) in intratumoral monocytes. This activation results in the transformation of mononuclear phagocytes within the TME into immunostimulatory monocytes and DCs. Additionally, it promotes the polarization of macrophages to antitumor macrophages and stimulates crosstalk between NK cells and DCs. A high-fiber diet can trigger this mechanism by enriching the population of Akkermansia muciniphila , which produces c-di-AMP and enhances the therapeutic effect of ICB in melanoma patients. 301 The presence of Akkermansia , a mucin-degrading bacterium, is strongly associated with favorable outcomes in cancer patients. 302 Moreover, inulin, a polysaccharide dietary fiber, can enhance the effectiveness of anti-PD-1 therapy by increasing the abundance of beneficial commensal microbiota genera (e.g., Akkermansia , Lactobacillus and Roseburia ) and SCFAs, further increasing the number of stem-like T-cell factor-1 (Tcf1) + PD-1 + CD8 + T cells. 303 Similarly, oral administration of pectin, another dietary polysaccharide fiber, can largely improve the efficacy of anti-PD-1 mAbs by increasing the number of butyrate-producing bacteria, which is sufficient to promote T-cell infiltration and activation in the TME. 304

Although research into the antitumor or protumor effects of the intratumor microbiome is still in its early stages, recent studies have started to focus on how the intratumor microbiome can influence the effectiveness of immunotherapy. The colonization of Bifidobacterium in the microenvironment, combined with anti-CD47 monoclonal antibody treatment, stimulates the STING signaling pathway and enhances the cross-priming of DCs to upregulate CD8 + T cells. 305 The probiotic Lactobacillus reuteri (Lr) within melanoma promotes the local generation of IFNγ by CD8 + T cells through the release of its tryptophan breakdown metabolite, indole-3-aldehyde (I3A), thus enhancing ICI efficacy. Dietary intake rich in tryptophan boosts the antitumor immunity induced by Lr and ICI, which is dependent on the CD8 + T-cell AhR signaling pathway. 306

Apart from immunotherapy, recent research has also started to investigate how diet, by influencing the gut microbiota, could affect other forms of cancer treatment. By enriching the gut microbiome with queuosine-producing bacteria, HFD can induce chemotherapy resistance in pancreatic cancer through the upregulation of the oxidative stress protector PRDX1. This resistance can be counteracted by SAM, which is typically produced by bacteria in lean diets, highlighting the influence of diet on chemotherapy effectiveness via gut microbiome adjustments. 307 Expanding on the theme of diet’s influence on chemotherapy effectiveness in pancreatic cancer, another study revealed that the microbiota-derived tryptophan metabolite indole-3-acetic acid (3-IAA) is enriched in patients responsive to chemotherapy. Through dietary manipulation of tryptophan, an increase in 3-IAA production enhances chemotherapy efficacy by disrupting cancer cell metabolic fitness via increased reactive oxygen species and reduced autophagy. 308 These findings further emphasize the crucial role of gut microbiota modulation via dietary interventions in cancer treatment outcomes.

Despite the significant progress in this field, the complex relationships among dietary factors, the gut microbiota, and cancer treatment still need to be understood. Each individual’s microbiome is unique, influenced by genetics, diet, environment, and lifestyle, which adds layers of complexity to the task of identifying universally beneficial interventions. Additionally, the development of high-throughput technologies and bioinformatics tools for microbiome analysis will be vital in deciphering these complex interactions. These advancements could enable the identification of biomarkers for microbiome-related treatment responses and the customization of diet-based interventions to enhance the efficacy of cancer therapies. The identification of specific dietary factors and gut microbiota constituents that can enhance the effectiveness of cancer therapies may lead to the development of personalized treatments to improve therapeutic outcomes for cancer patients.

Implications of dietary intervention for other diseases

Dietary interventions may induce, prevent or delay the progression of various diseases in addition to cancer, which also influence human health and longevity. Healthy dietary patterns that are rich in fiber and beneficial nutrients may reduce the risk of disease, while unhealthy dietary patterns may increase the risk of disease and worsen clinical outcomes. 309 Here, we summarize preclinical and human studies revealing the implications and mechanisms of various dietary patterns on other diseases in addition to cancer, including neurodegenerative diseases, autoimmune diseases, CVD, and metabolic disorders.

Neurodegenerative diseases

Several neurodegenerative diseases (NDs), such as epilepsy, Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), which feature chronic progressive damage to the nervous system, have been proven to be tightly connected with nutrient availability and dietary patterns. 310 The underlying mechanisms of various dietary interventions mainly include altering neurotransmitters, remodeling, interfering with brain energy metabolism and mitochondrial function, and altering inflammation and oxidative stress. The underlying mechanisms also include altering the composition and balance of the gut microbiome, which further influence the process of neurodegeneration via the gut-brain axis (Fig. 4 ).

figure 4

Impact of different diets on neurodegenerative diseases. The ketogenic diet (KD) can enhance inhibitory neurotransmission and anti-inflammatory effects in epilepsy, influence the gut microbiota, and elevate beneficial metabolites. KD is particularly beneficial for treating pediatric drug-resistant epilepsy with elevated specific Bifidobacteria and TNF. In Alzheimer’s disease (AD) and Parkinson’s disease (PD), KD could counteract decreased β-HB levels, inhibit the NLRP3 inflammasome, reduce pathology, and alleviate symptoms by inhibiting microglial activation. Fasting mimicking diet (FMD) enhances the gut microbiota composition and metabolites, inhibiting neuroinflammation. This results in the attenuated loss of dopaminergic neurons in the substantia nigra in patients with PD. Caloric restriction (CR) may prevent AD by lowering serum tyrosine levels, reversing the exhaustion of tyrosyl-tRNA synthetase (TyrRS), and upregulating the sirtuin pathway, which attenuates the amyloidogenic processing of amyloid-β protein precursor (APP). Dietary restriction can increase brain-derived neurotrophic factor (BDNF) and chaperone heat-shock protein-70 (HSP70) levels in the striatum and cortex, which are relevant to Huntington’s disease (HD). High-fat diet (HFD) can accelerate recognition-memory impairment in an AD mouse model by increasing blood N-acetylneuraminic acid (NANA) levels, leading to systemic immune exhaustion. Conversely, the Mediterranean diet (MD) may protect against memory decline and mediotemporal atrophy by lowering amyloid-β protein and phosphorylated tau levels, reducing AD risk. This figure was created with BioRender.com

KD has been clinically applied for nearly a century as alternative therapy for childhood intractable epilepsy, but there is sufficient evidence that a modified Atkins diet (MAD) is more tolerable and has a greater probability of causing seizure reduction than a classical KD according to a systematic review. 311 , 312 , 313 Increased levels of the inhibitory neurotransmitter GABA can be observed in preclinical KD models and patient cerebrospinal fluid (CSF), dampening neuronal excitability. 314 , 315 , 316 An increase in peroxisome proliferator activated receptor gamma 2 (PPARγ2) and upregulation of hippocampal catalase in KD-fed rats are observed, which may increase anti-inflammatory and antioxidant activity. 317 In addition, a KD may upregulate potassium channels that are sensitive to ATP opening, reducing the electrical excitability of the brain and increasing the seizure threshold. 318 The gut microbiota, which includes Akkermansia , Parabacteroides , and Bifidobacteria , also contributes to the neuroprotective effects of KD on epilepsy. 319 , 320

Epidemiologic evidence indicates that obesity is an independent risk factor for AD, while HFD is closely associated with an increased risk of obesity. 321 Recognition-memory impairment in an AD mouse model (5xFAD) can be accelerated by high-fat obesogenic diet by increasing blood levels of the metabolite N-acetylneuraminic acid (NANA), which results in systemic immune exhaustion. 322 HFD may also enhance neuroinflammation by increasing circulating free fatty acids and cytokines, which may lead to cognitive impairment. 323 Conversely, healthy dietary interventions, including the Mediterranean diet (MD), CR, and KD, may prevent AD progression. 324 , 325 , 326 Adhering to MD may act as a protective factor against memory decline and mediotemporal atrophy, as indicated by decreased levels of amyloid-β protein and phosphorylated tau, reducing the risk of AD. 327 CR may prevent AD by lowering serum tyrosine levels to reverse the exhaustion of tyrosyl-tRNA synthetase (TyrRS) and upregulating the sirtuin pathway, which attenuates the amyloidogenic processing of amyloid-β protein precursor (APP), as confirmed by in vivo and in vitro models. 328 , 329 KD may reverse the decreased β-HB levels in red blood cells and the brain parenchyma of AD patients, hence inhibiting NLRP3 inflammasome activation and reducing AD pathology. 330 In addition, diet can influence AD by modulating the gut microbiome and metabolites. For instance, a Mediterranean-ketogenic diet (MMKD) is associated with improved AD biomarkers in CSF, as indicated by increased Akkermansia muciniphila levels, which modulate GABA levels and gut transit time. 331 , 332

Gut microenvironmental changes may trigger the development of PD through the gut-brain axis, as determined by the presence of α-synuclein and Lewy bodies in the enteric nervous system and the convincing association between PD and gut inflammation. 333 , 334 Research has revealed changes in the gut microbiome in PD patients compared to healthy volunteers, highlighting the potential benefits of dietary interventions in treating PD patients. 335 High serum sodium is associated with cognitive decline, as observed in the aged population. 336 However, a recent study denies the association between HSD and neurodegeneration or α-synuclein accumulation in a PLP-hαSyn model, suggesting that the mechanism of HSD needs further exploration. 337 Adhering to MD is associated with a decreased incidence of PD, the mechanisms of which may include reducing neuroinflammation, similar to AD. 338 , 339 KD ameliorates motor and nonmotor symptoms in PD patients by inhibiting microglial activation 340 . FMD promotes a favorable gut microbiota composition and metabolites and inhibits neuroinflammation, consequently attenuating the loss of dopaminergic neurons in the substantia nigra in a PD model. 341

Other neurodegenerative diseases with lower incidence rates are also relevant to dietary interventions. A clinical trial suggested that increased consumption of dairy products may increase the risk of phenoconversion, resulting in earlier onset of HD. 342 In addition, high antigliadin antibody titers in patients with HD suggest the potential value of applying gluten-free diet in HD patients. 343 A dietary restriction regimen retarded the progression of neuropathological, behavioral, and metabolic abnormalities in an HD model, resulting in an extension of life span by increasing brain-derived neurotrophic factor and chaperone heat-shock protein-70 (HSP70) levels in the striatum and cortex, the mechanisms of which still need further explanation. 344 A cross-sectional baseline analysis revealed that a higher intake of antioxidants and carotenes may result in greater ALS function. 345 Another meta-analysis revealed that a greater intake of ω-3 PUFAs is associated with a reduced risk of ALS. 346 Although weight loss has been identified as a negative prognostic factor, high-calorie fatty acid diet provides a significant survival benefit for patients in the subgroup of fast-progressing ALS patients only. 347

Autoimmune diseases

Different types of autoimmune diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), Hashimoto’s thyroiditis (HT), and multiple sclerosis (MS), can cause distinct clinical features from abnormal activation of the immune system that erroneously attacks healthy host cells and tissues. Impaired gut barrier function, also referred to as a “leaky gut”, which may disrupt the balance between tolerance and immunity to non-self-antigens, is often observed in autoimmune diseases. 348 This finding suggested a close relationship between diet, the gut, and autoimmune diseases. Dietary interventions may influence the susceptibility, progression and treatment response of these autoimmune diseases through various mechanisms, from adjusting inflammation levels and immune cell composition to adjusting the gut microbiome composition (Fig. 5 ).

figure 5

Impact of different diets on autoimmune diseases. Extravirgin olive oil (EVOO) can reduce joint inflammation and degradation in rheumatoid arthritis (RA) due to its phenolic compounds. However, the protective effects of a high-fiber diet can be reversed by Prevotella copri colonization, which promotes proinflammatory responses. Fish oil supplementation can suppress proinflammatory cytokines and cartilage degradation, improving RA outcomes. Vitamin D can inhibit the proliferation, differentiation, and function of B and T cells, potentially reducing inflammatory cytokine expression in systemic lupus erythematosus (SLE) patients. A diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) can alleviate gut symptoms in quiescent inflammatory bowel disease (IBD) patients, possibly by regulating the immune response through reducing fecal microbial abundance. However, a high-fat diet (HFD) can exacerbate pre-IBD inflammation by impairing epithelial mitochondrial bioenergetics and triggering microbiota disruptions, especially when combined with antibiotics. High salt diet (HSD) can exacerbate autoimmune conditions such as multiple sclerosis (MS) by promoting the induction of pathogenic Th17 cells. Intermittent fasting (IF) can improve MS by reducing the number of IL-17-producing T cells, increasing the number of Tregs in the gut, and enhancing antioxidative microbial metabolic pathways. However, the Western diet can impair myelin-debris clearance in microglia, hindering lesion recovery after demyelination and potentially contributing to MS induction. This figure was created with BioRender.com

A healthy MD may benefit RA by reducing inflammatory activity and increasing physical function. 349 Phenolic compounds in extravirgin olive oil (EVOO), an essential component of the MD, can decrease joint edema, cell migration, cartilage degradation and bone erosion by reducing the levels of proinflammatory cytokines and prostaglandin E2 in the joint. 350 However, the protective effect of high-fiber diet may be reversed if there exists colonization of Prevotella copri , which leads to the overproduction of organic acids, including fumarate and succinate, during the digestion of complex fibers and the promotion of proinflammatory responses in macrophages, exacerbating arthritis in an RA model. 351 In addition, abundant supplementation of fish oil benefits the clinical outcome of RA by suppressing the production of proinflammatory cytokines and cartilage degradative enzymes. 352 The erythrocyte level of ω-6 PUFAs acts as a biomarker that inverses the risk of RA, and the remission rate of RA increases when ω-3 PUFAs are added to disease-modifying anti-rheumatic drug (DMARD) treatment. 353 , 354

Dysbiosis of the gut microbiome can be observed in SLE patients, including a decreased richness and diversity of the gut microbiota and a reduced proportion of Firmicutes/Bacteroides (F/B); the latter may promote lymphocyte activation and Th17 differentiation from naïve CD4 + lymphocytes. 355 , 356 Blooming of Ruminococcus (blautia) gnavus occurs at times of high disease activity and during lupus nephritis, indicating that it is the driver of often remitting-relapsing SLE. 357 Another analysis showed that Veillonella dispar has a positive association with the activity of SLE. 358 According to a systematic review, nutritional support in the SLE population is focused mainly on interventions involving ω-3 and vitamin D. 359 The anti-inflammatory effect of ω-3 may contribute to its clinical function, similar to that of RA. 360 Vitamin D blocks the proliferation, differentiation and function of B cells and T cells, which may attenuate the expression of inflammatory cytokines in patients with SLE. 361 Inadequate levels of serum vitamin D have been observed in SLE patients, suggesting the importance of supplementing their diet with vitamin D 362 . Dietary patterns other than single nutrients as supplementary treatments for SLE still require further investigation. 363

Ulcerative colitis (UC) and Crohn’s disease (CD) are the two major clinical phenotypes of IBD. Dietary management and microbiota modulation have been clinically recommended for IBD treatment according to clinical guidelines. 364 Obesity is a risk factor for IBD, especially for CD. 365 As a potential trigger of obesity, HFD, together with antibiotics, exacerbates inflammation in pre-IBDs by impairing epithelial mitochondrial bioenergetics and triggering microbiota disruptions in mouse models. 366 However, IBD increases the risk of malnutrition, which triggers inflammatory responses and subsequently leads to poor clinical outcomes. 367 Therefore, dietary interventions and nutritional care should be planned according to the precise nutritional assessment and dietary assessment for IBD patients. 368 Exclusive enteral nutrition (EEN), the first-line therapy in pediatric patients with active CD, can effectively decrease clinical activity and reduce the complications of CD simultaneously, but its benefit in adults still lacks competent evidence. 369 Similarly, CD exclusion diet (CDED) positively correlates with the clinical remission of pediatric patients with active CD. 370 In addition, diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) can relieve the gut symptoms of patients with quiescent IBD, possibly reducing the fecal abundance of microbes and thereby regulating the immune response of the host. 371

Dietary interventions may also influence the risk and clinical outcome of other autoimmune diseases. A recent study on HT suggested that low intake of animal foods, mainly meat, has a protective effect on thyroid autoimmunity and potentially has a positive influence on redox balance, which further reduces oxidative stress-related disorders. 372 Improvement in HT has also been observed in other dietary interventions, including elimination of gluten or lactose, energy restriction, and consumption of Nigella sativa, suggesting the potential benefit of diet as a complementary treatment for HT. 373 MS is more common in western countries, suggesting diet as a potential risk factor. 374 Western diet triggers impaired myelin-debris clearance in microglia, thereby impairing lesion recovery after demyelination, which may explain its role in MS induction. 375 Moreover, an elevated intake of dietary salt can exacerbate autoimmune conditions by promoting the induction of pathogenic Th17 cells, contributing to MS. 376 Conversely, IF diet ameliorates the clinical course and pathology of MS by reducing the number of IL-17-producing T cells, increasing the number of Tregs in the gut and increasing the richness of gut bacteria, which enhance antioxidative microbial metabolic pathways. 377 Vitamin D supplementation has been shown to lower the incidence and benefit MS patients with sufficient evidence, and a “Coimbra Protocol” referring to daily doses up to 1000 I.U. vitamin D3 per kg body weight is clinically applied to treat patients with MS. 378 , 379

Cardiovascular diseases (CVD)

According to epidemiological studies, obesity and unhealthy diet are risk factors for CVD. Greater dietary fiber intake from cereal, vegetables and fruits is associated with a lower risk of CVD, suggesting that high-fiber diet is a potential protective factor. 380 An experimental model fed with diet lack of prebiotic fiber induces hypertension through inducing deficiency of SCFA production and GPR43/109A signaling, suggesting the underlying mechanisms of dietary fiber. 381 Besides, high-fiber diet and acetate supplementation can lead to changes in the gut microbiota, particularly an increase in Bacteroides acidifaciens , which is protective against the development of CVD. 382 Other healthy dietary patterns, including the Nordic diet, the Dietary Approaches to Stop Hypertension (DASH) diet, the MD, and the vegetarian diet, also have protective effects on CVD risk. 383 High sodium intake is the leading dietary risk factor for CVD. 384 High salt load may induce persistent hepatic steatosis and inflammation by inhibiting SIRT3 expression, thereby contributing to cardiovascular damage. 385 Conversely, a low-sodium diet may dampen the risk of CVD, which is highly recommended by current dietary guidelines. 386 Amino acids play different roles in the progression of CVD. Diet with high-unsaturated fatty acid composition and less saturated fat might be cardioprotective. 387 In contrast, higher intake of BCAAs is associated with increased platelet activity and arterial thrombosis formation; therefore, BCAA levels are associated with the risk of CVD. 388

Therapeutic implications of diet for CVD treatment have also been a focus of recent studies. CR attenuates hypertension, left ventricular remodeling and diastolic dysfunction in DS/obese rats by reducing cardiac oxidative stress and inflammation. 389 In addition, a combination of CR and exercise can improve cardiac mitochondrial dynamics, decrease cardiac apoptosis, and maintain cardiac [Ca 2+ ] i homeostasis in obese insulin-resistant rats. 390 CR also helps to maintain the iron homeostasis of cardiomyocytes. 391 These findings suggest the function of CR in cardiac protection. However, strictly adhering to CR is very difficult for most patients. IF is easier to perform than CR and has similar potential clinical value. 392 FMD, a 5-day fasting dietary pattern, increases cardiac vascularity and function and resistance to cardiotoxins in a high-fat, high-calorie diet (HFCD) mouse model, thereby postponing the process of cardiac aging. 393 Alternate day fasting (ADF) improves cardiovascular marker levels, including reduced fat mass, an improved fat-to-lean ratio, and increased β-HB-hydroxybutyrate levels, suggesting its clinical relevance for CVD intervention. 394 KD has a beneficial effect on the blood lipid profile, the NLRP3 inflammasome, myocardial energy metabolism, and the vascular endothelium, benefiting CVD patients. 395 However, research on healthy individuals has reported that lipid profiles deteriorate in response to a KD, suggesting that its role in preventing CVD in the normal population needs further inquiry (Fig. 6 ). 396

figure 6

Impact of different diets on cardiovascular diseases. Calorie restriction (CR) can reduce cardiac oxidative stress and inflammation, improve cardiac mitochondrial dynamics and maintain cardiac ion homeostasis, which may be protective against cardiovascular disease (CVD) in obese and/or insulin-resistant models. Fast-mimicking diet (FMD) increases cardiac vascularity and function and resistance to cardiotoxins in a high-fat, high-calorie diet (HFCD) mouse model. Alternate day fasting (ADF) improves cardiovascular markers, for example, reduced fat mass. Ketogenic diet (KD) inhibits the NLRP3 inflammasome and improves the blood lipid profile but may lead to impaired blood lipid profiles in healthy individuals. High-salt diet (HSD) can inhibit SIRT3 expression and induce persistent hepatic steatosis and inflammation, thereby contributing to cardiovascular damage. A diet lacking prebiotic fiber induces hypertension through inducing a deficiency in short-chain fatty acid (SCFA) production and GPR43/109A signaling. High branched-chain amino acid (BCAA) intake is associated with increased platelet activity and arterial thrombosis formation. This figure was created with BioRender.com

Metabolic disorders

Overnutrition is a driving factor for obesity and related metabolic disorders, mainly including type 2 diabetes mellitus (T2DM), metabolic syndrome, nonalcoholic fatty liver disease (NAFLD), and polycystic ovarian syndrome (PCOS). 397 In addition, these metabolic disorders have a complicated internal relation, for instance, T2DM and NAFLD are independent factors for each other, and PCOS is closely related to insulin resistance and T2DM. 398 , 399 These epidemiological characteristics suggest a high correlation between dietary patterns and multiple metabolic disorders (Fig. 7 ). Changes in the gut microbiome may also explain the etiology of metabolic disorders by altering the levels of metabolites, such as SCFAs and succinate. 400

figure 7

Impact of different diets on metabolic disorders. High-fat diet (HFD) can directly increase caloric intake, induce inflammatory mediators such as JNK and IκB kinase (IKK) to promote hypothalamic inflammation, and contribute to adipose tissue hypoxia and inflammation, which all lead to the development of obesity and/or insulin resistance. Over-intake of fructose can also increase caloric intake and induce obesity by impairing hepatic insulin sensitivity. However, time-restricted feeding (TRF) with equivalent caloric intake from HFD can adjust various signaling pathways and rhythmic creatine-mediated thermogenesis and reverse excessive daytime sleepiness induced by paraventricular thalamic nucleus (PVT) dysfunction, resulting in a protective effect on HFD-induced obesity. High-fiber diet can reduce inflammation and insulin resistance by influencing the gut microbiota and associated molecules, for instance, SCFA-producing bacteria. Every-other-day fasting (EODF) regimen can also shift the gut microbiota composition and stimulate beige fat development within white adipose tissue to inhibit insulin resistance. Ketogenic diet (KD) is clinically beneficial for the glycemic control of type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD). However, in experimental models, KD can decrease sensitivity to peripheral insulin by upregulating insulin receptors. Intermittent fasting (IF) alone or combined with exercise can reduce intrahepatic triglyceride (IHTG) levels and hepatic steatosis in NAFLD patients by downregulating hepatic inflammatory pathways, modifying lipogenic gene expression and inducing autophagy. Calorie restriction (CR) can be effective at reducing weight loss and reversing ovulatory/metabolic dysfunction in polycystic ovarian syndrome (PCOS) patients. This figure was created with BioRender.com

HFD is the standard method to induce obesity in animal models and results from the overconsumption of fat, which directly increases caloric intake. The elevation of inflammatory mediators such as JNK and IκB kinase (IKK) in hypothalamic inflammation may also explain the obesity induced by HFD. 401 Interestingly, a TRF with equivalent caloric intake from HFD has been shown to have a protective effect on HFD-induced obesity and associated complications by adjusting various signaling pathways and causing rhythmic creatine-mediated thermogenesis, which may further improve nutrient utilization and energy expenditure and reverse excessive daytime sleepiness induced by paraventricular thalamic nucleus (PVT) dysfunction. 402 , 403 , 404 Adipose tissue hypoxia and inflammation may lead to adipocyte dysfunction and obesity-induced insulin resistance in HFD-fed models, as indicated by increased infiltration of adipose tissue macrophages (ATMs), activation of the NLRP3 inflammasome and increased levels of proinflammatory cytokines. 405 , 406 , 407 In addition to fat intake, the overintake of fructose may also impair hepatic insulin sensitivity, and several metabolic pathways are independent of increased weight gain and caloric intake. 408 Within this complex interplay of diet, metabolism, and inflammation, IL-17 has been identified as a key player in metabolic dysregulation associated with HFD, where inhibiting IL-17A production or blocking its receptor can attenuate obesity by enhancing adipose tissue browning and energy dissipation. 409 Complementarily, IL-17F promote the expression of TGFβ1 in adipocytes, which fosters sympathetic innervation and suggests a novel therapeutic target for obesity that could stimulate thermogenic activity in fat tissue, thereby improving metabolic health and providing a potential treatment strategy for obesity and its related metabolic disorders. 410

Cohort studies have demonstrated that healthy diets, including the Portfolio diet, DASH diet, and MD, are associated with a decreased risk of T2DM. 411 , 412 , 413 The promotion of SCFA-producing bacteria induced by dietary fibers observed in T2DM patients suggests the potential value of fiber supplementation in clinical practice. 414 In addition, increased fiber consumption is associated with decreased insulin resistance, the mechanism of which mainly includes the gut microbiota and associated molecules. 415 , 416 IF is an effective strategy for controlling weight and increasing insulin sensitivity in patients with diabetes and can also improve cardiometabolic outcomes. 417 , 418 The every-other-day fasting (EODF) regimen selectively stimulates beige fat development within white adipose tissue and shifts the gut microbiota composition in experimental models, explaining the mechanism through which IF ameliorates obesity, insulin resistance, and hepatic steatosis. 419 KD has therapeutic effects on glycemia, lipid control, and weight reduction in T2DM patients. 420 However, KD may contribute to decreased sensitivity to peripheral insulin and impaired glucose tolerance by upregulating insulin receptors, as determined by previous studies, which contradicts clinical findings. 421

NAFLD features hepatic steatosis or adiposity with a potential risk of developing into inflammation, fibrosis, and cancer. MD, as the most recommended dietary pattern for NAFLD, can reduce liver steatosis and improve insulin sensitivity even without weight loss in an insulin-resistant population. 422 Reduced liver fat may be associated with ameliorated inflammation induced by antioxidants, low glycemic response induced by dietary fiber, and improved hepatic lipid metabolism. 423 KD is more clinically meaningful for glycemic control in individuals with T2DM and NAFLD than low-calorie diet or high-carbohydrate, low-fat (HCLF) diet. 424 , 425 Mechanistically, ketone bodies may modulate inflammation and fibrosis in hepatic cells. 426 IF alone or combined with exercise is effective at lowering intrahepatic triglyceride (IHTG) levels and reducing hepatic steatosis in patients with NAFLD, possibly by downregulating hepatic inflammatory pathways, modifying lipogenic gene expression and increasing levels of autophagy. 427 , 428

PCOS features a series of metabolic irregularities, mainly androgen excess and ovarian dysfunction. A meta-analysis showed that women with PCOS have a lower overall diet quality with higher cholesterol, lower magnesium and lower zinc intake. 429 Dietary modification with lower caloric intake to achieve weight loss is recommended as a first-line therapy for managing PCOS, and higher supplementary nutrient intake, including vitamin D, chromium and ω-3, may also benefit patients suffering from PCOS. 430 MD, KD and their combination can all lead to significant improvements in body weight, metabolic function and ovulatory dysfunction in PCOS patients. 431 , 432 , 433 In addition, IF may be beneficial for treating anovulatory PCOS by reducing body fat and improving menstruation, hyperandrogenemia, insulin resistance and chronic inflammation. 434 CR may also improve weight and metabolic disorders in patients with PCOS, alone or in combination with supplementation. 435 However, the exact mechanisms of these dietary interventions remain unclear and need further exploration.

While the potential of dietary interventions to influence systemic diseases of the whole body is supported by various studies, a critical outlook reveals the necessity for more rigorous, long-term clinical trials to validate these findings. It is essential to approach these interventions with caution, considering individual differences and the intricate balance of potential benefits against nutritional deficiencies or other risks.

Conclusions and perspectives

Our review provides compelling evidence that dietary interventions, including calorie restriction, fasting or FMD, KD, protein restriction diet, HSD, HFD, and high-fiber diet, have substantial potential for modulating metabolism, redirecting disease progression, and enhancing therapeutic responses. These findings highlight the pivotal role of diet, an important environmental factor, in influencing tumor metabolism and the course of various diseases, such as cancer, neurodegenerative diseases, autoimmune diseases, CVD, and metabolic disorders.

Despite compelling evidence, the potential impact of dietary interventions on disease treatment, particularly cancer treatment, is not fully understood. 436 The latest American Society of Clinical Oncology (ASCO) guidelines suggest that “there is currently insufficient evidence to recommend for or against dietary interventions such as ketogenic or low-carbohydrate diets, low-fat diets, functional foods, or fasting to improve outcomes related to quality of life (QoL), treatment toxicity, or cancer control”. 437 The intricate relationship between dietary interventions and treatment outcomes can be influenced by numerous factors, such as overall lifestyle habits, health status, specific disease type and its corresponding treatment, degree of dietary alterations, and patient adherence. A comprehensive assessment of these variables is crucial for understanding the precise impact of diet on treatment efficacy. 438 , 439

With the recognition of metabolic reprogramming inherent in disease progression, particularly in malignancies, it is becoming essential to explore the value of implementing dietary interventions and translating the evidence into practice. Future research should focus on unraveling the specific molecular mechanisms involved, which will enable the development of more effective, personalized dietary interventions that serve as adjunct therapies in comprehensive disease management.

Building upon the initial observation, it is crucial to interpret and apply these findings with caution due to potential variations and discrepancies. The efficacy of dietary interventions may vary significantly, for instance, depending on the mouse model used. 440 Each model might have unique metabolic and immune responses that could influence the outcome of dietary interventions. Similarly, the type of cancer cells used to induce tumor formation, whether primary cells derived directly from patient tissues or cultured cell lines, can have profound impacts on the experimental results. 441 Orthotopic or heterotopic transplantation technique is another significant factor that can influence how tumors respond to dietary interventions. Furthermore, the duration of treatment and the specifics of dietary interventions can substantially influence the results, as short-term interventions might not yield the same results as long-term interventions, and different dietary components could have varying effects on tumor growth and progression. 120 Therefore, future research in this field should carefully consider the design of animal models and the specifics of dietary interventions to ensure that the findings are robust and translatable to human cancer treatment.

Additionally, clinical trials with larger sample sizes and longer follow-up periods are needed to further validate the efficacy of these strategies and to identify potential side effects and contraindications. It is important for these trials to be designed to represent diverse population groups, including elderly and obese individuals, as these groups may respond differently to dietary modifications. The safety of dietary interventions is another key consideration. While dietary changes generally cause fewer side effects than pharmacological treatments, potential risks should not be overlooked. For instance, severe dietary restrictions may lead to malnutrition or other health complications, particularly in vulnerable population groups. Therefore, in addition to efficacy, these trials should systematically evaluate the safety of dietary interventions, identifying any potential side effects and contraindications.

In conclusion, dietary interventions hold great promise as a novel approach to disease management. However, to realize their full potential, it is essential to continue rigorous scientific investigations into their mechanisms of action, safety profiles, and efficacy in different patient populations. With further research, dietary interventions could become integral components of personalized medicine, providing a new avenue for the prevention and treatment of a myriad of diseases.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (82103369) and the China Postdoctoral Science Foundation (2022M710757). The funders had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript. The figures were created with Biorender.com.

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Yu-Ling Xiao, Yue Gong, Ying-Jia Qi, Zhi-Ming Shao & Yi-Zhou Jiang

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Xiao, YL., Gong, Y., Qi, YJ. et al. Effects of dietary intervention on human diseases: molecular mechanisms and therapeutic potential. Sig Transduct Target Ther 9 , 59 (2024). https://doi.org/10.1038/s41392-024-01771-x

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rust disease research paper

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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2021: Artificial Intelligence and Soft Computing pp 202–213 Cite as

Quantifying the Severity of Common Rust in Maize Using Mask R-CNN

  • Nelishia Pillay 14 ,
  • Mia Gerber 14 ,
  • Katerina Holan 15 ,
  • Steven A. Whitham 15 &
  • Dave K. Berger 16  
  • Conference paper
  • First Online: 05 October 2021

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12854))

The second sustainable development goal defined by the United Nations focuses on achieving food security and supporting sustainable agriculture. This paper focuses on one such initiative contributing to attaining this goal, namely, the identification or prediction of disease in crops. More specifically the paper examines the automated quantification of the severity of common rust in maize. Previous work has focused on using standard image processing algorithms for this problem. This is the first study, to the knowledge of the authors, employing machine learning techniques to determine the severity of common rust disease in maize. Quantifying the severity of common rust is achieved by counting the number of pustules on maize leaves and determining the surface area of the leaf covered by pustules. In this study a Mask R-CNN is used to determine this. Both the standard image processing algorithms and the Mask R-CNN were evaluated on a real-world dataset created from images of maize leaves grown in a greenhouse. The Mask R-CNN was found to outperform the standard image processing algorithms in terms of counting the number of pustules, calculation of the pustule surface area and the average pustule size. These results were found to be statistically significant at a 5% level of significance. One of the challenges with Mask R-CNN is finding suitable parameter values, which is time consuming. Future work will examine automating parameter tuning for the Mask R-CNN.

  • Plant disease severity quantification
  • Image processing

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1 Introduction

One of the sustainable development goals defined by the United Nations focuses on food security and sustainable agriculture [ 6 ]. Some of the initiatives towards attaining this goal include automated irrigation, precision agriculture, and automated detection of pests and plant disease. Global crop production is constantly under threat by pests and pathogens, and a recent study quantified average global losses of maize, wheat and rice from these biotic threats to be 22.5%, 21.5% and 30%, respectively [ 17 ]. In addition to crop disease diagnostics there is a need to develop high-throughput methods for quantifying the severity of crop disease symptoms, for example the area of lesions on a maize leaf caused by a pathogen [ 9 ].

Common rust caused by the fungus Puccinia sorghi Schwein is a widespread disease of maize in North and South America, Africa and Asia, and it can account for significant yield losses [ 18 ]. Quantification of the severity of common rust disease is done by counting rust (red-brown) lesions, referred to as pustules , and measuring the lesion surface area over a time course after artificial inoculation [ 2 ]. An example of a greenhouse maize leaf with pustules is illustrated in Fig.  1 .

figure 1

Pustules on a maize leaf caused by common rust disease

These rust disease metrics are measured at different stages of the infection by plant pathologists or plant breeders in maize improvement projects either: (i) to test rust control measures such as fungicides or biological control agents;, or (ii) to screen different varieties of maize for genetic resistance to rust [ 2 ]. The study presented in this paper examines automating the process of quantifying the severity of common rust in maize. In initial work a standard image processing algorithm was used to determine the pustule count and the area covered by pustules. This study examines the use of neural networks, namely, a Mask R-CNN [ 7 ], for this purpose. The performance of the neural network is compared to that of the standard image processing algorithm that was previously applied. The Mask R-CNN was found to outperform the image processing algorithm.

The following section provides an overview of neural networks used for plant disease diagnosis and quantification. Section  3 presents the standard image processing algorithms used for determining the pustule count and surface area covered and Sect.  4 describes the Mask R-CNN employed. Section  5 presents the experimental setup used to assess the performance of the approaches. A comparison of the performance of the standard image processing algorithms and the Mask R-CNN is discussed in Sect.  6 . Section  7 provides a summary of the findings of the study and proposes future extensions of the work.

2 Related Work

Deep neural networks have proven to be effective for predicting plant disease and determining severity from images [ 11 , 23 ]. The research into the use of deep neural networks can be categorised into two areas. The first involves classifying images as having the disease or not. This is essentially a binary classification problem with the classes being infected and not infected or a multiclass classification problem with the classes being the different levels of severity of the disease [ 11 ]. Convolutional neural networks that have proven to be effective for this include VGG16, VGG19, Inception-v3 and ResNet50.

The second area is image segmentation for quantifying disease severity. This essentially involves segmenting the image into areas so as to count the spots on a leaf. This area has not been as well researched as plant disease diagnosis. Deep neural networks have also been effective for this area [ 15 , 22 ]. More recently Faster R-CNN and Mask R-CNN [ 3 , 16 ] have been used for this purpose. An advantage that Mask R-CNN has over Faster R-CNN is that Faster R-CNN is only able to generate rectangular bounding boxes for regions of interest whereas Mask R-CNN can generate binary polygon masks for regions in the feature map, which align perfectly on a pixel level. Stated differently, Mask R-CNN is capable of performing image segmentation (more specifically instance segmentation), whereas Faster R-CNN can only do object detection. However Mask R-CNN is not the only neural network that has been applied to the task of image segmentation, other neural networks such as YOLO and U-Net are also popular choices. Mask R-CNN has however been shown in previous work to outperform both YOLO [ 4 , 5 ] as well as U-Net [ 1 , 14 , 25 ]. YOLO specifically would not be suited to the task of pustule identification as it is known to struggle with identification of small objects as well as objects that overlap [ 20 ]. Although Mask R-CNN has not before been applied to the task of common rust pustule identification on Puccinia sorghi, it has performed well in similar applications to that investigated in this paper [ 3 ], specifically it has been used for problems such as leaf counting [ 24 ] and leaf blight phenotyping [ 21 ]. Mask R-CNN appears to be the most suitable for the application at hand, namely, counting pustules and the surface area covered by pustules from the images of greenhouse leaves. The neural network produces a mask allowing for the number of pustules to be counted as well as the surface area covered to be determined from the mask.

In this study we compare the performance of Mask R-CNN to that of the standard image processing algorithms previously used to count the number of pustules and determine the surface area covered by pustules on a maize leaf. The following section presents the standard image processing algorithms used.

3 Image Processing Algorithms

The image processing pipeline, employing standard image processing algorithms, employed to determine the pustule count and surface area covered by pustules, is specified in Algorithm 1.

figure a

Algorithm 2 describes the process of dividing the image into the RGB components which is the first step of the image processing pipeline. The first two steps of Algorithm 2 prevents leaf areas that are simply chlorotic or browning from being highlighted as pustules.

figure b

The next section describes the Mask R-CNN employed to determine the pustule count and surface area covered by pustules.

4 Mask R-CNN

The Matterport [ 10 ] version of Mask R-CNN is employed in this study. The ResNet 101 convolutional neural network forms the backbone of the Mask R-CNN. ResNet 101 is pretrained on ImageNet. The parameter values for Mask R-CNN was determined empirically. These parameter values are listed in Table  1 .

The Mask R-CNN essentially performs supervised learning. The label for each image in the training set is comprised of:

The coordinates for each pustule.

The surface area of the leaf covered by the pustules.

The size of each pustule. This is used to determine the average pustule size.

The Mask R-CNN produces a mask from which the number of pustules, surface area covered by pustules and the average pustule size is determined. The performance of the Mask R-CNN is assessed using the following measures:

PCAcc - This measures the pustule count accuracy. This measure is calculated by firstly taking the difference between the number of pustules specified in the label for the training instance and the number of pustules identified by the Mask R-CNN at the same coordinates. This value is expressed as a percentage of the total number of pustules specified for the training instance and averaged over all the training instances in the training set.

SAAcc -This is a measure of the accuracy of the surface area covered by pustules determined by the Mask R-CNN. The surface area is measured in terms of the number of pixels. For each training instance the difference in the number of pixels is calculated and expressed as a percentage of the surface area specified in the label of the training instance. This percentage is then averaged over all the training instances.

APSAcc - This measure assesses the average pustule size accuracy. The difference in the average pustule size specified in the training instance and that produced by the Mask R-CNN is calculated and expressed as a percentage. This percentage is averaged over training instances.

The following section describes the experimental setup used to assess the performance of the image processing algorithms and the Mask R-CNN.

5 Experimental Setup

This section describes the experimental setup used to evaluate the performance of the standard image processing algorithms and the Mask R-CNN. The main aim of the research presented is to compare the performance of the standard image processing algorithms and Mask R-CNN. The Mann Whitney-U test has been used to ascertain the statistical significance of the results obtained. Section  5.1 describes the dataset used to evaluate the approaches and Sect.  5.2 presents the technical specifications of the machines used to run experiments.

5.1 Dataset of Common Rust Images

The dataset was created from maize plants grown in a greenhouse. The maize plants were inoculated by spraying the leaves with common rust urediniospores. Disease symptoms were monitored over a two week period. At different stages of infection, maize leaves were removed and scanned on a flatbed scanner at 1200 DPI. Common rust exhibits different stages of symptom development, and it was hypothesised that it may be more difficult to quantify the severity of common rust in one stage than another. To test this hypothesis the images were divided into two datasets:

Dataset 1 - This dataset contains leaves from the early stage of common rust.

Dataset 2 - This dataset contains leaves from the later stage of common rust.

A total of 1040 images were created, with 400 images in Dataset 1 and 640 images in Dataset 2.

One of the characteristics of maize leaves is that they are longer than they are wider. Large images can slow down the training of neural networks. To work around this problem the individual images were sliced into 8 smaller, equal sided images. This had the added benefit of increasing the size of the dataset without requiring that additional scans be taken. The slices were individually inspected and if a slice was found to not contain any pustules it was removed from the dataset.

Manual annotation using Fiji was performed to specify “ground truth” labels for each of the images (pustule number, pustule area). Rules for annotation were established with the help of a domain expert. The details of the labels are presented in Sect.  4 .

For Dataset 1 320 of the 400 images were used for training and 80 for testing. For Dataset 2 512 of the 640 images were used for training and 128 for testing.

5.2 Technical Specifications

All the algorithms were coded in Python. A multicore cluster was used to run simulations. Approximately 144 cores were used for training and 24 cores for testing.

6 Results and Discussion

This section firstly presents the results obtained by applying the Mask R-CNN to Dataset 1 and Dataset 2. A comparison between the standard image processing algorithms and the Mask R-CNN is then presented.

6.1 Mask R-CNN Performance

This section discusses the performance of the Mask R-CNN in predicting the pustule count, surface area covered by pustules and the average pustule size. Table  2 presents the training results and Table  3 the test results.

The neural network appears to have performed better for Dataset 1, i.e. images from the early stage of common rust, than for Dataset 2 which contains the late stage common rust images. A potential explanation for this discrepancy is illustrated by looking at the difference between the two datasets. Figure  2 shows a typical image taken from the Dataset 1 and Fig.  3 shows a typical image taken from Dataset 2.

figure 2

Dataset 1 example

figure 3

Dataset 2 example

The leaf in Fig.  2 not only has far less pustules, but they are also more clearly defined and well separated. The leaf in Fig.  3 has far more pustules, in addition to the quantity of pustules, certain pustules are starting to coalesce with other pustules in close proximity. The coalescing of pustules can make it difficult for even a human domain expert to correctly count the number of pustules. The other potential issue with Fig.  3 is the presence of spores on the leaf surface from pustules that have burst open. Even though these spores have not been annotated as pustules during dataset creation, they add additional noise that could affect accuracy. Future work will investigate techniques for addressing coalescing pustules and noise.

figure 4

Dataset 1 example - leaf without ground truth annotations

figure 5

Dataset 1 example - leaf with ground truth annotations

figure 6

Dataset 1 example - leaf with neural network predictions

figure 7

Dataset 2 example - leaf without ground truth annotations

6.2 Qualitative Results

This subsection presents some qualitative results in the form of images with their masks overlayed for both Dataset 1 and Dataset 2. Three example leaves from Dataset 1 are given in Fig.  4 , the ground truth annotations for these leaves are shown in Fig.  5 and the masks produced for the leaves by the neural network are shown in Fig.  6 . Three example leaves from Dataset 2 are given in Fig.  7 , the ground truth annotations for these leaves are shown in Fig.  8 and the masks produced for the leaves by the neural network are shown in Fig.  9 .

figure 8

Dataset 2 example - leaf with ground truth annotations

figure 9

Dataset 2 example - leaf with neural network predictions

6.3 Performance Comparison

This section compares the performance of the standard image processing algorithms to that of the Mask R-CNN. The performance comparison for Dataset 1 is presented in Table  4 and for Dataset 2 in Table  5 .

From Table  4 and Table  5 it is evident that the Mask R-CNN has outperformed the standard image processing algorithms for both Dataset 1 and Dataset 2. These results were found to statistically significant a 5% level of significance.

As can be anticipated the standard image processing algorithms have lower runtimes than the mask R-CNN. Table  6 lists the runtimes for all the experiments.

The training of the Mask R-CNN can be conducted offline and the testing runtimes are reasonable to use the model in realtime.

7 Conclusion and Future Work

The research presented in this paper compares the performance of standard image processing algorithms and the Mask R-CNN in quantifying the severity of common rust on maize leaves. This is the first study applying machine learning techniques to the quantification of the severity of common rust in maize. The study revealed that the Mask R-CNN is more effective than the standard image processing algorithms in quantifying the severity of common rust in maize. This result was found to be statistically significant at a 5% level of significance. As expected the runtimes for Mask R-CNN are higher than that for the standard image processing algorithms. However, the testing times are reasonable for real-time use, especially given the improvement in accuracy. The study also revealed that quantifying the severity of common rust in later stages of the disease proved to be more challenging than quantifying the severity in early stages. It is hypothesised that the possible reason for this is noise and coalescing pustules in the late stage common rust. Future work will investigate techniques for addressing this.

One of the challenges with Mask R-CNN is finding effective parameter values. Future work will investigate the automating the process of parameter tuning. Previous work has shown the effectiveness of selection perturbative hyper-heuristics [ 13 ] and evolutionary algorithms for parameter tuning [ 12 ], hence future research will investigate this for Mask R-CNN.

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Acknowledgements

This work is based on the research supported wholly/in part by the National Research Foundation of South Africa (Grant Numbers 46712). Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.

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Nelishia Pillay & Mia Gerber

Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA

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Pillay, N., Gerber, M., Holan, K., Whitham, S.A., Berger, D.K. (2021). Quantifying the Severity of Common Rust in Maize Using Mask R-CNN. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_18

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Computer Science > Computer Vision and Pattern Recognition

Title: decomposing disease descriptions for enhanced pathology detection: a multi-aspect vision-language matching framework.

Abstract: Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts, current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper, we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects, leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module, our approach aligns an input image with the diverse elements of a disease, generating aspect-centric image representations. By consolidating the matches from each aspect, we improve the compatibility between an image and its associated disease. Additionally, capitalizing on the aspect-oriented representations, we present a dual-head Transformer tailored to process known and unknown diseases, optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets, ours outperforms recent methods by up to 8.07% and 11.23% in AUC scores for seen and novel categories, respectively. Our code is released at \href{ this https URL }{ this https URL }.

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