1 Introduction

Northern corn leaf spot (NCLB) is a foliar disease of maize (Zea mays) caused by the fungus Setosphaeria turcica (asexual form Exserohilum turcicum) that affects many regions of the world. Its most notable symptom is the appearance of long, gray spots on leaves that sometimes form a continuous patch, causing severe leaf damage, which in turn affects photosynthesis and ultimately reduces grain yield (Poland et al., 2011; Wang et al., 2018). NCLB is widespread, especially in parts of Asia and Europe, causing severe yield losses and placing considerable economic pressure on agriculture (Van Inghelandt et al., 2012; Rashid et al., 2020; Yang et al., 2021). Although various management methods have been tried, effective control remains a challenge (Chen et al., 2015; Ranganatha et al., 2021).

A big problem facing corn breeders is that NCLB pathogens continue to evolve and overcome traditional disease resistance genes. For example, major resistance genes such as Ht1, Ht2, Ht3, and Htn1, which were once widely used, have become unstable as new pathogen virulent subspecies emerge (Welz and Geiger, 2000; Yang et al., 2021). Therefore, relying solely on these qualitative resistance genes is no longer enough, and there is an urgent need to find more durable and broad-spectrum resistance methods.

In recent years, researchers have paid more attention to quantitative resistance loci (QTLs), trying to identify and combine these genes to breed corn varieties with more robust disease resistance (Chen et al., 2015; Wang et al., 2018; Zhu et al., 2022). This approach may solve the problem of disease resistance genes being easily overcome while ensuring yield and crop health. In conclusion, developing superior maize varieties resistant to NCLB is of great significance for ensuring food security and sustainable agriculture (Welz and Geiger, 2000; Hurni et al., 2015).

This study reviewed the genetic variation associated with NCLB resistance in current maize germplasm, focusing on the key QTL and single nucleotide polymorphism (SNP) loci, evaluating the actual effects of different disease resistance genes, and also discussed the application of marker-assisted selection (MAS) technology in breeding, aiming to provide theoretical support for the development of a durable and effective disease resistance breeding strategy.

2 Pathogen and Epidemiology of Northern Corn Leaf Spot

2.1 Basic characteristics and infection mode of pathogen Exserohilum turcicum

During corn cultivation, long brown spots appear on leaves from time to time, which often reminds people of northern corn leaf spot (NCLB). The culprit is a fungus called Exserohilum turcicum, which belongs to the heterothallic gametosporic fungi. Although the spores of this fungus are slender and shaped like elongated spindles, and multiple septa (ranging from 2 to 12) can be seen on the surface, what really makes it difficult is its highly adaptable genetic "equipment".

For example, a study pointed out (Yang et al., 2021) that E. turcicum shows obvious polymorphism at a gene locus called ZmWAK-RLK1, which is related to the structure of corn cell walls and is particularly active during pathogen infection. However, just one key gene cannot explain everything - its entire genome has been sequenced, encoding about 13 000 proteins, which provides clues to understanding how it "uses its brain" to invade crops (Cao et al., 2020).

It is worth mentioning that this fungus does not launch an attack immediately after landing on the leaves. It must first germinate on the surface of the leaves through spores, and then slowly penetrate into the tissue. After the infection is successful, it can generate spores again to form a secondary transmission. In other words, it can not only fire the "first shot", but also continue to create "recoil" (Navarro et al., 2023). However, corn is not completely passive. Resistance genes such as Ht1, Ht2, Ht3 and Htn1 can quickly induce reactions when facing invasion, such as accumulating reactive oxygen and adjusting photosynthesis efficiency (Yang et al., 2021), although the intensity of the reaction varies from variety to variety.

2.2 Epidemic patterns and data differences in different production areas

The spread speed and severity of NCLB actually depend to a large extent on "time, place and people". Environmental differences and variety differences in different corn-producing areas will cause the disease to show different rhythms. In China, field data over the years have shown that the development process of this disease can be traced. Generally speaking, the earliest incidence often determines the final severity. The earlier the problem occurs, the more difficult it is to control in the later stage (Liu et al., 2022). Moreover, it is not randomly distributed in the field, and often a few plants in one area are concentrated, suggesting that the local release of pathogenic spores is the key link.

But looking overseas, things become more complicated. In Bihar, India and Malaysia, the strains of E. turcicum are different from those in China in appearance, growth rate and pathogenicity (Kutawa et al., 2017; Anwer et al., 2022). For example, there are obvious differences in colony color. Some strains grow fast, while others grow slowly. This may be due to genetic drift caused by different environmental selection pressures.

South Africa has also conducted some hybrid disease resistance assessments. The results are not surprising: different genotypes show obvious differences, and some hybrids can maintain moderate resistance in areas with high incidence of NCLB (Mtyobile and Miya, 2023). This type of variety is particularly important in areas with severe diseases. After all, it is easy to change the environment and the cost of changing seeds is lower.

2.3 The impact of environmental factors such as humidity and temperature on the progression of the disease

NCLB does not break out every year, and not every field will have problems. Environmental conditions are often "catalysts". Especially humidity and temperature - once the two are properly matched, E. turcicum is as active as a cheat. Research data from Beijing show that under high humidity and medium temperature conditions, the disease development speed is significantly accelerated, and large-scale expansion can be seen in a short period of time (Figure 1) (Liu et al., 2022).

Figure 1 New ICT based fertility management model in private dairy farm India as well as abroad

In Malaysia, experiments using different culture media to compare pathogens found that corn flour agar grew fastest, which indirectly confirmed the role of warm and humid environments in promoting its reproduction (Kutawa et al., 2017). Similar findings have been made in South Africa - some corn hybrids perform better in specific climates, which means that even if the genotype is the same, environmental conditions can greatly change the severity of the disease (Mtyobile and Miya, 2023). Therefore, choosing suitable varieties in different regions is actually negotiating with natural conditions.

3 Overview of Genetic Diversity of Corn Germplasm

3.1 Corn germplasm resources and their value in disease resistance research

The germplasm resources of corn include a variety of genetic materials such as varieties, inbred lines and hybrids. These resources are very rich and very important. Although they seem to be just different types of corn, they are actually a genetic treasure trove that is particularly critical for studying disease resistance. Take the Northern Corn Leaf Spot (NCLB) as an example. Most of the alleles used to enhance disease resistance in breeding come from these germplasm resources. For example, the Htn1 gene that was introduced into modern corn breeding lines in the 1970s was originally discovered in local varieties in Mexico (Hurni et al., 2015). Of course, it is not just Htn1. In various corn populations, scientists have found many qualitative and quantitative disease resistance genes, which also shows the necessity of protecting and utilizing these resources (Welz and Geiger, 2000).

However, traditional breeding alone is not enough. With the development of technology, genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping allow us to gain a deeper understanding of the genetic basis of disease resistance. For example, in tropical maize, GWAS found 22 SNP loci associated with NCLB resistance, indicating that these germplasm resources can indeed help us discover new disease resistance genes (Rashid et al., 2020). In addition, through QTL analysis of recombinant inbred lines, multiple QTLs for NCLB resistance were also identified (Chen et al., 2015), which makes the research and application of these resources more reliable.

3.2 Differences in genetic background of maize varieties around the world

Maize is widely distributed, and the genetic backgrounds of varieties in different regions vary significantly, which also leads to differences in their disease resistance. For example, when studying nearly 1 500 maize inbred lines in Europe, multiple SNP markers associated with NCLB resistance were found, indicating that European maize has its own unique genetic background (Van Inghelandt et al., 2012). In contrast, tropical maize lines adapted to different agricultural environments in Asia show another set of genetic variation, and GWAS analysis has found haplotypes specifically associated with NCLB resistance (Rashid et al., 2020; Zhou and Liang, 2024).

It is worth noting that the genetic background of maize not only determines their disease resistance genes, but also affects the expression and stability of these genes. The main disease resistance genes of NCLB, such as Ht2, Ht3 and Htn1, show different performances in different maize lines because they have allelic differences (Yang et al., 2021). Therefore, when breeding, it is very important not to focus on a certain disease resistance gene and ignore the genetic background of the entire variety.

3.3 Application of genetic markers in genetic diversity assessment

Evaluating maize genetic diversity is now inseparable from genetic marker technology, such as simple sequence repeats (SSR) and single nucleotide polymorphisms (SNP). These markers can provide us with more detailed information on the genetic structure of disease resistance. In particular, high-density SNP genotyping is used in GWAS to find SNPs significantly associated with NCLB resistance, which is very helpful for marker-assisted selection in breeding (Van Inghelandt et al., 2012; Rashid et al., 2020). In QTL positioning, the use of SNP markers plays an even more important role. For example, multiple QTLs that confer NCLB resistance were found on chromosomes 2, 5, and 8, one of which can even explain 16.34% of the phenotypic differences (Figure 2) (Ranganatha et al., 2021). Although SSR markers are technically a little older, they are still widely used to describe the genetic diversity and resistance loci of corn populations (Welz and Geiger, 2000), indicating that these genetic marker tools are still indispensable in genetic research.

Figure 2 LOD peak for QTL conditioning resistance to northern corn leaf blight on chromosomes 2, 5, and 8 in rainy season (Kharif) of 2013, 2014, and pooled analysis over seasons (Adopted from Ranganatha et al., 2021)

4 Disease Resistance Traits in Maize and Their Genetic Basis

4.1 Classification and function of major disease resistance genes (such as Ht gene)

Resistance of maize to northern corn leaf spot (NCLB) is controlled by the combined action of multiple genes. Generally speaking, resistance is divided into two categories: qualitative and quantitative. Qualitative resistance is often attributed to several major genes, such as Ht1, Ht2, Ht3, and Htn1, which can show strong resistance to specific pathogens. The proteins they encode are involved in the plant immune response, including nucleotide-binding leucine-rich repeat (NLR) receptors and cell wall-related receptor kinases (RLKs) (Van Inghelandt et al., 2012; Ranganatha et al., 2021; Thatcher et al., 2022). Taking Ht1 as an example, the NLR receptor it encodes can recognize specific pathogen effectors and activate defense responses. In contrast, the kinase encoded by Htn1 mainly relies on the pathogen recognition mechanism of the cell wall to activate plant innate immunity.

However, qualitative resistance is sometimes susceptible to environmental influences, while quantitative resistance is usually more stable. This resistance is regulated by multiple small effect gene loci, distributed on different chromosomes, and the related quantitative trait loci (QTL) have a certain resistance to multiple pathogens (Welz and Geiger, 2000; Poland et al., 2011; Yang et al., 2021). Studies have shown that some QTLs related to NCLB resistance were found on chromosomes 1, 3, 5 and 8, and these loci were consistent in different maize populations. It is worth noting that these QTLs often cluster with genes for resistance to other fungal diseases and pests, indicating that the genetic structure of maize disease resistance is relatively complex.

4.2 Effects of gene expression and regulation on disease resistance mechanisms

Not only the gene itself, but also the expression level and regulation mechanism of the disease resistance gene are critical. For example, the disease resistance effect of the Ht1 gene is closely related to its expression, and the susceptible allele with low expression usually lacks resistance. In the experiment, by introducing the Ht1 gene into susceptible varieties, its disease resistance can be significantly enhanced, which shows that the gene expression level is crucial for resistance (Thatcher et al., 2022). Similarly, the expression pattern of the Htn1 gene in resistant and susceptible corn is also different, further reflecting the influence of gene regulation on disease resistance (Ranganatha et al., 2021).

In addition, RNA sequencing analysis showed that the expression of related defense genes in disease-resistant corn lines changed more significantly when infected with pathogens, especially genes involved in pathogenesis proteins and secondary metabolites (Ranganatha et al., 2021; Thatcher et al., 2022). The regulation of these genes is also affected by environmental factors, resulting in differences in resistance stability. Quantitative resistance genes are often less sensitive to environmental changes and show greater stability because they act on multiple sites (Welz and Geiger, 2000; Galiano-Carneiro and Miedaner, 2017).

4.3 Phenotypic evaluation and standard measurement of disease resistance traits

When evaluating maize disease resistance traits, it is usually necessary to measure a variety of traits that reflect the plant's response to pathogens. Among them, the incubation period (IP) and the area under the disease progression curve (AUDPC) are the core indicators for evaluating NCLB quantitative resistance. These traits have high heritability and are often used in research to locate genomic regions related to disease resistance. For example, in studies involving three mapping populations, IP and AUDPC-related QTLs on chromosomes 3, 5, and 8 performed well (Welz and Geiger, 2000; Feng, 2024).

Field trials are an important means of evaluating disease resistance, especially artificial inoculation in multiple high-incidence locations, scoring according to the severity of lesions, and obtaining reliable phenotypic data (Wang et al., 2018). With the development of high-density SNP markers and genotyping technology, the accuracy of disease resistance phenotypic evaluation has been greatly improved, which has also promoted the association analysis of significant SNPs and haplotypes with resistance traits (Rashid et al., 2020). In addition, chromosome segment substitution lines (CSSLs) are widely used for QTL verification. This method effectively confirms the genetic basis of disease resistance and supports the implementation of marker-assisted breeding.

5 Summary of the Results of the Meta-analysis of Genetic Diversity and Disease Resistance in Maize

5.1 Results of the study on the relationship between genetic diversity and disease resistance in different maize germplasms

The relationship between genetic diversity and disease resistance is very complex in maize germplasm. Genome-wide association studies (GWAS) have found multiple single nucleotide polymorphisms (SNPs) associated with resistance to northern corn leaf spot (NCLB) in different maize populations. For example, in the high-resolution GWAS of tropical maize lines, 22 SNPs were significantly associated with NCLB resistance, many of which fell in some regions on chromosomes that contain well-known resistance genes, such as Ht2, Ht3, and Htn1 (Rashid et al., 2020). This suggests that genetic diversity near these genes may play a role in disease resistance.

However, the complexity of genetic diversity cannot be ignored. Through quantitative trait loci mapping (QTL), studies have found that NCLB resistance is a trait controlled by multiple genes. In the hybrids of susceptible and resistant maize lines, three major QTLs on chromosomes 2, 5, and 8 were located, explaining a large part of the phenotypic differences (Figure 2) (Ranganatha et al., 2021). It can be seen that the accumulation of multiple resistance alleles is important for improving disease resistance.

5.2 Identification of key gene loci and their role in disease resistance

Several key gene loci have been confirmed to play an important role in NCLB disease resistance. The Ht1 gene encodes a nucleotide-binding leucine-rich repeat (NLR) immune receptor and is one of the main disease resistance genes. Experiments have shown that after transgenic maize expresses Ht1, the disease symptoms are significantly reduced, indicating that it plays a significant role in the immune response (Thatcher et al., 2022).

In addition, the Htn1 gene encodes a cell wall-associated receptor-like kinase (RLK), which provides quantitative resistance by delaying the formation of lesions, which is a key link in plant defense (Hurni et al., 2015). The study also found that the allelic variation of the ZmWAK-RLK1 kinase gene is related to the main resistance sites Ht2, Ht3, and Htn1, indicating that there is an allelic relationship between these genes and they may share similar disease resistance mechanisms (Yang et al., 2021). This is of great significance for breeding work because it simplifies the genetic structure of resistance and is conducive to the breeding of new disease-resistant varieties.

5.3 Study the impact of heterogeneity on the consistency of results

Heterogeneity in maize genetic diversity and disease resistance research often affects the stability and consistency of results. Different experimental designs, environmental conditions, and genetic background differences may be the reasons. For example, different mapping populations or different phenotypic evaluation environments often lead to different NCLB resistance QTLs identified. A study using nested association mapping populations found a total of 29 QTLs, most of which had small effects, indicating that disease resistance is a complex and variable trait (Poland et al., 2011).

6 Discussion and Significance of Maize Disease Resistance Research

6.1 What is the use of these studies in disease resistance breeding?

In actual breeding, genetic research on northern corn leaf spot (NCLB) is indeed not just a "paper talk" in the laboratory. Researchers have found some QTLs (quantitative trait loci) related to resistance, such as qNCLB7.02 and qNCLB5.04, which can explain many differences in traits (Chen et al., 2015; Wang et al., 2018). These results provide a directly available "tool" for marker-assisted selection (MAS). Of course, the value of such genetic markers ultimately depends on whether they can perform well in actual breeding.

But it's not just these QTLs. Gene alleles like ZmWAK-RLK1 are directly related to major resistance loci such as Ht2, Ht3, and Htn1, and have become objects that can be "locked" in subsequent breeding (Hurni et al., 2015; Yang et al., 2021). They provide more stable genetic support for the next step of "directional resistance enhancement".

As for technologies such as genome-wide association analysis (GWAS) and high-density SNP markers, although they sound a bit technical, their core purpose is one: to "fix" disease resistance more precisely. These methods have indeed promoted some inbred lines all the way to the F6 generation, and some materials have shown stable multi-disease resistance (Ranganatha et al., 2021). In other words, combining multiple resistance loci for "integrated breeding" has begun to show results.

6.2 Are there any flaws in the study? Of course there are

Although there have been many advances in the research, there are also many problems, especially in terms of data consistency. Sometimes, the results of the same trait in different experimental environments are quite different. Environmental changes and different genetic backgrounds of materials will affect the identification of QTLs and SNPs. As a result, some sites are "useful" in one place, but have little effect in another place (Van Inghelandt et al., 2012; Rashid et al., 2020). In other words, the results we get in the experimental field may not work when we move them to different agricultural areas. This makes "promotion and application" a lot more troublesome.

6.3 Where should the research go next?

How should future research be done? There are two key directions. One is that the genetic resources should be richer, and we should not always focus on the limited types of materials. Introducing some germplasm resources from other regions may allow us to discover new disease resistance genes (Welz and Geiger, 2000; Zhu et al., 2022). Another thing is that multi-environment repeated experiments must be done, and we cannot just look at single-point results.

In addition, gene editing technologies such as CRISPR/Cas9 are becoming more and more mature. With the help of high-throughput phenotyping tools, it is hoped that which genes really play a role in disease resistance can be found out more quickly.

Another point to note is that qualitative resistance and quantitative resistance cannot be neglected. Genes such as Htn1, although not "completely preventive", can delay the development of diseases and are actually very valuable in breeding (Hurni et al., 2015). Therefore, combining different types of resistance genes may be more conducive to achieving broad-spectrum and lasting resistance.

Finally, this matter cannot be solved by a group of researchers alone. Breeding experts, farmers, and technology promotion departments must work together to truly "plant" the results of the laboratory in the fields. After all, improving disease resistance is not just to reduce disease losses, but also to ensure stable corn production and food security in the future.

7 Future Prospects of Maize Disease Resistance Research

7.1 Innovation of Molecular Marker Technology and Its Application in Disease Resistance Breeding

Speaking of plant breeding, molecular marker technology has indeed brought about great changes, especially the improvement of maize disease resistance. Methods such as marker-assisted selection (MAS) and genomic selection (GS) are becoming increasingly important. They allow breeders to quickly select materials with strong disease resistance potential from a large population without wasting too much time and resources. Coupled with the current high-density marker array combined with large-scale phenotypic data in different environments, this greatly enhances the accuracy of breeding (Yang et al., 2017; Miedaner et al., 2020; Zhu et al., 2021).

However, not all disease resistance genes are so easy to find. With the continuous improvement of molecular marker technologies such as single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), we can understand the genetic background of disease resistance traits in more detail. This is particularly critical for the precise positioning and cloning of disease resistance genes, especially for the study of quantitative disease resistance (QDR) mechanisms. It is foreseeable that with these tools, breeding for resistance to northern corn leaf spot (NCLB) and other diseases will achieve results more quickly (Lanubile et al., 2017; Yang et al., 2017; Zhu et al., 2021).

7.2 The potential of gene editing and CRISPR technology in enhancing disease resistance traits

In recent years, the emergence of gene editing technology, especially CRISPR/Cas9, has opened up new avenues for disease resistance breeding. It allows us to precisely modify the corn genome, such as knocking out genes that make corn susceptible to diseases, or introducing disease resistance genes through non-GMO methods. In fact, this technology has shown great potential in the study of fighting against a variety of pathogens such as viruses, bacteria, and fungi (Bisht et al., 2019; Yin and Qiu, 2019; Ahmad et al., 2020; Zaidi et al., 2020).

Of course, the use of CRISPR is not limited to simply knocking out genes. It can also be used to adjust the interaction between pathogen effectors and plant targets, and even design synthetic immune receptors, or intervene in the action of antagonistic defense hormones. These complex operations are expected to enable corn to obtain a broader spectrum and more durable resistance. Given the rapid development and increasingly widespread application of this technology, it will surely play an important role in corn disease resistance breeding in the future (Langner et al., 2018; Bisht et al., 2019; Ahmad et al., 2020; Zaidi et al., 2020).

7.3 New challenges brought by climate change and disease adaptability

Climate change has brought a lot of trouble to corn production, especially in terms of disease resistance. Rising temperatures, changing rainfall patterns, and more frequent extreme weather will complicate the disease problem. Pathogens may also gradually adapt to these environmental changes and even break through the existing disease resistance barriers of corn. This interaction between climate and disease requires us to respond with more active breeding strategies (Bisht et al., 2019; Miedaner et al., 2020; Zaidi et al., 2020).

8 Suggestions and Prospects

The future development direction of maize disease resistance research cannot only focus on a single technology or method, but must be considered comprehensively from multiple angles. For example, the diversity of germplasm resources has always been the basis of breeding, but it is not only the increase in quantity, but more importantly, their performance must be repeatedly verified under different environments. The genetic differences between maize germplasms in different regions are very large, which brings valuable genetic resources to disease resistance breeding. Introducing germplasms from different regions can discover some unique disease resistance genes and help create varieties that are both stable and widely resistant to diseases.

In particular, testing under multiple environmental conditions can screen out truly disease-resistant and stable genotypes. Although this process is more complicated, it is precisely because the disease resistance mechanism is diverse and complex that it can provide more practical solutions for responding to climate change and pathogen mutations. As long as it is verified through these multi-environment tests, the breeding results may be promoted to a wider range. Speaking of gene editing technology, the emergence of CRISPR/Cas9 has made new breakthroughs in maize disease resistance breeding. Unlike traditional genetic modification, CRISPR can modify genes more accurately, such as knocking out genes that are easily attacked by pathogens, or directly adding disease resistance genes. These operations can improve corn's resistance to diseases such as northern leaf spot.

However, the application of CRISPR is not limited to this. It can also regulate the relationship between effectors and targets, and even design new immune receptors, or intervene in the role of defense hormones, thereby achieving long-term resistance to a variety of pathogens. In the future, if gene editing is combined with traditional breeding, it will be more efficient and can breed excellent disease-resistant varieties more quickly. Especially in the context of climate change accelerating the spread of pathogens, this flexible and efficient technology is particularly important.

Acknowledgments

We would like to thank CropSci Publisher continuous support throughout the development of this study.”

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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