Rotating and stacking genes can improve crop resistance durability while potentially selecting highly virulent pathogen strains

Strategies for breeding crops for future environments

The green revolution successfully increased agricultural output in the early 1960s by relying primarily on three pillars: plant breeding, irrigation, and chemical fertilization. Today, the need to reduce the use of chemical fertilizers, water scarcity, and future environmental changes, together with a growing population, requires innovative strategies to adapt to a new context and prevent food shortages. Therefore, scientists from around the world are directing their efforts to breed crops for future environments to sustainably produce more nutritious food. Herein, we propose scientific avenues to be reinforced in selecting varieties, including crop wild relatives, either for monoculture or mixed cropping systems, taking advantage of plant-microbial interactions, while considering the diversity of organisms associated with crops and unlocking combinatorial nutritional stresses.

Genetic manipulation of Indian mustard genotypes with WRR-gene(s) confers resistance against Albugo candida

BACKGROUND

Brassica species is the second most important edible oilseed crop in India. Albugo candida (Pers.) Kuntze, a major oomycete disease of oilseed brassica causing white rust, leads to 60% yield loss globally. The prevalence of A. candida race 2 (Ac2V) that specifically infects B. juncea, coupled with limitations of conventional methods has resulted in a dearth of white rust resistance resources in cultivated varieties.

METHODS AND RESULTS

In an effort to develop resistant plants, Agrobacterium mediated genetic transformation of three B. juncea genotypes viz., susceptible host var. Varuna, along with its doubled haploid mutant lines C66 and C69 (showing moderate tolerance to field isolates of A. candida) was initiated to transfer resistance genes (WRR8Sf-2 and WRR9Hi-0) identified in Arabidopsis thaliana against race Ac2V, that encode for Toll-like/interleukin-1 receptor-nucleotide binding-leucine-rich repeat proteins that recognize effectors of the pathogen races.

CONCLUSIONS

Our results demonstrate that introduction of resistance genes from a tertiary gene pool by genetic transformation enhances disease resistance in B. juncea genotypes to a highly virulent Ac2V isolate.

Effects of pathogen reproduction system on the evolutionary and epidemiological control provided by deployment strategies for two major resistance genes in agricultural landscapes

Resistant cultivars are of value for protecting crops from disease, but can be rapidly overcome by pathogens. Several strategies have been proposed to delay pathogen adaptation (evolutionary control), while maintaining effective protection (epidemiological control). Resistance genes can be (i) combined in the same cultivar (pyramiding), (ii) deployed in different cultivars sown in the same field (mixtures) or in different fields (mosaics), or (iii) alternated over time (rotations). The outcomes of these strategies have been investigated principally in pathogens displaying pure clonal reproduction, but many pathogens have at least one sexual event in their annual life cycles. Sexual reproduction may promote the emergence of superpathogens adapted to all the resistance genes deployed. Here, we improved the spatially explicit stochastic model landsepi to include pathogen sexual reproduction, and we used the improved model to investigate the effect of sexual reproduction on evolutionary and epidemiological outcomes across deployment strategies for two major resistance genes. Sexual reproduction favours the establishment of a superpathogen when single mutant pathogens are present together at a sufficiently high frequency, as in mosaic and mixture strategies. However, sexual reproduction did not affect the strategy recommendations for a wide range of mutation probabilities, associated fitness costs, and landscape organisations.

Future of Bacterial Disease Management in Crop Production

Bacterial diseases are a constant threat to crop production globally. Current management strategies rely on an array of tactics, including improved cultural practices; application of bactericides, plant activators, and biocontrol agents; and use of resistant varieties when available. However, effective management remains a challenge, as the longevity of deployed tactics is threatened by constantly changing bacterial populations. Increased scrutiny of the impact of pesticides on human and environmental health underscores the need for alternative solutions that are durable, sustainable, accessible to farmers, and environmentally friendly. In this review, we discuss the strengths and shortcomings of existing practices and dissect recent advances that may shape the future of bacterial disease management. We conclude that disease resistance through genome modification may be the most effective arsenal against bacterial diseases. Nonetheless, more research is necessary for developing novel bacterial disease management tactics to meet the food demand of a growing global population.

Models of Plant Resistance Deployment

Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.