Mechanisms of quantitative disease resistance in plants

French, Elizabeth; Kim, Bong-Suk; Iyer‐Pascuzzi, Anjali S.

doi:10.1016/j.semcdb.2016.05.015

Cited by 80 publications

(78 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tests that directly assess if the genes driving quantitative resistance include or resemble known components of the innate immune system are illuminating key aspects of quantitative resistance (Jones and Dangl, 2006;French et al, 2016). Typically, these innate immunity genes consist of plasma membranelocalized receptor-like kinases (RLKs) and/or cytoplasmic Nod-like receptors (NLRs) that detect the presence of a pathogenic microbe and initiate the appropriate immune response (Figure 2, I).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Resistance: More Than Just Perception of a Pathogen

2017

View full text Add to dashboard Cite

ORCID IDs: 0000-0001-6455-8474 (J.A.C.); 0000-0001-5759-3175 (D.J.K.)Molecular plant pathology has focused on studying large-effect qualitative resistance loci that predominantly function in detecting pathogens and/or transmitting signals resulting from pathogen detection. By contrast, less is known about quantitative resistance loci, particularly the molecular mechanisms controlling variation in quantitative resistance. Recent studies have provided insight into these mechanisms, showing that genetic variation at hundreds of causal genes may underpin quantitative resistance. Loci controlling quantitative resistance contain some of the same causal genes that mediate qualitative resistance, but the predominant mechanisms of quantitative resistance extend beyond pathogen recognition. Indeed, most causal genes for quantitative resistance encode specific defense-related outputs such as strengthening of the cell wall or defense compound biosynthesis. Extending previous work on qualitative resistance to focus on the mechanisms of quantitative resistance, such as the link between perception of microbe-associated molecular patterns and growth, has shown that the mechanisms underlying these defense outputs are also highly polygenic. Studies that include genetic variation in the pathogen have begun to highlight a potential need to rethink how the field considers broad-spectrum resistance and how it is affected by genetic variation within pathogen species and between pathogen species. These studies are broadening our understanding of quantitative resistance and highlighting the potentially vast scale of the genetic basis of quantitative resistance.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantitative Resistance: More Than Just Perception of a Pathogen

2017

View full text Add to dashboard Cite

show abstract

“…Several genes around the Qpm3.1 locus are indeed annotated to encode R-like proteins, including one TIR-NBS-LRR gene ( At3g04220 ), one TIR-NB gene ( At3g04210 ); and four receptor-like kinase/protein genes ( At3g05360 , At3g05370 , At3g05650 , and At3g05660 ); several major R-genes have been shown to be responsible for race-specific QTLs conditioning quantitative resistance to Xanthomonas bacteriain Arabidopsis and rice plants (Li et al, 1999; Debieu et al, 2015). However, the narrow spectrum of Qpm3.1 does not necessarily mean that a “defeated” or “weak” R-gene is responsible for the observed quantitative resistance (see discussion below) as diverse molecular mechanisms other than R -avr interaction have been found to underpin the quantitative resistance in multiple pathosystems (Poland et al, 2009; French et al, 2016). In fact, apart from the R-like genes mentioned above, 12 more genes are annotated to confer defense responses among the predicted 384 genes within the Qpm3.1 locus.…”

Section: Discussionmentioning

confidence: 99%

Genetic Interaction between Arabidopsis Qpm3.1 Locus and Bacterial Effector Gene hopW1-1 Underlies Natural Variation in Quantitative Disease Resistance to Pseudomonas Infection

et al. 2017

View full text Add to dashboard Cite

Wide quantitative variation in plant disease resistance across Arabidopsis wild populations has been documented and the underlying mechanisms remain largely unknown. To investigate the genetic and molecular basis of this variation, Arabidopsis recombinant inbred lines (RILs) derived from Aa-0 × Col-0 and Gie-0 × Col-0 crosses were constructed and used for inoculation with Pseudomonas syringae pathovars maculicola ES4326 (ES4326) and tomato DC3000 (DC3000). Bacterial growth assays revealed continuous distribution across the large differences between the most and the least susceptible lines in the RILs. Quantitative trait locus (QTL) mapping analyses identified a number of QTLs underpinning the variance in disease resistance, among which Qpm3.1, a major QTL on chromosome III from both Aa-0 and Gie-0 accessions, preferentially restricted the growth of ES4326. A genetic screen for the ES4326 gene selectively leading to bacterial growth inhibition on accession Aa-0 uncovered the effector gene hopW1-1. Further QTL analysis of disease in RILs inoculated with DC3000 carrying hopW1-1 showed that the genetic interaction between Qpm3.1 and hopW1-1 determined Arabidopsis resistance to bacterial infection. These findings illustrate the complexity of Arabidopsis-Pseudomonas interaction and highlight the importance of pathogen effectors in delineating genetic architectures of quantitative variation in plant disease resistance.

show abstract

“…QDR determine host resistance that results in a reduction, but not complete absence of disease. QDR can be controlled by quantitative variation in NLR or PRR activation or by completely different mechanisms (French, Kim, and Iyer-Pascuzzi 2016). QDR is frequently controlled by multiple quantitative trait loci (QTL) that interact with each other and are influenced by the environment (French, Kim, and Iyer-Pascuzzi 2016).…”

Section: Foundational Research Needs Opportunities and Challengesmentioning

confidence: 99%

“…QDR can be controlled by quantitative variation in NLR or PRR activation or by completely different mechanisms (French, Kim, and Iyer-Pascuzzi 2016). QDR is frequently controlled by multiple quantitative trait loci (QTL) that interact with each other and are influenced by the environment (French, Kim, and Iyer-Pascuzzi 2016). Some QTL may encode modifiers that enhance immunity; others may encode genes that are not components of the immune system.…”

Section: Foundational Research Needs Opportunities and Challengesmentioning

confidence: 99%

“…This has been a continual and often elusive goal in many disease control programs for decades; however, we now have opportunities to provide more durable resistance based on foundational knowledge and recent technological advances. Long-standing questions as to the molecular and genetic basis of specificity between hosts and pathogens/pests are being answered in increasing detail (Couto and Zipfel 2016; French, Kim, and Iyer-Pascuzzi 2016; Toruño, Stergiopoulos, and Coaker 2016). There is still much more to be discovered as to how the plant immune system functions and how it can be predictably deployed with minimal side effects on yield and other important agronomic traits.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Foundational and Translational Research Opportunities to Improve Plant Health

Michelmore

Coaker

Bart

et al. 2017

MPMI

View full text Add to dashboard Cite

Summary This whitepaper reports the deliberations of a workshop focused on biotic challenges to plant health held in Washington, D.C. in September 2016. Ensuring health of food plants is critical to maintaining the quality and productivity of crops and for sustenance of the rapidly growing human population. There is a close linkage between food security and societal stability; however, global food security is threatened by the vulnerability of our agricultural systems to numerous pests, pathogens, weeds, and environmental stresses. These threats are aggravated by climate change, the globalization of agriculture, and an over-reliance on non-sustainable inputs. New analytical and computational technologies are providing unprecedented resolution at a variety of molecular, cellular, organismal, and population scales for crop plants as well as pathogens, pests, beneficial microbes, and weeds. It is now possible to both characterize useful or deleterious variation as well as precisely manipulate it. Data-driven, informed decisions based on knowledge of the variation of biotic challenges and of natural and synthetic variation in crop plants will enable deployment of durable interventions throughout the world. These should be integral, dynamic components of agricultural strategies for sustainable agriculture.

show abstract

Mechanisms of quantitative disease resistance in plants

Cited by 80 publications

References 68 publications

Quantitative Resistance: More Than Just Perception of a Pathogen

Quantitative Resistance: More Than Just Perception of a Pathogen

Genetic Interaction between Arabidopsis Qpm3.1 Locus and Bacterial Effector Gene hopW1-1 Underlies Natural Variation in Quantitative Disease Resistance to Pseudomonas Infection

Foundational and Translational Research Opportunities to Improve Plant Health

Contact Info

Product

Resources

About