Mechanisms underlying the emergence of resistance to fungicides

Plant diseases are a major concern for global food security worldwide. Most crops are attacked by pathogens that severely challenge currently available control measures. One of the major concerns comes from the pathogens’ ability to continuously overcome pesticide treatments. Predicting the emergence of pesticide resistance in populations of the pathogen is therefore crucial for the development of sustainable control strategies. However, if multiple mechanisms of pesticide resistance have been reported, we still lack a global view of how these mechanisms connect to favor the emergence of resistance in natural populations. The diversity of resistance mechanisms reported so far suggests that complex molecular networks are at play. Indeed, protein mutations, gene duplications and changes in gene expression have all been associated with pesticide resistance.

Of the many disease-causing microorganisms, fungi alone are responsible for massive fungicide applications worldwide. In fungi as in all living organisms, individual fitness is tied to the ability to exploit environmental resources i.e the metabolism. Metabolism is one of the major targets for many fungicides and is notorious to harbor network scale properties defining its ability to sustain activity in changing environments (i.e robustness). Ultimately, fungicide resistance is therefore determined by the metabolic capacity of the pathogen at maintaining growth. Genetic and functional redundancy at metabolic pathways can therefore provide increased robustness and plasticity to the metabolic network that will favor growth in challenging conditions. Identifying the metabolic reactions that sustain robustness and plasticity of the global network represent a route to predict the risk of resistance to emerge and design novel control strategies. In this project I propose to combine genome-scale metabolic network modelling and comparative genomics studies to better understand the emergence of fungicide resistance in Zymoseptoria tritici, an important pathogen of wheat.

Publications

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6.    Steinhauer, D. et al. A dispensable paralog of succinate dehydrogenase subunit C mediates standing resistance towards a subclass of SDHI fungicides in Zymoseptoria tritici. PLoS Pathog. 15, 616904 (2019).
7.    Omrane, S. et al. Plasticity of the MFS1 Promoter Leads to Multidrug Resistance in the Wheat Pathogen Zymoseptoria tritici. mSphere 2, e00393-17 (2017).
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10.    Berman, J. & Krysan, D. J. Drug resistance and tolerance in fungi. Nature Reviews Microbiology 18, 319–331 (2020).
11.    Hu, M. & Chen, S. Non-target site mechanisms of fungicide resistance in crop pathogens: A review. Microorganisms 9, 1–19 (2021).
12.    Fouché, G. et al. Directed evolution predicts cytochrome b G37V target site modification as probable adaptive mechanism towards the QiI fungicide fenpicoxamid in Zymoseptoria tritici. Environ. Microbiol. (2021). doi:10.1111/1462-2920.15760
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19.    Medlock, G. L. et al. Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota. Cell Syst. 7, 245-257.e7 (2018).
20.    Rosenthal, A. Z. et al. Metabolic interactions between dynamic bacterial subpopulations. Elife 7, (2018).
21.    Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. 97, 5528–5533 (2000).
22.    Förster, J., Famili, I., Fu, P., Palsson, B. & Nielsen, J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253 (2003).
23.    Herrgård, M. J. et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology 26, 1155–1160 (2008).
24.    Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, (2003).
25.    Feist, A. M. et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, (2007).
26.    Orth, J. D. et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol. 7, (2011).
27.    Monk, J. M. et al. iML1515, a knowledgebase that computes Escherichia coli traits. Nature Biotechnology 35, 904–908 (2017).
28.    Yang, J. E. et al. One-step fermentative production of aromatic polyesters from glucose by metabolically engineered Escherichia coli strains. Nat. Commun. 9, (2018).
29.    Abdel-Haleem, A. M. et al. Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting. PLoS Comput. Biol. 14, (2018).
30.    Hawkins, N. J., Bass, C., Dixon, A. & Neve, P. The evolutionary origins of pesticide resistance. Biol. Rev. 94, 135–155 (2019).
31.    Garnault, M. et al. Large-scale study validates that regional fungicide applications are major determinants of resistance evolution in the wheat pathogen Zymoseptoria tritici in France. New Phytol. 229, 3508–3521 (2021).
32.    Hellin, P. et al. Spatio‐temporal distribution of DMI and SDHI fungicide resistance of Zymoseptoria tritici throughout Europe based on frequencies of key target‐site alterations. Pest Manag. Sci. (2021). doi:10.1002/ps.6601
33.    Estep, L. K. et al. Emergence and early evolution of fungicide resistance in North American populations of Zymoseptoria tritici. Plant Pathol. 64, 961–971 (2015).
34.    Foster, A. J. Identification of Fungicide Targets in Pathogenic Fungi. in Physiology and Genetics 277–296 (Springer International Publishing, 2018). doi:10.1007/978-3-319-71740-1_9
35.    Mohd-Assaad, N., McDonald, B. A. & Croll, D. Multilocus resistance evolution to azole fungicides in fungal plant pathogen populations. Mol. Ecol. 25, 6124–6142 (2016).