Submitted by Ksenia Krasileva on
Background
Unlike humans, plants lack adaptive immune systems and instead rely on germline-encoded innate immunity. A critical component of this innate defense is the nucleotide-binding leucine-rich repeat receptors (NLRs). NLRs recognize pathogenic molecules known as effectors within plant cells, triggering robust immune responses to counteract pathogen proliferation and spread.
Plants and their pathogens engage in a co-evolutionary battle for survival. Pathogens constantly alter their effectors to evade NLR recognition, while plants evolve their NLRs to regain the ability to detect pathogen effectors. Unfortunately, pathogens with shorter lifecycles and greater genomic adaptability often outpace plants in this arms race. Given the genetic uniformity of many crop fields, the consequences of this struggle can be devastating, leading to the widespread destruction of entire crops.
Project description
The field of plant-pathogen interactions has sought ways to engineer NLRs to enhance plant health and food security. However, the NLR-effector interaction is inherently complex, and finding solutions has proven challenging. The Krasileva lab leverages recent advances in machine learning, particularly AlphaFold, to develop methods for engineering NLRs. In this project, by analyzing computational models, we generate hypotheses about protein-protein interactions between NLRs and their effectors. To verify these hypotheses, we perform site-directed mutagenesis to introduce amino acid mutations on the NLRs and effectors and transiently express these mutants in Nicotiana benthamiana. We then phenotype and quantify the disease resistance responses to prove and disprove the hypothesis. These phenotypic data guides the generation of the next hypotheses. Once we generate a reasonable structural model, we attempt engineering NLRs to recognize structurally similar pathogen effectors, thereby expanding the resistance gene pool that can be employed in the agricultural sectors.
In this project, students will routinely perform the following tasks and acquire relevant skillsets:
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Conduct site-directed mutagenesis to introduce amino acid mutations in the genes of interest.
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Transform E. coli and Agrobacterium.
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Infiltrate Nicotiana benthamiana for transient gene expression and assess disease responses.
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Transplant seedlings and manage plants.
In addition to these tasks, students, based on their interests, will have the opportunity to learn to:
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Explore sequence databases, such as NCBI and UniProt, and gain insight into gene and protein evolution.
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Predict protein structures using AlphaFold and interpret these structures.
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Develop coding skills in Python and bash.
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Acquire knowledge of techniques in genomics, bioinformatics, and computational biology.
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Engage in hypothesis testing and data analysis.
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Create data visualizations and generate publication-quality figures.
Students applying for this project should meet the following qualifications:
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Have completed at least one college-level general biology course, such as Biology 1A, and possess a basic understanding of biological principles and systems.
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Have a genuine interest in plant biology, microbiology, and potentially computational biology.
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Demonstrate effective communication and critical thinking skills.
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Be willing to commit a set amount of time to research throughout the semester.
Prior programming experience is encouraged but not mandatory, and previous research experience is not required.