Biochemistry

Abstract

CS-16-2 - Modeling degrees of genetic redundancy among paralogs in Arabidopsis thaliana

Monday, July 16
3:38 PM - 3:58 PM

Genetic redundancy refers to paralogous genes maintaining seemingly redundant functions; a single gene mutant (single mutant) may not show an apparent phenotype until additional paralogs are knocked out in combination (i.e. double or higher-order mutants). In Arabidopsis thaliana, many single mutants have no reported phenotype. This may be due to genetic redundancy or because they have conditional or extremely subtle phenotypes, among other possibilities. Here, a machine-learning approach is applied to build a model for prediction of the extent to which an A. thaliana gene pair is genetically redundant based on evolutionary conservation, duplication patterns and mechanisms, epigenetic and post-translational modifications, gene expression patterns, and network properties of paralogous gene pairs. The predictions are then tested using hold-out, published phenotype data and a library of A. thaliana Mitogen Activated Protein Kinase single and double mutants. To capture subtle and/or conditional phenotypes in single mutants, we impose low-level abiotic stress and examine growth rate, photosynthetic efficiency, and most importantly, lifetime fitness estimates that measure the combined impact of subtle phenotypes on reproductive success. With this comprehensive phenotyping, a fine-scale measure of the degree of genetic redundancy between these gene pairs is generated. The genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs that are seemingly functionally redundant. It additionally allows for more targeted generation of functionally informative double mutants, advancing the study of gene functions.


 

Co-Authors

Fanrui Meng – Michigan State University; Peipei Wang – Michigan State University; Bethany Moore – Michigan State University; Paityn Donaldson – Michigan State University ; Jeffrey Conner – Michigan State University; Patrick Krysan – University of Wisconsin-Madison; Melissa Lehti-Shiu – Michigan State University; Shin-Han Shiu – Michigan State University

Siobhan A. Cusack

graduate student
Michigan State University

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CS-16-2 - Modeling degrees of genetic redundancy among paralogs in Arabidopsis thaliana



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