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Standley M, Blay V, Beleva Guthrie V, Kim J, Lyman A, Moya A, Karchin R, Camps M. Experimental and In Silico Analysis of TEM β-Lactamase Adaptive Evolution. ACS Infect Dis 2022; 8:2451-2463. [PMID: 36377311 PMCID: PMC9745794 DOI: 10.1021/acsinfecdis.2c00216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 β-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.
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Affiliation(s)
- Melissa Standley
- Department
of Microbiology and Environmental Toxicology, University of California, Santa
Cruz, California95064, United States
| | - Vincent Blay
- Department
of Microbiology and Environmental Toxicology, University of California, Santa
Cruz, California95064, United States,Institute
for Integrative Systems Biology (I2Sysbio), Universitat de València and Spanish Research Council (CSIC), 46980Valencia, Spain,
| | - Violeta Beleva Guthrie
- Department
of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland21218, United States
| | - Jay Kim
- Department
of Microbiology and Environmental Toxicology, University of California, Santa
Cruz, California95064, United States
| | - Audrey Lyman
- Department
of Microbiology and Environmental Toxicology, University of California, Santa
Cruz, California95064, United States
| | - Andrés Moya
- Institute
for Integrative Systems Biology (I2Sysbio), Universitat de València and Spanish Research Council (CSIC), 46980Valencia, Spain,Foundation
for the Promotion of Sanitary and Biomedical Research of Valencia
Region (FISABIO), 46021Valencia, Spain,CIBER
in Epidemiology and Public Health (CIBEResp), 28029Madrid, Spain
| | - Rachel Karchin
- Department
of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland21218, United States
| | - Manel Camps
- Department
of Microbiology and Environmental Toxicology, University of California, Santa
Cruz, California95064, United States,
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Cao C, Kwok D, Edie S, Li Q, Ding B, Kossinna P, Campbell S, Wu J, Greenberg M, Long Q. kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes. Brief Bioinform 2021; 22:5985285. [PMID: 33200776 DOI: 10.1093/bib/bbaa270] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/31/2022] Open
Abstract
The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).
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Affiliation(s)
- Chen Cao
- Department of Biochemistry & Molecular Biology, University of Calgary
| | - Devin Kwok
- Department of Mathematics & Statistics, University of Calgary
| | | | - Qing Li
- Department of Biochemistry & Molecular Biology, University of Calgary
| | - Bowei Ding
- Department of Mathematics & Statistics, University of Calgary
| | - Pathum Kossinna
- Department of Biochemistry & Molecular Biology, University of Calgary
| | | | - Jingjing Wu
- Department of Mathematics & Statistics, University of Calgary
| | | | - Quan Long
- Departments of Biochemistry & Molecular Biology, Medical Genetics and Mathematics & Statistics
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