51
|
Abstract
Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.
Collapse
|
52
|
Polte C, Wedemeyer D, Oliver KE, Wagner J, Bijvelds MJC, Mahoney J, de Jonge HR, Sorscher EJ, Ignatova Z. Assessing cell-specific effects of genetic variations using tRNA microarrays. BMC Genomics 2019; 20:549. [PMID: 31307398 PMCID: PMC6632033 DOI: 10.1186/s12864-019-5864-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background By definition, effect of synonymous single-nucleotide variants (SNVs) on protein folding and function are neutral, as they alter the codon and not the encoded amino acid. Recent examples indicate tissue-specific and transfer RNA (tRNA)-dependent effects of some genetic variations arguing against neutrality of synonymous SNVs for protein biogenesis. Results We performed systematic analysis of tRNA abunandance across in various models used in cystic fibrosis (CF) research and drug development, including Fischer rat thyroid (FRT) cells, patient-derived primary human bronchial epithelia (HBE) from lung biopsies, primary human nasal epithelia (HNE) from nasal curettage, intestinal organoids, and airway progenitor-directed differentiation of human induced pluripotent stem cells (iPSCs). These were compared to an immortalized CF bronchial cell model (CFBE41o−) and two widely used laboratory cell lines, HeLa and HEK293. We discovered that specific synonymous SNVs exhibited differential effects which correlated with variable concentrations of cognate tRNAs. Conclusions Our results highlight ways in which the presence of synonymous SNVs may alter local kinetics of mRNA translation; and thus, impact protein biogenesis and function. This effect is likely to influence results from mechansistic analysis and/or drug screeining efforts, and establishes importance of cereful model system selection based on genetic variation profile. Electronic supplementary material The online version of this article (10.1186/s12864-019-5864-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Christine Polte
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany
| | - Daniel Wedemeyer
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany
| | - Kathryn E Oliver
- Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Johannes Wagner
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany
| | - Marcel J C Bijvelds
- Gastroenterology and Hepatology Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - John Mahoney
- Cystic Fibrosis Foundation CFFT Lab, Lexington, MA, 02421, USA
| | - Hugo R de Jonge
- Gastroenterology and Hepatology Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Eric J Sorscher
- Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Zoya Ignatova
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146, Hamburg, Germany.
| |
Collapse
|
53
|
Schmiedel JM, Lehner B. Determining protein structures using deep mutagenesis. Nat Genet 2019; 51:1177-1186. [PMID: 31209395 PMCID: PMC7610650 DOI: 10.1038/s41588-019-0431-x] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 04/29/2019] [Indexed: 12/12/2022]
Abstract
Determining the three-dimensional structures of macromolecules is a major goal of biological research, because of the close relationship between structure and function; however, thousands of protein domains still have unknown structures. Structure determination usually relies on physical techniques including X-ray crystallography, NMR spectroscopy and cryo-electron microscopy. Here we present a method that allows the high-resolution three-dimensional backbone structure of a biological macromolecule to be determined only from measurements of the activity of mutant variants of the molecule. This genetic approach to structure determination relies on the quantification of genetic interactions (epistasis) between mutations and the discrimination of direct from indirect interactions. This provides an alternative experimental strategy for structure determination, with the potential to reveal functional and in vivo structures.
Collapse
Affiliation(s)
- Jörn M Schmiedel
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- ICREA, Barcelona, Spain.
| |
Collapse
|
54
|
Kinney JB, McCandlish DM. Massively Parallel Assays and Quantitative Sequence-Function Relationships. Annu Rev Genomics Hum Genet 2019; 20:99-127. [PMID: 31091417 DOI: 10.1146/annurev-genom-083118-014845] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.
Collapse
Affiliation(s)
- Justin B Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA; ,
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA; ,
| |
Collapse
|
55
|
Domingo J, Baeza-Centurion P, Lehner B. The Causes and Consequences of Genetic Interactions (Epistasis). Annu Rev Genomics Hum Genet 2019; 20:433-460. [PMID: 31082279 DOI: 10.1146/annurev-genom-083118-014857] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.
Collapse
Affiliation(s)
- Júlia Domingo
- Systems Biology Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, 08003 Barcelona, Spain; , ,
| | - Pablo Baeza-Centurion
- Systems Biology Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, 08003 Barcelona, Spain; , ,
| | - Ben Lehner
- Systems Biology Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, 08003 Barcelona, Spain; , , .,Universitat Pompeu Fabra, 08003 Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
| |
Collapse
|
56
|
Horovitz A, Fleisher RC, Mondal T. Double-mutant cycles: new directions and applications. Curr Opin Struct Biol 2019; 58:10-17. [PMID: 31029859 DOI: 10.1016/j.sbi.2019.03.025] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 03/20/2019] [Indexed: 11/17/2022]
Abstract
Double-mutant cycle (DMC) analysis is a powerful approach for detecting and quantifying the energetics of both direct and long-range interactions in proteins and other chemical systems. It can also be used to unravel higher-order interactions (e.g. three-body effects) that lead to cooperativity in protein folding and function. In this review, we describe new applications of DMC analysis based on advances in native mass spectrometry and high-throughput methods such as next generation sequencing and protein complementation assays. These developments have facilitated carrying out high-throughput DMC analysis, which can be used to characterize increasingly higher-order interactions and very large interaction networks in proteins. Such studies have provided insights into the extent of cooperativity (epistasis) in protein structures. High-throughput DMC studies have also been used to validate correlated mutation analysis and can provide restraints for protein docking.
Collapse
Affiliation(s)
- Amnon Horovitz
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Rachel C Fleisher
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tridib Mondal
- Department of Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| |
Collapse
|
57
|
Proteostasis Environment Shapes Higher-Order Epistasis Operating on Antibiotic Resistance. Genetics 2019; 212:565-575. [PMID: 31015194 PMCID: PMC6553834 DOI: 10.1534/genetics.119.302138] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 04/19/2019] [Indexed: 11/18/2022] Open
Abstract
Recent studies have affirmed that higher-order epistasis is ubiquitous and can have large effects on complex traits. Yet, we lack frameworks for understanding how epistatic interactions are influenced by central features of cell physiology. In this study, we assess how protein quality control machinery-a critical component of cell physiology-affects epistasis for different traits related to bacterial resistance to antibiotics. Specifically, we disentangle the interactions between different protein quality control genetic backgrounds and two sets of mutations: (i) SNPs associated with resistance to antibiotics in an essential bacterial enzyme (dihydrofolate reductase, or DHFR) and (ii) differing DHFR bacterial species-specific amino acid background sequences (Escherichia coli, Listeria grayi, and Chlamydia muridarum). In doing so, we improve on generic observations that epistasis is widespread by discussing how patterns of epistasis can be partly explained by specific interactions between mutations in an essential enzyme and genes associated with the proteostasis environment. These findings speak to the role of environmental and genotypic context in modulating higher-order epistasis, with direct implications for evolutionary theory, genetic modification technology, and efforts to manage antimicrobial resistance.
Collapse
|
58
|
Costanzo M, Kuzmin E, van Leeuwen J, Mair B, Moffat J, Boone C, Andrews B. Global Genetic Networks and the Genotype-to-Phenotype Relationship. Cell 2019; 177:85-100. [PMID: 30901552 PMCID: PMC6817365 DOI: 10.1016/j.cell.2019.01.033] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 01/09/2019] [Accepted: 01/21/2019] [Indexed: 01/25/2023]
Abstract
Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.
Collapse
Affiliation(s)
- Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada.
| | - Elena Kuzmin
- Goodman Cancer Research Centre, McGill University, Montreal QC, Canada
| | | | - Barbara Mair
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada
| | - Jason Moffat
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada; Department of Molecular Genetics, University of Toronto, 1 Kings College Circle, Toronto ON, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada; Department of Molecular Genetics, University of Toronto, 1 Kings College Circle, Toronto ON, Canada.
| | - Brenda Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada; Department of Molecular Genetics, University of Toronto, 1 Kings College Circle, Toronto ON, Canada.
| |
Collapse
|
59
|
Computational Complexity as an Ultimate Constraint on Evolution. Genetics 2019; 212:245-265. [PMID: 30833289 DOI: 10.1534/genetics.119.302000] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 02/22/2019] [Indexed: 01/28/2023] Open
Abstract
Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima-thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semismooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics-even ones that do not follow adaptive paths-can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.
Collapse
|
60
|
Abstract
Genetic background impacts the phenotypic outcome of a mutation in different individuals; however, the underlying molecular mechanisms are often unclear. We characterized genes exhibiting conditional essentiality when mutated in two genetically distinct yeast strains. Hybrid crosses and whole-genome sequencing revealed that conditional essentiality can be associated with nonchromosomal elements or a single-modifier locus, but most involve a complex set of modifier loci. Detailed analysis of the cysteine biosynthesis pathway showed that independent, rare, single-gene modifiers, related to both up- and downstream pathway functions, can arise in multiple allelic forms from separate lineages. For several genes, we also resolved complex sets of modifying loci underlying conditional essentiality, revealing specific genetic interactions that drive an individual strain’s background effect. The phenotypic consequence of a given mutation can be influenced by the genetic background. For example, conditional gene essentiality occurs when the loss of function of a gene causes lethality in one genetic background but not another. Between two individual Saccharomyces cerevisiae strains, S288c and Σ1278b, ∼1% of yeast genes were previously identified as “conditional essential.” Here, in addition to confirming that some conditional essential genes are modified by a nonchromosomal element, we show that most cases involve a complex set of genomic modifiers. From tetrad analysis of S288C/Σ1278b hybrid strains and whole-genome sequencing of viable hybrid spore progeny, we identified complex sets of multiple genomic regions underlying conditional essentiality. For a smaller subset of genes, including CYS3 and CYS4, each of which encodes components of the cysteine biosynthesis pathway, we observed a segregation pattern consistent with a single modifier associated with conditional essentiality. In natural yeast isolates, we found that the CYS3/CYS4 conditional essentiality can be caused by variation in two independent modifiers, MET1 and OPT1, each with roles associated with cellular cysteine physiology. Interestingly, the OPT1 allelic variation appears to have arisen independently from separate lineages, with rare allele frequencies below 0.5%. Thus, while conditional gene essentiality is usually driven by genetic interactions associated with complex modifier architectures, our analysis also highlights the role of functionally related, genetically independent, and rare variants.
Collapse
|
61
|
Mair B, Moffat J, Boone C, Andrews BJ. Genetic interaction networks in cancer cells. Curr Opin Genet Dev 2019; 54:64-72. [PMID: 30974317 PMCID: PMC6820710 DOI: 10.1016/j.gde.2019.03.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 03/02/2019] [Indexed: 01/12/2023]
Abstract
The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual's environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.
Collapse
Affiliation(s)
- Barbara Mair
- Donnelly Centre, University of Toronto, ON, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada.
| |
Collapse
|
62
|
Baeza-Centurion P, Miñana B, Schmiedel JM, Valcárcel J, Lehner B. Combinatorial Genetics Reveals a Scaling Law for the Effects of Mutations on Splicing. Cell 2019; 176:549-563.e23. [PMID: 30661752 DOI: 10.1016/j.cell.2018.12.010] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 08/29/2018] [Accepted: 12/07/2018] [Indexed: 02/08/2023]
Abstract
Despite a wealth of molecular knowledge, quantitative laws for accurate prediction of biological phenomena remain rare. Alternative pre-mRNA splicing is an important regulated step in gene expression frequently perturbed in human disease. To understand the combined effects of mutations during evolution, we quantified the effects of all possible combinations of exonic mutations accumulated during the emergence of an alternatively spliced human exon. This revealed that mutation effects scale non-monotonically with the inclusion level of an exon, with each mutation having maximum effect at a predictable intermediate inclusion level. This scaling is observed genome-wide for cis and trans perturbations of splicing, including for natural and disease-associated variants. Mathematical modeling suggests that competition between alternative splice sites is sufficient to cause this non-linearity in the genotype-phenotype map. Combining the global scaling law with specific pairwise interactions between neighboring mutations allows accurate prediction of the effects of complex genotype changes involving >10 mutations.
Collapse
Affiliation(s)
- Pablo Baeza-Centurion
- Systems Biology Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
| | - Belén Miñana
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
| | - Jörn M Schmiedel
- Systems Biology Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
| | - Juan Valcárcel
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010 Barcelona, Spain.
| | - Ben Lehner
- Systems Biology Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain; Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010 Barcelona, Spain.
| |
Collapse
|
63
|
Blanco C, Janzen E, Pressman A, Saha R, Chen IA. Molecular Fitness Landscapes from High-Coverage Sequence Profiling. Annu Rev Biophys 2019; 48:1-18. [PMID: 30601678 DOI: 10.1146/annurev-biophys-052118-115333] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes.
Collapse
Affiliation(s)
- Celia Blanco
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA; , , , ,
| | - Evan Janzen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA; , , , , .,Biomolecular Science and Engineering Program, University of California, Santa Barbara, California 93106, USA
| | - Abe Pressman
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA; , , , , .,Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - Ranajay Saha
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA; , , , ,
| | - Irene A Chen
- Biomolecular Science and Engineering Program, University of California, Santa Barbara, California 93106, USA
| |
Collapse
|
64
|
Singhal S, Gomez SM, Burch CL. Recombination drives the evolution of mutational robustness. ACTA ACUST UNITED AC 2019; 13:142-149. [PMID: 31572829 DOI: 10.1016/j.coisb.2018.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Recombination can impose fitness costs as beneficial parental combinations of alleles are broken apart, a phenomenon known as recombination load. Computational models suggest that populations may evolve a reduced recombination load by reducing either the likelihood of recombination events (bring interacting loci in physical proximity) or the strength of interactions between loci (make loci more independent of one another). We review evidence for each of these possibilities and their consequences for the genotype-fitness relationship. In particular, we expect that reducing interaction strengths between loci will lead to genomes that are also robust to mutational perturbations, but reducing recombination rates alone will not. We note that both mechanisms most likely played a role in the evolution of extant populations, and that both can result in the frequently-observed pattern of physical linkage between interacting loci.
Collapse
Affiliation(s)
- Sonia Singhal
- Biology Department, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shawn M Gomez
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514.,Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514.,Joint Department of Biomedical Engineering at University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | - Christina L Burch
- Biology Department, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|