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Liberles DA, Meyer MM, Rest JS, Teufel AI. 2021 Zuckerkandl Prize. J Mol Evol 2021. [PMID: 34919154 DOI: 10.1007/s00239-021-10041-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- David A Liberles
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA.
| | - Michelle M Meyer
- Department of Biology, Boston College, Chestnut Hill, MA, 02467, USA
| | - Joshua S Rest
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Ashley I Teufel
- Department of Life Sciences, Texas A&M University- San Antonio, San Antonio, TX, 78224, USA
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2
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Klein B, Holmér L, Smith KM, Johnson MM, Swain A, Stolp L, Teufel AI, Kleppe AS. A computational exploration of resilience and evolvability of protein-protein interaction networks. Commun Biol 2021; 4:1352. [PMID: 34857859 PMCID: PMC8639913 DOI: 10.1038/s42003-021-02867-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 11/03/2021] [Indexed: 11/09/2022] Open
Abstract
Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.
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Affiliation(s)
- Brennan Klein
- Network Science Institute, Northeastern University, Boston, MA, USA. .,Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA.
| | - Ludvig Holmér
- grid.419684.60000 0001 1214 1861Center for Data Analytics, Stockholm School of Economics, Stockholm, Sweden
| | - Keith M. Smith
- grid.12361.370000 0001 0727 0669Department of Physics and Mathematics, Nottingham Trent University, Nottingham, UK
| | - Mackenzie M. Johnson
- grid.89336.370000 0004 1936 9924Department of Integrative Biology, University of Texas at Austin, Austin, TX USA
| | - Anshuman Swain
- grid.164295.d0000 0001 0941 7177Department of Biology, University of Maryland, College Park, MD USA
| | - Laura Stolp
- grid.7177.60000000084992262Graduate School of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Ashley I. Teufel
- grid.89336.370000 0004 1936 9924Department of Integrative Biology, University of Texas at Austin, Austin, TX USA ,grid.209665.e0000 0001 1941 1940Santa Fe Institute, Santa Fe, NM USA ,grid.469272.c0000 0001 0180 5693Texas A&M University, San Antonio, San Antonio, TX USA
| | - April S. Kleppe
- grid.5949.10000 0001 2172 9288Institute for Evolution and Biodiversity, University of Münster, Münster, Germany ,grid.7048.b0000 0001 1956 2722Department of Clinical Medicine (MOMA), Aarhus University, Aarhus, Denmark
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3
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Ahrens JB, Teufel AI, Siltberg-Liberles J. A Phylogenetic Rate Parameter Indicates Different Sequence Divergence Patterns in Orthologs and Paralogs. J Mol Evol 2020; 88:720-730. [PMID: 33118098 DOI: 10.1007/s00239-020-09969-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 10/15/2020] [Indexed: 10/23/2022]
Abstract
Heterotachy-the change in sequence evolutionary rate over time-is a common feature of protein molecular evolution. Decades of studies have shed light on the conditions under which heterotachy occurs, and there is evidence that site-specific evolutionary rate shifts are correlated with changes in protein function. Here, we present a large-scale, computational analysis using thousands of protein sequence alignments from animal and plant proteomes, representing genes related either by orthology (speciation events) or paralogy (gene duplication), to compare sequence divergence patterns in orthologous vs. paralogous sequence alignments. We use sequence-based phylogenetic analyses to infer overall sequence divergence (tree length/number of sequences) and to fit site-specific rates to a discrete gamma distribution with a shape parameter α. This inference method is applied to real protein sequence alignments, as well as alignments simulated under various models of protein sequence evolution. Our simulations indicate that sequence divergence and the α parameter are positively correlated when sequences evolve with heterotachy, meaning that inferred site rate distributions appear more uniform as sequences diverge. Divergence and α are also positively correlated in both orthologous and paralogous genes, but the average increase in α (as a function of divergence) is significantly higher in paralogous protein alignments than in orthologous alignments. This result is consistent with the widely held view that recently duplicated proteins initially evolve under relaxed selective pressure, promoting functional divergence by accumulation of amino acid replacements, and hence experience more evolutionary rate fluctuations than orthologous proteins. We discuss these findings in the context of the ortholog conjecture, a long-standing assumption in molecular evolution, which posits that protein sequences related by orthology tend to be more functionally conserved than paralogous proteins.
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Affiliation(s)
- Joseph B Ahrens
- Department of Biological Sciences, Biomolecular Sciences Institute, Florida International University, Miami, FL, USA. .,Department of Biochemistry and Molecular Genetics, Computational Bioscience Program, University of Colorado Denver, Aurora, CO, USA.
| | - Ashley I Teufel
- Department of Integrative Biology, The University of Texas At Austin, Austin, TX, USA.,Santa Fe Institute, Santa Fe, NM, USA
| | - Jessica Siltberg-Liberles
- Department of Biological Sciences, Biomolecular Sciences Institute, Florida International University, Miami, FL, USA.
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4
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Laurent JM, Garge RK, Teufel AI, Wilke CO, Kachroo AH, Marcotte EM. Humanization of yeast genes with multiple human orthologs reveals functional divergence between paralogs. PLoS Biol 2020; 18:e3000627. [PMID: 32421706 PMCID: PMC7259792 DOI: 10.1371/journal.pbio.3000627] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 05/29/2020] [Accepted: 04/14/2020] [Indexed: 01/17/2023] Open
Abstract
Despite over a billion years of evolutionary divergence, several thousand human genes possess clearly identifiable orthologs in yeast, and many have undergone lineage-specific duplications in one or both lineages. These duplicated genes may have been free to diverge in function since their expansion, and it is unclear how or at what rate ancestral functions are retained or partitioned among co-orthologs between species and within gene families. Thus, in order to investigate how ancestral functions are retained or lost post-duplication, we systematically replaced hundreds of essential yeast genes with their human orthologs from gene families that have undergone lineage-specific duplications, including those with single duplications (1 yeast gene to 2 human genes, 1:2) or higher-order expansions (1:>2) in the human lineage. We observe a variable pattern of replaceability across different ortholog classes, with an obvious trend toward differential replaceability inside gene families, and rarely observe replaceability by all members of a family. We quantify the ability of various properties of the orthologs to predict replaceability, showing that in the case of 1:2 orthologs, replaceability is predicted largely by the divergence and tissue-specific expression of the human co-orthologs, i.e., the human proteins that are less diverged from their yeast counterpart and more ubiquitously expressed across human tissues more often replace their single yeast ortholog. These trends were consistent with in silico simulations demonstrating that when only one ortholog can replace its corresponding yeast equivalent, it tends to be the least diverged of the pair. Replaceability of yeast genes having more than 2 human co-orthologs was marked by retention of orthologous interactions in functional or protein networks as well as by more ancestral subcellular localization. Overall, we performed >400 human gene replaceability assays, revealing 50 new human-yeast complementation pairs, thus opening up avenues to further functionally characterize these human genes in a simplified organismal context.
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Affiliation(s)
- Jon M. Laurent
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Institute for Systems Genetics, NYU Langone Health, New York, New York, United States of America
| | - Riddhiman K. Garge
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, United States of America
| | - Ashley I. Teufel
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Claus O. Wilke
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Aashiq H. Kachroo
- The Department of Biology, Centre for Applied Synthetic Biology, Concordia University, Montreal, Quebec, Canada
| | - Edward M. Marcotte
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, United States of America
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5
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Shoemaker LG, Barner AK, Bittleston LS, Teufel AI. Quantifying the relative importance of variation in predation and the environment for species coexistence. Ecol Lett 2020; 23:939-950. [PMID: 32255558 DOI: 10.1111/ele.13482] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/20/2019] [Accepted: 01/19/2020] [Indexed: 12/25/2022]
Abstract
Coexistence and food web theory are two cornerstones of the long-standing effort to understand how species coexist. Although competition and predation are known to act simultaneously in communities, theory and empirical study of these processes continue to be developed largely independently. Here, we integrate modern coexistence theory and food web theory to simultaneously quantify the relative importance of predation and environmental fluctuations for species coexistence. We first examine coexistence in a theoretical, multitrophic model, adding complexity to the food web using machine learning approaches. We then apply our framework to a stochastic model of the rocky intertidal food web, partitioning empirical coexistence dynamics. We find the main effects of both environmental fluctuations and variation in predator abundances contribute substantially to species coexistence. Unexpectedly, their interaction tends to destabilise coexistence, leading to new insights about the role of bottom-up vs. top-down forces in both theory and the rocky intertidal ecosystem.
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Affiliation(s)
| | - Allison K Barner
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, 94720, USA.,Department of Biology, Colby College, Waterville, ME, 04901, USA
| | - Leonora S Bittleston
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Biological Sciences, Boise State University, Boise, ID, 83725, USA
| | - Ashley I Teufel
- Santa Fe Institute, Santa Fe, NM, 87501, USA.,Department of Integrative Biology, The University of Texas at Austin, Austin, TX, 78712, USA
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6
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Teufel AI, Johnson MM, Laurent JM, Kachroo AH, Marcotte EM, Wilke CO. The Many Nuanced Evolutionary Consequences of Duplicated Genes. Mol Biol Evol 2019; 36:304-314. [PMID: 30428072 DOI: 10.1093/molbev/msy210] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Gene duplication is seen as a major source of structural and functional divergence in genome evolution. Under the conventional models of sub or neofunctionalization, functional changes arise in one of the duplicates after duplication. However, we suggest here that the presence of a duplicated gene can result in functional changes to its interacting partners. We explore this hypothesis by in silico evolution of a heterodimer when one member of the interacting pair is duplicated. We examine how a range of selection pressures and protein structures leads to differential patterns of evolutionary divergence. We find that a surprising number of distinct evolutionary trajectories can be observed even in a simple three member system. Further, we observe that selection to correct dosage imbalance can affect the evolution of the initial function in several unexpected ways. For example, if a duplicate is under selective pressure to avoid binding its original binding partner, this can lead to changes in the binding interface of a nonduplicated interacting partner to exclude the duplicate. Hence, independent of the fate of the duplicate, its presence can impact how the original function operates. Additionally, we introduce a conceptual framework to describe how interacting partners cope with dosage imbalance after duplication. Contextualizing our results within this framework reveals that the evolutionary path taken by a duplicate's interacting partners is highly stochastic in nature. Consequently, the fate of duplicate genes may not only be controlled by their own ability to accumulate mutations but also by how interacting partners cope with them.
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Affiliation(s)
- Ashley I Teufel
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX
| | - Mackenzie M Johnson
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX
| | - Jon M Laurent
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX.,Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX.,Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, New York University Langone Health, New York, NY
| | - Aashiq H Kachroo
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX.,Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX.,The Department of Biology, Centre for Applied Synthetic Biology, Concordia University, Montreal, QC, Canada
| | - Edward M Marcotte
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX.,Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX
| | - Claus O Wilke
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX
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7
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Teufel AI, Johnson MM, Laurent JM, Kachroo AH, Marcotte EM, Wilke CO. Withdrawn as Duplicate: The many nuanced evolutionary consequences of duplicated genes. Mol Biol Evol 2018; 35:e1. [PMID: 30445614 DOI: 10.1093/molbev/msy216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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8
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Abstract
This themed issue centered on the evolution and structure of proteins and proteomes is comprised of seven published manuscripts. [...].
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Affiliation(s)
- David A Liberles
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA 19122, USA.
| | - Ashley I Teufel
- Department of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA.
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9
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Abstract
Sigmoidal and double-sigmoidal dynamics are commonly observed in many areas of biology. Here we present sicegar, an R package for the automated fitting and classification of sigmoidal and double-sigmoidal data. The package categorizes data into one of three categories, “no signal,” “sigmoidal,” or “double-sigmoidal,” by rigorously fitting a series of mathematical models to the data. The data is labeled as “ambiguous” if neither the sigmoidal nor double-sigmoidal model fit the data well. In addition to performing the classification, the package also reports a wealth of metrics as well as biologically meaningful parameters describing the sigmoidal or double-sigmoidal curves. In extensive simulations, we find that the package performs well, can recover the original dynamics even under fairly high noise levels, and will typically classify curves as “ambiguous” rather than misclassifying them. The package is available on CRAN and comes with extensive documentation and usage examples.
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Affiliation(s)
- M Umut Caglar
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - Ashley I Teufel
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - Claus O Wilke
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
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10
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Teufel AI, Wilke CO. Accelerated simulation of evolutionary trajectories in origin-fixation models. J R Soc Interface 2017; 14:20160906. [PMID: 28228542 PMCID: PMC5332577 DOI: 10.1098/rsif.2016.0906] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 01/31/2017] [Indexed: 11/12/2022] Open
Abstract
We present an accelerated algorithm to forward-simulate origin-fixation models. Our algorithm requires, on average, only about two fitness evaluations per fixed mutation, whereas traditional algorithms require, per one fixed mutation, a number of fitness evaluations of the order of the effective population size, Ne Our accelerated algorithm yields the exact same steady state as the original algorithm but produces a different order of fixed mutations. By comparing several relevant evolutionary metrics, such as the distribution of fixed selection coefficients and the probability of reversion, we find that the two algorithms behave equivalently in many respects. However, the accelerated algorithm yields less variance in fixed selection coefficients. Notably, we are able to recover the expected amount of variance by rescaling population size, and we find a linear relationship between the rescaled population size and the population size used by the original algorithm. Considering the widespread usage of origin-fixation simulations across many areas of evolutionary biology, we introduce our accelerated algorithm as a useful tool for increasing the computational complexity of fitness functions without sacrificing much in terms of accuracy of the evolutionary simulation.
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Affiliation(s)
- Ashley I Teufel
- Department of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Claus O Wilke
- Department of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA
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11
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Orlenko A, Teufel AI, Chi PB, Liberles DA. Selection on metabolic pathway function in the presence of mutation-selection-drift balance leads to rate-limiting steps that are not evolutionarily stable. Biol Direct 2016; 11:31. [PMID: 27393343 PMCID: PMC4938953 DOI: 10.1186/s13062-016-0133-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 07/02/2016] [Indexed: 11/15/2022] Open
Abstract
Background While commonly assumed in the biochemistry community that the control of metabolic pathways is thought to be critical to cellular function, it is unclear if metabolic pathways generally have evolutionarily stable rate limiting (flux controlling) steps. Results A set of evolutionary simulations using a kinetic model of a metabolic pathway was performed under different conditions to evaluate the evolutionary stability of rate limiting steps. Simulations used combinations of selection for steady state flux, selection against the cost of molecular biosynthesis, and selection against the accumulation of high concentrations of a deleterious intermediate. Two mutational regimes were used, one with mutations that on average were neutral to molecular phenotype and a second with a preponderance of activity-destroying mutations. The evolutionary stability of rate limiting steps was low in all simulations with non-neutral mutational processes. Clustering of parameter co-evolution showed divergent inter-molecular evolutionary patterns under different evolutionary regimes. Conclusions This study provides a null model for pathway evolution when compensatory processes dominate with potential applications to predicting pathway functional change. This result also suggests a possible mechanism in which studies in statistical genetics that aim to associate a genotype to a phenotype assuming independent action of variants may be mis-specified through a mis-characterization of the link between individual gene function and pathway function. A better understanding of the genotype-phenotype map has potential applications in differentiating between compensatory changes and directional selection on pathways as well as detecting SNPs and fixed differences that might have phenotypic effects. Reviewers This article was reviewed by Arne Elofsson, David Ardell, and Shamil Sunyaev. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0133-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alena Orlenko
- Center for Computational Genetics and Genomics and Department of Biology, Temple University, Bio-Life Building, 1900 N. 12th Street, Philadelphia, PA, 19122-1801, USA.,Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA
| | - Ashley I Teufel
- Center for Computational Genetics and Genomics and Department of Biology, Temple University, Bio-Life Building, 1900 N. 12th Street, Philadelphia, PA, 19122-1801, USA.,Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA
| | - Peter B Chi
- Center for Computational Genetics and Genomics and Department of Biology, Temple University, Bio-Life Building, 1900 N. 12th Street, Philadelphia, PA, 19122-1801, USA.,Department of Mathematics and Computer Science, Ursinus College, Collegeville, PA, 19426, USA
| | - David A Liberles
- Center for Computational Genetics and Genomics and Department of Biology, Temple University, Bio-Life Building, 1900 N. 12th Street, Philadelphia, PA, 19122-1801, USA. .,Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA.
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12
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Teufel AI, Liu L, Liberles DA. Models for gene duplication when dosage balance works as a transition state to subsequent neo-or sub-functionalization. BMC Evol Biol 2016; 16:45. [PMID: 26897341 PMCID: PMC4761171 DOI: 10.1186/s12862-016-0616-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 02/12/2016] [Indexed: 01/29/2023] Open
Abstract
Background Dosage balance has been described as an important process for the retention of duplicate genes after whole genome duplication events. However, dosage balance is only a temporary mechanism for duplicate gene retention, as it ceases to function following the stochastic loss of interacting partners, as dosage balance itself is lost with this event. With the prolonged period of retention, on the other hand, there is the potential for the accumulation of substitutions which upon release from dosage balance constraints, can lead to either subsequent neo-functionalization or sub-functionalization. Mechanistic models developed to date for duplicate gene retention treat these processes independently, but do not describe dosage balance as a transition state to eventual functional change. Results Here a model for these processes (dosage plus neofunctionalization and dosage plus subfunctionalization) has been built within an existing framework. Because of the computational complexity of these models, a simpler modeling framework that captures the same information is also proposed. This model is integrated into a phylogenetic birth-death model, expanding the range of available models. Conclusions Including further levels of biological reality in methods for gene tree/species tree reconciliation should not only increase the accuracy of estimates of the timing and evolutionary history of genes but can also offer insight into how genes and genomes evolve. These new models add to the tool box for characterizing mechanisms of duplicate gene retention probabilistically.
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Affiliation(s)
- Ashley I Teufel
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. .,Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA.
| | - Liang Liu
- Department of Statistics, University of Georgia, Athens, GA, 30602, USA.
| | - David A Liberles
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. .,Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA.
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13
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Zhao J, Teufel AI, Liberles DA, Liu L. A generalized birth and death process for modeling the fates of gene duplication. BMC Evol Biol 2015; 15:275. [PMID: 26643106 PMCID: PMC4672517 DOI: 10.1186/s12862-015-0539-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Accepted: 11/10/2015] [Indexed: 01/15/2023] Open
Abstract
Background Accurately estimating the timing and mode of gene duplications along the evolutionary history of species can provide invaluable information about underlying mechanisms by which the genomes of organisms evolved and the genes with novel functions arose. Mechanistic models have previously been introduced that allow for probabilistic inference of the evolutionary mechanism for duplicate gene retention based upon the average rate of loss over time of the duplicate. However, there is currently no probabilistic model embedded in a birth-death modeling framework that can take into account the effects of different evolutionary mechanisms of gene retention when analyzing gene family data. Results In this study, we describe a generalized birth-death process for modeling the fates of gene duplication. Use of mechanistic models in a phylogenetic framework requires an age-dependent birth-death process. Starting with a single population corresponding to the lineage of a phylogenetic tree and with an assumption of a clock that starts ticking for each duplicate at its birth, an age-dependent birth-death process is developed by extending the results from the time-dependent birth-death process. The implementation of such models in a full phylogenetic framework is expected to enable large scale probabilistic analysis of duplicates in comparative genomic studies. Conclusions We develop an age-dependent birth-death model for understanding the mechanisms of gene retention, which allows a gene loss rate dependent on each duplication event. Simulation results indicate that different mechanisms of gene retentions produce distinct likelihood functions, which can be used with genomic data to quantitatively distinguish those mechanisms.
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Affiliation(s)
- Jing Zhao
- Department of Statistics, University of Georgia, 101 Cedar Street, Athens, GA, 30602, USA.
| | - Ashley I Teufel
- Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA. .,Center for Computational Genetics and Genomics and Department of Biology, Temple University, Philadelphia, PA, 19122, USA.
| | - David A Liberles
- Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA. .,Center for Computational Genetics and Genomics and Department of Biology, Temple University, Philadelphia, PA, 19122, USA.
| | - Liang Liu
- Department of Statistics, University of Georgia, 101 Cedar Street, Athens, GA, 30602, USA. .,Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
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14
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Abstract
Most sequenced eukaryotic genomes show a large excess of recent duplicates. As duplicates age, both the population genetic process of failed fixation and the mutation-driven process of nonfunctionalization act to reduce the observed number of duplicates. Understanding the processes generating the age distributions of recent duplicates is important to also understand the role of duplicate genes in the functional divergence of genomes. To date, mechanistic models for duplicate gene retention only account for the mutation-driven nonfunctionalization process. Here, a neutral model for the distribution of synonymous substitutions in duplicated genes which are segregating and expected to never fix in a population is introduced. This model enables differentiation of neutral loss due to failed fixation from loss due to mutation-driven nonfunctionalization. The model has been validated on simulated data and subsequent analysis with the model on genomic data from human and mouse shows that conclusions about the underlying mechanisms for duplicate gene retention can be sensitive to consideration of population genetic processes.
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Affiliation(s)
- Ashley I Teufel
- Department of Molecular Biology, University of Wyoming Center for Computational Genetics and Genomics and Department of Biology, Temple University
| | - Joanna Masel
- Department of Ecology and Evolutionary Biology, University of Arizona
| | - David A Liberles
- Department of Molecular Biology, University of Wyoming Center for Computational Genetics and Genomics and Department of Biology, Temple University
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15
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Abstract
Computational genomics is now generating very large volumes of data that have the potential to be used to address important questions in both basic biology and biomedicine. Addressing these important biological questions becomes possible when mechanistic models rooted in biochemistry and evolutionary/population genetic processes are developed, instead of fitting data to off-the-shelf statistical distributions that do not enable mechanistic inference. Three examples are presented, the first involving ecological processes inferred from metagenomic data, the second involving mechanisms of gene regulation rooted in protein–DNA interactions with consideration of DNA structure, and the third involving existing models for the retention of duplicate genes that enables prediction of evolutionary mechanisms. This description of mechanistic models is generalized toward future developments in computational genomics and the need for biological mechanisms and processes in biological models.
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Jones MR, Forester BR, Teufel AI, Adams RV, Anstett DN, Goodrich BA, Landguth EL, Joost S, Manel S. INTEGRATING LANDSCAPE GENOMICS AND SPATIALLY EXPLICIT APPROACHES TO DETECT LOCI UNDER SELECTION IN CLINAL POPULATIONS. Evolution 2013; 67:3455-68. [DOI: 10.1111/evo.12237] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2012] [Accepted: 08/08/2013] [Indexed: 12/24/2022]
Affiliation(s)
- Matthew R. Jones
- Department of Zoology and Physiology, Berry Biodiversity Conservation Center; University of Wyoming; 1000 E. University Avenue, Dept. 4304 Laramie WY 82071 USA
| | - Brenna R. Forester
- University Program in Ecology; Nicholas School of the Environment; Duke University; Durham NC 27705 USA
| | - Ashley I. Teufel
- Department of Molecular Biology; University of Wyoming; 1000 E. University Avenue, Dept. 3944 Laramie WY 82071 USA
| | - Rachael V. Adams
- Department of Biological Sciences; University of Lethbridge; 4401 University Drive Lethbridge AB T1K 3M4 Canada
| | - Daniel N. Anstett
- Department of Ecology and Evolutionary Biology; University of Toronto; 25 Willcocks Street Toronto ON M5S 3B2 Canada
- University of Toronto-Mississauga; Department of Biology; 3359 Mississauga Road N. Mississauga ON L5L 1C6 Canada
| | - Betsy A. Goodrich
- Northern Arizona University; School of Forestry; PO Box 15018 Flagstaff AZ 86011 USA
| | - Erin L. Landguth
- University of Montana; Division of Biological Sciences; 32 Campus Drive Missoula MT 59846 USA
| | - Stéphane Joost
- Laboratory of Geographic Information Systems; School of Architecture; Civil and Environmental Engineering; Ecole Polytechnique Fédérale de Lausanne; Bâtiment GC, Station 18 1015 Lausanne Switzerland
| | - Stéphanie Manel
- Laboratoire Population Environnement Développement; Aix-Marseille University Marseille; UMR AMAP, TA A51/PS2 34398 Montpellier Cedex 5 France
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Liberles DA, Teichmann SA, Bahar I, Bastolla U, Bloom J, Bornberg-Bauer E, Colwell LJ, de Koning APJ, Dokholyan NV, Echave J, Elofsson A, Gerloff DL, Goldstein RA, Grahnen JA, Holder MT, Lakner C, Lartillot N, Lovell SC, Naylor G, Perica T, Pollock DD, Pupko T, Regan L, Roger A, Rubinstein N, Shakhnovich E, Sjölander K, Sunyaev S, Teufel AI, Thorne JL, Thornton JW, Weinreich DM, Whelan S. The interface of protein structure, protein biophysics, and molecular evolution. Protein Sci 2012; 21:769-85. [PMID: 22528593 PMCID: PMC3403413 DOI: 10.1002/pro.2071] [Citation(s) in RCA: 140] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Revised: 03/22/2012] [Accepted: 03/23/2012] [Indexed: 12/20/2022]
Abstract
Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.
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Affiliation(s)
- David A Liberles
- Department of Molecular Biology, University of WyomingLaramie, Wyoming 82071
| | - Sarah A Teichmann
- MRC Laboratory of Molecular BiologyHills Road, Cambridge CB2 0QH, United Kingdom
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of PittsburghPittsburgh, Pennsylvania 15213
| | - Ugo Bastolla
- Bioinformatics Unit. Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Universidad Autonoma de Madrid28049 Cantoblanco Madrid, Spain
| | - Jesse Bloom
- Division of Basic Sciences, Fred Hutchinson Cancer Research CenterSeattle, Washington 98109
| | - Erich Bornberg-Bauer
- Evolutionary Bioinformatics Group, Institute for Evolution and Biodiversity, University of MuensterGermany
| | - Lucy J Colwell
- MRC Laboratory of Molecular BiologyHills Road, Cambridge CB2 0QH, United Kingdom
| | - A P Jason de Koning
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of ColoradoAurora, Colorado
| | - Nikolay V Dokholyan
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel HillNorth Carolina 27599
| | - Julian Echave
- Escuela de Ciencia y Tecnología, Universidad Nacional de San MartínMartín de Irigoyen 3100, 1650 San Martín, Buenos Aires, Argentina
| | - Arne Elofsson
- Department of Biochemistry and Biophysics, Center for Biomembrane Research, Stockholm Bioinformatics Center, Science for Life Laboratory, Swedish E-science Research Center, Stockholm University106 91 Stockholm, Sweden
| | - Dietlind L Gerloff
- Biomolecular Engineering Department, University of CaliforniaSanta Cruz, California 95064
| | - Richard A Goldstein
- Division of Mathematical Biology, National Institute for Medical Research (MRC)Mill Hill, London NW7 1AA, United Kingdom
| | - Johan A Grahnen
- Department of Molecular Biology, University of WyomingLaramie, Wyoming 82071
| | - Mark T Holder
- Department of Ecology and Evolutionary Biology, University of KansasLawrence, Kansas 66045
| | - Clemens Lakner
- Bioinformatics Research Center, North Carolina State UniversityRaleigh, North Carolina 27695
| | - Nicholas Lartillot
- Département de Biochimie, Faculté de Médecine, Université de MontréalMontréal, QC H3T1J4, Canada
| | - Simon C Lovell
- Faculty of Life Sciences, University of ManchesterManchester M13 9PT, United Kingdom
| | - Gavin Naylor
- Department of Biology, College of CharlestonCharleston, South Carolina 29424
| | - Tina Perica
- MRC Laboratory of Molecular BiologyHills Road, Cambridge CB2 0QH, United Kingdom
| | - David D Pollock
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of ColoradoAurora, Colorado
| | - Tal Pupko
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv UniversityTel Aviv, Israel
| | - Lynne Regan
- Department of Molecular Biophysics and Biochemistry, Yale UniversityNew Haven 06511
| | - Andrew Roger
- Department of Biochemistry and Molecular Biology, Dalhousie UniversityHalifax, NS, Canada
| | - Nimrod Rubinstein
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv UniversityTel Aviv, Israel
| | - Eugene Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard UniversityCambridge, Massachusetts 02138
| | - Kimmen Sjölander
- Department of Bioengineering, University of CaliforniaBerkeley, Berkeley, California 94720
| | - Shamil Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School77 Avenue Louis Pasteur, Boston, Massachusetts 02115
| | - Ashley I Teufel
- Department of Molecular Biology, University of WyomingLaramie, Wyoming 82071
| | - Jeffrey L Thorne
- Bioinformatics Research Center, North Carolina State UniversityRaleigh, North Carolina 27695
| | - Joseph W Thornton
- Howard Hughes Medical Institute and Institute for Ecology and Evolution, University of OregonEugene, Oregon 97403
- Department of Human Genetics, University of ChicagoChicago, Illinois 60637
- Department of Ecology and Evolution, University of ChicagoChicago, Illinois 60637
| | - Daniel M Weinreich
- Department of Ecology and Evolutionary Biology, and Center for Computational Molecular Biology, Brown UniversityProvidence, Rhode Island 02912
| | - Simon Whelan
- Faculty of Life Sciences, University of ManchesterManchester M13 9PT, United Kingdom
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Abstract
Gene duplication is an important process in the functional divergence of genes and genomes. Several processes have been described that lead to duplicate gene retention over different timescales after both smaller-scale events and whole-genome duplication, including neofunctionalization, subfunctionalization, and dosage balance. Two common modes of duplicate gene loss include nonfunctionalization and loss due to population dynamics (failed fixation). Previous work has characterized expectations of duplicate gene retention under the neofunctionalization and subfunctionalization models. Here, that work is extended to dosage balance using simulations. A general model for duplicate gene loss/retention is then presented that is capable of fitting expectations under the different models, is defined at t = 0, and decays to an orthologous asymptotic rate rather than zero, based upon a modified Weibull hazard function. The model in a maximum likelihood framework shows the property of identifiability, recovering the evolutionary mechanism and parameters of simulation. This model is also capable of recovering the evolutionary mechanism of simulation from data generated using an unrelated network population genetic model. Lastly, the general model is applied as part of a mixture model to recent gene duplicates from the Oikopleura dioica genome, suggesting that neofunctionalization may be an important process leading to duplicate gene retention in that organism.
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Affiliation(s)
- Anke Konrad
- Department of Molecular Biology, University of Wyoming
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