1
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Milhaven M, Bakry HA, Batra A, Bermingham AM, Grama G, Kebe J, Martinez SS, Mudunuri RV, Nelson MR, Nguyen ET, Peterson MM, Pruitt A, Tran K, Brar A, Cerna G, Chaffee E, Caruso SM, Pfeifer SP. Complete genome sequence of the Streptomyces bacteriophage Amabiko. Microbiol Resour Announc 2024:e0018224. [PMID: 38651927 DOI: 10.1128/mra.00182-24] [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: 02/22/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Amabiko is a lytic subcluster BE2 bacteriophage that infects Streptomyces scabiei-a bacterium causing common scab in potatoes. Its 131,414 bp genome has a GC content of 49.5% and contains 245 putative protein-coding genes, 45 tRNAs, and one tmRNA. Amabiko is closely related to Streptomyces bacteriophage MindFlayer (gene content similarity: 86.5%).
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Affiliation(s)
- Mark Milhaven
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Heba A Bakry
- Department of Biological Sciences, University of Maryland, Baltimore, Maryland, USA
| | - Anuvi Batra
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | | | - Gloria Grama
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Jacob Kebe
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA
| | - Shawn S Martinez
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Rishika V Mudunuri
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Megan R Nelson
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Department of Psychology, Arizona State University, Tempe, Arizona, USA
| | - Evie T Nguyen
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Mia M Peterson
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, USA
| | - Alexis Pruitt
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, USA
| | - Kristan Tran
- Department of Biological Sciences, University of Maryland, Baltimore, Maryland, USA
| | - Akarshi Brar
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Gabriella Cerna
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Elaine Chaffee
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Steven M Caruso
- Department of Biological Sciences, University of Maryland, Baltimore, Maryland, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, USA
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2
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Soni V, Pfeifer SP, Jensen JD. The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects. Genome Biol Evol 2024; 16:evae004. [PMID: 38207127 PMCID: PMC10834165 DOI: 10.1093/gbe/evae004] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/12/2023] [Accepted: 01/07/2024] [Indexed: 01/13/2024] Open
Abstract
Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavor; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and δaδi). We demonstrate that, if not properly modeled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination before utilizing population genomic data to quantify the effects of genetic drift (i.e. as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modeled in downstream inference.
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, USA
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3
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Versoza CJ, Weiss S, Johal R, La Rosa B, Jensen JD, Pfeifer SP. Novel Insights into the Landscape of Crossover and Noncrossover Events in Rhesus Macaques (Macaca mulatta). Genome Biol Evol 2024; 16:evad223. [PMID: 38051960 PMCID: PMC10773715 DOI: 10.1093/gbe/evad223] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 08/04/2023] [Revised: 11/04/2023] [Accepted: 11/28/2023] [Indexed: 12/07/2023] Open
Abstract
Meiotic recombination landscapes differ greatly between distantly and closely related taxa, populations, individuals, sexes, and even within genomes; however, the factors driving this variation are yet to be well elucidated. Here, we directly estimate contemporary crossover rates and, for the first time, noncrossover rates in rhesus macaques (Macaca mulatta) from four three-generation pedigrees comprising 32 individuals. We further compare these results with historical, demography-aware, linkage disequilibrium-based recombination rate estimates. From paternal meioses in the pedigrees, 165 crossover events with a median resolution of 22.3 kb were observed, corresponding to a male autosomal map length of 2,357 cM-approximately 15% longer than an existing linkage map based on human microsatellite loci. In addition, 85 noncrossover events with a mean tract length of 155 bp were identified-similar to the tract lengths observed in the only other two primates in which noncrossovers have been studied to date, humans and baboons. Consistent with observations in other placental mammals with PRDM9-directed recombination, crossover (and to a lesser extent noncrossover) events in rhesus macaques clustered in intergenic regions and toward the chromosomal ends in males-a pattern in broad agreement with the historical, sex-averaged recombination rate estimates-and evidence of GC-biased gene conversion was observed at noncrossover sites.
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Affiliation(s)
- Cyril J Versoza
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - Sarah Weiss
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Ravneet Johal
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Bruno La Rosa
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
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4
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Howell AA, Versoza CJ, Pfeifer SP. Computational host range prediction-The good, the bad, and the ugly. Virus Evol 2023; 10:vead083. [PMID: 38361822 PMCID: PMC10868548 DOI: 10.1093/ve/vead083] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/05/2023] [Accepted: 12/19/2023] [Indexed: 02/17/2024] Open
Abstract
The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges-that is the bacterial strains or species that a bacteriophage can successfully infect and kill-is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision-however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of in silico host prediction to combat the challenge of guiding experimental designs to identify the most promising bacteriophage candidates for any given application.
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Affiliation(s)
| | - Cyril J Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Susanne P Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
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5
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Soni V, Pfeifer SP, Jensen JD. The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects. bioRxiv 2023:2023.11.11.566703. [PMID: 38014252 PMCID: PMC10680612 DOI: 10.1101/2023.11.11.566703] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and δaδi). We demonstrate that, if not properly modelled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination prior to utilizing population genomic data to quantify the effects of genetic drift (i.e., as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modelled in downstream inference.
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Affiliation(s)
- Vivak Soni
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine
| | - Susanne P. Pfeifer
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine
| | - Jeffrey D. Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine
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6
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Moström MJ, Yu S, Tran D, Saccoccio FM, Versoza CJ, Malouli D, Mirza A, Valencia S, Gilbert M, Blair RV, Hansen S, Barry P, Früh K, Jensen JD, Pfeifer SP, Kowalik TF, Permar SR, Kaur A. Protective effect of pre-existing natural immunity in a nonhuman primate reinfection model of congenital cytomegalovirus infection. PLoS Pathog 2023; 19:e1011646. [PMID: 37796819 PMCID: PMC10553354 DOI: 10.1371/journal.ppat.1011646] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 08/29/2023] [Indexed: 10/07/2023] Open
Abstract
Congenital cytomegalovirus (cCMV) is the leading infectious cause of neurologic defects in newborns with particularly severe sequelae in the setting of primary CMV infection in the first trimester of pregnancy. The majority of cCMV cases worldwide occur after non-primary infection in CMV-seropositive women; yet the extent to which pre-existing natural CMV-specific immunity protects against CMV reinfection or reactivation during pregnancy remains ill-defined. We previously reported on a novel nonhuman primate model of cCMV in rhesus macaques where 100% placental transmission and 83% fetal loss were seen in CD4+ T lymphocyte-depleted rhesus CMV (RhCMV)-seronegative dams after primary RhCMV infection. To investigate the protective effect of preconception maternal immunity, we performed reinfection studies in CD4+ T lymphocyte-depleted RhCMV-seropositive dams inoculated in late first / early second trimester gestation with RhCMV strains 180.92 (n = 2), or RhCMV UCD52 and FL-RhCMVΔRh13.1/SIVgag, a wild-type-like RhCMV clone with SIVgag inserted as an immunological marker, administered separately (n = 3). An early transient increase in circulating monocytes followed by boosting of the pre-existing RhCMV-specific CD8+ T lymphocyte and antibody response was observed in the reinfected dams but not in control CD4+ T lymphocyte-depleted dams. Emergence of SIV Gag-specific CD8+ T lymphocyte responses in macaques inoculated with the FL-RhCMVΔRh13.1/SIVgag virus confirmed reinfection. Placental transmission was detected in only one of five reinfected dams and there were no adverse fetal sequelae. Viral whole genome, short-read, deep sequencing analysis confirmed transmission of both reinfection RhCMV strains across the placenta with ~30% corresponding to FL-RhCMVΔRh13.1/SIVgag and ~70% to RhCMV UCD52, consistent with the mixed human CMV infections reported in infants with cCMV. Our data showing reduced placental transmission and absence of fetal loss after non-primary as opposed to primary infection in CD4+ T lymphocyte-depleted dams indicates that preconception maternal CMV-specific CD8+ T lymphocyte and/or humoral immunity can protect against cCMV infection.
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Affiliation(s)
- Matilda J. Moström
- Tulane National Primate Research Center, Tulane University, Covington, Louisiana, United States of America
| | - Shan Yu
- Tulane National Primate Research Center, Tulane University, Covington, Louisiana, United States of America
| | - Dollnovan Tran
- Tulane National Primate Research Center, Tulane University, Covington, Louisiana, United States of America
| | - Frances M. Saccoccio
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, United States of America
| | - Cyril J. Versoza
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Daniel Malouli
- Oregon Health and Sciences University, Beaverton, Oregon, United States of America
| | - Anne Mirza
- University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America
| | - Sarah Valencia
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, United States of America
| | - Margaret Gilbert
- Tulane National Primate Research Center, Tulane University, Covington, Louisiana, United States of America
| | - Robert V. Blair
- Tulane National Primate Research Center, Tulane University, Covington, Louisiana, United States of America
| | - Scott Hansen
- Oregon Health and Sciences University, Beaverton, Oregon, United States of America
| | - Peter Barry
- University of California, Davis, California, United States of America
| | - Klaus Früh
- Oregon Health and Sciences University, Beaverton, Oregon, United States of America
| | - Jeffrey D. Jensen
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Susanne P. Pfeifer
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Timothy F. Kowalik
- University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America
| | - Sallie R. Permar
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, United States of America
- Weill Cornell Medicine, New York, New York State, United States of America
| | - Amitinder Kaur
- Tulane National Primate Research Center, Tulane University, Covington, Louisiana, United States of America
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7
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Johri P, Pfeifer SP, Jensen JD. Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history. Mol Biol Evol 2023; 40:7147633. [PMID: 37128989 DOI: 10.1093/molbev/msad100] [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: 01/09/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 05/03/2023] Open
Abstract
Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection (e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome (i.e., purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
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8
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Howell AA, Terbot Ii JW, Soni V, Johri P, Jensen JD, Pfeifer SP. Developing an appropriate evolutionary baseline model for the study of human cytomegalovirus. Genome Biol Evol 2023; 15:7128054. [PMID: 37071785 PMCID: PMC10139446 DOI: 10.1093/gbe/evad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 04/05/2023] [Accepted: 04/09/2023] [Indexed: 04/20/2023] Open
Abstract
Human cytomegalovirus (HCMV) represents a major threat to human health, contributing to both birth defects in neonates as well as organ transplant failure and opportunistic infections in immunocompromised individuals. HCMV exhibits considerable inter- and intra-host diversity, which likely influences the pathogenicity of the virus. Therefore, understanding the relative contributions of various evolutionary forces in shaping patterns of variation is of critical importance both mechanistically as well as clinically. Herein we present the individual components of an evolutionary baseline model for HCMV, with a particular focus on congenital infections for the sake of illustration - including mutation and recombination rates, the distribution of fitness effects, infection dynamics, as well as compartmentalization - and describe the current state of knowledge of each. By building this baseline model, researchers will be able to better describe the range of possible evolutionary scenarios contributing to observed variation, as well as improve power and reduce false-positive rates when scanning for adaptive mutations in the HCMV genome.
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Affiliation(s)
- Abigail A Howell
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Phoenix, Arizona, USA
| | - John W Terbot Ii
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Phoenix, Arizona, USA
- University of Montana, Division of Biological Sciences, Missoula, Montana, USA
| | - Vivak Soni
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Phoenix, Arizona, USA
| | - Parul Johri
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Phoenix, Arizona, USA
| | - Jeffrey D Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Phoenix, Arizona, USA
| | - Susanne P Pfeifer
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Phoenix, Arizona, USA
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9
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Johri P, Pfeifer SP, Jensen JD. Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history. bioRxiv 2023:2023.04.11.536488. [PMID: 37090533 PMCID: PMC10120674 DOI: 10.1101/2023.04.11.536488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection ( e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome ( i.e. , purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.
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10
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Moström M, Yu S, Tran D, Saccoccio F, Versoza CJ, Malouli D, Mirza A, Valencia S, Gilbert M, Blair R, Hansen S, Barry P, Früh K, Jensen JD, Pfeifer SP, Kowalik TF, Permar SR, Kaur A. Protective effect of pre-existing natural immunity in a nonhuman primate reinfection model of congenital cytomegalovirus infection. bioRxiv 2023:2023.04.10.536057. [PMID: 37090643 PMCID: PMC10120644 DOI: 10.1101/2023.04.10.536057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Congenital cytomegalovirus (cCMV) is the leading infectious cause of neurologic defects in newborns with particularly severe sequelae in the setting of primary CMV infection in the first trimester of pregnancy. The majority of cCMV cases worldwide occur after non-primary infection in CMV-seropositive women; yet the extent to which pre-existing natural CMV-specific immunity protects against CMV reinfection or reactivation during pregnancy remains ill-defined. We previously reported on a novel nonhuman primate model of cCMV in rhesus macaques where 100% placental transmission and 83% fetal loss were seen in CD4 + T lymphocyte-depleted rhesus CMV (RhCMV)-seronegative dams after primary RhCMV infection. To investigate the protective effect of preconception maternal immunity, we performed reinfection studies in CD4+ T lymphocyte-depleted RhCMV-seropositive dams inoculated in late first / early second trimester gestation with RhCMV strains 180.92 ( n =2), or RhCMV UCD52 and FL-RhCMVΔRh13.1/SIV gag , a wild-type-like RhCMV clone with SIV gag inserted as an immunological marker ( n =3). An early transient increase in circulating monocytes followed by boosting of the pre-existing RhCMV-specific CD8+ T lymphocyte and antibody response was observed in the reinfected dams but not in control CD4+ T lymphocyte-depleted dams. Emergence of SIV Gag-specific CD8+ T lymphocyte responses in macaques inoculated with the FL-RhCMVΔRh13.1/SIV gag virus confirmed reinfection. Placental transmission was detected in only one of five reinfected dams and there were no adverse fetal sequelae. Viral whole genome, short-read, deep sequencing analysis confirmed transmission of both reinfection RhCMV strains across the placenta with ∼30% corresponding to FL-RhCMVΔRh13.1/SIV gag and ∼70% to RhCMV UCD52, consistent with the mixed human CMV infections reported in infants with cCMV. Our data showing reduced placental transmission and absence of fetal loss after non-primary as opposed to primary infection in CD4+ T lymphocyte-depleted dams indicates that preconception maternal CMV-specific CD8+ T lymphocyte and/or humoral immunity can protect against cCMV infection. Author Summary Globally, pregnancies in CMV-seropositive women account for the majority of cases of congenital CMV infection but the immune responses needed for protection against placental transmission in mothers with non-primary infection remains unknown. Recently, we developed a nonhuman primate model of primary rhesus CMV (RhCMV) infection in which placental transmission and fetal loss occurred in RhCMV-seronegative CD4+ T lymphocyte-depleted macaques. By conducting similar studies in RhCMV-seropositive dams, we demonstrated the protective effect of pre-existing natural CMV-specific CD8+ T lymphocytes and humoral immunity against congenital CMV after reinfection. A 5-fold reduction in congenital transmission and complete protection against fetal loss was observed in dams with pre-existing immunity compared to primary CMV in this model. Our study is the first formal demonstration in a relevant model of human congenital CMV that natural pre-existing CMV-specific maternal immunity can limit congenital CMV transmission and its sequelae. The nonhuman primate model of non-primary congenital CMV will be especially relevant to studying immune requirements of a maternal vaccine for women in high CMV seroprevalence areas at risk of repeated CMV reinfections during pregnancy.
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Affiliation(s)
- Matilda Moström
- Tulane National Primate Research Center, Tulane University, Covington LA
| | - Shan Yu
- Tulane National Primate Research Center, Tulane University, Covington LA
| | - Dollnovan Tran
- Tulane National Primate Research Center, Tulane University, Covington LA
| | | | - Cyril J. Versoza
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ
| | | | - Anne Mirza
- University of Massachusetts Chan Medical School, Worcester, MA
| | - Sarah Valencia
- Duke Human Vaccine Institute, Duke University, Durham, NC
| | - Margaret Gilbert
- Tulane National Primate Research Center, Tulane University, Covington LA
| | - Robert Blair
- Tulane National Primate Research Center, Tulane University, Covington LA
| | - Scott Hansen
- Oregon Health and Sciences University, Beaverton, OR
| | | | - Klaus Früh
- Oregon Health and Sciences University, Beaverton, OR
| | - Jeffrey D. Jensen
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ
| | - Susanne P. Pfeifer
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ
| | | | - Sallie R. Permar
- Duke Human Vaccine Institute, Duke University, Durham, NC
- Weill Cornell Medicine, New York, NY
| | - Amitinder Kaur
- Tulane National Primate Research Center, Tulane University, Covington LA
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11
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Terbot JW, Johri P, Liphardt SW, Soni V, Pfeifer SP, Cooper BS, Good JM, Jensen JD. Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples. PLoS Pathog 2023; 19:e1011265. [PMID: 37018331 PMCID: PMC10075409 DOI: 10.1371/journal.ppat.1011265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Abstract
Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.
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Affiliation(s)
- John W Terbot
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Parul Johri
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Schuyler W Liphardt
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Vivak Soni
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Susanne P Pfeifer
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Brandon S Cooper
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey M Good
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey D Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
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12
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Ghafoor S, Santos J, Versoza CJ, Jensen JD, Pfeifer SP. The Impact of Sample Size and Population History on Observed Mutational Spectra: A Case Study in Human and Chimpanzee Populations. Genome Biol Evol 2023; 15:7039701. [PMID: 36790107 PMCID: PMC9989333 DOI: 10.1093/gbe/evad019] [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: 02/10/2021] [Revised: 01/20/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Recent studies have highlighted variation in the mutational spectra among human populations as well as closely related hominoids-yet little remains known about the genetic and nongenetic factors driving these rate changes across the genome. Pinpointing the root causes of these differences is an important endeavor that requires careful comparative analyses of population-specific mutational landscapes at both broad and fine genomic scales. However, several factors can confound such analyses. Although previous studies have shown that technical artifacts, such as sequencing errors and batch effects, can contribute to observed mutational shifts, other potentially confounding parameters have received less attention thus far. Using population genetic simulations of human and chimpanzee populations as an illustrative example, we here show that the sample size required for robust inference of mutational spectra depends on the population-specific demographic history. As a consequence, the power to detect rate changes is high in certain hominoid populations while, for others, currently available sample sizes preclude analyses at fine genomic scales.
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Affiliation(s)
- Suhail Ghafoor
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - João Santos
- School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - Cyril J Versoza
- School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
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13
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Howell AA, Versoza CJ, Cerna G, Johnston T, Kakde S, Karuku K, Kowal M, Monahan J, Murray J, Nguyen T, Sanchez Carreon A, Streiff A, Su B, Youkhana F, Munig S, Patel Z, So M, Sy M, Weiss S, Pfeifer SP. Phylogenomic analyses and host range prediction of cluster P mycobacteriophages. G3 (Bethesda) 2022; 12:jkac244. [PMID: 36094333 PMCID: PMC9635641 DOI: 10.1093/g3journal/jkac244] [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] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/07/2022] [Indexed: 06/15/2023]
Abstract
Bacteriophages, infecting bacterial hosts in every environment on our planet, are a driver of adaptive evolution in bacterial communities. At the same time, the host range of many bacteriophages-and thus one of the selective pressures acting on complex microbial systems in nature-remains poorly characterized. Here, we computationally inferred the putative host ranges of 40 cluster P mycobacteriophages, including members from 6 subclusters (P1-P6). A series of comparative genomic analyses revealed that mycobacteriophages of subcluster P1 are restricted to the Mycobacterium genus, whereas mycobacteriophages of subclusters P2-P6 are likely also able to infect other genera, several of which are commonly associated with human disease. Further genomic analysis highlighted that the majority of cluster P mycobacteriophages harbor a conserved integration-dependent immunity system, hypothesized to be the ancestral state of a genetic switch that controls the shift between lytic and lysogenic life cycles-a temperate characteristic that impedes their usage in antibacterial applications.
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Affiliation(s)
| | | | - Gabriella Cerna
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Tyler Johnston
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Shriya Kakde
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Keith Karuku
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Maria Kowal
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Jasmine Monahan
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Jillian Murray
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Teresa Nguyen
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Aurely Sanchez Carreon
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Abigail Streiff
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Blake Su
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- School of Politics and Global Studies, Arizona State University, Tempe, AZ 85281, USA
| | - Faith Youkhana
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Saige Munig
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Zeel Patel
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Minerva So
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Makena Sy
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Sarah Weiss
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Susanne P Pfeifer
- Corresponding author: Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
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14
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Howell AA, Versoza CJ, Cerna G, Johnston T, Kakde S, Karuku K, Kowal M, Monahan J, Murray J, Nguyen T, Sanchez Carreon A, Song E, Streiff A, Su B, Youkhana F, Munig S, Patel Z, So M, Sy M, Weiss S, Zhou Y, Pfeifer SP. Complete Genome Sequence of the Cluster P Mycobacteriophage Phegasus. Microbiol Resour Announc 2022; 11:e0054022. [PMID: 35924939 PMCID: PMC9476930 DOI: 10.1128/mra.00540-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/21/2022] [Indexed: 11/21/2022] Open
Abstract
We characterized the complete genome of the cluster P mycobacteriophage Phegasus. Its 47.5-kb genome contains 81 protein-coding genes, 36 of which could be assigned a putative function. Phegasus is most closely related to two subcluster P1 bacteriophages, Mangethe and Majeke, with an average nucleotide identity of 99.63% each.
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Affiliation(s)
- Abigail A. Howell
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Biodesign Institute, Arizona State University, Tempe, Arizona, USA
| | - Cyril J. Versoza
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, USA
| | - Gabriella Cerna
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Biodesign Institute, Arizona State University, Tempe, Arizona, USA
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Tyler Johnston
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Shriya Kakde
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Keith Karuku
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Maria Kowal
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Jasmine Monahan
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Jillian Murray
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Teresa Nguyen
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Aurely Sanchez Carreon
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Biodesign Institute, Arizona State University, Tempe, Arizona, USA
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Elizabeth Song
- Division of Biology and Medicine, Brown University, Providence, Rhode Island, USA
| | - Abigail Streiff
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Blake Su
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Politics and Global Studies, Arizona State University, Tempe, Arizona, USA
| | - Faith Youkhana
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, USA
| | - Saige Munig
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Zeel Patel
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Minerva So
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Makena Sy
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Sarah Weiss
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Yang Zhou
- Division of Biology and Medicine, Brown University, Providence, Rhode Island, USA
| | - Susanne P. Pfeifer
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, USA
- Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA
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15
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Versoza CJ, Howell AA, Aftab T, Blanco M, Brar A, Chaffee E, Howell N, Leach W, Lobatos J, Luca M, Maddineni M, Mirji R, Mitra C, Strasser M, Munig S, Patel Z, So M, Sy M, Weiss S, Pfeifer SP. Comparative Genomics of Closely-Related Gordonia Cluster DR Bacteriophages. Viruses 2022; 14:v14081647. [PMID: 36016269 PMCID: PMC9413003 DOI: 10.3390/v14081647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/16/2022] [Accepted: 07/25/2022] [Indexed: 12/10/2022] Open
Abstract
Bacteriophages infecting bacteria of the genus Gordonia have increasingly gained interest in the scientific community for their diverse applications in agriculture, biotechnology, and medicine, ranging from biocontrol agents in wastewater management to the treatment of opportunistic pathogens in pulmonary disease patients. However, due to the time and costs associated with experimental isolation and cultivation, host ranges for many bacteriophages remain poorly characterized, hindering a more efficient usage of bacteriophages in these areas. Here, we perform a series of computational genomic inferences to predict the putative host ranges of all Gordonia cluster DR bacteriophages known to date. Our analyses suggest that BiggityBass (as well as several of its close relatives) is likely able to infect host bacteria from a wide range of genera—from Gordonia to Nocardia to Rhodococcus, making it a suitable candidate for future phage therapy and wastewater treatment strategies.
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Affiliation(s)
- Cyril J. Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Abigail A. Howell
- Biodesign Institute, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Tanya Aftab
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Madison Blanco
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Akarshi Brar
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Elaine Chaffee
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Nicholas Howell
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Willow Leach
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Jackelyn Lobatos
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Michael Luca
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Meghna Maddineni
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Ruchira Mirji
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Corinne Mitra
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Maria Strasser
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Saige Munig
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Zeel Patel
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Minerva So
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Makena Sy
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Sarah Weiss
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (T.A.); (M.B.); (A.B.); (E.C.); (N.H.); (J.L.); (M.L.); (R.M.); (C.M.); (M.S.); (S.M.); (Z.P.); (M.S.); (M.S.); (S.W.)
| | - Susanne P. Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
- Correspondence:
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16
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Johri P, Aquadro CF, Beaumont M, Charlesworth B, Excoffier L, Eyre-Walker A, Keightley PD, Lynch M, McVean G, Payseur BA, Pfeifer SP, Stephan W, Jensen JD. Recommendations for improving statistical inference in population genomics. PLoS Biol 2022; 20:e3001669. [PMID: 35639797 PMCID: PMC9154105 DOI: 10.1371/journal.pbio.3001669] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [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] [Indexed: 01/29/2023] Open
Abstract
The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Charles F. Aquadro
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America
| | - Mark Beaumont
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Brian Charlesworth
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Laurent Excoffier
- Institute of Ecology and Evolution, University of Berne, Berne, Switzerland
| | - Adam Eyre-Walker
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Peter D. Keightley
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael Lynch
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Bret A. Payseur
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Susanne P. Pfeifer
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | | | - Jeffrey D. Jensen
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- * E-mail:
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17
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Walker K, Kalra D, Lowdon R, Chen G, Molik D, Soto DC, Dabbaghie F, Khleifat AA, Mahmoud M, Paulin LF, Raza MS, Pfeifer SP, Agustinho DP, Aliyev E, Avdeyev P, Barrozo ER, Behera S, Billingsley K, Chong LC, Choubey D, De Coster W, Fu Y, Gener AR, Hefferon T, Henke DM, Höps W, Illarionova A, Jochum MD, Jose M, Kesharwani RK, Kolora SRR, Kubica J, Lakra P, Lattimer D, Liew CS, Lo BW, Lo C, Lötter A, Majidian S, Mendem SK, Mondal R, Ohmiya H, Parvin N, Peralta C, Poon CL, Prabhakaran R, Saitou M, Sammi A, Sanio P, Sapoval N, Syed N, Treangen T, Wang G, Xu T, Yang J, Zhang S, Zhou W, Sedlazeck FJ, Busby B. The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms. F1000Res 2022; 11:530. [PMID: 36262335 PMCID: PMC9557141 DOI: 10.12688/f1000research.110194.1] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/04/2022] [Indexed: 01/25/2023] Open
Abstract
In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.
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Affiliation(s)
- Kimberly Walker
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA,
| | - Divya Kalra
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA,
| | | | - Guangyi Chen
- Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Saarbrücken, Germany,Center for Bioinformatics, Saarland University, Saarbrücken, Germany,
| | - David Molik
- Tropical Crop and Commodity Protection Research Unit, Pacific Basin Agricultural Research Center, Hilo, HI, 96720, USA
| | - Daniela C. Soto
- Biochemistry & Molecular Medicine, Genome Center, MIND Institute, University of California, Davis, Davis, CA, 95616, USA
| | - Fawaz Dabbaghie
- Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Saarbrücken, Germany,Institute for Medical Biometry and Bioinformatics, University hospital Düsseldorf, Düsseldorf, Germany
| | - Ahmad Al Khleifat
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Luis F Paulin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Muhammad Sohail Raza
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Beijing, China
| | - Susanne P. Pfeifer
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
| | - Daniel Paiva Agustinho
- Department of Molecular Microbiology, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA
| | - Elbay Aliyev
- Research Department, Sidra Medicine, Doha, Qatar
| | - Pavel Avdeyev
- Computational Biology Institute, The George Washington University, Washington, DC, 20052, USA
| | - Enrico R. Barrozo
- Department of Obstetrics & Gynecology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Sairam Behera
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kimberley Billingsley
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Li Chuin Chong
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Beykoz, Istanbul, Turkey
| | - Deepak Choubey
- Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India
| | - Wouter De Coster
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, Antwerp, Belgium,Applied and Translational Neurogenomics Group, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Yilei Fu
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Alejandro R. Gener
- Association of Public Health Labs, Centers for Disease Control and Prevention, Downey, CA, USA
| | - Timothy Hefferon
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David Morgan Henke
- Department Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Wolfram Höps
- EMBL Heidelberg, Genome Biology Unit, Heidelberg, Germany
| | | | - Michael D. Jochum
- Department of Obstetrics & Gynecology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Maria Jose
- Centre for Bioinformatics, Pondicherry University, Pondicherry, India
| | - Rupesh K. Kesharwani
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | | | | | - Priya Lakra
- Department of Zoology, University of Delhi, Delhi, India
| | - Damaris Lattimer
- University of Applied Sciences Upper Austria - FH Hagenberg, Mühlkreis, Austria
| | - Chia-Sin Liew
- Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Bai-Wei Lo
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Chunhsuan Lo
- Human Genetics Laboratory, National Institute of Genetics, Japan, Mishima City, Japan
| | - Anneri Lötter
- Department of Biochemistry, University of Pretoria, Pretoria, South Africa
| | - Sina Majidian
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | | | - Rajarshi Mondal
- Department of Biotechnology, The University of Burdwan, West Bengal, India
| | - Hiroko Ohmiya
- Genetic Reagent Development Unit, Medical & Biological Laboratories Co., Ltd., Tokoyo, Japan
| | - Nasrin Parvin
- Department of Biotechnology, The University of Burdwan, West Bengal, India
| | | | | | | | - Marie Saitou
- Center of Integrative Genetics (CIGENE),Faculty of Biosciences, Norwegian University of Life Sciences, As, Norway
| | - Aditi Sammi
- School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
| | - Philippe Sanio
- University of Applied Sciences Upper Austria - FH Hagenberg, Hagenberg im Mühlkreis, Austria
| | - Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Najeeb Syed
- Research Department, Sidra Medicine, Doha, Qatar
| | - Todd Treangen
- Department of Computer Science, Rice University, Houston, TX, USA
| | | | - Tiancheng Xu
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Jianzhi Yang
- Department of Quantitative and Computational Biology,, University of Southern California, Los Angeles, CA, USA
| | - Shangzhe Zhang
- School of Biology, University of St Andrews, St Andrews, UK
| | - Weiyu Zhou
- Department of Statistical Science, George Mason University, Fairfax, Virginia, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA,
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18
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Sabin S, Morales-Arce AY, Pfeifer SP, Jensen JD. The impact of frequently neglected model violations on bacterial recombination rate estimation: a case study in Mycobacterium canettii and Mycobacterium tuberculosis. G3 (Bethesda) 2022; 12:jkac055. [PMID: 35253851 PMCID: PMC9073693 DOI: 10.1093/g3journal/jkac055] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/28/2022] [Indexed: 12/04/2022]
Abstract
Mycobacterium canettii is a causative agent of tuberculosis in humans, along with the members of the Mycobacterium tuberculosis complex. Frequently used as an outgroup to the M. tuberculosis complex in phylogenetic analyses, M. canettii is thought to offer the best proxy for the progenitor species that gave rise to the complex. Here, we leverage whole-genome sequencing data and biologically relevant population genomic models to compare the evolutionary dynamics driving variation in the recombining M. canettii with that in the nonrecombining M. tuberculosis complex, and discuss differences in observed genomic diversity in the light of expected levels of Hill-Robertson interference. In doing so, we highlight the methodological challenges of estimating recombination rates through traditional population genetic approaches using sequences called from populations of microorganisms and evaluate the likely mis-inference that arises owing to a neglect of common model violations including purifying selection, background selection, progeny skew, and population size change. In addition, we compare performance when full within-host polymorphism data are utilized, versus the more common approach of basing analyses on within-host consensus sequences.
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Affiliation(s)
- Susanna Sabin
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Ana Y Morales-Arce
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Susanne P Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Jeffrey D Jensen
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
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19
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Versoza CJ, Rivera JA, Rosenblum EB, Vital-García C, Hews DK, Pfeifer SP. The recombination landscapes of spiny lizards (genus Sceloporus). G3 Genes|Genomes|Genetics 2022; 12:6433156. [PMID: 34878100 PMCID: PMC9210290 DOI: 10.1093/g3journal/jkab402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/14/2021] [Indexed: 11/16/2022]
Abstract
Despite playing a critical role in evolutionary processes and outcomes, relatively little is known about rates of recombination in the vast majority of species, including squamate reptiles—the second largest order of extant vertebrates, many species of which serve as important model organisms in evolutionary and ecological studies. This paucity of data has resulted in limited resolution on questions related to the causes and consequences of rate variation between species and populations, the determinants of within-genome rate variation, as well as the general tempo of recombination rate evolution on this branch of the tree of life. In order to address these questions, it is thus necessary to begin broadening our phylogenetic sampling. We here provide the first fine-scale recombination maps for two species of spiny lizards, Sceloporus jarrovii and Sceloporus megalepidurus, which diverged at least 12 Mya. As might be expected from similarities in karyotype, population-scaled recombination landscapes are largely conserved on the broad-scale. At the same time, considerable variation exists at the fine-scale, highlighting the importance of incorporating species-specific recombination maps in future population genomic studies.
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Affiliation(s)
- Cyril J Versoza
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - Julio A Rivera
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Erica Bree Rosenblum
- Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Cuauhcihuatl Vital-García
- Departamento de Ciencias Veterinarias, Programa de Maestría en Ciencia Animal, Universidad Autónoma de Ciudad Juárez México, Chihuahua 32315, Mexico
| | - Diana K Hews
- Department of Biology, Indiana State University, Terre Haute, IN 47809, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA
- Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85281, USA
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20
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Bergeron LA, Besenbacher S, Turner T, Versoza CJ, Wang RJ, Price AL, Armstrong E, Riera M, Carlson J, Chen HY, Hahn MW, Harris K, Kleppe AS, López-Nandam EH, Moorjani P, Pfeifer SP, Tiley GP, Yoder AD, Zhang G, Schierup MH. The mutationathon highlights the importance of reaching standardization in estimates of pedigree-based germline mutation rates. eLife 2022; 11:73577. [PMID: 35018888 PMCID: PMC8830884 DOI: 10.7554/elife.73577] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [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: 09/02/2021] [Accepted: 01/11/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various nonhuman species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and appropriately accounting for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a ‘Mutationathon,’ a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a twofold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria, and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.
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Affiliation(s)
- Lucie A Bergeron
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Søren Besenbacher
- Department of Molecular Medicine (MOMA), Aarhus University, Aarhus N, Denmark
| | - Tychele Turner
- Department of Genetics, Washington University in St. Louis, Saint Louis, United States
| | - Cyril J Versoza
- Center for Evolution and Medicine, Arizona State University, Tempe, United States
| | - Richard J Wang
- Department of Biology, Indiana University, Bloomington, United States
| | - Alivia Lee Price
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Ellie Armstrong
- Department of Biology, Stanford University, Stanford, United States
| | - Meritxell Riera
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Jedidiah Carlson
- Department of Genome Sciences, University of Washington, Seattle, United States
| | - Hwei-Yen Chen
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Matthew W Hahn
- Department of Biology, Indiana University, Bloomington, United States
| | - Kelley Harris
- Department of Genome Sciences, University of Washington, Seattle, United States
| | | | | | - Priya Moorjani
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, United States
| | - George P Tiley
- Department of Biology, Duke University, Durham, United States
| | - Anne D Yoder
- Department of Biology, Duke University, Durham, United States
| | - Guojie Zhang
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
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21
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Abstract
Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.
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Affiliation(s)
- Cyril J. Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Susanne P. Pfeifer
- Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
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22
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Abstract
This commentary investigates the important role of computational pipeline and parameter choices in performing mutation rate estimation, using the recent article published in this journal by Bergeron et al. entitled "The germline mutational process in rhesus macaque and its implications for phylogenetic dating" as an illustrative example.
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Affiliation(s)
- Susanne P Pfeifer
- Correspondence address. Susanne P. Pfeifer, School of Life Sciences, Arizona
State University, Tempe, AZ 85281, USA. E-mail:
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23
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Crane A, Versoza CJ, Hua T, Kapoor R, Lloyd L, Mehta R, Menolascino J, Morais A, Munig S, Patel Z, Sackett D, Schmit B, Sy M, Pfeifer SP. Phylogenetic relationships and codon usage bias amongst cluster K mycobacteriophages. G3 (Bethesda) 2021; 11:6353607. [PMID: 34849792 PMCID: PMC8527509 DOI: 10.1093/g3journal/jkab291] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/09/2021] [Indexed: 01/21/2023]
Abstract
Bacteriophages infecting pathogenic hosts play an important role in medical research, not only as potential treatments for antibiotic-resistant infections but also offering novel insights into pathogen genetics and evolution. A prominent example is cluster K mycobacteriophages infecting Mycobacterium tuberculosis, a causative agent of tuberculosis in humans. However, as handling M. tuberculosis as well as other pathogens in a laboratory remains challenging, alternative nonpathogenic relatives, such as Mycobacterium smegmatis, are frequently used as surrogates to discover therapeutically relevant bacteriophages in a safer environment. Consequently, the individual host ranges of the majority of cluster K mycobacteriophages identified to date remain poorly understood. Here, we characterized the complete genome of Stinson, a temperate subcluster K1 mycobacteriophage with a siphoviral morphology. A series of comparative genomic analyses revealed strong similarities with other cluster K mycobacteriophages, including the conservation of an immunity repressor gene and a toxin/antitoxin gene pair. Patterns of codon usage bias across the cluster offered important insights into putative host ranges in nature, highlighting that although all cluster K mycobacteriophages are able to infect M. tuberculosis, they are less likely to have shared an evolutionary infection history with Mycobacterium leprae (underlying leprosy) compared to the rest of the genus’ host species. Moreover, subcluster K1 mycobacteriophages are able to integrate into the genomes of Mycobacterium abscessus and Mycobacterium marinum—two bacteria causing pulmonary and cutaneous infections which are often difficult to treat due to their drug resistance.
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Affiliation(s)
- Adele Crane
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - Cyril J Versoza
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - Tiana Hua
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Rohan Kapoor
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Lillian Lloyd
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Rithik Mehta
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | | | - Abraham Morais
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Saige Munig
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Zeel Patel
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Daniel Sackett
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Brandon Schmit
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Makena Sy
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA
- Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85281, USA
- Corresponding author: School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85281, USA.
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24
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Wang HY, Valencia SM, Pfeifer SP, Jensen JD, Kowalik TF, Permar SR. Common Polymorphisms in the Glycoproteins of Human Cytomegalovirus and Associated Strain-Specific Immunity. Viruses 2021; 13:v13061106. [PMID: 34207868 PMCID: PMC8227702 DOI: 10.3390/v13061106] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 12/18/2022] Open
Abstract
Human cytomegalovirus (HCMV), one of the most prevalent viruses across the globe, is a common cause of morbidity and mortality for immunocompromised individuals. Recent clinical observations have demonstrated that mixed strain infections are common and may lead to more severe disease progression. This clinical observation illustrates the complexity of the HCMV genome and emphasizes the importance of taking a population-level view of genotypic evolution. Here we review frequently sampled polymorphisms in the glycoproteins of HCMV, comparing the variable regions, and summarizing their corresponding geographic distributions observed to date. The related strain-specific immunity, including neutralization activity and antigen-specific cellular immunity, is also discussed. Given that these glycoproteins are common targets for vaccine design and anti-viral therapies, this observed genetic variation represents an important resource for future efforts to combat HCMV infections.
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Affiliation(s)
- Hsuan-Yuan Wang
- Department of Pediatrics, Weill Cornell Medicine, New York, NY 10065, USA;
- Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710, USA;
| | - Sarah M. Valencia
- Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710, USA;
| | - Susanne P. Pfeifer
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (S.P.P.); (J.D.J.)
| | - Jeffrey D. Jensen
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA; (S.P.P.); (J.D.J.)
| | - Timothy F. Kowalik
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA 01655, USA;
| | - Sallie R. Permar
- Department of Pediatrics, Weill Cornell Medicine, New York, NY 10065, USA;
- Correspondence: ; Tel.: +1-212-746-4111
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25
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Abstract
Despite its important biological role, the evolution of recombination rates remains relatively poorly characterized. This owes, in part, to the lack of high-quality genomic resources to address this question across diverse species. Humans and our closest evolutionary relatives, anthropoid apes, have remained a major focus of large-scale sequencing efforts, and thus recombination rate variation has been comparatively well studied in this group-with earlier work revealing a conservation at the broad- but not the fine-scale. However, in order to better understand the nature of this variation, and the time scales on which substantial modifications occur, it is necessary to take a broader phylogenetic perspective. I here present the first fine-scale genetic map for vervet monkeys based on whole-genome population genetic data from ten individuals and perform a series of comparative analyses with the great apes. The results reveal a number of striking features. First, owing to strong positive correlations with diversity and weak negative correlations with divergence, analyses suggest a dominant role for purifying and background selection in shaping patterns of variation in this species. Second, results support a generally reduced broad-scale recombination rate compared with the great apes, as well as a narrower fraction of the genome in which the majority of recombination events are observed to occur. Taken together, this data set highlights the great necessity of future research to identify genomic features and quantify evolutionary processes that are driving these rate changes across primates.
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Affiliation(s)
- Susanne P Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ
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26
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Apata M, Pfeifer SP. Recent population genomic insights into the genetic basis of arsenic tolerance in humans: the difficulties of identifying positively selected loci in strongly bottlenecked populations. Heredity (Edinb) 2020; 124:253-262. [PMID: 31776483 PMCID: PMC6972707 DOI: 10.1038/s41437-019-0285-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 10/22/2019] [Accepted: 11/13/2019] [Indexed: 02/06/2023] Open
Abstract
Recent advances in genomics have enabled researchers to shed light on the evolutionary processes driving human adaptation, by revealing the genetic architectures underlying traits ranging from lactase persistence, to skin pigmentation, to hypoxic response, to arsenic tolerance. Complicating the identification of targets of positive selection in modern human populations is their complex demographic history, characterized by population bottlenecks and expansions, population structure, migration, and admixture. In particular, founder effects and recent strong population size reductions, such as those experienced by the indigenous peoples of the Americas, have severe impacts on genetic variation that can lead to the accumulation of large allele frequency differences between populations due to genetic drift rather than natural selection. While distinguishing the effects of demographic history from selection remains challenging, neglecting neutral processes can lead to the incorrect identification of candidate loci. We here review the recent population genomic insights into the genetic basis of arsenic tolerance in Andean populations, and utilize this example to highlight both the difficulties pertaining to the identification of local adaptations in strongly bottlenecked populations, as well as the importance of controlling for demographic history in selection scans.
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Affiliation(s)
- Mario Apata
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, 85821, USA
| | - Susanne P Pfeifer
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, 85821, USA.
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27
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Abstract
As one of the most commonly utilized organisms in the study of local adaptation, an accurate characterization of the demographic history of Drosophila melanogaster remains as an important research question. This owes both to the inherent interest in characterizing the population history of this model organism, as well as to the well-established importance of an accurate null demographic model for increasing power and decreasing false positive rates in genomic scans for positive selection. Although considerable attention has been afforded to this issue in non-African populations, less is known about the demographic history of African populations, including from the ancestral range of the species. While qualitative predictions and hypotheses have previously been forwarded, we here present a quantitative model fitting of the population history characterizing both the ancestral Zambian population range as well as the subsequently colonized west African populations, which themselves served as the source of multiple non-African colonization events. We here report the split time of the West African population at 72 kya, a date corresponding to human migration into this region as well as a period of climatic changes in the African continent. Furthermore, we have estimated population sizes at this split time. These parameter estimates thus represent an important null model for future investigations in to African and non-African D. melanogaster populations alike.
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Affiliation(s)
- Adamandia Kapopoulou
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Susanne P Pfeifer
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona
| | - Jeffrey D Jensen
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona
| | - Stefan Laurent
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
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28
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Pfeifer SP, Laurent S, Sousa VC, Linnen CR, Foll M, Excoffier L, Hoekstra HE, Jensen JD. The Evolutionary History of Nebraska Deer Mice: Local Adaptation in the Face of Strong Gene Flow. Mol Biol Evol 2019; 35:792-806. [PMID: 29346646 PMCID: PMC5905656 DOI: 10.1093/molbev/msy004] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [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] [Indexed: 12/20/2022] Open
Abstract
The interplay of gene flow, genetic drift, and local selective pressure is a dynamic process that has been well studied from a theoretical perspective over the last century. Wright and Haldane laid the foundation for expectations under an island-continent model, demonstrating that an island-specific beneficial allele may be maintained locally if the selection coefficient is larger than the rate of migration of the ancestral allele from the continent. Subsequent extensions of this model have provided considerably more insight. Yet, connecting theoretical results with empirical data has proven challenging, owing to a lack of information on the relationship between genotype, phenotype, and fitness. Here, we examine the demographic and selective history of deer mice in and around the Nebraska Sand Hills, a system in which variation at the Agouti locus affects cryptic coloration that in turn affects the survival of mice in their local habitat. We first genotyped 250 individuals from 11 sites along a transect spanning the Sand Hills at 660,000 single nucleotide polymorphisms across the genome. Using these genomic data, we found that deer mice first colonized the Sand Hills following the last glacial period. Subsequent high rates of gene flow have served to homogenize the majority of the genome between populations on and off the Sand Hills, with the exception of the Agouti pigmentation locus. Furthermore, mutations at this locus are strongly associated with the pigment traits that are strongly correlated with local soil coloration and thus responsible for cryptic coloration.
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Affiliation(s)
- Susanne P Pfeifer
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ
| | - Stefan Laurent
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Vitor C Sousa
- Institute of Ecology & Evolution, University of Berne, Berne, Switzerland.,Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | | | - Matthieu Foll
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laurent Excoffier
- Institute of Ecology & Evolution, University of Berne, Berne, Switzerland
| | - Hopi E Hoekstra
- Department of Organismic & Evolutionary Biology and Molecular & Cellular Biology, Museum of Comparative Zoology, Howard Hughes Medical Institute, Harvard University, Cambridge, MA
| | - Jeffrey D Jensen
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ
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29
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Barrett RDH, Laurent S, Mallarino R, Pfeifer SP, Xu CCY, Foll M, Wakamatsu K, Duke-Cohan JS, Jensen JD, Hoekstra HE. Linking a mutation to survival in wild mice. Science 2019; 363:499-504. [DOI: 10.1126/science.aav3824] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 12/06/2018] [Indexed: 12/19/2022]
Abstract
Adaptive evolution in new or changing environments can be difficult to predict because the functional connections between genotype, phenotype, and fitness are complex. Here, we make these explicit connections by combining field and laboratory experiments in wild mice. We first directly estimate natural selection on pigmentation traits and an underlying pigment locus, Agouti, by using experimental enclosures of mice on different soil colors. Next, we show how a mutation in Agouti associated with survival causes lighter coat color through changes in its protein binding properties. Together, our findings demonstrate how a sequence variant alters phenotype and then reveal the ensuing ecological consequences that drive changes in population allele frequency, thereby illuminating the process of evolution by natural selection.
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30
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Sackman AM, Pfeifer SP, Kowalik TF, Jensen JD. On the Demographic and Selective Forces Shaping Patterns of Human Cytomegalovirus Variation within Hosts. Pathogens 2018; 7:pathogens7010016. [PMID: 29382090 PMCID: PMC5874742 DOI: 10.3390/pathogens7010016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 01/23/2018] [Accepted: 01/25/2018] [Indexed: 02/08/2023] Open
Abstract
Human cytomegalovirus (HCMV) is a member of the β -herpesvirus subfamily within Herpesviridae that is nearly ubiquitous in human populations, and infection generally results only in mild symptoms. However, symptoms can be severe in immunonaive individuals, and transplacental congenital infection of HCMV can result in serious neurological sequelae. Recent work has revealed much about the demographic and selective forces shaping the evolution of congenitally transmitted HCMV both on the level of hosts and within host compartments, providing insight into the dynamics of congenital infection, reinfection, and evolution of HCMV with important implications for the development of effective treatments and vaccines.
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Affiliation(s)
- Andrew M Sackman
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
| | - Timothy F Kowalik
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA 01655, USA.
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
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31
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Dexter E, Bollens SM, Cordell J, Soh HY, Rollwagen-Bollens G, Pfeifer SP, Goudet J, Vuilleumier S. A genetic reconstruction of the invasion of the calanoid copepod Pseudodiaptomus inopinus across the North American Pacific Coast. Biol Invasions 2017. [DOI: 10.1007/s10530-017-1649-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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32
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Crawford NG, Kelly DE, Hansen MEB, Beltrame MH, Fan S, Bowman SL, Jewett E, Ranciaro A, Thompson S, Lo Y, Pfeifer SP, Jensen JD, Campbell MC, Beggs W, Hormozdiari F, Mpoloka SW, Mokone GG, Nyambo T, Meskel DW, Belay G, Haut J, Rothschild H, Zon L, Zhou Y, Kovacs MA, Xu M, Zhang T, Bishop K, Sinclair J, Rivas C, Elliot E, Choi J, Li SA, Hicks B, Burgess S, Abnet C, Watkins-Chow DE, Oceana E, Song YS, Eskin E, Brown KM, Marks MS, Loftus SK, Pavan WJ, Yeager M, Chanock S, Tishkoff SA. Loci associated with skin pigmentation identified in African populations. Science 2017; 358:eaan8433. [PMID: 29025994 PMCID: PMC5759959 DOI: 10.1126/science.aan8433] [Citation(s) in RCA: 191] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Accepted: 10/03/2017] [Indexed: 12/13/2022]
Abstract
Despite the wide range of skin pigmentation in humans, little is known about its genetic basis in global populations. Examining ethnically diverse African genomes, we identify variants in or near SLC24A5, MFSD12, DDB1, TMEM138, OCA2, and HERC2 that are significantly associated with skin pigmentation. Genetic evidence indicates that the light pigmentation variant at SLC24A5 was introduced into East Africa by gene flow from non-Africans. At all other loci, variants associated with dark pigmentation in Africans are identical by descent in South Asian and Australo-Melanesian populations. Functional analyses indicate that MFSD12 encodes a lysosomal protein that affects melanogenesis in zebrafish and mice, and that mutations in melanocyte-specific regulatory regions near DDB1/TMEM138 correlate with expression of ultraviolet response genes under selection in Eurasians.
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Affiliation(s)
- Nicholas G Crawford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Derek E Kelly
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew E B Hansen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcia H Beltrame
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shaohua Fan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shanna L Bowman
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine and Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ethan Jewett
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94704, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA 94704, USA
| | - Alessia Ranciaro
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Simon Thompson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yancy Lo
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Michael C Campbell
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, Howard University, Washington, DC 20059, USA
| | - William Beggs
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA
| | | | - Gaonyadiwe George Mokone
- Department of Biomedical Sciences, University of Botswana School of Medicine, Gaborone, Botswana
| | - Thomas Nyambo
- Department of Biochemistry, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | | | - Gurja Belay
- Department of Biology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Jake Haut
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Harriet Rothschild
- Stem Cell Program, Division of Hematology and Oncology, Pediatric Hematology Program, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Leonard Zon
- Stem Cell Program, Division of Hematology and Oncology, Pediatric Hematology Program, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Yi Zhou
- Stem Cell Program, Division of Hematology and Oncology, Pediatric Hematology Program, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Michael A Kovacs
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mai Xu
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tongwu Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kevin Bishop
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jason Sinclair
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Cecilia Rivas
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eugene Elliot
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jiyeon Choi
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shengchao A Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA
| | - Belynda Hicks
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA
| | - Shawn Burgess
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Christian Abnet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
| | - Dawn E Watkins-Chow
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elena Oceana
- Department of Molecular Pharmacology, Physiology and Biotechnology, Brown University, Providence, RI 02912, USA
| | - Yun S Song
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94704, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA 94704, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eleazar Eskin
- Department of Computer Science and Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kevin M Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael S Marks
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine and Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stacie K Loftus
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William J Pavan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
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33
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Pfeifer SP. Direct estimate of the spontaneous germ line mutation rate in African green monkeys. Evolution 2017; 71:2858-2870. [PMID: 29068052 DOI: 10.1111/evo.13383] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [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: 07/06/2017] [Revised: 10/03/2017] [Accepted: 10/09/2017] [Indexed: 12/30/2022]
Abstract
Here, I provide the first direct estimate of the spontaneous mutation rate in an Old World monkey, using a seven individual, three-generation pedigree of African green monkeys. Eight de novo mutations were identified within ∼1.5 Gbp of accessible genome, corresponding to an estimated point mutation rate of 0.94 × 10-8 per site per generation, suggesting an effective population size of ∼12000 for the species. This estimation represents a significant improvement in our knowledge of the population genetics of the African green monkey, one of the most important nonhuman primate models in biomedical research. Furthermore, by comparing mutation rates in Old World monkeys with the only other direct estimates in primates to date-humans and chimpanzees-it is possible to uniquely address how mutation rates have evolved over longer time scales. While the estimated spontaneous mutation rate for African green monkeys is slightly lower than the rate of 1.2 × 10-8 per base pair per generation reported in chimpanzees, it is similar to the lower range of rates of 0.96 × 10-8 -1.28 × 10-8 per base pair per generation recently estimated from whole genome pedigrees in humans. This result suggests a long-term constraint on mutation rate that is quite different from similar evidence pertaining to recombination rate evolution in primates.
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Affiliation(s)
- Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,School of Life Sciences, Arizona State University (ASU), Tempe, Arizona 85281
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34
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Abstract
Relatively little is known about the evolutionary history of the African green monkey (genus Chlorocebus) due to the lack of sampled polymorphism data from wild populations. Yet, this characterization of genetic diversity is not only critical for a better understanding of their own history, but also for human biomedical research given that they are one of the most widely used primate models. Here, I analyze the demographic and selective history of the African green monkey, utilizing one of the most comprehensive catalogs of wild genetic diversity to date, consisting of 1,795,643 autosomal single nucleotide polymorphisms in 25 individuals, representing all five major populations: C. a. aethiops, C. a. cynosurus, C. a. pygerythrus, C. a. sabaeus, and C. a tantalus. Assuming a mutation rate of 5.9 × 10-9 per base pair per generation and a generation time of 8.5 years, divergence time estimates range from 523 to 621 kya for the basal split of C. a. aethiops from the other four populations. Importantly, the resulting tree characterizing the relationship and split-times between these populations differs significantly from that presented in the original genome paper, owing to their neglect of within-population variation when calculating between population-divergence. In addition, I find that the demographic history of all five populations is well explained by a model of population fragmentation and isolation, rather than novel colonization events. Finally, utilizing these demographic models as a null, I investigate the selective history of the populations, identifying candidate regions potentially related to adaptation in response to pathogen exposure.
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Affiliation(s)
- Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,School of Life Sciences, Arizona State University, Tempe, AZ
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35
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Pokalyuk C, Renzette N, Irwin KK, Pfeifer SP, Gibson L, Britt WJ, Yamamoto AY, Mussi-Pinhata MM, Kowalik TF, Jensen JD. Characterizing human cytomegalovirus reinfection in congenitally infected infants: an evolutionary perspective. Mol Ecol 2017; 26:1980-1990. [PMID: 27988973 DOI: 10.1111/mec.13953] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [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: 05/22/2016] [Accepted: 12/12/2016] [Indexed: 02/04/2023]
Abstract
Given the strong selective pressures often faced by populations when colonizing a novel habitat, the level of variation present on which selection may act is an important indicator of adaptive potential. While often discussed in an ecological context, this notion is also highly relevant in our clinical understanding of viral infection, in which the novel habitat is a new host. Thus, quantifying the factors determining levels of variation is of considerable importance for the design of improved treatment strategies. Here, we focus on such a quantification of human cytomegalovirus (HCMV) - a virus which can be transmitted across the placenta, resulting in foetal infection that can potentially cause severe disease in multiple organs. Recent studies using genomewide sequencing data have demonstrated that viral populations in some congenitally infected infants diverge rapidly over time and between tissue compartments within individuals, while in other infants, the populations remain highly stable. Here, we investigate the underlying causes of these extreme differences in observed intrahost levels of variation by estimating the underlying demographic histories of infection. Importantly, reinfection (i.e. population admixture) appears to be an important, and previously unappreciated, player. We highlight illustrative examples likely to represent a single-population transmission from a mother during pregnancy and multiple-population transmissions during pregnancy and after birth.
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Affiliation(s)
- Cornelia Pokalyuk
- Institute for Mathematics, Goethe Universität Frankfurt, Frankfurt am Main, Germany.,Faculty for Mathematics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.,School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Nicholas Renzette
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA
| | - Kristen K Irwin
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Laura Gibson
- Departments of Medicine and Pediatrics, Divisions of Infectious Diseases and Immunology, University of Massachusetts Medical School, Worcester, MA, USA
| | - William J Britt
- Department of Pediatrics, University of Alabama Birmingham, School of Medicine, Birmingham, AL, USA
| | - Aparecida Y Yamamoto
- Department of Pediatrics, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
| | - Marisa M Mussi-Pinhata
- Department of Pediatrics, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
| | - Timothy F Kowalik
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jeffrey D Jensen
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,School of Life Sciences, Arizona State University, Tempe, AZ, USA
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36
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Kingsley EP, Kozak KM, Pfeifer SP, Yang DS, Hoekstra HE. The ultimate and proximate mechanisms driving the evolution of long tails in forest deer mice. Evolution 2016; 71:261-273. [PMID: 27958661 PMCID: PMC5324611 DOI: 10.1111/evo.13150] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [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: 02/28/2016] [Revised: 11/15/2016] [Accepted: 12/02/2016] [Indexed: 12/21/2022]
Abstract
Understanding both the role of selection in driving phenotypic change and its underlying genetic basis remain major challenges in evolutionary biology. Here, we use modern tools to revisit a classic system of local adaptation in the North American deer mouse, Peromyscus maniculatus, which occupies two main habitat types: prairie and forest. Using historical collections, we find that forest‐dwelling mice have longer tails than those from nonforested habitat, even when we account for individual and population relatedness. Using genome‐wide SNP data, we show that mice from forested habitats in the eastern and western parts of their range form separate clades, suggesting that increased tail length evolved independently. We find that forest mice in the east and west have both more and longer caudal vertebrae, but not trunk vertebrae, than nearby prairie forms. By intercrossing prairie and forest mice, we show that the number and length of caudal vertebrae are not correlated in this recombinant population, indicating that variation in these traits is controlled by separate genetic loci. Together, these results demonstrate convergent evolution of the long‐tailed forest phenotype through two distinct genetic mechanisms, affecting number and length of vertebrae, and suggest that these morphological changes—either independently or together—are adaptive.
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Affiliation(s)
- Evan P Kingsley
- Howard Hughes Medical Institute, Department of Organismic and Evolutionary Biology, Department of Molecular and Cellular Biology, Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, 02138
| | - Krzysztof M Kozak
- Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, United Kingdom.,Current Address: Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panamá, República de Panamá
| | - Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland and School of Life Sciences, Arizona State University, Tempe, Arizona, 85287
| | - Dou-Shuan Yang
- Burke Museum and Department of Biology, University of Washington, Seattle, Washington, 98195.,Current Address: US Fish and Wildlife Service, Ventura Field Office, 2493 Portola Road #B, Ventura, California, 93003
| | - Hopi E Hoekstra
- Howard Hughes Medical Institute, Department of Organismic and Evolutionary Biology, Department of Molecular and Cellular Biology, Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, 02138
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37
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Abstract
Levels of nucleotide diversity vary greatly across the genomes of most species owing to multiple factors. These include variation in the underlying mutation rates, as well as the effects of both direct and linked selection. Fundamental to interpreting the relative importance of these forces is the common observation of a strong positive correlation between nucleotide diversity and recombination rate. While indeed observed in humans, the interpretation of this pattern has been difficult in the absence of high-quality polymorphism data and recombination maps in closely related species. Here, we characterize genetic features driving nucleotide diversity in Western chimpanzees using a recently generated whole genome polymorphism data set. Our results suggest that recombination rate is the primary predictor of nucleotide variation with a strongly positive correlation. In addition, telomeric distance, regional GC-content, and regional CpG-island content are strongly negatively correlated with variation. These results are compared with humans, with both similarities and differences interpreted in the light of the estimated effective population sizes of the two species as well as their strongly differing recent demographic histories.
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Affiliation(s)
- Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland .,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,School of Life Sciences, Arizona State University (ASU), Tempe, Arizona
| | - Jeffrey D Jensen
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,School of Life Sciences, Arizona State University (ASU), Tempe, Arizona
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38
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Ormond L, Foll M, Ewing GB, Pfeifer SP, Jensen JD. Inferring the age of a fixed beneficial allele. Mol Ecol 2016; 25:157-69. [PMID: 26576754 DOI: 10.1111/mec.13478] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [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: 05/08/2015] [Revised: 10/14/2015] [Accepted: 11/09/2015] [Indexed: 12/28/2022]
Abstract
Estimating the age and strength of beneficial alleles is central to understanding how adaptation proceeds in response to changing environmental conditions. Several haplotype-based estimators exist for inferring the age of segregating beneficial mutations. Here, we develop an approximate Bayesian-based approach that rather estimates these parameters for fixed beneficial mutations in single populations. We integrate a range of existing diversity, site frequency spectrum, haplotype- and linkage disequilibrium-based summary statistics. We show that for strong selective sweeps on de novo mutations the method can estimate allele age and selection strength even in nonequilibrium demographic scenarios. We extend our approach to models of selection on standing variation, and co-infer the frequency at which selection began to act upon the mutation. Finally, we apply our method to estimate the age and selection strength of a previously identified mutation underpinning cryptic colour adaptation in a wild deer mouse population, and compare our findings with previously published estimates as well as with geological data pertaining to the presumed shift in selective pressure.
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Affiliation(s)
- Louise Ormond
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Matthieu Foll
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Gregory B Ewing
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Susanne P Pfeifer
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Jeffrey D Jensen
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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39
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Laurent S, Pfeifer SP, Settles ML, Hunter SS, Hardwick KM, Ormond L, Sousa VC, Jensen JD, Rosenblum EB. The population genomics of rapid adaptation: disentangling signatures of selection and demography in white sands lizards. Mol Ecol 2015; 25:306-23. [PMID: 26363411 DOI: 10.1111/mec.13385] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [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: 05/09/2015] [Revised: 08/31/2015] [Accepted: 09/04/2015] [Indexed: 02/03/2023]
Abstract
Understanding the process of adaptation during rapid environmental change remains one of the central focal points of evolutionary biology. The recently formed White Sands system of southern New Mexico offers an outstanding example of rapid adaptation, with a variety of species having rapidly evolved blanched forms on the dunes that contrast with their close relatives in the surrounding dark soil habitat. In this study, we focus on two of the White Sands lizard species, Sceloporus cowlesi and Aspidoscelis inornata, for which previous research has linked mutations in the melanocortin-1 receptor gene (Mc1r) to blanched coloration. We sampled populations both on and off the dunes and used a custom sequence capture assay based on probed fosmid libraries to obtain >50 kb of sequence around Mc1r and hundreds of other random genomic locations. We then used model-based statistical inference methods to describe the demographic and adaptive history characterizing the colonization of White Sands. We identified a number of similarities between the two focal species, including strong evidence of selection in the blanched populations in the Mc1r region. We also found important differences between the species, suggesting different colonization times, different genetic architecture underlying the blanched phenotype and different ages of the beneficial alleles. Finally, the beneficial allele is dominant in S. cowlesi and recessive in A. inornata, allowing for a rare empirical test of theoretically expected patterns of selective sweeps under these differing models.
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Affiliation(s)
- Stefan Laurent
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL SV IBI-SV UPJENSEN, Station 15, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL SV IBI-SV UPJENSEN, Station 15, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Matthew L Settles
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, 83844, USA
| | - Samuel S Hunter
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, 83844, USA
| | - Kayla M Hardwick
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, 83844, USA
| | - Louise Ormond
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL SV IBI-SV UPJENSEN, Station 15, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Vitor C Sousa
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Institute of Ecology and Evolution, University of Berne, Baltzerstrasse 6, CH-3012, Berne, Switzerland
| | - Jeffrey D Jensen
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL SV IBI-SV UPJENSEN, Station 15, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Erica Bree Rosenblum
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, 83844, USA.,Department of Environmental Sciences, Policy & Management, Berkeley, CA, 94720, USA
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