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TOWNSEND Jeffrey

  • 135 College St, Yale University, New Haven, United States of America
  • Evolutionary Applications, Evolutionary Epidemiology, Evolutionary Theory, Experimental Evolution, Expression Studies, Genome Evolution, Genotype-Phenotype, Human Evolution, Life History, Molecular Evolution, Morphological Evolution, Phenotypic Plasticity, Phylogenetics / Phylogenomics, Phylogeography & Biogeography, Population Genetics / Genomics, Quantitative Genetics, Speciation, Systematics / Taxonomy
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Recommendations:  0

Review:  1

Areas of expertise
Professor Townsend received his Ph.D. in 2002 in organismic and evolutionary biology from Harvard University, under the advisement of Daniel Hartl. His Ph.D. was entitled "Population genetic variation in genome-wide gene expression: modeling, measurement, and analysis", and constituted the first population genetic analysis of genome-wide gene expression variation. After making use of the model budding yeast S. cerevisiae for his Ph.D. research, Dr. Townsend accepted an appointment as a Miller Fellow at the University of California-Berkeley in the Department of Plant and Microbial Biology, where he worked to develop molecular tools, techniques, and analysis methodologies for functional genomics studies with the filamentous fungal model species Neurospora crassa, co-advised by Berkeley fungal evolutionary biologist John Taylor and molecular mycologist Louise Glass. In 2004, he accepted his first appointment as an Assistant Professor in the Department of Molecular and Cell Biology at the University of Connecticut. In 2006 he was appointed as an Assistant Professor the Department of Ecology and Evolutionary Biology at Yale University. In 2013 he began to work on statistical approaches to fit mathematical models of disease spread and emergence, and to work on the somatic evolution of cancer, and was appointed as an Associate Professor of Biostatistics and Ecology & Evolutionary Biology. In 2017 he was named Elihu Associate Professor of Biostatistics and Ecology & Evolutionary Biology, and in 2018 he was appointed Elihu Professor of Biostatistics and Ecology & Evolutionary Biology. In 2019 he was appointed a member of the Connecticut Academy of Science and Engineering, in recognition of the development of innovative approaches to population biology, including the evolution of antimicrobial resistance, disease evolution and transmission, and evolution of tumorigenesis; and research that has enabled curtailment of pathogen evolution, outbreak mitigation, and informed therapeutic approaches to cancer metastasis and evolution of therapeutic resistance in cancer. In 2021 he was selected as the Co-Chair-Elect of the Cancer Evolution Working Group of the American Association for Cancer Research, and in 2022 he was appointed Co-Director of the Genetics, Genomics, and Epigenetics Program of the Yale Cancer Center.

Review:  1

21 Nov 2018
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Convergent evolution as an indicator for selection during acute HIV-1 infection

Is convergence an evidence for positive selection?

Recommended by based on reviews by Jeffrey Townsend and 1 anonymous reviewer

The preprint by Bertels et al. [1] reports an interesting application of the well-accepted idea that positively selected traits (here variants) can appear several times independently; think about the textbook examples of flight capacity. Hence, the authors assume that reciprocally convergence implies positive selection. The methodology becomes then, in principle, straightforward as one can simply count variants in independent datasets to detect convergent mutations.
In this preprint, the authors have applied this counting strategy on 95 available sequence alignments of the env gene of HIV-1 [2,3] that corresponds to samples taken in different patients during the early phase of infection, at the very beginning of the onset of the immune system. They have compared the number and nature of the convergent mutations to a "neutral" model that assumes (a) a uniform distribution of mutations and (b) a substitution matrix estimated from the data. They show that there is an excess of convergent mutations when compared to the “neutral” expectations, especially for mutations that have arisen in 4+ patients. They also show that the gp41 gene is enriched in these convergent mutations. The authors then discuss in length the potential artifacts that could have given rise to the observed pattern.
I think that this preprint is remarkable in the proposed methodology. Samples are taken in different individuals, whose viral populations were founded by a single particle. Thus, there is no need for phylogenetic reconstruction of ancestral states that is the typical first step of trait convergent analyses. It simply becomes counting variants. This simple counting procedure needs nonetheless to be compared to a “neutral” expectation (a reference model), which includes the mutational process. In this article, the poor predictions of a specifically designed reference model is interpreted as an evidence for positive selection.
Whether the few mutations that are convergent in 4-7 samples out of 95 were selected or not is hard to assess with certainty. The authors have provided good evidence that they are, but only experimental validations will strengthen the claim. Nonetheless, beyond a definitive clue to the implication of selection on these particular mutations, I found the methodological strategy and the discussions on the potential biases highly stimulating. This article is an excellent starting point for further methodological developments that could be then followed by large-scale analyses of convergence in many different organisms and case studies.

References

[1] Bertels, F., Metzner, K. J., & Regoes R. R. (2018). Convergent evolution as an indicator for selection during acute HIV-1 infection. BioRxiv, 168260, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/168260
[2] Keele, B. F., Giorgi, E. E., Salazar-Gonzalez, J. F., Decker, J. M., Pham, K.T., Salazar, M. G., Sun, C., Grayson, T., Wang, S., Li, H. et al. (2008). Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci USA 105: 7552–7557. doi: 10.1073/pnas.0802203105
[3] Li, H., Bar, K. J., Wang, S., Decker, J. M., Chen, Y., Sun, C., Salazar-Gonzalez, J.F., Salazar, M.G., Learn, G.H., Morgan, C. J. et al. (2010). High multiplicity infection by HIV-1 in men who have sex with men. PLoS Pathogens 6:e1000890. doi: 10.1371/journal.ppat.1000890

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TOWNSEND Jeffrey

  • 135 College St, Yale University, New Haven, United States of America
  • Evolutionary Applications, Evolutionary Epidemiology, Evolutionary Theory, Experimental Evolution, Expression Studies, Genome Evolution, Genotype-Phenotype, Human Evolution, Life History, Molecular Evolution, Morphological Evolution, Phenotypic Plasticity, Phylogenetics / Phylogenomics, Phylogeography & Biogeography, Population Genetics / Genomics, Quantitative Genetics, Speciation, Systematics / Taxonomy
  • recommender

Recommendations:  0

Review:  1

Areas of expertise
Professor Townsend received his Ph.D. in 2002 in organismic and evolutionary biology from Harvard University, under the advisement of Daniel Hartl. His Ph.D. was entitled "Population genetic variation in genome-wide gene expression: modeling, measurement, and analysis", and constituted the first population genetic analysis of genome-wide gene expression variation. After making use of the model budding yeast S. cerevisiae for his Ph.D. research, Dr. Townsend accepted an appointment as a Miller Fellow at the University of California-Berkeley in the Department of Plant and Microbial Biology, where he worked to develop molecular tools, techniques, and analysis methodologies for functional genomics studies with the filamentous fungal model species Neurospora crassa, co-advised by Berkeley fungal evolutionary biologist John Taylor and molecular mycologist Louise Glass. In 2004, he accepted his first appointment as an Assistant Professor in the Department of Molecular and Cell Biology at the University of Connecticut. In 2006 he was appointed as an Assistant Professor the Department of Ecology and Evolutionary Biology at Yale University. In 2013 he began to work on statistical approaches to fit mathematical models of disease spread and emergence, and to work on the somatic evolution of cancer, and was appointed as an Associate Professor of Biostatistics and Ecology & Evolutionary Biology. In 2017 he was named Elihu Associate Professor of Biostatistics and Ecology & Evolutionary Biology, and in 2018 he was appointed Elihu Professor of Biostatistics and Ecology & Evolutionary Biology. In 2019 he was appointed a member of the Connecticut Academy of Science and Engineering, in recognition of the development of innovative approaches to population biology, including the evolution of antimicrobial resistance, disease evolution and transmission, and evolution of tumorigenesis; and research that has enabled curtailment of pathogen evolution, outbreak mitigation, and informed therapeutic approaches to cancer metastasis and evolution of therapeutic resistance in cancer. In 2021 he was selected as the Co-Chair-Elect of the Cancer Evolution Working Group of the American Association for Cancer Research, and in 2022 he was appointed Co-Director of the Genetics, Genomics, and Epigenetics Program of the Yale Cancer Center.