Smbe 2013 talk
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This document summarizes a scientific paper on understanding the contributions of prokaryotes (bacteria and archaea) to the eukaryotic genome through a network approach. The paper characterized six types of evolutionary units, five of which involve mosaic lineages generated by horizontal gene transfer. It introduced terminology based on networks of three nodes ("P3s") and "mosaic P3s" to detect these units. Recognizing these evolutionary relationships beyond vertical descent stimulates rethinking key questions in evolution, like early evolution, novelty origins, and lineage formation. This expands understanding of biological complexity beyond genealogy to additional sources of diversity.
Smbe 2013 talk
- 1. Understanding the Prokaryotic Contributions to the Eukaryotic Genome: A Network Approach James McInerney NUI Maynooth* http://bioinf.nuim.ie/ Current address:Harvard University (2012-2013) Thursday 11 July 13
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- 6. Evolutionary analyses of non-genealogical bonds produced by introgressive descent Eric Baptestea,1 , Philippe Lopeza , Frédéric Bouchardb , Fernando Baqueroc , James O. McInerneyd , and Richard M. Buriane a Unité Mixte de Recherche 7138 Systématique, Adaptation, Evolution, Université Pierre et Marie Curie, 75005 Paris, France; b Département de Philosophie, Université de Montréal, Montréal, QC, Canada H3C 3J7; c Department of Microbiology, Ramón y Cajal University Hospital (IRYCIS, CIBERESP), 28034 Madrid, Spain; d Molecular Evolution and Bioinformatics Unit, Department of Biology, National University of Ireland Maynooth, County Kildare, Ireland; and e Department of Philosophy, Virginia Tech, Blacksburg, VA 24061 Edited by W. Ford Doolittle, Dalhousie University, Halifax, Canada, and approved September 24, 2012 (received for review April 20, 2012) All evolutionary biologists are familiar with evolutionary units that evolve by vertical descent in a tree-like fashion in single lineages. However, many other kinds of processes contribute to evolutionary diversity. In vertical descent, the genetic material of a particular evolutionary unit is propagated by replication inside its own lineage. In what we call introgressive descent, the genetic material of a particular evolutionary unit propagates into different host structures and is replicated within these host structures. Thus, introgressive descent generates a variety of evolutionary units and leaves recognizable patterns in resemblance networks. We characterize six kinds of evolutionary units, of which five involve mosaic lineages generated by introgressive descent. To facilitate detection of these units in resemblance networks, we introduce terminology based on two notions, P3s (subgraphs of three nodes: A, B, and C) and mosaic P3s, and suggest an apparatus for systematic detection of introgressive descent. Mosaic P3s correspond to a distinct type of evolutionary bond that is orthogonal to the bonds of kinship and genealogy usually examined by evolutionary biologists. We argue that recognition of these evolutionary bonds stimulates radical rethinking of key questions in evolutionary biology (e.g., the relations among evolutionary players in very early phases of evolutionary history, the origin and emergence of novelties, and the production of new lineages). This line of research will expand the study of biological complexity beyond the usual genealogical bonds, revealing additional sources of biodiversity. It provides an important step to a more realistic pluralist treatment of evolutionary complexity. biodiversity structure | evolutionary transitions | lateral gene transfer | network of life | symbiosis E volutionary biologists often study the origins of biodiversity through the identification of the units at which evolution operates. In agreement with the work by Lewontin (1), it is commonly assumed that such units present a few necessary conditions for evolution by natural selection, namely (i) phenotypic variation among members of an evolutionary unit, (ii) a link between phenotype, survival, and reproduction of organization in ways that may conflict across levels. For instance, some considered that kin selection among related insects was sufficient to account for the seemingly higher level of organization in collectives of eusocial insects (2, 3, 11–13). For others, the colony existed as a selectable whole, irreducible to the simple addition of individual insects’ fates (14–17). This multilevel perspective seems notably jus- were made to explain micro- and major evolutionary transitions. For instance, it was proposed that evolution of higher-level interactors results from the functional integration and suppression of competition between related lower-level interactors, like in scenarios for the “fraternal” tran- sition from unicellularity to multicellular- ity (23), or from the “egalitarian” assortments of unrelated entities interact- ing in ways that lead to new entities (23), PERSPECTIVE Thursday 11 July 13
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- 17. Link between lethality and informational genes 0 2000 137 316 237 1610 Lethal Viable Archaebacteria Eubacteria 0 1500 55 31257 1483 Info Oper Lethal Viable Informational genes are significantly more likely to be lethal than operational genes (or=2.98; 2.03-4.40). An archaebacterial homolog is almost 3 times as likely to be lethal upon deletion as a eubacterial homolog (or=2.96; 2.32-3.77). Thursday 11 July 13
- 18. Informational genes, or=2.01; 0.92-4.41 0 40 35 18 20 39 Lethal Viable Archaebacteria Eubacteria 0 1500 102 257 210 1226 Lethal Viable Archaebacteria Eubacteria Lethality of archaebacterial genes is almost identical across the two categories Operational genes, or=1.89; 1.43-2. Thursday 11 July 13
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- 20. P-values are bootstrap probabilities for the mean of the statistic in archaebacteria being less than or equal to the mean in eubacteria, based on 10,000 replicates. Cotton and McInerney, PNAS, 107:40 17252-17255 (2010) Thursday 11 July 13
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- 23. EubacterialEubacterialEubacterial ArchaebacterialArchaebacterialArchaebacterial n Median Average n Median Average P-valuea Expression level 6735 15.70 89.68 776 17.29 203.62 0.047 * Expression breadth 6735 12.00 12.68 776 17.00 13.78 0.014 * dN/dS 6612 0.10 0.13 764 0.09 0.12 0.006 ** Degree 3342 3.00 7.01 489 4.00 8.06 0.003 ** Betweenness 3342 2.07×10–5 4.10×10–4 489 4.03×10–5 3.74×10–4 0.037 * Closeness 3342 0.22 0.21 489 0.23 0.22 3.42×10–4 *** Protein length 7884 540.00 707.41 939 496.00 665.19 3.26×10–7 *** # Paralogs 7884 3.00 4.31 939 1.00 2.86 7.83×10–34 *** n PercentPercent n PercentPercent P-valuea Lethal mouse orthologsb 2588 44.3%44.3% 247 52.2%52.2% <0.05 * Involved in human diseaseb 7884 17.3%17.3% 939 12.2%12.2% <0.05 * Informationalb 6515 3.4%3.4% 795 18.6%18.6% <0.05 * Mitochondrialb 6798 11.5%11.5% 809 6.4%6.4% <0.05 * The effects of history on Humans Thursday 11 July 13
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- 29. - 61 yeast genes directly linked to viral genes in our network - 13 (i.e., 21%) encode proteins that locate to the yeast nucleus. - Yeast genes without viral homologs: 21% encode proteins that are targeted to the nucleus. Thursday 11 July 13
- 30. The eubacterial component is flexible Thursday 11 July 13
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- 33. Eukaryotes... • are chimaeric • are monophyletic • are not ancestral to prokaryotes • are not derived from planctomycetes • don’t seem to have nuclei with a numerically large contribution of proteins from viruses • are still a semi-segregated community of genetic “goods” • have archaebacterial proteins that prefer to play with archaebacterial proteins. • have eubacterial proteins that prefer to play with eubacterial proteins • have ESP proteins that prefer to play with ESP proteins • have expanding and contracting eubacterial families • have a relatively constant archaebacterial component • have an archaebacterial component that evolves more slowly, is more highly-expressed, is more likely to be lethal on deletion, is more central in protein-protein interaction networks. Thursday 11 July 13
- 34. ThanksNUI Maynooth: Chris Creevey, Mary O’Connell, Melissa Pentony, David Fitzpatrick, Gayle Philip, Jennifer Commins, Davide Pisani, James Cotton, Simon Travers, Rhoda Kinsella, Fergal Martin, Carla Cummins, Leanne Haggerty, Aoife Doherty, Sinead Hamilton David Álvarez-Ponce External Collaborators: Bill Martin, Duesseldorf, Germany Martin Embley, Newcastle, UK Mark Wilkinson, NHM, London, UK Peter Foster, NHM, London, UK Eugene Koonin, NIH, USA Michael Galperin, NIH, USA John Allen, QMUL, London, UK Nick Lane, Univ. Coll. London, UK Eric Bapteste, UPMC, Paris, France Philippe Lopez, UPMC, Paris, France Ford Doolittle, Dalhousie, Nova Scotia John Archibald, Dalhousie, Nova Scotia Bill Hanage, Harvard School of Public Health Thursday 11 July 13