My talk at SMBE 2013 on eukaryotic origins.

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)
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3. Falsifiability
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4. Concilience
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5. Mum
Dad
Me
Mum
Dad
Me
Mum
Dad
Me
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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
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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).
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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.
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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)
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22. The playground.
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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
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24. A broader selection
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27. Eukaryote_a
Archaebacteria
Eubacteria
Eukaryote_b
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28. Eukaryote_a
Archaebacteria
Eubacteria
Eukaryote_b
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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.
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30. The eubacterial component is flexible
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32. Un-baking the cake?
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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.
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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
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