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Items: 14

1.
Nat Genet. 2016 Sep;48(9):1066-70. doi: 10.1038/ng.3621. Epub 2016 Jul 25.

Genomic analysis of Andamanese provides insights into ancient human migration into Asia and adaptation.

Author information

1
Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain.
2
Servei de Genòmica, Universitat Pompeu Fabra, Barcelona, Spain.
3
BGI Shenzhen, Shenzhen, China.
4
Computational Biology, Target Sciences, GSK R&D, GlaxoSmithKline, Stevenage, UK.
5
Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.
6
Departament de Genètica i de Microbiologia, Universitat Autonòma de Barcelona, Bellaterra, Spain.
7
National Institute of BioMedical Genomics, Kalyani, India.
8
Leverhulme Centre for Human Evolutionary Studies, Department of Archaeology and Anthropology, University of Cambridge, Cambridge, UK.

Abstract

To shed light on the peopling of South Asia and the origins of the morphological adaptations found there, we analyzed whole-genome sequences from 10 Andamanese individuals and compared them with sequences for 60 individuals from mainland Indian populations with different ethnic histories and with publicly available data from other populations. We show that all Asian and Pacific populations share a single origin and expansion out of Africa, contradicting an earlier proposal of two independent waves of migration. We also show that populations from South and Southeast Asia harbor a small proportion of ancestry from an unknown extinct hominin, and this ancestry is absent from Europeans and East Asians. The footprints of adaptive selection in the genomes of the Andamanese show that the characteristic distinctive phenotypes of this population (including very short stature) do not reflect an ancient African origin but instead result from strong natural selection on genes related to human body size.

PMID:
27455350
DOI:
10.1038/ng.3621
[Indexed for MEDLINE]
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2.
Nucleic Acids Res. 2016 Jan 4;44(D1):D992-9. doi: 10.1093/nar/gkv1123. Epub 2015 Oct 29.

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings.

Author information

1
Division of Cancer Studies, King's College London, London SE11UL, UK.
2
Division of Cancer Studies, King's College London, London SE11UL, UK francesca.ciccarelli@kcl.ac.uk.

Abstract

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

PMID:
26516186
PMCID:
PMC4702816
DOI:
10.1093/nar/gkv1123
[Indexed for MEDLINE]
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3.
Bioinformatics. 2015 Dec 15;31(24):3946-52. doi: 10.1093/bioinformatics/btv493. Epub 2015 Aug 26.

Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations.

Author information

1
Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain.
2
Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Department of Biology, Stanford University, Stanford, CA 94305, USA.
3
Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Division of Cancer Studies, King's College of London, London SE1 1UL, UK and.
4
Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Departament de Genètica i de Microbiologia, Universitat Autonòma de Barcelona, Bellaterra 8193, Spain.

Abstract

MOTIVATION:

Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness).

RESULTS:

We have developed a machine-learning classification framework that exploits the combined ability of some selection tests to uncover different polymorphism features expected under the hard sweep model, while controlling for population-specific demography. As a result, we achieve high sensitivity toward hard selective sweeps while adding insights about their completeness (whether a selected variant is fixed or not) and age of onset. Our method also determines the relevance of the individual methods implemented so far to detect positive selection under specific selective scenarios. We calibrated and applied the method to three reference human populations from The 1000 Genome Project to generate a genome-wide classification map of hard selective sweeps. This study improves detection of selective sweep by overcoming the classical selection versus no-selection classification strategy, and offers an explanation to the lack of consistency observed among selection tests when applied to real data. Very few signals were observed in the African population studied, while our method presents higher sensitivity in this population demography.

AVAILABILITY AND IMPLEMENTATION:

The genome-wide results for three human populations from The 1000 Genomes Project and an R-package implementing the 'Hierarchical Boosting' framework are available at http://hsb.upf.edu/.

PMID:
26315912
DOI:
10.1093/bioinformatics/btv493
[Indexed for MEDLINE]
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4.
Bioinformatics. 2015 Feb 1;31(3):438-9. doi: 10.1093/bioinformatics/btu650. Epub 2014 Oct 4.

VCF2Networks: applying genotype networks to single-nucleotide variants data.

Author information

1
Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona.
2
Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona.
3
Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona.

Abstract

SUMMARY:

A wealth of large-scale genome sequencing projects opens the doors to new approaches to study the relationship between genotype and phenotype. One such opportunity is the possibility to apply genotype networks analysis to population genetics data. Genotype networks are a representation of the set of genotypes associated with a single phenotype, and they allow one to estimate properties such as the robustness of the phenotype to mutations, and the ability of its associated genotypes to evolve new adaptations. So far, though, genotype networks analysis has rarely been applied to population genetics data. To help fill this gap, here we present VCF2Networks, a tool to determine and study genotype network structure from single-nucleotide variant data.

AVAILABILITY AND IMPLEMENTATION:

VCF2Networks is available at https://bitbucket.org/dalloliogm/vcf2networks.

CONTACT:

giovanni.dallolio@kcl.ac.uk

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25282646
DOI:
10.1093/bioinformatics/btu650
[Indexed for MEDLINE]
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5.
PLoS One. 2014 Jun 9;9(6):e99424. doi: 10.1371/journal.pone.0099424. eCollection 2014.

Human genome variation and the concept of genotype networks.

Author information

1
Institut de Biologia Evolutiva, CSIC-Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
2
Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland; The Swiss Institute of Bioinformatics, Lausanne, Switzerland; The Santa Fe Institute, Santa Fe, New Mexico, United States of America.
3
Institut de Biologia Evolutiva, CSIC-Universitat Pompeu Fabra, Barcelona, Catalonia, Spain; Universitat Autonòma de Barcelona, Barcelona, Spain.

Erratum in

  • PLoS One. 2014;9(8):e107347.

Abstract

Genotype networks are a concept used in systems biology to study sets of genotypes having the same phenotype, and the ability of these to bring forth novel phenotypes. In the past they have been applied to determine the genetic heterogeneity, and stability to mutations, of systems such as metabolic networks and RNA folds. Recently, they have been the base for reconciling the neutralist and selectionist views on evolution. Here, we adapted this concept to the study of population genetics data. Specifically, we applied genotype networks to the human 1000 genomes dataset, and analyzed networks composed of short haplotypes of Single Nucleotide Variants (SNV). The result is a scan of how properties related to genetic heterogeneity and stability to mutations are distributed along the human genome. We found that genes involved in acquired immunity, such as some HLA and MHC genes, tend to have the most heterogeneous and connected networks, and that coding regions tend to be more heterogeneous and stable to mutations than non-coding regions. We also found, using coalescent simulations, that regions under selection have more extended and connected networks. The application of the concept of genotype networks can provide a new opportunity to understand the evolutionary processes that shaped our genome. Learning how the genotype space of each region of our genome has been explored during the evolutionary history of the human species can lead to a better understanding on how selective pressures and neutral factors have shaped genetic diversity within populations and among individuals. Combined with the availability of larger datasets of sequencing data, genotype networks represent a new approach to the study of human genetic diversity that looks to the whole genome, and goes beyond the classical division between selection and neutrality methods.

PMID:
24911413
PMCID:
PMC4049842
DOI:
10.1371/journal.pone.0099424
[Indexed for MEDLINE]
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6.
Nucleic Acids Res. 2014 Jan;42(Database issue):D903-9. doi: 10.1093/nar/gkt1188. Epub 2013 Nov 25.

1000 Genomes Selection Browser 1.0: a genome browser dedicated to signatures of natural selection in modern humans.

Author information

1
Program for Population Genetics, Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain, Population Genomics Node, National Institute for Bioinformatics (INB), Universitat Pompeu Fabra, 08003 Barcelona, Spain, Institute of Molecular Biology and Biotechnology-FORTH, Heraklion, Crete GR 700 13, Greece and Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany.

Abstract

Searching for Darwinian selection in natural populations has been the focus of a multitude of studies over the last decades. Here we present the 1000 Genomes Selection Browser 1.0 (http://hsb.upf.edu) as a resource for signatures of recent natural selection in modern humans. We have implemented and applied a large number of neutrality tests as well as summary statistics informative for the action of selection such as Tajima's D, CLR, Fay and Wu's H, Fu and Li's F* and D*, XPEHH, ΔiHH, iHS, F(ST), ΔDAF and XPCLR among others to low coverage sequencing data from the 1000 genomes project (Phase 1; release April 2012). We have implemented a publicly available genome-wide browser to communicate the results from three different populations of West African, Northern European and East Asian ancestry (YRI, CEU, CHB). Information is provided in UCSC-style format to facilitate the integration with the rich UCSC browser tracks and an access page is provided with instructions and for convenient visualization. We believe that this expandable resource will facilitate the interpretation of signals of selection on different temporal, geographical and genomic scales.

PMID:
24275494
PMCID:
PMC3965045
DOI:
10.1093/nar/gkt1188
[Indexed for MEDLINE]
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7.
BMC Evol Biol. 2012 Jun 25;12:98. doi: 10.1186/1471-2148-12-98.

Distribution of events of positive selection and population differentiation in a metabolic pathway: the case of asparagine N-glycosylation.

Author information

1
IBE, Institut de Biologia Evolutiva (UPF-CSIC), Parc de Recerca Biomèdica de Barcelona (PRBB), Dr, Aiguader, 88, 08003, Barcelona, Catalonia, Spain.

Abstract

BACKGROUND:

Asparagine N-Glycosylation is one of the most important forms of protein post-translational modification in eukaryotes. This metabolic pathway can be subdivided into two parts: an upstream sub-pathway required for achieving proper folding for most of the proteins synthesized in the secretory pathway, and a downstream sub-pathway required to give variability to trans-membrane proteins, and involved in adaptation to the environment and innate immunity. Here we analyze the nucleotide variability of the genes of this pathway in human populations, identifying which genes show greater population differentiation and which genes show signatures of recent positive selection. We also compare how these signals are distributed between the upstream and the downstream parts of the pathway, with the aim of exploring how forces of population differentiation and positive selection vary among genes involved in the same metabolic pathway but subject to different functional constraints.

RESULTS:

Our results show that genes in the downstream part of the pathway are more likely to show a signature of population differentiation, while events of positive selection are equally distributed among the two parts of the pathway. Moreover, events of positive selection are frequent on genes that are known to be at bifurcation points, and that are identified as being in key position by a network-level analysis such as MGAT3 and GCS1.

CONCLUSIONS:

These findings indicate that the upstream part of the Asparagine N-Glycosylation pathway has lower diversity among populations, while the downstream part is freer to tolerate diversity among populations. Moreover, the distribution of signatures of population differentiation and positive selection can change between parts of a pathway, especially between parts that are exposed to different functional constraints. Our results support the hypothesis that genes involved in constitutive processes can be expected to show lower population differentiation, while genes involved in traits related to the environment should show higher variability. Taken together, this work broadens our knowledge on how events of population differentiation and of positive selection are distributed among different parts of a metabolic pathway.

PMID:
22731960
PMCID:
PMC3426484
DOI:
10.1186/1471-2148-12-98
[Indexed for MEDLINE]
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8.
Mol Biol Evol. 2012 May;29(5):1379-92. doi: 10.1093/molbev/msr298. Epub 2011 Dec 1.

Network-level and population genetics analysis of the insulin/TOR signal transduction pathway across human populations.

Author information

1
Institute of Evolutionary Biology CEXS-UPF-PRBB, Barcelona, Catalonia, Spain.

Abstract

Genes and proteins rarely act in isolation, but they rather operate as components of complex networks of interacting molecules. Therefore, for understanding their evolution, it may be helpful to take into account the interaction networks in which they participate. It has been shown that selective constraints acting on genes depend on the position that they occupy in the network. Less understood is how the impact of local adaptation at the intraspecific level is affected by the network structure. Here, we analyzed the patterns of molecular evolution of 67 genes involved in the insulin/target of rapamycin (TOR) signal transduction pathway. This well-characterized pathway plays a key role in fundamental processes such as energetic metabolism, growth, reproduction, and aging and is involved in metabolic disorders such as obesity, insulin resistance, and diabetes. For that purpose, we combined genotype data from worldwide human populations with current knowledge of the structure and function of the pathway. We identified the footprint of recent positive selection in nine of the studied genomic regions. Most of the adaptation signals were observed among Middle East and North African, European, and Central South Asian populations. We found that positive selection preferentially targets the most central elements in the pathway, in contrast to previous observations in the whole human interactome. This observation indicates that the impact of positive selection on genes involved in the insulin/TOR pathway is affected by the pathway structure.

PMID:
22135191
DOI:
10.1093/molbev/msr298
[Indexed for MEDLINE]
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11.
PLoS One. 2011 Mar 28;6(3):e17913. doi: 10.1371/journal.pone.0017913.

Similarity in recombination rate estimates highly correlates with genetic differentiation in humans.

Author information

1
IBE, Institute of Evolutionary Biology UPF-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain.

Abstract

Recombination varies greatly among species, as illustrated by the poor conservation of the recombination landscape between humans and chimpanzees. Thus, shorter evolutionary time frames are needed to understand the evolution of recombination. Here, we analyze its recent evolution in humans. We calculated the recombination rates between adjacent pairs of 636,933 common single-nucleotide polymorphism loci in 28 worldwide human populations and analyzed them in relation to genetic distances between populations. We found a strong and highly significant correlation between similarity in the recombination rates corrected for effective population size and genetic differentiation between populations. This correlation is observed at the genome-wide level, but also for each chromosome and when genetic distances and recombination similarities are calculated independently from different parts of the genome. Moreover, and more relevant, this relationship is robustly maintained when considering presence/absence of recombination hotspots. Simulations show that this correlation cannot be explained by biases in the inference of recombination rates caused by haplotype sharing among similar populations. This result indicates a rapid pace of evolution of recombination, within the time span of differentiation of modern humans.

PMID:
21464928
PMCID:
PMC3065460
DOI:
10.1371/journal.pone.0017913
[Indexed for MEDLINE]
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12.
Glycobiology. 2011 Nov;21(11):1395-400. doi: 10.1093/glycob/cwq215. Epub 2011 Jan 2.

The annotation of the asparagine N-linked glycosylation pathway in the Reactome database.

Author information

1
Institute of Evolutionary Biology, Carrer Doctor Aiguader 88, Barcelona, Catalonia, Spain.

Abstract

Asparagine N-linked glycosylation is one of the most important forms of protein post-translational modification in eukaryotes and is one of the first metabolic pathways described at a biochemical level. Here, we report a new annotation of this pathway for the Human species, published after passing a peer-review process in Reactome. The new annotation presented here offers a high level of detail and provides references and descriptions for each reaction, along with integration with GeneOntology and other databases. The open-source approach of Reactome toward annotation encourages feedback from its users, making it easier to keep the annotation of this pathway updated with future knowledge. Reactome's web interface allows easy navigation between steps involved in the pathway to compare it with other pathways and resources in other scientific databases and to export it to BioPax and SBML formats, making it accessible for computational studies. This new entry in Reactome expands and complements the annotations already published in databases for biological pathways and provides a common reference to researchers interested in studying this important pathway in the human species. Finally, we discuss the status of the annotation of this pathway and point out which steps are worth further investigation or need better experimental validation.

PMID:
21199820
PMCID:
PMC3191915
DOI:
10.1093/glycob/cwq215
[Indexed for MEDLINE]
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13.
Database (Oxford). 2010 Dec 23;2010:baq035. doi: 10.1093/database/baq035. Print 2010.

The annotation and the usage of scientific databases could be improved with public issue tracker software.

Author information

1
Institute of Evolutionary Biology, UPF-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain.

Abstract

Since the publication of their longtime predecessor The Atlas of Protein Sequences and Structures in 1965 by Margaret Dayhoff, scientific databases have become a key factor in the organization of modern science. All the information and knowledge described in the novel scientific literature is translated into entries in many different scientific databases, making it possible to obtain very accurate information on a biological entity like genes or proteins without having to manually review the literature on it. However, even for the databases with the finest annotation procedures, errors or unclear parts sometimes appear in the publicly released version and influence the research of unaware scientists using them. The researcher that finds an error in a database is often left in a uncertain state, and often abandons the effort of reporting it because of a lack of a standard procedure to do so. In the present work, we propose that the simple adoption of a public error tracker application, as in many open software projects, could improve the quality of the annotations in many databases and encourage feedback from the scientific community on the data annotated publicly. In order to illustrate the situation, we describe a series of errors that we found and helped solve on the genes of a very well-known pathway in various biomedically relevant databases. We would like to show that, even if a majority of the most important scientific databases have procedures for reporting errors, these are usually not publicly visible, making the process of reporting errors time consuming and not useful. Also, the effort made by the user that reports the error often goes unacknowledged, putting him in a discouraging position.

PMID:
21186182
PMCID:
PMC3011984
DOI:
10.1093/database/baq035
[Indexed for MEDLINE]
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14.
Mol Biol Evol. 2011 Jan;28(1):813-23. doi: 10.1093/molbev/msq259. Epub 2010 Oct 5.

Molecular evolution and network-level analysis of the N-glycosylation metabolic pathway across primates.

Author information

1
Department of Experimental and Health Sciences, Institute of Evolutionary Biology, Universitat Pompeu Fabra-Consejo Superior de Investigaciones Cientificas, Barcelona, Catalonia, Spain.

Abstract

N-glycosylation is one of the most important forms of protein modification, serving key biological functions in multicellular organisms. N-glycans at the cell surface mediate the interaction between cells and the surrounding matrix and may act as pathogen receptors, making the genes responsible for their synthesis good candidates to show signatures of adaptation to different pathogen environments. Here, we study the forces that shaped the evolution of the genes involved in the synthesis of the N-glycans during the divergence of primates within the framework of their functional network. We have found that, despite their function of producing glycan repertoires capable of evading rapidly evolving pathogens, genes involved in the synthesis of the glycans are highly conserved, and no signals of positive selection have been detected within the time of divergence of primates. This suggests strong functional constraints as the main force driving their evolution. We studied the strength of the purifying selection acting on the genes in relation to the network structure considering the position of each gene along the pathway, its connectivity, and the rates of evolution in neighboring genes. We found a strong and highly significant negative correlation between the strength of purifying selection and the connectivity of each gene, indicating that genes encoding for highly connected enzymes evolve slower and thus are subject to stronger selective constraints. This result confirms that network topology does shape the evolution of the genes and that the connectivity within metabolic pathways and networks plays a major role in constraining evolutionary rates.

PMID:
20924085
DOI:
10.1093/molbev/msq259
[Indexed for MEDLINE]
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