Literature DB >> 11956689

Clustering of tissue-specific genes underlies much of the similarity in rates of protein evolution of linked genes.

Elizabeth J B Williams1, Laurence D Hurst.   

Abstract

Are genes nonrandomly distributed around the genome and might this explain why it was found that, in the mouse genome, proteins of linked genes evolve at similar rates? Anecdotal evidence suggests that the similarity of expression of linked genes might, in part, explain the similarity in their rates of evolution. Immune system genes, for example, are known to evolve at a high rate and sometimes cluster in the genome. Here we develop methods for statistical tests of similarity of expression of linked genes and report that there is a significant tendency for genes of similar expression breadth to be linked. Significantly, when we exclude tissue specific genes from our sample, the similarity in rates of protein evolution of linked genes is greatly diminished, if not abolished. This diminution is not a sampling artifact. In contrast, while half of the immune genes in our sample reside in 1 of 10 immune clusters in the mouse genome, this clustering appears not to affect the extent of local similarity in rates of evolution. The distribution of placentally expressed genes, in contrast, does have an effect.

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Year:  2002        PMID: 11956689     DOI: 10.1007/s00239-001-0043-8

Source DB:  PubMed          Journal:  J Mol Evol        ISSN: 0022-2844            Impact factor:   2.395


  11 in total

1.  Differential activity of clustered genes in Arabidopsis thaliana.

Authors:  N Yu Minakova; G N Shirshikova; V D Kreslavski; A M Boutanaev
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2.  Selection against Robertsonian fusions involving housekeeping genes in the house mouse: integrating data from gene expression arrays and chromosome evolution.

Authors:  Aurora Ruiz-Herrera; Marta Farré; Montserrat Ponsà; Terence J Robinson
Journal:  Chromosome Res       Date:  2010-09-02       Impact factor: 5.239

3.  Genomic regionality in rates of evolution is not explained by clustering of genes of comparable expression profile.

Authors:  Martin J Lercher; Jean-Vincent Chamary; Laurence D Hurst
Journal:  Genome Res       Date:  2004-06       Impact factor: 9.043

4.  Gepoclu: a software tool for identifying and analyzing gene positional clusters in large-scale gene expression analysis.

Authors:  Tania Dottorini; Nicola Senin; Giorgio Mazzoleni; Kalle Magnusson; Andrea Crisanti
Journal:  BMC Bioinformatics       Date:  2011-01-26       Impact factor: 3.307

5.  Coexpression of linked gene pairs persists long after their separation.

Authors:  Guang-Zhong Wang; Wei-Hua Chen; Martin J Lercher
Journal:  Genome Biol Evol       Date:  2011-07-06       Impact factor: 3.416

6.  Lineage-specific sequence evolution and exon edge conservation partially explain the relationship between evolutionary rate and expression level in A. thaliana.

Authors:  Stephen J Bush; Paula X Kover; Araxi O Urrutia
Journal:  Mol Ecol       Date:  2015-06-05       Impact factor: 6.185

7.  Enhancer Sharing Promotes Neighborhoods of Transcriptional Regulation Across Eukaryotes.

Authors:  Porfirio Quintero-Cadena; Paul W Sternberg
Journal:  G3 (Bethesda)       Date:  2016-12-07       Impact factor: 3.154

Review 8.  Functional bias and spatial organization of genes in mutational hot and cold regions in the human genome.

Authors:  Jeffrey H Chuang; Hao Li
Journal:  PLoS Biol       Date:  2004-02-17       Impact factor: 8.029

9.  The Pseudoautosomal Regions of the U/V Sex Chromosomes of the Brown Alga Ectocarpus Exhibit Unusual Features.

Authors:  Rémy Luthringer; Agnieszka P Lipinska; Denis Roze; Alexandre Cormier; Nicolas Macaisne; Akira F Peters; J Mark Cock; Susana M Coelho
Journal:  Mol Biol Evol       Date:  2015-08-06       Impact factor: 16.240

10.  Analysis of the relationship between coexpression domains and chromatin 3D organization.

Authors:  María E Soler-Oliva; José A Guerrero-Martínez; Valentina Bachetti; José C Reyes
Journal:  PLoS Comput Biol       Date:  2017-09-13       Impact factor: 4.475

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