CoChat
Sign Up
Cited by 52

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Functional protein divergence in the evolution of Homo sapiens

Lopez-Bigas, Nuria, De, Subhajyoti, Teichmann, Sarah A·2008·Genome Biology
DOI 10.1186/gb-2008-9-2-r33PMC PMC2374701
Open this paper in CoChat — free account

Abstract

Background: Protein-coding regions in a genome evolve by sequence divergence and gene gain and loss, altering the gene content of the organism. However, it is not well understood how this has given rise to the enormous diversity of metazoa present today. Results: To obtain a global view of human genomic evolution, we quantify the divergence of proteins by functional category at different evolutionary distances from human. Conclusion: This analysis highlights some general systems-level characteristics of human evolution: regulatory processes, such as signal transducers, transcription factors and receptors, have a high degree of plasticity, while core processes, such as metabolism, transport and protein synthesis, are largely conserved. Additionally, this study reveals a dynamic picture of selective forces at short, medium and long evolutionary timescales. Certain functional categories, such as 'development' and 'organogenesis', exhibit temporal patterns of sequence divergence in eukaryotes relative to human. This framework for a grammar of human evolution supports previously postulated theories of robustness and evolvability.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/gb-2008-9-2-r33

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Flow chart of the FRED method for analyzing the protein divergence landscape of functional categories. (a) We start from a matrix of all human genes with the conservation score (CS) in each of the 15 genomes analyzed. (b) First, all genes with a CS over 0 are ranked in each organism, and the highly ranked genes are shown in red and lowly ranked in blue following a gradient of colors. White cells mean that no ortholog/homolog is detected. Next, the genes are classified according to GO terms. (c) For each set of genes within a GO category, we calculate the median CS, and also select 10,000 sets of the same number of genes as in the GO category considered at random from the complete set of genes with GO annotation. (d) For each random set, we calculate the median CS. (e) From the 10,000 random sets we obtain the expected median CS and the standard error, which allow us to calculate the Z-score for the GO category under consideration. (f) This Z-score is then plotted in a matrix on a color-coded scale. Gray means no significant difference in the level of conservation compared to the background. A similar procedure is followed for the calculation of Z-scores for number of orthologs and homologs by counting the proportion of genes with homologs or orthologs in each set. Mmus, Mus musculus; Rnor, Rattus norvegicus; Cfam, Canis familiaris; Bta, Bos taurus; Mdom, Monodelphis domestica; Ggal, Gallus gallus; Xtro, Xenopus tropicalis; Drer, Danio rerio; Trub, Takifugu rubripes; Tnig, Tetraodon nigroviridis; Cint, Ciona intestinalis; Agam, Anopheles gambiae; Dmel, Drosophila melanogaster; Cele, Caenorhabditis elegans; Scer, Saccharomyces cerevisiae. All the results of these analyses for all GO categories are provided online in a searchable database at [28].
Figure 1 Flow chart of the FRED method for analyzing the protein divergence landscape of functional categories. (a) We start from a matrix of all human genes with the conservation score (CS) in each of the 15 genomes analyzed. (b) First, all genes with a CS over 0 are ranked in each organism, and the highly ranked genes are shown in red and lowly ranked in blue following a gradient of colors. White cells mean that no ortholog/homolog is detected. Next, the genes are classified according to GO terms. (c) For each set of genes within a GO category, we calculate the median CS, and also select 10,000 sets of the same number of genes as in the GO category considered at random from the complete set of genes with GO annotation. (d) For each random set, we calculate the median CS. (e) From the 10,000 random sets we obtain the expected median CS and the standard error, which allow us to calculate the Z-score for the GO category under consideration. (f) This Z-score is then plotted in a matrix on a color-coded scale. Gray means no significant difference in the level of conservation compared to the background. A similar procedure is followed for the calculation of Z-scores for number of orthologs and homologs by counting the proportion of genes with homologs or orthologs in each set. Mmus, Mus musculus; Rnor, Rattus norvegicus; Cfam, Canis familiaris; Bta, Bos taurus; Mdom, Monodelphis domestica; Ggal, Gallus gallus; Xtro, Xenopus tropicalis; Drer, Danio rerio; Trub, Takifugu rubripes; Tnig, Tetraodon nigroviridis; Cint, Ciona intestinalis; Agam, Anopheles gambiae; Dmel, Drosophila melanogaster; Cele, Caenorhabditis elegans; Scer, Saccharomyces cerevisiae. All the results of these analyses for all GO categories are provided online in a searchable database at [28].CC BY · PubMed Central
Figure 2 Degree of conservation of the glucagon and insulin signaling pathways. (a) Regulatory interactions between proteins involved in glucagon (GCG) and insulin (INS) signaling, and enzymes involved in glucose and glycogen metabolism. Proteins depicted in red show high conservation, those depicted blue have low levels of conservation and the ones in green intermediate conservation. The CREB protein is represented in yellow because it is highly conserved in vertebrates and not in invertebrates. There is a clear correlation between the functions of the molecules shown in the key and the degree of conservation indicated by the color code: enzymes and kinases tend to be red and conserved, while signal transducers, receptors and transcription factors tend to be blue and divergent. (b) Matrix of normalized ranking of the genes depicted in (a). The rows in the matrix are ordered by the sum of the CS rank in the 15 organisms.
Figure 2 Degree of conservation of the glucagon and insulin signaling pathways. (a) Regulatory interactions between proteins involved in glucagon (GCG) and insulin (INS) signaling, and enzymes involved in glucose and glycogen metabolism. Proteins depicted in red show high conservation, those depicted blue have low levels of conservation and the ones in green intermediate conservation. The CREB protein is represented in yellow because it is highly conserved in vertebrates and not in invertebrates. There is a clear correlation between the functions of the molecules shown in the key and the degree of conservation indicated by the color code: enzymes and kinases tend to be red and conserved, while signal transducers, receptors and transcription factors tend to be blue and divergent. (b) Matrix of normalized ranking of the genes depicted in (a). The rows in the matrix are ordered by the sum of the CS rank in the 15 organisms.CC BY · PubMed Central
Figure 3 Divergence of orthologs and homologs of representative functional categories. (a) Molecular function and (b) biological process. Colors towards red signify high relative conservation of the group of genes in a particular genome. Colors towards blue signify low relative conservation. Gray means no statistically significant difference in conservation level compared to the background of the rest of the genome. White cells denote that there is no gene with the GO term and with ortholog/homolog in the other organism. The colored lines on the left of the names of the functional classes correspond to the colors of the categories represented in Figure 5.
Figure 3 Divergence of orthologs and homologs of representative functional categories. (a) Molecular function and (b) biological process. Colors towards red signify high relative conservation of the group of genes in a particular genome. Colors towards blue signify low relative conservation. Gray means no statistically significant difference in conservation level compared to the background of the rest of the genome. White cells denote that there is no gene with the GO term and with ortholog/homolog in the other organism. The colored lines on the left of the names of the functional classes correspond to the colors of the categories represented in Figure 5.CC BY · PubMed Central
Figure 4 Histogram distribution of CSs of orthologs for selected GO categories in M. musculus, D. rerio and D. melanogaster. (a) The CS distributions for proteins in three molecular function categories. 'Catalytic activity' is significantly conserved in all three organisms, while 'Transcription factors DBD' and 'Receptor activity' are significantly divergent in zebrafish and Drosophila. (b) The CS distributions for proteins in three biological process categories. 'Biosynthesis' is a highly conserved category in all three organisms, while 'Development' is significantly conserved in mouse but significantly divergent in Drosophila. 'Response to stimulus' is significantly divergent across all three organisms.
Figure 4 Histogram distribution of CSs of orthologs for selected GO categories in M. musculus, D. rerio and D. melanogaster. (a) The CS distributions for proteins in three molecular function categories. 'Catalytic activity' is significantly conserved in all three organisms, while 'Transcription factors DBD' and 'Receptor activity' are significantly divergent in zebrafish and Drosophila. (b) The CS distributions for proteins in three biological process categories. 'Biosynthesis' is a highly conserved category in all three organisms, while 'Development' is significantly conserved in mouse but significantly divergent in Drosophila. 'Response to stimulus' is significantly divergent across all three organisms.CC BY · PubMed Central
Figure 5 Peripheral and core functional categories. A set of core molecular functions and biological processes that are highly conserved are represented in red in the centre of the figure. Other sets of functions and processes that are highly divergent across all eukaryotes (blue) or highly divergent in some organisms and highly conserved in others (yellow) are represented on the periphery as regulators of the core processes. The colors correspond to the colored lines on the left in Figure 3.
Figure 5 Peripheral and core functional categories. A set of core molecular functions and biological processes that are highly conserved are represented in red in the centre of the figure. Other sets of functions and processes that are highly divergent across all eukaryotes (blue) or highly divergent in some organisms and highly conserved in others (yellow) are represented on the periphery as regulators of the core processes. The colors correspond to the colored lines on the left in Figure 3.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 45

  1. Dickerson RE: The structures of cytochrome c and the rates of molecular evolution. J Mol Evol. 1971, 1: 26-45. 10.1007/BF01659392. · DOI · Paper page
  2. Dayhoff M, Schwartz R, Orcutt B: A model of evolutionary change in proteins. Atlas of Protein Sequence and Structure. Edited by: Dayhoff M. 1978, Silver Springs, MD: National Biomedical Research Foundation, 5: 345-352.
  3. Albà MM, Castresana J: Inverse relationship between evolutionary rate and age of mammalian genes. Mol Biol Evol. 2005, 22: 598-606. 10.1093/molbev/msi045. · DOI
  4. Luz H, Vingron M: Family specific rates of protein evolution. Bioinformatics. 2006, 22: 1166-1171. 10.1093/bioinformatics/btl073. · DOI
  5. Pál C, Papp B, Lercher MJ: An integrated view of protein evolution. Nat Rev Genet. 2006, 7: 337-348. 10.1038/nrg1838. · DOI
  6. Bustamante CD, Fledel-Alon A, Williamson S, Nielsen R, Hubisz MT, Glanowski S, Tanenbaum DM, White TJ, Sninsky JJ, Hernandez RD, Civello D, Adams MD, Cargill M, Clark AG: Natural selection on protein-coding genes in the human genome. Nature. 2005, 437: 1153-1157. 10.1038/nature04240. · DOI
  7. Castillo-Davis CI, Kondrashov FA, Hartl DL, Kulathinal RJ: The functional genomic distribution of protein divergence in two animal phyla: coevolution, genomic conflict, and constraint. Genome Res. 2004, 14: 802-811. 10.1101/gr.2195604. · DOI
  8. Clark AG, Glanowski S, Nielsen R, Thomas PD, Kejariwal A, Todd MA, Tanenbaum DM, Civello D, Lu F, Murphy B, Ferriera S, Wang G, Zheng X, White TJ, Sninsky JJ, Adams MD, Cargill M: Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science. 2003, 302: 1960-1963. 10.1126/science.1088821. · DOI
  9. The International HapMap Consortium: A haplotype map of the human genome. Nature. 2005, 437: 1299-1320. 10.1038/nature04226. · DOI · Paper page
  10. Voight BF, Kudaravalli S, Wen X, Pritchard JK: A map of recent positive selection in the human genome. PLoS Biol. 2006, 4: e72-10.1371/journal.pbio.0040072. · DOI
  11. Arbiza L, Dopazo J, Dopazo H: Positive selection, relaxation, and acceleration in the evolution of the human and chimp genome. PLoS Comput Biol. 2006, 2: e38-10.1371/journal.pcbi.0020038. · DOI
  12. Chervitz SA, Aravind L, Sherlock G, Ball CA, Koonin EV, Dwight SS, Harris MA, Dolinski K, Mohr S, Smith T, Weng S, Cherry JM, Botstein D: Comparison of the complete protein sets of worm and yeast: orthology and divergence. Science. 1998, 282: 2022-2028. 10.1126/science.282.5396.2022. · DOI
  13. Madan Babu M, Teichmann SA, Aravind L: Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J Mol Biol. 2006, 358: 614-633. 10.1016/j.jmb.2006.02.019. · DOI
  14. Coulson RM, Ouzounis CA: The phylogenetic diversity of eukaryotic transcription. Nucleic Acids Res. 2003, 31: 653-660. 10.1093/nar/gkg156. · DOI
  15. Peregrin-Alvarez JM, Tsoka S, Ouzounis CA: The phylogenetic extent of metabolic enzymes and pathways. Genome Res. 2003, 13: 422-427. 10.1101/gr.246903. · DOI
  16. van Nimwegen E: Scaling laws in the functional content of genomes. Trends Genet. 2003, 19: 479-484. 10.1016/S0168-9525(03)00203-8. · DOI
  17. Ranea JA, Buchan DW, Thornton JM, Orengo CA: Evolution of protein superfamilies and bacterial genome size. J Mol Biol. 2004, 336: 871-887. 10.1016/j.jmb.2003.12.044. · DOI
  18. Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, Hubisz MJ, Fledel-Alon A, Tanenbaum DM, Civello D, White TJ, J Sninsky J, Adams MD, Cargill M: A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol. 2005, 3: e170-10.1371/journal.pbio.0030170. · DOI
  19. Jiang G, Zhang BB: Glucagon and regulation of glucose metabolism. Am J Physiol Endocrinol Metab. 2003, 284: E671-E678. · DOI · Paper page
  20. Gilad Y, Oshlack A, Smyth GK, Speed TP, White KP: Expression profiling in primates reveals a rapid evolution of human transcription factors. Nature. 2006, 440: 242-245. 10.1038/nature04559. · DOI
  21. Brogiolo W, Stocker H, Ikeya T, Rintelen F, Fernandez R, Hafen E: An evolutionarily conserved function of the Drosophila insulin receptor and insulin-like peptides in growth control. Curr Biol. 2001, 11: 213-221. 10.1016/S0960-9822(01)00068-9. · DOI · Paper page
  22. Pierce SB, Costa M, Wisotzkey R, Devadhar S, Homburger SA, Buchman AR, Ferguson KC, Heller J, Platt DM, Pasquinelli AA, Liu LX, Doberstein SK, Ruvkun G: Regulation of DAF-2 receptor signaling by human insulin and ins-1, a member of the unusually large and diverse C. elegans insulin gene family. Genes Dev. 2001, 15: 672-686. 10.1101/gad.867301. · DOI
  23. Duret L, Guex N, Peitsch MC, Bairoch A: New insulin-like proteins with atypical disulfide bond pattern characterized in Caenorhabditis elegans by comparative sequence analysis and homology modeling. Genome Res. 1998, 8: 348-353. · DOI
  24. Leevers SJ: Growth control: invertebrate insulin surprises!. Curr Biol. 2001, 11: R209-R212. 10.1016/S0960-9822(01)00107-5. · DOI
  25. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA: The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003, 4: 41-10.1186/1471-2105-4-41. · DOI
  26. Klein J, Nikolaidis N: The descent of the antibody-based immune system by gradual evolution. Proc Natl Acad Sci USA. 2005, 102: 169-174. 10.1073/pnas.0408480102. · DOI
  27. Alder MN, Rogozin IB, Iyer LM, Glazko GV, Cooper MD, Pancer Z: Diversity and function of adaptive immune receptors in a jawless vertebrate. Science. 2005, 310: 1970-1973. 10.1126/science.1119420. · DOI
  28. Yokoyama M, Nishi Y, Miyamoto Y, Nakamura M, Akiyama K, Matsubara K, Okubo K: Molecular cloning of a human neuroD from a neuroblastoma cell line specifically expressed in the fetal brain and adult cerebellum. Brain Res Mol Brain Res. 1996, 42: 135-139. 10.1016/S0169-328X(96)00154-4. · DOI
  29. Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, Sidow A: Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005, 15: 901-913. 10.1101/gr.3577405. · DOI · Paper page
  30. Freilich S, Massingham T, Bhattacharyya S, Ponsting H, Lyons PA, Freeman TC, Thornton JM: Relationship between the tissue-specificity of mouse gene expression and the evolutionary origin and function of the proteins. Genome Biol. 2005, 6: R56-10.1186/gb-2005-6-7-r56. · DOI
  31. Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A, Patapoutian A, Hampton GM, Schultz PG, Hogenesch JB: Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci USA. 2002, 99: 4465-4470. 10.1073/pnas.012025199. · DOI · Paper page
  32. Kirschner M, Gerhart J: Evolvability. Proc Natl Acad Sci USA. 1998, 95: 8420-8427. 10.1073/pnas.95.15.8420. · DOI · Paper page
  33. Lee S, Kohane I, Kasif S: Genes involved in complex adaptive processes tend to have highly conserved upstream regions in mammalian genomes. BMC Genomics. 2005, 6: 168-10.1186/1471-2164-6-168. · DOI
  34. Taylor MS, Kai C, Kawai J, Carninci P, Hayashizaki Y, Semple CA: Heterotachy in mammalian promoter evolution. PLoS Genet. 2006, 2: e30-10.1371/journal.pgen.0020030. · DOI
  35. Khaitovich P, Hellmann I, Enard W, Nowick K, Leinweber M, Franz H, Weiss G, Lachmann M, Pääbo S: Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees. Science. 2005, 309: 1850-1854. 10.1126/science.1108296. · DOI
  36. Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, Tyler-Smith C, Carter N, Scherer SW, Tavaré S, Deloukas P, Hurles ME, Dermitzakis ET: Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science. 2007, 315: 848-853. 10.1126/science.1136678. · DOI
  37. Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, Hunt S, Kahl B, Antonarakis SE, Tavaré S, Deloukas P, Dermitzakis ET: Genome-wide associations of gene expression variation in humans. PLoS Genet. 2005, 1: e78-10.1371/journal.pgen.0010078. · DOI
  38. Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, Taylor MS, Rawlins JN, Mott R, Flint J: Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet. 2006, 38: 879-887. 10.1038/ng1840. · DOI
  39. Birney E, Andrews D, Caccamo M, Chen Y, Clarke L, Coates G, Cox T, Cunningham F, Curwen V, Cutts T, Down T, Durbin R, Fernandez-Suarez XM, Flicek P, Gräf S, Hammond M, Herrero J, Howe K, Iyer V, Jekosch K, Kähäri A, Kasprzyk A, Keefe D, Kokocinski F, Kulesha E, London D, Longden I, Melsopp C, Meidl P, Overduin B, et al: Ensembl 2006. Nucleic Acids Res. 2006, 34 (Database issue): D556-D561. 10.1093/nar/gkj133. · DOI
  40. Gene Ontology Consortium: The Gene Ontology (GO) project in 2006. Nucleic Acids Res. 2006, 34 (Database issue): D322-D326. 10.1093/nar/gkj021. · DOI
  41. Pruitt KD, Tatusova T, Maglott DR: NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007, 35 (Database issue): D61-D65. 10.1093/nar/gkl842. · DOI
  42. Kummerfeld SK, Teichmann SA: DBD: a transcription factor prediction database. Nucleic Acids Res. 2006, 34 (Database issue): D74-D81. 10.1093/nar/gkj131. · DOI
  43. Lopez-Bigas N, Ouzounis CA: Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res. 2004, 32: 3108-3114. 10.1093/nar/gkh605. · DOI
  44. Morgenstern B: DIALIGN: multiple DNA and protein sequence alignment at BiBiServ. Nucleic Acids Res. 2004, 32 (Web Server issue): W33-W36. 10.1093/nar/gkh373. · DOI
  45. Friedman N, Ninio M, Pe'er I, Pupko T: A structural EM algorithm for phylogenetic inference. J Comput Biol. 2002, 9: 331-353. 10.1089/10665270252935494. · DOI

Cited by

Sorted by citations

See all 52

Metadata

Authors
Lopez-Bigas, N., De, S., & Teichmann, S. A.
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2008-02-15
Volume
9
Issue
2
ISSN
1474-760X
DOI
10.1186/gb-2008-9-2-r33
PMC
PMC2374701
Cited by
52
References
9
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review