期刊论文详细信息
BMC Evolutionary Biology
The relationship between the hierarchical position of proteins in the human signal transduction network and their rate of evolution
David Alvarez-Ponce1 
[1] Department of Biology, National University of Ireland Maynooth, Maynooth, County Kildare, Ireland
关键词: Upstream and downstream position;    Molecular pathways;    Signal transduction;    Purifying selection;    Selective constraint;    Evolutionary rate;    Network evolution;   
Others  :  1140237
DOI  :  10.1186/1471-2148-12-192
 received in 2012-05-25, accepted in 2012-09-14,  发布年份 2012
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【 摘 要 】

Background

Proteins evolve at disparate rates, as a result of the action of different types and strengths of evolutionary forces. An open question in evolutionary biology is what factors are responsible for this variability. In general, proteins whose function has a great impact on organisms’ fitness are expected to evolve under stronger selective pressures. In biosynthetic pathways, upstream genes usually evolve under higher levels of selective constraint than those acting at the downstream part, as a result of their higher hierarchical position. Similar observations have been made in transcriptional regulatory networks, whose upstream elements appear to be more essential and subject to selection. Less well understood is, however, how selective pressures distribute along signal transduction pathways.

Results

Here, I combine comparative genomics and directed protein interaction data to study the distribution of evolutionary forces across the human signal transduction network. Surprisingly, no evidence was found for higher levels of selective constraint at the upstream network genes (those occupying more hierarchical positions). On the contrary, purifying selection was found to act more strongly on genes acting at the downstream part of the network, which seems to be due to downstream genes being more highly and broadly expressed, performing certain functions and, in particular, encoding proteins that are more highly connected in the protein–protein interaction network. When the effect of these confounding factors is discounted, upstream and downstream genes evolve at similar rates. The trends found in the overall signaling network are exemplified by analysis of the distribution of purifying selection along the mammalian Ras signaling pathway, showing that upstream and downstream genes evolve at similar rates.

Conclusions

These results indicate that the upstream/downstream position of proteins in the signal transduction network has, in general, no direct effect on their rates of evolution, suggesting that upstream and downstream genes are similarly important for the function of the network. This implies that natural selection differently distributes across signal transduction networks and across biosynthetic and transcriptional regulatory networks, which might reflect fundamental differences in their function and organization.

【 授权许可】

   
2012 Alvarez-Ponce; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Li WH, Wu CI, Luo CC: A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changes. Mol Biol Evol 1985, 2:150-174.
  • [2]Zuckerkandl E, Pauling L: Molecules as documents of evolutionary history. J Theor Biol 1965, 8:357-366.
  • [3]Kimura M: The neutral theory of molecular evolution. Cambridge: Cambridge University Press; 1983.
  • [4]Koonin EV, Wolf YI: Evolutionary systems biology: links between gene evolution and function. Curr Opin Biotechnol 2006, 17:481-487.
  • [5]Rocha EP: The quest for the universals of protein evolution. Trends Genet 2006, 22:412-416.
  • [6]McInerney JO: The causes of protein evolutionary rate variation. Trends Ecol Evol 2006, 21:230-232.
  • [7]Ingvarsson PK: Gene expression and protein length influence codon usage and rates of sequence evolution in Populus tremula. Mol Biol Evol 2007, 24:836-844.
  • [8]Butcher EC, Berg EL, Kunkel EJ: Systems biology in drug discovery. Nat Biotechnol 2004, 22:1253-1259.
  • [9]Lee JW, Kim TY, Jang YS, Choi S, Lee SY: Systems metabolic engineering for chemicals and materials. Trends Biotechnol 2011, 29:370-378.
  • [10]Korcsmáros T, Szalay MS, Böde C, Kovács IA, Csermely P: How to design multi-target drugs: target search options in cellular networks. Expert Opinion on Drug Discovery 2007, 2:799-808.
  • [11]Cork JM, Purugganan MD: The evolution of molecular genetic pathways and networks. Bioessays 2004, 26:479-484.
  • [12]Zera AJ: Microevolution of intermediary metabolism: evolutionary genetics meets metabolic biochemistry. J Exp Biol 2011, 214:179-190.
  • [13]Eanes WF: Molecular population genetics and selection in the glycolytic pathway. J Exp Biol 2011, 214:165-171.
  • [14]Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW: Evolutionary rate in the protein interaction network. Science 2002, 296:750-752.
  • [15]Hahn MW, Kern AD: Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 2005, 22:803-806.
  • [16]Lemos B, Bettencourt BR, Meiklejohn CD, Hartl DL: Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein-protein interactions. Mol Biol Evol 2005, 22:1345-1354.
  • [17]Vitkup D, Kharchenko P, Wagner A: Influence of metabolic network structure and function on enzyme evolution. Genome Biol 2006, 7:R39.
  • [18]Greenberg AJ, Stockwell SR, Clark AG: Evolutionary constraint and adaptation in the metabolic network of Drosophila. Mol Biol Evol 2008, 25:2537-2546.
  • [19]Hudson CM, Conant GC: Expression level, cellular compartment and metabolic network position all influence the average selective constraint on mammalian enzymes. BMC Evol Biol 2011, 11:89.
  • [20]Cui Q, Purisima EO, Wang E: Protein evolution on a human signaling network. BMC Syst Biol 2009, 3:21.
  • [21]Lovell SC, Robertson DL: An integrated view of molecular coevolution in protein-protein interactions. Mol Biol Evol 2010, 27:2567-2575.
  • [22]Codoñer FM, Fares MA: Why should we care about molecular coevolution? Evol Bioinform Online 2008, 4:29-38.
  • [23]Alvarez-Ponce D, McInerney JO: The human genome retains relics of its prokaryotic ancestry: human genes of archaebacterial and eubacterial origin exhibit remarkable differences. Genome Biol Evol 2011, 3:782-790.
  • [24]Doherty A, Alvarez-Ponce D, McInerney JO: Increased genome sampling reveals a dynamic relationship between gene duplicability and the structure of the primate protein-protein interaction network. Mol Biol Evolin press
  • [25]Sharkey TD, Yeh S, Wiberley AE, Falbel TG, Gong D, Fernandez DE: Evolution of the isoprene biosynthetic pathway in kudzu. Plant Physiol 2005, 137:700-712.
  • [26]Ramsay H, Rieseberg LH, Ritland K: The correlation of evolutionary rate with pathway position in plant terpenoid biosynthesis. Mol Biol Evol 2009, 26:1045-1053.
  • [27]Livingstone K, Anderson S: Patterns of variation in the evolution of carotenoid biosynthetic pathway enzymes of higher plants. J Hered 2009, 100:754-761.
  • [28]Rausher MD, Miller RE, Tiffin P: Patterns of evolutionary rate variation among genes of the anthocyanin biosynthetic pathway. Mol Biol Evol 1999, 16:266-274.
  • [29]Ma X, Wang Z, Zhang X: Evolution of dopamine-related systems: biosynthesis, degradation and receptors. J Mol Evol 2010, 71:374-384.
  • [30]Yu HS, Shen YH, Yuan GX, Hu YG, Xu HE, Xiang ZH, Zhang Z: Evidence of selection at melanin synthesis pathway loci during silkworm domestication. Mol Biol Evol 2011, 28:1785-1799.
  • [31]Wright KM, Rausher MD: The evolution of control and distribution of adaptive mutations in a metabolic pathway. Genetics 2010, 184:483-502.
  • [32]Bhardwaj N, Kim PM, Gerstein MB: Rewiring of transcriptional regulatory networks: hierarchy, rather than connectivity, better reflects the importance of regulators. Sci Signal 2010, 3:ra79.
  • [33]Rhone B, Brandenburg JT, Austerlitz F: Impact of selection on genes involved in regulatory network: a modelling study. J Evol Biol 2011, 24:2087-2098.
  • [34]Cui Q, Ma Y, Jaramillo M, Bari H, Awan A, Yang S, Zhang S, Liu L, Lu M, O'Connor-McCourt M, et al.: A map of human cancer signaling. Mol Syst Biol 2007, 3:152.
  • [35]Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 1999, 27:29-34.
  • [36]Ma'ayan A, Jenkins SL, Neves S, Hasseldine A, Grace E, Dubin-Thaler B, Eungdamrong NJ, Weng G, Ram PT, Rice JJ, et al.: Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 2005, 309:1078-1083.
  • [37]Riley RM, Jin W, Gibson G: Contrasting selection pressures on components of the Ras-mediated signal transduction pathway in Drosophila. Mol Ecol 2003, 12:1315-1323.
  • [38]Ferrell JE Jr: Tripping the switch fantastic: how a protein kinase cascade can convert graded inputs into switch-like outputs. Trends Biochem Sci 1996, 21:460-466.
  • [39]Alvarez-Ponce D, Aguadé M, Rozas J: Network-level molecular evolutionary analysis of the insulin/TOR signal transduction pathway across 12 Drosophila genomes. Genome Res 2009, 19:234-242.
  • [40]Alvarez-Ponce D, Aguadé M, Rozas J: Comparative genomics of the vertebrate insulin/TOR signal transduction pathway: a network-level analysis of selective pressures. Genome Biol Evol 2011, 3:87-101.
  • [41]Alvarez-Ponce D, Guirao-Rico S, Orengo DJ, Segarra C, Rozas J, Aguade M: Molecular population genetics of the insulin/TOR signal transduction pathway: a network-level analysis in Drosophila melanogaster. Mol Biol Evol 2012, 29:123-132.
  • [42]Jovelin R, Phillips PC: Expression level drives the pattern of selective constraints along the insulin/Tor signal transduction pathway in Caenorhabditis. Genome Biol Evol 2011, 3:715-722.
  • [43]Wu X, Chi X, Wang P, Zheng D, Ding R, Li Y: The evolutionary rate variation among genes of HOG-signaling pathway in yeast genomes. Biol Direct 2010, 5:46.
  • [44]Luisi P, Alvarez-Ponce D, Dall'olio GM, Sikora M, Bertranpetit J, Laayouni H: Network-level and population genetics analysis of the insulin/TOR signal transduction pathway across human populations. Mol Biol Evol 2012, 29:1379-1392.
  • [45]Yang Z, Nielsen R, Goldman N, Pedersen AM: Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 2000, 155:431-449.
  • [46]Anisimova M, Bielawski JP, Yang Z: Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution. Mol Biol Evol 2001, 18:1585-1592.
  • [47]Liao BY, Weng MP, Zhang J: Impact of extracellularity on the evolutionary rate of mammalian proteins. Genome Biol Evol 2010, 2:39-43.
  • [48]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.
  • [49]Al-Shahrour F, Minguez P, Tarraga J, Medina I, Alloza E, Montaner D, Dopazo J: FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Res 2007, 35:W91-96.
  • [50]Duret L, Mouchiroud D: Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Mol Biol Evol 2000, 17:68-74.
  • [51]Pál C, Papp B, Hurst LD: Highly expressed genes in yeast evolve slowly. Genetics 2001, 158:927-931.
  • [52]Subramanian S, Kumar S: Gene expression intensity shapes evolutionary rates of the proteins encoded by the vertebrate genome. Genetics 2004, 168:373-381.
  • [53]Sharp PM: Determinants of DNA sequence divergence between Escherichia coli and Salmonella typhimurium: codon usage, map position, and concerted evolution. J Mol Evol 1991, 33:23-33.
  • [54]Lynch M, Conery JS: The evolutionary fate and consequences of duplicate genes. Science 2000, 290:1151-1155.
  • [55]Jordan IK, Wolf YI, Koonin EV: Duplicated genes evolve slower than singletons despite the initial rate increase. BMC Evol Biol 2004, 4:22.
  • [56]Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Bahler J, Wood V, et al.: The BioGRID Interaction Database: 2008 update. Nucleic Acids Res 2008, 36:D637-640.
  • [57]Batada NN, Hurst LD, Tyers M: Evolutionary and physiological importance of hub proteins. PLoS Comput Biol 2006, 2:e88.
  • [58]Hardy MA: Regression with dummy variables. London: Sage Publications; 1993.
  • [59]Cohen J: Applied multiple regression/correlation analysis for the behavioral science. 3rd edition. Mahwah, New Jersey: Lawrence Erlbaum; 2003.
  • [60]McKay MM, Morrison DK: Integrating signals from RTKs to ERK/MAPK. Oncogene 2007, 26:3113-3121.
  • [61]Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, et al.: A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 2004, 101:6062-6067.
  • [62]Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW 3rd, Su AI: BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol 2009, 10:R130.
  • [63]Hartl DL, Dykhuizen DE, Dean AM: Limits of adaptation: the evolution of selective neutrality. Genetics 1985, 111:655-674.
  • [64]Eanes WF: Analysis of selection on enzyme polymorphisms. Rev Ecol Syst 1999, 30:301-326.
  • [65]Watt WB, Dean AM: Molecular-functional studies of adaptive genetic variation in prokaryotes and eukaryotes. Annu Rev Genet 2000, 34:593-622.
  • [66]Tamura K, Subramanian S, Kumar S: Temporal patterns of fruit fly (Drosophila) evolution revealed by mutation clocks. Mol Biol Evol 2004, 21:36-44.
  • [67]Russo CA, Takezaki N, Nei M: Molecular phylogeny and divergence times of drosophilid species. Mol Biol Evol 1995, 12:391-404.
  • [68]Ponting CP: The functional repertoires of metazoan genomes. Nat Rev Genet 2008, 9:689-698.
  • [69]Lu Y, Rausher MD: Evolutionary rate variation in anthocyanin pathway genes. Mol Biol Evol 2003, 20:1844-1853.
  • [70]Rausher MD, Lu Y, Meyer K: Variation in constraint versus positive selection as an explanation for evolutionary rate variation among anthocyanin genes. J Mol Evol 2008, 67:137-144.
  • [71]Clotault J, Peltier D, Soufflet-Freslon V, Briard M, Geoffriau E: Differential selection on carotenoid biosynthesis genes as a function of gene position in the metabolic pathway: a study on the carrot and dicots. PLoS One 2012, 7:e38724.
  • [72]Levy ED, Landry CR, Michnick SW: Cell signaling. Signaling through cooperation. Science 2010, 328:983-984.
  • [73]Breitkreutz A, Choi H, Sharom JR, Boucher L, Neduva V, Larsen B, Lin ZY, Breitkreutz BJ, Stark C, Liu G, et al.: A global protein kinase and phosphatase interaction network in yeast. Science 2010, 328:1043-1046.
  • [74]Flicek P, Amode MR, Barrell D, Beal K, Brent S, Chen Y, Clapham P, Coates G, Fairley S, Fitzgerald S, et al.: Ensembl 2011. Nucleic Acids Res 2011, 39:D800-806.
  • [75]Do CB, Mahabhashyam MS, Brudno M, Batzoglou S: ProbCons: Probabilistic consistency-based multiple sequence alignment. Genome Res 2005, 15:330-340.
  • [76]Yang Z: PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 2007, 24:1586-1591.
  • [77]Whelan S, Goldman N: Distributions of statistics used for the comparison of models of sequence evolution in phylogenetics. Mol Biol Evol 1999, 16:1292-1299.
  • [78]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 1995, 57:289-300.
  • [79]Kinsella RJ, Kahari A, Haider S, Zamora J, Proctor G, Spudich G, Almeida-King J, Staines D, Derwent P, Kerhornou A, et al.: Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database (Oxford) 2011, 2011:bar030.
  • [80]Wright F: The 'effective number of codons' used in a gene. Gene 1990, 87:23-29.
  • [81]Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25:25-29.
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