Why Is the Correlation between Gene Importance and Gene Evolutionary Rate So Weak?


One of the few commonly believed principles of molecular evolution is that functionally more important genes (or DNA sequences) evolve more slowly than less important ones. This principle is widely used by molecular biologists in daily practice. However, recent genomic analysis of a diverse array of organisms found only weak, negative correlations between the evolutionary rate of a gene and its functional importance, typically measured under a single benign lab condition. A frequently suggested cause of the above finding is that gene importance determined in the lab differs from that in an organism's natural environment. Here, we test this hypothesis in yeast using gene importance values experimentally determined in 418 lab conditions or computationally predicted for 10,000 nutritional conditions. In no single condition or combination of conditions did we find a much stronger negative correlation, which is explainable by our subsequent finding that always-essential (enzyme) genes do not evolve significantly more slowly than sometimes-essential or always-nonessential ones. Furthermore, we verified that functional density, approximated by the fraction of amino acid sites within protein domains, is uncorrelated with gene importance. Thus, neither the lab-nature mismatch nor a potentially biased among-gene distribution of functional density explains the observed weakness of the correlation between gene importance and evolutionary rate. We conclude that the weakness is factual, rather than artifactual. In addition to being weakened by population genetic reasons, the correlation is likely to have been further weakened by the presence of multiple nontrivial rate determinants that are independent from gene importance. These findings notwithstanding, we show that the principle of slower evolution of more important genes does have some predictive power when genes with vastly different evolutionary rates are compared, explaining why the principle can be practically useful despite the weakness of the correlation.


Vyšlo v časopise: Why Is the Correlation between Gene Importance and Gene Evolutionary Rate So Weak?. PLoS Genet 5(1): e32767. doi:10.1371/journal.pgen.1000329
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1000329

Souhrn

One of the few commonly believed principles of molecular evolution is that functionally more important genes (or DNA sequences) evolve more slowly than less important ones. This principle is widely used by molecular biologists in daily practice. However, recent genomic analysis of a diverse array of organisms found only weak, negative correlations between the evolutionary rate of a gene and its functional importance, typically measured under a single benign lab condition. A frequently suggested cause of the above finding is that gene importance determined in the lab differs from that in an organism's natural environment. Here, we test this hypothesis in yeast using gene importance values experimentally determined in 418 lab conditions or computationally predicted for 10,000 nutritional conditions. In no single condition or combination of conditions did we find a much stronger negative correlation, which is explainable by our subsequent finding that always-essential (enzyme) genes do not evolve significantly more slowly than sometimes-essential or always-nonessential ones. Furthermore, we verified that functional density, approximated by the fraction of amino acid sites within protein domains, is uncorrelated with gene importance. Thus, neither the lab-nature mismatch nor a potentially biased among-gene distribution of functional density explains the observed weakness of the correlation between gene importance and evolutionary rate. We conclude that the weakness is factual, rather than artifactual. In addition to being weakened by population genetic reasons, the correlation is likely to have been further weakened by the presence of multiple nontrivial rate determinants that are independent from gene importance. These findings notwithstanding, we show that the principle of slower evolution of more important genes does have some predictive power when genes with vastly different evolutionary rates are compared, explaining why the principle can be practically useful despite the weakness of the correlation.


Zdroje

1. KarpG

2008 Cell and Molecular Biology Hoboken, New Jersey John Wiley & Sons, Inc

2. JordanIK

RogozinIB

WolfYI

KooninEV

2002 Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res 12 962 968

3. WallDP

HirshAE

FraserHB

KummJ

GiaeverG

2005 Functional genomic analysis of the rates of protein evolution. Proc Natl Acad Sci U S A 102 5483 5488

4. LiaoBY

ScottNM

ZhangJ

2006 Impacts of gene essentiality, expression pattern, and gene compactness on the evolutionary rate of mammalian proteins. Mol Biol Evol 23 2072 2080

5. RochaEP

DanchinA

2004 An analysis of determinants of amino acids substitution rates in bacterial proteins. Mol Biol Evol 21 108 116

6. HurstLD

SmithNG

1999 Do essential genes evolve slowly? Curr Biol 9 747 750

7. HirshAE

FraserHB

2001 Protein dispensability and rate of evolution. Nature 411 1046 1049

8. YangJ

GuZ

LiWH

2003 Rate of protein evolution versus fitness effect of gene deletion. Mol Biol Evol 20 772 774

9. WolfYI

CarmelL

KooninEV

2006 Unifying measures of gene function and evolution. Proc Biol Sci 273 1507 1515

10. ZhangJ

HeX

2005 Significant impact of protein dispensability on the instantaneous rate of protein evolution. Mol Biol Evol 22 1147 1155

11. KrylovDM

WolfYI

RogozinIB

KooninEV

2003 Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution. Genome Res 13 2229 2235

12. KimuraM

1983 The Neutral Theory of Molecular Evolution Cambridge Cambridge University Press

13. KimuraM

1968 Evolutionary rate at the molecular level. Nature 217 624 626

14. KingJL

JukesTH

1969 Non-Darwinian evolution. Science 164 788 798

15. KimuraM

OhtaT

1974 On some principles governing molecular evolution. Proc Natl Acad Sci U S A 71 2848 2852

16. WilsonAC

CarlsonSS

WhiteTJ

1977 Biochemical evolution. Annu Rev Biochem 46 573 639

17. WolfYI

2006 Coping with the quantitative genomics ‘elephant’: the correlation between the gene dispensability and evolution rate. Trends Genet 22 354 357

18. PappB

PalC

HurstLD

2004 Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast. Nature 429 661 664

19. HillenmeyerME

FungE

WildenhainJ

PierceSE

HoonS

2008 The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320 362 365

20. FayJC

BenavidesJA

2005 Evidence for domesticated and wild populations of Saccharomyces cerevisiae. PLoS Genet 1 66 71

21. PriceND

ReedJL

PalssonBO

2004 Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2 886 897

22. EdwardsJS

CovertM

PalssonBO

2002 Metabolic Modeling of Microbes: the Flux Balance Approach. Environ Microbiol 4 133 140

23. SteinmetzLM

ScharfeC

DeutschbauerAM

MokranjacD

HermanZS

2002 Systematic screen for human disease genes in yeast. Nat Genet 31 400 404

24. LiaoBY

ZhangJ

2008 Null mutations in human and mouse orthologs frequently result in different phenotypes. Proc Natl Acad Sci U S A 105 6987 6992

25. DuarteNC

HerrgardMJ

PalssonBO

2004 Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res 14 1298 1309

26. BurgardAP

NikolaevEV

SchillingCH

MaranasCD

2004 Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14 301 312

27. SnitkinES

DudleyAM

JanseDM

WongK

ChurchGM

2008 Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions. Genome Biol 9 R140

28. ForsterJ

FamiliI

PalssonBO

NielsenJ

2003 Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Omics 7 193 202

29. SegreD

VitkupD

ChurchGM

2002 Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99 15112 15117

30. KondrashovAS

SunyaevS

KondrashovFA

2002 Dobzhansky-Muller incompatibilities in protein evolution. Proc Natl Acad Sci U S A 99 14878 14883

31. GaoL

ZhangJ

2003 Why are some human disease-associated mutations fixed in mice? Trends Genet 19 678 681

32. CopleyRR

DoerksT

LetunicI

BorkP

2002 Protein domain analysis in the era of complete genomes. FEBS Lett 513 129 134

33. HuloN

BairochA

BulliardV

CeruttiL

De CastroE

2006 The PROSITE database. Nucleic Acids Res 34 D227 230

34. MulderNJ

ApweilerR

2008 The InterPro database and tools for protein domain analysis. Curr Protoc Bioinformatics Chapter 2 Unit 2.7

35. PalC

PappB

HurstLD

2001 Highly expressed genes in yeast evolve slowly. Genetics 158 927 931

36. DrummondDA

WilkeCO

2008 Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134 341 352

37. DrummondDA

RavalA

WilkeCO

2006 A single determinant dominates the rate of yeast protein evolution. Mol Biol Evol 23 327 337

38. PalC

PappB

LercherMJ

2006 An integrated view of protein evolution. Nat Rev Genet 7 337 348

39. PlotkinJB

FraserHB

2007 Assessing the determinants of evolutionary rates in the presence of noise. Mol Biol Evol 24 1113 1121

40. KimSH

YiSV

2007 Understanding relationship between sequence and functional evolution in yeast proteins. Genetica 131 151 156

41. DrummondDA

BloomJD

AdamiC

WilkeCO

ArnoldFH

2005 Why highly expressed proteins evolve slowly. Proc Natl Acad Sci U S A 102 14338 14343

42. PennacchioLA

AhituvN

MosesAM

PrabhakarS

NobregaMA

2006 In vivo enhancer analysis of human conserved non-coding sequences. Nature 444 499 502

43. BoffelliD

McAuliffeJ

OvcharenkoD

LewisKD

OvcharenkoI

2003 Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science 299 1391 1394

44. XieX

LuJ

KulbokasEJ

GolubTR

MoothaV

2005 Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals. Nature 434 338 345

45. KellisM

PattersonN

EndrizziM

BirrenB

LanderES

2003 Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423 241 254

46. BeckerSA

FeistAM

MoML

HannumG

PalssonBO

2007 Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2 727 738

47. WangZ

ZhangJ

2007 In search of the biological significance of modular structures in protein networks. PLoS Comput Biol 3 e107

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