The Relationship among Gene Expression, the Evolution of Gene Dosage, and the Rate of Protein Evolution
The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.
Vyšlo v časopise:
The Relationship among Gene Expression, the Evolution of Gene Dosage, and the Rate of Protein Evolution. PLoS Genet 6(5): e32767. doi:10.1371/journal.pgen.1000944
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1000944
Souhrn
The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.
Zdroje
1. TorresEM
WilliamsBR
AmonA
2008 Aneuploidy: cells losing their balance. Genetics 179 737 746
2. PayerB
LeeJT
2008 X chromosome dosage compensation: how mammals keep the balance. Annu Rev Genet 42 733 772
3. StrangerBE
ForrestMS
DunningM
IngleCE
BeazleyC
2007 Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315 848 853
4. GonzalezE
KulkarniH
BolivarH
ManganoA
SanchezR
2005 The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science 307 1434 1440
5. PerryGH
DominyNJ
ClawKG
LeeAS
FieglerH
2007 Diet and the evolution of human amylase gene copy number variation. Nat Genet 39 1256 1260
6. NairS
MillerB
BarendsM
JaideeA
PatelJ
2008 Adaptive copy number evolution in malaria parasites. PLoS Genet 4 e1000243 doi:10.1371/journal.pgen.1000243
7. Ohno
1970 Evolution by gene duplication; Unwin A, editor London.
8. WolfeKH
2001 Yesterday's polyploids and the mystery of diploidization. Nat Rev Genet 2 333 341
9. ScannellDR
FrankAC
ConantGC
ByrneKP
WoolfitM
2007 Independent sorting-out of thousands of duplicated gene pairs in two yeast species descended from a whole-genome duplication. Proc Natl Acad Sci U S A 104 8397 8402
10. SemonM
WolfeKH
2007 Consequences of genome duplication. Curr Opin Genet Dev 17 505 512
11. WalshJB
1995 How often do duplicated genes evolve new functions? Genetics 139 421 428
12. ForceA
LynchM
PickettFB
AmoresA
YanYL
1999 Preservation of duplicate genes by complementary, degenerative mutations. Genetics 151 1531 1545
13. CusackBP
WolfeKH
2007 When gene marriages don't work out: divorce by subfunctionalization. Trends Genet 23 270 272
14. MaereS
De BodtS
RaesJ
CasneufT
Van MontaguM
2005 Modeling gene and genome duplications in eukaryotes. Proc Natl Acad Sci U S A 102 5454 5459
15. ConantGC
WolfeKH
2007 Increased glycolytic flux as an outcome of whole-genome duplication in yeast. Mol Syst Biol 3 129
16. PappB
PalC
HurstLD
2003 Dosage sensitivity and the evolution of gene families in yeast. Nature 424 194 197
17. AuryJM
JaillonO
DuretL
NoelB
JubinC
2006 Global trends of whole-genome duplications revealed by the ciliate Paramecium tetraurelia. Nature 444 171 178
18. QianW
ZhangJ
2008 Gene dosage and gene duplicability. Genetics 179 2319 2324
19. SeoigheC
WolfeKH
1999 Yeast genome evolution in the post-genome era. Curr Opin Microbiol 2 548 554
20. ByrneKP
WolfeKH
2005 The Yeast Gene Order Browser: combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res 15 1456 1461
21. DaubinV
OchmanH
2004 Bacterial genomes as new gene homes: the genealogy of ORFans in E. coli. Genome Res 14 1036 1042
22. AlbaMM
CastresanaJ
2005 Inverse relationship between evolutionary rate and age of mammalian genes. Mol Biol Evol 22 598 606
23. AshburnerM
BallCA
BlakeJA
BotsteinD
ButlerH
2000 Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25 25 29
24. DrummondDA
WilkeCO
2008 Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134 341 352
25. SteinmetzLM
ScharfeC
DeutschbauerAM
MokranjacD
HermanZS
2002 Systematic screen for human disease genes in yeast. Nat Genet 31 400 404
26. HolstegeFC
JenningsEG
WyrickJJ
LeeTI
HengartnerCJ
1998 Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95 717 728
27. DopmanEB
HartlDL
2007 A portrait of copy-number polymorphism in Drosophila melanogaster. Proc Natl Acad Sci U S A 104 19920 19925
28. HenrichsenCN
VinckenboschN
ZollnerS
ChaignatE
PradervandS
2009 Segmental copy number variation shapes tissue transcriptomes. Nat Genet 41 424 429
29. SkaletskyH
Kuroda-KawaguchiT
MinxPJ
CordumHS
HillierL
2003 The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes. Nature 423 825 837
30. SemonM
MouchiroudD
DuretL
2005 Relationship between gene expression and GC-content in mammals: statistical significance and biological relevance. Hum Mol Genet 14 421 427
31. WagnerA
2005 Energy constraints on the evolution of gene expression. Mol Biol Evol 22 1365 1374
32. DekelE
AlonU
2005 Optimality and evolutionary tuning of the expression level of a protein. Nature 436 588 592
33. BedfordT
HartlDL
2009 Optimization of gene expression by natural selection. Proc Natl Acad Sci U S A 106 1133 1138
34. GalitskiT
SaldanhaAJ
StylesCA
LanderES
FinkGR
1999 Ploidy regulation of gene expression. Science 285 251 254
35. WangJ
TianL
LeeHS
WeiNE
JiangH
2006 Genomewide nonadditive gene regulation in Arabidopsis allotetraploids. Genetics 172 507 517
36. StuparRM
BhaskarPB
YandellBS
RensinkWA
HartAL
2007 Phenotypic and transcriptomic changes associated with potato autopolyploidization. Genetics 176 2055 2067
37. DoyleJJ
FlagelLE
PatersonAH
RappRA
SoltisDE
2008 Evolutionary genetics of genome merger and doubling in plants. Annu Rev Genet 42 443 461
38. MastersonJ
1994 Stomatal Size in Fossil Plants: Evidence for Polyploidy in Majority of Angiosperms. Science 264 421 424
39. AndalisAA
StorchovaZ
StylesC
GalitskiT
PellmanD
2004 Defects arising from whole-genome duplications in Saccharomyces cerevisiae. Genetics 167 1109 1121
40. BergerJD
SchmidtHJ
1978 Regulation of macronuclear DNA content in Paramecium tetraurelia. J Cell Biol 76 116 126
41. SnokeMS
BerendonkTU
BarthD
LynchM
2006 Large global effective population sizes in Paramecium. Mol Biol Evol 23 2474 2479
42. DuretL
MouchiroudD
2000 Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Mol Biol Evol 17 68 74
43. DrummondDA
RavalA
WilkeCO
2006 A single determinant dominates the rate of yeast protein evolution. Mol Biol Evol 23 327 337
44. RochaEP
2006 The quest for the universals of protein evolution. Trends Genet 22 412 416
45. DrummondDA
BloomJD
AdamiC
WilkeCO
ArnoldFH
2005 Why highly expressed proteins evolve slowly. Proc Natl Acad Sci U S A 102 14338 14343
46. EdgarR
DomrachevM
LashAE
2002 Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30 207 210
47. SmythGK
SpeedT
2003 Normalization of cDNA microarray data. Methods 31 265 273
48. GoutJF
DuretL
KahnD
2009 Differential retention of metabolic genes following whole-genome duplication. Mol Biol Evol 26 1067 1072
49. MiaoW
XiongJ
BowenJ
WangW
LiuY
2009 Microarray analyses of gene expression during the Tetrahymena thermophila life cycle. PLoS ONE 4 e4429 doi:10.1371/journal.pone.0004429
50. ArnaizO
CainS
CohenJ
SperlingL
2007 ParameciumDB: a community resource that integrates the Paramecium tetraurelia genome sequence with genetic data. Nucleic Acids Res 35 D439 444
51. GavinAC
AloyP
GrandiP
KrauseR
BoescheM
2006 Proteome survey reveals modularity of the yeast cell machinery. Nature 440 631 636
52. IhakaR
GentlemanR
1996 R: A language for data analysis and graphics. Journal of computational and graphical statistics 5 299 314
53. CarbonS
IrelandA
MungallCJ
ShuS
MarshallB
2009 AmiGO: online access to ontology and annotation data. Bioinformatics 25 288 289
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Genetika Reprodukčná medicínaČlánok vyšiel v časopise
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