The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
Gene Expression Intensity Shapes Evolutionary Rates of the Proteins Encoded by the Vertebrate Genome
Open accessAbstract
Natural selection leaves its footprints on protein-coding sequences by modulating their silent and replacement evolutionary rates. In highly expressed genes in invertebrates, these footprints are seen in the higher codon usage bias and lower synonymous divergence. In mammals, the highly expressed genes have a shorter gene length in the genome and the breadth of expression is known to constrain the rate of protein evolution. Here we have examined how the rates of evolution of proteins encoded by the vertebrate genomes are modulated by the amount (intensity) of gene expression. To understand how natural selection operates on proteins that appear to have arisen in earlier and later phases of animal evolution, we have contrasted patterns of mouse proteins that have homologs in invertebrate and protist genomes (Precambrian genes) with those that do not have such detectable homologs (vertebrate-specific genes). We find that the intensity of gene expression relates inversely to the rate of protein sequence evolution on a genomic scale. The most highly expressed genes actually show the lowest total number of substitutions per polypeptide, consistent with cumulative effects of purifying selection on individual amino acid replacements. Precambrian genes exhibit a more pronounced difference in protein evolutionary rates (up to three times) between the genes with high and low expression levels as compared to the vertebrate-specific genes, which appears to be due to the narrower breadth of expression of the vertebrate-specific genes. These results provide insights into the differential relationship and effect of the increasing complexity of animal body form on evolutionary rates of proteins.
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.
Integrity signals
Registry
Cites
Statistics
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.
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?
In the citing paper
“It has been previously shown that highly expressed proteins evolve more slowly than proteins expressed at lower levels, i.e., they display a reduced protein divergence on the sequence level (Pal et al, 2001; Subramanian and Kumar, 2004)”
From The quantitative proteome of a human cell line (2011) · cites this paper as Subramanian and Kumar, 2004
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
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Thompson, J. D., Higgins, D. G., & Gibson, T. J. (1994) · Nucleic Acids Research · doi:10.1093/nar/22.22.4673 · Cited by 64,617
Initial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
Initial sequencing and comparative analysis of the mouse genome
Mouse Genome Sequencing Consortium (2002) · Nature · doi:10.1038/nature01262 · Cited by 7,187
The rapid generation of mutation data matrices from protein sequences
Jones, D. T., Taylor, W. R., & Thornton, J. M. (1992) · Computer applications in the biosciences · doi:10.1093/bioinformatics/8.3.275 · Cited by 7,003
Large-scale analysis of the human and mouse transcriptomes
Su, A. I., Cooke, M. P., Ching, K. A., Hakak, Y., Walker, J. R., Wiltshire, T., Orth, A. P., Vega, R. G., Sapinoso, L. M., Moqrich, A., et al. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.012025199 · Cited by 1,466
DNA methylation and the frequency of CpG in animal DNA
Bird, A. P. (1980) · Nucleic Acids Research · doi:10.1093/nar/8.7.1499 · Cited by 1,175
Duret, L., & Mouchiroud, D. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.8.4482 · Cited by 808
Human housekeeping genes are compact
Eisenberg, E., & Levanon, E. Y. (2003) · Trends in Genetics · doi:10.1016/s0168-9525(03)00140-9 · Cited by 699
Akashi, H. D., & Gojobori, T. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.062526999 · Cited by 687
Research works printed but not linked: 3
- 1990, J. Mol. Biol., 215, 403, 10.1016/S0022-2836(05)80360-2 · DOI · Paper page
- 1992, Comput. Appl. Biosci., 8, 275
- 1988, Mol. Biol. Evol., 5, 704
Cited by
Sorted by citations
Mistranslation-Induced Protein Misfolding as a Dominant Constraint on Coding-Sequence Evolution
Drummond, D. A., & Wilke, C. O. (2008) · Cell · doi:10.1016/j.cell.2008.05.042 · Cited by 1,065
Why highly expressed proteins evolve slowly
Drummond, D. A., Bloom, J. D., Adami, C., Wilke, C. O., & Arnold, F. H. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504070102 · Cited by 815
The quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816
The evolutionary origin of orphan genes
Tautz, D., & Domazet-Lošo, T. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3053 · Cited by 764
An integrated view of protein evolution
Pál, C., Papp, B., & Lercher, M. J. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1838 · Cited by 508
Metadata
- Authors
- Subramanian, S., & Kumar, S.
- Affiliations
- Arizona State University (US)
- Corresponding author
- Kumar, Sudhir
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-09-01
- Volume
- 168
- Issue
- 1
- Pages
- 373-381
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W2104099887
- Cited by
- 250
- References
- 34
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 9 · 2023: 5 · 2022: 12 · 2021: 5 · 2020: 5 · 2019: 6 · 2018: 9 · 2017: 10 · 2016: 10 · 2015: 9 · 2014: 13 · 2013: 15 · 2012: 20
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed (publishedVersion)
- Griffith Research Online (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein sequence evolution, evolutionary rates, purifying selection, synonymous divergence, codon usage bias, gene length, mouse protein, vertebrate evolution
- MeSH terms
- Animals, Drosophila melanogaster, Embryo, Mammalian, Embryo, Nonmammalian, Humans, Proteins, Genetic Variation, Gene Library, Gene Expression, Evolution, Molecular, Expressed Sequence Tags, Gene Expression Profiling, Takifugu, Mice
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
