Genome sequencing reveals insights into physiology and longevity of the naked mole rat.

The naked mole rat (Heterocephalus glaber) is a strictly subterranean, extraordinarily long-lived eusocial mammal. Although it is the size of a mouse, its maximum lifespan exceeds 30 years, making this animal the longest-living rodent. Naked mole rats show negligible senescence, no age-related increase in mortality, and high fecundity until death. In addition to delayed ageing, they are resistant to both spontaneous cancer and experimentally induced tumorigenesis. Naked mole rats pose a challenge to the theories that link ageing, cancer and redox homeostasis. Although characterized by significant oxidative stress, the naked mole rat proteome does not show age-related susceptibility to oxidative damage or increased ubiquitination. Naked mole rats naturally reside in large colonies with a single breeding female, the 'queen', who suppresses the sexual maturity of her subordinates. They also live in full darkness, at low oxygen and high carbon dioxide concentrations, and are unable to sustain thermogenesis nor feel certain types of pain. Here we report the sequencing and analysis of the naked mole rat genome, which reveals unique genome features and molecular adaptations consistent with cancer resistance, poikilothermy, hairlessness and insensitivity to low oxygen, and altered visual function, circadian rythms and taste sensing. This information provides insights into the naked mole rat's exceptional longevity and ability to live in hostile conditions, in the dark and at low oxygen. The extreme traits of the naked mole rat, together with the reported genome and transcriptome information, offer opportunities for understanding ageing and advancing other areas of biological and biomedical research.

The assessment of science: the relative merits of post-publication review, the impact factor, and the number of citations.

The assessment of scientific publications is an integral part of the scientific process. Here we investigate three methods of assessing the merit of a scientific paper: subjective post-publication peer review, the number of citations gained by a paper, and the impact factor of the journal in which the article was published. We investigate these methods using two datasets in which subjective post-publication assessments of scientific publications have been made by experts. We find that there are moderate, but statistically significant, correlations between assessor scores, when two assessors have rated the same paper, and between assessor score and the number of citations a paper accrues. However, we show that assessor score depends strongly on the journal in which the paper is published, and that assessors tend to over-rate papers published in journals with high impact factors. If we control for this bias, we find that the correlation between assessor scores and between assessor score and the number of citations is weak, suggesting that scientists have little ability to judge either the intrinsic merit of a paper or its likely impact. We also show that the number of citations a paper receives is an extremely error-prone measure of scientific merit. Finally, we argue that the impact factor is likely to be a poor measure of merit, since it depends on subjective assessment. We conclude that the three measures of scientific merit considered here are poor; in particular subjective assessments are an error-prone, biased, and expensive method by which to assess merit. We argue that the impact factor may be the most satisfactory of the methods we have considered, since it is a form of pre-publication review. However, we emphasise that it is likely to be a very error-prone measure of merit that is qualitative, not quantitative.

Estimation of the neutrality index.

The McDonald-Kreitman (MK) test is a simple and widely used test of selection in which the numbers of nonsilent and silent substitutions (D(n) and D(s)) are compared with the numbers of nonsilent and silent polymorphisms (P(n) and P(s)). The neutrality index (NI = D(s)P(n)/D(n)P(s)), the odds ratio (OR) of the MK table, measures the direction and degree of departure from neutral evolution. The mean of NI values across genes is often taken to summarize patterns of selection in a species. Here, we show that this leads to statistical bias in both simulated and real data to the extent that species, which show a pattern of adaptive evolution, can apparently be subject to weak purifying selection and vice versa. We show that this bias can be removed by using a variant of the Cochran-Mantel-Haenszel procedure for estimating a weighted average OR. We also show that several point estimators of NI are statistically biased even when cutoff values are employed. We therefore suggest that a new statistic be used to study patterns of selection when data are sparse, the direction of selection: DoS = D(n)/(D(n) + D(s)) - P(n)/(P(n) + P(s)).

The positive correlation between dN/dS and dS in mammals is due to runs of adjacent substitutions.

A positive correlation between ω, the ratio of the nonsynonymous and synonymous substitution rates, and dS, the synonymous substitution rate has recently been reported. This correlation is unexpected under simple evolutionary models. Here, we investigate two explanations for this correlation: first, whether it is a consequence of a statistical bias in the estimation of ω and second, whether it is due to substitutions at adjacent sites. Using simulations, we show that estimates of ω are biased when levels of divergence are low. This is true using the methods of Yang and Nielsen, Nei and Gojobori, and Muse and Gaut. Although the bias could generate a positive correlation between ω and dS, we show that it is unlikely to be the main determinant. Instead we show that the correlation is reduced when genes that are high quality in sequence, annotation, and alignment are used. The remaining--likely genuine--positive correlation appears to be due to adjacent tandem substitutions; single substitutions, though far more numerous, do not contribute to the correlation. Genuine adjacent substitutions may be due to mutation or selection.

The surprising negative correlation of gene length and optimal codon use--disentangling translational selection from GC-biased gene conversion in yeast.

BACKGROUND: Surprisingly, in several multi-cellular eukaryotes optimal codon use correlates negatively with gene length. This contrasts with the expectation under selection for translational accuracy. While suggested explanations focus on variation in strength and efficiency of translational selection, it has rarely been noticed that the negative correlation is reported only in organisms whose optimal codons are biased towards codons that end with G or C (-GC). This raises the question whether forces that affect base composition--such as GC-biased gene conversion--contribute to the negative correlation between optimal codon use and gene length. RESULTS: Yeast is a good organism to study this as equal numbers of optimal codons end in -GC and -AT and one may hence compare frequencies of optimal GC- with optimal AT-ending codons to disentangle the forces. Results of this study demonstrate in yeast frequencies of GC-ending (optimal AND non-optimal) codons decrease with gene length and increase with recombination. A decrease of GC-ending codons along genes contributes to the negative correlation with gene length. Correlations with recombination and gene expression differentiate between GC-ending and optimal codons, and also substitution patterns support effects of GC-biased gene conversion. CONCLUSION: While the general effect of GC-biased gene conversion is well known, the negative correlation of optimal codon use with gene length has not been considered in this context before. Initiation of gene conversion events in promoter regions and the presence of a gene conversion gradient most likely explain the observed decrease of GC-ending codons with gene length and gene position.

Conflicting selection pressures on synonymous codon use in yeast suggest selection on mRNA secondary structures.

BACKGROUND: Eukaryotic mRNAs often contain secondary structures in their untranslated regions that are involved in expression regulation. Whether secondary structures in the protein coding regions are of functional importance remains unclear: laboratory studies suggest stable secondary structures within the protein coding sequence interfere with translation, while several bioinformatic studies indicate stable mRNA structures are more frequent than expected. RESULTS: In contrast to several studies testing for unexpected structural stabilities, I directly compare the selective constraint of sites that differ in their structural importance. I.e. for each nucleotide, I identify whether it is paired with another nucleotide, or unpaired, in the predicted secondary structure. I assume paired sites are more important for the predicted secondary structure than unpaired sites. I look at protein coding yeast sequences and use optimal codons and synonymous substitutions to test for structural constraints. As expected under selection for secondary structures, paired sites experience higher constraint than unpaired sites, i.e. significantly lower numbers of conserved optimal codons and consistently lower numbers of synonymous substitutions. This is true for structures predicted by different algorithms. CONCLUSION: The results of this study are consistent with purifying selection on mRNA secondary structures in yeast protein coding sequences and suggest their biological importance. One should be aware, however, that accuracy of structure prediction is unknown for mRNAs and interrelated selective forces may contribute as well. Note that if selection pressures alternative to translational selection affect synonymous (and optimal) codon use, this may lead to under- or over-estimates of selective strength on optimal codon use depending on strength and direction of translational selection.

Reduced local mutation density in regulatory DNA of cancer genomes is linked to DNA repair.

Carcinogenesis and neoplastic progression are mediated by the accumulation of somatic mutations. Here we report that the local density of somatic mutations in cancer genomes is highly reduced specifically in accessible regulatory DNA defined by DNase I hypersensitive sites. This reduction is independent of any known factors influencing somatic mutation density and is observed in diverse cancer types, suggesting a general mechanism. By analyzing individual cancer genomes, we show that the reduced local mutation density within regulatory DNA is linked to intact global genome repair machinery, with nearly complete abrogation of the hypomutation phenomenon in individual cancers that possess mutations in components of the nucleotide excision repair system. Together, our results connect chromatin structure, gene regulation and cancer-associated somatic mutation.

Synonymous codon usage in Escherichia coli: selection for translational accuracy.

In many organisms, selection acts on synonymous codons to improve translation. However, the precise basis of this selection remains unclear in the majority of species. Selection could be acting to maximize the speed of elongation, to minimize the costs of proofreading, or to maximize the accuracy of translation. Using several data sets, we find evidence that codon use in Escherichia coli is biased to reduce the costs of both missense and nonsense translational errors. Highly conserved sites and genes have higher codon bias than less conserved ones, and codon bias is positively correlated to gene length and production costs, both indicating selection against missense errors. Additionally, codon bias increases along the length of genes, indicating selection against nonsense errors. Doublet mutations or replacement substitutions do not explain our observations. The correlations remain when we control for expression level and for conflicting selection pressures at the start and end of genes. Considering each amino acid by itself confirms our results. We conclude that selection on synonymous codon use in E. coli is largely due to selection for translational accuracy, to reduce the costs of both missense and nonsense errors.

A dissection of volatility in yeast.

It has been suggested that volatility, the proportion of mutations which change an amino acid, can be used to infer the level of natural selection acting upon a gene. This conjecture is supported by a correlation between volatility and the rate of nonsynonymous substitution (dN), or the ratio of nonsynonymous and synonymous substitution rates, in a variety of organisms. These organisms include yeast, in which the correlations are quite strong. Here we show that these correlations are a by-product of a correlation between synonymous codon bias toward translationally optimal codons and dN. Although this analysis suggests that volatility is not a good measure of the selection, we suggest that it might be possible to infer something about the level of natural selection, from a single genome sequence, using translational codon bias.