Biomedical subjects

Laurence Loewe

Publications and source records attributed to Laurence Loewe.

6 recordsLinked to original sources

PubMed2006

Background selection in single genes may explain patterns of codon bias.

Background selection involves the reduction in effective population size caused by the removal of recurrent deleterious mutations from a population. Previous work has examined this process for large genomic regions. Here we focus on the level of a single gene or small group of genes and investigate how the effects of background selection caused by nonsynonymous mutations are influenced by the lengths of coding sequences, the number and length of introns, intergenic distances, neighboring genes, mutation rate, and recombination rate. We generate our predictions from estimates of the distribution of the fitness effects of nonsynonymous mutations, obtained from DNA sequence diversity data in Drosophila. Results for genes in regions with typical frequencies of crossing over in Drosophila melanogaster suggest that background selection may influence the effective population sizes of different regions of the same gene, consistent with observed differences in codon usage bias along genes. It may also help to cause the observed effects of gene length and introns on codon usage. Gene conversion plays a crucial role in determining the sizes of these effects. The model overpredicts the effects of background selection with large groups of nonrecombining genes, because it ignores Hill-Robertson interference among the mutations involved.

Animals↗

PubMed2006

Inferring the distribution of mutational effects on fitness in Drosophila.

The properties of the distribution of deleterious mutational effects on fitness (DDME) are of fundamental importance for evolutionary genetics. Since it is extremely difficult to determine the nature of this distribution, several methods using various assumptions about the DDME have been developed, for the purpose of parameter estimation. We apply a newly developed method to DNA sequence polymorphism data from two Drosophila species and compare estimates of the parameters of the distribution of the heterozygous fitness effects of amino acid mutations for several different distribution functions. The results exclude normal and gamma distributions, since these predict too few effectively lethal mutations and power-law distributions as a result of predicting too many lethals. Only the lognormal distribution appears to fit both the diversity data and the frequency of lethals. This DDME arises naturally in complex systems when independent factors contribute multiplicatively to an increase in fitness-reducing damage. Several important parameters, such as the fraction of effectively neutral non-synonymous mutations and the harmonic mean of non-neutral selection coefficients, are robust to the form of the DDME. Our results suggest that the majority of non-synonymous mutations in Drosophila are under effective purifying selection.

Animals↗

PubMed2006

Quantifying the genomic decay paradox due to Muller's ratchet in human mitochondrial DNA.

The observation of high mitochondrial mutation rates in human pedigrees has led to the question of how such an asexual genetic system can survive the accumulation of slightly deleterious mutations caused by Muller's ratchet. I define a null model to quantify in unprecedented detail the threat from extinction caused by Muller's ratchet. This model is general enough to explore the biological significance of Muller's ratchet in various species where its operation has been suspected. For increased precision over a wide range of parameter space I employ individual-based simulations run by evolution@home, the first global computing system for evolutionary biology. After compiling realistic values for the key parameters in human mitochondrial DNA (mtDNA) I find that a surprisingly large range of biologically realistic parameter combinations would lead to the extinction of the human line over a period of 20 million years - if accepted wisdom about mtDNA and Muller's ratchet is correct. The resulting genomic decay paradox complements a similar threat from extinction due to mutation accumulation in nuclear DNA and suggests evaluation of unconventional explanations for long-term persistence. A substantial list of potential solutions is given, including compensatory back mutations, mutation rate heterogeneity and occasional recombination in mtDNA. Future work will have to explore which of these actually solves the paradox. Nonetheless, the results presented here provide yet another reason to minimize anthropogenic increase of mutation rates.

Computer Simulation↗

PubMed2005

Estimating selection on nonsynonymous mutations.

The distribution of mutational effects on fitness is of fundamental importance for many aspects of evolution. We develop two methods for characterizing the fitness effects of deleterious, nonsynonymous mutations, using polymorphism data from two related species. These methods also provide estimates of the proportion of amino acid substitutions that are selectively favorable, when combined with data on between-species sequence divergence. The methods are applicable to species with different effective population sizes, but that share the same distribution of mutational effects. The first, simpler, method assumes that diversity for all nonneutral mutations is given by the value under mutation-selection balance, while the second method allows for stronger effects of genetic drift and yields estimates of the parameters of the probability distribution of mutational effects. We apply these methods to data on populations of Drosophila miranda and D. pseudoobscura and find evidence for the presence of deleterious nonsynonymous mutations, mostly with small heterozygous selection coefficients (a mean of the order of 10(-5) for segregating variants). A leptokurtic gamma distribution of mutational effects with a shape parameter between 0.1 and 1 can explain observed diversities, in the absence of a separate class of completely neutral nonsynonymous mutations. We also describe a simple approximate method for estimating the harmonic mean selection coefficient from diversity data on a single species.

Animals↗

PubMed2003

High deleterious genomic mutation rate in stationary phase of Escherichia coli.

In natural habitats, bacteria spend most of their time in some form of growth arrest. Little is known about deleterious mutations in such stages, and consequently there is limited understanding of what evolutionary events occur. In a deleterious mutation accumulation experiment in prolonged stationary phase of Escherichia coli, about 0.03 slightly deleterious mutations were observed per genome per day. This is over an order of magnitude higher than extrapolations from fast-growing cells, but in line with inferences from observations in adaptive stationary phase mutation experiments. These findings may affect understanding of bacterial evolution and the emergence of bacterial pathogenicity.

Biological Evolution↗

PubMed2002

Global computing for bioinformatics.

Global computing, the collaboration of idle PCs via the Internet in a SETI@home style, emerges as a new way of massive parallel multiprocessing with potentially enormous CPU power. Its relations to the broader, fast-moving field of Grid computing are discussed without attempting a review of the latter. This review (i) includes a short table of milestones in global computing history, (ii) lists opportunities global computing offers for bioinformatics, (iii) describes the structure of problems well suited for such an approach, (iv) analyses the anatomy of successful projects and (v) points to existing software frameworks. Finally, an evaluation of the various costs shows that global computing indeed has merit, if the problem to be solved is already coded appropriately and a suitable global computing framework can be found. Then, either significant amounts of computing power can be recruited from the general public, or--if employed in an enterprise-wide Intranet for security reasons--idle desktop PCs can substitute for an expensive dedicated cluster.

Computational Biology↗