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Analysis of genome variation provides insights into mechanisms in genome evolution. This is increasingly appreciated with the rapid growth of genomic data. Mitochondrial genomes (mitogenomes) are well known to vary substantially in many genomic aspects, such as genome size, sequence context, nucleotide base composition and substitution rate. Such substantial variation makes mitogenomes an excellent model system to study the mechanisms dictating mitogenome variation. Recent sequencing efforts have not only covered a rich number of yeast species but also generated genomes from abundant strains within the same species. The rich yeast genomic data have enabled detailed investigation from genome variation into molecular mechanisms in genome evolution. This mini-review highlights some recent progresses in yeast mitogenome studies. [ABSTRACT FROM AUTHOR]

Genome-wide nucleotide composition varies widely among species. Despite extensive research, the source of genome-wide nucleotide composition diversity remains elusive. Yeast mitochondrial genomes (mitogenomes) are highly A + T rich, and they provide a unique opportunity to study the evolution of AT-biased landscape. In this study, we sequenced ten complete mitogenomes of the Saccharomycodes ludwigii yeast with 8% G + C content, the lowest genome-wide %(G + C) in all published genomes to date. The S. ludwigii mitogenomes have high densities of short tandem repeats but severely underrepresented mononucleotide repeats. Comparative population genomics of these record-setting A + T-rich genomes shows dynamic indel mutations and strong mutation bias toward A/T. Indel mutations play a greater role in genomic variation among very closely related strains than nucleotide substitutions. Indels have resulted in presence–absence polymorphism of tRNAArg (ACG) among S. ludwigii mitogenomes. Interestingly, these mitogenomes have undergone recombination, a genetic process that can increase G + C content by GC-biased gene conversion. Finally, the expected equilibrium G + C content under mutation pressure alone is higher than observed G + C content, suggesting existence of mechanisms other than AT-biased mutation operating to increase A/T. Together, our findings shed new lights on mechanisms driving extremely AT-rich genomes. [ABSTRACT FROM AUTHOR]

The study of genetic and phenotypic variation is fundamental for understanding the dynamics of bacterial genome evolution and untangling the evolution and epidemiology of bacterial pathogens. Neisseria meningitidis (Nm) is among the most intriguing bacterial pathogens in genomic studies due to its dynamic population structure and complex forms of pathogenicity. Extensive genomic variation within identical clonal complexes (CCs) in Nm has been recently reported and suggested to be the result of homologous recombination, but the extent to which recombination contributes to genomic variation within identical CCs has remained unclear. In this study, we sequenced two Nm strains of identical serogroup (C) and multi-locus sequence type (ST60), and conducted a systematic analysis with an additional 34 Nm genomes. Our results revealed that all gene content variation between the two ST60 genomes was introduced by homologous recombination at the conserved flanking genes, and 94.25% or more of sequence divergence was caused by homologous recombination. Recombination was found in genes associated with virulence factors, antigenic outer membrane proteins, and vaccine targets, suggesting an important role of homologous recombination in rapidly altering the pathogenicity and antigenicity of Nm. Recombination was also evident in genes of the restriction and modification systems, which may undermine barriers to DNA exchange. In conclusion, homologous recombination can drive both gene content variation and sequence divergence in Nm. These findings shed new light on the understanding of the rapid pathoadaptive evolution of Nm and other recombinogenic bacterial pathogens. [ABSTRACT FROM PUBLISHER]

We analyzed travel-associated clinical isolates of Escherichia coli O104:H4, including 1 from the 2011 German outbreak and 1 from a patient who returned from the Philippines in 2010, by genome sequencing and optical mapping. Despite extensive genomic similarity between these strains, key differences included the distribution of toxin and antimicrobial drug-resistance determinants. [ABSTRACT FROM AUTHOR]

Abstract: Methods for assessing gene presence and absence have been widely used to study bacterial genome evolution. A recent report by Zhaxybayeva et al. [Zhaxybayeva, O., Nesbo, C. L., and Doolittle, W. F., 2007. Systematic overestimation of gene gain through false diagnosis of gene absence. Genome. Biol. 8, 402] suggests that false diagnosis of gene absence or the presence of undetected truncated genes leads to a systematic overestimation of gene gain. Here (1) we argue that these annotation errors can cause more complicated effects and are not necessarily systematic, (2) we argue that current annotations (supplemented with BLAST searches) are the best way to consistently score gene presence/absence and (3) that genome wide estimates of gene gain/loss are not strongly affected by small differences in gene annotations but that the number of related gene families is strongly affected. We have estimated the rates of gene insertions/deletions using a variety of cutoff thresholds and match lengths as a way in which to alter the recognition of genes and gene fragments. The results reveal that different cutoffs for match length only cause a small variation of the estimated insertion/deletion rates. The rates of gene insertions/deletions on recent branches remain relatively high regardless of the thresholds for match length. Lastly (4), the dynamic process of gene truncation needs to be further considered in genome comparison studies. The data presented suggest that gene truncation tends to take place preferentially in recently transferred genes, which supports a fast turnover of recent laterally transferred genes. The presence of truncated genes or false diagnosis of gene absence therefore does not significantly affect the estimation of gene insertions/deletions rates, but there are several other factors that bias the results toward an under-estimation of the rate of gene insertion/deletion. All of these factors need to be considered. [Copyright &y& Elsevier]