Insights into the microbial degradation of rubber and gutta-percha by analysis of the complete genome of Nocardia nova SH22a

The complete genome sequence of Nocardia nova SH22a was determined in light of the remarkable ability of rubber and gutta-percha (GP) degradation of this strain. The genome consists of a circular chromosome of 8,348,532 bp with a G+C content of 67.77% and 7,583 predicted protein-encoding genes. Functions were assigned to 72.45% of the coding sequences. Among them, a large number of genes probably involved in the metabolism of xenobiotics and hardly degradable compounds, as well as genes that participate in the synthesis of polyketide- and/or nonribosomal peptide-type secondary metabolites, were detected. Based on in silico analyses and experimental studies, such as transposon mutagenesis and directed gene deletion studies, the pathways of rubber and GP degradation were proposed and the relationship between both pathways was unraveled. The genes involved include, inter alia, genes participating in cell envelope synthesis (long-chain-fatty-acid-AMP ligase and arabinofuranosyltransferase), β-oxidation (α-methylacyl-coenzyme A [α-methylacyl-CoA] racemase), propionate catabolism (acyl-CoA carboxylase), gluconeogenesis (phosphoenolpyruvate carboxykinase), and transmembrane substrate uptake (Mce [mammalian cell entry] transporter). This study not only improves our insights into the mechanism of microbial degradation of rubber and GP but also expands our knowledge of the genus Nocardia regarding metabolic diversity.

Statistics and truth in phylogenomics

Phylogenomics refers to the inference of historical relationships among species using genome-scale sequence data and to the use of phylogenetic analysis to infer protein function in multigene families. With rapidly decreasing sequencing costs, phylogenomics is becoming synonymous with evolutionary analysis of genome-scale and taxonomically densely sampled data sets. In phylogenetic inference applications, this translates into very large data sets that yield evolutionary and functional inferences with extremely small variances and high statistical confidence (P value). However, reports of highly significant P values are increasing even for contrasting phylogenetic hypotheses depending on the evolutionary model and inference method used, making it difficult to establish true relationships. We argue that the assessment of the robustness of results to biological factors, that may systematically mislead (bias) the outcomes of statistical estimation, will be a key to avoiding incorrect phylogenomic inferences. In fact, there is a need for increased emphasis on the magnitude of differences (effect sizes) in addition to the P values of the statistical test of the null hypothesis. On the other hand, the amount of sequence data available will likely always remain inadequate for some phylogenomic applications, for example, those involving episodic positive selection at individual codon positions and in specific lineages. Again, a focus on effect size and biological relevance, rather than the P value, may be warranted. Here, we present a theoretical overview and discuss practical aspects of the interplay between effect sizes, bias, and P values as it relates to the statistical inference of evolutionary truth in phylogenomics.