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Estimating the rates at which bacterial genomes evolve is critical to understanding major evolutionary and ecological processes such as disease emergence, long-term host-pathogen associations, and short-term transmission patterns. The surge in bacterial genomic data sets provides a new opportunity to estimate these rates and reveal the factors that shape bacterial evolutionary dynamics. For many organisms estimates of evolutionary rate display an inverse association with the time-scale over which the data are sampled. However, this relationship remains unexplored in bacteria due to the difficulty in estimating genome-wide evolutionary rates, which are impacted by the extent of temporal structure in the data and the prevalence of recombination. We collected 36 whole genome sequence data sets from 16 species of bacterial pathogens to systematically estimate and compare their evolutionary rates and assess the extent of temporal structure in the absence of recombination. The majority (28/36) of data sets possessed sufficient clock-like structure to robustly estimate evolutionary rates. However, in some species reliable estimates were not possible even with “ancient DNA” data sampled over many centuries, suggesting that they evolve very slowly or that they display extensive rate variation among lineages. The robustly estimated evolutionary rates spanned several orders of magnitude, from 10−6 to 10−8 nucleotide substitutions site-1 year-1. This variation was largely attributable to sampling time, which was strongly negatively associated with estimated evolutionary rates, with this relationship best described by an exponential decay curve. To avoid potential estimation biases such time-dependency should be considered when inferring evolutionary time-scales in bacteria.

International audience; The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values 1. 'How to use PSA' remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer 2 or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application 3,4. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g. PCA3 in prostate cancer 5,6 and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer 7-10. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1).