BackgroundMycobacterium abscessus subsp. abscessus (MAB) is a highly drug resistant mycobacterium and the most common respiratory pathogen among the rapidly growing non-tuberculous mycobacteria. MAB is also one of the most deadly of the emerging cystic fibrosis (CF) pathogens requiring prolonged treatment with multiple antibiotics. In addition to its “mycobacterial” virulence genes, the genome of MAB harbours a large accessory genome, presumably acquired via lateral gene transfer including homologs shared with the CF pathogens Pseudomonas aeruginosa and Burkholderia cepacia. While multiple genome sequences are available there is little functional genomics data available for this important pathogen.ResultsWe report here the first multi-omics approach to characterize the primary transcriptome, coding potential and potential regulatory regions of the MAB genome utilizing differential RNA sequencing (dRNA-seq), RNA-seq, Ribosome profiling and LC-MS proteomics. In addition we attempt to address the genomes contribution to the molecular systems that underlie MAB’s adaptation and persistence in the human host through an examination of MABs transcriptional response to a number of clinically relevant conditions. These include hypoxia, exposure to sub-inhibitory concentrations of antibiotics and growth in an artificial sputum designed to mimic the conditions within the cystic fibrosis lung.ConclusionsOur integrated map provides the first comprehensive view of the primary transcriptome of MAB and evidence for the translation of over one hundred new short open reading frames (sORFs). Our map will act as a resource for ongoing functional genomics characterization of MAB and our transcriptome data from clinically relevant stresses informs our understanding of MAB’s adaptation to life in the CF lung. MAB’s adaptation to growth in artificial CF sputum highlights shared metabolic strategies with other CF pathogens including P. aeruginosa and mirrors the transcriptional responses that lead to persistence in mycobacteria. These strategies include an increased reliance on amino acid metabolism, and fatty acid catabolism and highlights the relevance of the glyoxylate shunt to growth in the CF lung. Our data suggests that, similar to what is seen in chronically persisting P. aeruginosa, progression towards a biofilm mode of growth would play a more prominent role in a longer-term MAB infection. Finally, MAB’s transcriptional response to antibiotics highlights the role of antibiotic modifications enzymes, active transport and the evolutionarily conserved WhiB7 driven antibiotic resistance regulon.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2868-y) contains supplementary material, which is available to authorized users.

Variation in the rates and characteristics of germline and somatic mutations across the genome of an organism is informative about DNA damage and repair processes and can also shed light on aspects of organism physiology and evolution. We adapted a recently developed method for inferring somatic mutations from bulk RNA-seq data and applied it to a large collection of Arabidopsis thaliana accessions. The wide range of genomic data types available for A. thaliana enabled us to investigate the relationships of multiple genomic features with the variation in the somatic mutation rate across the genome of this model plant. We observed that late replicated regions showed evidence of an elevated rate of somatic mutation compared to genomic regions that are replicated early. We identified transcriptional strand asymmetries, consistent with the effects of transcription-coupled damage and/or repair. We also observed a negative relationship between the inferred somatic mutation count and the H3K36me3 histone mark which is well-documented in the literature of human systems. In addition, we were able to support previous reports of an inverse relationship between inferred somatic mutation count and GC content as well as a positive relationship between inferred somatic mutation count and DNA methylation for both cytosine and non-cytosine mutations.

Background Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining ‘primary’ and ‘auxiliary’ data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. Results We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction. Conclusions The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms.