Staunton, Patrick M.

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Staunton, Patrick M.
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Staunton, Patrick M.

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  • Publication
    Computationally inferring modes of transcriptional regulation in Mycobacterium abscessus
    (University College Dublin. School of Biomolecular and Biomedical Science, 2020)
    Staunton, Patrick M. 
    Mycobacterium abscessus subspecies abscessus is a highly drug resistant mycobacteria and the most common respiratory pathogen among the rapidly growing non-tuberculous mycobacteria. We report here the first multi-omics approach to characterize the primary transcriptome, coding potential and potential regulatory regions of the Mycobacterium abscessus genome utilizing RNA-seq, dRNA-seq, ribosome profiling and LC-MS proteomics. In addition, we attempt to address the genome’s contribution to the molecular systems that underlie Mycobacterium abscessus’ adaptation and persistence in the human host through an examination of Mycobacterium abscessus' transcriptional responses 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. 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 Expression and compaRative genomics (BINDER). In tandem with derived experimental expression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus. In particular, inference on regulatory interactions is conducted by combining ‘primary data’ from RNA-seq experiments derived from Mycobacterium abscessus and ‘auxiliary’ ChIP-seq data from the related Mycobacterium tuberculosis. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. We construct an inter-conditional snapshot of the transcriptional landscape in Mycobacterium abscessus across a range of stress-inducing conditions comprising exposure to antimicrobial compounds as well as nutrient starvation and iron depletion. The research herein provides valuable elucidation on the transcriptional means through which Mycobacterium abscessus persists in hostile environments and mediates virulence in the human host.
      226
  • Publication
    BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus
    (Springer, 2019-09-10)
    Staunton, Patrick M. 
    ;
    Miranda-CasoLuengo, Aleksandra A. 
    ;
    Loftus, Brendan J. 
    ;
    Gormley, Isobel Claire 
    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.
      253Scopus© Citations 4