Functional characterization of the Mycobacterium abscessus genome coupled with condition specific transcriptomics reveals conserved molecular strategies for host adaptation and persistence

Background: Mycobacterium 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. Results: We 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. Conclusions: Our 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

Mouse models of rhinovirus-induced disease and exacerbation of allergic airway inflammation

Rhinoviruses cause serious morbidity and mortality as the major etiological agents of asthma exacerbations and the common cold. A major obstacle to understanding disease pathogenesis and to the development of effective therapies has been the lack of a small-animal model for rhinovirus infection. Of the 100 known rhinovirus serotypes, 90% (the major group) use human intercellular adhesion molecule-1 (ICAM-1) as their cellular receptor and do not bind mouse ICAM-1; the remaining 10% (the minor group) use a member of the low-density lipoprotein receptor family and can bind the mouse counterpart. Here we describe three novel mouse models of rhinovirus infection: minor-group rhinovirus infection of BALB/c mice, major-group rhinovirus infection of transgenic BALB/c mice expressing a mouse-human ICAM-1 chimera and rhinovirus-induced exacerbation of allergic airway inflammation. These models have features similar to those observed in rhinovirus infection in humans, including augmentation of allergic airway inflammation, and will be useful in the development of future therapies for colds and asthma exacerbations

A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation.

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

Computationally inferring modes of transcriptional regulation in Mycobacterium abscessus

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

BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

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.Science Foundation IrelandWellcome TrustInsight Research Centr

Interlaboratory comparison of size measurements on nanoparticles using nanoparticle tracking analysis (NTA)

One of the key challenges in the field of nanoparticle (NP) analysis is in producing reliable and reproducible characterisation data for nanomaterials. This study looks at the reproducibility using a relatively new, but rapidly adopted, technique, Nanoparticle Tracking Analysis (NTA) on a range of particle sizes and materials in several different media. It describes the protocol development and presents both the data and analysis of results obtained from 12 laboratories, mostly based in Europe, who are primarily QualityNano members. QualityNano is an EU FP7 funded Research Infrastructure that integrates 28 European analytical and experimental facilities in nanotechnology, medicine and natural sciences with the goal of developing and implementing best practice and quality in all aspects of nanosafety assessment. This study looks at both the development of the protocol and how this leads to highly reproducible results amongst participants. In this study, the parameter being measured is the modal particle size

Highly Sensitive Single Domain Antibody–Quantum Dot Conjugates for Detection of HER2 Biomarker in Lung and Breast Cancer Cells

Despite the widespread availability of immunohistochemical and other methodologies for screening and early detection of lung and breast cancer biomarkers, diagnosis of the early stage of cancers can be difficult and prone to error. The identification and validation of early biomarkers specific to lung and breast cancers, which would permit the development of more sensitive methods for detection of early disease onset, is urgently needed. In this paper, ultra-small and bright nanoprobes based on quantum dots (QDs) conjugated to single domain anti-HER2 (human epidermal growth factor receptor 2) antibodies (sdAbs) were applied for immunolabeling of breast and lung cancer cell lines, and their performance was compared to that of anti-HER2 monoclonal antibodies conjugated to conventional organic dyes Alexa Fluor 488 and Alexa Fluor 568. The sdAbs–QD conjugates achieved superior staining in a panel of lung cancer cell lines with differential HER2 expression. This shows their outstanding potential for the development of more sensitive assays for early detection of cancer biomarkers