Metal worker’s lung: spatial association with Mycobacterium avium

Background Outbreaks of hypersensitivity pneumonitis (HP) are not uncommon in workplaces where metal working fluid (MWF) is used to facilitate metal turning. Inhalation of microbe-contaminated MWF has been assumed to be the cause, but previous investigations have failed to establish a spatial relationship between a contaminated source and an outbreak. Objectives After an outbreak of five cases of HP in a UK factory, we carried out blinded, molecular-based microbiological investigation of MWF samples in order to identify potential links between specific microbial taxa and machines in the outbreak zone. Methods Custom-quantitative PCR assays, microscopy and phylogenetic analyses were performed on blinded MWF samples to quantify microbial burden and identify potential aetiological agents of HP in metal workers. Measurements and main results MWF from machines fed by a central sump, but not those with an isolated supply, was contaminated by mycobacteria. The factory sump and a single linked machine at the centre of the outbreak zone, known to be the workstation of the index cases, had very high levels of detectable organisms. Phylogenetic placement of mycobacterial taxonomic marker genes generated from these samples indicated that the contaminating organisms were closely related to Mycobacterium avium. Conclusions We describe, for the first time, a close spatial relationship between the abundance of a mycobacterium-like organism, most probably M. avium, and a localised outbreak of MWF-associated HP. The further development of sequence-based analytic techniques should assist in the prevention of this important occupational disease

Predicting DNA methylation level across human tissues

Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue. The method was evaluated in publicly available data and in two studies using the latest IlluminaBeadChips: a childhood asthma study with methylation measured in both peripheral blood leukocytes (PBL) and lymphoblastoid cell lines; and a study of postoperative atrial fibrillation with methylation in PBL, atrium and artery. We found that our method can greatly improve accuracy of cross-tissue prediction at CpG sites that are variable in the target tissue [R2 increases from 0.38 (original R2 between tissues) to 0.89 for PBL-to-artery prediction; from 0.39 to 0.95 for PBL-to-atrium; and from 0.81 to 0.98 for lymphoblastoid cell line-to-PBL based on cross-validation, and confirmed using cross-study prediction]. An extended model with multiple CpGs further improved performance. Our results suggest that large-scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles

Genome-wide interaction study of early-life smoking exposure on time-to-asthma onset in childhood

Background: Asthma, a heterogeneous disease with variable age of onset, results from the interplay between genetic and environmental factors. Early-life tobacco smoke (ELTS) exposure is a major asthma risk factor. Only a few genetic loci have been reported to interact with ELTS exposure in asthma. Objective: Our aim was to identify new loci interacting with ELTS exposure on time-to-asthma onset (TAO) in childhood.Methods: We conducted genome-wide interaction analyses of ELTS exposure on time-to-asthma onset in childhood in five European-ancestry studies (totaling 8,273 subjects) using Cox proportional-hazard model. The results of all five genome-wide analyses were meta-analyzed.Results: The 13q21 locus showed genome-wide significant interaction with ELTS exposure (P=4.3x10-8 for rs7334050 within KLHL1 with consistent results across the five studies). Suggestive interactions (P<5x10-6) were found at three other loci: 20p12 (rs13037508 within MACROD2; P=4.9x10-7), 14q22 (rs7493885 near NIN; P=2.9x10-6) and 2p22 (rs232542 near CYP1B1; P=4.1x10-6). Functional annotations and the literature showed that the lead SNPs at these four loci influence DNA methylation in the blood and are located nearby CpG sites reported to be associated with exposure to tobacco smoke components, which strongly support our findings.Conclusion and Clinical Relevance: We identified novel candidate genes interacting with ELTS exposure on time-to-asthma onset in childhood. These genes have plausible biological relevance related to tobacco smoke exposure. Further epigenetic and functional studies are needed to confirm these findings and to shed light on the underlying mechanisms

Disordered microbial communities in asthmatic airways.

A rich microbial environment in infancy protects against asthma [1], [2] and infections precipitate asthma exacerbations [3]. We compared the airway microbiota at three levels in adult patients with asthma, the related condition of COPD, and controls. We also studied bronchial lavage from asthmatic children and controls.We identified 5,054 16S rRNA bacterial sequences from 43 subjects, detecting >70% of species present. The bronchial tree was not sterile, and contained a mean of 2,000 bacterial genomes per cm(2) surface sampled. Pathogenic Proteobacteria, particularly Haemophilus spp., were much more frequent in bronchi of adult asthmatics or patients with COPD than controls. We found similar highly significant increases in Proteobacteria in asthmatic children. Conversely, Bacteroidetes, particularly Prevotella spp., were more frequent in controls than adult or child asthmatics or COPD patients.The results show the bronchial tree to contain a characteristic microbiota, and suggest that this microbiota is disturbed in asthmatic airways

Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma

Asthma is caused by a combination of poorly understood genetic and environmental factors(1,2). We have systematically mapped the effects of single nucleotide polymorphisms ( SNPs) on the presence of childhood onset asthma by genome-wide association. We characterized more than 317,000 SNPs in DNA from 994 patients with childhood onset asthma and 1,243 non-asthmatics, using family and case-referent panels. Here we show multiple markers on chromosome 17q21 to be strongly and reproducibly associated with childhood onset asthma in family and case-referent panels with a combined P value of P < 10(-12). In independent replication studies the 17q21 locus showed strong association with diagnosis of childhood asthma in 2,320 subjects from a cohort of German children (P=0.0003) and in 3,301 subjects from the British 1958 Birth Cohort (P=0.0005). We systematically evaluated the relationships between markers of the 17q21 locus and transcript levels of genes in Epstein - Barr virus (EBV)-transformed lymphoblastoid cell lines from children in the asthma family panel used in our association study. The SNPs associated with childhood asthma were consistently and strongly associated (P < 10(-22)) in cis with transcript levels of ORMDL3, a member of a gene family that encodes transmembrane proteins anchored in the endoplasmic reticulum(3). The results indicate that genetic variants regulating ORMDL3 expression are determinants of susceptibility to childhood asthma.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62682/1/nature06014.pd

Significance of the microbiome in obstructive lung disease

The composition of the lung microbiome contributes to both health and disease, including obstructive lung disease. Because it has been estimated that over 70% of the bacterial species on body surfaces cannot be cultured by currently available techniques, traditional culture techniques are no longer the gold standard for microbial investigation. Advanced techniques that identify bacterial sequences, including the 16S ribosomal RNA gene, have provided new insights into the depth and breadth of microbiota present both in the diseased and normal lung. In asthma, the composition of the microbiome of the lung and gut during early childhood development may play a key role in the development of asthma, while specific airway microbiota are associated with chronic asthma in adults. Early bacterial stimulation appears to reduce asthma susceptibility by helping the immune system develop lifelong tolerance to innocuous antigens. By contrast, perturbations in the microbiome from antibiotic use may increase the risk for asthma development. In chronic obstructive pulmonary disease, bacterial colonisation has been associated with a chronic bronchitic phenotype, increased risk of exacerbations, and accelerated loss of lung function. In cystic fibrosis, studies utilising culture-independent methods have identified associations between decreased bacterial community diversity and reduced lung function; colonisation with Pseudomonas aeruginosa has been associated with the presence of certain CFTR mutations. Genomic analysis of the lung microbiome is a young field, but has the potential to define the relationship between lung microbiome composition and disease course. Whether we can manipulate bacterial communities to improve clinical outcomes remains to be seen

Genomic attributes of airway commensal bacteria and mucosa

Microbial communities at the airway mucosal barrier are conserved and highly ordered, in likelihood reflecting co-evolution with human host factors. Freed of selection to digest nutrients, the airway microbiome underpins cognate management of mucosal immunity and pathogen resistance. We show here the initial results of systematic culture and whole-genome sequencing of the thoracic airway bacteria, identifying 52 novel species amongst 126 organisms that constitute 75% of commensals typically present in heathy individuals. Clinically relevant genes encode antimicrobial synthesis, adhesion and biofilm formation, immune modulation, iron utilisation, nitrous oxide (NO) metabolism and sphingolipid signalling. Using whole-genome content we identify dysbiotic features that may influence asthma and chronic obstructive pulmonary disease. We match isolate gene content to transcripts and metabolites expressed late in airway epithelial differentiation, identifying pathways to sustain host interactions with microbiota. Our results provide a systematic basis for decrypting interactions between commensals, pathogens, and mucosa in lung diseases of global significance

Bacterial Signatures of Paediatric Respiratory Disease : An Individual Participant Data Meta-Analysis

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.Peer reviewe