Rationale: Idiopathic pulmonary fibrosis (IPF) causes considerable global morbidity and mortality, and its mechanisms of disease progression are poorly understood. Recent observational studies have reported associations between lung dysbiosis, mortality, and altered host defense gene expression, supporting a role for lung microbiota in IPF. However, the causal significance of altered lung microbiota in disease progression is undetermined. Objectives: To examine the effect of microbiota on local alveolar inflammation and disease progression using both animal models and human subjects with IPF. Methods: For human studies, we characterized lung microbiota in BAL fluid from 68 patients with IPF. For animal modeling, we used a murine model of pulmonary fibrosis in conventional and germ-free mice. Lung bacteria were characterized using 16S rRNA gene sequencing with novel techniques optimized for low-biomass sample load. Microbiota were correlated with alveolar inflammation, measures of pulmonary fibrosis, and disease progression. Measurements and Main Results: Disruption of the lung microbiome predicts disease progression, correlates with local host inflammation, and participates in disease progression. In patients with IPF, lung bacterial burden predicts fibrosis progression, and microbiota diversity and composition correlate with increased alveolar profibrotic cytokines. In murine models of fibrosis, lung dysbiosis precedes peak lung injury and is persistent. In germ-free animals, the absence of a microbiome protects against mortality. Conclusions: Our results demonstrate that lung microbiota contribute to the progression of IPF. We provide biological plausibility for the hypothesis that lung dysbiosis promotes alveolar inflammation and aberrant repair. Manipulation of lung microbiota may represent a novel target for the treatment of IPF.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal interstitial pneumonia. The disease pathophysiology is poorly understood and the etiology remains unclear. Recent advances have generated new therapies and improved knowledge of the natural history of IPF. These gains have been brokered by advances in technology and improved insight into the role of various genes in mediating disease, but gene expression and protein levels do not always correlate. Thus, in this paper we apply a novel large scale high throughput aptamer approach to identify more than 1100 proteins in the peripheral blood of well-characterized IPF patients and normal volunteers. We use systems biology approaches to identify a unique IPF proteome signature and give insight into biological processes driving IPF. We found IPF plasma to be altered and enriched for proteins involved in defense response, wound healing and protein phosphorylation when compared to normal human plasma. Analysis also revealed a minimal protein signature that differentiated IPF patients from normal controls, which may allow for accurate diagnosis of IPF based on easily-accessible peripheral blood. This report introduces large scale unbiased protein discovery analysis to IPF and describes distinct biological processes that further inform disease biology.

In animal models and human subjects, lung dysbiosis is a prominent feature of HCT. Lung dysbiosis is correlated with histologic, immunologic, and physiologic features of post-HCT pulmonary complications. Our findings suggest the lung microbiome may be an unappreciated target for the prevention and treatment of post-HCT pulmonary complications.

The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue-specific expression of proteins and the varied post-translational modifications, splice variants and protein-protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.

Idiopathic pulmonary fibrosis (IPF) is a progressive and heterogeneous interstitial lung disease of unknown origin with a low survival rate. There are few treatment options available due to the fact that mechanisms underlying disease progression are not well understood, likely because they arise from dysregulation of complex signaling networks spanning multiple tissue compartments. To better characterize these networks, we used systems-focused data-driven modeling approaches to identify cross-tissue compartment (blood and bronchoalveolar lavage) and temporal proteomic signatures that differentiated IPF progressors and non-progressors. Partial least squares discriminant analysis identified a signature of 54 baseline (week 0) blood and lung proteins that differentiated IPF progression status by the end of 80 weeks of follow-up with 100% cross-validation accuracy. Overall we observed heterogeneous protein expression patterns in progressors compared to more homogenous signatures in non-progressors, and found that non-progressors were enriched for proteomic processes involving regulation of the immune/defense response. We also identified a temporal signature of blood proteins that was significantly different at early and late progressor time points (p < 0.0001), but not present in non-progressors. Overall, this approach can be used to generate new hypothesis for mechanisms associated with IPF progression and could readily be translated to other complex and heterogeneous diseases. Idiopathic pulmonary fibrosis (IPF) is a heterogeneous and irreversible interstitial pneumonia, with symptoms including progressive cough, shortness of breath, and ultimately respiratory failure, with a median survival of only 3-5 years post diagnosis 1. The disease is believed to be caused by a dysregulated wound healing response to various epithelial injuries leading to fibrosis of the lung interstitium 1. Two medications (nintedanib 2 and pirfenidone 3) are effective treatments for IPF; though neither can reverse the disease 4. Thus, lung transplantation is currently the only option for a cure 5 , even though this procedure has the highest failure rate of all organ transplantation options (54% at 5 years 6). Better understanding of mechanisms underpinning progression of pulmonary fibrosis could lead to improved outcomes via identification of new therapeutic targets. To add to the complexity surrounding IPF, disease progression is also heterogeneous, with some individual patients experiencing long-term stability and others rapid loss of lung function. A number of longitudinal cohort studies have been created with the goal of better characterizing IPF pathobiology using proteomic measurements 7-10. These efforts have identified individual proteins, including blood MMP-7 11,12 , CCL18 13 , and blood surfactant protein D 14,15 , as potential prognostic biomarkers. However, it has been difficult to replicate these findings across multiple cohorts 16,17 , especially when attempting to validate specific, prognostically-relevant cutoff concentrati...

Associate Editor William H. Guilford oversaw the review of this article.

Scientific Reports 7: Article number: 46560; published online: 25 April 2017; updated: 30 June 2017 The original version of this Article contained an error in the reference list, where reference 51 was incorrectly listed as “Ogawa, S. et al. Reduced cerebrospinal fluid ethanolamine concentration in major depressive disorder.

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