Integrated plasma proteomics and lung transcriptomics reveal novel biomarkers in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease with a significant unmet medical need. Development of transformational therapies for IPF is challenging in part to due to lack of robust predictive biomarkers of prognosis and treatment response. Importantly, circulating biomarkers of IPF are limited and none are in clinical use. METHODS: We previously reported dysregulated pathways and new disease biomarkers in advanced IPF through RNA sequencing of lung tissues from a cohort of transplant-stage IPF patients (n = 36) in comparison to normal healthy donors (n = 19) and patients with acute lung injury (n = 11). Here we performed proteomic profiling of matching plasma samples from these cohorts through the Somascan-1300 SomaLogics platform. RESULTS: Comparative analyses of lung transcriptomic and plasma proteomic signatures identified a set of 34 differentially expressed analytes (fold change (FC) ≥ ± 1.5, false discovery ratio (FDR) ≤ 0.1) in IPF samples compared to healthy controls. IPF samples showed strong enrichment of chemotaxis, tumor infiltration and mast cell migration pathways and downregulated extracellular matrix (ECM) degradation. Mucosal (CCL25 and CCL28) and Th2 (CCL17 and CCL22) chemokines were markedly upregulated in IPF and highly correlated within the subjects. The mast cell maturation chemokine, CXCL12, was also upregulated in IPF plasma (fold change 1.92, FDR 0.006) and significantly correlated (Pearson r = - 0.38, p = 0.022) to lung function (%predicted FVC), with a concomitant increase in the mast cell Tryptase, TPSB2. Markers of collagen III and VI degradation (C3M and C6M) were significantly downregulated (C3M p < 0.001 and C6M p < 0.0001 IPF vs control) and correlated, Pearson r = 0.77) in advanced IPF consistent with altered ECM homeostasis. CONCLUSIONS: Our study identifies a panel of tissue and circulating biomarkers with clinical utility in IPF that can be validated in future studies across larger cohorts.

A robust data-driven genomic signature for idiopathic pulmonary fibrosis with applications for translational model selection.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease affecting ~5 million people globally. We have constructed an accurate model of IPF disease status using elastic net regularized regression on clinical gene expression data. Leveraging whole transcriptome microarray data from 230 IPF and 89 control samples from Yang et al. (2013), sourced from the Lung Tissue Research Consortium (LTRC) and National Jewish Health (NJH) cohorts, we identify an IPF gene expression signature. We performed optimal feature selection to reduce the number of transcripts required by our model to a parsimonious set of 15. This signature enables our model to accurately separate IPF patients from controls. Our model outperforms existing published models when tested with multiple independent clinical cohorts. Our study underscores the utility of elastic nets for gene signature/panel selection which can be used for the construction of a multianalyte biomarker of disease. We also filter the gene sets used for model input to construct a model reliant on secreted proteins. Using this approach, we identify the preclinical bleomycin rat model that is most congruent with human disease at day 21 post-bleomycin administration, contrasting with earlier timepoints suggested by other studies.

RNA sequencing of transplant-stage idiopathic pulmonary fibrosis lung reveals unique pathway regulation.

Idiopathic pulmonary fibrosis (IPF), the scarring of lung parenchyma resulting in the loss of lung function, remains a fatal disease with a significant unmet medical need. Patients with severe IPF often develop acute exacerbations resulting in the rapid deterioration of lung function, requiring transplantation. Understanding the pathophysiological mechanisms contributing to IPF is key to develop novel therapeutic approaches for end-stage disease. We report here RNA-sequencing analyses of lung tissues from a cohort of patients with transplant-stage IPF (n=36), compared with acute lung injury (ALI) (n=11) and nondisease controls (n=19), that reveal a robust gene expression signature unique to end-stage IPF. In addition to extracellular matrix remodelling pathways, we identified pathways associated with T-cell infiltration/activation, tumour development, and cholesterol homeostasis, as well as novel alternatively spliced transcripts that are differentially regulated in the advanced IPF lung versus ALI or nondisease controls. Additionally, we show a subset of genes that are correlated with percent predicted forced vital capacity and could reflect disease severity. Our results establish a robust transcriptomic fingerprint of an advanced IPF lung that is distinct from previously reported microarray signatures of moderate, stable or progressive IPF and identifies hitherto unknown candidate targets and pathways for therapeutic intervention in late-stage IPF as well as biomarkers to characterise disease progression and enable patient stratification.

A practical approach to a viral detection pipeline using existing viral and non-viral sequence resources.

For public health safety, vaccines and other pharmaceutical products as well as the raw materials used in their manufacture need to be tested for adventitious virus contamination. The current standard of practice is to develop culture-based or polymerase chain reaction assays for the types of viruses one might expect based upon the source of reagents used. High-throughput sequencing technology is well-suited for building an unbiased strategy for the purpose of adventitious virus detection. We have developed an approach to automate curation of publically available nucleotide sequences, and have practically balanced the desire to capture all viral diversity while simultaneously reducing the use of partial viral sequences that represent the largest source of false positive results. In addition, we describe an effective workflow for virus detection that can process sequence data from all currently available High-throughput sequencing technologies and produce a report that summarizes the weight of sequence data in support of each detected virus.