(A Molecular Systems Database for IPF)

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Browse here for miRNAs in IPF, commons genes in IPF pathway and Crosstalk pathway.
Chromosomal distribution of differential expressed genes in IPF
Idiopathic Pulmonary Fibrosis is an incurable progressive fibrotic disease of the lungs. We currently lack a systematic understanding of IPF biology and a systems approach may offer new therapeutic insights. Here, for the first time, a large volume of high throughput genomics data has been unified to derive the most common molecular signatures of IPF. A set of 39 differentially expressed genes (DEGs) was found critical to distinguish IPF. Using high confidence evidences and experimental data, system level networks for IPF were reconstructed, involving 737 DEGs found common across at least two independent studies.
This all provided one of the most comprehensive molecular system views for IPF underlining the regulatory and molecular consequences associated. 56 pathways crosstalks were identified which included critical pathways with specified directionality. The associated steps gained and lost due to crosstalk during IPF were also identified. A serially connected system of five crucial genes was found, potentially controlled by nine miRNAs and eight transcription factors exclusively in IPF when compared to NSIP and Sarcoidosis. Findings from this study have been implemented into a comprehensive molecular and systems database on IPF to facilitate devising diagnostic and therapeutic solutions for this deadly disease.
Idiopathic interstitial pneumonias (IIPs) are interstitial lung diseases with unidentified mechanism, distinguished by matrix deposition and alveolar epithelium disruption. Idiopathic pulmonary fibrosis (IPF) is a chronic fibrosing IIP that has no effective therapy with a mortality rate higher than many cancers; median survival from the time of diagnosis being about three years1. There is typically no response to general anti-inflammatory therapy such as glucocorticosteroids, which are effective in some IIPs such as non-specific interstitial pneumonia (NSIP). Antifibrotic agents such as Nintedanib and Pirfenidone were initially reported to be potentially beneficial, but the early promise has not been sustained2-3. Adenosine receptor antagonist based solutions have shown some limited promise4. While new targets have emerged, such as proinflammatory cytokine (IL-13)5, the negligible molecular systems information for IPF remains a problem. A gene expression level understanding of IPF is incomplete, with limited number of studies mostly microarray (MA) based6-11 and hardly a couple of RNA-seq studies12-13. Also, very few study has been done covering systems biological perspectives but not specific to IPF9. Limited MA based studies with non-coding RNAs have also been performed, but small RNA-seq studies are lacking14-16.
From a systems biology perspective, IPF offers both opportunities and challenges. The main challenge is a likely heterogeneity within IPF, which is a clinical diagnosis based on typical diffuse radiological or pathological findings that may be seen in a limited form in other IIPs or even aged lungs. Further, the IPF lung is itself heterogeneous. Thus there is likely to be substantial background noise, as evidenced by high variability between studies. This also presents an opportunity to systematically consolidate all these studies, using informatics to extract the underlining characteristic signatures for IPF. Such efforts are critical towards better molecular characterization of IPF that could lead to better diagnosis, classification and therapy.
This is important because the therapeutic response is very different between different types of IIP and possibly within different subsets of IPF. There is also a need for unified information portal and database for molecular and systems biology of IPF beyond the few existing resources on expression data reporting that have limited scope (https://research.cchmc.org/pbge/lunggens/mainportal.html http://montgomerylab.stanford.edu/resources.html). The present study has been carried out considering these factors. The study attempts to unify the best available gene expression data for IPF, derive its core characteristics towards generating a computational model of IPF pathology and create a comprehensive state-of-the-art database dedicated to IPF research.
After analysis of a large volume of high throughput genomic data, a set of 39 differentially expressed genes (DEGs) emerged critical to distinguish IPF. Experimentally validated and high confidence data with multiple evidences were used to reconstruct the system model for IPF, incorporating various PPI and regulatory interactions. Careful analysis of the networks identified several critical potential FFLs as well as compromised and benefited routes in various pathways, marking the phenotype of IPF. A system of serially connected five genes, eight TFs and nine miRNAs appeared exclusive to IPF, which could be promising for therapeutic interventions.
In future, the expression of associated miRNAs in this system could be controlled in order to verify their potential role as therapeutic targets for IPF to design therapeutic agents. Finally, this study has generated a state-of-the-art database on IPF where the involved components can be analyzed and visualized in a highly informative manner. This database would be very useful for any molecular systems research on IPF.