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Pillich, R. T.

Publications and source records attributed to Pillich, R. T..

3 recordsLinked to original sources

Single cell transcriptomics in a treatment-segregated cohort exposes a STAT3-regulated therapeutic gap in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic pulmonary disease of unknown etiology. Since approved IPF drugs only slow disease progression, novel therapeutics are required that improve clinical outcomes. Here we report a single cell lung RNA-Seq and gene regulatory network analysis of the largest IPF cohort assembled to date. Segregating this cohort based on status of treatment with approved first-generation IPF antifibrotics (untreated, nintedanib- and pirfenidone-treated), we describe for the first time the transcriptional landscape of untreated IPF across 40 lung cell types, and the elements of this program that are impacted by these antifibrotics. On average, nearly 60% of the untreated IPF-dysregulated transcriptome is refractory to treatment with these drugs, a transcriptional deficit we refer to as the IPF therapeutic gap. Gene regulatory network analysis indicated a dominant functional footprint for the transcription factor STAT3 in both untreated IPF and the IPF therapeutic gap. Validating our analysis in a translational precision cut lung slice platform that recapitulates IPF explants, pharmacological inhibition of STAT3 reduced the IPF therapeutic gap in numerous lung cell types. Finally, we resolved a STAT3-anchored master regulatory network comprising numerous profibrotic transcription factors in IPF alveolar fibroblasts, a critical fibrotic lineage. Our study represents a comprehensive resource for translational lung fibrosis research and introduces a strategy for drug discovery that is adaptable to human disease more broadly.

cell biology↗

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

bioinformatics↗

NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange

MotivationThe investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes active or modulated in a specific experimental context. ResultsThe NDEx Integrated Query (IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. IQuerys integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources. Availability and ImplementationIQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java. ContactDexter Pratt (depratt@health.ucsd.edu)

bioinformatics↗