Common pathway signature in lung and liver fibrosis

Eugene Makarev, Evgeny Izumchenko, Fumiaki Aihara, Piotr T. Wysocki, Qingsong Zhu, Anton Buzdin, David Sidransky, Alex Zhavoronkov, Anthony Atala

Research output: Contribution to journalComment/debatepeer-review

Abstract

Fibrosis, a progressive accumulation of extracellular matrix components, encompasses a wide spectrum of distinct organs, and accounts for an increasing burden of morbidity and mortality worldwide. Despite the tremendous clinical impact, the mechanisms governing the fibrotic process are not yet understood, and to date, no clinically reliable therapies for fibrosis have been discovered. Here we applied Regeneration Intelligence, a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in lung and liver fibrosis. In both tissues, our analysis detected major conserved signaling pathways strongly associated with fibrosis, suggesting that some of the pathways identified by our algorithm but not yet wet-lab validated as fibrogenesis related, may be attractive targets for future research. While the majority of significantly disrupted pathways were specific to histologically distinct organs, several pathways have been concurrently activated or downregulated among the hepatic and pulmonary fibrosis samples, providing new evidence of evolutionary conserved pathways that may be relevant as possible therapeutic targets. While future confirmatory studies are warranted to validate these observations, our platform proposes a promising new approach for detecting fibrosis-promoting pathways and tailoring the right therapy to prevent fibrogenesis.

Original languageEnglish (US)
Pages (from-to)1667-1673
Number of pages7
JournalCell Cycle
Volume15
Issue number13
DOIs
StatePublished - Jul 2 2016

Keywords

  • IPF
  • PAS
  • algorithm
  • bioinformatics
  • liver fibrosis
  • pathway activation
  • signaling pathway signature

ASJC Scopus subject areas

  • Molecular Biology
  • Developmental Biology
  • Cell Biology

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