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Related Experiment Video

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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A framework for non-preserved consensus gene module detection in Johne's disease.

Maryam Heidari1, Abbas Pakdel1, Mohammad Reza Bakhtiarizadeh2

  • 1Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran.

Frontiers in Veterinary Science
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

Johne's disease research identified non-preserved consensus modules in co-expression networks. This computational pipeline revealed key long non-coding RNAs and transcription factors involved in the disease, offering new insights into its molecular mechanisms.

Keywords:
Johne's diseasePPIs networksRNA-Seqconsensus moduleshub geneslncRNA-mRNA-TF networks

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Area of Science:

  • Genomics and Bioinformatics
  • Veterinary Medicine
  • Immunology

Background:

  • Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), significantly impacts the dairy industry.
  • The precise molecular pathogenesis of Johne's disease remains incompletely understood, hindering effective control strategies.
  • Reproducibility issues in biological studies necessitate robust computational approaches for reliable molecular mechanism discovery.

Purpose of the Study:

  • To develop a computational pipeline for identifying stable molecular modules in co-expression networks across different Johne's disease datasets.
  • To pinpoint non-preserved consensus modules affected by MAP infection.
  • To construct and validate integrated lncRNA-mRNA-TF networks and identify potential biomarkers for Johne's disease.

Main Methods:

  • Analysis of two RNA-Seq datasets from MAP-infected animals.
  • Detection and preservation analysis of consensus co-expression modules.
  • Identification of long non-coding RNAs (lncRNAs) and transcription factor (TF) genes within non-preserved modules.
  • Construction and validation of integrated lncRNA-mRNA-TF networks using protein-protein interaction (PPI) data.
  • Functional enrichment analysis of identified modules.

Main Results:

  • Identified 21 non-preserved consensus modules common to both datasets, containing 619 hub genes.
  • Discovered 34 lncRNAs and 152 TFs within these modules, with significant PPI network support for 17 modules.
  • Functional enrichment highlighted pathways related to inflammatory and interferon responses, crucial for Johne's disease.
  • Identified potential candidate genes including TLR2, NFKB1, IRF1, and STAT1 involved in disease pathogenesis.

Conclusions:

  • The developed computational pipeline effectively identifies non-preserved consensus modules, offering a robust method for discovering molecular mechanisms in complex diseases.
  • The study elucidates key lncRNAs, TFs, and mRNAs implicated in Johne's disease pathogenesis, particularly those involved in inflammatory and immune responses.
  • The identified candidate genes provide valuable targets for future research and potential therapeutic strategies against Johne's disease.