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Deciphering early development of complex diseases by progressive module network.

Tao Zeng1, Chuan-chao Zhang2, Wanwei Zhang1

  • 1Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

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Summary

A new framework identifies disease progression stages using dynamical network biomarkers and gene expression analysis. This method detects early warning signals for pre-disease states and identifies therapeutic targets for advanced diseases like Type 1 diabetes mellitus.

Keywords:
Disease development and progressionDisease diagnosis and prognosisDisease therapyDynamical network biomarkerProgressive module networkType 1 diabetes mellitus

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Complex diseases lack effective cures, necessitating early detection and treatment.
  • Disease progression involves normal, pre-disease, and disease stages, requiring understanding of molecular changes.
  • Identifying dynamical molecular organizations is crucial for early diagnosis and effective treatment strategies.

Purpose of the Study:

  • To develop a novel computational framework for identifying molecular modules associated with different disease stages.
  • To detect pre-disease modules as early warning signals and progressive modules as therapeutic targets.
  • To apply and validate the framework using a Type 1 diabetes mellitus (T1DM) mouse model.

Main Methods:

  • Developed a framework integrating dynamical network biomarkers (DNBs) for pre-disease modules.
  • Utilized cross-tissue gene expression analysis to identify disease-responsive modules.
  • Employed progressive module network (PMN) analysis to find modules related to early disease development.

Main Results:

  • Identified critical transition points using tissue-specific modules and DNBs relevant to the pre-disease stage.
  • Detected tissue-common modules enriched with T1DM-associated genes, indicating later disease events.
  • Revealed common essential progressive genes and environmental factor pathways in early T1DM development via tissue-specific modules.

Conclusions:

  • The developed framework accurately detects critical disease stages and key molecular modules.
  • Pre-disease modules can serve as effective warning signals for early disease diagnosis (e.g., T1DM).
  • Progressive modules identified by the framework are potential therapeutic targets for advanced disease stages (e.g., T1DM).