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Universal gene-level bimodality in natural microbial communities.

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Microbiome gene distributions often show bimodality, with specific genes linked to niche adaptation and disease. This finding enables a new gene-centric approach for microbiome functional typing and disease biomarker discovery.

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Area of Science:

  • Microbiome research
  • Ecological genomics
  • Systems biology

Background:

  • Bimodality, a trait distribution with two peaks, is common in nature and observed in microbiome species abundances.
  • The prevalence of bimodality in microbiome gene abundance distributions and its functional implications remain largely unexplored.

Purpose of the Study:

  • To investigate the widespread occurrence of gene-level bimodality across diverse microbiomes.
  • To explore the functional roles of bimodal genes in ecological adaptation.
  • To develop a novel gene-centric framework for microbiome functional typing and identify potential disease biomarkers.

Main Methods:

  • Systematic analysis of individual gene abundance distributions in various microbiomes (e.g., human gut, ocean).
  • Identification of bimodal genes and enrichment analysis of associated pathways.
  • Development and application of a gene-centric microbiome functional typing framework.
  • Machine learning model construction using bimodal genes for disease prediction.

Main Results:

  • Widespread gene-level bimodality was discovered across diverse microbiomes.
  • Bimodal genes were enriched in niche-specific pathways, indicating roles in community adaptation.
  • A robust gene-centric microbiome functional typing framework was established.
  • Eleven bimodal genes in the human gut microbiome were identified, associated with diseases like liver cirrhosis.
  • Machine learning models using these genes demonstrated predictive capability for diseases.

Conclusions:

  • Gene abundance bimodality is a prevalent feature in microbiomes, offering insights into functional architecture.
  • The identified bimodal genes serve as potential biomarkers for microbiome-based diagnostics and disease prediction.
  • A gene-centric approach provides a valuable alternative to traditional taxonomy-based microbiome analysis.