MNRS: Multi-Factor Network-Based Ranking Score for Detecting Critical Transitions of Complex Diseases Using Gut...

Area of Science:

• Microbiome research
• Computational biology
• Disease dynamics

Background:

• Disease progression can be abrupt, with critical thresholds preceding deterioration.
• Gut microbiome alterations are linked to diseases like type 1 diabetes, celiac disease, and colorectal cancer.
• Existing transcriptome methods struggle with noisy, sparse, and compositional microbiome data.

Purpose of the Study:

• To develop a novel computational framework for detecting pre-disease states using gut microbiome data.
• To identify early-warning signatures of critical transitions in disease progression.
• To overcome limitations of existing methods in analyzing complex microbiome data.

Main Methods:

• Proposed a novel computational framework: multi-factor network-based ranking score (MNRS).
• MNRS infers perturbed microbial networks and quantifies dynamic alterations in species/genus associations.
• Applied MNRS to simulated and real-world gut microbiome datasets.

Main Results:

• MNRS accurately identifies pre-disease states.
• The framework outperforms existing methods in robustness and detection performance.
• MNRS identified "dark species" potentially crucial in disease deterioration, missed by other analyses.

Conclusions:

• MNRS provides a robust and effective computational tool for early detection of pre-disease states from gut microbiome data.
• The framework's ability to detect subtle microbial network changes offers new insights into disease onset.
• MNRS highlights the importance of previously overlooked microbial species in disease progression.