Competing Gut Bacterial Guilds for Genome-Centric Disease Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current gene-centric approaches for metagenomic data analysis in microbiome-wide association studies (MWAS) fail to account for the ecological interactions of bacterial strains, leading to spurious correlations and overlooking functional guilds that are crucial for understanding disease biomarkers.
Innovation Solution
Employing a genome-centric approach that identifies metagenome-assembled genomes (MAGs) and their guild-level aggregation to analyze gut microbiome features, recognizing guilds as functional units that interact coherently and affect host health, using a Random Forest regression model to correlate guild abundance with disease phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If gene-centric approaches are used for metagenomic data analysis, then individual genes can be annotated using existing databases, but ecological interactions between bacterial strains are ignored leading to spurious correlations
Solution Approach 1:
The patent merges multiple genes that belong to the same functional pathway or metabolic module into a single functional unit. This combining approach preserves the ecological relationships between bacterial strains by analyzing genes at the pathway level rather than individual gene level, thereby eliminating spurious correlations while maintaining analytical feasibility through database annotation of consolidated functional units
2Productivity
If individual genes are treated as independent units, then database annotation can be performed efficiently, but the ecological behavior of bacterial carriers is disregarded
Solution Approach 1:
The patent segments the microbiome analysis into hierarchical levels: first organizing genes into functional pathways, then grouping pathways into metabolic modules, and finally aggregating modules into guild-level functional units. This segmentation strategy maintains ecological interaction information at each hierarchical level while enabling efficient database annotation and analysis at the consolidated guild level
3Device complexity
If competing bacterial strains encode the same functional gene, then gene annotation is simplified, but opposite growth trajectories are masked leading to neutralized signals
Solution Approach 1:
The patent applies local quality analysis by examining the specific ecological context and growth trajectory of each bacterial strain carrying a functional gene. Instead of uniformly annotating all copies of a gene, the method assigns different weights or signs to gene copies based on their carrier strains' ecological roles and growth patterns, thereby preserving opposite changes from competing strains while maintaining annotation efficiency
Data Source
AI summary
Methods and systems for determining a disease state by obtaining a first plurality of nucleic acid sequences for genomic DNA from a sample from the gut of a subject. Determine, from the nucleic acid sequences a first plurality of genomic abundance values for a first plurality of gut bacteria and a second plurality of genomic abundance values for a second plurality of at least 20 species of gut bacteria. Apply a model to at least the first plurality of genomic abundance values and the second plurality of genomic abundance values, or one or more combinations thereof, thereby determining the disease state of the subject as an output of the model.