Bacterial Identification via Whole Metagenome Sequencing
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Solution Overview
Problem
Current methods for bacterial identification, such as culture-based techniques and next-generation sequencing, are limited by time, bias, and lack of specificity, leading to ineffective antibiotic regimens and complications in treating clinical infections like diabetic foot ulcers, where the diversity of microbial communities is not fully understood.
Innovation Solution
Whole metagenome sequence analysis using fast k-mer based sequence analysis and Bayesian network analysis to diagnose and prognose infections by analyzing the entire microbiome, identifying bacterial species, antibiotic resistance, and virulence factors, and stratifying communities into healing versus non-healing clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If culture-based methods are used for bacterial identification, then the diagnosis can be performed with standard techniques, but the time to diagnosis is prolonged (24-72 hours) and sensitivity is reduced for fastidious organisms
Solution Approach 1:
The patent replaces culture-based mechanical growth methods with direct genomic sequencing of bacterial DNA from clinical samples. This substitution eliminates the need for bacterial cultivation, enabling rapid identification within hours rather than days, while maintaining high diagnostic accuracy through direct detection of genetic material.
Solution Approach 2:
The patent performs preliminary genomic sequencing and analysis before final diagnosis is established. By sequencing conserved genes and comparing them against databases in advance, the system prepares identification results ready for immediate clinical interpretation, significantly reducing the time from sample collection to diagnostic decision.
2Productivity
If sequencing of conserved genes (e.g., 16s rRNA) is used to differentiate species, then the method is rapid and culture-independent, but it lacks specificity for organisms with highly similar sequences
Solution Approach 1:
The patent segments the bacterial genome into multiple conserved gene regions (16s rRNA, rpoB, atpD, infB, gyrB) rather than relying on a single gene. By analyzing multiple segmented genomic regions simultaneously, the system achieves higher resolution for distinguishing closely related species while maintaining the speed and culture-independent advantages of molecular sequencing.
Solution Approach 2:
The patent creates a composite diagnostic approach by combining sequencing data from multiple conserved genes. This composite genomic fingerprinting method integrates information from different genomic regions, providing enhanced specificity for species differentiation while preserving the rapid, culture-independent nature of molecular methods.
3Speed
If empirically based antibiotic regimens are used, then treatment can be initiated immediately, but the regimens may be ineffective and contribute to antibiotic resistance
Solution Approach 1:
The patent performs preliminary genomic identification and antibiotic resistance gene detection before finalizing treatment decisions. By rapidly sequencing and analyzing bacterial DNA for resistance markers, the system provides clinicians with targeted antibiotic recommendations in advance, enabling effective treatment initiation without relying on empirical regimens.
Solution Approach 2:
The patent implements a feedback loop where genomic sequencing results and resistance profile analysis inform subsequent treatment decisions. The system continuously refines antibiotic recommendations based on identified resistance mechanisms, ensuring treatment effectiveness while reducing the risk of contributing to antibiotic resistance through inappropriate empirical therapy.
Data Source
AI summary
Disclosed herein are methods of identifying infections, such as methods of identifying bacterial infections which utilize whole metagenome sequence analysis to sequence the entire wound microbiome of clinical samples. The disclosed methods use fast k-mer based sequence analysis, predictive modeling, and Bayesian network analysis, to analyze bacterial metagenomic sequence compositions in conjunction with clinical factors to stratify communities of bacteria into healing versus non-healing clusters. The methods of identifying infections can include performing molecular analysis of a patient wound sample, preparing the data obtained from the molecular analysis, diagnosing the wound sample and/or prognosing the wound sample. The disclosed methods can also be used to identify protein function as well as novel biomarkers.