Microbial DNA Detection via Size Profiles and End Signatures
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Solution Overview
Problem
Current methods for detecting infection-causing pathogen-derived microbial DNA in biological samples are inefficient and require no-template control (NTC) samples, which can lead to false negatives and are challenging due to low abundance and contamination issues.
Innovation Solution
The detection of infection-causing pathogen-derived microbial DNA is performed based on size profiles and end signatures without the need for NTC samples, using statistical values and machine-learning models to identify and classify the level of infection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microbial DNA detection is performed using traditional culture-based methods, then pathogen identification accuracy is improved, but detection time increases significantly and many pathogens cannot be cultured
Solution Approach 1:
The patent replaces traditional mechanical culture-based detection methods with a molecular biology approach using PCR amplification and next-generation sequencing. This substitution enables direct detection of microbial DNA from clinical samples without requiring pathogen cultivation, dramatically reducing detection time while maintaining high identification accuracy through sequence analysis.
2Measurement precision
If no-template control (NTC) samples are used to distinguish contaminant DNA, then false positives are reduced, but device complexity and resource consumption increase
Solution Approach 1:
The patent extracts and utilizes specific fragmentomic features (size profiles and end signatures) of microbial DNA as diagnostic markers. By focusing on these inherent characteristics of pathogenic DNA fragments, the method distinguishes true pathogens from contaminants without requiring separate NTC samples, thereby reducing system complexity while maintaining detection accuracy.
Solution Approach 2:
The patent changes the detection parameters from relying on presence/absence data requiring controls to analyzing fragmentomic parameters (size distribution and end sequence signatures). This parameter transformation enables direct differentiation of pathogenic versus contaminant DNA based on their intrinsic physical and sequence characteristics, eliminating the need for additional control samples.
3Measurement precision
If multiple sequence end signatures are analyzed using machine-learning models, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent analyzes a focused set of specific fragmentomic features (size profiles and end signatures) rather than attempting to process all possible DNA sequence information. This selective analysis of the most discriminatory features enables effective pathogen detection using machine-learning models while keeping computational complexity manageable by concentrating on the most informative parameters.
Data Source
AI summary
Various embodiments are directed to detecting infection-causing microbial cell-free DNA from a biological sample based on their size profiles and/or end signatures, in which the detection of infection-causing microbial DNA can be performed without no template control (NTC) samples. Embodiments can include identifying the infection-causing pathogen-derived microbial DNA based on sizes of microbial cell-free DNA molecules. Embodiments can also include identifying from the infection-causing pathogen-derived microbial DNA based on end signatures of microbial cell-free DNA molecules. Embodiments can also include applying a machine-learning algorithm to a plurality of vectors that represent end signatures of the microbial cell-free DNA molecules, to identify the infection-causing pathogen-derived microbial DNA. By detecting the infection-causing pathogen-derived microbial DNA, a level of infection for the biological sample can be predicted.