Integrated LRTI Classifier Using Host Genes and Microbial Diversity
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Solution Overview
Problem
Existing diagnostic techniques for lower respiratory tract infections (LRTIs) suffer from low sensitivity, long turnaround times, and limited pathogen detection, leading to inappropriate treatments and increased mortality, particularly in vulnerable populations like critically ill children.
Innovation Solution
A diagnostic approach combining host gene expression analysis using a gene panel and metagenomic next-generation sequencing (mNGS) with a meta-classifier to determine the likelihood of LRTI, integrating host gene expression, respiratory microbiota, and pathogen abundance, enhancing diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bacterial culture methods are used for LRTI diagnosis, then pathogen identification is achieved, but turnaround time is extended to several days
Solution Approach 1:
The diagnostic approach segments the analysis into two independent components: host gene expression profiling (using a 14-gene panel including GNLY, PSMB8, FFAR3, SLC38A2, ISG15, IRF1, KIAA1841, AC090425.2, AKR1C3, CXCL5, SESN1, PCOLCE2, RBP4, TAP1, EPSTI1, and FABP4) and microbial community detection (using mNGS). This segmentation allows parallel processing of host response assessment and pathogen identification, reducing overall diagnostic time while maintaining accuracy.
Solution Approach 2:
The patent introduces host gene expression signatures as an intermediary marker that reflects the host's immune response to infection. These signatures serve as a rapid surrogate indicator of LRTI presence, enabling clinicians to make preliminary diagnostic decisions within hours rather than days, while mNGS provides confirmatory pathogen identification.
2Speed
If broad spectrum antibiotics are used empirically, then treatment initiation is immediate, but inappropriate treatment and adverse events occur
Solution Approach 1:
The diagnostic system performs preliminary assessment of LRTI likelihood using host gene expression signatures before initiating antimicrobial therapy. By evaluating the probability of bacterial versus viral etiology in advance (within hours), clinicians can select appropriate narrow-spectrum antibiotics or antivirals from the outset, avoiding unnecessary broad-spectrum antibiotic use and its associated harms.
3Measurement precision
If mNGS is used alone for LRTI diagnosis, then pathogen detection capability is enhanced, but diagnostic accuracy is insufficient due to asymptomatic pathogen carriage
Solution Approach 1:
The patent merges two diagnostic modalities: host gene expression profiling (which assesses the host's immune response to infection) and mNGS (which detects microbial presence). By combining these approaches, the system distinguishes between asymptomatic pathogen carriage (where mNGS detects microbes but host signatures remain normal) and true LRTI (where both pathogen detection and abnormal host responses are present), thereby achieving high diagnostic accuracy in pediatric patients.
Data Source
AI summary
Various embodiments are directed to analyzing host gene expression levels and microbial diversity in a biological sample, to determine a likelihood of lower-respiratory tract infection (LRTI) in subjects. Embodiments can include determining a probability value of a subject having LRTI based on differential gene expression of the subject and reference levels of control subjects. Embodiments can also include determining the likelihood of LRTI in subjects based on amicrobial diversity index or abundance levels of microbes that are considered as potential pathogens. Embodiments can also include applying an integrated classifier to gene expression levels, virus abundance levels, and microbial diversity to determine the likelihood of LRTI in subjects.