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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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Unbiased Pathogen Detection and Host Gene Profiling for Conjunctivitis
Prajna Lalitha1, Gerami D Seitzman2, Ritesh Kotecha1
1Department of Ocular Microbiology, Aravind Eye Hospital, Madurai, Tamil Nadu, India.
Ophthalmology
|April 7, 2019
Summary
Metagenomic RNA deep sequencing accurately identified pathogens in 86% of conjunctivitis cases, including rare fungal infections. This method offers a powerful tool for diagnosing infectious conjunctivitis and understanding host responses.
Area of Science:
- Ophthalmology
- Infectious Diseases
- Molecular Biology
Background:
- Conjunctivitis etiology is frequently misdiagnosed, necessitating improved diagnostic methods.
- Accurate identification of infectious agents is crucial for effective treatment and preventing spread.
Purpose of the Study:
- To apply unbiased metagenomic RNA deep sequencing (MDS) for identifying diverse pathogens causing conjunctivitis.
- To explore host transcriptome signatures for differentiating infectious agents.
Main Methods:
- Prospective collection of conjunctival swabs from patients with acute infectious conjunctivitis.
- Unbiased metagenomic RNA deep sequencing (MDS) for pathogen identification.
- Computational pipeline for analyzing nonhost sequences and differential gene expression analysis for host transcriptome signatures.
Main Results:
- MDS detected pathogens in 86% (12/14) of patients.
- Human adenovirus (HAdV) was identified in 10/14 patients, and Vittaforma corneae (fungus) in 2/14 patients.
- Distinct host transcriptome signatures were identified for HAdV and V. corneae infections.
Conclusions:
- MDS reliably detects and quantifies common and rare conjunctivitis pathogens, including fungi.
- The unbiased approach expands pathogen detection scope beyond typical agents.
- Host transcriptome signatures may aid in pathogen detection even with low pathogen loads.
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