Functional metagenomics highlights varied infection states with dynamics of pathogens and antibiotic resistance in lower respiratory tract infections

Uzma Shamim; Aanchal Yadav; Ranjeet Maurya; Priti Devi; Pallawi Kumari; Kanika; Kriti Khare; Bansidhar Tarai; Rajesh Pandey

doi:10.1016/j.heliyon.2024.e38380

Functional metagenomics highlights varied infection states with dynamics of pathogens and antibiotic resistance in lower respiratory tract infections

Heliyon. 2024 Sep 26;10(19):e38380. doi: 10.1016/j.heliyon.2024.e38380. eCollection 2024 Oct 15.

Authors

Uzma Shamim^{1

2}, Aanchal Yadav^{1

3}, Ranjeet Maurya^{1

3}, Priti Devi^{1

3}, Pallawi Kumari^{1

4}, Kanika¹, Kriti Khare^{1

3}, Bansidhar Tarai⁵, Rajesh Pandey^{1

3}

Affiliations

¹ Division of Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi-110007, India.
² Ashoka University, Sonipat, Haryana-131029, India.
³ Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India.
⁴ Indraprastha Institute of Information Technology (IIIT), New Delhi-110020, India.
⁵ Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi 110017, India.

Abstract

Background: Antimicrobial resistance (AMR) amongst pathogenic bacterial species poses significant challenges in treating infections of the lower respiratory tract (LRT), leading to higher hospitalization and mortality rates.

Methods: Bronchoalveolar lavage fluid (BALF) from 84 clinically adjudicated LRTI patients were subjected to respiratory pathogen ID/AMR (RPIP) enrichment and sequencing followed by Explify and CZID seq data analysis to identify potential LRTI pathogens and associated AMR genes. Patients were categorized as LRTI-WP (with pneumonia) and LRTI-WoP (without pneumonia).

Findings: mNGS achieved 100 % pathogen detection compared to 73 % through clinician-used BioFire panel. Predominant pathogens included Acinetobacter baumannii, Klebsiella pneumoniae along with detection of Aspergillus versicolor and Herpes simplex virus. Double and polymicrobial infections were captured, involving non-respiratory pathogens like Rothia mucilaginosa, Escherichia coli, and Moraxella osloensis. AMR detection highlighted macrolide (MPH; ERM) and Sulfonamide (SUL) rich resistome in 60 % of patients followed by extended spectrum beta lactamase (OXA) and tetracycline (TET). LRTI-WP showed high abundance of A. baumannii, majorly associated with MPH whereas K. pneumoniae with beta-lactams was comparable in both groups. Differences in clinical severity may stem from non-respiratory pathogens, newly recognized via mNGS. CZID seq pipeline validated and revealed additional microbes and AMR genes in the cohort.

Interpretation: The prevalence of common pathogens like A. baumannii and K. pneumoniae along with the non-respiratory pathogens identified by RPIP-Explify and CZID seq provided an understanding to evaluate the LRTI. mNGS is crucial for precise pathogen and antibiotic resistance detection, vital for combating antibiotic resistance.

Keywords: Antimicrobial resistance (AMR); CZID seq; Explify; Lower respiratory tract Infection (LRTI); Metagenomic next-generation sequencing (mNGS); Microbes; Pneumonia; RPIP.