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Research Article Open access CC BY 4.0

Microbial Genetics and Metagenomics of Bacterial Pneumonia Related to Their Antibiotics Resistance: An Insightful Review

Muhammad Aishatu Malami, Olusegun Timothy God-Giveth, Onare Opeyemi Mary, Mkpoikana Emmanuel Johnson, Elohor Precious Samuel, Favour Ugochi Ukaumunna, Sushma Bhuvanagiri, Ogboenyie Goodluck Chibunna

Journal of Advances in Microbiology · pp. 93–106 · Published 1 Mar 2025

10.9734/jamb/2025/v25i3911

Abstract

Bacterial pneumonia (BP) is an infection caused by the presence of one or more bacteria that mostly affect the lower respiratory tract and cause lung complications. The global prevalence of BP’s  AR is approximately 400 million, with a higher incidence in children at or below the age of 5 years and adults aged 65. This prevalence is further compounded in low and middle-income countries, where access to antibiotics is limited. Understanding the role of genetics in studying persistent antibiotic resistance (AR) by BP causative agents and their predictions is of the essence. There has been limited data on antibiotic resistance of bacterial pneumonia related to their genetic makeup, highlighting how antibiotic resistance in bacterial pneumonia can be related to their genetics and metagenomics can help to improve diagnosis and treatment regimens. The evaluated antibiotic resistance sensitivity and genetic characteristics findings have prompted the need for metagenomics. Metagenomics Next Generation Sequencing (mNGS) is a potential diagnostic strategy to achieve clearer and more predictable insights into the antibiotic resistance of common pneumonia-causing bacteria. Additionally, metagenomics has been shown to produce high detection and diagnostic insights for multiple genetic tests done simultaneously. Hence, it is a recommended approach for evaluating the relationship between the AR of pneumonia-causing bacteria and the genetic characteristics of BP. Therefore, this review aims to highlight the relationship between the genetic and metagenomic profiles of bacterial pneumonia and the development of antibiotic resistance, to identify key genetic factors and microbial interactions that contribute to resistance mechanisms. Additionally, it highlights the necessity of utilizing mNGS techniques for AR surveillance and prediction as they relate to BP.

Bacterial pneumonia antibiotics resistance AR genetics prevalence relationship diagnosis metagenomics NGS challenges

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