Whole-genome Sequencing of Bacteria Accountable for Lactational Mastitis in Humans Combined with an Examination of Their Antibiotic Resistance Profiles

Overview

Journal Braz J Microbiol

Specialty Microbiology

Date 2024 Sep 25

PMID 39320640

Authors

Priyanka Dalwadi

Neelam Nathani

Kshipra Chauhan

Jasmine Mansuri

Prakash Koringa

Vaibhav Bhatt

Anju P Kunjadiya

Affiliations

Soon will be listed here.

Abstract

Lactational mastitis, a common condition affecting nursing mothers, is characterized by mammary gland inflammation during lactation. This inflammatory response typically occurs due to bacterial infection. The discomfort and pain associated with lactational mastitis can significantly impact a mother's ability to breastfeed comfortably and may lead to the cessation of breastfeeding altogether if left untreated. Antibiotics are commonly prescribed to target the bacteria causing the infection and alleviate symptoms, aiming to treat the infection. Nevertheless, a notable worry linked to antibiotic use is the emergence of antibiotic resistance, compounded by the possible persistence of antibiotics in milk. Additionally, lactational mastitis is characterized by its polymicrobial nature. In this study, bacteria were isolated from infected breast milk samples and whole-genome sequencing was performed on eleven isolates to accurately identify the bacteria and assess their antibiotic resistance profiles. Using Galaxy tools and the ResFinder database, we identified Bacillus paraanthracis, Bacillus altitudinis, Staphylococcus aureus, Bacillus cereus, Escherichia coli, Alcaligenes faecalis, and Bacillus licheniformis, along with antibiotic-resistant genes like fosB1, cat86, erm (D), blaZ, and mdf (A). ABRicate aided in antimicrobial resistance (AMR) gene analysis, and CARD visualized their distribution. Our study demonstrates that the severity of infection is directly proportional to an increase in somatic cell count (SCC). This research sheds light on microbial diversity in lactational mastitis milk and provides crucial insights into antibiotic-resistance genes. Utilizing bioinformatics tools, such as those employed in this study, can inform the design of effective treatment strategies for lactational mastitis infections.

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