Deciphering Antibiotic Resistance in Escherichia Coli: A Multifaceted Genomic and Machine Learning Powered Approach

Section: Research Paper
Issue
Vol. 47 No. 2 (2025): Volume 47 Issue 2
Published
Dec 29, 2025
Pages
141-149

Abstract

Background: Antimicrobial resistance (AMR), a dangerous health problem, is driven by excessive and incorrect usages of the antibiotics in both clinical and farming environments. Escherichia coli is an opportunistic bacteria and a sentinel species used in AMR monitoring, and it usually contains a repertoire of acquired resistance genes and chromosomal mutations.


Aim: The objective of the present work was to examine the genomic landscape of antibiotic resistance in a cache of 31 E. coli strains isolated in Iraq, Iran and Turkey and to implement a multifaceted approach based on a combination of genome-wide screening, mutational profiling and machine learning.


Methods: Assemblies of genomes were retrieved in the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), and resistance genes were identified with ResFinder and ABRicate. To discover high impact mutations of major genes such as gyrA, parC, ompF, and acrR, variant calling was used. We augmented phenotypic resistance in ciprofloxacin, ampicillin, and cefotaxime and used the data with the genomic data to ensure Random Forest classifiers were trained and to determine feature importance.


Results: The most common acquired resistance genes included blaTEM-1B (87%), sul1 (77%), aadA1 (71%) and qnrS1 (65%). The frequency of mutations at gyrA S83L and parC S80I sites was more than 70% among isolates, testifying to their role in resistance to fluoroquinolone. The models of machine learning recognized aac(3)-IId, blaTEM-1B, and sul1 as the best predictors of phenotypic resistance.


Conclusions: Interaction occurs between acquired and chromosomal resistance mechanisms in E. coli landscape construction of AMR in the Middle East. Bioinformatics and machine learning would offer solid ground of resistance prediction and surveillance, and increase the need of context-sensitive plans of AMR endeavors.

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Authors

Yilmaz Kaya

Department of Agricultural Biotechnology Faculty of Agriculture Ondokuz Mayis University Samsun, Turkey

Ali Adel Dawood

Department of Anatomy, College of Medicine, University of Mosul , Mosul, Iraq

Identifiers

https://doi.org/10.33899/acmm.v47i2.60167
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1.
Kaya Y, Dawood AA. Deciphering Antibiotic Resistance in Escherichia Coli: A Multifaceted Genomic and Machine Learning Powered Approach. Ann. coll. Med. Mosul [Internet]. 2025 Dec. 29 [cited 2026 Jan. 2];47(2):141-9. Available from: https://mmed.uomosul.edu.iq/index.php/mmed/article/view/60167