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Escherichia coli (E. coli) is a group of bacteria that cause infections in the gastrointestinal (GI) tract and urinary tract (UTIs). The rise in antimicrobial resistance (AMR) due to antibioticresistant genes (ARGs) linked to E. coli strains that cause UTIs poses a significant threat. Identifying ARGs and resistant strains is crucial for effective treatment. To identify ARGs and classify resistant strains in E. coli utilizing gene expression (GE) data with advanced computational techniques such as feature engineering and transfer learning (TL). In TL, knowledge acquired by the baseline model is transferred to the target domain (BI-LSTMGRU). ARGai 2.0 utilizes the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling the GE dataset to evaluate the effectiveness of the proposed TL framework. Our proposed ARGai 2.0 model achieved a higher classification accuracy of 14% compared to 1D CNN and 11% compared to BI-LSTM-GRU individually. The analysis revealed that genes associated with the nitrate reductase operon (narU, narV, narW, narY, and narZ) exhibit high connectivity and interaction scores, indicating their central role in nitrate metabolism. This aligns with the high enrichment FDR of 3.07E-10 and fold enrichment of 229.28 for pathways related to nitrate reductase complex and nitrite transmembrane transporter activity. ARGai 2.0 successfully detected the significant genes responsible for antibiotic resistance and classified the resistant strains. The gene network analysis highlights the central role of nitrate metabolism genes, while peripheral genes like ansP and yncG are involved in more specialized functions. [ABSTRACT FROM AUTHOR]

Polymyxin' E' (Colistin) is considered the last resort therapy against Multidrug resistance (MDR) bacteria, mainly Klebsiella peumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, and Escherichia coli and play a critical role in causing life-threatening infection, and their prevalence is increasing as a big concern globally. Apart from immunological adaptation, chromosomal mutations and plasmid-mediated genes are mostly associated with this resistance at the molecular level. Therefore, the current review extensively focused on Colistin as a drug in 21st-century antibiotics, the activities spectrum with diverse resistance mechanisms of bacteria against Colistin, and emerging approaches of Colistin from discovery to tackling MDR. In the study, we got to know about the challenges and new developments with old weapons like phage therapy as well as new approaches like Phage display and drug repurposing, in addition to the chromosomal and plasmid-mediated genes that play a role in antimicrobial resistance (AMR). The present study would provide insight into the prognostic aspect of combating MDR. [ABSTRACT FROM AUTHOR]

Background: There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data., Methodology: The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models., Results: The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 ( p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR., Conclusions: aiGeneR successfully detected the E. coli genes validating our four hypotheses., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). Published by IMR Press.)