WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Article Metabolic Responses to Benzoic Acid Stress and Glutamine Transport-Dependent Vulnerabilities in Escherichia Coli Revealed by NMR Metabolomics(Springer, 2026) Yuksektepe, Ecem; Elgin, Emine Sonay; Onat-Tasdelen, Kadriye Aslihan; Chae, Young Kee; Dogu, Eralp; Catav, Sukru Serter; Ozturkel-Kabakas, HaticeBenzoic acid (BA) is a widely used weak organic acid preservative with antimicrobial activity, yet the metabolic basis of its antibacterial action and the determinants of bacterial sensitivity remain incompletely understood. Here we combined growth assays with H-1 NMR metabolomics to characterize BA-induced metabolic responses in Escherichia coli BW25113 and to examine metabolic changes associated with impaired glutamine transport. Wild-type BW25113 and its BA-sensitive isogenic Delta glnP mutant, lacking the membrane-bound glutamine permease of the high-affinity GlnHPQ transport system, were exposed to sublethal BA concentrations. BA slowed growth and significantly altered the levels of 42 metabolites in the wild-type and 38 in Delta glnP, with the mutant showing stronger growth inhibition and reduced BA tolerance. Both strains exhibited metabolic changes consistent with cellular responses to oxidative and acid stress, including alterations in central carbon metabolism, lysine degradation, cysteine and methionine metabolism, pyrimidine metabolism, and one-carbon pool by folate. However, several metabolic responses differed between the two strains. In wild-type cells, BA exposure was associated with changes in glycerolipid metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, lysine biosynthesis, glycine, serine and threonine metabolism, and purine metabolism. In contrast, Delta glnP cells showed distinct alterations in D-amino acid metabolism, arginine biosynthesis, and other carbon fixation pathways. In addition, the mutant displayed substantial baseline differences relative to the wild-type, including altered nucleotide and amino acid pools. Together, these results indicate that both BA exposure and deletion of glnP induce broad metabolic adjustments in Escherichia coli. Loss of glnP is associated with distinct metabolic states and altered responses to BA stress, highlighting the importance of glutamine transport in adaptation to weak organic acid stress.Article Comprehensive Proteomic and Metabolomic Analysis of Novel Substituted Fluoroquinolone Derivatives in Escherichia Coli Isolates(John Wiley and Sons Ltd, 2026) Nigiz Ş.; Kulabaş N.; Türe A.; Kablan S.; Koçak E.; Özkul C.; Küçükgüzel İ.; Koçak, Engin; Nigiz, Şeyma; Kablan, Sevilay Erdoğan; Özkul, Ceren; Kulabaş, Necla; Küçükgüzel, İlkay; Türe, AslıAntimicrobial resistance is one of the most important global problems, and new antibiotic requirements have been emerging as a key point in this issue. In the present work, we focused on the efficiency of two novel promising fluoroquinolone derivatives on resistant Escherichia coli isolates at the molecular level. Their mode of action and adaptation process were evaluated by using proteomics and metabolomics analysis. Proteomics analysis showed that two compounds have an effect mainly on the ribosomal process and energy metabolism. Moreover, we observed compounds that affect various important antimicrobial targets, such as ribosomal subunits, phosphotransacetylase, and chaperone proteins. In metabolomics analysis, we found that compounds altered bacterial metabolism directly. Pathway analysis showed that cofactor biosynthesis and energy metabolism were affected mainly by undertreated groups. Our experiments demonstrated that novel fluoroquinolone derivatives have promising results at the molecular level and results will contribute to further studies. © 2026 John Wiley & Sons Ltd.Article Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tolan, H.K.; Aydın, İ.; Tanyildizi-Kökkülünk, H.; Karakuş, M.; Akkaya, Y.; Kaya, O.; Işman, F.K.Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020–2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among β-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance. © 2025 by the authors.Article Citation - WoS: 8Citation - Scopus: 8Functional Groups Matter: Metabolomics Analysis of <i>escherichia Coli</I> Exposed To Trans-Cinnamic Acid and Its Derivatives Unveils Common and Unique Targets(Springer, 2024) Onat-Tasdelen, Kadriye Aslihan; Ozturkel-Kabakas, Hatice; Yuksektepe, Ecem; Catav, Sukru Serter; Guzel, Gulnur; Col, Bekir; Elgin, Emine SonayPhenolic acids are derivatives of benzoic and cinnamic acids, which possess important biological activities at certain concentrations. Trans-cinnamic acid (t-CA) and its derivatives, such as p-coumaric acid (p-CA) and ferulic acid (FA) have been shown to have antibacterial activity against various Gram-positive and -negative bacteria. However, there is limited information available concerning the antibacterial mode of action of these phenolic acids. In this study, we aimed to ascertain metabolic alterations associated with exposure to t-CA, p-CA, and FA in Escherichia coli BW25113 using a nuclear magnetic resonance (NMR)-based metabolomics approach. The results showed that t-CA, p-CA, and FA treatments led to significant changes (p < 0.05) in the concentration of 42, 55, and 74% of the identified metabolites in E. coli, respectively. Partial least-squares discriminant analysis (PLS-DA) revealed a clear separation between control and phenolic acid groups with regard to metabolic response. Moreover, it was found that FA and p-CA treatment groups were clustered closely together but separated from the t-CA treatment group. Arginine, putrescine, cadaverine, galactose, and sucrose had the greatest impact on group differentiation. Quantitative pathway analysis demonstrated that arginine and proline, pyrimidine, glutathione, and galactose metabolisms, as well as aminoacyl-tRNA and arginine biosyntheses, were markedly affected by all phenolic acids. Finally, the H2O2 content of E. coli cells was significantly increased in response to t-CA and p-CA whereas all phenolic acids caused a dramatic increase in the number of apurinic/apyrimidinic sites. Overall, this study suggests that the metabolic response of E. coli cells to t-CA is relatively different from that to p-CA and FA. However, all phenolic acids had a certain impact on oxidative/antioxidant status, genomic stability, arginine-related pathways, and nucleic acid metabolism.