Biology subjects

Aytan-Aktug, D.

Publications and source records attributed to Aytan-Aktug, D..

3 recordsLinked to original sources

bioRxiv2025

Dynamic changes in the plasmidome and resistome in the gastrointestinal tract of chickens

The expansion of intensive poultry farming has led to a substantial increase in antibiotic use, which in turn has promoted the accumulation of antibiotic resistance genes (ARGs). The chicken gut serves as a reservoir for these genes and provides favorable conditions for their horizontal transfer via mobile genetic elements, such as plasmids. Through this process, commensal bacteria can transfer ARGs to pathogens, facilitating their spread and increasing the risk of transmission to humans. In this study, long-read sequencing was used to characterize plasmidome and resistome in 12 fecal samples from three houses of a commercial chicken broiler farm. All chickens received enrofloxacin in the first days of life, with one house additionally treated with sulfamethoxazole/trimethoprim combination. For comparison, metagenomic analysis using short-read sequencing was performed on the same samples. This study revealed the presence of various ARGs associated with resistance to 26 antibiotic classes. Strong genetic association between MOBP-type plasmids and fluoroquinolone resistance was observed within chicken broiler farm. Temporal trends indicated progressive mobilization of these ARGs, suggesting an increasing potential for horizontal gene transfer. While fluoroquinolone resistance expanded over time, diaminopyrimidine resistance remained stable despite the antibiotic treatment. Most ARGs were carried on small plasmids, and complete plasmid reconstructions ranged from 2.6 to 47.6 kb. Despite technical limitations, our findings demonstrate that plasmidome sequencing can enrich metagenomic analysis by enabling the detection of low-abundance plasmid types and providing deeper insights into the dynamic plasmid-mediated dissemination of ARGs in the chicken gut microbiome. ImportanceDespite the crucial role of plasmids in antimicrobial resistance (AMR) dissemination, studies focusing on plasmidome, defined as the complete set of plasmids, remain limited. Combining a metagenomic approach with a focus on plasmids enhances our ability to understand the genetic context and mechanisms underlying AMR transmission. The findings emphasize the importance of targeted plasmid analysis to improve surveillance and risk assessment of AMR transmission in microbial ecosystems.

microbiology↗

bioRxiv2021

PlasmidHostFinder: Prediction of plasmid hosts using random forest

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods, compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids based on the random forest machine learning method. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level, where the models achieved 0.662 and 0.867 Matthews correlation coefficients at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool can be used online through Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). ImportanceAntimicrobial resistance is a global health threat to humans and animals causing high mortality and morbidity, and effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes, which can encode antimicrobial resistance and virulence factors. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detecting the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution, as well as for taking effective counter measures against antimicrobial resistance.

bioinformatics↗

bioRxiv2019

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

2.A possible way to tackle the crisis of antimicrobial resistance development is a strict policy when prescribing antibiotics. Thus, it is important that prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing (NGS), bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes (ARG). We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of any qnrS gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 92% of the test samples MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work goes further than the typical predictions that use machine learning as a black box model concept. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement. 3. Impact statementWhole genome sequencing has become the standard approach to study molecular epidemiology of bacteria. However, the application of WGS in the clinical microbiology laboratory as part of individual patient diagnostics still requires significant steps forward, in particular with respect to prediction of antibiotic susceptibility based on DNA sequence. Whilst the majority of studies of prediction of susceptibility have used a binary outcome (susceptible/resistant), a quantitative prediction of susceptibility, such as the MIC, will allow for earlier detection of trends in increasing resistance as well as the flexibility to follow potential adjustments in definitions of susceptible (wild type) and resistant (non-wild type) categories (breakpoints/ epidemiological cut-off values). 4. Data summaryIn this study, 704 E. coli genomes combined with MIC measurement for ciprofloxacin were analysed (24). Paired-end sequencing was performed on all isolates and the results were stored in FASTQ format. The isolates originated from five countries, Denmark, Italy, USA, UK, and Vietnam. The MIC distribution for these isolates is depicted in Table 1. Out of 704, 266 E. coli genomes had no country metadata available and were used as an independent test set. All data were deposited in the AMR Data Hub (24) which consists of raw sequencing data, ciprofloxacin minimum inhibitory concentrations, and additional metadata such as the origin of the samples. O_TBL View this table: org.highwire.dtl.DTLVardef@5a31d2org.highwire.dtl.DTLVardef@17cfa2eorg.highwire.dtl.DTLVardef@ce3ccorg.highwire.dtl.DTLVardef@3f6721org.highwire.dtl.DTLVardef@8bb604_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTABLE 1C_FLOATNO O_TABLECAPTIONThe collected and used data in the analysis grouped by country and MIC values. C_TABLECAPTION C_TBL Publicly available sequencing data was used from projects PRJEB21131, PRJNA266657, PRJNA292901, PRJNA292904, PRJNA292902, PRJDB7087, PRJEB21880, PRJEB21997, PRJEB14086 and PRJEB16326. Download and analysis scripts are available at https://github.com/patbaa/AMR_ciprofloxacin. iTOL phylogenetic tree is available at https://itol.embl.de/tree/14511722611491391569485969. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗