Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data
BackgroundEarly detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-m...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-05-01
|
| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fmicb.2020.01013/full |
| _version_ | 1818576716923142144 |
|---|---|
| author | Pieter-Jan Van Camp Pieter-Jan Van Camp David B. Haslam David B. Haslam Aleksey Porollo Aleksey Porollo Aleksey Porollo |
| author_facet | Pieter-Jan Van Camp Pieter-Jan Van Camp David B. Haslam David B. Haslam Aleksey Porollo Aleksey Porollo Aleksey Porollo |
| author_sort | Pieter-Jan Van Camp |
| collection | DOAJ |
| description | BackgroundEarly detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms.Methods and FindingsWe have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets.ConclusionWhole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/. |
| first_indexed | 2024-12-16T06:18:27Z |
| format | Article |
| id | doaj.art-c1ff0c4102b34ad2b23a1f661edc2b69 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-12-16T06:18:27Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-c1ff0c4102b34ad2b23a1f661edc2b692022-12-21T22:41:13ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2020-05-011110.3389/fmicb.2020.01013530987Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing DataPieter-Jan Van Camp0Pieter-Jan Van Camp1David B. Haslam2David B. Haslam3Aleksey Porollo4Aleksey Porollo5Aleksey Porollo6Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, United StatesDivision of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDivision of Infectious Diseases, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDepartment of Pediatrics, University of Cincinnati, Cincinnati, OH, United StatesDivision of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesDepartment of Pediatrics, University of Cincinnati, Cincinnati, OH, United StatesCenter for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United StatesBackgroundEarly detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms.Methods and FindingsWe have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets.ConclusionWhole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.https://www.frontiersin.org/article/10.3389/fmicb.2020.01013/fullantimicrobial resistanceantibiotic resistancewhole-genome sequencingmachine learningpredictiongenotype-phenotype relationship |
| spellingShingle | Pieter-Jan Van Camp Pieter-Jan Van Camp David B. Haslam David B. Haslam Aleksey Porollo Aleksey Porollo Aleksey Porollo Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data Frontiers in Microbiology antimicrobial resistance antibiotic resistance whole-genome sequencing machine learning prediction genotype-phenotype relationship |
| title | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data |
| title_full | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data |
| title_fullStr | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data |
| title_full_unstemmed | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data |
| title_short | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data |
| title_sort | prediction of antimicrobial resistance in gram negative bacteria from whole genome sequencing data |
| topic | antimicrobial resistance antibiotic resistance whole-genome sequencing machine learning prediction genotype-phenotype relationship |
| url | https://www.frontiersin.org/article/10.3389/fmicb.2020.01013/full |
| work_keys_str_mv | AT pieterjanvancamp predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata AT pieterjanvancamp predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata AT davidbhaslam predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata AT davidbhaslam predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata AT alekseyporollo predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata AT alekseyporollo predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata AT alekseyporollo predictionofantimicrobialresistanceingramnegativebacteriafromwholegenomesequencingdata |