Browsing by Author "Warren, Robin M"
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- ItemOpen AccessA landscape of genomic alterations at the root of a near-untreatable tuberculosis epidemic(2020-02-21) Klopper, Marisa; Heupink, Tim H; Hill-Cawthorne, Grant; Streicher, Elizabeth M; Dippenaar, Anzaan; de Vos, Margaretha; Abdallah, Abdallah M; Limberis, Jason; Merker, Matthias; Burns, Scott; Niemann, Stefan; Dheda, Keertan; Posey, James; Pain, Arnab; Warren, Robin MAbstract Background Atypical Beijing genotype Mycobacterium tuberculosis strains are widespread in South Africa and have acquired resistance to up to 13 drugs on multiple occasions. It is puzzling that these strains have retained fitness and transmissibility despite the potential fitness cost associated with drug resistance mutations. Methods We conducted Illumina sequencing of 211 Beijing genotype M. tuberculosis isolates to facilitate the detection of genomic features that may promote acquisition of drug resistance and restore fitness in highly resistant atypical Beijing forms. Phylogenetic and comparative genomic analysis was done to determine changes that are unique to the resistant strains that also transmit well. Minimum inhibitory concentration (MIC) determination for streptomycin and bedaquiline was done for a limited number of isolates to demonstrate a difference in MIC between isolates with and without certain variants. Results Phylogenetic analysis confirmed that two clades of atypical Beijing strains have independently developed resistance to virtually all the potent drugs included in standard (pre-bedaquiline) drug-resistant TB treatment regimens. We show that undetected drug resistance in a progenitor strain was likely instrumental in this resistance acquisition. In this cohort, ethionamide (ethA A381P) resistance would be missed in first-line drug-susceptible isolates, and streptomycin (gidB L79S) resistance may be missed due to an MIC close to the critical concentration. Subsequent inadequate treatment historically led to amplification of resistance and facilitated spread of the strains. Bedaquiline resistance was found in a small number of isolates, despite lack of exposure to the drug. The highly resistant clades also carry inhA promoter mutations, which arose after ethA and katG mutations. In these isolates, inhA promoter mutations do not alter drug resistance, suggesting a possible alternative role. Conclusion The presence of the ethA mutation in otherwise susceptible isolates from ethionamide-naïve patients demonstrates that known exposure is not an adequate indicator of drug susceptibility. Similarly, it is demonstrated that bedaquiline resistance can occur without exposure to the drug. Inappropriate treatment regimens, due to missed resistance, leads to amplification of resistance, and transmission. We put these results into the context of current WHO treatment regimens, underscoring the risks of treatment without knowledge of the full drug resistance profile.
- ItemOpen AccessA landscape of genomic alterations at the root of a near-untreatable tuberculosis epidemic(2020-02-04) Klopper, Marisa; Heupink, Tim H; Hill-Cawthorne, Grant; Streicher, Elizabeth M; Dippenaar, Anzaan; de Vos, Margaretha; Abdallah, Abdallah M; Limberis, Jason; Merker, Matthias; Burns, Scott; Niemann, Stefan; Dheda, Keertan; Posey, James; Pain, Arnab; Warren, Robin MAbstract Background Atypical Beijing genotype Mycobacterium tuberculosis strains are widespread in South Africa and have acquired resistance to up to 13 drugs on multiple occasions. It is puzzling that these strains have retained fitness and transmissibility despite the potential fitness cost associated with drug resistance mutations. Methods We conducted Illumina sequencing of 211 Beijing genotype M. tuberculosis isolates to facilitate the detection of genomic features that may promote acquisition of drug resistance and restore fitness in highly resistant atypical Beijing forms. Phylogenetic and comparative genomic analysis was done to determine changes that are unique to the resistant strains that also transmit well. Minimum inhibitory concentration (MIC) determination for streptomycin and bedaquiline was done for a limited number of isolates to demonstrate a difference in MIC between isolates with and without certain variants. Results Phylogenetic analysis confirmed that two clades of atypical Beijing strains have independently developed resistance to virtually all the potent drugs included in standard (pre-bedaquiline) drug-resistant TB treatment regimens. We show that undetected drug resistance in a progenitor strain was likely instrumental in this resistance acquisition. In this cohort, ethionamide (ethA A381P) resistance would be missed in first-line drug-susceptible isolates, and streptomycin (gidB L79S) resistance may be missed due to an MIC close to the critical concentration. Subsequent inadequate treatment historically led to amplification of resistance and facilitated spread of the strains. Bedaquiline resistance was found in a small number of isolates, despite lack of exposure to the drug. The highly resistant clades also carry inhA promoter mutations, which arose after ethA and katG mutations. In these isolates, inhA promoter mutations do not alter drug resistance, suggesting a possible alternative role. Conclusion The presence of the ethA mutation in otherwise susceptible isolates from ethionamide-naïve patients demonstrates that known exposure is not an adequate indicator of drug susceptibility. Similarly, it is demonstrated that bedaquiline resistance can occur without exposure to the drug. Inappropriate treatment regimens, due to missed resistance, leads to amplification of resistance, and transmission. We put these results into the context of current WHO treatment regimens, underscoring the risks of treatment without knowledge of the full drug resistance profile.
- ItemOpen AccessIdentifying nucleic acid-associated proteins in Mycobacterium smegmatis by mass spectrometry-based proteomics(2020-03-23) Kriel, Nastassja L; Heunis, Tiaan; Sampson, Samantha L; Gey van Pittius, Nico C; Williams, Monique J; Warren, Robin MAbstract Background Transcriptional responses required to maintain cellular homeostasis or to adapt to environmental stress, is in part mediated by several nucleic-acid associated proteins. In this study, we sought to establish an affinity purification-mass spectrometry (AP-MS) approach that would enable the collective identification of nucleic acid-associated proteins in mycobacteria. We hypothesized that targeting the RNA polymerase complex through affinity purification would allow for the identification of RNA- and DNA-associated proteins that not only maintain the bacterial chromosome but also enable transcription and translation. Results AP-MS analysis of the RNA polymerase β-subunit cross-linked to nucleic acids identified 275 putative nucleic acid-associated proteins in the model organism Mycobacterium smegmatis under standard culturing conditions. The AP-MS approach successfully identified proteins that are known to make up the RNA polymerase complex, as well as several other known RNA polymerase complex-associated proteins such as a DNA polymerase, sigma factors, transcriptional regulators, and helicases. Gene ontology enrichment analysis of the identified proteins revealed that this approach selected for proteins with GO terms associated with nucleic acids and cellular metabolism. Importantly, we identified several proteins of unknown function not previously known to be associated with nucleic acids. Validation of several candidate nucleic acid-associated proteins demonstrated for the first time DNA association of ectopically expressed MSMEG_1060, MSMEG_2695 and MSMEG_4306 through affinity purification. Conclusions Effective identification of nucleic acid-associated proteins, which make up the RNA polymerase complex as well as other DNA- and RNA-associated proteins, was facilitated by affinity purification of the RNA polymerase β-subunit in M. smegmatis. The successful identification of several transcriptional regulators suggest that our approach could be sensitive enough to investigate the nucleic acid-associated proteins that maintain cellular functions and mediate transcriptional and translational change in response to environmental stress.
- ItemOpen AccessA treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis(2022-03-02) Verboven, Lennert; Calders, Toon; Callens, Steven; Black, John; Maartens, Gary; Dooley, Kelly E; Potgieter, Samantha; Warren, Robin M; Laukens, Kris; Van Rie, AnneliesBackground Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.