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Motivation Tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC), which has a strain- or lineage-based clonal population structure. The evolution of drug-resistance in the MTBC poses a threat to successful treatment and eradication of TB. Machine learning approaches are being increasingly adopted to predict drug-resistance and characterize underlying mutations from whole genome sequences. However, such approaches may not generalize well in clinical practice due to confounding from the population structure of the MTBC. Results To investigate how population structure affects machine learning prediction, we compared three different approaches to reduce lineage dependency in random forest (RF) models, including stratification, feature selection, and feature weighted models. All RF models achieved moderate-high performance (area under the ROC curve range: 0.60–0.98). First-line drugs had higher performance than second-line drugs, but it varied depending on the lineages in the training dataset. Lineage-specific models generally had higher sensitivity than global models which may be underpinned by strain-specific drug-resistance mutations or sampling effects. The application of feature weights and feature selection approaches reduced lineage dependency in the model and had comparable performance to unweighted RF models. Availability and implementation https://github.com/NinaMercedes/RF_lineages. [ABSTRACT FROM AUTHOR]

Background Carbapenem-resistant Klebsiella pneumoniae (CRKP) strains are of particular concern, especially strains with mobilizable carbapenemase genes such as bla KPC, bla NDM or bla OXA-48, given that carbapenems are usually the last line drugs in the β-lactam class and, resistance to this sub-class is associated with increased mortality and frequently co-occurs with resistance to other antimicrobial classes. Objectives To characterize the genomic diversity and international dissemination of CRKP strains from tertiary care hospitals in Lisbon, Portugal. Methods Twenty CRKP isolates obtained from different patients were subjected to WGS for species confirmation, typing, drug resistance gene detection and phylogenetic reconstruction. Two additional genomic datasets were included for comparative purposes: 26 isolates (ST13, ST17 and ST231) from our collection and 64 internationally available genomic assemblies (ST13). Results By imposing a 21 SNP cut-off on pairwise comparisons we identified two genomic clusters (GCs): ST13/GC1 (n  = 11), all bearing bla KPC-3, and ST17/GC2 (n  = 4) harbouring bla OXA-181 and bla CTX-M-15 genes. The inclusion of the additional datasets allowed the expansion of GC1/ST13/KPC-3 to 23 isolates, all exclusively from Portugal, France and the Netherlands. The phylogenetic tree reinforced the importance of the GC1/KPC-3-producing clones along with their rapid emergence and expansion across these countries. The data obtained suggest that the ST13 branch emerged over a decade ago and only more recently did it underpin a stronger pulse of transmission in the studied population. Conclusions This study identifies an emerging OXA-181/ST17-producing strain in Portugal and highlights the ongoing international dissemination of a KPC-3/ST13-producing clone from Portugal. [ABSTRACT FROM AUTHOR]

With >1 million associated deaths in 2020, human tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis remains one of the deadliest infectious diseases. A plethora of genomic tools and bioinformatics pipelines have become available in recent years to assist the whole genome sequencing of M. tuberculosis. The Oxford Nanopore Technologies (ONT) portable sequencer is a promising platform for cost-effective application in clinics, including personalizing treatment through detection of drug resistance-associated mutations, or in the field, to assist epidemiological and transmission investigations. In this study, we performed a comparison of 10 clinical isolates with DNA sequenced on both long-read ONT and (gold standard) short-read Illumina HiSeq platforms. Our analysis demonstrates the robustness of the ONT variant calling for single nucleotide polymorphisms, despite the high error rate. Moreover, because of improved coverage in repetitive regions where short sequencing reads fail to align accurately, ONT data analysis can incorporate additional regions of the genome usually excluded (e.g. pe / ppe genes). The resulting extra resolution can improve the characterization of transmission clusters and dynamics based on inferring closely related isolates. High concordance in variants in loci associated with drug resistance supports its use for the rapid detection of resistant mutations. Overall, ONT sequencing is a promising tool for TB genomic investigations, particularly to inform clinical and surveillance decision-making to reduce the disease burden. [ABSTRACT FROM AUTHOR]

Motivation Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis , the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researchers and clinicians to use, test or reproduce published models. Results We packaged a number of published and unpublished ML models for predicting AMR of M.tuberculosis into Docker containers. Similarly, the pipelines required for pre-processing genomic data into the formats required by the models were also packaged into separate containers. By following a minimal container I/O standard, we ensured as much interoperability as possible. We also created a command-line application, TB-ML, which can be used to easily combine pre-processing and prediction containers into complete pipelines ready for predicting resistance from novel, raw data with a single command. As long as there is adherence to this minimal standard for the container interface, containers produced by researchers holding new models can likewise be included in these pipelines, making benchmark comparisons of different models simple and facilitating faster uptake in the clinic. Availability and implementation TB-ML contains a simple Docker API written in Python and is available at https://github.com/jodyphelan/tb-ml. Example Docker containers for resistance prediction and corresponding data pre-processing as well as a tutorial on how to create new containers for TB-ML are available at https://tb-ml.github.io/tb-ml-containers/. Contact jody.phelan@lshtm.ac.uk [ABSTRACT FROM AUTHOR]