Genetic risk of obesity as a modifier of associations between neighbourhood environment and body mass index: an observational study of 335 046 UK Biobank participants.

BackgroundThere is growing recognition that recent global increases in obesity are the product of a complex interplay between genetic and environmental factors. However, in gene-environment studies of obesity, 'environment' usually refers to individual behavioural factors that influence energy balance, whereas more upstream environmental factors are overlooked. We examined gene-environment interactions between genetic risk of obesity and two neighbourhood characteristics likely to be associated with obesity (proximity to takeaway/fast-food outlets and availability of physical activity facilities).MethodsWe used data from 335 046 adults aged 40-70 in the UK Biobank cohort to conduct a population-based cross-sectional study of interactions between neighbourhood characteristics and genetic risk of obesity, in relation to body mass index (BMI). Proximity to a fast-food outlet was defined as distance from home address to nearest takeaway/fast-food outlet, and availability of physical activity facilities as the number of formal physical activity facilities within 1 km of home address. Genetic risk of obesity was operationalised by weighted Genetic Risk Scores of 91 or 69 single nucleotide polymorphisms (SNP), and by six individual SNPs considered separately. Multivariable, mixed-effects models with product terms for the gene-environment interactions were estimated.ResultsAfter accounting for likely confounding, the association between proximity to takeaway/fast-food outlets and BMI was stronger among those at increased genetic risk of obesity, with evidence of an interaction with polygenic risk scores (p=0.018 and p=0.028 for 69-SNP and 91-SNP scores, respectively) and in particular with a SNP linked to MC4R (p=0.009), a gene known to regulate food intake. We found very little evidence of gene-environment interaction for the availability of physical activity facilities.ConclusionsIndividuals at an increased genetic risk of obesity may be more sensitive to exposure to the local fast-food environment. Ensuring that neighbourhood residential environments are designed to promote a healthy weight may be particularly important for those with greater genetic susceptibility to obesity

A snapshot of translation in Mycobacterium tuberculosis during exponential growth and nutrient starvation revealed by ribosome profiling.

Mycobacterium tuberculosis, which causes tuberculosis, can undergo prolonged periods of non-replicating persistence in the host. The mechanisms underlying this are not fully understood, but translational regulation is thought to play a role. A large proportion of mRNA transcripts expressed in M. tuberculosis lack canonical bacterial translation initiation signals, but little is known about the implications of this for fine-tuning of translation. Here, we perform ribosome profiling to characterize the translational landscape of M. tuberculosis under conditions of exponential growth and nutrient starvation. Our data reveal robust, widespread translation of non-canonical transcripts and point toward different translation initiation mechanisms compared to canonical Shine-Dalgarno transcripts. During nutrient starvation, patterns of ribosome recruitment vary, suggesting that regulation of translation in this pathogen is more complex than originally thought. Our data represent a rich resource for others seeking to understand translational regulation in bacterial pathogens

Understanding molecular consequences of putative drug resistant mutations in Mycobacterium tuberculosis.

Genomic studies of Mycobacterium tuberculosis bacteria have revealed loci associated with resistance to anti-tuberculosis drugs. However, the molecular consequences of polymorphism within these candidate loci remain poorly understood. To address this, we have used computational tools to quantify the effects of point mutations conferring resistance to three major anti-tuberculosis drugs, isoniazid (n = 189), rifampicin (n = 201) and D-cycloserine (n = 48), within their primary targets, katG, rpoB, and alr. Notably, mild biophysical effects brought about by high incidence mutations were considered more tolerable, while different structural effects brought about by haplotype combinations reflected differences in their functional importance. Additionally, highly destabilising mutations such as alr Y388, highlighted a functional importance of the wildtype residue. Our qualitative analysis enabled us to relate resistance mutations onto a theoretical landscape linking enthalpic changes with phenotype. Such insights will aid the development of new resistance-resistant drugs and, via an integration into predictive tools, in pathogen surveillance

Geographical classification of malaria parasites through applying machine learning to whole genome sequence data

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities

Whole genome sequencing reveals large deletions and other loss of function mutations in Mycobacterium tuberculosis drug resistance genes.

Drug resistance in Mycobacterium tuberculosis, the causative agent of tuberculosis disease, arises from genetic mutations in genes coding for drug-targets or drug-converting enzymes. SNPs linked to drug resistance have been extensively studied and form the basis of molecular diagnostics and sequencing-based resistance profiling. However, alternative forms of functional variation such as large deletions and other loss of function (LOF) mutations have received much less attention, but if incorporated into diagnostics they are likely to improve their predictive performance. Our work aimed to characterize the contribution of LOF mutations found in 42 established drug resistance genes linked to 19 anti-tuberculous drugs across 32689 sequenced clinical isolates. The analysed LOF mutations included large deletions (n=586), frameshifts (n=4764) and premature stop codons (n=826). We found LOF mutations in genes strongly linked to pyrazinamide (pncA), isoniazid (katG), capreomycin (tlyA), streptomycin (e.g. gid) and ethionamide (ethA, mshA) (P<10-5), but also in some loci linked to drugs where relatively less phenotypic data is available [e.g. cycloserine, delaminid, bedaquiline, para-aminosalicylic acid (PAS), and clofazimine]. This study reports that large deletions (median size 1115 bp) account for a significant portion of resistance variants found for PAS (+7.1% of phenotypic resistance percentage explained), pyrazinamide (+3.5%) and streptomycin (+2.6%) drugs, and can be used to improve the prediction of cryptic resistance. Overall, our work highlights the importance of including LOF mutations (e.g. large deletions) in predicting genotypic drug resistance, thereby informing tuberculosis infection control and clinical decision-making

TB-ML-a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis

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/

Feature Weighted Models (FWM) to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.

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 characterise underlying mutations from whole genome sequences. However, such approaches may not generalise 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 (AUC-ROC 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets.

During COVID-19, diagnostic serological tools and vaccines have been developed. To inform control activities in a post-vaccine surveillance setting, we have developed an online "immuno-analytics" resource that combines epitope, sequence, protein and SARS-CoV-2 mutation analysis. SARS-CoV-2 spike and nucleocapsid proteins are both vaccine and serological diagnostic targets. Using the tool, the nucleocapsid protein appears to be a sub-optimal target for use in serological platforms. Spike D614G (and nsp12 L314P) mutations were most frequent (> 86%), whilst spike A222V/L18F have recently increased. Also, Orf3a proteins may be a suitable target for serology. The tool can accessed from: http://genomics.lshtm.ac.uk/immuno (online); https://github.com/dan-ward-bio/COVID-immunoanalytics (source code)

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artifcial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expertlevel performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identifed eight loci associated with delta age (p ≤ 5 × 10−8), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine

Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs

BACKGROUND: Mycobacterium tuberculosis resistance to anti-tuberculosis drugs is a major threat to global public health. Whole genome sequencing (WGS) is rapidly gaining traction as a diagnostic tool for clinical tuberculosis settings. To support this informatically, previous work led to the development of the widely used TBProfiler webtool, which predicts resistance to 14 drugs from WGS data. However, for accurate and rapid high throughput of samples in clinical or epidemiological settings, there is a need for a stand-alone tool and the ability to analyse data across multiple WGS platforms, including Oxford Nanopore MinION. RESULTS: We present a new command line version of the TBProfiler webserver, which includes hetero-resistance calling and will facilitate the batch processing of samples. The TBProfiler database has been expanded to incorporate 178 new markers across 16 anti-tuberculosis drugs. The predictive performance of the mutation library has been assessed using > 17,000 clinical isolates with WGS and laboratory-based drug susceptibility testing (DST) data. An integrated MinION analysis pipeline was assessed by performing WGS on 34 replicates across 3 multi-drug resistant isolates with known resistance mutations. TBProfiler accuracy varied by individual drug. Assuming DST as the gold standard, sensitivities for detecting multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) were 94% (95%CI 93-95%) and 83% (95%CI 79-87%) with specificities of 98% (95%CI 98-99%) and 96% (95%CI 95-97%) respectively. Using MinION data, only one resistance mutation was missed by TBProfiler, involving an insertion in the tlyA gene coding for capreomycin resistance. When compared to alternative platforms (e.g. Mykrobe predictor TB, the CRyPTIC library), TBProfiler demonstrated superior predictive performance across first- and second-line drugs. CONCLUSIONS: The new version of TBProfiler can rapidly and accurately predict anti-TB drug resistance profiles across large numbers of samples with WGS data. The computing architecture allows for the ability to modify the core bioinformatic pipelines and outputs, including the analysis of WGS data sourced from portable technologies. TBProfiler has the potential to be integrated into the point of care and WGS diagnostic environments, including in resource-poor settings