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

CD4+ lymphocyte adenosine triphosphate determination in sepsis: a cohort study

INTRODUCTION: Patients suffering from sepsis are currently classified on a clinical basis (i.e., sepsis, severe sepsis, septic shock); however, this clinical classification may not accurately reflect the overall immune status of an individual patient. Our objective was to describe a cohort of patients with sepsis in terms of their measured immune status. METHODS: Fifty-two patients with sepsis (n = 13), severe sepsis (n = 21), or septic shock (n = 18) were studied. The immune status was determined by measuring the CD4+ lymphocyte adenosine triphosphate (ATP) content after mitogen stimulation in whole blood. RESULTS: The measured CD4+ lymphocyte ATP content at the time of ICU admission did not differ among the various groups defined by the sepsis classification system (sepsis = 454 ± 79 ng/ml; severe sepsis = 359 ± 54 ng/ml; septic shock = 371 ± 53 ng/ml; P = 0.44). Furthermore, survivors of sepsis had a significantly higher CD4+ lymphocyte ATP content at the time of ICU admission than did nonsurvivors of sepsis (431 ± 41 ng/mL vs. 266 ± 53 ng/mL, respectively; P = 0.04). CONCLUSIONS: The sepsis classification system that is currently used is not representative of the individual immune status as determined by measuring the CD4+ lymphocyte ATP content. Moreover, a lower CD4+ ATP content at the time of ICU admission is associated with a worse clinical outcome in those suffering from sepsis

Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel ‘cannibalistic’ elimination algorithm (“Hungry, Hungry SNPos”) that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n=3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWA

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.

BACKGROUND: Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. RESULTS: We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). CONCLUSION: Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications

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

Methylation in mycobacterium tuberculosis is lineage specific with associated mutations present globally

DNA methylation is an epigenetic modification of the genome involved in regulating crucial cellular processes, including transcription and chromosome stability. Advances in PacBio sequencing technologies can be used to robustly reveal methylation sites. The methylome of the Mycobacterium tuberculosis complex is poorly understood but may be involved in virulence, hypoxic survival and the emergence of drug resistance. In the most extensive study to date, we characterise the methylome across the 4 major lineages of M. tuberculosis and 2 lineages of M. africanum, the leading causes of tuberculosis disease in humans. We reveal lineage-specific methylated motifs and strain-specific mutations that are abundant globally and likely to explain loss of function in the respective methyltransferases. Our work provides a set of sixteen new complete reference genomes for the Mycobacterium tuberculosis complex, including complete lineage 5 genomes. Insights into lineage-specific methylomes will further elucidate underlying biological mechanisms and other important phenotypes of the epi-genom

Combining structure and genomics to understand antimicrobial resistance.

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR

Genomic epidemiology of carbapenemase producing Klebsiella pneumoniae strains at a northern Portuguese hospital enables the detection of a misidentified Klebsiella variicola KPC-3 producing strain

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).The evolutionary epidemiology, resistome, virulome and mobilome of thirty-one multidrug resistant Klebsiella pneumoniae clinical isolates from the northern Vila Real region of Portugal were characterized using whole-genome sequencing and bioinformatic analysis. The genomic population structure was dominated by two main sequence types (STs): ST147 (n = 17; 54.8%) and ST15 (n = 6; 19.4%) comprising four distinct genomic clusters. Two main carbapenemase coding genes were detected (blaKPC-3 and blaOXA-48) along with additional extended-spectrum β-lactamase coding loci (blaCTX-M-15, blaSHV-12, blaSHV-27, and blaSHV-187). Moreover, whole genome sequencing enabled the identification of one Klebsiella variicola KPC-3 producer isolate previously misidentified as K. pneumoniae, which in addition to the blaKPC-3 carbapenemase gene, bore the chromosomal broad spectrum β-lactamase blaLEN-2 coding gene, oqxAB and fosA resistance loci. The blaKPC-3 genes were located in a Tn4401b transposon (K. variicolan = 1; K. pneumoniaen = 2) and Tn4401d isoform (K. pneumoniaen = 28). Overall, our work describes the first report of a blaKPC-3 producing K. variicola, as well as the detection of this species during infection control measures in surveillance cultures from infected patients. It also highlights the importance of additional control measures to overcome the clonal dissemination of carbapenemase producing clones.This work was supported in part by UID/DTP/04138/2019 and UIDB/04033/2020 from Fundação para a Ciência e Tecnologia (FCT), Portugal.info:eu-repo/semantics/publishedVersio

COVID-profiler: a webserver for the analysis of SARS-CoV-2 sequencing data.

BACKGROUND: SARS-CoV-2 virus sequencing has been applied to track the COVID-19 pandemic spread and assist the development of PCR-based diagnostics, serological assays, and vaccines. With sequencing becoming routine globally, bioinformatic tools are needed to assist in the robust processing of resulting genomic data. RESULTS: We developed a web-based bioinformatic pipeline ("COVID-Profiler") that inputs raw or assembled sequencing data, displays raw alignments for quality control, annotates mutations found and performs phylogenetic analysis. The pipeline software can be applied to other (re-) emerging pathogens. CONCLUSIONS: The webserver is available at http://genomics.lshtm.ac.uk/ . The source code is available at https://github.com/jodyphelan/covid-profiler