Biology subjects

Essex, M.

Publications and source records attributed to Essex, M..

5 recordsLinked to original sources

bioRxiv2022

Gut microbiota dysbiosis is associated with altered tryptophan metabolism and dysregulated inflammatory response in severe COVID-19

The clinical course of the 2019 coronavirus disease (COVID-19) is variable and to a substantial degree still unpredictable, especially in persons who have neither been vaccinated nor recovered from previous infection. We hypothesized that disease progression and inflammatory responses were associated with alterations in the microbiome and metabolome. To test this, we integrated metagenome, metabolome, cytokine, and transcriptome profiles of longitudinally collected samples from hospitalized COVID-19 patients at the beginning of the pandemic (before vaccines or variants of concern) and non-infected controls, and leveraged detailed clinical information and post-hoc confounder analysis to identify robust within- and cross-omics associations. Severe COVID-19 was directly associated with a depletion of potentially beneficial intestinal microbes mainly belonging to Clostridiales, whereas oropharyngeal microbiota disturbance appeared to be mainly driven by antibiotic use. COVID-19 severity was also associated with enhanced plasma concentrations of kynurenine, and reduced levels of various other tryptophan metabolites, lysophosphatidylcholines, and secondary bile acids. Decreased abundance of Clostridiales potentially mediated the observed reduction in 5-hydroxytryptophan levels. Moreover, altered plasma levels of various tryptophan metabolites and lower abundances of Clostridiales explained significant increases in the production of IL-6, IFN{gamma} and/or TNF. Collectively, our study identifies correlated microbiome and metabolome alterations as a potential contributor to inflammatory dysregulation in severe COVID-19. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=150 HEIGHT=200 SRC="FIGDIR/small/518860v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@afc4edorg.highwire.dtl.DTLVardef@1a9ac2aorg.highwire.dtl.DTLVardef@65fb46org.highwire.dtl.DTLVardef@153ee6a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Evaluation of microbiome association models under realistic and confounded conditions

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance (DA) testing is lacking, and it remains unexplored how these cope with confounding. Previous DA benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations. ResultsHere we develop a simulation framework which implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole-metagenome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies to a much greater extent than in previous benchmarks. With extensively parametrized simulations we benchmark the performance of eighteen DA methods and further evaluate the best ones on confounded simulations. Only linear models, limma, fastANCOM, and the Wilcoxon test properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that post hoc adjustment can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications. ConclusionsFor microbiome association studies tight error control is critical. The unsatisfactory performance of many DA methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

bioinformatics↗

bioRxiv2020

Whole Genome Sequencing-based Characterization of Human Genome Variation and Mutation Burden in Botswana

The study of human genome variations can contribute towards understanding population diversity and the genetic aetiology of health-related traits. We sought to characterise human genomic variations of Botswana in order to assess diversity and elucidate mutation burden in the population using whole genome sequencing. Whole genome sequences of 390 unrelated individuals from Botswana were available for computational analysis. The sequences were mapped to the human reference genome GRCh38. Population joint variant calling was performed using Genome Analysis Tool Kit (GATK) and BCFTools. Variant characterisation was achieved by annotating the variants with a suite of databases in ANNOVAR and snpEFF. The genomic architecture of Botswana was delineated through principal component analysis, structure analysis and FST. We identified a total of 27.7 million unique variants. Variant prioritisation revealed 24 damaging variants with the most damaging variants being ACTRT2 rs3795263, HOXD12 rs200302685, ABCB5 rs111647033, ATP8B4 rs77004004 and ABCC12 rs113496237. We observed admixture of the Khoe-San, Niger-Congo and European ancestries in the population of Botswana, however population substructure was not observed. This exploration of whole genome sequences presents a comprehensive characterisation of human genomic variations in the population of Botswana and their potential in contributing to a deeper understanding of population diversity and health in Africa and the African diaspora.

genomics↗

bioRxiv2020

SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This revealed some biomarkers to be disease-specific, others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

bioinformatics↗

bioRxiv2019

PANDAA-monium: Intentional violations of conventional qPCR design enables rapid, HIV-1 subtype-independent drug resistance SNP detection

Global efforts to ensure that 90% of all HIV-infected people receiving antiretroviral therapy (ART) will be virally suppressed by 2020 could be crippled by increases in acquired and transmitted HIV drug resistance (HIVDR), which challenge ART efficacy. The long-term sustainability of ART treatment programs is contingent on effective HIVDR monitoring yet current Sanger sequencing genotypic resistance tests are inadequate for large-scale implementation in low- and middle-income countries (LMICs). A simple, rapid, affordable HIVDR diagnostic would radically improve the treatment paradigm in LMICs by facilitating informed clinical decision-making upon ART failure. Although point mutation assays can be broadly deployed in this context, the primary challenge arises from extensive sequence variation surrounding targeted drug resistance mutations (DRMs). Here, we systematically and intentionally violate the canonical principles of qPCR design to develop a novel assay, Pan-Degenerate Amplification and Adaptation (PANDAA), that mitigates the impact of DRM-proximal secondary polymorphisms on probe-based qPCR performance to enable subtype-independent, focused resistance genotyping. Using extremely degenerate primers with 3 termini overlapping the probe-binding site, the HIV-1 genome is adapted through site-directed mutagenesis to replace secondary polymorphisms flanking the target DRM during the initial qPCR cycles. We show that PANDAA can quantify key HIV DRMs present at [≥]5% and has diagnostic sensitivity and specificity of 96.9% and 97.5%, respectively, to detect DRMs associated with ART failure. PANDAA is an innovative solution for HIVDR genotyping and is an advancement in qPCR technology that could be applicable to any scenario where target-proximal genetic variability has been a roadblock in diagnostic development.

molecular biology↗