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Probiotics reduce self-reported symptoms of upper respiratory tract infection in overweight and obese adults: should we be considering probiotics during viral pandemics?
Mullish, BH, Marchesi, JR, McDonald, JAK, Pass, DA, Masetti, G, Michael, DR, Plummer, S, Jack, AA, Davies, TS, Hughes, TR, et al
Gut microbes. 2021;(1):1-9
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Abstract
Gut microbiome manipulation to alter the gut-lung axis may potentially protect humans against respiratory infections, and clinical trials of probiotics show promise in this regard in healthy adults and children. However, comparable studies are lacking in overweight/obese people, who have increased risks in particular of viral upper respiratory tract infections (URTI). This Addendum further analyses our recent placebo-controlled trial of probiotics in overweight/obese people (focused initially on weight loss) to investigate the impact of probiotics upon the occurrence of URTI symptoms. As well as undergoing loss of weight and improvement in certain metabolic parameters, study participants taking probiotics experienced a 27% reduction in URTI symptoms versus control, with those ≥45 years or BMI ≥30 kg/m2 experiencing greater reductions. This symptom reduction is apparent within 2 weeks of probiotic use. Gut microbiome diversity remained stable throughout the study in probiotic-treated participants. Our data provide support for further trials to assess the potential role of probiotics in preventing viral URTI (and possibly also COVID-19), particularly in overweight/obese people.
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A randomised controlled study shows supplementation of overweight and obese adults with lactobacilli and bifidobacteria reduces bodyweight and improves well-being.
Michael, DR, Jack, AA, Masetti, G, Davies, TS, Loxley, KE, Kerry-Smith, J, Plummer, JF, Marchesi, JR, Mullish, BH, McDonald, JAK, et al
Scientific reports. 2020;(1):4183
Abstract
In an exploratory, block-randomised, parallel, double-blind, single-centre, placebo-controlled superiority study (ISRCTN12562026, funded by Cultech Ltd), 220 Bulgarian participants (30 to 65 years old) with BMI 25-34.9 kg/m2 received Lab4P probiotic (50 billion/day) or a matched placebo for 6 months. Participants maintained their normal diet and lifestyle. Primary outcomes were changes in body weight, BMI, waist circumference (WC), waist-to-height ratio (WtHR), blood pressure and plasma lipids. Secondary outcomes were changes in plasma C-reactive protein (CRP), the diversity of the faecal microbiota, quality of life (QoL) assessments and the incidence of upper respiratory tract infection (URTI). Significant between group decreases in body weight (1.3 kg, p < 0.0001), BMI (0.045 kg/m2, p < 0.0001), WC (0.94 cm, p < 0.0001) and WtHR (0.006, p < 0.0001) were in favour of the probiotic. Stratification identified greater body weight reductions in overweight subjects (1.88%, p < 0.0001) and in females (1.62%, p = 0.0005). Greatest weight losses were among probiotic hypercholesterolaemic participants (-2.5%, p < 0.0001) alongside a significant between group reduction in small dense LDL-cholesterol (0.2 mmol/L, p = 0.0241). Improvements in QoL and the incidence rate ratio of URTI (0.60, p < 0.0001) were recorded for the probiotic group. No adverse events were recorded. Six months supplementation with Lab4P probiotic resulted in significant weight reduction and improved small dense low-density lipoprotein-cholesterol (sdLDL-C) profiles, QoL and URTI incidence outcomes in overweight/obese individuals.
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The Human Transcription Factors.
Lambert, SA, Jolma, A, Campitelli, LF, Das, PK, Yin, Y, Albu, M, Chen, X, Taipale, J, Hughes, TR, Weirauch, MT
Cell. 2018;(4):650-665
Abstract
Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.
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Using expression profiling data to identify human microRNA targets.
Huang, JC, Babak, T, Corson, TW, Chua, G, Khan, S, Gallie, BL, Hughes, TR, Blencowe, BJ, Frey, BJ, Morris, QD
Nature methods. 2007;(12):1045-9
Abstract
We demonstrate that paired expression profiles of microRNAs (miRNAs) and mRNAs can be used to identify functional miRNA-target relationships with high precision. We used a Bayesian data analysis algorithm, GenMiR++, to identify a network of 1,597 high-confidence target predictions for 104 human miRNAs, which was supported by RNA expression data across 88 tissues and cell types, sequence complementarity and comparative genomics data. We experimentally verified our predictions by investigating the result of let-7b downregulation in retinoblastoma using quantitative reverse transcriptase (RT)-PCR and microarray profiling: some of our verified let-7b targets include CDC25A and BCL7A. Compared to sequence-based predictions, our high-scoring GenMiR++ predictions had much more consistent Gene Ontology annotations and were more accurate predictors of which mRNA levels respond to changes in let-7b levels.
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RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors.
Chen, X, Hughes, TR, Morris, Q
Bioinformatics (Oxford, England). 2007;(13):i72-9
Abstract
MOTIVATION The sequence specificity of DNA-binding proteins is typically represented as a position weight matrix in which each base position contributes independently to relative affinity. Assessment of the accuracy and broad applicability of this representation has been limited by the lack of extensive DNA-binding data. However, new microarray techniques, in which preferences for all possible K-mers are measured, enable a broad comparison of both motif representation and methods for motif discovery. Here, we consider the problem of accounting for all of the binding data in such experiments, rather than the highest affinity binding data. We introduce the RankMotif++, an algorithm designed for finding motifs whenever sequences are associated with a semi-quantitative measure of protein-DNA-binding affinity. RankMotif++ learns motif models by maximizing the likelihood of a set of binding preferences under a probabilistic model of how sequence binding affinity translates into binding preference observations. Because RankMotif++ makes few assumptions about the relationship between binding affinity and the semi-quantitative readout, it is applicable to a wide variety of experimental assays of DNA-binding preference. RESULTS By several criteria, RankMotif++ predicts binding affinity better than two widely used motif finding algorithms (MDScan, MatrixREDUCE) or more recently developed algorithms (PREGO, Seed and Wobble), and its performance is comparable to a motif model that separately assigns affinities to 8-mers. Our results validate the PWM model and provide an approximation of the precision and recall that can be expected in a genomic scan. AVAILABILITY RankMotif++ is available upon request. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Functional discovery via a compendium of expression profiles.
Hughes, TR, Marton, MJ, Jones, AR, Roberts, CJ, Stoughton, R, Armour, CD, Bennett, HA, Coffey, E, Dai, H, He, YD, et al
Cell. 2000;(1):109-26
Abstract
Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.