Biology subjects

Wilcox, R.

Publications and source records attributed to Wilcox, R..

3 recordsLinked to original sources

bioRxiv2026

TLR5 drives metabolic dysfunction-associated steatohepatitis through lipid- and flagellin-induced hepatocyte injury signalling

Liver fibrosis is a strong predictor of clinical outcomes in metabolic dysfunction-associated steatohepatitis (MASH). Fibrosis is a consequence of persistent liver cell injury and inflammation in which Toll-like receptors (TLRs) play a key initiating role. Here we test the hypothesis that TLR5 is involved in the development of MASH and fibrosis using a combination of clinical data from multiple independent patient cohorts, single cell liver transcriptomics and human in vitro and ex vivo models. Hepatic TLR5 expression, but not TLR2 or TLR4, is associated with liver fibrosis and mortality. Plasma levels of TLR5s cognate ligand flagellin are increased in MASH with advanced fibrosis and fall with liver disease improvement. Mechanistically, we identify two parallel TLR5-mediated routes to hepatocyte injury: one elicited by flagellin and the other indirectly by lipid injury. Furthermore, hepatocyte TLR5 inhibition abrogates paracrine activation of hepatic stellate cells to suppress collagen production. This is also seen ex vivo in patient-derived precision-cut liver slices where TLR5 inhibition significantly reduces lipid-induced collagen deposition. These findings reveal a new role for TLR5 signalling, specifically in the development of advanced MASH fibrosis and may offer a novel disease-specific therapeutic approach.

molecular biology↗

bioRxiv2021

Robust linear modelling in ElectroEncephaloGraphy can be obtained using single weights reflecting each single trials' dynamics

Being able to remove or weigh down the influence of outlier data is desirable for any statistical models. While Magnetic and ElectroEncephaloGraphic (MEEG) data are often averaged across trials per condition, it is becoming common practice to use information from all trials to build statistical linear models. Individual trials can, however, have considerable weight and thus bias inferential results (effect sizes as well as thresholded t/F/p maps). Here, rather than looking for univariate outliers, defined independently at each measurement point, we apply the principal component projection (PCP) method at each channel, deriving a single weight per trial at each channel independently. Using both synthetic data and open EEG data, we show (1) that PCP is efficient at detecting a large variety of outlying trials; (2) how PCP-based weights can be implemented in the context of the general linear model (GLM) with accurate control of type 1 family-wise error rate; and (3) that our PCP-based Weighted Least Square (WLS) approach increases the statistical power of group analyses as well as a much slower Iterative Reweighted Least Squares (IRLS), although the weighting scheme is markedly different. Together, our results show that WLS based on PCP weights derived from whole trial profiles is an efficient method to weigh down the influence of outlier EEG data in linear models. Data availabilityall data used are publicly available (CC0), all code (simulations and data analyzes) is also available online in the LIMO MEEG GitHub repository (MIT license).

neuroscience↗

bioRxiv2018

Temporal and spatial variation in distribution of fish environmental DNA in England’s largest lake

Environmental DNA offers great potential as a biodiversity monitoring tool. Previous work has demonstrated that eDNA metabarcoding provides reliable information for lake fish monitoring, but important questions remain about temporal and spatial repeatability, which is critical for understanding the ecology of eDNA and developing effective sampling strategies. Here, we carried out comprehensive spatial sampling of Englands largest lake, Windermere, during summer and winter to 1) examine repeatability of the method, 2) compare eDNA results with contemporary gill-net survey data, 3) test the hypothesis of greater spatial structure of eDNA in summer compared to winter due to differences in water mixing between seasons, and 4) compare the effectiveness of shore and offshore sampling for species detection. We find broad consistency between results from three sampling events in terms of species detection and abundance, with eDNA detecting more species than established methods and being significantly correlated to rank abundance determined by long-term data. As predicted, spatial structure was much greater in the summer, reflecting less mixing of eDNA than in the winter. For example Arctic charr, a deep-water species, was only detected in deep, mid-lake samples in the summer, while littoral or benthic species such as minnow and stickleback were more frequently detected in shore samples. By contrast in winter, the eDNA of these species was more uniformly distributed. This has important implications for design of sampling campaigns, for example, deep-water species could be missed and littoral/benthic species overrepresented by focusing exclusively on shoreline samples collected in the summer.

ecology↗