Biology subjects

Lepage, M.

Publications and source records attributed to Lepage, M..

7 recordsLinked to original sources

bioRxiv2026

TMPRSS6 Cleavage of β-Klotho Modulates FGF19 Signaling

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder worldwide, yet therapeutic options remain limited. TMPRSS6, a liver serine protease best known for its role in iron homeostasis, has recently emerged as a potential therapeutic target for MASLD. However, the molecular mechanisms linking TMPRSS6 to hepatic lipid metabolism remain incompletely understood. To identify novel TMPRSS6 substrates, we performed extracellular proteomic analyses of TMPRSS6-overexpressing cells. Among the proteins identified, {beta}-klotho (KLB), a co-receptor required for FGF19 and FGF21 signaling, emerged as a compelling candidate substrate. We demonstrate that TMPRSS6 interacts with KLB and promotes its proteolytic shedding in a catalytic activity-dependent manner. Functionally, TMPRSS6 reduced full-length KLB abundance at the cell surface and attenuated FGF19-dependent FGFR4 signaling in a heterologous expression system. Together, these findings identify KLB as a novel functional substrate of TMPRSS6, providing a mechanistic framework through which this protease may influence hepatic lipid metabolism. These results provide a rationale for investigating the regulation of KLB and other candidate substrates by TMPRSS6 in physiological models and further support its evaluation as a therapeutic target for MASLD.

biochemistry↗

bioRxiv2024

Development of ketobenzothiazole-based peptidomimetic TMPRSS13 inhibitors with low nanomolar potency

TMPRSS13, a member of the Type II Transmembrane Serine Proteases (TTSP) family, is involved in cancer progression and in cell entry of respiratory viruses. To date, no inhibitors have been specifically developed toward this protease. In this study, a chemical library of 65 ketobenzothiazole-based peptidomimetic molecules was screened against a proteolytically active form of recombinant TMPRSS13 to identify novel inhibitors. Following an initial round of screening, subsequent synthesis of additional derivatives supported by molecular modelling, uncovered important molecular determinants involved in TMPRSS13 inhibition. One inhibitor, N-0430, achieved low nanomolar affinity towards TMPRSS13 activity in a cellular context. Using a SARS-CoV-2 pseudovirus cell entry model, we further show the ability of N-0430 to block TMPRSS13-dependent entry of the pseudovirus. The identified peptidomimetic inhibitors and the molecular insights of their potency gained from this study will aid in the development of specific TMPRSS13 inhibitors.

pharmacology and toxicology↗

bioRxiv2024

Evaluating permutation-based inference for partial least squares analysis of neuroimaging data

Partial least squares (PLS) is actively leveraged in neuroimaging work, typically to map latent variables (LVs) representing brain-behaviour associations. LVs are considered statistically significant if they tend to capture more covariance than LVs derived from permuted data, with a Procrustes rotation applied to map each set of permuted LVs to the space defined by the originals, creating an "apples to apples" comparison. Yet, it has not been established whether applying the rotation makes the permutation test more sensitive to whether true LVs are present in a dataset, and it is unclear if significance alone is sufficient to fully characterize a PLS decomposition, given that complementary metrics like strength and split-half stability may offer non-redundant information about the LVs. Accordingly, we performed PLS analyses across a range of simulated datasets with known latent effects, observing that the Procrustes rotation systematically weakened the null distributions for the first LV. By extension, the first LV was nearly always significant, regardless of whether the effect was weak, undersampled, noisy, or simulated at all. But, if no rotation was applied, all possible LVs tended to be significant as we increased the sample size of UK Biobank datasets. Meanwhile, LV strength and stability metrics accurately tracked our confidence that effects were present in simulated data, and allowed for a more nuanced assessment of which LVs may be relevant in the UK Biobank. We end by presenting a list of considerations for researchers implementing PLS permutation testing, and by discussing promising alternative tests which may alleviate the concerns raised by our findings.

neuroscience↗

bioRxiv2022

Normative Sex Differences in Cognition and Morphometric Brain Connectivity: Evidence from 30,000+ UK Biobank Participants

There is robust evidence for sex differences in domain-specific cognitive performance in the general population, where females typically show an advantage for verbal memory (VM), while males tend to perform better on tasks of spatial memory (SM). Sex differences in brain structure and connectivity are also well-documented and may provide insight into sex differences in cognition. In this study, we examined sex differences in cognition and morphometric brain connectivity of a large healthy sample (N = 31,180) from the UK Biobank dataset. Using T1-weighted magnetic resonance imaging (MRI) scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of morphometric brain connectivity, hypothesizing that sex-related differences in brain network global efficiency, or overall connectivity, would underlie observed cognitive differences. As predicted, females demonstrated better VM performance and males showed an advantage in SM. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males tended to have higher global efficiency, as well as higher regional connectivity (nodal strengths) in both the left and right hemispheres relative to females. Furthermore, higher global efficiency in males was found to mediate observed sex differences in cognition, predicting poorer verbal memory performance, better spatial memory, and slower processing speed in males. These findings contribute to an improved understanding of the way biological sex and differences in cognitive performance are related to morphometric brain connectivity as derived from graph-theoretic methods.

neuroscience↗

bioRxiv2022

High-fructose feeding suppresses cold-stimulated brown adipose tissue glucose uptake in young men independently of changes in thermogenesis and the gut microbiome

Diets rich in added sugars, especially high in fructose, are associated with metabolic diseases such as insulin resistance, and non-alcoholic fatty liver disease. Studies have shown a link between these pathologies and changes in the microbiome and its metabolites. Given the reported associations in animal models between the microbiome and brown or beige adipose tissue (BAT) function, and the alterations in the microbiome induced by high glucose or high fructose diets, we investigated the potential causal link between high glucose or fructose diets and BAT dysfunction in humans. We show that BAT glucose uptake, but not thermogenesis, is impaired by a high fructose but not high glucose diet, in the absence of changes in body mass, the gastrointestinal microbiome, and faecal short-chain fatty acids. We conclude that BAT metabolic dysfunction occurs independently from changes in gut microbiome composition, and earlier than other pathophysiological abnormalities associated with insulin resistance and dyslipidemia during fructose overconsumption in humans.

physiology↗

bioRxiv2021

DeepImageTranslator: a free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis

ObjectivesThe advent of deep-learning has set new standards in an array of image translation applications. At present, the use of these methods often requires computer programming experience. Non-commercial programs with graphical interface usually do not allow users to fully customize their deep-learning pipeline. Therefore, our primary objective is to provide a simple graphical interface that allows students and researchers with no programming experience to easily create, train, and evaluate custom deep-learning models for image translation. We also aimed to test the applicability of our tool (the DeepImageTranslator) in two different tasks: semantic segmentation and noise reduction of CT images. MethodsThe DeepImageTranslator was implemented using the Tkinter library; backend computations were implemented using Pillow, Numpy, OpenCV, Augmentor, Tensorflow, and Keras libraries. Convolutional neural networks (CNNs) were trained using DeepImageTranslator and assessed with three-way cross-validation. The effects of data augmentation, deep-supervision, and sample size on model accuracy were also systematically assessed. ResultsThe DeepImageTranslator a simple tool that allows users to customize all aspects of their deep-learning pipeline, including the CNN, the training optimizer, the loss function, and the types of training image augmentation scheme. We showed that DeepImageTranslator can be used to achieve state-of-the-art accuracy and generalizability in semantic segmentation and noise reduction. Highly accurate 3D segmentation models for body composition can be obtained using training sample sizes as small as 17 images. Therefore, for studies with small datasets, researchers can randomly select a very small subset of images for manual labeling, which can then be used to train a specialized CNN model with DeepImageTranslator to fully automate segmentation of the entire dataset, thereby saving tremendous time and effort. ConclusionsAn open-source deep-learning tool for accurate image translation with a user-friendly graphical interface was presented and evaluated. This standalone software can be downloaded for Windows 10 at: https://sourceforge.net/projects/deepimagetranslator/

bioinformatics↗

bioRxiv2019

Spatial patterning of tissue volume deformation in schizophrenia reflects brain network architecture

BackgroundThere is growing recognition that connectome architecture shapes cortical and sub-cortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown.\n\nMethodsHere we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 {+/-} 12.9 years) and N = 113 controls (64 female, 23.5 {+/-} 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI.\n\nResultsWe find that regional deformation is correlated with the deformation of structurally- and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex.\n\nConclusionsAltogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation.

neuroscience↗