Biology subjects

Daigle, B.

Publications and source records attributed to Daigle, B..

4 recordsLinked to original sources

bioRxiv2026

Early life stress exposure alters brain vasculature transcriptomic profiles in areas regulating stress resilience

Early life stress (ELS) events during sensitive postnatal time periods can recalibrate future stress responsiveness and precipitate mental disorders. Neurovascular adaptations can influence cognition, mood, and stress responses. Disruption of blood-brain barrier (BBB) integrity, which is formed by endothelial cells, astrocytes, and pericytes, has been implicated in affective disorders such as depression, which often arise from chronic stress experiences. Despite the BBB undergoing critical maturation stages during development, it remains poorly known how ELS influences brain vascular function, as previously shown for adult stress, and whether it augments BBB vulnerability to subsequent challenges. First, we took advantage of a public two-hit stress RNA-sequencing dataset and filtered for vascular enriched genes in the prefrontal cortex and nucleus accumbens, the two brain regions where BBB integrity is frequently compromised. This analysis revealed BBB-related gene ontology categories modulated by either ELS alone or its combination with adult stress. Then, using a mouse model combining ELS with chronic social defeat stress (CSDS) in adulthood, we found that ELS did not exacerbate CSDS susceptibility; instead, it increased social interactions and the likelihood of a resilient profile in both males and females. Transcriptomic profiling in our cohort further identified distinct sex- and region-specific BBB gene expression patterns associated with ELS and its interaction with CSDS. Additionally, we observed a reduction of corticosterone levels, the primary stress hormone, following CSDS. Altogether, these results indicate that ELS modulates stress responses when facing emotional challenges in adulthood, possibly through long-lasting changes of BBB function via the glucocorticoid system. HighlightsO_LIRNA-seq vascular filtering reveals BBB distinct ontology categories for ELS and AS C_LIO_LIELS increases the likelihood of a high social and resilient profile. C_LIO_LIPericytes gene expression associated to resilience is sex- and region-specific. C_LIO_LICORT response desensitizes after adult CSDS in both sexes. C_LI

neuroscience↗

bioRxiv2019

GEOlimma: Differential Expression Analysis and Feature Selection Using Pre-Existing Microarray Data

BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.\n\nResultsIn this study, we propose a novel differential expression and feature selection method--GEOlimma--which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.\n\nConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

bioinformatics↗

bioRxiv2019

Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks

BackgroundHistopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks (CNNs), a state-of-the-art image analysis technique in computer vision, automatically learns representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Despite hepatocellular carcinoma (HCC) being the sixth most common type of primary liver malignancy with a high mortality rate, little previous work has made use of CNN models to delineate the importance of histopathological images in diagnosis and clinical survival of HCC.\n\nResultsWe applied three pre-trained CNN models - VGG 16, Inception V3, and ResNet 50 - to extract features from HCC histopathological images. The visualization and classification showed clear separation between cancer and normal samples using image features. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival and disease-free survival, respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized CoxPH model of overall survival, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E-18) after applying Inception image features. We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception image features were significantly associated with both overall (C-index = 0.628 and p = 7.39E-07) and disease-free survival (C-index =0.558 and p = 0.012). Our results suggest the feasibility of feature extraction using pre-trained models, as well as the utility of the resulting features to build an accurate prognosis model of HCC and highlight significant correlations with clinical survival and biological pathways.\n\nConclusionsThe image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with relevant biological outcomes.

bioinformatics↗

bioRxiv2016

CASTOR: A machine learning platform for reproducible viral genome classification

MotivationAdvances in cloning and sequencing technology yielded a massive number of genome of virus strains. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for a well-studied virus. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families.\n\nResultsHere, we introduce a fast, accurate and generic virus classification platform, CASTOR, based on a machine learning approach. CASTOR is inspired by a well-known technique in molecular biology: Restriction Fragment Length Polymorphism (RFLP). It simulates the restriction digestion of genomic material by different enzymes into fragments in-silico. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of Human Papillomaviruses (HPV), Hepatitis B Viruses (HBV) and Human Immunodeficiency viruses (HIV). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV M group subtyping respectively. Furthermore, CASTOR shows a competitive performance compare to well-known HIV-specific classifier REGA and COMET on whole genome and pol fragments. With such prediction rates, genericity and robustness, as well as rapidity, such approach could constitute a reference in large-scale virus studies. Finally, we developed the CASTOR web platform for open access and reproducible viral machine learning classifiers.\n\nAvailabilityhttp://castor.bioinfo.uqam.ca\n\nContactdiallo.abdoulaye@uqam.ca

bioinformatics↗