Biology subjects

Lessard, S.

Publications and source records attributed to Lessard, S..

5 recordsLinked to original sources

bioRxiv2026

RNA Editing of DEGS1 Links Genetic Risk to Ceramide Dysregulation and Astrocyte Toxicity in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with ~90% of cases being sporadic. Although genome-wide association studies have identified numerous genetic risk loci, these variants account for only a portion of ALS heritability, suggesting that additional genetic and regulatory mechanisms contribute to disease risk. Furthermore, the mechanisms linking common variants to disease pathogenesis remain poorly understood. Adenosine-to-inosine (A-to-I) RNA editing is dysregulated in ALS, yet systematic identification of disease-relevant editing events and their functional consequences has been limited. We performed comprehensive RNA editing analysis across eight central nervous system regions from a Target ALS cohort, identifying 752 differentially edited sites in 304 genes. Global RNA editing was significantly reduced in ALS tissues, particularly in spinal cord and choroid plexus, with altered editing in genes involved in immune signaling, stress response and RNA processing. Through genetic colocalization analysis integrating editing quantitative trait loci (edQTLs) with ALS GWAS data, we identified common variants at the DEGS1 locus associated with both increased 3' UTR editing and elevated ALS risk (posterior probability >0.88). DEGS1 encodes dihydroceramide desaturase 1, a key enzyme converting dihydroceramide to ceramide, the precursor to all sphingolipid species. Using CRISPR-generated DEGS1 knockout iPSCs and lipidomic profiling, we demonstrated that DEGS1 is essential for ceramide synthesis and downstream sphingolipid production in astrocytes. Functional validation revealed that ALS-associated editing of the DEGS1 3' UTR significantly increased DEGS1 protein expression. Critically, astrocytes expressing the fully edited DEGS1 3' UTR increased ceramide levels and reduced cell viability compared to those expressing the unedited construct. These findings establish a novel regulatory link between RNA editing, sphingolipid metabolism and ALS pathogenesis. Our results demonstrate that common genetic variants can influence disease risk through post-transcriptional mechanisms that alter lipid homeostasis in astrocytes, a cell type increasingly recognized as central to ALS progression.

neuroscience↗

bioRxiv2026

The Human Pleiotropic Map of GWAS Associations and Therapeutic Implications

Genetic support for drug targets substantially increases clinical success rates, establishing genome-wide association studies (GWAS) as central to therapeutic hypothesis generation. However, the same genetic evidence that reveals causal gene-disease relationships simultaneously exposes organism-level safety liabilities--a dimension requiring principled, genome-wide quantification. Here we systematically analyse 100,526 GWAS to yield 789,453 credible sets and gene prioritisations for 15,641 genes, with discovery showing no saturation as GWAS expand and increase diversity. We find that 64% of GWAS-implicated genes are pleiotropic, associated with traits across multiple diseases and showing a non-linear relationship between the degree of pleiotropy and clinical success. Highly pleiotropic genes--concentrated in immune, inflammatory, and oncogenic signalling programmes--are enriched in safety-terminated clinical programmes, mouse lethal knockouts, and cancer driver genes, establishing gene-level pleiotropy as a potential measure of genetically-informed organism-level safety liability. Protein-altering variant (PAV) support amplifies therapeutic signal (OR = 6.0), yet PAV targets show higher average pleiotropy, introducing a competing safety liability. Combining PAV support with intermediate pleiotropy (2-5 therapeutic areas) resolves this tension, yielding OR = 10.3 and relative success = 4.8--a profile already satisfied by 52 approved therapies. As GWAS continue to expand in scale and resolution, these findings lay the groundwork for increasingly sophisticated target discovery strategies that yield safer and more effective therapeutic hypotheses.

genomics↗

bioRxiv2026

Synovial transcriptional clusters link cartilage degeneration to cell-type-specific gene expression in knee osteoarthritis

ObjectivesTo identify synovial transcriptional clusters in human knee osteoarthritis (OA) and determine how these relate to synovial histologic features, cell-type-associated gene expression, and cartilage degeneration severity. MethodsBulk RNA sequencing (RNA-seq) of synovial tissue from n = 135 patients with knee OA was analyzed using consensus clustering. Clusters were compared by clinical and histologic features, including cartilage degeneration severity (OARSI score). Single-cell RNA-seq (n = 18) and spatial transcriptomics were used to relate cartilage degeneration-associated gene expression patterns to synovial cell populations. ResultsFour synovial transcriptional clusters that differed in synovial histologic features and cartilage degeneration severity were identified. Greater cartilage degeneration was associated with enrichment of lining fibroblast- and inflammatory myeloid-associated gene expression, whereas lesser cartilage degeneration was associated with enrichment of sublining fibroblast, endothelial, mural cell, and adipocyte-associated gene expression. ConclusionsHuman knee OA synovium segregates into transcriptional clusters associated with cartilage degeneration severity. Synovial transcriptional heterogeneity corresponds to cell-type-associated gene expression. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIOsteoarthritis synovium exhibits marked histologic and molecular heterogeneity. C_LIO_LISynovial inflammation detected by MRI correlates with cartilage degeneration and predicts progressive cartilage loss in knee OA. C_LIO_LIPrior transcriptomic studies have identified molecular subsets of OA synovium, but their relationship to cartilage degeneration severity remains unclear. C_LI What this study addsO_LIOA synovium segregates into four transcriptional clusters: Sublining (C1), Lymphomyeloid (C2), Myeloid (C3), and Major trauma (C4). C_LIO_LIGreater cartilage degeneration is associated with enrichment of inflammatory myeloid and lining fibroblast gene expression, whereas lesser degeneration is associated with enrichment of adipocyte, sublining fibroblast, endothelial, and mural cell-associated gene expression. C_LI How this study might affect research, practice or policyO_LIProvides a framework for a clinically relevant biological stratification of OA patients based on synovial molecular features. C_LIO_LIInforms future efforts to link synovial biology with OA prognosis, cartilage degeneration, treatment allocation, and development of targeted therapeutic strategies. C_LI

genomics↗

bioRxiv2026

A classification of structured coalescent processes with migration, conditional on the population pedigree

Recent analyses of the effects that organismal genealogies or pedigrees of populations have on times to common ancestry for samples of genetic data are extended to cases of population subdivision and migration. Traditional coalescent models marginalize over pedigrees. A finding of a pedigree effect implies that data analysis and interpretation should not be based on the corresponding traditional coalescent model but rather on a coalescent model obtained by conditioning on the pedigree. We apply a straightforward test based on the distribution of pairwise coalescence times to four previously described scenarios of subdivision and migration. These scenarios are defined by the relative magnitudes of four parameters: the number of the local populations or demes, the deme size, the migration fraction, and the probability that migration can occur at all. We find pedigree effects in three scenarios. In two, the effect is weak if the deme size is large. The one scenario without a pedigree effect corresponds to the well known structured-coalescent model. The one scenario with a persistent pedigree effect even in the limit as the deme size tends to infinity involves long periods without gene flow interrupted by pulses of migration. We illustrate our results using simulations and numerical analysis.

evolutionary biology↗

bioRxiv2019

A saturating mutagenesis CRISPR-Cas9 mediated functional genomic screen identifies cis- and trans-regulatory elements of Oct4 in embryonic stem cells

Regulatory elements (REs) consist of enhancers and promoters that occupy a significant portion of the non-coding genome and control gene expression programs either in -cis or in - trans. Putative REs have been identified largely based on their regulatory features (co-occupancy of ESC-specific transcription factors, enhancer histone marks and DNase hypersensitivity) in mouse embryonic stem cells (mESCs). However, less has been established regarding their regulatory functions in their native context. We deployed cis- and trans-regulatory elements scanning through saturating mutagenesis and sequencing (ctSCAN-SMS) to target elements within the [~]12kb cis-region (Cis-REs; CREs) of the Oct4 gene locus, as well as genome-wide 2,613 high-confidence trans-REs (TREs), in mESCs. ctSCAN-SMS identified 10 CREs and 12 TREs, as novel candidate REs of the Oct4 gene in mESCs. Furthermore, deletions of these candidate REs confirmed that the majority of the REs are functionally active, and CREs are more active than TREs in controlling Oct4 gene expression. A subset of active CREs and TREs physically interact with the Oct4 promoter to varying degrees; specifically, a greater number of active CREs compared to active TREs, physically interact with the Oct4 promoter. Moreover, comparative genomics analysis reveals that more number of active CREs than active TREs are evolutionary conserved between mouse and primates, including human. Taken together, our study demonstrates the reliability and robustness of ctSCAN-SMS screening to identify critical REs, and investigate their roles in the regulation of transcriptional output of a target gene (in this case Oct4) in their native context.

genomics↗