Biology subjects

Bar, C.

Publications and source records attributed to Bar, C..

5 recordsLinked to original sources

bioRxiv2025

TDP-43 toxic gain of function links ALS/FTLD-TDP and Alzheimer's Disease through splicing

Loss of nuclear TDP-43 splicing activity is a common feature across neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), but its relevance to Alzheimers disease (AD) remains unclear. Here, we show that TDP-43 pathology in AD is broadly associated with splicing abnormalities, including aberrant splicing of amyloid precursor protein (APP). TDP-43 drives the formation of elongated APP isoforms, disrupting alternative splicing across ALS, FTLD-TDP and AD, providing a compelling mechanism for a long-standing observation of APP isoform dysregulation. We further establish a mechanistic link between TDP-43, APP splicing, and A{beta} pathology. Surprisingly, the disruption to alternative APP splicing is mediated by a toxic gain of cytoplasmic TDP-43 function, rather than loss of its nuclear role. Using proximity proteomics and base editing in human iPSC-derived neurons, we show that TDP-43 pathology causes cytoplasmic co-sequestration of splicing regulators SCAF11, SRSF5, and TIAL1. Knockdown of these regulators also results in APP mis-splicing and increased A{beta} burden, without affecting other TDP-43 targets such as STMN2 or UNC13A. Together, our findings suggest that TDP-43-mediated splicing dysfunction upstream of APP contributes to the pathogenesis of seemingly disparate neurodegenerative diseases, uniting AD and ALS/FTLD-TDP through a shared molecular mechanism.

neuroscience↗

bioRxiv2024

Functional assessment of a kcnb1 knock-out zebrafish to model KCNB1-related neurodevelopmental and epileptic disorders

KEY POINTSO_LIkcnb1 is expressed in distinct cell subtypes and various regions of the central nervous system in zebrafish C_LIO_LIBrain anatomy and neuronal circuits are not disrupted in the kcnb1 loss-of-function zebrafish model C_LIO_LILoss of kcnb1 leads to altered behavior phenotype, light and sound-induced locomotor impairments C_LIO_LIkcnb1 knock-out zebrafish exhibit increased locomotor sensitivity to PTZ and elevated expression of epileptogenesis-related genes C_LIO_LIkcnb1-/- larvae show spontaneous and provoked epileptiform-like electrographic activity associated with disrupted GABA regulation C_LI ObjectiveKCNB1 encodes an -subunit of the delayed-rectifier voltage-dependent potassium channel Kv2.1. De novo pathogenic variants of KCNB1 have been linked to developmental and epileptic encephalopathies (DEE), diagnosed in early childhood and sharing limited treatment options. Loss-of-function (LOF) of KCNB1 with dominant negative effects has been proposed as the pathogenic mechanism in these disorders. Here, we aim to characterize a knock-out (KO) zebrafish line targeting kcnb1 (kcnb1+/- and kcnb1-/-) for investigating DEEs. MethodsThis study presents the phenotypic analysis of a kcnb1 knock-out zebrafish model, obtained by CRISPR/Cas9 mutagenesis. Through a combination of immunohistochemistry, behavioral assays, electrophysiological recordings, and neurotransmitter quantifications, we have characterized the expression, function, and impact of this kcnb1 LOF model at early stages of development. ResultsIn wild-type larval zebrafish, kcnb1 was found in various regions of the central nervous system and in diverse cell subtypes including neurons, oligodendrocytes and microglial cells. Both kcnb1+/- and kcnb1-/- zebrafish displayed impaired swimming behavior and "epilepsy-like" features that persisted through embryonic and larval development, with variable severity. When exposed to the chemoconvulsant pentylenetetrazol (PTZ), both mutant models showed elevated locomotor activity. In addition, PTZ-exposed kcnb1-/- larvae exhibited higher bdnf mRNA expression and activated c-Fos positive neurons in the telencephalon. This same model presents spontaneous and provoked epileptiform-like electrographic activity associated with disrupted GABA regulation. In this KO model, neuronal circuit organization remained unaffected. SignificanceWe conclude that kcnb1 knock-out in zebrafish leads to early-onset phenotypic features reminiscent of DEEs, affecting neuronal functions and primarily inhibitory pathways in developing embryonic and larval brains. This study highlights the relevance of this model for investigating developmental neuronal signaling pathways in KCNB1-related DEEs.

neuroscience↗

bioRxiv2024

Metabolic and age-associated epigenetic barriers during direct reprogramming of mouse fibroblasts into induced cardiomyocytes

Heart disease is the leading cause of mortality in developed countries novel regenerative procedures are warranted for improving patients well fare. Direct cardiac conversion (DCC) can create induced cardiomyocytes (iCMs) and besides holding great promise, lacks clinical effectiveness for cardiac regeneration as metabolic and age-associated barriers remains elusive. Here, we identify by histone post-translational analysis that DCC triggers major age-dependent alterations in the epigenetic landscape. Metabolomics revealed decrease abundance of anabolic metabolites related to TCA cycle and glutaminolysis and profound mitochondrial network remodeling, with increased elongation and interconnectivity and reliance in mitochondrial respiration in iCMs. Importantly, adult-derived iCMs present increase accumulation of oxidative stress in the mitochondria and pharmacological activation of mitophagy increase DCC of adult fibroblasts in vitro. Metabolic modulation in vitro and dietary manipulations in vivo improves DCC efficiency and are accompanied by significant alterations in the histone acetylation and methylation landscape and mitochondria homeostasis. Our study provides evidence that metaboloepigenetics as a direct role in cell fate transitions driving direct cardiac conversion into iCMs, highlighting the potential use of metabolic modulation in increasing cardiac regenerative strategies.

cell biology↗

bioRxiv2024

Organellomics: AI-driven deep organellar phenotyping of human neurons

Systematic assessment of organelle architectures, termed the organellome, offers valuable insights into cellular states and pathomechanisms, but remains largely uncharted. Here, we present a deep phenotypic learning based on vision transformers, resulting in the Neuronal Organellomics Vision Atlas (NOVA) model that studies confocal images of more than 30 markers of distinct membrane-bound and membraneless organelles in 11.5 million images of human neurons. Organellomics analysis quantifies perturbation-induced changes in organelle localization and morphology using a rigorous mixed-effects meta-analytic framework that accounts for sampling variance and experimental heterogeneity. Applying this approach, we delineate phenotypic alterations in neurons carrying ALS-associated mutations and uncover a physical and functional crosstalk between cytoplasmic mislocalized TDP-43, a hallmark of ALS, and processing bodies (P-bodies), membraneless organelles regulating mRNA stability. These findings are validated in patient-derived neurons and human neuropathology. NOVA establishes a scalable framework for quantitative mapping of subcellular phenotypes and provides a new avenue for investigating the neurocellular basis of disease.

systems biology↗

bioRxiv2020

Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks

Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.

pathology↗