Biology subjects

Jebara, T.

Publications and source records attributed to Jebara, T..

3 recordsLinked to original sources

bioRxiv2025

WWOX deficiency uncovers a cell-autonomous mechanism impairing myelin repair

Remyelination is essential for neuronal function and plasticity, and its failure contributes to multiple sclerosis (MS) and other neurodegenerative disorders. Yet, the molecular programs governing oligodendrocyte precursor cell (OPC) differentiation and remyelination remain incompletely defined. Here, we identify the WW domain-containing oxidoreductase (WWOX) as a critical cell-autonomous regulator of oligodendrocyte differentiation and myelin repair. Reanalysis of single-nucleus RNA sequencing from MS lesions revealed WWOX as one of the most significantly dysregulated oligodendroglial genes. Conditional deletion of Wwox in oligodendroglia impaired OPC differentiation, favouring aberrant proliferation and blocking myelin regeneration after cuprizone-induced demyelination. Single-nucleus transcriptomics confirmed profound transcriptional reprogramming in WWOX-deficient oligodendroglia during remyelination, with enrichment of WNT and TGF{beta} signalling and cell cycle programs. Mechanistically, WWOX physically interacts with the master transcription factor SOX10 via its WW1 domain, stabilising SOX10 protein and sustaining its downstream myelin gene network. Loss of WWOX reduced SOX10 stability and activity, providing a direct mechanistic link to defective OPC differentiation. Together, our findings uncover WWOX as an essential orchestrator of remyelination and position the WWOX-SOX10 axis as a promising therapeutic target for enhancing myelin repair in MS and related demyelinating disorders.

neuroscience↗

bioRxiv2018

Robust Algorithms for Capturing Population Dynamics and Transport in Oceanic Variables along Drifter Trajectories using Linear Dynamical Systems with Latent Variables

The blooms of Noctiluca in the Gulf of Oman and the Arabian Sea have been intensifying in recent years posing a threat to regional fisheries and the long-term health of an ecosystem supporting a coastal population of nearly 120 million people. We present the results of a microscopic data analysis to investigate the onset and patterns of the Noctiluca (mixotrophic dinoflagellate Noctiluca scintillans) blooms, which form annually during the winter monsoon in the Gulf of Oman and in the Arabian Sea. Our approach combines methods in physical and biological oceanography with machine learning techniques. In particular, we present a robust algorithm, the variable-length Linear Dynamic Systems (vLDS) model, that extracts the causal factors and latent dynamics at the microscopic population-level along each individual drifter trajectory, and demonstrate its effectiveness by using it to test and confirm previously benchmarked macroscopic scientific hypotheses. The test results provide microscopic statistical evidence to support and recheck the macroscopic physical and biological Oceanography hypotheses on the Noctiluca blooms; it also helps identify complementary microscopic dynamics that might not be visible or discoverable at the macroscopic scale. The vLDS model also exhibits a generalization capability (inherited from a machine learning methodology) to investigate important causal factors and hidden dynamics associated with ocean biogeochemical processes and phenomena at the population-level.

ecology↗

bioRxiv2017

Identification and Targeting of Cortical Ensembles

Breaking the neural code requires the characterization of physiological and behavioral correlates of neuronal ensemble activity. To understand how the emergent properties of neuronal ensembles allow an internal representation of the external world, it is necessary to generate empirically grounded models that fully capture ensemble dynamics. We used machine learning techniques, often applied in big data pattern recognition, to identify and target cortical ensembles from mouse primary visual cortex in vivo leveraging recent developments in optical techniques that allowed the simultaneous recording and manipulation of neuronal ensembles with single-cell precision. Conditional random fields (CRFs) allowed us not only to identify cortical ensembles representing visual stimuli, but also to individually target neurons that are functionally key for pattern completion. These results represent the proof-of-principle that machine learning techniques could be used to design close-loop behavioral experiments that involve the precise manipulation of functional cortical ensembles.

neuroscience↗