Biology subjects

Wert-Carvajal, C.

Publications and source records attributed to Wert-Carvajal, C..

5 recordsLinked to original sources

bioRxiv2025

Nanoscale dendritic shaft constrictions shape synaptic integration in fine caliber principal neuron dendrites

Traditionally, theoretical studies typically described dendritic morphology as optimized for efficient synaptic voltage transfer from spines to the soma, implemented as a tubular design respecting Ralls 3/2 rule for impedance matching at branch points. Here, we reveal that this view is an oversimplification. Using three high-resolution imaging techniques, we demonstrate that dendrites in cortical and hippocampal neurons contain nanoscale constrictions, comparable in diameter to spine necks. We provide theoretical and experimental evidence that these constrictions partition the dendrite into distinct electrical compartments, significantly shaping dendritic integration of synaptic potentials.

neuroscience↗

bioRxiv2025

Transformations of the spatial activity manifold convey aversive information in CA3

Hippocampal circuits form cognitive maps representing spatial position and integrating contextual information, including affective cues, within episodic memory representations. We investigated how spatial and affective information combine in the population activity of CA3 axons by imaging intermediate-to-dorsal and dorsal-to-dorsal projections in mice navigating a linear track before, during, and after exposure to an aversive air puff stimulus. Our analyses reveal that both axonal populations maintain a robust, time-invariant spatial coding manifold across recordings, independent of affective context. Alterations to this common manifold encode the presence of the aversive stimulus without disrupting the spatial representation. Both axonal pathways encoded affective information with similar efficacy. This population-level encoding was distributed similarly across place and non-place cells. Our findings demonstrate that hippocampal CA3 axons integrate spatial and affective information within a common representational geometry while preserving the ability to extract each information type separately.

neuroscience↗

bioRxiv2024

Deciphering the astrocytic contribution to learning and relearning

Astrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this astrocytic regulation is consistent with the classic Bienenstock Cooper Munro (BCM) model for synaptic plasticity, which postulated the existence of an activity-dependent LTP/LTD threshold. We show how the resulting mathematical framework can explain the experimentally observed behavioral effects of astrocytic cannabinoid receptor knock-out on mice during a place avoidance task and give rise to new testable predictions about the learning process advancing our understanding of the functional role of neuron-glia interaction in learning.

neuroscience↗

bioRxiv2023

Unraveling the Power of NAP-CNB's Machine Learning-enhanced Tumor Neoantigen Prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNAseq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in vivo following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA-identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies

immunology↗

bioRxiv2020

NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope immunogenicity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the systems predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.

bioinformatics↗