Biology subjects

Madeo, G.

Publications and source records attributed to Madeo, G..

2 recordsLinked to original sources

bioRxiv2025

Neuronal LAG3 facilitates pathogenic α-synuclein neuron-to-neuron propagation

Lymphocyte activation gene 3 (LAG3) is a key receptor involved in the propagation of pathological proteins in Parkinsons disease (PD). This study investigates the role of neuronal LAG3 in mediating the binding, uptake, and propagation of -synuclein (Syn) preformed fibrils (PFFs). Using neuronal LAG3 conditional knockout mice and human induced pluripotent stem cells-derived dopaminergic (DA) neurons, we demonstrate that LAG3 expression is critical for pathogenic Syn propagation. Our results show that the absence of neuronal LAG3 significantly reduces Syn pathology, alleviates motor dysfunction, and inhibits neurodegeneration in vivo. Electrophysiological recordings revealed that Syn PFFs induce pronounced neuronal hyperactivity in wild-type (WT) neurons, increasing firing rates in cell-attached and whole-cell configurations, and reducing miniature excitatory postsynaptic currents. In contrast, neurons lacking LAG3 resisted these electrophysiological effects. Moreover, treatment with an anti-human LAG3 antibody in human DA neurons inhibited Syn PFFs binding and uptake, preventing pathology propagation. These findings confirm the essential function of neuronal LAG3 in mediating Syn propagation and associated disruptions, identifying LAG3 as a potential therapeutic target for PD and related -synucleinopathies.

neuroscience↗

bioRxiv2023

CoCoNat: a novel method based on deep-learning for coiled-coil prediction

MotivationCoiled-coil domains (CCD) are widespread in all organisms performing several crucial functions. Given their relevance, the computational detection of coiled-coil domains is very important for protein functional annotation. State-of-the art prediction methods include the precise identification of coiled-coil domain boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state. ResultsIn this paper we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field (GRHCRF) for CCD identification and refinement. A final neural network (NN) predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level coiled-coil detection. CoCoNat significantly outperforms the most recent state-of-the art method on register annotation and prediction of oligomerization states. AvailabilityCoCoNat is available at https://coconat.biocomp.unibo.it. Contactpierluigi.martelli@unibo.it

bioinformatics↗