Biology subjects

Donoso, M.

Publications and source records attributed to Donoso, M..

3 recordsLinked to original sources

bioRxiv2026

Extremophilic Laccase-Mediator System Enabling Near-Complete Degradation of Doxycycline.

Using an extremophilic laccase with a phenolic redox mediator, we rapidly abolished doxycycline antibacterial activity and achieved near-complete chemical degradation (>97%) within 2 h under mild conditions. Antibiotic residues in aquatic and terrestrial environments drive the emergence and spread of microbial resistance genes, yet many treatment strategies are limited by slow kinetics or incomplete transformation. Here, we evaluated doxycycline degradation by the recombinant FNT laccase across pH and temperature conditions, comparing direct enzymatic oxidation with a laccase-mediator system. RP-HPLC quantified doxycycline removal. Residual toxicity was assessed using growth inhibition assays with Bacillus subtilis and Escherichia coli BL21. In the absence of a mediator, oxidation produced only partial loss of function, indicating that structural modification alone does not guarantee pharmacological inactivation. In contrast, the addition of acetosyringone markedly increased biodegradation efficiency, yielding >97% doxycycline depletion and eliminating antibacterial activity. To rationalize condition-dependent performance, we complemented experiments with molecular simulations of doxycycline binding at the laccase active site, revealing pH-dependent differences in stability and substrate interactions consistent with enhanced turnover under alkaline conditions. These results demonstrate that an extremophilic laccase-mediator system provides rapid, effective detoxification of doxycycline, supporting its potential for treatment of antibiotic-contaminated effluents.

biochemistry↗

bioRxiv2025

Neural Networks and Foundation Models: Two Strategies for EEG-to-fMRI Prediction

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from EEG activity could give us the best of both worlds, and open new horizons for neuroscience research and neurotechnology applications. Here, we formulate this prediction objective both as a classification task (predicting whether the fMRI signal increases or de- creases) and a regression task (predicting the value of this signal). We follow two distinct strategies: training classical machine learning and deep learning mod- els (including MLP, CNN, RNN, and transformer) on an EEG-fMRI dataset, or leveraging the capabilities of pre-trained large language models (LLMs) and large multimodal models. We show that predicting fMRI activity from EEG activity is possible for the brain regions defined by the Harvard-Oxford cortical atlas, in the context of subjects performing a neurofeedback task. Interestingly, both strategies yield promising results, possibly highlighting two complementary paths for our prediction objective. Furthermore, a Chain-of-Thought approach demonstrates that LLMs can infer the cognitive functions associated with EEG data, and subsequently predict the fMRI data from these cognitive functions. The natural combination of the two strategies, i.e., fine-tuning an LLM on an EEG-fMRI dataset, is not straightforward and would certainly require further study. These findings could provide important insights for enhancing neural interfaces and advancing toward a multimodal foundation model for neuroscience, integrating EEG, fMRI, and possibly other neuroimaging modalities.

neuroscience↗

bioRxiv2020

The G protein-Coupled Metabotropic Glutamate Receptor 1 controls neuronal macroautophagy

Autophagy is an evolutionarily conserved, highly regulated catabolic process critical to neuronal homeostasis, function and survival throughout organismal lifespan. However, the external factors and signals that control autophagy in neurons are still poorly understood. Here we report that the G protein-coupled metabotropic glutamate receptor 1 (mGlu1) contributes to control basal autophagy in the brain. Autophagy is upregulated in the brain of adult mGlu1 knockout mice and genetic deletion or pharmacological inhibition of native mGlu1 receptors enhances autophagy flux in neurons. The evolutionarily conserved adaptor protein FEZ1, identified by a genome-wide screen as mGlu1 receptor interacting partner, was found to participate in the regulation of neuronal autophagy and to be required for repression of autophagy flux by the mGlu1 receptor. Furthermore, FEZ1 appears to enable association of mGlu1 with Ulk1, a core component of the autophagy pathway. Thus, we propose that the mGlu1 receptor contributes to restrain constitutive autophagy in neurons.

neuroscience↗