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Sanchez, J. P.

Publications and source records attributed to Sanchez, J. P..

2 recordsLinked to original sources

Inhibition of sensory neuron driven acute, inflammatory, and neuropathic pain using a humanised chemogenetic system

Hyperexcitability in sensory neurons is known to underlie many of the maladaptive changes associated with persistent pain. Chemogenetics has shown promise as a means to suppress such excitability, yet chemogenetic approaches suitable for human applications are needed. PSAM4-GlyR is a modular system based on the human 7 nicotinic acetylcholine and glycine receptors, which responds to inert chemical ligands and the clinically-approved drug, varenicline. Here, we demonstrated the efficacy of this channel in silencing both mouse and human sensory neurons by the activation of large shunting conductances after agonist administration. Virally-mediated expression of PSAM4-GlyR in mouse sensory neurons produced behavioural hyposensitivity upon agonist administration, which was recovered upon agonist washout. Importantly, stable expression of the channel led to similar reversible behavioural effects even after 10 months of viral delivery. Mechanical and spontaneous pain readouts were also ameliorated by PSAM4-GlyR activation in acute and joint pain inflammation models. Furthermore, suppression of mechanical hypersensitivity generated by a spared nerve injury model of neuropathic pain was also observed upon activation of the channel. Effective silencing of behavioural hypersensitivity was reproduced in a human model of hyperexcitability and clinical pain: PSAM4-GlyR activation decreased the excitability of human induced pluripotent stem-cell-derived sensory neurons and spontaneous activity due to a gain of function NaV1.7 mutation causing inherited erythromelalgia. Our results demonstrate the contribution of sensory neuron hyperexcitability to neuropathic pain and the translational potential of an effective, stable and reversible human-based chemogenetic system for the treatment of pain.

neuroscience↗

mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data

MotivationMicrobiota data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discoverys reliability. Data sparsity makes it difficult to obtain a well-cluster structure and distorts the abundance distributions. Currently, there is a rised need to develop new algorithms with improved capacities to reduce noise and recover missing information. ResultsWe present mb-PHENIX, an open-source algorithm developed in Python, that recovers taxa abundances from the noisy and sparse microbiota data. Our method deals with sparsity in the count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion onto the supervised Uniform Manifold Approximation Projection (sUMAP) space. Our hybrid machine learning approach allows the user to denoise microbiota data. Thus, the differential abundance of microbes is more accurate among study groups, where abundance analysis fails. AvailabilityThe mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub) ContactOresendis@inmegen.gob.mx Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗