Biology subjects

Mitxelena, I.

Publications and source records attributed to Mitxelena, I..

2 recordsLinked to original sources

bioRxiv2023

Schwann cells modulate nociception in neurofibromatosis 1

Pain of unknown etiology is frequent in individuals with the tumor predisposition syndrome Neurofibromatosis 1 (NF1), even when tumors are absent. Schwann cells (SC) were recently shown to play roles in nociceptive processing, and we find that chemogenetic activation of SCs is sufficient to induce afferent and behavioral mechanical hypersensitivity in mice. In mouse models, animals show afferent and behavioral hypersensitivity when SC, but not neurons, lack Nf1. Importantly, hypersensitivity corresponds with SC-specific upregulation of mRNA encoding glial cell line derived neurotrophic factor (GDNF), independent of the presence of tumors. Neuropathic pain-like behaviors in the NF1 mice were inhibited by either chemogenetic silencing of SC calcium or by systemic delivery of GDNF targeting antibodies. Together, these findings suggest that Nf1 loss in SCs causes mechanical pain by influencing adjacent neurons and, data may identify cell-specific treatment strategies to ameliorate pain in individuals with NF1. Graphical AbstractGDNF released from Schwann cells acts on sensory neurons leading to mechanical hypersensitivity and pain-like behaviors in preclinical models of NF1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/533004v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@d17177org.highwire.dtl.DTLVardef@706f56org.highwire.dtl.DTLVardef@19db1ccorg.highwire.dtl.DTLVardef@3e28d_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Markov state models from hierarchical density-based assignment

Markov state models (MSMs) have become one of the preferred methods for the analysis and interpretation of molecular dynamics (MD) simulations of conformational transitions in biopolymers. While there is great variation in terms of implementation, a well-defined workflow involving multiple steps is often adopted. Typically, molecular coordinates are first subjected to dimensionality reduction and then clustered into small "microstates", which are subsequently lumped into "macrostates" using the information from the slowest eigenmodes. However, the microstate dynamics is often non-Markovian and long lag times are required to converge the MSM. Here we propose a variation on this typical workflow, taking advantage of hierarchical density-based clustering. When applied to simulation data, this type of clustering separates high population regions of conformational space from others that are rarely visited. In this way, density-based clustering naturally implements assignment of the data based on transitions between metastable states. As a result, the state definition becomes more consistent with the assumption of Markovianity and the timescales of the slow dynamics of the system are recovered more effectively. We present results of this simplified workflow for a model potential and MD simulations of the alanine dipeptide and the FiP35 WW domain.

biophysics↗