2025 Hyper Recent •CC0 1.0 Universal

This work is dedicated to the public domain. No rights reserved.

Access Preprint From Server
April 2nd, 2025
Version: 1
ISEF - CURE - UDELAR
bioinformatics
biorxiv

Serotonergic Modulation of Motoneuronal Excitability and Its Impact on Muscle Force Generation: A Computational Study

Melendez-Gallardo, J.Open in Google Scholar•Plada-Delgado, D.Open in Google Scholar

This study investigates the impact of serotonin (5-HT) on motoneuron electrical activity and muscle force generation. Using a computational model, we explore how 5-HT receptors influence motoneuron excitability and muscle function at different stimulation frequencies. Our results demonstrate that physiological 5-HT release increases motoneuron excitability, particularly at higher frequencies (40 Hz and 100 Hz), consistent with the known excitatory role of serotonin through 5-HT2a receptor activation. However, high concentrations of 5-HT lead to decreased motoneuron excitability, potentially due to excessive activation of 5-HT1a receptors, which are involved in neuronal hyperpolarization. The generated muscle force exhibited a direct correlation with motoneuron activity. At 10 Hz, physiological 5-HT release did not significantly affect force production, suggesting minimal impact at low frequencies. However, high 5-HT concentrations reduced contraction duration, indicating muscle fatigue. At 40 Hz and 100 Hz, physiological 5-HT release enhanced motoneuron excitability, promoting more sustained muscle responses. Nevertheless, high serotonin concentrations decreased the ability to maintain muscle contraction, leading to fatigue. Our findings align with experimental studies, validating the model as an effective tool for understanding the role of serotonin in neuromuscular function. The model provides insights into the mechanisms underlying muscle fatigue and could serve as a foundation for developing therapeutic strategies to modulate serotonergic signaling, improving muscle performance and recovery. Future research should explore the interactions with other neurotransmitters to further investigate their effects on motor function and fatigue resistance.

Similar Papers

biorxiv
Thu Apr 03 2025
TarDis: Achieving Robust and Structured Disentanglement of Multiple Covariates
Addressing challenges in domain invariance within single-cell genomics necessitates innovative strategies to manage the heterogeneity of multi-source datasets while maintaining the integrity of biological signals. We introduce TarDis, a novel deep generative model designed to disentangle intricate covariate structures across diverse biological datasets, distinguishing technical artifacts from true...
Inecik, K.
•
Kara, A.
•
Rose, A.
•
Haniffa, M.
•
Theis, F. J.
biorxiv
Thu Apr 03 2025
Sketch, capture and layout phylogenies
Phylogenetic trees and networks play a central role in biology, bioinformatics, and mathematical biology, and producing clear, informative visualizations of them is an important task. We present new algorithms for visualizing rooted phylogenetic networks as either combining or transfer networks, in both cladogram and phylogram style. In addition, we introduce a layout algorithm that aims to improv...
Huson, D.
biorxiv
Thu Apr 03 2025
Differential T cell receptor gene expression analysis using the Wilcoxon test with similarity-based weighting
To better understand immune responses, comparing the abundance of T cell receptors (TCRs) between conditions can provide insights into which T cells have proliferated or were involved in immune activation. This requires methods that can accurately identify significant differences in TCR-seq data. For conventional RNA-seq data, well-established differential gene expression (DGE) analysis tools such...
Buytenhuijs, F.
•
Ankan, A.
•
Textor, J.
biorxiv
Thu Apr 03 2025
Evidence of off-target probe binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel compromises accuracy of spatial transcriptomic profiling
The accuracy of spatial gene expression profiles generated by probe-based in situ spatially-resolved transcriptomic technologies depends on the specificity with which probes bind to their intended target gene. Off-target binding, defined as a probe binding to something other than the target gene, can distort a gene\'s true expression profile, making probe specificity essential for reliable transcr...
Hallinan, C.
•
Ji, H. J.
•
Salzberg, S. L.
•
Fan, J.
biorxiv
Thu Apr 03 2025
LncMamba: A Deep Learning Model for lncRNA Localization Prediction Based on the Mamba Model
Accurate prediction of long non-coding RNA (lncRNA) subcellular localization is crucial for understanding its biological functions. In this study, we propose a novel deep learning framework, LncMamba, which utilizes a two-layer FPN network for multi-scale feature extraction and introduces the Mamba network to lncRNA localization prediction tasks for the first time. Based on this, we improved the l...
Huang, B.
•
Luo, Y.
•
Zhuang, Y.
•
He, S.
•
Yuan, C.
biorxiv
Thu Apr 03 2025
BioPKS Pipeline: An integrated platform for merging the computational design of chimeric type I polyketide synthases with enzymatic pathways for chemical biosynthesis
Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods which can often rely on arduous literature searches. Here, we present an automated retrobiosynthesis tool, BioPKS Pipeline, that simul...
Chainani, Y.
•
Diaz, J.
•
Guilarte-Silva, M.
•
Blay, V.
...•
Backman, T. W. H.
biorxiv
Wed Apr 02 2025
Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to con...
Liu, T.
•
De Brouwer, E.
•
Kuo, T.
•
Diamant, N.
...•
Heimberg, G.
biorxiv
Wed Apr 02 2025
Building A Unified Model for Drug Synergy Analysis Powered by Large Language Models
Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models...
Liu, T.
•
Chu, T.
•
Luo, X.
•
Zhao, H.
biorxiv
Wed Apr 02 2025
Predicting microbial transcriptome using genome sequence
We present TXpredict, a transformer-based framework for predicting microbial transcriptomes using annotated genome sequences. By leveraging information learned from a large protein language model, TXpredict achieves an average Spearman correlation of 0.53 and 0.62 in predicting gene expression for new bacterial and fungal genomes. We further extend this framework to predict transcriptomes for 2,68...
FU, G.
•
YAN, Y.
•
CHEN, Y.
•
SHAO, B.