Biology subjects

Sturm, M.

Publications and source records attributed to Sturm, M..

3 recordsLinked to original sources

bioRxiv2024

A computational model of altered neuronal activity in altered gravity

Electrophysiological experiments have shown that neuronal activity changes upon exposure to altered gravity. More specifically, neurons firing rates increase during microgravity and decrease during centrifugal-induced hypergravity. Different biophysical explanations have been proposed for this phenomenon: however, they have not been backed by quantitative analyses nor simulations. More generally, classical computational models of neurons and networks do not account for the effect of altered gravity, which limits the possibility to perform in-silico experiments and simulations. Here, we propose computational implementations for different effects of altered gravity on cellular functions, and modify existing models to account for the effect of micro- and hyper-gravity. Firstly, in line with previous experiments, we suggest that microgravity could be modeled as an increase of the voltage-dependent channel transition rates, which is assumed to be the result of a higher membrane fluidity and can be readily implemented into the Hodgkin-Huxley model. Using in-silico simulations of single neurons, we show that this model of the influence of gravity on neuronal activity allows to reproduce the observed increased firing and burst rates. Secondly, we explore the role of mechano-gated (MG) ion channels on population activity. We show that recordings can be fitted by a network of connected excitatory neurons, whose activity is balanced by firing rate adaptation. Adding a small depolarizing current to account for the activation of MG channels also reproduces the observed increased firing and burst rates. Overall, our results fill an important gap in the literature, by providing a computational link between altered gravity and neuronal activity. Starting from historical observations of the effects of gravity on cellular functions, we derived gravity-sensitive models of neurons and networks, whose predictions could be refined using future experiments.

neuroscience↗

bioRxiv2023

Multiomic ALS signatures highlight sex differences and molecular subclusters and identify the MAPK pathway as therapeutic target

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease and lacks effective disease-modifying treatments. Here, we performed a multiomic analysis of the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, as well as four transgenic mouse models of C9orf72-, SOD1-, TDP-43-, and FUS-ALS to characterize early and sex-specific disease mechanisms in ALS. Integrated analyses of transcriptomes, (phospho)proteomes, and miRNAomes revealed more pronounced changes in males. We identified transcriptome-based human ALS subclusters driven by the immune response, ECM, mitochondrial respiration, and RNA metabolism. The molecular signatures of human subclusters were reflected in specific mouse models. Individual and integrative multiomics analysis highlighted the mitogen-activated protein kinase pathway as an early disease-relevant mechanism. Its modulation by trametinib in vitro and in vivo validated that mitogen-activated protein kinase kinase 2 is a promising therapeutic target with beneficial effects in females.

neuroscience↗

bioRxiv2022

ClinCNV: multi-sample germline CNV detection in NGS data

Germline copy number variants (CNVs) are a common source of genomic variation involved in many genetic disorders, and their detection is crucial for clinical molecular diagnostics. Genomic microarrays, quantitative polymerase chain reaction (qPCR), and multiplex ligation-dependent probe amplification (MLPA) have been widely used for CNV detection in clinics for many years. Similarly, next-generation sequencing (NGS) applications such as whole-genome sequencing (WGS) and whole-exome sequencing (WES) are well-established, highly accurate techniques for the detection of single nucleotide variants (SNVs) and small insertions and deletions (indels). However, CNV detection using NGS remains challenging due to short read lengths, smaller than CNVs sizes. CNV detection using read coverage depths summarized in genomic regions is affected by various biases that arise during the library preparation and sequencing. We have developed a novel strategy for detecting CNVs, implemented in the tool ClinCNV (freely available on https://github.com/imgag/ClinCNV). ClinCNV does multi-sample normalization and CNV calling, using an original algorithm taking the best from the circular binary segmentation method and Hidden Markov model-based approaches. Here, we describe the methods and discuss the results obtained by applying ClinCNV to thousands of clinical WES, WGS, and shallow-WGS samples in various clinical and research settings.

bioinformatics↗