Biology subjects

Andersh, K.

Publications and source records attributed to Andersh, K..

3 recordsLinked to original sources

bioRxiv2024

Neuronal protein phosphatase 1β regulates glutamate release, cortical myelination, node of Ranvier formation, and action potential propagation in the optic nerve

Precise regulation of protein phosphorylation is critical for many cellular processes, and dysfunction in this process has been linked to various neurological disorders and diseases. Protein phosphatase 1 (PP1) is a ubiquitously expressed serine/threonine phosphatase with three major isoforms, (, {beta}, {gamma}) and hundreds of known substrates. Previously, we reported that PP1 and PP1{gamma} are essential for the known role of PP1 in synaptic physiology and learning/memory, while PP1{beta} displayed a surprising opposing function. De novo mutations in PP1{beta} cause neurodevelopmental disorders in humans, but the mechanisms involved are currently unknown. A Cre-Lox system was used to delete PP1{beta} specifically in neurons in order to study its effects on developing mice. These animals fail to survive to 3 postnatal weeks, and exhibit deficits in cortical myelination and glutamate release. There was defective compound action potential (CAP) propagation in the optic nerve of the null mice, which was traced to a deficit in the formation of nodes of Ranvier. Finally, it was found that phosphorylation of the PP1{beta}-specific substrate, myosin light chain 2 (MLC2), is significantly enhanced in PP1{beta} null optic nerves. Several novel important in vivo roles of PP1{beta} in neurons were discovered, and these data will aid future investigations in delineating the mechanisms by which de novo mutations in PP1{beta} lead to intellectual and developmental delays in patients.

neuroscience↗

bioRxiv2024

Comparative study of enriched dopaminergic neurons from siblings with Gaucher disease discordant for parkinsonism

Inducible pluripotent stem cells (iPSCs) derived from patient samples have significantly enhanced our ability to model neurological diseases. Comparative studies of dopaminergic (DA) neurons differentiated from iPSCs derived from siblings with Gaucher disease discordant for parkinsonism provides a valuable avenue to explore genetic modifiers contributing to GBA1-associated parkinsonism in disease-relevant cells. However, such studies are often complicated by the inherent heterogeneity in differentiation efficiency among iPSC lines derived from different individuals. To address this technical challenge, we devised a selection strategy to enrich dopaminergic (DA) neurons expressing tyrosine hydroxylase (TH). A neomycin resistance gene (neo) was inserted at the C-terminus of the TH gene following a T2A self-cleavage peptide, placing its expression under the control of the TH promoter. This allows for TH+ DA neuron enrichment through geneticin selection. This method enabled us to generate comparable, high-purity DA neuron cultures from iPSC lines derived from three sisters that we followed for over a decade: one sibling is a healthy individual, and the other two have Gaucher disease (GD) with GBA1 genotype N370S/c.203delC+R257X (p.N409S/c.203delC+p.R296X). Notably, the younger sister with GD later developed Parkinson disease (PD). A comprehensive analysis of these high-purity DA neurons revealed that although GD DA neurons exhibited decreased levels of glucocerebrosidase (GCase), there was no substantial difference in GCase protein levels or lipid substrate accumulation between DA neurons from the GD and GD/PD sisters, suggesting that the PD discordance is related to of other genetic modifiers.

neuroscience↗

bioRxiv2023

A Model-Based Hierarchical Bayesian Approach to Sholl Analysis

SO_SCPLOWUMMARYC_SCPLOWDue to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still the gold standard for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a fully parametric model-based approach for analyzing Sholl data. We generalize our model to a hierarchical Bayesian framework so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to three real data examples and perform simulation studies comparing the proposed method with a popular alternative.

neuroscience↗