Wysocka, E. | bioRxiv Science

bioRxiv2025

T cells are necessary for development of PCOS reproductive symptoms in a letrozole-induced mouse model of PCOS

Polycystic ovary syndrome (PMOS) is a complex reproductive disorder with clear genetic susceptibilities that impact the heterogeneous clinical presentation of symptoms and severity through unknown mechanisms. Chronic inflammation is linked to PMOS, but a clear cause-and-effect relationship between immune mediators and PMOS phenotypes has yet to be demonstrated. This study employed a comprehensive systems immunology approach, utilizing a letrozole-induced PMOS mouse model to identify changes in inflammatory factors associated with PMOS symptoms. By analyzing immune cells and secreted cytokines from 22 different mouse strains, we identified T cells and TNF-{beta} as associated with PMOS-like phenotypes, regardless of genetic background. We used a knockout of TCR to show that functional T cells are necessary for development of pathologically elevated luteinizing hormone (LH) in letrozole-treated female mice. In women with PMOS, we observed elevated TNF-{beta} transcripts in immune cells from women with PMOS. Finally, we demonstrate that TNF-{beta} increased Lhb mRNA in a female mouse gonadotrope-derived cell line, suggesting that TNF-{beta} may directly modulate gonadotrope gene expression and may contribute to elevated LH in PMOS-like conditions. These findings support a requirement for functional {beta} T cells in LET-induced LH elevation in a PMOS-like mouse model and identify TNF-{beta} as a candidate immune mediator for further investigation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/631835v2_ufig1.gif" ALT="Figure 1"> View larger version (90K): org.highwire.dtl.DTLVardef@14dcf2aorg.highwire.dtl.DTLVardef@15bdc67org.highwire.dtl.DTLVardef@1e2f718org.highwire.dtl.DTLVardef@1cc5c16_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryFunctional {beta} T cells are linked to LET-induced LH elevation in a PMOS-like mouse model, uncovering candidate immune mechanisms for further study.

immunology↗

bioRxiv2018

Analysis of proteins in computational models of synaptic plasticity

The desire to explain how synaptic plasticity arises from interactions between ions, proteins and other signalling molecules has propelled the development of biophysical models of molecular pathways in hippocampal, striatal and cerebellar synapses. The experimental data underpinning such models is typically obtained from low-throughput, hypothesis-driven experiments. We used high-throughput proteomic data and bioinformatics datasets to assess the coverage of biophysical models.\n\nTo determine which molecules have been modelled, we surveyed biophysical models of synaptic plasticity, identifying which proteins are involved in each model. We were able to map 4.2% of previously reported synaptic proteins to entities in biophysical models. Linking the modelled protein list to Gene Ontology terms shows that modelled proteins are focused on functions such as calmodulin binding, cellular responses to glucagon stimulus, G-alpha signalling and DARPP-32 events.\n\nWe cross-linked the set of modelled proteins with sets of genes associated with common neurological diseases. We find some examples of disease-associated molecules that are well represented in models, such as voltage-dependent calcium channel family (CACNA1C), dopamine D1 receptor, and glutamate ionotropic NMDA type 2A and 2B receptors. Many other disease-associated genes have not been included in models of synaptic plasticity, for example catechol-O-methyltransferase (COMT) and MAO A. By incorporating pathway enrichment results, we identify LAMTOR, a gene uniquely associated with Schizophrenia, which is closely linked to the MAPK pathway found in some models.\n\nOur analysis provides a map of how molecular pathways underpinning neurological diseases relate to synaptic biophysical models that can in turn be used to explore how these molecular events might bridge scales into cellular processes and beyond. The map illustrates disease areas where biophysical models have good coverage as well as domain gaps that require significant further research.\n\nAuthor summaryThe 100 billion neurons in the human brain are connected by a billion trillion structures called synapses. Each synapse contains hundreds of different proteins. Some proteins sense the activity of the neurons connecting the synapse. Depending on what they sense, the proteins in the synapse are rearranged and new proteins are synthesised. This changes how strongly the synapse influences its target neuron, and underlies learning and memory. Scientists build computational models to reason about the complex interactions between proteins. Here we list the proteins that have been included in computational models to date. For good reasons, models do not always specify proteins precisely, so to make the list we had to translate the names used for proteins in models to gene names, which are used to identify proteins. Our translation could be used to label computational models in the future. We found that the list of modelled proteins contains only 4.2% of proteins associated with synapses, suggesting more proteins should be added to models. We used lists of genes associated with neurological diseases to suggest proteins to include in future models.

neuroscience↗