Expression of Bruton´s tyrosine kinase in different type of brain lesions of multiple sclerosis patients and during experimental demyelination

BackgroundInhibition of Bruton’s tyrosine kinase (BTK) is an emerging multiple sclerosis (MS) therapy. BTK inhibitors (BTKi) cross the blood-brain barrier and modulate B cells and microglia, major cellular players in active and chronic active lesions.ObjectiveTo assess potential lesional and cellular targets of BTKi, we examined BTK expression in different type of MS white matter (WM) lesions, in unmanipulated CNS resident cells, and in a degenerative MS model associated with microglia activation in vivo.MethodsWe examined BTK expression by next-generation RNA-sequencing in postmortem 25 control WM, 19 NAWM, 6 remyelinating, 18 active, 13 inactive and 17 chronic active lesions. Presence of B cells and microglia were examined by immunohistochemistry. CNS resident cells were isolated from the mouse brain by magnetic sorting. BTK expression was examined by quantitative PCR in isolated cells and dissected corpus callosum from mice treated with cuprizone (CPZ).ResultsBTK expression was significantly increased in active and chronic active lesions with upregulated complement receptors and Fcγ receptors. Active lesions contained high number of perivascular B cells, microglia, and macrophages. Chronic active lesions were characterized by microglia/macrophages in the rim. Microglia expressed BTK at high level (120-fold) in contrast to other CNS cell types (2-4-fold). BTK expression was increasing during CPZ treatment reaching significance after stopping CPZ.ConclusionConsidering BTK expression in MS lesions and resident cells, BTKi may exert effect on B cells, microglia/macrophages in active lesions, and limit microglia activation in chronic active lesions, where tissue damage propagates

Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet

Differences in co-expression networks between two or multiple cell (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/ or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN and GRN pipeline including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, coregulatory networks, and drug-gene interactions. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is available as a free, open source, and user-friendly Python package that can be easily integrated in systems biology pipelines.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table

RETRACTED ARTICLE: Unique RNA signature of different lesion types in the brain white matter in progressive multiple sclerosis

Abstract The heterogeneity of multiple sclerosis is reflected by dynamic changes of different lesion types in the brain white matter (WM). To identify potential drivers of this process, we RNA-sequenced 73 WM areas from patients with progressive MS (PMS) and 25 control WM. Lesion endophenotypes were described by a computational systems medicine analysis combined with RNAscope, immunohistochemistry, and immunofluorescence. The signature of the normal-appearing WM (NAWM) was more similar to control WM than to lesions: one of the six upregulated genes in NAWM was CD26/DPP4 expressed by microglia. Chronic active lesions that become prominent in PMS had a signature that were different from all other lesion types, and were differentiated from them by two clusters of 62 differentially expressed genes (DEGs). An upcoming MS biomarker, CHI3L1 was among the top ten upregulated genes in chronic active lesions expressed by astrocytes in the rim. TGFβ-R2 was the central hub in a remyelination-related protein interaction network, and was expressed there by astrocytes. We used de novo networks enriched by unique DEGs to determine lesion-specific pathway regulation, i.e. cellular trafficking and activation in active lesions; healing and immune responses in remyelinating lesions characterized by the most heterogeneous immunoglobulin gene expression; coagulation and ion balance in inactive lesions; and metabolic changes in chronic active lesions. Because we found inverse differential regulation of particular genes among different lesion types, our data emphasize that omics related to MS lesions should be interpreted in the context of lesion pathology. Our data indicate that the impact of molecular pathways is substantially changing as different lesions develop. This was also reflected by the high number of unique DEGs that were more common than shared signatures. A special microglia subset characterized by CD26 may play a role in early lesion development, while astrocyte-derived TGFβ-R2 and TGFβ pathways may be drivers of repair in contrast to chronic tissue damage. The highly specific mechanistic signature of chronic active lesions indicates that as these lesions develop in PMS, the molecular changes are substantially skewed: the unique mitochondrial/metabolic changes and specific downregulation of molecules involved in tissue repair may reflect a stage of exhaustion

Hypothesis of a potential BrainBiota and its relation to CNS autoimmune inflammation

Infectious agents have been long considered to play a role in the pathogenesis of neurological diseases as part of the interaction between genetic susceptibility and the environment. The role of bacteria in CNS autoimmunity has also been highlighted by changes in the diversity of gut microbiota in patients with neurological diseases such as Parkinson's disease, Alzheimer disease and multiple sclerosis, emphasizing the role of the gut-brain axis. We discuss the hypothesis of a brain microbiota, the BrainBiota: bacteria living in symbiosis with brain cells. Existence of various bacteria in the human brain is suggested by morphological evidence, presence of bacterial proteins, metabolites, transcripts and mucosal-associated invariant T cells. Based on our data, we discuss the hypothesis that these bacteria are an integral part of brain development and immune tolerance as well as directly linked to the gut microbiome. We further suggest that changes of the BrainBiota during brain diseases may be the consequence or cause of the chronic inflammation similarly to the gut microbiota.Published versionMLE is grateful for financial support from Lundbeckfonden (no. R347-2020-2454). ZI is grateful for financial support from Independent Research Fund Denmark (DFF 9039-00370B), Lundbeckfonden (R118-A11472), Scleroseforeningen (A25341, A29926, A31829, A33600), University of Southern Denmark (14/24200), Odense University Hospital (5798002573633). JB is grateful for financial support from the Center for Data and Computing in Natural Sciences (CDCS), and by his VILLUM Young Investigator Grant nr.13154. Furthermore, this project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777111

Proteomic changes during experimental de- and remyelination in the corpus callosum.

BACKGROUND:In the cuprizone model of multiple sclerosis, de- and remyelination can be studied without major interference from the adaptive immune responses. Since previous proteomic studies did not focus on the corpus callosum, where cuprizone causes the most pronounced demyelination, we performed a bottom up proteomic analysis on this brain region. METHODS:Eight week-old mice treated with 0.2% cuprizone, for 4 weeks and controls (C) were sacrificed after termination of the treatment (4wD), and 2 (2dR) or 14 (2wR) days later. Homogenates of dissected corpus callosum were analysed by quantitative proteomics. For data processing, clustering, gene ontology analysis, and regulatory network prediction, we used Perseus, PANTHER and Ingenuity Pathway Analysis softwares, respectively. RESULTS:We identified 4886 unmodified, single- or multi phosphorylated and/or gycosylated (PTM) proteins. Out of them, 191 proteins were differentially regulated in at least one experimental group. We found 57 proteins specific for demyelination, 27 for early- and 57 for late remyelinationwhile 36 proteins were affected in two, and 23 proteins in all three groups. Phosphorylation represented 92% of the post translational modifications among differentially regulated modified (PTM) proteins with decreased level, while it was only 30% of the PTM proteins with increased level. Gene ontology analysis could not classify the demyelination specific proteins into any biological process category, while allocated the remyelination specific ones to nervous system development and myelination as the most specific subcategory. We also identified a protein network in experimental remyelination, and the gene orthologues of the network were differentially expressed in remyelinating multiple sclerosis brain lesions consistent with an early remyelination pattern. CONCLUSION:Proteomic analysis seems more informative for remyelination than demyelination in the cuprizone model