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Supplemental material, sj-docx-1-msj-10.1177_1352458520987269 for Unbiased examination of genome-wide human endogenous retrovirus transcripts in MS brain lesions by Maria L Elkjaer, Tobias Frisch, Arianna Tonazzolli, Richard Röttger, Richard Reynolds, Jan Baumbach and Zsolt Illes in Multiple Sclerosis Journal

Supplemental material, sj-pdf-1-msj-10.1177_1352458520987269 for Unbiased examination of genome-wide human endogenous retrovirus transcripts in MS brain lesions by Maria L Elkjaer, Tobias Frisch, Arianna Tonazzolli, Richard Röttger, Richard Reynolds, Jan Baumbach and Zsolt Illes in Multiple Sclerosis Journal

Additional file 8. Review history.

Supplemental material, sj-pdf-2-msj-10.1177_1352458520987269 for Unbiased examination of genome-wide human endogenous retrovirus transcripts in MS brain lesions by Maria L Elkjaer, Tobias Frisch, Arianna Tonazzolli, Richard Röttger, Richard Reynolds, Jan Baumbach and Zsolt Illes in Multiple Sclerosis Journal

Supplemental material, sj-pdf-4-msj-10.1177_1352458520987269 for Unbiased examination of genome-wide human endogenous retrovirus transcripts in MS brain lesions by Maria L Elkjaer, Tobias Frisch, Arianna Tonazzolli, Richard Röttger, Richard Reynolds, Jan Baumbach and Zsolt Illes in Multiple Sclerosis Journal

Supplemental material, sj-pdf-3-msj-10.1177_1352458520987269 for Unbiased examination of genome-wide human endogenous retrovirus transcripts in MS brain lesions by Maria L Elkjaer, Tobias Frisch, Arianna Tonazzolli, Richard Röttger, Richard Reynolds, Jan Baumbach and Zsolt Illes in Multiple Sclerosis Journal

Additional file 1: Tables S1-S3. Table S1. Enrichment of the pathway ���Muscle contraction��� from Reactome for the exons upregulated in the muscles generated by the NEASE package. Table S2. Enrichment of the pathway ���Synaptic vesicle cycle��� from KEGG for the exons upregulated in the neural tissues generated by the NEASE package. Table S3. RNA-Seq samples used in the study. Figs. S1-S6. Fig. S1. Node degree distribution of the classic PPI and structurally annotated PPI, the latter contains only interactions with evidence from DDIs and DMIs or residue-level evidence from the co-resolved structure. Fig S2. Network Enrichment Analysis using EviNet webtool for exons upregulated in muscles and neural tissues. Fig. S3. NEASE visualization highlights the interactions of differentially spliced genes with the DCM pathway. Fig. S4. The PSI values of two exon skipping events in the genes TPM1 and DST from the GTEx dataset confirm that both the exons are upregulated in muscles and heart tissues. Fig. S5. The PSI values of two exon skipping events in the genes CLTA and CLTB from the GTEx dataset confirm that both the exons are upregulated in neural tissues. Fig. S6. Pseudocode of NEASE algorithm.

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. Electronic supplementary material The online version of this article (10.1186/s40478-019-0709-3) contains supplementary material, which is available to authorized users.

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., Competing Interests: JSR reports funding from GSK, Pfizer and Sanofi and fees from Travere Therapeutics and Astex Pharmaceuticals.