A proposed workflow to analyze bacterial transcripts in RNAseq from blood extracellular vesicles of people with Multiple Sclerosis

Abstract

Motivation: The taxonomical characterisation of bacterial species derived from genetic material blood, including reads derived from bacterial extracellular vesicles (bEVs) poses certain challenges, such as the proper discrimination of "true" reads from contaminating reads. This is a common issue in taxa profiling and can lead to the false discovery of species that are present in the sample. To avoid such biases a careful approximation to taxa profiling is necessary. Results: In this work we propose a workflow to analyze the presence of bacterial transcripts as indicative of putative bEVs circulating in the blood of people with MS (pwMS). The workflow includes several reference mapping steps against the host genome and a consensus selection of genera based on different taxa profilers. The consensus selection is performed with a flagging system that removes species with low abundance or with high variation across profilers. Additionally, the inclusion of biological samples from known cultured species as well as the generation of artificial reads constitute two key aspects of this workflow. Availability: The workflow is available at the following repository: https://github.com/NanoNeuro/EV_taxprofiling

SLIRP promotes autoimmune diseases by amplifying antiviral signaling via positive feedback regulation

“…However, since the upregulation of PNPT1 and downregulation of MTPAP should lead to mt-dsRNA downregulation, it cannot account for the reported elevation of mtRNA expression. In addition, we reanalyzed the expression of the mtRNA regulators in other autoimmune diseases, including peripheral blood mononuclear cells (PBMCs) of rheumatoid arthritis (RA) 54 , extracellular vesicles (EVs) of secondary progressive multiple sclerosis (SPMS) 55 , and fibroblast of Aicardi- Goutières Syndrome (AGS) 56 (Figure 1B). Notably, SLIRP was upregulated in all three autoimmune diseases, along with degrading factors such as hSUV3 and PNPT1 .…”