Exploring the Therapeutic Potential of Sephin1 in Multiple Sclerosis: A Differential Gene Expression Analysis in the EAE Mouse Model
Abstract
Multiple sclerosis (MS) is an autoimmune disorder characterized by the degradation of the myelin sheath on neurons by the body’s own immune system. This degradation can cause dysfunctional neurons, which leads to decreased motor function in a patient. One specific pathway associated with MS is the Integrated Stress Response: an innate signaling pathway that reduces the effects of inflammation on oligodendrocytes, glial cells which produce myelin. Sephin1 is a molecule theorized to prolong the Integrated Stress Response, thereby preserving oligodendrocytes. This is significant as oligodendrocytes are pivotal to MS and MS therapies. Sephin1 affects the Integrated Stress Response and could be a possible treatment for patients with multiple sclerosis. Her recent study includes the use of the Experimental Autoimmune Encephalomyelitis (EAE) Mouse Model, a common model used to replicate the effects of MS (Chen, 2019). The purpose of our efforts was to provide differential oligodendrocyte gene expression analysis between all 3 experimental groups (non-disease model-negative control, EAE Vehicle- negative control and EAE Sephin1) to determine whether there is a significant difference in the mice with Sephin1. Platforms including Python and RStudio were used, alongside algorithms including Kallisto and Sleuth, and web-based tools such as WebGestalt.
Introduction
Multiple sclerosis (MS) is a chronic, autoimmune disease in which the immune system attacks myelin, the protective covering of neurons. In MS, nerve damage disrupts the communication between the brain and body, resulting in sensory, motor, and cognitive impairments. Multiple sclerosis is deemed as an immune-mediated inflammatory demyelinating disorder (Ghasemi, 2017). Although the cause of multiple sclerosis is unknown, some of its pathological properties include destruction of oligodendrocytes (the myelin-producing cells of the CNS), demyelination, and axon degeneration (Chen, 2019). With the addition of Sephin1, the Integrated Stress Response pathway is prolonged, saving oligodendrocytes that can be used to produce myelin for neurons whos myelin sheaths have degraded (Chen, 2019). This thereby limits the symptoms and pathogenesis of multiple sclerosis.
Studies investigating the effectiveness of Sephin1 in shielding oligodendrocytes against inflammatory stress show promise (Chen, 2019). This could potentially lead to further advancement in the treatment of multiple sclerosis (Robinson, 2014). By utilizing the EAE mouse model of MS, her lab set up three groups to be compared: CFA (negative control) vs. EAE + Vehicle (positive control), CFA vs. EAE + Sephin1, and EAE + Vehicle vs. EAE + Sephin1. Her lab collected oligodendrocyte RNA seq data from each group of mice to be used to perform differential expression analysis and gene set enrichment analysis. We’ve developed a pipeline that takes the translated transcript FASTQ files for each experimental group and runs them through Kallisto to quantify them. Then, the Kallisto output files are read into Sleuth, where the comparisons between the experimental groups are done (Chougule, 2018). Finally, the Sleuth output files are used in the web-based tool, WebGestalt, for gene set enrichment analysis that will allow for the extraction of biological insight from genes of interest and their significantly changed pathways (Liao, 2019).
Project Github Repository: https://github.com/abielanski/Multiple-Sclerosis-RNA-Seq
Implementation
The programming languages that we used to create our pipeline are Python and R. The initial input files were provided by Dr. Chen and made accessible to us on our class server. Each experimental group had three FASTQ files of RNA Seq transcripts, which we checked the quality of using FastQC before we continued to use them. Before running Kallisto, an index was generated. Then, Kallisto quantification was run using the generated index and the transcript FASTQ files. After that, the three output files produced by Kallisto for each of the experimental groups were read in by Sleuth, and the comparisons: CFA vs. Vehicle, CFA vs. Sephin1, and Vehicle vs. Sephin1 between the groups were done. Within Sleuth, the Wald test function is included to compute the specific beta coefficient of every transcript, which is necessary for WebGestalt. Furthermore, the transcript IDs were converted to gene IDs using Biomart. Next, three tables were generated, one for each comparison, with two columns for gene IDs and their beta coefficients. These tables were uploaded to the web-based WebGestalt for gene set enrichment analysis (GSEA). GSEA allowed for the extraction of biological insight from genes of interest and generated volcano plots and figures visualizing the processes they are involved in and their changed pathways. The final output files of our pipeline include gene lists and the plots generated by WebGestalt.
Results and Discussion
Sephin1 treatment increased expression of these three gene sets; Peptidyl-cysteine modification (Fig. 1.2, p= <2.2e-16), Cellular divalent inorganic cation homeostasis(Fig. 1.2, p= .001) and Intermediate filament-based process(Fig. 1.2, p= <2.2e-16). With no transcripts having significant differences at FDR(qval <0.05)(Fig. 2, FDR >.05).
Our results showcase which genes are expressed across the three conditions, giving biological insight to the processes affected by Sephin 1. Currently there is no oligodendrocyte-based treatment of MS, our project provides analytical support in determining whether or not Sephin 1 is a viable therapy for MS.
Our pipeline is specifically built around comparing the differential expression genes and significantly changed pathways in regards to Sephin 1 treatment for MS within an EAE mouse model. This tool could be developed to intake any FASTQ files of RNA seq transcripts and the parallel reference transcriptome to run the same GSEA analysis.