Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of unknown etiology. However, several studies suggest that infectious agents, e.g., Human Herpes Viruses (HHV), may be involved in triggering the disease. Molecular mimicry, bystander effect, and epitope spreading are three mechanisms that can initiate immunoreactivity leading to CNS autoimmunity in MS. Theiler’s murine encephalomyelitis virus (TMEV)-induced demyelinating disease (TMEV-IDD) is a pre-clinical model of MS in which intracerebral inoculation of TMEV results in a CNS autoimmune disease that causes demyelination, neuroaxonal damage, and progressive clinical disability. Given the spectra of different murine models used to study MS, this review highlights why TMEV-IDD represents a valuable tool for testing the viral hypotheses of MS. We initially describe how the main mechanisms of CNS autoimmunity have been identified across both MS and TMEV-IDD etiology. Next, we discuss how adaptive, innate, and CNS resident immune cells contribute to TMEV-IDD immunopathology and how this relates to MS. Lastly, we highlight the sexual dimorphism observed in TMEV-IDD and MS and how this may be tied to sexually dimorphic responses to viral infections. In summary, TMEV-IDD is an underutilized murine model that recapitulates many unique aspects of MS; as we learn more about the nature of viral infections in MS, TMEV-IDD will be critical in testing the future therapeutics that aim to intervene with disease onset and progression.

IntroductionThe human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolvedMethodsUsing DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells.ResultsWe demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer’s disease, autism, Huntington’s disease, epilepsy, and schizophrenia.DiscussionWe expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.

DNA methylation-based copy number variation (CNV) calling software offers the advantages of providing both genetic (copy-number) and epigenetic (methylation) state information from a single genomic library. This method is advantageous when looking at large-scale chromosomal rearrangements such as the loss of the short arm of chromosome 3 (3p) in renal cell carcinoma and the codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q) commonly seen in histologically defined oligodendrogliomas. Herein, we present MethylMasteR: a software framework that facilitates the standardization and customization of methylation-based CNV calling algorithms in a single R package deployed using the Docker software framework. This framework allows for the easy comparison of the performance and the large-scale CNV event identification capability of four common methylation-based CNV callers. Additionally, we incorporated our custom routine, which was among the best performing routines. We employed the Affymetrix 6.0 SNP Chip results as a gold standard against which to compare large-scale event recall. As there are disparities within the software calling algorithms themselves, no single software is likely to perform best for all samples and all combinations of parameters. The employment of a standardized software framework via creating a Docker image and its subsequent deployment as a Docker container allows researchers to efficiently compare algorithms and lends itself to the development of modified workflows such as the custom workflow we have developed. Researchers can now use the MethylMasteR software for their methylation-based CNV calling needs and follow our software deployment framework. We will continue to refine our methodology in the future with a specific focus on identifying large-scale chromosomal rearrangements in cancer methylation data.

The human brain comprises heterogeneous cell subtypes whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. We demonstrate that DNA-based cell-type deconvolution achieves an accurate resolution of seven major cell types. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Current methods for brain cell deconvolution are limited only to neuronal and non-neuronal cells. Using DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells. We demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington’s disease, epilepsy, and schizophrenia. We expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.