CXCL10/IgG1 Axis in Multiple Sclerosis as a Potential Predictive Biomarker of Disease Activity.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is a heterogeneous disease, and its course is difficult to predict. Prediction models can be established by measuring intrathecally synthesized proteins involved in inflammation, glial activation, and CNS injury. METHODS: To determine how these intrathecal proteins relate to the short-term, i.e., 12 months, disease activity in relapsing-remitting MS (RRMS), we measured the intrathecal synthesis of 46 inflammatory mediators and 14 CNS injury or glial activation markers in matched serum and CSF samples from 47 patients with MS (pwMS), i.e., 23 RRMS and 24 clinically isolated syndrome (CIS), undergoing diagnostic lumbar puncture. Subsequently, all pwMS were followed for ≥12 months in a retrospective follow-up study and ultimately classified into "active", i.e., developing clinical and/or radiologic disease activity, n = 18) or "nonactive", i.e., not having disease activity, n = 29. Disease activity in patients with CIS corresponded to conversion to RRMS. Thus, patients with CIS were subclassified as "converters" or "nonconverters" based on their conversion status at the end of a 12-month follow-up. Twenty-seven patients with noninflammatory neurologic diseases were included as negative controls. Data were subjected to differential expression analysis and modeling techniques to define the connectivity arrangement (network) between neuroinflammation and CNS injury relevant to short-term disease activity in RRMS. RESULTS: Lower age and/or higher CXCL13 levels positively distinguished active/converting vs nonactive/nonconverting patients. Network analysis significantly improved the prediction of short-term disease activity because active/converting patients featured a stronger positive connection between IgG1 and CXCL10. Accordingly, analysis of disease activity-free survival demonstrated that pwMS, both RRMS and CIS, with a lower or negative IgG1-CXCL10 correlation, have a higher probability of activity-free survival than the patients with a significant correlation (p < 0.0001, HR ≥ 2.87). DISCUSSION: Findings indicate that a significant IgG1-CXCL10 positive correlation predicts the risk of short-term disease activity in patients with RRMS and CIS. Thus, the present results can be used to develop a predictive model for MS activity and conversion to RRMS.

Immunological shifts during early-stage Parkinson's disease identified with DNA methylation data on longitudinally collected blood samples.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the United States. Decades before motor symptoms manifest, non-motor symptoms such as hyposmia and rapid eye movement (REM) sleep behavior disorder are highly predictive of PD. Previous immune profiling studies have identified alterations to the proportions of immune cells in the blood of clinically defined PD patients. However, it remains unclear if these phenotypes manifest before the clinical diagnosis of PD. We utilized longitudinal DNA methylation (DNAm) microarray data from the Parkinson's Progression Marker's Initiative (PPMI) to perform immune profiling in clinically defined PD and prodromal PD patients (Prod). We identified previously reported changes in neutrophil, monocyte, and T cell numbers in PD patients. Additionally, we noted previously unrecognized decreases in the naive B cell compartment in the defined PD and Prod patient group. Over time, we observed the proportion of innate immune cells in PD blood increased, but the proportion of adaptive immune cells decreased. We identified decreases in T and B cell subsets associated with REM sleep disturbances and early cognitive decline. Lastly, we identified increases in B memory cells associated with both genetic (LRRK2 genotype) and infectious (cytomegalovirus seropositivity) risk factors of PD. Our analysis shows that the peripheral immune system is dynamic as the disease progresses. The study provides a platform to understand how and when peripheral immune alterations occur in PD and whether intervention at particular stages may be therapeutically advantageous.

The CXCL13 Index as a Predictive Biomarker for Activity in Clinically Isolated Syndrome.

Multiple sclerosis (MS) is a clinically heterogenous disease. Currently, we cannot identify patients with more active disease who may potentially benefit from earlier interventions. Previous data from our lab identified the CXCL13 index (ICXCL13), a measure of intrathecal production of CXCL13, as a potential biomarker to predict future disease activity in MS patients two years after diagnosis. Patients with clinically isolated syndrome (CIS) or radiologically isolated syndrome (RIS) underwent a lumbar puncture and blood draw, and the ICXCL13 was determined. They were then followed for at least 5 years for MS activity. Patients with high ICXCL13 were more likely to convert to clinically definite MS (82.4%) compared to those with low ICXCL13 (10.0%). The data presented below demonstrate that this predictive ability holds true in CIS and RIS patients, and for at least five years compared to our initial two-year follow-up study. These data support the concept that ICXCL13 has the potential to be used to guide immunomodulatory therapy in MS.

Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation.

Introduction: The 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 deconvolved. Methods: 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. Results: 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. Discussion: 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.

Theiler's virus-induced demyelinating disease as an infectious model of progressive multiple sclerosis.

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.

MethylMasteR: A Comparison and Customization of Methylation-Based Copy Number Variation Calling Software in Cancers Harboring Large Scale Chromosomal Deletions.

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.