A Polymorphism Within the MBP Gene Is Associated With a Higher Relapse Number in Male Patients of Multiple Sclerosis

Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data

This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc (h2 = 0.77; 95%CI: 0.36, 1.00), Plat (h2 = 0.70; 95%CI: 0.27, 0.95), Anxa1 (h2 = 0.68; 95%CI: 0.27, 1.00), Sod1 (h2 = 0.58; 95%CI: 0.18, 0.96), Irf8 (h2 = 0.56; 95%CI: 0.19, 0.99), Ptger4 (h2 = 0.45; 95%CI: 0.10, 0.96), and Fadd (h2 = 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability.

Investigation of differentially expressed genes and dysregulated pathways involved in multiple sclerosis

Multiple Sclerosis (MS) is a neurodegenerative autoimmune and organ-specific demyelinating disorder, known to affect the central nervous system (CNS). While genetic studies have revealed several critical genes and diagnostic biomarkers associated with MS, the etiology of the disease remains poorly understood. This study is aimed at screening and identifying the key genes and canonical pathways associated with MS. Gene expression profiling of the microarray dataset GSE38010 was used to analyze two control brain samples (control 1; GSM931812, control 2; GSM931813), active inflammation stage samples (CAP1; GSM931815, CAP2; GSM931816) and late subsided stage samples (CP1; GSM931817, CP2; GSM931818) collected from patients ranging between 23 and 54years and both genders. This analysis yielded a list of 58,866 DEGs (29,433 for active-inflammation stage and 29,433 for late-subsided Stage). The interactions between the DEGs were then studied using STRING, Cytoscape software, and MCODE was employed to find the genes that form clusters. Functional enrichment and integrative analysis were performed using ClueGO/CluePedia and MetaCore™. Our data revealed dysregulated key canonical pathways in MS patients. In addition, we identified three hub genes (SCN2A, HTR2A, and HCN1) that may serve as potential biomarkers for the prognosis of MS. Furthermore, the expression patterns of HPCA and PLCB1 provide insights into the progressive stages of MS, indicating that these genes could be used in predicting MS progression. We were able to map potential biomarkers that could be used for the prognosis and diagnosis of MS.

Smoking, cardiovascular risk factors and LRP2 gene variation: Associations with disease severity, cognitive function and brain structure in primary progressive multiple sclerosis

BACKGROUND

Smoking, cardiovascular risk factors, and genetic factors can have adverse effects in MS.

OBJECTIVE

To determine if smoking after disease onset, cardiovascular risk factors, and genetic variants influence primary progressive MS (PPMS).

METHOD

In this cross-sectional study, smoking habits, Framingham Risk Score (FRS), genetic variants, including the low-density lipoprotein receptor-related protein 2 (LRP2) SNP rs12988804 and MRI were collected in 60 PPMS trial participants. Disability and cognition were assessed with the Age-Related Multiple Sclerosis Severity (ARMSS) score, the Progressive-Onset MS Multiple Sclerosis Severity Score, and the Brief International Cognitive Assessment for MS.

RESULTS

Smoking after PPMS onset was significantly associated with higher ARMSS (95% CI 0.8-2.4, p = 0.00016) statistically significant after Bonferroni correction. Lower magnetization transfer ratio in lesions was also significantly associated with smoking after onset of PPMS after correction (95% CI -0.9--4.4, p = 0.0035). Pack-years in people who smoked after onset was likewise significantly associated with higher ARMSS score (b = 0.06 95% CI 0.02-0.09, p = 0.0021) as well as lower Symbol Digit Modalities Test scores (b = -0.40; 95% CI -0.66--0.13, p = 0.0037), both statistically significant after Bonferroni correction. The LRP2 risk allele was associated with decreased performance on the California Verbal Learning Test 2 after correction (CC vs. CT+TT 95% CI -14.2--3.4, p = 0.0018).

CONCLUSION

If validated, these findings suggest that intervention regarding smoking may be beneficial in PPMS. If confirmed, assessment of the LRP2 gene variant may aid in the understanding of underlying pathological mechanisms in PPMS.