Vitamin D is becoming increasingly accepted as an important physiological regulator outside of its classical role in skeletal homeostasis. A growing body of evidence connects vitamin D with hepatic disease. This review summarises the role of vitamin D in liver homeostasis and disease and discusses the therapeutic potential of vitamin D-based treatments to protect against hepatic disease progression and to improve response to treatment. While pre-clinical experimental data is promising, clinical trials around liver diseases have mostly been under-powered, and further studies will be required to clarify whether vitamin D or vitamin D analogues have beneficial effects on liver disease.

Although the causes of Multiple Sclerosis (MS) still remain largely unknown, multiple lines of evidence suggest that Epstein–Barr virus (EBV) infection may contribute to the development of MS. Here, we aimed to identify the potential contribution of EBV-encoded and host cellular miRNAs to MS pathogenesis. We identified differentially expressed host miRNAs in EBV infected B cells (LCLs) and putative host/EBV miRNA interactions with MS risk loci. We estimated the genotype effect of MS risk loci on the identified putative miRNA:mRNA interactions in silico. We found that the protective allele of MS risk SNP rs4808760 reduces the expression of hsa-mir-3188-3p. In addition, our analysis suggests that hsa-let-7b-5p may interact with ZC3HAV1 differently in LCLs compared to B cells. In vitro assays indicated that the protective allele of MS risk SNP rs10271373 increases ZC3HAV1 expression in LCLs, but not in B cells. The higher expression for the protective allele in LCLs is consistent with increased IFN response via ZC3HAV1 and so decreased immune evasion by EBV. Taken together, this provides evidence that EBV infection dysregulates the B cell miRNA machinery, including MS risk miRNAs, which may contribute to MS pathogenesis via interaction with MS risk genes either directly or indirectly.

Background: Epstein-Barr virus (EBV) infection may be necessary for the development of Multiple sclerosis (MS). Earlier we had identified six MS risk loci that are co-located with binding sites for the EBV transcription factor Epstein-Barr Nuclear Antigen 2 (EBNA2) in EBV-infected B cells (lymphoblastoid cell lines À LCLs). Methods: We used an allele-specific chromatin immunoprecipitation PCR assay to assess EBNA2 allelic preference. We treated LCLs with a peptide inhibitor of EBNA2 (EBNA2-TAT), reasoning that inhibiting EBNA2 function would alter gene expression at these loci if it was mediated by EBNA2. Findings: We found that EBNA2 binding was dependent on the risk allele for five of these six MS risk loci (p < 0¢05). Treatment with EBNA2-TAT significantly altered the expression of TRAF3 (p < 0¢05), CD40 (p < 0¢001), CLECL1 (p <0¢0001), TNFAIP8 (p < 0¢001) and TNFRSF1A (p < 0¢001). Interpretation: These data suggest that EBNA2 can enhance or reduce expression of the gene depending on the risk allele, likely promoting EBV infection. This is consistent with the concept that these MS risk loci affect MS risk through altering the response to EBNA2. Together with the extensive data indicating a pathogenic role for EBV in MS, this study supports targeting EBV and EBNA2 to reduce their effect on MS pathogenesis.

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Multiple Sclerosis (MS) is a complex immune-mediated disease of the central nervous system. Treatment is based on immunomodulation, including specifically targeting B cells. B cells are the main host for the Epstein-Barr Virus (EBV), which has been described as necessary for MS development. Over 200 genetic loci have been identified as increasing susceptibility to MS. Many MS risk genes have altered expression in EBV infected B cells, dependent on the risk genotype, and are themselves regulated by the EBV transcription factor EBNA2. Females are 2-3 times more likely to develop MS than males. We investigated if MS risk loci might mediate the gender imbalance in MS. From a large public dataset, we identified gender-specific associations with EBV traits, and MS risk SNP/gene pairs with gender differences in their associations with gene expression. Some of these genes also showed gender differences in correlation of gene expression level with Estrogen Receptor 2. To test if estrogens may drive these gender specific differences, we cultured EBV infected B cells (lymphoblastoid cell lines, LCLs), in medium depleted of serum to remove the effects of sex hormones as well as the estrogenic effect of phenol red, and then supplemented with estrogen (100 nM estradiol). Estradiol treatment altered MS risk gene expression, LCL proliferation rate, EBV DNA copy number and EBNA2 expression in a sex-dependent manner. Together, these data indicate that there are estrogen-mediated gender-specific differences in MS risk gene expression and EBV functions. This may in turn contribute to gender differences in host response to EBV and to MS susceptibility.

Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.