Variability in the CIITA gene interacts with HLA in multiple sclerosis

Interactome analysis implicates class II transactivator (CIITA) in depression and other neuroinflammatory disorders

PURPOSE

Inappropriate inflammatory responses within the nervous system (neuroinflammation) have been implicated in several neurological conditions. Class II transactivator (CIITA), a principal regulator of the major histocompatibility complex II (MHCII), is known to play essential roles in inflammation. Hence, CIITA and its interactors could be potentially involved in multiple neurological disorders. However, the molecular mechanisms underlying CIITA-mediated neuroinflammation (NI) are yet to be understood.

MATERIALS AND METHODS

In this regard, we analyzed the potential involvement of CIITA and its interactome in the regulation of neuroinflammation. In the present study, using various computational tools, we aimed (1) to identify NI-related proteins, (2) to filter the critical interactors in the CIITA-NI network, and (3) to analyze the protein-disease interactions and the associated molecular pathways through which CIITA could influence neuroinflammation.

RESULTS

CIITA was found to interact with P T GS2, GSK3B, and NR3C1 and may influence depressive disorders. Further, the IL4/IL13 pathway was found to be potentially underlying the CIITA-interactomemediated effects on neurological disorders. Moreover, CIITA was found to be connected to genes associated with depressive disorder through IL4, wherein CIITA was found to be potentially involved in depressive disorders through IL-4/IL-13 and hippo pathways. However, the present study is based on the existing data on protein interactomes and could be re-evaluated as newer interactions are discovered. Also, the functional mechanisms of CIITA's roles in neuroinflammation must be evaluated further.

CONCLUSION

Notwithstanding these limitations, the results presented here, could form a basis for further experimental studies to assess CIITA as a potential therapeutic target in managing depression and other neuroinflammatory disorders.

Tissue-Specific Variations in Transcription Factors Elucidate Complex Immune System Regulation

Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed “transcriptome imputation” are used to estimate the genetic component of gene expression, but these models typically consider only the cis regions of the gene. However, these cis-based models miss large variability in expression for multiple genes. Transcription factors (TFs) that regulate gene expression are natural candidates for looking for additional sources of the missing variability. We developed a hypothesis-driven approach to identify second-tier regulation by variability in TFs. Our approach tested two models representing possible mechanisms by which variations in TFs can affect gene expression: variability in the expression of the TF and genetic variants within the TF that may affect the binding affinity of the TF to the TF-binding site. We tested our TF models in whole blood and skeletal muscle tissues and identified TF variability that can partially explain missing gene expression for 1035 genes, 76% of which explains more than the cis-based models. While the discovered regulation patterns were tissue-specific, they were both enriched for immune system functionality, elucidating complex regulation patterns. Our hypothesis-driven approach is useful for identifying tissue-specific genetic regulation patterns involving variations in TF expression or binding.

Link between CIITA rs3087456 polymorphism and the risk of laryngeal squamous cell carcinoma in a Chinese population

The class II trans-activator (CIITA) is the master regulator of the major histocompatibility complex (MHC) gene expression. CIITA mutations have been previously associated with several kinds of tumors, while the role of CIITA polymorphisms (rs3087456) in laryngeal squamous cell carcinoma (LSCC) is little known. We evaluate the link between CIITA polymorphisms and the existence of LSCC in patients. This study was conducted with 200 Chinese Han patients (LSCC) and 200 healthy control subjects. The association of CIITA genetic polymorphism rs3087456 with the risk of LSCC was assessed through pyrosequencing. The CIITA expression in LSCC tumor tissue and adjacent normal tissue was detected by immunohistochemistry (IHC) staining. The relationship between the genotype of rs3087456 in controls and in clinical pathology features in LSCC were analyzed, and in-silico analysis was also used for the CIITA gene. The in-silico analysis results showed that the CIITA gene is closely related to genes such as RFX5 and RFXAP. The IHC results showed that CIITA was highly expressed in LSCC tumor tissues, compared with the corresponding adjacent normal tissues. The AG, AG + AA, and A genotypes of rs3087456 of CIITA gene notably increased the risk of LSCC compared to the controls. Our study suggests that CIITA polymorphism (rs3087456) is associated with a higher risk of developing LSCC in a Chinese cohort.

Genome-Wide Analysis in Brazilians Reveals Highly Differentiated Native American Genome Regions

Despite its population, geographic size, and emerging economic importance, disproportionately little genome-scale research exists into genetic factors that predispose Brazilians to disease, or the population genetics of risk. After identification of suitable proxy populations and careful analysis of tri-continental admixture in 1,538 North-Eastern Brazilians to estimate individual ancestry and ancestral allele frequencies, we computed 400,000 genome-wide locus-specific branch length (LSBL) Fst statistics of Brazilian Amerindian ancestry compared to European and African; and a similar set of differentiation statistics for their Amerindian component compared with the closest Asian 1000 Genomes population (surprisingly, Bengalis in Bangladesh). After ranking SNPs by these statistics, we identified the top 10 highly differentiated SNPs in five genome regions in the LSBL tests of Brazilian Amerindian ancestry compared to European and African; and the top 10 SNPs in eight regions comparing their Amerindian component to the closest Asian 1000 Genomes population. We found SNPs within or proximal to the genes CIITA (rs6498115), SMC6 (rs1834619), and KLHL29 (rs2288697) were most differentiated in the Amerindian-specific branch, while SNPs in the genes ADAMTS9 (rs7631391), DOCK2 (rs77594147), SLC28A1 (rs28649017), ARHGAP5 (rs7151991), and CIITA (rs45601437) were most highly differentiated in the Asian comparison. These genes are known to influence immune function, metabolic and anthropometry traits, and embryonic development. These analyses have identified candidate genes for selection within Amerindian ancestry, and by comparison of the two analyses, those for which the differentiation may have arisen during the migration from Asia to the Americas.

Fast and general tests of genetic interaction for genome-wide association studies

A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10⁻⁰⁹) as well as replicate the interaction (p = 6.97 ⋅ 10⁻⁰⁷). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.

Effects of C2ta genetic polymorphisms on MHC class II expression and autoimmune diseases

Antigen presentation by the MHC-II to CD4⁺ T cells is important in adaptive immune responses. The class II transactivator (CIITA in human and C2TA in mouse) is the master regulator of MHC-II gene expression. It coordinates the transcription factors necessary for the transcription of MHC-II molecules. In humans, genetic variations in CIITA have been associated with differential expression of MHC-II and susceptibility to autoimmune diseases. Here we made use of a C2ta congenic mouse strain (expressing MHC-II haplotype H-2^q ) to investigate the effect of the natural genetic polymorphisms in type I promoter of C2ta on MHC-II expression and function. We demonstrate that an allelic variant in the type I promoter of C2ta resulted in an increased expression of MHC-II on macrophages (72-151% higher mean florescence intensity) and conventional dendritic cells (13-65% higher mean florescence intensity) in both spleen and peripheral blood. The increase in MHC-II expression resulted in an increase in antigen presentation to T cells in vitro and increased T-cell activation. The differential MHC-II expression in B6Q.C2ta, however, did not alter the disease development in models of rheumatoid arthritis (collagen-induced arthritis and human glucose-6-phosphate-isomerase_325-339 -peptide-induced arthritis), or multiple sclerosis (MOG_1-125 protein-induced and MOG_79-96 peptide-induced experimental autoimmune encephalomyelitis). This is the first study to address the role of an allelic variant in type I promoter of C2ta in MHC-II expression and autoimmune diseases; and shows that C2ta polymorphisms regulate MHC-II expression and T-cell responses but do not necessarily have a strong impact on autoimmune diseases.

Genotypic and phenotypic predictors of inflammation in patients with chronic kidney disease

BACKGROUND

In complex diseases such as chronic kidney disease (CKD), the risk of clinical complications is determined by interactions between phenotypic and genotypic factors. However, clinical epidemiological studies rarely attempt to analyse the combined effect of large numbers of phenotype and genotype features. We have recently shown that the relaxed linear separability (RLS) model of feature selection can address such complex issues. Here, it is applied to identify risk factors for inflammation in CKD.

METHODS

The RLS model was applied in 225 CKD stage 5 patients sampled in conjunction with dialysis initiation. Fifty-seven anthropometric or biochemical measurements and 79 genetic polymorphisms were entered into the model. The model was asked to identify phenotypes and genotypes that, when combined, could separate inflamed from non-inflamed patients. Inflammation was defined as a high-sensitivity C-reactive protein concentration above the median (5 mg/L).

RESULTS

Among the 60 genotypic and phenotypic features predicting inflammation, 31 were genetic. Among the 10 strongest predictors of inflammation, 8 were single nucleotide polymorphisms located in the NAMPT, CIITA, BMP2 and PIK3CB genes, whereas fibrinogen and bone mineral density were the only phenotypic biomarkers.

CONCLUSION

These results indicate a larger involvement of hereditary factors in inflammation than might have been expected and suggest that inclusion of genotype features in risk assessment studies is critical. The RLS model demonstrates that inflammation in CKD is determined by an extensive panel of factors and may prove to be a suitable tool that could enable a much-needed multifactorial approach as opposed to the commonly utilized single-factor analysis.

Multiple Sclerosis Risk Allele in CLEC16A Acts as an Expression Quantitative Trait Locus for CLEC16A and SOCS1 in CD4+ T Cells

For multiple sclerosis, genome wide association studies and follow up studies have identified susceptibility single nucleotide polymorphisms located in or near CLEC16A at chromosome 16p13.13, encompassing among others CIITA, DEXI and SOCS1 in addition to CLEC16A. These genetic variants are located in intronic or intergenic regions and display strong linkage disequilibrium with each other, complicating the understanding of their functional contribution and the identification of the direct causal variant(s). Previous studies have shown that multiple sclerosis-associated risk variants in CLEC16A act as expression quantitative trait loci for CLEC16A itself in human pancreatic β-cells, for DEXI and SOCS1 in thymic tissue samples, and for DEXI in monocytes and lymphoblastoid cell lines. Since T cells are major players in multiple sclerosis pathogenesis, we have performed expression analyses of the CIITA-DEXI-CLEC16A-SOCS1 gene cluster in CD4+ and CD8+ T cells isolated from multiple sclerosis patients and healthy controls. We observed a higher expression of SOCS1 and CLEC16A in CD4+ T cells in samples homozygous for the risk allele of CLEC16A rs12927355. Pair-wise linear regression analysis revealed high correlation in gene expression in peripheral T cells of CIITA, DEXI, CLEC16A and SOCS1. Our data imply a possible regulatory role for the multiple sclerosis-associated rs12927355 in CLEC16A.