Exploring the diabetogenicity of the HLA-B18-DR3 CEH: independent association with T1D genetic risk close to HLA-DOA

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

BACKGROUND

Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF.

METHODS

We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes.

RESULTS

A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B).

CONCLUSIONS

This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

What to do with HLA-DO/H-2O two decades later?

The main objective of antigen processing is to orchestrate the selection of immunodominant epitopes for recognition by CD4 T cells. To achieve this, MHC class II molecules have evolved with a flexible peptide-binding groove in need of a bound peptide. Newly synthesized MHC-II molecules bind a class II invariant chain (Ii) upon synthesis and are shuttled to a specialized compartment, where they encounter exogenous antigens. Ii serves multiple functions, one of which is to maintain the shape of the MHC-II groove so that it can readily bind exogenous antigens upon dissociation of the Ii peptide in MHC- II compartment. MIIC contains processing enzymes, one or both accessory molecules, HLA-DM/H2-M (DM) and HLA-DO/H2-O (DO), and optimal denaturing conditions. In a process known as "editing," DM facilitates the dissociation of the invariant chain peptide, CLIP, for exchange with exogenous antigens. Despite the availability of mechanistic insights into DM functions, understanding how DO contributes to epitope selection has proven to be more challenging. The current dogma assumes that DO inhibits DM, whereas an opposing model suggests that DO fine-tunes the epitope selection process. Understanding which of these, or potentially other models of DO function is important, as DO variants have been linked to autoimmunity, cancer, and the generation of broadly neutralizing antibodies to viruses. This review therefore attempts to evaluate experimental evidence in support of these hypotheses, with an emphasis on the less discussed model, and to explore intriguing questions about the importance of DO in biology.

Celiac Diasease-associated lncRNA Named HCG14 Regulates NOD1 Expression in Intestinal Cells

OBJECTIVE

The aim of the study is to identify additional celiac disease associated loci in the major histocompatibility complex (MHC) independent from classical HLA risk alleles (HLA-DR3-DQ2) and to characterize their potential functional impact in celiac disease pathogenesis at the intestinal level.

METHODS

We performed a high-resolution single-nucleotide polymorphism (SNP) genotyping of the MHC region, comparing HLA-DR3 homozygous celiac patients and non-celiac controls carrying a single copy of the B8-DR3-DQ2 conserved extended haplotype. Expression level of potential novel risk genes was determined by RT-PCR in intestinal biopsies and in intestinal and immune cells isolated from control and celiac individuals. Small interfering RNA-driven silencing of selected genes was performed in the intestinal cell line T84.

RESULTS

MHC genotyping revealed 2 associated SNPs, one located in TRIM27 gene and another in the non-coding gene HCG14. After stratification analysis, only HCG14 showed significant association independent from HLA-DR-DQ loci. Expression of HCG14 was slightly downregulated in epithelial cells isolated from duodenal biopsies of celiac patients, and eQTL analysis revealed that polymorphisms in HCG14 region were associated with decreased NOD1 expression in duodenal intestinal cells.

CONCLUSIONS

We have successfully employed a conserved extended haplotype-matching strategy and identified a novel additional celiac disease risk variant in the lncRNA HCG14. This lncRNA seems to regulate the expression of NOD1 in an allele-specific manner. Further functional studies are needed to clarify the role of HCG14 in the regulation of gene expression and to determine the molecular mechanisms by which the risk variant in HCG14 contributes to celiac disease pathogenesis.

Identification of key genes for diabetic kidney disease using biological informatics methods

Diabetic kidney disease (DKD) is a common complication of diabetes, which is characterized by albuminuria, impaired glomerular filtration rate or a combination of the two. The aim of the present study was to identify the potential key genes involved in DKD progression and to subsequently investigate the underlying mechanism involved in DKD development. The array data of GSE30528 including 9 DKD and 13 control samples was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in DKD glomerular and tubular kidney biopsy tissues were compared with normal tissues, and were analyzed using the limma package. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for DEGs using the GO Function software in Bioconductor. The protein‑protein interaction (PPI) network was then constructed using Cytoscape software. A total of 426 genes (115 up‑ and 311 downregulated) were differentially expressed between the DKD and normal tissue samples. The PPI network was constructed with 184 nodes and 335 edges. Vascular endothelial growth factor A (VEGFA), α‑actinin‑4 (ACTN4), proto‑oncogene, Src family tyrosine kinase (FYN), collagen, type 1, α2 (COL1A2) and insulin‑like growth factor 1 (IGF1) were hub proteins. Major histocompatibility complex, class II, DP α1 (HLA‑DPA1) was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways, and the immune response was a GO term enriched in module A. VEGFA, ACTN4, FYN, COL1A2, IGF1 and HLA‑DPA1 may be potential key genes associated with the progression of DKD, and immune mechanisms may serve a part in DKD development.

The association of the HLA-A*24:02, B*39:01 and B*39:06 alleles with type 1 diabetes is restricted to specific HLA-DR/DQ haplotypes in Finns

BACKGROUND

We analysed the previously reported association of the HLA-A*24:02, B*18 and B*39 alleles with type 1 diabetes and diabetes associated autoimmunity in the Finnish population applying HLA-DR/DQ stratification.

MATERIALS & METHODS

Haplotype transmission was analysed in 2424 nuclear families from the Finnish Paediatric Diabetes Register. Survival analysis was applied to study the development of islet autoantibodies and further progression to clinical diabetes in the prospective follow-up cohort from the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study. The subjects were genotyped for specific HLA class I alleles by sequence-specific hybridization using lanthanide labelled nucleotide probes.

RESULTS

The HLA-B*39:06 allele was found almost exclusively on the (DR8)-DQB1*04 haplotype in which its presence changed the disease risk status of the whole haplotype from neutral to predisposing. The HLA-A*24:02 and the B*39:01 alleles increased the diabetes-associated risk of the DRB1*04:04-DQA1*03-DQB1*03:02 haplotype but the alleles were in linkage disequilibrium and no independent effect could be detected. Within the DIPP cohort, neither the A*24:02 nor the B*39:01 allele were associated with seroconversion but were in contrast associated with increased progression from seroconversion to clinical disease.

DISCUSSION & CONCLUSIONS

The independent predisposing effect of the HLA-B*39:06 allele with type 1 diabetes was confirmed in the Finnish population but the association of the A*24:02 and B*39:01 alleles remained inconclusive whilst both A*24:02 and B*39:01 affected the progression rate from seroconversion to autoantibody positivity to overt type 1 diabetes.

RNA-sequencing study of peripheral blood monocytes in chronic periodontitis

BACKGROUND

Monocytes are an important cell type in chronic periodontitis (CP) by interacting with oral bacteria and mediating host immune response. The aim of this study was to reveal new functional genes and pathways for CP at monocyte transcriptomic level.

METHODS

We performed an RNA-sequencing (RNA-seq) study of peripheral blood monocytes (PBMs) in 5 non-smoking moderate to severe CP (case) individuals vs. 5 controls. We took advantage of a microarray study of periodontitis to support our findings. We also performed pathway-based analysis on the identified differentially expressed (DEx) transcripts/isoforms using DAVID (Database for Annotation, Visualization and Integrated Discovery).

RESULTS

Through differential expression analyses at both whole gene (or whole non-coding RNA) and isoform levels, we identified 380 DEx transcripts and 5955 DEx isoforms with a PPEE (posterior probability of equal expression) of <0.05. Pervasive up-regulation of transcripts at isoform level in CP vs. control individuals was observed, suggesting a more functionally active monocyte transcriptome for CP. By comparing with the microarray dataset, we identified several CP-associated novel genes (e.g., FACR and CUX1) that have functions to interact with invading microorganisms or enhance TNF production on lipopolysaccharide stimulation. DAVID analysis of both the RNA-seq and the microarray datasets leads to converging evidence supporting "endocytosis", "cytokine production" and "apoptosis" as significant biological processes in CP.

CONCLUSIONS

As the first RNA-seq study of PBMs for CP, this study provided novel findings at both gene (e.g., FCAR and CUX1) and biological process level. The findings will contribute to better understanding of CP disease mechanisms.

Host genetics and immune control of HIV-1 infection: fine mapping for the extended human MHC region in an African cohort

Multiple MHC loci encoding human leukocyte antigens (HLA) have allelic variants unequivocally associated with differential immune control of HIV-1 infection. Fine mapping based on single nucleotide polymorphisms (SNPs) in the extended MHC (xMHC) region is expected to reveal causal or novel factors and to justify a search for functional mechanisms. We have tested the utility of a custom fine-mapping platform (the ImmunoChip) for 172 HIV-1 seroconverters (SCs) and 449 seroprevalent individuals (SPs) from Lusaka, Zambia, with a focus on more than 6,400 informative xMHC SNPs. When conditioned on HLA and non-genetic factors previously associated with HIV-1 viral load (VL) in the study cohort, penalized approaches (HyperLasso models) identified an intergenic SNP (rs3094626 between RPP21 and HLA-E) and an intronic SNP (rs3134931 in NOTCH4) as novel correlates of early set-point VL in SCs. The minor allele of rs2857114 (downstream from HLA-DOB) was an unfavorable factor in SPs. Joint models based on demographic features, HLA alleles and the newly identified SNP variants could explain 29% and 15% of VL variance in SCs and SPs, respectively. These findings and bioinformatics strongly suggest that both classic and non-classic MHC genes deserve further investigation, especially in Africans with relatively short haplotype blocks.

SNP association mapping across the extended major histocompatibility complex and risk of B-cell precursor acute lymphoblastic leukemia in children

The extended major histocompatibility complex (xMHC) is the most gene-dense region of the genome and harbors a disproportionately large number of genes involved in immune function. The postulated role of infection in the causation of childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) suggests that the xMHC may make an important contribution to the risk of this disease. We conducted association mapping across an approximately 4 megabase region of the xMHC using a validated panel of single nucleotide polymorphisms (SNPs) in childhood BCP-ALL cases (n=567) enrolled in the Northern California Childhood Leukemia Study (NCCLS) compared with population controls (n=892). Logistic regression analyses of 1,145 SNPs, adjusted for age, sex, and Hispanic ethnicity indicated potential associations between several SNPs and childhood BCP-ALL. After accounting for multiple comparisons, one of these included a statistically significant increased risk associated with rs9296068 (OR=1.40, 95% CI=1.19-1.66, corrected p=0.036), located in proximity to HLA-DOA. Sliding window haplotype analysis identified an additional locus located in the extended class I region in proximity to TRIM27 tagged by a haplotype comprising rs1237485, rs3118361, and rs2032502 (corrected global p=0.046). Our findings suggest that susceptibility to childhood BCP-ALL is influenced by genetic variation within the xMHC and indicate at least two important regions for future evaluation.

Conditional meta-analysis stratifying on detailed HLA genotypes identifies a novel type 1 diabetes locus around TCF19 in the MHC

The human leukocyte antigen (HLA) class II genes HLA-DRB1, -DQA1 and -DQB1 are the strongest genetic factors for type 1 diabetes (T1D). Additional loci in the major histocompatibility complex (MHC) are difficult to identify due to the region's high gene density and complex linkage disequilibrium (LD). To facilitate the association analysis, two novel algorithms were implemented in this study: one for phasing the multi-allelic HLA genotypes in trio families, and one for partitioning the HLA strata in conditional testing. Screening and replication were performed on two large and independent datasets: the Wellcome Trust Case-Control Consortium (WTCCC) dataset of 2,000 cases and 1,504 controls, and the T1D Genetics Consortium (T1DGC) dataset of 2,300 nuclear families. After imputation, the two datasets have 1,941 common SNPs in the MHC, of which 22 were successfully tested and replicated based on the statistical testing stratifying on the detailed DRB1 and DQB1 genotypes. Further conditional tests using the combined dataset confirmed eight novel SNP associations around 31.3 Mb on chromosome 6 (rs3094663, p = 1.66 × 10(-11) and rs2523619, p = 2.77 × 10(-10) conditional on the DR/DQ genotypes). A subsequent LD analysis established TCF19, POU5F1, CCHCR1 and PSORS1C1 as potential causal genes for the observed association.

Novel gene associations in type 1 diabetes

Recent genome-wide association studies have been able to identify multiple new gene loci affecting type 1 diabetes susceptibility, but the impact of these new defined loci seems to decrease in parallel with their number. The HLA gene region remains the main nominator of genetic susceptibility, although the identity of important genes and especially the mechanisms of their action are still largely unclear. Products of HLA and most other known risk genes are involved in regulation of the immune system in accordance with the autoimmune nature of the disease. The multitude of genes involved in the pathogenesis implies complex pathways where multiple steps in each may be essential in turning the balance of immune response to beta-cell destructing autoimmunity.

HLA DPA1, DPB1 alleles and haplotypes contribute to the risk associated with type 1 diabetes: analysis of the type 1 diabetes genetics consortium families

OBJECTIVE

To determine the relative risk associated with DPA1 and DPB1 alleles and haplotypes in type 1 diabetes.

RESEARCH DESIGN AND METHODS

The frequency of DPA1 and DPB1 alleles and haplotypes in type 1 diabetic patients was compared to the family based control frequency in 1,771 families directly and conditional on HLA (B)-DRB1-DQA1-DQB1 linkage disequilibrium. A relative predispositional analysis (RPA) was performed in the presence or absence of the primary HLA DR-DQ associations and the contribution of DP haplotype to individual DR-DQ haplotype risks examined.

RESULTS

Eight DPA1 and thirty-eight DPB1 alleles forming seventy-four DPA1-DPB1 haplotypes were observed; nineteen DPB1 alleles were associated with multiple DPA1 alleles. Following both analyses, type 1 diabetes susceptibility was significantly associated with DPB1*0301 (DPA1*0103-DPB1*0301) and protection with DPB1*0402 (DPA1*0103-DPB1*0402) and DPA1*0103-DPB1*0101 but not DPA1*0201-DPB1*0101. In addition, DPB1*0202 (DPA1*0103-DPB1*0202) and DPB1*0201 (DPA1*0103-DPB1*0201) were significantly associated with susceptibility in the presence of the high risk and protective DR-DQ haplotypes. Three associations (DPB1*0301, *0402, and *0202) remained statistically significant when only the extended HLA-A1-B8-DR3 haplotype was considered, suggesting that DPB1 alone may delineate the risk associated with this otherwise conserved haplotype.

CONCLUSIONS

HLA DP allelic and haplotypic diversity contributes significantly to the risk for type 1 diabetes; DPB1*0301 (DPA1*0103-DPB1*0301) is associated with susceptibility and DPB1*0402 (DPA1*0103-DPB1*0402) and DPA1*0103-DPB1*0101 with protection. Additional evidence is presented for the susceptibility association of DPB1*0202 (DPA1*0103-DPB1*0202) and for a contributory role of individual amino acids and DPA1 or a gene in linkage disequilibrium in DR3-DPB1*0101 positive haplotypes.