Association of the HLA-DQA1 and HLA-DQB1 Alleles in Type 2 Diabetes Mellitus and Diabetic Nephropathy in the Han Ethnicity of China

Mitochondrial related variants associated with cardiovascular traits

Introduction

Cardiovascular disease (CVD) is responsible for over 30% of mortality worldwide. CVD arises from the complex influence of molecular, clinical, social, and environmental factors. Despite the growing number of autosomal genetic variants contributing to CVD, the cause of most CVDs is still unclear. Mitochondria are crucial in the pathophysiology, development and progression of CVDs; the impact of mitochondrial DNA (mtDNA) variants and mitochondrial haplogroups in the context of CVD has recently been highlighted.

Aims

We investigated the role of genetic variants in both mtDNA and nuclear-encoded mitochondrial genes (NEMG) in CVD, including coronary artery disease (CAD), hypertension, and serum lipids in the UK Biobank, with sub-group analysis for diabetes.

Methods

We investigated 371,542 variants in 2,527 NEMG, along with 192 variants in 32 mitochondrial genes in 381,994 participants of the UK Biobank, stratifying by presence of diabetes.

Results

Mitochondrial variants showed associations with CVD, hypertension, and serum lipids. Mitochondrial haplogroup J was associated with CAD and serum lipids, whereas mitochondrial haplogroups T and U were associated with CVD. Among NEMG, variants within Nitric Oxide Synthase 3 (NOS3) showed associations with CVD, CAD, hypertension, as well as diastolic and systolic blood pressure. We also identified Translocase Of Outer Mitochondrial Membrane 40 (TOMM40) variants associated with CAD; Solute carrier family 22 member 2 (SLC22A2) variants associated with CAD and CVD; and HLA-DQA1 variants associated with hypertension. Variants within these three genes were also associated with serum lipids.

Conclusion

Our study demonstrates the relevance of mitochondrial related variants in the context of CVD. We have linked mitochondrial haplogroup U to CVD, confirmed association of mitochondrial haplogroups J and T with CVD and proposed new markers of hypertension and serum lipids in the context of diabetes. We have also evidenced connections between the etiological pathways underlying CVDs, blood pressure and serum lipids, placing NOS3, SLC22A2, TOMM40 and HLA-DQA1 genes as common nexuses.

Virus-induced diabetes mellitus: revisiting infection etiology in light of SARS-CoV-2

Diabetes mellitus (DM) is comprised of two predominant subtypes: type 1 diabetes mellitus (T1DM), accounting for approximately 5 % of cases worldwide and resulting from autoimmune destruction of insulin-producing β-cells, and type 2 (T2DM), accounting for approximately 95 % of cases globally and characterized by the inability of pancreatic β-cells to meet the demand for insulin due to a relative β-cell deficit in the setting of peripheral insulin resistance. Both types of DM involve derangement of glucose metabolism and are metabolic diseases generally considered to be initiated by a combination of genetic and environmental factors. Viruses have been reported to play a role as infectious etiological factors in the initiation of both types of DM in predisposed individuals. Among the reported viral infections causing DM in humans, the most studied include coxsackie B virus, cytomegalovirus and hepatitis C virus. The recent COVID-19 pandemic has highlighted the diabetogenic potential of SARS-CoV-2, rekindling interest in the field of virus-induced diabetes (VID). This review discusses the reported mechanisms of viral-induced DM, addressing emerging concepts in VID, as well as highlighting areas where knowledge is lacking, and further investigation is warranted.

Liquid Biopsy: A Game Changer for Type 2 Diabetes

As the burden of type 2 diabetes (T2D) continues to escalate globally, there is a growing need for novel, less-invasive biomarkers capable of early diabetes detection and monitoring of disease progression. Liquid biopsy, recognized for its minimally invasive nature, is increasingly being applied beyond oncology, and nevertheless shows its potential when the collection of the tissue biopsy is not possible. This diagnostic approach involves utilizing liquid biopsy markers such as cell-free nucleic acids, extracellular vesicles, and diverse metabolites for the molecular diagnosis of T2D and its related complications. In this context, we thoroughly examine recent developments in T2D liquid biopsy research. Additionally, we discuss the primary challenges and future prospects of employing liquid biopsy in the management of T2D. Prognosis, diagnosis and monitoring of T2D through liquid biopsy could be a game-changing technique for personalized diabetes management.

Integrated analysis of RNA methylation regulators crosstalk and immune infiltration for predictive and personalized therapy of diabetic nephropathy

BACKGROUND

RNA methylation is a widely known post-transcriptional regulation which exists in many cancer and immune system diseases. However, the potential role and crosstalk of five types RNA methylation regulators in diabetic nephropathy (DN) and immune microenvironment remain unclear.

METHODS

The mRNA expression of 37 RNA modification regulators and RNA modification regulators related genes were identified in 112 samples from 5 Gene Expression Omnibus datasets. Nonnegative Matrix Factorization clustering method was performed to determine RNA modification patterns. The ssGSEA algorithms and the expression of human leukocyte antigen were employed to assess the immune microenvironment characteristics. Risk model based on differentially expression genes responsible for the modification regulators was constructed to evaluate its predictive capability in DN patients. Furthermore, the results were validated by using immunofluorescence co-localizations and protein experiments in vitro.

RESULTS

We found 24 RNA methylation regulators were significant differently expressed in glomeruli in DN group compared with control group. Four methylation-related genes and six RNA regulators were introduced into riskScore model using univariate Logistic regression and integrated LASSO regression, which could precisely distinguish the DN and healthy individuals. Group with high-risk score was associated with high immune infiltration. Three distinct RNA modification patterns were identified, which has significant differences in immune microenvironment, biological pathway and eGFR. Validation analyses showed the METTL3, ADAR1, DNMT1 were upregulated whereas YTHDC1 was downregulated in DN podocyte cell lines comparing with cells cultured by the normal glucose.

CONCLUSION

Our study reveals that RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN. The bioinformatic analyses combine with verification in vitro could provide robust evidence for identification of predictive RNA methylation regulators in DN.

Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration

BACKGROUND

Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis.

METHODS

The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program.

RESULTS

Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib.

CONCLUSIONS

We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.

Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer’s disease

Background

Recent research has investigated the connection between Diabetes Mellitus (DM) and Alzheimer’s Disease (AD). Insulin resistance plays a crucial role in this interaction. Studies have focused on dysregulated proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the other hand, play an important role in the development of many diseases. They encode the majority of the human genome and regulate gene expression through a variety of mechanisms. Consequently, identifying significant ncRNAs and utilizing them as biomarkers could facilitate the early detection of this cross-talk. On the other hand, computational-based methods may help to understand the possible relationships between different molecules and conduct future wet laboratory experiments.

Materials and methods

In this study, we retrieved Genome-Wide Association Study (GWAS, 2008) results from the United Kingdom Biobank database using the keywords “Alzheimer’s” and “Diabetes Mellitus.” After excluding low confidence variants, statistical analysis was performed, and adjusted p-values were determined. Using the Linkage Disequilibrium method, 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen and the SNP-SNP interaction network was built. From this network, dense subgraphs were extracted as signatures. By mapping each signature to the reference genome, genes associated with the selected SNPs were retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA (lncRNA) bipartite networks were built and significant ncRNAs were extracted. After the validation process, by applying the scoring function, the final protein-miRNA-lncRNA tripartite network was constructed, and significant miRNAs and lncRNAs were identified.

Results

Hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the three most important lncRNAs, and their interacting proteins in the final tripartite network, have been proposed as new candidate biomarkers in the cross-talk between DM and AD. The literature review also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs; hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all expressed in the brain, and their interacting proteins in our final network are suggested for future research investigation.

Conclusion

This study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs involved in the cross-talk between DM and AD. Different network analysis and scoring function suggested the most significant miRNAs and lncRNAs as potential candidate biomarkers for wet laboratory experiments. Considering these candidate biomarkers may help in the early detection of DM and AD co-occurrence.

Influence of Insulin Receptor Single Nucleotide Polymorphisms on Glycaemic Control and Formation of Anti-Insulin Antibodies in Diabetes Mellitus

Single nucleotide polymorphisms (SNPs) in insulin and insulin receptor genes may influence the interaction between the two molecules, as may anti-insulin antibodies (IAs), commonly found in patients with type 1 diabetes mellitus (T1D) or type 2 diabetes mellitus (T2D) treated with exogenous insulin. We examined the impact of two SNPs in the human insulin gene (INS), rs3842752 and rs689, and two in the insulin receptor gene (INSR) rs2245649 and rs2229429, on disease susceptibility, glycaemic control, and IAs formation in 100 T1D patients and 101 T2D patients treated with insulin. 79 individuals without diabetes were typed as healthy controls. The minor alleles of rs3842752 and rs689 in INS protected against T1D (OR: 0.50, p = 0.01 and OR: 0.44; p = 0.002, respectively). The minor alleles of both rs2245649 and rs2229429 in INSR were risk factors for poor glycaemic control (HbA1c ≥ 80 mmol/mol) in T1D (OR: 5.35, p = 0.009 and OR: 3.10, p = 0.01, respectively). Surprisingly, the minor alleles of rs2245649 and rs2229429 in INSR associated strongly with the absence of IAs in T1D (OR = 0.28, p = 0.008 and OR = 0.30, p = 0.002, respectively). In conclusion, the minor alleles of the investigated INS SNPs protect against T1D, and the minor alleles of the investigated INSR SNPs are associated with poor glycaemic control and the absence of IAs in T1D.

Human Leukocyte Antigen (HLA) System: Genetics and Association with Bacterial and Viral Infections

The human leukocyte antigen (HLA) system is one of the most crucial host factors influencing disease progression in bacterial and viral infections. This review provides the basic concepts of the structure and function of HLA molecules in humans. Here, we highlight the main findings on the associations between HLA class I and class II alleles and susceptibility to important infectious diseases such as tuberculosis, leprosy, melioidosis, Staphylococcus aureus infection, human immunodeficiency virus infection, coronavirus disease 2019, hepatitis B, and hepatitis C in populations worldwide. Finally, we discuss challenges in HLA typing to predict disease outcomes in clinical implementation. Evaluation of the impact of HLA variants on the outcome of bacterial and viral infections would improve the understanding of pathogenesis and identify those at risk from infectious diseases in distinct populations and may improve the individual treatment.

Untangling the genetic link between type 1 and type 2 diabetes using functional genomics

There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D. We identified 195 pleiotropic genes that are modulated by tissue-specific spatial eQTLs associated with both T1D and T2D. The pleiotropic genes are enriched in inflammatory and metabolic pathways that include mitogen-activated protein kinase activity, pertussis toxin signaling, and the Parkinson's disease pathway. We identified 8 regulatory elements within the TCF7L2 locus that modulate transcript levels of genes involved in immune regulation as well as genes important in the etiology of T2D. Despite the observed gene and pathway overlaps, there was no significant genetic correlation between variant effects on T1D and T2D risk using European ancestral summary data. Collectively, our findings support the hypothesis that T1D and T2D specific genetic variants act through genetic regulatory mechanisms to alter the regulation of common genes, and genes that co-locate in biological pathways, to mediate pleiotropic effects on disease development. Crucially, a high risk genetic profile for T1D alters biological pathways that increase the risk of developing both T1D and T2D. The same is not true for genetic profiles that increase the risk of developing T2D. The conversion of information on genetic susceptibility to the protein pathways that are altered provides an important resource for repurposing or designing novel therapies for the management of diabetes.

Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions

After the announcement of a new coronavirus in China in December 2019, which was then called SARS-CoV-2, this virus changed to a global concern and it was then declared as a pandemic by WHO. Human leukocyte antigen (HLA) alleles, which are one of the most polymorphic genes, play a pivotal role in both resistance and vulnerability of the body against viruses and other infections as well as chronic diseases. The association between HLA alleles and preexisting medical conditions such as cardiovascular diseases and diabetes mellitus is reported in various studies. In this review, we focused on the bioinformatic HLA studies to summarize the HLA alleles which responded to SARS-CoV-2 peptides and have been used to design vaccines. We also reviewed HLA alleles that are associated with comorbidities and might be related to the high mortality rate among COVID-19 patients. Since both genes and patients' medical conditions play a key role in both severity of the disease and the mortality rate in COVID-19 patients, a better understanding of the connection between HLA alleles and SARS-CoV-2 can provide a wider perspective on the behavior of the virus. Such understanding can help scientists, especially in terms of protecting healthcare workers and designing effective vaccines.

Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules

BACKGROUND

Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus.

METHODS

To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules.

RESULTS

A total of 820 DEGs were identified between healthy obese and metabolically unhealthy obese, among 409 up regulated and 411 down regulated genes. The GO enrichment analysis results showed that these DEGs were significantly enriched in ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway enrichment analysis results showed that these DEGs were significantly enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play an essential role in obesity associated type 2 diabetes mellitus was further screened.

CONCLUSIONS

The present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing therapeutic targets for obesity associated type 2 diabetes mellitus.

Association of HLA-B Gene Polymorphisms with Type 2 Diabetes in Pashtun Ethnic Population of Khyber Pakhtunkhwa, Pakistan

Human leukocyte antigen (HLA) system is the most polymorphic and gene dense region of human DNA that has shown many disease associations. It has been further divided into HLA classes I, II, and III. Polymorphism in HLA class II genes has been reported to play an important role in the pathogenesis of type 1 diabetes (T1D). It also showed association with T2D in different ethnic populations. However, a little is known about the relationship of HLA class I gene polymorphism and T2D. This study has evaluated the association of HLA-B (class I gene) variants with T2D in Pashtun ethnic population of Khyber Pakhtunkhwa. In the first phase of the study, whole exome sequencing (WES) of 2 pooled DNA samples was carried out, and DNA pools used were constructed from 100 diabetic cases and 100 control subjects. WES results identified a total of n = 17 SNPs in HLA-B gene. In the next phase, first 5 out of n = 17 reported SNPs were genotyped using MassARRAY® system in order to validate WES results and to confirm association of selected SNPs with T2D. Minor allele frequencies (MAFs) and selected SNPs×T2D association were determined using chi-square test and logistic regression analysis. The frequency of minor C allele was significantly higher in the T2D group as compared to control group (45.0% vs. 13.0%) (p = 0.006) for rs2308655 in HLA-B gene. No significant difference in MAF distribution between cases and controls was observed for rs1051488, rs1131500, rs1050341, and rs1131285 (p > 0.05). Binary logistic regression analyses showed significant results for SNP rs2308655 (OR = 2.233, CI (95%) = 1.223-4.077, and p = 0.009), while no considerable association was observed for the other 4 SNPs. However, when adjusted for these variants, the association of rs2308655 further strengthened significantly (adjusted OR = 7.485, CI (95%) = 2.353-23.812, and p = 0.001), except for rs1131500, which has no additive effect. In conclusion, the finding of this study suggests rs2308655 variant in HLA-B gene as risk variant for T2D susceptibility in Pashtun population.

Corni Fructus as a Natural Resource Can Treat Type 2 Diabetes by Regulating Gut Microbiota

Accumulating evidence suggests that gut microbiota plays a crucial role in the development of metabolic diseases, especially type 2 diabetes mellitus (T2DM). The nutrient-rich resource Cornus Fructus (CF) showed curative effects on diabetes mellitus. However, the mechanism underlying its hyperglycemic activity remains obscure. Herein, the antidiabetic potential of four extracts from CF, including saponin (CTS), iridoid glycoside (CIG), tannin (CT), and alcohol extract (CCA) was evaluated in vivo. The results showed that all four extracts could increase the body weight, decrease the blood glucose levels, and elevate the glucose tolerance. Moreover, insulin sensitivity and lipid profile were significantly improved in fed mice. In the [Formula: see text]-diversity index of samples, compared to the DM group, the diversity and richness of gut microbiota in mice to a certain extent were reduced in both CF extracts and Metformin (PC). Among them, there was statistical significance in PC (ACE, [Formula: see text]) and CCA (ACE, [Formula: see text]; chao1: [Formula: see text]). Beta diversity showed the same trend as the UPGMA clustering trees, which revealed that CF extracts could improve intestinal homeostasis in T2DM mice. Also, CF extracts could elevate the production of short-chain fatty acids, as well as regulate the composition of gut microbiota. The key bacteria related to T2DM including Firmicutes, Bacteroides, Lactobacillus, and Clostridium were modulated by metformin and CF. Altogether, CF is a potential nutrient-rich candidate that can be used in functional foods for the treatment of T2DM, and the change of gut microbiota might be a novel mechanism underlying its hyperglycemic activity.

HLA Class II Allele Analyses Implicate Common Genetic Components in Type 1 and Non-Insulin-Treated Type 2 Diabetes

CONTEXT

Common genetic susceptibility may underlie the frequently observed co-occurrence of type 1 and type 2 diabetes in families. Given the role of HLA class II genes in the pathophysiology of type 1 diabetes, the aim of the present study was to test the association of high density imputed human leukocyte antigen (HLA) genotypes with type 2 diabetes.

OBJECTIVES AND DESIGN

Three cohorts (Ntotal = 10 413) from Leipzig, Germany were included in this study: LIFE-Adult (N = 4649), LIFE-Heart (N = 4815) and the Sorbs (N = 949) cohort. Detailed metabolic phenotyping and genome-wide single nucleotide polymorphism (SNP) data were available for all subjects. Using 1000 Genome imputation data, HLA genotypes were imputed on 4-digit level and association tests for type 2 diabetes, and related metabolic traits were conducted.

RESULTS

In a meta-analysis including all 3 cohorts, the absence of HLA-DRB5 was associated with increased risk of type 2 diabetes (P = 0.001). In contrast, HLA-DQB*06:02 and HLA-DQA*01:02 had a protective effect on type 2 diabetes (P = 0.005 and 0.003, respectively). Both alleles are part of the well-established type 1 diabetes protective haplotype DRB1*15:01~DQA1*01:02~DQB1*06:02, which was also associated with reduced risk of type 2 diabetes (OR 0.84; P = 0.005). On the contrary, the DRB1*07:01~DQA1*02:01~DQB1*03:03 was identified as a risk haplotype in non-insulin-treated diabetes (OR 1.37; P = 0.002).

CONCLUSIONS

Genetic variation in the HLA class II locus exerts risk and protective effects on non-insulin-treated type 2 diabetes. Our data suggest that the genetic architecture of type 1 diabetes and type 2 diabetes might share common components on the HLA class II locus.

HLA class II alleles and risk for peripheral neuropathy in type 2 diabetes patients

The potential impact of human leukocyte antigen (HLA) genotype variations on development of diabetic peripheral neuropathy (DPN) is not well determined. This study aimed to identify the association of HLA class II alleles with DPN in type 2 diabetes (T2D) patients. Totally 106 T2D patients, 49 with DPN and 57 without DPN, and 100 ethnic-matched healthy controls were analyzed. Both groups of the patients were matched based on sex, age, body mass index (BMI) and duration of T2D. Polyneuropathy was diagnosed using electrodiagnostic methods. HLA-DRB1 and DQB1 genotyping was performed in all subjects by the polymerase chain reaction with sequence-specific primers (PCR-SSP) method. T2D patients with DPN showed higher frequencies of HLA-DRB1*10 and DRB1*12 alleles compared to control group (P = 0.04). HLA-DQB1*02 allele and HLA-DRB1*07-DQB1*02 haplotype were associated with a decreased risk for developing DPN in T2D patients (P = 0.02 and P = 0.05 respectively). Also, patients with severe neuropathy showed higher frequencies of DRB1*07 (P = 0.003) and DQB1*02 (P = 0.02) alleles than those with mild-to-moderate form of neuropathy. The distribution of DRB1 and DQB1 alleles and haplotypes were not statistically different between all patients and healthy controls. Our findings implicate a possible protective role of HLA-DQB1*02 allele and HLA-DRB1*07-DQB1*02 haplotype against development of peripheral neuropathy in T2D patients. Therefore, variations in HLA genotypes might be used as genetic markers for prediction and potentially management of neuropathy in T2D patients.

Distribution of HLA DRB1 and DQB1 alleles and DRB1-DQB1 haplotypes among Bahraini women with polycystic ovary syndrome

OBJECTIVE

This study investigated the association between HLA-DRB1 and -DQB1 alleles and DRB1-DQB1 haplotypes, and polycystic ovary syndrome (PCOS) in Bahraini women.

DESIGN

Case-control, retrospective study.

METHODS

Study subjects comprised 80 women with PCOS, and 169 age- and ethnically-matched control women. DRB1 and DQB1 genotyping was done by PCR-SSP.

RESULTS

Of the 13 DRB1 alleles and 5 DQB1 alleles identified, DRB1*10 (14.3% vs. 4.4%) and DRB1*14 (8.7% vs. 1.1%), along with DQB1*05 (35.0% vs. 23.9%), were the most frequent alleles in cases, while DRB1*11 (15.3% vs. 6.8%) was the frequent allele found in controls. The association of PCOS with DRB1*10 (Pc<0.001), DRB1*14 (Pc<0.001), DQB1*05 (Pc=0.040), but not DRB1*11 (Pc=0.076) persisted after correcting for multiple comparisons. DRB1-DQB1 haplotype analysis identified nine common shared haplotypes in women with PCOS and control women, with a frequency exceeding 1%. Significantly higher frequency of DRB1*10-DQB1*05 (12.4% vs. 3.1%) and DRB1*14-DQB1*03 (5.6% vs. 1.0%), and reduced frequency of DRB1*11-DQB1*03 (4.1% vs. 14.1%) haplotypes were seen in women with PCOS vs. control women, thus assigning PCOS-susceptible and -protective nature to these haplotypes, respectively. This association persisted after controlling for multiple comparisons.

CONCLUSION

Our results confirm an association of HLA-DRB1 and -DQB1 alleles and haplotypes with PCOS susceptibility in Bahraini Arabs, further underscoring the immunological/inflammatory nature of this disorder.

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

BACKGROUND

Diabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.

RESULTS

In this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.

CONCLUSIONS

The results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

Role of antigen presentation in the production of pro-inflammatory cytokines in obese adipose tissue

Type II diabetes regroups different physiological anomalies that ultimately lead to low-grade chronic inflammation, insulin resistance and loss of pancreatic β-cells. Obesity is one of the best examples of such a condition that can develop into Metabolic Syndrome, causing serious health problems of great socio-economic consequences. The pathological outcome of obesity has a genetic basis and depends on the delicate balance between pro- and anti-inflammatory effectors of the immune system. The causal link between obesity and inflammation is well established. While innate immunity plays a key role in the development of a pro-inflammatory state in obese adipose tissues, it has now become clear that adaptive immune cells are also involved and participate in the cascade of events that lead to metabolic perturbations. The efficacy of some immunotherapeutic protocols in reducing the symptoms of obesity-driven metabolic syndrome in mice implicated all arms of the immune response. Recently, the production of pathogenic immunoglobulins and pro-inflammatory cytokines by B and T lymphocytes suggested an auto-immune basis for the establishment of a non-healthy obese state. Understanding the cellular landscape of obese adipose tissues and how immune cells sustain chronic inflammation holds the key to the development of targeted therapies. In this review, we emphasize the role of antigen-presenting cells and MHC molecules in obese adipose tissue and the general contribution of the adaptive arm of the immune system in inflammation-induced insulin resistance.

Protective effect of HLA-DQB1 alleles against alloimmunization in patients with sickle cell disease

BACKGROUND

Alloimmunization or the development of alloantibodies to Red Blood Cell (RBC) antigens is considered one of the major complications after RBC transfusions in patients with sickle cell disease (SCD) and can lead to both acute and delayed hemolytic reactions. It has been suggested that polymorphisms in HLA genes, may play a role in alloimmunization. We conducted a retrospective study analyzing the influence of HLA-DRB1 and DQB1 genetic diversity on RBC-alloimmunization.

STUDY DESIGN

Two-hundred four multi-transfused SCD patients with and without RBC-alloimmunization were typed at low/medium resolution by PCR-SSO, using IMGT-HLA Database. HLA-DRB1 and DQB1 allele frequencies were analyzed using logistic regression models, and global p-value was calculated using multiple logistic regression.

RESULTS

While only trends towards associations between HLA-DR diversity and alloimmunization were observed, analysis of HLA-DQ showed that HLA-DQ2 (p=0.02), -DQ3 (p=0.02) and -DQ5 (p=0.01) alleles were significantly higher in non-alloimmunized patients, likely behaving as protective alleles. In addition, multiple logistic regression analysis showed both HLA-DQ2/6 (p=0.01) and HLA-DQ5/5 (p=0.03) combinations constitute additional predictor of protective status.

CONCLUSION

Our data suggest that particular HLA-DQ alleles influence the clinical course of RBC transfusion in patients with SCD, which could pave the way towards predictive strategies.

HLA class I and class II associations with ESRD in Saudi Arabian population

Background

Chronic renal failure (CRF) leads in the majority of instances to end stage renal disease (ESRD) requiring renal replacement therapy. Our interest was to evaluate the possible associations of HLA class I and class II antigens with ESRD independent of other factors, in Saudi Arabia population.

Methodology

A retrospective study to determine the HLA class I and class II polymorphisms and their association with ESRD, was performed on 350 patients with ESRD, and 105 healthy unrelated control. Patients and control groups were typed by SSOP lumenix techniques. The alleles positively associated to the ESRD were: HLA-B*15, B*18, B*49 - DRB1*03, negatively associated alleles were A*26, HLA-B*39, B*50. The haplotypes positively associated with ESRD were: HLA-A*01-DRB1*13 and HLA-A*30-DRBI*03. The negatively associated haplotypes were: HLA-A*02-B*39, A*02-B*50, A*24-B*35, A*24-B*58, A*24-DRB1*16, A*68-DRB1*04, A*02-DQB1*03, A*29-DQB1*02, A*29-DOB1*05 and B*27-DRB1*07 and the last one is the most significant protective haplotypes.

Conclusion

The high Relative Risk (RR) observed and its statistical correlation reflect the strength of the described association between HLA antigens and ESRD.