RNASEH1 gene variants are associated with autoimmune type 1 diabetes in Colombia

Identification of key regulatory genes and their working mechanisms in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D.

METHODS

The microarray profile of T1D-related gene expression was searched using the Gene Expression Omnibus (GEO) database. Then, the expression data of two messenger RNAs (mRNAs) were integrated for Weighted Gene Co-Expression Network Analysis (WGCNA) to generate candidate genes related to T1D. In parallel, T1D microRNA (miRNA) data were analyzed to screen for possible regulatory miRNAs and their target genes. An miRNA-mRNA regulatory network was then established to predict the key regulatory genes and their mechanisms.

RESULTS

A total of 24 modules (i.e., clusters/communities) were selected using WGCNA analysis, in which three modules were significantly associated with T1D. Further correlation analysis of the gene module revealed 926 differentially expressed genes (DEGs), of which 327 genes were correlated with T1D. Analysis of the miRNA microarray showed that 13 miRNAs had significant expression differences in T1D. An miRNA-mRNA network was established based on the prediction of miRNA target genes and the combined analysis of mRNA, in which the target genes of two miRNAs were found in T1D correlated genes.

CONCLUSION

An miRNA-mRNA network for T1D was established, based on which 2 miRNAs and 12 mRNAs were screened, suggesting that they may play key regulatory roles in the initiation and development of T1D.

A non-coding RNASEH1 gene variant associates with type 1 diabetes and interacts with HLA tagSNPs in families from Colombia

OBJECTIVES

RNASEH1 gene has recently been associated with type 1 diabetes (T1D) in Colombia. The purpose of this study was to fine mapping the putative functional variant in RNASEH1 and testing its interaction with HLA tagSNPs.

METHODS

Two-hundred nuclear families with T1D were included in this study. Probands were tested for GAD65 and IA-2 autoantibodies. Genotyping was performed using 20 coding tagSNPs uncovered through Sanger sequencing (N = 96), in addition to 23 tagSNPs chosen from 1000genomes to cover the extent of the gene region. Also, 45 tagSNPs for classic HLA alleles associated with T1D were also genotyped. The transmission disequilibrium test (TDT) was used to test for association and a multiple testing correction was made using permutation. Interaction between RNASEH1 variants and HLA was evaluated by means of the M-TDT test.

RESULTS

We identified 20 variants (15 were novel) in the 96 patients sequenced. None of these variants were in linkage disequilibrium. In total, 43 RNASEH1 variants were genotyped in the 200 families. Association between T1D and rs7607888 was identified (P = .002). Haplotype analysis involving rs7607888 variant revealed even stronger association with T1D (most significative P = .0003). HLA tagSNPs displayed stronger associations (OR = 6.39, 95% CI = 4.33-9.44, P-value = 9.74E-28). Finally, we found several statistically significant interactions of HLA variants with rs7607888 (P-value ranged from 8.77E-04 to 5.33E-12).

CONCLUSION

Our results verify the association of rs7607888 in RNASEH1 gene with T1D. It is also shown in the interaction between RNASEH1 and HLA for conveying risk to T1D in Northwest Colombia. Work is underway aiming to identify the actual classic HLA alleles associated with the tagSNPs tested here.

Type 1 diabetes loci display a variety of native American and African ancestries in diseased individuals from Northwest Colombia

BACKGROUND

Type 1 diabetes (T1D) is a complex disease with a higher incidence in Europeans than other populations. The Colombians Living in Medellin (CLM) is admixed with ancestry contributions from Europeans, Native Americans (NAT) and Africans (AFR).

AIM

Our aim was to analyze the genetic admixture component at candidate T1D loci in Colombian individuals with the disease.

METHODS

Seventy-four ancestry informative markers (AIMs), which tagged 41 T1D candidate loci/genes, were tested by studying a cohort of 200 Northwest Colombia diseased individuals. T1D status was classified by testing for glutamic acid decarboxylase (GAD-65 kDa) and protein tyrosine-like antigen-2 auto-antibodies in serum samples. Candidate loci/genes included HLA, INS, PTPN22, CTLA4, IL2RA, SUMO4, CLEC16A, IFIH1, EFR3B, IL7R, NRP1 and RNASEH1, amongst others. The 1,000 genome database was used to analyze data from 94 individuals corresponding to the reference CLM. As the data did not comply with a normal distribution, medians were compared between groups using the Mann-Whitney U-test.

RESULTS

Both T1D patients and individuals from CLM displayed mainly European ancestry (61.58 vs 62.06) followed by Native American (27.34 vs 27.46) and to a lesser extent the AFR ancestry (10.28 vs 10.65) components. However, compared to CLM, ancestry of T1D patients displayed a decrease of NAT ancestry at gene EFR3B (24.30 vs 37.10) and an increase at genes IFIH1 (32.07 vs 14.99) and IL7R (52.18 vs 39.18). Also, for gene NRP1 (36.67 vs 0.003), we observed a non-AFR contribution (attributed to NAT). Autoimmune patients (positive for any of two auto-antibodies) displayed lower NAT ancestry than idiopathic patients at the MHC region (20.36 vs 31.88). Also, late onset patients presented with greater AFR ancestry than early onset patients at gene IL7R (19.96 vs 6.17). An association analysis showed that, even after adjusting for admixture, an association exists for at least seven such AIMs, with the strongest findings on chromosomes 5 and 10 (gene IL7R, P = 5.56 × 10^-6 and gene NRP1, P = 8.70 × 10^-19, respectively).

CONCLUSION

Although Colombian T1D patients have globally presented with higher European admixture, specific T1D loci have displayed varying levels of Native American and AFR ancestries in diseased individuals.