Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.814191
Title: Defining the molecular, genetic and transcriptomic mechanisms underlying the variation in glycation gap between individuals
Author: Naseem, Fakhra
ISNI:       0000 0004 9352 9674
Awarding Body: University of Wolverhampton
Current Institution: University of Wolverhampton
Date of Award: 2019
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Abstract:
The discrepancy between HbA1c and fructosamine estimations in the assessment of glycaemia has frequently been observed and is referred to as the glycation gap (G-gap). This could be explained by the higher activity of the fructosamine-3-kinase (FN3K) deglycating enzyme in the negative G-gap group (patients with lower than predicted HbA1c for their mean glycaemia) as compared to the positive G-gap group. This G-gap is linked with differences in complications in patients with diabetes and this potentially happens because of dissimilarities in deglycation. The difference in deglycation rate in turn leads to altered production of advanced glycation end products (AGEs). These AGEs are both receptor dependent and receptor independent. It was hypothesised that variations in the level of the deglycating enzyme fructosamine-3-kinase (FN3K) might be as a result of known Single Nucleotide Polymorphisms (SNPs): rs1056534, rs3848403 and rs1046896 in FN3K gene, SNP in ferroportin1/SLC40A1 gene (rs11568350 linked with FN3K activity), differentially expressed genes (DEGs), differentially expressed transcripts or alternatively spliced transcript variants. Previous studies reported accelerated telomere length shortening in patients with diabetes. In this study, 184 patients with diabetes were included as dichotomised groups with either a strongly negative or positive G-gap. This study was conducted to analyse the differences in genotype frequency of specific SNPs via real time qPCR,determine soluble receptors for AGE (sRAGE) concentration via ELISA, finding association of sRAGE concentration with SNPs genotype, and evaluate relative average telomere length ratio via real time qPCR. This study also aimed at the investigation of underlying mechanisms of G-gap via transcriptome study for the identification of the DEGs and differentially expressed transcripts and to consequently identify pathways, biological processes and diseases linked to situations in which DEGs were enriched. The relative length of the telomere was normalised to the expression of a single copy gene (S). Chi-squared test was used for estimating the expected genotype frequencies in diabetic patients with negative and positive G-gap. Genotype frequencies of FN3K SNPs (rs1056534, rs3848403 and rs1046896) and SLC40A1/ferroportin1 SNP (rs11568350) polymorphisms within the studied groups were non-significant. With respect to genotypes, the rs1046896 genotype (CT) and rs11568350 genotype (AC) were only found in heterozygous state in all the investigated cohorts. No association between sRAGE concentration and FN3K SNPs (rs3848403 and rs1056534) was observed as the sRAGE concentration was also found not to be different between the groups. Similarly, the relative average telomere length was not different in both groups. Plasma sRAGE levels were not different in the cohort studied even though the Wolverhampton Diabetes Research Group (WDRG) previously reported that AGE is higher in positive G-gap. The latter is a more likely consequence of lower FN3K activities. In this study, it was found that SNPs in the FN3K/ferroportin1 gene are not responsible for the discrepancy in average glycaemia. The transcriptomic study via RNA-Seq mapped a total of 64451 gene transcripts to the human transcriptome. The DEGs and differentially expressed transcripts were 103 and 342 respectively (p < 0.05, fold change > 1.5). Of 103 DEGs, 61 were downregulated in G-gap positive and 42 were upregulated in positive G-gap individuals while 14 genes produced alternatively spliced transcript variants. Four pathways (Viral carcinogenesis, Ribosome, Phagosome and Dorso-ventral axis) were identified in the bioinformatics analysis of samples in which DEGs were enriched. These DEGs were also found to be associated with raised blood pressure and glycated haemoglobin (conditions that coexist with diabetes). Future analysis based on these results will be necessary to elucidate the significant drivers of gene expression leading to the G-gap in these patients.
Supervisor: Dunmore, Simon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.814191  DOI: Not available
Keywords: Diabetes ; glycation gap ; G-gap ; G-gap positive ; G-gap negative ; single nucleotide polymorphisms (SNPs) ; next generation sequencing ; RNA sequencing ; soluble receptors for advanced glycation end products ; differentially expressed genes ; Telomere length
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