71 research outputs found
Evolutionary Pressure on Mitochondrial Cytochrome b Is Consistent with a Role of CytbI7T Affecting Longevity during Caloric Restriction
Background: Metabolism of energy nutrients by the mitochondrial electron transport chain (ETC) is implicated in the aging process. Polymorphisms in core ETC proteins may have an effect on longevity. Here we investigate the cytochrome b (cytb) polymorphism at amino acid 7 (cytbI7T) that distinguishes human mitochondrial haplogroup H from haplogroup U. Principal Findings: We compared longevity of individuals in these two haplogroups during historical extremes of caloric intake. Haplogroup H exhibits significantly increased longevity during historical caloric restriction compared to haplogroup U(p = 0.02) while during caloric abundance they are not different. The historical effects of natural selection on the cytb protein were estimated with the software TreeSAAP using a phylogenetic reconstruction for 107 mammal taxa from all major mammalian lineages using 13 complete protein-coding mitochondrial gene sequences. With this framework, we compared the biochemical shifts produced by cytbI7T with historical evolutionary pressure on and near this polymorphic site throughout mammalian evolution to characterize the role cytbI7T had on the ETC during times of restricted caloric intake. Significance: Our results suggest the relationship between caloric restriction and increased longevity in human mitochondrial haplogroup H is determined by cytbI7T which likely enhances the ability of water to replenish the Q i binding site and decreases the time ubisemiquinone is at the Qo site, resulting in a decrease in the average production rate of radica
THREE METHODS TO INCREASE THE LIKELY TO IDENTIFY GENE INVOLVED IN COMPLEX DISEASE
The large part of human pathology is composed by complex disease, such as heart disease, obesity, cancer, diabetes, and many common psychiatric and neurological conditions. The common feature of all these conditions is the multifactorial etiology that involves both genetic and environmental factors. The common disease-common variant (CDCV) hypothesis posits that common, interacting alleles underlie most common diseases, in association with environmental factors. Furthermore, according to the thrift genotype, such alleles have been subjected to selective pressure, mainly those involved in metabolic disease such as T2DM and obesity.
Although the concept of gene-environment interaction is central to ecogenetics, and has long been recognized by geneticists (Haldane 1946), there are relatively few detailed descriptions of gene–environment interaction in biomedical literature. This lacking may be explained by difficulties in collecting environmental information of enough quality and by great difficulties in analyze them. Indeed, when the number of factors to analyze is large, become overwhelming the course of dimensionality and the multiple testing problems.
In the present thesis the hypothesis that knowledge-driven approaches may improve the ability to identify genes involved in complex disease was checked. Three approaches have been presented, each of them leading to the identification of a factor or of a interaction of factors. As the study a complex disease is composed by three steps: (1) selection of candidate genes, (2) collecting of genetic and non-genetic information and (3) statistical analysis of data, it is showed that each of these steps may be improved by consideration of the biological background.
The first study, regarded the possibility to exploit evolutionary information to identify genes involved in type 2 diabetes. This hypothesis was based on the thrifty genotype hypothesis. A gene was identified, ACO1, and was successfully associated to the disease.
In the second study, we analyses the case of a gene, PPAGγ that have been inconsistency associated with obesity. We hypothesized that the inconsistence of association may be due to its relationship with environment. Then we jointly analyzed the genotype of the gene and comprehensive nutritional information about a cohort and proved an interaction. The genotype of PPARγ modulated the response to the diet. Ala-carriers gained more weight than ProPro individuals when had the same caloric intake.
In the third study, we implemented a software tool to create simulated populations based on gene-environment interactions. The system was based on genetic information to simulate realistic populations. We used these simulated populations to collect information on statistical methods more frequently used to study case-controls samples. Afterward, we built an ensemble of these methods and applied it to a real sample. We showed that ensemble had better performances of each single methods in condition of small sample size
Pacific People, Metabolic Disease and Evolutionary Processes: a mitochondrial DNA study
Data clearly indicate that there is a high burden of metabolic disease including gout, type 2 diabetes, and cardiovascular heart disease, among Polynesians. Many of the metabolic diseases have a shared aetiology and often present as comorbidities. To date, a number of studies have been undertaken, with varying success, attempting to disentangle the genetic and environmental components of these complex diseases.
The high disease burden has led some to propose that Pacific populations possess a ‘Thrifty Genotype’. Here I argue that not only do the data not support this hypothesis, but that this is not in line with what is known of the evolutionary history of the region. Instead, a model for selection by a mechanism of infectious disease resistance is proposed. All of these metabolic conditions have a significant immunological component, namely the involvement of the NLRP3 inflammasome, an important component of innate immunity.
A number of recent studies have indicated that mitochondria play an integral role in the activation of the NLRP3 inflammasome. Thus, a study of mitochondrial genetic variation was undertaken, exploring how genetic variation in mitochondrial DNA (mtDNA) could contribute to differences in immune response and altered metabolic efficiency. Gout was used as a proxy for altered immune response and obesity as an indicator for metabolic efficiency. Whole mitochondrial genome sequencing was undertaken on 442 Māori and Polynesian men, selected from a cohort of individuals participating in a study of gout genetics.
Few genetic associations were detected between either of these proxies, however there was a statistically significant association detected between variation in the region of the poly-cytosine tracts (16179-16189) in hypervariable region 1 (HVR1) and gout status. There was some indication that sequence length heteroplasmy in this region (which coincided with individuals possessing mitochondrial genomes belonging to the B macrohaplogroup) was associated with gout.
Mitochondrial DNA copy number was also examined in the context of gout. Quantitative PCR was undertaken to measure the relative amount of mtDNA in 484 Polynesians. There appeared to be a general reduction in the amount of mtDNA in participants with gout compared to healthy controls. Given the involvement of the mitochondria in the co-localisation of components of the NLRP3 inflammasome, this novel finding may explain some of what is occurring in the inflammatory processes underlying gouty disease, and possibly other metabolic diseases more generally.
From this preliminary study, it appears that mitochondrial genetics may provide insights into susceptibility to metabolic conditions. This has wider implications when it comes to considering genetic ancestry and disease susceptibility, and may explain some of the high prevalence of metabolic disease among Polynesian and other Pacific populations
GENETIC SUSCEPTIBILITY TO TYPE II DIABETES AND OBESITY: THE ROLE OF UCP2, UCP3 AND CAPN10 GENES
PhDThe global prevalence of type 2 diabetes (T2DM) and obesity is increasing, with obesity the
most important predisposing factor contributing to the development of T2DM.
Epidemiological and genetic evidence supports a major genetic component in both
multifactorial and heterogeneous disorders. The identification of disease susceptibility genes
in humans could greatly assist in the elucidation of underlying pathophysiological
mechanisms and allow the development of more effective preventative and therapeutic
strategies for these conditions.
Three candidate genes, uncoupling proteins 2 and 3 (UCP2; UCP3) and calpain 10
(CAPN10), are proposed and the rationale for their selection discussed. Gene variants were
identified in UCP2 and UCP3. These variants were tested for association with T2DM,
obesity and intermediate quantitative traits in a South Indian population and family
collection, and also a cohort of British obese case/control subjects. No variant was
associated with T2DM. However, investigations revealed positive associations with a UCP2
3'UTR 45bp Ins/Del and a novel UCP3 promoter variant (-55C/T) with variation in body
mass (BMI) and fat distribution (WHR) respectively. The results support the view that
uncoupling proteins may influence weight gain and hence progression to obesity/T2DM. A
significant correlation with plasma leptin levels and the UCP2 Ins/Del variant might indicate
one potential mechanism whereby weight could be modulated by uncoupling proteins.
A linkage study in affected sibling pairs of North European descent, was negative for the
putative T2DM susceptibility gene region, NIDDMI. In contrast, haplotypes of four
sequence variants of a T2DM susceptibility gene (CAPN10) identified in this region
positively associated with T2DM in a South Indian population.
In conclusion, these investigations provide evidence that the three genes studied may
contribute to susceptibility for development of T2DM or obesity. However, the findings are
in agreement with the most likely genetic model for non-Mendelian complex diseases, that
many genes are involved in determining susceptibility to disease with no single gene
capable of determining the overall disease phenotype
Angiotensin converting enzyme and mitochondria – molecular and genetic mechanisms involving bradykinin receptors and uncoupling proteins
Low angiotensin converting enzyme (ACE) activity is associated with various cardiovascular phenotypes including reduced left ventricular (LV) hypertrophy, reduced cardiovascular events and enhanced metabolic efficiency, but precise mechanisms are unclear and direct genetic associations remain controversial. ACE degrades kinins and promotes formation of angiotensin II. Combined genetic and in vitro studies were used to test the hypothesis that the previously observed effects may be through alterations in kinins or mitochondrial function via novel uncoupling proteins (UCPs).
The -9 allele of the bradykinin β2 receptor BDKRB2+9/-9 gene variant is correlated with low kinin activity and was associated with lower prospective LV growth during strenuous physical exercise and lower prospective hypertensive cardiovascular risk, as well as increased efficiency of skeletal muscle contraction (delta efficiency) in healthy volunteers (P = 0.003, accounting for 11% of the inter-individual variability).
Addition of angiotensin II to skeletal myocytes resulted in a 3.5 fold increase in oxygen consumption (P = 0.03). Incubation of isolated myocytes with an ACE inhibitor lead to mitochondrial membrane hyperpolarisation, suggesting mitochondrial coupling may be an important mediator of the cellular actions of ACE.
A common promoter variant in the UCP2 gene was associated with a two-fold increase in prospective cardiovascular risk (P < 0.0001). Variation in the UCP3/2 gene cluster accounted for 15% of the inter-individual endurance training related changes in delta efficiency and there was a surprising, but consistent, association with serum ACE activity. Finally, in vitro assays confirmed physiological downregulation of UCP2 in endothelial cells was associated with increased oxidative stress and reduced ACE mRNA.
In conclusion, BDKRB2 may mediate some of the beneficial metabolic and cardiovascular effects associated with low ACE activity, possibly through changes in mitochondrial function. Mitochondrial coupling appears pivotal in cardiovascular (patho)physiology, possibly via oxidative stress or a novel ACE metabolic regulatory pathway. UCPs may be a target for future cardiovascular interventions
Molecular Evidence for Dietary Adaptation in Humans
Starch digestion begins in the mouth where it is hydrolysed into smaller polysaccharides by the enzyme salivary amylase. Three salivary amylase genes (AMY1A,B & C) and a psuedogene (AMYP1) have been described and are located in tandem on the short arm of chromosome 1. Polymorphic variation has been demonstrated in Caucasian populations in the form of the number of repeats of the AMY1 genes, as follows: (1A-1B-P1)n-1C. This variation results in differing levels salivary amylase enzyme production and, as a result, differences in the efficiency of starch digestion. It is suggested that an increase in salivary gene copy number may be an adaptation to high starch diets as a result of the adoption of agriculture. A reliable high-throughput PCR based method has been designed that utilises ABI GeneScan technology, to quantify AMY1 gene copy number and to type 6 microsatellite markers closely linked to the AMY gene cluster. Data have been collected for 14 human populations, with different histories of cereal agriculture and levels of starch in the diet. Data have also been collected on AMY1 gene copy number in 5 common chimpanzees (Pan troglodytes).
The AMY1 allele frequency difference (measured using FST) between the two most extreme populations, the Mongolians and Saami, was not an outlier on a distribution of 11,024 SNPs from the human genome. As the AMY1 locus does not appear to differ from the rest of the genome in terms of allele frequency difference between populations, genetic drift could not be ruled out as an explanation for the observed AMY1 allele frequency differences. The chimpanzee data suggest that the most frequent allele (AMY1*H1) in humans may not be the ancestral allele, as all chimpanzee chromosomes tested carried the AMY1*H0 allele. Furthermore, a powerful method for the analysis of intra-allelic variability at the AMY locus suggests that weak positive selection has occurred on the AMY*H1 allele. As a result, genetic drift could not be ruled out as an explanation for the observed AMY1 allele frequency difference among populations.
Alanine:glyoxylate aminotransferase (AGT) is an intermediary metabolic enzyme that is targeted to different organelles in different species. Previous studies have shown that there is a clear relationship between the organellar distribution of AGT and diet. Non-human primates show the herbivorous peroxisomal distribution of AGT. In humans a point mutation and insertion deletion polymorphism have been associated with peroxisome-to-mitochondria AGT mis-targeting. Data have been collected using a PCR/RFLP based method, in 11 human populations. In a comparison with FST values from 11,024 SNP loci, 94.5% of SNPs had a lower FST than a comparison of AGT allele frequencies for Saami and Chinese. This unusually high allele frequency difference between Chinese and Saami is consistent with the signature of positive selection driven by the unusually high meat content in the Saami diet
Insulin Resistance
This book has been made possible by the contributions of leading scientists and clinicians from upcoming and interdisciplinary fields of research concerning the molecular and clinical features of insulin resistance. Multiple metabolic disturbances associated with Insulin Resistance include inflammatory cytokines, adipokines, endothelial dysfunction, tissue-specific defects in insulin action and signaling, oxidative stress, ectopic lipid deposition, and disordered neuroregulation. This book aims to encourage scientists and physicians - working separately on various aspects of Insulin resistance and metabolic syndrome for early detection of the first signs indicating the onset of a metabolic misbalance in order to prevent the consecutive cascades which lead to metabolic syndrome, resulting in the so-called diseases of modern civilization - cancer, diabetes and hypertension
Association study of transcription factors regulating insulin secretion and action in type 2 diabetes in Chinese.
Ho Sin Ka Janice.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 105-119).Abstracts in English and Chinese.Chapter CHAPTER 1. --- IntroductionChapter 1.1. --- Epidemiology of Type 2 Diabetes --- p.1Chapter 1.2. --- Risk factors contributing to Type 2 Diabetes --- p.3Chapter 1.2.1. --- Environmental and physiological factors --- p.3Chapter 1.2.2. --- Genetic factors --- p.3Chapter 1.3. --- Disruption of energy homeostasis in the pathogenesis of type 2 diabetes --- p.6Chapter 1.3.1. --- Clinical spectrum of diabetes --- p.6Chapter 1.3.2. --- Insulin as a key regulator of energy homeostasis --- p.7Chapter 1.3.3. --- Insulin secretion and glucose metabolism --- p.8Chapter 1.3.4. --- Insulin action and lipid metabolism --- p.9Chapter 1.3.5. --- Lipotoxicity and glucotoxicity --- p.12Chapter 1.3.6. --- Role of transcription factors as metabolic switch --- p.13Chapter 1.4. --- Candidate genes implicated in type 2 diabetes susceptibility --- p.15Chapter 1.4.1. --- Candidate genes involved in insulin secretion pathway --- p.15Chapter 1.4.1.1. --- HNF4A --- p.15Chapter 1.4.1.2. --- HNF1A --- p.16Chapter 1.4.1.3. --- PDX1/PBX1 --- p.17Chapter 1.4.1.4. --- NEUROD1 --- p.17Chapter 1.4.1.5. --- GCK --- p.17Chapter 1.4.1.6. --- KCNJ11/ABCC8 --- p.18Chapter 1.4.2 --- Candidate genes involved in insulin action pathway --- p.19Chapter 1.4.2.1. --- PPARG --- p.19Chapter 1.4.2.2. --- PPARA --- p.20Chapter 1.4.2.3. --- PPARGC1A --- p.20Chapter 1.4.2.4. --- ADIP0Q --- p.21Chapter 1.4.2.5. --- LPL --- p.21Chapter 1.4.2.6. --- UPC --- p.22Chapter 1.5. --- Hypothesis and objectives of the study --- p.23Chapter CHAPTER 2. --- Materials and methodsChapter 2.1. --- Study design --- p.25Chapter 2.1.1. --- Two-stage candidate gene association design --- p.25Chapter 2.1.2. --- Power calculation --- p.27Chapter 2.2. --- Study cohort --- p.29Chapter 2.2.1. --- Subject recruitment --- p.29Chapter 2.2.2. --- Clinical and biochemical measurements --- p.30Chapter 2.2.3. --- Clinical definitions --- p.31Chapter 2.3. --- Genetic study --- p.32Chapter 2.3.1. --- Candidate gene selection --- p.32Chapter 2.3.2. --- SNP selection --- p.32Chapter 2.3.3. --- DNA sample preparation --- p.35Chapter 2.3.4. --- Genotyping methods --- p.36Chapter 2.3.4.1. --- Allele specific Tm shift assay --- p.36Chapter 2.3.4.2. --- Mass spectrometry assay --- p.40Chapter 2.4. --- Data quality control --- p.42Chapter 2.4.1. --- Stage 1 --- p.42Chapter 2.4.2. --- Stage 2 --- p.42Chapter 2.5. --- Statistical analysis --- p.45Chapter 2.5.1. --- Stage 1 analysis --- p.45Chapter 2.5.2. --- Stage 2 analysis --- p.45Chapter 2.5.3. --- Stage 1 and 2 combined analysis --- p.46Chapter CHAPTER 3. --- ResultsChapter 3.1. --- Clinical characteristics of subjects in stages 1 and 2 studies --- p.48Chapter 3.2. --- Case-control associations in stage 1 --- p.51Chapter 3.2.1. --- Association with T2D --- p.51Chapter 3.2.2. --- Association with T2D subset by metabolic syndrome --- p.54Chapter 3.3. --- Case-control associations in stage 2 --- p.60Chapter 3.3.1. --- SNP selection for genotyping --- p.60Chapter 3.3.2. --- Association with T2D --- p.63Chapter 3.3.3. --- Association with T2D subset by metabolic syndrome --- p.64Chapter 3.4. --- Case-control associations in combined stages 1 and 2 --- p.66Chapter 3.4.1. --- Association with T2D --- p.66Chapter 3.4.2. --- Association with T2D subset by metabolic syndrome --- p.70Chapter 3.4.3. --- Association with T2D subset by age at diagnosis --- p.74Chapter 3.4.4. --- Association with T2D subset by gender --- p.76Chapter 3.4.5. --- Genetic epistasis for T2D association --- p.79Chapter 3.5. --- Metabolic traits associations in control subjects in combined stages 1 and 2 studies --- p.83Chapter CHAPTER 4. --- Discussion --- p.86Chapter 4.1. --- Role of insulin secretion genes in type 2 diabetes --- p.87Chapter 4.2. --- Role of insulin action genes in type 2 diabetes --- p.92Chapter 4.3. --- Combined genetic effects on risk for type 2 diabetes --- p.97Chapter 4.4. --- Summary --- p.98Chapter 4.5. --- Limitation of this study and future direction --- p.101REFERENCES --- p.104APPENDICES --- p.119Chapter Appendix 1: --- Gene structure and linkage disequilibrium of genotyped SNPs of candidate genes --- p.119Chapter Appendix 2: --- Information of SNPs genotyped in stage 1 --- p.130Chapter Appendix 3: --- T2D association results (additive model) of 152 SNPs for stage 1 case- control samples --- p.137Chapter Appendix 4: --- T2D association results (additive model) of 152 SNPs for stage 1 case- control samples subset by metabolic syndrome status in cases --- p.144Chapter Appendix 5: --- T2D association results (additive model) of 22 SNPs for stage 2 case- control samples --- p.151Chapter Appendix 6: --- T2D association results (additive model) of 22 SNPs for stage 2 case- control samples subset by metabolic syndrome status in cases --- p.15
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