Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients

Mitonuclear epistasis involving TP63 and haplogroup Uk: Risk of rapid progression of knee OA in patients from the OAI

OBJECTIVE

To investigate genetic interactions between mitochondrial deoxyribonucleic acid (mtDNA) haplogroups and nuclear single nucleotide polymorphisms (nSNPs) to analyze their impact on the development of the rapid progression of knee osteoarthritis (OA).

DESIGN

A total of 1095 subjects from the Osteoarthritis Initiative, with a follow-up time of at least 48-months, were included. Appropriate statistical approaches were performed, including generalized estimating equations adjusting for age, gender, body mass index, contralateral knee OA, Western Ontario and McMaster Universities Osteoarthritis Index pain, previous injury in target knee and the presence of the mtDNA variant m.16519C. Additional genomic data consisted in the genotyping of Caucasian mtDNA haplogroups and eight nSNPs previously associated with the risk of knee OA in robust genome-wide association studies.

RESULTS

The simultaneous presence of the G allele of rs12107036 at TP63 and the haplogroup Uk significantly increases the risk of a rapid progression of knee OA (odds ratio = 1.670; 95% confidence interval [CI]: 1.031-2.706; adjusted p-value = 0.027). The assessment of the population attributable fraction showed that the highest proportion of rapid progressors was under the simultaneous presence of the G allele of rs12107036 and the haplogroup Uk (23.4%) (95%CI: 7.89-38.9; p-value < 0.05). The area under the curve of the cross-validation model (0.730) was very similar to the obtained for the predictive model (0.735). A nomogram was constructed to help clinicians to perform clinical trials or epidemiologic studies.

CONCLUSIONS

This study demonstrates the existence of a mitonuclear epistasis in OA, providing new mechanisms by which nuclear and mitochondrial variation influence the susceptibility to develop different OA phenotypes.

Exercise-induced expression of genes associated with aging in the hippocampus of rats

Recent research has underscored the influence of aging and exercise on brain function. In this study, we aimed to explore alterations in the expression of novel molecular factors and gain insight into underlying molecular mechanisms in the hippocampus of rats engaged in voluntary wheel running. We assessed the expression of aging-related genes in the hippocampus using a high-throughput whole genome DNA microarray approach in rats engaged in voluntary running for four weeks. The results indicated that compared to the control group, wheel running significantly altered the expressions of aging-related genes in the hippocampus. Functional categorization, utilizing pathway-focused gene classifications and disease state-focused gene classifications, along with Ingenuity Pathway Analysis (IPA), revealed changes in expression pattern in major categories of cell death and survival, renal necrosis/cell death, and cardiovascular disease genes. These findings suggest that exercise may mitigate the risk of age-related cognitive decline by regulating of aging-related genes in the hippocampus. Further research is warranted to elucidate the mechanisms driving changes in gene expression and to determine the long-term effects of exercise on brain function.

Association Analysis Provides Insights into Plant Mitonuclear Interactions

Cytonuclear interaction refers to the complex and ongoing process of coevolution between nuclear and organelle genomes, which are responsible for cellular respiration, photosynthesis, lipid metabolism, etc. and play a significant role in adaptation and speciation. There have been a large number of studies to detect signatures of cytonuclear interactions. However, identification of the specific nuclear and organelle genetic polymorphisms that are involved in these interactions within a species remains relatively rare. The recent surge in whole genome sequencing has provided us an opportunity to explore cytonuclear interaction from a population perspective. In this study, we analyzed a total of 3,439 genomes from 7 species to identify signals of cytonuclear interactions by association (linkage disequilibrium) analysis of variants in both the mitochondrial and nuclear genomes across flowering plants. We also investigated examples of nuclear loci identified based on these association signals using subcellular localization assays, gene editing, and transcriptome sequencing. Our study provides a novel perspective on the investigation of cytonuclear coevolution, thereby enriching our understanding of plant fitness and offspring sterility.

Intergenomic and epistatic interactions control free radical mediated pancreatic β-cell damage

Alloxan (AL)-generated Reactive Oxygen Species (ROS) selectively destroy insulin-producing pancreatic β-cells. A previous genome-wide scan (GWS) using a cohort of 296 F2 hybrids between NOD (AL-sensitive) and ALR (AL-resistant) mice identified linkages contributing to β-cell susceptibility or resistance to AL-induced diabetes on Chromosomes (Chr) 2, 3, 8, and a single nucleotide polymorphism in mt-Nd2 of the mitochondrial genome (mtDNA). AL treatment of congenic and consomic NOD mouse stocks confirmed resistance linked to both the mtDNA and the Chr 8 locus from ALR [NOD.mt^ALR.ALR-(D8Mit293-D8Mit137)]. To identify possible epistatic interactions, the GWS analysis was expanded to 678 F2 mice. ALR-derived diabetes-resistance linkages on Chr 8 as well as the mt-Nd2^a allele were confirmed and novel additional linkages on Chr 4, 5, 6, 7, and 13 were identified. Epistasis was observed between the linkages on Chr 8 and 2 and Chr 8 and 6. Furthermore, the mt-Nd2 genotype affected the epistatic interactions between Chr 8 and 2. These results demonstrate that a combination of nuclear-cytoplasmic genome interactions regulates β-cell sensitivity to ROS-mediated ALD.

Variants influencing age at diagnosis of HNF1A-MODY

BACKGROUND

HNF1A-MODY is a monogenic form of diabetes caused by variants in the HNF1A gene. Different HNF1A variants are associated with differences in age of disease onset, but other factors are postulated to influence this trait. Here, we searched for genetic variants influencing age of HNF1A-MODY onset.

METHODS

Blood samples from 843 HNF1A-MODY patients from Czech Republic, France, Poland, Slovakia, the UK and the US were collected. A validation set consisted of 121 patients from the US. We conducted a genome-wide association study in 843 HNF1A-MODY patients. Samples were genotyped using Illumina Human Core arrays. The core analysis was performed using the GENESIS package in R statistical software. Kinship coefficients were estimated with the KING and PC-Relate algorithms. In the linear mixed model, we accounted for year of birth, sex, and location of the HNF1A causative variant.

RESULTS

A suggestive association with age of disease onset was observed for rs2305198 (p = 2.09E-07) and rs7079157 (p = 3.96E-06) in the HK1 gene, rs2637248 in the LRMDA gene (p = 2.44E-05), and intergenic variant rs2825115 (p = 2.04E-05). Variant rs2637248 reached nominal significance (p = 0.019), while rs7079157 (p = 0.058) and rs2825115 (p = 0.068) showed suggestive association with age at diabetes onset in the validation set.

CONCLUSIONS

rs2637248 in the LRMDA gene is associated with age at diabetes onset in HNF1A-MODY patients.

Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

BACKGROUND

Knee osteoarthritis is the most prevalent chronic musculoskeletal debilitating disease. Current treatments are only symptomatic, and to improve this, we need a robust prediction model to stratify patients at an early stage according to the risk of joint structure disease progression. Some genetic factors, including single nucleotide polymorphism (SNP) genes and mitochondrial (mt)DNA haplogroups/clusters, have been linked to this disease. For the first time, we aim to determine, by using machine learning, whether some SNP genes and mtDNA haplogroups/clusters alone or combined could predict early knee osteoarthritis structural progressors.

METHODS

Participants (901) were first classified for the probability of being structural progressors. Genotyping included SNP genes TP63, FTO, GNL3, DUS4L, GDF5, SUPT3H, MCF2L, and TGFA; mtDNA haplogroups H, J, T, Uk, and others; and clusters HV, TJ, KU, and C-others. They were considered for prediction with major risk factors of osteoarthritis, namely, age and body mass index (BMI). Seven supervised machine learning methodologies were evaluated. The support vector machine was used to generate gender-based models. The best input combination was assessed using sensitivity and synergy analyses. Validation was performed using tenfold cross-validation and an external cohort (TASOAC).

RESULTS

From 277 models, two were defined. Both used age and BMI in addition for the first one of the SNP genes TP63, DUS4L, GDF5, and FTO with an accuracy of 85.0%; the second profits from the association of mtDNA haplogroups and SNP genes FTO and SUPT3H with 82.5% accuracy. The highest impact was associated with the haplogroup H, the presence of CT alleles for rs8044769 at FTO, and the absence of AA for rs10948172 at SUPT3H. Validation accuracy with the cross-validation (about 95%) and the external cohort (90.5%, 85.7%, respectively) was excellent for both models.

CONCLUSIONS

This study introduces a novel source of decision support in precision medicine in which, for the first time, two models were developed consisting of (i) age, BMI, TP63, DUS4L, GDF5, and FTO and (ii) the optimum one as it has one less variable: age, BMI, mtDNA haplogroup, FTO, and SUPT3H. Such a framework is translational and would benefit patients at risk of structural progressive knee osteoarthritis.

Mitochondrial genome variations, mitochondrial-nuclear compatibility, and their association with metabolic diseases

Two genomes regulate the energy metabolism of eukaryotic cells: the nuclear genome, which codes for most cellular proteins, and the mitochondrial genome, which, together with the nuclear genome, coregulates cellular bioenergetics. Therefore, mitochondrial genome variations can affect, directly or indirectly, all energy-dependent cellular processes and shape the metabolic state of the organism. This review provides a current and up-to-date overview on how codependent these two genomes are, how they appear to have coevolved, and how variations within the mitochondrial genome might be associated with the manifestation of metabolic diseases. This review summarizes and structures results obtained from epidemiological studies that identified links between mitochondrial haplogroups and individual risks for developing obesity and diabetes. This is complemented by findings on the compatibility of mitochondrial and nuclear genomes and cellular bioenergetic fitness, which have been acquired from well-controlled studies in conplastic animal models. These elucidate, more mechanistically, how single-nucleotide variants can influence cellular metabolism and physiology. Overall, it seems that certain mitochondrial genome variations negatively affect mitochondrial-nuclear compatibility and are statistically linked with the onset of metabolic diseases, whereas, for others, greater uncertainty exists, and additional research into this exciting field is required.

mtDNA haplogroup A enhances the effect of obesity on the risk of knee OA in a Mexican population

To evaluate the influence of mitochondrial DNA haplogroups on the risk of knee OA in terms of their interaction with obesity, in a population from Mexico. Samples were obtained from (n = 353) knee OA patients (KL grade ≥ I) and (n = 364) healthy controls (KL grade = 0) from Mexico city and Torreon (Mexico). Both Caucasian and Amerindian mtDNA haplogroups were assigned by single base extension assay. A set of clinical and demographic variables, including obesity status, were considered to perform appropriate statistical approaches, including chi-square contingency tables, regression models and interaction analyses. To ensure the robustness of the predictive model, a statistical cross-validation strategy of B = 1000 iterations was used. All the analyses were performed using boot, GmAMisc and epiR package from R software v4.0.2 and SPSS software v24. The frequency distribution of the mtDNA haplogroups between OA patients and healthy controls for obese and non-obese groups showed the haplogroup A as significantly over-represented in knee OA patients within the obese group (OR 2.23; 95% CI 1.22–4.05; p-value = 0.008). The subsequent logistic regression analysis, including as covariate the interaction between obesity and mtDNA haplogroup A, supported the significant association of this interaction (OR 2.57; 95% CI 1.24–5.32; p-value = 0.011). The statistical cross-validation strategy confirmed the robustness of the regression model. The data presented here indicate a link between obesity in knee OA patients and mtDNA haplogroup A.

Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease

Large-scale genome-wide association studies have identified hundreds of single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD). However, collectively, these explain <20% of the heritability. Hypothesis: Here, we hypothesize that mitochondrial (MT)-SNVs might present one potential source of this “missing heritability”. Methods: We analyzed 265 MT-SNVs in ~500,000 UK Biobank individuals, exploring two different CAD definitions: a more stringent (myocardial infarction and/or revascularization; HARD = 20,405), and a more inclusive (angina and chronic ischemic heart disease; SOFT = 34,782). Results: In HARD cases, the most significant (p < 0.05) associations were for m.295C>T (control region) and m.12612A>G (ND5), found more frequently in cases (OR = 1.05), potentially related to reduced cardiorespiratory fitness in response to exercise, as well as for m.12372G>A (ND5) and m.11467A>G (ND4), present more frequently in controls (OR = 0.97), previously associated with lower ROS production rate. In SOFT cases, four MT-SNVs survived multiple testing corrections (at FDR < 5%), all potentially conferring increased CAD risk. Of those, m.11251A>G (ND4) and m.15452C>A (CYB) have previously shown significant associations with body height. In line with this, we observed that CAD cases were slightly less physically active, and their average body height was ~2.00 cm lower compared to controls; both traits are known to be related to increased CAD risk. Gene-based tests identified CO2 associated with HARD/SOFT CAD, whereas ND3 and CYB associated with SOFT cases (p < 0.05), dysfunction of which has been related to MT oxidative stress, obesity/T2D (CO2), BMI (ND3), and angina/exercise intolerance (CYB). Finally, we observed that macro-haplogroup I was significantly (p < 0.05) more frequent in HARD cases vs. controls (3.35% vs. 3.08%), potentially associated with response to exercise. Conclusions: We found only spurious associations between MT genome variation and HARD/SOFT CAD and conclude that more MT-SNV data in even larger study cohorts may be needed to conclusively determine the role of MT DNA in CAD.

The transcriptome-wide association search for genes and genetic variants which associate with BMI and gestational weight gain in women with type 1 diabetes

BACKGROUND

Clinical data suggest that BMI and gestational weight gain (GWG) are strongly interconnected phenotypes; however, the genetic basis of the latter is rather unclear. Here we aim to find genes and genetic variants which influence BMI and/or GWG.

METHODS

We have genotyped 316 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays. The GIANT, ARIC and T2D-GENES summary statistics were used for TWAS (performed with PrediXcan) in adipose tissue. Next, the analysis of association of imputed expression with BMI in the general and diabetic cohorts (Analysis 1 and 2) or GWG (Analysis 3 and 4) was performed, followed by variant association analysis (1 Mb around identified loci) with the mentioned phenotypes.

RESULTS

In Analysis 1 we have found 175 BMI associated genes and 19 variants (p < 10-4) which influenced GWG, with the strongest association for rs11465293 in CCL24 (p = 3.18E-06). Analysis 2, with diabetes included in the model, led to discovery of 1812 BMI associated loci and 207 variants (p < 10-4) influencing GWG, with the strongest association for rs9690213 in PODXL (p = 9.86E-07). In Analysis 3, among 648 GWG associated loci, 2091 variants were associated with BMI (FDR < 0.05). In Analysis 4, 7 variants in GWG associated loci influenced BMI in the ARIC cohort.

CONCLUSIONS

Here, we have shown that loci influencing BMI might have an impact on GWG and GWG associated loci might influence BMI, both in the general and T1DM cohorts. The results suggest that both phenotypes are related to insulin signaling, glucose homeostasis, mitochondrial metabolism, ubiquitinoylation and inflammatory responses.