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.

A meta-analysis and a functional study support the influence of mtDNA variant m.16519C on the risk of rapid progression of knee osteoarthritis

OBJECTIVES

To identify mitochondrial DNA (mtDNA) genetic variants associated with the risk of rapid progression of knee osteoarthritis (OA) and to characterise their functional significance using a cellular model of transmitochondrial cybrids.

METHODS

Three prospective cohorts contributed participants. The osteoarthritis initiative (OAI) included 1095 subjects, the Cohort Hip and Cohort Knee included 373 and 326 came from the PROspective Cohort of Osteoarthritis from A Coruña. mtDNA variants were screened in an initial subset of 450 subjects from the OAI by in-depth sequencing of mtDNA. A meta-analysis of the three cohorts was performed. A model of cybrids was constructed to study the functional consequences of harbouring the risk mtDNA variant by assessing: mtDNA copy number, mitochondrial biosynthesis, mitochondrial fission and fusion, mitochondrial reactive oxygen species (ROS), oxidative stress, autophagy and a whole transcriptome analysis by RNA-sequencing.

RESULTS

mtDNA variant m.16519C is over-represented in rapid progressors (combined OR 1.546; 95% CI 1.163 to 2.054; p=0.0027). Cybrids with this variant show increased mtDNA copy number and decreased mitochondrial biosynthesis; they produce higher amounts of mitochondrial ROS, are less resistant to oxidative stress, show a lower expression of the mitochondrial fission-related gene fission mitochondrial 1 and an impairment of autophagic flux. In addition, its presence modulates the transcriptome of cybrids, especially in terms of inflammation, where interleukin 6 emerges as one of the most differentially expressed genes.

CONCLUSIONS

The presence of the mtDNA variant m.16519C increases the risk of rapid progression of knee OA. Among the most modulated biological processes associated with this variant, inflammation and negative regulation of cellular process stand out. The design of therapies based on the maintenance of mitochondrial function is recommended.

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.

POS0231 GENETIC BIOMARKERS, SNP GENES AND mtDNA HAPLOGROUPS, PREDICT OSTEOARTHRITIS STRUCTURAL PROGRESSORS THROUGH THE USE OF SUPERVISED MACHINE LEARNING

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.

Objectives

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, TGFA, mtDNA haplogroups H, J, T, Uk, others, and clusters HV, TJ, KU, 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 10-fold cross-validation as well as 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, 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.

Conclusion

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, FTO and ii) the optimum one as it has one less variable: age, BMI, mtDNA haplogroup, FTO, SUPT3H. Such a framework is translational and would be of benefit to patients at risk of structural progressive knee osteoarthritis.

Acknowledgements

The authors would like to thank the Osteoarthritis Initiative (OAI) participants and Coordinating Center for their work in generating the clinical and radiological data of the OAI cohort and for making them publicly available. The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners. None of the authors are part of the OAI investigator team. Moreover, the authors are also grateful to the TASOAC participants.

A special thanks to ArthroLab Inc. for having provided the MRI data used for classifying structural progressors for each individual.

Disclosure of Interests

Hossein Bonakdari: None declared, Jean-Pierre Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: Work supported in part by the Osteoarthritis Research Unit of the University of Montreal Hospital Research Centre and the Chair in Osteoarthritis from the University of Montreal., Francisco J. Blanco: None declared, Ignacio Rego-Perez: None declared, Alejandro Durán-Sotuela: None declared, Dawn Aitken: None declared, Graeme Jones: None declared, Flavia Cicuttini: None declared, Afshin Jamshidi Grant/research support from: Received a bursary from the Canada First Research Excellence Fund through the TransMedTech Institute in Canada., François Abram Employee of: was an employee of ArthroLab Inc., Johanne Martel-Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: Work supported in part by the Osteoarthritis Research Unit of the University of Montreal Hospital Research Centre and the Chair in Osteoarthritis from the University of Montreal.

Genetic biomarkers in osteoarthritis: a quick overview

Osteoarthritis (OA) is a chronic musculoskeletal disease with a polygenic and heterogeneous nature. In addition, when clinical manifestations appear, the evolution of the disease is usually already irreversible. Therefore, the efforts on OA research are focused mainly on the discovery of therapeutic targets and reliable biomarkers that permit the early identification of different OA-related parameters such as diagnosis, prognosis, or phenotype identification. To date, potential candidate protein biomarkers have been associated with different aspects of the disease; however, there is currently no gold standard. In this sense, genomic data could act as complementary biomarkers of diagnosis and prognosis or even help to identify therapeutic targets of the disease. In this review, we will describe the most recent advances in genetic biomarkers in OA over the past three years.

POS0347 SPECIFIC MITO-NUCLEAR INTERACTIONS AND mt16519C VARIANT AS PREDICTIVE BIOMARKERS FOR THE RAPIDLY PROGRESSIVE OSTEOARTHRITIS OF THE KNEE. DATA FROM THE OSTEOARTHRITIS INITIATIVE

Background:

The early identification of patients with rapid progressive osteoarthritis (RPOA) could allow the implementation of prevention strategies and their inclusion in clinical trials. Polymorphisms in nuclear and mitochondrial DNA (mtDNA) have been associated with OA. Preliminary analyses by our group showed nuclear single nucleotide polymorphism (nSNP) rs12107036 of TP63 as a potential risk factor for RPOA of the knee.

Objectives:

i) To analyze interactions between mtDNA haplogroups and rs12107036 ii) To apply Next Generation Sequencing (NGS) to discover novel mitochondrial variants to construct predictive models of RPOA of the knee.

Methods:

1102 Caucasian subjects from the OAI were classified as follows: i) Rapid progressors (N=255), baseline KL grade 0-1 or 2 in at least one knee, that increases up to KL≥ 3 or 4 respectively during 48-month follow-up. ii) Non-rapid progressors (N=847), with the same baseline characteristics as rapid progressors, but with slower or no evolution over time.

mtDNA haplogroups and rs12107036 were assigned by mini-sequencing techniques. Novel mtDNA variants were studied by NGS. Statistical analyses included chi-square tests and generalized estimating equations. Relative excess risk due to interaction (RERI) and attributable proportion (AP) were evaluated for the additive interaction between mtDNA clusters and nSNP rs12107036. A nomogram for the estimation of the risk of RPOA was also developed. Analyses were performed using SPSS Statistics v24 and epi.R package included in R software v3.6.3.

Results:

Chi-square analyses revealed an increased risk of RPOA in patients with the allele G of rs12107036 and mtDNA cluster UK (OR 2,013; p=0,001). An excess of 70,3% of RERI between nSNP rs12107036 and mtDNA clusters was detected, indicating that 47,1% (AP) of the risk is attributable to this interaction, therefore harboring both genetic factors increase the risk of RPOA up to 4,7 times compared to harboring just one. mtDNA sequencing revealed the variant mt16519 overrepresented in rapid-progressors (OR 1,620; p=0,002).

Table 1 shows the predictive model for the risk of RPOA. The interaction between the allele G of rs12107036 and mtDNA cluster KU (OR 1,727; p=0,036), in addition to the variant mt16519C (OR 1,690; p=0,003), showed a significant association with the RPOA phenotype regardless of age, BMI, contralateral knee OA, previous injury and WOMAC pain. Image 1 displays the nomogram for predicting risk of RPOA; as an example, a 70 year old male, with a BMI of 28, WOMAC pain score of 10, contralateral OA and presence of both mito-nuclear interaction and mt16519C, has a risk of RPOA of 0,7.

Conclusion:

mtDNA genetic variants are useful, not only as modulators of the influence of specific nuclear polymorphisms on the risk of developing RPOA, but also as candidate genetic biomarkers of this phenotype.

Table 1.

Predictive model for the risk of RPOA phenotype

Variablep-valueORmin 95% CIMAX 95% CIClinical and genetic variablesAge<0,001#1,0561,0381,074Female0,1431,2600,9251,718BMI<0,001#1,0651,0301,101Contralateral OA<0,001#1,9271,4132,626Previous Injury<0,001#1,7701,2932,422WOMAC pain0,001#1,0971,0391,159rs12107036 G0,1721,2260,9151,643mt16519 C0,003#1,6901,2022,375mtDNA Clusters$Others0,8030,9210,4821,760TJ0,4821,2090,7122,052UK0,1360,6980,4351,120HVReferencers12107036 G*mtDNA ClusterG * Others0,5020,7890,3951,576G * TJ0,1580,6470,3531,185G * UK0,036#1,7271,0362,881G * HVReference

$mtDNA Clusters: haplogroups with a common phylogenetic origin BMI: Body Mass Index; WOMAC: Western Ontario and McMaster Universities Osteoarthritis Index; OR: Odds Ratio; CI: confidence interval; #: statistical significance declared at P ≤ 0.05, in bold.

Image 1.

Nomogran for the estimation of the risk of RPOA phenotype. Circles represent the values for the example. Clusters: haplogroups with a common phylogenetic origin BMI: Body Mass Index; WOMAC: Western Ontario and McMaster Universities Osteoarthritis Index.

Disclosure of Interests:

None declared

THU0012 IMPACT OF RS12107036 POLYMORPHISM OF TP63 ON THE RISK OF RAPID PROGRESSIVE OSTEOARTHRITIS OF THE KNEE. DATA FROM THE OSTEOARTHRITIS INITIATIVE

Background:

There is a need to identify patients with the rapid progressive phenotype of Osteoarthritis (RPOA) to include them in clinical trials and to implement prevention strategies. During the last years, nuclear single nucleotide polymorphisms (SNPs) were associated with susceptibility and progression of the disease, but not with the rapid progression phenotype.

Objectives:

Analyze the influence of previously knee OA-associated nuclear SNPs on the risk of RPOA in patients of the OAI.

Methods:

Caucasian patients from the OAI were selected and assigned into three different groups (N=252/group) based on the following criteria:

A.rapid progressors; baseline KL grade 0-1 in at least one knee and increase up to KL≥ 3 during a 48-month period; or baseline KL grade 2 in at least one knee and increase up to KL 4 or total knee replacement during the follow-up.

B.no-rapid progressors; baseline KL grade 0-1 in at least one knee and increase up to KL 2 during 48-month period; or baseline KL grade 2 in at least one knee and increase up to KL 3 during the follow-up.

C.no-progressors; KL grade 0-2 at baseline in at least one knee and bilaterally stable during 48-month period.

Groups were re-categorized into two groups: non-progressors and progressors (pooling A and B). Nuclear SNPs were previously assigned by mini-sequencing techniques. Preliminary chi-square analyses and binary and multinomial logistic regression models adjusted by gender, age, body mass index (BMI), contralateral OA, previous injury in target knee and WOMAC pain, were performed with IBM SPSS Statistics v24.

Results:

We analyzed the effect of 7 SNPs that had been strongly associated with knee OA susceptibility in different GWAS studies: rs11177, rs4730250, rs11842874, rs12107036, rs8044769, rs10948172 and rs143383. Chi-square analyses only showed differences in the frequency distribution of rs12107036 between groups (p=0,028), being the GG genotype over-represented in the rapid progressors group and the AA genotype in the non-progressors group (Figure 1).

The binary logistic regression showed that G allele was significantly over-represented in the (pooled) progressors group when compared with non-progressors (p=0,008) (Table 1). And the multinomial logistic regression showed that, in addition to age and previous injury in target knee, the GG genotype (p=0,032) emerged as a potential risk factor for the RPOA when compared with non-rapid progressors (Table 2).

Table 1.

Binary regression model comparing progressors pool vs. no-progress

Variablesp-valueORC.I. 95%Min.Max.Age0,2171,0120,9931,030Sex (Female)0,000#2,0491,4782,842BMI0,000#1,0851,0441,127Contralateral OA (Yes)0,044#1,4001,0091,942Previous Injury (Yes)0,002#1,7231,2232,429WOMAC pain0,003#1,1021,0331,177rs12107036 G (Yes)0,008#1,6821,1482,463

CI: confidence interval; OR: Odd Ratio; #: statistical significance declared at P ≤ 0.05

Table 2.

Multinomial regression model comparing rapid vs. no-rapid progressors.

Variablesp-valueORC.I. 95%Min.Max.Age0,000#1,0641,0411,088Sex (Female)0,4980,8750,5951,287BMI0,0961,0340,9941,077Contralateral OA (Yes)0,7921,0520,7191,539Previous Injury (Yes)0,028#1,5231,0472,216WOMAC pain0,0911,0550,9921,123rs12107036 GG (Yes)0,032#1,5741,0392,382

CI: confidence interval; OR: Odd Ratio; #: statistical significance declared at P ≤ 0.05

Conclusion:

The G allele of the nuclear SNP rs12107036 of TP63 gen increases the risk of knee OA progression. Depending on the number of risk allele copies the level of progression varies, being the GG genotype a risk factor for the RPOA of the knee. The assignment of this nuclear polymorphism could be useful as complementary genetic biomarker for the early identification of this phenotype.

Disclosure of Interests:

Alejandro Durán-Sotuela: None declared, Mercedes Fernandez-Moreno: None declared, Maria Eugenia Vazquez Mosquera: None declared, Paula Ramos-Louro: None declared, Andrea Dalmao-Fernandez: None declared, Sara Relaño-Fernandez: None declared, Natividad Oreiro: None declared, Francisco J. Blanco Grant/research support from: Sanofi-Aventis, Lilly, Bristol MS, Amgen, Pfizer, Abbvie, TRB Chemedica International, Glaxo SmithKline, Archigen Biotech Limited, Novartis, Nichi-iko pharmaceutical Co, Genentech, Jannsen Research & Development, UCB Biopharma, Centrexion Theurapeutics, Celgene, Roche, Regeneron Pharmaceuticals Inc, Biohope, Corbus Pharmaceutical, Tedec Meiji Pharma, Kiniksa Pharmaceuticals, Ltd, Gilead Sciences Inc, Consultant of: Lilly, Bristol MS, Pfizer, Ignacio Rego-Perez: None declared

Mitochondrial Genetics and Epigenetics in Osteoarthritis

During recent years, the significant influence of mitochondria on osteoarthritis (OA), the most common joint disease, has been consistently demonstrated. Not only mitochondrial dysfunction but also mitochondrial genetic polymorphisms, specifically the mitochondrial DNA haplogroups, have been shown to have an important influence on different OA-related features, including the prevalence, severity, incidence, and progression of the disease. This influence could probably be mediated by the role of mitochondria in the regulation of different processes involved in the pathogenesis of OA, such as energy production, the generation of reactive oxygen and nitrogen species, apoptosis, and inflammation. The regulation of these processes is at least partially controlled by the bi-directional communication between the nucleus and mitochondria, which permits the regulation of adaptation to a wide range of stressors and the maintenance of cellular homeostasis. This bi-directional communication consists of an “anterograde regulation” by which the nucleus regulates mitochondrial biogenesis and activity and a “retrograde regulation” by which both mitochondria and mitochondrial genetic variation exert a regulatory signaling control over the nuclear epigenome, which leads to the modulation of nuclear genes. Throughout this mini review, we will describe the evidence that demonstrates the profound influence of the mitochondrial genetic background in the pathogenesis of OA, as well as its influence on the nuclear DNA methylome of the only cell type present in the articular cartilage, the chondrocyte. This evidence leads to serious consideration of the mitochondrion as an important therapeutic target in OA.

Differential Association of Mitochondrial DNA Haplogroups J and H With the Methylation Status of Articular Cartilage: Potential Role in Apoptosis and Metabolic and Developmental Processes

OBJECTIVE

To analyze the influence of mitochondrial genome variation on the DNA methylome of articular cartilage.

METHODS

DNA methylation profiling was performed using data deposited in the NCBI Gene Expression Omnibus database (accession no. GSE43269). Data were obtained for 14 cartilage samples from subjects with haplogroup J and 20 cartilage samples from subjects with haplogroup H. Subsequent validation was performed in an independent subset of 7 subjects with haplogroup J and 9 with haplogroup H by RNA-seq. Correlated genes were validated by real-time polymerase chain reaction in an independent cohort of 12 subjects with haplogroup J and 12 with haplogroup H. Appropriate analyses were performed using R Bioconductor and qBasePlus software, and gene ontology analysis was conducted using DAVID version 6.8.

RESULTS

DNA methylation profiling revealed 538 differentially methylated loci, while whole-transcriptome profiling identified 2,384 differentially expressed genes, between cartilage samples from subjects with haplogroup H and those with haplogroup J. Seventeen genes showed an inverse correlation between methylation and expression. In terms of gene ontology, differences in correlations between methylation and expression were also detected between cartilage from subjects with haplogroup H and those with haplogroup J, highlighting a significantly enhanced apoptotic process in cartilage from subjects with haplogroup H (P = 0.007 for methylation and P = 0.019 for expression) and repressed apoptotic process in cartilage from subjects with haplogroup J (P = 0.021 for methylation), as well as a significant enrichment of genes related to metabolic processes (P = 1.93 × 10^-4 for methylation and P = 6.79 x 10^-4 for expression) and regulation of gene expression (P = 0.012 for methylation) in cartilage from subjects with haplogroup H, and to developmental processes (P = 0.015 for methylation and P = 8.25 x 10^-12 for expression) in cartilage from subjects with haplogroup J.

CONCLUSION

Mitochondrial DNA variation differentially associates with the methylation status of articular cartilage by acting on key mechanisms involved in osteoarthritis, such as apoptosis and metabolic and developmental processes.