Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach

Causal association of genetically determined plasma metabolites with osteoarthritis: a two-sample Mendelian randomization study

Background

We aimed to elucidate the causal relationship between plasma metabolites and the vulnerability to Osteoarthritis (OA), encompassing both hip OA and knee OA.

Methods

We conducted a two-way two-sample Mendelian randomization (MR) analysis to investigate the association of 1,400 plasma metabolites with OA. The Inverse Variance Weighted (IVW) model served as the primary two-sample MR Analysis method, with supplementary analysis using the Weighted Median (WM) and MR Egger methods. To ensure the robustness of our findings, sensitivity analyses were performed, incorporating Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and Leave-One-Out analyses. To validate the identified metabolites, we utilized the Steiger test and linkage disequilibrium score regression.

Results

A total of 94 plasma metabolites were associated with osteoarthritis, with 60 associated with hip OA and 106 associated with knee OA. IVW analysis revealed that tryptophan levels showed the strongest positive association with hip OA (OR [95% CI]: 1.119 [1.024, 1.223]), while X-24757 levels exhibited the highest positive association with knee osteoarthritis (OR [95% CI]: 1.095 [1.032, 1.162]). Ethylparaben sulfate levels were found to have the greatest positive association with hip OA (OR [95% CI]: 1.118 [1.015, 1.231]). Notably, the plasma metabolite X-2475 showed a strong robust random effect across all three types of osteoarthritis. Metabolic pathway analysis revealed that the pathogenesis of osteoarthritis in the hip was mediated by acetylarginine, specifically in four important metabolic pathways: ethanol degradation (p = 0.044), amino sugar metabolism (p = 0.090), fatty acid biosynthesis (p = 0.095), and aspartate metabolism (p = 0.097816).

Conclusion

There is a significant association between tryptophan levels and the risk of hip OA, as well as X-24757 levels and the risk of knee osteoarthritis. Additionally, X-24757 levels are also linked to the risk of hip OA. Moreover, this study has identified four crucial metabolic pathways in hip osteoarthritis, which are all regulated by acetylarginine. These findings provide valuable insights into potential biomarkers for OA and highlight potential pathways for its prevention and clinical intervention.

Exploring molecular mechanisms of intra-articular changes in osteonecrosis of femoral head using DIA proteomics and bioinformatics

PURPOSE

This study is aimed to delve into the crucial proteins associated with hormonal osteonecrosis of the femoral head (ONFH) and its intra-articular lesions through data-independent acquisition (DIA) proteomics and bioinformatics analysis.

METHODS

We randomly selected samples from eligible ONFH patients and collected samples from the necrotic area of the femoral head and load-bearing cartilage. The control group comprised specimens from the same location in patients with femoral neck fractures. With DIA proteomics, we quantitatively and qualitatively tested both groups and analyzed the differentially expressed proteins (DEPs) between groups. Additionally, we enriched the analysis of DEP functions using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways and verified the key proteins in ONFH through Western blot.

RESULTS

Proteomics experiment uncovered 937 common DEPs (422 upregulated and 515 downregulated) between the two groups. These DEPs mainly participate in biological processes such as hidden attributes, catalytic activity, molecular function regulators, and structural molecule activity, and in pathways such as starch and sucrose metabolism, ECM-receptor interaction, PI3K-Akt signaling, complement and coagulation cascades, IL-17 signaling, phagosome, transcriptional misregulation in cancers, and focal adhesion. Through protein-protein interaction network target gene analysis and Western blot validation, we identified C3, MMP9, APOE, MPO, LCN2, ELANE, HPX, LTF, and THBS1 as key proteins in ONFH.

CONCLUSIONS

With DIA proteomics and bioinformatics analysis, this study reveals the molecular mechanisms of intra-articular lesions in ONFH. A correlation in the necrotic area and load-bearing cartilage of ONFH at ARCO stages IIIB-IV as well as potential key regulatory proteins was identified. These findings will help more deeply understand the pathogenesis of ONFH and may provide important clues for seeking more effective treatment strategies.

Differential Metabolites in Osteoarthritis: A Systematic Review and Meta-Analysis

(1) Many studies have attempted to utilize metabolomic approaches to explore potential biomarkers for the early detection of osteoarthritis (OA), but consistent and high-level evidence is still lacking. In this study, we performed a systematic review and meta-analysis of differential small molecule metabolites between OA patients and healthy individuals to screen promising candidates from a large number of samples with the aim of informing future prospective studies. (2) Methods: We searched the EMBASE, the Cochrane Library, PubMed, Web of Science, Wan Fang Data, VIP Date, and CNKI up to 11 August 2022, and selected relevant records based on inclusion criteria. The risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. We performed qualitative synthesis by counting the frequencies of changing directions and conducted meta-analyses using the random effects model and the fixed-effects model to calculate the mean difference and 95% confidence interval. (3) Results: A total of 3798 records were identified and 13 studies with 495 participants were included. In the 13 studies, 132 kinds of small molecule differential metabolites were extracted, 58 increased, 57 decreased and 17 had direction conflicts. Among them, 37 metabolites appeared more than twice. The results of meta-analyses among four studies showed that three metabolites increased, and eight metabolites decreased compared to healthy controls (HC). (4) Conclusions: The main differential metabolites between OA and healthy subjects were amino acids (AAs) and their derivatives, including tryptophan, lysine, leucine, proline, phenylalanine, glutamine, dimethylglycine, citrulline, asparagine, acetylcarnitine and creatinine (muscle metabolic products), which could be potential biomarkers for predicting OA.

Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study

BACKGROUND

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and is the leading cause of pain and disability in the aged population. However, the underlying biological mechanism has not been fully understood. This study aims to reveal the causal effect of circulation metabolites on OA susceptibility.

METHODS

A two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of GDMs on OA. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas 8 different OA phenotypes, including any-site OA (All OA), knee and/or hip OA (knee/hip OA), knee OA, hip OA, spine OA, finger and/or thumb OA (hand OA), finger OA, thumb OA, were set the outcomes. Inverse-variance weighted (IVW) was used for calculating causal estimates. Methods including weight mode, weight median, MR-egger, and MR-PRESSO were used for the sensitive analysis. Furthermore, metabolic pathway analysis was performed via the web-based Metaconflict 4.0. All statistical analyses were performed in R software.

RESULTS

In this MR analysis, a total of 235 causative associations between metabolites and different OA phenotypes were observed. After false discovery rate (FDR) correction and sensitive analysis, 9 robust causative associations between 7 metabolites (e.g., arginine, kynurenine, and isovalerylcarnitine) and 5 OA phenotypes were finally identified. Additionally, eleven significant metabolic pathways in 4 OA phenotypes were identified by metabolic pathway analysis.

CONCLUSION

The finding of our study suggested that identified metabolites and metabolic pathways can be considered useful circulating metabolic biomarkers for OA screening and prevention in clinical practice, and can also serve as candidate molecules for future mechanism exploration and drug target selection.

Aggrecan and Hyaluronan: The Infamous Cartilage Polyelectrolytes - Then and Now

Cartilages are unique in the family of connective tissues in that they contain a high concentration of the glycosaminoglycans, chondroitin sulfate and keratan sulfate attached to the core protein of the proteoglycan, aggrecan. Multiple aggrecan molecules are organized in the extracellular matrix via a domain-specific molecular interaction with hyaluronan and a link protein, and these high molecular weight aggregates are immobilized within the collagen and glycoprotein network. The high negative charge density of glycosaminoglycans provides hydrophilicity, high osmotic swelling pressure and conformational flexibility, which together function to absorb fluctuations in biomechanical stresses on cartilage during movement of an articular joint. We have summarized information on the history and current knowledge obtained by biochemical and genetic approaches, on cell-mediated regulation of aggrecan metabolism and its role in skeletal development, growth as well as during the development of joint disease. In addition, we describe the pathways for hyaluronan metabolism, with particular focus on the role as a "metabolic rheostat" during chondrocyte responses in cartilage remodeling in growth and disease.Future advances in effective therapeutic targeting of cartilage loss during osteoarthritic diseases of the joint as an organ as well as in cartilage tissue engineering would benefit from 'big data' approaches and bioinformatics, to uncover novel feed-forward and feed-back mechanisms for regulating transcription and translation of genes and their integration into cell-specific pathways.

1H-NMR metabolomic profile of healthy and osteoarthritic canine synovial fluid before and after UC-II supplementation

The aim of the study was to compare the metabolomic synovial fluid (SF) profile of dogs affected by spontaneous osteoarthritis (OA) and supplemented with undenatured type II collagen (UC-II), with that of healthy control dogs. Client-owned dogs were enrolled in the study and randomized in two different groups, based on the presence/absence of OA (OA group and OA-free group). All dogs were clinically evaluated and underwent SF sampling for ¹H-Nuclear Magnetic Resonance spectroscopy (¹H-NMR) analysis at time of presentation. All dogs included in OA group were supplemented with UC-II orally administered for 30 days. After this period, they were reassessed (OA-T30). The differences in the ¹H-NMR metabolic SFs profiles between groups (OA-free, OA-T0 and OA-T30) were studied. The multivariate statistical analysis performed on SFs under different conditions (OA-T0 vs OA-T30 SFs; OA-T0 vs OA-free SFs and OA-T30 vs OA-free SFs) gave models with excellent goodness of fit and predictive parameters, revealed by a marked separation between groups. β-Hydroxybutyrate was identified as a characteristic compound of osteoarthritic joints, showing the important role of fat metabolism during OA. The absence of β-hydroxybutyrate after UC-II supplementation suggests the supplement's effectiveness in rebalancing the metabolism inside the joint. The unexpectedly high level of lactate in the OA-free group suggests that lactate could not be considered a good marker for OA. These results prove that ¹H-NMR-based metabolomic analysis is a valid tool to study and monitor OA and that UC-II improves clinical symptoms and the SF metabolic profile in OA dogs.

Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics

Total joint arthroplasty is a common surgical procedure resulting in improved quality of life; however, a leading cause of surgery failure is infection. Periprosthetic joint infections often involve biofilms, making treatment challenging. The metabolic state of pathogens in the joint space and mechanism of their tolerance to antibiotics and host defenses are not well understood. Thus, there is a critical need for increased understanding of the physiological state of pathogens in the joint space for development of improved treatment strategies toward better patient outcomes. Here, we present a quantitative, untargeted NMR-based metabolomics strategy for Pseudomonas aeruginosa suspended culture and biofilm phenotypes grown in bovine synovial fluid as a model system. Significant differences in metabolic pathways were found between the suspended culture and biofilm phenotypes including creatine, glutathione, alanine, and choline metabolism and the tricarboxylic acid cycle. We also identified 21 unique metabolites with the presence of P. aeruginosa in synovial fluid and one uniquely present with the biofilm phenotype in synovial fluid. If translatable in vivo, these unique metabolite and pathway differences have the potential for further development to serve as targets for P. aeruginosa and biofilm control in synovial fluid.

Synovial tissue metabolomic profiling reveal biomarkers of synovial inflammation in patients with osteoarthritis

Objective

Inflammatory responses are associated with changes in tissue metabolism. Prior studies find altered metabolomic profiles in both the synovial fluid (SF) and serum of osteoarthritis subjects. Our study determined the metabolomic profile of synovial tissue (ST) and SF of individuals with osteoarthritis (OA) and its association with synovial inflammation.

Design

37 OA ST samples were collected during joint replacement, 21 also had SF. ST samples were fixed in formalin for histological analysis, cultured (explants) for cytokine analysis by enzyme-linked immunosorbent assay, or snap-frozen for metabolomic analysis. ST samples were categorized by Krenn synovitis score and picrosirius red. CD68 and vimentin expression was assessed by immunohistochemistry and semi-quantified using Image J. Proton-nuclear magnetic resonance (1H NMR) was used to acquire a spectrum from ST and SF samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. Metaboanalyst 5.0, SPSS v26, and R (v4.1.2) were used for statistical analysis.

Results

42 and 29 metabolites were detected in the ST and SF respectively by 1H NMR. Only 3 metabolites, lactate, dimethylamine, and creatine positively correlated between SF and ST. ST concentrations of several metabolites (lactate, alanine, fumarate, glutamine, glycine, leucine, lysine, methionine, trimethylamine N-oxide, tryptophan and valine) were associated with synovitis score, mostly to the lining score. IL-6, acetoacetate, and tyrosine in SF predicted high Krenn synovitis scores in ST.

Conclusion

Metabolomic profiling of ST identified metabolic changes associated with inflammation. Further studies are needed to determine whether metabolomic profiling of synovial tissue can identify new therapeutic targets in osteoarthritis.

HK2: a potential regulator of osteoarthritis via glycolytic and non-glycolytic pathways

Osteoarthritis (OA) is an age-related chronic degenerative joint disease where the main characteristics include progressive degeneration of cartilage, varying degrees of synovitis, and periarticular osteogenesis. However, the underlying factors involved in OA pathogenesis remain elusive which has resulted in poor clinical treatment effect. Recently, glucose metabolism changes provide a new perspective on the pathogenesis of OA. Under the stimulation of external environment, the metabolic pathway of chondrocytes tends to change from oxidative phosphorylation (OXPHOS) to aerobic glycolysis. Previous studies have demonstrated that glycolysis of synovial tissue is increased in OA. The hexokinase (HK) is the first rate limiting enzyme in aerobic glycolysis, participating and catalyzing the main pathway of glucose utilization. An isoform of HKs, HK2 is considered to be a key regulator of glucose metabolism, promotes the transformation of glycolysis from OXPHOS to aerobic glycolysis. Moreover, the expression level of HK2 in OA synovial tissue (FLS) was higher than that in control group, which indicated the potential therapeutic effect of HK2 in OA. However, there is no summary to help us understand the potential therapeutic role of glucose metabolism in OA. Therefore, this review focuses on the properties of HK2 and existing research concerning HK2 and OA. We also highlight the potential role and mechanism of HK2 in OA.

Metabolomic Analysis of Severe Osteoarthritis in a Spanish Population of Women Compared to Healthy and Osteoporotic Subjects

Bone pathologies such as osteoporosis (OTP) and osteoarthritis (OA) are rising in incidence with the worldwide rise in life expectancy. The diagnosis is usually obtained using imaging techniques such as densitometry, but with both being multifactorial diseases, several molecular mechanisms remain to be understood. Metabolomics offers the potential to detect global changes which can lead to the identification of biomarkers and a better insight in the progress of the diseases. Our aim was to compare the metabolic profiles of a cohort of 100 postmenopausal women, including subcapital hip fragility fracture patients, women with severe OA of the hip that required the implantation of a hip prosthesis and controls, to find altered metabolites and networks. Nuclear magnetic resonance (NMR) spectroscopy was used to obtain the metabolomic profiles of peripheral blood derived serum, and statistical analysis was performed using MATLAB V.6.5. 30 of the 73 metabolites analysed showed statistically significant differences in a 3-way ANOVA, and 11 of them were present in the comparison between OA and controls after adjustment by covariates, including amino acids, energy metabolism metabolites and phospholipid precursors. PLS-DA analysis shows a good discrimination between controls and fracture subjects with OA patients, and ROC curve analysis demonstrates that control and fracture subjects were accurately discriminated using the metabolome, but not OA. These results point to OA as an intermediate metabolic state between controls and fracture, and suggest that some metabolic shifts that happen after a fracture are also present at weaker intensity in the OA process.

Tai Chi Improves Brain Functional Connectivity and Plasma Lysophosphatidylcholines in Postmenopausal Women With Knee Osteoarthritis: An Exploratory Pilot Study

Objective: A pre/post pilot study was designed to investigate neurobiological mechanisms and plasma metabolites in an 8-week Tai-Chi (TC) group intervention in subjects with knee osteoarthritis.

Methods: Twelve postmenopausal women underwent Tai-Chi group exercise for 8 weeks (60 min/session, three times/week). Outcomes were measured before and after Tai Chi intervention including pain intensity (VAS), Brief Pain Inventory (BPI), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), plasma metabolites (amino acids and lipids), as well as resting-state functional magnetic resonance imaging (rs-fMRI, 10 min, eyes open), diffusion tensor imaging (DTI, 12 min), and structural MRI (4.5 min) in a subgroup. Clinical data was analyzed using paired t-tests; plasma metabolites were analyzed using Wilcoxon signed-rank tests; and rs-fMRI data were analyzed using seed-based correlations of the left and right amygdala in a two-level mixed-effects model (FSL software). Correlations between amygdala-medial prefrontal cortex (mPFC) connectivity and corresponding changes in clinical outcomes were examined. DTI connectivity of each amygdala was modeled using a Bayesian approach and probabilistic tractography. The associations between neurobiological effects and pain/physical function were examined.

Results: Significant pre/post changes were observed with reduced knee pain (VAS with most pain: p = 0.018; WOMAC-pain: p = 0.021; BPI with worst level: p = 0.018) and stiffness (WOMAC-stiffness, p = 0.020), that likely contributed to improved physical function (WOMAC-physical function: p = 0.018) with TC. Moderate to large effect sizes pre/post increase in rs-fMRI connectivity were observed between bilateral mPFC and the amygdala seed regions (i.e., left: d = 0.988, p = 0.355; right: d = 0.600, p = 0.282). Increased DTI connectivity was observed between bilateral mPFC and left amygdala (d = 0.720, p = 0.156). There were moderate-high correlations (r = 0.28–0.60) between TC-associated pre-post changes in amygdala-mPFC functional connectivity and pain/physical function improvement. Significantly higher levels of lysophosphatidylcholines were observed after TC but lower levels of some essential amino acids. Amino acid levels (alanine, lysine, and methionine) were lower after 8 weeks of TC and many of the lipid metabolites were higher after TC. Further, plasma non-HDL cholesterol levels were lower after TC.

Conclusion: This pilot study showed moderate to large effect sizes, suggesting an important role that cortico-amygdala interactions related to TC have on pain and physical function in subjects with knee osteoarthritis pain. Metabolite analyses revealed a metabolic shift of higher lyso-lipids and lower amino acids that might suggest greater fatty acid catabolism, protein turnover and changes in lipid redistribution in response to TC exercise. The results also support therapeutic strategies aimed at strengthening functional and structural connectivity between the mPFC and the amygdala. Controlled clinical trials are warranted to confirm these observed preliminary effects.

West Lake staging: A new staging system orchestrated by X-ray and MRI on knee osteoarthritis

Purpose: To investigate the differences on X-ray and MRI among each stage of knee osteoarthritis (KOA) and further propose a new staging system called West Lake (WL) staging. Methods: A cross-sectional study was conducted on patients with KOA. Stage I, II, III, and IV were divided based on stepwise treatment strategy of Knee osteoarthritis (KOA). Joint space widths (JSW) were measured on X-rays, whereas cartilage injuries (CI) and bone marrow lesions (BML) were evaluated on MRI. The differences of them across the groups were calculated by T-test. Receiver operating characteristic (ROC) curves were rendered to obtain the areas under the curves (AUC), Youden index and corresponding cut-off points. Results: Eventually, there were significant differences on JSW, CI, and BML between stage II/III and III/IV, while no significant differences between stage I/II. In stage II/III, the AUC of JSW, CI, BML was 0.99, 0.76, 0.71 and the Youden index was 0.94, 0.38, 0.45, meanwhile the cut-off points were ≤5.1 mm, >1, >2. In stage III/IV, the AUC of JSW, CI, BML was 0.96, 0.79, 0.74 and the Youden index was 0.84, 0.58, 0.38, meanwhile the cut-off points were ≤3.2 mm, >3, >4. Conclusion: The WL staging was described as follows: Stage I, X-ray shows no joint space narrow, normal MRI or MRI shows cartilage degeneration and only 1 or 2 sections are involved in BML. Stage II, X-ray shows joint space narrow, MRI shows cartilage defect but no full-thickness cartilage defect, meanwhile 3 or 4 sections are involved in BML. Stage III, X-ray shows serious joint space narrow even JSW disappeared, MRI shows full-thickness cartilage defect, more than 4 sections are involved in BML.

A review of applications of metabolomics in osteoarthritis

Osteoarthritis (OA) represents the most prevalent and disabling arthritis worldwide due to its heterogeneous and progressive articular degradation. However, effective and timely diagnosis and fundamental treatment for this disorder are lacking. Metabolomics, a growing field in life science research in recent years, has the potential to detect many metabolites and thus explains the underlying pathophysiological processes. Hence, new specific metabolic markers and related metabolic pathways can be identified for OA. In this review, we aimed to provide an overview of studies related to the metabolomics of OA in animal models and humans to describe the metabolic changes and related pathways for OA. The present metabolomics studies reveal that the pathogenesis of OA may be significantly related to perturbations of amino acid metabolism. These altered amino acids (e.g., branched-chain amino acids, arginine, and alanine), as well as phospholipids, were identified as potential biomarkers to distinguish patients with OA from healthy individuals.

Two reactive behaviors of chondrocytes in an IL-1β-induced inflammatory environment revealed by the single-cell RNA sequencing

Objective: To investigate the heterogeneous responses of in vitro expanded chondrocytes, which were cultured in an interleukin (IL)-1β -induced inflammatory environment.

Method: Human articular chondrocytes were expanded, in vitro, for 13 days and treated with IL-1β for 0, 24, and 48 h. Cells were collected and subjected to single-cell RNA sequencing. Multiple bioinformatics tools were used to determine the signatures that define chondrocyte physiology.

Results: Two major cell clusters with distinct expression patterns were identified at the initial phase and were with heterogeneous variation that coincides with inflammation progress. They transformed into two terminal cell clusters one of which exhibited OA-phenotype and proinflammatory characteristics through two paths, “response-to-inflammation” and “atypical response-to-inflammation”, respectively. The involved cell clusters exhibited intrinsic relationship with cell types within native cartilage from OA patients. Genes controlling cell transformation to OA-phenotype were relating to the tumor necrosis factor (TNF) signaling pathway via NFKB, up-regulated KRAS signaling and the IL2/STAT5 signaling pathway and pathways relating to apoptosis and reactive oxygen species.

Conclusion: The in vitro expanded chondrocytes under IL-1β-induced inflammatory progression behave heterogeneously. One of the initial cell clusters could transform into a proinflammatory subpopulation through a termed response-to-inflammation path, which may serve as the core target to alleviate OA progression.

Metabolic signatures of osteoarthritis in urine using liquid chromatography-high resolution tandem mass spectrometry

INTRODUCTION

Osteoarthritis (OA) is a common cause of disability in older people, but its aetiology is not yet fully understood. Biomarkers of OA from metabolomics studies have shown potential use in understanding the progression and pathophysiology of OA.

OBJECTIVES

To investigate possible surrogate biomarkers of knee OA in urine using metabolomics to contribute towards a better understanding of OA progression and possible targeted treatment.

METHOD

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) was applied in a case-control approach to explore the possible metabolic differences between the urinary profiles of symptomatic knee OA patients (n = 74) (subclassified into inflammatory OA, n = 22 and non-inflammatory OA, n = 52) and non-OA controls (n = 68). Univariate, multivariate and pathway analyses were performed with a rigorous validation including cross-validation, permutation test, prediction and receiver operating characteristic curve to identify significantly altered metabolites and pathways in OA.

RESULTS

OA datasets generated 7405 variables and multivariate analysis showed clear separation of inflammatory OA, but not non-inflammatory OA, from non-OA controls. Adequate cross-validation (R²Y = 0.874, Q² = 0.465) was obtained. The prediction model and the ROC curve showed satisfactory results with a sensitivity of 88%, specificity of 71% and accuracy of 77%. 26 metabolites were identified as potential biomarkers of inflammatory OA using HMDB, authentic standards and/or MS/MS database.

CONCLUSION

Urinary metabolic profiles were altered in inflammatory knee OA subjects compared to those with non-inflammatory OA and non-OA controls. These altered profiles associated with perturbed activity of the TCA cycle, pyruvate and amino acid metabolism linked to inflammation, oxidative stress and collagen destruction. Of note, 2-keto-glutaramic acid level was > eightfold higher in the inflammatory OA patients compared to non-OA control, signalling a possible perturbation in glutamine metabolism related to OA progression.

The role of metabolism in chondrocyte dysfunction and the progression of osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease characterized by low-grade inflammation and high levels of clinical heterogeneity. Aberrant chondrocyte metabolism is a response to changes in the inflammatory microenvironment and may play a key role in cartilage degeneration and OA progression. Under conditions of environmental stress, chondrocytes tend to adapt their metabolism to microenvironmental changes by shifting from one metabolic pathway to another, for example from oxidative phosphorylation to glycolysis. Similar changes occur in other joint cells, including synoviocytes. Switching between these pathways is implicated in metabolic alterations that involve mitochondrial dysfunction, enhanced anaerobic glycolysis, and altered lipid and amino acid metabolism. The shift between oxidative phosphorylation and glycolysis is mainly regulated by the AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) pathways. Chondrocyte metabolic changes are likely to be a feature of different OA phenotypes. Determining the role of chondrocyte metabolism in OA has revealed key features of disease pathogenesis. Future research should place greater emphasis on immunometabolism and altered metabolic pathways as a means to understand the pathophysiology of age-related OA. This knowledge will advance the development of new drugs against therapeutic targets of metabolic significance.

Understanding osteoarthritis pathogenesis: a multiomics system-based approach

PURPOSE OF REVIEW

Osteoarthritis is a heterogeneous, multifactorial condition regulated by complex biological interactions at multiple levels. Comprehensive understanding of these regulatory interactions is required to develop feasible advances to improve patient outcomes. Improvements in technology have made extensive genomic, transcriptomic, epigenomic, proteomic, and metabolomic profiling possible. This review summarizes findings over the past 20 months related to omics technologies in osteoarthritis and examines how using a multiomics approach is necessary for advancing our understanding of osteoarthritis as a disease to improve precision osteoarthritis treatments.

RECENT FINDINGS

Using the search terms 'genomics' or 'transcriptomics' or 'epigenomics' or 'proteomics' or 'metabolomics' and 'osteoarthritis' from January 1, 2018 to August 31, 2019, we identified advances in omics approaches applied to osteoarthritis. Trends include untargeted whole genome, transcriptome, proteome, and metabolome analyses leading to identification of novel molecular signatures, cell subpopulations and multiomics validation approaches.

SUMMARY

To address the complexity of osteoarthritis, integration of multitissue analyses by multiomics approaches with the inclusion of longitudinal clinical data is necessary for a comprehensive understanding of the disease process, and for appropriate development of efficacious diagnostics, prognostics, and biotherapeutics.

Hair Metabolomics in Animal Studies and Clinical Settings

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.