The mechanism by which chondrocytes respond to reduced mechanical loading environments and the subsequent risk of developing osteoarthritis remains unclear. This is of particular concern for astronauts. In space the reduced joint loading forces during prolonged microgravity (10−6 g) exposure could lead to osteoarthritis (OA), compromising quality of life post-spaceflight. In this study, we encapsulated human chondrocytes in an agarose gel of similar stiffness to the pericellular matrix to mimic the cartilage microenvironment. We then exposed agarose-chondrocyte constructs to simulated microgravity (SM) for four days using a rotating wall vessel (RWV) bioreactor to better assess the cartilage health risks associated with spaceflight. Metabolites extracted from media and agarose gel constructs were analyzed on liquid chromatography-mass spectrometry. Global metabolomic profiling detected a total of 1205 metabolite features, with 497 significant metabolite features identified by ANOVA (FDR-corrected p-value < 0.05). Specific metabolic shifts detected in response to SM exposure resulted in clusters of co-regulated metabolites, with glutathione, nitrogen, histidine, vitamin B3, and aminosugars metabolism identified by variable importance in projection scores. Microgravity-induced metabolic shifts in gel constructs and media were indicative of protein synthesis, energy and nucleotide metabolism, and oxidative catabolism. Microgravity associated-metabolic shifts were consistent with our previously published early osteoarthritic metabolomic profiles in human synovial fluid, suggesting that even short-term exposure to microgravity (or other reduced mechanical loading environments) may lead to the development of OA. This work further suggests the potential to detect these metabolic perturbations in synovial fluid in vivo to ascertain osteoarthritis risk in astronauts.
Similar content being viewed by others Explore related subjectsDiscover the latest articles and news from researchers in related subjects, suggested using machine learning. IntroductionOsteoarthritis (OA) is the most common degenerative joint disease, affecting over 500 million individuals worldwide [1]. OA is a whole joint disease associated with changes to the subchondral bone (e.g., osteophyte formation and subchondral bone sclerosis), inflammation of the synovial membrane, and articular cartilage (AC) degradation. These changes in whole joint structure lead to significant pain, joint stiffness and reduced mobility, and an overall decrease in patient quality of life. The pathogenesis of OA is complex and multifactorial, with key aspects, including inflammation and oxidative stress, excessive reactive oxygen species, extracellular matrix (ECM) degradation, and decreases in the production of ECM components. Current treatments include pain management for early OA and joint replacements for end-stage disease.
A major risk factor for the development of OA is abnormal mechanical loading. Deviations from physiological loading conditions (e.g., high impact forces [2, 3] or reduced loading [4, 5]) result in a metabolic imbalance in articular chondrocytes. This imbalance favors matrix catabolism, which weakens cartilage and diminishes the ability to resist mechanical loads [6]. Human studies of post-traumatic OA highlight the disruption of AC homeostasis following abnormal loading with accelerated cell death, ECM degradation, elevated inflammatory cytokines, and distinct metabolic profiles [7,8,9]. While other musculoskeletal tissues, such as bone and muscle, can reverse the effects of abnormal loading, AC is avascular and thus has a poor regeneration capacity [10]. This results in potentially irreversible AC degeneration associated with that of OA.
Microgravity exposure (10−6 g) significantly reduces the mechanical loads applied to the musculoskeletal system. Before, during, and after missions, astronauts are highly vulnerable to musculoskeletal alterations [11, 12]. Prior to spaceflight, simulated microgravity (SM) and neutral buoyancy trainings have caused injuries [13, 14]. Additionally, conducting daily activities in space puts astronauts at an increased risk of musculoskeletal alterations and injuries [11], with a multi-year study showing increased post-flight knee injuries [12]. Analysis of urine and serum markers of cartilage degradation found elevated levels of cartilage markers in human biofluids in the 30 days following long-duration (>5 months) microgravity exposure [15,16,17]. These studies show alterations to musculoskeletal homeostasis, which could have deleterious effects on AC and contribute to the risk of developing OA post-spaceflight.
Although very few studies have investigated the effects of microgravity exposure on human cartilage and chondrocytes, studies of reduced loading have been conducted. In studies analyzing the effects of joint immobilization after clinical bed rest, reduced loading results in significant reductions in cartilage thickness at weight-bearing regions of the tibia at short-term time points (two weeks [4]) and at the patella, medial tibia, and medial and lateral femoral condyle at longer time points (12–24-months post-injury [18, 19]). Mouse studies of reduced loading (either by spaceflight or terrestrial hind-limb unloading) found decreases in the levels of glycosaminoglycans and collagen, increased concentrations of catabolic enzymes (MMP-13), and reduced cartilage thickness [20,21,22,23,24]. Taken together, these studies provide clear evidence that reduced mechanical loading due to microgravity leads to cartilage degeneration. However, more data are needed to fully understand the underlying mechanisms and pathways driving these degenerative changes. This opens the door to in vitro modeling of microgravity exposure as a promising tool for collecting data and fulling elucidating the mechanisms of degeneration following microgravity exposure.
Thus, the overall goal of this work is to expand our understanding of chondrocyte mechanotransduction in reduced loading environments to help uncover the underlying mechanism driving these degenerative changes to better assess astronaut risk of OA post-spaceflight. We hypothesized that exposing encapsulated chondrocyte cells to SM would induce shifts in metabolism. To address this hypothesis, human chondrocytes were encapsulated in an agarose gel of similar physiological stiffness to the pericellular matrix (PCM) to better mimic the cartilage biomechanical environment and exposed to SM using a rotating wall vessel (RWV) bioreactor for four days. The response to SM was analyzed using global metabolomic profiling to gain an untargeted and unbiased view of metabolic shifts. Our in vitro approach to studying the metabolic effects of short-term SM exposure is the first we know of to encapsulate chondrocytes in a three-dimensional agarose gel of similar stiffness to the PCM, allowing for the understanding of microgravity induced metabolic shifts following microgravity exposure.
Materials and Methods Cell CultureThe human chondrosarcoma cell line SW1353 (HTB-94) was purchased from the American Type Culture Collection and cultured in a complete high-glucose Dulbecco’s modified eagle’s medium (DMEM) supplemented with 1% Penicillin–Streptomycin and 10% fetal bovine serum. This cell line has previously been used for studies of chondrocyte mechanotransduction [25, 26]. The cell line was incubated in a 37 ºC humidified atmosphere with 5% CO2, and cultures were maintained at < 80% confluency.
Chondrocyte EncapsulationSW1353 cells were encapsulated using an established protocol [27, 28]. Cells were encapsulated in 4.95% agarose gels at a concentration of 800,000 cells/gel. Gels were cylindrical with a height of 10.67 mm and diameter of 6.86 mm. This cell concentration was chosen based on the density of chondrocytes in human articular cartilage [29], and the agarose concentration was selected to mimic the stiffness of the cartilage PCM [28]. Briefly, agarose powder and water were combined in a beaker and heated for 10 second intervals until the mixture became homogenous. A portion of the agarose mixture was removed and SW1353 cells were added and gently mixed. The cell-agarose mixture was then transferred into wells of a 96-well plate and allowed to solidify and polymerize [28].
Experimental DesignSamples of agarose-encapsulated chondrocytes were created and individually weighed for consistency (0.329 ± 0.006 mg). Four experimental trials were performed with four biological replicates per group (SM and control), totaling 32 encapsulated chondrocyte samples. Half (n = 16) of the encapsulated chondrocyte samples were placed into Rotary Cell Culture System (RCCS) vessels and filled with media for exposure to SM on the RWV bioreactor (Synthecon, Houston, TX) (SM cohort). Note that this experimental design is necessitated by the 4 available sample ports in the RVW bioreactor. Normal gravitational force controls were generated using the remaining encapsulated chondrocytes (n = 16) by placing each gel into the RCCS, filling with media, and incubating for exposure to normal gravity.
RCCS vessels were attached to the RWV bioreactor inside of the incubator (37 ºC, 5% CO2). The RWV bioreactor was manually set to 13.2 rpm to correctly position the encapsulated chondrocytes in constant free fall based on pilot experiments. RWV bioreactors simulate microgravity by constant rotation along the horizontal axis to maintain samples in constant free fall and vector-averaged gravity to create equal and opposite effects, resulting in a microgravity environment of approximately 10−6 g.
The RCCS vessels were carefully monitored throughout the duration of the four-day experiments to maintain constant free fall and ensure the absence of bubbles, which indicate substantial carbon dioxide production. The four-day timepoint was determined based on the longest continuous stimulus that could be applied to encapsulated chondrocytes prior to requiring medium changes. Pilot experiments showed the depletion of culture medium nutrients after approximately 96 hours and the formation of bubbles. Therefore, four days were selected as the longest timepoint possible for applying continuous SM stimulation. The remaining RCCS vessels were left in the incubator (37 ºC, 5% CO2) to serve as controls. Both SM and control encapsulated chondrocytes were maintained under either 10−6 g or normal gravitational forces for a period of four days (Figure 1).
Fig. 1Experimental design of study. SW1353 cells were cultured to P10-14, cells were encapsulated in an agarose gel construct and suspended in either normal gravitational forces in a CO2 incubator or microgravity (10−6 g) on a RWV bioreactor. Cell viability was confirmed through RFUs on a microplate reader (SM n = 4, Ctrl n = 4). Metabolite extractions were performed on both SM (n = 16) and control (n = 16) groups and ran on an LC-MS for metabolomic profiling. SM = simulated microgravity and Ctrl = normal gravity (created with BioRender, accessed December 9th, 2023)
Cell ViabilityA subset of the encapsulated chondrocytes was assessed for cell viability at the end of the four-day timepoint. SM (n = 4) and control (n = 4) encapsulated chondrocytes were washed once with phosphate-buffered saline (PBS) and incubated in a solution containing 8-µmol calcein AM and 75-µmol propidium iodide for 1 hour at 37 ℃ before analyzing on a Thermo Fisher Scientific Varioskan LUX multimode microplate reader. Using an established protocol, relative fluorescence units (RFUs) were obtained for calcien AM (ex/em 490/517 nm) representing live cells and propidium iodide (ex/em 535/617 nm) representing dead cells [30]. The means and standard deviations were then determined.
Metabolite ExtractionMetabolites were extracted from encapsulated chondrocytes and media using an established metabolite extraction protocol, with slight modifications/adaptions for agarose gel [31]. From each vessel, 50 mL of media was harvested and centrifuged at 500 x g at 4 ºC for 5 minutes to remove debris and a second time for 10 minutes at 12,000 rpm. Encapsulated chondrocytes were flash frozen in liquid nitrogen to halt metabolism and homogenized using the SPEX SamplePrep tissue homogenizer (SPEX SamplePrep, Metuchen, NJ) with 1 mL of 3:1 methanol:water prior to centrifugation. Supernatants were collected and mixed with 80% v/v methanol:water at – 20 ºC for 30 minutes (other 20% miliQ/RO water) to extract metabolites followed by vortexing for 3 minutes. Samples were centrifuged at 16,100 x g for 5 minutes at 4 ºC, and the supernatants were transferred to a new tube before metabolites were dried down in a vacuum concentrator (Savant AES 1010, Thermo Fisher Scientific, Waltham, MA). Proteins, lipids, and waxes were precipitated by re-extracting with 5 volumes (1 mL each = 5 mL total) of aqueous 1:1 (v/v) acetonitrile:water v/v at 0 ºC for 30 minutes. Samples were centrifuged at 16,100 x g a final time for 5 minutes and the supernatants containing metabolites were collected and dried down in the vacuum concentrator. All samples were then stored at − 80ºC until ready for mass spectrometry analysis. All extraction solvents were HPLC-grade or higher.
Liquid Chromatography-Mass Spectrometry AnalysisDried metabolite extracts were re-suspended in mass spectrometry grade 50:50 water:acetonitrile as the liquid chromatography-mass spectrometry (LC-MS) injection solution. The LC-MS system used for analysis was an Agilent 1290 UPLC connected to an Agilent 6538 Quadrupole-Time of Flight (Q-TOF) mass spectrometer (Agilent, Santa Clara, CA) operated in positive mode. Separation of metabolites occurred on a Cogent Diamond Hydride HILIC column (150 x 2.1 mm, MicroSolv, Eatontown, NJ) using an optimized normal phase 25-minute gradient elution method [30].
Statistical AnalysisData generated by the LC-MS were initially processed using MSConvert [32] and XCMS [33], as previously described [31]. Metabolomic profiling was then performed using MetaboAnalyst (version 4.0) [34]. Prior to statistical analyses, the data were normalized by the median, log transformed using the base−10 logarithm (log10), and auto-scaled by mean-centering and dividing by the standard deviation of each metabolite feature to correct for non-normal distributions commonly observed in data [31]. Unsupervised analyses included Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to visualize overall metabolomic profiles. Additionally, supervised statistical analyses Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were used to visualize differences between SM and controls, as well as to identify specific populations of metabolites that discriminate between SM and control samples. In OPLS-DA, metabolite features are scored based on their discriminatory abilities using Variable Importance in Projection (VIP) scores. Metabolite features with the highest VIP scores have the greatest ability to discriminate between cohorts.
Pathway AnalysisImportant metabolite features were selected using OPLS-DA VIP scores. Metabolites with the highest VIP scores (VIP > 1) from OPLS-DA were mapped to metabolic pathways using MetaboAnalyst’s Functional Analysis module with the mummichog algorithm which predicts a network of functional activity based on the projection of detected metabolite features onto local pathways [34]. Pathway library Human MFN was used for compound identification and pathway enrichment (mass tolerance: positive mode; 5 ppm; ranked by p-values; enforced primary ions (V2 only)). Pathways are reported as significant by pathway overrepresentation analysis with an adjusted a priori significance level of 0.1. The rationale for this significance level is that these are the first studies on the effects of microgravity on chondrocyte metabolomic profiles.
Results Cell ViabilityA subset of SM and control constructs was analyzed for cell viability by staining with calcein AM and propidium iodide. The relative fluorescence was measured using a fluorescent plate reader. Cell viability assays confirmed that chondrocyte constructs were alive, with cell viability estimated at > 90% (Figure 2).
Fig. 2Viability of encapsulated chondrocytes. Mean relative fluorescent units for the SM (experimental) and normal gravity (control) cohorts with associated standard deviation bars
Global Metabolomic Profiling of Constructs and Media Exposed to Short-Term Simulated MicrogravityGlobal metabolomic profiling detected a total of 1205 metabolite features across all samples, with 497 significant metabolite features identified by ANOVA (FDR-corrected p-value < 0.05). Unsupervised statistical analyses visualized the overall variability within the datasets and found minimal separation between control and SM cohorts in PCA plots and HCA dendrograms (Figure 3A–B). However, PCA demonstrated greater variability in the SM cohorts in comparison to the control cohorts, as exhibited by tighter clustering of control samples (Figure 3B). PLS-DA showed clear separation between gel and media cohorts, with some separation between control and SM cohorts (Figure 3C). These results suggest that short-term exposure may not induce large-scale shifts in overall metabolism. However, by utilizing PLS-DA we found moderate shifts in metabolomic profiles (as indicated by partial PLS-DA separation between control and SM cohorts).
Fig. 3Unsupervised HCA and PCA and supervised PLS-DA exhibiting clustering of constructs and media cohorts. A Unsupervised HCA dendrogram exhibiting separation between constructs and media, but minimal separation between SM and control samples. Clusters of cohorts were identified and labeled as gel control, gel microgravity, media control, and media microgravity. Each cohort contained 8 samples. Line length represents Euclidean distances between samples. B Unsupervised PCA plot exhibiting separation between SM and media, but minimal separation between SM and control samples. The two PCs are associated with 42.3% of the variation between cohorts (PC1 = 29.1%; PC2 = 13.2%). Samples correspond to their respective cohort: dark blue for gel control, light blue for gel microgravity, red for media control, and pink for media microgravity. C Supervised PLS-DA plot exhibiting separation between constructs and media, with some separation between SM and control samples. Component 1 and component 2 account for 36.5% of the overall variance (component 1 = 28.8%; component 2 = 7.7%). Samples correspond to their respective cohort: dark blue for gel control, light blue for gel microgravity, red for media control, and pink for media microgravity
To pinpoint specific metabolic shifts associated with SM exposure, median intensity values were calculated for each metabolite feature. These medians were then clustered using HCA, and clusters were then subjected to pathway analysis (Figure 4) and enrichment (Table 1). Clusters of co-regulated metabolites were identified that represented microgravity-induced metabolic shifts: (1) differences between SM constructs and control constructs—clusters 3 (197 metabolites) and 5 (165 metabolites) and (2) differences between SM media and control media—clusters 1 (454 metabolites) and 4 (686 metabolites). Metabolites with higher abundances in encapsulated chondrocytes exposed to SM compared to control conditions mapped to amino sugar and nucleotide metabolism (Cluster 3, Figure 4, and Table 1). Conversely, metabolite features that were lower in abundance in SM media compared to controls mapped to glutathione, nitrogen, arginine, tyrosine, glutamine, glutamate, proline, phenylalanine, tryptophan, and aspartate metabolism (Cluster 1, Figure 4, and Table 1). Metabolite features lower in abundance in SM constructs compared to controls mapped to vitamin B6, tyrosine, porphyrin, and phenylalanine metabolism (Cluster 5, Figure 4, and Table 1). Metabolites higher in abundance in SM media compared to controls mapped to valine, leucine, isoleucine, glycine, serine, threonine, cysteine, methionine, and histidine metabolism (Cluster 4, Figure 4, and Table 1).
Fig. 4HCA clustergram showcasing global metabolomic profiles of simulated microgravity and control constructs and media. Rows represent metabolite features and columns represent experimental cohorts. The colors express if the specific metabolite feature was higher (magenta) or lower (blue) based on standard deviations from the mean metabolite intensity across all experimental cohorts. SM = simulated microgravity and Ctrl = control/normal gravity
Table 1 Pathway enrichment of HCA. Microgravity-Induced Metabolic Shifts in Chondrocyte Constructs and Simulated Microgravity MediaOPLS-DA was used to examine differences in metabolomic profiles between SM and control cohorts and identify specific metabolites features that contributed to separation between cohorts. OPLS-DA revealed clear separation between SM and control gel cohorts (Figure 5). Metabolites with the highest VIP scores contributed the most to the separation between SM and control gels and were mapped to metabolic pathways reported in Table 2. Select pathways perturbed in response to SM were mapped to di-unsaturated fatty acid β-oxidation, lysine, purine, vitamin B6 (pyridoxine), and urea cycle/amino group metabolism (FDR-corrected p-value < 0.1).
Fig. 5Supervised OPLS-DA plots exhibiting separation between control and SM samples. A Supervised OPLS-DA plot for gel samples exhibiting separation between control and SM samples. The T score showcases the inter-group variability (T score = 5.5%). The Orthogonal T score showcases the intra-group variability (Orthogonal T score = 21.9%). Samples correspond to their respective cohorts: dark blue for gel control and light blue for gel microgravity. B Supervised OPLS-DA plot for media samples exhibiting separation between control and SM samples. The T score showcases the inter-group variability (T score = 8.6%). The Orthogonal T score showcases the intra-group variability (Orthogonal T score = 26.2%). Samples correspond to their respective cohorts: red for media control and pink for media microgravity
Table 2 OPLS-DA pathways—gelsOPLS-DA plot showed greater separation in SM media compared to control media (Figure 5). Metabolites with the highest VIP scores contributed the most to the separation between SM and control media and were mapped to metabolic pathways reported in Table 3. Select pathways perturbed in response to SM were mapped to glutathione, nitrogen, histidine, vitamin B3 (nicotinate and nicotinamide), and aminosugars metabolism (FDR-adjusted p-value < 0.1).
Table 3 OPLS-DA pathways—media Discussion OverviewWhile recent research suggests that microgravity exposure leads to AC degeneration [15,16,17, 22,23,24,25,26], the underlying mechanisms driving this degeneration in reduced loading environments are currently unknown. Furthermore, there is a critical need for in vitro models that recapitulate the biomechanical microenvironment of chondrocytes for improved understanding of chondrocyte mechanotransduction in reduced loading environments. In the present study, we generated global metabolomic profiles of encapsulated chondrocytes exposed to SM to expand our understanding of chondrocyte mechanotransduction in reduced loading environments to better assess the risk of OA development post-spaceflight. To our knowledge, this is the first study to encapsulate chondrocytes in agarose gel constructs of similar stiffness to the PCM for suspension in a RWV bioreactor to examine the metabolic effects of short-term SM exposure.
Mimicking the Chondrocyte Biomechanical EnvironmentA handful of in vitro studies document the effects of SM exposure on liquid cell culture of chondrocytes [35,36,37,38,39]. Experiments under SM are advantageous in that they circumvent many of the challenges of conducting experiments in space (e.g., limited quantity, high cost). These studies suggest that chondrocytes exposed to SM exhibit cytoskeletal rearrangements, assembly of cells, and changes in gene expression [35,36,37,38,39]. While these studies provide insight into chondrocyte mechanotransduction in reduced loading environments, they do not consider the mechanical stiffness of the extracellular microenvironment and how it may impact mechanotransduction to chondrocytes.
Recapitulation of the chondrocyte pericellular microenvironment is necessary for studies of chondrocyte mechanotransduction [40, 41]. It is well established that chondrocytes are mechanosensitive cells that respond to mechanical stimuli by altering cellular metabolism and other processes to maintain homeostasis. In vivo chondrocytes are surrounded by a narrow (1–5 µm) layer of glycoproteins, proteoglycans, and collagen known as the pericellular matrix (PCM) that plays a key role in translating tissue level mechanical stimuli to chondrocytes. Importantly, there is a disparity in modulus between the PCM and surrounding ECM in cartilage. Because the PCM is less stiff than the ECM, it can decrease the magnitude of stress applied to chondrocytes. Studies of chondrocyte mechanotransduction in response to increased physiological loading (e.g., high impact loads) routinely embed chondrocytes in a 3D hydrogel to better mimic the cartilage microenvironment. Chondrocyte mechanotransduction studies in reduced loading environments, however, are limited to in vitro studies. This study takes the first step to improve in vitro modeling of chondrocyte mechanotransduction in reduced loading environments by embedding chondrocytes in an agarose gel of similar physiological stiffness to the PCM. Additionally, this study and our in vitro model can inform future experiments on the International Space Station (ISS).
Metabolic Response of Chondrocytes to Short-Term Simulated MicrogravityCurrent research suggests that deviations from physiological loading (e.g., reduced or increased loading) result in an imbalance in chondrocyte metabolism, with catabolic processes predominating. Numerous studies characterize the metabolic response of chondrocytes to increased mechanical loads (e.g., high impact loads) [2, 3]. However, no study to date has investigated the metabolic effects of reduced loading on chondrocyte mechanotransduction.
The results of this study reveal that four-day exposure to reduced loading environments is sufficient to induce shifts in chondrocyte metabolism, which may contribute to the degeneration of AC. Exposure to SM induced significant alterations in amino acid metabolism and protein synthesis (valine, leucine, and isoleucine degradation, metabolism of glycine, serine, threonine, histidine, cysteine, methionine, arginine, tyrosine, phenylalanine, tryptophan, and vitamin B6 and aminoacyl tRNA biosynthesis). Additionally, significant changes in nucleotide metabolism (amino sugar and nucleotide metabolism, nitrogen metabolism, and pyrimidine metabolism), oxidative stress (glutathione metabolism), and energy metabolism (pantothenate and coenzyme A biosynthesis) were observed.
The metabolic response to short-term SM exposure suggests that chondrocytes respond to reduced loading by decreasing anabolic pathways required for ECM maintenance. This is shown by significant reductions in the metabolism of various amino acids and nucleotide metabolism in SM cohorts (constructs and media). While multiple studies show that chondrocytes respond to physiological loading by increasing the synthesis of ECM components [42,43,44], the results herein suggest that reduced loading by SM leads to a reduction in the metabolic pathways necessary for the synthesis of ECM components, which may lead to inadequate maintenance of the cartilage structural integrity and degeneration consistent with that of OA.
Exposure to SM also induced shifts in chondrocyte energy metabolism, as shown by an increase in pantothenate and coenzyme A biosynthesis detected by metabolites secreted into surrounding media. Pantothenate (vitamin B5) is a precursor to coenzyme A, an important cofactor in the tricarboxylic acid (TCA) cycle. If not used to produce coenzyme A, pantothenate is exported from the cell. Increased levels of metabolites involved in pantothenate and coenzyme A biosynthesis detected in surrounding media may suggest that pantothenate is not being used for the production of coenzyme A, indicating that the energy requirements are lowered in response to the reduced mechanical loads. This is in line with a decrease in the anabolic pathways discussed above, which would significantly reduce chondrocyte energy requirements [45].
Chondrocytes exposed to short-term SM exhibited a reduced ability to respond to oxidative stress, as shown by a decrease in the abundance of glutathione, a known antioxidant. It is well established that microgravity exposure is associated with accumulation of reactive oxygen species (ROS) and a corresponding increase in oxidative stress. Increased oxidative stress alters chondrocyte metabolism, leading to an imbalance in matrix synthesis and degradation. Under normal conditions, chondrocytes are capable of counteracting oxidative stress through the production of antioxidants. However, the results herein suggest that even short-term exposure to SM may compromise the chondrocytes’ ability to counteract oxidative stress, which could lead to AC degeneration.
Metabolic Pathway Alterations Consistent with OsteoarthritisMany of the observed SM-induced metabolic perturbations have been previously implicated in OA. Several studies have shown differences in amino acid concentrations and amino acid metabolism in OA cartilage and biofluids (synovial fluid, urine, plasma), indicating their importance in OA pathogenesis [reviewed in 45]. Additionally, OA chondrocytes exhibit alterations in energy metabolism, such as an increase in anaerobic glycolysis and reductions in the TCA cycle and oxidative phosphorylation [46, 47]. Notably, a hallmark of OA is the chondrocyte’s inability to maintain homeostasis in response to oxidative stress [46, 48,49,50,51,52,53]. Studies suggest that glutathione levels are significantly reduced with aging and in OA [52, 53]. Taken together, these results suggest that even short-term exposure to reduced loading environments may induce early metabolic shifts consistent with that of OA.
Importantly, many similarities were observed between metabolites secreted from encapsulated chondrocytes exposed to short-term SM into the surrounding media and our previous work analyzing metabolomic profiles of OA synovial fluid [54]. Metabolites involved in the following pathways were perturbed in both SM media and early OA synovial fluid: histidine, amino sugars, fatty acid, butanoate, phosphatidyl phosphate, methionine, and cysteine metabolism; valine, leucine, and isoleucine degradation; glycine, serine, threonine, methionine, cysteine, glutathione, arginine, proline, and glutamate metabolism. These results provide further validation that reduced loading, even in the short-term, may induce metabolic shifts consistent with early OA. Furthermore, the detection of these metabolic perturbations as secretions into surrounding media suggest that microgravity-induced metabolic shifts may be detectable in human synovial fluid in vivo to assess osteoarthritis risk in astronauts.
LimitationsThis work has important limitations providing opportunities for future projects. Future studies may increase the sample size of experimental cohorts to confirm the present observations of microgravity-induced metabolic shifts. Future studies also could use targeted metabolomic profiling for confident identification of metabolite identities and implicated pathways. Another important limitation is the single timepoint of four days (a necessary limitation of the RWV to avoid the formation of bubbles and nutrient depletion). Follow up studies could also include multiple timepoints to better understand both the transient metabolic effects throughout the four-day period, as well as the durability of the changes after microgravity exposure and the return to normal gravitational forces.
Furthermore, applying cyclical mechanical stimulation (e.g., compression) with and without simulated microgravity may provide further insight into the role of microgravity in chondrocyte mechanotransduction. Future studies combining finite element modeling with atomic force microscopy can be performed to predict changes in pericellular stresses in these conditions of simulated microgravity. Finally, this work is limited by the use of the SW1353 cell line, a chondrosarcoma cell line, which does not perfectly model the behavior of primary chondrocytes. Thus, primary chondrocytes obtained from human cartilage will be used for future work to model the in vitro microenvironment of cartilage more accurately.
Significance and ConclusionTo our knowledge, this is the first study to generate global metabolomic profiles of 3D encapsulated chondrocytes in response to short-term SM exposure. Importantly, this work demonstrates the feasibility of using RWV bioreactors with three dimensionally encapsulated cells and enhances our ability to study chondrocyte mechanotransduction in vitro using simulated microgravity. Metabolic pathways perturbed in response to SM were similar to those previously observed in early OA and thus may further define potential pathophysiological processes induced in chondrocytes during space travel.
ReferencesLong, H., Q. Liu, H. Yin, K. Wang, N. Diao, Y. Zhang, et al. Prevalence trends of site-specific osteoarthritis from 1990 to 2019: findings from the global burden of disease study 2019. Arthritis Rheumatol. 74(7):1172–1183, 2022. https://doi.org/10.1002/art.42089.
Punzi, L., P. Galozzi, R. Luisetto, M. Favero, R. Ramonda, F. Oliviero, et al. Post-traumatic arthritis: overview on pathogenic mechanisms and role of inflammation. RMD Open. 2(2):e000279, 2016. https://doi.org/10.1136/rmdopen-2016-000279.
Felson, D. T. Osteoarthritis as a disease of mechanics. Osteoarthritis Cartilage. 21:10–15, 2013.
Liphardt, A. M., A. Mundermann, S. Koo, N. Backer, T. P. Andriacchi, J. Zange, et al. Vibration training intervention to maintain cartilage thickness and serum concentrations of cartilage oligometric matrix protein (COMP) during immobilization. Osteoarthritis Cartilage. 17(12):1598–1603, 2009. https://doi.org/10.1016/j.joca.2009.07.007.
Buckwalter, J. A., and H. J. Mankin. Articular cartilage: tissue design and chondrocyte-matrix interactions. Instr Course Lect. 47:477–486, 1998.
Bader, D. L., D. M. Salter, and T. T. Chowdhury. Biomechanical influence of cartilage homeostasis in health and disease. Arthritis. 2011:979032, 2011. https://doi.org/10.1155/2011/979032.
Dwivedi, G., L. Flaman, B. Alaybeyoglu, A. Struglics, E. H. Frank, et al. Inflammatory cytokines and mechanical injury induce post-traumatic osteoarthritis-like changes in a human cartilage-bone-synovium microphysiological system. Arthritis Res Ther. 24(1):198, 2022. https://doi.org/10.1186/s13075-022-02881-z.
Johnson, D. L., W. P. Urban, D. N. Caborn, W. J. Vanarthos, and C. S. Carlson. Articular cartilage changes seen with magnetic resonance imaging-detected bone bruises associated with acute anterior cruciate ligament rupture. Am. J. Sports Med. 26(3):409–14, 1998. https://doi.org/10.1177/03635465980260031101.
Struglics, A., S. Larsson, N. Kumahashi, R. Frobell, and L. S. Lohmander. Changes in cytokines and aggrecan ARGS neoepitope in synovial fluid and serum and in C-terminal crosslinking telopeptide of type II collagen and N-terminal crosslinking telopeptide of type I collagen in urine over five years after anterior cruciate ligament rupture: an exploratory analysis in the knee anterior cruciate ligament, nonsurgical versus surgical treatment trial. Arthritis Rheumatol. 67(7):1816–25, 2015. https://doi.org/10.1002/art.39146.
Lyndin, M., N. Gluschenko, V. Sikora, Y. Lyndina, N. Hyryavenko, G. Tkach, et al. Morphofunctional features of articular cartilage structure. Folia Med Cracov. 59(3):81–93, 2019. https://doi.org/10.24425/fmc.2019.131138.
Kerstman, E. L., R. A. Scheuring, M. G. Barnes, T. B. DeKorse, and L. G. Saile. Space adaptation back pain: a retrospective study. Aviat. Space Environ. Med. 83(1):2–7, 2012. https://doi.org/10.3357/asem.2876.2012.
NASA (2016). US military well represented among NASA astronauts. Veterans United Network.
Viegas, S. F., D. Williams, J. Jones, S. Strauss, and J. Clark. Physical demands and injuries to the upper extremity associated with the space program. J. Hand Surg. Am. 29(3):359–366, 2004. https://doi.org/10.1016/j.jhsa.2004.01.015.
Strauss, S., R. L. Krog, and A. H. Feiveson. Extravehicular mobility unit training and astronaut injuries. Aviat. Space Environ. Med. 76(5):469–474, 2005.
Niehoff, A., G. P. Brüggemann, F. Zaucke, F. Eckstein, W. Bloch, A. Mündermann, et al. Long-duration space flight and cartilage adaptation: first results on changes in tissue metabolism. Osteoarthritis Cartilage. 24:S144–S145, 2016. https://doi.org/10.1016/j.joca.2016.01.282.
Niehoff, A., M. Dreiner, A. Mündermann, F. Zaucke, and A. M. Liphardt. The effect of micro-and hypergravity on serum comp levels in healthy adults. Osteoarthritis Cartilage. 27:S169–S170, 2019. https://doi.org/10.1016/j.joca.2019.02.253.
Liphardt AM, Djalal N, Smith SM, Zwart SR, Mündermann A, Zaucke F, et al. Urine Concentration of Cartilage Degradation Marker CTX-II is Sensitive to Microgravity. Abstract. In Proceedings of the NASA Human Research Program Investigators’ Workshop, Virtual; 2022.
Vanwanseele, B., F. Eckstein, H. Knecht, E. Stüssi, and A. Spaepen. Knee cartilage of spinal cord-injured patients displays progressive thinning in the absence of normal joint loading and movement. Arthritis Rheum. 46(8):2073–2078, 2002. https://doi.org/10.1002/art.10462.
Vanwanseele, B., F. Eckstein, H. Knecht, A. Spaepen, and E. Stüssi. Longitudinal analysis of cartilage atrophy in the knees of patients with spinal cord injury. Arthritis Rheum. 48(12):3377–3381, 2003. https://doi.org/10.1002/art.11367.
Fitzgerald J, Endicott J, Hansen U, Janowitz C. Articular cartilage and sternal fibrocartilage respond differently to extended microgravity. NPJ Microgravity. 2019;5:3. Published 2019 Feb 18. https://doi.org/10.1038/s41526-019-0063-6
Kwok, A. T., N. S. Mohamed, J. F. Plate, R. R. Yammani, S. Rosas, T. A. Bateman, et al. Spaceflight and hind limb unloading induces an arthritic phenotype in knee articular cartilage and menisci of rodents. Sci Rep. 11(1):10469, 2021. https://doi.org/10.1038/s41598-021-90010-2.
Willey, J. S., A. T. Kwok, J. E. Moore, V. Payne, C. A. Lindburg, S. A. Balk, et al. Spaceflight-Relevant Challenges of Radiation and/or Reduced Weight Bearing Cause Arthritic Responses in Knee Articular Cartilage. Radiat Res. 186(4):333–344, 2016. https://doi.org/10.1667/RR14400.1.
Takahashi, I., T. Matsuzaki, H. Kuroki, and M. Hoso. Physiological reloading recovers histologically disuse atrophy of the articular cartilage and bone by hindlimb suspension in rat knee joint. Cartilage. 13(2):1530S-1539S, 2021. https://doi.org/10.1177/19476035211063857.
Kwok, A. T., J. E. Moore, S. Rosas, B. A. Kerr, R. N. Andrews, C. M. Nguyen, et al. Knee and hip joint cartilage damage from combined spaceflight hazards of low-dose radiation less than 1 Gy and prolonged hindlimb unloading. Radiat Res. 191(6):497–506, 2019. https://doi.org/10.1667/RR15216.1.
Jutila, A. A., D. L. Zignego, B. K. Hwang, J. K. Hilmer, T. Hamerly, C. A. Minor, S. T. Walk, and R. K. June. Candidate mediators of chondrocyte mechanotransduction via targeted and untargeted metabolomic measurements. Arch Biochem Biophys. 545:116–123, 2014. https://doi.org/10.1016/j.abb.2014.01.011.
Welhaven, H. D., C. N. McCutchen, and R. K. June. Effects of mechanical stimulation on metabolomic profiles of SW1353 chondrocytes: shear and compression. Biology Open. 11(1):bio058895, 2022. https://doi.org/10.1242/bio.058895.
Fredrikson, J. P., P. P. Brahmachary, A. E. Erdoğan, Z. K. Archambault, J. N. Wilking, R. K. June, et al. Metabolomic Profiling and Mechanotransduction of Single Chondrocytes Encapsulated in Alginate Microgels. Cells. 11(5):900, 2022. https://doi.org/10.3390/cells11050900.
Jutila, A. A., D. L. Zignego, W. J. Schell, and R. K. June. Encapsulation of chondrocytes in high-stiffness agarose microenvironments for in vitro modeling of osteoarthritis mechanotransduction. Ann Biomed Eng. 43(5):1132–1144, 2015. https://doi.org/10.1007/s10439-014-1183-5.
Sophia Fox, A. J., A. Bedi, and S. A. Rodeo. The basic science of articular cartilage: structure, composition, and function. Sports Health. 1(6):461–468, 2009. https://doi.org/10.1177/1941738109350438.
Pearson, W., M. W. Orth, N. A. Karrow, N. J. Maclusky, and M. I. Lindinger. Anti-inflammatory and chondroprotective effects of nutraceuticals from Sasha’s Blend in a cartilage explant model of inflammation. Mol Nutr Food Res. 51(8):1020–1030, 2007. https://doi.org/10.1002/mnfr.200700026.
Carlson, A. K., R. A. Rawle, E. Adams, M. C. Greenwood, B. Bothner, and R. K. June. Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers. Biochem Biophys Res Commun. 499(2):182–188, 2018. https://doi.org/10.1016/j.bbrc.2018.03.117.
Adusumilli, R., and P. Mallick. Data conversion with ProteoWizard msConvert. Methods Mol Biol. 1550:339–368, 2017. https://doi.org/10.1007/978-1-4939-6747-6_23.
Domingo-Almenara, X., and G. Siuzdak. Metabolomics data processing using XCMS. Methods Mol Biol. 2104:11–24, 2020. https://doi.org/10.1007/978-1-0716-0239-3_2.
Chong, J., O. Soufan, C. Li, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46(W1):W486–W494, 2018. https://doi.org/10.1093/nar/gky310.
Nordberg RC, Mellor LF, Krause AR, Donahue HJ, Loboa EG. LRP receptors in chondrocytes are modulated by simulated microgravity and cyclic hydrostatic pressure. PLoS One. 2019;14(10):e0223245. Published 2019 Oct 4. https://doi.org/10.1371/journal.pone.0223245
Hatakeyama, J., M. Nomura, Y. Wakimoto, S. Inoue, C. Li, D. Takamura, et al. Effects of cyclic tensile strain and microgravity on the distribution of actin fiber and Fat1 cadherin in murine articular chondrocytes. J Biomech. 129:110774, 2021. https://doi.org/10.1016/j.jbiomech.2021.110774.
Ulbrich, C., K. Westphal, J. Pietsch, H. D. F. Winkler, A. Leder, J. Bauer, et al. Characterization of human chondrocytes exposed to simulated microgravity. Cell Physiol Biochem. 25(4–5):551–560, 2010. https://doi.org/10.1159/000303059.
Aleshcheva G, Sahana J, Ma X, Hauslage J, Hemmersbach R, Egli M, et al. Changes in morphology, gene expression and protein content in chondrocytes cultured on a random positioning machine. PLoS One. 2013;8(11):e79057. Published 2013 Nov 11. https://doi.org/10.1371/journal.pone.0079057
Wuest, S. L., M. Caliò, T. Wernas, S. Tanner, C. Giger-Lange, F. Wyss, et al. Influence of Mechanical Unloading on Articular Chondrocyte Dedifferentiation. Int J Mol Sci. 19(5):1289, 2018. https://doi.org/10.3390/ijms19051289.
Wilusz, R. E., J. Sanchez-Adams, and F. Guilak. The structure and function of the pericellular matrix of articular cartilage. Matrix Biol. 39:25–32, 2014. https://doi.org/10.1016/j.matbio.2014.08.009.
Statham, P., E. Jones, L. M. Jennings, and H. L. Fermor. Reproducing the Biomechanical Environment of the Chondrocyte for Cartilage Tissue Engineering. Tissue Eng Part B Rev. 28(2):405–420, 2022. https://doi.org/10.1089/ten.TEB.2020.0373.
Smith, R. L., S. F. Rusk, B. E. Ellison, P. Wessells, K. Tsuchiya, D. R. Carter, et al. In vitro stimulation of articular chondrocyte mRNA and extracellular matrix synthesis by hydrostatic pressure. J Orthop Res. 14(1):53–60, 1996. https://doi.org/10.1002/jor.1100140110.
Dolzani P, Assirelli E, Pulsatelli L, Meliconi R, Mariani E, Neri S. Ex vivo physiological compression of human osteoarthritis cartilage modulates cellular and matrix components. PLoS One. 2019;14(9):e0222947. Published 2019 Sep 24. https://doi.org/10.1371/journal.pone.0222947
Ragan, P. M., V. I. Chin, H. H. Hung, K. Masuda, E. J. Thonar, E. C. Arner, et al. Chondrocyte extracellular matrix synthesis and turnover are influenced by static compression in a new alginate disk culture system. Arch Biochem Biophys. 383(2):256–264, 2000. https://doi.org/10.1006/abbi.2000.2060.
Wu, X., C. Liyanage, M. Plan, T. Stark, T. McCubbin, R. A. Barrero, et al. Dysregulated energy metabolism impairs chondrocyte function in osteoarthritis. Osteoarthritis Cartilage. 31(5):613–626, 2023. https://doi.org/10.1016/j.joca.2022.11.004.
Zheng, L., Z. Zhang, P. Sheng, and A. Mobasheri. The role of metabolism in chondrocyte dysfunction and the progression of osteoarthritis. Ageing Res Rev. 66:101249, 2021. https://doi.org/10.1016/j.arr.2020.101249.
Maneiro, E., M. A. Martín, M. C. de Andres, M. J. López-Armada, J. L. Fernández-Sueiro, P. del Hoyo, et al. Mitochondrial respiratory activity is altered in osteoarthritic human articular chondrocytes. Arthritis Rheum. 48(3):700–708, 2003. https://doi.org/10.1002/art.10837.
Loeser, R. F., J. A. Collins, and B. O. Diekman. Ageing and the pathogenesis of osteoarthritis. Nat Rev Rheumatol. 12(7):412–420, 2016. https://doi.org/10.1038/nrrheum.2016.65.
Hui, W., D. A. Young, A. D. Rowan, X. Xu, T. E. Cawston, and C. J. Proctor. Oxidative changes and signalling pathways are pivotal in initiating age-related changes in articular cartilage. Ann Rheum Dis. 75(2):449–458, 2016. https://doi.org/10.1136/annrheumdis-2014-206295.
Mobasheri, A., M. P. Rayman, O. Gualillo, J. Sellam, P. van der Kraan, and U. Fearon. The role of metabolism in the pathogenesis of osteoarthritis. Nat Rev Rheumatol. 13(5):302–311, 2017. https://doi.org/10.1038/nrrheum.2017.50.
Johnson, K., A. Jung, A. Murphy, A. Andreyev, J. Dykens, and R. Terkeltaub. Mitochondrial oxidative phosphorylation is a downstream regulator of nitric oxide effects on chondrocyte matrix synthesis and mineralization. Arthritis Rheum. 43(7):1560–1570, 2000.
Zhu, S., D. Makosa, B. Miller, and T. M. Griffin. Glutathione as a mediator of cartilage oxidative stress resistance and resilience during aging and osteoarthritis. Connect Tissue Res. 61(1):34–47, 2020. https://doi.org/10.1080/03008207.2019.1665035.
Fu, Y., M. Kinter, J. Hudson, et al. Aging Promotes Sirtuin 3-Dependent Cartilage Superoxide Dismutase 2 Acetylation and Osteoarthritis. Arthritis Rheumatol. 68(8):1887–1898, 2016. https://doi.org/10.1002/art.39618.
Carlson, A. K., R. A. Rawle, C. W. Wallace, E. G. Brooks, E. Adams, M. C. Greenwood, et al. Characterization of synovial fluid metabolomic phenotypes of cartilage morphological changes associated with osteoarthritis. Osteoarthritis Cartilage. 27(8):1174–1184, 2019. https://doi.org/10.1016/j.joca.2019.04.007.
The authors would like to acknowledge the Montana State University Mass Spectrometry CORE Facility for their assistance with mass spectrometry.
FundingResearch reported in this publication was supported by the M.J. Murdock Charitable Trust under Award Number FSU-2017207 (AKH), the National Aeronautics and Space Administration under Award Number 80NSSC20M0042 (AKH), the National Science Foundation under Award Number CMMI 1554708 (RKJ), and the National Institutes of Health under Award Numbers R01AR073964 (RKJ). Funding for the Mass Spectrometry Facility used in this publication was made possible by the M.J. Murdock Charitable Trust, and the National Institute of General Medical Sciences of the National Institutes of Health (P20GM103474 and S10OD28650). The content is solely the responsibility of the authors and does not necessarily represent the official news of the funding courses.
Author information Authors and AffiliationsDepartment of Biological and Environmental Science, Carroll College, 1601 N Benton Ave, Helena, MT, 59625, USA
Annika R. Bergstrom, Matthew G. Glimm, Eden A. Houske, Marrin Chapman, Katherine Bourekis & Alyssa K. Hahn
Department of Chemical and Biological Engineering, Villanova University, Villanova, PA, 19085, USA
Annika R. Bergstrom
Molecular Biosciences Program, Montana State University, Bozeman, MT, 59717, USA
Gwendolyn Cooper, Ethan Viles & Hope D. Welhaven
Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, 59717, USA
Gwendolyn Cooper & Hope D. Welhaven
Department of Mechanical and Industrial Engineering, Montana State University, PO Box 173800, Bozeman, MT, 59717-3800, USA
Ethan Viles, Priyanka P. Brahmachary & Ronald K. June
ARB assisted in designing experiments, performed metabolite extractions, ran LC-MS samples, analyzed data, and drafted the manuscript. MGG and EAH assisted in designing experiments, extracting metabolites, carried out cell viability assays, and assisted in analyzing data. EV and GC assisted in designing experiments. MC, KB, and PPB assisted in cell viability assays. HDW ran LC-MS samples and assisted in analyzing data. RKJ assisted in analyzing data. AKH designed experiments and analyzed data. All authors have read and revised the manuscript.
Corresponding authorsCorrespondence to Alyssa K. Hahn or Ronald K. June.
Ethics declarations Conflict of interestAuthors have no conflicts of interest to disclose. PPB owns stock in OpenBioWorks, which was not involved in this study. RKJ owns stock in Beartooth Biotech and OpenBioWorks, which were not involved in this study.
Additional informationAssociate Editor Eiji Tanaka oversaw the review of this article.
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Supplementary InformationBelow is the link to the electronic supplementary material.
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About this article Cite this articleBergstrom, A.R., Glimm, M.G., Houske, E.A. et al. Metabolic Profiles of Encapsulated Chondrocytes Exposed to Short-Term Simulated Microgravity. Ann Biomed Eng 53, 785–797 (2025). https://doi.org/10.1007/s10439-024-03667-x
Received: 20 August 2024
Accepted: 02 December 2024
Published: 18 December 2024
Issue Date: March 2025
DOI: https://doi.org/10.1007/s10439-024-03667-x
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