Subchondral bone structure and synovial fluid metabolism are altered in injured and contralateral limbs 7 days after non-invasive joint injury in skeletally-mature C57BL/6 mice

Hislop, Brady; Devine, Connor; June, Ronald K.; Heveran, Chelsea M.

doi:10.1016/j.joca.2022.09.002

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…When performing an ensemble of clustering solutions there are a bevy of potential clustering methods including partitional, hierarchical, and density-based methods that can be utilized. In this implementation we focused on a combination of hierarchical clustering solutions as they are commonly used clustering method in the metabolomics field(Carlson et al ., 2018; Carlson et al ., 2019a; Carlson et al ., 2019b; Hahn et al ., 2021; Hislop et al ., 2022; Kim et al ., 2014; Pang et al ., 2021; Wallace et al ., 2022; Wang-Sattler et al ., 2012; Zignego et al ., 2015). However, we acknowledge that there are many other potential combinations of clustering solutions that can be explored to provide a consensus on the appropriate number of clusters within a given dataset.…”

Section: Discussionmentioning

confidence: 99%

“…These clusters of similar metabolites are also submitted for pathway detection. In our previous work (Hislop et al, 2022), clustering analysis combined with pathway analysis showed that many pathways are shared between ACL-injured and contralateral-to-injured knees in a mouse model. These data evidence an acute systemic metabolomic response to ACL injury.…”

Section: Introductionmentioning

confidence: 97%

“…Metabolomics has yielded many important insights into biological processes and diseases (Carlson et al ., 2018; Carlson et al ., 2019a; Carlson et al ., 2019b; Hahn et al ., 2021; Hislop et al ., 2022; Kim et al ., 2014; Wang-Sattler et al ., 2012; Zignego et al ., 2015). Arguably, metabolomics could be employed more broadly and to greater effect by resolving persistent challenges in the analysis workflow.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Clustering Combined with Clustering Optimization – A Novel Workflow for Analyzing Metabolomics Data

Hislop

Heveran

June

2022

Preprint

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Metabolomic studies have improved the understanding of a broad range of biological tissues, fluids, and systems. Typically, metabolomic analyses employ a workflow that starts with detecting peaks from mass spectrometry data and is followed by a series of statistical analysis aimed at identifying dysregulated metabolites, group differences, and group similarities in dysregulated metabolites and pathways. Generating these group similarities relies on clustering analyses. However, current clustering methods are highly subjective and can be prone to errors, indicating the need for an updated workflow that improves upon these issues. Here we present a novel metabolomics workflow that can produce unbiased, reproducible clustering results: ensemble clustering combined with cluster optimization (ECCO). The first step, clustering optimization, is used to identify an optimal number of clusters without bias. The second step, ensemble clustering, is then performed by finding the consensus clustering solution across thirteen distance algorithms. This step improves the repeatability of analyses and eliminates bias through eliminating the need to choose one distance algorithm in clustering solutions. We employ ECCO to analyze synovial fluid metabolites from patients with early and late osteoarthritis (OA). This method improves upon the detection of distinct metabolomic endotypes compared with conventional analyses. Furthermore, novel pathways were identified corresponding with different stages of OA. These results demonstrate the utility of ECCO in metabolomics workflows that involve clustering data. ECCO, which we provide as an open-source tool, can improve the repeatability, reliability, and ease-of-use of metabolomics analyses, and is therefore expected to increase the confidence of biological interpretation from these data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Ensemble Clustering Combined with Clustering Optimization – A Novel Workflow for Analyzing Metabolomics Data

Hislop

Heveran

June

2022

Preprint

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Aging alters the subchondral bone response 7 days after noninvasive traumatic joint injury in C57BL/6JN mice

Dauenhauer,

Hislop,

Brahmachary

et al. 2024

Journal Orthopaedic Research

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Posttraumatic osteoarthritis (PTOA) commonly develops following anterior cruciate ligament (ACL) injuries, affecting around 50% of individuals within 10–20 years. Recent studies have highlighted early changes in subchondral bone structure after ACL injury in adolescent or young adult mice, which could contribute to the development of PTOA. However, ACL injuries do not only occur early in life. Middle‐aged and older patients also experience ACL injuries and PTOA, but whether the aged subchondral bone also responds rapidly to injury is unknown. This study utilized a noninvasive, single overload mouse injury model to assess subchondral bone microarchitecture, turnover, and material properties in both young adults (5 months) and early old age (22 months) female C57BL/6JN mice at 7 days after injury. Mice underwent either joint injury (i.e., produces ACL tears) or sham injury procedures on both the loaded and contralateral limbs, allowing evaluation of the impacts of injury versus loading. The subchondral bone response to ACL injury is distinct for young adult and aged mice. While 5‐month mice show subchondral bone loss and increased bone resorption postinjury, 22‐month mice did not show loss of bone structure and had lower bone resorption. Subchondral bone plate modulus increased with age, but not with injury. Both ages of mice showed several bone measures were altered in the contralateral limb, demonstrating the systemic skeletal response to joint injury. These data motivate further investigation to discern how osteochondral tissues differently respond to injury in aging, such that diagnostics and treatments can be refined for these demographics.

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Role and Mechanism of Mechanical Load in the Homeostasis of the Subchondral Bone in Knee Osteoarthritis: A Comprehensive Review

Chen,

Zhang,

Liu

2024

JIR

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Osteoarthritis (OA) is one of the most prevalent degenerative joint diseases, and the knee joint is particularly susceptible to it. It typically affects the entire joint and is marked by the erosion of cartilage integrity, chondrocytopenia, subchondral bone sclerosis and the mild synovial inflammation. Pathological changes in the subchondral bone often serve as initiating factors for joint degeneration. Various predisposing factors, including metabolic disorders, oxidative stress, and abnormal mechanical loading, regulate OA pathogenesis. Of them, mechanical loading is closely associated with the maintenance of the subchondral bone. Disrupted mechanical loading, leading to subchondral bone remodeling, can potentially trigger OA, whereas appropriate loading might ameliorate its progression. Therefore, this narrative review aimed to discuss existing knowledge and explore how mechanical loading mediates changes in the subchondral bone, influencing the development of knee osteoarthritis. Special emphasis is placed on its role and underlying mechanisms in maintaining joint homeostasis.

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Subchondral bone structure and synovial fluid metabolism are altered in injured and contralateral limbs 7 days after non-invasive joint injury in skeletally-mature C57BL/6 mice

Cited by 3 publications

References 45 publications

Ensemble Clustering Combined with Clustering Optimization – A Novel Workflow for Analyzing Metabolomics Data

Ensemble Clustering Combined with Clustering Optimization – A Novel Workflow for Analyzing Metabolomics Data

Aging alters the subchondral bone response 7 days after noninvasive traumatic joint injury in C57BL/6JN mice

Role and Mechanism of Mechanical Load in the Homeostasis of the Subchondral Bone in Knee Osteoarthritis: A Comprehensive Review

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