Increased mechanistic insight into the pathogenesis of knee osteoarthritis (KOA) is needed to develop efficacious disease-modifying treatments. Though age-related pathogenic mechanisms are most relevant to the majority of clinically presenting KOA, the bulk of our mechanistic understanding of KOA has been derived using surgically induced posttraumatic OA (PTOA) models. Here, we took an integrated approach of meta-analysis and multi-omics data analysis to elucidate pathogenic mechanisms of age-related KOA in mice. Protein-level data were integrated with transcriptomic profiling to reveal inflammation, autophagy, and cellular senescence as primary hallmarks of age-related KOA. Importantly, the molecular profiles of cartilage aging were unique from those observed following PTOA, with less than 3% overlap between the 2 models. At the nexus of the 3 aging hallmarks, advanced glycation end product (AGE)/receptor for AGE (RAGE) emerged as the most statistically robust pathway associated with age-related KOA. This pathway was further supported by analysis of mass spectrometry data. Notably, the change in AGE–RAGE signaling over time was exclusively observed in male mice, suggesting sexual dimorphism in the pathogenesis of age-induced KOA in murine models. Collectively, these findings implicate dysregulation of AGE–RAGE signaling as a sex-dependent driver of age-related KOA.

Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC. Elucidating the differences between cancer evolutionary patterns among patients is valuable in personalized medicine, since therapeutic response mostly depends on cancer evolution process. Recently, computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a so-called “cancer evolutionary tree” constructed from multi-regional sequencing data. However, there have been few studies on comparisons of a set of cancer evolutionary trees to better understand the relationship between a set of cancer evolutionary patterns and patient phenotypes. Given a set of tree objects for multiple patients, we propose an unsupervised learning approach to identify subgroups of patients through clustering the respective cancer evolutionary trees. Using this approach, we effectively identified the patterns of different evolutionary modes in a simulation analysis, and also successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets. We believe that the value and impact of our work will grow as more and more datasets for the cancer evolution of patients become available.

Inflammatory cytokines released by synovium after trauma disturb the gene regulatory network and have been implicated in the pathophysiology of osteoarthritis. A mechanistic understanding of how aging perturbs this process can help identify novel interventions. Here, we introduced network paradigms to simulate cytokine‐mediated pathological communication between the synovium and cartilage. Cartilage‐specific network analysis of injured young and aged murine knees revealed aberrant matrix remodeling as a transcriptomic response unique to aged knees displaying accelerated cartilage degradation. Next, network‐based cytokine inference with pharmacological manipulation uncovered IL6 family member, Oncostatin M (OSM), as a driver of the aberrant matrix remodeling. By implementing a phenotypic drug discovery approach, we identified that the activation of OSM recapitulated an “inflammatory” phenotype of knee osteoarthritis and highlighted high‐value targets for drug development and repurposing. These findings offer translational opportunities targeting the inflammation‐driven osteoarthritis phenotype.

The Human Glycome Atlas (HGA) Project was launched in April 2023, spearheaded by three Japanese institutes: the Tokai National Higher Education and Research System, the National Institutes of Natural Sciences, and Soka University. This was the first time that a field in the life sciences was adopted by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) for a Large-scale Academic Frontiers Promotion Project. This project aims to construct a knowledgebase of human glycans and glycoproteins as a standard for the human glycome. A high-throughput pipeline for comprehensively analyzing 20,000 blood samples in its first five years is planned, at which time an access-controlled version of a human glycomics knowledgebase, called TOHSA, will be released. By the end of the final tenth year, TOHSA will provide a central resource linking human glycan data with other omics data including disease-related information.

Accumulated fat in skeletal muscle (i.e. myosteatosis), common in sedentary older individuals, compromises skeletal muscle health and function. A mechanistic understanding of how physical activity levels dictate fat accumulation represents a critical step towards establishment of therapies that promote healthy ageing. Using a network medicine paradigm that characterized the transcriptomic response of aged muscle to exercise versus immobilization protocols, this study explored the shared molecular cascade that regulates the fate of fibro‐adipogenic progenitors (FAPs), the cell population primarily responsible for fat accumulation. Specifically, gene set enrichment analyses with network propagation revealed Pgc‐1α as a functional hub of a large gene regulatory network underlying the regulation of FAPs by physical activity in aged muscle, but not in young counterparts. Integrated in silico and in situ approaches to induce Pgc‐1α overexpression in aged muscle promoted mitochondrial fatty acid oxidation and inhibited FAP adipogenesis. These findings suggest that the Pgc‐1α–mitochondrial fatty acid oxidation axis is a shared mechanism by which physical activity regulates age‐related myosteatosis. The network medicine paradigm introduced provides mechanistic insight into exercise adaptation in elderly skeletal muscle and offers translational opportunities to advance exercise prescription for older populations. image Key points: Fat accumulation is a quintessential feature of aged skeletal muscle. While increasing physical activity levels has been proposed as an effective strategy to reduce the fat in skeletal muscle (i.e. myosteatosis), the molecular cascade underlying these benefits has been poorly defined. This study implemented a series of network medicine approaches and uncovered Pgc‐1α as a mechanistic driver of the regulation of fibro‐adipogenic progenitors (FAPs) by physical activity. Integrated in silico and in situ approaches to induce Pgc‐1α overexpression promoted mitochondrial fatty acid oxidation and inhibited FAP adipogenesis. Together, the findings of the current study suggest a novel hypothesis that physical activity reduces myosteatosis via upregulation of Pgc‐1α‐mediated mitochondrial fatty acid oxidation and subsequent inhibition of FAP adipogenesis.

Two-dimensional video-based pose estimation is a technique that can be used to estimate human skeletal coordinates from video data alone. It is also being applied to gait analysis and in particularly, due to its simplicity of measurement, it has the potential to be applied to gait analysis of large populations. However, it is considered difficult to completely homogenize the environment and settings during the measurement of large populations. Therefore, it is necessary to appropriately deal with technical errors that are not related to the biological factors of interest. In this study, by analyzing a large cohort database, we have identified four major types of anomalies that occur during gait analysis using OpenPose in uncontrolled environments: anatomical, biomechanical, and physical anomalies and errors due to estimation. We have also developed a workflow for identifying and correcting these anomalies and confirmed that this workflow is reproducible through simulation experiments. Our results will help obtain a comprehensive understanding of the anomalies to be addressed during pre-processing for 2D video-based gait analysis of large populations.