The Methylation Pattern for Knee and Hip Osteoarthritis

Epigenetics in Knee Osteoarthritis: A 2020-2023 Update Systematic Review

Osteoarthritis is a leading cause of disability in the world. The scientific literature highlights the critical importance of epigenetic regulatory effects, intertwined with biomechanical and biochemical peculiar conditions within each musculoskeletal district. While the contribution of genetic and epigenetic factors to knee OA is well-recognized, their precise role in disease management remains an area of active research. Such a field is particularly heterogeneous, calling for regular analysis and summarizing of the data that constantly emerge in the scientific literature, often sparse and scant of integration. The aim of this study was to systematically identify and synthesize all new evidence that emerged in human and animal model studies published between 2020 and 2023. This was necessary because, to the best of our knowledge, articles published before 2019 (and partly 2020) had already been included in systematic reviews that allowed to identify the ones concerning the knee joint. The review was carried out in accordance with Preferential Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only peer-reviewed articles were considered for inclusion. A total of 40 studies were identified, showing promising results in terms either of biomarker identification, new insight in mechanism of action or potential therapeutic targets for knee OA. DNA methylation, histone modification and ncRNA were all mechanisms involved in epigenetic regulation of the knee. Most recent evidence suggests that epigenetics is a most promising field with the long-term goal of improving understanding and management of knee OA, but a variety of research approaches need greater consolidation.

Analyzing omics data based on sample network

Identifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a strong discriminating ability tends to have many edges between the same group samples and few edges between the different group samples. At the same time, FS-SN removes redundant features according to the gravitational interaction between features. To show the validation of FS-SN, it was compared on ten public datasets with ERGS, mRMR, ReliefF, ATSD-DN, and INDEED which are efficient in omics data analysis. Experimental results show that FS-SN performed better than the compared methods in accuracy, sensitivity and specificity in most cases. Hence, FS-SN making use of the topology of the sample network is effective for analyzing omics data, it can identify key features that reflect the occurrence and development of diseases, and reveal the underlying biological mechanism.

A whole genome scan reveals distinct features of selection in Zhaotong cattle of Yunnan province

Over the years, indigenous cattle have not only played an essential role in securing primary food sources but have also been utilized for labor by humans, making them invaluable genetic resources. The Zhaotong cattle, a native Chinese breed from the Yunnan province, possess excellent meat quality and resistance to heat and humidity. Here we used whole genome sequencing data of 104 animals to delve into the population structure, genomic diversity and potential positive selection signals in Zhaotong cattle. The findings of this study demonstrate that the genetic composition of Zhaotong cattle was primarily derived from Chinese indicine cattle and East Asian cattle. The nucleotide diversity of Zhaotong cattle was only lower than that of Chinese indicine cattle, which was much higher than that of other taurine cattle. Genome-wide selection scans detected a series of positive candidate regions containing multiple key genes related to bone development and metabolism (CA10, GABRG3, GLDN and NOTUM), meat quality traits (ALG8, LINGO2, MYO5B, PRKG1 and GABRB1), immune response (ADA2, BMF, LEF1 and PAK6) and heat resistance (EIF2AK4 and LEF1). In summary, this study supplies essential genetic insights into the genome diversity within Zhaotong cattle and provides a foundational framework for comprehending the genetic basis of indigenous cattle breeds.

Group-shrinkage feature selection with a spatial network for mining DNA methylation data

Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication.

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR

Coal miners’ occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners’ working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.