Mechanical cues sensed by integrins induce cells to produce proteases to remodel the extracellular matrix. Excessive protease production occurs in many degenerative diseases, including osteoarthritis, in which articular cartilage degradation is associated with the genesis of matrix protein fragments that can activate integrins. We investigated the mechanisms by which integrin signals may promote protease production in response to matrix changes in osteoarthritis. Using a fragment of the matrix protein fibronectin (FN) to activate the α5β1 integrin in primary human chondrocytes, we found that endocytosis of the integrin and FN fragment complex drove the production of the matrix metalloproteinase MMP-13. Activation of α5β1 by the FN fragment, but not by intact FN, was accompanied by reactive oxygen species (ROS) production initially at the cell surface, then in early endosomes. These ROS-producing endosomes (called redoxosomes) contained the integrin-FN fragment complex, the ROS-producing enzyme NADPH oxidase 2 (NOX2), and SRC, a redox-regulated kinase that promotes MMP-13 production. In contrast, intact FN was endocytosed and trafficked to recycling endosomes without inducing ROS production. Articular cartilage from patients with osteoarthritis showed increased amounts of SRC and the NOX2 complex component p67 ^phox . Furthermore, we observed enhanced localization of SRC and p67 ^phox at early endosomes, suggesting that redoxosomes could transmit and sustain integrin signaling in response to matrix damage. This signaling mechanism not only amplifies the production of matrix-degrading proteases but also establishes a self-perpetuating cycle that contributes to the ongoing degradation of cartilage matrix in osteoarthritis.

The regeneration of periodontal, periapical, and pulpal tissues is a complex process requiring the direct involvement of cells derived from pluripotent stem cells in the periodontal ligament and dental pulp. Dental pulp stem cells (DPSCs) and periodontal ligament stem cells (PDLSCs) are spatially distinct with the potential to differentiate into similar functional and phenotypic cells. We aimed to identify the cell heterogeneity of DPSCs and PDLSCs and explore the differentiation potentials of their specialized organ-specific functions using single-cell transcriptomic analysis. Our results revealed 7 distinct clusters, with cluster 3 showing the highest potential for differentiation. Clusters 0 to 2 displayed features similar to fibroblasts. The trajectory route of the cell state transition from cluster 3 to clusters 0, 1, and 2 indicated the distinct nature of cell differentiation. PDLSCs had a higher proportion of cells (78.6%) at the G1 phase, while DPSCs had a higher proportion of cells at the S and G2/M phases (36.1%), mirroring the lower cell proliferation capacity of PDLSCs than DPSCs. Our study suggested the heterogeneity of stemness across PDLSCs and DPSCs, the similarities of these 2 stem cell compartments to be potentially integrated for regenerative strategies, and the distinct features between them potentially particularized for organ-specific functions of the dental pulp and periodontal ligament for a targeted regenerative dental tissue repair and other regeneration therapies.

Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.

A new CRISPR screening strategy allows characterizing enhancers for multiple genes associated with converging phenotypes.

Background and Objective: Periodontitis is a multifactorial chronic inflammatory disease that can lead to the irreversible destruction of dental support tissues. As an epigenetic factor, the expression of circRNA is tissue‐dependent and disease‐dependent. This study aimed to identify novel periodontitis‐associated circRNAs and predict relevant circRNA‐periodontitis regulatory network by using recently developed bioinformatic tools and integrating sequencing profiling with clinical information for getting a better and more thorough image of periodontitis pathogenesis, from gene to clinic.Material and Methods: High‐throughput sequencing and RT‐qPCR were conducted to identify differentially expressed circRNAs in gingival tissues from periodontitis patients. The relationship between upregulated circRNAs expression and probing depth (PD) was performed using Spearman's correlation analysis. Bioinformatic analyses including GO analysis, circRNA‐disease association prediction, and circRNA‐miRNA‐mRNA network prediction were performed to clarify potential regulatory functions of identified circRNAs in periodontitis. A receiver‐operating characteristic (ROC) curve was established to assess the diagnostic significance of identified circRNAs.Results: High‐throughput sequencing identified 70 differentially expressed circRNAs (68 upregulated and 2 downregulated circRNAs) in human periodontitis (fold change >2.0 and p < .05). The top five upregulated circRNAs were validated by RT‐qPCR that had strong associations with multiple human diseases, including periodontitis. The upregulation of circRNAs were positively correlated with PD (R = .40–.69, p < .05, moderate). A circRNA‐miRNA‐mRNA network with the top five upregulated circRNAs, differentially expressed mRNAs, and overlapped predicted miRNAs indicated potential roles of circRNAs in immune response, cell apoptosis, migration, adhesion, and reaction to oxidative stress. The ROC curve showed that circRNAs had potential value in periodontitis diagnosis (AUC = 0.7321–0.8667, p < .05).Conclusion: CircRNA‐disease associations were predicted by online bioinformatic tools. Positive correlation between upregulated circRNAs, circPTP4A2, chr22:23101560‐23135351+, circARHGEF28, circBARD1 and circRASA2, and PD suggested function of circRNAs in periodontitis. Network prediction further focused on downstream targets regulated by circRNAs during periodontitis pathogenesis.

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by ~3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimer’s disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.