KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

Antidementia medication acetylcholinesterase inhibitors have therapeutic benefits on osteoporotic bone by attenuating osteoclastogenesis and bone resorption.

This study was designed to determine whether the use of acetylcholinesterase inhibitors (AChEIs), a group of drugs that stimulate acetylcholine receptors and are used to treat Alzheimer's disease (AD), is associated with osteoporosis protection and inhibition of osteoclast differentiation and function. Firstly, we examined the effects of AChEIs on RANKL-induced osteoclast differentiation and function with osteoclastogenesis and bone resorption assays. Next, we investigated the impacts of AChEIs on RANKL-induced nuclear factor κB and NFATc1 activation and expression of osteoclast marker proteins CA-2, CTSK and NFATc1, and dissected the MAPK signaling in osteoclasts in vitro by using luciferase assay and Western blot. Finally, we assessed the in vivo efficacy of AChEIs using an ovariectomy-induced osteoporosis mouse model, which was analyzed using microcomputed tomography, in vivo osteoclast and osteoblast parameters were assessed using histomorphometry. We found that Donepezil and Rivastigmine inhibited RANKL-induced osteoclastogenesis and impaired osteoclastic bone resorption. Moreover, AChEIs reduced the RANKL-induced transcription of Nfatc1, and expression of osteoclast marker genes to varying degrees (mainly Donepezil and Rivastigmine but not Galantamine). Furthermore, AChEIs variably inhibited RANKL-induced MAPK signaling accompanied by downregulation of AChE transcription. Finally, AChEIs protected against OVX-induced bone loss mainly by inhibiting osteoclast activity. Taken together, AChEIs (mainly Donepezil and Rivastigmine) exerted a positive effect on bone protection by inhibiting osteoclast function through MAPK and NFATc1 signaling pathways through downregulating AChE. Our findings have important clinical implications that elderly patients with dementia who are at risk of developing osteoporosis may potentially benefit from therapy with the AChEI drugs. Our study may influence drug choice in those patients with both AD and osteoporosis.

Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach.

BACKGROUND: Herbal medicine Sanqi (SQ), the dried root or stem of Panax notoginseng (PNS), has been reported to have anti-diabetic and anti-obesity effects and is usually administered as a decoction for Chinese medicine. Alternative to utilizing PNS pure compound for treatment, we are motivated to propose an unconventional scheme to investigate the functions of PNS mixture. However, studies providing a detailed overview of the transcriptomics-based signaling network in response to PNS are seldom available. METHODS: To explore the reasoning of PNS in treating metabolic disorders such as insulin resistance, we implemented a systems biology-based approach with RNA sequencing (RNA-seq) and miRNA sequencing data to elucidate key pathways, genes and miRNAs involved. RESULTS: Functional enrichment analysis revealed PNS up-regulating oxidative stress-related pathways and down-regulating insulin and fatty acid metabolism. Superoxide dismutase 1 (SOD1), peroxiredoxin 1 (PRDX1), heme oxygenase-1 (Hmox1) and glutamate cysteine ligase (GCLc) mRNA and protein levels, as well as related miRNA levels, were measured in PNS treated rat pancreatic ß cells (INS-1). PNS treatment up-regulated Hmox1, SOD1 and GCLc expression while down-regulating miR-24-3p and miR-139-5p to suppress oxidative stress. Furthermore, we verified the novel interactions between miR-139-5p and miR-24-3p with GCLc and SOD1. CONCLUSION: This work has demonstrated the mechanism of how PNS regulates cellular molecules in metabolic disorders. Therefore, combining omics data with a systems biology strategy could be a practical means to explore the potential function and molecular mechanisms of Chinese herbal medicine in the treatment of metabolic disorders.

Pyroptosis-based risk score predicts prognosis and drug sensitivity in lung adenocarcinoma.

Pyroptosis is a recently identified form of programmed cell death; however, its role in lung adenocarcinoma (LUAD) remains unclear. Therefore, we set out to explore the prognostic potential of pyroptosis-related genes in LUAD. The pyroptosis-related risk score (PRRS) was developed by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression. We found that PRRS was an independent prognostic factor for LUAD. LUAD patients in the high-PRRS group showed a significantly shorter overall survival (OS) and enriched in cell proliferation-related pathways. Then pathway enrichment analyses, mutation profile, tumor microenvironment, and drug sensitivity analysis were further studied in PRRS stratified LUAD patients. Tumor purity (TP) analyses revealed that L-PRRS LUAD patients had a lower TP, and patients in L-TP + L-PRRS subgroup had the most prolonged OS. Mutation analyses suggested that the L-PRRS LUAD patients had a lower tumor mutation burden (TMB), and patients in H-TMB + L-PRRS subgroup had the most prolonged OS. Drug sensitivity analyses showed that PRRS was significantly negatively correlated with the sensitivity of cisplatin, besarotene, etc., while it was significantly positively correlated with the sensitivity of kin001-135. Eventually, a nomogram was constructed based on PRRS and clinical characters of LUAD. Overall, the pyroptosis-related signature is helpful for prognostic prediction and in guiding treatment for LUAD patients.

Holistic similarity-based prediction of phosphorylation sites for understudied kinases.

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and 'STRING'-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the 'TK', 'Other', 'STE', 'CAMK', 'TKL', 'CMGC', 'AGC', 'CK1' and 'Atypical' groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach.

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

Identification of chondrocyte subpopulations in osteoarthritis using single-cell sequencing analysis.

Osteoarthritis (OA) is the most common joint disease. Previous studies were focused on general functions of chondrocyte population in OA without elucidating the existence of chondrocyte subpopulations. To investigate the heterogeneity of chondrocyte, here we conducted detailed analysis on the single-cell sequencing data of cartilage cells from OA patients. After quality control, unsupervised K-mean clustering identified seven different subpopulations of chondrocytes in OA. Those subpopulations of chondrocytes were nominated based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis: stress-metabolizing chondrocytes (cluster 1), rhythmic chondrocytes (cluster 2), apoptotic chondrocytes (cluster 3), matrix-synthesis-related chondrocytes (cluster 4), developmental chondrocytes (cluster 5), protein-synthesis-related chondrocytes (cluster 6 and 8), and osteogenesis chondrocytes (cluster 7). We further noticed that the stress-metabolizing chondrocytes (cluster 1) were dominant in early stages of cartilage damage with increased metabolic levels inhibiting cartilage tissue degeneration, while the matrix-synthesis-related chondrocytes (cluster 4) were mainly existed in the late stages of cartilage damage which reorganized collagen fibers with type III collagen disrupting the extracellular matrix and further cartilage damages. Besides, we identified genes NFKBIA and TUBB2B as potential markers for the stress-metabolizing chondrocytes and the matrix synthesis related chondrocytes, respectively. Our study identifies different chondrocyte subpopulations in OA, and highlights the potential different functions of chondrocyte subpopulations in the early versus late stages of cartilage damage.

Percutaneous Transforaminal Endoscopic Discectomy Versus Microendoscopic Discectomy for Lumbar Disk Herniation: Five-year Results of a Randomized Controlled Trial.

STUDY DESIGN: A prospective randomized controlled study. OBJECTIVE: To compare the efficacy and safety between percutaneous transforaminal endoscopic discectomy (PTED) and microendoscopic discectomy (MED). SUMMARY OF BACKGROUND DATA: Two kinds of minimally invasive discectomy, PTED and MED, are now widely used for treating lumbar disk herniation (LDH). The long-term comparative results of these two techniques still remained uncertain. MATERIALS AND METHODS: In this single-center, open-label, randomized controlled trial, patients were included if they had persistent signs and symptoms of radiculopathy with corresponding imaging-confirmed LDH and were randomly allocated to PTED or MED groups. The primary outcome was the score of Oswestry Disability Index (ODI) and the secondary outcomes included the score of Medical Outcomes Study 36-Item Short-Form Health Survey bodily pain (SF36-BP) and physical function (SF36-PF), European Quality of Life-Five Dimensions (EQ-5D), Visual Analog Scales for back pain (VAS-back) and leg pain (VAS-leg). RESULTS: A total of 241 patients were accepted to enroll in our randomized controlled trial, of which 119 were randomly assigned to the PTED group, and the rest 122 were assigned to the MED group. A total of 194 out of 241 patients (80.5%) completed the five-year follow-up. PTED group was associated with shorter postoperative in-bed time and length of hospital stay. Both primary and secondary outcomes did not differ significantly between the two treatment groups at each follow-up time point. During the five-year follow-up, seven recurrent cases occurred in PTED and MED groups, respectively. CONCLUSION: Over the five-year follow-up period, PTED and MED were both efficacious in the treatment of LDH. The long-term clinical outcomes and recurrent rates were comparable between the treatment groups. PTED represents a more minimally invasive technique with the advantages of rapid recovery.

MAGE-A3 regulates tumor stemness in gastric cancer through the PI3K/AKT pathway.

Gastric cancer remains a malignant disease of the digestive tract with high mortality and morbidity worldwide. However, due to its complex pathological mechanisms and lack of effective clinical therapies, the survival rate of patients after receiving treatment is not satisfactory. A increasing number of studies have focused on cancer stem cells and their regulatory properties. In this study, we first constructed a co-expression network based on the WGCNA algorithm to identify modules with different degrees of association with tumor stemness indices. After selecting the most positively correlated modules of the stemness index, we performed a consensus clustering analysis on gastric cancer samples and constructed the co-expression network again. We then selected the modules of interest and applied univariate COX regression analysis to the genes in this module for preliminary screening. The results of the screening were then used in LASSO regression analysis to construct a risk prognostic model and subsequently a sixteen-gene model was obtained. Finally, after verifying the accuracy of the module and screening for risk genes, we identified MAGE-A3 as the final study subject. We then performed in vivo and in vitro experiments to verify its effect on tumor stemness and tumour proliferation. Our data supports that MAGE-A3 is a tumor stemness regulator and a potent prognostic biomarker which can help the prediction and treatment of gastric cancer patients.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications.

Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.

A representation and deep learning model for annotating ubiquitylation sentences stating E3 ligase - substrate interaction.

BACKGROUND: Ubiquitylation is an important post-translational modification of proteins that not only plays a central role in cellular coding, but is also closely associated with the development of a variety of diseases. The specific selection of substrate by ligase E3 is the key in ubiquitylation. As various high-throughput analytical techniques continue to be applied to the study of ubiquitylation, a large amount of ubiquitylation site data, and records of E3-substrate interactions continue to be generated. Biomedical literature is an important vehicle for information on E3-substrate interactions in ubiquitylation and related new discoveries, as well as an important channel for researchers to obtain such up to date data. The continuous explosion of ubiquitylation related literature poses a great challenge to researchers in acquiring and analyzing the information. Therefore, automatic annotation of these E3-substrate interaction sentences from the available literature is urgently needed. RESULTS: In this research, we proposed a model based on representation and attention mechanism based deep learning methods, to automatic annotate E3-substrate interaction sentences in biomedical literature. Focusing on the sentences with E3 protein inside, we applied several natural language processing methods and a Long Short-Term Memory (LSTM)-based deep learning classifier to train the model. Experimental results had proved the effectiveness of our proposed model. And also, the proposed attention mechanism deep learning method outperforms other statistical machine learning methods. We also created a manual corpus of E3-substrate interaction sentences, in which the E3 proteins and substrate proteins are also labeled, in order to construct our model. The corpus and model proposed by our research are definitely able to be very useful and valuable resource for advancement of ubiquitylation-related research. CONCLUSION: Having the entire manual corpus of E3-substrate interaction sentences readily available in electronic form will greatly facilitate subsequent text mining and machine learning analyses. Automatic annotating ubiquitylation sentences stating E3 ligase-substrate interaction is significantly benefited from semantic representation and deep learning. The model enables rapid information accessing and can assist in further screening of key ubiquitylation ligase substrates for in-depth studies.

UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase-substrate interactions.

Ubiquitination is an important post-translational modification, which controls protein turnover by labeling malfunctional and redundant proteins for proteasomal degradation, and also serves intriguing non-proteolytic regulatory functions. E3 ubiquitin ligases, whose substrate specificity determines the recognition of target proteins of ubiquitination, play crucial roles in ubiquitin-proteasome system. UbiNet 2.0 is an updated version of the database UbiNet. It contains 3332 experimentally verified E3-substrate interactions (ESIs) in 54 organisms and rich annotations useful for investigating the regulation of ubiquitination and the substrate specificity of E3 ligases. Based on the accumulated ESIs data, the recognition motifs in substrates for each E3 were also identified and a functional enrichment analysis was conducted on the collected substrates. To facilitate the research on ESIs with different categories of E3 ligases, UbiNet 2.0 performed strictly evidence-based classification of the E3 ligases in the database based on their mechanisms of ubiquitin transfer and substrate specificity. The platform also provides users with an interactive tool that can visualize the ubiquitination network of a group of self-defined proteins, displaying ESIs and protein-protein interactions in a graphical manner. The tool can facilitate the exploration of inner regulatory relationships mediated by ubiquitination among proteins of interest. In summary, UbiNet 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about experimentally validated ESIs and a visualized tool for the construction of ubiquitination regulatory networks available at http://awi.cuhk.edu.cn/~ubinet/index.php.

A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra.

Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.

PKC-δ deficiency in B cells displays osteopenia accompanied with upregulation of RANKL expression and osteoclast-osteoblast uncoupling.

PKC-δ is an important molecule for B-cell proliferation and tolerance. B cells have long been recognized to play a part in osteoimmunology and pathological bone loss. However, the role of B cells with PKC-δ deficiency in bone homeostasis and the underlying mechanisms are unknown. We generated mice with PKC-δ deletion selectively in B cells by crossing PKC-δ-loxP mice with CD19-Cre mice. We studied their bone phenotype using micro-CT and histology. Next, immune organs were obtained and analyzed. Western blotting was used to determine the RANKL/OPG ratio in vitro in B-cell cultures, ELISA assay and immunohistochemistry were used to analyze in vivo RANKL/OPG balance in serum and bone sections respectively. Finally, we utilized osteoclastogenesis to study osteoclast function via hydroxyapatite resorption assay, and isolated primary calvaria osteoblasts to investigate osteoblast proliferation and differentiation. We also investigated osteoclast and osteoblast biology in co-culture with B-cell supernatants. We found that mice with PKC-δ deficiency in B cells displayed an osteopenia phenotype in the trabecular and cortical compartment of long bones. In addition, PKC-δ deletion resulted in changes of trabecular bone structure in association with activation of osteoclast bone resorption and decrease in osteoblast parameters. As expected, inactivation of PKC-δ in B cells resulted in changes in spleen B-cell number, function, and distribution. Consistently, the RANKL/OPG ratio was elevated remarkably in B-cell culture, in the serum and in bone specimens after loss of PKC-δ in B cells. Finally, in vitro analysis revealed that PKC-δ ablation suppressed osteoclast differentiation and function but co-culture with B-cell supernatant reversed the suppression effect, as well as impaired osteoblast proliferation and function, indicative of osteoclast-osteoblast uncoupling. In conclusion, PKC-δ plays an important role in the interplay between B cells in the immune system and bone cells in the pathogenesis of bone lytic diseases.

Conditional Knockout of PKC-δ in Osteoclasts Favors Bone Mass Accrual in Males Due to Decreased Osteoclast Function.

Protein kinase C delta (PKC-δ) functions as an important regulator in bone metabolism. However, the precise involvement of PKC-δ in the regulation of osteoclasts remains elusive. We generated an osteoclast specific PKC-δ knockout mouse strain to investigate the function of PKC-δ in osteoclast biology. Bone phenotype was investigated using microcomputed tomography. Osteoclast and osteoblast parameters were assessed using bone histomorphometry, and analysis of osteoclast formation and function with osteoclastogensis and hydroxyapatite resorption assays. The molecular mechanisms by which PKC-δ regulated osteoclast function were dissected by Western Blotting, TUNEL assay, transfection and transcriptome sequencing. We found that ablation of PKC-δ in osteoclasts resulted in an increase in trabecular and cortical bone volume in male mice, however, the bone mass phenotype was not observed in female mice. This was accompanied by decreased osteoclast number and surface, and Cathepsin-K protein levels in vivo, as well as decreased osteoclast formation and resorption in vitro in a male-specific manner. PKC-δ regulated androgen receptor transcription by binding to its promoter, moreover, PKC-δ conditional knockout did not increase osteoclast apoptosis but increased MAPK signaling and enhanced androgen receptor transcription and expression, finally leding to significant alterations in gene expression and signaling changes related to extracellular matrix proteins specifically in male mice. In conclusion, PKC-δ plays an important role in osteoclast formation and function in a male-specific manner. Our work reveals a previously unknown target for treatment of gender-related bone diseases.

Casein kinase 1 epsilon facilitates cartilage destruction in osteoarthritis through JNK pathway.

Osteoarthritis (OA) is a high-morbidity skeletal disease worldwide and the exact mechanisms underlying OA pathogenesis are not fully understood. Casein kinase 1 epsilon (CK1ε) is a serine/threonine protein kinase, but its relationship with OA is still unknown. We demonstrated that CK1ε was upregulated in articular cartilage of human patients with OA and mice with experimentally induced OA. Activity of CK1ε, demonstrated by analysis of phosphorylated substrates, was significantly elevated in interleukin (IL)-1ß-induced OA-mimicking chondrocytes. CK1ε inhibitor or CK1ε short hairpin RNA (shRNA) partially blocked matrix metalloproteinase (MMP) expression by primary chondrocytes induced by IL-1ß, and also inhibited cartilage destruction in knee joints of experimental OA model mice. Conversely, overexpression of CK1ε promoted chondrocyte catabolism. Previous studies indicated that CK1ε was involved in canonical Wnt/ß-catenin signaling and noncanonical Wnt/c-Jun N-terminal kinase (JNK) signaling pathway. Interestingly, the activity of JNK but not ß-catenin decreased after CK1ε knockdown in IL-1ß-treated chondrocytes in vitro, and JNK inhibition reduced MMP expression in chondrocytes overexpressing CK1ε, which illustrated that CK1ε-mediated OA was based on JNK pathway. In conclusion, our results demonstrate that CK1ε promotes OA development, and inhibition of CK1ε could be a potential strategy for OA treatment in the future.

Fangchinoline protects against bone loss in OVX mice via inhibiting osteoclast formation, bone resorption and RANKL-induced signaling.

Osteoporosis is a disease characterized by abnormally increased formation and function of osteoclasts. Anti-RANKL treatment using natural medicine is a potential therapy for osteoporosis. Here, we studied the effect of fangchinoline, which is extracted from the root of Stephania tetrandra S. Moore, on osteoclast formation and function. We found that fangchinoline inhibited osteoclastogenesis at doses of 0.5 and 1 µM. In addition, we also examined the mechanism of the inhibitory effect of fangchinoline on osteoclasts. We found that fangchinoline down regulated NFATc1 activity and expression. However, fangchinoline did not affect IκBα degradation and MAPK pathways. In addition, we also found that fangchinoline could protect against bone loss in OVX mice. Taken together, fangchinoline may be a potential compound for osteoporosis.

Amyloid ß peptide promotes bone formation by regulating Wnt/ß-catenin signaling and the OPG/RANKL/RANK system.

BACKGROUND: Amyloid ß peptide (Aß) is involved in osteoporosis, but the effects of Aß on osteoblast and bone formation remain unclear. In this study, we investigated the effect of Aß on bone formation. METHODS: An animal model of osteoporosis was established by ovariectomy in C57BL/6 mice. The mice received intraperitoneal injection of Aß. The effect of Aß on the osteogenic differentiation of human bone marrow stromal stem cells (hBMSCs) and differentiation of both pre-osteoblasts and pre-osteoclasts in a co-culture system were investigated. RESULTS: In the animal study, intraperitoneal injection of Aß for 8 weeks promoted early and late osteogenic differentiation of hBMSCs. Aß treatment significantly elevated osterix+ (osteoblastic) cells but decreased TRAP+ cells (osteoclasts) in the distal femur bone. In vitro study showed that Aß treatment significantly enhanced matrix mineralization and osteogenic markers (Runx2 and osteocalcin). Aß treatment activated Wnt/ß-catenin signaling in hBMSCs. The effect of Aß was blocked by DKK1 (a Wnt/ß-catenin inhibitor) treatment. In the co-culture system, Aß treatment significantly increased the ALP activities of MC3T3-E1 cells (pre-osteoblasts) but reduced the TRAP+ RAW264.7 cells (pre-osteoclasts). Aß treatment upregulated TCF1 and OPG proteins in MC3T3-E1 cells. Aß treatment upregulated IκB-α but downregulated NFATc1protein in RAW264.7 cells. These effects were blocked by XAV-939 (a Wnt signaling antagonist), and then rescued by additional Wnt3a (a Wnt agonist). CONCLUSION: Aß treatment simultaneously promoted osteogenic differentiation via Wnt/ß-catenin signaling, and inhibited osteoclasts differentiation via the OPG/RANKL/RANK system, suggesting Aß is a positive regulator of osteoblast differentiation and bone formation.