Osteoclasts recycle via osteomorphs during RANKL-stimulated bone resorption.

Osteoclasts are large multinucleated bone-resorbing cells formed by the fusion of monocyte/macrophage-derived precursors that are thought to undergo apoptosis once resorption is complete. Here, by intravital imaging, we reveal that RANKL-stimulated osteoclasts have an alternative cell fate in which they fission into daughter cells called osteomorphs. Inhibiting RANKL blocked this cellular recycling and resulted in osteomorph accumulation. Single-cell RNA sequencing showed that osteomorphs are transcriptionally distinct from osteoclasts and macrophages and express a number of non-canonical osteoclast genes that are associated with structural and functional bone phenotypes when deleted in mice. Furthermore, genetic variation in human orthologs of osteomorph genes causes monogenic skeletal disorders and associates with bone mineral density, a polygenetic skeletal trait. Thus, osteoclasts recycle via osteomorphs, a cell type involved in the regulation of bone resorption that may be targeted for the treatment of skeletal diseases.

Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease.

To identify key gene expression pathways altered with infection of the novel coronavirus SARS-CoV-2, we performed the largest comparative genomic and transcriptomic analysis to date. We compared the novel pandemic coronavirus SARS-CoV-2 with SARS-CoV and MERS-CoV, as well as influenza A strains H1N1, H3N2 and H5N1. Phylogenetic analysis confirms that SARS-CoV-2 is closely related to SARS-CoV at the level of the viral genome. RNAseq analyses demonstrate that human lung epithelial cell responses to SARS-CoV-2 infection are distinct. Extensive Gene Expression Omnibus literature screening and drug predictive analyses show that SARS-CoV-2 infection response pathways are closely related to those of SARS-CoV and respiratory syncytial virus infections. We validated SARS-CoV-2 infection response genes as disease-associated using Kaplan-Meier survival estimates in lung disease patient data. We also analysed COVID-19 patient peripheral blood samples, which identified signalling pathway concordance between the primary lung cell and blood cell infection responses.

Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected individuals that have hypertension or cardiovascular comorbidities have an elevated risk of serious coronavirus disease 2019 (COVID-19) disease and high rates of mortality but how COVID-$19$ and cardiovascular diseases interact are unclear. We therefore sought to identify novel mechanisms of interaction by identifying genes with altered expression in SARS-CoV-$2$ infection that are relevant to the pathogenesis of cardiovascular disease and hypertension. Some recent research shows the SARS-CoV-$2$ uses the angiotensin converting enzyme-$2$ (ACE-$2$) as a receptor to infect human susceptible cells. The ACE2 gene is expressed in many human tissues, including intestine, testis, kidneys, heart and lungs. ACE2 usually converts Angiotensin I in the renin-angiotensin-aldosterone system to Angiotensin II, which affects blood pressure levels. ACE inhibitors prescribed for cardiovascular disease and hypertension may increase the levels of ACE-$2$, although there are claims that such medications actually reduce lung injury caused by COVID-$19$. We employed bioinformatics and systematic approaches to identify such genetic links, using messenger RNA data peripheral blood cells from COVID-$19$ patients and compared them with blood samples from patients with either chronic heart failure disease or hypertensive diseases. We have also considered the immune response genes with elevated expression in COVID-$19$ to those active in cardiovascular diseases and hypertension. Differentially expressed genes (DEGs) common to COVID-$19$ and chronic heart failure, and common to COVID-$19$ and hypertension, were identified; the involvement of these common genes in the signalling pathways and ontologies studied. COVID-$19$ does not share a large number of differentially expressed genes with the conditions under consideration. However, those that were identified included genes playing roles in T cell functions, toll-like receptor pathways, cytokines, chemokines, cell stress, type 2 diabetes and gastric cancer. We also identified protein-protein interactions, gene regulatory networks and suggested drug and chemical compound interactions using the differentially expressed genes. The result of this study may help in identifying significant targets of treatment that can combat the ongoing pandemic due to SARS-CoV-$2$ infection.

Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases.

With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions.

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.

A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients.

The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.

Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality.

Ovarian cancer (OC) is a common cause of cancer death among women worldwide, so there is a pressing need to identify factors influencing OC mortality. Much OC patient clinical data is publicly accessible via the Broad Institute Cancer Genome Atlas (TCGA) datasets which include patient age, cancer site, stage and subtype and patient survival, as well as OC gene transcription profiles. These allow studies correlating OC patient survival (and other clinical variables) with gene expression to identify new OC biomarkers to predict patient mortality. We integrated clinical and tissue transcriptome data from patients available from the TCGA portal. We determined OC mRNA expression levels (compared to normal ovarian tissue) of 41 genes already implicated in OC progression, and assessed how their OC tissue expression levels predicts patient survival. We employed Cox Proportional Hazard regression models to analyse clinical factors and transcriptomic information to determine the relative effects on survival that is associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age and ovary tumour site significantly correlated with patient survival. The univariate analysis also confirmed significant differences in patient survival time when altered transcription levels of TLR4, BSCL2, CDH1, ERBB2, and SCGB2A1 were evident, while multivariate analysis that considered the 41 genes simultaneously revealed a significant relationship of survival with TLR4, BSCL2, CDH1, ERBB2 and PTPRE genes. However, analyses that considered all 41 genes with clinical variables together identified genes TLR4, BSCL2, CDH1, ERBB2, BRCA2 and SCGB2A1 as independently related to survival in OC. These studies indicate that the latter genes influence OC patient survival, i.e., expression levels of these genes provide mechanistic and predictive information in addition to that of the clinical traits. Our study provides strong evidence that these genes are important prognostic indicators of patient survival that give clues to biological processes that underlie OC progression and mortality.

The selective TrkA agonist, gambogic amide, promotes osteoblastic differentiation and improves fracture healing in mice.

OBJECTIVES: To study effects of the selective TrkA agonist, gambogic amide (GA), on fracture healing in mice and on an osteoprogenitor cell line in vitro. METHODS: Mice were given bilateral fibular fractures and treated for two weeks with vehicle or 1 mg/kg/day GA and euthanized at 14-, 21-, and 42-days post-fracture. Calluses were analysed by micro-computed tomography (µCT), three-point bending and histology. For RT-PCR analyses, Kusa O cells were treated with 0.5nM of GA or vehicle for 3, 7, and 14 days, while for mineralization assessment, cells were treated for 21 days. RESULTS: µCT analysis found that 21-day GA-treated calluses had both decreased tissue volume (p<0.05) and bone surface (p<0.05) and increased fractional bone volume (p<0.05) compared to controls. Biomechanical analyses of 42-day calluses revealed that GA treatment increased stiffness per unit area by 53% (p<0.01) and load per unit area by 52% (p<0.01). GA treatment increased Kusa O gene expression of alkaline phosphatase and osteocalcin (p<0.05) by 14 days as well as mineralization at 21 days (p<0.05). CONCLUSIONS: GA treatment appeared to have a beneficial effect on fracture healing at 21- and 42-days post-fracture. The exact mechanism is not yet understood but may involve increased osteoblastic differentiation and matrix mineralization.

Discovering Biomarkers and Pathways Shared by Alzheimer's Disease and Ischemic Stroke to Identify Novel Therapeutic Targets.

Background and objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein-protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein-drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.

Eriodictyol Inhibits RANKL-Induced Osteoclast Formation and Function Via Inhibition of NFATc1 Activity.

Receptor activator of nuclear factor kappa-B ligand (RANKL) induces differentiation and function of osteoclasts through triggering multiple signaling cascades, including NF-κB, MAPK, and Ca(2+) -dependent signals, which induce and activate critical transcription factor NFATc1. Targeting these signaling cascades may serve as an effective therapy against osteoclast-related diseases. Here, by screening a panel of natural plant extracts with known anti-inflammatory, anti-tumor, or anti-oxidant properties for possible anti-osteoclastogenic activities we identified Eriodictyol. This flavanone potently suppressed RANKL-induced osteoclastogenesis and bone resorption in a dose-dependent manner without detectable cytotoxicity, suppressing RANKL-induced NF-κB, MAPK, and Ca(2+) signaling pathways. Eriodictyol also strongly inhibited RANKL-induction of c-Fos levels (a critical component of AP-1 transcription factor required by osteoclasts) and subsequent activation of NFATc1, concomitant with reduced expression of osteoclast specific genes including cathepsin K (Ctsk), V-ATPase-d2 subunit, and tartrate resistant acid phosphatase (TRAcP/Acp5). Taken together, these data provide evidence that Eriodictyol could be useful for the prevention and treatment of osteolytic disorders associated with abnormally increased osteoclast formation and function. J. Cell. Physiol. 231: 1983-1993, 2016. © 2016 Wiley Periodicals, Inc.

Molecular stress-inducing compounds increase osteoclast formation in a heat shock factor 1 protein-dependent manner.

Many anticancer therapeutic agents cause bone loss, which increases the risk of fractures that severely reduce quality of life. Thus, in drug development, it is critical to identify and understand such effects. Anticancer therapeutic and HSP90 inhibitor 17-(allylamino)-17-demethoxygeldanamycin (17-AAG) causes bone loss by increasing osteoclast formation, but the mechanism underlying this is not understood. 17-AAG activates heat shock factor 1 (Hsf1), the master transcriptional regulator of heat shock/cell stress responses, which may be involved in this negative action of 17-AAG upon bone. Using mouse bone marrow and RAW264.7 osteoclast differentiation models we found that HSP90 inhibitors that induced a heat shock response also enhanced osteoclast formation, whereas HSP90 inhibitors that did not (including coumermycin A1 and novobiocin) did not affect osteoclast formation. Pharmacological inhibition or shRNAmir knockdown of Hsf1 in RAW264.7 cells as well as the use of Hsf1 null mouse bone marrow cells demonstrated that 17-AAG-enhanced osteoclast formation was Hsf1-dependent. Moreover, ectopic overexpression of Hsf1 enhanced 17-AAG effects upon osteoclast formation. Consistent with these findings, protein levels of the essential osteoclast transcription factor microphthalmia-associated transcription factor were increased by 17-AAG in an Hsf1-dependent manner. In addition to HSP90 inhibitors, we also identified that other agents that induced cellular stress, such as ethanol, doxorubicin, and methotrexate, also directly increased osteoclast formation, potentially in an Hsf1-dependent manner. These results, therefore, indicate that cellular stress can enhance osteoclast differentiation via Hsf1-dependent mechanisms and may significantly contribute to pathological and therapeutic related bone loss.

Protein kinase C inhibitor, GF109203X attenuates osteoclastogenesis, bone resorption and RANKL-induced NF-κB and NFAT activity.

Osteolytic bone diseases are characterized by excessive osteoclast formation and activation. Protein kinase C (PKC)-dependent pathways regulate cell growth, differentiation and apoptosis in many cellular systems, and have been implicated in cancer development and osteoclast formation. A number of PKC inhibitors with anti-cancer properties have been developed, but whether they might also influence osteolysis (a common complication of bone invading cancers) is unclear. We studied the effects of the PKC inhibitor compound, GF109203X on osteoclast formation and activity, processes driven by receptor activator of NFκB ligand (RANKL). We found that GF109203X strongly and dose dependently suppresses osteoclastogenesis and osteoclast activity in RANKL-treated primary mouse bone marrow cells. Consistent with this GF109203X reduced expression of key osteoclastic genes, including cathepsin K, calcitonin receptor, tartrate resistant acid phosphatase (TRAP) and the proton pump subunit V-ATPase-d2 in RANKL-treated primary mouse bone marrow cells. Expression of these proteins is dependent upon RANKL-induced NF-κB and NFAT transcription factor actions; both were reduced in osteoclast progenitor populations by GF109203X treatment, notably NFATc1 levels. Furthermore, we showed that GF109203X inhibits RANKL-induced calcium oscillation. Together, this study shows GF109203X may block osteoclast functions, suggesting that pharmacological blockade of PKC-dependent pathways has therapeutic potential in osteolytic diseases.

Macrophage migration inhibitory factor is essential for osteoclastogenic mechanisms in vitro and in vivo mouse model of arthritis.

Macrophage migration inhibitory factor (MIF) enhances activation of leukocytes, endothelial cells and fibroblast-like synoviocytes (FLS), thereby contributing to the pathogenesis of rheumatoid arthritis (RA). A MIF promoter polymorphism in RA patients resulted in higher serum MIF concentration and worsens bone erosion; controversially current literature reported an inhibitory role of MIF in osteoclast formation. The controversial suggested that the precise role of MIF and its putative receptor CD74 in osteoclastogenesis and RA bone erosion, mediated by locally formed osteoclasts in response to receptor activator of NF-κB ligand (RANKL), is unclear. We reported that in an in vivo K/BxN serum transfer arthritis, reduced clinical and histological arthritis in MIF(-/-) and CD74(-/-) mice were accompanied by a virtual absence of osteoclasts at the synovium-bone interface and reduced osteoclast-related gene expression. Furthermore, in vitro osteoclast formation and osteoclast-related gene expression were significantly reduced in MIF(-/-) cells via decreasing RANKL-induced phosphorylation of NF-κB-p65 and ERK1/2. This was supported by a similar reduction of osteoclastogenesis observed in CD74(-/-) cells. Furthermore, a MIF blockade reduced RANKL-induced osteoclastogenesis via deregulating RANKL-mediated NF-κB and NFATc1 transcription factor activation. These data indicate that MIF and CD74 facilitate RANKL-induced osteoclastogenesis, and suggest that MIF contributes directly to bone erosion, as well as inflammation, in RA.

Berberine Sulfate Attenuates Osteoclast Differentiation through RANKL Induced NF-κB and NFAT Pathways.

Osteoporosis, a metabolic bone disease, is characterized by an excessive formation and activation of osteoclasts. Anti-catabolic treatment using natural compounds has been proposed as a potential therapeutic strategy against the osteoclast related osteolytic diseases. In this study, the activity of berberine sulfate (an orally available form of berberine) on osteoclast differentiation and its underlying molecular mechanisms of action were investigated. Using bone marrow macrophages (BMMs) derived osteoclast culture system, we showed that berberine sulfate at the dose of 0.25, 0.5 and 1 µM significantly inhibited the formation of osteoclasts. Notably, berberine sulfate at these doses did not affect the BMM viability. In addition, we observed that berberine sulfate inhibited the expression of osteoclast marker genes, including cathepsin K (Ctsk), nuclear factor of activated T cells cytoplasmic 1 (NFATc1), tartrate resistant acid phosphatase (TRAcP, Acp5) and Vacuolar-type H+-ATPase V0 subunit D2 (V-ATPase d2). Luciferase reporter gene assay and Western blot analysis further revealed that berberine sulfate inhibits receptor for activation of nuclear factor ligand (RANKL)-induced NF-κB and NFAT activity. Taken together, our results suggest that berberine sulfate is a natural compound potentially useful for the treatment of osteoporosis.

HSP90 inhibitors enhance differentiation and MITF (microphthalmia transcription factor) activity in osteoclast progenitors.

The HSP90 (heat-shock protein 90) inhibitor 17-AAG (17-allylamino-demethoxygeldanamycin) increases osteoclast formation both in vitro and in vivo, an action that can enhance cancer invasion and growth in the bone microenvironment. The cellular mechanisms through which 17-AAG exerts this action are not understood. Thus we sought to clarify the actions of 17-AAG on osteoclasts and determine whether other HSP90 inhibitors had similar properties. We determined that 17-AAG and the structurally unrelated HSP90 inhibitors CCT018159 and NVP-AUY922 dose-dependently increased RANKL [receptor activator of NF-κB (nuclear factor κB) ligand]-stimulated osteoclastogenesis in mouse bone marrow and pre-osteoclastic RAW264.7 cell cultures. Moreover, 17-AAG also enhanced RANKL- and TNF (tumour necrosis factor)-elicited osteoclastogenesis, but did not affect RANKL-induced osteoclast survival, suggesting that only differentiation mechanisms are targeted. 17-AAG affected the later stages of progenitor maturation (after 3 days of incubation), whereas the osteoclast formation enhancer TGFß (transforming growth factor ß) acted prior to this, suggesting different mechanisms of action. In studies of RANKL-elicited intracellular signalling, 17-AAG treatment did not increase c-Fos or NFAT (nuclear factor of activated T-cells) c1 protein levels nor did 17-AAG increase activity in luciferase-based NF-κB- and NFAT-response assays. In contrast, 17-AAG treatment (and RANKL treatment) increased both MITF (microphthalmia-associated transcription factor) protein levels and MITF-dependent vATPase-d2 (V-type proton ATPase subunit d2) gene promoter activity. These results indicate that HSP90 inhibitors enhance osteoclast differentiation in an NFATc1-independent manner that involves elevated MITF levels and activity.

CRUX, a platform for visualising, exploring and analysing cancer genome cohort data.

To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).