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).

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL . It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.

Machine learning-based statistical analysis for early stage detection of cervical cancer.

Cervical cancer (CC) is the most common type of cancer in women and remains a significant cause of mortality, particularly in less developed countries, although it can be effectively treated if detected at an early stage. This study aimed to find efficient machine-learning-based classifying models to detect early stage CC using clinical data. We obtained a Kaggle data repository CC dataset which contained four classes of attributes including biopsy, cytology, Hinselmann, and Schiller. This dataset was split into four categories based on these class attributes. Three feature transformation methods, including log, sine function, and Z-score were applied to these datasets. Several supervised machine learning algorithms were assessed for their performance in classification. A Random Tree (RT) algorithm provided the best classification accuracy for the biopsy (98.33%) and cytology (98.65%) data, whereas Random Forest (RF) and Instance-Based K-nearest neighbor (IBk) provided the best performance for Hinselmann (99.16%), and Schiller (98.58%) respectively. Among the feature transformation methods, logarithmic gave the best performance for biopsy datasets whereas sine function was superior for cytology. Both logarithmic and sine functions performed the best for the Hinselmann dataset, while Z-score was best for the Schiller dataset. Various Feature Selection Techniques (FST) methods were applied to the transformed datasets to identify and prioritize important risk factors. The outcomes of this study indicate that appropriate system design and tuning, machine learning methods and classification are able to detect CC accurately and efficiently in its early stages using clinical data.

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes, and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.

Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer.

Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.

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.

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.

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 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.

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.

Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors.

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high-calorie diet and high-fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein-protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression.

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma.

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

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.

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.

Genetic effects of welding fumes on the development of respiratory system diseases.

BACKGROUND: The welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. METHODS: We developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. RESULTS: We observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. CONCLUSIONS: Our network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

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.

Melphalan modifies the bone microenvironment by enhancing osteoclast formation.

Melphalan is a cytotoxic chemotherapy used to treat patients with multiple myeloma (MM). Bone resorption by osteoclasts, by remodeling the bone surface, can reactivate dormant MM cells held in the endosteal niche to promote tumor development. Dormant MM cells can be reactivated after melphalan treatment; however, it is unclear whether melphalan treatment increases osteoclast formation to modify the endosteal niche. Melphalan treatment of mice for 14 days decreased bone volume and the endosteal bone surface, and this was associated with increases in osteoclast numbers. Bone marrow cells (BMC) from melphalan-treated mice formed more osteoclasts than BMCs from vehicle-treated mice, suggesting that osteoclast progenitors were increased. Melphalan also increased osteoclast formation in BMCs and RAW264.7 cells in vitro, which was prevented with the cell stress response (CSR) inhibitor KNK437. Melphalan also increased expression of the osteoclast regulator the microphthalmia-associated transcription factor (MITF), but not nuclear factor of activated T cells 1 (NFATc1). Melphalan increased expression of MITF-dependent cell fusion factors, dendritic cell-specific transmembrane protein (Dc-stamp) and osteoclast-stimulatory transmembrane protein (Oc-stamp) and increased cell fusion. Expression of osteoclast stimulator receptor activator of NFκB ligand (RANKL) was unaffected by melphalan treatment. These data suggest that melphalan stimulates osteoclast formation by increasing osteoclast progenitor recruitment and differentiation in a CSR-dependent manner. Melphalan-induced osteoclast formation is associated with bone loss and reduced endosteal bone surface. As well as affecting bone structure this may contribute to dormant tumor cell activation, which has implications for how melphalan is used to treat patients with MM.

A RhoA-FRET Biosensor Mouse for Intravital Imaging in Normal Tissue Homeostasis and Disease Contexts.

The small GTPase RhoA is involved in a variety of fundamental processes in normal tissue. Spatiotemporal control of RhoA is thought to govern mechanosensing, growth, and motility of cells, while its deregulation is associated with disease development. Here, we describe the generation of a RhoA-fluorescence resonance energy transfer (FRET) biosensor mouse and its utility for monitoring real-time activity of RhoA in a variety of native tissues in vivo. We assess changes in RhoA activity during mechanosensing of osteocytes within the bone and during neutrophil migration. We also demonstrate spatiotemporal order of RhoA activity within crypt cells of the small intestine and during different stages of mammary gestation. Subsequently, we reveal co-option of RhoA activity in both invasive breast and pancreatic cancers, and we assess drug targeting in these disease settings, illustrating the potential for utilizing this mouse to study RhoA activity in vivo in real time.

Neohesperidin suppresses osteoclast differentiation, bone resorption and ovariectomised-induced osteoporosis in mice.

Excessive bone resorption by osteoclasts plays an important role in osteoporosis. Bone loss occurs in ovariectomised (OVX) mice in a similar manner to that in humans, so this model is suitable for evaluating potential new therapies for osteoporosis. Neohesperidin (NE) is a flavonoid compound isolated from citrus fruits. Its role in bone metabolism is unknown. In this study we found that neohesperidin inhibits osteoclast differentiation, bone resorption and the expression of osteoclast marker genes, tartrate-resistant acid phosphatase and cathepsin K. In addition, neohesperidin inhibited receptor activator of NF-κB ligand (RANKL)-induced activation of NF-κB, and the degradation of inhibitor of kappa B-alpha (IκBα). Furthermore, neohesperidin inhibited RANKL induction of nuclear factor of activated T-cells (NFAT) and calcium oscillations. In vivo treatment of ovariectomised mice with neohesperidin protected against bone loss in mice. The results suggest neohesperidin has anti-osteoclastic effects in vitro and in vivo and possesses therapeutic potential as a natural anti-catabolic treatment in osteoporosis.