Abnormalities in Osteoclastogenesis and Decreased Tumorigenesis in Mice Deficient for Ovarian Cancer G Protein-Coupled Receptor 1

Ovarian cancer G protein-coupled receptor 1 (OGR1) has been shown to be a proton sensing receptor in vitro. We have shown that OGR1 functions as a tumor metastasis suppressor gene when it is over-expressed in human prostate cancer cells in vivo. To examine the physiological functions of OGR1, we generated conditional OGR1 deficient mice by homologous recombination. OGR1 deficient mice were viable and upon gross-inspection appeared normal. Consistent with in vitro studies showing that OGR1 is involved in osteoclastogenesis, reduced osteoclasts were detected in OGR1 deficient mice. A pH-dependent osteoclasts survival effect was also observed. However, overall abnormality in the bones of these animals was not observed. In addition, melanoma cell tumorigenesis was significantly inhibited in OGR1 deficient mice. OGR1 deficient mice in the mixed background produced significantly less peritoneal macrophages when stimulated with thioglycolate. These macrophages also showed altered extracellular signal-regulated kinases (ERK) activation and nitric oxide (NO) production in response to lipopolysaccharide. OGR1-dependent pH responses assessed by cAMP production and cell survival in macrophages or brown fat cells were not observed, presumably due to the presence of other proton sensing receptors in these cells. Our results indicate that OGR1's role in osteoclastogenesis is not strong enough to affect overall bone development and its role in tumorigenesis warrants further investigation. The mice generated can be potentially used for several disease models, including cancers or osteoclast-related diseases

Event-based record linkage in health and aged care services data: a methodological innovation

<p>Abstract</p> <p>Background</p> <p>The interface between acute hospital care and residential aged care has long been recognised as an important issue in aged care services research in Australia. However, existing national data provide very poor information on the movements of clients between the two sectors. Nevertheless, there are national data sets which separately contain data on individuals' hospital episodes and stays in residential aged care, so that linking the two data sets–if feasible–would provide a valuable resource for examining relationships between the two sectors. As neither name nor common person identifiers are available on the data sets, other information needs to be used to link events relating to inter-sector movement.</p> <p>Methods</p> <p>Event-based matching using limited demographic data in conjunction with event dates to match events in two data sets provides a possible method for linking related events. The authors develop a statistical model for examining the likely prevalence of false matches, and consequently the number of true matches, among achieved matches when using anonymous event-based record linkage to identify transition events.</p> <p>Results</p> <p>Theoretical analysis shows that for event-based matching the prevalence of false matches among achieved matches (a) declines as the events of interest become rarer, (b) declines as the number of matches increases, and (c) increases with the size of the population within which matching is taking place. The method also facilitates the examination of the trade-off between false matches and missed matches when relaxing or tightening linkage criteria.</p> <p>Conclusion</p> <p>Event-based record linkage is a method for linking related transition events using event dates and basic demographic variables (other than name or person identifier). The likely extent of false links among achieved links depends on the two event rates, the match rate and population size. Knowing these, it is possible to gauge whether, for a particular study, event-based linkage could provide a useful tool for examining movements. Analysis shows that there is a range of circumstances in which event-based record linkage could be applied to two event-level databases to generate a linked database useful for transition analysis.</p

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

A method for detergent-free isolation of membrane proteins in their local lipid environment.

Despite the great importance of membrane proteins, structural and functional studies of these proteins present major challenges. A significant hurdle is the extraction of the functional protein from its natural lipid membrane. Traditionally achieved with detergents, purification procedures can be costly and time consuming. A critical flaw with detergent approaches is the removal of the protein from the native lipid environment required to maintain functionally stable protein. This protocol describes the preparation of styrene maleic acid (SMA) co-polymer to extract membrane proteins from prokaryotic and eukaryotic expression systems. Successful isolation of membrane proteins into SMA lipid particles (SMALPs) allows the proteins to remain with native lipid, surrounded by SMA. We detail procedures for obtaining 25 g of SMA (4 d); explain the preparation of protein-containing SMALPs using membranes isolated from Escherichia coli (2 d) and control protein-free SMALPS using E. coli polar lipid extract (1-2 h); investigate SMALP protein purity by SDS-PAGE analysis and estimate protein concentration (4 h); and detail biophysical methods such as circular dichroism (CD) spectroscopy and sedimentation velocity analytical ultracentrifugation (svAUC) to undertake initial structural studies to characterize SMALPs (∼2 d). Together, these methods provide a practical tool kit for those wanting to use SMALPs to study membrane proteins

Bone Marrow Derived Mesenchymal Stem Cells Inhibit Inflammation and Preserve Vascular Endothelial Integrity in the Lungs after Hemorrhagic Shock

Hemorrhagic shock (HS) and trauma is currently the leading cause of death in young adults worldwide. Morbidity and mortality after HS and trauma is often the result of multi-organ failure such as acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), conditions with few therapeutic options. Bone marrow derived mesenchymal stem cells (MSCs) are a multipotent stem cell population that has shown therapeutic promise in numerous pre-clinical and clinical models of disease. In this paper, in vitro studies with pulmonary endothelial cells (PECs) reveal that conditioned media (CM) from MSCs and MSC-PEC co-cultures inhibits PEC permeability by preserving adherens junctions (VE-cadherin and β-catenin). Leukocyte adhesion and adhesion molecule expression (VCAM-1 and ICAM-1) are inhibited in PECs treated with CM from MSC-PEC co-cultures. Further support for the modulatory effects of MSCs on pulmonary endothelial function and inflammation is demonstrated in our in vivo studies on HS in the rat. In a rat “fixed volume” model of mild HS, we show that MSCs administered IV potently inhibit systemic levels of inflammatory cytokines and chemokines in the serum of treated animals. In vivo MSCs also inhibit pulmonary endothelial permeability and lung edema with concurrent preservation of the vascular endothelial barrier proteins: VE-cadherin, Claudin-1, and Occludin-1. Leukocyte infiltrates (CD68 and MPO positive cells) are also decreased in lungs with MSC treatment. Taken together, these data suggest that MSCs, acting directly and through soluble factors, are potent stabilizers of the vascular endothelium and inflammation. These data are the first to demonstrate the therapeutic potential of MSCs in HS and have implications for the potential use of MSCs as a cellular therapy in HS-induced lung injury

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa

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Refined histopathological predictors of BRCA1 and BRCA2 mutation status: A large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia

Introduction: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling. Methods: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the