Growth inhibition of thyroid follicular cell-derived cancers by the opioid growth factor (OGF) - opioid growth factor receptor (OGFr) axis

<p>Abstract</p> <p>Background</p> <p>Carcinoma of the thyroid gland is an uncommon cancer, but the most frequent malignancy of the endocrine system. Most thyroid cancers are derived from the follicular cell. Follicular carcinoma (FTC) is considered more malignant than papillary thyroid carcinoma (PTC), and anaplastic thyroid cancer (ATC) is one of the most lethal human cancers. Opioid Growth Factor (OGF; chemical term - [Met⁵]-enkephalin) and its receptor, OGFr, form an inhibitory axis regulating cell proliferation. Both the peptide and receptor have been detected in a wide variety of cancers, and OGF is currently used clinically as a biotherapy for some non-thyroid neoplasias. This study addressed the question of whether the OGF-OGFr axis is present and functional in human thyroid follicular cell - derived cancer.</p> <p>Methods</p> <p>Utilizing human ATC (KAT-18), PTC (KTC-1), and FTC (WRO 82-1) cell lines, immunohistochemistry was employed to ascertain the presence and location of OGF and OGFr. The growth characteristics in the presence of OGF or the opioid antagonist naltrexone (NTX), and the specificity of opioid peptides for proliferation of ATC, were established in KAT-18 cells. Dependence on peptide and receptor were investigated using neutralization studies with antibodies and siRNA experiments, respectively. The mechanism of peptide action on DNA synthesis and cell survival was ascertained. The ubiquity of the OGF-OGFr axis in thyroid follicular cell-derived cancer was assessed in KTC-1 (PTC) and WRO 82-1 (FTC) tumor cells.</p> <p>Results</p> <p>OGF and OGFr were present in KAT-18 cells. Concentrations of 10^-6M OGF inhibited cell replication up to 30%, whereas NTX increased cell growth up to 35% relative to cultures treated with sterile water. OGF treatment reduced cell number by as much as 38% in KAT-18 ATC in a dose-dependent and receptor-mediated manner. OGF antibodies neutralized the inhibitory effects of OGF, and siRNA knockdown of OGFr negated growth inhibition by OGF. Cell survival was not altered by OGF, but DNA synthesis as recorded by BrdU incorporation was depressed by 28% in OGF-treated cultures compared to those exposed to sterile water. The OGF-OGFr axis was detected and functional in PTC (KTC-1) and FTC (WRO 82-1) cell lines.</p> <p>Conclusion</p> <p>These data suggest that OGF and OGFr are present in follicular-derived thyroid cancers, and that OGF serves in a tonically active inhibitory manner to maintain homeostasis of cell proliferation. These results may provide a biotherapeutic strategy in the treatment of these cancers.</p

Time series modeling for syndromic surveillance

BACKGROUND: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. METHODS: Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. RESULTS: Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. CONCLUSIONS: Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization

Single-nucleotide polymorphism associations with preterm delivery: a case-control replication study and meta-analysis

BackgroundThe aim of this study was to replicate single-nucleotide polymorphism (SNP) associations with preterm birth (PTB; birth at MethodsSpontaneous PTB cases and controls were selected from an existing cohort. Candidate SNPs were taken from an existing genotype panel. A systematic review was conducted for each SNP in the panel to determine suitability as a PTB candidate. Those with significant associations previously reported in Caucasians were selected for replication. Candidate SNPs were already genotyped in cases and controls and clinical data were accessed from state perinatal and cerebral palsy databases. Association analysis was conducted between each SNP and PTB, and meta-analysis was conducted if there were ≥ 3 studies in the literature. Maternal and fetal SNPs were considered as separate candidates.ResultsA cohort of 170 cases and 583 controls was formed. Eight SNPs from the original panel of genotyped SNPs were selected as PTB candidates and for replication on the basis of systematic literature review results. In our cohort, fetal factor V Leiden (FVL) was significantly associated with PTB (odds ratio (OR): 2.6, 95% confidence interval (CI): 1.31-5.17), and meta-analysis confirmed this association (OR: 2.71, 95% CI: 1.15-6.4).ConclusionReplication and meta-analysis support an increased risk of PTB in Caucasians with the fetal FVL mutation.Michael E. O’Callaghan, Alastair H. MacLennan, Gai L. McMichael, Eric A. Haan and Gustaaf A. Dekke

Fatal myocarditis in a child with systemic onset juvenile idiopathic arthritis during treatment with an interleukin 1 receptor antagonist

<p>Abstract</p> <p>Background</p> <p>The pathologic diagnosis of isolated myocarditis without pericardial involvement is uncommonly encountered in systemic onset Juvenile Idiopathic Arthritis (soJIA).</p> <p>Case</p> <p>An eleven year-old boy with soJIA died suddenly while being treated with the interleukin 1 (IL-1) receptor inhibitor, anakinra. His autopsy revealed an enlarged heart and microscopic findings were consistent with myocarditis, but not pericarditis. Viral PCR testing performed on his myocardial tissue was negative.</p> <p>Conclusion</p> <p>This case illustrates myocarditis as a fatal complication of soJIA, potentially enabled by anakinra.</p

Early pregnancy peripheral blood gene expression and risk of preterm delivery: a nested case control study

<p>Abstract</p> <p>Background</p> <p>Preterm delivery (PTD) is a significant public health problem associated with greater risk of mortality and morbidity in infants and mothers. Pathophysiologic processes that may lead to PTD start early in pregnancy. We investigated early pregnancy peripheral blood global gene expression and PTD risk.</p> <p>Methods</p> <p>As part of a prospective study, ribonucleic acid was extracted from blood samples (collected at 16 weeks gestational age) from 14 women who had PTD (cases) and 16 women who delivered at term (controls). Gene expressions were measured using the GeneChip^®Human Genome U133 Plus 2.0 Array. Student's T-test and fold change analysis were used to identify differentially expressed genes. We used hierarchical clustering and principle components analysis to characterize signature gene expression patterns among cases and controls. Pathway and promoter sequence analyses were used to investigate functions and functional relationships as well as regulatory regions of differentially expressed genes.</p> <p>Results</p> <p>A total of 209 genes, including potential candidate genes (e.g. PTGDS, prostaglandin D2 synthase 21 kDa), were differentially expressed. A set of these genes achieved accurate pre-diagnostic separation of cases and controls. These genes participate in functions related to immune system and inflammation, organ development, metabolism (lipid, carbohydrate and amino acid) and cell signaling. Binding sites of putative transcription factors such as EGR1 (early growth response 1), TFAP2A (transcription factor AP2A), Sp1 (specificity protein 1) and Sp3 (specificity protein 3) were over represented in promoter regions of differentially expressed genes. Real-time PCR confirmed microarray expression measurements of selected genes.</p> <p>Conclusions</p> <p>PTD is associated with maternal early pregnancy peripheral blood gene expression changes. Maternal early pregnancy peripheral blood gene expression patterns may be useful for better understanding of PTD pathophysiology and PTD risk prediction.</p

Beauty Is in the Eye of the Beholder: Proteins Can Recognize Binding Sites of Homologous Proteins in More than One Way

Understanding the mechanisms of protein–protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein–protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein–protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe