Transforming growth factor β receptor 1 is a new candidate prognostic biomarker after acute myocardial infarction

<p>Abstract</p> <p>Background</p> <p>Prediction of left ventricular (LV) remodeling after acute myocardial infarction (MI) is clinically important and would benefit from the discovery of new biomarkers.</p> <p>Methods</p> <p>Blood samples were obtained upon admission in patients with acute ST-elevation MI who underwent primary percutaneous coronary intervention. Messenger RNA was extracted from whole blood cells. LV function was evaluated by echocardiography at 4-months.</p> <p>Results</p> <p>In a test cohort of 32 MI patients, integrated analysis of microarrays with a network of protein-protein interactions identified subgroups of genes which predicted LV dysfunction (ejection fraction ≤ 40%) with areas under the receiver operating characteristic curve (AUC) above 0.80. Candidate genes included transforming growth factor beta receptor 1 (TGFBR1). In a validation cohort of 115 MI patients, TGBFR1 was up-regulated in patients with LV dysfunction (P < 0.001) and was associated with LV function at 4-months (P = 0.003). TGFBR1 predicted LV function with an AUC of 0.72, while peak levels of troponin T (TnT) provided an AUC of 0.64. Adding TGFBR1 to the prediction of TnT resulted in a net reclassification index of 8.2%. When added to a mixed clinical model including age, gender and time to reperfusion, TGFBR1 reclassified 17.7% of misclassified patients. TGFB1, the ligand of TGFBR1, was also up-regulated in patients with LV dysfunction (P = 0.004), was associated with LV function (P = 0.006), and provided an AUC of 0.66. In the rat MI model induced by permanent coronary ligation, the TGFB1-TGFBR1 axis was activated in the heart and correlated with the extent of remodeling at 2 months.</p> <p>Conclusions</p> <p>We identified TGFBR1 as a new candidate prognostic biomarker after acute MI.</p

Tisagenlecleucel in children and young adults with B-cell lymphoblastic leukemia

BACKGROUND: In a single-center phase 1-2a study, the anti-CD19 chimeric antigen receptor (CAR) T-cell therapy tisagenlecleucel produced high rates of complete remission and was associated with serious but mainly reversible toxic effects in children and young adults with relapsed or refractory B-cell acute lymphoblastic leukemia (ALL). METHODS: We conducted a phase 2, single-cohort, 25-center, global study of tisagenlecleucel in pediatric and young adult patients with CD19+ relapsed or refractory B-cell ALL. The primary end point was the overall remission rate (the rate of complete remission or complete remission with incomplete hematologic recovery) within 3 months. RESULTS: For this planned analysis, 75 patients received an infusion of tisagenlecleucel and could be evaluated for efficacy. The overall remission rate within 3 months was 81%, with all patients who had a response to treatment found to be negative for minimal residual disease, as assessed by means of flow cytometry. The rates of event-free survival and overall survival were 73% (95% confidence interval [CI], 60 to 82) and 90% (95% CI, 81 to 95), respectively, at 6 months and 50% (95% CI, 35 to 64) and 76% (95% CI, 63 to 86) at 12 months. The median duration of remission was not reached. Persistence of tisagenlecleucel in the blood was observed for as long as 20 months. Grade 3 or 4 adverse events that were suspected to be related to tisagenlecleucel occurred in 73% of patients. The cytokine release syndrome occurred in 77% of patients, 48% of whom received tocilizumab. Neurologic events occurred in 40% of patients and were managed with supportive care, and no cerebral edema was reported. CONCLUSIONS: In this global study of CAR T-cell therapy, a single infusion of tisagenlecleucel provided durable remission with long-term persistence in pediatric and young adult patients with relapsed or refractory B-cell ALL, with transient high-grade toxic effects. (Funded by Novartis Pharmaceuticals; ClinicalTrials.gov number, NCT02435849.

Maximal Extraction of Biological Information from Genetic Interaction Data

Targeted genetic perturbation is a powerful tool for inferring gene function in model organisms. Functional relationships between genes can be inferred by observing the effects of multiple genetic perturbations in a single strain. The study of these relationships, generally referred to as genetic interactions, is a classic technique for ordering genes in pathways, thereby revealing genetic organization and gene-to-gene information flow. Genetic interaction screens are now being carried out in high-throughput experiments involving tens or hundreds of genes. These data sets have the potential to reveal genetic organization on a large scale, and require computational techniques that best reveal this organization. In this paper, we use a complexity metric based in information theory to determine the maximally informative network given a set of genetic interaction data. We find that networks with high complexity scores yield the most biological information in terms of (i) specific associations between genes and biological functions, and (ii) mapping modules of co-functional genes. This information-based approach is an automated, unsupervised classification of the biological rules underlying observed genetic interactions. It might have particular potential in genetic studies in which interactions are complex and prior gene annotation data are sparse

Predicting Quantitative Genetic Interactions by Means of Sequential Matrix Approximation

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis

Practices participating in a dental PBRN have substantial and advantageous diversity even though as a group they have much in common with dentists at large

<p>Abstract</p> <p>Background</p> <p>Practice-based research networks offer important opportunities to move recent advances into routine clinical practice. If their findings are not only generalizable to dental practices at large, but can also elucidate how practice characteristics are related to treatment outcome, their importance is even further elevated. Our objective was to determine whether we met a key objective for The Dental Practice-Based Research Network (DPBRN): to recruit a diverse range of practitioner-investigators interested in doing DPBRN studies.</p> <p>Methods</p> <p>DPBRN participants completed an enrollment questionnaire about their practices and themselves. To date, more than 1100 practitioners from the five participating regions have completed the questionnaire. The regions consist of: Alabama/Mississippi, Florida/Georgia, Minnesota, Permanente Dental Associates, and Scandinavia (Denmark, Norway, and Sweden). We tested the hypothesis that there are statistically significant differences in key characteristics among DPBRN practices, based on responses from dentists who participated in DPBRN's first network-wide study (n = 546).</p> <p>Results</p> <p>There were statistically significant, substantive regional differences among DPBRN-participating dentists, their practices, and their patient populations.</p> <p>Conclusion</p> <p>Although as a group, participants have much in common with practices at large; their substantial diversity offers important advantages, such as being able to evaluate how practice differences may affect treatment outcomes, while simultaneously offering generalizability to dentists at large. This should help foster knowledge transfer in both the research-to-practice and practice-to-research directions.</p

The Lack of ADAM17 Activity during Embryonic Development Causes Hemorrhage and Impairs Vessel Formation

Background: ADAM17/TACE activity is important during embryonic development. We wished to investigate possible roles of this metalloprotease, focusing on vascular development. Methodology/Principal Findings: Mice mutant in the enzymatic activity of ADAM17 were examined at various stages of embryonic development for vascular pattern and integrity using markers for vessel wall cells. We observed hemorrhage and edema starting at embryonic day E14.5 and becoming more severe as development proceeded; prior to embryonic day E14.5, embryos appeared normal. Staining for PECAM-1/CD31 revealed abnormalities in the patterns of branching of the embryonic vasculature at E14.5. Conclusions/Significance: These abnormalities preceded association of pericytes or monocyte/macrophage cells with the affected vessels and, therefore, presumably arise from defects in endothelial function consequent upon failure of ADAM17 to cleave one or more substrates involved in vascular development, such as Notch, Delta, VEGFR2 or JAM-A. Our study demonstrates a role for ADAM17 in modulating embryonic vessel development and function

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Altered Gene Expression in Pulmonary Tissue of Tryptophan Hydroxylase-1 Knockout Mice: Implications for Pulmonary Arterial Hypertension

The use of fenfluramines can increase the risk of developing pulmonary arterial hypertension (PAH) in humans, but the mechanisms responsible are unresolved. A recent study reported that female mice lacking the gene for tryptophan hydroxylase-1 (Tph1(−/−) mice) were protected from PAH caused by chronic dexfenfluramine, suggesting a pivotal role for peripheral serotonin (5-HT) in the disease process. Here we tested two alternative hypotheses which might explain the lack of dexfenfluramine-induced PAH in Tph1(−/−) mice. We postulated that: 1) Tph1(−/−) mice express lower levels of pulmonary 5-HT transporter (SERT) when compared to wild-type controls, and 2) Tph1(−/−) mice display adaptive changes in the expression of non-serotonergic pulmonary genes which are implicated in PAH. SERT was measured using radioligand binding methods, whereas gene expression was measured using microarrays followed by quantitative real time PCR (qRT-PCR). Contrary to our first hypothesis, the number of pulmonary SERT sites was modestly up-regulated in female Tph1(−/−) mice. The expression of 51 distinct genes was significantly altered in the lungs of female Tph1(−/−) mice. Consistent with our second hypothesis, qRT-PCR confirmed that at least three genes implicated in the pathogenesis of PAH were markedly up-regulated: Has2, Hapln3 and Retlna. The finding that female Tph1(−/−) mice are protected from dexfenfluramine-induced PAH could be related to compensatory changes in pulmonary gene expression, in addition to reductions in peripheral 5-HT. These observations emphasize the intrinsic limitation of interpreting data from studies conducted in transgenic mice that are not fully characterized