Protein profiling in hepatocellular carcinoma by label-free quantitative proteomics in two west african populations.

Background Hepatocellular Carcinoma is the third most common cause of cancer related death worldwide, often diagnosed by measuring serum AFP; a poor performance stand-alone biomarker. With the aim of improving on this, our study focuses on plasma proteins identified by Mass Spectrometry in order to investigate and validate differences seen in the respective proteomes of controls and subjects with LC and HCC. Methods Mass Spectrometry analysis using liquid chromatography electro spray ionization quadrupole time-of-flight was conducted on 339 subjects using a pooled expression profiling approach. ELISA assays were performed on four significantly differentially expressed proteins to validate their expression profiles in subjects from the Gambia and a pilot group from Nigeria. Results from this were collated for statistical multiplexing using logistic regression analysis. Results Twenty-six proteins were identified as differentially expressed between the three subject groups. Direct measurements of four; hemopexin, alpha-1-antitrypsin, apolipoprotein A1 and complement component 3 confirmed their change in abundance in LC and HCC versus control patients. These trends were independently replicated in the pilot validation subjects from Nigeria. The statistical multiplexing of these proteins demonstrated performance comparable to or greater than ALT in identifying liver cirrhosis or carcinogenesis. This exercise also proposed preliminary cut offs with achievable sensitivity, specificity and AUC statistics greater than reported AFP averages. Conclusions The validated changes of expression in these proteins have the potential for development into high-performance tests usable in the diagnosis and or monitoring of HCC and LC patients. The identification of sustained expression trends strengthens the suggestion of these four proteins as worthy candidates for further investigation in the context of liver disease. The statistical combinations also provide a novel inroad of analyses able to propose definitive cut-offs and combinations for evaluation of performance

Rationale and study design of the MINERVA study: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction-UK multicentre collaboration

Introduction The purpose of this study is to assess the ability of two new ECG markers (Regional Repolarisation Instability Index (R2I2) and Peak Electrical Restitution Slope) to predict sudden cardiac death (SCD) or ventricular arrhythmia (VA) events in patients with ischaemic cardiomyopathy undergoing implantation of an implantable cardioverter defibrillator for primary prevention indication. Methods and analysis Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction is a prospective, open label, single blinded, multicentre observational study to establish the efficacy of two ECG biomarkers in predicting VA risk. 440 participants with ischaemic cardiomyopathy undergoing routine first time implantable cardioverter-defibrillator (ICD) implantation for primary prevention indication are currently being recruited. An electrophysiological (EP) study is performed using a non-invasive programmed electrical stimulation protocol via the implanted device. All participants will undergo the EP study hence no randomisation is required. Participants will be followed up over a minimum of 18 months and up to 3 years. The first patient was recruited in August 2016 and the study will be completed at the final participant follow-up visit. The primary endpoint is ventricular fibrillation or sustained ventricular tachycardia >200 beats/min as recorded by the ICD. The secondary endpoint is SCD. Analysis of the ECG data obtained during the EP study will be performed by the core lab where blinding of patient health status and endpoints will be maintained. Ethics and dissemination Ethical approval has been granted by Research Ethics Committees Northern Ireland (reference no. 16/NI/0069). The results will inform the design of a definitive Randomised Controlled Trial (RCT). Dissemination will include peer reviewed journal articles reporting the qualitative and quantitative results, as well as presentations at conferences and lay summaries

Simultaneous Extraction of the Fermi constant and PMNS matrix elements in the presence of a fourth generation

Several recent studies performed on constraints of a fourth generation of quarks and leptons suffer from the ad-hoc assumption that 3 x 3 unitarity holds for the first three generations in the neutrino sector. Only under this assumption one is able to determine the Fermi constant G_F from the muon lifetime measurement with the claimed precision of G_F = 1.16637 (1) x 10^-5 GeV^-2. We study how well G_F can be extracted within the framework of four generations from leptonic and radiative mu and tau decays, as well as from K_l3 decays and leptonic decays of charged pions, and we discuss the role of lepton universality tests in this context. We emphasize that constraints on a fourth generation from quark and lepton flavour observables and from electroweak precision observables can only be obtained in a consistent way if these three sectors are considered simultaneously. In the combined fit to leptonic and radiative mu and tau decays, K_l3 decays and leptonic decays of charged pions we find a p-value of 2.6% for the fourth generation matrix element |U_{e 4}|=0 of the neutrino mixing matrix.Comment: 19 pages, 3 figures with 16 subfigures, references and text added refering to earlier related work, figures and text in discussion section added, results and conclusions unchange

Staircase Quantum Dots Configuration in Nanowires for Optimized Thermoelectric Power

The performance of thermoelectric energy harvesters can be improved by nanostructures that exploit inelastic transport processes. One prototype is the three-terminal hopping thermoelectric device where electron hopping between quantum-dots are driven by hot phonons. Such three-terminal hopping thermoelectric devices have potential in achieving high efficiency or power via inelastic transport and without relying on heavy-elements or toxic compounds. We show in this work how output power of the device can be optimized via tuning the number and energy configuration of the quantum-dots embedded in parallel nanowires. We find that the staircase energy configuration with constant energy-step can improve the power factor over a serial connection of a single pair of quantum-dots. Moreover, for a fixed energy-step, there is an optimal length for the nanowire. Similarly for a fixed number of quantum-dots there is an optimal energy-step for the output power. Our results are important for future developments of high-performance nanostructured thermoelectric devices

Dissecting complex transcriptional responses using pathway-level scores based on prior information

<p>Abstract</p> <p>Background</p> <p>The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathways to changes in the environment of the cells. The use of prior information, either about the function of the protein encoded by each gene, or about the physical interactions between regulatory factors and the sequences controlling its expression, has emerged as a powerful approach for dissecting complex transcriptional responses.</p> <p>Results</p> <p>We review two different approaches for combining the noisy expression levels of multiple individual genes into robust pathway-level differential expression scores. The first is based on a comparison between the distribution of expression levels of genes within a predefined gene set and those of all other genes in the genome. The second starts from an estimate of the strength of genomewide regulatory network connectivities based on sequence information or direct measurements of protein-DNA interactions, and uses regression analysis to estimate the activity of gene regulatory pathways. The statistical methods used are explained in detail.</p> <p>Conclusion</p> <p>By avoiding the thresholding of individual genes, pathway-level analysis of differential expression based on prior information can be considerably more sensitive to subtle changes in gene expression than gene-level analysis. The methods are technically straightforward and yield results that are easily interpretable, both biologically and statistically.</p

Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines

Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells. © 2012 Shen et al

Ranked retrieval of Computational Biology models

<p>Abstract</p> <p>Background</p> <p>The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind.</p> <p>Results</p> <p>Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models.</p> <p>Conclusions</p> <p>The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.</p

Aflatoxin-Induced TP53 R249S Mutation in HepatoCellular Carcinoma in Thailand: Association with Tumors Developing in the Absence of Liver Cirrhosis

Primary Liver Cancer (PLC) is the leading cause of death by cancer among males in Thailand and the 3rd among females. Most cases are hepatocellular carcinoma (HCC) but cholangiocarcinomas represent between 4 and 80% of liver cancers depending upon geographic area. Most HCC are associated with chronic infection by Hepatitis B Virus while a G→T mutation at codon 249 of the TP53 gene, R249S, specific for exposure to aflatoxin, is detected in tumors for up to 30% of cases. We have used Short Oligonucleotide Mass Analysis (SOMA) to quantify free circulating R249S-mutated DNA in plasma using blood specimens collected in a hospital case:control study. Plasma R249S-mutated DNA was detectable at low concentrations (≥67 copies/mL) in 53 to 64% of patients with primary liver cancer or chronic liver disease and in 19% of controls. 44% of patients with HCC and no evidence of cirrhosis had plasma concentrations of R249S-mutated DNA ≥150 copies/mL, compared to 21% in patients with both HCC and cirrhosis, 22% in patients with cholangiocarcinoma, 12% in patients with non-cancer chronic liver disease and 3% of subjects in the reference group. Thus, plasma concentrations of R249S-mutated DNA ≥150 copies/mL tended to be more common in patients with HCC developing without pre-existing cirrhosis (p = 0.027). Overall, these results support the preferential occurrence of R249S-mutated DNA in HCC developing in the absence of cirrhosis in a context of HBV chronic infection

Vehicle emissions and PM2.5 mass concentrations in six Brazilian cities

In Brazil, the principal source of air pollution is the combustion of fuels (ethanol, gasohol, and diesel). In this study, we quantify the contributions that vehicle emissions make to the urban fine particulate matter (PM2.5) mass in six state capitals in Brazil, collecting data for use in a larger project evaluating the impact of air pollution on human health. From winter 2007 to winter 2008, we collected 24-h PM2.5 samples, employing gravimetry to determine PM2.5 mass concentrations; reflectance to quantify black carbon concentrations; X-ray fluorescence to characterize elemental composition; and ion chromatography to determine the composition and concentrations of anions and cations. Mean PM2.5 concentrations in the cities of São Paulo, Rio de Janeiro, Belo Horizonte, Curitiba, Porto Alegre, and Recife were 28, 17.2, 14.7, 14.4, 13.4, and 7.3 μg/m3, respectively. In São Paulo and Rio de Janeiro, black carbon explained approximately 30% of the PM2.5 mass. We used receptor models to identify distinct source-related PM2.5 fractions and correlate those fractions with daily mortality rates. Using specific rotation factor analysis, we identified the following principal contributing factors: soil and crustal material; vehicle emissions and biomass burning (black carbon factor); and fuel oil combustion in industries (sulfur factor). In all six cities, vehicle emissions explained at least 40% of the PM2.5 mass. Elemental composition determination with receptor modeling proved an adequate strategy to identify air pollution sources and to evaluate their short- and long-term effects on human health. Our data could inform decisions regarding environmental policies vis-à-vis health care costs