Molecular Interaction Studies of HIV-1 Matrix Protein p17 and Heparin: IDENTIFICATION OF THE HEPARIN-BINDING MOTIF OF p17 AS A TARGET FOR THE DEVELOPMENT OF MULTITARGET ANTAGONISTS

Once released by HIV cells, p17 binds heparan sulfate proteoglycans (HSPGs) and CXCR1 on leukocytes causing their dysfunction. By exploiting an approach integrating computational modeling, site-directed mutagenesis of p17, chemical desulfation of heparin, and surface plasmon resonance, we characterized the interaction of p17 with heparin, a HSPG structural analog, and CXCR1. p17 binds to heparin with an affinity (Kd 190 nM) that is similar to those of other heparin-binding viral proteins. Two stretches of basic amino acids (basic motifs) are present in p17 N and C termini. Neutralization (Arg3Ala substitution) of the N-terminal, but not of the C-terminal basic motif, causes the loss of p17 heparin-binding capacity. The N-terminal heparin-binding motif of p17 partially overlaps the CXCR1-binding domain. Accordingly, its neutralization prevents also p17 binding to the chemochine receptor. Competition experiments demonstrated that free heparin and heparan sulfate (HS), but not selectively 2-O-, 6-O-, and N-O desulfated heparins, prevent p17 binding to substrate-immobilized heparin, indicating that the sulfate groups of the glycosaminoglycan mediate p17 interaction. Evaluation of the p17 antagonist activity of a panel of biotechnological heparins derived by chemical sulfation of the Escherichia coli K5 polysaccharide revealed that the highlyN,O-sulfated derivative prevents the binding of p17 to both heparin and CXCR1, thus inhibiting p17-driven chemotactic migration of human monocytes with an efficiency that is higher than those of heparin and HS. Here, we characterized at a molecular level the interaction of p17 with its cellular receptors, laying the basis for the development of heparin-mimicking p17 antagonists

The role of the degree of use of the facilities in the port choice process: The Spanish dockside cranes case

The second author also acknowledges the financial support provided by the Government of Spain under the project MTM2014-54199-P

Transcriptomic Analysis of Rhodococcus opacus R7 Grown on o-Xylene by RNA-Seq

Xylenes are considered one of the most common hazardous sources of environmental contamination. The biodegradation of these compounds has been often reported, rarer the ability to oxidize the ortho-isomer. Among few o-xylene-degrading bacteria, Rhodococcus opacus R7 is well known for its capability to degrade diverse aromatic hydrocarbons and toxic compounds, including o-xylene as only carbon and energy source. This work shows for the first time the RNA-seq approach to elucidate the genetic determinants involved in the o-xylene degradation pathway in R. opacus R7. Transcriptomic data showed 542 differentially expressed genes that are associated with the oxidation of aromatic hydrocarbons and stress response, osmotic regulation and central metabolism. Gene ontology (GO) enrichment and KEGG pathway analysis confirmed significant changes in aromatic compound catabolic processes, fatty acid metabolism, beta-oxidation, TCA cycle enzymes, and biosynthesis of metabolites when cells are cultured in the presence of o-xylene. Interestingly, the most up-regulated genes belong to the akb gene cluster encoding for the ethylbenzene (Akb) dioxygenase system. Moreover, the transcriptomic approach allowed identifying candidate enzymes involved in R7 o-xylene degradation for their likely participation in the formation of the metabolites that have been previously identified. Overall, this approach supports the identification of several oxidative systems likely involved in o-xylene metabolism confirming that R. opacus R7 possesses a redundancy of sequences that converge in o-xylene degradation through R7 peculiar degradation pathway. This work advances our understanding of o-xylene metabolism in bacteria belonging to Rhodococcus genus and provides a framework of useful enzymes (molecular tools) that can be fruitfully targeted for optimized o-xylene consumption

A HPC and Grid enabling framework for genetic linkage analysis of SNPs

Understanding the structure, function and development of the human genome is a key factor to improve the quality of life. In order to achieve this goal developing and using a modern ICT infrastructure is essential, and can exploit next generation High Performance Computing (HPC) systems beyond the Petaflop scale in a collaborative and efficient way. The genetic linkage analysis of Single Nucleotide Polymorphism (SNP) markers has recently become a very popular approach for genetic epidemiology and population studies, aiming to discover the genetic correlation in complex diseases. The high computational cost and memory requirements of the major algorithms proposed in the literature make analyses of medium/large data sets very hard on a single CPU. A Grid based facility has hence been set up upon a high-performance infrastructure, the EGEE Grid, in order to create a tool for achieving whole-genome linkage analysis

A HPC and Grid enabling framework for genetic linkage analysis of SNPs

Understanding the structure, function and development of the human genome is a key factor to improve the quality of life. In order to achieve this goal developing and using a modern ICT infrastructure is essential, and can exploit next generation High Performance Computing (HPC) systems beyond the Petaflop scale in a collaborative and efficient way. The genetic linkage analysis of Single Nucleotide Polymorphism (SNP) markers has recently become a very popular approach for genetic epidemiology and population studies, aiming to discover the genetic correlation in complex diseases. The high computational cost and memory requirements of the major algorithms proposed in the literature make analyses of medium/large data sets very hard on a single CPU. A Grid based facility has hence been set up upon a high-performance infrastructure, the EGEE Grid, in order to create a tool for achieving whole-genome linkage analysis

Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer’s Disease: Insights From Data Mining and Enrichment Analysis

The incidence of cancer and Alzheimer\u2019s disease (AD) increases exponentially with age. A growing body of epidemiological evidence and molecular investigations inspired the hypothesis of an inverse relationship between these two pathologies. It has been proposed that the two diseases might utilize the same proteins and pathways that are, however, modulated differently and sometimes in opposite directions. Investigation of the common processes underlying these diseases may enhance the understanding of their pathogenesis and may also guide novel therapeutic strategies. Starting from a text-mining approach, our in silico study integrated the dispersed biological evidence by combining data mining, gene set enrichment, and protein-protein interaction (PPI) analyses while searching for common biological hallmarks linked to AD and cancer. We retrieved 138 genes (ALZCAN gene set), computed a significant number of enriched gene ontology clusters, and identified four PPI modules. The investigation confirmed the relevance of autophagy, ubiquitin proteasome system, and cell death as common biological hallmarks shared by cancer and AD. Then, from a closer investigation of the PPI modules and of the miRNAs enrichment data, several genes (SQSTM1, UCHL1, STUB1, BECN1, CDKN2A, TP53, EGFR, GSK3B, and HSPA9) and miRNAs (miR-146a-5p, MiR-34a-5p, miR-21-5p, miR-9-5p, and miR-16-5p) emerged as promising candidates. The integrative approach uncovered novel miRNA-gene networks (e.g., miR-146 and miR-34 regulating p62 and Beclin1 in autophagy) that might give new insights into the complex regulatory mechanisms of gene expression in AD and cancer

Sudden cardiac arrest prediction via deep learning electrocardiogram analysis

Aims: Sudden cardiac arrest (SCA) is a commonly fatal event that often occurs without prior indications. To improve outcomes and enable preventative strategies, the electrocardiogram (ECG) in conjunction with deep learning was explored as a potential screening tool. Methods and results: A publicly available data set containing 10 s of 12-lead ECGs from individuals who did and did not have an SCA, information about time from ECG to arrest, and age and sex was utilized for analysis to individually predict SCA or not using deep convolution neural network models. The base model that included age and sex, ECGs within 1 day prior to arrest, and data sampled from windows of 720 ms around the R-waves from 221 individuals with SCA and 1046 controls had an area under the receiver operating characteristic curve of 0.77. With sensitivity set at 95%, base model specificity was 31%, which is not clinically applicable. Gradient-weighted class activation mapping showed that the model mostly relied on the QRS complex to make predictions. However, models with ECGs recorded between 1 day to 1 month and 1 month to 1 year prior to arrest demonstrated predictive capabilities. Conclusion: Deep learning models processing ECG data are a promising means of screening for SCA, and this method explains differences in SCAs due to age and sex. Model performance improved when ECGs were nearer in time to SCAs, although ECG data up to a year prior had predictive value. Sudden cardiac arrest prediction was more dependent upon QRS complex data compared to other ECG segments