A transversal approach to predict gene product networks from ontology-based similarity

<p>Abstract</p> <p>Background</p> <p>Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression.</p> <p>Results</p> <p>The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity.</p> <p>Conclusion</p> <p>Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression.</p

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

Metabolic syndrome in overweight children from the city of Botucatu - São Paulo State - Brazil: agreement among six diagnostic criteria

<p>Abstract</p> <p>Background</p> <p>The metabolic syndrome has been described in children; however, a standard criterion has not been established for its diagnosis. Also, few studies have been conducted to specifically observe the possible existence of agreement among the existing diagnostic criteria. The purpose of the study is to evaluate agreement concerning prevalence rates of the metabolic syndrome diagnosed by six different criteria in overweight schoolchildren in the city of Botucatu - SP -Brazil.</p> <p>Methods</p> <p>This is a cross-sectional study on 128 overweight schoolchildren. Clinical examination included anthropometry, pubertal staging evaluation, and blood pressure. Triacylglycerol, glycemia, HDL-cholesterol, insulin levels, and HOMA-IR were determined. The Kappa index, the Mann-Whitney test and the chi-square test were used for statistical analysis.</p> <p>Results</p> <p>The prevalence of the metabolic syndrome varied from 10 to 16.5% according to different diagnostic criteria. Results were similar for boys and girls and pubertal stage. Great agreement was observed among the six different diagnostic criteria for the metabolic syndrome.</p> <p>Conclusions</p> <p>Different diagnostic criteria, when adopted for subjects with similar demographic characteristics, generate similar and compatible prevalence. Results suggest that it is possible to adopt any of the analyzed criteria, and the choice should be according to the components available for each situation.</p

Transcriptional Enhancers in Protein-Coding Exons of Vertebrate Developmental Genes

Many conserved noncoding sequences function as transcriptional enhancers that regulate gene expression. Here, we report that protein-coding DNA also frequently contains enhancers functioning at the transcriptional level. We tested the enhancer activity of 31 protein-coding exons, which we chose based on strong sequence conservation between zebrafish and human, and occurrence in developmental genes, using a Tol2 transposable GFP reporter assay in zebrafish. For each exon we measured GFP expression in hundreds of embryos in 10 anatomies via a novel system that implements the voice-recognition capabilities of a cellular phone. We find that 24/31 (77%) exons drive GFP expression compared to a minimal promoter control, and 14/24 are anatomy-specific (expression in four anatomies or less). GFP expression driven by these coding enhancers frequently overlaps the anatomies where the host gene is expressed (60%), suggesting self-regulation. Highly conserved coding sequences and highly conserved noncoding sequences do not significantly differ in enhancer activity (coding: 24/31 vs. noncoding: 105/147) or tissue-specificity (coding: 14/24 vs. noncoding: 50/105). Furthermore, coding and noncoding enhancers display similar levels of the enhancer-related histone modification H3K4me1 (coding: 9/24 vs noncoding: 34/81). Meanwhile, coding enhancers are over three times as likely to contain an H3K4me1 mark as other exons of the host gene. Our work suggests that developmental transcriptional enhancers do not discriminate between coding and noncoding DNA and reveals widespread dual functions in protein-coding DNA

Perivascular Fat and the Microcirculation: Relevance to Insulin Resistance, Diabetes, and Cardiovascular Disease

Type 2 diabetes and its major risk factor, obesity, are a growing burden for public health. The mechanisms that connect obesity and its related disorders, such as insulin resistance, type 2 diabetes, and hypertension, are still undefined. Microvascular dysfunction may be a pathophysiologic link between insulin resistance and hypertension in obesity. Many studies have shown that adipose tissue-derived substances (adipokines) interact with (micro)vascular function and influence insulin sensitivity. In the past, research focused on adipokines from perivascular adipose tissue (PVAT). In this review, we focus on the interactions between adipokines, predominantly from PVAT, and microvascular function in relation to the development of insulin resistance, diabetes, and cardiovascular disease

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Alignment of the ALICE Inner Tracking System with cosmic-ray tracks

37 pages, 15 figures, revised version, accepted by JINSTALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 micron in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10^5 charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.Peer reviewe

DNA damage by lipid peroxidation products: implications in cancer, inflammation and autoimmunity

Oxidative stress and lipid peroxidation (LPO) induced by inflammation, excess metal storage and excess caloric intake cause generalized DNA damage, producing genotoxic and mutagenic effects. The consequent deregulation of cell homeostasis is implicated in the pathogenesis of a number of malignancies and degenerative diseases. Reactive aldehydes produced by LPO, such as malondialdehyde, acrolein, crotonaldehyde and 4-hydroxy-2-nonenal, react with DNA bases, generating promutagenic exocyclic DNA adducts, which likely contribute to the mutagenic and carcinogenic effects associated with oxidative stress-induced LPO. However, reactive aldehydes, when added to tumor cells, can exert an anticancerous effect. They act, analogously to other chemotherapeutic drugs, by forming DNA adducts and, in this way, they drive the tumor cells toward apoptosis. The aldehyde-DNA adducts, which can be observed during inflammation, play an important role by inducing epigenetic changes which, in turn, can modulate the inflammatory process. The pathogenic role of the adducts formed by the products of LPO with biological macromolecules in the breaking of immunological tolerance to self antigens and in the development of autoimmunity has been supported by a wealth of evidence. The instrumental role of the adducts of reactive LPO products with self protein antigens in the sensitization of autoreactive cells to the respective unmodified proteins and in the intermolecular spreading of the autoimmune responses to aldehyde-modified and native DNA is well documented. In contrast, further investigation is required in order to establish whether the formation of adducts of LPO products with DNA might incite substantial immune responsivity and might be instrumental for the spreading of the immunological responses from aldehyde-modified DNA to native DNA and similarly modified, unmodified and/or structurally analogous self protein antigens, thus leading to autoimmunity