A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle

Skeletal muscle is the principal contributor to exercise-induced changes in human metabolism. Strikingly, although it has been demonstrated that a lot of metabolites accumulating in blood and human skeletal muscle during an exercise activate different signaling pathways and induce the expression of many genes in working muscle fibres, the systematic understanding of signaling-metabolic pathway interrelations with downstream genetic regulation in the skeletal muscle is still elusive. Herein, a physiologically based computational model of skeletal muscle comprising energy metabolism, Ca2+, and AMPK (AMP-dependent protein kinase) signaling pathways and the expression regulation of genes with early and delayed responses was developed based on a modular modeling approach and included 171 differential equations and more than 640 parameters. The integrated modular model validated on diverse including original experimental data and different exercise modes provides a comprehensive in silico platform in order to decipher and track cause-effect relationships between metabolic, signaling, and gene expression levels in skeletal muscle.

Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data

Creating a complete picture of the regulation of transcription seems to be an urgent task of modern biology. Regulation of transcription is a complex process carried out by transcription factors (TFs) and auxiliary proteins. Over the past decade, ChIP-Seq has become the most common experimental technology studying genome-wide interactions between TFs and DNA. We assessed the transcriptional significance of cell line-specific features using regression analysis of ChIP-Seq datasets from the GTRD database and transcriptional start site (TSS) activities from the FANTOM5 expression atlas. For this purpose, we initially generated a large number of features that were defined as the presence or absence of TFs in different promoter regions around TSSs. Using feature selection and regression analysis, we identified sets of the most important TFs that affect expression activity of TSSs in human cell lines such as HepG2, K562 and HEK293. We demonstrated that some TFs can be classified as repressors and activators depending on their location relative to TSS.

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis

We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.

Comparative analysis of protein-coding and long non-coding transcripts based on RNA sequence features

RNA plays an important role in the intracellular cell life and in the organism in general. Besides the well-established protein coding RNAs (messenger RNAs, mRNAs), long non-coding RNAs (lncRNAs) have gained the attention of recent researchers. Although lncRNAs have been classified as non-coding, some authors reported the presence of corresponding sequences in ribosome profiling data (Ribo-seq). Ribo-seq technology is a powerful experimental tool utilized to characterize RNA translation in cell with focus on initiation (harringtonine, lactimidomycin) and elongation (cycloheximide). By exploiting translation starts obtained from the Ribo-seq experiment, we developed a novel position weight matrix model for the prediction of translation starts. This model allowed us to achieve 96% accuracy of discrimination between human mRNAs and lncRNAs. When the same model was used for the prediction of putative ORFs in RNAs, we discovered that the majority of lncRNAs contained only small ORFs ([Formula: see text][Formula: see text]nt) in contrast to mRNAs.

Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data

Objectives

Mammalian genomics studies, especially those focusing on transcriptional regulation, require information on genomic locations of regulatory regions, particularly, transcription factor (TF) binding sites. There are plenty of published ChIP-Seq data on in vivo binding of transcription factors in different cell types and conditions. However, handling of thousands of separate data sets is often impractical and it is desirable to have a single global map of genomic regions potentially bound by a particular TF in any of studied cell types and conditions.

Data description

Here we report human and mouse cistromes, the maps of genomic regions that are routinely identified as TF binding sites, organized by TF. We provide cistromes for 349 mouse and 599 human TFs. Given a TF, its cistrome regions are supported by evidence from several ChIP-Seq experiments or several computational tools, and, as an optional filter, contain occurrences of sequence motifs recognized by the TF. Using the cistrome, we provide an annotation of TF binding sites in the vicinity of human and mouse transcription start sites. This information is useful for selecting potential gene targets of transcription factors and detecting co-regulated genes in differential gene expression data.

[Air pollution by particulate matter as the risk factor for the cardiovascular diseases]

In the review there highlighted contemporary concepts about the relation between the air pollution by the particulate matter (PM) and human morbidity and mortality rate due to cardiovascular diseases. There are considered results of the short- and long-term PM impact on the human cardiovascular system in the dependence on size, origin, chemical composition, and concentration in the air. Authors performed the formalized description of the action and possible effects of PM on vascular endothelium presented as an example of systemization. Summarizing data respective knowledge collected in the literature were used in the article as an example.

Assessment of translational importance of mammalian mRNA sequence features based on Ribo-Seq and mRNA-Seq data

Ribosome profiling technology (Ribo-Seq) allowed to highlight more details of mRNA translation in cell and get additional information on importance of mRNA sequence features for this process. Application of translation inhibitors like harringtonine and cycloheximide along with mRNA-Seq technique helped to assess such important characteristic as translation efficiency. We assessed the translational importance of features of mRNA sequences with the help of statistical analysis of Ribo-Seq and mRNA-Seq data. Translationally important features known from literature as well as proposed by the authors were used in analysis. Such comparisons as protein coding versus non-coding RNAs and high- versus low-translated mRNAs were performed. We revealed a set of features that allowed to discriminate the compared categories of RNA. Significant relationships between mRNA features and efficiency of translation were also established.

[Prediction of a non-small cell lung cancer sensitivity to cisplatin and paclitaxel based on the marker genes expression]

The goal of the present study was to define gene expression signatures that predict a chemosensitivity of non-small cell lung cancer (NSCLC) to cisplatin and paclitaxel. To generate set of candidate genes likely to be predictive a current knowledge of the pathways involved in resistance and sensitivity to individual drugs was used. Forty four genes coding proteins belonging to following categories: ATP-dependent transport proteins, detoxification system proteins, reparation system proteins, tubulin and proteins responsible for its synthesis, cell cycle and apoptosis proteins were considered. Eight NSCLC cell lines (A549, Calul, H1299, H322, H358, H460, H292, and H23) were used in our study. For each NSCLC cell line a cisplatin and paclitaxel chemosensitivity as well as an expression level of 44 candidate genes were evaluated. To develop a chemosensitivity prediction model based on selected genes expression level a multiple regression analysis was performed. The model based on the expression level of 11 genes (TUBB3, TXR1, MRP5, MSH2, ERCC1, STMN, SMAC, FOLR1, PTPN14, HSPA2, GSTP1) allowed us to predict the paclitaxel cytotoxic concentration with high level of correlation (r = 0.91, p < 0.01). However, none model developed was able to reliably predict a sensitivity of the NSCLC cells to cisplatin.

Identification of differentially expressed genes by meta-analysis of microarray data on breast cancer

Albeit the great number of microarray data available on breast cancer, reliable identification of genes associated with breast cancer development remains a challenge. The aim of this work was to develop a novel method of meta-analysis for the identification of differentially expressed genes integrating results of several independent microarray experiments. We developed a statistical method for identification of up- and down-regulated genes to perform meta-analysis. The method takes advantage of hypergeometric and binomial distributions. Using our method we performed meta-analysis of five data sets from independent cDNA-microarray experiments on breast cancer. The meta-analysis revealed that 3.2% and 2.8% of the 24,726 analyzed genes are significantly (P-value < 0.01) down- and up-regulated, respectively. We also show that properly applied meta-analysis is a good tool for comparison of different breast cancer subtypes. Our meta-analysis showed that the expression of the majority of genes does not show significant differences in different subtypes of breast cancer. Here, we report the rationale, development and application of meta-analysis that enable us to identify biologically meaningful features of breast cancer. The algorithm we propose for the meta-analysis can reveal the features specific to the breast cancer subtypes and those common to breast cancer. The results allow us to revise the previously generated lists of genes associated with breast cancer and also identify most promising anticancer drug-target genes.

[Analysis of polymorphism of the number of tandem repeats in the aggrecan gene exon G3 in the families with idiopathic scoliosis]

In our previous study we showed that the inheritance of pronounced forms of idiopathic scoliosis was described by an autosomal-dominant major gene model assuming incomplete sex- and age-dependent penetrance. In the present study a search for the major gene was carried out by means of testing candidate genes. The aggrecan gene with known polymorphism of the number of tandem repeats in exon G3 was considered to be one of these candidate genes. Various alleles of this gene provide attachment of different number of chondroitin sulfate chains to a proteoglycan core protein, thereby changing functional properties of cartilage. Using the TDT analysis of 33 unrelated families consisting of a proband and his parents, we examined the existence of associations between the aggrecan alleles and the disease. Among nine alleles identified, three alleles with tandem repeats numbers of 25, 26, and 27 prevailed. We did not reveal preferable transmission of any of these alleles to the proband (TDT-statistics for different alleles varied from 0 to 0.71). There was also no correlation between the number of tandem repeats and the disease severity. Thus, either the polymorphism of the number of tandem repeats is not the direct reason for development of idiopathic scoliosis in the families tested, or its effect is too low to be detected using the samples examined.