A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related

<p>Abstract</p> <p>Background</p> <p>The ageing of the worldwide population means there is a growing need for research on the biology of ageing. DNA damage is likely a key contributor to the ageing process and elucidating the role of different DNA repair systems in ageing is of great interest. In this paper we propose a data mining approach, based on classification methods (decision trees and Naive Bayes), for analysing data about human DNA repair genes. The goal is to build classification models that allow us to discriminate between ageing-related and non-ageing-related DNA repair genes, in order to better understand their different properties.</p> <p>Results</p> <p>The main patterns discovered by the classification methods are as follows: (a) the number of protein-protein interactions was a predictor of DNA repair proteins being ageing-related; (b) the use of predictor attributes based on protein-protein interactions considerably increased predictive accuracy of attributes based on Gene Ontology (GO) annotations; (c) GO terms related to "response to stimulus" seem reasonably good predictors of ageing-relatedness for DNA repair genes; (d) interaction with the XRCC5 (Ku80) protein is a strong predictor of ageing-relatedness for DNA repair genes; and (e) DNA repair genes with a high expression in T lymphocytes are more likely to be ageing-related.</p> <p>Conclusions</p> <p>The above patterns are broadly integrated in an analysis discussing relations between Ku, the non-homologous end joining DNA repair pathway, ageing and lymphocyte development. These patterns and their analysis support non-homologous end joining double strand break repair as central to the ageing-relatedness of DNA repair genes. Our work also showcases the use of protein interaction partners to improve accuracy in data mining methods and our approach could be applied to other ageing-related pathways.</p

Economic factors influencing zoonotic disease dynamics: demand for poultry meat and seasonal transmission of avian influenza in Vietnam

While climate is often presented as a key factor influencing the seasonality of diseases, the importance of anthropogenic factors is less commonly evaluated. Using a combination of methods-wavelet analysis, economic analysis, statistical and disease transmission modelling-we aimed to explore the influence of climatic and economic factors on the seasonality of H5N1 Highly Pathogenic Avian Influenza in the domestic poultry population of Vietnam. We found that while climatic variables are associated with seasonal variation in the incidence of avian influenza outbreaks in the North of the country, this is not the case in the Centre and the South. In contrast, temporal patterns of H5N1 incidence are similar across these 3 regions: periods of high H5N1 incidence coincide with Lunar New Year festival, occurring in January-February, in the 3 climatic regions for 5 out of the 8 study years. Yet, daily poultry meat consumption drastically increases during Lunar New Year festival throughout the country. To meet this rise in demand, poultry production and trade are expected to peak around the festival period, promoting viral spread, which we demonstrated using a stochastic disease transmission model. This study illustrates the way in which economic factors may influence the dynamics of livestock pathogens

Increased Numbers of IL-7 Receptor Molecules on CD4+CD25−CD107a+ T-Cells in Patients with Autoimmune Diseases Affecting the Central Nervous System

BACKGROUND: High content immune profiling in peripheral blood may reflect immune aberrations associated with inflammation in multiple sclerosis (MS) and other autoimmune diseases affecting the central nervous system. METHODS AND FINDINGS: Peripheral blood mononuclear cells from 46 patients with multiple sclerosis (MS), 9 patients diagnosed with relapsing remitting MS (RRMS), 13 with secondary progressive multiple sclerosis (SPMS), 9 with other neurological diseases (OND) and well as 15 healthy donors (HD) were analyzed by 12 color flow cytometry (TCRalphabeta, TCRgammadelta, CD4, CD8alpha, CD8beta, CD45RA, CCR7, CD27, CD28, CD107a, CD127, CD14) in a cross-sectional study to identify variables significantly different between controls (HD) and patients (OND, RRMS, SPMS). We analyzed 187 individual immune cell subsets (percentages) and the density of the IL-7 receptor alpha chain (CD127) on 59 individual immune phenotypes using a monoclonal anti-IL-7R antibody (clone R34.34) coupled to a single APC molecule in combination with an APC-bead array. A non-parametric analysis of variance (Kruskal-Wallis test) was conducted in order to test for differences among the groups in each of the variables. To correct for the multiplicity problem, the FDR correction was applied on the p-values. We identified 19 variables for immune cell subsets (percentages) which allowed to segregate healthy individuals and individuals with CNS disorders. We did not observe differences in the relative percentage of IL-7R-positive immune cells in PBMCs. In contrast, we identified significant differences in IL-7 density, measured on a single cell level, in 2/59 variables: increased numbers of CD127 molecules on TCRalphabeta+CD4+CD25 (intermed) T-cells and on TCRalphabeta+CD4+CD25-CD107a+ T-cells (mean: 28376 Il-7R binding sites on cells from HD, 48515 in patients with RRMS, 38195 in patients with SPMS and 33692 IL-7 receptor binding sites on cells from patients with OND). CONCLUSION: These data show that immunophenotyping represents a powerful tool to differentiate healthy individuals from individuals suffering from neurological diseases and that the number of IL-7 receptor molecules on differentiated TCRalphabeta+CD4+CD25-CD107a+ T-cells, but not the percentage of IL-7R-positive cells, segregates healthy individuals from patients with neurological disorders

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases

A genetic analysis of nitric oxide-mediated signaling during chronological aging in the yeast

In mammals, NO•, a signaling molecule is implicated in the regulation of vasodilation, neurotransmission and immune response. It is believed that NO• is a signaling molecule also in unicellular organism like yeast and may be involved in the regulation of apoptosis and sporulation. It has been reported that NO• is produced during chronological aging (CA) leading to an increase of the superoxide level, which in turn mediates apoptosis. Since this conclusion was based on indirect measurements of NO• by the Griess reaction, the role of NO• signaling during CA in the yeast remains uncertain. We investigated this issue more precisely using different genetic and biochemical methodologies. We used cells lacking the factors influencing nitrosative stress response like flavohemoglobin metabolizing NO•, S-nitrosoglutathione reductase metabolizing S-nitrosoglutathione and the transcription factor Fzf1p mediating NO• response. We measured the standard parameters describing CA and found an elevation in the superoxide level, percentage of death cells, the level of TUNEL positive cells and a decrease in proliferating potential. These observations showed no significant differences between wild type cells and the disruptants except for a small elevation of the superoxide level in the Δsfa1 mutant. The intracellular NO• level and flavohemoglobin expression decreased rather than increased during CA. Products of general nitrogen metabolism and protein tyrosine nitration were slightly decreased during CA, the magnitude of changes showing no differences between the wild type and the mutant yeast. Altogether, our data indicate that apoptosis during yeast CA is mediated by superoxide signaling rather than NO• signaling

Gene duplication and phenotypic changes in the evolution of Mammalian metabolic networks

Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines

The AQUA-FONTIS study: protocol of a multidisciplinary, cross-sectional and prospective longitudinal study for developing standardized diagnostics and classification of non-thyroidal illness syndrome

<p>Abstract</p> <p>Background</p> <p>Non-thyroidal illness syndrome (NTIS) is a characteristic functional constellation of thyrotropic feedback control that frequently occurs in critically ill patients. Although this condition is associated with significantly increased morbidity and mortality, there is still controversy on whether NTIS is caused by artefacts, is a form of beneficial adaptation, or is a disorder requiring treatment. Trials investigating substitution therapy of NTIS revealed contradictory results. The comparison of heterogeneous patient cohorts may be the cause for those inconsistencies.</p> <p>Objectives</p> <p>Primary objective of this study is the identification and differentiation of different functional states of thyrotropic feedback control in order to define relevant evaluation criteria for the prognosis of affected patients. Furthermore, we intend to assess the significance of an innovative physiological index approach (SPINA) in differential diagnosis between NTIS and latent (so-called "sub-clinical") thyrotoxicosis.</p> <p>Secondary objective is observation of variables that quantify distinct components of NTIS in the context of independent predictors of evolution, survival or pathophysiological condition and influencing or disturbing factors like medication.</p> <p>Design</p> <p>The <b>a</b>pproach to a <b>qua</b>ntitative <b>f</b>ollow-up <b>o</b>f <b>n</b>on-<b>t</b>hyroidal <b>i</b>llness <b>s</b>yndrome (AQUA FONTIS study) is designed as both a cross-sectional and prospective longitudinal observation trial in critically ill patients. Patients are observed in at least two evaluation points with consecutive assessments of thyroid status, physiological and clinical data in additional weekly observations up to discharge. A second part of the study investigates the neuropsychological impact of NTIS and medium-term outcomes.</p> <p>The study design incorporates a two-module structure that covers a reduced protocol in form of an observation trial before patients give informed consent. Additional investigations are performed if and after patients agree in participation.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov NCT00591032</p

GiSAO.db: a database for ageing research

<p>Abstract</p> <p>Background</p> <p>Age-related gene expression patterns of <it>Homo sapiens </it>as well as of model organisms such as <it>Mus musculus</it>, <it>Saccharomyces cerevisiae</it>, <it>Caenorhabditis elegans </it>and <it>Drosophila melanogaster </it>are a basis for understanding the genetic mechanisms of ageing. For an effective analysis and interpretation of expression profiles it is necessary to store and manage huge amounts of data in an organized way, so that these data can be accessed and processed easily.</p> <p>Description</p> <p>GiSAO.db (Genes involved in senescence, apoptosis and oxidative stress database) is a web-based database system for storing and retrieving ageing-related experimental data. Expression data of genes and miRNAs, annotation data like gene identifiers and GO terms, orthologs data and data of follow-up experiments are stored in the database. A user-friendly web application provides access to the stored data. KEGG pathways were incorporated and links to external databases augment the information in GiSAO.db. Search functions facilitate retrieval of data which can also be exported for further processing.</p> <p>Conclusions</p> <p>We have developed a centralized database that is very well suited for the management of data for ageing research. The database can be accessed at <url>https://gisao.genome.tugraz.at</url> and all the stored data can be viewed with a guest account.</p

Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data

<p>Abstract</p> <p>Background</p> <p>Researchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups differ from one another. Thus, <it>post hoc </it>tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template correlations work extremely well, especially when the researcher has <it>a priori </it>information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased by the researcher's assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally, different patterns may have different probabilities of occurring by chance, importantly influencing researchers' conclusions about a pattern and its constituent genes.</p> <p>Results</p> <p>We developed a four step, <it>post hoc </it>pattern matching (PPM) algorithm to automate single channel gene expression pattern identification/significance. First, 1-Way Analysis of Variance (ANOVA), coupled with <it>post hoc </it>'all pairwise' comparisons are calculated for all genes. Second, for each ANOVA-significant gene, all pairwise contrast results are encoded to create unique pattern ID numbers. The # genes found in each pattern in the data is identified as that pattern's 'actual' frequency. Third, using Monte Carlo simulations, those patterns' frequencies are estimated in random data ('random' gene pattern frequency). Fourth, a Z-score for overrepresentation of the pattern is calculated ('actual' against 'random' gene pattern frequencies). We wrote a Visual Basic program (StatiGen) that automates PPM procedure, constructs an Excel workbook with standardized graphs of overrepresented patterns, and lists of the genes comprising each pattern. The visual basic code, installation files for StatiGen, and sample data are available as supplementary material.</p> <p>Conclusion</p> <p>The PPM procedure is designed to augment current microarray analysis procedures by allowing researchers to incorporate all of the information from post hoc tests to establish unique, overarching gene expression patterns in which there is no overlap in gene membership. In our hands, PPM works well for studies using from three to six treatment groups in which the researcher is interested in treatment-related patterns of gene expression. Hardware/software limitations and extreme number of theoretical expression patterns limit utility for larger numbers of treatment groups. Applied to a published microarray experiment, the StatiGen program successfully flagged patterns that had been manually assigned in prior work, and further identified other gene expression patterns that may be of interest. Thus, over a moderate range of treatment groups, PPM appears to work well. It allows researchers to assign statistical probabilities to patterns of gene expression that fit <it>a priori </it>expectations/hypotheses, it preserves the data's ability to show the researcher interesting, yet unanticipated gene expression patterns, and assigns the majority of ANOVA-significant genes to non-overlapping patterns.</p

Differences in genome-wide gene expression response in peripheral blood mononuclear cells between young and old men upon caloric restriction

Background: Caloric restriction (CR) is considered to increase lifespan and to prevent various age-related diseases in different nonhuman organisms. Only a limited number of CR studies have been performed on humans, and results put CR as a beneficial tool to decrease risk factors in several age-related diseases. The question remains at what age CR should be implemented to be most effective with respect to healthy aging. The aim of our study was to elucidate the role of age in the transcriptional response to a completely controlled 30 % CR diet on immune cells, as immune response is affected during aging. Ten healthy young men, aged 20–28, and nine healthy old men, aged 64–85, were subjected to a 2-week weight maintenance diet, followed by 3 weeks of 30 % CR. Before and after 30 % CR, the whole genome gene expression in peripheral blood mononuclear cells (PBMCs) was assessed. Results: Expression of 554 genes showed a different response between young and old men upon CR. Gene set enrichment analysis revealed a downregulation of gene sets involved in the immune response in young but not in old men. At baseline, immune response-related genes were higher expressed in old compared to young men. Upstream regulator analyses revealed that most potential regulators were controlling the immune response. Conclusions: Based on the gene expression data, we theorise that a short period of CR is not effective in old men regarding immune-related pathways while it is effective in young men