Medicinal plants growing in the Judea region: network approach for searching potential therapeutic targets

Plants growing in the Judea region are widely used in traditional medicine of the Levant region. Nevertheless, they have not so far been sufficiently analyzed and their medicinal potential has not been evaluated. This study is the first attempt to fill the gap in the knowledge of the plants growing in the region. Comprehensive data mining of online botanical databases and peer-reviewed scientific literature including ethno-pharmacological surveys from the Levant region was applied to compile a full list of plants growing in the Judea region, with the focus on their medicinal applications. Around 1300 plants growing in the Judea region were identified. Of them, 25% have medicinal applications which were analyzed in this study. Screening for chemical-protein interactions, together with the network-based analysis of potential targets, will facilitate discovery and therapeutic applications of the Judea region plants. Such an approach could also be applied as an integrative platform for further searching the potential therapeutic targets of plants growing in other regions of the world

Alpha 2 macroglobulin activity in rats infected with Trypanosoma lewisi and treated with cyclophosphamide and its effect on the malignancy of the disease

Background & objectives: Trypanosoma lewisi is a common, flagellated parasite of the rat. Ourprevious study showed that rabbits injected with serum collected from rats infected with Trypanosomalewisi and treated with cyclophosphamide (CyI) produced high levels of antibodies against a newprotein in the CyI rat serum.Results: In the present study, this protein was characterised as α2 macroglobulin (α2M) and thekinetics of its production and its influence on the malignancy of the disease were determined. In ratsinfected with T. lewisi, α2M was first demonstrated and peaked on the second day post-infection(972 μg/ml) and then reduced gradually, reaching a level of 32 μg/ml on the eighth day post-infection.However, in the CyI rats the level of α2M was gradually increased as the disease progressed,reaching a level of 890 μg/ml on the eighth day post-infection. Injection of both crude and purifiedα2M into rats infected with T. lewisi led to increased parasitaemia.Interpretation & conclusion: The present study suggests that increased levels of α2M in the CyI ratscontribute to the malignancy of the disease

Wideband digital phase comparator for high current shunts

A wideband phase comparator for precise measurements of phase difference of high current shunts has been developed at INRIM. The two-input digital phase detector is realized with a precision wideband digitizer connected through a pair of symmetric active guarded transformers to the outputs of the shunts under comparison. Data are first acquired asynchronously, and then transferred from on-board memory to host memory. Because of the large amount of data collected the filtering process and the analysis algorithms are performed outside the acquisition routine. Most of the systematic errors can be compensated by a proper inversion procedure. The system is suitable for comparing shunts in a wide range of currents, from several hundred of milliampere up to 100 A, and frequencies ranging between 500 Hz and 100 kHz. Expanded uncertainty (k=2) less than 0.05 mrad, for frequency up to 100 kHz, is obtained in the measurement of the phase difference of a group of 10 A shunts, provided by some European NMIs, using a digitizer with sampling frequency up to 1 MHz. An enhanced version of the phase comparator employs a new digital phase detector with higher sampling frequency and vertical resolution. This permits to decrease the contribution to the uncertainty budget of the phase detector of a factor two from 20 kHz to 100 kHz. Theories and experiments show that the phase difference between two high precision wideband digitizers, coupled as phase detector, depends on multiple factors derived from both analog and digital imprint of each sampling system.Comment: 20 pages, 9 figure

Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks

Intricate and interconnected pathways modulate longevity, but screens to identify the components of these pathways have not been saturating. Because biological processes are often executed by protein complexes and fine-tuned by regulatory factors, the first-order protein-protein interactors of known longevity genes are likely to participate in the regulation of longevity. Data-rich maps of protein interactions have been established for many cardinal organisms such as yeast, worms, and humans. We propose that these interaction maps could be mined for the identification of new putative regulators of longevity. For this purpose, we have constructed longevity networks in both humans and worms. We reasoned that the essential first-order interactors of known longevity-associated genes in these networks are more likely to have longevity phenotypes than randomly chosen genes. We have used C. elegans to determine whether post-developmental inactivation of these essential genes modulates lifespan. Our results suggest that the worm and human longevity networks are functionally relevant and possess a high predictive power for identifying new longevity regulators

A multidimensional systems biology analysis of cellular senescence in aging and disease.

BACKGROUND: Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of aging and has been linked to aging-related diseases. Many genes play a role in cellular senescence, yet a comprehensive understanding of its pathways is still lacking. RESULTS: We develop CellAge (http://genomics.senescence.info/cells), a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build cellular senescence protein-protein interaction and co-expression networks. Clusters in the networks are enriched for cell cycle and immunological processes. Network topological parameters also reveal novel potential cellular senescence regulators. Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence. CONCLUSIONS: Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence

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 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

Machine learning for predicting lifespan-extending chemical compounds

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age‐related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans’ lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound’s chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans’ lifespan in the DGIdb database, where the effect of the compounds on an organism’s lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti‐inflammatories, and compounds for gonadotropin‐ releasing hormone therapies

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