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

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

Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals

One of the important questions in aging research is how differences in transcriptomics are associated with the longevity of various species. Unfortunately, at the level of individual genes, the links between expression in different organs and maximum lifespan (MLS) are yet to be fully understood. Analyses are complicated further by the fact that MLS is highly associated with other confounding factors (metabolic rate, gestation period, body mass, etc.) and that linear models may be limiting. Using gene expression from 41 mammalian species, across five organs, we constructed gene-centric regression models associating gene expression with MLS and other species traits. Additionally, we used SHapley Additive exPlanations and Bayesian networks to investigate the non-linear nature of the interrelations between the genes predicted to be determinants of species MLS. Our results revealed that expression patterns correlate with MLS, some across organs, and others in an organ-specific manner. The combination of methods employed revealed gene signatures formed by only a few genes that are highly predictive towards MLS, which could be used to identify novel longevity regulator candidates in mammals

Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals

One of the important questions in aging research is how differences in transcriptomics are associated with the longevity of various species. Unfortunately, at the level of individual genes, the links between expression in different organs and maximum lifespan (MLS) are yet to be fully understood. Analyses are complicated further by the fact that MLS is highly associated with other confounding factors (metabolic rate, gestation period, body mass, etc.) and that linear models may be limiting. Using gene expression from 41 mammalian species, across five organs, we constructed gene-centric regression models associating gene expression with MLS and other species traits. Additionally, we used SHapley Additive exPlanations and Bayesian networks to investigate the non-linear nature of the interrelations between the genes predicted to be determinants of species MLS. Our results revealed that expression patterns correlate with MLS, some across organs, and others in an organ-specific manner. The combination of methods employed revealed gene signatures formed by only a few genes that are highly predictive towards MLS, which could be used to identify novel longevity regulator candidates in mammals

Erratum: Wound healing and longevity: Lessons from long-lived \u3b1MUPA mice

In this Article, the additional affiliation is added for Arie Budovsky, a co-author of this manuscript. Erratum for Wound healing and longevity: lessons from long-lived \u3b1MUPA mice. [Aging (Albany NY). 2015

Middle age has a significant impact on gene expression during skin wound healing in male mice

The vast majority of research on the impact of age on skin wound healing (WH) compares old animals to young ones. The middle age is often ignored in biogerontological research despite the fact that many functions that decline in an age-dependent manner have starting points in mid-life. With this in mind, we examined gene expression patterns during skin WH in late middle-aged versus young adult male mice, using the head and back punch models. The rationale behind this study was that the impact of age would first be detectable at the transcriptional level. We pinpointed several pathways which were over-activated in the middle-aged mice, both in the intact skin and during WH. Among them were various metabolic, immune-inflammatory and growth-promoting pathways. These transcriptional changes were much more pronounced in the head than in the back. In summary, the middle age has a significant impact on gene expression in intact and healing skin. It seems that the head punch model is more sensitive to the effect of age than the back model, and we suggest that it should be more widely applied in aging research on wound healing

SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes

Abstract Interventional studies on genetic modulators of longevity have significantly changed gerontology. While available lifespan data are continually accumulating, further understanding of the aging process is still limited by the poor understanding of epistasis and of the non-linear interactions between multiple longevity-associated genes. Unfortunately, based on observations so far, there is no simple method to predict the cumulative impact of genes on lifespan. As a step towards applying predictive methods, but also to provide information for a guided design of epistasis lifespan experiments, we developed SynergyAge - a database containing genetic and lifespan data for animal models obtained through multiple longevity-modulating interventions. The studies included in SynergyAge focus on the lifespan of animal strains which are modified by at least two genetic interventions, with single gene mutants included as reference. SynergyAge, which is publicly available at www.synergyage.info, provides an easy to use web-platform for browsing, searching and filtering through the data, as well as a network-based interactive module for visualization and analysis.</jats:p