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Toren, D.

Publications and source records attributed to Toren, D..

4 recordsLinked to original sources

Drug prediction for reversing AD/PD transcriptional profiles using an aging of systems-centric approach

Age-related pathologies are so widely presented in old age that in most cases they are hardly distinguishable at the molecular level from the so-called normal aging. Both aging and age-related diseases are characterized by a wide range of transcriptional and epigenetic changes that underlie the physiological or pathological phenotype, with plenty of overlap in their signatures, but also with differences. In most pathological conditions it is rather the dysregulation of a complex network of genes than a problem with a single gene dysregulation that causes its emergence or progression, and aging differently gives a "predisposition" towards an age-related pathology or another, or in a favorable situation towards none. The important question is how similar are the transcriptional changes during "healthy" aging with those that occur in age-related diseases. In this study, we explore gene expression data to answer this question and aim to predict which drugs and compounds could have a reversing effect on their common drift.

bioinformatics↗

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

Interventional studies on genetic modulators of longevity have significantly changed gerontology. While available lifespan data is 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. Database URL: http://www.synergyage.info/ BACKGROUND & SUMMARYThe aging process can be genetically modulated. This has been previously shown in many studies in which average lifespan, and in some cases even maximum lifespan, has been modified by genetic interventions. It is for example possible to have genetic mutants with an increased lifespan, up to ten times higher compared to wild type in C. elegans 1, and up to 150% and 46% in D. melanogaster and M. musculus, respectively 2,3. Up until now, at least 2,205 genes, whose mutations, downregulation or overexpression results in a long- or short-lived phenotype, have been identified in model organisms. A comprehensive list with these longevity-associated genes (LAGs), including more detailed information about lifespan experiments, can be found in the GenAge database 4. This type and amount of data have made it possible for higher level analyses to be performed 5-7, and the collection of LAGs in public repositories has significantly pushed biogerontology towards more integrative approaches to study longevity. One important aspect observed is that many LAGs seem to act in a cooperative manner 8-10 and are not independent regulators of lifespan. In fact, in most cases when combining two or more genetic interventions, the effect is rarely additive, as genes are generally epistatic and interact in nonlinear ways 11,12. While in most cases combined interventions seem to have lower than expected results in how much they extend lifespan, there are also a minority of cases where genes act synergistically 13,14. Even so, the much more common case is that of studies where partially dependent gene interactions are revealed, making it even more important to understand and predict genetic dependencies. Unfortunately, data on epistasis is much harder to obtain through wide-screen experimental studies, which has been for example the case for the discovery of most LAG interventions in worms. The main impediment comes from the combinatorial explosion of multiple gene groups for which lifespan assays would need to be measured in a "blind" search, through wet-lab experiments. Instead, it would be more efficient to use existing epistasis data to explore predicted synergies in guided lifespan experiments. Luckily, an accumulating number of papers has been published in the last two decades with reported lifespans for double mutants and in some cases even triple or quadruple mutants. As such, it has been now possible for us: (i) to collect the data from existing studies containing lifespan records for strains that have multiple genes modulated, and (ii) to create an intuitive, network-based tool, which allows users to explore in a fast, visual and interactive way the lifespan relationships between these strains. Here, we present SynergyAge, a database containing manually curated data, extracted from experimental studies, regarding gene combinations that affect lifespan. With the creation of SynergyAge, we aim to encourage the investigation of the cumulative effects of different gene interventions on lifespan, by providing the scientific community with a "one-stop" web platform to access, compare and analyze lifespan synergisms or antagonisms. This resource is of particular interest in designing wet-lab experiments in which multi-genetic lifespan interventions are needed. SynergyAge (http://www.synergyage.info) is publicly available and contains data from three animal model organisms: Caenorhabditis elegans, Drosophila melanogaster and Mus musculus.

systems biology↗

Gray whale transcriptome reveals longevity adaptations associated with DNA repair, autophagy and ubiquitination

One important question in aging research is how differences in genomics and transcriptomics determine the maximum lifespan in various species. Despite recent progress, much is still unclear on the topic, partly due to the lack of samples in non-model organisms and due to challenges in direct comparisons of transcriptomes from different species. The novel ranking-based method that we employ here is used to analyze gene expression in the gray whale and compare its de novo assembled transcriptome with that of other long- and short-lived mammals. Gray whales are among the top 1% longest-lived mammals. Despite the extreme environment, or maybe due to a remarkable adaptation to its habitat (intermittent hypoxia, Arctic water, and high pressure), gray whales reach at least the age of 77 years. In this work, we show that long-lived mammals share common gene expression patterns between themselves, including high expression of DNA maintenance and repair, ubiquitination, apoptosis, and immune responses. Additionally, the level of expression for gray whale orthologs of pro- and anti-longevity genes found in model organisms is in support of their alleged role and direction in lifespan determination. Remarkably, among highly expressed pro-longevity genes many are stress-related, reflecting an adaptation to extreme environmental conditions. The conducted analysis suggests that the gray whale potentially possesses high resistance to cancer and stress, at least in part ensuring its longevity. This new transcriptome assembly also provides important resources to support the efforts of maintaining the endangered population of gray whales.

evolutionary biology↗

Human Ageing Genomic Resources: 2018 Update

In spite of a growing body of research and data, human ageing remains a poorly understood process. To facilitate studies of ageing, over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), which are now the leading online resource for biogerontologists. In this update, we present HAGRs main functionalities, including new additions and improvements to HAGR. HAGR consists of five databases: 1) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; 2) the AnAge database of animal ageing and longevity, featuring >4000 species; 3) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; 4) the LongevityMap database of human genetic association studies of longevity with >500 entries; 5) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; 6) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts to ensure a high quality data and presented in an intuitive and clear interface that includes cross-links across our databases and to external resources. HAGR is freely available online (http://genomics.senescence.info/).

genomics↗