Creation of databases of ageing-related drugs and statistical analysis and applied machine learning for the prioritization of potential lifespan-extension drugs

Dissertação de mestrado em Biofísica e BionanossistemasOver the last few centuries, the success of modern medicine has consistently increased the average life expectancy of mankind. This extended longevity came a paradigm-shift: multimorbidity is now our top concern, instead of the immediate fatal diseases (e.g. infections) of the past. The aged populations currently observed in developed countries, are already having negative recursions in the social state ideal and are expected to spread to the rest of the world. The scientific solution to this predicament lies in developing anti-aging therapies. In the recent decades, the idea that aging is not a fixed biological process was challenged and thoroughly refuted. There are now more than a thousand different genes known to alter lifespan in model organisms, and simple lifestyle interventions like a caloric restriction diet prolong the lifespan of non-human primates. Unfortunately, the discoveries made so far are yet to be translated into meaningful human anti-aging therapies. In this work, we offer several scientific contributions to help mitigate the looming aging crisis. Our most prominent contribution is the creation of the DrugAge database (http://genomics.senescence.info/drugs/). This unparalleled resource systematically compiles information regarding drug lifespan assays that increased the lifespan of model organisms. DrugAge is free, manually curated and is composed of 1316 entries featuring 418 different compounds from studies across 27 model organisms. We used the information provided on DrugAge to: train an algorithm for the prediction of the anti-aging potential of new compounds; conduct the functional enrichment of DrugAge; compare DrugAge with the known anti-aging genes; show that gender does not influence the performance of anti-aging compounds in model organisms. A separate section is dedicated to applying drug repurposing to accelerate the discovery of antiaging drugs in humans. After matching a meta-repository of drug-gene interactions with the known anti-aging genes in model organisms, we found 16 drugs with significant potential to affect the aging process. Two drug combinations are suggested to be tried in model organisms.Durante os últimos séculos, o sucesso da medicina moderna tem consistentemente aumentado a esperença média de vida da humanidade. Esta maior longevidade é acompanhado por uma mudança de paradigma: multimorbidade, causada pela acumulação de doenças relacionadas com o envelhecimento, é agora a nossa principal preocupação, ao invés das doenças fatais imediatas (por exemplo infeções) do passado. As populações envelhecidas presentemente observadas nos países desenvolvidos, já estão a ter repercussões negativas no ideal do estado social e é esperado que estas se alastrem para o resto do mundo. A solução científica para este problema assenta em desenvolver terapias anti-envelhecimento. Nas décadas recentes, o conceito de envelhecimento como um processo biológico fixado foi desafiado e indubitavelmente refutado. Atualmente, conhecem-se mais de um milhar de genes que modificam a longevidade em organismos modelo, e simples modificações no estilo de vida como uma dieta de restrição calórica prolongam a esperança de vida em primatas não-humanos. Infelizmente, as descobertas até hoje realizadas estão ainda para ser traduzidas em terapias antienvelhecimento com impacto em seres humanos. Neste trabalho nós oferecemos várias contribuções científicas para ajudar a mitigar a iminente crise da população envelhecida. A nossa contribuição mais proeminente é a criação da base de dados DrugAge (http://genomics.senescence.info/drugs/). Este recurso sem paralelo congila sistematicamente informação relativa a ensaios de envelhecimento de drogas que aumentaram a longevidade em organismos modelo. DrugAge é grátis, está curada manualmente e é composta por 1316 entradas representando 418 substâncias diferentes provenientes de estudos conduzidos em 27 organismos modelo. Usámos a informação presente na DrugAge para: treinar um algoritmo para estimar o potencial anti-envelhecimento de novos compostos; realizar o enriquecimento funcional de DrugAge; comparar DrugAge com os genes anti-envelhecimento conhecidos; revelar que género não influencia a performance the compostos anti-envelhecimento em organismos modelo. Um capítulo independente é dedicado a aplicar a reutilização de drogas para acelerar a descoberta de drogas anti-envelhecimento em humanos. Depois de fazer a correspondência entre um metarepositório de interações droga-gene e os genes anti-envelhecimento de organismos modelo, encontrámos 16 compostos com um considerável potencial para afetar o processo de envelhecimento. Duas combinações de drogas são sugeridas para serem testadas em organismos modelo

Systematic analysis of the gerontome reveals links between aging and age-related diseases

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases

Human Ageing Genomic Resources:updates on key databases in ageing research

Ageing is a complex and multifactorial process. For two decades, the Human Ageing Genomic Resources (HAGR) have aided researchers in the study of various aspects of ageing and its manipulation. Here, we present the key features and recent enhancements of these resources, focusing on its six main databases. One database, GenAge, focuses on genes related to ageing, featuring 307 genes linked to human ageing and 2205 genes associated with longevity and ageing in model organisms. AnAge focuses on ageing, longevity, and life-history across animal species, containing data on 4645 species. DrugAge includes information about 1097 longevity drugs and compounds in model organisms such as mice, rats, flies, worms and yeast. GenDR provides a list of 214 genes associated with the life-extending benefits of dietary restriction in model organisms. CellAge contains a catalogue of 866 genes associated with cellular senescence. The LongevityMap serves as a repository for genetic variants associated with human longevity, encompassing 3144 variants pertaining to 884 genes. Additionally, HAGR provides various tools as well as gene expression signatures of ageing, dietary restriction, and replicative senescence based on meta-analyses. Our databases are integrated, regularly updated, and manually curated by experts. HAGR is freely available online (https://genomics.senescence.info/).</p

Human Ageing Genomic Resources:updates on key databases in ageing research

Ageing is a complex and multifactorial process. For two decades, the Human Ageing Genomic Resources (HAGR) have aided researchers in the study of various aspects of ageing and its manipulation. Here, we present the key features and recent enhancements of these resources, focusing on its six main databases. One database, GenAge, focuses on genes related to ageing, featuring 307 genes linked to human ageing and 2205 genes associated with longevity and ageing in model organisms. AnAge focuses on ageing, longevity, and life-history across animal species, containing data on 4645 species. DrugAge includes information about 1097 longevity drugs and compounds in model organisms such as mice, rats, flies, worms and yeast. GenDR provides a list of 214 genes associated with the life-extending benefits of dietary restriction in model organisms. CellAge contains a catalogue of 866 genes associated with cellular senescence. The LongevityMap serves as a repository for genetic variants associated with human longevity, encompassing 3144 variants pertaining to 884 genes. Additionally, HAGR provides various tools as well as gene expression signatures of ageing, dietary restriction, and replicative senescence based on meta-analyses. Our databases are integrated, regularly updated, and manually curated by experts. HAGR is freely available online (https://genomics.senescence.info/).</p

EXTENDING HEALTHY LIFESPAN BY SYSTEMATICALLY TARGETING AGEING PATHWAYS SYNERGIES

Ph.DDOCTOR OF PHILOSOPHY (SOM

A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence

Aging and senescence are characterized by pervasive transcriptional dysfunction, including increased expression of transposons and introns. Our aim was to elucidate mechanisms behind this increased expression. Most transposons are found within genes and introns, with a large minority being close to genes. This raises the possibility that transcriptional readthrough and intron retention are responsible for age-related changes in transposon expression rather than expression of autonomous transposons. To test this, we compiled public RNA-seq datasets from aged human fibroblasts, replicative and drug-induced senescence in human cells and RNA-seq from aging mice and senescent mouse cells. Indeed, our reanalysis revealed a correlation between transposons expression, intron retention and transcriptional readthrough across samples and within samples. Both intron retention and readthrough increased with aging or cellular senescence and these transcriptional defects were more pronounced in human samples as compared to those of mice. In support of a causal connection between readthrough and transposon expression, analysis of models showing induced transcriptional readthrough confirmed that they also show elevated transposon expression. Taken together, our data shows that elevated transposon reads during aging seen in various RNA-seq dataset are concomitant with multiple transcriptional defects. Intron retention and transcriptional readthrough are the most likely explanation for the expression of transposable elements that lack a functional promoter.Published versionWe would like to thank VitaDAO for financial support

Alpha-ketoglutarate supplementation and BiologicaL agE in middle-aged adults (ABLE)-intervention study protocol

10.1007/s11357-023-00813-6GEROSCIENC

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

Human Ageing Genomic Resources: new and updated databases

In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR’s main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) 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; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/)

Drug Synergy Slows Aging and Improves Healthspan through IGF and SREBP Lipid Signaling

10.1016/j.devcel.2018.09.001DEVELOPMENTAL CELL47167-