The quest to slow ageing through drug discovery

Although death is inevitable, individuals have long sought to alter the course of the ageing process. Indeed, ageing has proved to be modifiable; by intervening in biological systems, such as nutrient sensing, cellular senescence, the systemic environment and the gut microbiome, phenotypes of ageing can be slowed sufficiently to mitigate age-related functional decline. These interventions can also delay the onset of many disabling, chronic diseases, including cancer, cardiovascular disease and neurodegeneration, in animal models. Here, we examine the most promising interventions to slow ageing and group them into two tiers based on the robustness of the preclinical, and some clinical, results, in which the top tier includes rapamycin, senolytics, metformin, acarbose, spermidine, NAD+ enhancers and lithium. We then focus on the potential of the interventions and the feasibility of conducting clinical trials with these agents, with the overall aim of maintaining health for longer before the end of life

Using the drug-protein interactome to identify anti-ageing compounds for humans

Advancing age is the dominant risk factor for most of the major killer diseases in developed countries. Hence, ameliorating the effects of ageing may prevent multiple diseases simultaneously. Drugs licensed for human use against specific diseases have proved to be effective in extending lifespan and healthspan in animal models, suggesting that there is scope for drug repurposing in humans. New bioinformatic methods to identify and prioritise potential anti-ageing compounds for humans are therefore of interest. In this study, we first used drug-protein interaction information, to rank 1,147 drugs by their likelihood of targeting ageing-related gene products in humans. Among 19 statistically significant drugs, 6 have already been shown to have pro-longevity properties in animal models (p < 0.001). Using the targets of each drug, we established their association with ageing at multiple levels of biological action including pathways, functions and protein interactions. Finally, combining all the data, we calculated a ranked list of drugs that identified tanespimycin, an inhibitor of HSP-90, as the top-ranked novel anti-ageing candidate. We experimentally validated the pro-longevity effect of tanespimycin through its HSP-90 target in Caenorhabditis elegans

Identifying Potential Ageing-Modulating Drugs In Silico

Increasing human life expectancy has posed increasing challenges for healthcare systems. As people age, they become more susceptible to chronic diseases, with an increasing burden of multimorbidity, and the associated polypharmacy. Accumulating evidence from work with laboratory animals has shown that ageing is a malleable process that can be ameliorated by genetic and environmental interventions. Drugs that modulate the ageing process may delay or even prevent the incidence of multiple diseases of ageing. To identify novel, anti-ageing drugs, several studies have developed computational drug-repurposing strategies. We review published studies showing the potential of current drugs to modulate ageing. Future studies should integrate current knowledge with multi-omics, health records, and drug safety data to predict drugs that can improve health in late life

In silico identification of genetic and pharmacological interventions to modulate ageing

As life expectancy increases and fertility rates decrease, the growing ageing population poses a significant challenge to the healthcare systems of developed countries. Ageing as the major risk factor for chronic diseases constitutes the primary target to reduce the burden of diseases and improve human health. However, ageing is a complex process and predicting potential interventions into it requires system-level approaches. In this thesis, I present the development of two computational methods using biological data to predict novel genetic and pharmacological interventions to ameliorate ageing. My first study focused on identifying repurposable drugs to delay human ageing. Several computational drug-repurposing studies have been developed, but most of them focus on predicting geroprotectors using animal models data, even though certain aspects of ageing may be human-specific. Using drug-protein interaction information, I searched for drugs targeting a significant proportion of human ageing-related genes and pathways. The top-ranked drugs included a significant number of known geroprotectors, validating the capability of the method to discover drugs to modulate ageing. On the top of the list was tanespimycin, a heat shock protein inhibitor, whose geroprotective properties we validated experimentally. My second study centres on determining the molecular mechanisms associated with healthy lifespan, and how to use this information to find new genetic interventions to delay ageing. In recent years, the number of transcriptomic studies of mouse models of ageing has increased dramatically, providing the opportunity to compare gene expression changes of long- and short-lived strains. I showed that differences in healthy lifespan are associated with expression changes in genes regulating mitochondrial metabolism. Using these gene sets as biomarkers of lifespan, I compared the mouse models of ageing against 51 genetically engineered mice and predicted candidate genetic and pharmacological interventions with the potential to delay ageing. Through computational studies I predicted a narrowed down list of candidate genetic and pharmacological interventions to delay mouse and human ageing and validated several predictions made by other researchers using different methods, confirming the robustness of computational methods to identify new anti-ageing interventions. With the discovery of tanespimycin as a new geroprotector, I revealed that a little proteostatic stress is good for longevity and that we can trigger this hormetic response pharmacologically. I exposed the complexity of ageing as I found multiple mechanisms to delay ageing, most of which were tissue-specific, and found evidence for new candidate hallmarks of ageing and novel biomarkers of lifespan

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

A gene signature for Alzheimer’s disease using RNAi in C. elegans

Alzheimer’s disease (AD) is a complex multifactorial disorder that is responsible for the large majority of the 50 million cases of dementia worldwide. This disease is still incurable, a situation caused at least in part by the fact that its genetics are incompletely known. In our laboratory, we have developed a novel computational approach—Network-based Transcriptome-Wide Association Studies (nTWAS)—that seeks to identify the genes associated with AD by comparing gene expression patterns across tissues in the brain. nTWAS acts as an in silico pre-screen by providing a list of gene candidates, thus enabling us to pursue investigations into each gene candidate with significantly more depth. To that end, we use RNA interference in an AD model in the nematode worm Caenorhabditis elegans to validate the results of this pre-screen. C. elegans is a well-established research tool in biological and biochemical research for its ease of culture, small size, short generation time, and relative simplicity. Furthermore, the worm’s facility for genetic manipulation and remarkably similar cellular characteristics to those of humans have allowed for numerous advances in the study of cancer, neurodegeneration, and ageing. Our approach takes advantage of an automated worm tracking platform, developed in our laboratory, that can simultaneously track hundreds of worms and make precise measurements of their motility, defects of which has been shown to correlate with neurological and muscular toxicity. While standard approaches typically only take data on dozens of worms, the vastly increased population size of our approach greatly improves the statistical power of our screen. We have leveraged these improvements in screening methods to associate the differences in distributions of these parameters with phenotypic changes across various siRNA conditions. Through both motility screening and validation by imaging, we identified ckr-2, skr-21, and Y92H12A.2 as modulators of amyloid beta aggregation. While skr-21 and Y92H12A.2 are both components of the ubiquitin-proteasome system, ckr-2 is an ortholog of a neuronal cholecystokinin receptor which has been suggested to be a biomarker of AD but for which no mechanism is known. The results of this work thus contribute to extending our understanding of the gene signature of AD

Mitoriboscins : mitochondrial-based therapeutics targeting cancer stem cells (CSCs), bacteria and pathogenic yeast

The “endo-symbiotic theory of mitochondrial evolution” states that mitochondrial organelles evolved from engulfed aerobic bacteria, after millions of years of symbiosis and adaptation. Here, we have exploited this premise to design new antibiotics and novel anti-cancer therapies, using a convergent approach. First, virtual high-throughput screening (vHTS) and computational chemistry were used to identify novel compounds binding to the 3D structure of the mammalian mitochondrial ribosome. The resulting library of ~880 compounds was then subjected to phenotypic drug screening on human cancer cells, to identify which compounds functionally induce ATP-depletion, which is characteristic of mitochondrial inhibition. Notably, the top ten “hit” compounds define four new classes of mitochondrial inhibitors. Next, we further validated that these novel mitochondrial inhibitors metabolically target mitochondrial respiration in cancer cells and effectively inhibit the propagation of cancer stem-like cells in vitro. Finally, we show that these mitochondrial inhibitors possess broad-spectrum antibiotic activity, preventing the growth of both gram-positive and gram-negative bacteria, as well as C. albicans – a pathogenic yeast. Remarkably, these novel antibiotics also were effective against methicillin-resistant Staphylococcus aureus (MRSA). Thus, this simple, yet systematic, approach to the discovery of mitochondrial ribosome inhibitors could provide a plethora of anti-microbials and anti-cancer therapies, to target drug-resistance that is characteristic of both i) tumor recurrence and ii) infectious disease. In summary, we have successfully used vHTS combined with phenotypic drug screening of human cancer cells to identify several new classes of broad-spectrum antibiotics that target both bacteria and pathogenic yeast. We propose the new term “mitoriboscins” to describe these novel mitochondrial-related antibiotics. Thus far, we have identified four different classes of mitoriboscins, such as: 1) mitoribocyclines, 2) mitoribomycins, 3) mitoribosporins and 4) mitoribofloxins. However, we broadly define mitoriboscins as any small molecule(s) or peptide(s) that bind to the mitoribosome (large or small subunits) and, as a consequence, inhibit mitochondrial function, i.e., mitoribosome inhibitors

Metabolic influences on ageing in Caenorhabditis elegans: A time series multi-omics and metabolic modelling study

Ageing presents one of the most fundamental public health challenges of our time. Progress in living standards, combating infectious disease and promoting safety, and general nutritional availability, has led to an increase in lifespan across the developed world. However, this has been accompanied by an increase in the duration of late-life frailty and associated conditions affecting health. Metabolism is known to be a key mediator of ageing across the diversity of living species. Many of the pathways that extend lifespan and promote healthspan are known to be metabolic and relate to managing the balance of energy availability to optimise resource usage and survival during times of scarcity. The model organism Caenorhabditis elegans, a small transparent nematode worm that ordinarily lives in the soil and eats bacteria, is one of the most common organisms used in the study of ageing as it is easy to culture in laboratory conditions and has a short lifespan of around three weeks under normal conditions. In this thesis, I analyse in detail the metabolic changes that occur during ageing in C. elegans, using a multi-omics metabolomics and transcriptomics time series of measurements in three C. elegans strains, and mathematical modelling. Whole-genome metabolic models are representations of all the metabolic reactions taking place within an organism together with their metabolic inputs and outputs, and enzymatic catalysts. I describe the development and validation of a community-wide shared whole-genome metabolic model for C. elegans. Using this model together with measured gene expression levels for each enzyme that catalyses a reaction, it is possible to predict intracellular reaction fluxes using a method called Flux Balance Analysis (FBA). I describe a novel method for the integration of metabolomics data with FBA, and the results of a comparative analysis of the resulting fluxes in normal wild-type ageing. I then go on to describe the differences to a germline-free strain that is long-lived and metabolically different. Finally, I have used the model to probe the metabolic flexibility and evidence for trans-omics bidirectional regulation between the transcriptomic and metabolomic layers

Biophysical studies of protein misfolding and aggregation in in vivo models of Alzheimer's and Parkinson's diseases.

Neurodegenerative disorders, including Alzheimer's (AD) and Parkinson's diseases (PD), are characterised by the formation of aberrant assemblies of misfolded proteins. The discovery of disease-modifying drugs for these disorders is challenging, in part because we still have a limited understanding of their molecular origins. In this review, we discuss how biophysical approaches can help explain the formation of the aberrant conformational states of proteins whose neurotoxic effects underlie these diseases. We discuss in particular models based on the transgenic expression of amyloid-β (Aβ) and tau in AD, and α-synuclein in PD. Because biophysical methods have enabled an accurate quantification and a detailed understanding of the molecular mechanisms underlying protein misfolding and aggregation in vitro, we expect that the further development of these methods to probe directly the corresponding mechanisms in vivo will open effective routes for diagnostic and therapeutic interventions