Research
Research
Rejuvenomicslab
Current Research Projects
The complexity and multi-dimensional nature of the ageing process require that this biological problem be tackled using a combination of disciplines and approaches. Below is a list of strategies and methods we employ in our work.
Ultimately, however, we believe it is the integration of these different approaches that will mostly enhance our knowledge of longevity and ageing, which is why we aim to develop our research in a highly collaborative and interdisciplinary environment.
Biologists can be divided into two classes: experimentalists who observe things that cannot be explained, and theoreticians who explain things that cannot be observed.
Many genes have been shown to regulate ageing in model systems. It is now necessary, however, to study how these genes interact and how they exert their influence as an aggregate to modulate the ageing process.
For that purpose, we have been developing a number of bioinformatics resources to try to understand how the parts, the genes, influence the ageing process as a whole.
Specifically, we developed the GenAge database, the benchmark database of ageing-related genes in model organisms and in humans, and are now integrating GenAge with other types of data, such as gene expression profiles, protein-protein interactions, epigenetics, co-expression data and phenotypic data from animal studies and human cohorts.
Our goal is to enhance our understanding of gene networks and transcriptional regulation during ageing and build better models of ageing that help guide experiments, for example by identifying key network regulators of ageing.
We also continue to develop algorithms that predict gene behaviour and provide testable hypotheses. Such methods could be applied to ageing and also to many other processes and diseases.
Moreover, we are taking advantage of single-cell sequencing to investigate age-related changes in intercellular communication.
Findings from model organisms show that ageing is surprisingly plastic and can be manipulated not only by genes (as indicated above) but also by diet.
The best-studied dietary manipulation of ageing is caloric restriction (CR), which consists of restricting the food intake of organisms without triggering malnutrition and has been shown to retard ageing across multiple animal models.
We are taking advantage of genomic technologies, including next-generation sequencing, to characterize the molecular changes during CR and gain mechanistic insights about ageing and its modulation by diet.
Moreover, we are employing integrative and system-level approaches to study the signaling pathways and identify key genes mediating the life-extending effects of CR.
Our lab established the first database of genes related to CR and then used a variety of approaches to analyse, for the first time in a systematic fashion, the gene network of CR.
Understanding the underlying mechanisms of CR and identifying key regulators may lead to new interventions with unprecedented impact on human health. Indeed, we are also employing network pharmacology and machine learning approaches to identify new caloric restriction mimetic drugs.
In the long-term, we hope this line of research will open translational opportunities to develop therapies targeting age-related conditions.
Because ageing impacts on so many diseases and genome analysis and interpretation have broad biomedical implications, we also have a great interest in the genomics of complex human diseases.
By taking advantage of our expertise in genomics, bioinformatics, statistics and cell biology, we aim to help unravel the genetic and molecular mechanisms of complex human diseases.
Given our group’s interest in ageing, age-related diseases like cancer and neurological disorders are of particular interest. Our data-mining analyses have already revealed new candidate cancer-related genes (e.g., C1ORF112) which we are studying experimentally in vitro and in collaboration with clinicians.
Moreover, we have been using machine learning methods for over 10 years, and we are very keen to apply machine learning/artificial intelligence approaches to large datasets in order to gain insights into human phenotypes and diseases.
Of note, we are employing machine, AI and deep learning approaches to predict new genes associated with age-related conditions.
Cells are the fundamental building blocks of animals. It is clear that human ageing has a cellular component and that, for instance, some aspects of cell cycle regulation are related to the ageing process.
Therefore, cell biology is a centrepiece of our lab’s work, and cell studies complement many of our genomic and computational approaches.
Our studies have focused significantly on cell senescence, a permanent state of replicative arrest in otherwise proliferating cells. Cellular senescence is a hallmark of ageing and has been linked to ageing-related diseases like cancer.
We previously developed CellAge, a new database of cell senescence human genes, and have been performing verious systems-level analyses of cellular senescence.
Our goal is to combine in silico and in vitro methods to identify communication and signalling pathways involved in mediating the actions of senescent cells as well as predict potential new drugs targeting senescence.
More recently we have been studying rejuvenation by cellular reprogramming with Yamanaka factors using a combination of methods.
One of the most striking observations (and mysteries) in the field of ageing research is the variety of ageing rates among similar species such as mammals or even primates.
Clearly, the genome determines the features of each species’ ageing process to a large extent. Understanding why and how evolution gives rise to genomes that result in similar organisms with vastly different paces of ageing has an enormous potential to provide biologically-significant clues about the genetics of ageing.
Therefore, one of the aims of our work is to develop and implement computational high-throughput approaches, such as comparative genomics, to study the evolution of complex processes such as ageing.
As more organisms are sequenced, it is becoming possible to obtain detailed models of genome evolution to, for example, identify candidate human longevity genes by searching for genes with unique signatures of selection.
Because longevity evolved in the human lineage, we are particularly interested in employing modern computational methods in primates to study the evolution, structure, and function of genes associated with ageing, which may shed light on the genetic changes that contributed to the evolution of human longevity.
Ultimately, our goal is to understand why we are different from each other and from other species and what is the role of each DNA base in the genome in determining these differences, in particular in the context of ageing and age-related diseases.
Stemming from the above rationale for studying the biological underpinnings of species differences in longevity and ageing, we are interested in studying the unique genetics, physiology, and cell biology of long-lived animals.
One of such animals is the naked mole-rat (Heterocephalus glaber, shown on the left), the longest-lived rodent not known to develop cancer. Furthermore, our lab led the sequencing and analysis of the genome of the longest-lived mammal, the bowhead whale (Balaena mysticetus).
We are also using next-generation sequencing platforms to unravel the longevity secrets of the naked mole-rat. Lastly, we are studying cells from the naked mole-rat.
Because DNA damage accumulation is thought to be one of the causes of ageing and cancer, our hypothesis is that the naked mole-rat’s unique mechanisms for coping with DNA damage are crucial for its longevity and healthy ageing.
Discovery consists of seeing what everybody has seen and thinking what nobody has thought.
Lastly, we are interested in putting our findings and ideas together to address the big questions in gerontology and develop a coherent theoretical framework that explains ageing.
One hypothesis we are particularly interested in is the idea that some developmental mechanisms shaped by evolution to optimize reproduction have an impact on ageing and age-related diseases. The free radical theory of ageing, for instance, can be interpreted in light of the roles of reactive oxygen species in development and cellular growth.
We are also fascinated by the potential role of epigenetic modifications in ageing, and we are developing a new model for how epigenetic damage can be the root driving force of the ageing phenotype.
