Olivia Casanueva

Research Summary

Groundbreaking work in the nematode Caenorhabditis elegans has demonstrated that ageing is not simply a stochastic and progressive decay, but that it is genetically controlled by the longevity pathways. Strikingly, lifespan is highly variable even in genetically identical individuals reared under controlled environmental conditions.

We are interested in finding the mechanisms underlying transcriptional inter-individual variability in genes that modulate lifespan and determining to what extent it explains individual-to-individual differences in the rates of ageing. We are also interested in studying the influence of both stochastic and environmental variability during early life and its long-term effect on health. Dietary restriction (DR), reduced food intake without malnutrition, increases health and function during ageing and protects against ageing-related disease in most organisms. We are interested in understanding how early life nutrition (and DR) can set rates of ageing via epigenetic mechanisms.

Answering these questions requires the development of new technologies that make whole animals centre stage and will have a significant conceptual impact on ageing research and personalized medicine.  

Latest Publications

Multi-Omics and Genome-Scale Modeling Reveal a Metabolic Shift During C. Elegans Ageing.
Hastings J, Mains A, Virk B, Rodriguez N, Murdoch S, Pearce J, Bergmann S, Le Novère N, Casanueva O

In this contribution, we describe a multi-omics systems biology study of the metabolic changes that occur during aging in . Sampling several time points from young adulthood until early old age, our study covers the full duration of aging and include transcriptomics, and targeted MS-based metabolomics. In order to focus on the metabolic changes due to age we used two strains that are metabolically close to wild-type, yet are conditionally non-reproductive. Using these data in combination with a whole-genome model of the metabolism of and mathematical modeling, we predicted metabolic fluxes during early aging. We find that standard Flux Balance Analysis does not accurately predict measured fluxes nor age-related changes associated with the Citric Acid cycle. We present a novel Flux Balance Analysis method where we combined biomass production and targeted metabolomics information to generate an objective function that is more suitable for aging studies. We validated this approach with a detailed case study of the age-associated changes in the Citric Acid cycle. Our approach provides a comprehensive time-resolved multi-omics and modeling resource for studying the metabolic changes during normal aging in .

+ View Abstract

Frontiers in molecular biosciences, 6, 2296-889X, 2019

PMID: 30788345

Modeling Meets Metabolomics-The WormJam Consensus Model as Basis for Metabolic Studies in the Model Organism .
Witting M, Hastings J, Rodriguez N, Joshi CJ, Hattwell JPN, Ebert PR, van Weeghel M, Gao AW, Wakelam MJO, Houtkooper RH, Mains A, Le Novère N, Sadykoff S, Schroeder F, Lewis NE, Schirra HJ, Kaleta C, Casanueva O

Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in , which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in physiology.

+ View Abstract

Frontiers in molecular biosciences, 5, 2296-889X, 2018

PMID: 30488036

Epigenetic inheritance of proteostasis and ageing.
Li C, Casanueva O

Abundant evidence shows that the genome is not as static as once thought and that gene expression can be reversibly modulated by the environment. In some cases, these changes can be transmitted to the next generation even if the environment has reverted. Such transgenerational epigenetic inheritance requires that information be stored in the germline in response to exogenous stressors. One of the most elusive questions in the field of epigenetic inheritance is the identity of such inherited factor(s). Answering this question would allow us to understand how the environment can shape human populations for multiple generations and may help to explain the rapid rise in obesity and neurodegenerative diseases in modern society. It will also provide clues on how we might be able to reprogramme the epigenome to prevent transmission of detrimental phenotypes and identify individuals who might be at increased risk of disease. In this article, we aim to review recent developments in this field, focusing on research conducted mostly in the nematode Caenorhabditis elegans and mice, that link environmental modulators with the transgenerational inheritance of phenotypes that affect protein-folding homoeostasis and ageing.

+ View Abstract

Essays in biochemistry, 60, 1744-1358, 2016

PMID: 27744335