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Final Report Summary - ELDERLYEAST (Modelling the Complexity of Ageing in a Simple Genetic System)

The Marie Curie CIG proposal “Modelling the Complexity of Ageing in a Simple Genetic System” aims to characterise the natural variation in yeast ageing in order to understand the underlying molecular mechanisms. More generally, this project fit with the broad interest of my laboratory to exploit natural variation as a tool for understanding how complex phenotypes are genetically regulated. Specifically we proposed the following aims:

- Aim 1: An artificial population as a framework for QTL mapping
- Aim 2: Ageing and the interplay with environment, genome stability and telomere regulation
- Aim 3: Identify ageing genetic variants
- Aim 4: Population genomics of longevity

Description of the work performed since the beginning of the project:
In line with the Marie Curie proposal, we made excellent progress in all four objectives listed above. We partially concluded aim 1 and released a yeast mapping multiparent population (SGRP-4X) obtained from outcrossing four wild founders representative of the main S. cerevisiae lineages for 12 generations [Cubillos FA et al. 2013; Genetics]. In addition to our original proposal, we were awarded of a sequencing grant “The 1002 yeast genomes project: a framework for genome-wide association studies (GWAS)” (with Joseph Schacherer as co-coordinator, University of Strasbourg, France). Whole genome resequencing has been applied to 1002 S. cerevisiae strains isolated from a vast array of sources in different continents (http://1002genomes.u-strasbg.fr/). This project provided an exhaustive view of the genome architecture and genetic diversity and enable genome-wide association studies of longevity variants. The initial paper with the global description of the resource will be submitted soon. Furthermore we have phenotyped the entire collection for CLS and applied GWAS. This population genomics dataset will provide a great asset for what proposed in Aim 4 of the CIG as well as will guide a correct strain selection for a new multiparent population (with 8 or even 16 strains, Aim 1).
Furthermore, we have devised a new powerful way to dissect complex traits devising the Phased Outbred Lines (POLs) approach [Hallin J et al. 2016; Nature Communications, Mertens K et al. 2016; Nature Communications]. We are applying the POLs library to CLS studies in different environment and mapped several new candidates previously unlinked to ageing (Aim 2 and Aim 3). We are focusing our environment interactions studies on Rapamycin and caloric restriction and identified environmental dependent QTLs. We also characterized variants involved in desiccation and starvation resistance and investigating their overlap with cellular ageing. We also initiate collaboration with Gilles Fischer (UPMC, Paris) to monitor genome stability in old cells. We developed a series of strains [Louvel H et al. 2014; Yeast] where additional genetic cassettes will be transformed to allow measuring the rate of different genome instability events (e.g. duplications, deletions, inversions). Finally, we have performed QTL mapping for CLS in individuals and we are currently running the pool selection experiments as proposed in Aim 3.

Description of the main results achieved so far:
In line with our proposal, we released a yeast mapping multiparent population obtained from outcrossing the four wild founders representative of the main S. cerevisiae lineages for 12 generations. SGRP-4X contains 10 million segregants with fine-grained mosaic genomes and greatly reduced linkage, while retaining the phenotypic diversity of the parental strains. We demonstrate the power and resolution of QTL mapping in this population by both traditional linkage analysis on 179 genotyped and phenotyped individuals and a recently developed approach of sequencing the entire population under selection. Using these individuals, we mapped 25 loci linked to growth traits under heat, arsenite and paraquat stress, the majority of which were best explained by a diverging phenotype caused by a single allele in one condition. By sequencing pooled DNA from millions of segregants grown under heat stress, we further identified 34 and 39 regions under selection in haploid and diploid pools respectively. We are now using this resource to map QTLs for CLS and telomere length. With rapid advances in highly multiplexed whole-genome sequencing, an even larger number of new individuals derived from the SGRP4-X could be genotyped in the future to approach nearly full power to map trait loci.
Aside from to the QTL mapping performed in the SGRP-4X mapping population, we explored new methodology to generate recombinant hybrids. We used the property of S. cerevisiae to enter the meiotic developmental program, induce meiotic Spo11-dependent double-strand breaks genome-wide and return to mitotic growth, a process known as Return To Growth (RTG). Whole genome sequencing of 36 RTG strains derived from the hybrid S288c/SK1 diploid strain demonstrates that the RTGs are bona fide diploids with mosaic recombined genome, derived from either parental origin. Individual RTG genome-wide genotypes are comprised of 5 to 87 homozygous regions due to the loss of heterozygous (LOH) events of various lengths, varying between a few nucleotides up to several hundred kilobases. Phenotype/genotype analysis of the RTG strains for the auxotrophic and arsenate resistance traits validates the potential of this procedure of genome diversification to rapidly map complex traits loci (QTLs) in diploid strains without undergoing sexual reproduction. This project has lead to new research direction, a major ANR grant as coordinator, a patent and several manuscript either published or under preparation.

The expected final results and their potential impact and use (including the socio-economic impact and the wider societal implications of the project so far:
Ageing is a complex and multifactorial trait and has a massive impact on human society. Studies in genetics system have identified some of the pathways that regulate longevity. Most of these studies relied on the artificial inactivation of genes (reverse genetics) in highly manipulated laboratory organisms. In contrast, this project aims to elucidate the genetic mechanisms underlying longevity by characterising the solutions exploited by nature through evolution (forward genetics). The four complementary objectives use similar experimental approaches but are not dependent upon one another for success. The artificially outbred yeast population (O1) provides an important framework for mapping longevity variants (O3), for understanding how they interact with the environment (O2) and impact ageing in natural populations (O4). The proposed experimental and computational approaches are highly innovative and are at the forefront of the field of genetics. In the future, the lessons learned from this simple genetic system will be applied to other phenotypes including human diseases. The experiments proposed will reveal novel mechanistic details of ageing. Given that many of the longevity pathways are conserved from yeast to humans, we have the ambition to test previously uncharacterised genes in other model systems in collaboration with team within the host institute. The outcome of this research is crucial to promoting health span in humans and livestock.

Web resources:
Although we do not have an ad hoc web resources for the project MC-CIG, the following supporting links are available:

Our webpage of the lab at IRCAN: http://www.ircan.org/index.php?option=com_content&view=article&id=72&Itemid=98

Our population genomics resource (available via Alan Moses wepage):
http://www.moseslab.csb.utoronto.ca/sgrp/
1002 S. cerevisiae genome project:
(http://1002genomes.u-strasbg.fr/)

Population Level Reference Yeast Genomes:
https://yjx1217.github.io/Yeast_PacBio_2016/welcome/

My research output in Google Scholar:
http://scholar.google.fr/citations?user=DKDEoMcAAAAJ&hl=en

Related information

Result In Brief

Contact

Sophie Deschaintres, (Responsable Partenariat et Valorisation)
Tel.: +33 4 93954260
Fax: +33 4 92960339
E-mail

Subjects

Life Sciences
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