Genetic Systems

Our goal is to understand and predict phenotypic variation amongst individuals. This includes the distribution, importance and interactions of inherited genetic variation, somatic mutations, and induced, stochastic and inherited epigenetic variation. Predicting how individuals vary is a fundamental challenge for biology but also central to the development of personalised medicine:  a patient does not want to know the typical outcome of a mutation that they carry; they want to know what will actually happen to them. As model systems we primarily use worms, yeast and tumours, choosing the system and approach according to the question at hand.

• Somatic mutations/cancer: Many processes can cause the same nucleotide change in a genome, making the identification of the mechanisms causing mutations a difficult challenge.  We showed that clustered mutations provide a more precise fingerprint of mutagenic processes.   Of nine clustered mutation signatures identified from >1,000 tumour genomes, three relate to variable APOBEC activity and three are associated with tobacco smoking.  An additional signature matches the spectrum of translesion DNA polymerase eta (POLH). In lymphoid cells these mutations target promoters, consistent with AID-initiated somatic hypermutation.  In solid tumours, however, they are associated with UV-exposure and alcohol consumption and target the H3K36me3 chromatin of active genes in a mismatch repair (MMR)-dependent manner. These regions normally have a low mutation rate because error-free MMR also targets H3K36me3 chromatin.  Carcinogens and error-prone repair therefore redistribute mutations to the more important regions of the genome, contributing a substantial mutation load in many tumours, including driver mutations (Supek, Cell 2017).
• Transgenerational epigenetic inheritance: We discovered that a temperature-induced change in expression from a  elegans heterochromatic gene array can endure for at least 14 generations. Inheritance is primarily in cis with the locus, occurs through both oocytes and sperm, and is associated with altered trimethylation of histone H3 lysine 9 (H3K9me3) before the onset of zygotic transcription. Expression profiling revealed that temperature-induced expression from endogenous repressed repeats can also be inherited for multiple generations (Klosin, Science 2017).  This work represents one of the best characterised and longest-lasting examples of a trans-generational epigenetic memory of an environmental perturbation in an animal.
• Epigenetics: Through an RNAi screen, we discovered that impaired DNA replication during embryonic development results in a global de-repression of chromatin because of the loss of modified nucleosomes from the genome during the early embryonic cleavages. The resulting ‘epi-alleles’ are stable during development with at least some also inherited between generations (Klosin, Reis, Science Advances 2017.  This work illustrates how impairments in replication can also drive directional epigenetic changes in a genome, with these changes sometimes being stably inherited.
• Intergenerational epigenetics: Starting from single animal-expression profiling, we discovered that maternal age is a major cause of phenotypic variation in wild-type elegans populations, altering developmental speed, stress-resistance, fecundity, and the relative developmental rates between organs. We showed that age-dependent change in the maternal provisioning of a lipoprotein complex is the cause of variation in multiple traits throughout the life of an animal (Perez, Francesconi, Nature 2017). This is an unusual example of how parental physiology (age) determines the characteristics of individuals throughout their lives.
• Noise and robustness:  Isogenic cells in a common environment show substantial cell-to-cell variation in gene expression, often referred to as “expression noise.” We used multiple single-cell RNA-sequencing datasets to identify features associated with high or low expression noise in mouse embryonic stem cells. These include the core promoter architecture of a gene, with CpG island promoters and a TATA box associated with low and high noise, respectively. High noise is also associated with “conflicting” chromatin states-the absence of transcription-associated histone modifications or the presence of repressive ones in active genes. Genes regulated by pluripotency factors through super-enhancers show high and correlated expression variability, consistent with fluctuations in the pluripotent state. Together, our results provide an integrated view of how core promoters, chromatin, regulation, and pluripotency fluctuations contribute to the variability of gene expression across individual stem cells (Faure, Cell Systems 2017).