Wong, M. S. | Search bioRxiv

bioRxiv2021

Nuclear stabilisation of p53 requires a functional nucleolar surveillance pathway

The nucleolar surveillance pathway (NSP) monitors nucleolar fidelity and responds to nucleolar stresses (i.e., inactivation of ribosome biogenesis) by mediating the inhibitory binding of ribosomal proteins (RPs) to mouse double minute 2 homolog (MDM2), a nuclear-localised E3 ubiquitin ligase, which results in p53 accumulation. Inappropriate activation of the NSP has been implicated in the pathogenesis of collection of human diseases termed "ribosomopathies", while drugs that selectively activate the NSP are now in trials for cancer. Despite the clinical significance, the precise molecular mechanism(s) regulating the NSP remain poorly understood. Using genome-wide loss of function screens, we demonstrate the ribosome biogenesis (RiBi) axis as the most potent class of genes whose disruption stabilises p53. Furthermore, we identified a novel suite of genes critical for the NSP, including a novel mammalian protein implicated in 5S ribonucleoprotein particle (5S-RNP) biogenesis, HEATR3. By selectively disabling the NSP, we unexpectedly demonstrate that a functional NSP is required for the ability of all nuclear acting stresses tested, including DNA damage, to robustly induce p53 accumulation. Together, our data demonstrates that the NSP has evolved as the dominant central integrator of stresses that regulate nuclear p53 abundance, thus ensuring RiBi is hardwired to cellular proliferative capacity.

cell biology↗

bioRxiv2020

Empirical variance component regression for sequence-function relationships

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects genetic interactions not only between pairs of sites, but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here, we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis, and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA 5' splice sites, for which we also validate our model predictions via additional low-throughput experiments.

evolutionary biology↗