Biology subjects

Lovell, S. C.

Publications and source records attributed to Lovell, S. C..

6 recordsLinked to original sources

bioRxiv2025

Domain-specific mechanisms of YAP1 variants in ocular coloboma revealed by in-vitro and organoid studies

The conserved transcriptional co-activator YAP1 is a central regulator of organ development and tissue homeostasis, integrating mechanical and biochemical cues to control cell proliferation and survival. YAP1 variants underlie a spectrum of congenital disorders, including autosomal dominant coloboma that can occur alone or with syndromic features. Despite this clinical significance, the functional role of YAP1 in human eye development, as well as the impact of disease-associated missense variants, remains poorly understood. Here we show YAP1 expression at the optic fissure in human embryos, a key structure involved in coloboma pathogenesis. We also identify a novel YAP1 variant in a proband with syndromic coloboma and investigate five previously reported coloboma-associated YAP1 variants. Using in silico prediction, cell-based assays, and fluorescence cross-correlation spectroscopy (FCCS) to directly quantify YAP1-TEAD binding, we demonstrate that the position of YAP1 missense variants dictates their functional changes. TEAD-binding domain mutations most strongly disrupted transcriptional activity in a luciferase assay, whereas all tested variants impaired induction of endogenous YAP1-TEAD target genes. Furthermore, mimicking reduced YAP1-TEAD binding using verteporfin small molecule in retinal organoids led to reduced progenitor proliferation and survival. These findings establish defective YAP1-dependent transcription as a mechanism driving congenital eye malformations and provide a framework for interpreting the pathogenicity of human YAP1 variants. More broadly, this study highlights the need for functional analyses to connect genetic variation with disease.

developmental biology↗

bioRxiv2025

Structural constraints acting on the SARS-CoV-2 spike protein reveal limited space for viral adaptation

The SARS-CoV-2 pandemic resulted in an unprecedented scientific response. The enormous scale of global genome sequencing, protein structural determination and targeted studies of protein and variant dynamics has resulted in a unique dataset which provides a valuable resource for understanding of viral evolutionary dynamics. Previous analysis of SARS-CoV-2 evolution has revealed apparently saltatory dynamics, with viral variants arising following large evolutionary jumps without genetic intermediates represented in the sequence database. We utilise rich SARS-CoV-2 datasets to interrogate the role of protein structural constraint in SARS-CoV-2 evolution and whether these evolutionary jumps may result from the viral spike protein accessing new regions of viable sequence space. We apply multiple computational predictors of structural constraint across different structural backgrounds and assess how constraint has changed during SARS-CoV-2 variant evolution. These predictions are validated using substitution data from the SARS-CoV-2 global sequence database. We find that all predictive methods suggest that the structural constraint experienced by specific sites has undergone very limited change, despite significant phenotypic evolution of the SARS-CoV-2 S protein. Signature mutations for variants of concern are not found to be under structural constraint by any computational predictor regardless of which viral variant structure is used to calculate predictions. We also develop a machine learning model to assess substitution viability, combining predictors of evolutionary constraint with information about local structural context. This confirms our conclusions, with model performance largely unaffected by the use of different viral variant structures. We also find no reduction in the shared proportion of accessible substitutions over evolutionary time, as would be expected if the S protein had entered and explored novel sequence space during variant evolution. These results suggest that despite its rapid rate of mutation, the SARS-CoV-2 S protein is subject to strict structural constraints and shows that viral genomes exhibit limited plasticity following infection of a new host.

evolutionary biology↗

bioRxiv2025

Resistance mutation supply modulates the benefit of CRISPR immunity against virulent phages

Only a fraction of bacterial genomes encode CRISPR-Cas systems but the selective causes of this variation are unexplained. How naturally virulent bacteriophages (phages) select for CRISPR immunity has rarely been tested experimentally. Here, we show against a panel of genetically and functionally diverse virulent phages that CRISPR immunity was not universally beneficial, and its fitness effect varied strongly between phages in predictable ways. In addition to mechanisms known to alter the effectiveness of CRISPR immunity, such as encoding a matching spacer or a protective nuclear shell, we show that the fitness effect of CRISPR immunity negatively correlated with the probability of evolving receptor-based resistance to the phage via spontaneous mutation. Supply of resistance mutations differed strongly between very closely related lipopolysaccharide-binding phages and was associated with variation at the C-terminus of the tail fibre protein altering residues involved in hydrogen bonding and the predicted binding site. Our results show that CRISPR immunity is more beneficial against virulent phages that are harder to evolve resistance to via receptor mutations, suggesting that virulent phage community composition and diversity will be important drivers of the prevalence of CRISPR immunity.

microbiology↗

bioRxiv2025

Evolutionary potential of the Escherichia coli antimutator ΔnudJ is reduced via altered mutational spectrum

The rate of spontaneous mutation is a key factor in determining the capacity of a population to adapt to a novel environment, for example a bacterial population exposed to antibiotics. Genetic and environmental factors controlling the mutation rate commonly also cause shifts in the relative rates of different mutational classes, i.e. the spectrum of spontaneous mutations. When the mutational spectrum is altered, the relatively enriched and depleted mutations may differ in their fitness effects. Here we aim to explore how a reduced mutation rate and altered mutational spectrum can contribute to adaptation in Escherichia coli. We measure mutation rates across a set of Nudix hydrolase deletants, finding multiple strains with an antimutator phenotype. We focus on the antimutator {Delta}nudJ which can cause a 6-fold mutation rate reduction relative to the wildtype with an altered mutational spectrum. Not only does nudJ deletion reduce the probability of antibiotic resistance arising but, in the case of rifampicin, the accessible resistance mutations under this altered mutation spectrum have greater fitness costs. While this effect is, so far, specific to this drug, it opens up the possibility of reducing the likelihood of resistance mutations establishing through simultaneous modification of mutation rate and spectrum. The identification and characterisation of antimutator alleles such as {Delta}nudJ provides potential targets for antievolution drugs which could be applied during antibiotic treatment to inhibit NudJ activity. Such an approach could suppress spontaneous resistance evolution both through fewer resistance mutations and by limiting access to the fittest mutations.

evolutionary biology↗

bioRxiv2021

Essentiality, Protein-Protein Interactions and Evolutionary Properties are Key Predictors for Identifying Cancer Genes Using Machine Learning

The identification of genes that may be linked to cancer is of great importance for the discovery of new drug targets. The rate at which cancer genes are being found experimentally is slow, however, due to the complexity of the identification and confirmation process, giving a narrow range of therapeutic targets to investigate and develop. One solution to this problem is to use predictive analysis techniques that can accurately identify cancer gene candidates in a timely fashion. Furthermore, the effort in identifying characteristics that are linked to cancer genes is crucial to further our understanding of this disease. These characteristics can be employed in recognising therapeutic drug targets. Here, we investigated whether certain genes properties can indicate the likelihood of it to be involved in the initiation or progression of cancer. We found that for cancer, the essentiality scores tend to be higher for cancer genes than for all protein coding human genes. A machine-learning model was developed and we found that essentiality related properties and properties arising from protein-protein interaction networks or evolution are particularly effective in predicting cancer-associated genes. We were also able to identify potential drug targets that have not been previously linked with cancer, but have the characteristics of cancer-related genes. Author SummaryMutations in numerous genes are known to be involved in cancer, yet there are undoubtedly many more to be discovered. We analysed a set of hundreds of cancer genes with the aim of finding out what makes them different from genes not known to be mutated in cancer. In particular, we found that genes that are essential for the survival of an organism are more likely to be involved in cancer. We used the gene properties that we examined to develop an artificial intelligence method that can accurately predict whether a gene is involved in cancer or not. Applying the method gives hundreds of non-cancer genes that resemble cancer genes. New discoveries of cancer genes are likely to be found within this set.

cancer biology↗

bioRxiv2020

Variability in gene expression is associated with incomplete penetrance in inherited eye disorders

Inherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from HGMD 2019.1 and cross-checked with the gnomAD 2.1 control-only dataset. Genes for which >1 variant was encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n=56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources, GTEx and EyeGEx. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p=0.0000015); this finding was consistent across tissue types. The results of this study point to a possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.

genetics↗