Biology subjects

Cavero, D.

Publications and source records attributed to Cavero, D..

3 recordsLinked to original sources

bioRxiv2026

Fanconi Anaemia E3 Ligase complex activity is regulated by a druggable metabolite binding site in FANCX

The Fanconi anaemia (FA) DNA repair pathway is an emerging target for precision cancer therapy. Using a high-throughput FANCD2-monoubiquitination assay, we identified a class of small molecules, including MSG010, that inhibit the FA E3 ligase complex in vitro. Because these molecules, and the metabolite, palmitoyl-CoA, are known to engage allosteric drug and metabolite (ADaM) binding site in AMP-activated kinase (AMPK), we hypothesised that a similar pocket exists within the FA complex. Supporting this, long-chain, but not short-chain, fatty acyl-CoA molecules inhibited the FA E3 ligase complex activity, and sequence analysis revealed similarity between the AMPK ADaM site and a WD40 repeat in the FA subunit FANCX. Targeted mutagenesis of this FANCX region disrupted E3 ligase activity or abolished inhibition by MSG010, suggesting the presence of an ADaM-like site in FANCX. Moreover, MSG010 preferentially killed BRCA1-deficient cells in vitro. These findings identify a putative small-molecule binding site in the FA pathway that may be developed further to test for exploitation as anticancer therapeutics.

cancer biology↗

bioRxiv2019

Improving imputation quality in BEAGLE for crop and livestock data

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

genetics↗

bioRxiv2017

A simple test identifies selection on complex traits in breeding and experimentally-evolved populations

Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome wide association studies and selection mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique utilizes additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted[G] , which can be used to test for significant evidence of selection on a trait. Our test requires pre- and post-selection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that[G] is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

evolutionary biology↗