Biology subjects

Hinch, R.

Publications and source records attributed to Hinch, R..

3 recordsLinked to original sources

bioRxiv2026

A genome-wide atlas of meiotic recombination intermediates reveals distinct modes of DNA repair that direct crossovers away from transcriptionally marked genes

Crossovers are essential for accurate chromosome segregation in meiosis. Yet the programmed DNA double-strand breaks that initiate them frequently occur in genes and pose a risk to transcription required for gametogenesis. How meiotic cells reconcile these competing demands has remained unclear. Here, we generate a genome-wide in vivo atlas of meiotic recombination intermediates across [~]42,000 hotspots by mapping repair proteins BLM, HFM1, and RPA in wild-type and genome-engineered mutant mouse testes. These maps reveal two distinct modes of break repair: a fast-resolving class with short-lived intermediates that are repaired predominantly as non-crossovers, and a slower class with persistent intermediates that give rise to nearly all crossovers. Fast-resolving hotspots occur almost exclusively within a deeply conserved set of [~]4,500 genes marked by structural and chromatin features established during an early stage of meiotic transcription. This transcriptional memory predicts repair fate with high accuracy across mouse subspecies and sexes. Across widely diverged mammals, including humans and cattle, orthologous genes show similar crossover suppression. Our findings reveal an early bifurcation between crossover and non-crossover repair that is governed by the transcriptional context of meiotic breaks. Together, they establish an evolutionarily conserved principle in which crossovers are directed away from transcriptionally important genes, thereby safeguarding gene function and shaping their evolution.

genomics↗

bioRxiv2023

Learning patterns of HIV-1 co-resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning

The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available training datasets are underrepresented, which likely affects models generalizability across subtypes. A second challenge is that combinations of bnAbs are required to avoid the inevitable resistance to a single bnAb, and computationally determining optimal combinations of bnAbs is an unsolved problem. Recently, machine learning models trained using resistance outcomes for multiple antibodies at once, a strategy called multi-task learning (MTL), have been shown to achieve better performance in several cases than previous approaches. We develop a new model and show that, beyond the boost in performance, MTL also helps address the previous two challenges. Specifically, we demonstrate empirically that MTL can mitigate bias from underrepresented subtypes, and that MTL allows the model to learn patterns of co-resistance between antibodies, thus providing tools to predict antibodies epitopes and to potentially select optimal bnAb combinations. Our analyses, publicly available at https://github.com/iaime/LBUM, can be adapted to other infectious diseases that are treated with antibody therapy.

bioinformatics↗

bioRxiv2022

Viral burdens are associated with age and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection related sampling bias.

In this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burdens, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. Analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior infection, the average Ct value was 0.94 lower among Alpha variant infections, compared those with the predecessor strain, B.1.177. However, among vaccinated individuals, it was 0.34 lower among Delta variant infections, compared to those with the Alpha variant. In addition, the average Ct value decreased by 0.20 for every 10 year age increment of the infected individual. In summary, within-host viral burdens are associated with age, in addition to the interplay of vaccination status and viral variant.

genetics↗