Biology subjects

Taylor, H. S.

Publications and source records attributed to Taylor, H. S..

3 recordsLinked to original sources

bioRxiv2026

Multi-omic profiling reveals impaired luteal resolution and CHK2-regulated inflammation in ovarian aging

The ovary ages earlier than most other organs, but the molecular mechanisms that drive this decline remain unclear. To gain more insight into this process, we profiled ovaries from young and reproductively aged mice after synchronized ovulation and found that aging was marked by the accumulation of luteal cells with increased steroidogenic, antigen presentation and inflammatory gene expression. DNA methylation was globally preserved in aged ovaries but showed focal age-associated changes at gene regulatory regions. Protein profiling identified an increase in checkpoint kinase 2 (CHK2), a DNA damage response protein encoded by the CHEK2 gene associated with the timing of natural menopause in humans. CHK2 and DNA damage markers were increased in aged mice and human ovarian somatic cells. Transient CHK2 inhibition in aged mice increased the proportion of mature oocytes and blastocyst formation and reversed inflammatory signatures in granulosa cells. These findings identify defective luteal resolution and inflammation as features of ovarian aging and suggest that CHK2 induces a reversible age-associated inflammatory signature. Our results show that reproductive decline may remain pharmacologically modifiable during ovarian aging via regulation of the inflammatory response.

molecular biology↗

bioRxiv2025

slideimp: Efficient Imputation for DNA Methylation Data

MotivationThere is a growing need for efficient imputation methods for high-dimensional DNA methylation (DNAm) datasets. Existing microarray imputation approaches, such as k-nearest neighbors (K-NN) or principal component analysis (PCA)-based methods, provide high accuracy but can be computationally intensive, while methods for whole-genome data are not designed for large cohorts. We developed slideimp, an R package that implements sliding window, groupable, parallelized K-NN and optimized PCA imputation to address these limitations. ResultsBenchmarks on microarray DNAm datasets demonstrate that slideimp achieves up to 150x faster runtime and 10x-100x lower memory usage while maintaining comparable or superior accuracy over existing methods and implementations. For K-NN and PCA imputation, slideimp supports grouped imputation which enhances imputation efficiency and accuracy. In a whole-genome dataset, sliding window K-NN imputation substantially increased the correlation of the Horvath 2013 clock with chronological age from 0.131 to 0.477. Additional features include targeted imputation of CpG subsets for K-NN and estimation of imputation accuracy via repeated cross-validation. The efficient and flexible DNAm imputation methods implemented by slideimp can easily be applied to other high-dimensional data types. Availability and ImplementationThe code to fully reproduce all analyses presented in this paper is available on GitHub at https://github.com/hhp94/slideimp_paper. The R package slideimp is also available on GitHub at https://github.com/hhp94/slideimp. Supplemental figures are available online.

bioinformatics↗

bioRxiv2022

A novel gyrovirus associated with a fatal respiratory disease in yellow-eyed penguin (Megadyptes antipodes) chicks

Yellow-eyed penguins (Megadyptes antipodes), or hoiho in te reo M[a]ori, are predicted to become extinct on mainland Aotearoa New Zealand in the next few decades, with infectious disease a significant contributor to their decline. A recent disease phenomenon termed respiratory distress syndrome (RDS) causing lung pathology has been identified in very young chicks. To date, no causative pathogens for RDS have been identified. In 2020 and 2021, the number of chick deaths from suspected RDS increased four- and five-fold, respectively, with a mortality rate of >90%. Here, we aimed to identify possible pathogens responsible for RDS disease impacting yelloweyed penguins. Total RNA was extracted from tissue samples collected during post-mortem of 43 chicks and subject to metatranscriptomic sequencing. From these data we identified a novel and highly abundant gyrovirus in 80% of tissue samples. This virus exhibited only 41% amino acid identity within VP1 to its closest relative, Gyrovirus 8, discovered in a diseased seabird. No other exogenous viral transcripts, nor pathogenic bacterial, protozoal and fungal organisms, were identified in these tissues. Due to the high relative abundance of viral reads, it is likely that this novel gyrovirus is associated with RDS in yellow-eyed penguin chicks. Author SummaryNew Zealands population of yellow-eyed penguins, also called hoiho, are predicted to become extinct in the next 20-30 years, with disease a major factor contributing to their decline. A new disease, causing fluid and bleeding into the lungs, was initially identified in 2019 in very young chicks. It was characterised as causing respiratory distress with a mortality of >90% usually within the first week of life. To date, no causative pathogens of the disease have been identified. We aimed to identify possible pathogens responsible for respiratory disease in these penguin chicks. A metatranscriptomic survey of dead chicks identified a novel and highly abundant gyrovirus present in diseased tissue, with closely related viruses causing disease in other avian hosts. It is, therefore, highly likely that this novel gyrovirus is associated with respiratory disease in these chicks. This finding offers the potential to increase the success of disease management in the critically endangered yellow-eyed penguin and possibly other at-risk penguin species. The potential to lessen mortality and slow the decline of the species is essential in protecting the biodiversity of New Zealands fauna and flora.

microbiology↗