Biology subjects

Woerner, A. E.

Publications and source records attributed to Woerner, A. E..

4 recordsLinked to original sources

bioRxiv2026

Involvement of Mitophagy in Endothelin-1 Mediated Neurodegeneration in Rodent Models of Glaucoma

The ultimate cause of blindness in glaucoma is the death of retinal ganglion cells, and understanding the mechanism behind retinal ganglion cell loss during glaucoma could lead to the development of novel treatments for glaucoma. Endothelin-1 has been shown to mediate retinal ganglion cell death during glaucoma through impairment of mitochondrial function. Retinal ganglion cells are highly metabolically active, and susceptible to oxidative damage and decreased respiratory capacity. Mitophagy is the process whereby damaged mitochondria are degraded to prevent further propagation of oxidative damage. The current study evaluates the effect of endothelin-1 on mitophagy in retinal ganglion cells. Electron microscopy revealed endothelin-1 administration lead to a decrease in healthy mitochondria in the optic nerve. The MitoQC mouse was used to evalute mitophagy in response to endothelin-1, along with immunohistochemical analysis of mitophagy proteins. Mitophagy follows different trends in the optic nerve and retinal ganglion cell bodies following endothelin-1 administration, mitophagy was increased in the optic nerve but decreased in the retina following endothelin administration. With elevation of intraocular pressure, mitophagy was increased in the retina but decreased in the optic nerve. In retinal ganglion cells, parkin expression and activation was unchanged 24 hours after endothelin-1 administration, but was decreased 72 hours following endothelin-1 administration. Taken together, these results suggest that endothelin-1 impacts mitophagy through parkin-independent mechanisms in retinal ganglion cell bodies, and the ganglion cell bodies and optic nerve appear to have different responses to endothelin-1.

neuroscience↗

bioRxiv2025

StrPhaser constructs tandem repeat alleles from VCF data

Variant calling is a ubiquitous genomic technique that underpins many scientific disciplines. From a computational perspective, variant calling is a form of logical compression; neglecting large variation, a persons genome can be losslessly described as a set of differences (SNP and small InDel alleles) relative to the reference sequence. Another common genomic technique is haplotype phasing, wherein alleles are partitioned into their paternal and maternal components (as haplotypes). Some classes of alleles are more difficult to describe than others, e.g., short tandem repeats (STRs). STRs serve as a critical marker for many genetic assays. However, STRs tend not to be explicitly reported in most genomic workflows. Here, we present StrPhaser, a novel algorithm that leverages phased variant calling datasets in the VCF file format to construct STR alleles. We evaluated StrPhaser on [~]10,000 STR alleles from 284 human genomes, achieving an average allele accuracy of 91%. In addition, StrPhaser better recovers longer STR alleles than competing approaches; in principle, STR alleles that are longer than the maximum read length can be characterized. This capability, combined with its user-friendly interface, speed, and generation of both STR genotypes and visualizations, makes StrPhaser a valuable tool for a wide range of genomic studies. AvailabilityThe StrPhaser is publicly available at https://github.com/XuewenWangUGA/StrPhaser.

bioinformatics↗

bioRxiv2022

vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework

MotivationGenotyping error can impact downstream SNP-based analyses. Simulating various modes and and levels of error can help investigators better understand potential biases caused by miscalled genotypes. ResultsWe have developed and validated vcferr, a tool to probabilistically simulate genotyping error and missigness in VCF files. We demonstrate how vcferr could be used to address a research question by introducing varying levels of error of different type into a sample in a simulated pedigree, and assessed how kinship analysis degrades as a function of kind and type of error. Software Availabilityvcferr is available for installation via PyPi (https://pypi.org/project/vcferr/) or conda (https://anaconda.org/bioconda/vcferr). The software is released under the MIT license with source code available on GitHub (https://github.com/signaturescience/vcferr).

bioinformatics↗

bioRxiv2021

skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation

MotivationSNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream processing and manipulation. A dedicated software package that consistently and intuitively implements this analysis functionality is needed. ResultsHere we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. AvailabilityThe skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.

bioinformatics↗