Biology subjects

Mercer, H.

Publications and source records attributed to Mercer, H..

3 recordsLinked to original sources

bioRxiv2024

The role of ADAR editing and nonsense-mediated decay in Parkinson's Disease

Parkinsons Disease (PD) is a multifactorial disease with heterogenous phenotypes that vary across individuals, as well as by age and sex. Therefore, it is likely that multiple interacting factors, such as environmental influences and aging, as well as genetic factors, including dynamic RNA (ADAR, Adenosine Deaminases Acting on RNA) editing, may play a role in PD pathology. In this analysis of 317 transcriptomes of healthy controls, PD and prodromal patients aged 65 years or older, from Parkinsons Project Markers Initiative dataset, we observe differences in ADAR expression, number of putative ADAR edits, editing index, and the number of high and moderate impact edits between control groups and diseased samples, particularly when ADAR editing is associated with nonsense-mediated decay (NMD). Likewise, differentially expressed genes between comparison groups were linked to NMD-related pathways. NMD is an important process in detecting deleterious nonsense sequences in mRNA transcripts and eliminating them from the cell. Thus, NMD regulation serves an important role in neurodevelopment, neural differentiation, and neural maturation. RNA misprocessing, which includes dysregulation of NMD, is known to play an important role in neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and fronto-temporal dementia. Our results suggest that NMD may also be an important factor in PD physiology.

bioinformatics↗

bioRxiv2020

Biological Correlations and Confounding Variables for Quantification of Retinal Ganglion Cells Based on Optical Coherence Tomography Using Diversity Outbred Mice

PurposeDespite popularity of optical coherence tomography (OCT) in glaucoma studies, its unclear how well OCT-derived metrics compare to traditional measures of retinal ganglion cell (RGC) abundance. Here, Diversity Outbred (J:DO) mice are used to directly compare ganglion cell complex (GCC)-thickness measured by OCT to metrics of retinal anatomy measured ex vivo with retinal wholemounts or optic nerve cross sections. MethodsJ:DO mice (n = 48) underwent OCT and fundoscopic exams, with GCC-thickness measured using automated segmentation. Following euthanasia, RGC axons were quantified from para-phenylenediamine-stained optic nerve cross sections and RGC somas from BRN3A-immunolabeled retinal wholemounts with total cellularity assessed by TO-PRO or hematoxylin nuclear staining. ResultsJ:DO tissues lacked overt disease. GCC-thickness (62.4 {+/-} 3.7 {micro}m), RGC abundance (3,097 {+/-} 515 BRN3A+ nuclei/mm2; 45,533 {+/-} 9,077 axons), and total inner retinal cell abundance (6,952 {+/-} 810 nuclei/mm2) varied broadly. GCC-thickness correlated significantly to RGC somal density (r = 0.46) and axon number (r = 0.49), whereas total inner retinal cellularity did not. Retinal area (20.3 {+/-} 2.4 mm2) and optic nerve (0.09 {+/-} 0.02 mm2) cross-sectional area varied widely. Sex did not significantly influence any of these metrics. In bilateral comparisons, GCC-thickness (r = 0.89), inner retinal cellularity (r = 0.47), and RGC axon abundance (r = 0.72) all correlated significantly. ConclusionsAmongst outbred mice with widely variable phenotypes, OCT-derived measurements of GCC thickness correlate significantly to RGC abundance and axon number. The extensive phenotypic variability exhibited by J:DO mice make them a powerful resource for studies of retinal anatomy using quantitative genetics.

neuroscience↗

bioRxiv2020

Automated Isoform Diversity Detector (AIDD): A pipeline for investigating transcriptome diversity of RNA-seq data

BackgroundAs the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism - RNA editing due to post-transcriptional changes of individual nucleotides - remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise. ResultsHere we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDDs capabilities. ConclusionsAIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

bioinformatics↗