Biology subjects

Taylor, A. J.

Publications and source records attributed to Taylor, A. J..

10 recordsLinked to original sources

bioRxiv2026

Elevated Delta Power in a Maternal UBE3A-Deletion Pig Model of Angelman Syndrome

Angelman syndrome is a neurodevelopmental disorder caused by loss of the maternally inherited UBE3A allele and is characterized by severe cognitive, motor, and communication impairments. Increased delta (1- 4 Hz) activity on electroencephalogram (EEG) assessed by visual inspection and by spectral power analysis is a robust feature of the disorder in humans and rodent models and is used as a biomarker of Angelman syndrome. This aspect of the phenotype has not been evaluated in the recently developed pig model of Angelman syndrome. Here, we analyzed scalp EEG recordings from freely moving pigs carrying a maternal UBE3A deletion (UBE3A-/+) across three age groups to determine whether they recapitulate the delta power abnormalities characteristic of the disorder. UBE3A-/+ pigs exhibited elevated delta power during both wakefulness and sleep compared with wild-type littermates, with the largest differences observed during the awake state. The typical increase in delta power that accompanies the transition from wakefulness to sleep was also reduced in UBE3A-/+ pigs. These effects were observed across study groups, demonstrating that the maternal UBE3A-deletion pig model reproduces the elevated delta power EEG phenotype of Angelman syndrome. Our results establish noninvasive scalp EEG as a translationally relevant tool for assessing neural dysfunction in this large-animal model and provide a framework for preclinical therapeutic testing. This work strengthens the utility of the pig model for mechanistic studies and therapeutic development in Angelman syndrome.

neuroscience↗

bioRxiv2025

A preclinical pig model of Angelman syndrome mirrors the early developmental trajectory of the human condition

Angelman syndrome is a neurodevelopmental disorder characterized by severe motor and cognitive deficits. It is caused by the loss of the maternally inherited allele of the imprinted ubiquitin-protein ligase E3A (UBE3A) gene. Rodent models of Angelman syndrome do not fully recapitulate all the symptoms associated with the condition and are limited as a preclinical model for therapeutic development. Here, we show that pigs (Sus scrofa) with a maternally inherited deletion of UBE3A (UBE3A-/+) have altered postnatal behaviors, impaired vocalizations, reduced brain growth, motor incoordination, and ataxia. Neonatal UBE3A-/+ pigs exhibited several symptoms observed in infants with Angelman syndrome, including hypotonia, suckling deficits, and failure to thrive. Collectively, these findings are consistent with the pathophysiology and developmental trajectory observed in individuals with Angelman syndrome. We anticipate that this pig model will advance our understanding of the pathophysiology of Angelman syndrome and be used as a preclinical large animal model for therapeutic development.

genetics↗

bioRxiv2024

Miniature: Unsupervised glimpses into multiplexed tissue imaging datasets as thumbnails for data portals

Multiplexed tissue imaging can illuminate complex spatial protein expression patterns in healthy and diseased specimens. Large-scale atlas programs such as Human Tumor Atlas Network and are relying heavily on highly-multiplexed approaches including CyCIF and CODEX to image up to 100 antigens. Such high dimensionality allows a deep understanding of cellular diversity and spatial structure, but can provide a challenge for image visualization and exploration. One challenge for data portals and visualization tools is the generation of an informative and pleasing image preview that captures the full heterogeneity of the image, rather than relying on a multi-channel overlay that may be restricted to 4-6 channels. We describe Miniature, a tool to automatically generate informative image thumbnails from multiplexed tissue images in an unsupervised and scalable manner. Miniature aims to aid researchers in understanding tissue heterogeneity and identifying potential pathological features without extensive manual intervention. Miniature uses a choice of unsupervised dimensionality reduction methods including uniform manifold embedding and projection (UMAP), t-distributed stochastic neighbor Embedding (t-SNE), and principal Component analysis (PCA) to reduce on-tissue pixels from a low-resolution, high dimensional image to two or three dimensions. Pixels are then color encoded by their coordinate in low dimensional space using a choice of color maps. We show that perceptually distinct regions in Miniature thumbnails reflect known pathological features seen in both the source multiplexed tissue image and H&E imaging of the same sample. We evaluate Miniature parameters for dimensionality reduction and pixel color encoding to recommend default configurations that maximize perceptual trustworthiness to both the low-dimensional embedding and high-dimensional image and provide high Mantel correlation between the perceived color difference (delta E 2000) and distance in high- and low-dimensional space. By simulating color vision deficiency, we show that Miniature thumbnails are accessible to all. We demonstrate that Miniature thumbnails are suitable for a wide range of multiplexed tissue imaging modalities and show their application in the Human Tumor Atlas Network Data Portal.

bioinformatics↗

bioRxiv2024

Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scienti[fi]c community. Data from the [fi]rst phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools--including CellxGene, Minerva, and cBioPortal--and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCCs approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.

cancer biology↗

bioRxiv2023

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

bioinformatics↗

bioRxiv2023

A Spatiotemporal Proteomic Map of Human Adipogenesis

White adipocytes function as the major energy reservoir in humans by storing substantial amounts of triglycerides and their dysfunction is associated with metabolic disorders. However, the mechanisms underlying cellular specialization during adipogenesis remain unknown. Here, using a high-sensitivity-high throughput workflow, we generated a spatiotemporal proteomic atlas of human adipogenesis encompassing information for ~8.000 proteins. Our systematic approach gives insights into cellular remodeling and the spatial reorganization of metabolic pathways to optimize cells for lipid accumulation and highlights the coordinated regulation of protein localization and abundance during adipogenesis. More specifically, we identified a compartment-specific regulation of protein levels and localization changes of metabolic enzymes to reprogram branched chain amino acid and one-carbon metabolism to provide building blocks and reduction equivalents for lipid synthesis. Additionally, we identified C19orf12 as a differentiation induced and adipocyte-specific lipid droplet (LD) protein, which interacts with the translocase of the outer membrane (TOM) complex of LD associated mitochondria and regulates adipocyte lipid storage. Overall, our study provides a comprehensive resource for understanding human adipogenesis and for future discoveries in the field. KEY POINTSO_LIHuman adipogenesis induces distinct temporal changes in protein abundance C_LIO_LI20% of all detected proteins change organellar localization during adipogenesis C_LIO_LIBCAA and one-carbon metabolism enzyme levels and localizations are coordinately regulated to promote adipogenesis C_LIO_LIC19orf12 is an adipocyte specific LD protein and regulator of lipid storage C_LI

molecular biology↗

bioRxiv2022

Deletion of carboxypeptidase E in beta cells disrupts proinsulin processing and alters beta cell identity in mice

Carboxypeptidase E (CPE) facilitates the conversion of prohormones into mature hormones and is highly expressed in multiple neuroendocrine tissues. Carriers of CPE mutations have elevated plasma proinsulin and develop severe obesity and hyperglycemia. We aimed to determine whether loss of Cpe in pancreatic beta cells disrupts proinsulin processing and accelerates development of diabetes and obesity in mice. Pancreatic beta cell-specific Cpe knockout mice ({beta}CpeKO; Cpefl/fl x Ins1Cre/+) lack mature insulin granules and have elevated proinsulin in plasma; however, glucose-and KCl-stimulated insulin secretion in {beta}CpeKO islets remained intact. High fat diet-fed {beta}CpeKO mice showed comparable weight gain and glucose tolerance compared to Wt littermates. Notably, beta-cell area was increased in chow-fed {beta}CpeKO mice and beta-cell replication was elevated in {beta}CpeKO islets. Transcriptomic analysis of {beta}CpeKO beta cells revealed elevated glycolysis and Hif1-target gene expression. Upon high glucose challenge, beta cells from {beta}CpeKO mice showed reduced mitochondrial membrane potential, increased reactive oxygen species, reduced MafA, and elevated Aldh1a3 transcript levels. Following multiple low-dose streptozotocin treatment, {beta}CpeKO mice had accelerated hyperglycemia with reduced beta-cell insulin and Glut2 expression. These findings suggest that Cpe and proper proinsulin processing are critical in maintaining beta cell function during the development of diabetes.

physiology↗

bioRxiv2021

Cross-species evolutionary rewiring in the enteric bacterium Campylobacter

Significance StatementGenetic exchange among bacteria shapes the microbial world. From the acquisition of antimicrobial resistance genes to fundamental questions about the nature of bacterial species, this powerful evolutionary force has preoccupied scientists for decades. However, the mixing of genes between species rests on a paradox. On one hand, promoting adaptation by conferring novel functionality, on the other potentially introducing disharmonious gene combinations (negative epistasis) that will be selected against. Taking an interdisciplinary approach to analyse natural populations of the enteric bacteria Campylobacter, an ideal example of long-range admixture, we demonstrate that genes can independently transfer across species boundaries and rejoin in epistasis in a recipient genome. This challenges conventional ideas and highlights the possibility of single step evolution by saltation. AbstractRecombination of short DNA fragments via horizontal gene transfer (HGT) can both introduce beneficial alleles and create genomic disharmony through negative epistasis. For non-core (accessory) genes, the negative epistatic cost is likely to be minimal because the incoming genes have not co-evolved with the recipient genome. By contrast, for the core genome, interspecific recombination is expected to be rare because disruptive allelic replacement is likely to introduce negative epistasis. Why then is homologous recombination common in the core of bacterial genomes? To understand this enigma we take advantage of an exceptional model system, the common enteric pathogens Campylobacter jejuni and Campylobacter coli, that are known for very high magnitude interspecies gene flow in the core genome. As expected, HGT does indeed disrupt co-adapted allele pairings (negative epistasis). However, multiple HGT events enable recovery of the genomes co-adaption between introgressing alleles, even in core metabolism genes (e.g., formate dehydrogenase). These findings demonstrate that, even for complex traits, genetic coalitions can be decoupled, transferred and independently reinstated in a new genetic background - facilitating transition between fitness peaks. In this example, the two-step recombinational process is associated with C. coli that are adapted to the agricultural niche.

microbiology↗

bioRxiv2020

Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods. One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

bioinformatics↗

bioRxiv2019

The cryo-EM structure of the bacterial flagellum cap complex suggests a molecular mechanism for filament elongation.

The bacterial flagellum is a remarkable molecular motor, present at the surface of many bacteria, whose primary function is to allow motility through the rotation of a long filament protruding from the bacterial cell. A cap complex, consisting of an oligomeric assembly of the protein FliD, is localized at the tip of the flagellum, and is essential for filament assembly, as well as adherence to surfaces in some bacteria. However, the structure of the intact cap complex, and the molecular basis for its interaction with the filament, remains elusive. Here we report the cryo-EM structure of the Campylobacter jejuni cap complex. This structure reveals that FliD is pentameric, with the N-terminal region of the protomer forming an unexpected extensive set of contacts across several subunits, that contribute to FliD oligomerization. We also demonstrate that the native C. jejuni flagellum filament is 11-stranded and propose a molecular model for the filament-cap interaction.

microbiology↗