Biology subjects

Johnson, R. L.

Publications and source records attributed to Johnson, R. L..

7 recordsLinked to original sources

bioRxiv2026

A Context-Aware Target Engagement and Pharmacodynamic Biomarker Resource to Accelerate Drug Discovery and Development

Confirming target engagement in tumor experimental models remains a major challenge in oncology drug development. Pharmacodynamic biomarkers can help address this, but few systematic resources link drug targets to candidate biomarkers. We developed TargetTrace, a comprehensive resource to identify and prioritize pharmacodynamic biomarkers across nine key target classes, including transcription factors/cofactors, kinases, phosphatases, ubiquitin ligases, deubiquitinases, acetyltransferases, deacetylases, methyltransferases, and demethylases. Biomarker candidates were gathered from curated molecular interaction resources and refined using external annotations to improve accuracy. For enzyme targets with measurable substrate changes, we applied a two-agent large language model workflow, followed by manual review, to harmonize antibody information from the antibody resources and ensure that the selected biomarkers are measurable with existing laboratory tests. From more than 92,000 input interactions and over 2,300 targets, we compiled 71,323 target-biomarker relationships involving 2,270 potential drug targets, encompassing both transcription factor/cofactor-target gene and enzyme-substrate interactions. Commercial antibodies were available for over 1,400 biomarkers, supporting laboratory validation. This resource provides a structured and reusable resource for systematic identification and prioritization of pharmacodynamic biomarkers in oncology.

bioinformatics↗

bioRxiv2025

Population genetics of the endangered narrowly endemic Island Marble butterfly (Euchloe ausonides insulanus)

The Island Marble butterfly (Euchloe ausonides insulanus) is an endangered species endemic to the San Juan Islands off the coast of Washington State, United States, and British Columbia, Canada. The species was thought to be extinct for [~]90 years before it was rediscovered at American Camp, San Juan Island National Historical Park in 1998. Here, we report the results of the first population genetic analyses for insulanus, using DNA collected non-invasively from individuals in the last known stronghold for the species. We used DNA extracted from meconium, larval exuviae, and natural mortalities to generate and test thirteen new microsatellite markers to estimate genetic diversity, population structure, and kinship. We assembled and annotated mitochondrial genomes, which were used alongside museum specimens of insulanus collected [~]100 years ago from Vancouver Island, and other members of the E. ausonides species complex, to infer the evolutionary history of the species. The results indicated that insulanus experiences low heterozygosity, a small effective population size (Ne), and low allelic diversity. High levels of inbreeding were found in some individuals, but inbreeding was uneven across the population. No population structure or partitioning of genetic variation by host plant was detected. The mitogenomes of extant insulanus were all identical and modern samples showed a loss of allelic diversity compared to insulanus from museums. Extant insulanus formed a clade with museum specimens and we identified multiple putatively diagnostic alleles to differentiate insulanus from other subspecies. Based on these results, we outline considerations for species management and genetic monitoring.

genetics↗

bioRxiv2024

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy

Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments.

developmental biology↗

bioRxiv2022

Hypoxia inducible factors regulate infectious SARS-CoV-2, epithelial damage and respiratory symptoms in a hamster COVID-19 model.

Understanding the host pathways that define susceptibility to SARS-CoV-2 infection and disease are essential for the design of new therapies. Oxygen levels in the microenvironment define the transcriptional landscape, however the influence of hypoxia on virus replication and disease in animal models is not well understood. In this study, we identify a role for the hypoxic inducible factor (HIF) signalling axis to inhibit SARS-CoV-2 infection, epithelial damage and respiratory symptoms in Syrian hamsters. Pharmacological activation of HIF with the prolyl-hydroxylase inhibitor FG-4592 significantly reduced the levels of infectious virus in the upper and lower respiratory tract. Nasal and lung epithelia showed a reduction in SARS-CoV-2 RNA and nucleocapsid expression in treated animals. Transcriptomic and pathological analysis showed reduced epithelial damage and increased expression of ciliated cells. Our study provides new insights on the intrinsic antiviral properties of the HIF signalling pathway in SARS-CoV-2 replication that may be applicable to other respiratory pathogens and identifies new therapeutic opportunities.

microbiology↗

bioRxiv2021

Yap/Taz-Activated Expansion of Tert-Expressing Acinar Cells Are Required for Pancreatic Regeneration

The expression of TERT (telomerase reverse transcriptase) has been implicated in stem and progenitor cells, which are essential for tissue homeostasis and regeneration. However, the roles of TERT-expressing cells in the pancreas remain elusive. Employing genetically engineered Tert knock-in mouse model, herein, we located a rare population of Tert+ acinar cells. While Tert+ cells are quiescent in normal conditions, acinar cell injury leads to mitotic activation of Tert+ cells and subsequent generation of new acinar cells. Moreover, the genetic ablation of Tert+ cells impairs pancreatic regeneration. We further found that Yap/Taz activation is required for the expansion of Tert+ acinar cells. Our results identified Tert+ acinar cells as a distinct subset of acinar cells, which contributes to pancreatic regeneration via Yap/Taz activation.

cell biology↗

bioRxiv2019

Deletion of Lats 1 and 2 in mouse pancreatic acinar cells induces pancreatic fibrosis and inflammation

ObjectiveThe Hippo signaling pathway is known for regulating proliferation, differentiation, organ size, and tumorigenesis. Large tumor suppressor kinase 1 and 2 (LATS1&2) are the core kinases of this pathway, whose functions in both the normal pancreas and pancreatic diseases are unclear. We studied the function of LATS1&2 specifically in pancreatic acinar cells of adult mice. DesignWe generated mice with adult pancreatic acinar cell-specific deletion of Lats1&2 genes by using CreER/LoxP. Pancreata were analyzed by histological examination, immunostaining, western blot, and RNA-sequencing. ResultsDeletion of Lats1&2 genes in adult pancreatic acinar cells resulted in rapid development of pancreatic inflammation and fibrosis. Loss of Lats1&2 did not directly induce acinar cell proliferation or apoptosis, but resulted in pancreatic stellate cell (PSC) activation followed by immune cell infiltration and acinar-to-ductal metaplasia. These effects were mediated by the Hippo downstream effectors YAP1/TAZ. By using neutralizing antibody to block CTGF, a YAP1/TAZ target, the inflammation and fibrosis were reduced. Our RNA-sequencing data identified upregulation of fibroinflammatory genes in Lats1&2 null pancreata, which may play important roles in stimulating PSC activation and promoting pancreatic fibrosis, as well as inflammation. ConclusionsDeletion of the Lats1&2 genes from adult acinar cells leads to the YAP1/TAZ dependent upregulation of a fibroinflammatory program. Our results emphasize the critical role of Lats1&2 in regulating PSC activation. Our findings identify new strategies for controlling pancreatic inflammation and fibrosis in diseases such as pancreatitis and pancreatic cancer.

molecular biology↗

bioRxiv2018

A large-scale analysis of bioinformatics code on GitHub

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/githubbioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.\n\nAuthor summaryWe present, to our knowledge, the first large-scale analysis of bioinformatics source code. The purpose of our work is to contribute data to the growing conversation in the bioinformatics community around reproducibility, code quality, and software usability. We analyze a large collection of bioinformatics software projects, identifying relationships between code properties, development activity, developer communities, and software impact. Throughout the work, we compare the large set of projects to a small set of highly popular bioinformatics tools, highlighting features associated with high-profile projects. We make our data and code publicly available to enable others to build upon our analysis or generate new datasets. The significance of our work is to (1) contribute a large base of knowledge to the bioinformatics community about the state of their software, (2) contribute tools and resources enabling the community to conduct their own analyses, and (3) demonstrate that it is possible to systematically analyze large volumes of bioinformatics code. This work and the provided resources will enable a more effective, data-driven conversation around software practices in the bioinformatics community.

bioinformatics↗