Biology subjects

Surles-Zeigler, M. C.

Publications and source records attributed to Surles-Zeigler, M. C..

4 recordsLinked to original sources

bioRxiv2026

PRECISE TBI Model Catalog: Increasing Accessibility and Reproducibility in TBI research

Preclinical traumatic brain injury (TBI) research relies on experimental models that vary by mechanism, parameters, surgical procedures, species, strains, and ages, to name a few. While these models are crucial for understanding injury mechanisms and testing therapies, the progress in translating this knowledge to the clinic has been limited. This is in part due to fragmented resources and inconsistent reporting of critical variables. Here, we introduce the PRECISE-TBI model catalog, a centralized, queryable resource that consolidates metadata from published studies. The catalog integrates curated annotations from more than 450 papers, including details such as age, sex, strain, model type, device, and injury parameters. Where available, entries are also linked to protocols and datasets to enhance transparency and reproducibility. The Model Catalog serves as a living resource that enables cross-study comparison, identifies gaps in reporting, and connects the literature to datasets, protocols, device information, and other relevant resources. Analysis of the initial catalog entries revealed gaps in the reporting of device, age, and weight. In contrast, the reporting of sex improved over time, with over 90% of recent studies within the catalog papers reporting sex. Strain was also reported in most studies, with consistent reporting of specificity, especially for the C57 mice substrain. We expect the Model Catalog to serve as a valuable tool to enhance study design and reproducibility in preclinical TBI research while advancing FAIR data principles in the TBI field.

neuroscience↗

bioRxiv2024

Developing a Multiscale Neural Connectivity Knowledgebase of the Autonomic Nervous System

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARCs mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing SCKAN, a dynamic knowledge base of ANS connectivity that contains information about the origins, terminations, and routing of ANS projections. The distillation of SPARCs connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKANs ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.

bioinformatics↗

bioRxiv2021

Extending and using anatomical vocabularies in the Stimulating Peripheral Activity to Relieve Conditions (SPARC) project

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a US National Institutes of Health-funded effort to improve our understanding of the neural circuitry of the autonomic nervous system in support of bioelectronic medicine. As part of this effort, the SPARC program is generating multi-species, multimodal data, models, simulations, and anatomical maps supported by a comprehensive knowledge base of autonomic circuitry. To facilitate the organization of and integration across multi-faceted SPARC data and models, SPARC is implementing the FAIR data principles to ensure that all SPARC products are findable, accessible, interoperable, and reusable. We are therefore annotating and describing all products with a common FAIR vocabulary. The SPARC Vocabulary is built from a set of community ontologies covering major domains relevant to SPARC, including anatomy, physiology, experimental techniques, and molecules. The SPARC Vocabulary is incorporated into tools researchers use to segment and annotate their data, facilitating the application of these ontologies for annotation of research data. However, since investigators perform deep annotations on experimental data, not all terms and relationships are available in community ontologies. We therefore implemented a term management and vocabulary extension pipeline where SPARC researchers may extend the SPARC Vocabulary using InterLex, an online vocabulary management system. To ensure the quality of contributed terms, we have set up a curated term request and review pipeline specifically for anatomical terms involving expert review. Accepted terms are added to the SPARC Vocabulary and, when appropriate, contributed back to community ontologies to enhance autonomic nervous system coverage. Here, we provide an overview of the SPARC Vocabulary, the infrastructure and process for implementing the term management and review pipeline. In an analysis of > 300 anatomical contributed terms, the majority represented composite terms that necessitated combining terms within and across existing ontologies. Although these terms are not good candidates for community ontologies, they can be linked to structures contained within these ontologies. We conclude that the term request pipeline serves as a useful adjunct to community ontologies for annotating experimental data and increases the FAIRness of SPARC data.

bioinformatics↗

bioRxiv2021

Upregulation of CREB1 and FOXO1 transcription factor pathways in Neuregulin-1 mediated neuroprotection following ischemic stroke

Neuregulin-1 (NRG-1) is growth factor that has been investigated for its neuroprotective properties following ischemic stroke. While NRG-1 has shown significant promise in preventing neuronal damage following stroke, the mechanisms behind its neuroprotective effects are unclear. The goal of this research was to investigate the effects of NRG-1 treatment on ischemia-induced gene expression profiles following a permanent middle cerebral artery occlusion (MCAO) in rats. Rats were sacrificed twelve hours following MCAO and either vehicle or NRG-1 treatment. RNA extracted from the peri-infarct cortex of the brain was hybridized to an Affymetrix Rat Genome 2.0st Microarray Gene Chip. Data were analyzed using the Affymetrix Transcriptome Analysis Console (TAC) 4.0 software and the STRING Protein-Protein Interaction Networks database. Our results showed that NRG-1 delivery increased the regulation of pro-survival genes. Most notably, NRG-1 treatment upregulated the CREB1 and FOXO1 transcription factor pathways which are involved in increasing anti-inflammatory and cell proliferation responses and decreasing apoptosis and oxidative stress responses, respectively. Luminex multiplex transcription factor assays demonstrated that the activities of CREB1 and FOXO1 were increased by NRG-1 treatment with MCAO. These findings provide novel insight into the molecular mechanisms involved in NRG-1 mediated neuroprotection.

neuroscience↗