Selected Grantee Publications
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- Aquatic Vertebrate Models
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- niehs
Disentangling the Link Between Zebrafish Diet, Gut Microbiome Succession, and Mycobacterium chelonae Infection
Sieler et al., Animal Microbiome. 2023.
https://pubmed.ncbi.nlm.nih.gov/37563644/
Despite the long-established importance of zebrafish (Danio rerio) as a model organism and their increasing use in microbiome-targeted studies, relatively little is known about how husbandry practices involving diet impact the zebrafish gut microbiome. Given the microbiome's important role in mediating host physiology and the potential for diet to drive variation in microbiome composition, the authors sought to clarify how three different dietary formulations that are commonly used in zebrafish facilities impact the gut microbiome. They report that diet drives the successional development of the gut microbiome, as well as its sensitivity to exogenous exposure. Consequently, investigators should carefully consider the role of diet in their microbiome zebrafish investigations, especially when integrating results across studies that vary by diet. Supported by ORIP (R24OD010998) and NIEHS.
Gigapixel Imaging With a Novel Multi-Camera Array Microscope
Thomson et al., eLife. 2022.
https://www.doi.org/10.7554/eLife.74988
The dynamics of living organisms are organized across many spatial scales. The investigators created assembled a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution, large field-of-view recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, they computationally generated gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This system allows the team to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales (e.g., larval zebrafish, fruit flies, slime mold). Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms. Supported by ORIP (R44OD024879), NIEHS, NCI, and NIBIB.
Promoting Validation and Cross-Phylogenetic Integration in Model Organism Research
Cheng et al., Disease Models & Mechanisms. 2022.
https://www.doi.org/10.1242/dmm.049600
Model organisms are essential for biomedical research and therapeutic development, but translation of such research to the clinic is low. The authors summarized discussions from an NIH virtual workshop series, titled “Validation of Animal Models and Tools for Biomedical Research,” held from 2020 to 2021. They described challenges and opportunities for developing and integrating tools and resources and provided suggestions for improving the rigor, validation, reproducibility, and translatability of model organism research. Supported by ORIP (R01OD011116, R24OD031447, R03OD030597, R24OD018559, R24OD017870, R24OD026591, R24OD022005, U42OD026645, U42OD012210, U54OD030165, UM1OD023221, P51OD011107), NIAMS, NIDDK, NIGMS, NHGRI, and NINDS.
A Multidimensional Metabolomics Workflow to Image Biodistribution and Evaluate Pharmacodynamics in Adult Zebrafish
Jackstadt et al., Disease Models & Mechanisms. 2022.
https://www.doi.org/10.1242/dmm.049550
The evaluation of tissue distribution and pharmacodynamic properties of a drug is essential but often expensive in clinical research. The investigators developed a multidimensional metabolomics platform to evaluate drug activity that integrates mass spectrometry–based imaging, absolute drug quantitation, in vivo isotope tracing, and global metabolome analysis in zebrafish. They validated this platform by evaluating whole-body distribution of the anti-rheumatic agent hydroxychloroquine sulfate and its impact on the systemic metabolism of adult zebrafish. This work suggests that the multidimensional metabolomics platform is a cost-effective method for evaluating on- and off-target effects of drugs. Supported by ORIP (R24OD024624) and NIEHS.