Biology subjects

Baumgartner, W. A.

Publications and source records attributed to Baumgartner, W. A..

3 recordsLinked to original sources

bioRxiv2026

Sex-stratified Gut Microbiome Disruption is Associated with Altered Hepatic Gene Expression during Acute Azoxystrobin Exposure

Azoxystrobin is a widely used fungicide that has been associated with to reproductive, neurological, and developmental defects. This chemical also disrupts gut microbial communities; however, if these perturbations contribute to the harms associated with exposure to azoxystrobin, this remains unclear. In this study, we investigated the effects of acute exposure to a series of concentrations (5-500 mg/kg) of azoxystrobin on the host and gut microbiota in zebrafish. Fecal amplicon and shotgun metagenomic sequencing was integrated with liver gene expression to quantify associations between microbiome disruption azoxystrobin toxicity in the host. Azoxystrobin exposure resulted in significant alteration in microbiome composition and functional potential in a dose- and sex-dependent manner. Microbial communities in exposed animals exhibited an increased abundance of xenobiotic metabolism pathways and decreased bacterial motility and lipopolysaccharide biosynthesis pathway metabolism. At the host level, histopathology identified increased biliary proliferation, most evident in medium- and high-dose fish. We also observed hepatic transcriptional changes consistent with a stress response, including altered redox-associated genes and reduced expression of lipid and small-molecule metabolic genes, with sex-stratified differences. Importantly, alterations in host transcriptional programming correlated with the compositional changes in exposed microbiota. Together, these results suggest concurrent impacts of azoxystrobin on gut microbiota and the liver implicate the microbiome as a potential contributor to changes in liver gene expression during exposure. ImportanceWidespread fungicide use contaminates ecosystems worldwide, but the biological pathways underlying their effects on humans and other animals are not well understood. Using zebrafish (Danio rerio), we found that short-term exposure to the fungicide azoxystrobin was associated with changes in the gut microbiome, liver gene activity, and liver changes. Exposure produced dose- and sex-dependent shifts in microbial communities, including changes in predicted microbial functions involved in chemical metabolism, bacterial motility and defense. Compositional changes in the microbiome correlated with gene-expression changes consistent with stress and altered metabolism in exposed fish, suggesting that exposure induced disruption may contribute to exposure impact to the host. These results highlight a potential role for the microbiome in mediation of the impacts of azoxystrobin on host physiology. As such microbial based interventions could be a viable strategy to mitigate exposure impacts on health.

microbiology↗

bioRxiv2020

Concept Recognition as a Machine Translation Problem

BackgroundAutomated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models had the potential to outperform multi-class classification approaches. Here we systematically characterize the factors that contribute to the accuracy and efficiency of several approaches to sequence-to-sequence machine learning. ResultsWe report on our extensive studies of alternative methods and hyperparameter selections. The results not only identify the best-performing systems and parameters across a wide variety of ontologies but also illuminate about the widely varying resource requirements and hyperparameter robustness of alternative approaches. Analysis of the strengths and weaknesses of such systems suggest promising avenues for future improvements as well as design choices that can increase computational efficiency with small costs in performance. Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) for span detection (as previously found) along with the Open-source Toolkit for Neural Machine Translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies in CRAFT Corpus. This approach uses substantially fewer computational resources, including hardware, memory, and time than several alternative approaches. ConclusionsMachine translation is a promising avenue for fully machine-learning-based concept recognition that achieves state-of-the-art results on the CRAFT Corpus, evaluated via a direct comparison to previous results from the 2019 CRAFT Shared Task. Experiments illuminating the reasons for the surprisingly good performance of sequence-to-sequence methods targeting ontology identifiers suggest that further progress may be possible by mapping to alternative target concept representations. All code and models can be found at: https://github.com/UCDenver-ccp/Concept-Recognition-as-Translation.

bioinformatics↗

bioRxiv2020

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

MotivationAlthough knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms. PheKnowLator is available at: https://github.com/callahantiff/PheKnowLator.

bioinformatics↗