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White, E. K.

Publications and source records attributed to White, E. K..

2 recordsLinked to original sources

Staphyloxanthin production by Staphylococcus aureus promotes resistance to oxidative stress to delay diabetic wound healing

Diabetic foot ulcers (DFU) are a serious complication of diabetes mellitus that burden patients and health care systems. Staphylococcus aureus is prevalent and abundant in the DFU microbiome, and strain-level differences in S. aureus may drive clinical outcomes. To identify mechanisms underlying strain-specific outcomes in DFU with S. aureus, we performed high-throughput phenotyping screens on a collection of 221 S. aureus cultured isolates from clinically uninfected DFU. Of the 4 phenotypes examined (in vitro biofilm formation and production of staphylokinase, staphyloxanthin, and siderophores), we discovered that isolates from non-healing wounds produced more staphyloxanthin, a carotenoid cell membrane pigment. In a murine diabetic wound healing model, staphyloxanthin-producing isolates delayed wound closure significantly compared to staphyloxanthin-deficient isolates. Staphyloxanthin promoted resistance to oxidative stress in vitro and enhanced bacterial survival in human neutrophils. Comparative genomic and transcriptomic analysis of genetically similar clinical isolates with disparate staphyloxanthin phenotypes revealed a mutation in the Sigma B regulatory pathway that resulted in marked differences in stress response gene expression. Our findings suggest that staphyloxanthin production delays wound healing by protecting S. aureus from neutrophil-mediated oxidative stress, and may provide a target for therapeutic intervention in S. aureus-positive wounds.

microbiology↗

Identifying and Classifying Goals For Scientific Knowledge

MotivationScience progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize ignorance statements, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals. ResultsWe present a novel ignorance taxonomy driven by the role ignorance statements play in the research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10,000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. AvailabilityCorpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python. ContactMayla.Boguslav@CUAnshcutz.edu

bioinformatics↗