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Biology subjects

Gray, I. J.

Publications and source records attributed to Gray, I. J..

3 recordsLinked to original sources

Ecology and engineering to modify the bile acid output of a defined microbial community

The bile acid pool, which is synthesized collaboratively by the host and its microbiome, impacts metabolism, immunity, and disease risk. Targeted microbiome interventions could in principle reshape the bile acid pool for therapeutic benefit, but practical strategies remain elusive. In the course of screening a complex defined community for metabolic phenotypes by dropping out individual strains, we observed that several of the single-strain dropout communities had markedly increased deoxycholic and lithocholic acid levels and a larger bile acid pool. In each of these communities, a second strain--Lactobacillus plantarum--had bloomed. The bile salt hydrolase activity of L. plantarum was necessary and sufficient to expand the size of the bile acid pool. An engineered community in which the bsh gene is overexpressed in multiple Lactobacillus strains confers on mice increased levels of secondary bile acid levels and a larger pool size. By overexpressing a different pair of bile acid metabolic genes in multiple strains of Lactobacillus--7- and 7{beta}-hydroxysteroid dehydrogenase--we changed the composition of the bile acid pool, enlarging it and redirecting it toward ursodeoxycholic acid. Together, these results demonstrate that fine details of the microbiomes strain composition can have a substantial effect on bile acid metabolism, and that rational manipulation of the microbiome can alter the size and composition of the bile acid pool.

microbiology↗

Rapid QC-MS - Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high throughput workflows, helping researchers recognize low integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck - in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semi-autonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/578059v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@50d2b1org.highwire.dtl.DTLVardef@388373org.highwire.dtl.DTLVardef@3e31b5org.highwire.dtl.DTLVardef@1316c74_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Multiomics characterization of cell type repertoires for urine liquid biopsies

Urine is assayed alongside blood in medicine, yet current clinical diagnostic tests utilize only a small fraction of its total biomolecular repertoire, potentially foregoing high-resolution insights into human health and disease. In this work, we characterized the joint landscapes of transcriptomic and metabolomic signals in human urine. We also compared the urine transcriptome to plasma cell-free RNA, identifying a distinct cell type repertoire and enrichment for metabolic signal. Untargeted metabolomic measurements identified a complementary set of pathways to the transcriptomic analysis. Our findings suggest that urine is a promising biofluid yielding prognostic and detailed insights for hard-to-biopsy tissues with low representation in the blood, offering promise for a new generation of liquid biopsies.

genomics↗