Biology subjects

Chung, E.

Publications and source records attributed to Chung, E..

21 records · Page 2Linked to original sources

bioRxiv2019

Phenylpropionc acid produced by gut microbiota alleviates acetaminophen-induced hepatotoxicity

ABSTRACTAcetaminophen (APAP) overdose causes hepatic injury and is major contributor to acute liver injury cases. To investigate potential roles of gut microbiota in APAP-induced liver injury, C57BL/6 mice from Jackson (JAX) or Taconic (TAC) were challenged with APAP. TAC mice were more susceptible to APAP toxicity, and this disappeared upon co-housing of JAX and TAC mice. When the cecum contents from JAX and TAC mice were transplanted to germ-free mice, the mice that received TAC gut microbiota exhibited more significant hepatotoxicity after APAP administration. Non-targeted metabolomic analysis using portal vein serum and liver tissue of the mice led to identification of 19 metabolites the levels of which are associated with JAX or TAC gut microbiota. A gut bacteria-derived metabolite phenylpropionic acid (PPA) levels in cecum contents and blood were higher in mice harboring JAX gut microbiota. PPA supplementation in drinking water alleviated APAP-induced hepatotoxicity in TAC mice. This was accompanied by reduced hepatic protein levels of cytochrome P450 (CYP) 2E1, the enzyme responsible for APAP bioactivation to a toxic metabolite. This illustrates a gut microbe-liver interaction mediated by a gut bacteria-derived metabolite in modulating drug-induced liver injury.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2018

Wnt/β-catenin signaling is required for the development of multiple nephron segments

The nephron is composed of distinct segments that perform unique physiological functions to generate urine. Little is known about how multipotent nephron progenitor cells differentiate into different nephron segments. It is well known that Wnt/{beta}-catenin signaling regulates the maintenance and commitment of mesenchymal nephron progenitors during kidney development. However, it is not fully understood how it regulates nephron patterning after nephron progenitors undergo mesenchymal-to-epithelial transition. To address this, we performed {beta}-catenin loss-of-function and gain-of-function studies in epithelial nephron progenitors in the mouse kidney. Consistent with a previous report, the formation of the renal corpuscle was defective in the absence of {beta}-catenin. Interestingly, we found that epithelial nephron progenitors lacking {beta}-catenin were able to form presumptive proximal tubules but that they failed to further develop into differentiated proximal tubules, suggesting that Wnt/{beta}-catenin signaling plays a critical role in proximal tubule development. We also found that epithelial nephron progenitors lacking {beta}-catenin failed to form the distal tubules. Constitutive activation of Wnt/{beta}-catenin signaling blocked the proper formation of all nephron segments, suggesting tight regulation of Wnt/{beta}-catenin signaling during nephron patterning. This work shows that Wnt/{beta}-catenin signaling regulates the patterning of multiple nephron segments along the proximo-distal axis of the mammalian nephron.

developmental biology↗

bioRxiv2018

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis.

A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large omics or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high \"startup\" costs and poor adherence to procedures when they deviate substantially from an analysts usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individuals existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically Rs package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

bioinformatics↗