Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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MicroRNA expression profiling of human differentiated HepaRG cells after exposure to rifampicin or efavirenz compared to exposure with dimethyl sulfoxide as control

ABSTRACT: Rifampicin and efavirenz are commonly used medicines to treat tuberculosis (rifampicin) and HIV/AIDS (efavirenz). Rifampicin and efavirenz are metabolized in the liver and exposure to these medicines can alter microRNA expression in hepatocytes. Changes in microRNA expression may affect metabolism of these medicines and potentially influence how patient's respond to therapy. It is important to understand changes in microRNA expression in a hepatic cell-based model. Differentiated HepaRG cells were used because mRNA expression and induction of drug metabolizing enzymes are comparable to that of primary human hepatocytes yet differentiated HepaRG cells have an extended lifespan. Differentiated HepaRG cells were obtained from Merck Millipore and cultured as an adherent cell line at a density of 315000 cells/well using collagen I coated 24-well plates. Clinically relevant concentrations of rifampicin and efavirenz were selected as 6.4uM efavirenz (dissolved in 100% dimethyl sulfoxide) and 24.4uM rifampicin (dissolved in 100% dimethyl sulfoxide), while 0.02% dimethyl sulfoxide was used as control to minimize toxicity. HepaRG serum-free induction medium was used to dilute efavirenz and rifampicin. On day 7 since thawing differentiated HepaRG cells, these cells were treated with 6.4uM efavirenz or 24.4uM rifampicin or 0.02% dimethyl sulfoxide for a period of 24 hours. Three biological replicates were available for each treatment condition. Total RNA was isolated from the cells, after treatment, using the Quick-RNATM MiniPrep Kit from Zymo Research Corporation. MicroRNA expression profiling was performed using the TaqMan® OpenArray® Human MicroRNA Panel and QuantStudioTM 12K Flex system. The R/Bioconductor package “Automated Analysis of High-Throughput qPCR Data” were used for data analyses. CT values were used for differential expression analysis and microRNAs with a CT value > 35 was considered undetected. MicroRNAs undetected in any of the replicate samples or microRNAs with AmpScore < 1.24 or CqConf < 0.8 were excluded. Quantile normalization was followed by limma analyses to identify differentially expressed microRNAs for efavirenz versus dimethyl sulfoxide and rifampicin versus dimethyl sulfoxide.

INSTRUMENT(S): Agilent® 2100 Bioanalyzer Instrument, QuantStudioTM 12K Flex system, None

ORGANISM(S): Homo sapiens

SUBMITTER: Marelize Swart

PROVIDER: E-MTAB-7929 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Human milk (breastmilk) is much more than nutrition for the infant, containing an array of regulatory agents with immunoprotective and developmental functions. Amongst those, microRNAs (miRNAs) have recently been identified, with their properties, roles, origin and distribution in breastmilk as well as in the mammary gland being still undetermined. In this study, we examined the miRNA profile of different fractions of human milk (cells and lipids) using the OpenArray system (Applied Biosystems, 770 miRNA species measured per sample) and compared it with maternal peripheral blood mononuclear cells (PBMCs) and plasma. Although PBMCs were the richest group in miRNA species, plasma showed very low expression pattern. Thus, the human milk fractions (cells, lipid) and skim milk (not being investigated in this study) were found to conserve higher levels of miRNAs than blood in general. Specifically, human milk cell miRNA quantity was found relatively close to PBMCs, and higher than milk lipids. Correlation and clustering analyses indicated that miRNA expression and types of milk cells were highly similar to those in lipids. Milk miRNAs showed a slight correlation to PBMCs, so PBMCs potentially are not contributing to milk miRNAs. Plasma was different to all other three groups in miRNA content and expression pattern. Further, two infant formulae (a plant-based and a cow milk-based) were compared to human milk and found to contain significantly fewer miRNA species than human milk cells and lipids (p>0.001). Taken together with previous studies on miRNAs, our findings demonstrate that human milk is one of the richest sources of miRNAs among human body fluids. As a non-invasive and plentiful source of miRNAs, human milk could be used as a disease biomarker for the mammary gland, with potential in assessing lactation performance. Finally, gene target and pathways analyses identified several target mRNAs regulated by miRNAs found to be abundant in breastmilk. Given the recently identified stability and function of food-derived miRNAs in regulating mammalian genes, we propose that breastmilk is a rich source of miRNA ingested by the infant during the first months of life, and which potentially contribute to early infant development. 10 exclusively breastfeeding dyads were recruited. 10 whole milk and 10 whole blood samples were collected and fractionated to obtain 10 milk cells, 10 milk lipid, 10 mononeucleoted blood cells (PBMCs), and 10 plasma. In addition to the above 40 samples, 2 infant formula were profiled. 4 different extraction kits were used, miRNeasy mini Kit for human milk cell and PBMC samples. miRCURY RNA Isolation-Biofluids Kit for human milk lipid samples and both infant formulae. mirVana PARIS Kit for plasma samples. NanoDrop 2000 and Bioanalyzer 2100 were used to determine concentration and purity of the extracted miRNA from all samples (n=42). miRNA OpenArray panel system (Life Technologies, CA, USA) was used to profile 754 human mature miRNAs in samples. RNU48, RNU44 and U6 rRNA were used as housekeeping controls for normalisation. ath-miR159a was used as a negative control for human samples. GeneGO and Ingenuity Pathway Analysis were used to determine biological pathways. Please note that normalization of miRNAs was done in R but without generating deltaCT values, thus [1] only the list of normalized miRNA with Ct vlaue between 8 and 29 and that detected in at least 4 samples out of 10 analysed in each group is provided ('normalized_miRNAs_list.txt') [2] the sample data tables contain raw data.

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MicroRNA expression profiling of human differentiated HepaRG cells after exposure to rifampicin or efavirenz compared to exposure with dimethyl sulfoxide as control

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