Regulation of Activation-Associated MicroRNA Accumulation Rates during Monocyte-to-Macrophage Differentiation
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Joel Graff
PROVIDER: E-GEOD-60110 | 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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Project description:Purpose: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell carcinoma (MRCC) and to validate them in a cellular model. Selected microRNAs were studied in serum from MRCC patients and healthy individuals. Methods: We screened 673 microRNAs using TaqMan Low-density Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n=41). Differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenoytpes selected from an independent cohort (n=117). Results were validated in a cellular model of sunitinib resistance and studied in serum from healthy individuals and MRCC patients. Results: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p=0.0074). High expression of miR-942, miR-133a, miR484, and miR-628-5p was significantly associated with decreased time-to-progression and overall survival. These microRNAs were overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive parental cell line. Serum levels of miR-942, miR-133a, miR-484, miR-146a-5p, miR-374a and miR-486-5p were significantly reduced in MRCC patients compared to healthy controls. Conclusions: Our strategy identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity and resistance to sunitinib. Mir-942 was the best predictor of efficacy. Results were confirmed in a cellular model of sunitinib resistance. We also identified exosome derived serum microRNAs differentially expressed in MRCC patients and healthy individuals. Taqman Low Density Array for 6 FFPE tissues obtained from extreme phenotype MRCC patients, (n=3 marked resistance to sunitinib treatment patients and n=3 marked sensitivity to sunitinib treatment patients), was performanced to screen 667 microRNAs.
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