Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

MRNA profile in patients with multiple sclerosis versus healthy controls.

ABSTRACT: We tested the expression profile of 1146 miRNAs in PBMCs of 19 MS patients and 14 controls (discovery sample) and we explored their function either in vivo by performing a whole-genome mRNA profiling in same patients and in silico using bioinformatic prediction tool (miRbase software). These approaches lead to the identification of putative genetic targets of these miRNAs which could be implicated in the ethiopathogenesis of the disease.

ORGANISM(S): Homo_sapiens

SUBMITTER: Dr Filippo Martinelli Boneschi

PROVIDER: E-MTAB-358 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Project description:Osteoporosis is the consequence of altered bone metabolism resulting in the systemic reduction of bone strength and increased risk of fragility fractures. MicroRNAs (miRNAs) regulate gene expression on a post-transcriptional level are known to take part in the control of bone formation and bone resorption. Recently, targeted secretion of miRNAs from cells originating from various tissues has been described, which allows for their minimal-invasive detection in serum/plasma and use as biomarkers for presence and progression of pathological conditions. One pilot study has reported circulating miRNAs in serum and tissue of fracture patients. However, further studies are required to explore whether a dysbalance in bone homeostasis of fracture patients can reliably be reflected by specific circulating miRNAs, and whether these miRNAs might serve as drugable targets. Here, we report results from a comprehensive multiplex study of 175 miRNAs in serum samples obtained from 7 patients with osteoporotic fractures at the femoral neck, and 7 age-matched controls. Following elaborate quality control statistical analysis of this exploratory dataset identified 9 microRNAs with altered serum levels in response to fracture (adjusted p-value < 0.1). Of these, hsa-miR-10a/b gave excellent discrimination of both groups (AUC = 1.0), and clustering of samples based on the top10 miRNAs confirmed the high discriminatory power of circulating microRNAs for osteoporotic fractures. In the next step 3 miRNAs with unknown roles in osteogenic differentiation and 4 miRNA from a previous study were tested for their effects on osteogenic differentiation. Of these, 3 miRNAs showed robust effects on osteogenic differentiation. Overall, these data provide important insights into changes in serum miRNA in post-traumatic patients. Future studies will show, whether this knowledge can be used to improve current diagnostic methodologies to predict fracture risk and design novel treatment strategies for osteoporosis patients. Two groups with n=7 per group; one groups represents cases with osteoporotic fractures, the control group is age-matched without fractures

Project description:MicroRNAs (miRNAs) regulate activity of protein-coding genes including those involved in hematopoietic cancers. The goal of the current study was to explore which miRNAs are unique for seven different subtypes of pediatric acute lymphoblastic leukemia (ALL). Therefore, the expression levels of 397 miRNAs (including novel miRNAs) were measured by quantitative RT-PCR in 81 pediatric leukemia cases and 17 normal hematopoietic control cases. Except for BCR-ABL-positive and B-other ALL, all major subtypes i.e. T-ALL, MLL-rearranged, TEL-AML1-positive, E2A-PBX1-positive and hyperdiploid ALL have unique miRNA-signatures that differ from each other and from those in healthy hematopoietic cells. Strikingly, the miRNA signature between TEL-AML1-positive and hyperdiploid cases partly overlapped, which suggests a common underlying biology. Moreover, aberrant downregulation of let-7b (~70-fold) in MLL-rearranged ALL was linked to upregulation of oncoprotein c-Myc (P<0.0001). Besides genetic aberrations, in vitro drug resistance predicts clinical outcome. Resistance to vincristine and daunorubicin was characterized by ~20-fold upregulation of miR-125b, miR-99a and miR-100 (P≤0.002). No discriminative miRNAs were found for prednisolone and only one miRNA was linked to L-asparaginase resistance. Finally we show that the expression levels of 14 miRNAs were --independently of subtype-- associated with clinical outcome in pediatric ALL. We conclude that genetic subtypes and drug resistant leukemic cells display characteristic miRNA signatures in pediatric ALL. Functional studies of discriminative and prognostic important miRNAs may provide new insights into the biology of disease. Experiment type: stem-loop real-time (RT) quantitative PCR (RT-qPCR) Bone marrow and peripheral blood samples were collected from children at newly diagnosis of acute lymphoblastic leukemia (ALL). CD34+ -cells were sorted from G-CSF-stimulated blood cell samples of children with a brain tumor or Wilm’s tumor. Thymocytes were isolated from thymic lobes that were resected from children during surgery for congenital heart disease. RNA from the cell samples was extracted using TRIzol reagents (firma). Only RNA with an RNA Integrity Number (RIN) of ≥ 7.5 as measured by the 2100 Bioanalyzer (Agilent, Amstelveen, the Netherlands) was used as input for the RT-qPCR reactions. All miRNAs were validated with the stem-loop real-time (RT) quantitative PCR (RT-qPCR) technique by using either TaqMan MicroRNA Array MicroRNA arrays (v 1.0, Early Access) or Custom TaqMan MicroRNA Array arrays (Applied Biosystems, Foster City, USA). Values represent ∆Ct in each individual patient for whom the specific subtype, identification number and cellular drug-resistance is shown on top of each column. Drug-resistance was based on median LC50 values (concentration of a drug lethal to 50% of the leukemic cells) that have reported prognostic impact in children with newly diagnosed ALL. Median LC50 values were used to assign patients as sensitive (≤ median LC50) or resistant (> median LC50) to the drug in question. The ∆Ct value was calculated according to the following equation: Ct value of the specific miRNA minus the Ct value of the internal reference. In case of the TaqMan MicroRNA Array MicroRNA arrays the mean Ct values for snoR-13 and snoR-14 were used as reference whereas in case of the Custom TaqMan MicroRNA Array arrays snoR-1 was used. MiRNAs validated by the TaqMan MicroRNA Array MicroRNA arrays are listed together with the part numbers of the corresponding (stem-loop) primers as available by Applied Biosystems, Foster City, USA. Primers for the remaining miRNAs were custom designed by Applied Biosystems. Abbreviations: MLL: MLL-rearranged precursor B-ALL, B-other: precursor B-ALL negative for MLL-rearrangements, TEL-AML1, BCR-ABL, E2A-PBX and hyperdiploidy (> 50 chromosomes). CD34+: normal CD34+-sorted blood progenitor cells, nBM: normal bone marrow.

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.