Research | Funded Projects

VALIDAZIONE DI UN SET SPECIFICO DI MIRNA PER LA DIAGNOSI PRECOCE DELL'ATROFIA MULTISISTEMICA

The identification and validation of specific set of miRNAs associated with PD and MSA may not only facilitate early disease recognition but also have significant consequences for the treatment and for patient counselling. The overall aim of this project is to validate a specific set of miRNAs for early diagnosis of MSA and discrimination between MSA and PD.Serum samples (2 ml for each sample) from MSA and PD patients (possibly matched for sex, age +/- 3 years and disease duration) and healthy controls (matched for sex and age +/- 3 years) will be collected and stored at -80°C.RNA isolation, reverse transcription, and miRNA profiling by TaqMan Low Density Array: Serum samples will be centrifuged at 2000 rpm for 10′ to pellet and remove any circulating cell or debris. miRNAs will be extracted from 400 μl of serum samples by using Qiagen miRNeasy mini kit (Qiagen, GmbH, Hilden, Germany), according to Qiagen supplementary protocol for purification of small RNAs from serum and plasma, and finally eluted in 30 μl volume of elution buffer (Ragusa et al., 2013). RNAs will be quantified by fluorometer and spectrophotometer. To profile the transcriptome of miRNAs, 3.2 μl of serum RNAs (corresponding to about 20 ng of RNA) will be retrotranscribed and pre-amplified, according to the manufacturer's instructions. Pre-amplified products will be loaded onto TLDAs, TaqMan Human MicroRNA Array v3.0 A and B (Applied Biosystems | Life Technologies™ Monza, Italy). Result validation will be obtained by single TaqMan assays (Applied Biosystems | Life Technologies™ Monza, Italy) using the same amount of serum miRNAs according to the manufacturer's instructions.Data analysis:To obtain an accurate miRNA profiling, we will use the global median normalization method. Similarly to microarray analysis, Ct values from each sample will be normalized to the median Ct of the array (Ragusa et al., 2010). DE miRNAs will be identified by Significance of Microarrays Analysis (SAM) computed by Multi experiment viewer v4.8.1 (http://www.tm4.org), applying a two-class unpaired test among ΔCts and using a p-value based on 100 permutations and imputation engine: K-nearest neighbors (10 neighbors). False discovery rate<0.15 will be used as correction for multiple comparisons. miRNA target prediction:In order to increase data strength, DE miRNA targets will be analyzed by a combination of two different approaches. By interpolating 11 prediction tools (http://mirecords.biolead.org), a first series of predicted and experimentally validated DE miRNA targets will be extracted from miRecords. To improve our prediction, an additional filtering will be performed by using starBase, a database for predicted miRNA–target interactions, overlapped with data from Argonaute cross-linked immunoprecipitation sequencing (CLIP-Seq) (Yang et al., 2011). Gene ontology analysis:The identification of statistically significant Gene Ontologies of miRNA targets will be obtained by using FatiGo (Biological Process) from Babelomics 4.2 server (http://babelomics.bioinfo.cipf.es/). We will use the gene functional classification tool DAVID (http://niaid.abcc.ncifcrf.gov) to identify tissue-specific expression of miRNA targets.