An integrated hierarchical Bayesian approach to normalizing left-censored microRNA microarray data.
ABSTRACT: BACKGROUND: MicroRNAs (miRNAs) are small endogenous ssRNAs that regulate target gene expression post-transcriptionally through the RNAi pathway. A critical pre-processing procedure for detecting differentially expressed miRNAs is normalization, aiming at removing the between-array systematic bias. Most normalization methods adopted for miRNA data are the same methods used to normalize mRNA data; but miRNA data are very different from mRNA data mainly because of possibly larger proportion of differentially expressed miRNA probes, and much larger percentage of left-censored miRNA probes below detection limit (DL). Taking the unique characteristics of miRNA data into account, we present a hierarchical Bayesian approach that integrates normalization, missing data imputation, and feature selection in the same model. RESULTS: Results from both simulation and real data seem to suggest the superiority of performance of Bayesian method over other widely used normalization methods in detecting truly differentially expressed miRNAs. In addition, our findings clearly demonstrate the necessity of miRNA data normalization, and the robustness of our Bayesian approach against the violation of standard assumptions adopted in mRNA normalization methods. CONCLUSION: Our study indicates that normalization procedures can have a profound impact on the detection of truly differentially expressed miRNAs. Although the proposed Bayesian method was formulated to handle normalization issues in miRNA data, we expect that biomarker discovery with other high-dimensional profiling techniques where there are a significant proportion of left-censored data points (e.g., proteomics) might also benefit from this approach.
Project description:Recent studies have established that mutations or deletions in microRNA (miRNA) processing enzymes resulting in a global decrease of miRNA expression are frequent across cancers and can be associated with a poorer prognosis. While very popular in miRNA profiling studies, it remains unclear whether miRNA microarrays are suited or not to accurately detecting global miRNA decreases seen in cancers. In this work, we analyzed the miRNA profiles of samples with global miRNA decreases using Affymetrix miRNA microarrays following the inducible genetic deletion of Dicer1. Surprisingly, up to a third of deregulated miRNAs identified upon Dicer1 depletion were found to be up-regulated following standard robust multichip average (RMA) background correction and quantile normalization, indicative of normalization bias. Our comparisons of five preprocess steps performed at the probe level demonstrated that the use of cyclic loess relying on non-miRNA small RNAs present on the Affymetrix platform significantly improved specificity and sensitivity of detection of decreased miRNAs. These findings were validated in samples from patients with prostate cancer, where conjugation of robust normal-exponential background correction with cyclic loess normalization and array weights correctly identified the greatest number of decreased miRNAs, and the lowest amount of false-positive up-regulated miRNAs. These findings highlight the importance of miRNA microarray normalization for the detection of miRNAs that are truly differentially expressed and suggest that the use of cyclic loess based on non-miRNA small RNAs can help to improve the sensitivity and specificity of miRNA profiling in cancer samples with global miRNA decrease.
Project description:Neuropathic pain (NP) is a complex, chronic pain condition caused by injury or dysfunction affecting the somatosensory nervous system. This study aimed to identify crucial genes and miRNAs involved in NP. Microarray data (access number GSE91396) were downloaded from the Gene Expression Omnibus (GEO). Murine RNA?seq samples from three brain regions [nucleus accumbens, (NAc); medial prefrontal cortex, (mPFC) and periaqueductal gray, (PAG)]were compared between the spared nerve injury (SNI) model and a sham surgery. After data normalization, differentially expressed RNAs were screened using the limma package and functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery. The microRNA (miRNA/miR)?mRNA regulatory network and miRNA?target gene?pathway regulatory network were constructed using Cytoscape software. A total of 2,776 differentially expressed RNAs (219 miRNAs and 2,557 mRNAs) were identified in the SNI model compared with the sham surgery group. A total of two important modules (red and turquoise module) were found to be related to NP using weighed gene co?expression network analysis (WGCNA) for the 2,325 common differentially expressed RNAs in three brain regions. The differentially expressed genes (DEGs) in the miRNA?mRNA regulatory network were significantly enriched in 21 Gene Ontology terms and five pathways. A total of four important DEGs (CXCR2, IL12B, TNFSF8 and GRK1) and five miRNAs (miR?208a?5p, miR?7688?3p, miR?344f?3p, miR?135b?3p and miR?135a?2?3p) were revealed according to the miRNA?target gene?pathway regulatory network to be related to NP. Four important DEGs (CXCR2, IL12B, TNFSF8 and GRK1) and five miRNAs (miR?208a?5p, miR?7688?3p, miR?344f?3p, miR?135b?3p and miR?135a?2?3p) were differentially expressed in SNI, indicating their plausible roles in NP pathogenesis.
Project description:Plant microRNAs are small non-coding, endogenic RNA molecule (containing 20-24 nucleotides) produced from miRNA precursors (pri-miRNA and pre-miRNA). Evidence suggests that up and down regulation of the miRNA targets the mRNA genes involved in resistance against biotic and abiotic stresses. Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a powerful technique to analyze variations in mRNA levels. Normalizing the data using reference genes is essential for the analysis of reliable RT-qPCR data. In this study, two groups of candidate reference mRNAs and miRNAs in soybean leaves and roots treated with various abiotic stresses (PEG-simulated drought, salinity, alkalinity, salinity+alkalinity, and abscisic acid) were analyzed by RT-qPCR. We analyzed the most appropriate reference mRNA/miRNAs using the geNorm, NormFinder, and BestKeeper algorithms. According to the results, Act and EF1b were the most suitable reference mRNAs in leaf and root samples, for mRNA and miRNA precursor data normalization. The most suitable reference miRNAs found in leaf and root samples were 166a and 167a for mature miRNA data normalization. Hence the best combinations of reference mRNAs for mRNA and miRNA precursor data normalization were EF1a + Act or EF1b + Act in leaf samples, and EF1a + EF1b or 60s + EF1b in root samples. For mature miRNA data normalization, the most suitable combinations of reference miRNAs were 166a + 167d in leaf samples, and 171a + 156a or 167a + 171a in root samples. We identified potential reference mRNA/miRNAs for accurate RT-qPCR data normalization for mature miRNA, miRNA precursors, and their targeted mRNAs. Our results promote miRNA-based studies on soybean plants exposed to abiotic stress conditions.
Project description:BACKGROUND:MicroRNAs (miRNAs) play vital roles in acute inflammatory and antiviral responses during influenza A virus (IAV) infection. The Xijiao Dihuang decoction combined with Yinqiao powder (XDY) is applied to remedy viral pneumonia in China and its therapeutic efficacy in pneumonic mice challenged with IAV was demonstrated; however, the underlying mechanisms remain elusive. Thus, this study aimed to explore the miRNA-mRNA profiles in the lungs of IAV-infected mice and investigate the therapeutic mechanisms of XDY involving miRNAs and associated pathways. METHODS:We detected the cellular miRNA contents in the lungs of mice treated with XDY (23?g/kg/d) for A/FM/1/47 (H1N1) (FM1) infection at 4?days postinoculation (dpi) and 7 dpi. MiRNA and mRNA high-throughput sequencing analyses, and miRNA and mRNA qRT-PCR analyses were used to detect and verify the relevant miRNAs and mRNAs. Conjoint analysis, GO enrichment analysis, and KEGG database analysis were applied to identify the miRNA-mRNA regulatory relationships. RESULTS:The quantities of differentially expressed miRNAs and mRNAs were upregulated over time. The data showed that 104 miRNAs and 3485 mRNAs were differentially expressed after challenge with FM1 on day 4, while 191 miRNAs and 6126 mRNAs were differentially expressed on day 7. The GO enrichment analysis and KEGG database data showed that the differentially expressed miRNAs and mRNAs were mainly enriched in JNK activity, MAPK phosphatase activity, and the TLR, Jak-STAT and TNF signalling pathways after treatment of FM1 infection with XDY. Generally, the expression trends of differentially expressed miRNAs and mRNAs based on the qRT-PCR results exhibited good consistency with the results of the high-throughput sequencing analysis. CONCLUSIONS:MiRNAs and mRNAs were differentially expressed during FM1 infection. The therapeutic mechanisms of XDY in FM1-infected mice, might be related to regulating antiviral immunity and ameliorating excessive inflammatory responses by modulating the expression of dysregulated miRNAs and mRNAs involved in the ERK/JNK-AP-1, and IFN-?/STAT signalling pathways.
Project description:Fat deposition is very important in pig production, and its mechanism is not clearly understood. MicroRNAs (miRNAs) play critical roles in fat deposition and energy metabolism. In the current study, we investigated the mRNA and miRNA transcriptome in the livers of Landrace pigs with extreme backfat thickness to explore miRNA-mRNA regulatory networks related to lipid deposition and metabolism. A comparative analysis of liver mRNA and miRNA transcriptomes from pigs (four pigs per group) with extreme backfat thickness was performed. We identified differentially expressed genes from RNA-seq data using a Cufflinks pipeline. Seventy-one differentially expressed genes (DEGs), including twenty-eight well annotated on the porcine reference genome genes, were found. The upregulation genes in pigs with higher backfat thickness were mainly involved in fatty acid synthesis, and included fatty acid synthase (FASN), glucokinase (GCK), phosphoglycerate dehydrogenase (PHGDH), and apolipoprotein A4 (APOA4). Cytochrome P450, family 2, subfamily J, polypeptide 34 (CYP2J34) was lower expressed in pigs with high backfat thickness, and is involved in the oxidation of arachidonic acid. Moreover, 13 differentially expressed miRNAs were identified. Seven miRNAs were associated with fatty acid synthesis, lipid metabolism, and adipogenic differentiation. Based on comprehensive analysis of the transcriptome of both mRNAs and miRNAs, an important regulatory network, in which six DEGs could be regulated by differentially expressed miRNAs, was established for fat deposition. The negative correlate in the regulatory network including, miR-545-5p and GRAMD3, miR-338 and FASN, and miR-127, miR-146b, miR-34c, miR-144 and THBS1 indicate that direct suppressive regulation may be involved in lipid deposition and energy metabolism. Based on liver mRNA and miRNA transcriptomes from pigs with extreme backfat thickness, we identified 28 differentially expressed genes and 13 differentially expressed miRNAs, and established an important miRNA-mRNA regulatory network. This study provides new insights into the molecular mechanisms that determine fat deposition in pigs.
Project description:MicroRNAs (miRNAs) play a vital role in muscle development by binding to messenger RNAs (mRNAs). Based on prenatal skeletal muscle at 33, 65 and 90 days post-coitus (dpc) from Landrace, Tongcheng and Wuzhishan pigs, we carried out integrated analysis of miRNA and mRNA expression profiling. We identified 33, 18 and 67 differentially expressed miRNAs and 290, 91 and 502 mRNA targets in Landrace, Tongcheng and Wuzhishan pigs, respectively. Subsequently, 12 mRNAs and 3 miRNAs differentially expressed were validated using quantitative real-time PCR (qPCR), and 5 predicted miRNA targets were confirmed via dual luciferase reporter or western blot assays. We identified a set of miRNAs and mRNA genes differentially expressed in muscle development. Gene ontology (GO) enrichment analysis suggests that the miRNA targets are primarily involved in muscle contraction, muscle development and negative regulation of cell proliferation. Our data indicated that more mRNAs are regulated by miRNAs at earlier stages than at later stages of muscle development. Landrace and Tongcheng pigs also had longer phases of myoblast proliferation than Wuzhishan pigs. This study will be helpful to further explore miRNA-mRNA interactions in myogenesis and aid to uncover the molecular mechanisms of muscle development and phenotype variance in pigs.
Project description:Hirschsprung's disease (HSCR), the most common congenital malformation of the gut, is regulated by multiple signal transduction pathways. Several components of these pathways are important targets for microRNAs (miRNAs). Multiple miRNAs have been associated with the pathophysiology of HSCR, and serum miRNAs profiles of HSCR patients have been reported, but miRNA expression in HSCR colon tissue is almost completely unexplored. Using microarray technology, we screened colon tissue to detect miRNAs whose expression profiles were altered in HSCR and identify targets of differentially expressed miRNAs. Following filtering of low-intensity signals, data normalization, and volcano plot filtering, we identified 168 differentially expressed miRNAs (104 up-regulated and 64 down-regulated). Fifty of these mRNAs represent major targets of dysegulated miRNAs and may thus important roles in the pathophysiology of HSCR. Pathway analysis revealed that 7 of the miRNA targets encode proteins involved in regulation of cell proliferation and migration via RET and related signaling pathways (MAPK and PI3K/AKT). Our results identify miRNAs that play key roles in the pathophysiology of the complex multi-factorial disease HSCR.
Project description:MiRNAs regulate gene expression by post-transcriptionally suppressing mRNA translation or by causing mRNA degradation. It has been proposed that unique miRNAs influence specific tumor molecular phenotype. In this paper, we test the hypotheses that miRNA expression differs by tumor molecular phenotype and that those differences may influence prognosis. Data come from population-based studies of colorectal cancer conducted in Utah and the Northern California Kaiser Permanente Medical Care Program. A total of 1893 carcinoma samples were run on the Agilent Human miRNA Microarray V19.0 containing 2006 miRNAs. We assessed differences in miRNA expression between TP53-mutated and non-mutated, KRAS-mutated and non-mutated, BRAF-mutated and non-mutated, CpG island methylator phenotype (CIMP) high and CIMP low, and microsatellite instability (MSI) and microsatellite stable (MSS) colon and rectal tumors. Using a Cox proportional hazard model we evaluated if those miRNAs differentially expressed by tumor phenotype influenced survival after adjusting for age, sex, and AJCC stage. There were 22 differentially expressed miRNAs for TP53-mutated colon tumors and 5 for TP53-mutated rectal tumors with a fold change of >1.49 (or <0.67). Additionally, 13 miRNAS were differentially expressed for KRAS-mutated rectal tumors, 8 differentially expressed miRNAs for colon CIMP high tumors, and 2 differentially expressed miRNAs for BRAF-mutated colon tumors. The majority of differentially expressed miRNAS were observed between MSI and MSS tumors (94 differentially expressed miRNAs for colon; 41 differentially expressed miRNAs for rectal tumors). Of these miRNAs differentially expressed between MSI and MSS tumors, the majority were downregulated. Ten of the differentially expressed miRNAs were associated with survival; after adjustment for MSI status, five miRNAS, miR-196b-5p, miR-31-5p, miR-99b-5p, miR-636, and miR-192-3p, were significantly associated with survival. In summary, it appears that the majority of miRNAs that are differentially expressed by tumor molecular phenotype are MSI tumors. However, these miRNAs appear to have minimal effect on prognosis.
Project description:Pemetrexed is indicated for non-small cell lung carcinoma and mesothelioma, but often has limited efficacy due to drug resistance. To probe the molecular mechanisms underlying chemotherapeutic response, we performed mRNA and microRNA (miRNA) expression profiling of pemetrexed treated and untreated lymphoblastoid cell lines (LCLs) and applied a hierarchical Bayesian method. We identified genetic variation associated with gene expression in human lung tissue for the most significant differentially expressed genes (Benjamini-Hochberg [BH] adjusted p?<?0.05) using the Genotype-Tissue Expression data and found evidence for their clinical relevance using integrated molecular profiling and lung adenocarcinoma survival data from The Cancer Genome Atlas project. We identified 39 miRNAs with significant differential expression (BH adjusted p?<?0.05) in LCLs. We developed a gene expression based imputation model of drug sensitivity, quantified its prediction performance, and found a significant correlation of the imputed phenotype generated from expression data with survival time in lung adenocarcinoma patients. Differentially expressed genes (MTHFD2 and SUFU) that are putative targets of differentially expressed miRNAs also showed differential perturbation in A549 fusion lung tumor cells with further replication in A549 cells. Our study suggests pemetrexed may be used in combination with agents that target miRNAs to increase its cytotoxicity.
Project description:Recent studies have indicated that circulating noncoding RNAs (ncRNAs) such as miRNAs are stable biomarkers for the diagnosis and prognosis of human diseases. However, due to low concentrations of circulating ncRNAs in blood, data normalization in plasma/serum ncRNA experiments using next-generation sequencing and quantitative real time RT-qPCR is a challenge. We found that the current normalization methods based on synthetic external spiked-in controls or published endogenous miRNA controls are inappropriate as they are not stably expressed and therefore fail to reliably detect differentially expressed ncRNAs. Using the alternative of individual ncRNAs as biomarkers, we considered a ratio-based normalization method calculated taking the ratio of any two ncRNAs in the same sample and used the resulting ratios as biomarkers. We mathematically verified the method to be independent of spiked-in and internal controls, and more robust than existing reference control based normalization methods to identify differentially expressed ncRNAs as potential biomarkers for human diseases. Thus, the ratio-based method can solve the difficult normalization problem for circuiting ncRNA data to identify reliable biomarkers to meet real clinical practice.