microRNAs (miRNAs) regulate expression by promoting degradation or repressing translation of target transcripts. miRNA target sites have been catalogued in databases based on experimental validation and computational prediction using various algorithms. Several online resources provide collections of multiple databases but need to be imported into other software, such as R, for processing, tabulation, graphing and computation. Currently available miRNA target site packages in R are limited in the number of databases, types of databases and flexibility. We present multiMiR, a new miRNA–target interaction R package and database, which includes several novel features not available in existing R packages: (i) compilation of nearly 50 million records in human and mouse from 14 different databases, more than any other collection; (ii) expansion of databases to those based on disease annotation and drug microRNAresponse, in addition to many experimental and computational databases; and (iii) user-defined cutoffs for predicted binding strength to provide the most confident selection. Case studies are reported on various biomedical applications including mouse models of alcohol consumption, studies of chronic obstructive pulmonary disease in human subjects, and human cell line models of bladder cancer metastasis. We also demonstrate how multiMiR was used to generate testable hypotheses that were pursued experimentally.
Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from −183 to 178 grams per 10% increase in methylation (P Bonferroni < 1.06 x 10 −7 ). In additional analyses in 7,278 participants, <1.3% of birthweight-associated differential methylation is also observed in childhood and adolescence, but not adulthood. Birthweight-related CpGs overlap with some Bonferroni-significant CpGs that were previously reported to be related to maternal smoking (55/914, p = 6.12 x 10 −74 ) and BMI in pregnancy (3/914, p = 1.13x10 −3 ), but not with those related to folate levels in pregnancy. Whether the associations that we observe are causal or explained by confounding or fetal growth influencing DNA methylation (i.e. reverse causality) requires further research.
Drosophila imaginal disc cells can switch fates by transdetermining from one determined state to another. We analyzed the expression profiles of cells induced by ectopic Wingless expression to transdetermine from leg to wing by dissecting transdetermined cells and hybridizing probes generated by linear RNA amplification to DNA microarrays. Changes in expression levels implicated a number of genes: lamina ancestor, CG12534 (a gene orthologous to mouse augmenter of liver regeneration), Notch pathway members, and the Polycomb and trithorax groups of chromatin regulators. Functional tests revealed that transdetermination was significantly affected in mutants for lama and seven different PcG and trxG genes. These results validate our methods for expression profiling as a way to analyze developmental programs, and show that modifications to chromatin structure are key to changes in cell fate. Our findings are likely to be relevant to the mechanisms that lead to disease when homologs of Wingless are expressed at abnormal levels and to the manifestation of pluripotency of stem cells.
Rationale: Chronic obstructive pulmonary disease (COPD) occurs in a minority of smokers and is characterized by intermittent exacerbations and clinical subphenotypes such as emphysema and chronic bronchitis. Although sphingolipids as a class are implicated in the pathogenesis of COPD, the particular sphingolipid species associated with COPD subphenotypes remain unknown.Objectives: To use mass spectrometry to determine which plasma sphingolipids are associated with subphenotypes of COPD.Methods: One hundred twenty-nine current and former smokers from the COPDGene cohort had 69 distinct sphingolipid species detected in plasma by targeted mass spectrometry. Of these, 23 were also measured in 131 plasma samples (117 independent subjects) using an untargeted platform in an independent laboratory. Regression analysis with adjustment for clinical covariates, correction for false discovery rate, and metaanalysis were used to test associations between COPD subphenotypes and sphingolipids. Peripheral blood mononuclear cells were used to test associations between sphingolipid gene expression and plasma sphingolipids.Measurements and Main Results: Of the measured plasma sphingolipids, five sphingomyelins were associated with emphysema; four trihexosylceramides and three dihexosylceramides were associated with COPD exacerbations. Three sphingolipids were strongly associated with sphingolipid gene expression, and 15 sphingolipid gene/metabolite pairs were differentially regulated between COPD cases and control subjects.Conclusions: There is evidence of systemic dysregulation of sphingolipid metabolism in patients with COPD. Subphenotyping suggests that sphingomyelins are strongly associated with emphysema and glycosphingolipids are associated with COPD exacerbations.
Although most cases of chronic obstructive pulmonary disease (COPD) occur in smokers, only a fraction of smokers develop the disease. We hypothesized distinct molecular signatures for COPD and emphysema in the peripheral blood mononuclear cells (PBMCs) of current and former smokers. To test this hypothesis, we identified and validated PBMC gene expression profiles in smokers with and without COPD. We generated expression data on 136 subjects from the COPDGene study, using Affymetrix U133 2.0 microarrays (Affymetrix, Santa Clara, CA). Multiple linear regression with adjustment for covariates (gender, age, body mass index, family history, smoking status, and pack-years) was used to identify candidate genes, and ingenuity pathway analysis was used to identify candidate pathways. Candidate genes were validated in 149 subjects according to multiplex quantitative real-time polymerase chain reaction, which included 75 subjects not previously profiled. Pathways that were differentially expressed in subjects with COPD and emphysema included those that play a role in the immune system, inflammatory responses, and sphingolipid (ceramide) metabolism. Twenty-six of the 46 candidate genes (e.g., FOXP1, TCF7, and ASAH1) were validated in the independent cohort. Plasma metabolomics was used to identify a novel glycoceramide (galabiosylceramide) as a biomarker of emphysema, supporting the genomic association between acid ceramidase (ASAH1) and emphysema. COPD is a systemic disease whose gene expression signatures in PBMCs could serve as novel diagnostic or therapeutic targets.
BackgroundChronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly predictive of outcomes than individual clinical variables, we hypothesized that multiple biomarkers may be more informative than individual biomarkers to predict subtypes, disease severity, disease progression, and mortality.MethodsFibrinogen, C-Reactive Protein (CRP), surfactant protein D (SP-D), soluble Receptor for Advanced Glycation Endproducts (sRAGE), and Club Cell Secretory Protein (CC16) were measured in the plasma of 1465 subjects from the COPDGene cohort and 2746 subjects from the ECLIPSE cohort. Regression analysis was performed to determine whether these biomarkers, individually or in combination, were predictive of subtypes, disease severity, disease progression, or mortality, after adjustment for clinical covariates.ResultsIn COPDGene, the best combinations of biomarkers were: CC16, sRAGE, fibrinogen, CRP, and SP-D for airflow limitation (p < 10−4), SP-D, CRP, sRAGE and fibrinogen for emphysema (p < 10−3), CC16, fibrinogen, and sRAGE for decline in FEV1 (p < 0.05) and progression of emphysema (p < 10−3), and all five biomarkers together for mortality (p < 0.05). All associations except mortality were validated in ECLIPSE. The combination of SP-D, CRP, and fibrinogen was the best model for mortality in ECLIPSE (p < 0.05), and this combination was also significant in COPDGene.ConclusionThis comprehensive analysis of two large cohorts revealed that combinations of biomarkers improve predictive value compared with clinical variables and individual biomarkers for relevant cross-sectional and longitudinal COPD outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-017-0597-7) contains supplementary material, which is available to authorized users.
Searches for the identity of genes that influence the levels of alcohol consumption by humans and other animals have often been driven by presupposition of the importance of particular gene products in determining positively or negatively reinforcing effects of ethanol. We have taken an unbiased approach and performed a meta-analysis across three types of mouse populations to correlate brain gene expression with levels of alcohol intake. Our studies, using filtering procedures based on QTL analysis, produced a list of eight candidate genes with highly heritable expression, which could explain a significant amount of the variance in alcohol preference in mice. Using the Allen Brain Atlas for gene expression, we noted that the candidate genes' expression was localized to the olfactory and limbic areas as well as to the orbitofrontal cortex. Informatics techniques and pathway analysis illustrated the role of the candidate genes in neuronal migration, differentiation, and synaptic remodeling. The importance of olfactory cues, learning and memory formation (Pavlovian conditioning), and cortical executive function, for regulating alcohol intake by animals (including humans), is discussed.
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