Predicting microRNA targets in time-series microarray experiments via functional data analysis

Functional Data Analysis for Predicting Pediatric Failure to Complete Ten Brief Exercise Bouts

Physiological response to physical exercise through analysis of cardiopulmonary measurements has been shown to be predictive of a variety of diseases. Nonetheless, the clinical use of exercise testing remains limited because interpretation of test results requires experience and specialized training. Additionally, until this work no methods have identified which dynamic gas exchange or heart rate responses influence an individual's decision to start or stop physical activity. This research examines the use of advanced machine learning methods to predict completion of a test consisting of multiple exercise bouts by a group of healthy children and adolescents. All participants could complete the ten bouts at low or moderate-intensity work rates, however, when the bout work rates were high-intensity, 50% refused to begin the subsequent exercise bout before all ten bouts had been completed (task failure). We explored machine learning strategies to model the relationship between the physiological time series, the participant's anthropometric variables, and the binary outcome variable indicating whether the participant completed the test. The best performing model, a generalized spectral additive model with functional and scalar covariates, achieved 93.6% classification accuracy and an F1 score of 93.5%. Additionally, functional analysis of variance testing showed that participants in the 'failed' and 'success' groups have significantly different functional means in three signals: heart rate, oxygen uptake rate, and carbon dioxide uptake rate. Overall, these results show the capability of functional data analysis with generalized spectral additive models to identify key differences in the exercise-induced responses of participants in multiple bout exercise testing.

miRNA target identification and prediction as a function of time in gene expression data

The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

Expression of microRNAs and isomiRs in the porcine endometrium: implications for gene regulation at the maternal-conceptus interface

Background

Embryo implantation is a complex, synchronized process that requires establishment of a reciprocal dialogue between a receptive endometrium and developing blastocysts. Recently, microRNAs (miRNAs), known to modulate gene expression through post-transcriptional mechanisms, were implicated in regulation of early pregnancy events including maternal recognition of pregnancy and implantation. To characterize complex transcriptomic changes, expression of miRNAs in pregnant and cyclic endometria collected on days 12, 16 and 20 was analyzed using Illumina deep sequencing and analyzed with bioinformatic pipeline. Moreover, expression profiles of ten genes related to miRNA synthesis and transport such as DROSHA, DGCR8, XPO5, DICER, TARBP2, TNRC6A, and AGO1-4 were determined.

Results

Among genes involved in miRNA transport and synthesis DROSHA, XPO5, DICER1, TARBP, and AGO1 expression was affected by the reproductive status. Moreover, DICER1 and AGO2 proteins were localized in luminal and glandular epithelium with immunofluorescence staining. Several hundred mature, canonical and non-canonical miRNAs were found to be expressed in the endometrial samples. Detailed analysis revealed that miRNA length variants, isomiRs, accounted for the vast majority of defined sequences. Both miRNA and isomiR of miR-140-3p were shown to affect expression of putative targets in endometrial stromal cells in vitro. Computational analysis of putative target genes for miRNAs differentially expressed (DE) between pregnant and cyclic animals resulted in lists of biological processes and regulatory pathways indicating their role in cellular development, cell cycle, immunological response and organismal development. Among predicted target genes for DE miRNAs, vascular endothelial growth factor (VEGF), progesterone and estradiol receptors (PGR, ESR1) and leukemia inhibitory factor (LIF) were found.

Conclusions

This research revealed a repertoire of pregnancy-related miRNAs in porcine endometrium during initial stages of conceptus implantation and during the estrous cycle, and sheds light on mechanisms regulating miRNA-mediated gene expression at the maternal-conceptus interface.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2172-2) contains supplementary material, which is available to authorized users.

Transcriptome dynamics of the microRNA inhibition response

We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line—the first such dynamic study of the microRNA inhibition response—revealing both general and specific aspects of the physiological response. We show miR-9 inhibition inducing a multiphasic transcriptome response, with a direct target perturbation before 4 h, earlier than previously reported, amplified by a downstream peak at ∼32 h consistent with an indirect response due to secondary coherent regulation. Predictive modelling indicates a major role for miR-9 in post-transcriptional control of RNA processing and RNA binding protein regulation. Cluster analysis identifies multiple co-regulated gene regulatory modules. Functionally, we observe a shift over time from mRNA processing at early time points to translation at later time points. We validate the key observations with independent time series qPCR and we experimentally validate key predicted miR-9 targets. Methodologically, we developed sensitive functional data analytic predictive methods to analyse the weak response inherent in microRNA inhibition experiments. The methods of this study will be applicable to similar high-resolution time series transcriptome analyses and provides the context for more accurate experimental design and interpretation of future microRNA inhibition studies.

Big effects of small RNAs: a review of microRNAs in anxiety

Epigenetic and regulatory elements provide an additional layer of complexity to the heterogeneity of anxiety disorders. MicroRNAs (miRNAs) are a class of small, noncoding RNAs that have recently drawn interest as epigenetic modulators of gene expression in psychiatric disorders. miRNAs elicit their effects by binding to target messenger RNAs (mRNAs) and hindering translation or accelerating degradation. Considering their role in neuronal differentiation and synaptic plasticity, miRNAs have opened up new investigative avenues in the aetiology and treatment of anxiety disorders. In this review, we provide a thorough analysis of miRNAs, their targets and their functions in the central nervous system (CNS), focusing on their role in anxiety disorders. The involvement of miRNAs in CNS functions (such as neurogenesis, neurite outgrowth, synaptogenesis and synaptic and neural plasticity) and their intricate regulatory role under stressful conditions strongly support their importance in the aetiology of anxiety disorders. Furthermore, miRNAs could provide new avenues for the development of therapeutic targets in anxiety disorders.

Applications of functional data analysis: A systematic review

BACKGROUND

Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods.

METHODS

A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995-2010. Papers reporting methodological considerations only were excluded, as were non-English articles.

RESULTS

In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data.

CONCLUSIONS

Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.

Regulation of epidermal growth factor receptor signaling and erlotinib sensitivity in head and neck cancer cells by miR-7

Elevated expression and activity of the epidermal growth factor receptor (EGFR)/protein kinase B (Akt) signaling pathway is associated with development, progression and treatment resistance of head and neck cancer (HNC). Several studies have demonstrated that microRNA-7 (miR-7) regulates EGFR expression and Akt activity in a range of cancer cell types via its specific interaction with the EGFR mRNA 3'-untranslated region (3'-UTR). In the present study, we found that miR-7 regulated EGFR expression and Akt activity in HNC cell lines, and that this was associated with reduced growth in vitro and in vivo of cells (HN5) that were sensitive to the EGFR tyrosine kinase inhibitor (TKI) erlotinib (Tarceva). miR-7 acted synergistically with erlotinib to inhibit growth of erlotinib-resistant FaDu cells, an effect associated with increased inhibition of Akt activity. Microarray analysis of HN5 and FaDu cell lines transfected with miR-7 identified a common set of downregulated miR-7 target genes, providing insight into the tumor suppressor function of miR-7. Furthermore, we identified several target miR-7 mRNAs with a putative role in the sensitization of FaDu cells to erlotinib. Together, these data support the coordinate regulation of Akt signaling by miR-7 in HNC cells and suggest the therapeutic potential of miR-7 alone or in combination with EGFR TKIs in this disease.