Integrating full spectrum of sequence features into predicting functional microRNA-mRNA interactions

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

An efficient framework to identify key miRNA-mRNA regulatory modules in cancer

MOTIVATION

Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA-mRNA regulatory modules.

RESULTS

We presented a two-step framework to model miRNA-mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supported miRTarBase database.

AVAILABILITY AND IMPLEMENTATION

Our implementation of proposed two-step RFR algorithm in R is available at https://github.com/MiladMokhtaridoost/2sRFR together with the scripts that replicate the reported experiments.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational annotation of miRNA transcription start sites

Motivation

MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in gene regulation and phenotype development. The identification of miRNA transcription start sites (TSSs) is critical to understand the functional roles of miRNA genes and their transcriptional regulation. Unlike protein-coding genes, miRNA TSSs are not directly detectable from conventional RNA-Seq experiments due to miRNA-specific process of biogenesis. In the past decade, large-scale genome-wide TSS-Seq and transcription activation marker profiling data have become available, based on which, many computational methods have been developed. These methods have greatly advanced genome-wide miRNA TSS annotation.

Results

In this study, we summarized recent computational methods and their results on miRNA TSS annotation. We collected and performed a comparative analysis of miRNA TSS annotations from 14 representative studies. We further compiled a robust set of miRNA TSSs (RSmirT) that are supported by multiple studies. Integrative genomic and epigenomic data analysis on RSmirT revealed the genomic and epigenomic features of miRNA TSSs as well as their relations to protein-coding and long non-coding genes.

Contact

xiaoman@mail.ucf.edu, haihu@cs.ucf.edu

What's the target: understanding two decades of in silico microRNA-target prediction

MOTIVATION

Since the initial discovery of microRNAs as post-transcriptional, regulatory key players in the 1990s, a total number of $2656$ mature microRNAs have been publicly described for Homo sapiens. As discovery of new miRNAs is still on-going, target identification remains to be an essential and challenging step preceding functional annotation analysis. One key challenge for researchers seems to be the selection of the most appropriate tool out of the larger multiverse of published solutions for a given research study set-up.

RESULTS

In this review we collectively describe the field of in silico target prediction in the course of time and point out long withstanding principles as well as recent developments. By compiling a catalog of characteristics about the 98 prediction methods and identifying common and exclusive traits, we signpost a simplified mechanism to address the problem of application selection. Going further we devised interpretation strategies for common types of output as generated by frequently used computational methods. To this end, our work specifically aims to make prospective users aware of common mistakes and practical questions that arise during the application of target prediction tools.

AVAILABILITY

An interactive implementation of our recommendations including materials shown in the manuscript is freely available at https://www.ccb.uni-saarland.de/mtguide.

mirDIP 4.1-integrative database of human microRNA target predictions

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

Classifying mild traumatic brain injuries with functional network analysis

Background

Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury.

Results

In this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information.

Conclusions

The systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

Reward-related neural activity and structure predict future substance use in dysregulated youth

BACKGROUND

Identifying youth who may engage in future substance use could facilitate early identification of substance use disorder vulnerability. We aimed to identify biomarkers that predicted future substance use in psychiatrically un-well youth.

METHOD

LASSO regression for variable selection was used to predict substance use 24.3 months after neuroimaging assessment in 73 behaviorally and emotionally dysregulated youth aged 13.9 (s.d. = 2.0) years, 30 female, from three clinical sites in the Longitudinal Assessment of Manic Symptoms (LAMS) study. Predictor variables included neural activity during a reward task, cortical thickness, and clinical and demographic variables.

RESULTS

Future substance use was associated with higher left middle prefrontal cortex activity, lower left ventral anterior insula activity, thicker caudal anterior cingulate cortex, higher depression and lower mania scores, not using antipsychotic medication, more parental stress, older age. This combination of variables explained 60.4% of the variance in future substance use, and accurately classified 83.6%.

CONCLUSIONS

These variables explained a large proportion of the variance, were useful classifiers of future substance use, and showed the value of combining multiple domains to provide a comprehensive understanding of substance use development. This may be a step toward identifying neural measures that can identify future substance use disorder risk, and act as targets for therapeutic interventions.

Longitudinal relationships among activity in attention redirection neural circuitry and symptom severity in youth

BACKGROUND

Changes in neural circuitry function may be associated with longitudinal changes in psychiatric symptom severity. Identification of these relationships may aid in elucidating the neural basis of psychiatric symptom evolution over time. We aimed to distinguish these relationships using data from the Longitudinal Assessment of Manic Symptoms (LAMS) cohort.

METHODS

Forty-one youth completed two study visits (mean=21.3 months). Elastic-net regression (Multiple response Gaussian family) identified emotional regulation neural circuitry that changed in association with changes in depression, mania, anxiety, affect lability, and positive mood and energy dysregulation, accounting for clinical and demographic variables.

RESULTS

Non-zero coefficients between change in the above symptom measures and change in activity over the inter-scan interval were identified in right amygdala and left ventrolateral prefrontal cortex. Differing patterns of neural activity change were associated with changes in each of the above symptoms over time. Specifically, from Scan1 to Scan2, worsening affective lability and depression severity were associated with increased right amygdala and left ventrolateral prefrontal cortical activity. Worsening anxiety and positive mood and energy dysregulation were associated with decreased right amygdala and increased left ventrolateral prefrontal cortical activity. Worsening mania was associated with increased right amygdala and decreased left ventrolateral prefrontal cortical activity. These changes in neural activity between scans accounted for 13.6% of the variance; that is 25% of the total explained variance (39.6%) in these measures.

CONCLUSIONS

Distinct neural mechanisms underlie changes in different mood and anxiety symptoms overtime.

Predicting Functional MicroRNA-mRNA Interactions

MicroRNAs (miRNAs) are small RNA molecules that play key regulatory roles in general biological processes and disease pathogenesis. These small RNA molecules interact with their target mRNAs to induce mRNA degradation and/or inhibit the translation of mRNAs into proteins. Therefore, identifying miRNA targets is an essential step to fully understand the regulatory effects of miRNAs. Here, we describe a regularized regression approach that integrates the sequence information with the miRNA and mRNA expression profiles for detecting miRNA targets. This method takes into account the full spectrum of gene sequence features of miRNA targets, including the thermodynamic stability, the accessibility energy, and the context features of the target sites,. Given these sequence features for each putative miRNA-mRNA interaction and their expression values, this model is able to quantify the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature for predicting functional miRNA-mRNA interactions.

Predicting clinical outcome from reward circuitry function and white matter structure in behaviorally and emotionally dysregulated youth

Behavioral and emotional dysregulation in childhood may be understood as prodromal to adult psychopathology. Additionally, there is a critical need to identify biomarkers reflecting underlying neuropathological processes that predict clinical/behavioral outcomes in youth. We aimed to identify such biomarkers in youth with behavioral and emotional dysregulation in the Longitudinal Assessment of Manic Symptoms (LAMS) study. We examined neuroimaging measures of function and white matter in the whole brain using 80 youth aged 14.0 (s.d.=2.0) from three clinical sites. Linear regression using the LASSO (Least Absolute Shrinkage and Selection Operator) method for variable selection was used to predict severity of future behavioral and emotional dysregulation measured by the Parent General Behavior Inventory-10 Item Mania Scale (PGBI-10M)) at a mean of 14.2 months follow-up after neuroimaging assessment. Neuroimaging measures, together with near-scan PGBI-10M, a score of manic behaviors, depressive behaviors and sex, explained 28% of the variance in follow-up PGBI-10M. Neuroimaging measures alone, after accounting for other identified predictors, explained ~1/3 of the explained variance, in follow-up PGBI-10M. Specifically, greater bilateral cingulum length predicted lower PGBI-10M at follow-up. Greater functional connectivity in parietal-subcortical reward circuitry predicted greater PGBI-10M at follow-up. For the first time, data suggest that multimodal neuroimaging measures of underlying neuropathologic processes account for over a third of the explained variance in clinical outcome in a large sample of behaviorally and emotionally dysregulated youth. This may be an important first step toward identifying neurobiological measures with the potential to act as novel targets for early detection and future therapeutic interventions.

Network-based approaches for analysis of complex biological systems

Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.