Grouping miRNAs of similar functions via weighted information content of gene ontology

Integrative mRNA-miRNA interaction analysis associated with the immune response of Strongylocentrotus intermedius to Vibrio harveyi infection

Strongylocentrotus intermedius is one of the most economically valuable sea urchin species in China and has experienced mass mortality owing to outbreaks of bacterial diseases such as black mouth disease. This has caused serious economic losses to the sea urchin farming industry. To investigate the immune response mechanism of S. intermedius with different tube feet colors in response to Vibrio harveyi infection, we examined the different tube feet-colored S. intermedius under V. harveyi challenge and compared their transcriptome and microRNA (miRNA) profiles using RNA-Seq. We obtained 1813 differentially expressed genes (DEGs), 28 DE miRNAs, and 303 DE miRNA-DEG pairs in different tube feet-colored S. intermedius under V. harveyi challenge. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the most significant DEGs were associated with the Notch signaling and phagosome pathways. The target genes of immune-related miRNAs (miR-71, miR-184, miR-193) and genes (CALM1, SPSB4, DMBT, CSRP1) in S. intermedius were predicted and validated. This study provides insight into the molecular mechanisms that regulate genes involved in the immune response of S. intermedius infected with V. harveyi.

Expression Regulation Mechanisms of Sea Urchin (Strongylocentrotus intermedius) Under the High Temperature: New Evidence for the miRNA-mRNA Interaction Involvement

In the context of global warming and continuous high temperatures in the northern part of China during summer, the mortality rate of our main breeding species, Strongylocentrotus intermedius, reached 80% in 2020. How sea urchins respond to high temperatures is of great concern to academia and industry. In this study, we examined the antioxidant enzyme activities of different color tube-footed sea urchins under heat stress and compared their transcriptome and microRNA (miRNA) profiles using RNA-Seq. The results showed that the antioxidant enzyme activities of sea urchins were altered by thermal stress, and the changes in peroxidase activities of red tube-footed sea urchins were particularly significant. Investigations revealed that 1,079 differentially expressed genes (DEGs), 11 DE miRNAs, and 104 “DE miRNA-DEG” pairs in total were detected in sea urchins under high temperature stress. Several mRNA and miRNAs were significantly changed (e.g. HSP70, DnaJ11, HYAL, CALR, miR-184-p5, miR-92a, miR-92c, and miR-124-p5), suggesting these genes and miRNAs exerted important functions in response to high temperature. At the transcriptional level, red tube-footed sea urchins were found to be more sensitive to high temperature and could respond to high temperature rapidly. DE miRNA-mRNA network showed that miR-92b-3p and PC-5p-7420 were the most corresponding miRNAs. Five mRNAs (DnaJ11, SAR1B, CALR, HYOU1, TUBA) may be potential markers of sea urchin response to high temperature. Possible interaction between miRNA-mRNA could be linked to protein folding in the endoplasmic reticulum, Phagosomes, and calcium transport. This study provides a theoretical basis for the molecular mechanism of sea urchin heat tolerance and information that will aid in the selection and breeding of sea urchins with high temperature tolerance.

Integrated miRNA-mRNA analysis provides potential biomarkers for selective breeding in bay scallop (Argopecten irradians)

Growth traits were compared between selected Argopecten irradians (BA) and non-selected A. irradians (NA; as a control). The results indicated that 1) the BA line exhibited greater average body weight and adductor muscle wet weight increase compared with the NA line at the same age of 10 months. 2) Comparative and integrated microRNA (miRNA) and mRNA transcriptome analyses identified 3373 differentially expressed genes (DEGs), 33 differentially expressed miRNAs (DEMs), and 39 "DEM-DEG" pairs in the BA line compared with the control. DEGs, DEMs, and "DEM-DEG" pairs involved in insulin signaling, immune related pathways, and actin cytoskeleton regulation were identified as candidates correlated with growth improvement in the BA line. A total of 259 positively selected genes were also identified. Collectively, our observations in this study will enrich the molecular information for A. irradians and provide potential biomarkers for future selective breeding and new seed creation in scallops.

MiR-455-5p serves as a biomarker of atherosclerosis and inhibits vascular smooth muscle cell proliferation and migration

Background: This study discussed the clinical value and expression level of miR-455-5p in atherosclerosis (AS) patients. Meanwhile, its regulatory effect on the proliferation and migration of vascular smooth muscle cells (VSMCs) was further analyzed. Materials & methods: Clinical experiments were detected by quantitative real-time PCR and receiver operating characteristic. Cell experiments were detected by CCK-8, transwell and luciferase reporter gene assay. Results: miR-455-5p was low expressed in AS patients and had diagnostic value to distinguish AS patients from healthy controls. MiR-455-5p inhibited the proliferation and migration of VSMCs. SOCS3 was the target gene of miR-455-5p. Conclusion: MiR-455-5p may be used as a potential diagnostic biomarker for AS. MiR-455-5p may inhibit the proliferation and migration of VSMCs through targeting SOCS3.

Predicting novel microRNA: a comprehensive comparison of machine learning approaches

MOTIVATION

The importance of microRNAs (miRNAs) is widely recognized in the community nowadays because these short segments of RNA can play several roles in almost all biological processes. The computational prediction of novel miRNAs involves training a classifier for identifying sequences having the highest chance of being precursors of miRNAs (pre-miRNAs). The big issue with this task is that well-known pre-miRNAs are usually few in comparison with the hundreds of thousands of candidate sequences in a genome, which results in high class imbalance. This imbalance has a strong influence on most standard classifiers, and if not properly addressed in the model and the experiments, not only performance reported can be completely unrealistic but also the classifier will not be able to work properly for pre-miRNA prediction. Besides, another important issue is that for most of the machine learning (ML) approaches already used (supervised methods), it is necessary to have both positive and negative examples. The selection of positive examples is straightforward (well-known pre-miRNAs). However, it is difficult to build a representative set of negative examples because they should be sequences with hairpin structure that do not contain a pre-miRNA.

RESULTS

This review provides a comprehensive study and comparative assessment of methods from these two ML approaches for dealing with the prediction of novel pre-miRNAs: supervised and unsupervised training. We present and analyze the ML proposals that have appeared during the past 10 years in literature. They have been compared in several prediction tasks involving two model genomes and increasing imbalance levels. This work provides a review of existing ML approaches for pre-miRNA prediction and fair comparisons of the classifiers with same features and data sets, instead of just a revision of published software tools. The results and the discussion can help the community to select the most adequate bioinformatics approach according to the prediction task at hand. The comparative results obtained suggest that from low to mid-imbalance levels between classes, supervised methods can be the best. However, at very high imbalance levels, closer to real case scenarios, models including unsupervised and deep learning can provide better performance.

Identification of molecular markers for superior quantitative traits in a novel sea cucumber strain by comparative microRNA-mRNA expression profiling

To investigate the adaptability of Apostichopus japonicus (A. japonicus) strain "Anyuan No. 1" in the South China Sea, field monitoring and microRNA-mRNA integrated analyses were conducted between "Anyuan No. 1" and a regular A. japonicus population from Wendeng (Shandong Province, as a control) in the Xiapu farming area in Fujian Province, China. The results showed that "Anyuan No. 1" exhibited greater body weight increase and a higher number of papillae compared to the control during two and a half months of field monitoring. Comparative microRNA (miRNA) and mRNA transcriptome analyses identified 12 differentially expressed miRNAs (DEMs) and 165 differentially expressed genes (DEGs) in "Anyuan No. 1" compared to the control. Long-chain specific acyl-CoA dehydrogenase (ACADL), transmembrane protein 251 (TMEM251), dehydrogenase/reductase SDR family protein 7-like (Dhrs7), insulin-like growth factor-binding protein 7 (IGFBP-7), CDK5 regulatory subunit-associated protein 1 (CDK5RAP1), visual pigment-like receptor peropsin, 39S ribosomal protein, miR-10, miR-153, miR-7, and miR-3529 were identified as gene and miRNA candidates correlated with superior economic traits in "Anyuan No. 1". Collectively, "Anyuan No. 1" is suitable for large-scale cultivation extension due to its better adaptability to the South China Sea area. Furthermore, we identified "miR10-ACADL" as a potential module for further molecular marker-assisted selective breeding of A. japonicus.

MiRGOFS: a GO-based functional similarity measurement for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA-disease association

Motivation

Benefiting from high-throughput experimental technologies, whole-genome analysis of microRNAs (miRNAs) has been more and more common to uncover important regulatory roles of miRNAs and identify miRNA biomarkers for disease diagnosis. As a complementary information to the high-throughput experimental data, domain knowledge like the Gene Ontology and KEGG pathway is usually used to guide gene function analysis. However, functional annotation for miRNAs is scarce in the public databases. Till now, only a few methods have been proposed for measuring the functional similarity between miRNAs based on public annotation data, and these methods cover a very limited number of miRNAs, which are not applicable to large-scale miRNA analysis.

Results

In this paper, we propose a new method to measure the functional similarity for miRNAs, called miRGOFS, which has two notable features: (i) it adopts a new GO semantic similarity metric which considers both common ancestors and descendants of GO terms; (i) it computes similarity between GO sets in an asymmetric manner, and weights each GO term by its statistical significance. The miRGOFS-based predictor achieves an F1 of 61.2% on a benchmark dataset of miRNA localization, and AUC values of 87.7 and 81.1% on two benchmark sets of miRNA-disease association, respectively. Compared with the existing functional similarity measurements of miRNAs, miRGOFS has the advantages of higher accuracy and larger coverage of human miRNAs (over 1000 miRNAs).

Availability and implementation

http://www.csbio.sjtu.edu.cn/bioinf/MiRGOFS/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Inverse similarity and reliable negative samples for drug side-effect prediction

BACKGROUND

In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement.

METHODS

Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions.

RESULTS

The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance than those based on a single type of drug property. Besides, the four machine-learning algorithms achieved significant improvement in macro-averaging F1-score (e.g., SVM from 0.655 to 0.898), macro-averaging precision (e.g., RBF from 0.592 to 0.828) and macro-averaging recall (e.g., KNN from 0.651 to 0.772) complimentarily attributed to the highly-reliable negative samples selected by the proposed method.

CONCLUSIONS

The results suggest that the inverse similarity hypothesis and the integration of different drug properties are valuable for side-effect prediction. The selection of highly-reliable negative samples can also make significant contributions to the performance improvement.

An isomiR expression panel based novel breast cancer classification approach using improved mutual information

Background

Gene expression-based profiling has been used to identify biomarkers for different breast cancer subtypes. However, this technique has many limitations. IsomiRs are isoforms of miRNAs that have critical roles in many biological processes and have been successfully used to distinguish various cancer types. Biomarker isomiRs for identifying different breast cancer subtypes has not been investigated. For the first time, we aim to show that isomiRs are better performing biomarkers and use them to explain molecular differences between breast cancer subtypes.

Results

In this study, a novel method is proposed to identify specific isomiRs that faithfully classify breast cancer subtypes. First, as a null hypothesis method we removed the lowly expressed isomiRs from small sequencing data generated from diverse breast cancers types. Second, we developed an improved mutual information-based feature selection method to calculate the weight of each isomiR expression. The weight of isomiR measures the importance of a given isomiR in classifying breast cancer subtypes. The improved mutual information enables to apply the dataset in which the feature is continuous data and label is discrete data; whereby, the traditional mutual information cannot be applied in this dataset. Finally, the support vector machine (SVM) classifier is applied to find isomiR biomarkers for subtyping.

Conclusions

Here we demonstrate that isomiRs can be used as biomarkers in the identification of different breast cancer subtypes, and in addition, they may provide new insights into the diverse molecular mechanisms of breast cancers. We have also shown that the classification of different subtypes of breast cancer based on isomiRs expression is more effective than using published gene expression profiling. The proposed method provides a better performance outcome than Fisher method and Hellinger method for discovering biomarkers to distinguish different breast cancer subtypes. This novel technique could be directly applied to identify biomarkers in other diseases.

Circulating microRNA signature of steroid-induced osteonecrosis of the femoral head

OBJECTIVES

Steroid-induced osteonecrosis of the femoral head (ONFH) is a common orthopaedic disease of which early detection remains clinically challenging. Accumulating evidences indicated that circulating microRNAs (miRNAs) plays vital roles in the development of several bone diseases. However, the association between circulating miRNAs and steroid-induced ONFH remains elusive.

MATERIALS AND METHODS

miRNA microarray was performed to identify the differentially abundant miRNAs in the serums of systemic lupus erythematosus (SLE) patients with steroid-induced ONFH as compared with SLE control and healthy control group. We predicted the potential functions of these differentially abundant miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and reconstructed the regulatory networks of miRNA-mRNA interactions.

RESULTS

Our data indicated that there were 11 differentially abundant miRNAs (2 upregulated and 9 downregulated) between SLE-ONFH group and healthy control group and 42 differentially abundant miRNAs (14 upregulated and 28 downregulated) between SLE-ONFH group and SLE control group. We also predicted the potential functions of these differentially abundant miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and reconstructed the regulatory networks of miRNA-mRNA interactions.

CONCLUSIONS

These findings corroborated the idea that circulating miRNAs play significant roles in the development of ONFH and may serve as diagnostic markers and therapeutic targets.

Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016)

The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.