Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.

BACKGROUND: RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level. RESULTS: Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets. CONCLUSIONS: Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Modelling cell type-specific lncRNA regulatory network in autism with Cycle.

BACKGROUND: Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types. RESULTS: Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation. CONCLUSION: Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.

The PPO family in Nicotiana tabacum is an important regulator to participate in pollination.

Polyphenol oxidases (PPOs) are type-3 copper enzymes and are involved in many biological processes. However, the potential functions of PPOs in pollination are not fully understood. In this work, we have screened 13 PPO members in Nicotiana. tabacum (named NtPPO1-13, NtPPOs) to explore their characteristics and functions in pollination. The results show that NtPPOs are closely related to PPOs in Solanaceae and share conserved domains except NtPPO4. Generally, NtPPOs are diversely expressed in different tissues and are distributed in pistil and male gametes. Specifically, NtPPO9 and NtPPO10 are highly expressed in the pistil and mature anther. In addition, the expression levels and enzyme activities of NtPPOs are increased after N. tabacum self-pollination. Knockdown of NtPPOs would affect pollen growth after pollination, and the purines and flavonoid compounds are accumulated in self-pollinated pistil. Altogether, our findings demonstrate that NtPPOs potentially play a role in the pollen tube growth after pollination through purines and flavonoid compounds, and will provide new insights into the role of PPOs in plant reproduction.

Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers.

Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.

BACKGROUND: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. RESULTS: In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk. CONCLUSIONS: To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer.

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline's performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules.

BACKGROUND: A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design. RESULTS: To fill the gap, we present an R/Bioconductor package, miRspongeR, for simplifying the procedure of identifying and analyzing miRNA sponge interaction networks and modules. It provides seven popular methods and an integrative method to identify miRNA sponge interactions. Moreover, it supports the validation of miRNA sponge interactions and the identification of miRNA sponge modules, as well as functional enrichment and survival analysis of miRNA sponge modules. CONCLUSIONS: This package enables researchers to quickly evaluate their new methods, apply existing methods to new datasets, and consequently speed up miRNA sponge research.

Protocol to infer and analyze miRNA sponge modules in heterogeneous data using miRSM 2.0.

MicroRNA (miRNA) sponges synergistically modulate physiological and pathological processes in the form of modules or clusters. Here, we present a protocol for inferring and analyzing miRNA sponge modules in heterogeneous data using the R package miRSM 2.0. We describe steps for identifying gene modules, inferring miRNA sponge modules at multi-sample and single-sample levels, and performing modular analysis. From the perspective of computational biology, miRSM 2.0 has the potential to advance our understanding of the role of miRNA sponges in diseases. For complete details on the use and execution of this protocol, please refer to Zhang et al.1,2,3.

miRspongeR 2.0: an enhanced R package for exploring miRNA sponge regulation.

Summary: MicroRNA (miRNA) sponges influence the capability of miRNA-mediated gene silencing by competing for shared miRNA response elements and play significant roles in many physiological and pathological processes. It has been proved that computational or dry-lab approaches are useful to guide wet-lab experiments for uncovering miRNA sponge regulation. However, all of the existing tools only allow the analysis of miRNA sponge regulation regarding a group of samples, rather than the miRNA sponge regulation unique to individual samples. Furthermore, most existing tools do not allow parallel computing for the fast identification of miRNA sponge regulation. Here, we present an enhanced version of our R/Bioconductor package, miRspongeR 2.0. Compared with the original version introduced in 2019, this package extends the resolution of miRNA sponge regulation from the multi-sample level to the single-sample level. Moreover, it supports the identification of miRNA sponge networks using parallel computing, and the construction of sample-sample correlation networks. It also provides more computational methods to infer miRNA sponge regulation and expands the ground truth for validation. With these new features, we anticipate that miRspongeR 2.0 will further accelerate the research on miRNA sponges with higher resolution and more utilities. Availability and implementation: http://bioconductor.org/packages/miRspongeR/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.