Large-scale discovery of previously undetected microRNAs specific to human liver
MicroRNAs (miRNAs) are crucial regulators of gene expression in normal development and cellular homeostasis. While miRNA repositories contain thousands of unique sequences, they primarily contain molecules that are conserved across several tissues, largely excluding lineage and tissue-specific miRNAs. By analyzing small non-coding RNA sequencing data for abundance and secondary RNA structure, we discovered 103 miRNA candidates previously undescribed in liver tissue. While expression of some of these unannotated sequences is restricted to non-malignant tissue, downregulation of most of the sequences was detected in liver tumors, indicating their importance in the maintenance of liver homeostasis. Furthermore, target prediction revealed the involvement of the unannotated miRNA candidates in fatty-acid metabolism and tissue regeneration, which are key pathways in liver biology. Here, we provide a comprehensive analysis of the undiscovered liver miRNA transcriptome, providing new resources for a deeper exploration of organ-specific biology and disease.
KeywordsLiver Non-coding RNA Novel miRNA Tissue specificity Liver cancer
DNAX-activating protein of 12 kDa
Epidermal growth factor receptor
Fibroblast growth factor receptor
Granulocyte-macrophages colony-stimulating factor
The Cancer Genome Atlas
t-Distributed Stochastic Neighbor Embedding
MicroRNAs (miRNAs) are known to promote post-transcriptional fine-tuning of gene expression through complementary binding to target mRNA sequences . Their wide-reaching effects are attributed to the fact that a single miRNA can target dozens to hundreds of genes, often affecting multiple nodes of a given signaling pathway . In the liver, miRNAs are believed to orchestrate cell lineage differentiation during organ development, the modulation of homeostatic liver functions such as cholesterol and lipid metabolism, and disease [2, 3]. Clinically, miRNAs hold prognostic and therapeutic value both as biomarkers and therapeutic targets. For example, Miravirsen is a miR-122 antagonist emerging as a promising treatment for hepatitis C infection, which has progressed through Phase 2a clinical trials .
Initial attempts to characterize the human miRNA transcriptome were mostly limited to the discovery of abundant miRNA sequences and/or sequences that are conserved across several tissue types. This restriction may preclude miRNA transcripts with expression patterns that are more specialized to individual tissues or cell lineages [5, 6]. Indeed, recent genome-wide studies using next-generation sequencing have suggested the existence of human-specific previously undetected miRNAs, and they have been shown to exhibit high tissue specificity [5, 6, 7, 8]. Therefore, the discovery of such miRNA sequences may uncover novel tissue-specific regulatory mechanisms relevant to developmental biology and disease pathology. In this study, we performed a large-scale discovery of miRNA candidates previously undescribed in liver tissue and showed that these sequences exhibit tissue-specific expression patterns, as well as involvement in liver biology and disease.
Non-malignant liver small RNA sequence data was obtained from The Cancer Genome Atlas (TCGA; n = 47). Previously unannotated miRNA sequence discovery was performed using the miRDeep2 algorithm, which scans the transcriptome for novel miRNA candidates and compares them with known miRNA sequences available in public databases, such as miRBase . This established miRNA detection algorithm uses a statistical model to measure the likelihood of a detected small RNA sequence to be a putative novel miRNA. Primarily, this model assesses the hairpin structure of the predicted miRNA precursor and recognizes whether the precursor gives rise to the three products of miRNA processing by DICER, namely (i) mature miRNA, (ii) star sequence, and (iii) hairpin loop . The likelihood of a detected small RNA sequence to be a true positive hit is reflected in the miRDeep2 score . However, the selection of true positives based solely on the provided miRDeep2 score may still yield a large amount of false positive candidates . To overcome these limitations, we applied several additional filtering steps to reduce the rate of false positives.
To identify the pathways regulated by the unannotated miRNAs, we analyzed their predicted targets. We restricted our analysis to protein-coding genes that were identified as targets by at least two of the three algorithms used and were predicted to be targeted by at least 10% of our novel miRNA sequences (Additional file 3: Figure S2). From this, we identified a total of 723 protein-coding gene targets of the newly detected miRNA candidates in the liver.
In order to further assess the biological relevance of the unannotated miRNA candidates, we sought to evaluate whether these sequences are deregulated in corresponding tumor samples. We compared the expression of the miRNAs between matched non-malignant and tumor tissues. Strikingly, 83 of the 103 miRNA sequences had lost (n = 65) or reduced (n = 18, Wilcoxon signed-rank test corrected p value < 0.05) expression in tumor samples (Additional file 4: Figure S3). Thus, the widespread decrease in expression of these unannotated miRNA sequences may contribute to liver tumorigenesis.
In conclusion, we have discovered 103 previously undetected miRNA candidates in the liver. Although further experimental validation is required to confirm these sequences, our results shed light into the existence of unexplored regulatory molecules in liver tissue. Most importantly, these unannotated miRNAs have not only a lineage-specific expression pattern but may also be regulators of key liver processes, including those relevant to pathogenesis. Collectively, our results have substantial implications for liver-specific miRNA biology, emphasizing the need to further explore the undescribed areas of the human transcriptome.
The authors thank Heather Saprunoff and Kim Lonergan for their expert critique of the manuscript.
This work was supported by grants from the Canadian Institutes for Health Research (CIHR FRN-143345). BCM, VDM, APS, EAM, and CA are supported by scholarships from the University of British Columbia. EAM and APS are also supported by scholarships from CIHR. IJ is in part supported by Canada Research Chair Program (#225404), Ontario Research Fund (GL2-01-030) and Canada Foundation for Innovation (CFI #225404, #30865).
BCM and VDM designed, performed data analysis, and prepared the manuscript. KWN, APS, and TT contributed to the data analysis and interpretation, as well as manuscript preparation. EAM, CA, KSSE, GLS, PPR, IJ, and WLL contributed to the data interpretation and manuscript preparation. All authors approved the final manuscript.
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