Comprehensive analysis of lncRNA-miRNA-mRNA during proliferative phase of rat liver regeneration

Epigenetic regulation in liver regeneration

The liver is considered unique in its enormous capacity for regeneration and self-repair. In contrast to other regenerative organs (i.e., skin, skeletal muscle, and intestine), whether the adult liver contains a defined department of stem cells is still controversial. In order to compensate for the massive loss of hepatocytes following liver injury, the liver processes a precisely controlled transcriptional reprogram that can trigger cell proliferation and cell-fate switch. Epigenetic events are thought to regulate the organization of chromatin architecture and gene transcription during the liver regenerative process. In this review, we will summarize how changes to the chromatin by epigenetic modifiers are translated into cell fate transitions to restore liver homeostasis during liver regeneration.

Expression profiles of lncRNAs, miRNAs, and mRNAs during the proliferative phase of liver regeneration in mice with liver fibrosis

The role of lncRNAs in the regeneration of fibrotic liver is unclear. To address this issue, we established a 70% hepatectomy model of liver fibrosis in mice, used high-throughput sequencing technology to obtain the expression profiles of lncRNAs, miRNAs, and mRNAs, and constructed a lncRNA-miRNA-mRNA regulatory network. A total of 1329 lncRNAs, 167 miRNAs, and 6458 mRNAs were differentially expressed. On this basis, a lncRNA-miRNA-mRNA ceRNA regulatory network consisting of 38 DE lncRNAs, 24 DE miRNAs, and 299 DE mRNAs was constructed, and a transcription factor (TF) - mRNA regulatory network composed of 20 TFs and 98 DE mRNAs was built. Through the protein network analysis, a core protein interaction network composed of 20 hub genes was derived. Furthermore, Xist/miR-144-3p/Cdc14b and Snhg3/miR-365-3p/Map3k14 axes in the ceRNA regulatory network were verified by Real-Time quantitative PCR. Therefore, we concluded that these new insights may further our understanding of liver regeneration.

Sparse Independence Component Analysis for Competitive Endogenous RNA Co-Module Identification in Liver Hepatocellular Carcinoma

OBJECTIVE

Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood.

METHODS

In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements.

RESULTS

We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules.

CONCLUSION

It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement-The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC.

Identification of crucial lncRNAs and mRNAs in liver regeneration after portal vein ligation through weighted gene correlation network analysis

BACKGROUND

Portal vein ligation (PVL)-induced liver hypertrophy increases future liver remnant (FLR) volume and improves resectability of large hepatic carcinoma. However, the molecular mechanism by which PVL facilitates liver hypertrophy remains poorly understood.

METHODS

To gain mechanistic insight, we established a rat PVL model and carried out a comprehensive transcriptome analyses of hepatic lobes preserving portal blood supply at 0, 1, 7, and 14-day after PVL. The differentially expressed (DE) long-non coding RNAs (lncRNAs) and mRNAs were applied to conduct weighted gene co-expression network analysis (WGCNA). LncRNA-mRNA co-expression network was constructed in the most significant module. The modules and genes associated with PVL-induced liver hypertrophy were assessed through quantitative real-time PCR.

RESULTS

A total of 4213 DElncRNAs and 6809 DEmRNAs probesets, identified by transcriptome analyses, were used to carry out WGCNA, by which 10 modules were generated. The largest and most significant module (marked in black_M6) was selected for further analysis. Gene Ontology (GO) analysis of the module exhibited several key biological processes associated with liver regeneration such as complement activation, IL-6 production, Wnt signaling pathway, autophagy, etc. Sixteen mRNAs (Notch1, Grb2, IL-4, Cops4, Stxbp1, Khdrbs2, Hdac2, Gnb3, Gng10, Tlr2, Sod1, Gosr2, Rbbp5, Map3k3, Golga2, and Rev3l) and ten lncRNAs (BC092620, AB190508, EF076772, BC088302, BC158675, BC100646, BC089934, L20987, BC091187, and M23890) were identified as hub genes in accordance with gene significance value, module membership value, protein-protein interaction (PPI) and lncRNA-mRNA co-expression network. Furthermore, the overexpression of 3 mRNAs (Notch1, Grb2 and IL-4) and 4 lncRNAs (BC089934, EF076772, BC092620, and BC088302) was validated in hypertrophic liver lobe tissues from PVL rats and patients undergoing hepatectomy after portal vein embolization (PVE).

CONCLUSIONS

Microarray and WGCNA analysis revealed that the 3 mRNAs (Notch1, Grb2 and IL-4) and the 4 lncRNAs (BC089934, EF076772, BC092620 and BC088302) may be promising targets for accelerating liver regeneration before extensive hepatectomy.

RNA-seq transcriptome profiling of liver regeneration in mice identifies the miR-34b-5p/phosphoinositide-dependent protein kinase 1 axis as a potential target for hepatocyte proliferation

Characterized by compensatory hyperplasia dependent on hepatocyte proliferation, the liver will initiate regeneration after partial hepatectomy (PH) and acute or chronic injuries. A variety of genes and noncoding RNAs play pivotal roles in these cell proliferation and growth processes. However, it is still unclear how competition endogenous RNAs (ceRNAs) modulate cellular activities during each phase of liver regeneration, and the specific mechanisms of posttranscriptional gene expression regulation in hepatocyte proliferation remain to be elucidated. To investigate the mechanism of liver regeneration through RNA-seq profiling and to determine the role of miR-34b-5p/PDK1 on hepatocyte proliferation, we established a 2/3 PH mouse model for whole transcriptome profiling based on high-throughput sequencing techniques. We subsequently constructed a lncRNA-miRNA-mRNA ceRNA regulatory network through integrative analyses of RNA interactions. Finally, plasmid transfection in NCTC 1469 cells, dual luciferase reporter gene assay, quantitative real-time PCR, western blotting, Cell Counting Kit-8, and EdU-DNA synthesis cell proliferation assay were used to demonstrate the role of the miR-34b-5p/PDK1 axis in hepatocyte proliferation in vitro. A total of 1443 mRNAs (962 up, 481 down), 48 miRNAs (35 up, 13 down), and 1955 lncRNAs (986 up, 969 down) were identified as significantly differentially expressed. We then successfully constructed a ceRNA regulatory network consisting of 7 lncRNAs, 15 miRNAs, and 347 mRNAs based on the predicted inverse interactions among ceRNAs. Additionally, miR-34b-5p/PDK1 was predicted to be closely related to hepatocyte proliferation. We further demonstrated that miR-34b-5p could bind specifically to the 3'-untranslated region (3'-UTR) of PDK1 using the dual luciferase reporter assay. Ectopic overexpression of miR-34b-5p significantly reduced the mRNA and protein expression of PDK1, while it markedly inhibited the proliferation of mouse NCTC 1469 cells in vitro. In contrast, knocking down miR-34b-5p exhibited the inverse effects on PDK1 expression and hepatocyte proliferation. Through analyzing the ceRNA network during mouse liver regeneration, this study reveals that miR-34b-5p can inhibit hepatocyte proliferation through negatively regulating PDK1 and may be a potential pharmacological intervention target.

Two way network construction and analysis of mRNA, miRNA and lncRNA reveals critical regulators and regulatory modules in cardiovascular diseases

BACKGROUND

Cardiovascular diseases contribute to the leading cause of deaths (31%) in the world population.

OBJECTIVE

The objective of this study is to compile non-coding RNA-gene interaction into a core regulatory network whose dysregulation might play an important role in disease progression.

METHOD

We applied a structured approach to reconstruct the interaction network of lncRNAs, miRNAs and genes involved in cardiovascular diseases. For network construction, we used 'diseasome to interactome' and 'interactome to diseasome' approaches and developed two regulatory networks in heart disorders. In diseasome to interactome approach, starting from a disease-centric network we, expanded the data into an interaction network. However in interactome to diseasome, we used a set of guide genes, miRNAs and lncRNAs to arrive at the common diseases. The disease-centric network in combination with the interaction network will shed light on the interconnected components in a huge diseasome network implicated in heart disorders and manifested through small sub-networks while progressing. Using the above networks we created a sub-networks consisting only of hub genes, miRNAs and lncRNAs on both approaches. The dysregulation of any one of the hubs can lead to a disease condition.

RESULTS

The top ranking hubs common in both the sub-networks were found to be VEGFA, MALAT1, HOTAIR, H19 and hsa-miR-15a. Our network based study reveals an entanglement of regulatory sub-network of miRNAs, lncRNAs and genes in multiple conditions.

CONCLUSION

The identification of hubs in the core triple node network of elements in disease development and progression demonstrates a promising role for network based approaches in targeting critical molecules for drug development.

An integrated analysis of the circRNA-miRNA-mRNA network reveals novel insights into potential mechanisms of cell proliferation during liver regeneration

Cell proliferation constitutes the fundamental process and driving force behind regrowth during liver regeneration (LR). However, it remains unclear how competing endogenous RNA (ceRNA) networks affect hepatocyte proliferation and liver regeneration. Therefore, this study was designed to explore an LR-specific ceRNA network, which regulates cell proliferation. Based on the microarray data of mRNAs, and high-throughput sequencing data of miRNAs and circRNAs from regenerating livers, this study initially applied known 1484 LR associated mRNAs to perform GO analysis, and then selected 169 LR associated mRNAs involved in cell proliferation and the cell cycle. Subsequently, 188 interactive miRNA-mRNA pairs and 5206 circRNA-miRNA pairs, respectively, were predicted using bioinformatics methods. Next, in view of the differential expressions of these ceRNAs during LR, 26 miRNA-mRNA pairs and 71 circRNA-miRNA pairs were applied to generate a circRNA-miRNA-mRNA regulatory network, and only 14 triple interactive groups were obtained based on the predicted inverse interactions among ceRNAs. Finally, circ_19698/miR-423-5p axis was demonstrated to promote cell proliferation by modulating the expression of MYC, CCNA2, and CCND1 in rat BRL-3A cells. This study suggests a potential regulatory mechanism of cell proliferation in regenerating livers, as well as a novel pathway for modulating ceRNA networks to promote liver regeneration.

Whole Transcriptome Analysis of Mesenchyme Tissue in Sika Deer Antler Revealed the CeRNAs Regulatory Network Associated With Antler Development

Deer antler is the only completely regenerable organ in mammals. During the rapid growth period, the antler proliferates even faster than cancerous tissue growth. However, the proliferation and development of antler have been in a stable and controllable growth cycle. In this study, we analyzed the time series expression data of nine samples from mesenchyme layer in three male sika deer in the early period of the antler with a saddle-like appearance (30 days), the rapid growth period of the antler with two branches (60 days), and the final period of the antler with three branches (90 days). Whole Transcriptome sequencing results show that in the 30 d versus 60 d group, 1,464 genes, 85 long noncoding RNAs (lncRNAs), and 61 miRNAs were identified as differentially expressed; 1,748 genes, 138 lncRNAs, and 78 miRNAs were identified as differentially expressed in 30d versus 90d group; and 816 differentially expressed genes (DEGs), 49 differentially expressed lncRNAs (DE lncRNAs), and 24 differentially expressed miRNA (DE miRNAs) were identified in 60d versus 90d group. A total of 182 miRNA-mRNA interaction pairs and 89 miRNA-lncRNA interaction pairs were screened from DEGs, DE miRNAs, and DE lncRNAs to construct the ceRNA regulatory network (ceRNET). In summary, we identified candidate mRNAs, miRNAs and lncRNAs that regulate the development of antler tip. It may lay the foundation for further investigating the molecular mechanism of antler rapid growth and development.

Comprehensive analysis of lncRNA-miRNA-mRNA during proliferative phase of rat liver regeneration

This study aims to reveal the regulatory mechanism of lncRNAs-miRNAs-mRNAs network during the proliferative phase of liver regeneration (LR). High-throughput sequencing technology was performed, and a total of 1,738 differentially expressed lncRNAs (DE lncRNAs), 167 known differentially expressed miRNAs (DE miRNAs), and 2,727 differentially expressed mRNAs were identified. Then, the target DE lncRNAs and DE mRNAs regulated by the same miRNAs were screened and a ceRNA regulatory network containing 32 miRNAs, 107 lncRNAs, and 270 mRNAs was constructed. Insulin signaling pathway, pyrimidine metabolism, axon guidance, carbohydrate digestion and absorption, and pyruvate metabolism were significantly enriched in the network. Through literature review and the regulatory relationship between lncRNAs and miRNAs, nine core lncRNAs were identified, which might play important roles during the proliferative phase of rat LR. This study analyzed lncRNA-miRNA-mRNA regulatory network for the first time during the proliferative phase of rat LR, providing clues for exploring the mechanism of LR and the treatment of liver diseases.