Genome-wide investigation of microRNAs and expression profiles during rhizome development in ginger (Zingiber officinale Roscoe)

Background MicroRNAs (miRNAs) are endogenous, non-coding small functional RNAs that govern the post-transcriptional regulatory system of gene expression and control the growth and development of plants. Ginger is an herb that is well-known for its flavor and medicinal properties. The genes involved in ginger rhizome development and secondary metabolism have been discovered, but the genome-wide identification of miRNAs and their overall expression profiles and targets during ginger rhizome development are largely unknown. In this study, we used BGISEQ-500 technology to perform genome-wide identification of miRNAs from the leaf, stem, root, flower, and rhizome of ginger during three development stages. Results In total, 104 novel miRNAs and 160 conserved miRNAs in 28 miRNA families were identified. A total of 181 putative target genes for novel miRNAs and 2772 putative target genes for conserved miRNAs were predicted. Transcriptional factors were the most abundant target genes of miRNAs, and 17, 9, 8, 4, 13, 8, 3 conserved miRNAs and 5, 7, 4, 5, 5, 15, 9 novel miRNAs showed significant tissue-specific expression patterns in leaf, stem, root, flower, and rhizome. Additionally, 53 miRNAs were regarded as rhizome development-associated miRNAs, which mostly participate in metabolism, signal transduction, transport, and catabolism, suggesting that these miRNAs and their target genes play important roles in the rhizome development of ginger. Twelve candidate miRNA target genes were selected, and then, their credibility was confirmed using qRT-PCR. As the result of qRT-PCR analysis, the expression of 12 candidate target genes showed an opposite pattern after comparison with their miRNAs. The rhizome development system of ginger was observed to be governed by miR156, miR319, miR171a_2, miR164, and miR529, which modulated the expression of the SPL, MYB, GRF, SCL, and NAC genes, respectively. Conclusion This is a deep genome-wide investigation of miRNA and identification of miRNAs involved in rhizome development in ginger. We identified 52 rhizome-related miRNAs and 392 target genes, and this provides an important basis for understanding the molecular mechanisms of the miRNA target genes that mediate rhizome development in ginger.

tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction

tRNA fragments (tRFs) are small RNAs comparable to the size and function of miRNAs. tRFs are generally Dicer independent, are found associated with Ago, and can repress expression of genes post-transcriptionally. Given that this expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, Gene Ontology analysis suggests that among the tRFs conserved between mice and humans, the predicted targets are enriched significantly in neuronal function, and we show this specifically for tRF-3009a. These improvements to tRF target prediction further our understanding of tRF function broadly across species and provide avenues for testing novel roles for tRFs in biology. We have created a publicly available website for the targets of tRFs predicted by tRForest.

Widespread microRNA degradation elements in target mRNAs can assist the encoded proteins

Binding of microRNAs (miRNAs) to mRNAs normally results in post-transcriptional repression of gene expression. However, extensive base-pairing between miRNAs and target RNAs can trigger miRNA degradation, a phenomenon called target RNA-directed miRNA degradation (TDMD). Here, we systematically analyzed Argonaute-CLASH (cross-linking, ligation, and sequencing of miRNA–target RNA hybrids) data and identified numerous candidate TDMD triggers, focusing on their ability to induce nontemplated nucleotide addition at the miRNA 3′ end. When exogenously expressed in various cell lines, eight triggers induce degradation of corresponding miRNAs. Both the TDMD base-pairing and surrounding sequences are essential for TDMD. CRISPR knockout of endogenous trigger or ZSWIM8, a ubiquitin ligase essential for TDMD, reduced miRNA degradation. Furthermore, degradation of miR-221 and miR-222 by a trigger in BCL2L11, which encodes a proapoptotic protein, enhances apoptosis. Therefore, we uncovered widespread TDMD triggers in target RNAs and demonstrated an example that could functionally cooperate with the encoded protein.

Identification of The Critical Genes and miRNAs in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a global health problem with a complex etiology and pathogenesis. Microarray data is increasingly being used as a novel and effective method for cancer pathogenesis analysis. To unravel the potential prognosis of HCC, an integrative study of mRNA and miRNA for HCC was conducted. Two microarray datasets (GSE89377 and GSE101685) and two miRNAs expression profiles (GSE112264 and GSE113740) were obtained from Gene Expression Omnibus (GEO) database. A total of 177 DEGs and 80 DEMs were screened out. Functional enrichment of DEGs was proceeded by Clue GO, including GO and KEGG pathway analysis. These genes were significantly enriched in chemical carcinogenesis. PPI network was then established on the STRING platform, and ten hub genes (CDC20, TOP2A, ASPM, NCAPG, AURKA, CYP2E1, HMMR, PRC1, TYMS, and CYP4A11) were visualized via Cytoscape software. Then, a miRNA-target network was established to identify the dysregulated miRNA. A key miRNA (hsa-miR-124-3p) was filtered. Finally, the miRNA-target- transcription factor networks were constructed for hsa-miR-124-3p. The network for hsa-miR-124-3p included two transcription factors (TFs) and five targets. These identified DEGs and DEMs, TFs, targets, and regulatory networks may help advance our understanding the underlying pathogenesis of HCC.

LncRNA NNT-AS1 regulates proliferation, ECM accumulation and inflammation of human mesangial cells induced by high glucose through miR-214-5p/smad4

Background LncRNA NNT-AS1 (NNT-AS1) has been extensively studied as the causative agent in propagation and progression of lung and bladder cancers, and cholangiocarcinoma. However, its significance in proliferation and inflammation of diabetic nephropathy is enigmatic. This study focuses on the molecular mechanisms followed by NNT-AS1 to establish diabetic nephropathy (DN) and its potential miRNA target. Methods Bioinformatics analysis to identify potential miRNA target of NNT-AS1 and smad4 transcription factor was conducted using LncBase and TargetScan, and was subsequently confirmed by luciferase reporter assay. Relative quantitative expression of NNT-AS1 in human glomerular mesangial cells (HGMCs) was detected through quantitative real-time PCR and WB analysis. Cell proliferation was detected through CCK-8 assay, whereas, ELISA was conducted to evaluate the expression of inflammatory cytokines. Following this, relative expression of miR-214-5p and smad4 were confirmed through qRT-PCR and western blot analysis. Results Results from the experiments manifested up-regulated levels of NNT-AS1 and smad4 in the blood samples of DN patients as well as in HGMCs, whereas, downregulated levels of miR-214-5p were measured in the HGMCs suggesting the negative correlation between NNT-AS1 and miR-214-5p. Potential binding sites of NNT-AS1 showed miR-214-5p as its direct target and NNT-AS1 as potential absorber for this microRNA, in turn increasing the expression of transcription factor smad4. Conclusion The data suggests that NNT-AS1 can be positively used as a potential biomarker and indicator of DN and causes extracellular matrix (ECM) accumulation and inflammation of human mesangial cells.

Identification of Micro Ribonucleic Acids and Their Targets in Response to Plasmodiophora brassicae Infection in Brassica napus

Clubroot disease, which is caused by the soil-borne pathogen Plasmodiophora brassicae War (P. brassicae), is one of the oldest and most destructive diseases of Brassica and cruciferous crops in the world. Plant microRNAs [micro ribonucleic acids (miRNAs)] play important regulatory roles in several developmental processes. Although the role of plant miRNAs in plant-microbe interaction has been extensively studied, there are only few reports on the specific functions of miRNAs in response to P. brassicae. This study investigated the roles of miRNAs and their targets during P. brassicae infection in a pair of Brassica napus near-isogenic lines (NILs), namely clubroot-resistant line 409R and clubroot-susceptible line 409S. Small RNA sequencing (sRNA-seq) and degradome-seq were performed on root samples of 409R and 409S with or without P. brassicae inoculation. sRNA-seq identified a total of 48 conserved and 72 novel miRNAs, among which 18 had a significant differential expression in the root of 409R, while only one miRNA was differentially expressed in the root of 409S after P. brassicae inoculation. The degradome-seq analysis identified 938 miRNA target transcripts, which are transcription factors, enzymes, and proteins involved in multiple biological processes and most significantly enriched in the plant hormone signal transduction pathway. Between 409R and 409S, we found eight different degradation pathways in response to P. brassicae infection, such as those related to fatty acids. By combining published transcriptome data, we identified a total of six antagonistic miRNA-target pairs in 409R that are responsive to P. brassicae infection and involved in pathways associated with root development, hypersensitive cell death, and chloroplast metabolic synthesis. Our results reveal that P. brassicae infection leads to great changes in miRNA pool and target transcripts. More interestingly, these changes are different between 409R and 409S. Clarification of the crosstalk between miRNAs and their targets may shed new light on the possible mechanisms underlying the pathogen resistance against P. brassicae.

A Combined mRNA- and miRNA-Sequencing Approach Reveals miRNAs as Potential Regulators of the Small Intestinal Transcriptome in Celiac Disease

Celiac disease (CeD) is triggered by gluten and results in inflammation and villous atrophy of the small intestine. We aimed to explore the role of miRNA-mediated deregulation of the transcriptome in CeD. Duodenal biopsies of CeD patients (n = 33) and control subjects (n = 10) were available for miRNA-sequencing, with RNA-sequencing also available for controls (n = 5) and CeD (n = 6). Differential expression analysis was performed to select CeD-associated miRNAs and genes. MiRNA‒target transcript pairs selected from public databases that also displayed a strong negative expression correlation in the current dataset (R < −0.7) were used to construct a CeD miRNA‒target transcript interaction network. The network includes 2030 miRNA‒target transcript interactions, including 423 experimentally validated pairs. Pathway analysis found that interactions are involved in immune-related pathways (e.g., interferon signaling) and metabolic pathways (e.g., lipid metabolism). The network includes 13 genes previously prioritized to be causally deregulated by CeD-associated genomic variants, including STAT1. CeD-associated miRNAs might play a role in promoting inflammation and decreasing lipid metabolism in the small intestine, thereby contributing unbalanced cell turnover in the intestinal crypt. Some CeD-associated miRNAs deregulate genes that are also affected by genomic CeD-risk variants, adding an additional layer of complexity to the deregulated transcriptome in CeD.

Integrated Four Comparative-Omics Reveals the Mechanism of the Terpenoid Biosynthesis in Two Different Overwintering Cryptomeria fortunei Phenotypes

Chinese cedar (Cryptomeria fortunei) is a tree species with important ornamental, medicinal, and economic value. Terpenoids extracted from the essential oil of C. fortunei needles have been considered valuable ingredients in the pharmaceutical and cosmetic industries. However, the possible gene regulation mechanisms that limit terpenoid biosynthesis in this genus are poorly understood. Here, we adopted integrated metabolome analysis, transcriptome, small-RNA (sRNA), and degradome sequencing to analyze the differences in terpenoid regulatory mechanisms in two different overwintering C. fortunei phenotypes (wild-type and an evergreen mutant). A total of 1447/6219 differentially synthesized metabolites (DSMs)/unigenes (DEGs) were detected through metabolome/transcriptome analyses, and these DSMs/DEGs were significantly enriched in flavonoid and diterpenoid biosynthesis pathways. In C. fortunei needles, 587 microRNAs (miRNAs), including 67 differentially expressed miRNAs (DERs), were detected. Among them, 8346 targets of 571 miRNAs were predicted using degradome data, and a 72-miRNA-target regulatory network involved in the metabolism of terpenoids and polyketides was constructed. Forty-one targets were further confirmed to be involved in terpenoid backbone and diterpenoid biosynthesis, and target analyses revealed that two miRNAs (i.e., aly-miR168a-5p and aof-miR396a) may be related to the different phenotypes and to differential regulation of diterpenoid biosynthesis. Overall, these results reveal that C. fortunei plants with the evergreen mutation maintain high terpenoid levels in winter through miRNA-target regulation, which provides a valuable resource for essential oil-related bioengineering research.

A MiRNA Target Prediction Model based on Distributed Representation Learning and Deep Learning

Background: MicroRNAs (miRNAs) are a kind of non-coding RNA, which plays an essential role in gene regulation by binding to messenger RNAs(mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the base sequence. Results: In this study, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site level prediction, which is inspired by the similarity between natural language and biological sequences. First, the wor2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the data features are fully extracted automatically from temporal and spatial via the stacked Bidirectional Long short-term memory(BiLSTM) network. We compare the effects of different embedding methods on model accuracy in different deep learning models, and the results prove that using word2vec can improve the accuracy of deep learning models. In addition, we performed visual analysis on the distributed represented sequences and found hidden similarity relationships between bases. Finally, compared with different advanced methods and data sets, the results show that our proposed method has gotten better performance in multiple evaluation aspects. Conclusions: We present a novel method for predicting miRNA target sites consisting of word2vec and the BiLSTM model and demonstrate that this method can realize automatic feature extraction and has higher accuracy. Furthermore, we process miRNA and mRNA as two languages for the first time and explore their biological significance through visual analysis.