Identification of microRNA genes and their mRNA targets in Festuca arundinacea

Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues

MOTIVATION

RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability.

RESULTS

In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies.

AVAILABILITY AND IMPLEMENTATION

The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.

TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model

The most communal post-transcriptional modification, N6-methyladenosine (m6A), is associated with a number of crucial biological processes. The precise detection of m6A sites around the genome is critical for revealing its regulatory function and providing new insights into drug design. Although both experimental and computational models for detecting m6A sites have been introduced, but these conventional methods are laborious and expensive. Furthermore, only a handful of these models are capable of detecting m6A sites in various tissues. Therefore, a more generic and optimized computational method for detecting m6A sites in different tissues is required. In this paper, we proposed a universal model using a deep neural network (DNN) and named it TS-m6A-DL, which can classify m6A sites in several tissues of humans (Homo sapiens), mice (Mus musculus), and rats (Rattus norvegicus). To extract RNA sequence features and to convert the input into numerical format for the network, we utilized one-hot-encoding method. The model was tested using fivefold cross-validation and its stability was measured using independent datasets. The proposed model, TS-m6A-DL, achieved accuracies in the range of 75-85% using the fivefold cross-validation method and 72-84% on the independent datasets. Finally, to authenticate the generalization of the model, we performed cross-species testing and proved the generalization ability by achieving state-of-the-art results.

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.

Identification and Characterization of MiRNAs in Coccomyxa subellipsoidea C-169

Coccomyxa subellipsoidea C-169 (C-169) is an oleaginous microalga which is promising for renewable biofuel production. MicroRNAs (miRNAs), as the pivotal modulators of gene expression at post-transcriptional level, are prospective candidates for bioengineering practice. However, so far, no miRNA in C-169 has been reported and its potential impact upon CO₂ supplementation remains unclear. High-throughput sequencing of small RNAs from C-169 cultured in air or 2% CO₂ revealed 124 miRNAs in total, including 118 conserved miRNAs and six novel ones. In total, 384 genes were predicted as their potential target genes, 320 for conserved miRNAs and 64 for novel miRNAs. The annotated target genes were significantly enriched in six KEGG pathways, including pantothenate and CoA biosynthesis, C5-branched dibasic acid metabolism, 2-oxocarboxylic acid metabolism, butanoate metabolism, valine, leucine and isoleucine biosynthesis and alpha-linolenic acid metabolism. The miRNAs’ target genes were enriched in lipid metabolism as well as RNA-interacting proteins involved in translation, transcription and rRNA processing. The pioneering identification of C-169 miRNAs and analysis of their putative target genes lay the foundation for further miRNA research in eukaryotic algae and will contribute to the development of C-169 as an oleaginous microalga through bioengineering in the future.

Expression of miRNAs Targeting mTOR and S6K1 Genes of mTOR Signaling Pathway Including miR-96, miR-557, and miR-3182 in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer. Aberrant expression of genes in mTOR pathway and their targeting miRNAs plays an important role in TNBC. The aim of this study was to determine the expression of mTOR and S6K1 and their targeting miRNAs in breast cancer cell lines and clinical samples. miRNAs targeting 3'-UTR of mTOR and S6K1 mRNAs were predicted using bioinformatic algorithms. MDA-MB-231, MCF-7, and MCF-10A as well as 20 TNBC samples were analyzed for gene and miRNA expression using quantitative real-time PCR (RT-qPCR). A receiver operating characteristic (ROC) curve analysis was performed for evaluation of candidate miRNAs as diagnostic biomarkers. miR-96 and miR-557 targeting mTOR and S6K1 mRNAs, respectively, were selected, and miR-3182 was selected as the miRNA targeting both genes. The miRNAs were down-regulated in cell lines, while their target mRNAs were up-regulated. Similar findings were observed in clinical samples. The ROC curve analysis revealed decline in expression of these miRNAs. We suggest that miR-96, miR-557, and miR-3182 can be used as inhibitory agents for mTOR and S6K1 in TNBC-targeted therapy.

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A, formerly known as PGC-1a) is a transcriptional coactivator and metabolic regulator. Previous studies are mainly focused on the association between PPARGC1A and hepatoma. However, the regulatory mechanism remains unknown. A microRNA associated with cancer (oncomiR), miR-93-5p, has recently been found to play an essential role in tumorigenesis and progression of various carcinomas, including liver cancer. Therefore, this paper aims to explore the regulatory mechanism underlying these two proteins in hepatoma cells. Firstly, an integrative analysis was performed with miRNA–mRNA modules on microarray and The Cancer Genome Atlas (TCGA) data and obtained the core regulatory network and miR-93-5p/PPARGC1A pair. Then, a series of experiments were conducted in hepatoma cells with the results including miR-93-5p upregulated and promoted cell proliferation. Thirdly, the inverse correlation between miR-93-5p and PPARGC1A expression was validated. Finally, we inferred that miR-93-5p plays an essential role in inhibiting PPARGC1A expression by directly targeting the 3′-untranslated region (UTR) of its mRNA. In conclusion, these results suggested that miR-93-5p overexpression contributes to hepatoma development by inhibiting PPARGC1A. It is anticipated to be a promising therapeutic strategy for patients with liver cancer in the future.

WITHDRAWN: Identification of microRNA precursor based on gapped n-tuple structure status composition kernel

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Computational Identification of MicroRNAs and Their Targets from Finger Millet (Eleusine coracana)

MicroRNAs are endogenous small RNAs regulating intrinsic normal growth and development of plant. Discovering miRNAs, their targets and further inferring their functions had become routine process to comprehend the normal biological processes of miRNAs and their roles in plant development. In this study, we used homology-based analysis with available expressed sequence tag of finger millet (Eleusine coracana) to predict conserved miRNAs. Three potent miRNAs targeting 88 genes were identified. The newly identified miRNAs were found to be homologous with miR166 and miR1310. The targets recognized were transcription factors and enzymes, and GO analysis showed these miRNAs played varied roles in gene regulation. The identification of miRNAs and their targets is anticipated to hasten the pace of key epigenetic regulators in plant development.

Small RNA deep sequencing reveals the important role of microRNAs in the halophyte Halostachys caspica

MicroRNAs (miRNAs), an extensive class of small regulatory RNAs, play versatile roles in plant growth and development as well as stress responses. However, the regulatory mechanism is unclear on miRNA-mediated response to abiotic stress in plants. Halostachys caspica is a halophytic plant species and a great model for investigating plant response to salinity stress. However, no research has been performed on miRNAs in H. caspica. In this study, we employed deep sequencing to identify both conserved and novel miRNAs from salinity-exposed H. caspica and its untreated control. Among the 13-19 million sequences generated from both treatments, a total of 170 conserved miRNAs, belonging to 151 miRNA families, were identified; among these miRNAs, 31 were significantly up-regulated and 48 were significantly down-regulated by salinity stress. We also identified 102 novel miRNAs from H. caspica; among them, 12 miRNAs were significantly up-regulated and 13 were significantly down-regulated by salinity. qRT-PCR expression analysis validated the deep sequencing results and also demonstrated that miRNAs and their targeted genes were responsive to high salt stress and existed a negative expression correlation between miRNAs and their targets. miRNA-target prediction, GO and KEGG analysis showed that miRNAs were involved in salt stress-related biological pathway, including calcium signalling pathway, MAPK signalling pathway, plant hormone signal transduction and flavonoid biosynthesis, etc. This suggests that miRNAs play an important role in plant salt stress tolerance in H. caspica. This result could be used to improve salt tolerance in crops and woods.