International Journal of Molecular Sciences

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Bioinformatics of RNA: Recent Advances and Open Challenges

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Dear Colleagues,

The importance of RNA-based regulation is becoming more and more evident, and more and more types of RNAs and their functions have been discovered. The need for specialized and integrated analysis of RNA research has led to the foundation of the development of bioinformatics tools, resources, and databases. These computational tools and resources are no doubt valuable for complementing and guiding wet-lab experimental studies, which are usually time-consuming and labor-intensive.

The aim of this Research Topic is to provide highly valuable computational resources for biologists who are dissecting the biological functions, pathological roles, and/or regulatory mechanisms of RNAs:

The following areas will be covered, among others:

Novel computational approaches for identifying RNA modifications;
Useful tools/pipelines to predict RNA structures;
New methods for predicting RNA subcellular localizations;
Comprehensive databases devoted to the integration of RNAs;
Cutting-edge surveys for certain aspects of bioinformatics research for RNAs.

Prof. Dr. Fuyi Li
Guest Editor

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Keywords

bioinformatics
data mining
machine learning
RNA modification
RNA structure
RNA subcellular localization

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19 pages, 21006 KiB

Open AccessArticle

Genome-Wide mRNA and Long Non-Coding RNA Analysis of Porcine Trophoblast Cells Infected with Porcine Reproductive and Respiratory Syndrome Virus Associated with Reproductive Failure

by Xinming Zhang, Xianhui Liu, Jiawei Peng, Sunyangzi Song, Ge Xu, Ningjia Yang, Shoutang Wu, Lin Wang, Shuangyun Wang, Leyi Zhang, Yanling Liu, Pengshuai Liang, Linjun Hong, Zheng Xu and Changxu Song

Int. J. Mol. Sci. 2023, 24(2), 919; https://doi.org/10.3390/ijms24020919 - 4 Jan 2023

Cited by 3 | Viewed by 1679

Abstract

Porcine reproductive and respiratory syndrome (PRRS) is a vertically transmitted reproductive disorder that is typically characterized by miscarriage, premature birth, and stillbirth in pregnant sows after infection. Such characteristics indicate that PRRSV can infect and penetrate the porcine placental barrier to infect fetus piglets. The porcine trophoblast is an important component of the placental barrier, and secretes various hormones, including estrogen and progesterone, to maintain normal pregnancy and embryonic development during pregnancy. It is conceivable that the pathogenic effects of PRRSV infection on porcine trophoblast cells may lead to reproductive failure; however, the underlying detailed mechanism of the interaction between porcine trophoblast (PTR2) cells and PRRSV is unknown. Therefore, we conducted genome-wide mRNA and long non-coding RNA (lncRNA) analysis profiling in PRRSV-infected PTR2. The results showed that 672 mRNAs and 476 lncRNAs were significantly different from the control group after viral infection. Target genes of the co-expression and co-location of differential mRNAs and lncRNAs were enriched by GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, revealing that most of the pathways were involved in cell nutrient metabolism, cell proliferation, and differentiation. Specifically, the estrogen signaling pathway, the PI3K (PhosphoInositide-3 Kinase)-Akt (serine/threonine kinase) signaling pathway, and the insulin secretion related to embryonic development were selected for analysis. Further research found that PRRSV inhibits the expression of G-protein-coupled estrogen receptor 1 (GPER1), thereby reducing estrogen-induced phosphorylation of AKT and the mammalian target of rapamycin (mTOR). The reduction in the phosphorylation of AKT and mTOR blocks the activation of the GPER1- PI3K-AKT-mTOR signaling pathway, consequently restraining insulin secretion, impacting PTR2 cell proliferation, differentiation, and nutrient metabolism. We also found that PRRSV triggered trophoblast cell apoptosis, interrupting the integrity of the placental villus barrier. Furthermore, the interaction network diagram of lncRNA, regulating GPER1 and apoptosis-related genes, was constructed, providing a reference for enriching the functions of these lncRNA in the future. In summary, this article elucidated the differential expression of mRNA and lncRNA in trophoblast cells infected with PRRSV. This infection could inhibit the PI3K-AKT-mTOR pathway and trigger apoptosis, providing insight into the mechanism of the vertical transmission of PRRSV and the manifestation of reproductive failure. Full article

(This article belongs to the Special Issue Bioinformatics of RNA: Recent Advances and Open Challenges)

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18 pages, 2610 KiB

Open AccessArticle

Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning

by Zhengtao Luo, Liliang Lou, Wangren Qiu, Zhaochun Xu and Xuan Xiao

Int. J. Mol. Sci. 2022, 23(24), 15490; https://doi.org/10.3390/ijms232415490 - 7 Dec 2022

Cited by 6 | Viewed by 1540

Abstract

N6-methyladenosine (m⁶A) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA m⁶A transcriptome-wide at base resolution via experimental approaches, which are generally time-consuming and expensive. Developing computational methods is a good strategy for accurate in silico detection of m⁶A modification sites from the large amount of RNA sequence data. Unfortunately, the existing computational models are usually only for m⁶A site prediction in a single species, without considering the tissue level of species, while most of them are constructed based on low-confidence level data generated by an m⁶A antibody immunoprecipitation (IP)-based sequencing method, thereby restricting reliability and generalizability of proposed models. Here, we review recent advances in computational prediction of m⁶A sites and construct a new computational approach named im6APred using ensemble deep learning to accurately identify m⁶A sites based on high-confidence level data in multiple tissues of mammals. Our model im6APred builds upon a comprehensive evaluation of multiple classification methods, including four traditional classification algorithms and three deep learning methods and their ensembles. The optimal base–classifier combinations are then chosen by five-fold cross-validation test to achieve an effective stacked model. Our model im6APred can produce the area under the receiver operating characteristic curve (AUROC) in the range of 0.82–0.91 on independent tests, indicating that our model has the ability to learn general methylation rules on RNA bases and generalize to m⁶A transcriptome-wide identification. Moreover, AUROCs in the range of 0.77–0.96 were achieved using cross-species/tissues validation on the benchmark dataset, demonstrating differences in predictive performance at the tissue level and the need for constructing tissue-specific models for m⁶A site prediction. Full article

(This article belongs to the Special Issue Bioinformatics of RNA: Recent Advances and Open Challenges)

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