Rui Li1
Yadong Zheng
William C. Cho- 1Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology and College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, China
- 2Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
Editorial on the Research Topic
Insights in RNA: 2022
RNA is a captivating realm that consists of coding RNA and noncoding RNA, with the latter experiencing significant growth in recent years. The advent of next-generation sequencing technologies has shed light on the functional importance of noncoding RNA, dispelling the notion that it is merely “junk” in the genome. RNA plays a fundamental role in various biological processes, including genetic inheritance, mediating interactions between DNA and proteins, catalyzing biochemical reactions, and regulating gene expression. Given the rapid advancements in this field, we have launched the Research Topic “Insights in RNA: 2022” to provide an overview of the latest technologies, discoveries, and theories in the RNA world, inspiring future research endeavors.
This Research Topic includes a total of 9 published papers, comprising 3 research articles and 6 reviews (https://www.frontiersin.org/research-topics/46100/insights-in-rna-2022/articles). In one of the research articles by Zahid et al., they identified five potential targets of brain-specific microRNA-153 in Alzheimer’s disease (AD). These targets, including ortilin-related receptor 1 (SORL1), amyloid precursor protein (APP), phosphatidylinositol binding clathrin assembly protein (PICALM), upstream stimulatory factor 1 (USF1), and presenilin-1 (PSEN1), are part of a protein interaction network implicated in AD. Previous studies have shown that APP, the precursor of the β-amyloid (Aβ) peptide, is downregulated in AD patients and negatively correlated with miR-153 expression (Liang et al., 2012; Long et al., 2012). AD is characterized by the accumulation of Aβ in senile plaques, and chronic brain hypoperfusion (CBH) has been implicated in Aβ deposition and synaptic plasticity reduction (de la Torre, 2021). In a recent study using a rat model of CBH, miR-153 was found to be upregulated and associated with impaired presynaptic vesicle release. Overexpression of miR-153 led to the suppression of several proteins involved in presynaptic vesicle release. Conversely, knockdown of miR-153 attenuated the decrease in presynaptic vesicle release and cognitive decline in the rat model, suggesting that miR-153 plays a role in impaired presynaptic plasticity in CBH (Yan et al., 2020). Notably, the expression levels of miR-153 in AD patients and the CBH rat model show opposite trends. This discrepancy may be due to differential expression at different stages of AD, with increased expression during synaptic dysfunction, which is implicated in the initiation of AD (Chakroborty et al., 2019). In addition to its role in presynaptic vesicle release, miR-153 has been shown to inhibit the differentiation and proliferation of neural stem cells, which have potential as disease-modifying biologics for AD treatment (Dong et al., 2023). Although the exact role of miR-153 in AD is still being elucidated, it is considered a promising therapeutic target for combating this disease. Furthermore, the paper discusses the therapeutic potential of other miRNAs in AD (Zainal Abidin et al.).
Understanding the intricate structures of RNA is crucial for unraveling its functions, and the field of RNA structure prediction has garnered considerable interest. Machine learning (ML) algorithms have emerged as a potential approach for predicting potential structures of RNA sequences. In this Research Topic, Chasles and Major evaluated the effectiveness of ML algorithms with different parameters in predicting RNA folding, highlighting the need to optimize models for specific data. Various ML methods with different model architectures and output predictions have been developed, such as RNA3DCNN, trRosettaRNA, and DRfold. ML has also been successfully employed in identifying binding sites of metal ions, including Mg2+, Na+, and K+ (Zhao et al., 2023). However, when it comes to truly generalizing ML methods to unseen, structurally distinct RNA families (not just unseen sequences), they do not appear to have an advantage over traditional non-learning techniques (Wu et al., 2023). To further advance the application of ML in RNA structure prediction, it is necessary to establish standardized benchmark training examples/datasets, possibly using a cluster-based k-fold cross-validation approach (Wu et al., 2023).
Sequencing technologies, particularly RNA sequencing (RNA-seq), have revolutionized our understanding of cellular and tissue physiology and pathology. By providing genome-wide RNA expression profiles, transcriptomics enables us to examine the transcriptional landscape and identify differentially expressed molecules relevant to the biology and pathogenesis of interest. However, reliable transcriptomic data necessitates the extraction of high-quality total RNA. In this Research Topic, He et al. conducted a comparative evaluation of the performance of various commercial RNA extraction kits and examined the factors influencing RNA quality in sera used in clinical settings. They observed significant variations in the quality of extracted total RNA when different commercial kits were employed, and identified storage time and temperature of sera as negative factors. Furthermore, they found that all preanalytical processes introduced a bias to the transcriptomes, highlighting the importance of RNA quality control prior to RNA-seq. These findings emphasize the need for ensuring high-quality RNA for accurate and reliable downstream analyses.
The full review papers included in this Research Topic cover a range of important areas in RNA research. These include intron biology, RNA sequencing technologies for T cell receptors, RNA-based therapeutics for the treatment of lung and central nervous diseases, and the mechanisms underlying mRNA deadenylation. These reviews provide readers some of the latest advancements and hot topics in of RNA research, offering valuable insights for future studies and guiding researchers towards new directions.
Author contributions
RL: Writing–original draft. YZ: Writing–review and editing. WC: Writing–review and editing.
Funding
The authors declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
We are indebted to the authors for their valuable contributions to this Research Topic and for the reviewers for their constructive suggestions.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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References
Chakroborty, S., Hill, E. S., Christian, D. T., Helfrich, R., Riley, S., Schneider, C., et al. (2019). Reduced presynaptic vesicle stores mediate cellular and network plasticity defects in an early-stage mouse model of Alzheimer's disease. Mol. Neurodegener. 14 (1), 7. doi:10.1186/s13024-019-0307-7
de la Torre, J. C. (2021). Deciphering alzheimer's disease pathogenic pathway: role of chronic brain hypoperfusion on p-tau and mTOR. J. Alzheimers Dis. 79 (4), 1381–1396. doi:10.3233/JAD-201165
Dong, X., Wang, H., Zhan, L., Li, Q., Li, Y., Wu, G., et al. (2023). miR-153-3p suppresses the differentiation and proliferation of neural stem cells via targeting GPR55. Aging (Albany NY) 15 (16), 8518–8527. doi:10.18632/aging.204002
Liang, C., Zhu, H., Xu, Y., Huang, L., Ma, C., Deng, W., et al. (2012). MicroRNA-153 negatively regulates the expression of amyloid precursor protein and amyloid precursor-like protein 2. Brain Res. 1455, 103–113. doi:10.1016/j.brainres.2011.10.051
Long, J. M., Ray, B., and Lahiri, D. K. (2012). MicroRNA-153 physiologically inhibits expression of amyloid-beta precursor protein in cultured human fetal brain cells and is dysregulated in a subset of Alzheimer disease patients. J. Biol. Chem. 287 (37), 31298–31310. doi:10.1074/jbc.M112.366336
Wu, K. E., Zou, J. Y., and Chang, H. (2023). Machine learning modeling of RNA structures: methods, challenges and future perspectives. Brief. Bioinform 24 (4). doi:10.1093/bib/bbad210
Yan, M. L., Zhang, S., Zhao, H. M., Xia, S. N., Jin, Z., Xu, Y., et al. (2020). MicroRNA-153 impairs presynaptic plasticity by blocking vesicle release following chronic brain hypoperfusion. Cell Commun. Signal 18 (1), 57. doi:10.1186/s12964-020-00551-8
Keywords: message RNA, noncoding RNA, microRNA, RNA folding, RNA-seq
Citation: Li R, Zheng Y and Cho WC (2024) Editorial: Insights in RNA: 2022. Front. Genet. 15:1382435. doi: 10.3389/fgene.2024.1382435
Received: 05 February 2024; Accepted: 16 February 2024;
Published: 22 February 2024.
Edited and reviewed by:
Fangqing Zhao, Beijing Institutes of Life Science (CAS), ChinaCopyright © 2024 Li, Zheng and Cho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: William C. Cho, d2lsbGlhbWNzY2hvQGdtYWlsLmNvbQ==; Yadong Zheng, emhlbmd5YWRvbmdAemFmdS5lZHUuY24=