Editorial: MiRNAs as pivotal components of ncRNA networks associated with CNS injuries and neurodegeneration, and their therapeutic potential
Bibliographic record
Abstract
Since miRNA discovery 30 years ago, studies have been focused on understanding miRNA mechanisms of action and biogenesis, as well as the pathways they regulate and their potential role in diagnosis and therapy of different diseases (Rani and Sengar, 2022, Dexheimer and Cochella, 2020, Matulic et al., 2022, Diener et al., 2022. Many studies have revealed alterations in the expression of miRNAs under different physiological and pathological conditions, supporting their participation in all cellular processes (DeVeale et al., 2021).Evidence shows that miRNAs respond to different environmental stimuli, such as cellular stress and viral infections, regulating gene expression in different cell types and organisms (Gebert andMacRae, 2019, DeVeale et al., 2021). The high stability and rapid expression of miRNAs compared to other RNA species make them ideal candidates in charge of the fine-tuning of mRNA and protein levels, and ultimately of the cellular response to diverse clues (Campos-Melo, 2022). These characteristics and the regulatory networks that miRNAs establish interacting with other non-coding RNAs (ncRNAs) such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are critical for neuronal function, death, and survival and are increasingly recognized as associated with human neurological disorders, such as neurodegeneration, spinal cord injury, and stroke (Yamamura et al., 2018, Khan et al., 2022, Nuzziello and Liguori, 2019, Li et al., 2022, Xu et al., 2021, Silvestro and Mazzon, 2022. This research topic clusters five articles from experts interested in in silico and in vitro miRNA characterization, miRNA functional interactions with other ncRNA molecules, and potential therapeutic roles of miRNAs in neurological diseases. Megret et al. (2022) reviewed the main families of machine learning methods for the analysis of miRNA regulation in neurodegenerative disorders. The authors argue that the challenge in system-level studies to analyze the role of miRNA regulation in neurodegenerative diseases is due to problems of insufficient and inhomogeneous data and in the accuracy of system-level modeling. The authors propose shape analysis of complex omics data as a promising approach to construct improved models of high-level precision in matching miRNA-mRNA profiles in neurodegeneration. Rysenkova et al. (2022). MiRNAs Plaur-miR1-3p and Plaur-miR1-5p, were described to be expressed in mouse brains and target Mef2D, a gene that encodes a protein that regulates gene expression in embryogenesis and brain architecture maintenance, and contributes to the regulation of neuronal apoptosis, neurogenesis, and differentiation.In recent years, several articles have studied regulatory networks and interactions between miRNAs and other ncRNAs in vitro and in vivo. Both, lncRNAs and circRNAs function as miRNA sponges or decoys altering the availability of miRNAs and their downstream regulatory effects (Sharma et al., 2023, Tang et al., 2023, Liu and Chen, 2022. For this research topic, Lan et al. ( 2022) reviewed lncRNAs that are involved in Alzheimer's disease (AD).LncRNAs modulate the transcription of target genes in cis or in trans, and mRNA stability and processing, by regulating the assembly of multi-molecular complexes. This article reviews evidence that lncRNAs contribute to the pathogenesis of AD by regulating tau hyperphosphorylation, Aβ plague formation, mitochondrial and synaptic function, neuroinflammation, and neuronal apoptosis. From viruses to humans, the regulatory role of miRNAs and their wiring networks with other ncRNAs is only beginning to be elucidated. Beyond the experimental characterization of most miRNAs, we need to understand where, when, and how miRNAs exert their actions over specific targets in the cell. This is particularly relevant in animals because the partial base pairing between the miRNA seed region and 3'UTR sequences creates a high repertoire of targets.Studies of spatiotemporal expression and function of miRNAs and their ncRNA partners within subcellular compartments, in different tissue cell types, and ultimately in the whole organism, will allow the understanding of the importance of genome regulation by miRNAs and their link to human diseases of the CNS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".