CircRNA accumulation: A new hallmark of aging?

Knupp, David; Miura, Pedro

doi:10.1016/j.mad.2018.05.001

Cited by 76 publications

(60 citation statements)

References 74 publications

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“…The overall levels of tRNAs, rRNAs, and snoRNAs increase with age, whereas the levels of piRNAs decrease with age (Kato et al, ). circRNAs are a recently identified type of noncoding RNAs, whose ends are covalently linked and are implicated in transcriptional regulation (Knupp & Miura, ). A recent study indicated that the overall levels of circRNAs increase during C. elegans aging (Cortes‐Lopez et al, ).…”

Section: Age‐dependent Changesmentioning

confidence: 99%

Age‐dependent changes and biomarkers of aging in Caenorhabditis elegans

et al. 2019

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Caenorhabditis elegans is an exceptionally valuable model for aging research because of many advantages, including its genetic tractability, short lifespan, and clear age‐dependent physiological changes. Aged C. elegans display a decline in their anatomical and functional features, including tissue integrity, motility, learning and memory, and immunity. Caenorhabditis elegans also exhibit many age‐associated changes in the expression of microRNAs and stress‐responsive genes and in RNA and protein quality control systems. Many of these age‐associated changes provide information on the health of the animals and serve as valuable biomarkers for aging research. Here, we review the age‐dependent changes in C. elegans and their utility as aging biomarkers indicative of the physiological status of aging.

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Section: Age‐dependent Changesmentioning

confidence: 99%

Age‐dependent changes and biomarkers of aging in Caenorhabditis elegans

et al. 2019

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“…In agreement with this hypothesis, agerelated modulation of microRNA levels in the rodent brain has been reported [46,47]. Notably, circular RNAs have been shown to accumulate in the aging brain [48], and long noncoding RNAs are also implicated in various aging processes [49]. Finally, although pluripotent cells and organoid culture methods have allowed to perform functional assays in human in vitro neural cell systems [50], mouse genetic models will remain essential for functional studies in vivo in the foreseeable future.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 77%

Molecular Signatures of the Aging Brain: Finding the Links Between Genes and Phenotypes

et al. 2019

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Aging is associated with cognitive decline and increased vulnerability to neurodegenerative diseases. The progressive extension of the average human lifespan is bound to lead to a corresponding increase in the fraction of cognitively impaired elderly individuals among the human population, with an enormous societal and economic burden. At the cellular and tissue levels, cognitive decline is linked to a reduction in specific neuronal subpopulations, a widespread decrease in synaptic plasticity and an increase in neuroinflammation due to an enhanced activation of astrocytes and microglia, but the molecular mechanisms underlying these functional changes during normal aging and in neuropathological conditions remain poorly understood. In this review, we summarize very recent and outstanding progress in elucidating the molecular changes associated with cognitive decline through the genome-wide profiling of aging brain cells at different molecular levels (genomic, epigenomic, transcriptomic, proteomic). We discuss how the correlation of different molecular and phenotypic traits driven by mathematical and computational analyses of large datasets has led to the prediction of key molecular nodes of neurodegenerative pathways, and provide a few examples of candidate regulators of cognitive decline identified with these approaches. Furthermore, we highlight the dysregulation of the synaptic transcriptome in neuronal cells and of the inflammatory transcriptome in glial cells as some of the key events during normal and neuropathological human brain aging.

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“…DGS(i, j) is the GIP kernel similarity between disease i and disease j. σ d is influential in tuning the kernel bandwidth calculated by the Eq. (5).…”

Section: ( )mentioning

confidence: 99%

Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association

Lei

Bian

2020

Sci Rep

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circRnA is a special type of non-coding RnA, which is closely related to the occurrence and development of many complex human diseases. However, it is time-consuming and expensive to determine the circRnA-disease associations through experimental methods. therefore, based on the existing databases, we propose a method named RWRKnn, which integrates the random walk with restart (RWR) and k-nearest neighbors (Knn) to predict the associations between circRnAs and diseases. Specifically, we apply RWR algorithm on weighting features with global network topology information, and employ Knn to classify based on features. finally, the prediction scores of each circRNA-disease pair are obtained. As demonstrated by leave-one-out, 5-fold cross-validation and 10fold cross-validation, RWRKNN achieves AUC values of 0.9297, 0.9333 and 0.9261, respectively. And case studies show that the circRnA-disease associations predicted by RWRKnn can be successfully demonstrated. in conclusion, RWRKnn is a useful method for predicting circRnA-disease associations. CircRNA, as a star molecule in the recent years, is a kind of non-coding endogenous RNA with single-stranded, closed and circular structure 1,2. Unlike the linear RNA, circRNA is the result of "back-splice" or derived from linear RNA. Hence, they lack 5′-3′ ends representing the RNA transcription's start and stop 3-6. The first circRNA was discovered by electron microscopy in RNA viruses 7 and afterwards in eukaryotic cells 8. Unfortunately, researchers regarded circRNA initially as a by-product of abnormal splicing without regulatory potential. Thus, circRNA did not attract much scientific attention 9. With the increasing researches on circRNAs, lots of circRNAs have been found in viruses, animals and plants 6,10-12. So far, circRNA has been confirmed to regulate multiple major biological processes, like cell invasion, proliferation as well as apoptosis 13,14. And circRNA is an important part in process of transcription 15 , mRNA splicing 16 , RNA translation and decay 17. Thus, the regulatory mechanism of circRNA is closely related to the occurrence of disease, which was identified by advanced biotechnology. For instance, the expression level of hsa_circ_0001982 in breast cancer tissues is significantly high 18. In addition, there are some circRNAs (Hsa_ circ_0014717 19 , CircMTO1 20 , Circ-PRKCI 21) that act as miRNA's sponge to regulate tumorigenesis. Therefore, it can provide new ideas for the treatment of diseases with acquisition and utilization of information related to circRNAs and diseases. In recent years, some circRNA-disease related databases have also been proposed to further investigate the associations between circRNAs and diseases, involving CircR2Disease 22 , circRNADisease 23 and Circ2Disease 24. The effective calculation methods based on these databases will effectively reduce the time consumption caused by the methods in biological experiments. Thus, it is urgent to use computational methods for exploring disease-related circRNA. Fan et al. 25 raised a simila...

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CircRNA accumulation: A new hallmark of aging?

Cited by 76 publications

References 74 publications

Age‐dependent changes and biomarkers of aging in Caenorhabditis elegans

Age‐dependent changes and biomarkers of aging in Caenorhabditis elegans

Molecular Signatures of the Aging Brain: Finding the Links Between Genes and Phenotypes

Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association

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