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Prediction of Alzheimer’s Disease Based on 3D Genome Selected circRNA

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Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease and there is by far no effective treatment for it, especially in its late stage. Circular RNAs (circRNAs), known as a class of non-coding RNAs are widely observed in eukaryotic transcriptomes, and are reported to play an important role in neurodegenerative diseases including AD. circRNAs usually act as microRNA (miRNA) inhibitors or «sponges» to regulate the function of miRNAs, leading to subsequent changes in protein activities and functions. Accumulating evidence indicates that circRNAs can serve as potential biomarker in AD early prediction. The functional roles of circRNAs are very versatile including miRNAs binding - thus affecting downstream gene expression, generating abnormally translated protein peptides, and affecting epigenetic modifications which subsequently affect AD related gene expressions. Therefore, identifying AD-related circRNAs can contribute to AD early diagnosis and intervention. In this work, we collected and curated an AD-related circRNA dataset; by exploring the circRNAs’ corresponding DNA loci distribution in chromatin 3D conformation (3D genome) and utilize the such 3D genome information, we were able to selected a concise yet predictively effective circRNA panel, based on which, significantly better AD prediction machine learning models were achieved.

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Funding

Funding: This project was supported by the Key Research and Development Plan of the Ministry of Science and Technology (2022YFE0125300), the National Key Research and Development Program (2016YFC0906400), the Innovation Funding in Shanghai (20JC1418600 and 18JC1413100), the National Natural Science Foundation of China (82071262 and 81671326), the Natural Science Foundation of Shanghai (20ZR1427200 and 20511101900), the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), the Shanghai Key Laboratory of Psychotic Disorders (13DZ2260500), the Shanghai Leading Academic Discipline Project (B205), the Shanghai Jiao Tong University STAR Grant (YG2023ZD26, YG2022ZD024, and YG2022QN111), and Shanghai Gaofeng & Gaoyuan Project for University Academic Program Development (Shanghai University of Sport-2023).

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Author notes

  1. These authors contributed equally as co-first authors.

Authors and Affiliations

  1. Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China

    R. Chi, K. Li, K. Su, L. Liu, M. Feng, Guang He & Yi Shi

  2. Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China

    R. Chi, K. Li, K. Su, L. Liu, M. Feng, Guang He & Yi Shi

  3. Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China

    X. Zhang & J. Wang

  4. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China

    X. Li

  5. Research Institute for Doping Control, Shanghai University of Sport, Shanghai, 200438, China

    Yi Shi

Authors
  1. R. Chi
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  2. K. Li
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  3. K. Su
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  4. L. Liu
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  7. J. Wang
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  8. X. Li
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  9. Guang He
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  10. Yi Shi
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Contributions

Authors’ contributions: RC, KL, KS and LL participated in the omics and computational experiments. RC and KL conducted the computational experiments. RC provided the figures, tables, and drafted the manuscript. RC, KL and LL designed chromatin modeling. JW, XL and GH provided clinical suggestions and biological insights about AD. YS and GH initiated this project and supervised the whole workflow. YS and GH edited and finalized the manuscript. All the authors reviewed and proof read the manuscript and the experimental results.

Corresponding authors

Correspondence to Guang He or Yi Shi.

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Competing interests: 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.

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Chi, R., Li, K., Su, K. et al. Prediction of Alzheimer’s Disease Based on 3D Genome Selected circRNA. J Prev Alzheimers Dis (2024). https://doi.org/10.14283/jpad.2024.52

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