1
|
Bin Li, Yan R, Liu X, Meng Z, Meng P, Wang Y, Huang Y. CircRNAs Biogenesis, Functions, and Its Research Progress in Aquaculture. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1068162022020042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
2
|
Tang L, Li P, Jang M, Zhu W. Circular RNAs and Cardiovascular Regeneration. Front Cardiovasc Med 2021; 8:672600. [PMID: 33928139 PMCID: PMC8076501 DOI: 10.3389/fcvm.2021.672600] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 03/22/2021] [Indexed: 01/22/2023] Open
Abstract
circular RNAs (circRNAs) are a type of non-coding RNAs that are widely present in eukaryotic cells. They have the characteristics of stable structure, high abundance, and cell or tissue specific expression. circRNAs are single-stranded RNAs that are covalently back spliced to form closed circular loops. They may participate in gene expression and regulation through a variety of action modes. circRNAs can encode proteins or function by acting as miRNA sponges for protein translation. Since 2016, a growing number of research studies have shown that circRNAs play important role in the pathogenesis of cardiovascular disease. With the construction of circRNA database, the differential expression of circRNAs in the heart tissue samples from different species and the gradual elucidation of its mode of action in disease may become an ideal diagnosis biomarker and an effective therapeutic target. What can be expected surely has a broader application prospect. In this review, we summarize recent publications on circRNA biogenesis, expression profiles, functions, and the most recent studies of circRNAs in the field of cardiovascular diseases with special emphasis on cardiac regeneration.
Collapse
Affiliation(s)
- Ling Tang
- Department of Cardiovascular Diseases, Physiology and Biomedical Engineering, Center of Regenerative Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | - Pengsheng Li
- Department of Cardiovascular Diseases, Physiology and Biomedical Engineering, Center of Regenerative Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | - Michelle Jang
- Department of Cardiovascular Diseases, Physiology and Biomedical Engineering, Center of Regenerative Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | - Wuqiang Zhu
- Department of Cardiovascular Diseases, Physiology and Biomedical Engineering, Center of Regenerative Medicine, Mayo Clinic, Scottsdale, AZ, United States
| |
Collapse
|
3
|
CirRNAPL: A web server for the identification of circRNA based on extreme learning machine. Comput Struct Biotechnol J 2020; 18:834-842. [PMID: 32308930 PMCID: PMC7153170 DOI: 10.1016/j.csbj.2020.03.028] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 03/29/2020] [Accepted: 03/29/2020] [Indexed: 12/27/2022] Open
Abstract
Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://server.malab.cn/CirRNAPL/.
Collapse
Key Words
- ACC, Accuracy
- CNN, Convolutional Neural Networks
- Circular RNA
- DAC, Dinucleotide-based auto-covariance
- DACC, Dinucleotide-based auto-cross-covariance
- DCC, Dinucleotide-based cross-covariance
- ELM, extreme learning machine
- Expression level
- Extreme learning machine
- GAC, Geary autocorrelation
- Identification
- MAC, Moran autocorrelation
- MCC, Matthews Correlation Coefficient
- MRMD, Maximum-Relevance-Maximum-Distance
- NMBAC, Normalized Moreau–Broto autocorrelation
- PC-PseDNC-General, General parallel correlation pseudo-dinucleotide composition
- PCGs, protein coding genes
- PSO, particle swarm optimization algorithm
- Particle swarm optimization algorithm
- PseDPC, Pseudo-distance structure status pair composition
- PseSSC, Pseudo-structure status composition
- RBF, radial basis function
- RF, random forest
- SC-PseDNC-General, General series correlation pseudo-dinucleotide composition
- SE, Sensitivity
- SP, Specifity
- SVM, support vector machine
- Triplet, Local structure-sequence triplet element
- circRNA, circular RNA
- lncRNAs, long non-coding RNAs
Collapse
|
4
|
Yan XM, Zhang Z, Meng Y, Li HB, Gao L, Luo D, Jiang H, Gao Y, Yuan B, Zhang JB. Genome-wide identification and analysis of circular RNAs differentially expressed in the longissimus dorsi between Kazakh cattle and Xinjiang brown cattle. PeerJ 2020; 8:e8646. [PMID: 32211228 PMCID: PMC7081781 DOI: 10.7717/peerj.8646] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 01/27/2020] [Indexed: 12/18/2022] Open
Abstract
Xinjiang brown cattle have better meat quality than Kazakh cattle. Circular RNAs (circRNAs) are a type of RNA that can participate in the regulation of gene transcription. Whether circRNAs are differentially expressed in the longissimus dorsi between these two types of cattle and whether differentially expressed circRNAs regulate muscle formation and differentiation are still unknown. In this study, we established two RNA-seq libraries, each of which consisted of three samples. A total of 5,177 circRNAs were identified in longissimus dorsi samples from Kazakh cattle and Xinjiang brown cattle using the Illumina platform, 46 of which were differentially expressed. Fifty-five Gene Ontology terms were significantly enriched, and 12 Kyoto Encyclopedia of Genes and Genomes pathways were identified for the differentially expressed genes. Muscle biological processes were associated with the origin genes of the differentially expressed circRNAs. In addition, we randomly selected six overexpressed circRNAs and compared their levels in longissimus dorsi tissue from Kazakh cattle and Xinjiang brown cattle using RT-qPCR. Furthermore, we predicted 66 interactions among 65 circRNAs and 14 miRNAs using miRanda and established a coexpression network. A few microRNAs known for their involvement in myoblast regulation, such as miR-133b and miR-664a, were identified in this network. Notably, bta_circ_03789_1 and bta_circ_05453_1 are potential miRNA sponges that may regulate insulin-like growth factor 1 receptor expression. These findings provide an important reference for prospective investigations of the role of circRNA in longissimus muscle growth and development. This study provides a theoretical basis for targeting circRNAs to improve beef quality and taste.
Collapse
Affiliation(s)
- Xiang-Min Yan
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China.,Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry, Ürümqi, Xinjiang, China
| | - Zhe Zhang
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| | - Yu Meng
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| | - Hong-Bo Li
- Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry, Ürümqi, Xinjiang, China
| | - Liang Gao
- Yili Vocational and Technical College, Yili, Xinjiang, China
| | - Dan Luo
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| | - Hao Jiang
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| | - Yan Gao
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| | - Bao Yuan
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| | - Jia-Bao Zhang
- Department of Laboratory Animals, Jilin University, Changchun, Jilin, China
| |
Collapse
|