Background: Structural changes of the left and right atria associated with atrial fibrillation (AF) in mitral stenosis (MS) patients are well known, and alterations in microRNA (miRNA) expression profiles of the right atria have also been investigated. However, miRNA changes in the left atria still require delineation. This study evaluated alterations in miRNA expression profiles of left atrial tissues from MS patients with AF relative to those with normal sinus rhythm (NSR).
Methods: Sample tissues from left atrial appendages were obtained from 12 MS patients (6 with AF) during mitral valve replacement surgery. From these tissues, miRNA expression profiles were created and analyzed using a human miRNA microarray. Results were validated via reverse-transcription and quantitative PCR for 5 selected miRNAs. Potential miRNA targets were predicted and their functions and potential pathways analyzed via the miRFocus database.
Results: The expression levels of 22 miRNAs differed between the AF and NSR groups. Relative to NSR patients, in those with AF the expression levels of 45% (10/22) of these miRNAs were significantly higher, while those of the balance (55%, 12/22) were significantly lower. Potential miRNA targets and molecular pathways were identified.
Conclusions: AF alters the miRNA expression profiles of the left atria of MS patients. These findings may be useful for the biological understanding of AF in MS patients.
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http://dx.doi.org/10.1186/1471-2261-14-10 | DOI Listing |
Brief Bioinform
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School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
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Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.
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