Investigation of the Circular Transcriptome in Alzheimer's Disease Brain.

Circular RNAs (circRNAs) are a subclass of non-coding RNAs which have demonstrated potential as biomarkers for Alzheimer's disease (AD). In this study, we conducted a comprehensive exploration of the circRNA transcriptome within AD brain tissues. Specifically, we assessed circRNA expression patterns in the dorsolateral prefrontal cortex collected from nine AD-afflicted individuals and eight healthy controls. Utilising two circRNA detection tools, CIRI2 and CIRCexplorer2, we detected thousands of circRNAs and performed a differential expression analysis. CircRNAs which exhibited statistically significantly differential expression were identified as AD-specific differentially expressed circRNAs. Notably, our investigation revealed 120 circRNAs with significant upregulation and 1325 circRNAs displaying significant downregulation in AD brains when compared to healthy brain tissue. Additionally, we explored the expression profiles of the linear RNA counterparts corresponding to differentially expressed circRNAs in AD-afflicted brains and discovered that the linear RNA counterparts exhibited no significant changes in the levels of expression. We used CRAFT tool to predict that circUBE4B had potential to target miRNA named as hsa-miR-325-5p, ultimately regulated CD44 gene. This study provides a comprehensive overview of differentially expressed circRNAs in the context of AD brains, underscoring their potential as molecular biomarkers for AD. These findings significantly enhance our comprehension of AD's underlying pathophysiological mechanisms, offering promising avenues for future diagnostic and therapeutic developments.

Machine learning and related approaches in transcriptomics.

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.

Protein-coding circular RNAs - mechanism, detection, and their role in cancer and neurodegenerative diseases.

Circular RNAs (circRNAs) are a unique class of non-coding RNAs and were originally thought to have no protein-coding potential due to their lack of a 5' cap and 3' poly(A) tail. However, recent studies have challenged this notion and revealed that some circRNAs have protein-coding potential. They have emerged as a key area of interest in cancer and neurodegeneration research as recent studies have identified several circRNAs that can produce functional proteins with important roles in cancer progression. The protein-coding potential of circRNAs is determined by the presence of an open reading frame (ORF) within the circular structure that can encode a protein. In some cases, the ORF can be translated into a functional protein despite the lack of traditional mRNA features. While the protein-coding potential of most circRNAs remains unclear, several studies have identified specific circRNAs that can produce functional proteins. Understanding the protein-coding potential of circRNAs is important for unravelling their biological functions and potential roles in disease. Our review provides comprehensive coverage of recent advances in the field of circRNA protein-coding capacity and its impact on cancer and neurodegenerative diseases pathogenesis and progression.

A unique circular RNA expression pattern in the peripheral blood of myalgic encephalomyelitis/chronic fatigue syndrome patients.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with obscure aetiology. The underdiagnosis rate of ME/CFS is high due to the lack of diagnostic criteria based on objective markers. In recent years, circRNAs have emerged as potential genetic biomarkers for neurological diseases, including Parkinson's disease and Alzheimer's disease, making them likely to have the same prospect of being biomarkers in ME/CFS. However, despite the extensive amount of research that has been performed on the transcriptomes of ME/CFS patients, all of them are solely focused on linear RNAs, and the profiling of circRNAs in ME/CFS has been completely omitted. In this study, we investigated the expression profiles of circRNAs, comparing ME/CFS patients and controls before and after two sessions of cardiopulmonary exercise longitudinally. In patients with ME/CFS, the number of detected circRNAs was higher compared to healthy controls, indicating potential differences in circRNA expression associated with the disease. Additionally, healthy controls showed an increase in the number of circRNAs following exercise testing, while no similar pattern was evident in ME/CFS patients, further highlighting physiological differences between the two groups. A lack of correlation was observed between differentially expressed circRNAs and their corresponding coding genes in terms of expression and function, suggesting the potential of circRNAs as independent biomarkers in ME/CFS. Specifically, 14 circRNAs were highly expressed in ME/CFS patients but absent in controls throughout the exercise study, indicating a unique molecular signature specific to ME/CFS patients and providing potential diagnostic biomarkers for the disease. Significant enrichment of protein and gene regulative pathways were detected in relation to five of these 14 circRNAs based on their predicted miRNA target genes. Overall, this is the first study to describe the circRNA expression profile in peripheral blood of ME/CFS patients, providing valuable insights into the molecular mechanisms underlying the disease.