Identification and Analysis of BCAS4/hsa-miR-185-5p/SHISA7 Competing Endogenous RNA Axis in Late-Onset Alzheimer’s Disease Using Bioinformatic and Experimental Approaches

Blood-based multivariate methylation risk score for cognitive impairment and dementia

INTRODUCTION

The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection.

METHODS

In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts.

RESULTS

We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10-3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia = 2.59).

DISCUSSION

Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk.

HIGHLIGHTS

We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level.

Integrative Analysis of Morphine-Induced Differential Circular RNAs and ceRNA Networks in the Medial Prefrontal Cortex

Circular RNAs (circRNAs) are a novel type of non-coding RNAs. Despite the fact that the functional mechanisms of most circRNAs remain unknown, emerging evidence indicates that circRNAs could sponge microRNAs (miRNAs), bind to RNA binding proteins (RBP), and even be translated into protein. Recent research has demonstrated the crucial roles played by circRNAs in neuropsychiatric disorders. The medial prefrontal cortex (mPFC) is a crucial component of drug reward circuitry and exerts top-down control over cognitive functions. However, there is currently limited knowledge about the correlation between circRNAs and morphine-associated contextual memory in the mPFC. Here, we performed morphine-induced conditioned place preference (CPP) in mice and extracted mPFC tissue for RNA-sequencing. Our study represented the first attempt to identify differentially expressed circRNAs (DEcircRNAs) and mRNAs (DEmRNAs) in the mPFC after morphine-induced CPP. We identified 47 significantly up-regulated DEcircRNAs and 429 significantly up-regulated DEmRNAs, along with 74 significantly down-regulated DEcircRNAs and 391 significantly down-regulated DEmRNAs. Functional analysis revealed that both DEcircRNAs and DEmRNAs were closely associated with neuroplasticity. To further validate the DEcircRNAs, we conducted qRT-PCR, Sanger sequencing, and RNase R digestion assays. Additionally, using an integrated bioinformatics approach, we constructed ceRNA networks and identified critical circRNA/miRNA/mRNA axes that contributed to the development of morphine-associated contextual memory. In summary, our study provided novel insights into the role of circRNAs in drug-related memory, specifically from the perspective of ceRNAs.

Tissue-resident memory T cell signatures from single-cell analysis associated with better melanoma prognosis

Tissue-resident memory T cells (TRM) are a specialized T cell population residing in peripheral tissues. The presence and potential impact of TRM in the tumor immune microenvironment (TIME) remain to be elucidated. Here, we systematically investigated the relationship between TRM and melanoma TIME based on multiple clinical single-cell RNA-seq datasets and developed signatures indicative of TRM infiltration. TRM infiltration is associated with longer overall survival and abundance of T cells, NK cells, M1 macrophages, and memory B cells in the TIME. A 22-gene TRM-derived risk score was further developed to effectively classify patients into low- and high-risk categories, distinguishing overall survival and immune activation, particularly in T cell-mediated responses. Altogether, our analysis suggests that TRM abundance is associated with melanoma TIME activation and patient survival, and the TRM-based machine learning model can potentially predict prognosis in melanoma patients.

Gene expression investigation of four key regulators of polyadenylation and alternative adenylation in the periphery of late-onset Alzheimer's disease patients

BACKGROUND

Alzheimer's disease (AD) is a genetic and sporadic neurodegenerative disease considered by an archetypal cognitive impairment and a decrease in less common cognitive impairment. Notably, the discovery of goals in this paradigm is still a challenge, and understanding basic mechanisms is an important step toward improving disease management. Polyadenylation (PA) and alternative polyadenylation (APA) are two of the most critical RNA processing stages in 3'UTRs that influence various AD-related genes.

METHODS

In this study, we assessed Cleavage and polyadenylation specificity factors 1 and 6 (CPSF1 and CPSF6), cleavage stimulation factor 1 (CSTF1), and WD Repeat Domain 33 (WDR33) genes expression in the periphery of 50 AD patients and 50 healthy individuals with age and gender-matched by quantitative real-time PCR.

RESULTS

Comparing AD patients with healthy people using expression analysis revealed a substantial increase in CSTF1 (posterior beta = 0.773, adjusted P-value = 0.042). Significant positive correlations were found between CSTF1 and CPSF1 (r = 0.365, P < 0.001), WDR33 (r = 0.506, P < 0.001), and CPSF6 (r = 0.446, P < 0.001) expression levels.

CONCLUSION

Although further research is required to determine their potential contribution to AD, our findings offer a fresh perspective on molecular regulatory pathways associated with AD pathogenic mechanisms associated with PA and APA.

The Role of Long Noncoding RNAs in Progression of Leukemia: Based on Chromosomal Location

Long non-coding RNA [LncRNA] dysregulation has been seen in many human cancers, including several kinds of leukemia, which is still a fatal disease with a poor prognosis. LncRNAs have been demonstrated to function as tumor suppressors or oncogenes in leukemia. This study covers current research findings on the role of lncRNAs in the prognosis and diagnosis of leukemia. Based on recent results, several lncRNAs are emerging as biomarkers for the prognosis, diagnosis, and even treatment outcome prediction of leukemia and have been shown to play critical roles in controlling leukemia cell activities, such as proliferation, cell death, metastasis, and drug resistance. As a result, lncRNA profiles may have superior predictive and diagnostic potential in leukemia. Accordingly, this review concentrates on the significance of lncRNAs in leukemia progression based on their chromosomal position.

Exosomal Circular Ribonucleic Acid-Microribonucleic Acid Expression Profile from Plasma in Alzheimer's Disease Patients by Bioinformatics and Integrative Analysis

OBJECTIVE

Alzheimer's disease is a neurodegenerative sickness and increasing with age throughout the world. A substantial body of evidence suggests the role of exosomal noncoding ribonucleic acids in the development of Alzheimer's disease, but the regulatory mechanisms mediated by these noncoding ribonucleic acids remain extensively unknown. Using plasma samples from Alzheimer's disease patients, this study explored the exosomal circular ribonucleic acid-microribonucleic acid profiles.

MATERIALS AND METHODS

The ArrayExpress platform was used to convey data from 3 samples from each group (healthy, mild cognitive impairment, and Alzheimer's disease). Using plasma exosomes, differentially expressed microribonucleic acids and differentially expressed circular ribonucleic acids were compared between the Alzheimer's disease and mild cognitive impairment groups. Afterward, to define pathways, gene ontologies, and networks, differentially expressed microribonucleic acids and differentially expressed circular ribonucleic acids common to both mild cognitive impairment and Alzheimer's disease groups were analyzed. Eventually, the selection of hub genes and protein-protein interaction network was analyzed.

RESULTS

A total of common 19 (7 upregulated and 12 downregulated) differentially expressed microribonucleic acids and 24 differentially expressed circular ribonucleic acids were recognized. A total of 4559 target genes were predicted for upregulated differentially expressed microribonucleic acids, while 6504 target genes were identified for downregulated differentially expressed microribonucleic acids, and most of the target genes involved in the phosphoinositide 3-kinases-Akt pathway and that were mostly regulated by hsa-mir-374a-3p, mir-196a-5p, let-205-5p, mir-185-3p, mir-374a-5p, mir-615-3p, let-7c-5p, mir-185-5p. Additionally, 9 hub genes (HSP90AA, ACTB, MAPK1, GSK3B, CCNE2, CDK6, AKT1, IGF1R, CCND1) were revealed as the genes considerably related to Alzheimer's disease by a protein-protein interaction network using the cytohubba in Cytoscape software.

CONCLUSION

Our findings provide a new perspective on how microribonucleic acids could connect with circular ribonucleic acids in the pathogenesis of Alzheimer's disease.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Human microRNA-4433 (hsa-miR-4443) Targets 18 Genes to be a Risk Factor of Neurodegenerative Diseases

BACKGROUND

Neurodegenerative diseases, such as Alzheimer's disease patients (AD), Huntington's disease (HD) and Parkinson's disease (PD), are common causes of morbidity, mortality, and cognitive impairment in older adults.

OBJECTIVE

We aimed to understand the transcriptome characteristics of the cortex of neurodegenerative diseases and to provide an insight into the target genes of differently expressed microRNAs in the occurrence and development of neurodegenerative diseases.

METHODS

The Limma package of R software was used to analyze GSE33000, GSE157239, GSE64977 and GSE72962 datasets to identify the differentially expressed genes (DEGs) and microRNAs in the cortex of neurodegenerative diseases. Bioinformatics methods, such as GO enrichment analysis, KEGG enrichment analysis and gene interaction network analysis, were used to explore the biological functions of DEGs. Weighted gene co-expression network analysis (WGCNA) was used to cluster DEGs into modules. RNA22, miRDB, miRNet 2.0 and TargetScan7 databases were performed to predict the target genes of microRNAs.

RESULTS

Among 310 Alzheimer's disease (AD) patients, 157 Huntington's disease (HD) patients and 157 non-demented control (Con) individuals, 214 co-DEGs were identified. Those co-DEGs were filtered into 2 different interaction network complexes, representing immune-related genes and synapserelated genes. The WGCNA results identified five modules: yellow, blue, green, turquoise, and brown. Most of the co-DEGs were clustered into the turquoise module and blue module, which respectively regulated synapse-related function and immune-related function. In addition, human microRNA-4433 (hsa-miR-4443), which targets 18 co-DEGs, was the only 1 co-up-regulated microRNA identified in the cortex of neurodegenerative diseases.

CONCLUSION

214 DEGs and 5 modules regulate the immune-related and synapse-related function of the cortex in neurodegenerative diseases. Hsa-miR-4443 targets 18 co-DEGs and may be a potential molecular mechanism in neurodegenerative diseases' occurrence and development.

The Network of miRNA-mRNA Interactions in Circulating T Cells of Patients Following Major Trauma - A Pilot Study

Purpose

Following major trauma, genes involved in adaptive immunity are downregulated, which accompanies the upregulation of genes involved in systemic inflammatory responses. This study investigated microRNA (miRNA)-mRNA interactome dysregulation in circulating T cells of patients with major trauma.

Patients and Methods

This study included adult trauma patients who had an injury severity score ≥16 and required ventilator support for more than 48 h in the intensive care unit. Next-generation sequencing was used to profile the miRNAs and mRNAs expressed in CD3+ T cells isolated from patient blood samples collected during the injury and recovery stages.

Results

In the 26 studied patients, 9 miRNAs (hsa-miR-16-2-3p, hsa-miR-16-5p, hsa-miR-185-5p, hsa-miR-192-5p, hsa-miR-197-3p, hsa-miR-23a-3p, hsa-miR-26b-5p, hsa-miR-223-3p, and hsa-miR-485-5p) were significantly upregulated, while 58 mRNAs were significantly downregulated in T cells following major trauma. A network consisting of 8 miRNAs and 22 mRNAs interactions was revealed by miRWalk, with three miRNAs (hsa-miR-185-5p, hsa-miR-197-3p, and hsa-miR-485-5p) acting as hub genes that regulate the network. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that “chemokine signaling pathway” was the predominant pathway.

Conclusion

The study revealed a miRNA-mRNA interactome consisting of 8 miRNAs and 22 mRNAs that are predominantly involved in chemokine signaling in circulating T cells of patients following major trauma.