Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer’s disease

Streptococcus gordonii Supragingival Bacterium Oral Infection-Induced Periodontitis and Robust miRNA Expression Kinetics

Streptococcus gordonii (S. gordonii, Sg) is one of the early colonizing, supragingival commensal bacterium normally associated with oral health in human dental plaque. MicroRNAs (miRNAs) play an important role in the inflammation-mediated pathways and are involved in periodontal disease (PD) pathogenesis. PD is a polymicrobial dysbiotic immune-inflammatory disease initiated by microbes in the gingival sulcus/pockets. The objective of this study is to determine the global miRNA expression kinetics in S. gordonii DL1-infected C57BL/6J mice. All mice were randomly divided into four groups (n = 10 mice/group; 5 males and 5 females). Bacterial infection was performed in mice at 8 weeks and 16 weeks, mice were euthanized, and tissues harvested for analysis. We analyzed differentially expressed (DE) miRNAs in the mandibles of S. gordonii-infected mice. Gingival colonization/infection by S. gordonii and alveolar bone resorption (ABR) was confirmed. All the S. gordonii-infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, and a significant increase in mandible and maxilla ABR (p < 0.0001). miRNA profiling revealed 191 upregulated miRNAs (miR-375, miR-34b-5p) and 22 downregulated miRNAs (miR-133, miR-1224) in the mandibles of S. gordonii-infected mice at the 8-week mark. Conversely, at 16 weeks post-infection, 10 miRNAs (miR-1902, miR-203) were upregulated and 32 miRNAs (miR-1937c, miR-720) were downregulated. Two miRNAs, miR-210 and miR-423-5p, were commonly upregulated, and miR-2135 and miR-145 were commonly downregulated in both 8- and 16-week-infected mice mandibles. Furthermore, we employed five machine learning (ML) algorithms to assess how the number of miRNA copies correlates with S. gordonii infections in mice. In the ML analyses, miR-22 and miR-30c (8-week), miR-720 and miR-339-5p (16-week), and miR-720, miR-22, and miR-339-5p (combined 8- and 16-week) emerged as the most influential miRNAs.

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

BACKGROUND AND OBJECTIVES

Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes.

RESULTS

The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases.

CONCLUSION

MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

Circulating microRNA miR-425-5p Associated with Brain White Matter Lesions and Inflammatory Processes

White matter lesions (WML) emerge as a consequence of vascular injuries in the brain. While they are commonly observed in aging, associations have been established with neurodegenerative and neurological disorders such as dementia or stroke. Despite substantial research efforts, biological mechanisms are incomplete and biomarkers indicating WMLs are lacking. Utilizing data from the population-based Study of Health in Pomerania (SHIP), our objective was to identify plasma-circulating micro-RNAs (miRNAs) associated with WMLs, thus providing a foundation for a comprehensive biological model and further research. In linear regression models, direct association and moderating factors were analyzed. In 648 individuals, we identified hsa-miR-425-5p as directly associated with WMLs. In subsequent analyses, hsa-miR-425-5p was found to regulate various genes associated with WMLs with particular emphasis on the SH3PXD2A gene. Furthermore, miR-425-5p was found to be involved in immunological processes. In addition, noteworthy miRNAs associated with WMLs were identified, primarily moderated by the factors of sex or smoking status. All identified miRNAs exhibited a strong over-representation in neurodegenerative and neurological diseases. We introduced hsa-miR-425-5p as a promising candidate in WML research probably involved in immunological processes. Mir-425-5p holds the potential as a biomarker of WMLs, shedding light on potential mechanisms and pathways in vascular dementia.

Identification and analysis of miRNAs expression profiles in human, bovine, and donkey milk exosomes

The purpose of this study is to identify and characterize mirnas in mammalian exosomes. Using Illumina sequencing technology, we sequenced miRNAs in the exosomes of mammalian human milk, bovine milk, and donkey milk. 36 known mature miRNAs and 256 novel miRNAs were identified in human milk. 61 known mature miRNAs and 346 novel miRNAs were identified in milk. 16 known mature miRNAs and 196 novel miRNAs were identified in donkey milk, and miRNAs target genes were predicted. Gene Ontology analysis showed that the miRNAs of human, bovine and donkey milk exosomes all labeled the functions related to body metabolism. Kyoto Encyclopedia pathway analysis showed that human, bovine and donkey milk miRNAs enriched AGE-RAGE signaling pathway in Complications of diabetes. Diabetes is a Metabolic disorder. Based on this pathway, we screened out hsa-miR-8485, bta-miR-342, miR-29c and other genes related to diabetes. This study has a new understanding of the physiological function of mammalian milk miRNAs, and also provides a new way to explore diabetes related miRNAs.

Unique miRomics Expression Profiles in Tannerella forsythia-Infected Mandibles during Periodontitis Using Machine Learning

T. forsythia is a subgingival periodontal bacterium constituting the subgingival pathogenic polymicrobial milieu during periodontitis (PD). miRNAs play a pivotal role in maintaining periodontal tissue homeostasis at the transcriptional, post-transcriptional, and epigenetic levels. The aim of this study was to characterize the global microRNAs (miRNA, miR) expression kinetics in 8- and 16-week-old T. forsythia-infected C57BL/6J mouse mandibles and to identify the miRNA bacterial biomarkers of disease process at specific time points. We examined the differential expression (DE) of miRNAs in mouse mandibles (n = 10) using high-throughput NanoString nCounter® miRNA expression panels, which provided significant advantages over specific candidate miRNA or pathway analyses. All the T. forsythia-infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, along with a significant increase in alveolar bone resorption (ABR) (p < 0.0001). We performed a NanoString analysis of specific miRNA signatures, miRNA target pathways, and gene network analysis. A total of 115 miRNAs were DE in the mandible tissue during 8 and 16 weeks The T. forsythia infection, compared with sham infection, and the majority (99) of DE miRNAs were downregulated. nCounter miRNA expression kinetics identified 67 downregulated miRNAs (e.g., miR-375, miR-200c, miR-200b, miR-34b-5p, miR-141) during an 8-week infection, whereas 16 upregulated miRNAs (e.g., miR-1902, miR-let-7c, miR-146a) and 32 downregulated miRNAs (e.g., miR-2135, miR-720, miR-376c) were identified during a 16-week infection. Two miRNAs, miR-375 and miR-200c, were highly downregulated with >twofold change during an 8-week infection. Six miRNAs in the 8-week infection (miR-200b, miR-141, miR-205, miR-423-3p, miR-141-3p, miR-34a-5p) and two miRNAs in the 16-week infection (miR-27a-3p, miR-15a-5p) that were downregulated have also been reported in the gingival tissue and saliva of periodontitis patients. This preclinical in vivo study identified T. forsythia-specific miRNAs (miR-let-7c, miR-210, miR-146a, miR-423-5p, miR-24, miR-218, miR-26b, miR-23a-3p) and these miRs have also been reported in the gingival tissues and saliva of periodontitis patients. Further, several DE miRNAs that are significantly upregulated (e.g., miR-101b, miR-218, miR-127, miR-24) are also associated with many systemic diseases such as atherosclerosis, Alzheimer's disease, rheumatoid arthritis, osteoarthritis, diabetes, obesity, and several cancers. In addition to DE analysis, we utilized the XGBoost (eXtreme Gradient boost) and Random Forest machine learning (ML) algorithms to assess the impact that the number of miRNA copies has on predicting whether a mouse is infected. XGBoost found that miR-339-5p was most predictive for mice infection at 16 weeks. miR-592-5p was most predictive for mice infection at 8 weeks and also when the 8-week and 16-week results were grouped together. Random Forest predicted miR-592 as most predictive at 8 weeks as well as the combined 8-week and 16-week results, but miR-423-5p was most predictive at 16 weeks. In conclusion, the expression levels of miR-375 and miR-200c family differed significantly during disease process, and these miRNAs establishes a link between T. forsythia and development of periodontitis genesis, offering new insights regarding the pathobiology of this bacterium.

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.

Non-Coding RNA in Microglia Activation and Neuroinflammation in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by complex pathophysiological features. Amyloid plaques resulting from extracellular amyloid deposition and neurofibrillary tangles formed by intracellular hyperphosphorylated tau accumulation serve as primary neuropathological criteria for AD diagnosis. The activation of microglia has been closely associated with these pathological manifestations. Non-coding RNA (ncRNA), a versatile molecule involved in various cellular functions such as genetic information storage and transport, as well as catalysis of biochemical reactions, plays a crucial role in microglial activation. This review aims to investigate the regulatory role of ncRNAs in protein expression by directly targeting genes, proteins, and interactions. Furthermore, it explores the ability of ncRNAs to modulate inflammatory pathways, influence the expression of inflammatory factors, and regulate microglia activation, all of which contribute to neuroinflammation and AD. However, there are still significant controversies surrounding microglial activation and polarization. The categorization into M1 and M2 phenotypes may oversimplify the intricate and multifaceted regulatory processes in microglial response to neuroinflammation. Limited research has been conducted on the role of ncRNAs in regulating microglial activation and inducing distinct polarization states in the context of neuroinflammation. Moreover, the regulatory mechanisms through which ncRNAs govern microglial function continue to be refined. The current understanding of ncRNA regulatory pathways involved in microglial activation remains incomplete and may be influenced by spatial, temporal, and tissue-specific factors. Therefore, further in-depth investigations are warranted. In conclusion, there are ongoing debates and uncertainties regarding the activation and polarization of microglial cells, particularly concerning the categorization into M1 and M2 phenotypes. The study of ncRNA regulation in microglial activation and polarization, as well as its mechanisms, is still in its early stages and requires further investigation. However, this review offers new insights and opportunities for therapeutic approaches in AD. The development of ncRNA-based drugs may hold promise as a new direction in AD treatment.

Multi-omics integration method based on attention deep learning network for biomedical data classification

BACKGROUND AND OBJECTIVE

Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics.

METHODS

We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction.

RESULTS

We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets.

CONCLUSIONS

MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data.

Investigation of Relationship Between Small Noncoding RNA (sncRNA) Expression Levels and Serum Iron, Copper, and Zinc Levels in Clinical Diagnosed Multiple Sclerosis Patients

In our study, we aimed to investigate the relationship between microRNA (miRNA) expression levels and serum iron (Fe), copper (Cu), and zinc (Zn) levels in Multiple sclerosis (MS) patients. Total RNA was isolated from peripheral venous blood containing ethylenediaminetetraacetic acid (EDTA) of MS patients and controls. Total RNA was labeled with Cy3-CTP fluorescent dye. Hybridization of samples was performed on microarray slides and arrays were scanned. Data argument and bioinformatics analysis were performed. Atomic absorption spectrophotometer method was used to measure serum Fe, Cu, and Zn levels. In our study, in bioinformatics analysis, although differently expressed miRNAs were not detected between 16 MS patients and 16 controls, hsa-miR-744-5p upregulation was detected between 4 MS patients and 4 controls. This may be stem from the patient group consisting of MS patients who have never had an attack for 1 year. Serum iron levels were detected significantly higher in the 16 MS patients compared to the 16 controls. This may be stem from the increase in iron accumulation based on inflammation in MS disease. According to the findings in our study, hsa-miR-744-5p upregulation has been determined as an early diagnostic biomarker for the development together of insulin resistance, diabetes mellitus associated with insulin signaling, and Alzheimer's diseases. Therefore, hsa-miR-744-5p is recommended as an important biomarker for the development together of diabetes mellitus, Alzheimer's disease, and MS disease. In addition, increased serum Fe levels may be suggested as an important biomarker for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and MS disease.

Prevailing Antagonistic Risks in Pleiotropic Associations with Alzheimer's Disease and Diabetes

BACKGROUND

The lack of efficient preventive interventions against Alzheimer's disease (AD) calls for identifying efficient modifiable risk factors for AD. As diabetes shares many pathological processes with AD, including accumulation of amyloid plaques and neurofibrillary tangles, insulin resistance, and impaired glucose metabolism, diabetes is thought to be a potentially modifiable risk factor for AD. Mounting evidence suggests that links between AD and diabetes may be more complex than previously believed.

OBJECTIVE

To examine the pleiotropic architecture of AD and diabetes mellitus (DM).

METHODS

Univariate and pleiotropic analyses were performed following the discovery-replication strategy using individual-level data from 10 large-scale studies.

RESULTS

We report a potentially novel pleiotropic NOTCH2 gene, with a minor allele of rs5025718 associated with increased risks of both AD and DM. We confirm previously identified antagonistic associations of the same variants with the risks of AD and DM in the HLA and APOE gene clusters. We show multiple antagonistic associations of the same variants with AD and DM in the HLA cluster, which were not explained by the lead SNP in this cluster. Although the ɛ2 and ɛ4 alleles played a major role in the antagonistic associations with AD and DM in the APOE cluster, we identified non-overlapping SNPs in this cluster, which were adversely and beneficially associated with AD and DM independently of the ɛ2 and ɛ4 alleles.

CONCLUSION

This study emphasizes differences and similarities in the heterogeneous genetic architectures of AD and DM, which may differentiate the pathogenic mechanisms of these diseases.

Identification of repurposed drugs targeting significant long non-coding RNAs in the cross-talk between diabetes mellitus and Alzheimer's disease

The relationship between diabetes mellitus (DM) and Alzheimer's disease (AD) is so strong that scientists called it "brain diabetes". According to several studies, the critical factor in this relationship is brain insulin resistance. Due to the rapid global spread of both diseases, overcoming this cross-talk has a significant impact on societies. Long non-coding RNAs (lncRNAs), on the other hand, have a substantial impact on complex diseases due to their ability to influence gene expression via a variety of mechanisms. Consequently, the regulation of lncRNA expression in chronic diseases permits the development of innovative therapeutic techniques. However, developing a new drug requires considerable time and money. Recently repurposing existing drugs has gained popularity due to the use of low-risk compounds, which may result in cost and time savings. in this study, we identified drug repurposing candidates capable of controlling the expression of common lncRNAs in the cross-talk between DM and AD. We also utilized drugs that interfered with this cross-talk. To do this, high degree common lncRNAs were extracted from microRNA-lncRNA bipartite network. The drugs that interact with the specified lncRNAs were then collected from multiple data sources. These drugs, referred to as set D, were classified in to positive (D⁺) and negative (D^-) groups based on their effects on the expression of the interacting lncRNAs. A feature selection algorithm was used to select six important features for D. Using a random forest classifier, these features were capable of classifying D⁺ and D^- with an accuracy of 82.5%. Finally, the same six features were extracted for the most recently Food and Drug Administration (FDA) approved drugs in order to identify those with the highest likelihood of belonging to D⁺ or D^-. The most significant FDA-approved positive drugs, chromium nicotinate and tapentadol, were presented as repurposing candidates, while cefepime and dihydro-alpha-ergocryptine were recommended as significant adverse drugs. Moreover, two natural compounds, curcumin and quercetin, were recommended to prevent this cross-talk. According to the previous studies, less attention has been paid to the role of lncRNAs in this cross-talk. Our research not only did identify important lncRNAs, but it also suggested potential repurposed drugs to control them.