Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Sex-associated microRNAs potentially implicated in sporadic Alzheimer's disease (sAD)

BACKGROUND

The onset and pathology of sporadic Alzheimer's disease (sAD) seem to be affected by both sex and genetic mechanisms. Evidence supports that the high prevalence of sAD in women, worldwide, may be attributed to an interplay among aging, sex, and lifestyle, influenced by genetics, metabolic changes, and hormones. Interestingly, epigenetic mechanisms such as microRNAs (miRNAs), known as master regulators of gene expression, may contribute to this observed sexual dimorphism in sAD.

OBJECTIVES

To investigate the potential impact of sex-associated miRNAs on processes manifesting sAD pathology, as described by the Tau-driven Adverse Outcome Pathway (AOP) leading to memory loss.

METHODS

Using publicly available human miRNA datasets, sex-biased miRNAs, defined as differentially expressed by sex in tissues possibly affected by sAD pathology, were collected. In addition, sex hormone-related miRNAs were also retrieved from the literature. The compiled sex-biased and sex hormone-related miRNAs were further plugged into the dysregulated processes of the Tau-driven AOP for memory loss.

RESULTS

Several miRNAs, previously identified as sex-associated, were implicated in dysregulated processes associated with the manifestation of sAD pathology. Importantly, the described pathology processes were not confined to a particular sex. A mechanistic-based approach utilizing miRNAs was adopted in order to elucidate the link between sex and biological processes potentially involved in the development of memory loss.

CONCLUSIONS

The identification of sex-associated miRNAs involved in the early processes manifesting memory loss may shed light to the complex molecular mechanisms underlying sAD pathogenesis in a sex-specific manner.

Non-fatal outcomes of COVID-19 disease in pediatric organ transplantation associates with down-regulation of senescence pathways

This is a cross-sectional study examining kinetics and durability of immune response in children with solid organ transplants (SOTs) who had COVID-19 disease between November 2020 through June 2022, who were followed for 60-days at a single transplant center. Blood was collected between 1-14 (acute infection), and 15-60 days of a positive PCR (convalescence). SOT children with peripheral blood mononuclear cells (PBMC) cryopreserved before 2019 were non-infected controls (ctrls). PBMCs stimulated with 15-mer peptides from spike protein and anti-CD49d/anti-CD28. Testing done included mass cytometry, mi-RNA sequencing with confirmatory qPCR. 38 children formed the study cohort, 10 in the acute phase and 8 in the convalescence phase. 20 subjects were non-infected controls. Two subjects had severe disease. Subjects in the acute and convalescent phases were different subjects. The median age and tacrolimus level at blood draw was not significantly different. There was no death, and no subject was lost to follow-up. During acute infection CD57 expression was low in NKT, Th17 effector memory, memory Treg, CD4-CD8-, and γδT cells (p = 0.01, p = 0.04, p = 0.03, p = 0.03, p = 0.004 respectively). The frequencies of NK and Th2 effector memory cells increased (p = 0.01, p = 0.02) during acute infection. Non-switched memory B and CD8 central memory cell frequencies were decreased during acute infection (p = 0.02; p = 0.02), but the decrease in CD8 central memory cells did not persist. CD4-CD8- and CD14 monocyte frequencies increased during recovery (p = 0.03; p = 0.007). Our observations suggest down regulation of CD57 with absence of NK cell contraction protect against death from COVID-19 disease in children with SOTs.

Death-associated protein kinase 1 as a therapeutic target for Alzheimer's disease

Alzheimer's disease (AD) is the most prevalent form of dementia in the elderly and represents a major clinical challenge in the ageing society. Neuropathological hallmarks of AD include neurofibrillary tangles composed of hyperphosphorylated tau, senile plaques derived from the deposition of amyloid-β (Aβ) peptides, brain atrophy induced by neuronal loss, and synaptic dysfunctions. Death-associated protein kinase 1 (DAPK1) is ubiquitously expressed in the central nervous system. Dysregulation of DAPK1 has been shown to contribute to various neurological diseases including AD, ischemic stroke and Parkinson's disease (PD). We have established an upstream effect of DAPK1 on Aβ and tau pathologies and neuronal apoptosis through kinase-mediated protein phosphorylation, supporting a causal role of DAPK1 in the pathophysiology of AD. In this review, we summarize current knowledge about how DAPK1 is involved in various AD pathological changes including tau hyperphosphorylation, Aβ deposition, neuronal cell death and synaptic degeneration. The underlying molecular mechanisms of DAPK1 dysregulation in AD are discussed. We also review the recent progress regarding the development of novel DAPK1 modulators and their potential applications in AD intervention. These findings substantiate DAPK1 as a novel therapeutic target for the development of multifunctional disease-modifying treatments for AD and other neurological disorders.

The Clinical Aspects of COVID and Alzheimer's Disease: A Round-Up of Where Things Stand and Are Headed

 The link between long COVID-19 and brain/cognitive impairments is concerning and may foster a worrisome worldwide emergence of novel cases of neurodegenerative diseases with aging. This review aims to update the knowledge, crosstalk, and possible intersections between the Post-COVID Syndrome (PCS) and Alzheimer's disease (AD). References included in this review were obtained from PubMed searches conducted between October 2023 and November 2023. PCS is a very heterogenous and poorly understood disease with recent evidence of a possible association with chronic diseases such as AD. However, more scientific data is required to establish the link between PCS and AD.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease

The patients' vocal Parkinson's disease (PD) changes could be identified early on, allowing for management before physically incapacitating symptoms appear. In this work, static as well as dynamic speech characteristics that are relevant to PD identification are examined. Speech changes or communication issues are among the challenges that Parkinson's individuals may encounter. As a result, avoiding the potential consequences of speech difficulties brought on by the condition depends on getting the appropriate diagnosis early. PD patients' speech signals change significantly from those of healthy individuals. This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM. The proposed hybrid model employs a new, pre-trained CNN with LSTM to recognize PD in linguistic features utilizing Mel-spectrograms derived from normalized voice signal and dynamic mode decomposition. The proposed Hybrid model works in various phases, which include Noise removal, extraction of Mel-spectrograms, feature extraction using pre-trained CNN model ResNet-50, and the final stage is applied for classification. An experimental analysis was performed using the PC-GITA disease dataset. The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy level achieved in Neural Network, CART, SVM, and XGBoost models is 72.69%, 84.21%, 73.51%, and 90.81%. The results show that under these four machine approaches of tenfold cross-validation and dataset splitting without samples overlapping one individual, the proposed hybrid model achieves an accuracy of 93.51%, significantly outperforming traditional ML models utilizing static features in detecting Parkinson's disease.

Common microRNA regulated pathways in Alzheimer's and Parkinson's disease

MicroRNAs (miRNAs) are small non-coding RNAs involved in gene regulation. Recently, miRNA dysregulation has been found in neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). The diagnosis of Alzheimer's and Parkinson's is currently challenging, mainly occurring when pathology is already present, and although treatments are available for both diseases, the role of treatment is primarily to prevent or delay the progress of the diseases instead of fully overcoming the diseases. Therefore, the challenge in the near future will be to determine effective drugs to tackle the dysregulated biological pathways in neurodegenerative diseases. In the present study, we describe the dysregulation of miRNAs in blood of Alzheimer's and Parkinson's patients with the aim to identify common mechanisms between the 2 pathologies and potentially to identify common therapeutic targets which can stop or delay the progression of two most frequent neuropathologies. Two independent systematic reviews, bioinformatic analysis, and experiment validation were performed to identify whether AD and PD share common pathways. A total of 15 common miRNAs were found in the literature and 13 common KEGG pathways. Among the common miRNAs, two were selected for validation in a small cohort of AD and PD patients. Let-7f-5p and miR-29b-3p showed to be good predictors in blood of PD patients.

Single-cell sequencing of entorhinal cortex reveals widespread disruption of neuropeptide networks in Alzheimer's disease

INTRODUCTION

Abnormalities of neuropeptides (NPs) that play important roles in modulating neuronal activities are commonly observed in Alzheimer's disease (AD). We hypothesize that NP network disruption is widespread in AD brains.

METHODS

Single-cell transcriptomic data from the entorhinal cortex (EC) were used to investigate the NP network disruption in AD. Bulk RNA-sequencing data generated from the temporal cortex by independent groups and machine learning were employed to identify key NPs involved in AD. The relationship between aging and AD-associated NP (ADNP) expression was studied using GTEx data.

RESULTS

The proportion of cells expressing NPs but not their receptors decreased significantly in AD. Neurons expressing higher level and greater diversity of NPs were disproportionately absent in AD. Increased age coincides with decreased ADNP expression in the hippocampus.

DISCUSSION

NP network disruption is widespread in AD EC. Neurons expressing more NPs may be selectively vulnerable to AD. Decreased expression of NPs participates in early AD pathogenesis. We predict that the NP network can be harnessed for treatment and/or early diagnosis of AD.

microRNA-based predictor for diagnosis of frontotemporal dementia

AIMS

This study aimed to explore the non-linear relationships between cell-free microRNAs (miRNAs) and their contribution to prediction of Frontotemporal dementia (FTD), an early onset dementia that is clinically heterogeneous, and too often suffers from delayed diagnosis.

METHODS

We initially studied a training cohort of 219 subjects (135 FTD and 84 non-neurodegenerative controls) and then validated the results in a cohort of 74 subjects (33 FTD and 41 controls).

RESULTS

On the basis of cell-free plasma miRNA profiling by next generation sequencing and machine learning approaches, we develop a non-linear prediction model that accurately distinguishes FTD from non-neurodegenerative controls in ~90% of cases.

CONCLUSIONS

The fascinating potential of diagnostic miRNA biomarkers might enable early-stage detection and a cost-effective screening approach for clinical trials that can facilitate drug development.

Identification of miRNAs in extracellular vesicles as potential diagnostic markers for pediatric epilepsy and drug-resistant epilepsy via bioinformatics analysis

Background

Pediatric epilepsy (PE) is a common neurological disease. However, many challenges regarding the clinical diagnosis and treatment of PE and drug-resistant epilepsy (DRE) remain unsettled. Our study aimed to identify potential miRNA biomarkers in children with epilepsy and drug-resistant epilepsy by scrutinizing differential miRNA expression profiles.

Methods

In this study, miRNA expression profiles in plasma extracellular vesicles (EV) of normal controls, children with drug-effective epilepsy (DEE), and children with DRE were obtained. In addition, differential analysis, transcription factor (TF) enrichment analysis, Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and target gene prediction were used to identify specifically expressed miRNAs and their potential mechanisms of action. Potential diagnostic markers for DRE were identified using machine learning algorithms, and their diagnostic efficiency was assessed by the receiver operating characteristic curve (ROC).

Results

The hsa-miR-1307-3p, hsa-miR-196a-5p, hsa-miR-199a-3p, and hsa-miR-21-5p were identified as diagnostic markers for PE, with values of area under curve (AUC) 0.780, 0.840, 0.832, and 0.816, respectively. In addition, the logistic regression model incorporating these four miRNAs had an AUC value of 0.940, and its target gene enrichment analysis highlighted that these miRNAs were primarily enriched in the PI3K-Akt, MAPK signaling pathways, and cell cycle. Furthermore, hsa-miR-99a-5p, hsa-miR-532-5p, hsa-miR-181d-5p, and hsa-miR-181a-5p showed good performance in differentiating children with DRE from those with DEE, with AUC values of 0.737 (0.534-0.940), 0.737 (0.523-0.952), 0.788 (0.592-0.985), and 0.788 (0.603-0.974), respectively.

Conclusion

This study characterized the expression profile of miRNAs in plasma EVs of children with epilepsy and identified miRNAs that can be used for the diagnosis of DRE.

Salivary biomarkers: novel noninvasive tools to diagnose chronic inflammation

Several chronic disorders including type 2 diabetes (T2D), obesity, heart disease and cancer are preceded by a state of chronic low-grade inflammation. Biomarkers for the early assessment of chronic disorders encompass acute phase proteins (APP), cytokines and chemokines, pro-inflammatory enzymes, lipids and oxidative stress mediators. These substances enter saliva through the blood flow and, in some cases, there is a close relation between their salivary and serum concentration. Saliva can be easily collected and stored with non-invasive and cost-saving procedures, and it is emerging the concept to use it for the detection of inflammatory biomarkers. To this purpose, the present review aims to discuss the advantages and challenges of using standard and cutting-edge techniques to discover salivary biomarkers which may be used in diagnosis/therapy of several chronic diseases with inflammatory consequences with the pursuit to possibly replace conventional paths with detectable soluble mediators in saliva. Specifically, the review describes the procedures used for saliva collection, the standard approaches for the measurement of salivary biomarkers and the novel methodological strategies such as biosensors to improve the quality of care for chronically affected patients.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

Exosomal miR-532-5p induced by long-term exercise rescues blood-brain barrier function in 5XFAD mice via downregulation of EPHA4

The breakdown of the blood-brain barrier, which develops early in Alzheimer's disease (AD), contributes to cognitive impairment. Exercise not only reduces the risk factors for AD but also confers direct protection against cognitive decline. However, the exact molecular mechanisms remain elusive, particularly whether exercise can liberate the function of the blood-brain barrier. Here, we demonstrate that long-term exercise promotes the clearance of brain amyloid-β by improving the function of the blood-brain barrier in 5XFAD mice. Significantly, treating primary brain pericytes or endothelial cells with exosomes isolated from the brain of exercised 5XFAD mice improves cell proliferation and upregulates PDGFRβ, ZO-1, and claudin-5. Moreover, exosomes isolated from exercised mice exhibit significant changes in miR-532-5p. Administration or transfection of miR-532-5p to sedentary mice or primary brain pericytes and endothelial cells reproduces the improvement of blood-brain barrier function. Exosomal miR-532-5p targets EPHA4, and accordingly, expression of EphA4 is decreased in exercised mice and miR-532-5p overexpressed mice. A specific siRNA targeting EPHA4 recapitulates the effects on blood-brain barrier-associated cells observed in exercised 5XFAD mice. Overall, our findings suggest that exosomes released by the brain contain a specific miRNA that is altered by exercise and has an impact on blood-brain barrier function in AD.

A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography

For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People's Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.

MicroRNA: A Linking between Astrocyte Dysfunction, Mild Cognitive Impairment, and Neurodegenerative Diseases

Simple Summary

Neurodegenerative diseases are complex neurological disorders with a high incidence worldwide in older people, increasing hospital visits and requiring expensive treatments. As a precursor phase of neurodegenerative diseases, cognitive impairment needs to be studied to understand the factors that influence its development and improve patients’ quality of life. The present review compiles possible factors and biomarkers for diagnosing mild cognitive impairment based on the most recent studies involving miRNAs. These molecules can direct the gene expression in multiple cells, affecting their behavior under certain conditions, such as stressing factors. This review encourages further research into biomarkers that identify cognitive impairment in cellular models such as astrocytes, which are brain cells capable of maintaining the optimal conditions for the central nervous system functioning.

Abstract

The importance of miRNAs in cellular processes and their dysregulation has taken significant importance in understanding different pathologies. Due to the constant increase in the prevalence of neurodegenerative diseases (ND) worldwide and their economic impact, mild cognitive impairment (MCI), considered a prodromal phase, is a logical starting point to study this public health problem. Multiple studies have established the importance of miRNAs in MCI, including astrocyte regulation during stressful conditions. Additionally, the protection mechanisms exerted by astrocytes against some damage in the central nervous system (CNS) lead to astrocytic reactivation, in which a differential expression of miRNAs has been shown. Nevertheless, excessive reactivation can cause neurodegeneration, and a clear pattern defining the equilibrium point between a neuroprotective or detrimental astrocytic phenotype is unknown. Therefore, the miRNA expression has gained significant attention to understand the maintenance of brain balance and improve the diagnosis and treatment at earlier stages in the ND. Here, we provide a comprehensive review of the emerging role of miRNAs in cellular processes that contribute to the loss of cognitive function, including lipotoxicity, which can induce chronic inflammation, also considering the fundamental role of astrocytes in brain homeostasis.

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases

Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may be related to the lack of sufficiently rich or homogeneous data, such as time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.

Differential microRNA expression analyses across two brain regions in Alzheimer's disease

Dysregulation of microRNAs (miRNAs) is involved in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease (AD). Hitherto, sample sizes from differential miRNA expression studies in AD are exceedingly small aggravating any biological inference. To overcome this limitation, we investigated six candidate miRNAs in a large collection of brain samples. Brain tissue was derived from superior temporal gyrus (STG) and entorhinal cortex (EC) from 99 AD patients and 91 controls. MiRNA expression was examined by qPCR (STG) or small RNA sequencing (EC). Brain region-dependent differential miRNA expression was investigated in a transgenic AD mouse model using qPCR and FISH. Total RNA sequencing was used to assess differential expression of miRNA target genes. MiR-129-5p, miR-132-5p, and miR-138-5p were significantly downregulated in AD vs. controls both in STG and EC, while miR-125b-5p and miR-501-3p showed no evidence for differential expression in this dataset. In addition, miR-195-5p was significantly upregulated in EC but not STG in AD patients. The brain region-specific pattern of miR-195-5p expression was corroborated in vivo in transgenic AD mice. Total RNA sequencing identified several novel and functionally interesting target genes of these miRNAs involved in synaptic transmission (GABRB1), the immune-system response (HCFC2) or AD-associated differential methylation (SLC16A3). Using two different methods (qPCR and small RNA-seq) in two separate brain regions in 190 individuals we more than doubled the available sample size for most miRNAs tested. Differential gene expression analyses confirm the likely involvement of miR-129-5p, miR-132-5p, miR-138-5p, and miR-195-5p in AD pathogenesis and highlight several novel potentially relevant target mRNAs.

Advances in the development of new biomarkers for Alzheimer's disease

Alzheimer's disease (AD) is a complex, heterogeneous, progressive disease and is the most common type of neurodegenerative dementia. The prevalence of AD is expected to increase as the population ages, placing an additional burden on national healthcare systems. There is a large need for new diagnostic tests that can detect AD at an early stage with high specificity at relatively low cost. The development of modern analytical diagnostic tools has made it possible to determine several biomarkers of AD with high specificity, including pathogenic proteins, markers of synaptic dysfunction, and markers of inflammation in the blood. There is a considerable potential in using microRNA (miRNA) as markers of AD, and diagnostic studies based on miRNA panels suggest that AD could potentially be determined with high accuracy for individual patients. Studies of the retina with improved methods of visualization of the fundus are also showing promising results for the potential diagnosis of the disease. This review focuses on the recent developments of blood, plasma, and ocular biomarkers for the diagnosis of AD.

Noncoding RNA as Diagnostic and Prognostic Biomarkers in Cerebrovascular Disease

Noncoding RNAs (ncRNAs), such as microRNAs, long noncoding RNAs, and circular RNAs, play an important role in the pathophysiology of cerebrovascular diseases (CVDs). They are effectively detectable in body fluids, potentially suggesting new biomarkers for the early detection and prognosis of CVDs. In this review, the physiological functions of circulating ncRNAs and their potential role as diagnostic and prognostic markers in patients with cerebrovascular diseases are discussed, especially in acute ischemic stroke, subarachnoid hemorrhage, and moyamoya disease.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1-42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Exploring the Mystery of Osteoarthritis using Bioinformatics Analysis of Cartilage Tissue

BACKGROUND

Osteoarthritis (OA) is a kind of chronic disease relating to joints, which seriously affectsthe daily life activities of the elderly and can also lead to disability. However, the pathogenesis of OA is still unclear, which leads to limited treatment and the therapeutic effect far from people's expectations. This study aims to filter out key genes in the pathogenesis of OA and explore their potential role in the occurrence and development of OA.

METHODS

The dataset of GSE117999 was obtained and analyzed in order to identify the differentially expressed genes (DEGs), hub genes and key genes. We also identified potential miRNAs which may play a major role in the pathogenesis of OA, and verified their difference in OA by real-time quantitative PCR (RT-qPCR). DGldb was found to serve as an indicator to identify drugs with potential therapeutic effects on key genes and Receiver Operating Characteristic (ROC) analysis was used for identifying underlying biomarkers of OA.

RESULTS

We identified ten key genes, including MDM2, RB1, EGFR, ESR1, UBE2E3, WWP1, BCL2, OAS2, TYMS and MSH2. Then, we identified hsa-mir-3613-3p, hsa-mir-548e-5p and hsamir- 5692a to be potentially related to key genes. In addition, RT-qPCR confirmed the differential expression of identified genes in mouse cartilage with or without OA. We then identified Etoposide and Everolimus, which were potentially specific to the most key genes. Finally, we speculated that ESR1 might be a potential biomarker of OA.

CONCLUSION

In this study, potential key genes related to OA and their biological functions were identified, and their potential application value in the diagnosis and treatment of OA has been demonstrated, which will help us to improve the therapeutic effect of OA.

Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer's Disease

Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer's disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD. Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set. Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively. Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.

The promise of microRNA-based therapies in Alzheimer's disease: challenges and perspectives

Multi-pathway approaches for the treatment of complex polygenic disorders are emerging as alternatives to classical monotarget therapies and microRNAs are of particular interest in that regard. MicroRNA research has come a long way from their initial discovery to the cumulative appreciation of their regulatory potential in healthy and diseased brain. However, systematic interrogation of putative therapeutic or toxic effects of microRNAs in (models of) Alzheimer's disease is currently missing and fundamental research findings are yet to be translated into clinical applications. Here, we review the literature to summarize the knowledge on microRNA regulation in Alzheimer's pathophysiology and to critically discuss whether and to what extent these increasing insights can be exploited for the development of microRNA-based therapeutics in the clinic.

Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters

BACKGROUND

Peripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patients in need of vascular intervention without specialized intra-hospital diagnostics.

METHODS

This is a retrospective study involving patients with stable PAD (sPAD, Fontaine Class I and II, n = 38) and unstable PAD (unPAD, Fontaine Class III and IV, n = 18) in need of invasive therapeutic measures. ML algorithms such as Random Forest were utilized to evaluate a matrix consisting of multiple routinely clinically available parameters (age, complete blood count, inflammation, lipid, iron metabolism).

RESULTS

ML has enabled a generation of an Artificial Intelligence (AI) PAD score (AI-PAD) that successfully divided sPAD from unPAD patients (high AI-PAD in sPAD, low AI-PAD in unPAD, cutoff at 50 AI-PAD units). Furthermore, the probability score positively coincided with gold-standard intra-hospital mean ankle-brachial index (ABI).

CONCLUSION

AI-based tools may be promising to enable the correct identification of patients with unstable PAD by using existing clinical information, thus supplementing clinical decision making. Additional studies in larger prospective cohorts are necessary to determine the usefulness of this approach in comparison to standard diagnostic measures.

MicroRNA-532-5p upregulation protects neurological deficits after ischemic stroke through inhibition of BTB and CNC homology 1

OBJECTIVE

MicroRNA (miR)-532-5p has been reported to protect against ischemic stroke (IS), while the underlying mechanism of miR-532-5p targeting BTB and CNC homology 1 (BACH1) in IS remains unknown. Thus, we aim to detect the role of miR-532-5p in IS via targeting BACH1.

METHODS

Blood samples were collected from IS patients and healthy controls. Rat middle cerebral artery occlusion (MCAO) models were established and intracerebrally injected with altered miR-532-5p or BACH1 plasmid vectors to reveal their roles in neurological function, brain tissue pathology and inflammation in MCAO. Expression of miR-532-5p and BACH1 in patients' blood samples and rat brain tissues was assessed, and the targeting relationship between miR-532-5p and BACH1 was confirmed.

RESULTS

MiR-532-5p was downregulated and BACH1 was upregulated in IS. BACH1 was targeted by miR-532-5p. Restored miR-532-5p or inhibited BACH1 improved neurological function and inhibited inflammation and apoptosis in MCAO rats. On the contrary, miR-532-5p reduction or BACH1 overexpression had totally opposite effects on MCAO rats. The protective role of miR-532-5p for MCAO rats was reversed by upregulated BACH1.

CONCLUSION

MiR-532-5p upregulation protects against neurological deficits after IS through inhibition of BACH1.

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs.

miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale

Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.

Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer's disease by meta-analysis and adaptive boosting ensemble learning

BACKGROUND

Blood circulating microRNAs that are specific for Alzheimer's disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method.

METHODS

Studies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA-AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions.

RESULTS

A systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected.

CONCLUSION

This study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.

Bioinformatics Analysis of Autophagy-related lncRNAs in Esophageal Carcinoma

BACKGROUND

Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules and to identify gene co-expression networks associated with autophagy in ESCA.

METHODS

We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis.

RESULTS

The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed.

CONCLUSION

These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

Objective

Autophagy and long noncoding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.

Methods

We downloaded RNA sequencing data and clinical information of melanoma from the Cancer Genome Atlas. The coexpression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate Cox regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.

Results

According to the results of the univariate Cox analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate Cox analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p < 0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.

Conclusion

The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNA risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

Targeting microRNAs to Regulate the Integrity of the Blood-Brain Barrier

The blood-brain barrier (BBB) is a highly specialized neurovascular unit that protects the brain from potentially harmful substances. In addition, the BBB also engages in the exchange of essential nutrients between the vasculature and brain parenchyma, which is critical for brain homeostasis. Brain diseases, including neurological disorders and cerebrovascular diseases, are often associated with disrupted BBB integrity, evidenced by increased permeability. Therefore, defining the mechanisms underlying the regulation of BBB integrity is crucial for the development of novel therapeutics targeting brain diseases. MicroRNAs (miRNA), a type of small non-coding RNAs, are emerging as an important regulator of BBB integrity. Here we review recent developments related to the role of miRNAs in regulating BBB integrity.

A Sex Perspective in Neurodegenerative Diseases: microRNAs as Possible Peripheral Biomarkers

Sex is a significant variable in the prevalence and incidence of neurological disorders. Sex differences exist in neurodegenerative disorders (NDs), where sex dimorphisms play important roles in the development and progression of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the last few years, some sex specific biomarkers for the identification of NDs have been described and recent studies have suggested that microRNA (miRNA) could be included among these, as influenced by the hormonal and genetic background. Failing to consider the possible differences between males and females in miRNA evaluation could introduce a sex bias in studies by not considering some of these sex-related biomarkers. In this review, we recapitulate what is known about the sex-specific differences in peripheral miRNA levels in neurodegenerative diseases. Several studies have reported sex-linked disparities, and from the literature analysis miR-206 particularly has been shown to have a sex-specific involvement. Hopefully, in the near future, patient stratification will provide important additional clues in diagnosis, prognosis, and tailoring of the best therapeutic approaches for each patient. Sex-specific biomarkers, such as miRNAs, could represent a useful tool for characterizing subgroups of patients.

Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression

Noncoding RNAs have diagnostic and prognostic importance in Parkinson's disease (PD). We studied circulating small noncoding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson's Progression Markers Initiative (PPMI) and Luxembourg Parkinson's Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing PD.

CoolMPS: evaluation of antibody labeling based massively parallel non-coding RNA sequencing

Results of massive parallel sequencing-by-synthesis vary depending on the sequencing approach. CoolMPS™ is a new sequencing chemistry that incorporates bases by labeled antibodies. To evaluate the performance, we sequenced 240 human non-coding RNA samples (dementia patients and controls) with and without CoolMPS. The Q30 value as indicator of the per base sequencing quality increased from 91.8 to 94%. The higher quality was reached across the whole read length. Likewise, the percentage of reads mapping to the human genome increased from 84.9 to 86.2%. For both technologies, we computed similar distributions between different RNA classes (miRNA, piRNA, tRNA, snoRNA and yRNA) and within the classes. While standard sequencing-by-synthesis allowed to recover more annotated miRNAs, CoolMPS yielded more novel miRNAs. The correlation between the two methods was 0.97. Evaluating the diagnostic performance, we observed lower minimal P-values for CoolMPS (adjusted P-value of 0.0006 versus 0.0004) and larger effect sizes (Cohen's d of 0.878 versus 0.9). Validating 19 miRNAs resulted in a correlation of 0.852 between CoolMPS and reverse transcriptase-quantitative polymerase chain reaction. Comparison to data generated with Illumina technology confirmed a known shift in the overall RNA composition. With CoolMPS we evaluated a novel sequencing-by-synthesis technology showing high performance for the analysis of non-coding RNAs.

MicroRNA-138 Overexpression Alters Aβ42 Levels and Behavior in Wildtype Mice

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by changes in cognitive and behavioral functions. With the exception or rare mutations in PSEN and APP genes causing early-onset autosomal dominant AD (EOADAD), little is known about the genetic factors that underlie the vast majority (>95%) of early onset AD (EOAD) cases. We have previously identified copy number variations (CNVs) in microRNA genes in patients with EOAD, including a duplication of the MIR-138-2 gene. Overexpression of miR-138 in cultured cells increased Aβ production and tau phosphorylation, similar to what is seen in AD brain. In this study, we sought to determine if miR-138 overexpression could recapitulate certain features of disease in vivo in non-transgenic mice. A mild overexpression of pre-miR-138 in the brain of C57BL/6J wildtype mice altered learning and memory in a novel object recognition test and in the Barnes Maze. Increased levels of anxiety were also observed in the open-field test. MiR-138 upregulation in vivo caused an increase in endogenous Aβ42 production as well as changes in synaptic and inflammation markers. Tau expression was significantly lower with no overt effects on phosphorylation. We finally observed that Sirt1, a direct target of miR-138 involved in Aβ production, learning and memory as well as anxiety, is decreased following miR-138 overexpression. In sum, this study further strengthens a role for increased gene dosage of MIR-138-2 gene in modulating AD risk, possibly by acting on different biological pathways. Further studies will be required to better understand the role of CNVs in microRNA genes in AD and related neurodegenerative disorders.

Encyclopedia of tools for the analysis of miRNA isoforms

RNA sequencing data sets rapidly increase in quantity. For microRNAs (miRNAs), frequently dozens to hundreds of billion reads are generated per study. The quantification of annotated miRNAs and the prediction of new miRNAs are leading computational tasks. Now, the increased depth of coverage allows to gain deeper insights into the variability of miRNAs. The analysis of isoforms of miRNAs (isomiRs) is a trending topic, and a range of computational tools for the analysis of isomiRs has been developed. We provide an overview on 27 available computational solutions for the analysis of isomiRs. These include both stand-alone programs (17 tools) and web-based solutions (10 tools) and span a publication time range from 2010 to 2020. Seven of the tools were published in 2019 and 2020, confirming the rising importance of the topic. While most of the analyzed tools work for a broad range of organisms or are completely independent of a reference organism, several tools have been tailored for the analysis of human miRNA data or for plants. While 14 of the tools are general analysis tools of miRNAs, and isomiR analysis is one of their features, the remaining 13 tools have specifically been developed for isomiR analysis. A direct comparison on 20 deep sequencing data sets for selected tools provides insights into the heterogeneity of results. With our work, we provide users a comprehensive overview on the landscape of isomiR analysis tools and in that support the selection of the most appropriate tool for their respective research task.

miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson’s disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

A hypothesis-generating scoping review of miRs identified in both multiple sclerosis and dementia, their protein targets, and miR signaling pathways

Cognitive impairment (CI) is a frequent complication affecting people with multiple sclerosis (MS). The causes of CI in MS are not fully understood. Besides MRI measures, few other biomarkers exist to help us predict the development of CI and understand its biology. MicroRNAs (miRs) are relatively stable, non-coding RNA molecules about 22 nucleotides in length that can serve as biomarkers and possible therapeutic targets in several autoimmune and neurodegenerative diseases, including the dementias. In this review, we identify dysregulated miRs in MS that overlap with dysregulated miRs in cognitive disorders and dementia and explore how these overlapping miRs play a role in CI in MS. MiR-15, miR-21, miR-128, miR-132, miR-138, miR-142, miR-146a, miR-155, miR-181, miR-572, and let-7 are known to contribute to various forms of dementia and show abnormal expression in MS. These overlapping miRs are involved in pathways related to apoptosis, neuroinflammation, glutamate toxicity, astrocyte activation, microglial burst activity, synaptic dysfunction, and remyelination. The mechanisms of action suggest that these miRs may be related to CI in MS. From our review, we also delineated miRs that could be neuroprotective in MS, namely miR-23a, miR-219, miR-214, and miR-22. Further studies can help clarify if these miRs are responsible for CI in MS, leading to potential therapeutic targets.

miRSwitch: detecting microRNA arm shift and switch events

Arm selection, the preferential expression of a 3′ or 5′ mature microRNA (miRNA), is a highly dynamic and tissue-specific process. Time-dependent expression shifts or switches between the arms are also relevant for human diseases. We present miRSwitch, a web server to facilitate the analysis and interpretation of arm selection events. Our species-independent tool evaluates pre-processed small non-coding RNA sequencing (sncRNA-seq) data, i.e. expression matrices or output files from miRNA quantification tools (miRDeep2, miRMaster, sRNAbench). miRSwitch highlights potential changes in the distribution of mature miRNAs from the same precursor. Group comparisons from one or several user-provided annotations (e.g. disease states) are possible. Results can be dynamically adjusted by choosing from a continuous range of highly specific to very sensitive parameters. Users can compare potential arm shifts in the provided data to a human reference map of pre-computed arm shift frequencies. We created this map from 46 tissues and 30 521 samples. As case studies we present novel arm shift information in a Alzheimer’s disease biomarker data set and from a comparison of tissues in Homo sapiens and Mus musculus. In summary, miRSwitch offers a broad range of customized arm switch analyses along with comprehensive visualizations, and is freely available at: https://www.ccb.uni-saarland.de/mirswitch/.

A blood miRNA signature associates with sporadic Creutzfeldt-Jakob disease diagnosis

Sporadic Creutzfeldt-Jakob disease (sCJD) presents as a rapidly progressive dementia which is usually fatal within six months. No clinical blood tests are available for diagnosis or disease monitoring. Here, we profile blood microRNA (miRNA) expression in sCJD. Sequencing of 57 sCJD patients, and healthy controls reveals differential expression of hsa-let-7i-5p, hsa-miR-16-5p, hsa-miR-93-5p and hsa-miR-106b-3p. Downregulation of hsa-let-7i-5p, hsa-miR-16-5p and hsa-miR-93-5p replicates in an independent cohort using quantitative PCR, with concomitant upregulation of four mRNA targets. Absence of correlation in cross-sectional analysis with clinical phenotypes parallels the lack of association between rate of decline in miRNA expression, and rate of disease progression in a longitudinal cohort of samples from 21 patients. Finally, the miRNA signature shows a high level of accuracy in discriminating sCJD from Alzheimer’s disease. These findings highlight molecular alterations in the periphery in sCJD which provide information about differential diagnosis and improve mechanistic understanding of human prion diseases.

Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive dementia. No clinical blood tests are available for diagnosis. The authors identified three miRNAs in whole-blood that are downregulated in sCJD patients, and discriminate sCJD from Alzheimer’s disease patients and healthy controls.

Circulating MicroRNA Profile Associated with Obstructive Sleep Apnea in Alzheimer's Disease

The diagnosis of obstructive sleep apnea (OSA) in Alzheimer's disease (AD) by polysomnography (PSG) is challenging due to the required collaboration of the patients. In addition, screening questionnaires have demonstrated limited usefulness with this subpopulation. Considering this, we investigated the circulating microRNA (miRNA) profile associated with OSA in AD patients. This study included a carefully selected cohort of females with mild-moderate AD confirmed by biological evaluation (n = 29). The individuals were submitted to one-night PSG to diagnose OSA (apnea-hypopnea index ≥ 15/h) and the blood was collected in the following morning. The plasma miRNA profile was evaluated using RT-qPCR. The patients had a mean (SD) age of 75.8 (5.99) years old with a body mass index of 28.6 (3.83) kg m^-2. We observed a subset of 15 miRNAs differentially expressed between OSA and non-OSA patients, of which 10 were significantly correlated with the severity of OSA. Based on this, we built a prediction model that generated an AUC (95% CI) of 0.95 (0.88-1.00) including 5 of the differentially expressed miRNAs that correlated with OSA severity: miR-26a-5p, miR-30a-3p, miR-374a-5p, miR-377-3p, and miR-545-3p. Our preliminary results suggest a plasma miRNA signature associated with the presence of OSA in AD patients. Further studies will be necessary to validate these findings.

In Vivo Chimeric Alzheimer's Disease Modeling of Apolipoprotein E4 Toxicity in Human Neurons

Despite its clear impact on Alzheimer's disease (AD) risk, apolipoprotein (apo) E4's contributions to AD etiology remain poorly understood. Progress in answering this and other questions in AD research has been limited by an inability to model human-specific phenotypes in an in vivo environment. Here we transplant human induced pluripotent stem cell (hiPSC)-derived neurons carrying normal apoE3 or pathogenic apoE4 into human apoE3 or apoE4 knockin mouse hippocampi, enabling us to disentangle the effects of apoE4 produced in human neurons and in the brain environment. Using single-nucleus RNA sequencing (snRNA-seq), we identify key transcriptional changes specific to human neuron subtypes in response to endogenous or exogenous apoE4. We also find that Aβ from transplanted human neurons forms plaque-like aggregates, with differences in localization and interaction with microglia depending on the transplant and host apoE genotype. These findings highlight the power of in vivo chimeric disease modeling for studying AD.

The Prediction of miRNAs in SARS-CoV-2 Genomes: hsa-miR Databases Identify 7 Key miRs Linked to Host Responses and Virus Pathogenicity-Related KEGG Pathways Significant for Comorbidities

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the betacoronavirus family, which causes COVID-19 disease. SARS-CoV-2 pathogenicity in humans leads to increased mortality rates due to alterations of significant pathways, including some resulting in exacerbated inflammatory responses linked to the “cytokine storm” and extensive lung pathology, as well as being linked to a number of comorbidities. Our current study compared five SARS-CoV-2 sequences from different geographical regions to those from SARS, MERS and two cold viruses, OC43 and 229E, to identify the presence of miR-like sequences. We identified seven key miRs, which highlight considerable differences between the SARS-CoV-2 sequences, compared with the other viruses. The level of conservation between the five SARS-CoV-2 sequences was identical but poor compared with the other sequences, with SARS showing the highest degree of conservation. This decrease in similarity could result in reduced levels of transcriptional control, as well as a change in the physiological effect of the virus and associated host-pathogen responses. MERS and the milder symptom viruses showed greater differences and even significant sequence gaps. This divergence away from the SARS-CoV-2 sequences broadly mirrors the phylogenetic relationships obtained from the whole-genome alignments. Therefore, patterns of mutation, occurring during sequence divergence from the longer established human viruses to the more recent ones, may have led to the emergence of sequence motifs that can be related directly to the pathogenicity of SARS-CoV-2. Importantly, we identified 7 key-microRNAs (miRs 8066, 5197, 3611, 3934-3p, 1307-3p, 3691-3p, 1468-5p) with significant links to KEGG pathways linked to viral pathogenicity and host responses. According to Bioproject data (PRJNA615032), SARS-CoV-2 mediated transcriptomic alterations were similar to the target pathways of the selected 7 miRs identified in our study. This mechanism could have considerable significance in determining the symptom spectrum of future potential pandemics. KEGG pathway analysis revealed a number of critical pathways linked to the seven identified miRs that may provide insight into the interplay between the virus and comorbidities. Based on our reported findings, miRNAs may constitute potential and effective therapeutic approaches in COVID-19 and its pathological consequences.

DynaVenn: web-based computation of the most significant overlap between ordered sets

BACKGROUND

In many research disciplines, ordered lists are compared. One example is to compare a subset of all significant genes or proteins in a primary study to those in a replication study. Often, the top of the lists are compared using Venn diagrams, ore more precisely Euler diagrams (set diagrams showing logical relations between a finite collection of different sets). If different cohort sizes, different techniques or algorithms for evaluation were applied, a direct comparison of significant genes with a fixed threshold can however be misleading and approaches comparing lists would be more appropriate.

RESULTS

We developed DynaVenn, a web-based tool that incrementally creates all possible subsets from two or three ordered lists and computes for each combination a p-value for the overlap. Respectively, dynamic Venn diagrams are generated as graphical representations. Additionally an animation is generated showing how the most significant overlap is reached by backtracking. We demonstrate the improved performance of DynaVenn over an arbitrary cut-off approach on an Alzheimer's Disease biomarker set.

CONCLUSION

DynaVenn combines the calculation of the most significant overlap of different cohorts with an intuitive visualization of the results. It is freely available as a web service at http://www.ccb.uni-saarland.de/dynavenn.