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

Harnessing the potential of long non-coding RNAs in the pathophysiology of Alzheimer's disease

Alzheimer's disease (AD), a diverse neurodegenerative disease, is the leading cause of dementia, accounting for 60-80 % of all cases. The pathophysiology of Alzheimer's disease is unknown, and there is no cure at this time. Recent developments in transcriptome-wide profiling have led to the identification of a number of non-coding RNAs (ncRNAs). Among these, long non-coding RNAs (lncRNAs)-long transcripts that don't seem to be able to code for proteins-have drawn attention because they function as regulatory agents in a variety of biological processes. Recent research suggests that lncRNAs play a role in the pathogenesis of Alzheimer's disease by modulating tau hyperphosphorylation, amyloid production, synaptic impairment, neuroinflammation, mitochondrial dysfunction, and oxidative stress, though their precise effects on the disorder are unknown. The biology and modes of action of the best-characterized lncRNAs in AD will be outlined here, with an emphasis on their possible involvement in the pathophysiology of the disease. As lncRNAs may offer prospective prognostic/diagnostic biomarkers and therapeutic targets for the treatment of AD, a greater comprehension of the molecular processes and the intricate network of interactions in which they are implicated could pave the way for future research.

Deciphering Depression: Epigenetic Mechanisms and Treatment Strategies

Depression, a significant mental health disorder, is under intense research scrutiny to uncover its molecular foundations. Epigenetics, which focuses on controlling gene expression without altering DNA sequences, offers promising avenues for innovative treatment. This review explores the pivotal role of epigenetics in depression, emphasizing two key aspects: (I) identifying epigenetic targets for new antidepressants and (II) using personalized medicine based on distinct epigenetic profiles, highlighting potential epigenetic focal points such as DNA methylation, histone structure alterations, and non-coding RNA molecules such as miRNAs. Variations in DNA methylation in individuals with depression provide opportunities to target genes that are associated with neuroplasticity and synaptic activity. Aberrant histone acetylation may indicate that antidepressant strategies involve enzyme modifications. Modulating miRNA levels can reshape depression-linked gene expression. The second section discusses personalized medicine based on epigenetic profiles. Analyzing these patterns could identify biomarkers associated with treatment response and susceptibility to depression, facilitating tailored treatments and proactive mental health care. Addressing ethical concerns regarding epigenetic information, such as privacy and stigmatization, is crucial in understanding the biological basis of depression. Therefore, researchers must consider these issues when examining the role of epigenetics in mental health disorders. The importance of epigenetics in depression is a critical aspect of modern medical research. These findings hold great potential for novel antidepressant medications and personalized treatments, which would significantly improve patient outcomes, and transform psychiatry. As research progresses, it is expected to uncover more complex aspects of epigenetic processes associated with depression, enhance our comprehension, and increase the effectiveness of therapies.

Machine and deep learning algorithms for classifying different types of dementia: A literature review

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

miR-24-3p and miR-484 are potential biomarkers for neurodegeneration in multiple sclerosis

Multiple sclerosis (MS) is a complex, neurodegenerative chronic disorder. Circulating diagnostic biomarkers for MS have remained elusive, and those proposed so far have limited sensitivity and specificity to MS. Plasma-circulating microRNAs (miRNAs) have advantageous biochemical and physiological attributes that can be utilized in clinical testing and disease monitoring. MS miRNA expression microarray datasets analysis resulted in four candidate miRNAs that were assessed for their expression in a separate MS case-control study. Only miR-24-3p was downregulated in all MS patients compared to healthy controls. MiR-484 was significantly upregulated in relapsing-remitting MS (RRMS) patients compared to healthy controls. Mir-146-5p and miR-484 were significantly downregulated in secondary-progressive MS (SPMS) compared to RRMS. MiR-484 downregulation was associated with worsening disability and increased lipocalin-2 levels. Mir-342-3p and miR-24-3p downregulation were associated with increased semaphorin-3A levels in MS and RRMS patients. In conclusion, mir-24-3p downregulation is diagnostic of MS, and mir-484 upregulation and downregulation are potential biomarkers for RRMS and SPMS conversion, respectively. The differential expression of miR-146a-3p in MS subtypes suggests its potential as an SPMS transition biomarker. The association of downregulated mir-24-3p and mir-484 with increased neurodegeneration biomarkers suggests they play a role in MS pathogenesis and neurodegeneration.

LncRNA ENST00000440246.1 Promotes Alzheimer's Disease Progression by Targeting PP2A

Alzheimer's disease (AD) is an extremely prevalent neurodegenerative disease. Long noncoding RNAs (lncRNAs) play pivotal roles in the regulation of AD. However, the function of most lncRNAs in AD remains to be elucidated. In this study, the effects of lncRNA ENST00000440246.1 on the biological characteristics of AD were explored. Differentially expressed lncRNAs in AD were identified through bioinformatics analysis and peripheral blood from thirty AD patients was collected to verify the expression of these lncRNAs by quantitative real-time polymerase chain reaction (RT-qPCR). The correlations between lncRNAs and the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA) were assessed by Pearson's correlation analysis. Immunofluorescence (IF), Cell Counting Kit-8 (CCK-8) and flow cytometry assays were conducted to evaluate the biological effect of ENST00000440246.1 and protein phosphatase 2 A (PP2A) in SK-N-SH cells. Gene expression at the protein and mRNA levels was analyzed by Western blotting and RT-qPCR. The interaction between PP2A and ENST00000440246.1 was confirmed by IntaRNA and RNA pulldown assays. ENST00000440246.1 was upregulated and significantly negatively correlated with the MMSE and MoCA scores and the overexpression of ENST00000440246.1 inhibited cell proliferation and facilitated apoptosis and Aβ expression in SK-N-SH cells. Mechanistically, ENST00000440246.1 targeted PP2A and regulated AD-related gene expression. The silencing of ENST00000440246.1 had the opposite effect. Furthermore, PP2A overexpression reversed the influence of ENST00000440246.1 overexpression in SK-N-SH cells. In conclusion, ENST00000440246.1 could promote AD progression by targeting PP2A, which indicates that ENST00000440246.1 has the potential to be a diagnostic target in AD.

Techniques for investigating lncRNA transcript functions in neurodevelopment

Long noncoding RNAs (lncRNAs) are sequences of 200 nucleotides or more that are transcribed from a large portion of the mammalian genome. While hypothesized to have a variety of biological roles, many lncRNAs remain largely functionally uncharacterized due to unique challenges associated with their investigation. For example, some lncRNAs overlap with other genomic loci, are expressed in a cell-type-specific manner, and/or are differentially processed at the post-transcriptional level. The mammalian CNS contains a vast diversity of lncRNAs, and lncRNAs are highly abundant in the mammalian brain. However, interrogating lncRNA function in models of the CNS, particularly in vivo, can be complex and challenging. Here we review the breadth of methods used to investigate lncRNAs in the CNS, their merits, and the understanding they can provide with respect to neurodevelopment and pathophysiology. We discuss remaining challenges in the field and provide recommendations to assay lncRNAs based on current methods.

Patient-derived neuron model: Capturing age-dependent adult-onset degenerative pathology in Huntington's disease

MicroRNAs play a crucial role in directly reprogramming (converting) human fibroblasts into neurons. Specifically, miR-9/9* and miR-124 (miR-9/9*-124) display neurogenic and cell fate-switching activities when ectopically expressed in human fibroblasts by erasing fibroblast identity and inducing a pan-neuronal state. These converted neurons maintain the biological age of the starting fibroblasts and thus provide a human neuron-based platform to study cellular properties in aged neurons and model adult-onset neurodegenerative disorders using patient-derived cells. Furthermore, the expression of striatal-enriched transcription factors in conjunction with miR-9/9*-124 guides the identity of medium spiny neurons (MSNs), the primary targets in Huntington's disease (HD). Converted MSNs from HD patient-derived fibroblasts (HD-MSNs) can replicate HD-related phenotypes including neurodegeneration associated with age-related declines in critical cellular functions such as autophagy. Here, we review the role of microRNAs in the direct conversion of patient-derived fibroblasts into MSNs and the practical application of converted HD-MSNs as a model for studying adult-onset neuropathology in HD. We provide valuable insights into age-related, cell-intrinsic changes contributing to neurodegeneration in HD-MSNs. Ultimately, we address a comprehensive understanding of the complex molecular landscape underlying HD pathology, offering potential avenues for therapeutic application.

Abnormal expression of long non-coding RNAs RMRP, CTC-487M23.5, and DGCR5 in the peripheral blood of patients with Bipolar disorder

Long non-coding RNAs (lncRNAs) have been recently considered as one of the regulatory mechanisms of the nervous system. Hence, lncRNAs may be considered diagnostic biomarkers for bipolar disorder (BD). We aimed to investigate the expression of RMRP, CTC-487M23.5, and DGCR5 lncRNAs in bipolar patients. The levels of these three lncRNAs were measured in peripheral blood mononuclear cells (PBMCs) of 50 BD patients and 50 healthy subjects by real-time PCR. Moreover, we performed a ROC curve analysis between the gene expression and some clinical features of BD patients. Significant upregulation of RMRP and CTC-487M23.5 and no significant change in levels of DGCR5 was observed in BD individuals compared with controls. Also, we found upregulation of RMRP and downregulation of CTC-487M23.5 and DGCR5 in females with BD. The areas under the ROC curve (AUC) for RMRP and CTC-487M23.5 lncRNAs were 0.80 and 0.61, respectively. There was no significant correlation between the expression of these three lncRNAs and clinical features in PBMCs of BD patients. These results suggest a role for RMRP and CTC-487M23.5 in the pathogenesis of bipolar disorder. Moreover, the peripheral expression of these two lncRNAs might be beneficial as potential biomarkers for BD.

Role of long non-coding RNAs in the pathophysiology of Alzheimer's disease and other dementias

Dementia is the term used to describe a group of cognitive disorders characterized by a decline in memory, thinking, and reasoning abilities that interfere with daily life activities. Examples of dementia include Alzheimer's Disease (AD), Frontotemporal dementia (FTD), Amyotrophic lateral sclerosis (ALS), Vascular dementia (VaD) and Progressive supranuclear palsy (PSP). AD is the most common form of dementia. The hallmark pathology of AD includes formation of β-amyloid (Aβ) oligomers and tau hyperphosphorylation in the brain, which induces neuroinflammation, oxidative stress, synaptic dysfunction, and neuronal apoptosis. Emerging studies have associated long non-coding RNAs (lncRNAs) with the pathogenesis and progression of the neurodegenerative diseases. LncRNAs are defined as RNAs longer than 200 nucleotides that lack the ability to encode functional proteins. LncRNAs play crucial roles in numerous biological functions for their ability to interact with different molecules, such as proteins and microRNAs, and subsequently regulate the expression of their target genes at transcriptional and post-transcriptional levels. In this narrative review, we report the function and mechanisms of action of lncRNAs found to be deregulated in different types of dementia, with the focus on AD. Finally, we discuss the emerging role of lncRNAs as biomarkers of dementias.

The pyroptosis mediated biomarker pattern: an emerging diagnostic approach for Parkinson's disease

BACKGROUND

Parkinson's disease (PD) affects 1% of people over 60, and long-term levodopa treatment can cause side effects. Early diagnosis is of great significance in slowing down the pathological process of PD. Multiple pieces of evidence showed that non-coding RNAs (ncRNAs) could participate in the progression of PD pathology. Pyroptosis is known to be regulated by ncRNAs as a key pathological feature of PD. Therefore, evaluating ncRNAs and pyroptosis-related proteins in serum could be worthy biomarkers for early diagnosis of PD.

METHODS

NcRNAs and pyroptosis/inflammation mRNA levels were measured with reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Luciferase assays were performed to confirm GSDME as a target of miR-675-5p and HMGB1 as a target of miR-1247-5p. In the serum of healthy controls (n = 106) and PD patients (n = 104), RT-qPCR was utilized to assess miR-675-5p, miR-1247-5p, and two related ncRNAs (circSLC8A1and lncH19) levels. The enzyme-linked immunosorbent assay measured serum levels of pyroptosis-related proteins in controls (n = 54) and PD patients (n = 70).

RESULTS

Our data demonstrated that miR-675-5p and miR-1247-5p significantly changed in PD neuron and animal models. Overexpressed miR-675-5p or downregulated miR-1247-5p could regulate pyroptosis and inflammation in PD neuron models. Using the random forest algorithm, we constructed a classifier based on PD neuron-pyroptosis pathology (four ncRNAs and six proteins) having better predictive power than single biomarkers (AUC = 92%). Additionally, we verified the performance of the classifier in early-stage PD patients (AUC ≥ 88%).

CONCLUSION

Serum pyroptosis-related ncRNAs and proteins could serve as reliable, inexpensive, and non-invasive diagnostic biomarkers for PD.

LIMITATIONS

All participants were from the same region. Additionally, longitudinal studies in the aged population are required to explore the practical application value of the classifier.

The Profiling of 179 miRNA Expression in Serum from Limb Girdle Muscular Dystrophy Patients and Healthy Controls

Limb girdle muscular dystrophies (LGMDs) are a group of genetically inherited neuromuscular diseases with a very variable clinical presentation and overlapping traits. Over the last few years there has been an increasing interest in the use of non-invasive circulating biomarkers to monitor disease progression and to evaluate the efficacy of therapeutic approaches. Our aim was to identify the miRNA signature with potential value for LGMD patient screening and stratification. Using miRCURY LNA miRNA qPCR Serum/Plasma Panel, we analyzed 179 miRNAs from 16 patients, divided in four pools based on their genetic diagnosis, and from healthy controls. The miRNAs analysis showed a total of 107 dysregulated miRNAs in LGMD patients when compared to the healthy controls. After filtering via skeletal tissue expression and gene/pathways target analysis, the number of dysregulated miRNAs drastically reduced. Six selected miRNAs-let-7f-5p (in LGMDR1), miR-20a-5p (in LGMDR2), miR-130b-5p, miR-378a-5p (both in LGMDR3), miR-376c-3p and miR-382-5p (both in LGMDR4)-whose expression was significantly lower compared to controls in the different LGMD pools, were further investigated. The bioinformatic analysis of the target genes in each selected miRNA revealed ECM-receptor interaction and TGF-beta signaling as the most involved pathways. The correlation analysis showed a good correlation of let-7f-5p with fibrosis and with the cross sectional area of type I and type II fibers, while miR-130b-5p showed a good correlation with the age of onset of the disease. The receiver operating characteristic curves showed how single miRNAs were able to discriminate a specific group of LGMD patients and how the combination of six miRNAs was able to discriminate LGMD patients from controls.

Competing endogenous RNAs in human astrocytes: crosstalk and interacting networks in response to lipotoxicity

Neurodegenerative diseases (NDs) are characterized by a progressive deterioration of neuronal function, leading to motor and cognitive damage in patients. Astrocytes are essential for maintaining brain homeostasis, and their functional impairment is increasingly recognized as central to the etiology of various NDs. Such impairment can be induced by toxic insults with palmitic acid (PA), a common fatty acid, that disrupts autophagy, increases reactive oxygen species, and triggers inflammation. Although the effects of PA on astrocytes have been addressed, most aspects of the dynamics of this fatty acid remain unknown. Additionally, there is still no model that satisfactorily explains how astroglia goes from being neuroprotective to neurotoxic. Current incomplete knowledge needs to be improved by the growing field of non-coding RNAs (ncRNAs), which is proven to be related to NDs, where the complexity of the interactions among these molecules and how they control other RNA expressions need to be addressed. In the present study, we present an extensive competing endogenous RNA (ceRNA) network using transcriptomic data from normal human astrocyte (NHA) cells exposed to PA lipotoxic conditions and experimentally validated data on ncRNA interaction. The obtained network contains 7 lncRNA transcripts, 38 miRNAs, and 239 mRNAs that showed enrichment in ND-related processes, such as fatty acid metabolism and biosynthesis, FoxO and TGF-β signaling pathways, prion diseases, apoptosis, and immune-related pathways. In addition, the transcriptomic profile was used to propose 22 potential key controllers lncRNA/miRNA/mRNA axes in ND mechanisms. The relevance of five of these axes was corroborated by the miRNA expression data obtained in other studies. MEG3 (ENST00000398461)/hsa-let-7d-5p/ATF6B axis showed importance in Parkinson's and late Alzheimer's diseases, while AC092687.3/hsa-let-7e-5p/[SREBF2, FNIP1, PMAIP1] and SDCBP2-AS1 (ENST00000446423)/hsa-miR-101-3p/MAPK6 axes are probably related to Alzheimer's disease development and pathology. The presented network and axes will help to understand the PA-induced mechanisms in astrocytes, leading to protection or injury in the CNS under lipotoxic conditions as part of the intricated cellular regulation influencing the pathology of different NDs. Furthermore, the five corroborated axes could be considered study targets for new pharmacologic treatments or as possible diagnostic molecules, contributing to improving the quality of life of millions worldwide.

Inflammatory diseases: Function of LncRNAs in their emergence and the role of mesenchymal stem cell secretome in their treatment

One of the best treatments for inflammatory diseases such as COVID-19, respiratory diseases and brain diseases is treatment with stem cells. Here we investigate the effect of stem cell therapy in the treatment of brain diseases.Preclinical studies have shown promising results, including improved functional recovery and tissue repair in animal models of neurodegenerative diseases, strokes,and traumatic brain injuries. However,ethical implications, safety concerns, and regulatory frameworks necessitate thorough evaluation before transitioning to clinical applications. Additionally, the complex nature of the brain and its intricate cellular environment present unique obstacles that must be overcome to ensure the successful integration and functionality of genetically engineered MSCs. The careful navigation of this path will determine whether the application of genetically engineered MSCs in brain tissue regeneration ultimately lives up to the hype surrounding it.

The Role of Hydrogen Sulfide (H2S) in Epigenetic Regulation of Neurodegenerative Diseases: A Systematic Review

This review explores the emerging role of hydrogen sulfide (H2S) in modulating epigenetic mechanisms involved in neurodegenerative diseases. Accumulating evidence has begun to elucidate the multifaceted ways in which H2S influences the epigenetic landscape and, subsequently, the progression of various neurodegenerative disorders, including Alzheimer's, Parkinson's, and Huntington's disease. H2S can modulate key components of the epigenetic machinery, such as DNA methylation, histone modifications, and non-coding RNAs, impacting gene expression and cellular functions relevant to neuronal survival, inflammation, and synaptic plasticity. We synthesize recent research that positions H2S as an essential player within this intricate network, with the potential to open new therapeutic avenues for these currently incurable conditions. Despite significant progress, there remains a considerable gap in our understanding of the precise molecular mechanisms and the potential therapeutic implications of modulating H2S levels or its downstream targets. We conclude by identifying future directions for research aimed at exploiting the therapeutic potential of H2S in neurodegenerative diseases.

Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach

The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics. This study aimed to use machine-learning techniques of expressed genes in patients with AD to identify potential biomarkers that can be used for future therapy. The dataset is accessed from the Gene Expression Omnibus (GEO) database (Accession Number: GSE36980). The subgroups (AD blood samples from frontal, hippocampal, and temporal regions) are individually investigated against non-AD models. Prioritized gene cluster analyses are conducted with the STRING database. The candidate gene biomarkers were trained with various supervised machine-learning (ML) classification algorithms. The interpretation of the model prediction is perpetrated with explainable artificial intelligence (AI) techniques. This experiment revealed 34, 60, and 28 genes as target biomarkers of AD mapped from the frontal, hippocampal, and temporal regions. It is identified ORAI2 as a shared biomarker in all three areas strongly associated with AD's progression. The pathway analysis showed that STIM1 and TRPC3 are strongly associated with ORAI2. We found three hub genes, TPI1, STIM1, and TRPC3, in the network of the ORAI2 gene that might be involved in the molecular pathogenesis of AD. Naive Bayes classified the samples of different groups by fivefold cross-validation with 100% accuracy. AI and ML are promising tools in identifying disease-associated genes that will advance the field of targeted therapeutics against genetic diseases.

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.

Mechanistic insights into dietary (poly)phenols and vascular dysfunction-related diseases using multi-omics and integrative approaches: Machine learning as a next challenge in nutrition research

Dietary (poly)phenols have been extensively studied for their vasculoprotective effects and consequently their role in preventing or delaying onsets of cardiovascular and metabolic diseases. Even though early studies have ascribed the vasculoprotective properties of (poly)phenols primarily on their putative free radical scavenging properties, recent data indicate that in biological systems, (poly)phenols act primarily through genomic and epigenomic mechanisms. The molecular mechanisms underlying their health properties are still not well identified, mainly due to the use of physiologically non-relevant conditions (native molecules or extracts at high concentrations, rather than circulating metabolites), but also due to the use of targeted genomic approaches aiming to evaluate the effect only on few specific genes, thus preventing to decipher detailed molecular mechanisms involved. The use of state-of-the-art untargeted analytical methods represents a significant breakthrough in nutrigenomics, as these methods enable detailed insights into the effects at each specific omics level. Moreover, the implementation of multi-omics approaches allows integration of different levels of regulation of cellular functions, to obtain a comprehensive picture of the molecular mechanisms of action of (poly)phenols. In combination with bioinformatics and the methods of machine learning, multi-omics has potential to make a huge contribution to the nutrition science. The aim of this review is to provide an overview of the use of the omics, multi-omics, and integrative approaches in studying the vasculoprotective properties of dietary (poly)phenols and address the potentials for use of the machine learning in nutrigenomics.

MicroRNAs and long non-coding RNAs in pancreatic cancer: From epigenetics to potential clinical applications

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two relevant classes of non-coding RNAs (ncRNAs) that play a pivotal role in a number of molecular processes through different epigenetic regulatory mechanisms of gene expression. As a matter of fact, the altered expression of these types of RNAs leads to the development and progression of a varied range of multifactorial human diseases. Several recent reports elucidated that miRNA and lncRNAs have been implicated in pancreatic cancer (PC). For instance, dysregulation of such ncRNAs has been found to be associated with chemoresistance, apoptosis, autophagy, cell differentiation, tumor suppression, tumor growth, cancer cell proliferation, migration, and invasion in PC. Moreover, several aberrantly expressed miRNAs and lncRNAs have the potential to be used as biomarkers for accurate PC diagnosis. Additionally, miRNAs and lncRNAs are considered as promising clinical targets for PC. Therefore, in this review, we discuss recent experimental evidence regarding the clinical implications of miRNAs and lncRNAs in the pathophysiology of PC, their future potential, as well as the challenges that have arisen in this field of study in order to drive forward the design of ncRNA-based diagnostics and therapeutics for PC.

(-)-Epicatechin exerts positive effects on anxiety in high fat diet-induced obese mice through multi-genomic modifications in the hippocampus

Obesity is associated with increased occurrence of cognitive and mood disorders. While consumption of high-fat diets (HFD) and associated obesity could have a detrimental impact on the brain, dietary bioactives may mitigate these harmful effects. We previously observed that (-)-epicatechin (EC) can mitigate HFD-induced anxiety-associated behaviors in mice. The aim of our study is to investigate the molecular mechanisms of EC actions in the hippocampus which underlies its anti-anxiety effects in HFD-fed mice using a multi-genomic approach. Healthy eight-week old male C57BL/6J mice were fed for 24 weeks either: (A) a control diet containing 10% total calories from fat; (B) a HFD containing 45% total calories from fat; or (C) the HFD supplemented with 20 mg EC per kg body weight. Hippocampi were isolated for genomic analysis using Affymetrix arrays, followed by in-depth bioinformatic analyses. Genomic analysis demonstrated that EC induced significant changes in mouse hippocampal global gene expression. We observed changes in the expression of 1001 protein-coding genes, 241 miRNAs, and 167 long non-coding RNAs. Opposite gene expression profiles were observed when the gene expression profile obtained upon EC supplementation was compared to the profile obtained after consumption of the HFD. Functionality analysis revealed that the differentially expressed genes regulate processes involved in neurofunction, inflammation, endothelial function, cell-cell adhesion, and cell signaling. In summary, the capacity of EC to mitigate anxiety-related behaviors in HFD-induced obese mice can be in part explained by its capacity to exert complex genomic modifications in the hippocampus, counteracting changes driven by consumption of the HFD and/or associated obesity.

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.

LncRNAs in neuropsychiatric disorders and computational insights for their prediction

Long non-coding RNAs (lncRNAs) are 200 nucleotide extended transcripts that do not encode proteins or possess limited coding ability. LncRNAs epigenetically control several biological functions such as gene regulation, transcription, mRNA splicing, protein interaction, and genomic imprinting. Over the years, drastic progress in understanding the role of lncRNAs in diverse biological processes has been made. LncRNAs are reported to show tissue-specific expression patterns suggesting their potential as novel candidate biomarkers for diseases. Among all other non-coding RNAs, lncRNAs are highly expressed within the brain-enriched or brain-specific regions of the neural tissues. They are abundantly expressed in the neocortex and pre-mature frontal regions of the brain. LncRNAs are co-expressed with the protein-coding genes and have a significant role in the evolution of functions of the brain. Any deregulation in the lncRNAs contributes to disruptions in normal brain functions resulting in multiple neurological disorders. Neuropsychiatric disorders such as schizophrenia, bipolar disease, autism spectrum disorders, and anxiety are associated with the abnormal expression and regulation of lncRNAs. This review aims to highlight the understanding of lncRNAs concerning normal brain functions and their deregulation associated with neuropsychiatric disorders. We have also provided a survey on the available computational tools for the prediction of lncRNAs, their protein coding potentials, and sub-cellular locations, along with a section on existing online databases with known lncRNAs, and their interactions with other molecules.

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.

Artificial Intelligence Predictor for Alzheimer’s Disease Trained on Blood Transcriptome: The Role of Oxidative Stress

Alzheimer’s disease (AD) is an incurable neurodegenerative disease diagnosed by clinicians through healthcare records and neuroimaging techniques. These methods lack sensitivity and specificity, so new antemortem non-invasive strategies to diagnose AD are needed. Herein, we designed a machine learning predictor based on transcriptomic data obtained from the blood of AD patients and individuals without dementia (non-AD) through an 8 × 60 K microarray. The dataset was used to train different models with different hyperparameters. The support vector machines method allowed us to reach a Receiver Operating Characteristic score of 93% and an accuracy of 89%. High score levels were also achieved by the neural network and logistic regression methods. Furthermore, the Gene Ontology enrichment analysis of the features selected to train the model along with the genes differentially expressed between the non-AD and AD transcriptomic profiles shows the “mitochondrial translation” biological process to be the most interesting. In addition, inspection of the KEGG pathways suggests that the accumulation of β-amyloid triggers electron transport chain impairment, enhancement of reactive oxygen species and endoplasmic reticulum stress. Taken together, all these elements suggest that the oxidative stress induced by β-amyloid is a key feature trained by the model for the prediction of AD with high accuracy.

Context-Dependent Regulation of Gene Expression by Non-Canonical Small RNAs

In recent functional genomics studies, a large number of non-coding RNAs have been identified. It has become increasingly apparent that noncoding RNAs are crucial players in a wide range of cellular and physiological functions. They have been shown to modulate gene expression on different levels, including transcription, post-transcriptional processing, and translation. This review aims to highlight the diverse mechanisms of the regulation of gene expression by small noncoding RNAs in different conditions and different types of human cells. For this purpose, various cellular functions of microRNAs (miRNAs), circular RNAs (circRNAs), snoRNA-derived small RNAs (sdRNAs) and tRNA-derived fragments (tRFs) will be exemplified, with particular emphasis on the diversity of their occurrence and on the effects on gene expression in different stress conditions and diseased cell types. The synthesis and effect on gene expression of these noncoding RNAs varies in different cell types and may depend on environmental conditions such as different stresses. Moreover, noncoding RNAs play important roles in many diseases, including cancer, neurodegenerative disorders, and viral infections.

Machine Learning Identifies Cellular and Exosomal MicroRNA Signatures of Lyssavirus Infection in Human Stem Cell-Derived Neurons

Despite being vaccine preventable, rabies (lyssavirus) still has a significant impact on global mortality, disproportionally affecting children under 15 years of age. This neurotropic virus is deft at avoiding the immune system while travelling through neurons to the brain. Until recently, research efforts into the role of non-coding RNAs in rabies pathogenicity and detection have been hampered by a lack of human in vitro neuronal models. Here, we utilized our previously described human stem cell-derived neural model to investigate the effect of lyssavirus infection on microRNA (miRNA) expression in human neural cells and their secreted exosomes. Conventional differential expression analysis identified 25 cellular and 16 exosomal miRNAs that were significantly altered (FDR adjusted P-value <0.05) in response to different lyssavirus strains. Supervised machine learning algorithms determined 6 cellular miRNAs (miR-99b-5p, miR-346, miR-5701, miR-138-2-3p, miR-651-5p, and miR-7977) were indicative of lyssavirus infection (100% accuracy), with the first four miRNAs having previously established roles in neuronal function, or panic and impulsivity-related behaviors. Another 4-miRNA signatures in exosomes (miR-25-3p, miR-26b-5p, miR-218-5p, miR-598-3p) can independently predict lyssavirus infected cells with >99% accuracy. Identification of these robust lyssavirus miRNA signatures offers further insight into neural lineage responses to infection and provides a foundation for utilizing exosome miRNAs in the development of next-generation molecular diagnostics for rabies.

The Vulnerability of the Developing Brain: Analysis of Highly Expressed Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic Annotation Derived From Mutational Studies

The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer's, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.