Therapeutic potential of ginseng leaf extract in inhibiting mast cell-mediated allergic inflammation and atopic dermatitis-like skin inflammation in DNCB-treated mice.

Ginseng leaves are known to contain high concentrations of bioactive compounds, such as ginsenosides, and have potential as a treatment for various conditions, including fungal infections, cancer, obesity, oxidative stress, and age-related diseases. This study assessed the impact of ginseng leaf extract (GLE) on mast cell-mediated allergic inflammation and atopic dermatitis (AD) in DNCB-treated mice. GLE reduced skin thickness and lymph node nodules and suppressed the expression and secretion of histamine and pro-inflammatory cytokines. It also significantly lowered the production of inflammatory response mediators including ROS, leukotriene C4 (LTC4), prostaglandin E2 (PGE2), cyclooxygenase-2 (COX-2), and inducible nitric oxide synthase (iNOS). GLE inhibited the phosphorylation of MAPKs (ERK, P38, JNK) and the activation of NF-κB, which are both linked to inflammatory cytokine expression. We demonstrated that GLE's inhibitory effect on mast cell-mediated allergic inflammation is due to the blockade of the NF-κB and inflammasome pathways. Our findings suggest that GLE can be an effective therapeutic agent for mast-cell mediated and allergic inflammatory conditions.

Molecular insights into regulatory RNAs in the cellular machinery.

It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.

Boosting of tau protein aggregation by CD40 and CD48 gene expression in Alzheimer's disease.

Neurodegenerative diseases result from the interplay of abnormal gene expression and various pathological factors. Therefore, a disease-specific integrative genetic approach is required to understand the complexities and causes of target diseases. Recent studies have identified the correlation between genes encoding several transmembrane proteins, such as the cluster of differentiation (CD) and Alzheimer's disease (AD) pathogenesis. In this study, CD48 and CD40 gene expression in AD, a neurodegenerative disease, was analyzed to infer this link. Total RNA sequencing was performed using an Alzheimer's disease mouse model brain and blood, and gene expression was determined using a genome-wide association study (GWAS). We observed a marked elevation of CD48 and CD40 genes in Alzheimer's disease. Indeed, the upregulation of both CD48 and CD40 genes was significantly increased in the severe Alzheimer's disease group. With the elevation of CD48 and CD40 genes in Alzheimer's disease, associations of protein levels were also markedly increased in tissues. In addition, overexpression of CD48 and CD40 genes triggered tau aggregation, and co-expression of these genes accelerated aggregation. The nuclear factor kappa B (NF-ĸB) signaling pathway was enriched by CD48 and CD40 gene expression: it was also associated with tau pathology. Our data suggested that the CD48 and CD40 genes are novel AD-related genes, and this approach may be useful as a diagnostic or therapeutic target for the disease.

Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach.

Phospholipase C (PLC) is an essential isozyme involved in the phosphoinositide signalling pathway, which maintains cellular homeostasis. Gain- and loss-of-function mutations in PLC affect enzymatic activity and are therefore associated with several disorders. Alternative splicing variants of PLC can interfere with complex signalling networks associated with oncogenic transformation and other diseases, including brain disorders. Cells and tissues with various mutations in PLC contribute different phosphoinositide signalling pathways and disease progression, however, identifying cryptic mutations in PLC remains challenging. Herein, we review both the mechanisms underlying PLC regulation of the phosphoinositide signalling pathway and the genetic variation of PLC in several brain disorders. In addition, we discuss the present challenges associated with the potential of deep-learning-based analysis for the identification of PLC mutations in brain disorders.

Identifying New COVID-19 Receptor Neuropilin-1 in Severe Alzheimer's Disease Patients Group Brain Using Genome-Wide Association Study Approach.

Recent preclinical studies show that Neuropilin-1 (NRP1), which is a transmembrane protein with roles in neuronal development, axonal outgrowth, and angiogenesis, also plays a role in the infectivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Thus, we hypothesize that NRP1 may be upregulated in Alzheimer's disease (AD) patients and that a correlation between AD and SARS-CoV-2 NRP1-mediated infectivity may exist as angiotensin converting enzyme 2 (ACE2). We used an AD mouse model that mimics AD and performed high-throughput total RNA-seq with brain tissue and whole blood. For quantification of NRP1 in AD, brain tissues and blood were subjected to Western blotting and real-time quantitative PCR (RT-qPCR) analysis. In silico analysis for NRP1 expression in AD patients has been performed on human hippocampus data sets. Many cases of severe symptoms of COVID-19 are concentrated in an elderly group with complications such as diabetes, degenerative disease, and brain disorders. Total RNA-seq analysis showed that the Nrp1 gene was commonly overexpressed in the AD model. Similar to ACE2, the NRP1 protein is also strongly expressed in AD brain tissues. Interestingly, in silico analysis revealed that the level of expression for NRP1 was distinct at age and AD progression. Given that NRP1 is highly expressed in AD, it is important to understand and predict that NRP1 may be a risk factor for SARS-CoV-2 infection in AD patients. This supports the development of potential therapeutic drugs to reduce SARS-CoV-2 transmission.

Prediction of genetic alteration of phospholipase C isozymes in brain disorders: Studies with deep learning.

Genetic mutations leading to the development of various diseases, such as cancer, diabetes, and neurodegenerative disorders, can be attributed to multiple mechanisms and exposure to diverse environments. These disorders further increase gene mutation rates and affect the activity of translated proteins, both phenomena associated with cellular responses. Therefore, maintaining the integrity of genetic and epigenetic information is critical for disease suppression and prevention. With the advent of genome sequencing technologies, large-scale genomic data-based machine learning tools, including deep learning, have been used to predict and identify somatic inactivation or negative dominant expression of target genes in various diseases. Although deep learning studies have recently been highlighted for their ability to distinguish between the genetic information of diseases, conventional wisdom is also necessary to explain the correlation between genotype and phenotype. Herein, we summarize the current understanding of phosphoinositide-specific phospholipase C isozymes (PLCs) and an overview of their associations with genetic variation, as well as their emerging roles in several diseases. We also predicted and discussed new findings of cryptic PLC splice variants by deep learning and the clinical implications of the PLC genetic variations predicted using these tools.

Long non-coding RNAs in brain tumors: roles and potential as therapeutic targets.

Brain tumors are associated with adverse outcomes despite improvements in radiation therapy, chemotherapy, and photodynamic therapy. However, treatment approaches are evolving, and new biological phenomena are being explored to identify the appropriate treatment of brain tumors. Long non-coding RNAs (lncRNAs), a type of non-coding RNA longer than 200 nucleotides, regulate gene expression at the transcriptional, post-transcriptional, and epigenetic levels and are involved in a variety of biological functions. Recent studies on lncRNAs have revealed their aberrant expression in various cancers, with distinct expression patterns associated with their instrumental roles in cancer. Abnormal expression of lncRNAs has also been identified in brain tumors. Here, we review the potential roles of lncRNAs and their biological functions in the context of brain tumors. We also summarize the current understanding of the molecular mechanisms and signaling pathways related to lncRNAs that may guide clinical trials for brain tumor therapy.

Advances in multiplex PCR for Alzheimer's disease diagnostics targeting CDK genes.

Alzheimer's disease (AD) is a common neurodegenerative disease that lacks biomarkers for diagnosis. Biomarkers for accurate detection of AD are required for potential therapeutic approaches. Recent studies in mammalian cells have demonstrated an association between the expression of cell cycle proteins and AD occurrence. Therefore, we aimed to identify a potent biomarker among relevant cell cycle-regulating proteins such as cyclin-dependent kinases (CDKs) for the diagnosis of AD. We also developed a multiplex-PCR-based diagnostic method, which showed the rapid and accurate detection of AD biomarkers. Genome-wide association study (GWAS) results showed increased gene expression of CDKs in an AD mouse model. Based on genomic analysis, our multiplex-PCR method, which contained optimized primer sets and PCR conditions targeting genes of CDKs, accurately matched RT-PCR results in the AD mouse model. Interestingly, validation by in silico meta-analysis for the expression of each CDK gene showed significant expression in moderate and severe groups of AD patients. Accordingly, clinical applications relying on the diagnosis of AD using our results may shed light on AD therapeutics.

Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening.

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer's disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.

Molecular landscape of long noncoding RNAs in brain disorders.

According to current paradigms, various risk factors, such as genetic mutations, oxidative stress, neural network dysfunction, and abnormal protein degradation, contribute to the progression of brain disorders. Through the cooperation of gene transcripts in biological processes, the study of noncoding RNAs can lead to insights into the cause and treatment of brain disorders. Recently, long noncoding RNAs (lncRNAs) which are longer than 200 nucleotides in length have been suggested as key factors in various brain disorders. Accumulating evidence suggests the potential of lncRNAs as diagnostic or prognostic biomarkers and therapeutic targets. High-throughput screening-based sequencing has been instrumental in identification of lncRNAs that demand new approaches to understanding the progression of brain disorders. In this review, we discuss the recent progress in the study of lncRNAs, and addresses the pathogenesis of brain disorders that involve lncRNAs and describes the associations of lncRNAs with neurodegenerative disorders such as Alzheimer disease (AD), Parkinson disease (PD), and neurodevelopmental disorders. We also discuss potential targets of lncRNAs and their promise as novel therapeutics and biomarkers in brain disorders.

Discoveries for Long Non-Coding RNA Dynamics in Traumatic Brain Injury.

In recent years, our understanding of long non-coding RNAs (lncRNAs) has been challenged with advances in genome sequencing and the widespread use of high-throughput analysis for identifying novel lncRNAs. Since then, the characterization of lncRNAs has contributed to the establishment of their molecular roles and functions in transcriptional regulation. Although genetic studies have so far explored the sequence-based primary function of lncRNAs that guides the expression of target genes, recent insights have shed light on the potential of lncRNAs for widening the identification of biomarkers from non-degenerative to neurodegenerative diseases. Therefore, further advances in the genetic characteristics of lncRNAs are expected to lead to diagnostic accuracy during disease progression. In this review, we summarized the latest studies of lncRNAs in TBI as a non-degenerative disease and discussed their potential limitations for clinical treatment.

Predictive Potential of Circulating Ube2h mRNA as an E2 Ubiquitin-Conjugating Enzyme for Diagnosis or Treatment of Alzheimer's Disease.

Neurodegenerative disorders are caused by neuronal cell death, miscommunications between synapse, and abnormal accumulations of proteins in the brain. Alzheimer's disease (AD) is one of the age-related disorders, which are the most common degenerative disorders today, and strongly affects memory consolidation and cognitive function in the brain. Amyloid-ß and tau proteins are triggers for AD pathogenesis, and usually used as AD candidate biomarkers in the clinical research. Especially, clinical exam, brain imaging and molecular biological methods are being used to diagnosis for AD. Genome-wide association study (GWAS) is a new biomedical method, and its use contributes to understanding many human diseases, including brain diseases. Here, we identified ubiquitin conjugating enzyme E2 (Ube2) gene expression in neurons through GWAS. The subfamilies of Ube2's genetic expression and inborn errors affect the ubiquitin proteasome system (UPS), leading to protein degradation in the brain. We found that only Ube2h mRNA transcription was significantly increased in the blood from AD, however we did not find any change of Ube2 subfamily genes' expression in the blood and brain tissue. These data may provide information for diagnosis or clinical approach, and suggest that cell-free circulating Ube2h mRNA is a novel potential biomarker for AD.

The potential roles of deubiquitinating enzymes in brain diseases.

Most proteins undergo posttranslational modification such as acetylation, methylation, phosphorylation, biotinylation, and ubiquitination to regulate various cellular processes. Ubiquitin-targeted proteins from the ubiquitin-proteasome system (UPS) are degraded by 26S proteasome, along with this, deubiquitinating enzymes (DUBs) have specific activity against the UPS through detaching of ubiquitin on ubiquitin-targeted proteins. Balancing between protein expression and degradation through interplay between the UPS and DUBs is important to maintain cell homeostasis, and abnormal expression and elongation of proteins lead to diverse diseases such as cancer, diabetes, and autoimmune response. Therefore, development of DUB inhibitors as therapeutic targets has been challenging. In addition, understanding of the roles of DUBs in neurodegeneration, specifically brain diseases, has emerged gradually. This review highlights recent studies on the molecular mechanisms for DUBs, and discusses potential therapeutic targets for DUBs in cases of brain diseases.

Secretomics to Discover Regulators in Diseases.

Secretory proteins play important roles in the cross-talk of individual functional units, including cells. Since secretory proteins are essential for signal transduction, they are closely related with disease development, including metabolic and neural diseases. In metabolic diseases, adipokines, myokines, and hepatokines are secreted from respective organs under specific environmental conditions, and play roles in glucose homeostasis, angiogenesis, and inflammation. In neural diseases, astrocytes and microglia cells secrete cytokines and chemokines that play roles in neurotoxic and neuroprotective responses. Mass spectrometry-based secretome profiling is a powerful strategy to identify and characterize secretory proteins. This strategy involves stepwise processes such as the collection of conditioned medium (CM) containing secretome proteins and concentration of the CM, peptide preparation, mass analysis, database search, and filtering of secretory proteins; each step requires certain conditions to obtain reliable results. Proteomic analysis of extracellular vesicles has become a new research focus for understanding the additional extracellular functions of intracellular proteins. Here, we provide a review of the insights obtained from secretome analyses with regard to disease mechanisms, and highlight the future prospects of this technology. Continued research in this field is expected to provide valuable information on cell-to-cell communication and uncover new pathological mechanisms.

An Intrinsic Transcriptional Program Underlying Synaptic Scaling during Activity Suppression.

Homeostatic scaling allows neurons to maintain stable activity patterns by globally altering their synaptic strength in response to changing activity levels. Suppression of activity by the blocking of action potentials increases synaptic strength through an upregulation of surface α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors. Although this synaptic upscaling was shown to require transcription, the molecular nature of the intrinsic transcription program underlying this process and its functional significance have been unclear. Using RNA-seq, we identified 73 genes that were specifically upregulated in response to activity suppression. In particular, Neuronal pentraxin-1 (Nptx1) increased within 6 hr of activity blockade, and knockdown of this gene blocked the increase in synaptic strength. Nptx1 induction is mediated by calcium influx through the T-type voltage-gated calcium channel, as well as two transcription factors, SRF and ELK1. Altogether, these results uncover a transcriptional program that specifically operates when neuronal activity is suppressed to globally coordinate the increase in synaptic strength.

UV-RNA Immunoprecipitation (UV-RIP) Protocol in Neurons.

With the many advances in genome-wide sequencing, it has been discovered that much more of the genome is transcribed into RNA than previously appreciated. These nonprotein-coding RNAs (ncRNAs) come in many different forms, and they have been shown to have a variety of functions within the cell, influencing processes such as gene expression, mRNA splicing, and transport, just as a few examples. As we delve deeper into studying their mechanisms of action, it becomes important to understand how they play these roles, in particular by understanding what proteins these ncRNAs interact with. This protocol describes one technique that can be used to study this, ultra-violet light cross-linking RNA immunoprecipitation (UV-RIP), which uses an antibody to pull down a specific protein of interest and then detects RNA that is bound to it. This technique utilizes UV light to cross-link the cells, which takes advantage of the fact that UV light will only cross-link proteins and nucleic acids that are directly interacting. This approach can provide key mechanistic insight into the function of these newly identified ncRNAs.

Stimulus-specific combinatorial functionality of neuronal c-fos enhancers.

The c-fos gene (also known as Fos) is induced by a broad range of stimuli and is a reliable marker for neural activity. Its induction mechanism and available reporter mouse lines are based exclusively on c-fos promoter activity. Here we demonstrate that multiple enhancers surrounding the c-fos gene are crucial for ensuring robust c-fos response to various stimuli. Membrane depolarization, brain-derived neurotrophic factor (BDNF) and forskolin activate distinct subsets of the enhancers to induce c-fos transcription in neurons, suggesting that stimulus-specific combinatorial activation of multiple enhancers underlies the broad inducibility of the c-fos gene. Accordingly, the functional requirement of key transcription factors varies depending on the type of stimulation. Combinatorial enhancer activation also occurs in the brain. Providing a comprehensive picture of the c-fos induction mechanism beyond the minimal promoter, our study should help in understanding the physiological nature of c-fos induction in relation to neural activity and plasticity.