The construction of common and specific significance subnetworks of Alzheimer's disease from multiple brain regions

Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients

Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.

Investigation of the underlying mechanism of Buyang Huanwu decoction in ischemic stroke by integrating systems pharmacology-proteomics and in vivo experiments

Buyang Huanwu Decoction (BHD) has been effective in treating ischemic stroke (IS). However, its mechanism of action remains unclear. The study intended to explore the potential mechanism of BHD against IS using systems pharmacology, proteomics, and animal experiments. The active components of BHD were identified from UPLC-Q-TOF-MS and literature mining. Systems pharmacology and proteomics were employed to investigate the underlying mechanism of BHD against IS. The AutoDock tool was used for molecular docking. A middle cerebral artery occlusion (MCAO) model rat was utilized to explore the therapeutic benefits of BHD. The rats were divided into sham, model, BHD (5, 10, 20 g/kg, ig) groups. The neurological scores, pathological section characteristics, brain infarct volumes, inflammatory cytokines, and signaling pathways were investigated in vivo experiments. The results of systems pharmacology showed that 13 active compounds and 112 common targets were screened in BHD. The docking results suggested that the active compounds in BHD had a high affinity for the key targets. In vivo experiments demonstrated that BHD exhibited neuroprotective benefits by lowering the neurological score, the volume of the cerebral infarct, the release of inflammatory cytokines, and reducing neuroinflammatory damage in MCAO rats. Furthermore, BHD decreased TNF-α and CD38 levels while increasing ATP2B2, PDE1A, CaMK4, p-PI3K, and p-AKT. Combined with systems pharmacology and proteomic studies, we confirmed that PI3K-Akt and calcium signaling pathways are the key mechanisms for BHD against IS. Furthermore, this study demonstrated the feasibility of combining proteomics with systems pharmacology to study the mechanism of herbal medicine.

Whole-Transcriptome Analysis of Repeated Low-Level Sarin-Exposed Rat Hippocampus and Identification of Cerna Networks to Investigate the Mechanism of Sarin-Induced Cognitive Impairment

Sarin is a potent organophosphorus nerve agent that causes cognitive dysfunction, but its underlying molecular mechanisms are poorly understood. In this study, a rat model of repeated low-level sarin exposure was established using the subcutaneous injection of 0.4 × LD₅₀ for 21 consecutive days. Sarin-exposed rats showed persistent learning and memory impairment and reduced hippocampal dendritic spine density. A whole-transcriptome analysis was applied to study the mechanism of sarin-induced cognitive impairment, and a total of 1035 differentially expressed mRNA (DEmRNA), including 44 DEmiRNA, 305 DElncRNA, and 412 DEcircRNA, were found in the hippocampus of sarin-treated rats. According to Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Protein-Protein Interaction (PPI) analysis, these DERNAs were mainly involved in neuronal synaptic plasticity and were related to the pathogenesis of neurodegenerative diseases. The circRNA/lncRNA-miRNA-mRNA ceRNA network was constructed, in which Circ_Fmn1, miR-741-3p, miR-764-3p, miR-871-3p, KIF1A, PTPN11, SYN1, and MT-CO3 formed one circuit, and Circ_Cacna1c, miR-10b-5p, miR-18a-5p, CACNA1C, PRKCD, and RASGRP1 constituted another circuit. The balance between the two circuits was crucial for maintaining synaptic plasticity and may be the regulatory mechanism by which sarin causes cognitive impairment. Our study reveals the ceRNA regulation mechanism of sarin exposure for the first time and provides new insights into the molecular mechanisms of other organophosphorus toxicants.

RNAseq Analysis of FABP4 Knockout Mouse Hippocampal Transcriptome Suggests a Role for WNT/β-Catenin in Preventing Obesity-Induced Cognitive Impairment

Microglial fatty-acid binding protein 4 (FABP4) is a regulator of neuroinflammation. We hypothesized that the link between lipid metabolism and inflammation indicates a role for FABP4 in regulating high fat diet (HFD)-induced cognitive decline. We have previously shown that obese FABP4 knockout mice exhibit decreased neuroinflammation and cognitive decline. FABP4 knockout and wild type mice were fed 60% HFD for 12 weeks starting at 15 weeks old. Hippocampal tissue was dissected and RNA-seq was performed to measure differentially expressed transcripts. Reactome molecular pathway analysis was utilized to examine differentially expressed pathways. Results showed that HFD-fed FABP4 knockout mice have a hippocampal transcriptome consistent with neuroprotection, including associations with decreased proinflammatory signaling, ER stress, apoptosis, and cognitive decline. This is accompanied by an increase in transcripts upregulating neurogenesis, synaptic plasticity, long-term potentiation, and spatial working memory. Pathway analysis revealed that mice lacking FABP4 had changes in metabolic function that support reduction in oxidative stress and inflammation, and improved energy homeostasis and cognitive function. Analysis suggested a role for WNT/β-Catenin signaling in the protection against insulin resistance, alleviating neuroinflammation and cognitive decline. Collectively, our work shows that FABP4 represents a potential target in alleviating HFD-induced neuroinflammation and cognitive decline and suggests a role for WNT/β-Catenin in this protection.

Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder

Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD. In this study, brain transcriptomics gene expression microarray datasets (GSE160208 and GSE124571) of human PD were evaluated and 35 differentially expressed genes (DEGs) were identified. By employing network-based protein-protein interaction (PPI) analysis on these DEGs, 10 central hub proteins, including SPP1, FKBP5, HPRT1, CDKN1A, BAG3, HSPB1, SYK, TNFRSF1A, PTPN6, and CD44, were identified. Employing bioinformatics approaches, a variety of transcription factors (EGR1, SSRP1, POLR2A, TARDP, and NR2F1) and miRNAs (hsa-mir-8485, hsa-mir-148b-3p, hsa-mir-4295, hsa-mir-26b-5p, and hsa-mir-16-5p) were predicted. EGR1 was found as the most imperative transcription factor (TF), and hsa-mir-16-5p and hsa-mir-148b-3p were found as the most crucial miRNAs targeted in PD. Finally, resveratrol and hypochlorous acid were predicted as possible therapeutic drugs for PD. This study could be helpful in better understanding of molecular systems and prospective pharmacological targets for developing effective PD treatments.

Disruption of Alternative Splicing in the Amygdala of Pigs Exposed to Maternal Immune Activation

The inflammatory response of gestating females to infection or stress can disrupt gene expression in the offspring’s amygdala, resulting in lasting neurodevelopmental, physiological, and behavioral disorders. The effects of maternal immune activation (MIA) can be impacted by the offspring’s sex and exposure to additional stressors later in life. The objectives of this study were to investigate the disruption of alternative splicing patterns associated with MIA in the offspring’s amygdala and characterize this disruption in the context of the second stress of weaning and sex. Differential alternative splicing was tested on the RNA-seq profiles of a pig model of viral-induced MIA. Compared to controls, MIA was associated with the differential alternative splicing (FDR-adjusted p-value < 0.1) of 292 and 240 genes in weaned females and males, respectively, whereas 132 and 176 genes were differentially spliced in control nursed female and male, respectively. The majority of the differentially spliced (FDR-adjusted p-value < 0.001) genes (e.g., SHANK1, ZNF672, KCNA6) and many associated enriched pathways (e.g., Fc gamma R-mediated phagocytosis, non-alcoholic fatty liver disease, and cGMP-PKG signaling) have been reported in MIA-related disorders including autism and schizophrenia in humans. Differential alternative splicing associated with MIA was detected in the gene MAG across all sex-stress groups except for unstressed males and SLC2A11 across all groups except unstressed females. Precise understanding of the effect of MIA across second stressors and sexes necessitates the consideration of splicing isoform profiles.

Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method

Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10^-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.

Integrated Bioinformatics Analysis to Identify Alternative Therapeutic Targets for Alzheimer's Disease: Insights from a Synaptic Machinery Perspective

Alzheimer's disease (AD), the most common type of dementia, is a serious neurodegenerative disease that has no cure yet, but whose symptoms can be alleviated with available medications. Therefore, early and accurate diagnosis of the disease and elucidation of the molecular mechanisms involved in the progression of pathogenesis are critically important. This study aimed to identify dysregulated miRNAs and their target mRNAs through the integrated analysis of miRNA and mRNA expression profiling in AD patients versus unaffected controls. Expression profiles in postmortem brain samples from AD patients and healthy individuals were extracted from the Gene Expression Omnibus database and were analyzed using bioinformatics approaches to identify gene ontologies, pathways, and networks. Finally, the module analysis of the PPI network and hub gene selection was carried out. A total of five differentially expressed miRNAs were extracted from the miRNA dataset, and 4312 differentially expressed mRNAs were obtained from the mRNA dataset. By comparing the DEGs and the putative targets of the altered miRNAs, 116 (3 upregulated and 113 downregulated) coordinated genes were determined. Also, six hub genes (SNAP25, GRIN2A, GRIN2B, DLG2, ATP2B2, and SCN2A) were identified by constructing a PPI network. The results of the present study provide insight into mechanisms such as synaptic machinery and neuronal communication underlying AD pathogenesis, specifically concerning miRNAs.

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.

Lentivirus-Mediated Expression of Human Secreted Amyloid Precursor Protein-Alpha Promotes Long-Term Induction of Neuroprotective Genes and Pathways in a Mouse Model of Alzheimer's Disease

BACKGROUND

Secreted amyloid precursor protein-alpha (sAPPα) can enhance memory and is neurotrophic and neuroprotective across a range of disease-associated insults, including amyloid-β toxicity. In a significant step toward validating sAPPα as a therapeutic for Alzheimer's disease (AD), we demonstrated that long-term overexpression of human sAPPα (for 8 months) in a mouse model of amyloidosis (APP/PS1) could prevent the behavioral and electrophysiological deficits that develop in these mice.

OBJECTIVE

To explore the underlying molecular mechanisms responsible for the significant physiological and behavioral improvements observed in sAPPα-treated APP/PS1 mice.

METHODS

We assessed the long-term effects on the hippocampal transcriptome following continuous lentiviral delivery of sAPPα or empty-vector to male APP/PS1 mice and wild-type controls using Affymetrix Mouse Transcriptome Assays. Data analysis was carried out within the Affymetrix Transcriptome Analysis Console and an integrated analysis of the resulting transcriptomic data was performed with Ingenuity Pathway analysis (IPA).

RESULTS

Mouse transcriptome assays revealed expected AD-associated gene expression changes in empty-vector APP/PS1 mice, providing validation of the assays used for the analysis. By contrast, there were specific sAPPα-associated gene expression profiles which included increases in key neuroprotective genes such as Decorin, betaine-GABA transporter and protocadherin beta-5, subsequently validated by qRT-PCR. An integrated biological pathways analysis highlighted regulation of GABA receptor signaling, cell survival and inflammatory responses. Furthermore, upstream gene regulatory analysis implicated sAPPα activation of Interleukin-4, which can counteract inflammatory changes in AD.

CONCLUSION

This study identified key molecular processes that likely underpin the long-term neuroprotective and therapeutic effects of increasing sAPPα levels in vivo.

Identifying circRNA- and lncRNA-associated-ceRNA networks in the hippocampi of rats exposed to PM2.5 using RNA-seq analysis

Non-coding RNAs appear to be involved in the regulation of the nervous system. However, no competing endogenous RNA (ceRNA) network related to PM_2.5 damage in the hippocampal function has yet been constructed. Herein, we used whole-transcriptome sequencing technology to systematically study the ceRNA network in rat hippocampi after PM_2.5 exposure. We identified 100 circRNAs, 67 lncRNAs, 28 miRNAs, and 539 mRNAs and constructed the most comprehensive ceRNA network to date, to our knowledge. Gene Ontology and KEGG analyses showed that the network molecules are involved in synapses, neural projections, and neural development and involve signal pathways such as the synaptic vesicle cycle. Finally, the expression of the differentially expressed RNAs confirmed by quantitative real-time PCR was consistent with the sequencing data. This study systematically dissected the ceRNA atlas related to cognitive memory function in the hippocampal tissue of PM_2.5-exposed rats for the first time, to our knowledge, and promotes the development of potential new treatments for cognitive impairment.

Increased expression of heme-binding protein 1 early in Alzheimer's disease is linked to neurotoxicity

Alzheimer’s disease is the most prevalent neurodegenerative disorder leading to progressive cognitive decline. Despite decades of research, understanding AD progression at the molecular level, especially at its early stages, remains elusive. Here, we identified several presymptomatic AD markers by investigating brain proteome changes over the course of neurodegeneration in a transgenic mouse model of AD (3×Tg-AD). We show that one of these markers, heme-binding protein 1 (Hebp1), is elevated in the brains of both 3×Tg-AD mice and patients affected by rapidly-progressing forms of AD. Hebp1, predominantly expressed in neurons, interacts with the mitochondrial contact site complex (MICOS) and exhibits a perimitochondrial localization. Strikingly, wildtype, but not Hebp1-deficient, neurons showed elevated cytotoxicity in response to heme-induced apoptosis. Increased survivability in Hebp1-deficient neurons is conferred by blocking the activation of the mitochondrial-associated caspase signaling pathway. Taken together, our data highlight a role of Hebp1 in progressive neuronal loss during AD progression.

VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in Genome-Wide Association Studies: applied to Alzheimer's Disease

It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.

Region-specific expression of circular RNAs in the mouse brain

Circular RNAs (circRNAs) are abundant in mammalian brain and their expression is regulated in a tissue- and developmental stage-specific manner. Mammalian brain is the most transcriptionally complex organ. While many studies have extensively studied linear transcriptome and its biological functions in the brain, the circular transcriptome remains largely unexplored. This study focused on investigation of circRNA expression patterns in the mammalian brain regions critical for cognitive and memory functions and performed comparative analysis with the linear transcriptome. Altogether our study showed that circular and linear RNAs have independent expression patterns despite being derived from the same genomic locus, and that circular transcriptomes from different brain region have distinct characteristics in terms of transcript abundance and composition.