Expanding causal genes for Parkinson's disease via multi-omics analysis

An integrative approach to identifying NPC1 as a susceptibility gene for gestational diabetes mellitus

OBJECTIVE

The objective of this study was to identify a novel gene and its potential mechanisms associated with susceptibility to gestational diabetes mellitus (GDM) through an integrative approach.

METHODS

We analyzed data from genome-wide association studies (GWAS) of GDM in the FinnGen R11 dataset (16,802 GDM cases and 237,816 controls) and Genotype Tissue Expression v8 expression quantitative trait locus data. We used summary-data-based Mendelian randomization to determine associations between transcript levels and phenotypes, transcriptome-wide association studies to provide insights into gene-trait associations, multi-marker analysis of genomic annotation to perform gene-based analysis, genome-wide complex trait analysis-multivariate set-based association test-combo to determine gene prioritization, and polygenic priority scores to prioritize the causal genes to screen candidate genes. Subsequent Mendelian randomization analysis was performed to infer causality between the candidate genes and GDM and phenome-wide association study (PheWAS) analysis was used to explore the associations between selected genes and other characteristics. Furthermore, to gain a deeper understanding of the functional implications of these susceptibility genes, GeneMANIA analysis was used to determine the fundamental biological functions of the therapeutic targets and protein-protein interaction network analysis was performed to identify intracellular protein interactions.

RESULTS

We identified two novel susceptibility genes associated with GDM: NPC1 and KIAA1191. Magnetic resonance imaging revealed a strong correlation between NPC1 expression levels and a lower incidence of GDM (odds ratio: 0.922, 95% confidence interval: 0.866-0.981, p = 0.011). PheWAS at the gene level indicated that NPC1 was not associated with any other trait. The biological significance of this gene was evidenced by its strong association with sterol metabolism.

CONCLUSION

Our study identified NPC1 as a novel gene whose predicted expression level is linked to a reduced risk of GDM, providing new insights into the genetic framework of this disease.

Investigating potential drug targets for the treatment of glioblastoma: a Mendelian randomization study

Glioblastoma (GBM), one of the most aggressive brain tumors, has a 5-year survival rate of less than 5%. Current standard therapies, including surgery, radiotherapy, and temozolomide (TMZ) chemotherapy, are limited by drug resistance and the blood-brain barrier. Integrating expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data has shown promise in uncovering disease mechanisms and therapeutic targets. This study combined eQTL and pQTL analyses to identify potential GBM-related genes and circulating plasma proteins for therapeutic exploration. Using transcriptomic data from The Cancer Genome Atlas (TCGA), we identified 2,528 differentially expressed genes, including GPX7 and CXCL10. eQTL-MR analysis identifies GBM-associated differentially expressed genes and constructs a protein-protein interaction (PPI) network.Integrating pQTL data from the deCODE database, pQTL-MR, and colocalization analyses validated the therapeutic potential of GPX7 and CXCL10.These findings provide new perspectives on GBM biology and suggest actionable targets for therapy. Despite limitations due to sample size and population-specific data, this study highlights GPX7 and CXCL10 as promising candidates for further investigation and lays the foundation for targeted GBM treatments.

Characterization of Isolated Human Astrocytes from Aging Brain

Astrocytes have multiple crucial roles, including maintaining brain homeostasis and synaptic function, performing phagocytic clearance, and responding to injury and repair. It has been suggested that astrocyte performance is progressively impaired with aging, leading to imbalances in the brain's internal milieu that eventually impact neuronal function and lead to neurodegeneration. Until now, most evidence of astrocytic dysfunction in aging has come from experiments done with whole tissue homogenates, astrocytes collected by laser capture, or cell cultures derived from animal models or cell lines. In this study, we used postmortem-derived whole cells sorted with anti-GFAP antibodies to compare the unbiased, whole-transcriptomes of human astrocytes from control, older non-impaired individuals and subjects with different neurodegenerative diseases, such as Parkinson's disease (PD), Alzheimer's disease (ADD), and progressive supranuclear palsy (PSP). We found hundreds of dysregulated genes between disease and control astrocytes. In addition, we identified numerous genes shared between these common neurodegenerative disorders that are similarly dysregulated; in particular, UBC a gene for ubiquitin, which is a protein integral to cellular homeostasis and critically important in regulating function and outcomes of proteins under cellular stress, was upregulated in PSP, PD, and ADD when compared to control.

Abnormal epigenetic modification of lysosome and lipid regulating genes in Alzheimer's disease

BackgroundAbnormal lipid metabolism has been identified as a potential pathogenic mechanism of Alzheimer's disease (AD), which might be epigenetically regulated. Lysosomes are critical organelles for lipid metabolism. However, the epigenetic modifications of lysosome and lipid regulating genes remain unclear in AD patients.ObjectiveExplore the role of abnormal epigenetic modifications, especially methylation of lysosome and lipid metabolism-related genes in AD.MethodsMethylation beadchip and MALDI-TOF mass spectrometry were used to detect genome-wide DNA methylation levels and validate key gene methylation, respectively. Clinical data were collected from all participants. Associations between clinical biochemical characteristics and altered DNA methylation in AD patients were analyzed, and a risk factor model of AD was established.Results41 differentially methylated positions (DMPs) corresponding to 33 genes were identified in AD patients, with 18 hypermethylated and 23 hypomethylated positions. Significant alterations were observed in lipid regulating genes (CTNNB1, DGKQ, SLC27A1) and lysosomal transmembrane gene (TMEM175). Clinical analysis revealed that TP, ALB, IB, ADA, ALP, HCY, GLU, TC, BUN, HDL-C, LDL-C, and APOA1 levels were significantly higher in AD patients, whereas A/G and DB levels were lower. TMEM175 hypermethylation was further verified and found to correlate with TC, HDL-C, LDL-C, APOA1, IB, and HCY. The AUC of the AD risk model, which integrated clinical lipid markers and TMEM175 methylation, reached 0.9519 (p < 0.0001).ConclusionsAbnormal epigenetic regulation of lysosomal gene and lipid dyshomeostasis were high-risk factors in AD. Methylation modifications of lysosome and lipid regulating genes might be key processes in AD pathogenesis.

Mitochondria-Related Genome-Wide Mendelian Randomization Identifies Putatively Causal Genes for Neurodegenerative Diseases

BACKGROUND

Mitochondrial dysfunction is increasingly recognized as a key factor in neurodegenerative diseases (NDDs), underscoring the therapeutic potential of targeting mitochondria-related genes. This study aimed to identify novel biomarkers and drug targets for these diseases through a comprehensive analysis that integrated genome-wide Mendelian randomization (MR) with genes associated with mitochondrial function.

METHODS

Using existing publicly available genome-wide association studies (GWAS) summary statistics and comprehensive data on 1136 mitochondria-related genes, we initially identified a subset of genes related to mitochondrial function that exhibited significant associations with NDDs. We then conducted colocalization and summary-data-based Mendelian randomization (SMR) analyses using expression quantitative trait loci (eQTL) to validate the causal role of these candidate genes. Additionally, we assessed the druggability of the encoded proteins to prioritize potential therapeutic targets for further exploration.

RESULTS

Genetically predicted levels of 10 genes were found to be significantly associated with the risk of NDDs. Elevated DMPK and LACTB2 levels were associated with increased Alzheimer's disease risk. Higher expression of NDUFAF2, BCKDK, and MALSU1, along with lower TTC19, raised Parkinson's disease risk. Higher ACLY levels were associated with both amyotrophic lateral sclerosis and multiple sclerosis (MS) risks, while decreased MCL1, TOP3A, and VWA8 levels raised MS risk. These genes primarily impact mitochondrial function and energy metabolism. Notably, several druggable protein targets identified are being explored for potential NDDs treatment.

CONCLUSIONS

This data-driven MR study demonstrated the causal role of mitochondrial dysfunction in NDDs. Additionally, this study identified candidate genes that could serve as potential pharmacological targets for the prevention and treatment of NDDs. © 2025 International Parkinson and Movement Disorder Society.

A Multi-Omics-Based Exploration of the Predictive Role of MSMB in Prostate Cancer Recurrence: A Study Using Bayesian Inverse Convolution and 10 Machine Learning Combinations

Background: Prostate cancer (PCa) is a prevalent malignancy among elderly men. Biochemical recurrence (BCR), which typically occurs after radical treatments such as radical prostatectomy or radiation therapy, serves as a critical indicator of potential disease progression. However, reliable and effective methods for predicting BCR in PCa patients remain limited. Methods: In this study, we used Bayesian deconvolution combined with 10 machine learning algorithms to build a five-gene model for predicting PCa progression. The model and the five selected genes were externally validated. Various analyses such as prognosis, clinical subgroups, tumor microenvironment, immunity, genetic variants, and drug sensitivity were performed on MSMB/Epithelial_cells subgroups. Results: Our model outperformed 102 previously published prognostic features. Notably, PCa patients with a high proportion of MSMB/epithelial cells were characterized by a greater progression-free Interval (PFI), a higher proportion of early-stage tumors, a lower stromal component, and a reduced presence of tumor-associated fibroblasts (CAF). The high proportion of MSMB/epithelial cells was also associated with higher frequencies of SPOP and TP53 mutations. Drug sensitivity analysis revealed that patients with a poorer prognosis and lower MSMB/epithelial cell ratio showed increased sensitivity to cyclophosphamide, cisplatin, and dasatinib. Conclusions: The model developed in this study provides a robust and accurate tool for predicting PCa progression. It offers significant potential for enhancing risk stratification and informing personalized treatment strategies for PCa patients.

Association of 25-hydroxyvitamin D with Parkinson's disease based on the results from the NHANES 2007 to 2018 and Mendelian randomization analysis

An abundance of observational researches had suggested that vitamin D insufficient was related to Parkinson's disease (PD) risk. However, their relationships were debatable and the causality remains uncertain. We intended to evaluate the association between 25-hydroxyvitamin D [25(OH)D] and Parkinson's disease (PD) risk using NHANES data (2007-2018) and Mendelian randomization (MR) analyses with the genome-wide association study (GWAS) summary data. Demographic characteristics and multivariable-adjusted logistic regression were conducted to assess the relationship between the serum 25(OH)D levels and risk of PD prevalence by utilizing NHANES database. Besides, a two-sample MR analysis was applied to evaluate the causal association between serum 25(OH)D levels and PD risk. The main analysis was conducted by citing the inverse-variance-weighted (IVW) approach, while additional MR approaches and multiple sensitivity analysis were cited to evaluate the robustness and pleiotropy for the discoveries. In total, 30,796 adults from NHANES 2007-2018 were selected for the present research. As a result, 1.1% participants with PD (mean age: 61.9 ± 15.5 years), while 68.5% reported vitamin D insufficient. Compared with participants without PD, those with PD had a greater level of 25(OH)D (P < 0.01). However, after adjusted for demographic characteristics and comorbid factors, this association was not observed. Furthermore, no potential causal relationships between the serum level of 25(OH)D and PD risk were found via MR analysis (IVW-MR: OR = 1.082; 95% CI, 0.902 to 1.297; P = 0.395). After eliminating variants with horizontal pleiotropy risk, pleiotropy-robust MR analysis presented similar results. In conclusion, this research suggested that serum 25(OH)D levels was not correlated with PD risk. Additionally, the MR analyses revealed no significant causal association between serum 25(OH)D levels and PD risk at the genetic level. Awareness of these findings may improve personalized prevention and treatment of PD.

Proteome-Wide Association Study for Finding Druggable Targets in Progression and Onset of Parkinson's Disease

OBJECTIVE

To identify and validate causal protein targets that may serve as potential therapeutic interventions for both the onset and progression of Parkinson's disease (PD) through integrative proteomic and genetic analyses.

METHOD

We utilized large-scale plasma and brain protein quantitative trait loci (pQTL) datasets from the deCODE Health study and the Religious Orders Study/Rush Memory and Aging Project (ROS/MAP), respectively. Proteome-wide association studies (PWAS) were conducted using the OTTERS framework for plasma proteins and the FUSION tool for brain proteins, examining associations with PD onset and three progression phenotypes: composite, motor, and cognitive. Significant protein associations (FDR-corrected p < 0.05) from PWAS were further validated using summary-based Mendelian randomization (SMR), colocalization analyses, and reverse Mendelian randomization (MR) to establish causality. Phenome-wide Mendelian randomization (PheW-MR) was performed to assess potential side effects across 679 disease traits when targeting these proteins to reduce PD-related phenotype risk by 20%. Additionally, we conducted cellular distribution-based clustering using gene expression data from the Allen Brain Atlas (ABA) to explore the distribution of key proteins across brain regions, constructed protein-protein interaction (PPI) networks via the STRING database to explore interactions among proteins, and evaluated the druggability of identified targets using the DrugBank database to identify opportunities for drug repurposing.

RESULT

Our analyses identified 25 candidate proteins associated with PD phenotypes, including 16 plasma proteins linked to PD progression (10 cognitive, 4 motor, and 3 composite) and 9 plasma proteins associated with PD onset. Notably, GPNMB was implicated in both plasma and brain tissues for PD onset. PheW-MR revealed predominantly beneficial side effects for the identified targets, with 83.7% of associations indicating positive outcomes and 16.3% indicating adverse effects. Cellular clustering categorized candidate targets into three distinct expression profiles across brain cell types using ABA. PPI network analysis highlighted one key interaction cluster among the proteins for PD cognitive progression and PD onset. Druggability assessment revealed 15 out of 25 proteins had repurposing opportunities for PD treatment.

CONCLUSION

We have identified 25 causal protein targets associated with the onset and progression of PD, providing new insights into the research and development of treatment strategies for PD.

Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a neurological condition characterized by complex genetic basic, and the reliable diagnosis of PD remained limited.

OBJECTIVE

To identify genes crucial to PD and assess their potential as diagnostic markers.

METHODS

Differentially expressed genes (DEGs) were screened from the PD tissue dataset and blood dataset. Two machine learning methods were used to identify key PD-related genes. The genes were validated in an independent dataset. Further validation using 120 peripheral blood mononuclear cells (PBMCs) from PD patients. The clinical significance and the diagnostic value of the genes was determined. The function of genes was analyzed and verified by cells experiments.

RESULTS

Thirteen common upregulated genes were identified between PD tissue dataset and blood dataset. Two machine learning methods identify three key PD-related genes (GPX2, CR1, ZNF556). An independent dataset and PBMCs samples results showed increased expression in PD patients. Clinical analysis showed that GPX2 and CR1 expression correlated with early-stage PD. The validated dataset of blood samples revealed each three gene showed moderate diagnostic potential for PD, with combined analysis outperforming individual gene analysis (AUC:0.701). The PBMCs samples showed similar diagnostic value of each gene, and the combination of the three genes presented better diagnostic value (AUC:0.801). Functional studies highlighted the involvement of these genes in key pathways in PD pathology. The results of SH-SY5Y cells showed that these three genes increased from PD cell model.

CONCLUSIONS

GPX2, CR1, ZNF556 were critical to the development of PD and might serve as diagnostic markers for PD.

Impact of potential biomarkers, SNRPE, COX7C, and RPS27, on idiopathic Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a progressive neuro-degenerative disorder most common in older adults which is associated with impairments in movement and other body functions. Most PD cases are classified as idiopathic PD (IPD), meaning that the etiology remains unidentified.

OBJECTIVE

To identify key genes and molecular mechanisms to identify biomarkers applicable to IPD.

METHODS

We applied a bioinformatics approach using a gene expression in whole blood dataset to pinpoint differentially expressed genes (DEGs) and pathways involved in IPD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs were subsequently performed. A protein-protein interaction (PPI) network was then constructed to select hub genes that may influence IPD. We further investigated the levels of differentially methylated regions (DMRs) and differentially expressed microRNA (DEMs) of whole blood of patients with IPD to validate hub genes. Additionally, we examined the hub gene expression patterns in the substantia nigra (STN) using single-cell RNA sequencing datasets.

RESULTS

In total, we identified 124 DEGs in the blood samples of patients with IPD, with GO and KEGG analyses highlighting their significant enrichment. Analysis of PPI networks revealed three major clusters and hub genes: small nuclear ribonucleoprotein polypeptide E (SNRPE), cytochrome C oxidase subunit 7 C (COX7C), and ribosomal protein S27 (RPS27). DMRs and DEMs analyses revealed hub gene regulation via epigenetic and RNA interference. In particular, SNRPE and RPS27 showed identically regulated gene expression in the STN.

CONCLUSION

This study suggests that SNRPE, COX7C, and RPS27 in whole-blood samples derived from patients may be useful biomarkers for IPD.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

Molecular Insights into Aggrephagy: Their Cellular Functions in the Context of Neurodegenerative Diseases

Protein homeostasis or proteostasis is an equilibrium of biosynthetic production, folding and transport of proteins, and their timely and efficient degradation. Proteostasis is guaranteed by a network of protein quality control systems aimed at maintaining the proteome function and avoiding accumulation of potentially cytotoxic proteins. Terminal unfolded and dysfunctional proteins can be directly turned over by the ubiquitin-proteasome system (UPS) or first amassed into aggregates prior to degradation. Aggregates can also be disposed into lysosomes by a selective type of autophagy known as aggrephagy, which relies on a set of so-called selective autophagy receptors (SARs) and adaptor proteins. Failure in eliminating aggregates, also due to defects in aggrephagy, can have devastating effects as underscored by several neurodegenerative diseases or proteinopathies, which are characterized by the accumulation of aggregates mostly formed by a specific disease-associated, aggregate-prone protein depending on the clinical pathology. Despite its medical relevance, however, the process of aggrephagy is far from being understood. Here we review the findings that have helped in assigning a possible function to specific SARs and adaptor proteins in aggrephagy in the context of proteinopathies, and also highlight the interplay between aggrephagy and the pathogenesis of proteinopathies.

Mendelian randomization analysis of the brain, cerebrospinal fluid, and plasma proteome identifies potential drug targets for attention deficit hyperactivity disorder

BACKGROUND

The need for new therapeutics for attention deficit hyperactivity disorder (ADHD) is evident. Brain, cerebrospinal fluid (CSF), and plasma protein biomarkers with causal genetic evidence could represent potential drug targets. However, a comprehensive screen of the proteome has not yet been conducted.

METHODS

We employed a three-pronged approach using Mendelian Randomization (MR) and Bayesian colocalization analysis. Firstly, we studied 608 brains, 214 CSF, and 612 plasma proteins as potential causal mediators of ADHD using MR analysis. Secondly, we analysed the consistency of the discovered biomarkers across three distinct subtypes of ADHD: childhood, persistent, and late-diagnosed ADHD. Finally, we extended our analysis to examine the correlation between identified biomarkers and Tourette syndrome and pervasive autism spectrum disorder (ASD), conditions often linked with ADHD. To validate the MR findings, we conducted sensitivity analysis. Additionally, we performed cell type analysis on the human brain to identify risk genes that are notably enriched in various brain cell types.

FINDINGS

After applying Bonferroni correction, we found that the risk of ADHD was increased by brain proteins GMPPB, NAA80, HYI, CISD2, and HYI, TIE1 in CSF and plasma. Proteins GMPPB, NAA80, ICA1L, CISD2, TIE1, and RMDN1 showed overlapped loci with ADHD risk through Bayesian colocalization. Overexpression of GMPPB protein was linked to an increase in the risk for all three ADHD subtypes. While ICA1L provided protection against both ASD and ADHD, CISD2 increased the probability of both disorders. Cell-specific studies revealed that GMPPB, NAA80, ICA1L, and CISD2 were predominantly present on the surface of excitatory-inhibitory neurons.

INTERPRETATION

Our comprehensive MR investigation of the brain, CSF, and plasma proteomes revealed seven proteins with causal connections to ADHD. Particularly, GMPPB and TIE1 emerged as intriguing targets for potential ADHD therapy.

FUNDING

This work was partly funded by the Key R & D Program of Zhejiang (T.L. 2022C03096); the National Natural Science Foundation of China Project (C.Z. 82001413); Postdoctoral Foundation of West China Hospital (C.Z. 2020HXBH163).

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis

Objective

Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy.

Methods

scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features.

Results

By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients.

Conclusion

Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Hippocampal transcriptomic analyses reveal the potential antiapoptotic mechanism of a novel anticonvulsant agent Q808 on pentylenetetrazol-induced epilepsy in rats

Brain apoptosis is one of the main causes of epileptogenesis. The antiapoptotic effect and potential mechanism of Q808, an innovative anticonvulsant chemical, have never been reported. In this study, the seizure stage and latency to reach stage 2 of pentylenetetrazol (PTZ) seizure rat model treated with Q808 were investigated. The morphological change and neuronal apoptosis in the hippocampus were detected by hematoxylin and eosin (HE) and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) staining, respectively. The hippocampal transcriptomic changes were observed using RNA sequencing (RNA-seq). The expression levels of hub genes were verified by quantitative reverse-transcription PCR (qRT-PCR). Results revealed that Q808 could allay the seizure score and prolong the stage 2 latency in seizure rats. The morphological changes of neurons and the number of apoptotic cells in the DG area were diminished by Q808 treatment. RNA-seq analysis revealed eight hub genes, including Map2k3, Nfs1, Chchd4, Hdac6, Siglec5, Slc35d3, Entpd1, and LOC103690108, and nine hub pathways among the control, PTZ, and Q808 groups. Hub gene Nfs1 was involved in the hub pathway sulfur relay system, and Map2k3 was involved in the eight remaining hub pathways, including Amyotrophic lateral sclerosis, Cellular senescence, Fc epsilon RI signaling pathway, GnRH signaling pathway, Influenza A, Rap1 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. qRT-PCR confirmed that the mRNA levels of these hub genes were consistent with the RNA-seq results. Our findings might contribute to further studies exploring the new apoptosis mechanism and actions of Q808.