Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer's Disease Pathogenesis

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Gene Co-Expression Analysis of Multiple Brain Tissues Reveals Correlation of FAM222A Expression with Multiple Alzheimer's Disease-Related Genes

Background

Alzheimer's disease (AD) is the most common form of dementia in the elderly marked by central nervous system (CNS) neuronal loss and amyloid plaques. FAM222A, encoding an amyloid plaque core protein, is an AD brain atrophy susceptibility gene that mediates amyloid-β aggregation. However, the expression interplay between FAM222A and other AD-related pathway genes is unclear.

Objective

Our goal was to study FAM222A's whole-genome co-expression profile in multiple tissues and investigate its interplay with other AD-related genes.

Methods

We analyzed gene expression correlations in Genotype-Tissue Expression (GTEx) tissues to identify FAM222A co-expressed genes and performed functional enrichment analysis on identified genes in CNS system.

Results

Genome-wide gene expression profiling identified 673 genes significantly correlated with FAM222A (p < 2.5×10-6) in 48 human tissues, including 298 from 13 CNS tissues. Functional enrichment analysis revealed that FAM222A co-expressed CNS genes were enriched in multiple AD-related pathways. Gene co-expression network analysis for identified genes in each brain region predicted other disease associated genes with similar biological function. Furthermore, co-expression of 25 out of 31 AD-related pathways genes with FAM222A was replicated in brain samples from 107 aged subjects from the Aging, Dementia and TBI Study.

Conclusion

This gene co-expression study identified multiple AD-related genes that are associated with FAM222A, indicating that FAM222A and AD-associated genes can be active simultaneously in similar biological processes, providing evidence that supports the association of FAM222A with AD.

Age-Dependent Sex Differences in Perineuronal Nets in an APP Mouse Model of Alzheimer's Disease Are Brain Region-Specific

Alzheimer's disease (AD) is the most common form of dementia, which disproportionately affects women. AD symptoms include progressive memory loss associated with amyloid-β (Aβ) plaques and dismantled synaptic mechanisms. Perineuronal nets (PNNs) are important components of the extracellular matrix with a critical role in synaptic stabilisation and have been shown to be influenced by microglia, which enter an activated state during AD. This study aimed to investigate whether sex differences affected the density of PNNs alongside the labelling of microglia and Aβ plaques density.We performed neurochemistry experiments using acute brain slices from both sexes of the APPNL-F/NL-F mouse model of AD, aged-matched (2-5 and 12-16 months) to wild-type mice, combined with a weighted gene co-expression network analysis (WGCNA). The lateral entorhinal cortex (LEC) and hippocampal CA1, which are vulnerable during early AD pathology, were investigated and compared to the presubiculum (PRS), a region unscathed by AD pathology. The highest density of PNNs was found in the LEC and PRS regions of aged APPNL-F/NL-F mice with a region-specific sex differences. Analysis of the CA1 region using multiplex-fluorescent images from aged APPNL-F/NL-F mice showed regions of dense Aβ plaques near clusters of CD68, indicative of activated microglia and PNNs. This was consistent with the results of WGCNA performed on normalised data on microglial cells isolated from age-matched, late-stage male and female wild-type and APP knock-in mice, which revealed one microglial module that showed differential expression associated with tissue, age, genotype, and sex, which showed enrichment for fc-receptor-mediated phagocytosis. Our data are consistent with the hypothesis that sex-related differences contribute to a disrupted interaction between PNNs and microglia in specific brain regions associated with AD pathogenesis.

Pooled analysis of frontal lobe transcriptomic data identifies key mitophagy gene changes in Alzheimer's disease brain

Background

The growing prevalence of Alzheimer's disease (AD) is becoming a global health challenge without effective treatments. Defective mitochondrial function and mitophagy have recently been suggested as etiological factors in AD, in association with abnormalities in components of the autophagic machinery like lysosomes and phagosomes. Several large transcriptomic studies have been performed on different brain regions from AD and healthy patients, and their data represent a vast source of important information that can be utilized to understand this condition. However, large integration analyses of these publicly available data, such as AD RNA-Seq data, are still missing. In addition, large-scale focused analysis on mitophagy, which seems to be relevant for the aetiology of the disease, has not yet been performed.

Methods

In this study, publicly available raw RNA-Seq data generated from healthy control and sporadic AD post-mortem human samples of the brain frontal lobe were collected and integrated. Sex-specific differential expression analysis was performed on the combined data set after batch effect correction. From the resulting set of differentially expressed genes, candidate mitophagy-related genes were identified based on their known functional roles in mitophagy, the lysosome, or the phagosome, followed by Protein-Protein Interaction (PPI) and microRNA-mRNA network analysis. The expression changes of candidate genes were further validated in human skin fibroblast and induced pluripotent stem cells (iPSCs)-derived cortical neurons from AD patients and matching healthy controls.

Results

From a large dataset (AD: 589; control: 246) based on three different datasets (i.e., ROSMAP, MSBB, & GSE110731), we identified 299 candidate mitophagy-related differentially expressed genes (DEG) in sporadic AD patients (male: 195, female: 188). Among these, the AAA ATPase VCP, the GTPase ARF1, the autophagic vesicle forming protein GABARAPL1 and the cytoskeleton protein actin beta ACTB were selected based on network degrees and existing literature. Changes in their expression were further validated in AD-relevant human in vitro models, which confirmed their down-regulation in AD conditions.

Conclusion

Through the joint analysis of multiple publicly available data sets, we identify four differentially expressed key mitophagy-related genes potentially relevant for the pathogenesis of sporadic AD. Changes in expression of these four genes were validated using two AD-relevant human in vitro models, primary human fibroblasts and iPSC-derived neurons. Our results provide foundation for further investigation of these genes as potential biomarkers or disease-modifying pharmacological targets.

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease

The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis

Background

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.

Materials and methods

Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.

Results

A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).

Conclusion

By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

A novel single-point mutation of NEFH and biallelic SACS mutation presenting as intermediate form Charcot-Marie-Tooth: A case report in Vietnam

Background

Charcot-Marie-Tooth disease (CMT) is among the most common group of inherited neuromuscular diseases. SACS mutations were demonstrated to cause autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). However, there have been few case reports regarding to NEFH and SACS gene mutation to CMT in Vietnamese patients, and the diagnosis of CMT and ARSACS in the clinical setting still overlapped.

Case Description

We report two patients presenting with sensorimotor neuropathy without cerebellar ataxia, spasticity and other neurological features, being diagnosed with intermediate form CMT by electrophysiological and clinical examination and neuroimaging. By whole-exome sequencing panel of two affected members, and PCR Sanger on NEFH and SACS genes to confirm the presence of selected variants on their parents, we identified a novel missense variant NEFH c.1925C>T (inherited from the mother) in an autosomal dominant heterozygous state, and two recessive SACS variants (SACS c.13174C>T, causing missense variant, and SACS c.11343del, causing frameshift variant) (inherited one from the mother and another from the father) in these two patients. Clinical and electrophysiological findings on these patients did not match classical ARSACS. To the best of our knowledge, this is the first case report of two affected siblings diagnosed with CMT carrying both a novel NEFH variant and biallelic SACS variants.

Conclusion

We concluded that this novel NEFH variant is likely benign, and biallelic SACS mutation (c.13174C>T and c.11343del) is likely pathogenic for intermediate form CMT. This study is also expected to emphasize the current knowledge of intermediate form CMT, ARSACS, and the phenotypic spectrum of NEFH-related and SACS-related disorders. We expect to give a new understanding of CMT; however, further research should be conducted to provide a more thorough knowledge of the pathogenesis of CMT in the future.

Perfluorooctanoic acid-induced developmental cardiotoxicity in chicken embryo: Roles of miR-490-5p

Perfluorooctanoic acid (PFOA) could induce developmental toxicities, affecting various organs, including the heart. Although peroxisome-proliferation activated receptor alpha (PPARα) had been identified as a major target of PFOA, PPARα-independent effects are frequently reported. To further elucidate the mechanism of toxicity in PFOA-induced developmental cardiotoxicity, RNA-seq analysis was performed in hatchling chicken hearts developmentally exposed to vehicle or 2 mg/kg (egg weight) PFOA. RT-PCR and western blotting were then performed to confirm the identified potential targets. Furthermore, lentivirus was designed to overexpress and silence identified target miRNA in developing chicken embryo, and the resulting phenotypes were investigated. 21 miRNAs and 1142 mRNAs were identified to be affected by developmental exposure to PFOA in chicken embryo hearts. Among the identified differentially expressed miRNAs, miR-490-5p was confirmed to be significantly affected by PFOA exposure, along with its downstream targets, Synaptosome associated protein 91 (SNAP91) and LY6/PLAUR domain containing 6 (LYPD6), as indicated by RT-PCR and western blotting results. Lentivirus overexpressing miR-490-5p mimicked the phenotype induced by PFOA exposure, while lentivirus silencing miR-490-5p alleviated PFOA-induced changes. Similar patterns were also observed in the expression of downstream target genes, SNAP91 and LYPD6. In summary, miR-490-5p and its downstream genes, SNAP91 and LYPD6 are associated with PFOA-induced developmental cardiotoxicity in chicken embryo, which might help to further elucidate the mechanism of PFOA-induced developmental cardiotoxicity.

RNA-binding protein ELAVL4/HuD ameliorates Alzheimer's disease-related molecular changes in human iPSC-derived neurons

The RNA binding protein ELAVL4/HuD regulates the translation and splicing of multiple Alzheimer's disease (AD) candidate genes. We generated ELAVL4 knockout (KO) human induced pluripotent stem cell-derived neurons to study the effect that ELAVL4 has on AD-related cellular phenotypes. ELAVL4 KO significantly increased the levels of specific APP isoforms and intracellular phosphorylated tau, molecular changes that are related to the pathological hallmarks of AD. Overexpression of ELAVL4 in wild-type neurons and rescue experiments in ELAVL4 KO cells showed opposite effects and also led to a reduction of the extracellular amyloid-beta (Aβ)42/40 ratio. All these observations were made in familial AD (fAD) and fAD-corrected neurons. To gain insight into the molecular cascades involved in neuronal ELAVL4 signaling, we conducted pathway and upstream regulator analyses of transcriptomic and proteomic data from the generated neurons. These analyses revealed that ELAVL4 affects multiple biological pathways linked to AD, including those involved in synaptic function, as well as gene expression downstream of APP and tau signaling. The analyses also suggest that ELAVL4 expression is regulated by insulin receptor-FOXO1 signaling in neurons. Taken together, ELAVL4 expression ameliorates AD-related molecular changes in neurons and affects multiple synaptic pathways, making it a promising target for novel drug development.

Identification and immunological characterization of cuproptosis-related molecular clusters in Alzheimer's disease

Introduction

Alzheimer's disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer's disease and construct a prediction model.

Methods

Based on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer's disease. Using 310 Alzheimer's disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.

Results

The dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer's disease and non-Alzheimer's disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer's disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer's disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.

Conclusion

Our study systematically illustrated the complicated relationship between cuproptosis and Alzheimer's disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer's disease patients.

A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma

Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.

Neurodevelopmental phenotype in 36 new patients with 8p inverted duplication-deletion: Genotype-phenotype correlation for anomalies of the corpus callosum

Inverted duplication deletion 8p [invdupdel(8p)] is a complex and rare chromosomal rearrangement that combines a distal deletion and an inverted interstitial duplication of the short arm of chromosome 8. Carrier patients usually have developmental delay and intellectual disability (ID), associated with various cerebral and extra-cerebral malformations. Invdupdel(8p) is the most common recurrent chromosomal rearrangement in ID patients with anomalies of the corpus callosum (AnCC). Only a minority of invdupdel(8p) cases reported in the literature to date had both brain cerebral imaging and chromosomal microarray (CMA) with precise breakpoints of the rearrangements, making genotype-phenotype correlation studies for AnCC difficult. In this study, we report the clinical, radiological, and molecular data from 36 new invdupdel(8p) cases including three fetuses and five individuals from the same family, with breakpoints characterized by CMA. Among those, 97% (n = 32/33) of patients presented with mild to severe developmental delay/ID and 34% had seizures with mean age of onset of 3.9 years (2 months-9 years). Moreover, out of the 24 patients with brain MRI and 3 fetuses with neuropathology analysis, 63% (n = 17/27) had AnCC. We review additional data from 99 previously published patients with invdupdel(8p) and compare data of 17 patients from the literature with both CMA analysis and brain imaging to refine genotype-phenotype correlations for AnCC. This led us to refine a region of 5.1 Mb common to duplications of patients with AnCC and discuss potential candidate genes within this region.

Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer's Disease

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.