Potential Risk Genes Research Articles

Identification of Potential Drug Targets for Sleep Apnea Through Mendelian Randomization

Background: Sleep apnea is a significant health impediment, and it presently lacks efficacious therapeutic interventions. Thus, it is imperative to discover novel therapeutic targets that could guide clinical treatment strategies. Objective: This study aims to utilize an integrative analytic approach to unearth previously unappreciated protein-encoding genes implicated in sleep apnea susceptibility. Methods: Through the Multi-Marker Analysis of Genomic Annotation (MAGMA), we aligned Single-Nucleotide Polymorphism (SNP) summary statistics from Genome-Wide Association Studies (GWAS) of gene bodies to discern potential risk genes. Following the MAGMA results, we conducted a round of Transcriptome-Wide Association Studies (TWAS) and Proteome-Wide Association Studies (PWAS) to expedite the conversion of genetic associations into probable protein targets. Mendelian Randomization (MR) and co-localization analysis were employed to ascertain the causal linkage between the candidate target genes and sleep apnea. Finally, a mediation analysis was undertaken to explore the possible intermediary role of 150 inflammatory metabolites and 1,124 proteins. Results: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetyl carnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. result: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetylcarnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. Conclusion: Our findings indicate that ACADVL, CCDC134, and UPP1 genes are potentially significant targets for further functional investigation and therapeutic interventions for sleep apnea.

RNF144A as a potential risk gene for endometrial carcinoma: Insights from Mendelian randomization, bulk RNA sequencing, single-cell RNA, and experimental analysis.

Endometrial carcinoma (EC) is a prevalent gynecological malignancy that poses a significant threat to women's health worldwide. However, its pathogenesis and underlying mechanisms remains unclear. In this study, expression quantitative trait loci data, Mendelian randomization analysis, and differential expression analysis were performed to identify potential targets. A prognostic risk signature was subsequently constructed for EC patients based on the expression of these genes. Four bioinformatics algorithms, including generalized linear model, extreme gradient boosting, support vector machine, and random forest, were used to identify hub genes in EC. The expression of ring finger protein 144A (RNF144A) was validated using quantitative real-time polymerase chain reaction. Cellular proliferation and migration ability were evaluated using CCK-8 and Transwell assays, respectively. The genes RNF144A, ketohexokinase, and Rab interacting lysosomal protein like 2 were identified as potential targets for EC. Their differential expression was observed in EC patients, and Mendelian randomization analysis revealed a negative correlation between these genes and the development of EC. Mechanistic analyses suggested a strong association between these genes and the tumor immune microenvironment. The constructed risk signature was significantly associated with the prognosis, age, cancer stage, and grade of EC patients. Furthermore, based on interacted model algorithms, RNF144A was identified as a hub gene in EC. It was found to be significantly downregulated in EC samples, and its expression was positively correlated with the stage and grade of EC patients. In vitro experiments showed that overexpression of RNF144A significantly promoted cell growth and migration in EC cells. In conclusion, this study provides insights into the molecular mechanisms underlying EC progression and identifies preliminary candidate biomarkers for the development of EC treatment strategies.

Integrative analysis of transcriptome-wide association study and mRNA expression profile identified risk genes for bipolar disorder

ObjectiveBipolar disorder (BD) is a debilitating neuropsychiatric disorder, which is associated with genetic variation through “vast but mixed” Genome-Wide Association Studies (GWAS). Transcriptome-Wide Association Study (TWAS) is more effective in explaining genetic factors that influence complex diseases and can help identifying risk genes more reliably. So, this study aims to identify potential BD risk genes in pedigrees with TWAS. MethodsWe conducted a TWAS analysis with expression quantitative trait loci (eQTL) analysis on extended BD pedigrees, and the BD genome-wide association study (GWAS) summary data acquired from the Psychiatric Genomics Consortium (PGC). Furthermore, the BD-associated genes identified by TWAS were validated by mRNA expression profiles from the Gene Expression Omnibus (GEO) Datasets (GSE23848 and GSE46416). Functional enrichment and annotation analysis were implemented by RStudio (version 4.2.0). ResultsTWAS identified 362 genes with P value < 0.05, and 18 genes remain significant after Bonferroni correction, such as SEMA3G (PTWAS=1.07 × 10-11), ALOX5AP (PTWAS=3.12 × 10-8), and PLEC (PTWAS=1.27 × 10-7). Further 6 overlapped genes were detected in integrative analysis, such as UQCRB (PTWAS=0.0020, PmRNA=0.0000), TMPRSS9 (PTWAS=0.0405, PmRNA=0.0032), and SNX10 (PTWAS=0.0104, PmRNA=0.0015). Using genes identified by TWAS, Gene Ontology (GO) enrichment analysis identified 40 significant GO terms, such as mitochondrial ATP synthesis coupled electron transport, mitochondrial respiratory, aerobic electron transport chain, oxidative phosphorylation, mitochondrial membrane proteins, and ubiquinone activity. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis identified significant 15 pathways for BD, such as Oxidative phosphorylation, endocannabinoids signaling, neurodegeneration, and reactive oxide species. ConclusionsWe found a set of BD-associated genes and pathways, validating the important role of neurodevelopmental abnormalities, inflammatory responses, and mitochondrial dysfunction in the pathology of BD, offering novel information for comprehending the genetic basis of BD.

Whole-genome sequencing identifies novel genes for autism in Chinese trios.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high genetic heritability but heterogeneity. Fully understanding its genetics requires whole-genome sequencing (WGS), but the ASD studies utilizing WGS data in Chinese population are limited. In this study, we present a WGS study for 334 individuals, including 112 ASD patients and their non-ASD parents. We identified 146 de novo variants in coding regions in 85 cases and 60 inherited variants in coding regions. By integrating these variants with an association model, we identified 33 potential risk genes (P<0.001) enriched in neuron and regulation related biological process. Besides the well-known ASD genes (SCN2A, NF1, SHANK3, CHD8 etc.), several high confidence genes were highlighted by a series of functional analyses, including CTNND1, DGKZ, LRP1, DDN, ZNF483, NR4A2, SMAD6, INTS1, and MRPL12, with more supported evidence from GO enrichment, expression and network analysis. We also integrated RNA-seq data to analyze the effect of the variants on the gene expression and found 12 genes in the individuals with the related variants had relatively biased expression. We further presented the clinical phenotypes of the proband carrying the risk genes in both our samples and Caucasian samples to show the effect of the risk genes on phenotype. Regarding variants in non-coding regions, a total of 74 de novo variants and 30 inherited variants were predicted as pathogenic with high confidence, which were mapped to specific genes or regulatory features. The number of de novo variants found in patient was significantly associated with the parents' ages at the birth of the child, and gender with trend. We also identified small de novo structural variants in ASD trios. The results in this study provided important evidence for understanding the genetic mechanism of ASD.

Prognostic Value of Fatty Acid Metabolism-related Genes in Patients with Bladder Cancer.

This study aimed to explore the expression profiles of fatty acid metabolism- related genes (FAMRGs) in patients with bladder cancer (BLCA). The corresponding clinicopathological features of BLCA patients and RNA sequencing data were downloaded from TCGA and GSE13507. Univariate Cox regression was used to determine the prognostic value of FRGS in BLCA patients. LASSO regression analysis was then performed to select potential risk genes and eliminate genes that might overfit the model. Based on the independent prognostication-related FRGs, the nomogram survival model was established using the root mean square value of the R packet to predict the 1-year, 3-year, and 5-year survival rates of BLCA patients. By determining the area under the curve (AUC) value, the time-dependent receiver operating characteristic curve (ROC) was used to evaluate the prognostic efficiency of our model. A total of 243 DEFRGs were identified. Twenty FRGs were found to be related to the prognosis of BLCA in the TCGA database. Survival curves showed that high-risk patients had significantly shorter OS than low-risk cases (p < 0.001). The AUC of risk was 0.784, which was superior to age, sex, and stage, suggesting that the risk score was more favorable in predicting OS than traditional pathologic prognostic factors. The AUC was 0.757 at 1 year, 0.732 at 3 years, and 0.733 at 5 year-OS. In this study, we demonstrated that a FAMRG prognosis biomarker is associated with the tumor immune microenvironment in patients with BLCA.

The paired immunoglobulin-like type 2 receptor alpha (PILRA) gene polymorphism rs1859788 reduces risk of Alzheimer's Disease in men homozygous for the ApoE ε4 allele.

The APOE gene has long been associated with Alzheimer Disease (AD) risk. Emerging research indicates that other genetic loci, including the paired immunoglobulin-like type 2 receptor alpha (PILRA) gene, may play a crucial role. In the current study we used UK Biobank data to assess the relationship between PILRA and AD. We examined the PILRA polymorphism rs1859788, a single nucleotide missense variant, G > A, minor allele frequency 0.3. Single nucleotide polymorphism (SNP) data for rs429358 and rs7412 determined APOE isoform. We used PheWeb to perform a phenome wide association study (phewas) of rs1859788 and identify other conditions that might be related to both AD and rs1859788. In male subjects homozygous for ApoE isoform ε4/ε4, of the men without AD, 9.7% had AA genotype; of the men with AD, 1.8% had AA genotype. This difference was significant (p = 0.006, two tail Fisher exact test). In female subjects homozygous for ApoE isoform ε4/ε4, of the women without AD, 10.4% had AA genotype; of the women with AD 7.9% had AA genotype. This difference was not significant (p = 0.481). In subjects not homozygous for ApoE isoform ε4/ε4, the effect of PILRA genotype was not significant. A phewas of rs1859788 found an association with megaloblastic anemia. We have confirmed the previously noted PILRA snp rs1859788 risk reduction of AD, as well as a PILRA link to the ApoE ε4 isoform that has been previously described. We are uncertain why the significant association is only with men who are homozygous for the ε4/ε4 isoform. A phewas indicated that PILRA SNP rs1859788 is associated with megaloblastic anemia, which may explain an observed association between AD and anemia. The identification of PILRA as a potential risk gene for Alzheimer's disease underscores the complexity of the genetic landscape contributing to AD. Alongside APOE, PILRA may play a significant role in modulating key pathological processes such as neuroinflammation and amyloid-beta accumulation.

Bioinformatics analysis of potential pathogenesis and risk genes of neuroinflammation-promoted brain injury in intracerebral hemorrhage

IntroductionSpontaneous (non-traumatic) intracerebral hemorrhage (ICH) is one of the major causes of global death. The purpose of our bioinformatics analysis was to detect viable pathophysiological targets and small-molecule drug candidates and to identify the precise secondary mechanisms of brain injury in ICH. Material and methodsThe GSE24265 dataset, consisting of data from four perihematomal brain tissues and seven contralateral brain tissues, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) in ICH. Online analysis tool GEO2R and Drug Susceptibility Assessment Module within the ACBI Bioinformation tool was used for data differential expression analysis. TargetScan, miRDB, and RNA22 were used to investigate the miRNAs regulating the DEGs. The functional annotation of DEGs was performed using Gene Ontology (GO) resources, and the cell signaling pathway analysis of DEGs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. Enrichment analysis was performed for delving the molecular mechanism of DEGs, and protein–protein interaction (PPI) networks and microRNA (miRNA)-messenger RNA (mRNA) networks were used to reveal the hub nodes and the related interaction relationships. Hub genes and miRNA-mRNA interaction of PPI network were identified by STRING version 12.0 online software and Cytoscape. Next, the DEGs were analyzed using the L1000CDS2 database to identify small-molecule compounds with potential therapeutic effects. ResultsA total of 325 upregulated genes and 103 downregulated genes associated with ICH were identified. The biological functions of DEGs associated with ICH are mainly involved in the inflammatory response, chemokine activity, and immune response. The KEGG analysis identified several pathways significantly associated with ICH, including but not limited to cytokine-cytokine receptor interaction and MAPK signaling pathway. A PPI network consisting of 188 nodes and 563 edges was constructed using STRING, and 27 hub genes were identified with Cytoscape software. The miRNA-mRNA network with high connectivity contained key 27 mRNAs (from C-C motif chemokine ligand 5 (CCL5), C-C motif chemokine ligand 8 (CCL8), …., to dishevelled-associated activator of morphogenesis 1 (DAAM1), and FRAT regulator of WNT signaling pathway 1 (FRAT1)) and 135 candidate miRNAs. These genes and miRNAs are closely related to secondary brain injury induced by ICH. In addition, a L1000CDS2 analysis of six small-molecule compounds revealed their therapeutic potential. ConclusionsOur study explores the pathogenesis of brain tissue injury promoted by neuroinflammation in ICH and extends the clinical utility of its key genes. At the same time, we constructed a miRNA-mRNA network which may play crucial roles in the pathogenesis of ICH. In addition, we obtained six small molecule compounds that will have anti-inflammatory effects on ICH, including Geldanamycin, Dasatinib, BMS-345541, Saracatinib, and Afatinib.

Whole-exome sequencing identifies high-confidence genes for tic disorders in a Chinese Han population

BackgroundTic disorder (TD) is a polygenic neurodevelopmental disorder with high susceptibility. However, identifying high-confidence risk genes has been challenging due to poor replication across multiple studies. MethodsWhole-exome sequencing was performed on 390 TD patients and 372 unaffected individuals in a Chinese Han population. Analysis of variance, burden analysis and in silico prediction were used to identify candidate genes for TD. To facilitate data analysis and to focus on high-confidence genes, we defined a panel of 160 genes as known causal or candidate TD genes from previous studies. Gene enrichment and protein–protein interaction analysis were utilized to detect potential novel TD risk genes. ResultsTotally, 14 variants across 12 known TD candidate genes were considered potential susceptibility variants. Ten variants across 10 known TD candidate genes were identified as potential disease-causing variants. Burden analysis identified variants of 28 known genes were significantly excess in TD patients. In addition, 354 previously unproven TD genes are over-represented in patients. Genes enriched in the PI3K-Akt signaling, sphingolipid metabolism and serotonergic synaptic pathways, as well as those interacting with FN1, were considered potential new candidate genes for TD. ConclusionsThis is the largest WES study focusing on TD patients in a Chinese Han population. Several variants recurring in our cohort were identified as high-confidence risk loci for TD. Moreover, we provided potential new risk genes that may be prioritized for further investigation.

Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis.

Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Sex differences orchestrated by androgens at single-cell resolution.

Sex differences in mammalian complex traits are prevalent and are intimately associated with androgens1-7. However, a molecular and cellular profile of sex differences and their modulation by androgens is still lacking. Here we constructed a high-dimensional single-cell transcriptomic atlas comprising over 2.3 million cells from 17 tissues in Mus musculus and explored the effects of sex and androgens on the molecular programs and cellular populations. In particular, we found that sex-biased immune gene expression and immune cell populations, such as group 2 innate lymphoid cells, were modulated by androgens. Integration with the UK Biobank dataset revealed potential cellular targets and risk gene enrichment in antigen presentation for sex-biased diseases. This study lays the groundwork for understanding the sex differences orchestrated by androgens and provides important evidence for targeting the androgen pathway as a broad therapeutic strategy for sex-biased diseases.

Identification and In-Silico study of non-synonymous functional SNPs in the human SCN9A gene.

Single nucleotide polymorphisms are the most common form of DNA alterations at the level of a single nucleotide in the genomic sequence. Genome-wide association studies (GWAS) were carried to identify potential risk genes or genomic regions by screening for SNPs associated with disease. Recent studies have shown that SCN9A comprises the NaV1.7 subunit, Na+ channels have a gene encoding of 1988 amino acids arranged into 4 domains, all with 6 transmembrane regions, and are mainly found in dorsal root ganglion (DRG) neurons and sympathetic ganglion neurons. Multiple forms of acute hypersensitivity conditions, such as primary erythermalgia, congenital analgesia, and paroxysmal pain syndrome have been linked to polymorphisms in the SCN9A gene. Under this study, we utilized a variety of computational tools to explore out nsSNPs that are potentially damaging to heath by modifying the structure or activity of the SCN9A protein. Over 14 potentially damaging and disease-causing nsSNPs (E1889D, L1802P, F1782V, D1778N, C1370Y, V1311M, Y1248H, F1237L, M936V, I929T, V877E, D743Y, C710W, D623H) were identified by a variety of algorithms, including SNPnexus, SNAP-2, PANTHER, PhD-SNP, SNP & GO, I-Mutant, and ConSurf. Homology modeling, structure validation, and protein-ligand interactions also were performed to confirm 5 notable substitutions (L1802P, F1782V, D1778N, V1311M, and M936V). Such nsSNPs may become the center of further studies into a variety of disorders brought by SCN9A dysfunction. Using in-silico strategies for assessing SCN9A genetic variations will aid in organizing large-scale investigations and developing targeted therapeutics for disorders linked to these variations.

Genome-Wide Mendelian Randomization Identifies Ferroptosis-Related Drug Targets for Alzheimer's Disease.

Alzheimer's disease (AD) currently lacks effective disease-modifying treatments. Recent research suggests that ferroptosis could be a potential therapeutic target. Mendelian randomization (MR) is a widely used method for identifying novel therapeutic targets. Employ genetic information to evaluate the causal impact of ferroptosis-related genes on the risk of AD. 564 ferroptosis-related genes were obtained from FerrDb. We derived genetic instrumental variables for these genes using four brain quantitative trait loci (QTL) and two blood QTL datasets. Summary-data-based Mendelian randomization (SMR) and two-sample MR methods were applied to estimate the causal effects of ferroptosis-related genes on AD. Using extern transcriptomic datasets and triple-transgenic mouse model of AD (3xTg-AD) to further validate the gene targets identified by the MR analysis. We identified 17 potential AD risk gene targets from GTEx, 13 from PsychENCODE, and 22 from BrainMeta (SMR p < 0.05 and HEIDI test p > 0.05). Six overlapping ferroptosis-related genes associated with AD were identified, which could serve as potential therapeutic targets (PEX10, CDC25A, EGFR, DLD, LIG3, and TRIB3). Additionally, we further pinpointed risk genes or proteins at the blood tissue and pQTL levels. Notably, EGFR demonstrated significant dysregulation in the extern transcriptomic datasets and 3xTg-AD models. This study provides genetic evidence supporting the potential therapeutic benefits of targeting the six druggable genes for AD treatment, especially for EGFR (validated by transcriptome and 3xTg-AD), which could be useful for prioritizing AD drug development in the field of ferroptosis.

Discovery of disease‐associated, tau, and inflammatory microglial subpopulations and molecular drivers in Alzheimer’s disease using network‐based deep learning integration of human brain single‐cell RNA‐sequencing data

AbstractBackgroundMicroglia have been implicated in the pathophysiology of Alzheimer’s disease (AD) for over 100 years, and substantial progress has been made in characterizing microglial heterogeneities and biology, including disease‐associated microglia (or DAM, amyloid‐pathology related, neuro‐protective), tau microglia (tau‐pathology related), and inflammatory microglia (neuro‐toxic). However, the crucial next step knowing how to apply these findings to develop new treatments for AD, through uncovering the functional roles and molecular drivers of each microglial subtype, has not yet been accomplished.MethodIn this study, we conducted single‐cell RNA‐sequencing data integration analyses of ∼0.8 million cells/nuclei by leveraging frozen brain samples from AD subjects across different brain regions from The Alzheimer’s Cell Atlas (https://taca.lerner.ccf.org/) using a deep learning analytic approach. We annotated three microglial subtypes (including DAM, tau, and inflammatory microglia) using well‐established marker genes and then used a human protein‐protein interactome network‐based approach to identify potential molecular drivers of different microglial subtypes across the spectrum of AD. We also applied transition gene network analysis to characterize microglial subtype transition from non‐disease associated to disease‐associated stages at transcriptomic levels.ResultVia trajectory analysis we pinpointed the existence of three microglial subtypes across AD progression. We also identified that tau microglia were significantly associated with synaptic processes. Compared to DAM, upregulated genes within inflammatory microglia were more significantly enriched within key immune pathways (e.g., NF‐kappa B and toll‐like receptor signaling pathways). In addition, transition gene networks of inflammatory microglia and DAM were found to contain potential AD pathobiology regulators (e.g., SYK, LYN, IRF8, and CSF1R) and genetic risk genes (including INPP5D, PICALM, and MEF2A). We further conducted network‐based drug repurposing prediction by simultaneously activating DAM while inhibiting inflammatory microglia, which indicated that several predicted repurposable drugs (i.e., fluticasone and mometasone) are significantly associated with reduced risk of AD in large patient databases.ConclusionThis study identifies potential underlying mechanisms of microglial subtypes involved in human AD brains, leading to potential drug targets for future development of microglia‐targeted therapies for AD.

Screening Alzheimer’s genetic risk factors against amyloid‐βeta synaptotoxicity

AbstractBackgroundSynapse degeneration is one of the earliest events in Alzheimer’s disease (AD) and toxic oligomers of amyloid‐beta (Aβ) peptide, cleavage product of APP, plays a major role in this process. Genome‐wide association studies recently reported 75 genomic risk loci for developing AD, but how the potential risk genes contribute to AD remains unknown. This project aims to identify AD genetic risk factors that mediate Aβ‐induced synaptotoxicity.MethodWe conducted a high‐content shRNA screening to assess the impact of ca. 200 AD risk genes on synaptic density and connectivity. Twenty‐four genes expressed in neurons with highest impact on synapses when silenced were shortlisted for a medium‐throughput screening to assess their capacity to block Aβ‐induced synaptotoxicity. Synaptotoxicity was modelled in microfluidic devices that allow primary postnatal rat neurons to be co‐cultured with Chinese hamster ovary cells, that overexpress wild‐type APP or APP with V717I (or London) mutation with increased secretion of Aβ1‐42 oligomers. Microfluidic devices allow the fluidic isolation of neurites from neuronal cell bodies and therefore provide exclusive access to pre‐ and post‐synaptic neurons. Target genes expression will be modulated either in pre‐ or post‐synaptic neurons and its impact on synaptic connectivity will be assessed.ResultBased on the shRNA screening, we shortlisted 17 detrimental genes, for which we obtained overexpression vectors packed in lentiviruses, and 6 protective genes. We have developed a medium‐throughput co‐culture device that permits screening twelve genes per imaging session using the INCell 6000 automated microscope, as well as an automated image analysis workflow for detecting and assigning presynaptic to postsynaptic puncta based on their relative proximity. Gene expression modulation is validated and the screen is on‐going.ConclusionWe have developed a microfluidic co‐culture model to rapidly and systematically interrogate genetic risk factors for their impact on synaptic connectivity. This study will help us identify AD genetic risk factors with a potential to block Aβ‐induced synaptotoxicity in vitro. Hit genes will be functionally validated via microelectrode array recordings.

Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.

Schizophrenia is a serious mental disease. With increased research funding for this disease, schizophrenia has become one of the key areas of focus in the medical field. Searching for associations between diseases and genes is an effective approach to study complex diseases, which may enhance research on schizophrenia pathology and lead to the identification of new treatment targets. The aim of this study was to identify potential schizophrenia risk genes by employing machine learning methods to extract topological characteristics of proteins and their functional roles in a protein-protein interaction (PPI)-keywords (PPIK) network and understand the complex disease-causing property. Consequently, a PPIK-based metagraph representation approach is proposed. To enrich the PPI network, we integrated keywords describing protein properties and constructed a PPIK network. We extracted features that describe the topology of this network through metagraphs. We further transformed these metagraphs into vectors and represented proteins with a series of vectors. We then trained and optimized our model using random forest (RF), extreme gradient boosting, light gradient boosting machine, and logistic regression models. Comprehensive experiments demonstrated the good performance of our proposed method with an area under the receiver operating characteristic curve (AUC) value between 0.72 and 0.76. Our model also outperformed baseline methods for overall disease protein prediction, including the random walk with restart, average commute time, and Katz models. Compared with the PPI network constructed from the baseline models, complementation of keywords in the PPIK network improved the performance (AUC) by 0.08 on average, and the metagraph-based method improved the AUC by 0.30 on average compared with that of the baseline methods. According to the comprehensive performance of the four models, RF was selected as the best model for disease protein prediction, with precision, recall, F1-score, and AUC values of 0.76, 0.73, 0.72, and 0.76, respectively. We transformed these proteins to their encoding gene IDs and identified the top 20 genes as the most probable schizophrenia-risk genes, including the EYA3, CNTN4, HSPA8, LRRK2, and AFP genes. We further validated these outcomes against metagraph features and evidence from the literature, performed a features analysis, and exploited evidence from the literature to interpret the correlation between the predicted genes and diseases. The metagraph representation based on the PPIK network framework was found to be effective for potential schizophrenia risk genes identification. The results are quite reliable as evidence can be found in the literature to support our prediction. Our approach can provide more biological insights into the pathogenesis of schizophrenia.

Whole-genome sequencing and RNA sequencing analysis reveals novel risk genes and differential expression patterns in hepatoblastoma

Hepatoblastoma (HB) is an uncommon malignant liver cancer primarily affecting infants and children, characterized by the presence of tissue that resembling fetal hepatocytes, mature liver cells or bile duct cells. The primary symptom in affected children is abdominal lumps. HB constitutes approximately 28% of all liver tumors and two-thirds of liver malignancies in the pediatric and adolescent population. Despite its high prevalence, the underlying mechanism of HB pathogenesis remain largely unknown. To reveal the genetic alternations associated with HB, we conducted a comprehensive genomic study using whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) techniques on five HB patients. We aimed to use WGS to identify somatic variant loci associated with HB, including single nucleotide polymorphisms (SNPs), insertions and deletions (Indels), and copy number variations (CNVs). Notably, we found deleterious mutation in CTNNB1, AXIN2 and PARP1, previously implicated in HB. In addition, we discovered multiple novel genes potentially associated with HB, including BRCA2 and GPC3 which require further functional validation to reveal their contributions to HB development. Furthermore, the American College of Medical Genetics and Genomics (ACMG) analysis identified the ABCC2 gene was the pathogenic gene as a potential risk gene linked with HB. To study the gene expression patterns in HB, we performed RNA-seq analysis and qPCR validation to reveal differential expression of four candidate genes (IGF1R, METTL1, AXIN2 and TP53) in tumors compared to nonneoplastic liver tissue in HB patients (P-Val < 0.01). These findings shed lights on the molecular mechanisms underlying HB development and facilitate to advance future personalized diagnosis and therapeutic interventions of HB.

Potential risk genes for primary Sjogren's syndrome from a meta-analysis by linear regression and random forest classification

Potential risk genes for primary Sjogren's syndrome from a meta-analysis by linear regression and random forest classification

Identifying potential risk genes and pathways for neuropsychiatric and substance use disorders using intermediate molecular mediator information.

Neuropsychiatric and substance use disorders (NPSUDs) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait genetic correlations. Genome-wide association studies (GWAS) of NPSUDs yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and molecular mediators (transcript, protein, and methylation abundances) infer the effect of these mediators on risk for disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome-wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (∼20,000) instead of millions of GWAS SNPs, which leads to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUDs by performing XWAS analyses in two tissues-blood and brain. First, to identify putative causal risk genes, we performed an XWAS using the Summary-data-based Mendelian randomization, which uses GWAS summary statistics, reference xQTL data, and a reference LD panel. Second, given the large comorbidities among NPSUDs and the shared cis-xQTLs between blood and the brain, we improved XWAS signal detection for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUDs. All XWAS signals i) were adjusted for heterogeneity in dependent instruments (HEIDI) (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the major histocompatibility complex region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (FURIN, NEK4, RERE, and ZDHHC5). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Our study revealed an enrichment of XWAS signals in vitamin D and omega-3 gene sets. So, including vitamin D and omega-3 in treatment plans may have a modest but beneficial effect on patients with bipolar disorder.

Integrated analysis of diagnostic, prognostic value and potential drug treatment of GSDME in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSC) poses a global health challenge. Effective biomarkers for early detection are necessary to improve the survival rate of HNSC patient. The purpose of this study was using integrated bioinformatic analysis to investigate the potential biological roles of GSDME in HNSC. The Gene Expression Omnibus (GEO) and Cancer Gnome Atlas (TCGA) databases were used to analyze the expression of GSDME in different cancer types. The correlation between GSDME expression and immune cell infiltration or immune checkpoint genes was examined by Spearman correlation analysis. DNA methylation analysis of the GSDME gene was conducted using the MethSurv database. Kaplan-Meier (K-M) survival curves, diagnostic receiver operating characteristic (ROC) curves, nomogram model, and Cox regression analysis were chosen to evaluate the diagnostic and prognostic predictive value of GSDME. Connectivity Map (Cmap) online platform, Protein Data Bank (PDB) database and Chem3D, AutoDock Tool and PyMol software were used to predict and visualize potential molecular drugs aimed for GSDME. GSDME expression level in HNSC was significantly higher than in the controls (p < 0.001). Differentially expressed genes (DEGs) correlation with GSDME were enriched in the GO pathways, such as protein activation cascade, complement activation and classical pathway (p < 0.05). According to GSEA, GSDME-associated differentially expressed genes were significantly enriched in KRAS signaling pathway and cytokine signaling molecule (p < 0.05). There is a significant relation between GSDME expression and immune cell infiltration in HNSC tissues, as well as immune checkpoint genes expression (p < 0.001). DNA methylation status of cg17790129 CpG islands of GSDME gene is correlated with HNSC prognosis (p < 0.05). Based on Cox regression analysis of HNSC patients, GSDME as a potential risk gene has high correlation with overall survival (OS) and disease specific survival (DSS) (p < 0.05). In a ROC curve analysis, HNSC tissues were differentiated from adjacent peritumoral tissues based on GSDME expression levels (AUC = 0.928). Totally six potential drugs targeted for GSDME were screened and the molecular docking tests between GSDME protein and candidate drugs were conducted. GSDME is a promising therapeutic target as well as a potential clinical biomarker in HNSC patients.