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Abstract Background Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene–gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene–gene interactions using Mendelian randomization. Results MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. Conclusion These findings demonstrate that MR-GGI accurately inferences gene–gene interactions despite the confounding effects in real biological environments.

Abstract Colorectal cancer (CRC) is one of the top five most common and life-threatening malignancies worldwide. Most CRC develops from advanced colorectal adenoma (ACA), a precancerous stage, through the adenoma-carcinoma sequence. However, its underlying mechanisms, including how the tumor microenvironment changes, remain elusive. Therefore, we conducted an integrative analysis comparing RNA-seq data collected from 40 ACA patients who visited Dongguk University Ilsan Hospital with normal adjacent colons and tumor samples from 18 CRC patients collected from a public database. Differential expression analysis identified 21 and 79 sequentially up- or down-regulated genes across the continuum, respectively. The functional centrality of the continuum genes was assessed through network analysis, identifying 11 up- and 13 down-regulated hub-genes. Subsequently, we validated the prognostic effects of hub-genes using the Kaplan–Meier survival analysis. To estimate the immunological transition of the adenoma-carcinoma sequence, single-cell deconvolution and immune repertoire analyses were conducted. Significant composition changes for innate immunity cells and decreased plasma B-cells with immunoglobulin diversity were observed, along with distinctive immunoglobulin recombination patterns. Taken together, we believe our findings suggest underlying transcriptional and immunological changes during the adenoma-carcinoma sequence, contributing to the further development of pre-diagnostic markers for CRC.

Abstract Background Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. Methods To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk. Results We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13 + BHLHE40 + TH cells which are significantly associated with JIA risks. Conclusion Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Integrative genomic and transcriptomic analyses on publicly available data-sets together with in silico drug repositioning identifies alternative therapeutic options to treat atopic dermatitis.

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Cerebral adrenoleukodystrophy (cALD) is a rare neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Another neurodegenerative disease with a high prevalence, Alzheimer’s disease (AD), shares many common features with cALD such as cognitive impairment and the alleviation of symptoms by erucic acid. We investigated cALD and AD in parallel to study the shared pathological pathways between a rare disease and a more common disease. The approach may expand the biological understandings and reveal novel therapeutic targets. Gene set enrichment analysis (GSEA) and weighted gene correlation network analysis (WGCNA) were conducted to identify both the resemblance in gene expression patterns and genes that are pathologically relevant in the two diseases. Within differentially expressed genes (DEGs), GSEA identified 266 common genes with similar up- or down-regulation patterns in cALD and AD. Among the interconnected genes in AD data, two gene sets containing 1,486 genes preserved in cALD data were selected by WGCNA that may significantly affect the development and progression of cALD. WGCNA results filtered by functional correlation via protein–protein interaction analysis overlapping with GSEA revealed four genes (annexin A5, beta-2-microglobulin, CD44 molecule, and fibroblast growth factor 2) that showed robust associations with the pathogeneses of cALD and AD, where they were highly involved in inflammation, apoptosis, and the mitogen-activated protein kinase pathway. This study provided an integrated strategy to provide new insights into a rare disease with scant publicly available data (cALD) using a more prevalent disorder with some pathological association (AD), which suggests novel druggable targets and drug candidates.

Abstract In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Abstract Background Regulatory hotspots are genetic variations that may regulate the expression levels of many genes. It has been of great interest to find those hotspots utilizing expression quantitative trait locus (eQTL) analysis. However, it has been reported that many of the findings are spurious hotspots induced by various unknown confounding factors. Recently, methods utilizing complicated statistical models have been developed that successfully identify genuine hotspots. Next-generation Intersample Correlation Emended (NICE) is one of the methods that show high sensitivity and low false-discovery rate in finding regulatory hotspots. Even though the methods successfully find genuine hotspots, they have not been widely used due to their non-user-friendly interfaces and complex running processes. Furthermore, most of the methods are impractical due to their prohibitively high computational complexity. Results To overcome the limitations of existing methods, we developed a fully automated web-based tool, referred to as NICER (NICE Renew), which is based on NICE program. First, we dramatically reduced running and installing burden of NICE. Second, we significantly reduced running time by incorporating multi-processing. Third, besides our web-based NICER, users can use NICER on Google Compute Engine and can readily install and run the NICER web service on their local computers. Finally, we provide different input formats and visualizations tools to show results. Utilizing a yeast dataset, we show that NICER can be successfully used in an eQTL analysis to identify many genuine regulatory hotspots, for which more than half of the hotspots were previously reported elsewhere. Conclusions Even though many hotspot analysis tools have been proposed, they have not been widely used for many practical reasons. NICER is a fully-automated web-based solution for eQTL mapping and regulatory hotspots analysis. NICER provides a user-friendly interface and has made hotspot analysis more viable by reducing the running time significantly. We believe that NICER will become the method of choice for increasing power of eQTL hotspot analysis.

Abstract Background Dupuytren’s disease (DD) is a fibroproliferative disorder characterized by thickening and contracting palmar fascia. The exact pathogenesis of DD remains unknown. Results In this study, we identified co-expressed gene set (DD signature) consisting of 753 genes via weighted gene co-expression network analysis. To confirm the robustness of DD signature, module enrichment analysis and meta-analysis were performed. Moreover, this signature effectively classified DD disease samples. The DD signature were significantly enriched in unfolded protein response (UPR) related to endoplasmic reticulum (ER) stress. Next, we conducted multiple-phenotype regression analysis to identify trans-regulatory hotspots regulating expression levels of DD signature using Genotype-Tissue Expression data. Finally, 10 trans-regulatory hotspots and 16 eGenes genes that are significantly associated with at least one cis-eQTL were identified. Conclusions Among these eGenes, major histocompatibility complex class II genes and ZFP57 zinc finger protein were closely related to ER stress and UPR, suggesting that these genetic markers might be potential therapeutic targets for DD.

Abstract Gold nanoparticles (AuNPs) with diverse physicochemical properties are reported to affect biological systems differently, but the relationship between the physicochemical properties of AuNPs and their biological effects is not clearly understood. Here, we aimed to elucidate the molecular origins of AuNP-induced cytotoxicity and their mechanisms, focusing on the surface charge and structural properties of modified AuNPs. We prepared a library of well-tailored AuNPs exhibiting various functional groups and surface charges. Through this work, we revealed that the direction or the magnitude of surface charge is not an exclusive factor that determines the cytotoxicity of AuNPs. We, instead, suggested that toxic AuNPs share a common structural characteristics of a hydrophobic moiety neighbouring the positive charge, which can induce lytic interaction with plasma membrane. Mechanistic study showed that the toxic AuNPs interfered with the formation of cytoskeletal structure to slow cell migration, inhibited DNA replication and caused DNA damage via oxidative stress to hinder cell proliferation. Gene expression analysis showed that the toxic AuNPs down-regulated genes associated with cell cycle processes. We discovered structural characteristics that define the cytotoxic AuNPs and suggested the mechanisms of their cytotoxicity. These findings will help us to understand and to predict the biological effects of modified AuNPs based on their physicochemical properties.

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.

The recent generation of induced neurons by direct lineage conversion holds promise for in vitro modelling of sporadic Alzheimer's disease. Here, we report the generation of induced neuron-based model of sporadic Alzheimer's disease in mice and humans, and used this system to explore the pathogenic mechanisms resulting from the sporadic Alzheimer's disease risk factor apolipoprotein E (APOE) ɛ3/4 allele. We show that mouse and human induced neurons overexpressing mutant amyloid precursor protein in the background of APOE ɛ3/4 allele exhibit altered amyloid precursor protein (APP) processing, abnormally increased production of amyloid-β42 and hyperphosphorylation of tau. Importantly, we demonstrate that APOE ɛ3/4 patient induced neuron culture models can faithfully recapitulate molecular signatures seen in APOE ɛ3/4-associated sporadic Alzheimer's disease patients. Moreover, analysis of the gene network derived from APOE ɛ3/4 patient induced neurons reveals a strong interaction between APOE ɛ3/4 and another Alzheimer's disease risk factor, desmoglein 2 (DSG2). Knockdown of DSG2 in APOE ɛ3/4 induced neurons effectively rescued defective APP processing, demonstrating the functional importance of this interaction. These data provide a direct connection between APOE ɛ3/4 and another Alzheimer's disease susceptibility gene and demonstrate in proof of principle the utility of induced neuron-based modelling of Alzheimer's disease for therapeutic discovery. [ABSTRACT FROM AUTHOR]