A data browsing application for accessing gene and module-level blood transcriptome profiles of healthy pregnant women from high- and low-resource settings.

Transcriptome profiling data, generated via RNA sequencing, are commonly deposited in public repositories. However, these data may not be easily accessible or usable by many researchers. To enhance data reuse, we present well-annotated, partially analyzed data via a user-friendly web application. This project involved transcriptome profiling of blood samples from 15 healthy pregnant women in a low-resource setting, taken at 6 consecutive time points beginning from the first trimester. Additional blood transcriptome profiles were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public repository, representing a cohort of healthy pregnant women from a high-resource setting. We analyzed these datasets using the fixed BloodGen3 module repertoire. We deployed a web application, accessible at https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/which displays the module-level analysis results from both original and public pregnancy blood transcriptome datasets. Users can create custom fingerprint grid and heatmap representations via various navigation options, useful for reports and manuscript preparation. The web application serves as a standalone resource for exploring blood transcript abundance changes during pregnancy. Alternatively, users can integrate it with similar applications developed for earlier publications to analyze transcript abundance changes of a given BloodGen3 signature across a range of disease cohorts. Database URL: https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/.

LettuceDB: an integrated multi-omics database for cultivated lettuce.

Crop genomics has advanced rapidly during the past decade, which generated a great abundance of omics data from multi-omics studies. How to utilize the accumulating data becomes a critical and urgent demand in crop science. As an attempt to integrate multi-omics data, we developed a database, LettuceDB (https://db.cngb.org/lettuce/), aiming to assemble multidimensional data for cultivated and wild lettuce germplasm. The database includes genome, variome, phenome, microbiome and spatial transcriptome. By integrating user-friendly bioinformatics tools, LettuceDB will serve as a one-stop platform for lettuce research and breeding in the future. Database URL: https://db.cngb.org/lettuce/.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes.

BACKGROUND: As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. RESULTS: We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. CONCLUSIONS: Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

PPanG: a precision pangenome browser enabling nucleotide-level analysis of genomic variations in individual genomes and their graph-based pangenome.

Graph-based pangenome is gaining more popularity than linear pangenome because it stores more comprehensive information of variations. However, traditional linear genome browser has its own advantages, especially the tremendous resources accumulated historically. With the fast-growing number of individual genomes and their annotations available, the demand for a genome browser to visualize genome annotation for many individuals together with a graph-based pangenome is getting higher and higher. Here we report a new pangenome browser PPanG, a precise pangenome browser enabling nucleotide-level comparison of individual genome annotations together with a graph-based pangenome. Nine rice genomes with annotations were provided by default as potential references, and any individual genome can be selected as the reference. Our pangenome browser provides unprecedented insights on genome variations at different levels from base to gene, and reveals how the structures of a gene could differ for individuals. PPanG can be applied to any species with multiple individual genomes available and it is available at https://cgm.sjtu.edu.cn/PPanG .

Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors.

Introduction: Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD. Methods: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects. Results: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD. Discussion: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

Identification of hub genes and key pathways in spinal cord injury via bioinformatics analysis.

This study aimed to explore the hub genes and related key pathways in Spinal Cord Injury (SCI) based on the bioinformatics analysis. Two microarray datasets (GSE45006, GSE45550) were obtained from the GEO database and were merged and batch-corrected. The differentially expressed genes (DEGs) in SCI were explored with the Limma, and the weighted gene co-expression network analysis (WGCNA) was conducted to explore the module genes. Functional enrichment analysis and Gene set variation analysis (GSVA) were used to investigate the biological functions and key pathways of the key genes related to SCI. Then the protein-protein interaction (PPI) network was generated using the STING online tool, and the hub genes in SCI were identified. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic value of the selected hub genes. We identified 554 DEGs in SCI, and 1236 key genes in SCI were selected via WGCNA. Totally 111 key genes related to SCI were discovered. Furthermore, the functional enrichment analysis showed that these key mRNAs were primarily enriched in the extracellular matrix (ECM)-related pathways and processes associated with wound healing and cell growth. The PPI network further filtered six hub genes (Cd44, Timp1, Loxl1, Col6a1, Col3a1, Col5a1) ranked by the degree, and the diagnostic value of the six hub genes was confirmed by the ROC curves. Six hub genes including Cd44, Timp1, Loxl1, Col6a1, Col3a1, and Col5a1 were identified in SCI, with differential expression and excellent diagnostic value, which might provide insight into the targeted therapy of SCI.

Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes.

Cerebral ischemic stroke (CIS) has the characteristics of a high incidence, disability, and mortality rate. Here, we aimed to explore the potential pathogenic mechanisms of ferroptosis-related genes (FRGs) in CIS. Three microarray datasets from the Gene Expression Omnibus (GEO) database were utilized to analyze differentially expressed genes (DEGs) between CIS and normal controls. FRGs were obtained from a literature report and the FerrDb database. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to screen hub genes. The receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic value of key genes in CIS, followed by analysis of immune microenvironment, transcription factor (TF) regulatory network, drug prediction, and molecular docking. In total, 128 CIS samples were divided into 2 subgroups after clustering analysis. Compared with cluster A, 1560 DEGs were identified in cluster B. After the construction of the WGCNA and PPI network, 5 hub genes, including MAPK3, WAS, DNAJC5, PRKCD, and GRB2, were identified for CIS. Interestingly, MAPK3 was a FRG that differentially expressed between cluster A and cluster B. The expression levels of 5 hub genes were all specifically highly in cluster A subtype. It is noted that neutrophils were the most positively correlated with all 5 real hub genes. PRKCD was one of the target genes of FASUDIL. In conclusion, five real hub genes were identified as potential diagnostic markers, which can distinguish the two subtypes well.

A novel TCGA-validated programmed cell-death-related signature of ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC. METHODS: By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores. RESULTS: A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines. CONCLUSION: Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Orthology and Paralogy Relationships at Transcript Level.

Eukaryotic genes undergo a mechanism called alternative processing, resulting in transcriptome diversity by allowing the production of multiple distinct transcripts from a gene. More than half of human genes are affected, and the resulting transcripts are highly conserved among orthologous genes of distinct species. In this work, we present the definition of orthology and paralogy between transcripts of homologous genes, together with an algorithm to compute clusters of conserved orthologous and paralogous transcripts. Gene-level homology relationships are utilized to define various types of homology relationships between transcripts originating from the same ancestral transcript. A Reciprocal Best Hits approach is employed to infer clusters of isoorthologous and recent paralogous transcripts. We applied this method to transcripts from simulated gene families as well as real gene families from the Ensembl-Compara database. The results are consistent with those from previous studies that compared orthologous gene transcripts. Furthermore, our findings provide evidence that searching for conserved transcripts between homologous genes, beyond the scope of orthologous genes, is likely to yield valuable information.

Active substance and mechanisms of Actinidia chinensis Planch for the treatment of breast cancer was explored based on network pharmacology and in silico study.

In this paper, our objective was to investigate the potential mechanisms of Actinidia chinensis Planch (ACP) for breast cancer treatment with the application of network pharmacology, molecular docking, and molecular dynamics. "Mihoutaogen" was used as a key word to query the Traditional Chinese Medicine Systems Pharmacology database for putative ingredients of ACP and its related targets. DrugBank, GeneCards, Online Mendelian Inheritance in Man, and therapeutic target databases were used to search for genes associated with "breast cancer." Using Cytoscape 3.9.0 we then constructed the protein-protein interaction and drug-ingredient-target-disease networks. An enrichment analysis of Kyoto encyclopedia of genes and genomes pathway and gene ontology were performed to exploration of the signaling pathways associated with ACP for breast cancer treatment. Discovery Studio software was applied to molecular docking. Finally, the ligand-receptor complex was subjected to a 50-ns molecular dynamics simulation using the Desmond_2020.4 tools. Six main active ingredients and 176 targets of ACP and 2243 targets of breast cancer were screened. There were 118 intersections of targets for both active ingredients and diseases. Tumor protein P53 (TP53), AKT serine/threonine kinase 1 (AKT1), estrogen receptor 1 (ESR1), Erb-B2 receptor tyrosine kinase 2 (ERBB2), epidermal growth factor receptor (EGFR), Jun Proto-Oncogene (JUN), and Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1) selected as the most important genes were used for verification by molecular docking and molecular dynamics simulation. The primary active compounds of ACP against breast cancer were predicted preliminarily, and its mechanism was studied, thereby providing a theoretical basis for future clinical studies.

Observational study on the potential mechanism of Sanao decoction in the treatment of asthma based on network pharmacology and molecular docking.

Bronchial asthma (BA) is a chronic respiratory disease closely related to immune system dysregulation. Traditional Chinese medicine has long adopted the strategy of Sanao decoction in the treatment of bronchial asthma. However, due to the multi-target and multi-pathway characteristics of Chinese herbal medicine, we are still unclear about the specific mechanism of Sanao decoction in treating bronchial asthma. To investigate the mechanism of action of Sanao decoction in the treatment of BA using a network pharmacology approach and preliminary validation by molecular docking technology. Traditional Chinese medicine systems pharmacology database and analysis platform and UniProt databases were used to search the active ingredients and targets of Sanao decoction, and BA-related targets were screened according to GeneCards and online Mendelian inheritance in man database databases. The intersection targets were imported into the STRING database to construct a protein-protein interaction network, and Cytoscape 3.9.1 software was used to screen out hub genes. This study also constructed a "drug-ingredient-target" visual network diagram. Gene Ontology and Kyoto Encyclopedia of Genomes enrichment analysis was performed on targets in the protein-protein interaction network using the ClusterProfiler package in R, with a P value < .05. Autodock software was used for molecular docking to complete the preliminary verification of core components and targets. A total of 73 active compounds and 308 targets of Sanao decoction, including 1640 BA-related disease targets, were retrieved from mainstream databases. Gene Ontology analysis and Kyoto encyclopedia of genes and genomes enrichment analysis suggested that Sanao decoction plays a role in the treatment of BA through signaling pathways such as PI3K-Akt, MAPK, and IL-17 signaling pathway. The 9 core goals represent the main elements related to Sanao decoction in the treatment of BA. Subsequently, the molecular docking results showed that most of the active compounds of Sanao decoction have strong binding efficiency with the hub gene. Sanao decoction has a key impact on BA through multiple channels. In summary, this intricate network reflects the potential of Sanao decoction in treating BA, a multifactorial disease. In addition, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical application of Sanao decoction.

Identification and analysis of the molecular targets of statins in colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer in the world. According to several types of research, statins may impact the development and treatment of CRC. This work aimed to use bioinformatics to discover the relationship between statin targets and differentially expressed genes (DEGs) in CRC patients and determine the possible molecular effect of statins on CRC suppression. We used CRC datasets from the GEO database to select CRC-related DEGs. DGIdb and STITCH databases were used to identify gene targets of subtypes of statin. Further, we identified the statin target of CRC DEGs hub genes by using a Venn diagram of CRC DEGs and statin targets. Funrich and enrichr databases were carried out for the KEGG pathway and gene ontology (GO) enrichment analysis, respectively. GSE74604 and GSE10950 were used to identify CRC DEGs. After analyzing datasets,1370 genes were identified as CRC DEGs, and 345 targets were found for statins. We found that 35 genes are CRC DEGs statin targets. We found that statin targets in CRC were enriched in the receptor and metallopeptidase activity for molecular function, cytoplasm and plasma membrane for cellular component, signal transduction, and cell communication for biological process genes were substantially enriched based on FunRich enrichment. Analysis of the KEGG pathways revealed that the overexpressed DEGs were enriched in the IL-17, PPAR, and Toll-like receptor signaling pathways. Finally, CCNB1, DNMT1, AURKB, RAC1, PPARGC1A, CDKN1A, CAV1, IL1B, and HSPD1 were identified as hub CRC DEGs statin targets. The genetic and molecular aspects of our findings reveal that statins might have a therapeutic effect on CRC.

ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.

Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

Network pharmacology and molecular docking to explore the mechanism of a clinical proved recipe for external use of clearing heat and removing dampness in the treatment of immune-related cutaneous adverse events.

Immune-related cutaneous adverse events (ircAEs) will undermine the patients' quality of lives, and interrupt the antitumor therapy. A clinical proved recipe for external use of clearing heat and removing dampness (Qing-Re-Li-Shi Formula, hereinafter referred to as "QRLSF") is beneficial to the treatment of ircAEs in clinical practice. Our study will elucidate the mechanism of QRLSF against ircAEs based on network pharmacology and molecular docking. The active components and corresponding targets of QRLSF were collected through traditional Chinese medicine systems pharmacology database. GeneCards, online Mendelian inheritance in man, and pharmacogenomics knowledgebase were used to screen the targets of ircAEs. The intersecting targets between drug and disease were acquired by venn analysis. Cytoscape software was employed to construct "components-targets" network. Search tool for the retrieval of interacting genes/proteins database was applied to establish the protein-protein interaction network and then its core targets were identified. Gene ontology and Kyoto encyclopedia of genes and genomes analysis was performed to predict the mechanism. The molecular docking verification of key targets and related phytomolecules was accomplished by AutoDock Vina software. Thirty-nine intersecting targets related to QRLSF against ircAEs were recognized. The analysis of network clarified 5 core targets (STAT3, RELA, TNF, TP53, and NFKBIA) and 4 key components (quercetin, apigenin, luteolin, and ursolic acid). The activity of QRLSF against ircAEs could be attributed to the regulation of multiple biological effects via multi-pathways (PI3K-Akt pathway, cytokine-cytokine receptor interaction, JAK-STAT pathway, chemokine pathway, Th17 cell differentiation, IL-17 pathway, TNF pathway, and Toll-like receptor pathway). The binding activities were estimated as good level by molecular docking. These discoveries disclosed the multi-component, multi-target, and multi-pathway characteristics of QRLSF against ircAEs, providing a new strategy for such medical problem.

Analysis of ceRNA Network and Identification of Potential Treatment Target and Biomarkers of Endothelial Cell Injury in Sepsis.

Background: Sepsis is a complex clinical syndrome caused by a dysregulated host immune response to infection. This study aimed to identify a competing endogenous RNA (ceRNA) network that can greatly contribute to understanding the pathophysiological process of sepsis and determining sepsis biomarkers. Methods: The GSE100159, GSE65682, GSE167363, and GSE94717 datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene coexpression network analysis was performed to find modules possibly involved in sepsis. A long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) network was constructed based on the findings. Single-cell analysis was performed. Human umbilical vein endothelial cells were treated with lipopolysaccharide (LPS) to create an in vitro model of sepsis for network verification. Reverse transcription-polymerase chain reaction, fluorescence in situ hybridization, and luciferase reporter genes were used to verify the bioinformatic analysis. Result: By integrating data from three GEO datasets, we successfully constructed a ceRNA network containing 18 lncRNAs, 7 miRNAs, and 94 mRNAs based on the ceRNA hypothesis. The lncRNA ZFAS1 was found to be highly expressed in LPS-stimulated endothelial cells and may thus play a role in endothelial cell injury. Univariate and multivariate Cox analyses showed that only SLC26A6 was an independent predictor of prognosis in sepsis. Overall, our findings indicated that the ZFAS1/hsa-miR-449c-5p/SLC26A6 ceRNA regulatory axis may play a role in the progression of sepsis. Conclusion: The sepsis ceRNA network, especially the ZFAS1/hsa-miR-449c-5p/SLC26A6 regulatory axis, is expected to reveal potential biomarkers and therapeutic targets for sepsis management.

Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay.

This study examines the remarkable effectiveness of Withaferin-A (WA), a withanolide obtained from Withania somnifera (Ashwagandha), in encountering the mortiferous breast malignancy, a global peril. The predominant objective is to investigate WA's intrinsic target proteins and hedgehog (Hh) pathway proteins in breast cancer targeting through the application of in silico computational techniques and network pharmacology predictions. The databases and webtools like Swiss target prediction, GeneCards, DisGeNet and Online Mendelian Inheritance in Man were exploited to identify the common target proteins. The culmination of the WA network and protein-protein interaction network were devised using Stitch and String web tools, through which the drug-target network of 30 common proteins was constructed employing Cytoscape-version 3.9. Enrichment analysis was performed by incorporating Gprofiler, Metascape and Cytoscape plugins. David compounded the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, and enrichment was computed through bioinformatics tools. The 20 pivotal proteins were docked harnessing Glide, Schrodinger Suite 2023-2. The investigation was governed by docking scores and affinity. The shared target proteins underscored the precise Hh and WA network roles with the affirmation enrichment P-value of <0.025. The implications for hedgehog and cancer pathways were profound with enrichment (P < 0.01). Further, the ADMET and drug-likeness assessments assisted the claim. Robust interactions were noticed with docking studies, authenticated through molecular dynamics, molecular mechanics generalized born surface area scores and bonds. The computational investigation emphasized WA's credible anti-breast activity, specifically with Hh proteins, implying stem-cell-level checkpoint restraints. Rigorous testament is imperative through in vitro and in vivo studies.

Genome-wide association studies highlight novel risk loci for septal defects and left-sided congenital heart defects.

BACKGROUND: Congenital heart defects (CHD) are structural defects of the heart affecting approximately 1% of newborns. They exhibit low penetrance and non-Mendelian patterns of inheritance as varied and complex traits. While genetic factors are known to play an important role in the development of CHD, the specific genetics remain unknown for the majority of patients. To elucidate the underlying genetic risk, we performed a genome wide association study (GWAS) of CHDs in general and specific CHD subgroups using the FinnGen Release 10 (R10) (N > 393,000), followed by functional fine-mapping through eQTL and co-localization analyses using the GTEx database. RESULTS: We discovered three genome-wide significant loci associated with general CHD. Two of them were located in chromosome 17: 17q21.32 (rs2316327, intronic: LRRC37A2, Odds ratio (OR) [95% Confidence Interval (CI)] = 1.17[1.12-1.23], p = 1.5 × 10-9) and 17q25.3 (rs1293973611, nearest: BAHCC1, OR[95%CI] = 4.48[2.80-7.17], p = 7.0 × 10-10), respectively, and in addition to general CHD, the rs1293973611 locus was associated with the septal defect subtype. The third locus was in band 1p21.2 (rs35046143, nearest: PALMD, OR[95%CI] = 1.15[1.09-1.21], p = 7.1 × 10-9), and it was associated with general CHD and left-sided lesions. In the subgroup analysis, two additional loci were associated with septal defects (rs75230966 and rs6824295), and one with left-sided lesions (rs1305393195). In the eQTL analysis the variants rs2316327 (general CHD), and rs75230966 (septal defects) both located in 17q21.32 (with a LD r2 of 0.41) were both predicted to significantly associate with the expression of WNT9B in the atrial appendage tissue category. This effect was further confirmed by co-localization analysis, which also implicated WNT3 expression in the atrial appendage. A meta-analysis of general CHD together with the UK Biobank (combined N = 881,678) provided a different genome-wide significant locus in LRRC37A2; rs16941382 (OR[95%CI] = 1.15[1.11-1.20], p = 1.5 × 10-9) which is in significant LD with rs2316327. CONCLUSIONS: Our results of general CHD and different CHD subcategories identified a complex risk locus on chromosome 17 near BAHCC1 and LRRC37A2, interacting with the genes WNT9B, WNT3 and MYL4, may constitute potential novel CHD risk associated loci, warranting future experimental tests to determine their role.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas.

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

Novel insights into the pathogenesis of thyroid eye disease through ferroptosis-related gene signature and immune infiltration analysis.

Thyroid eye disease (TED) has brought great physical and mental trauma to patients worldwide. Although a few potential signaling pathways have been reported, knowledge of TED remains limited. Our objective is to explore the fundamental mechanism of TED and identify potential therapeutic targets using diverse approaches. To perform a range of bioinformatic analyses, such as identifying differentially expressed genes (DEGs), conducting enrichment analysis, establishing nomograms, analyzing weighted gene correlation network analysis (WGCNA), and studying immune infiltration, the datasets GSE58331, GSE105149, and GSE9340 were integrated. Further validation was conducted using qPCR, western blot, and immunohistochemistry techniques. Eleven ferroptosis-related DEGs derived from the lacrimal gland were originally screened. Their high diagnostic value was proven, and diagnostic prediction nomogram models with high accuracy and robustness were established by using machine learning. A total of 15 hub gene-related DEGs were identified by WGCNA. Through CIBERSORTx, we uncovered five immune cells highly correlated with TED and found several special associations between these immune cells and the above DEGs. Furthermore, EGR2 from the thyroid sample was revealed to be closely negatively correlated with most DEGs from the lacrimal gland. High expression of APOD, COPB2, MYH11, and MYCN, as well as CD4/CD8 T cells and B cells, was verified in the periorbital adipose tissues of TED patients. To summarize, we discovered a new gene signature associated with ferroptosis that has a critical impact on the development of TED and provides valuable insights into immune infiltration. These findings might highlight the new direction and therapeutic strategies of TED.

Chromosome-level genome assembly of Platycarya strobilacea.

Platycarya strobilacea belongs to the walnut family (Juglandaceae), is commonly known as species endemic to East Asia, and is an ecologically important, wind pollinated, woody deciduous tree. To facilitate this ancient tree for the ecological value and conservation of this ancient tree, we report a new high-quality genome assembly of P. strobilacea. The genome size was 677.30 Mb, with a scaffold N50 size of 45,791,698 bp, and 98.43% of the assembly was anchored to 15 chromosomes. We annotated 32,246 protein-coding genes in the genome, of which 96.30% were functionally annotated in six databases. This new high-quality assembly of P. strobilacea provide valuable resource for the phylogenetic and evolutionary analysis of the walnut family and angiosperm.