Hub Module Research Articles

Disentangling the factors defining Bacillus subtilis group species abundance in natural soils.

Bacillus subtilis is ubiquitously and broadly distributed in various environments but is mostly isolated from soil. Given that B. subtilis is known as a plant growth-promoting rhizobacterium in agriculture, we aimed to describe the natural distribution of this species and uncover how biotic and abiotic factors affect its distribution. When comparing different soils, we discovered that B. subtilis group species are most abundant in grasslands but can rarely be isolated from forest soil, even if the soil sample sites are situated in proximity. Differential analysis revealed that spore-forming bacteria exhibited enrichments in the grassland, suggesting niche overlap or synergistic interactions leading to the proliferation of certain Bacillus species in grassland environments. Network analysis further revealed that Bacillus and other Bacillota established a densely interconnected hub module in the grassland, characterised by positive associations indicating co-occurrence, a pattern not observed in the forest soil. Speculating that this difference was driven by abiotic factors, we combined amplicon sequencing with physico-chemical analysis of soil samples and found multiple chemical variables, mainly pH, to affect microbial composition. Our study pinpoints the factors that influence B. subtilis abundance in natural soils and, therefore, offers insights for designing B. subtilis-based biocontrol products in agricultural settings.

Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.

Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood. This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 (CKAP4) and serine-arginine protein kinase 1 (SRPK1) as critical hub genes. In the merged dataset, the area under the curve (AUC) values for CKAP4 and SRPK1 were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for CKAP4 and 0.878 (95% CI: 0.750 to 1.000) for SRPK1. The CIBERSORT analysis connected CKAP4 and SRPK1 with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls. CKAP4 and SRPK1 emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BackgroundOur study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.MethodsWe utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.ResultsWGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.ConclusionsHTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Weighted gene coexpression network analysis and machine learning for the determination of tfh cell and B cell infiltrating biomarkers in thymoma-associated myasthenia gravis

Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.

FCER1G as a novel immune-associated blood biomarker in cardiogenic stroke

BackgroundCardioembolic stroke (CE) exhibits the highest recurrence rate and mortality rate among all subtypes of cerebral ischemic stroke (CIS), yet its pathogenesis remains uncertain. The immune system plays a pivotal role in the progression of CE. Growing evidence indicates that several immune-associated blood biomarkers may inform the causes of stroke. The study aimed to identify new immune-associated blood biomarkers in patients with CE and create an online predictive tool in distinguishing CE from noncardioembolic stroke (non-CE) in CIS. MethodsGene expression profiles that were publicly available were obtained from the Gene Expression Omnibus (GEO). The identification of differentially expressed genes (DEGs) was conducted using the Limma package. The hub module and hub genes were identified through the application of weighted gene coexpression network analysis (WGCNA). In order to identify potential diagnostic biomarkers for CE, both the random forest (RF) model and least absolute shrinkage and selection operator (LASSO) regression analysis were employed. Concurrently, the CIBERSORT algorithm was employed to evaluate the infiltration of immune cells in CE samples and examine the correlation between the biomarkers and the infiltrating immune cells. The diagnostic gene expression in blood samples was confirmed using qRT-PCR in a self-constructed dataset. Univariate and multiple logistic regression analyses were used to identify the risk factors for CE. Subsequently, the mathematical model of the nomogram was employed via Java's "Spring Boot" framework to develop the corresponding online tool, which was then deployed on a cloud server utilizing "nginx". ResultsEleven differentially expressed genes (DEGs) that were upregulated and seven DEGs that were downregulated were identified. Through bioinformatics analysis and clinical sample verification, it was discovered that Fc Fragment of IgE Receptor Ig (FCER1G) could serve as a novel potential blood biomarker for CE. FCER1G, along with other risk factors associated with CE, were utilized to develop a nomogram. The training and validation sets, which consisted of 65 CIS patients, yielded areas under the curve (AUCs) of 0.9722 and 0.9689, respectively. These results indicate a high level of precision in risk delineation by the nomogram. Furthermore, the associated online predictive platform has the potential to serve as a more efficacious and appropriate predictive instrument (https://www.origingenetic.com/CardiogenicStroke-FCER1G) for distinguishing between CE and non-CE. ConclusionBlood biomarker FCER1G has the potential to identify patients who are at a higher risk of cardioembolism and direct the search for occult AF.The utilization of this online tool is anticipated to yield significant implications in terms of distinguishing between CE and non-CE, as well as enhancing the optimization of treatment decision support.

Hua-Feng-Dan Alleviates LPS-induced Neuroinflammation by Inhibiting the TLR4/Myd88/NF-κB Pathway: Integrating Network Pharmacology and Experimental Validation.

Neuroinflammation is the pathological basis of many neurological diseases, including neurodegenerative diseases and stroke. Hua-Feng-Dan (HFD) is a well-established traditional Chinese medicine that has been used for centuries to treat stroke and various other brain-related ailments. Our study aims to elucidate the molecular mechanism by which HFD mitigates neuroinflammation by combining network pharmacology and in vitro experiments. TCMSP and SymMap databases were used to extract active compounds and their related targets. The neuroinflammation-related targets were obtained from the GeneCards database. The common targets of HFD and neuroinflammation were used to construct a protein-protein interaction (PPI) network. MCODE plug-in was used to find the hub module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to dissect the hub module genes. The lipopolysaccharide (LPS)-induced BV2 microglial neuroinflammation model was utilized to assess the therapeutic effects of HFD on neuroinflammation. Western blotting analysis was performed to examine the core target proteins in the TLR4/My- D88/NF-κB signaling pathway, potentially implicated in HFD's therapeutic effects on neuroinflammation. Hoechst 33342 staining and JC-1 staining were employed to evaluate neuronal apoptosis. Through network pharmacology, 73 active compounds were identified, with quercetin, beta-sitosterol, luteolin, and (-)-Epigallocatechin-3-Gallate recognized as important compounds. Meanwhile, 115 common targets of HFD and neuroinflammation were identified, and 61 targets were selected as the hub targets utilizing the MCODE algorithm. The results of in vitro experiments demonstrated that HFD significantly inhibited microglial-mediated neuronal inflammation induced by LPS. Integrating the predictions from network pharmacology with the in vitro experiment results, it was determined that the mechanism of HFD in mitigating neuroinflammation is closely related to the TLR4/MyD88/NF-κB pathway. Furthermore, HFD demonstrated the capacity to shield neurons from apoptosis by curbing the secretion of pro-inflammatory factors subsequent to microglial activation. The findings demonstrated that HFD had an inhibitory effect on LPS-induced neuroinflammation in microglia and elucidated its underlying mechanism. These findings will offer a theoretical foundation for the clinical utilization of HFD in treating neurodegenerative diseases associated with neuroinflammation.

Differential Gene Analysis of Langerhans Cell Histiocytosis and the Significance of MMP1-Targeted Drug Repositioning.

Langerhans cell histiocytosis (LCH) is a rare condition predominantly affecting young children. Activation of the MAPK pathway has offered key new insights into the pathogenesis of LCH; however, the precise mechanisms underlying its occurrence and development are still far from being completely elucidated. There is still a relapse/reactivation rate in patients with multisystem LCH. Therefore, this study aimed to investigate other potential LCH pathophysiologies and prospective therapeutic targets. The gene expression omnibus (GEO) database was used to retrieve gene expression profiles of LCH (GSE16395). Three distinct types of analyses were performed after identifying the common differentially expressed genes (DEGs) in LCH: hub gene identification, functional annotation, module construction, drug repositioning, and expression analysis via immunohistochemistry (IHC). We identified 417 common DEGs and 50 central hub genes. This functional study highlighted the significance of keratinization, skin development, and inflammation. In addition, we predicted new drug candidates (RS2 drugs targeting matrix metalloprotease1, MMP1) that could be used for LCH treatment. Finally, gene-miRNA and gene-TF networks and immune cell infiltration were analyzed for MMP1-related genes. MMP1 expression levels in LCH tissues were validated by IHC. Our study identified the central communal genes and novel drug candidates. These shared pathways and hub genes offer new perspectives on future mechanisms of action and therapeutic targets.

534 Forming a Translational Operations Group: Bridging the Gap to Enhance CTSA Hub Operational Efficiencies

OBJECTIVES/GOALS: CTSA Hubs represent complex centers where teams work to meet multiple goals of the CTSA grant. An existing challenge is to work collaboratively across teams. To address this challenge, a Translational Operations Group (TOG) was established. Results show enhanced intra-hub collaboration and communication while reducing inefficiencies. METHODS/STUDY POPULATION: The TOG is composed of all CTSA hub module program managers who are charged with operationalizing vision into reality. The TOG was formed in 2021 as a mechanism to integrate new team members, provide connection, and improve cross-core awareness and collaboration. Leveraging team science principles, a team charter was developed outlining specific TOG aims and objectives. Collectively, shared goals were identified with establishment of group norms, effective communication pathways, shared resources and knowledge and meeting cadence. Leadership of the group rotates among the TOG members annually further engaging all TOG members. Pre and Post (one year) surveys were developed and provided to TOG members to gauge TOG effectiveness and perceptions of TOG members. RESULTS/ANTICIPATED RESULTS: Survey results demonstrate the effectiveness of the TOG concept in promoting core/module awareness and goals, intra-core/module interconnectedness, and forming connections and integration into the CTSA hub ecosystem. Psycho-social questions demonstrated an increase in organizational self-esteem within the pre to post survey period in relation to the specific TOG member’s core/module and the CTSA hub as a whole. DISCUSSION/SIGNIFICANCE: Formation of a TOG has been successful within a virtual environment where connection is challenging. Integration of members to the operational activities is critical to foster a positive work environment, reduce silo effects, and provide a space for sharing resources and knowledge. Annual reflection of priorities contribute to the ongoing success.

Bioinformatics analysis and validation of genes related to paclitaxel's anti-breast cancer effect through immunogenic cell death

Research indicated that Paclitaxel (PTX) can induce immunogenic cell death (ICD) through immunogenic modulation. However, the combination of PTX and ICD has not been extensively studied in breast cancer (BRCA). The TCGA-BRCA and GSE20685 datasets were enrolled in this study. Samples from the TCGA-BRCA dataset were consistently clustered based on selected immunogenic cell death-related genes (ICD-RGs). Next, candidate genes were obtained by overlapping differentially expressed genes (DEGs) between BRCA and normal groups, intersecting genes common to DEGs between cluster1 and cluster2 and hub module genes, and target genes of PTX from five databases. The univariate Cox algorithm and the least absolute shrinkage and selection operator (LASSO) were performed to obtain biomarkers and build a risk model. Following observing the immune microenvironment in differential risk subgroups, single-gene gene set enrichment analysis (GSEA) was carried out in all biomarkers. Finally, the expression of biomarkers was analyzed. Enrichment analysis showed that 626 intersecting genes were linked with inflammatory response. Further five biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified and a risk model was built. The model's performance was validated using GSE20685 dataset. Furthermore, the biomarkers were enriched with adaptive immune response. Lastly, the experimental results indicated that the alterations in IL18, SH2D2A, and CHI3L1 expression after treatment matched those in the public database. In this study, Five PTX-ICD-related biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified to aid in predicting BRCA treatment outcomes.

LOXL1 promotes tumor cell malignancy and restricts CD8 + T cell infiltration in colorectal cancer

BackgroundColorectal cancer (CRC) is a leading cause of cancer mortality globally. Lymph node metastasis and immunosuppression are main factors of poor prognosis in CRC patients. Lysyl oxidase like 1 (LOXL1), part of the lysyl oxidase (LOX) family, plays a yet unclear role in CRC. This study aimed to identify effective biomarkers predictive of prognosis and efficacy of immunotherapy in CRC patients, and to elucidate the prognostic value, clinical relevance, functional and molecular features, and immunotherapy predictive role of LOXL1 in CRC and pan-cancer.MethodsWeighted gene co-expression network analysis (WGCNA) was employed to explore gene modules related to tumor metastasis and CD8 + T cell infiltration. LOXL1 emerged as a hub gene through differential gene expression and survival analysis. The molecular signatures, functional roles, and immunological characteristics affected by LOXL1 were analyzed in multiple CRC cohorts, cell lines and clinical specimens. Additionally, LOXL1's potential as an immunotherapy response indicator was assessed, along with its role in pan-cancer.ResultsTurquoise module in WGCNA analysis was identified as the hub module associated with lymph node metastasis and CD8 + T cell infiltration. Aberrant elevated LOXL1 expression was observed in CRC and correlated with poorer differentiation status and prognosis. Molecular and immunological characterization found that LOXL1 might mediate epithelial-mesenchymal transition (EMT) process and immunosuppressive phenotypes of CRC. Functional study found that LOXL1 enhanced tumor cell proliferation, migration and invasion. Moreover, high LOXL1 levels corresponded to reduced CD8 + T cell infiltration and predicted poor clinical outcomes of immunotherapy. Similar trends were also observed at the pan-cancer level.ConclusionsOur findings underscore the critical role of LOXL1 in modulating both malignancy and immunosuppression in CRC. This positions LOXL1 as a promising biomarker for predicting prognosis and the response to immunotherapy in CRC patients.Graphical

Identification and validation of stemness-based and ferroptosis-related molecular clusters in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with an extremely poor prognosis. Cancer stem cells (CSCs) are considered to be responsible for the poor survival, recurrence and therapy resistance of PDAC. Ferroptosis plays a crucial role in the sustain and survival of CSCs. Here, we employed a rigorous evaluation of multiple datasets to identify a novel stemness-based and ferroptosis-related genes (SFRGs) signature to access the potential prognostic application. This work we retrieved RNA-sequencing and clinical annotation data from the TCGA, ICGC, GTEx and GEO database, and acquired 26 stem cell gene sets and 259 ferroptosis genes from StemChecker database and FerrDb database, respectively. Based on consensus clustering and ssGSEA analysis, we identified two expression patterns of CSCs traits (C1 and C2). Then, WGCNA analysis was implemented to screen out hub module genes correlated with stemness. Furthermore, differential expression analysis, Pearson correlation analysis, and the Least absolute shrinkage and selection operator (LASSO) and Cox regression were performed to identify the SFRGs and to construct model. In addition, the differences in prognosis, tumor microenvironment (TME) components and therapy responses were evaluated between two risk groups. Finally, we verified the most influential marker ARNTL2 experimentally by western blot, qRT-PCR, sphere formation assay, mitoscreen assay, intracellular iron concentration determination and MDA determination assays. In conclusion, we developed a stemness-based and ferroptosis-related prognostic model, which could help predict overall survival for PDAC patients. Targeting ferroptosis may be a promising therapeutic strategy to inhibit PDAC progression by suppressing CSCs.

Identification of Candidate Genes Associated With Development of Vascular Cognitive Impairment by Integrated Bioinformatics Analysis Combined With Biological Experiments.

The morbidity and mortality associated with vascular cognitive impairment (VCI) generally increase steeply, and health systems will face increasing demand for services. The present study aims to screen key genes to give new insight into the mechanisms and treatment of VCI based on bioinformatic approaches combined with biological experiments in rats. The gene expression data of VCI patients contained in the GSE122063 data set were downloaded from the Gene Expression Omnibus. We performed a weighted gene co-expression network analysis to identify a hub module and 44 hub genes. Two hundred and seventy-seven differentially expressed genes (DEGs) were analyzed using R software by the "limma" package. STRING database was used to construct protein-protein interaction (PPI) network, after which 36 hub genes were identified through Cytoscape. Functional enrichment analysis revealed that these genes from the yellow module and 277 DEGs were mainly associated with these pathways, such as Staphylococcus aureus infection, complement, and coagulation cascades. These biological functions are related to inflammatory cell activation and inflammatory response. The key genes of VCI were the overlapping hub genes from the yellow module and the PPI network. The expressions of hub genes in rats were determined by quantitative reverse transcription-polymerase chain reaction, Western blot, immunohistochemistry, and immunofluorescence. In conclusion, C1QA, C1QB, C1QC, CD163, and FCGR2A were highly expressed in the hippocampus of VCI rats, and they can serve as candidate biomarkers for the diagnosis and prognosis of VCI. Finally, molecular docking results suggested that 5 genes interact with Bisphenol A. These findings open a new avenue to investigate molecular mechanisms for preventing or treating VCI.

Identification and immune landscape analysis of fatty acid metabolism genes related subtypes of gastric cancer

Fatty acid metabolism (FAM) is associated with prognosis and immune microenvironment remodeling in many tumors. It is currently unknown how FAM affects the immunological microenvironment and prognosis of Gastric cancer (GC). Therefore, the current work aims to categorize GC samples based on the expression status of genes involved in FAM and to identify populations that might benefit from immunotherapy. In total, 50 FAM genes associated with overall survival (OS) were determined through univariate Cox proportional hazard regression analysis by mining the public TCGA and GEO databases. The GSE84437 and TCGA-STAD cohort samples were divided into two clusters using the "NMF" R package. According to the survival curve, patients in Cluster-1 showed considerably longer OS than those in Cluster-2. Patients in Cluster-1 exhibited earlier T stages, more intestinal GCs, and were older. MSI molecular subtypes were mainly distributed in Cluster-1, while GS molecular subtypes were distributed primarily in Cluster-2. There were 227 upregulated and 22 down-regulated genes (logFC > 1 or logFC < − 1, FDR < 0.05) in Cluster-2 compared with Cluster-1. One hub module (edges = 64, nodes = 12) was identified with a module score of 11.636 through Cytoscape plug-in MCODE. KEGG and GO analysis showed that the hub genes were associated with the cell cycle and cell division. Different immune cell infiltrates profile, and immune pathway enrichment existed between the subtypes. In conclusion, the current findings showed that practically all immunological checkpoint and immunoregulatory genes were elevated in patients with Cluster-2 GC, indicating that FAM subtypes may be crucial in GC immunotherapy.

The role of m6A RNA methylation regulator in meningioma.

Meningiomas are common intracranial tumors, and the effect of surgical resection is often unsatisfactory. N6-Methyladenosine (m6A)-related regulator expression levels are related to cancer occurrence and development. This study aimed to investigate the roles of m6A RNA methylation regulators in meningiomas, as these are currently unclear. Two m6A methylation-regulated genes (METTL3 and IGF2BP2) were identified as survival-associated linear models for RiskScore through bioinformatics analysis. Univariate and multivariate Cox regression analyses showed that the overall survival of patients with meningioma in the high-risk group was substantially shorter than that in the low-risk group. Weighted gene co-expression network analysis constructed a co-expression network based on the m6A methylation model (RiskScore). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses identified the biological processes of hub module gene behavior, and Cytoscape constructed an m6A methylation-related gene regulatory network. In vitro experiments verified that the mRNA and protein expression levels of METTL3 and IGF2BP2 were lower in meningioma cells than in normal meningioma cells. Therefore, central regulators of m6A methylation (METTL3 and IGF2BP2) could potentially serve as novel therapeutic targets in meningioma. Subsequently, a novel methylation signature (RiskScore) was developed for prognostic prediction in patients with meningioma.

Transcriptome Analysis of Peritoneal Cells Reveals the Early Immune Response of Flounder (Paralichthys olivaceus) to Inactivated Vibrio anguillarum Immunization.

Vibrio anguillarum (V. anguillarum) is a bacterium that seriously harms flounder and other aquaculture species. Vaccination is an effective means of preventing vibriosis and is mainly administered by intraperitoneal injection. Effective antigen processing at the initial stage of immunization is essential to elicit adaptive immune responses and improve vaccine efficacy. To understand the early immune response of flounder caused by inactivated V. anguillarum, we detected the transcriptome profiles of the cells in the peritoneal cavity (PoPerCs) after inactivated V. anguillarum immunization. More than 10 billion high-quality reads were obtained, of which about 89.33% were successfully mapped to the reference genome of flounder. A total of 1985, 3072, 4001, and 5476 differentially expressed genes were captured at 6, 12, 24, and 48 h post immunization, respectively. The hub module correlated with the immunization time was identified by WGCNA. GO and KEGG analysis showed that hub module genes were abundantly expressed in various immune-related aspects, including the response to stimuli, the immune system process, signal transducer activity, autophagy, the NOD-like receptor signaling pathway, the toll-like receptor signaling pathway, the T cell receptor signaling pathway, and Th17 cell differentiation. Additionally, genes related to Th cell differentiation are presented as heatmaps. These genes constitute a complex immune regulatory network, mainly involved in pathogen recognition, antigen processing and presentation, and Th cell differentiation. The results of this study provide the first transcriptome profile of PoPerCs associated with inactivated V. anguillarum immunity and lay a solid foundation for further studies on effective V. anguillarum vaccines.

Identification of hub genes and potential inhibitory compounds in the process of liver transplantation through transcriptome sequencing

Liver transplantation (LT) is the best choice for patients with end-stage liver diseases. In order to better understand pathophysiological alterations in LT, we aimed to identify potential hub genes and inhibitory compounds involved in the LT process. Four pairs of peripheral blood mononuclear cell (PBMC) samples of the LT recipients before and after surgery were collected and taken for transcriptome sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for the screened differentially expressed genes (DEGs) between pre- and post-operation groups. Common DEGs were obtained from GO and KEGG enriched pathways, followed by protein-protein interaction (PPI) network construction, hub gene identification, module analysis, and structure-based virtual screening process (SBVS). Compared to the pre-operation stage, 4745 genes were down-regulated and 798 up-regulated after LT. GO analysis showed that the DEGs were enriched in ribosome-related translation regulation, and KEGG analysis indicated that infection and immune-related pathways and diseases were largely enriched. A large number of down-regulated DEGs were not only associated with ribosome-related pathways but also with the alterations of epigenetic modifications, in particular ubiquitination. Moreover, through the PPI network of 29 common genes from GO and KEGG-enriched pathways, 7 hub genes were identified, including PTEN, MYC, EIF2S1, EIF4EBP1, HSP90AB1, TP53, and HSPA8, which were mainly involved in the PI3K-AKT signaling pathway. SBVS of the seed molecule PTEN (PDB code: 1D5R) predicted top hits compounds that may serve as potential inhibitors of PTEN, of which the compound ZINC4235331 had the lowest binding affinity of -10 kcal/mol. The significance of screened hub genes and potential inhibitors involved in the process of LT provides novel therapeutic strategies for improving the outcomes of LT recipients during surgery.

Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis

BackgroundKawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood. Material/MethodsGene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein–protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers. ResultsWGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells. ConclusionActivated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.

Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets

Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation. Communicated by Ramaswamy H. Sarma

KIF11 serves as a cell cycle mediator in childhood acute lymphoblastic leukemia.

To identify key gene in childhood acute lymphoblastic leukemia (ALL) through weighted gene co-expression network analysis (WGCNA), and their enriched biological functions and signaling pathways. Array data of the GSE73578 dataset, involving 46 childhood ALL samples, were acquired from the Gene Expression Omnibus (GEO) database. Hub modules associated with childhood ALL were screened out by WGCNA. Enriched biological functions and signaling pathways were then identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were selected by overlapping those between down-regulated genes in GSE73578, GSE4698 and the hub module. Guilt by association (GBA) was adopted to verify the function of the identified KIF11 gene and to predict its target genes. Regulatory effects of KIF11 on the proliferation and cell cycle progression of ALL in vitro were determined by cytological experiments. WGCNA showed that the yellow module was the most relevant to childhood ALL treatment, containing 698 genes that were enriched in cell division, mitotic nuclear division, DNA replication and DNA repair, cell cycle, DNA replication and the P53 signaling pathway. The KIF11 gene was screened out and predicted as a cell cycle mediator in childhood ALL. Knockdown of KIF11 in ALL cells inhibited cell proliferation and arrested cell cycle progression in G2/M phase. The KIF11 gene is critical in the treatment process of childhood ALL, which is a promising therapeutic target for childhood ALL.

Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches.

Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis. The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR). Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway. The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.