Protein-protein Interaction Network Research Articles

Metabolic pathway modulation by olanzapine: Multitarget approach for treating violent aggression in patients with schizophrenia

BACKGROUND The use of network pharmacology and blood metabolomics to study the pathogenesis of violent aggression in patients with schizophrenia and the related drug mechanisms of action provides new directions for reducing the risk of violent aggression and optimizing treatment plans. AIM To explore the metabolic regulatory mechanism of olanzapine in treating patients with schizophrenia with a moderate to high risk of violent aggression. METHODS Metabolomic technology was used to screen differentially abundant metabolites in patients with schizophrenia with a moderate to high risk of violent aggression before and after olanzapine treatment, and the related metabolic pathways were identified. Network pharmacology was used to establish protein-protein interaction networks of the core targets of olanzapine. Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were subsequently performed. RESULTS Compared with the healthy group, the patients with schizophrenia group presented significant changes in the levels of 24 metabolites related to the disruption of 9 metabolic pathways, among which the key pathways were the alanine, aspartate and glutamate metabolism and arginine biosynthesis pathways. After treatment with olanzapine, the levels of 10 differentially abundant metabolites were significantly reversed in patients with schizophrenia. Olanzapine effectively regulated six metabolic pathways, among which the key pathways were alanine, aspartate and glutamate metabolism and arginine biosynthesis pathways. Ten core targets of olanzapine were involved in several key pathways. CONCLUSION The metabolic pathways of alanine, aspartate, and glutamate metabolism and arginine biosynthesis are the key pathways involved in olanzapine treatment for aggressive schizophrenia.

TRPV4 as a Novel Regulator of Ferroptosis in Colon Adenocarcinoma: Implications for Prognosis and Therapeutic Targeting.

Colon adenocarcinoma (COAD) is a leading cause of cancer-related mortality worldwide. Transient receptor potential vanilloid 4 (TRPV4), a calcium-permeable non-selective cation channel, has been implicated in various cancers, including COAD. This study investigates the role of TRPV4 in colon adenocarcinoma and elucidates its potential mechanism via the ferroptosis pathway. Gene expression profiles and clinical data were obtained from The Cancer Genome Atlas (TCGA), encompassing 647 colon adenocarcinoma tissue samples and 51 normal colorectal tissue samples. Ferroptosis-related genes were retrieved from the FerrDb database. Differential expression analysis, survival analysis, and Cox proportional hazards regression were performed to assess the prognostic significance of TRPV4. Protein-protein interaction networks and gene enrichment analyses (GO and KEGG) were conducted to explore functional associations. In vitro experiments were carried out using HT-29 and SW480 colon cancer cell lines. TRPV4 was knocked down using siRNA, and cell viability was assessed via hematoxylin and eosin (HE) staining. Immunofluorescence assays evaluated the expression of ferroptosis-related proteins (SLC3A2, GPX4) and proliferation markers (KI67, SRC, CTNNB1, COL1). TRPV4 expression was significantly elevated in colon adenocarcinoma tissues compared to normal tissues (p < 0.05), demonstrating high diagnostic accuracy (AUC = 0.848). High TRPV4 expression correlated with poorer overall survival (OS) and disease-specific survival (DSS), particularly in patients over 65years old and those in clinical stage II. Cox regression analysis confirmed TRPV4 as an independent prognostic factor (HR = 1.395, p = 0.074). Bioinformatics analyses revealed that TRPV4 is closely associated with ferroptosis-related genes, participating in key biological processes such as responses to external stimuli, oxidative stress, and cell adhesion. In vitro, TRPV4 knockdown significantly reduced cell viability (p < 0.05) and decreased expression levels of SLC3A2, GPX4, KI67, SRC, and COL1 (p < 0.05). The addition of the ferroptosis inhibitor FER-1 did not restore cell viability in TRPV4 knockdown cells, suggesting that TRPV4 modulates cell survival through the ferroptosis pathway. The bioinformation and in vitro experiments inTRPV4 and ferroptosis-related genes support the hypothesis that TRPV4 influences tumor cell survival via the ferroptosis pathway. The inability of FER-1 to rescue viability in TRPV4-deficient cells further confirms this mechanism. These findings provide novel insights into the molecular mechanisms of COAD and highlight TRPV4 as a potential therapeutic target. TRPV4 is significantly upregulated in COAD and is associated with unfavorable patient outcomes. It appears to promote tumor progression by regulating the ferroptosis pathway, affecting the expression of key ferroptosis-related genes and proliferation markers. Targeting TRPV4 may offer a new therapeutic approach for COAD, and further research is warranted to explore its role in other cancers and to develop TRPV4-based therapies.

Lysine succinylome analysis of MRSA reveals critical roles in energy metabolism and virulence.

MRSA's resistance poses a global health challenge. This study investigates lysine succinylation in MRSA using proteomics and bioinformatics approaches to uncover metabolic and virulence mechanisms, with the goal of identifying novel therapeutic targets. Mass spectrometry and bioinformatics analyses mapped the MRSA succinylome, identifying 8 048 succinylation sites on 1 210 proteins. These analyses included Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network construction (e.g. using the STRING database), providing a comprehensive functional and interactive landscape of succinylated proteins. The succinylated proteins were predominantly involved in cytoplasmic metabolic processes, with enrichment in glycolysis/gluconeogenesis and the tricarboxylic acid (TCA) cycle. Both of these pathways are critical for MRSA's energy production, growth, and virulence, supplying the necessary metabolic intermediates and energy to support bacterial survival and pathogenicity. Motif analysis revealed 13 conserved motifs, while PPI analysis highlighted FnbA as a central virulence factor. Succinylation significantly influences MRSA's metabolism and virulence, potentially impacting biofilm by modifying key proteins such as FnbA, bifunctional autolysin, and LuxS. These findings provide new avenues for developing antibiofilm strategies and therapeutic interventions against MRSA.

Transcriptome, miRNA, and degradome sequencing reveal the leaf stripe (Pyrenophora graminea) resistance genes in Tibetan hulless barley

Barley leaf stripe, a disease mainly caused by Pyrenophora graminea (P. graminea) infection, severely affects barley yield and quality and is one of the most widespread diseases in barley production. However, little is known about the underlying molecular mechanisms of leaf stripe resistance. In this study, the transcript expression profiles of normal and infected leaves of resistant Tibetan hulless barley (Hordeum vulgare L. var. nudum Hook. f.) variety Kunlun 14 and susceptible variety Z1141 were analyzed by RNA sequencing (RNA-seq). The results showed a total of 7,669 and 5,943 differentially expressed genes (DEGs) were found in resistant and susceptible Kunlun 14 and Z1141, respectively, with 8,916 DEGs found between Kunlun 14 and Z1141. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the 8,916 DEGs identified many significantly enriched categories and pathways, of which a plant–pathogen interaction pathway, containing a total of 102 genes (100 known genes and two novel genes), was found, that was very important for the study of the leaf stripe resistance mechanism. Using RNA-seq, small RNA sequencing (miRNA-seq) combined with degradome sequencing (degradome-seq), four pairs associated with leaf-stripe miRNAs and target genes were obtained, namely Hvu-miR168-5p and Argonaute1 (HvAGO1), Hvu-novel-52 and growth-regulating factor 6 (HvGRF6), Hvu-miR6195 and chemocyanin-like protein (CLP), and Hvu-miR159b and gibberellin-dependent MYB (GAMYB). Transformation of the important target gene HvAGO1 into Arabidopsis verified that HvAGO1 could against Botrytis cinerea. Then RNA-seq and miRNA-seq of Arabidopsis transformed with overexpressed of HvAGO1 were performed. Based on the above research results, we constructed a Protein-Protein Interaction (PPI) network of barley leaf stripe resistance. This study lays the foundation for the study of the barley leaf stripe resistance mechanism and provides new targets for the genetic improvement of disease-resistant barley varieties.

Expression level of miR-548aa in tissue samples of patients with colorectal cancer.

The pathogenesis of colorectal cancer (CRC) is influenced by various risk factors, and genetic alterations in progression of colon polyps. The expression patterns of microRNA-548 (miR-548) in colorectal tissues have been sufficiently characterized. The aim of this study is to clarify the role of miR-548aa in tumorigenesis, gene targeting, predictive value and its expression levels in tumoral versus adjacent marginal tissues in CRC patients. This study included 35 CRC patients who underwent surgery to remove their tumor tissue. Tumor samples and adjacent marginal tissue were collected and gene expression was analyzed through real-time PCR. The correlation between miR-548aa expression levels and clinical parameters were investigated. Our findings showed a significant increase in the expression of miR-548aa in tumoral tissues compared to marginal tissues (p < 0.05). The upregulation of miR-548aa was significantly detected in CRC samples, showing an area under the curve of 0.89 (p = 0.002), indicating strong sensitivity and specificity for CRC diagnosis. To prediction of miRNA target genes and construction of regulatory networks of miR-548aa, we used bioinformatics tools including TargetScan and miRDB. Protein-protein interaction (PPI) network was constructed through STRING database and visualized using Cytoscape software. Dysregulation of miR-548aa is closely related to tumorigenesis in colorectal tissues, which affects disease progression and clinical outcomes. The miR-548aa can be introduced as a proposed biomolecule involved in mechanism of tumorigenesis, gene targeting and predictive value for CRC diagnosis and a target for therapeutic intervention.

Matrix Metalloproteinase-9 is associated with tumor microenvironment remodeling of bladder cancer

Tumor microenvironment (TME) takes an essential part in the bladder cancer progression, which is associated with intercellular cross-talk between stroma cells and cancer. We aimed use bioinformatics tools to analyze tumor microenvironment remodeling in bladder cancer. CIBERSORT and ESTIMATE are bioinformatics tools based on deconvolution for calculating proportions of tumor-infiltrating immune cells and stromal components in TME. We utilized these two algorithms to analyze the immune components of 433 bladder cancer cases from The Cancer Genome Atlas database, aiming to compensate for the current lack of large-sample single-cell information. Then we used Cox regression to analyze the prognostic value of differentially expressed genes, and the protein–protein interaction network was constructed. Matrix Metalloproteinase-9 (MMP9) was identified as a predictive biomarker related to immune microenvironment. Using Gene Set Enrichment Analysis, the genes from the group with high MMP9 expression gathered in items related to immune diseases, and genes in the group with low MMP9 expression were negatively associated with valine, leucine and isoleucine degradation and glycosylphosphatidylinositol anchor biosynthesis. MMP9 expression and presence of macrophages M0 were positively correlated, while naïve B cells, activated dendritic cells, monocytes and plasma cells were negatively correlated. The results were confirmed by brightfield and multiplex fluorescence immunohistochemistry using stained bladder cancer and normal tissue.

Advancing COVID-19 Treatment: The Role of Non-covalent Inhibitors Unveiled by Integrated Machine Learning and Network Pharmacology.

The COVID-19 pandemic has necessitated rapid advancements in therapeutic discovery. This study presents an integrated approach combining machine learning (ML) and network pharmacology to identify potential non-covalent inhibitors against pivotal proteins in COVID-19 pathogenesis, specifically B-cell lymphoma 2 (BCL2) and Epidermal Growth Factor Receptor (EGFR). Employing a dataset of 13,107 compounds, ML algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) were utilized for screening and predicting active inhibitors based on molecular features. Molecular docking and molecular dynamics simulations, conducted over a 100 nanosecond period, enhanced the ML-based screening by providing insights into the binding affinities and interaction dynamics with BCL2 and EGFR. Network pharmacology analysis identified these proteins as hub targets within the COVID-19 protein-protein interaction network, highlighting their roles in apoptosis regulation and cellular signaling. The identified inhibitors exhibited strong binding affinities, suggesting potential efficacy in disrupting viral life cycles and impeding disease progression. Comparative analysis with existing literature affirmed the relevance of BCL2 and EGFR in COVID-19 therapy and underscored the novelty of integrating network pharmacology with ML. This multidisciplinary approach establishes a framework for emerging pathogen treatments and advocates for subsequent in vitro and in vivo validation, emphasizing a multi-targeted drug design strategy against viral adaptability. This study's findings are crucial for the ongoing development of therapeutic agents against COVID-19, leveraging computational and network-based strategies.

Identifying Novel Therapeutic Opportunities for Dilated Cardiomyopathy: A Bioinformatics Approach to Drug Repositioning and Herbal Medicine Prediction.

Dilated Cardiomyopathy (DCM) is a debilitating cardiovascular disorder that challenges current therapeutic strategies. The exploration of novel drug repositioning opportunities through gene expression analysis offers a promising avenue for discovering effective treatments. This study aims to identify potential drug repositioning opportunities and lead compounds for DCM treatment by optimizing gene expression characteristics using published data. Our approach involved analyzing DCM expression profiles from the Gene Expression Omnibus database and identifying differentially expressed genes with GEO2R. A protein interaction network was constructed using the STRING database and visualized with Cytoscape. Enrichment analyses were conducted on these genes through the Omicshare platform, followed by the identification of candidate compounds via the Connectivity Map (CMAP) and validation through molecular docking. The Coremine Medical database was utilized to predict potential herbal medicines. We identified 29 differentially expressed genes, highlighting MYH6, NPPA, and NPPB as central to DCM pathology. Enrichment analyses indicated significant impacts on biological processes, such as organ morphogenesis and inflammatory responses. The AGE-RAGE signaling pathway was notably affected. From over 6,100 compounds analyzed, tenoxicam emerged as a promising candidate, with Radix Salviae Miltiorrhizae (Danshen) being suggested as a potential herbal treatment. This study underscores the utility of bioinformatics in uncovering new therapeutic candidates for DCM, offering a foundational step towards novel drug development.

Identification of key miRNAs and target genes in extracellular vesicles derived from low-intensity pulsed ultrasound-treated stem cells

ObjectivesThis study aimed to investigate the impact of low-intensity pulsed ultrasound (LIPUS) treatment on the miRNA and mRNA profiles of stem cell-derived extracellular vesicles (EVs). Specifically, it sought to identify key miRNAs and their target mRNAs associated with enhanced therapeutic efficacy in LIPUS-treated stem cell-derived EVs.MethodsUtilizing miRNA deep-sequencing data from the Gene Expression Omnibus database, differential gene analysis was performed. MiRNA-mRNA target analysis, functional and pathway enrichment analysis, protein-protein interaction network construction, and hub gene identification were conducted. Validation of differentially expressed miRNAs was performed via RT-qPCR in human umbilical cord mesenchymal stem cells (hUC-MSCs) treated with LIPUS.ResultsTen differentially expressed miRNAs were identified, with six upregulated and four downregulated miRNAs in LIPUS-treated stem cell-derived EVs. Functional enrichment analysis revealed involvement in biological processes such as regulation of metabolic processes, cellular component organization, and response to stress, as well as signaling pathways like cell cycle, MAPK signaling, and Hippo signaling. Protein-protein interaction network analysis identified key hub genes including MYC, GAPDH, HSP90AA1, EP300, JUN, PTEN, DAC1, STAT3, HSPA8, and HIF1A associated with LIPUS treatment. RT-qPCR validation confirmed differential expression of selected miRNAs (hsa-miR-933, hsa-miR-3943, hsa-miR-4633-5p, hsa-miR-592, hsa-miR-659-5p, hsa-miR-4766-3p) in LIPUS-treated hUC-MSCs.ConclusionThis study sheds light on the potential therapeutic mechanisms underlying LIPUS-treated stem cell-derived EVs. The identified differentially expressed miRNAs and their potential target mRNAs offer valuable insights into the biological processes influenced by LIPUS treatment. While further investigation is necessary to validate their roles as therapeutic targets, this study lays the groundwork for future research on optimizing SC-EV therapy with LIPUS preconditioning.

Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma

PurposeUveal melanoma (UM) is the most common intraocular malignancy in adults. Previous studies have examined the intra-tumoral heterogeneity. However, the spatial distribution of tumor cells within the tumor microenvironment and its relationship with tumor progression still remains largely unclear. Our study aimed to analyze the correlation between cell distribution patterns and the prognosis of UM.MethodsIn this paper, we performed spatial transcriptomics (ST) sequencing on two UM samples to describe the different cellular distribution patterns. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) functional enrichment analysis, and protein–protein interaction (PPI) network were performed to define the biological function of each cluster. Differentially expressed genes (DEGs) and survival analysis based on datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database further confirmed the correlation between cellular distribution and clinical prognosis.ResultsWe found two different patterns of tumor cell distribution. The focal tumor cells have a distinct ribosome synthesis and rRNA pathway. In contrast, the subpopulation tented to distribute diffusely was related to fatty acids metabolism profile, presumably supporting tumor growth by providing energy. The scattered tumor cell cluster was associated with malignant biological behaviors and was involved in extensive cellular interactions, including COLLAGEN. Moreover, pseudo-time analysis showed that migration started from the basal region through cell differentiation. According to the TCGA and GEO database, genes expressed characteristically in the scattered tumor cell cluster were related to poor prognosis.ConclusionsOur study drew the ST maps for UM for the first time. These findings revealed the distribution patterns of tumor cells associated with different biological functions and pointed towards specific tumor subpopulations with higher invasiveness as potential therapeutic targets. Together, our study displayed an overview of UM transcriptome and explored the intra-tumoral heterogeneity of UM at the spatial level.

Ferroptosis Transcriptional Regulation and Prognostic Impact in Medulloblastoma Subtypes Revealed by RNA-Seq

Medulloblastoma (MB) is the most common malignant brain tumor in children, typically arising during infancy and childhood. Despite multimodal therapies achieving a response rate of 70% in children older than 3 years, treatment remains challenging. Ferroptosis, a form of regulated cell death, can be induced in medulloblastoma cells in vitro using erastin or RSL3. Using two independent medulloblastoma RNA-sequencing cohorts (MB-PBTA and MTAB-10767), we investigated the expression of ferroptosis-related molecules through multiple approaches, including Weighted Gene Co-Expression Network Analysis (WGCNA), molecular subtype stratification, protein–protein interaction (PPI) networks, and univariable and multivariable overall survival analyses. A prognostic expression score was computed based on a cross-validated ferroptosis signature. In training and validation cohorts, the regulation of the ferroptosis transcriptional program distinguished the four molecular subtypes of medulloblastoma. WGCNA identified nine gene modules in the MB tumor transcriptome; five correlated with molecular subtypes, implicating pathways related to oxidative stress, hypoxia, and trans-synaptic signaling. One module, associated with disease recurrence, included epigenetic regulators and nucleosome organizers. Univariable survival analyses identified a 45-gene ferroptosis prognostic signature associated with nutrient sensing, cysteine and methionine metabolism, and trans-sulfuration within a one-carbon metabolism. The top ten unfavorable ferroptosis genes included CCT3, SNX5, SQOR, G3BP1, CARS1, SLC39A14, FAM98A, FXR1, TFAP2C, and ATF4. Patients with a high ferroptosis score showed a worse prognosis, particularly in the G3 and SHH subtypes. The PPI network highlighted IL6 and CBS as unfavorable hub genes. In a multivariable overall survival model, which included gender, age, and the molecular subtype classification, the ferroptosis expression score was validated as an independent adverse prognostic marker (hazard ratio: 5.8; p-value = 1.04 × 10⁻9). This study demonstrates that the regulation of the ferroptosis transcriptional program is linked to medulloblastoma molecular subtypes and patient prognosis. A cross-validated ferroptosis signature was identified in two independent RNA-sequencing cohorts, and the ferroptosis score was confirmed as an independent and adverse prognostic factor in medulloblastoma.

Identification, Clinical Values, and Prospective Pathway Signaling of Lipid Metabolism Genes in Epilepsy and AED Treatment.

The dysregulation of lipid metabolism has been associated with the etiology and progression of the neurological pathology. However, the roles of lipid metabolism and the molecular mechanism in epilepsy and the use of antiepileptic drugs (AEDs) are relatively understudied. Gene expression profiles of GSE143272 from blood samples were included for differential analysis, and the lipid metabolism-related differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The STRING database and Cytoscape software were used to establish and visualize protein-protein interaction (PPI) networks. RT-PCR and western blotting were used to verify the expression levels of lipid metabolism-related DEGs in serum and cerebrospinal fluid (CSF). Eleven lipid metabolism-related DEGs were identified including CXCL8, PTGS2, FOSB, G0S2, HLA-C, CLEC12A, ARG1, ELANE, RSAD2, CTSG, and DEFA1. And among them, five lipid metabolism-related Hub DEGs including CXCL8, PTGS2, ELANE, CTSG, and ARG1 were finally verified in serum samples of epilepsy patients. Moreover, CXCL8 was selected and validated in the epilepsy without AEDs and epilepsy with AEDs. G0S2 was significantly decreased in serum and CSF in epilepsy with AEDs compared to epilepsy without AEDs. Collectively, these findings suggest that lipid metabolism is closely related to epilepsy. This revelation opens up opportunities to further investigate the associated molecular mechanisms and possible therapeutic targets for epilepsy.

Exploring the mechanism of rosmarinic acid in the treatment of lung adenocarcinoma based on bioinformatics methods and experimental validation

ObjectiveRosmarinic acid (RosA) is a natural polyphenol compound that has been shown to be effective in the treatment of inflammatory disease and a variety of malignant tumors. However, its specific mechanism for the treatment of lung adenocarcinoma (LUAD) has not been fully elucidated. Therefore, this study aims to clarify the mechanism of RosA in the treatment of LUAD by integrating bioinformatics, network pharmacology and in vivo experiments, and to explore the potential of the active ingredients of traditional Chinese medicine in treating LUAD.MethodsFirstly, the network pharmacology was used to screen the RosA targets, and LUAD-related differential expressed genes (DEGs) were acquired from the GEO database. The intersection of LUAD regulated by RosA (RDEGs) was obtained through the Venn diagram. Secondly, GO and KEGG enrichment analysis of RDEGs were performed, and protein–protein interaction networks (PPIs) were constructed to identify and visualize hub RDEGs. Then, molecular docking between hub RDEGs and RosA was performed, and further evaluation was carried out by using bioinformatics for the predictive value of the hub RDEGs. Finally, the mechanism of RosA in the treatment of LUAD was verified by establishing a xenograft model of NSCLC in nude mouse.ResultsBioinformatics and other analysis showed that, compared with the control group, the expressions of MMP-1, MMP-9, IGFBP3 and PLAU in LUAD tissues were significantly up-regulated, and the expressions of PPARG and FABP4 were significantly down-regulated, and these hub RDEGs had potential predictive value for LUAD. In vivo experimental results showed that RosA could inhibit the growth of transplanted tumors in nude mice bearing tumors of lung cancer cells, reduce the positive expression of Ki67 in lung tumor tissue, and hinder the proliferation of lung tumor cells. Upregulated expression of PPARG and FABP4 by activating the PPAR signaling pathway increases the level of ROS in lung tumor tissues and promotes apoptosis of lung tumor cells. In addition, RosA can also reduce the expression of MMP-9 and IGFBP3, inhibit the migration and invasion of lung tumor tissue cells.ConclusionsThis study demonstrated that RosA could induce apoptosis by regulating the PPAR signaling pathway and the expression of MMP-9, inhibit the proliferation, migration and invasion of lung cancer cells, thereby exerting anti-LUAD effects. This study provides new insight into the potential mechanism of RosA in treating LUAD and provides a new therapeutic avenue for treatment of LUAD.

A Genome-Wide Association Screen for Genes Affecting Leaf Trichome Development and Epidermal Metal Accumulation in Arabidopsis.

To identify novel genes engaged in plant epidermal development, we characterized the phenotypic variability of rosette leaf epidermis of 310 sequenced Arabidopsis thaliana accessions, focusing on trichome shape and distribution, compositional characteristics of the trichome cell wall, and histologically detectable metal ion distribution. Some of these traits correlated with cLimate parameters of our accession's locations of origin, suggesting environmental selection. A novel metal deposition pattern in stomatal guard cells was observed in some accessions. Subsequent GWAS analysis identified 1546 loci with protein sequence-altering SNPs associated with one or more traits, including 5 genes with previously reported relevant mutant phenotypes and 80 additional genes with known or predicted roles in relevant developmental and cellular processes. Some candidates, including GFS9/TT9, exhibited environmentally correlated allele distribution. Several large gene famiLies, namely DUF674, DUF784, DUF1262, DUF1985, DUF3741, cytochrome P450, receptor-Like kinases, Cys/His-rich C1 domain proteins and formins were overrepresented among the candidates for various traits, suggesting epidermal development-related functions. A possible participation of formins in guard cell metal deposition was supported by observations in available loss of function mutants. Screening of candidate gene lists against the STRING interactome database uncovered several predominantly nuclear protein interaction networks with possible novel roles in epidermal development.

Systematic qualitative proteome-wide analysis of lysine malonylation profiling in Platycodon grandiflorus

In recent years, it was found that lysine malonylation modification can affect biological metabolism and play an important role in plant life activities. Platycodon grandiflorus, an economic crop and medicinal plant, had no reports on malonylation in the related literature. This study qualitatively introduces lysine malonylation in P. grandiflorus. A total of 888 lysine malonylation-modified proteins in P. grandiflorus were identified, with a total of 1755 modification sites. According to the functional annotation, malonylated proteins were closely related to catalysis, binding, and other reactions. Subcellular localization showed that related proteins were enriched in chloroplasts, cytoplasm, and nuclei, indicating that this modification could regulate various metabolic processes. Motif analysis showed the enrichment of Alanine (A), Cysteine (C), Glycine (G), and Valine (V) amino acids surrounding malonylated lysine residues. Metabolic pathway and protein-protein interaction network analyses suggested these modifications are mainly involved in plant photosynthesis. Moreover, malonylated proteins are also involved in stress and defense responses. This study shows that lysine malonylation can affect a variety of biological processes and metabolic pathways, and the contents are reported for the first time in P. grandiflorus, which can provide important information for further research on P. grandiflorus and lysine malonylation’s role in environment stress, photosynthesis, and secondary metabolites enrichment.

The Atlas of Protein-Protein Interactions in Cancer (APPIC)-a webtool to visualize and analyze cancer subtypes.

Cancer is a complex disease with heterogeneous mutational and gene expression patterns. Subgroups of patients who share a phenotype might share a specific genetic architecture including protein-protein interactions (PPIs). We developed the Atlas of Protein-Protein Interactions in Cancer (APPIC), an interactive webtool that provides PPI subnetworks of 10 cancer types and their subtypes shared by cohorts of patients. To achieve this, we analyzed publicly available RNA sequencing data from patients and identified PPIs specific to 26 distinct cancer subtypes. APPIC compiles biological and clinical information from various databases, including the Human Protein Atlas, Hugo Gene Nomenclature Committee, g:Profiler, cBioPortal and Clue.io. The user-friendly interface allows for both 2D and 3D PPI network visualizations, enhancing the usability and interpretability of complex data. For advanced users seeking greater customization, APPIC conveniently provides all output files for further analysis and visualization on other platforms or tools. By offering comprehensive insights into PPIs and their role in cancer, APPIC aims to support the discovery of tumor subtype-specific novel targeted therapeutics and drug repurposing. APPIC is freely available at https://appic.brown.edu.

Antibody Responses and the Vaccine Efficacy of Recombinant Glycosyltransferase and Nicastrin Against Schistosoma japonicum

Schistosomiasis is a neglected tropical disease and the second most common parasitic disease after malaria. While praziquantel remains the primary treatment, concerns about drug resistance highlight the urgent need for new drugs and effective vaccines to achieve sustainable control. Previous proteomic studies from our group revealed that the expression of Schistosoma japonicum glycosyltransferase and nicastrin as proteins was higher in single-sex males than mated males, suggesting their critical roles in parasite reproduction and their potential as vaccine candidates. In this study, bioinformatic tools were employed to analyze the structural and functional properties of these proteins, including their signal peptide regions, transmembrane domains, tertiary structures, and protein interaction networks. Recombinant forms of glycosyltransferase and nicastrin were expressed and purified, followed by immunization experiments in BALB/c mice. Immunized mice exhibited significantly elevated specific IgG antibody levels after three immunizations compared to adjuvant and PBS controls. Furthermore, immunization with recombinant glycosyltransferase and nicastrin significantly reduced the reproductive capacity of female worms and liver egg burden, though egg hatchability and adult worm survival were unaffected. These findings demonstrate that recombinant glycosyltransferase and nicastrin are immunogenic and reduce female worm fecundity, supporting their potential as vaccine candidates against schistosomiasis.

Identification and mechanistic insights of cell senescence-related genes in psoriasis

Background Psoriasis is a chronic inflammatory skin disease affecting 2–3% of the global population, characterised by red scaly patches that significantly affect patients’ quality of life. Recent studies have suggested that cell senescence, a state in which cells cease to divide and secrete inflammatory mediators, plays a critical role in various chronic diseases, including psoriasis. However, the involvement and mechanisms of action of senescence-related genes in psoriasis remain unclear. Methods This study aimed to identify senescence-related genes associated with psoriasis and explore their molecular mechanisms. RNA sequencing data from psoriasis and control samples were obtained from the GEO database. Differential expression analysis was performed using DESeq2 to identify differentially expressed genes (DEGs). The intersection of DEGs with cell senescence-related genes from the CellAge database was used to identify the candidate genes. Protein-protein interaction networks, Gene Ontology, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to explore the functions and pathways of these genes. Machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support vector machine-recursive feature elimination (SVE-RFE), were used to select feature genes that were validated by qRT-PCR. Additionally, an immune cell infiltration analysis was performed to understand the roles of these genes in the immune response to psoriasis. Results This study identified 4,913 DEGs in psoriasis, of which 46 were related to cell senescence. Machine learning highlighted four key genes, CXCL1, ID4, CCND1, and IRF7, as significant. These genes were associated with immune cell infiltration and validated by qRT-PCR, suggesting their potential as therapeutic targets for psoriasis. Conclusions This study identified and validated key senescence-related genes involved in psoriasis, providing insights into their molecular mechanisms and potential therapeutic targets and offering a foundation for developing targeted therapies for psoriasis.

Identification of Key Biomarkers Associated with Glioma Hemorrhage: Evidence from Bioinformatic Analysis and Clinical Validation.

Hemorrhagic stroke is a known complication of glioma, yet the underlying mechanisms remain poorly understood. This study aims to investigate key biomarkers of glioma-related hemorrhage to provide insights into glioma molecular therapies. Data were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases to analyze differentially expressed genes (DEGs) in glioma by contrasting glioblastoma (GBM) with low-grade gliomas (LGGs). We conducted enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) databases through the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A STRING-based protein-protein interaction (PPI) network was developed to identify hub genes, which were subsequently analyzed for their functions in the GeneCards database. To identify angiogenesis-associated genes, we utilized the Human Protein Atlas (HPA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. A clinical pathological study was conducted using immunohistochemistry (IHC) staining to confirm the findings. In the GEO database, the GEO Series Experiments GSE26576 and GSE184941 included 4523 and 1471 differentially expressed genes (DEGs), respectively. We identified 2715 DEGs using the cBioPortal within the TCGA database. A Venn diagram identified 39 common DEGs. The KEGG pathways and Gene Ontology (GO) analysis highlighted functions related to angiogenesis. PPI network analyses pinpointed 13 hub genes. Through cross-referencing a gene set related to tumor angiogenesis in the GeneCards database, we identified MMP-2 and EGFR as key genes. In the HPA database, we observed EGFR and MMP-2 expression in the normal cerebral cortex, confirmed by IHC. In GEPIA database, high MMP-2 levels were associated with decreased survival time, while EGFR expression showed no significant differences in survival. A clinical study of 21 patients, 11 in the control group and 10 in the stroke group with glioma hemorrhage, revealed no significant differences in their characteristics or comorbidities. IDH1 positivity was higher in the control group (4/11) vs the stroke group (0/10). Tumor cells exhibited increased MMP-2 and EGFR expression, with stronger staining in the stroke group. Our study concluded that IDH1, MMP-2, and EGFR are implicated in the molecular mechanism of glioma hemorrhage as key biomarkers. MMP-2 and IDH1 are potential targets for molecular therapy.

Fracture-healing effects of Rhizoma Musae ethanolic extract: An integrated study using UHPLC-Q-Exactive-MS/MS, network pharmacology, and molecular docking.

Fracture disrupts the integrity and continuity of the bone, leading to symptoms such as pain, tenderness, swelling, and bruising. Rhizoma Musae is a medicinal material frequently utilized in the Miao ethnic region of Guizhou Province, China. However, its specific mechanism of action in treating fractures remains unknown. This study aimed to elucidate the chemical constituents of the ethanol extract of Rhizoma Musae (EERM) and investigate its fracture-healing mechanism using network pharmacology. The chemical profile of EERM was characterized via UHPLC-Q-Exactive-MS/MS. Subsequently, a comprehensive network of compounds, targets, and pathways was constructed using network pharmacology approaches. The interactions between the active compounds of EERM and their targets were validated through molecular docking, molecular dynamics simulation and in vitro cell experiments. EERM contained 522 identified compounds. Topological analysis of the protein-protein interaction (PPI) network identified 59 core targets, including key proteins like AKT1, IL-6, and EGFR, known for their anti-inflammatory properties and ability to enhance bone cell proliferation and differentiation. Gene Ontology analysis indicated the involvement of EERM in biological processes such as peptidyl-serine phosphorylation, response to xenobiotic stimulus, and nutrient level regulation. KEGG analysis suggested that EERM's mechanism may involve signaling pathways such as PI3K-Akt, lipid and atherosclerosis, EGFR tyrosine kinase inhibitor resistance, and MAPK pathways. Molecular docking and molecular dynamics simulations results demonstrated a strong binding affinity between the main compounds of EERM and key targets. In vitro cell experiments demonstrate that EERM enhances cell proliferation by upregulating the expression levels of EGFR and STAT3, while simultaneously downregulating AKT1 and CASP3. This study investigates the potential active compounds of EERM and its key targets in regulating multiple pathways of fracture, leading to promoting bone cell proliferation. These results offer valuable insights for the future development and clinical application of Rhizoma Musae.