Protein-protein Interaction Network Research Articles

Integrated Transcriptome and Proteome Analyses Reveal Differentially Expressed Genes and Proteins in Granulosa Cells from Female Patients with Metabolic Syndrome-associated Infertility.

Metabolic Syndrome (MS) is a cluster of conditions that significantly increase the risk of infertility in women. Granulosa cells are crucial for ovarian folliculogenesis and fertility. Understanding molecular alterations in these cells can provide insights into MS-associated infertility. This study aimed to investigate Differentially Expressed Genes (DEGs) and Proteins (DEPs) in granulosa cells from female patients with MS-associated infertility. Transcriptome and proteome analyses were integrated to compare granulosa cells from three MS patients with infertility to three control subjects. RNA sequencing and quantitative proteomics analyses were conducted, followed by differential expression analysis, Gene Set Enrichment Analysis (GSEA), and Protein-protein Interaction (PPI) network construction. Functional enrichment of overlapping DEGs and DEPs and potential drug-protein interactions were also explored. Hub genes identified by PPI were validated via quantitative Polymerase Chain Reaction (qPCR) and western blot assays. Principal Component Analysis (PCA) demonstrated a distinct separation between MS and control groups, indicating significant differences in gene and protein expression. A total of 1,046 upregulated and 23 downregulated DEGs, along with 222 upregulated and 412 downregulated DEPs, were identified in the MS group. GSEA highlighted enrichment in processes, like the cell cycle and immune response. Venn diagram revealed 71 overlapping DEGs and DEPs, mainly related to immune regulation. Key hub proteins and potential therapeutic candidates were identified, with hub genes upregulated at the mRNA level, but downregulated at the protein level in granulosa cells of MS patients. The integrative analyses revealed significant molecular alterations in granulosa cells from MS patients with infertility. Identified DEGs, DEPs, and hub proteins suggested potential therapeutic targets and pathways for addressing MS-associated infertility.

The relevance of endoplasmic reticulum lumen and Anoctamin-8 for major depression: Results from a systems biology study.

Major depressive disorder (MDD) is a highly prevalent and debilitating disorder, yet its pathophysiology has not been fully elucidated. The aim of this study is to identify novel potential proteins and biological processes associated with MDD through a systems biology approach. Original articles involving the measurement of proteins in the blood of patients diagnosed with MDD were selected. Data on the differentially expressed proteins (DEPs) in each article were extracted and imported into R, and the pathfindR package was used to identify the main gene ontology terms involved. Data from the STRING database were combined with the DEPs identified in the original studies to create expanded networks of protein-protein interactions (PPIs). An R script was developed to obtain the five most reliable connections from each DEP and to create the networks, which were visualized through Cytoscape software. Out of 510 articles found, eight that contained all the values necessary for the analysis were selected, including 1112 adult patients with MDD and 864 controls. A total of 240 DEPs were identified, with the most significant gene ontology term being "endoplasmic reticulum lumen" (46 DEPs, p-value=5.5x10-13). An extended PPI network was obtained, where Anoctamin-8 was the most central protein. Using systems biology contributed to the interpretation of data obtained in proteomic studies on MDD and expanded the findings of these studies. The combined use of these methodologies can provide new insights into the pathophysiology of psychiatric disorders, identifying novel biomarkers to improve diagnostic, prognostic, and treatment strategies in MDD.

Genome-wide characterization of the MADS-box gene family in Paeonia ostii and expression analysis of genes related to floral organ development

BackgroundPaeonia section Moutan DC. is a significant perennial subshrub, the ornamental value of which heavily depends on the type of flower it possesses. MADS-box transcription factors have a particular impact on the intricate process of floral organ development and differentiation. The release of the whole-genome data from Paeonia ostii now allows us to conduct a thorough investigation of the tree peony MADS-box gene family.ResultsIn this study, we identified 110 MADS-box genes in Paeonia ostii that were classified into 5 subgroups. Gene structure, domain and motif analyses revealed the conservation of the structure of these subgroups. Analysis of the cis-acting elements revealed that the 110 PoMADS genes contained different kinds of hormones and stress-related cis-acting elements in their promoter regions. Quantitative real-time PCR analysis was employed to validate the expression patterns of some PoMADS genes related to floral organ development. Genome collinearity analysis with Arabidopsis and grape revealed the conservation of PoMADS genes during evolution. A total of 857 SSRs were identified by analysing the genome sequences of identified genes. We additionally created protein‒protein interaction networks for PoMADS proteins and analysed proteins that could interact among PoMADSs in Arabidopsis thaliana and grape.ConclusionThese findings offer fundamental insights for understanding the function of the MADS-box gene family, which can aid in the selection and breeding of tree peony varieties with high ornamental value in addition to supporting the understanding of the process of tree peony floral organogenesis.

In silico analysis of hub genes and regulatory networks implicates the putamen in non-motor Parkinson’s disease disorders

BackgroundParkinson’s disease (PD) is a neurodegenerative condition marked by the gradual degeneration of dopaminergic neurons in the substantia nigra, leading to depletion of the dopaminergic neurons in the substantia nigra as well as degeneration as and decreased activity in the putamen. This study aims to identify the role of putamen in non-motor PD symptoms as well as potential therapeutic target in the putamen of PD.MethodsTranscriptome profiles of PD (dataset number: GSE205450, obtained from postmortem putamen and caudate samples from forty controls and thirty-five PD patients) were retrieved from the Gene Expression Omnibus (GEO) database. Specifically, we focused on putamen data for controls and PD patients. Differential gene expression analysis was carried out using with Limma, filtering for genes with |logFC|> 1 (fold change) and p < 0.05 (p-value). Protein–Protein Interaction networks were constructed using stringDB (combined score > 0.7) and analyzed in Cytoscape to identify hub genes based on various topological measures (EPC, MCC, MNC, Degree, and EcCentricity). Enrichment analysis of target genes was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Also, we constructed transcription factor (TF)-hub gene expression networks, miRNA-hub gene expression networks, and disease hub gene association networks using the JASPAR database, Tarbase database, and DisGeNET via NetworkAnalyst platform, respectively.ResultsSeven genes, namely SST, NPY, IL6, PVALB, ALB, NTS, and TH were identified as hub genes in PD putamen. Notable miRNAs included hsa-mir-34a-5p, hsa-mir-15a-5p, hsa-mir-424-5p, and hsa-mir-19b-3p, while key transcription factors include GATA2, CREB1, FOXC1, FOXL1, TID1, NFKB1, YY1, SPIB, GATA3, and STAT3.ConclusionsOur findings revealed close associations between the hub genes of PD putamen and non-motor symptoms of PD such as major depressive disorder, mood disorders and schizophrenia. These findings may provide a new direction for developing therapy for non-motor symptoms of PD and wet lab research is encouraged.

Elemene Injection Suppresses Pancreatic Cancer Progress through Regulating Cell Adhesion: A Research Based upon Network Pharmacology and Verification Test.

This study investigates the potential effects of elemene injection on pancreatic cancer using network pharmacology and experimental validation. GEO database were used to acquire genes which are differentially expressed between pancreatic cancer tissue and normal tissue. The vigorous energetic ingredients were identified in research and the object genes were obtained from BATMAN-TCM. The key targets and signaling pathways of elemene injection were identified using compound- target network analysis, protein-protein interaction network analysis, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. in vitro experiments were carried out to confirm the accuracy of the network pharmacology predictions. Two hundred and eleven target genes that may be involved in Elemene's impact on pancreatic cancer were identified. Bioinformatics analysis was conducted to determine the two active mixtures and one key target. GO and KEGG enrichment analyses indicated that elemene injection exerts therapeutic effects on pancreatic cancer, regulating the cell adhesion by ECM-receptor interaction pathway. The experiments verified that elemene injection suppressed the growth and movement of pancreatic cancer cell lines Panc02 and MiaPaca-2 and the mechanism is related to regulating ECM-receptor interaction pathway-related genes. FN1 was identified as core targets by bioinformatics analysis. The FN1 was downregulated by elemene injection and was validated by QPCR and Western Blot. The findings of the current study emphasized that elemene injection might control cell attachment, decrease metastasis, and suppresses pancreatic cancer progress. FN1 might be a therapeutic target for pancreatic cancer.

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

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. To explore the metabolic regulatory mechanism of olanzapine in treating patients with schizophrenia with a moderate to high risk of violent aggression. 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. 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. The metabolic pathways of alanine, aspartate, and glutamate metabolism and arginine biosynthesis are the key pathways involved in olanzapine treatment for aggressive schizophrenia.

The Potassium Utilization Gene Network in Brassica napus and Functional Validation of BnaZSHAK5.2 Gene in Response to Potassium Deficiency.

Potassium, an essential inorganic cation, is crucial for the growth of oil crops like Brassica napus L. Given the scarcity of potassium in soil, enhancing rapeseed's potassium utilization efficiency is of significant importance. This study identified 376 potassium utilization genes in the genome of B. napus ZS11 through homologous retrieval, encompassing 7 functional and 12 regulatory gene families. These genes are unevenly distributed across 19 chromosomes, and the proteins encoded by these genes are mainly localized in the cell membrane, vacuoles, and nucleus. Microsynteny analysis highlighted the role of small-scale replication events and allopolyploidization in the expansion of potassium utilization genes, identifying 77 distinct types of cis-acting elements within their promoter regions. The regulatory mechanisms of potassium utilization genes were provided by analyses of transcription factors, miRNA, and protein interaction networks. Under low potassium stress, the potassium utilization genes, particularly those belonging to the KUP and CBL families, demonstrate pronounced co-expression. RNA-seq and RT-qPCR analysis identified the BnaZSHAK5.2 gene, which is a high-affinity potassium ion transporter, playing a crucial role in the stress response to potassium deficiency in B. napus, as its expression is strongly induced by low potassium stress. A functional complementation study demonstrates that the BnaZSHAK5.2 gene could rescue the primary root growth of the Athak5 mutant under low potassium conditions, confirming its role in response to low potassium stress by sustaining root development.

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.

Construction of the bromodomain-containing protein-associated prognostic model in triple-negative breast cancer

BackgroundBromodomain-containing protein (BRD) play a pivotal role in the development and progression of malignant tumours. This study aims to identify prognostic genes linked to BRD-related genes (BRDRGs) in patients with triple-negative breast cancer (TNBC) and to construct a novel prognostic model.MethodsData from TCGA-TNBC, GSE135565, and GSE161529 were retrieved from public databases. GSE161529 was used to identify key cell types. The BRDRGs score in TCGA-TNBC was calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Differential expression analysis was performed to identify differentially expressed genes (DEGs): DEGs1 in key cells, DEGs2 between tumours and controls and DEGs3 in high and low BRDRGs score subgroups in TCGA-TNBC. Differentially expressed BRDRGs (DE-BRDRGs) were determined by overlapping DEGs1, DEGs2 and DEGs3. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were conducted to investigate active pathways and molecular interactions. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to construct a risk model and calculate risk scores. TNBC samples from TCGA-TNBC were classified into high and low-risk groups based on the median risk score. Additionally, correlations with clinical characteristics, Gene Set Enrichment Analysis (GSEA), immune analysis, and pseudotime analysis were performed.ResultsA total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3. GO analysis revealed enriched terms such as ‘apoptotic process,’ ‘immune response,’ and ‘regulation of the cell cycle,’ while 56 KEGG pathways, including the ‘MAPK signaling pathway,’ were associated with DE-BRDRGs. A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed. A nomogram based on independent prognostic factors was also developed. Immune cell abundance was significantly higher in high-risk group. In both risk groups, TP53 exhibited the highest mutation frequency. The expression of KRT6A, ABCA1, EDNRB, and CTSD went decreased progressively in pseudotime.ConclusionA novel prognostic model for TNBC associated with BRDRGs was developed and validated, providing fresh insights into the relationship between BRD and TNBC.

Proteomic analysis of spinal dorsal horn in prior exercise protection against neuropathic pain

Neuropathic pain (NP) is a complex and prevalent chronic pain condition that affects millions of individuals worldwide. Previous studies have shown that prior exercise protects against NP caused by nerve injury. However, the underlying mechanisms of this protective effect remain to be uncovered. Therefore, the purpose of this study is to investigate how prior exercise affects protein expression in NP model rats and thus gain comprehensive insights into the molecular mechanisms involved. To achieve this objective, 6-week-old male Sprague–Dawley rats were randomly assigned into three groups, named as chronic constriction injury (CCI) of the sciatic nerve, CCI with prior 6-week swimming training (CCI_Ex), and sham operated (Sham). The CCI_Ex group underwent 6 weeks of swimming training before CCI surgery, while the CCI and sham groups had no intervention. Mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) were used as the main observation indicators to evaluate the behavioral changes associated with pain. Tissues from the spinal dorsal horn of the rats in the three groups were collected at 4 weeks after operation. LC–MS/MS proteomic analysis based on the label-free approach was used to detect protein profiles, and volcano plots, Venn diagrams, and clustering heatmaps were used to identify differentially expressed proteins (DEPs). Gene Ontology (GO) annotations, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein–protein interaction networks were employed to explore the biological importance of DEPs. At 14, 21, and 28 days following CCI, CCI rats with prior exercise showed a significant increase in the MWT and TWL of the injured lateral hind paw compared with those without exercise. A total of 122 proteins with significant changes in abundance were detected after CCI surgery, and 55 proteins were detected in the comparison between the CCI_Ex and CCI groups. GO and KEGG enrichment analysis revealed that oxygen transport capacity and the complement and coagulation cascades may be the critical mechanism by which prior exercise protects against NP. Serpina1, DHX9, and Alb are the key proteins in this process and warrant further attention, as confirmed by the results of Western blot analysis. In conclusion, this study provides new evidence that active physical activity can accelerate the relief of hyperalgesia after NP. Proteomic analyses revealed the potential target proteins and pathways for this process, offering valuable data resources and new insights into the pathogenesis and therapeutic targets of NP.

Excessive Weight Gain During Pregnancy Increased Ponoxarase 1 Level in Neonatal Cord Blood.

Maternal obesity is increasingly recognized as a risk factor for adverse fetal outcomes, primarily through its association with heightened oxidative stress. This study aimed to evaluate oxidative stress markers in umbilical cord blood of neonates born to obese mothers. Sixty-three pregnant women, who were of normal weight at the start of pregnancy but classified as obese at term, were included. Umbilical cord blood samples were collected immediately post-delivery and analyzed for serum oxidative stress markers (total oxidant status (TOS), total antioxidant status (TAS), paraoxanase (PON), aryl esterase, thiol, and catalase activities). Protein interaction networks were generated using Cytoscape (v3.10.3), and the overlapping proteins were further analyzed for functional annotations with ShinyGO (0.80). The top ten significantly enriched pathways were identified with a false discovery rate (FDR) threshold of <0.05. Significant associations were found between maternal BMI change and paraoxonase 1 (PON1) levels in umbilical cord blood, while no correlation was observed with other oxidative (total oxidant status) and antioxidant markers (total antioxidant status, aryl esterase, thiol, and catalase). Additionally, the correlation analysis showed a significant relationship between BMI change and fetal gestational age, but not with other demographic or clinical features. A total of 24 common protein interactors associated with PON1, obesity, and oxidative stress were identified. Functional annotation analysis revealed significant enrichment in antioxidant and oxidoreductase activities, along with pathways involved in insulin resistance, AGE-RAGE signaling, and atherosclerosis. Maternal obesity may specifically affect PON1 activity, potentially serving as a compensatory response to oxidative stress in neonates, suggesting PON1 as a possible biomarker for oxidative stress-related metabolic disturbances in neonates of obese mothers, with implications for monitoring and managing pregnancy outcomes in obese populations.

Integrating network pharmacology and experimental verification to reveal the ferroptosis-associated mechanism of Changpu-Yizhi-Wan in the treatment of Alzheimer's disease.

To explore the pharmacological mechanism of Changpu-Yizhi-Wan (CYW) in the treatment of Alzheimer's disease (AD) from the perspective of ferroptosis based on network pharmacology and experimental verification. The Encyclopedia of Traditional Chinese Medicine 2.0 (ETCM2.0) database was used to collect the active components of CYW, and the putative targets were predicted in ETCM2.0 and SwissTargetPrediction database. The AD related targets were collected from GeneCards, comparative toxicogenomics database (CTD), Online Mendelian Inheritance in Man (OMIM), DisGeNET and Therapeutic Target Database (TTD), the ferroptosis related targets were collected from FerrDb V2 database, and the common targets of CYW, AD and ferroptosis were calculated by Venny2.1 platform. Protein-protein interaction (PPI) analysis was performed by STRING database, and the active compounds-target network and the PPI network were constructed using Cytoscape software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway enrichment analysis were performed through DAVID database. RSL3 was used to induce HT22 cells to establish a neuronal ferroptosis cell model, and the inhibitory effect of CYW on neuronal ferroptosis was evaluated by cell viability assay, intracellular iron assay and lipid peroxidation staining. The ferroptosis-associated key protein expressions of Nrf2, SLC7A11, GPX4 and FTH1 were detected by Western blot. A total of 100 candidate compounds were identified from CYW, and 1129 putative targets were obtained. 3924 AD-related targets and 564 ferroptosis-related targets were collected, respectively. There were 78 common targets between them and CYW targets, which were potential targets for CYW to regulate ferroptosis in the treatment of AD. PPI network analysis identified 10 key targets, including TP53, IL6, STAT3, HIF1A, NFE2L2, and others. GO, KEGG and Reactome enrichment analysis showed that 78 potential targets were involved in the regulation of ferroptosis and Nrf2-mediated gene transcription. Molecular docking showed that some active components of CYW had good affinity with Nrf2. In RSL3-induced HT22 cells, CYW significantly improved cell viability, reduced intracellular iron levels and inhibited lipid peroxidation, and improved the protein expression of Nrf2, SLC7A11, GPX4 and FTH1. The pharmacological mechanism of CYW in the treatment of AD may be related to the regulation of Nrf2/SLC7A11/GPX4/FTH1 axis to inhibit neuronal ferroptosis.

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.

Exploring gene regulatory interaction networks and predicting therapeutic molecules among major depressive disorder and bipolar disorder: A bioinformatics approach.

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are two common psychiatric disorders that have a substantial influence on people's mental health and quality of life. The identification of regulatory networks and potential drugs for both disorders enhances our understanding of these conditions and facilitates the development of targeted and effective therapies. This study employed network-based methods to identify gene regulatory networks and potential therapeutics for Major Depressive Disorder (MDD) and Bipolar Disorder (BD). We identified intersecting genes, predicted miRNAs and transcription factors, and constructed the TF-miRNA-hub gene network. Modules, enrichment analysis, and motifs were identified, and potential drugs targeting disease-associated genes were discovered using the DSigDB from the Enricher database. We identified five common hub genes (AKT1, IL1B, IL6, MAPK3, TNF) in MDD and BD protein-protein interaction networks. Our analysis revealed three microRNAs (hsa-let-7d-5p, hsa-let-7a-5p, hsa-mir-34a-5p) and two transcription factors (NFKB1, RELA) targeting these hub genes, which are also involved in various disorders and pathways, including cancer, hepatitis B, and the TNF signalling pathway. Notably, we identified 10 potential drug candidates targeting these hubs, providing valuable insights into MDD and BD's molecular mechanisms and potential therapeutic targets. Further experimental validation required to confirm the computational predictions. The findings emphasize the importance of regulatory network motifs discovery in understanding the disorders-dynamics and therapeutics. These results provide the ground work for developing the common targeted interventions for MDD and BD.

Analyzing gene-based apoptotic biomarkers in insomnia using bioinformatics.

Insomnia is increasingly common and poses significant health risks. The aims of this study are to identify apoptosis-related genes and potential biomarkers for insomnia and to find new therapeutic targets. Insomnia gene expression profiles were downloaded from the Gene Expression Omnibus database, and differentially expressed genes in normal and insomnia samples were identified by limma rapid differential analysis, and then the major modular genes with clinical relevance to insomnia were analyzed using the Weighted Gene Co-Expression Network Analysis, and intersections were obtained with the differentially expressed genes as well as with apoptotic gene databases. We validated apoptosis-related differentially expressed genes, enriched and analyzed the specific biological process of insomnia and related signaling pathways. In addition, we constructed a protein-protein interaction network and obtained Top10 hub genes using Cytoscape. We selected 3 of them as hub genes and compared their expression in normal hippocampal neuronal cells and hippocampal neuronal cells of the model group exposed to corticosterone induction by Western Blot and qRT-PCR experiments. A total of 190 differentially expressed apoptosis-related genes were identified in insomnia, and BCL2, SOCS3, and IL7R were identified as important hub genes. Enrichment analysis showed that the occurrence of apoptosis in insomnia was mainly related to "PI3K-Akt signaling pathway," "JAK-STAT signaling pathway," "P53 signaling pathway" and so on. GO analysis showed that apoptosis in insomnia was mainly related to "immune response," "T cell differentiation in thymus," and "positive regulation of MAPK cascade." Western Blot and qRT-PCR experiments showed that BCL2, SOCS3, IL7R antiapoptotic indexes were under-expressed in modeled hippocampal neuronal cells compared to normal hippocampal neuronal cells. This study emphasizes the role of apoptosis-related genes in insomnia and preliminarily predicts that the occurrence of insomnia is closely related to apoptosis. Compared to the normal group, the antiapoptotic ability of hippocampal neurons in the model group is reduced. Although BCL2 has been studied in the context of sleep deprivation, SOCS3 and IL7R have not yet been explored in insomnia. Insomnia and sleep deprivation involve similar pathways, but due to different mechanisms and types of insomnia, gene expression may vary.

Study on Immune-Related Genes and Clinical Validation of Acute Myocardial Infarction Based on Bioinformatics.

Acute myocardial infarction (AMI) is a cardiovascular disease featuring the narrowing and hardening of coronary arteries triggered by a combination of factors, which ultimately leads to the death of heart muscle. We retrieved the GSE109048 and GSE123342 datasets from the Gene Expression Omnibus (GEO) database. After integrating these datasets, we selected 154 module key genes with the help of weighted correlation network analysis (WGCNA). After that, we used protein-protein interaction networks (PPI) analysis to screen out 18 core genes in the protein interaction network from 154 genes. Finally, we used three machine learning algorithms to jointly identify three genes (CLEC4D, CLEC4E and LY96) that may predict or influence the progression of AMI. In the dataset, CLEC4D, CLEC4E and LY96 were significantly overexpressed in AMI patients. Immune infiltration analysis revealed that CLEC4D, CLEC4E and LY96 could affect the extent of immune cell infiltration. For further verification, we found that the expression levels of CLEC4D, CLEC4E and LY96 in the AMI cohort were significantly higher than those in coronary heart disease (CAD) patients by qRT-PCR. This finding corroborated the results derived from bioinformatics analysis. In summary, CLEC4D, CLEC4E and LY96 can be used to predict the occurrence of AMI.

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.

Metastatic melanoma: An integrated analysis to identify critical regulators associated with prognosis, pathogenesis and targeted therapies.

Metastatic melanoma causes a high rate of mortality. We conducted an integrated analysis to identify critical regulators associated with the prognosis, pathogenesis, and targeted therapies of metastatic-melanoma. A microarray dataset, GSE15605, including 12 metastatic-melanoma and sixteen normal skin (NS) samples, were obtained from the GEO database. After exploration of DEGs of NS and metastatic-melanoma, identification of relevant transcription factors (TFs) and kinases, the Gene Ontology (GO), and pathways analyses of DEGs were performed. Protein-protein interaction (PPI) networks were evaluated by the STRING and Cytoscape. Subsequently, the hub genes were selected using GEPIA. Survival analysis was performed using the TCGA. To identify microRNA and lncRNA DEGs of the melanoma-associated genes miRwalk and FANTOM6 were employed. In metastatic-melanoma samples 285 and 1173 genes were up and down-regulated, respectively. The upregulated genes were mostly involved in granulocyte chemotaxis, positive regulation of calcium ion transmembrane transport, and melanin biosynthetic process. Five hub genes including CXCL11, ICAM1, LEF1, MITF, and STAT1 were identified, SUZ12, SOX2, TCF3, NANOG, and SMAD4 were determined as the most significant TFs in metastatic-melanoma. Furthermore, CDK2, GSK3B, CSNK2A1, and CDK1 target the highest amounts of genes associated with disease. The DGIdb analysis results show the match drugs for five hub genes. MiRNAs analysis revealed hsa-miR-181c-5p, hsa-miR-30b-3p, hsa-miR-3680-3P, hsa-miR-4659a-3p, hsa-miR-4687-3P, and hsa-miR-6808-3P could regulate the hub genes, whereas RP11-553K8.5 and SRP14-AS1 were identified as the top significant lncRNA. The items recognized in the current study can be used as potential biomarkers for diagnostic, predictive, and might helpful to develop targeted combined therapies.

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.