Protein-protein Interaction Network Analysis Research Articles

Microarray-Based Detection of Critical Overexpressed Genes in the Progression of Hepatic Fibrosis in Non-Alcoholic Fatty Liver Disease: A Protein-Protein Interaction Network Analysis.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent cause of chronic liver disease and encompasses a broad spectrum of disorders, including simple steatosis, steatohepatitis, fibrosis, cirrhosis, and liver cancer. However, due to the global epidemic of NAFLD, where invasive liver biopsy is the gold standard for diagnosis, it is necessary to identify a more practical method for early NAFLD diagnosis with useful therapeutic targets; as such, molecular biomarkers could most readily serve these aims. To this end, we explored the hub genes and biological pathways in fibrosis progression in NAFLD patients. Raw data from microarray chips with GEO accession GSE49541 were downloaded from the Gene Expression Omnibus database, and the R package (Affy and Limma) was applied to investigate differentially expressed genes (DEGs) involved in the progress of low- (mild 0-1 fibrosis score) to high- (severe 3-4 fibrosis score) fibrosis stage NAFLD patients. Subsequently, significant DEGs with pathway enrichment were analyzed, including gene ontology (GO), KEGG and Wikipathway. In order to then explore critical genes, the protein-protein interaction network (PPI) was established and visualized using the STRING database, with further analysis undertaken using Cytoscape and Gephi software. Survival analysis was undertaken to determine the overall survival of the hub genes in the progression of NAFLD to hepatocellular carcinoma. A total of 311 significant genes were identified, with an expression of 278 being upregulated and 33 downregulated in the high vs. low group. Gene functional enrichment analysis of these significant genes demonstrated major involvement in extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, and the AGE-RAGE signaling pathway. The PPI network was constructed with 196 nodes and 572 edges with PPI enrichment using a p-value < 1.0 e-16. Based on this cut-off, we identified 12 genes with the highest score in four centralities: Degree, Betweenness, Closeness, and Eigenvector. Those twelve hub genes were CD34, THY1, CFTR, COL3A1, COL1A1, COL1A2, SPP1, THBS1, THBS2, LUM, VCAN, and VWF. Four of these hub genes, namely CD34, VWF, SPP1, and VCAN, showed significant association with the development of hepatocellular carcinoma. This PPI network analysis of DEGs identified critical hub genes involved in the progression of fibrosis and the biological pathways through which they exert their effects in NAFLD patients. Those 12 genes offer an excellent opportunity for further focused research to determine potential targets for therapeutic applications.

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation

ObjectiveAlthough programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. MethodsWe acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filted the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. ResultsWe harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expressions corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. ConclusionInnovatively stemming from from intra-PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN.regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

Integrating UPLC-Q-TOF-MS and Network Pharmacology to Explore the Potential Mechanisms of Paeonia lactiflora Pall. in the Treatment of Blood Stasis Syndrome

Paeonia lactiflora Pall. (PLP) is thought to promote blood circulation and remove blood stasis. This study used blood component analysis, network pharmacology, and molecular docking to predict the mechanism of PLP in the treatment of blood stasis syndrome (BSS). PLP was processed into Paeoniae Radix Alba (PRA) and Paeoniae Radix Rubra (PRR). PRA and PRR could significantly reduce whole blood viscosity (WBV) at 1/s shear rates and could increase the erythrocyte aggregation index (EAI), plasma viscosity (PV), and erythrocyte sedimentation rate (ESR) of rats with acute blood stasis. They prolonged the prothrombin time (PT), and PRR prolonged the activated partial thromboplastin time (APTT). PRA and PRR increased the thrombin time (TT) and decreased the fibrinogen (FBG) content. All the results were significant (p &lt; 0.05). Ten components of Paeoniflorin, Albiflorin, Paeonin C, and others were identified in the plasma of rats using ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). A protein–protein interaction network (PPI) analysis showed that AKT1, EGFR, SRC, MAPK14, NOS3, and KDR were key targets of PLP in the treatment of BSS, and the molecular docking results further verified this. This study indicated that PLP improves BSS in multiple ways and that the potential pharmacological mechanisms may be related to angiogenesis, vasoconstriction and relaxation, coagulation, and the migration and proliferation of vascular cells.

Effect of SNPs on Litter Size in Swine

Although sows do not directly enter the market, they play an important role in piglet breeding on farms. They consume large amounts of feed, resulting in a significant environmental burden. Pig farms can increase their income and reduce environmental pollution by increasing the litter size (LS) of swine. PCR-RFLP/SSCP and GWAS are common methods to evaluate single-nucleotide polymorphisms (SNPs) in candidate genes. We conducted a systematic meta-analysis of the effect of SNPs on pig LS. We collected and analysed data published over the past 30 years using traditional and network meta-analyses. Trial sequential analysis (TSA) was used to analyse population data. Gene set enrichment analysis and protein–protein interaction network analysis were used to analyse the GWAS dataset. The results showed that the candidate genes were positively correlated with LS, and defects in PCR-RFLP/SSCP affected the reliability of candidate gene results. However, the genotypes with high and low LSs did not have a significant advantage. Current breeding and management practices for sows should consider increasing the LS while reducing lactation length and minimizing the sows’ non-pregnancy period as much as possible.

Computational Drug Discovery in Diaphragm dysfunction via Text Mining and Biomedical Database.

The diaphragm, which is crucial for ventilation, is the primary muscle responsible for inspiration. Patients with severe burns who experience diaphragmatic dysfunction have an increased risk of mortality. Unfortunately, there are currently no effective medications available to prevent or treat this condition. The objective of our study is to utilize bioinformatics to identify potential genes and drugs associated with diaphragmatic dysfunction. In this study, text mining techniques were utilized to identify genes associated with diaphragmatic dysfunction and recovery. Common genes were then analyzed using GO and KEGG pathway analysis, as well as protein-protein interaction (PPI) network analysis. The obtained hub genes were processed using Cytoscape software, and their expression levels in diaphragmatic dysfunction were validated using quantitative real-time polymerase chain reaction (qRT-PCR) in severe burn rats. Genes that were confirmed were then examined in drug-gene interaction databases to identify potential drugs associated with these genes. Our analysis revealed 96 genes that were common to both the "Diaphragm dysfunction" and "Functional Recovery" text mining concepts. Gene enrichment analysis identified 19 genes representing ten pathways. qRT-PCR showed a significant increase in expression levels of 13 genes, including CCL2, CCL3, CD4, EGF, HGF, IFNG, IGF1, IL17A, IL6, LEP, PTGS2, TGFB1, and TNF, in samples with diaphragmatic dysfunction. Additionally, we found that a total of 56 drugs targeted 5 potential genes. These findings provide new insights into the development of more effective drugs for treating diaphragmatic dysfunction, and also present substantial opportunities for researching new target pharmacology and promoting drugs in the pharmaceutical industry.

Transcriptome analysis of Kunming mice responses to the bite of Xenopsylla cheopis

BackgroundFlea bites could trigger a series of complex molecular responses in the host. However, our understanding of the responses at the molecular level is still relatively limited. This study quantifies the changes in gene expression in mice after flea bites by RNA sequencing (RNA-seq) from their spleens, revealing the potential biological effects of host response to flea bites.MethodsRNA-seq was used for transcriptome analysis to screen for differentially expressed genes (DEGs) between the control mice group and the flea bite mice group. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on DEGs. Protein–protein interaction (PPI) network analysis on DEGs related to immune processes was performed. Finally, we randomly selected several genes from the screened DEGs to validate the results from the transcriptome data by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR).ResultsA total of 521 DEGs were identified, including 277 upregulated and 244 downregulated. There were 258 GO terms significantly enriched by upregulated DEGs and 419 GO terms significantly enriched by downregulated DEGs. Among the upregulated DEGs, 22 GO terms were associated with immune cells (e.g., B cells and T cells) and immune regulatory processes, while among the downregulated DEGs, 58 GO terms were associated with immune cells and immune regulatory processes. Through PPI analysis, we found that CD40 molecules with significantly downregulated expression levels after flea bites may play an important role in host immune regulation. Through KEGG pathway enrichment analysis, a total of 26 significantly enriched KEGG pathways were identified. The RT-qPCR analysis results indicated that the transcriptome sequencing results were reliable.ConclusionsThrough in-depth analysis of transcriptome changes in mice caused by flea bites, we revealed that flea bites could stimulate a series of biological and immunological responses in mice. These findings not only provided a deeper understanding of the impact of flea bites on the host but also provided a basis for further research on the interaction between ectoparasites and the host. We believe that digging deeper into the significance of these transcriptome changes will help reveal more about the adaptive response of the host to ectoparasites.Graphical

Exploring the potential mechanisms of Danshen against COVID-19 via network pharmacology analysis and molecular docking

Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein–Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs.

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout

BackgroundGout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis.MethodsThe study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout.ResultsWe identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A.ConclusionAccording to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition.

A molecular subtyping associated with the cGAS-STING pathway provides novel perspectives on the treatment of ulcerative colitis

Ulcerative colitis (UC) is characterized by an abnormal immune response, and the pathogenesis lacks clear understanding. The cGAS-STING pathway is an innate immune signaling pathway that plays a significant role in various pathophysiological processes. However, the role of the cGAS-STING pathway in UC remains largely unclear. In this study, we obtained transcriptome sequencing data from multiple publicly available databases. cGAS-STING related genes were obtained through literature search, and differentially expressed genes (DEGs) were analyzed using R package limma. Hub genes were identified through protein–protein interaction (PPI) network analysis and module construction. The ConsensuClusterPlus package was utilized to identify molecular subtypes based on hub genes. The therapeutic response, immune microenvironment, and biological pathways of subtypes were further investigated. A total of 18 DEGs were found in UC patients. We further identified IFI16, MB21D1 (CGAS), TMEM173 (STING) and TBK1 as the hub genes. These genes are highly expressed in UC. IFI16 exhibited the highest diagnostic value and predictive value for response to anti-TNF therapy. The expression level of IFI16 was higher in non-responders to anti-TNF therapy. Furthermore, a cluster analysis based on genes related to the cGAS-STING pathway revealed that patients with higher gene expression exhibited elevated immune burden and inflammation levels. This study is a pioneering analysis of cGAS-STING pathway-related genes in UC. These findings provide new insights for the diagnosis of UC and the prediction of therapeutic response.

Proteomics analysis of coronary atherosclerotic heart disease with different Traditional Chinese Medicine syndrome types before and after percutaneous coronary intervention.

To investigate the underlying protein molecular mechanisms of "Qi stagnation and blood stasis syndrome" (QS) and "Qi deficiency and blood stasis syndrome" (QD), as two subtypes of coronary artery disease (CAD) in Traditional Chinese Medicine (TCM), following percutaneous coronary intervention (PCI). In this study, a total of 227 CAD patients with QS and 211 CAD patients with QD were enrolled; all participants underwent PCI. Label-free quantification proteomics were employed to analyze the changes in serum in two subtypes of CAD patients before and 6 months after PCI, aiming to elucidate the intervention mechanism of PCI in treating CAD characterized by two different TCM syndromes. Biochemical analysis revealed significant changes in tumor necrosis factor-α, high density lipoprotein cholesterol, blood stasis clinical symptoms observation, and Gensini levels in both patient groups post-PCI; Proteomic analysis identified 79 and 95 differentially expressed proteins in the QS and QD patient groups, respectively, compared to their control groups. complement C8 alpha chain, complement factor H, apolipoprotein H, apolipoprotein B, plasminogen, carbonic anhydrase 2, and complement factor I were altered in both comparison groups. Furthermore, enrichment analysis demonstrated that cell adhesion and connectivity-related processes underwent changes in QS patients post-PCI, whereas lipid metabolism-related pathways, including the peroxisome proliferator-activated receptor signaling pathway and extracellular matrix receptor interaction, underwent changes in the QD group. The protein-protein interaction network analysis further enriched 52 node proteins, including apolipoprotein B, lipoprotein (a), complement C5, apolipoprotein A4, complement C8 alpha chain, complement C8 beta chain, complement C8 gamma chain, apolipoprotein H, apolipoprotein A-Ⅱ, albumin, complement C4-B, apolipoprotein C3, among others. The functional network of these proteins is posited to contribute to the pathophysiology of CAD characterized by TCM syndromes. The current quantitative proteomic study has preliminarily identified biomarkers of CAD in different TCM subtypes treated with PCI, potentially laying the groundwork for understanding the protein profiles associated with the treatment of various TCM subtypes of CAD.

Human dermal fibroblast-derived secretory proteins for regulating nerve restoration: A bioinformatic approach.

Human dermal fibroblasts secrete diverse proteins that regulate wound repair and tissue regeneration. In this study, dermal fibroblast-conditioned medium (DFCM) proteins potentially regulating nerve restoration were bioinformatically selected among the 337 protein lists identified by quantitative liquid chromatography-tandem mass spectrometry. Using these proteins, protein-protein interaction network analysis was conducted. In addition, the roles of DFCM proteins were reviewed according to their protein classifications. Gene Ontology protein classification categorized these 57 DFCM proteins into various classes, including protein-binding activity modulator (N=11), cytoskeletal protein (N=8), extracellular matrix protein (N=6), metabolite interconversion enzyme (N=5), chaperone (N=4), scaffold/adapter protein (N=4), calcium-binding protein (N=3), cell adhesion molecule (N=2), intercellular signal molecule (N=2), protein modifying enzyme (N=2), transfer/carrier protein (N=2), membrane traffic protein (N=1), translational protein (N=1), and unclassified proteins (N=6). Further protein-protein interaction network analysis of 57 proteins revealed significant interactions among the proteins that varied according to the settings of confidence score. Our bioinformatic analysis demonstrated that DFCM contains many secretory proteins that form significant protein-protein interaction networks crucial for regulating nerve restoration. These findings underscore DFCM proteins' critical roles in various nerve restoration stages during the wound repair process.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

BackgroundTranscriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.MethodsWe first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.ResultsWe identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.ConclusionWe applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Investigation of the Molecular Mechanism Underlying the Therapeutic Effect of Perilla frutescens L. Essential Oil on Acute Lung Injury Using Gas Chromatography Mass Spectrometry and Network Pharmacology.

The present study aimed to investigate the molecular mechanism through which Perilla essential oil treats acute lung injury (ALI) through network pharmacology, molecular docking, and in vitro assays. Relevant ALI targets of the active ingredients of Perilla essential oil were predicted using the SwissTargetPrediction database and meta TarFisher database. These ALI targets were then screened using GeneCards and DisGeNET, and differentially expressed ALI target genes were identified using the Gene Expression Omnibus (GEO) database. Next, key targets were enriched using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction network analysis was performed to obtain targets with the highest degree values for molecular docking with Perilla essential oil active ingredients. For in vitro experiments, lipopolysaccharide (LPS) was used to induce an ALI inflammation model using RAW264.7 cells. The model cells were then treated with Perilla essential oil to detect the protein expression levels of vascular endothelial factor (NO), tumor necrosis factor (TNF-α), and p65 nuclear transcription factor in them. Sixty-eight key targets of Perilla oil were identified for the treatment of ALI. These targets were found to be involved in biological processes related to peptides, response to lipopolysaccharides, the positive regulation of cytokine production, etc., using GO. The signaling pathways found to be associated with the targets included the AGE-RAGE signaling pathway in diabetic complications, the NF-kappa B signaling pathway, and small cell lung cancer and other inflammatory signaling pathways. The five key targets that showed good binding activity with Perilla oil active ingredients included TNF, RELA, PARP1, PTGS2, and IRAK4. In vitro assays showed that Perilla essential oil could significantly reduce NO and TNF-α levels and inhibit the phosphorylation of nuclear transcription factor P65, thus inhibiting the activation of NF-κB signaling pathway. Conclusion Perilla essential oil can play a role in the treatment of ALI by inhibiting the activation of the NF-κB signaling pathway and preventing an excessive inflammatory response. This study thus provides a reference for the in-depth study of the mechanisms through which Perilla essential oil treats ALI.

Protective efficacy and immune responses of largemouth bass (Micropterus salmoides) immunized with an inactivated vaccine against the viral hemorrhagic septicemia virus genotype IVa

Viral hemorrhagic septicemia virus (VHSV) poses a significant threat to the aquaculture industry, prompting the need for effective preventive measures. Here, we developed an inactivated VHSV and revealed the molecular mechanisms underlying the host's protective response against VHSV. The vaccine was created by treating VHSV with 0.05 % formalin at 16 °C for 48 h, which was determined to be the most effective inactivation method. Compared with nonvaccinated fish, vaccinated fish exhibited a remarkable increase in survival rate (99 %) and elevated levels of serum neutralizing antibodies, indicating strong immunization. To investigate the gene changes induced by vaccination, RNA sequencing was performed on spleen samples from control and vaccinated fish 14 days after vaccination. The analysis revealed 893 differentially expressed genes (DEGs), with notable up-regulation of immune-related genes such as annexin A1a, coxsackievirus and adenovirus receptor homolog, V-set domain-containing T-cell activation inhibitor 1-like, and heat shock protein 90 alpha class A member 1 tandem duplicate 2, indicating a vigorous innate immune response. Furthermore, KEGG enrichment analysis highlighted significant enrichment of DEGs in processes related to antigen processing and presentation, necroptosis, and viral carcinogenesis. GO enrichment analysis further revealed enrichment of DEGs related to the regulation of type I interferon (IFN) production, type I IFN production, and negative regulation of viral processes. Moreover, protein-protein interaction network analysis identified central hub genes, including IRF3 and HSP90AA1.2, suggesting their crucial roles in coordinating the immune response elicited by the vaccine. These findings not only confirm the effectiveness of our vaccine formulation but also offer valuable insights into the underlying immunological mechanisms, which can be valuable for future vaccine development and disease management in the aquaculture industry.

Clinical efficacy and gene chip expression analysis of Shenzhu Guanxin recipe granules in patients with intermediate coronary lesions.

To evaluate the clinical efficacy and safety of Shenzhu Guanxin recipe granules (, SGR) in treating patients with intermediate coronary lesions (ICL), and to investigate the potential mechanism though a transcriptome sequencing approach. ICL patients with Qi deficiency and phlegm stasis were adopted and randomly assigned to a case group or a control by random number generator in a 1:1 randomization ratio to evaluate the clinical efficacy. There was no significant difference between the two groups in coronary computed tomography angiography related indexes in the two groups before and after intervention. Through the gene chip expression analysis, it is finally concluded that there are 355 differential mRNAs (190 up-regulated genes and 165 down regulated genes) when compared the SGR group and placebo group. Through protein-protein interaction network analysis of differentially expressed genes, 10 hub genes were finally obtained: CACNA2D2, CACNA2D3, DNAJC6, FGF12, SGSM2, CACNA1G, LRP6, KIF25, OXTR, UPB1. SGR combined with Western Medicine can be safely used to treat ICL patients with Qi deficiency and phlegm stasis. The possible mechanism of action and relevant gene loci and pathway were proposed.

Phytol and α-Bisabolol Synergy Induces Autophagy and Apoptosis in A549 Cells and Additional Molecular Insights through Comprehensive Proteome Analysis via Nano LC-MS/MS.

Non-Small Cell Lung Cancer (NSCLC) is a malignancy with a significant prevalence and aggressive nature, posing a considerable challenge in terms of therapeutic interventions. Autophagy and apoptosis, two intricate cellular processes, are integral to NSCLC pathophysiology, each affecting the other through shared signaling pathways. Phytol (Phy) and α-bisabolol (Bis) have shown promise as potential anticancer agents individually, but their combined effects in NSCLC have not been extensively investigated. The present study was to examine the synergistic impact of Phy and Bis on NSCLC cells, particularly in the context of autophagy modulation, and to elucidate the resulting differential protein expression using LCMS/ MS analysis. The A549 cell lines were subjected to the patented effective concentration of Phy and Bis, and subsequently, the viability of the cells was evaluated utilizing the MTT assay. The present study utilized real-time PCR analysis to assess the expression levels of crucial apoptotic genes, specifically Bcl-2, Bax, and Caspase-9, as well as autophagy-related genes, including Beclin-1, SQSTM1, Ulk1, and LC3B. The confirmation of autophagy marker expression (Beclin-1, LC3B) and the autophagy-regulating protein SQSTM1 was achieved through the utilization of Western blot analysis. Differentially expressed proteins were found using LC-MS/MS analysis. The combination of Phy and Bis demonstrated significant inhibition of NSCLC cell growth, indicating their synergistic effect. Real-time PCR analysis revealed a shift towards apoptosis, with downregulation of Bcl-2 and upregulation of Bax and Caspase-9, suggesting a shift towards apoptosis. Genes associated with autophagy regulation, including Beclin-1, SQSTM1 (p62), Ulk1, and LC3B, showed significant upregulation, indicating potential induction of autophagy. Western blot analysis confirmed increased expression of autophagy markers, such as Beclin-1 and LC3B, while the autophagy-regulating protein SQSTM1 exhibited a significant decrease. LC-MS/MS analysis revealed differential expression of 861 proteins, reflecting the modulation of cellular processes. Protein-protein interaction network analysis highlighted key proteins involved in apoptotic and autophagic pathways, including STOML2, YWHAB, POX2, B2M, CDA, CAPN2, TXN, ECHS1, PEBP1, PFN1, CDC42, TUBB1, HSPB1, PXN, FGF2, and BAG3, emphasizing their crucial roles. Additionally, PANTHER pathway analysis uncovered enriched pathways associated with the differentially expressed proteins, revealing their involvement in a diverse range of biological processes, encompassing cell signaling, metabolism, and cellular stress responses. The combined treatment of Phy and Bis exerts a synergistic inhibitory effect on NSCLC cell growth, mediated through the interplay of apoptosis and autophagy. The differential protein expression observed, along with the identified proteins and enriched pathways, provides valuable insights into the underlying molecular mechanisms. These findings offer a foundation for further exploration of the therapeutic potential of Phy and Bis in the management of NSCLC.

Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors.

Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients. Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein-protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes. We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations. The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.

Spatial transcriptome analysis of amelanotic acral melanoma reveals distant molecular determinants compared to pigmented acral melanoma.

Amelanotic acral melanoma (AAM) is a rare type of acral melanoma associated with poor prognosis. We aimed to investigate the transcriptomic differences between AAM and pigmented acral melanoma (PAM). The differences in spatially resolved transcriptome profiles of 9 AAM patients with 29 regions of interest (ROIs) and 11 PAM patients with 46 ROIs were investigated using S100b and CD3 morphology markers. In S100b-positive tumor cell areas, we detected 11 upregulated differentially expressed genes (DEGs), including chaperone/ubiquitin-associated DEGs, and 82 downregulated DEGs, including human leukocyte antigen, in AAMs compared with PAMs. Protein-protein interaction network and pathway analyses revealed significant enrichment of dysregulated translational and nonsense-mediated decay pathways but significant decreases in antigen processing and presentation, interferon signaling, and melanin biosynthesis pathways in S100b-positive ROIs of AAMs compared with those of PAMs. In tumor-associated immune cell areas, the numbers of CD8 T cells (p = 0.044) and M1 macrophages (p = 0.014) were significantly decreased, whereas those of monocytes (p = 0.045) and endothelial cells (p = 0.04) were increased in AAMs compared with those in PAMs. In conclusion, these findings could widen our understanding of the biological differences between AAMs and PAMs that might result in a different clinical course.

Deciphering FFAR4 Mediated Functional Pathways and Potential Drug Targets in Diabetes Mellitus: An Integrated Protein Interaction Network Analysis and Kinetic Simulation

Deciphering FFAR4 Mediated Functional Pathways and Potential Drug Targets in Diabetes Mellitus: An Integrated Protein Interaction Network Analysis and Kinetic Simulation

Uncovering Druggable Targets in Aortic Dissection: An Association Study Integrating Mendelian Randomization, pQTL, and Protein-Protein Interaction Network.

Aortic dissection (AD) is a life-threatening acute aortic syndrome. There are limitations and challenges in the discovery and application of biomarkers and drug targets for AD. Mendelian randomization (MR) analysis is a reliable analytical method to identify effective therapeutic targets. We aimed to identify novel therapeutic targets for AD and investigate their potential side-effects based on MR analysis. Data from protein quantitative trait loci (pQTLs) were used for MR analyses to identify potential therapeutic targets. We probed druggable proteins involved in the pathogenesis of aortic dissection from deCODE. In this study, a two-sample MR analysis was conducted, with druggable proteins as the exposure factor and data on genome-wide association studies (GWAS) of AD as the outcome. After conducting a two-sample MR, summary data-based Mendelian randomization (SMR) analysis and colocalization analysis were performed. A protein-protein interaction (PPI) network was also constructed to delve into the interactions between identified proteins. After MR analysis and the Steiger test, we identified five proteins as potential therapeutic targets for AD. SMR analysis and colocalization analysis also confirmed our findings. Finally, we identified ASPN (OR = 1.36, 95% CI: 1.20, 1.54, p = 4.22 × 10-5) and SPOCK2 (OR = 0.57, 95% CI: 0.41, 0.78, p = 4.52 × 10-4) as the core therapeutic targets. Through PPI network analysis, we identified six druggable targets, enabling the subsequent identification of six promising drugs from DrugBank for treating AD. This discovery of specific proteins as novel therapeutic targets represents a significant advancement in AD treatment. These findings provide more effective treatment options for AD.