Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments,ongoing progress is required to ensure these are effective,economical,and accessible for the globally ageing population.As such,continued identification of new potential drug targets and biomarkers is critical."Big data"studies,such as proteomics,can generate information on thousands of possible new targets for dementia diagnostics and therapeutics,but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development.In this review,we discuss current tauopathy biomarkers and therapeutics,and highlight areas in need of improvement,particularly when addressing the needs of frail,comorbid and cognitively impaired populations.We highlight biomarkers which have been developed from proteomic data,and outline possible future directions in this field.We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development,and demonstrate its application to our group's recent tau interactome dataset as an example.

Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus:A pilot bioinformatics study

BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unknown.AIM To explore potential molecular connections between H.pylori infection and T2DM.METHODS We extracted gene expression arrays from three online datasets(GSE60427,GSE27411 and GSE115601).Differentially expressed genes(DEGs)commonly present in patients with H.pylori infection and T2DM were identified.Hub genes were validated using human gastric biopsy samples.Correlations between hub genes and immune cell infiltration,miRNAs,and transcription factors(TFs)were further analyzed.RESULTS A total of 67 DEGs were commonly presented in patients with H.pylori infection and T2DM.Five significantly upregulated hub genes,including TLR4,ITGAM,C5AR1,FCER1G,and FCGR2A,were finally identified,all of which are closely related to immune cell infiltration.The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links.TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs,the largest number of TFs among the 5 hub genes.CONCLUSION We identified five hub genes that may have molecular connections between H.pylori infection and T2DM.This study provides new insights into the pathogenesis of H.pylori-induced onset of T2DM.

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Research advance of Bacillus velezensis:bioinformatics,characteristics,and applications

Bacillus velezensis is a Gram-positive and spore-forming bacterium.It has potent antimicrobial properties that can be used to promote plant growth and as a pesticide by inhibiting pathogens.B.velezensis has the capability to generate a diverse range of enzymes that have potential applications in various fields,such as enzyme production,fermented food,degradation of pollutants,and bioenergy.In addition,B.velezensis is a promising probiotic.It possesses high bile-salt tolerance characteristics and has a high success rate of colonization in the intestinal mucosa.Besides,the strain can also regulate gut microbiota constitute by increasing the number of beneficial microorganisms and decreasing the number of pathogens.Furthermore,based on its special properties,including high-yield protease production and high salt-tolerance,B.velezensis shows potential for use in marine protein fermentation,opening up new avenues for the development of novel food products and bioactive peptides.In addition,B.velezensis can shorten the fermentation time as well as improve the nutritional value and flavor of fermented food.The safety of B.velezensis for food production was evaluated.This review provides valuable insights into the potential uses and benefits of B.velezensis,particularly in the context of fermented foods.

Proteomics Study of Benzene Metabolite Hydroquinone Induced Hematotoxicity in K562 Cells

Objective Hydroquinone(HQ),one of the phenolic metabolites of benzene,is widely recognized as an important participant in benzene-induced hematotoxicity.However,there are few relevant proteomics in HQ-induced hematotoxicity and the mechanism hasn’t been fully understood yet.Methods In this study,we treated K562 cells with 40μmol/L HQ for 72 h,examined and validated protein expression changes by Label-free proteomic analysis and Parallel reaction monitoring(PRM),and performed bioinformatics analysis to identify interaction networks.Results One hundred and eighty-seven upregulated differentially expressed proteins(DEPs)and 279 downregulated DEPs were identified in HQ-exposed K562 cells,which were involved in neutrophilmediated immunity,blood microparticle,and other GO terms,as well as the lysosome,metabolic,cell cycle,and cellular senescence-related pathways.Focusing on the 23 DEGs and 5 DEPs in erythroid differentiation-related pathways,we constructed the network of protein interactions and determined 6 DEPs(STAT1,STAT3,CASP3,KIT,STAT5B,and VEGFA)as main hub proteins with the most interactions,among which STATs made a central impact and may be potential biomarkers of HQ-induced hematotoxicity.Conclusion Our work reinforced the use of proteomics and bioinformatic approaches to advance knowledge on molecular mechanisms of HQ-induced hematotoxicity at the protein level and provide a valuable basis for further clarification.

Identification and Validation of Vascular-Associated Biomarkers for the Prognosis and Potential Pathogenesis of Hypertension Using Comprehensive Bioinformatics Methods

Background: Hypertension, also known as increased blood pressure, is a phenomenon in which blood flows in blood vessels and causes persistently higher-than-normal pressure on the vessel wall. The identification of novel prognostic and pathogenesis biomarkers plays a key role in the management of hypertension. Methods: The GSE7483 and GSE75815 datasets from the gene expression omnibus (GEO) database were used to identify the genes associated with hypertension that were differentially expressed genes (DEGs). The functional role of the DEGs was elucidated by gene body (GO) enrichment analysis. In addition, we performed an immune infiltration assay and GSEA on the DEGs of hypertensive patients and verified the expression of novel DEGs in the blood of hypertensive patients by RT-qPCR. Results: A total of 267 DEGs were identified from the GEO database. GO analysis revealed that these genes were associated mainly with biological processes such as fibroblast proliferation, cell structural organization, extracellular matrix organization, vasculature development regulation, and angiogenesis. We identified five possible biomarkers, Ecm1, Sparc, Sphk1, Thbsl, and Mecp2, which correlate with vascular development and angiogenesis characteristic of hypertension by bioinformatics, and explored the clinical expression levels of these genes by RT-qPCR, and found that Sparc, Sphk1, and Thbs1 showed significant up-regulation, in agreement with the results of the bioinformatics analysis. Conclusion: Our study suggested that Sparc, Sphk1 and Thbs1 may be potential novel biomarkers for the diagnosis, treatment and prognosis of hypertension and that they are involved in the regulation of vascular development and angiogenesis in hypertension.

Identification and Validation of Novel Biomarkers Related to the Calcium Metabolism Pathway in Hypertension Patients Based on Comprehensive Bioinformatics Methods

Background: Hypertension is a universal risk factor for cardiovascular diseases and is thus the leading cause of death worldwide. The identification of novel prognostic and pathogenesis biomarkers plays a key role in disease management. Methods: The GSE145854 and GSE164494 datasets were downloaded from the Gene Expression Omnibus (GEO) database and used for screening and validating hypertension signature genes, respectively. Gene Ontology (GO) enrichment analysis was performed on the differentially expressed genes (DEGs) related to calcium ion metabolism in patients with hypertension. The core genes related to immune infiltration were analyzed and screened, and the activity of the signature genes and related pathways was quantified using gene set enrichment analysis (GSEA). The infiltration of immune cells in the blood samples was analyzed, and the DEGs that were abnormally expressed in the clinical blood samples of patients with hypertension were verified via RT-qPCR. Results: A total of 176 DEGs were screened. GO showed that DEGs was involved in the regulation of calcium ion metabolism in biological processes (BP), actin mediated cell contraction, negative regulation of cell movement, and calcium ion transmembrane transport, and in the regulation of protease activity in molecular functions (MF). KEGG analysis revealed that the DEGs were involved mainly in the cGMP-PKG signaling pathway, ubiquitin-protein transferase, tight junction-associated proteins, and the regulation of myocardial cells. MF analysis revealed the immune infiltration function of the cells. RT-qPCR revealed that the expression of Cacna1d, Serpine1, Slc8a3, and Trpc4 was up regulated in hypertension, the expression of Myoz2 and Slc25a23 was down regulated. Conclusion: Cacna1d, Serpine1, Slc8a3, Trpc4, Myoz2 and Slc25a23 may be involved in the regulation of calcium metabolism pathways and play key roles in hypertension. These differentially expressed calcium metabolism-related genes may serve as prognostic markers of hypertension.

Comparative proteomics reveals the response and adaptation mechanisms of white Hypsizygus marmoreus against the biological stress caused by Penicillium

White Hypsizygus marmoreus is a popular edible mushroom.Its mycelium is easy to be contaminated by Penicillium,which leads to a decrease in its quality and yield.Penicillium could compete for limited space and nutrients through rapid growth and produce a variety of harmful gases,such as benzene,aldehydes,phenols,etc.,to inhibit the growth of H.marmoreus mycelium.A series of changes occurred in H.marmoreus proteome after contamination when detected by the label-free tandem mass spectrometry(MS/MS)technique.Some proteins with up-regulated expression worked together to participate in some processes,such as the non-toxic transformation of harmful gases,glutathione metabolism,histone modification,nucleotide excision repair,clearing misfolded proteins,and synthesizing glutamine,which were mainly used in response to biological stress.The proteins with down-regulated expression are mainly related to the processes of ribosome function,protein processing,spliceosome,carbon metabolism,glycolysis,and gluconeogenesis.The reduction in the function of these proteins affected the production of the cell components,which might be an adjustment to adapt to growth retardation.This study further enhanced the understanding of the biological stress response and the growth restriction adaptation mechanisms in edible fungi.It also provided a theoretical basis for protein function exploration and edible mushroom food safety research.

Bioinformatics Analysis of the Association between Ewing’s Sarcoma and Tuberculosis Comorbidity

Objective To screen and analyze the differentially expressed genes of Ewing’s sarcoma (ES) and Tuberculosis (TB) by bioinformatics. Methods GEO gene chip public database in NCBI was used for data retrieval, and chip data GSE17674 and GSE57736 were selected as analysis objects. The R language limma toolkit was used to screen DEmRNAs, and the data were standardized, and the common differentially expressed genes were screened by Venn diagram. The GO function and KEGG pathway enrichment of common differentially expressed genes were analyzed by using the R cluster Profiler package. String database was selected for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Import Hub gene into BioGPS database. Results: A total of 3 Hub genes were screened, namely CD3D, LCK, KLRB1;The genes were imported into BioGPS database to obtain the specific genes. Conclusion The selected differential genes and related signaling pathways are helpful to understand the molecular mechanism of ES and TB, and can provide the basis for early diagnosis of ES complicated with TB. It also provides new ideas for clinical treatment and diagnosis.

Bioinformatics Analysis of the Biological Properties of Ewing Sarcoma

Purpose: Bioinformatics-based approach to screen and analyze differentially expressed genes associated with the biological characteristics of Ewing sarcoma. Means: The GSE17674 dataset was selected for analysis, obtained by data retrieval based on the GEO public database. The R language limma toolkit was used to screen DEmRNAs. After the data were normalized, the Metascape online analysis software and the R language clusterProfiler package were used to analyze the GO function and KEGG pathway enrichment of DEmRNAs lines, respectively. The string database was selected for PPI analysis, and the results were imported into Cytoscape software to derive the core modules and predicted core genes. The genes selected above were analyzed for tissue localization specificity. Results: Through the analysis of GSE17674, differentially expressed genes were screened out, and GO and KEGG analyses were performed on the differentially expressed genes. The GO functional enrichment analysis was mainly enriched in the process of muscle system, muscle contraction, myocyte development, contractile fibers, myogenic fibers, myofibers, myofibrillar segments, actin binding, structural composition of muscle, and actin filament binding. KEGG pathway analysis showed that the core pathways associated with the development of ES were the core genes for myocardial contraction, congestive cardiomyopathy, and hypertrophic cardiomyopathy. Five Hub genes were obtained based on Cytoscape prediction. Tissue localization specificity analysis of Hub genes was performed, and a total of 2 Hub genes with tissue specificity were screened;MYH6 was specifically expressed in cardiac cells and MYL1 was specifically expressed in skeletal muscle cells. Conclusions: The differential genes screened will help to understand the molecular mechanisms underlying the highly invasive and metastasis-prone biological characteristics of ES, as well as provide new ideas for clinical drug-targeted treatment of ES.

Identification of anti-gastric cancer effects and molecular mechanisms of resveratrol: From network pharmacology and bioinformatics to experimental validation

BACKGROUND Gastric cancer(GC)is one of the most aggressive malignancies with limited therapeutic options and a poor prognosis.Resveratrol,a non-flavonoid poly-phenolic compound found in a variety of Chinese medicinal materials,has shown excellent anti-GC effect.However,its exact mechanisms of action in GC have not been clarified.AIM To identify the effects of resveratrol on GC progression and explore the related molecular mechanisms.METHODS Action targets of resveratrol and GC-related targets were screened from public databases.The overlapping targets between the two were confirmed using a Venn diagram,and a“Resveratrol-Target-GC”network was constructed using Cyto-scape software version 3.9.1.The protein-protein interaction(PPI)network was constructed using STRING database and core targets were identified by PPI network analysis.The Database for Annotation,Visualization and Integrated A total of 378 resveratrol action targets and 2154 GC disease targets were obtained from public databases,and 181 intersection targets between the two were screened by Venn diagram.The top 20 core targets were identified by PPI network analysis of the overlapping targets.GO function analysis mainly involved protein binding,identical protein binding,cytoplasm,nucleus,negative regulation of apoptotic process and response to xenobiotic stimulus.KEGG enrichment analysis suggested that the involved signaling pathways mainly included PI3K-AKT signaling pathway,MAPK signaling pathway,IL-17 signaling pathway,TNF signaling pathway,ErbB signaling pathway,etc.FBJ murine osteosarcoma viral oncogene homolog(FOS)and matrix metallopeptidase 9(MMP9)were selected by differential expression analysis,and they were closely associated with immune infiltration.Molecular docking results showed that resveratrol docked well with these two targets.Resveratrol treatment arrested the cell cycle at the S phase,induced apoptosis,and weakened viability,migration and invasion in a dose-dependent manner.Furthermore,resveratrol could exhibit anti-GC effect by regulating FOS and MMP9 expression.CONCLUSION The anti-GC effects of resveratrol are related to the inhibition of cell proliferation,migration,invasion and induction of cell cycle arrest and apoptosis by targeting FOS and MMP9.

Identification of Prognosis-Related Genes and Key Target Genes for Pancreatic Cancer: A Bioinformatics Analysis

Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.

Machine learning and bioinformatics to identify biomarkers in response to Burkholderia pseudomallei infection in mice

Objective:In the realm of Class I pathogens,Burkholderia pseudomallei(BP)stands out for its propensity to induce severe pathogenicity.Investigating the intricate interactions between BP and host cells is imperative for comprehending the dynamics of BP infection and discerning biomarkers indicative of the host cell response process.Methods:mRNA extraction from BP-infected mouse macrophages constituted the initial step of our study.Employing gene expression arrays,the extracted RNA underwent conversion into digital signals.The percentile shift method facilitated data processing,with the identification of genes manifesting significant differences accomplished through the application of the t-test.Subsequently,a comprehensive analysis involving Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway was conducted on the differentially expressed genes(DEGs).Leveraging the ESTIMATE algorithm,gene signatures were utilized to compute risk scores for gene expression data.Support vector machine analysis and gene enrichment scores were instrumental in establishing correlations between biomarkers and macrophages,followed by an evaluation of the predictive power of the identified biomarkers.Results:The functional and pathway associations of the DEGs predominantly centered around G protein-coupled receptors.A noteworthy positive correlation emerged between the blue module,consisting of 416 genes,and the StromaScore.FZD4,identified through support vector machine analysis among intersecting genes,indicated a robust interaction with macrophages,suggesting its potential as a robust biomarker.FZD4 exhibited commendable predictive efficacy,with BP infection inducing its expression in both macrophages and mouse lung tissue.Western blotting in macrophages confirmed a significant upregulation of FZD4 expression from 0.5 to 24 h post-infection.In mouse lung tissue,FZD4 manifested higher expression in the cytoplasm of pulmonary epithelial cells in BP-infected lungs than in the control group.Conclusion:Thesefindings underscore the upregulation of FZD4 expression by BP in both macrophages and lung tissue,pointing to its prospective role as a biomarker in the pathogenesis of BP infection.

Unraveling autophagy-related pathogenesis in active ulcerative colitis:A bioinformatics approach

In this editorial,we provide commentary on the study by Gong et al.In this original research article,Gong et al employed a bioinformatics approach to investigate the involvement of autophagy in active ulcerative colitis(UC).Through differential gene expression analysis,they identified 58 differentially expressed autophagy-related genes in UC patients compared to healthy controls.Notably,HSPA5,CASP1,SERPINA1,CX3CL1,and BAG3,were found to be upregulated in active UC patients,suggesting their significance as core autophagyrelated targets.Enrichment analysis unveiled associations with crucial signaling pathways and diseases such as middle cerebral artery occlusion and glomerulonephritis.Moreover,immune cell infiltration analysis revealed notable differences in immune cell composition between UC patients and healthy controls.These findings offer valuable insights into the role of autophagy in UC pathogenesis and potential therapeutic targets.

Identification and Validation of SLC9A2 as A Potential Tumor Suppressor in Colorectal Cancer:Integrating Bioinformatics Analysis with Experimental Confirmation

Objective To uncover the mechanisms underlying the development of colorectal cancer(CRC),we applied bioinformatic analyses to identify key genes and experimentally validated their possible roles in CRC onset and progression.Methods We performed Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis on differentially expressed genes(DEGs),constructed a protein-protein interaction(PPI)network to find the top 10 hub genes,and analyzed their expression in colon adenocarcinoma(COAD)and rectum adenocarcinoma(READ).We also studied the correlation between these genes and immune cell infiltration and prognosis and validated the expression of SLC9A2 in CRC tissues and cell lines using qRT-PCR and Western blotting.Functional experiments were conducted in vitro to investigate the effects of SLC9A2 on tumor growth and metastasis.Results We found 130 DEGs,with 45 up-regulated and 85 down-regulated in CRC.GO analysis indicated that these DEGs were primarily enriched in functions related to the regulation of cellular pH,zymogen granules,and transmembrane transporter activity.KEGG pathway analysis revealed that the DEGs played pivotal roles in pancreatic secretion,rheumatoid arthritis,and the IL-17 signaling pathway.We identified 10 hub genes:CXCL1,SLC26A3,CXCL2,MMP7,MMP1,SLC9A2,SLC4A4,CLCA1,CLCA4,and ZG16.GO enrichment analysis showed that these hub genes were predominantly involved in the positive regulation of transcription.Gene expression analysis revealed that CXCL1,CXCL2,MMP1,and MMP7 were highly expressed in CRC,whereas CLCA1,CLCA4,SLC4A4,SLC9A2,SLC26A3,and ZG16 were expressed at lower levels.Survival analysis revealed that 5 key genes were significantly associated with the prognosis of CRC.Both mRNA and protein expression levels of SLC9A2 were markedly reduced in CRC tissues and cell lines.Importantly,SLC9A2 overexpression in SW480 cells led to a notable inhibition of cell proliferation,migration,and invasion.Western blotting analysis revealed that the expression levels of phosphorylated ERK(p-ERK)and phosphorylated JNK(p-JNK)proteins were significantly increased,whereas there were no significant changes in the expression levels of ERK and JNK following SLC9A2 overexpression.Correlation analysis indicated a potential link between SLC9A2 expression and the MAPK signaling pathway.Conclusion Our study suggests that SLC9A2 acts as a tumor suppressor through the MAPK pathway and could be a potential target for CRC diagnosis and therapy.

Proteomics for early prenatal screening of gestational diabetes mellitus

In this editorial,we comment on the article by Cao et al.Through applying isobaric tags for relative and absolute quantification technology coupled with liquid chromatography-tandem mass spectrometry,the researchers observed significant differential expression of 47 proteins when comparing serum samples from pregnant women with gestational diabetes mellitus(GDM)to the healthy ones.GDM symptoms may involve abnormalities in inflammatory response,complement system,coagulation cascade activation,and lipid metabolism.Retinol binding protein 4 and angiopoietin like 8 are potential early indicators of GDM.GDM stands out as one of the most prevalent metabolic complications during pregnancy and is linked to severe maternal and fetal outcomes like pre-eclampsia and stillbirth.Nevertheless,none of the biomarkers discovered so far have demonstrated effectiveness in predicting GDM.Our topic was designed to foster insights into advances in the application of proteomics for early prenatal screening of GDM.

Comparative proteomics analysis reveals the domesticated Lepista sordida primordium differentiation regulation mechanism and the subsequent different development patterns in the pileus and stipe

The wild Lepista sordida is a kind of precious and rare edible fungus.An excellent strain of it by artificial domestication was obtained,which was high-yield and high in iron content.In this study,high-throughput comparative proteomics was used to reveal the regulatory mechanism of its primordium differentiation in the early fruiting body formation.The mycelium before the primordium differentiation mainly expressed high levels of mitochondrial functional proteins and carbon dioxide concentration regulatory proteins.In young mushrooms,the highly expressed proteins were mainly involved in cell component generation,cell proliferation,nitrogen compound metabolism,nucleotide metabolism,glutathione metabolism,and purine metabolism.The differential regulation patterns of pileus and stipe growth to maturity were also revealed.The highly expressed proteins related to transcription,RNA splicing,the production of various organelles,DNA conformational change,nucleosome organization,protein processing,maturation and transport,and cell detoxification regulated the pileus development and maturity.The proteins related to carbohydrate and energy metabolism,large amounts of obsolete cytoplasmic parts,nutrient deprivation,and external stimuli regulated the stipe development and maturity.Multiple CAZymes regulated nutrient absorption,morphogenesis,spore production,stress response,and other life activities at different growth and development stages.

iTRAQ-based proteomics reveals the mechanism of action of Yinlai decoction in treating pneumonia in mice consuming a high-calorie diet

Objective:To uncover the underlying mechanisms of action of the Yinlai decoction on high-calorie dietinduced pneumonia through proteomics analysis.Methods:Based on the Gene Expression Omnibus(GEO)database,lung tissue samples from normal and high-fat diet(HFD)fed mice in the GSE16377 dataset were selected as test cohorts to identify differentially expressed genes and conduct bioinformatics analyses.In the animal experiments,mice were randomly divided into the control(N),high-calorie diet pneumonia(M),and Yinlai decoction treatment(Y)groups.Mice in the M group received high-calorie feed and a 0.5 mg/mL lipopolysaccharide solution spray for 30 min for 3 d.The mice in the Y group were intragastrically administered 2 mL/10 g Yinlai decoction twice daily for 3 d.Pathological evaluation of the lung tissue was performed.Differentially expressed proteins(DEPs)in the lung tissue were identified using quantitative proteomics and bioinformatics analyses.The drug-target relationships between Yinlai decoction and core DEPs in the lung tissue were verified using AutoDock Vina and Molecular Graphics Laboratory(MGL)Tools.DEPs were verified by western blot.Results:GEO data mining showed that an HFD altered oxidative phosphorylation in mouse lung tissue.The Yinlai decoction alleviated pathological damage to lung tissue and pneumonia in mice that were fed a high-calorie diet.A total of 47 DEPs were identified between the Y and M groups.Enrichment analysis revealed their association with energy metabolism pathways such as the tricarboxylic acid cycle(TCA)and oxidative phosphorylation.The protein-protein interaction network revealed that Atp5a1,Pdha1,and Sdha were the target proteins mediating the therapeutic effects of Yinlai decoction.Molecular docking results suggested that the mechanism of the therapeutic effect of Yinlai decoction involves the binding of brassinolide,praeruptorin B,chrysoeriol,and other components in Yinlai decoction to Atp5a1.Conclusion:The Yinlai decoction alleviated lung tissue damage and pneumonia in mice that were fed a high-calorie diet by regulating the TCA and oxidative phosphorylation.Our study highlights the importance of a healthy diet for patients with pneumonia and provides a scientific basis for the prevention and treatment of pneumonia through dietary adjustments.

Bioinformatics Analysis of the Relationship between Dilated Cardiomyopathy and Chronic Heart Failure

Objective: To screen and analyze the differentially expressed genes between dilated cardiomyopathy (DCM) and chronic heart failure (CHF) based on bioinformatics methods. Methods: The Gene Expression Omnibus (GEO) database was used for data retrieval, and the chip data GSE3585 was downloaded, which was the original data of DCM and normal control group. At the same time, the chip data GSE76701 was downloaded, which was the original data of CHF and control group. Differentially expressed mRNAs (DEmRNAs) were screened by R language limma package, the data were standardized, and the common differentially expressed genes were screened. GO function and KEGG pathway enrichment analysis were performed on the common differentially expressed genes. String11.0 online tool was used for data analysis to obtain differentially expressed genes, and the results were imported into Cytoscape 3.9.1 software. The results were imported into Cytoscape 3.9.1 software, and the common expression gene module was obtained by MOCDE algorithm. Nine Hub genes were obtained by 10 algorithms such as MCC. Results: A total of 248 differentially expressed genes were screened. GO analysis showed that differentially expressed genes were mainly concentrated in 9 different physiological and pathological processes. KEGG analysis showed that the main signaling pathways involved in differentially expressed genes were 2, and 9 key differentially expressed genes were predicted: NPPB, NPPA, MYH6, FRZB, ASPN, SFRP4, RPS4Y1, DDX3Y. Conclusion: This study preliminarily explored the molecular mechanism of DCM and CHF, and obtained the common differentially expressed genes of the two diseases. Further experimental studies are needed to verify the correlation between gene expression and clinicopathological features. Provide new ideas for clinical drug treatment research.

Bioinformatics and network pharmacology identify the therapeutic role and potential targets of diosgenin in Alzheimer disease and COVID-19

Objective: This study aims to investigate the potential targets of diosgenin for the treatment of Alzheimer's disease (AD) and Coronavirus Disease 2019 (COVID-19) through the utilization of bioinformatics, network pharmacology, and molecular docking techniques. Methods: Differential expression genes (DEGs) shared by AD and COVID-19 were enriched by bioinformatics. Additionally, regulatory networks were analyzed to identify key genes in the Transcription Factor (TF) of both diseases. The networks were visualized using Cytoscape. Utilizing the DGIdb database, an investigation was conducted to identify potential drugs capable of treating both Alzheimer's disease (AD) and COVID-19. Subsequently, a Venn diagram analysis was performed using the drugs associated with AD and COVID-19 in the CTD database, leading to the identification of diosgenin as a promising candidate for the treatment of both AD and COVID-19.SEA, SuperPred, Swiss Target Prediction and TCMSP were used to predict the target of diosgenin in the treatment of AD and COVID-19, and the target of diosgenin in the treatment of AD and COVID-19 was determined by Wayne diagram intersection analysis with the differentially expressed genes of AD and COVID- 19. Their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed jointly. Genomes The Protein Protein Interaction (PPI) network of these drug targets was constructed, and core targets with the highest correlation were screened out. The binding of diosgenin to these core targets was analyzed by molecular docking. Results: Through enrichment and cluster analysis, it was found that the biological processes, pathways and diseases enriched by DEGs in AD and COVID-19 were all related to inflammation and immune regulation. These common DEGs and Trust databases were used to construct AD and COVID-19 TFs regulatory networks. Diosgenin was predicted as a potential drug for the treatment of AD and COVID-19 by network pharmacology, and 36 targets of diosgenin for the treatment of AD and 27 targets for COVID-19 were revealed. The six core targets with the highest correlation were selected for molecular docking with diosgenin using CytohHubba to calculate the scores. Conclusions: This study firstly revealed that the common TFs regulatory network of AD and COVID-19, and predicted and verified diosgenin as a potential drug for the treatment of AD and COVID-19. The binding of diosgenin to the core pharmacological targets for the treatment of AD and COVID-19 was determined by molecular docking, which provides a theoretical basis for developing a new approach to clinical treatment of AD and COVID-19.