Gene expression and immune infiltration analysis comparing lesioned and preserved subchondral bone in osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods: Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein-protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results: A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand-receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1ß were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia. METHODS: Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated. RESULTS: The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93). CONCLUSION: We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

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

BACKGROUND: Gout 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. METHODS: The 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. RESULTS: We 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. CONCLUSION: According 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.

Unraveling malignant phenotype of peritumoral tissue: transcriptomic insights into early-stage breast cancer.

BACKGROUND: Early-stage invasive ductal carcinoma displays high survival rates due to early detection and treatments. However, there is still a chance of relapse of 3-15% after treatment. The aim of this study was to uncover the distinctive transcriptomic characteristics and monitoring prognosis potential of peritumoral tissue in early-stage cases. METHODS: RNA was isolated from tumoral, peritumoral, and non-tumoral breast tissue from surgical resection of 10 luminal early-stage invasive ductal carcinoma patients. Transcriptome expression profiling for differentially expressed genes (DEGs) identification was carried out through microarray analysis. Gene Ontology and KEGG pathways enrichment analysis were explored for functional characterization of identified DEGs. Protein-Protein Interactions (PPI) networks analysis was performed to identify hub nodes of peritumoral tissue alterations and correlated with Overall Survival and Relapse Free Survival. RESULTS: DEGs closely related with cell migration, extracellular matrix organization, and cell cycle were upregulated in peritumoral tissue compared to non-tumoral. Analyzing PPI networks, we observed that the proximity to tumor leads to the alteration of gene modules involved in cell proliferation and differentiation signaling pathways. In fact, in the peritumoral area were identified the top ten upregulated hub nodes including CDK1, ESR1, NOP58, PCNA, EZH2, PPP1CA, BUB1, TGFBR1, CXCR4, and CCND1. A signature performed by four of these hub nodes (CDK1, PCNA, EZH2, and BUB1) was associated with relapse events in untreated luminal breast cancer patients. CONCLUSIONS: In conclusion, our study characterizes in depth breast peritumoral tissue providing clues on the changes that tumor signaling could cause in patients with early-stage breast cancer. We propose that the use of a four gene signature could help to predict local relapse. Overall, our results highlight the value of peritumoral tissue as a potential source of new biomarkers for early detection of relapse and improvement in invasive ductal carcinoma patient's prognosis.

Network pharmacology and experimental verification to explore the effect of Hedyotis diffusa on Alzheimer's disease.

This study aimed to explore the active components and the effect of Hedyotis diffusa (HD) against Alzheimer's disease (AD) via network pharmacology, molecular docking, and experimental evaluations. We conducted a comprehensive screening process using the TCMSP, Swiss Target Prediction, and PharmMapper databases to identify the active components and their related targets in HD. In addition, we collected potential therapeutic targets of AD from the Gene Cards, Drugbank, and OMIM databases. Afterward, we utilized Cytoscape to establish both protein-protein interaction (PPI) networks and compound-target (C-T) networks. To gain further insights into the functional aspect, we performed GO and KEGG pathway analyses using the David database. Next, we employed Autodock vina to estimate the binding force between the components and the hub genes. To validate our network pharmacology findings, we conducted relevant experiments on Caenorhabditis elegans, further confirming the reliability of our results. Then a total of six active compounds and 149 therapeutic targets were detected. Through the analysis of the association between active compounds, therapeutic targets, and signaling pathways, it was observed that the therapeutic effect of HD primarily encompassed the inhibition of Aß, suppression of AChE activity, and mitigating oxidative stress. Additionally, our investigation revealed that the key active compounds in HD primarily consisted of iridoids, which exhibited resistance against AD by acting on the Alzheimer's disease pathway and the AGE-RAGE signaling pathway in diabetic complications.

Bioinformatics analysis of signature genes related to cell death in keratoconus.

Keratoconus is corneal disease in which the progression of conical dilation of cornea leads to reduced visual acuity and even corneal perforation. However, the etiology mechanism of keratoconus is still unclear. This study aims to identify the signature genes related to cell death in keratoconus and examine the function of these genes. A dataset of keratoconus from the GEO database was analysed to identify the differentially expressed genes (DEGs). A total of 3558 DEGs were screened from GSE151631. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that they mainly involved in response to hypoxia, cell-cell adhesion, and IL-17 signaling pathway. Then, the cell death-related genes datasets were intersected with the above 3558 DEGs to obtain 70 ferroptosis-related DEGs (FDEGs), 32 autophagy-related DEGs (ADEGs), six pyroptosis-related DEGs (PDEGs), four disulfidptosis-related DEGs (DDEGs), and one cuproptosis-related DEGs (CDEGs). After using Least absolute shrinkage and selection operator (LASSO), Random Forest analysis, and receiver operating characteristic (ROC) curve analysis, one ferroptosis-related gene (TNFAIP3) and five autophagy-related genes (CDKN1A, HSPA5, MAPK8IP1, PPP1R15A, and VEGFA) were screened out. The expressions of the above six genes were significantly decreased in keratoconus and the area under the curve (AUC) values of these genes was 0.944, 0.893, 0.797, 0.726, 0.882 and 0.779 respectively. GSEA analysis showed that the above six genes mainly play an important role in allograft rejection, asthma, and circadian rhythm etc. In conclusion, the results of this study suggested that focusing on these genes and autoimmune diseases will be a beneficial perspective for the keratoconus etiology research.

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.

Deciphering the molecular nexus of BTG2 in periodontitis and diabetic kidney disease.

OBJECTIVE: To investigate the role of BTG2 in periodontitis and diabetic kidney disease (DKD) and its potential underlying mechanism. METHODS: Gene expression data for periodontitis and DKD were acquired from the Gene Expression Omnibus (GEO) database. Differential expression analysis identified co-expressed genes between these conditions. The Nephroseq V5 online nephropathy database validated the role of these genes in DKD. Pearson correlation analysis identified genes associated with our target gene. We employed Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks to elucidate potential mechanisms. Expression levels of BTG2 mRNA were examined using quantitative polymerase Chain Reaction (qPCR) and immunofluorescence assays. Western blotting quantified proteins involved in epithelial-to-mesenchymal transition (EMT), apoptosis, mTORC1 signaling, and autophagy. Additionally, wound healing and flow cytometric apoptosis assays evaluated podocyte migration and apoptosis, respectively. RESULTS: Analysis of GEO database data revealed BTG2 as a commonly differentially expressed gene in both DKD and periodontitis. BTG2 expression was reduced in DKD compared to normal conditions and correlated with proteinuria. GSEA indicated enrichment of BTG2 in the EMT and mTORC1 signaling pathways. The PPI network highlighted BTG2's relevance to S100A9, S100A12, and FPR1. Immunofluorescence assays demonstrated significantly lower BTG2 expression in podocytes under high glucose (HG) conditions. Reduced BTG2 expression in HG-treated podocytes led to increased levels of EMT markers (α-SMA, vimentin) and the apoptotic protein Bim, alongside a decrease in nephrin. Lower BTG2 levels were associated with increased podocyte mobility and apoptosis, as well as elevated RPS6KB1 and mTOR levels, but reduced autophagy marker LC3. CONCLUSION: Our findings suggest that BTG2 is a crucial intermediary gene linking DKD and periodontitis. Modulating autophagy via inhibition of the mTORC1 signaling pathway, and consequently suppressing EMT, may be pivotal in the interplay between periodontitis and DKD.

Identification of potential obese-specific biomarkers and pathways associated with abdominal subcutaneous fat deposition in pig using a comprehensive bioinformatics strategy.

Abdominal subcutaneous fat deposition (ASFD) is not only related to meat quality in the pig industry but also to human health in medicine. It is of great value to elucidate the potential molecular mechanisms of ASFD. The present study aims to identify obese-specific biomarkers and key pathways correlated with ASFD in pigs. The ASF-related mRNA expression dataset GSE136754 was retrieved from the Gene Expression Omnibus (GEO) database and systematically analyzed using a comprehensive bioinformatics method. A total of 565 differentially expressed genes (DEGs) were identified between three obese and three lean pigs, and these DEGs were mainly involved in the p53 signaling pathway, MAPK signaling pathway and fatty acid metabolism. A protein-protein interaction (PPI) network, consisting of 540 nodes and 1,065 edges, was constructed, and the top ten genes with the highest degree scores-ABL1, HDAC1, CDC42, HDAC2, MRPS5, MRPS10, MDM2, JUP, RPL7L1 and UQCRFS1-were identified as hub genes in the whole PPI network. Especially HDAC1, MDM2, MRPS10 and RPL7L1 were identified as potential robust obese-specific biomarkers due to their significant differences in single gene expression levels and high ROC area; this was further verified by quantitative real-time PCR (qRT-PCR) on abdominal subcutaneous fat samples from obese-type (Saba) and lean-type (Large White) pigs. Additionally, a mRNA-miRNA-lncRNA ceRNA network consisting of four potential biomarkers, 15 miRNAs and 51 lncRNAs was established, and two targeted lncRNAs with more connections, XIST and NEAT1, were identified as potentially important regulatory factors. The findings of this study may provide novel insights into the molecular mechanism involved in ASFD.

Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning.

Background and aims: Cuproptosis has emerged as a significant contributor in the progression of various diseases. This study aimed to assess the potential impact of cuproptosis-related genes (CRGs) on the development of hepatic ischemia and reperfusion injury (HIRI). Methods: The datasets related to HIRI were sourced from the Gene Expression Omnibus database. The comparative analysis of differential gene expression involving CRGs was performed between HIRI and normal liver samples. Correlation analysis, function enrichment analyses, and protein-protein interactions were employed to understand the interactions and roles of these genes. Machine learning techniques were used to identify hub genes. Additionally, differences in immune cell infiltration between HIRI patients and controls were analyzed. Quantitative real-time PCR and western blotting were used to verify the expression of the hub genes. Results: Seventy-five HIRI and 80 control samples from three databases were included in the bioinformatics analysis. Three hub CRGs (NLRP3, ATP7B and NFE2L2) were identified using three machine learning models. Diagnostic accuracy was assessed using a receiver operating characteristic (ROC) curve for the hub genes, which yielded an area under the ROC curve (AUC) of 0.832. Remarkably, in the validation datasets GSE15480 and GSE228782, the three hub genes had AUC reached 0.904. Additional analyses, including nomograms, decision curves, and calibration curves, supported their predictive power for diagnosis. Enrichment analyses indicated the involvement of these genes in multiple pathways associated with HIRI progression. Comparative assessments using CIBERSORT and gene set enrichment analysis suggested elevated expression of these hub genes in activated dendritic cells, neutrophils, activated CD4 memory T cells, and activated mast cells in HIRI samples versus controls. A ceRNA network underscored a complex regulatory interplay among genes. The genes mRNA and protein levels were also verified in HIRI-affected mouse liver tissues. Conclusion: Our findings have provided a comprehensive understanding of the association between cuproptosis and HIRI, establishing a promising diagnostic pattern and identifying latent therapeutic targets for HIRI treatment. Additionally, our study offers novel insights to delve deeper into the underlying mechanisms of HIRI.

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment.

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

Identification of hub glutamine metabolism-associated genes and immune characteristics in pre-eclampsia.

OBJECTIVE: Preeclampsia (PE) is a severe complication of unclear pathogenesis associated with pregnancy. This research aimed to elucidate the properties of immune cell infiltration and potential biomarkers of PE based on bioinformatics analysis. MATERIALS AND METHODS: Two PE datasets were imported from the Gene ExpressioOmnibus (GEO) and screened to identify differentially expressed genes (DEGs). Significant module genes were identified by weighted gene co-expression network analysis (WGCNA). DEGs that interacted with key module genes (GLu-DEGs) were analyzed further by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The diagnostic value of the genes was assessed using receiver operating characteristic (ROC) curves and protein-protein interaction (PPI) networks were constructed using GeneMANIA, and GSVA analysis was performed using the MSigDB database. Immune cell infiltration was analyzed using the TISIDB database, and StarBase and Cytoscape were used to construct an RBP-mRNA network. The identified hub genes were validated in two independent datasets. For further confirmation, placental tissue from healthy pregnant women and women with PE were collected and analyzed using both RT-qPCR and immunohistochemistry. RESULTS: A total of seven GLu-DEGs were obtained and were found to be involved in pathways associated with the transport of sulfur compounds, PPAR signaling, and energy metabolism, shown by GO and KEGG analyses. GSVA indicated significant increases in adipocytokine signaling. Furthermore, single-sample Gene Set Enrichment Analysis (ssGSEA) indicated that the levels of activated B cells and T follicular helper cells were significantly increased in the PE group and were negatively correlated with GLu-DEGs, suggesting their potential importance. CONCLUSION: In summary, the results showed a correlation between glutamine metabolism and immune cells, providing new insights into the understandingPE pathogenesis and furnishing evidence for future advances in the treatment of this disease.

On the mechanism of wogonin against acute monocytic leukemia using network pharmacology and experimental validation.

Wogonin is a natural flavone compound from the plant Scutellaria baicalensis, which has a variety of pharmacological activities such as anti-cancer, anti-virus, anti-inflammatory, and immune regulation. However, the potential mechanism of wogonin remains unknown. This study was to confirm the molecular mechanism of wogonin for acute monocytic leukemia treatment, known as AML-M5. The potential action targets between wogonin and acute monocytic leukemia were predicted from databases. The compound-target-pathway network and protein-protein interaction network (PPI) were constructed. The enrichment analysis of related targets and molecular docking were performed. The network pharmacological results of wogonin for AML-M5 treatment were verified using the THP-1 cell line. 71 target genes of wogonin associated with AML-M5 were found. The key genes TP53, SRC, AKT1, RELA, HSP90AA1, JUN, PIK3R1, and CCND1 were preliminarily found to be the potential central targets of wogonin for AML-M5 treatment. The PPI network analysis, GO analysis and KEGG pathway enrichment analysis demonstrated that the PI3K/AKT signaling pathway was the significant pathway in the wogonin for AML-M5 treatment. The antiproliferative effects of wogonin on THP-1 cells of AML-M5 presented a dose-dependent and time-dependent manner, inducing apoptosis, blocking the cell cycle at the G2/M phase, decreasing the expressions of CCND1, CDK2, and CyclinA2 mRNA, as well as AKT and p-AKT proteins. The mechanisms of wogonin on AML-M5 treatment may be associated with inhibiting cell proliferation and regulating the cell cycle via the PI3K/AKT signaling pathway.

Exploring the mechanism of Jinlida granules against type 2 diabetes mellitus by an integrative pharmacology strategy.

Jinlida granule (JLD) is a Traditional Chinese Medicine (TCM) formula used for the treatment of type 2 diabetes mellitus (T2DM). However, the mechanism of JLD treatment for T2DM is not fully revealed. In this study, we explored the mechanism of JLD against T2DM by an integrative pharmacology strategy. Active components and corresponding targets were retrieved from Traditional Chinese Medicine System Pharmacology (TCMSP), SwissADME and Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine Database (BATMAN-TCM) database. T2DM-related targets were obtained from Drugbank and Genecards databases. The protein-protein interaction (PPI) network was constructed and analyzed with STRING (Search Toll for the Retrieval of Interacting Genes/proteins) and Cytoscape to get the key targets. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID). Lastly, the binding capacities and reliability between potential active components and the targets were verified with molecular docking and molecular dynamics simulation. In total, 185 active components and 337 targets of JLD were obtained. 317 targets overlapped with T2DM-related targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumor necrosis factor (TNF), interleukin-6 (IL-6), cellular tumor antigen p53 (TP53), prostaglandin G/H synthase 2 (PTGS2), Caspase-3 (CASP3) and signal transducer and activator of transcription 3 (STAT3) were identified as seven key targets by the topological analysis of the PPI network. GO and KEGG enrichment analyses showed that the effects were primarily associated with gene expression, signal transduction, apoptosis and inflammation. The pathways were mainly enriched in PI3K-AKT signaling pathway and AGE-RAGE signaling pathway in diabetic complications. Molecular docking and molecular dynamics simulation verified the good binding affinity between the key components and targets. The predicted results may provide a theoretical basis for drug screening of JLD and a new insight for the therapeutic effect of JLD on T2DM.

Unveiling potential therapeutic targets for diabetes-induced frozen shoulder through Mendelian randomization analysis of the human plasma proteome.

INTRODUCTION: This study aimed to assess the causal relationship between diabetes and frozen shoulder by investigating the target proteins associated with diabetes and frozen shoulder in the human plasma proteome through Mendelian randomization (MR) and to reveal the corresponding pathological mechanisms. RESEARCH DESIGN AND METHODS: We employed the MR approach for the purposes of establishing: (1) the causal link between diabetes and frozen shoulder; (2) the plasma causal proteins associated with frozen shoulder; (3) the plasma target proteins associated with diabetes; and (4) the causal relationship between diabetes target proteins and frozen shoulder causal proteins. The MR results were validated and consolidated through colocalization analysis and protein-protein interaction network. RESULTS: Our MR analysis demonstrated a significant causal relationship between diabetes and frozen shoulder. We found that the plasma levels of four proteins were correlated with frozen shoulder at the Bonferroni significance level (p<3.03E-5). According to colocalization analysis, parathyroid hormone-related protein (PTHLH) was moderately correlated with the genetic variance of frozen shoulder (posterior probability=0.68), while secreted frizzled-related protein 4 was highly correlated with the genetic variance of frozen shoulder (posterior probability=0.97). Additionally, nine plasma proteins were activated during diabetes-associated pathologies. Subsequent MR analysis of nine diabetic target proteins with four frozen shoulder causal proteins indicated that insulin receptor subunit alpha, interleukin-6 receptor subunit alpha, interleukin-1 receptor accessory protein, glutathione peroxidase 7, and PTHLH might contribute to the onset and progression of frozen shoulder induced by diabetes. CONCLUSIONS: Our study identified a causal relationship between diabetes and frozen shoulder, highlighting the pathological pathways through which diabetes influences frozen shoulder.

Potential therapeutic targets for membranous nephropathy: proteome-wide Mendelian randomization and colocalization analysis.

Background: The currently available medications for treating membranous nephropathy (MN) still have unsatisfactory efficacy in inhibiting disease recurrence, slowing down its progression, and even halting the development of end-stage renal disease. There is still a need to develop novel drugs targeting MN. Methods: We utilized summary statistics of MN from the Kiryluk Lab and obtained plasma protein data from Zheng et al. We performed a Bidirectional Mendelian randomization analysis, HEIDI test, mediation analysis, Bayesian colocalization, phenotype scanning, drug bank analysis, and protein-protein interaction network. Results: The Mendelian randomization analysis uncovered 8 distinct proteins associated with MN after multiple false discovery rate corrections. Proteins related to an increased risk of MN in plasma include ABO [(Histo-Blood Group Abo System Transferase) (WR OR = 1.12, 95%CI:1.05-1.19, FDR=0.09, PPH4 = 0.79)], VWF [(Von Willebrand Factor) (WR OR = 1.41, 95%CI:1.16-1.72, FDR=0.02, PPH4 = 0.81)] and CD209 [(Cd209 Antigen) (WR OR = 1.19, 95%CI:1.07-1.31, FDR=0.09, PPH4 = 0.78)], and proteins that have a protective effect on MN: HRG [(Histidine-Rich Glycoprotein) (WR OR = 0.84, 95%CI:0.76-0.93, FDR=0.02, PPH4 = 0.80)], CD27 [(Cd27 Antigen) (WR OR = 0.78, 95%CI:0.68-0.90, FDR=0.02, PPH4 = 0.80)], LRPPRC [(Leucine-Rich Ppr Motif-Containing Protein, Mitochondrial) (WR OR = 0.79, 95%CI:0.69-0.91, FDR=0.09, PPH4 = 0.80)], TIMP4 [(Metalloproteinase Inhibitor 4) (WR OR = 0.67, 95%CI:0.53-0.84, FDR=0.09, PPH4 = 0.79)] and MAP2K4 [(Dual Specificity Mitogen-Activated Protein Kinase Kinase 4) (WR OR = 0.82, 95%CI:0.72-0.92, FDR=0.09, PPH4 = 0.80)]. ABO, HRG, and TIMP4 successfully passed the HEIDI test. None of these proteins exhibited a reverse causal relationship. Bayesian colocalization analysis provided evidence that all of them share variants with MN. We identified type 1 diabetes, trunk fat, and asthma as having intermediate effects in these pathways. Conclusions: Our comprehensive analysis indicates a causal effect of ABO, CD27, VWF, HRG, CD209, LRPPRC, MAP2K4, and TIMP4 at the genetically determined circulating levels on the risk of MN. These proteins can potentially be a promising therapeutic target for the treatment of MN.

Hypothesis: evidence that the PRS gene products of Saccharomyces cerevisiae support both PRPP synthesis and maintenance of cell wall integrity.

The gene products of PRS1-PRS5 in Saccharomyces cerevisiae are responsible for the production of PRPP (5-phospho-D-ribosyl-α-1-pyrophosphate). However, it has been demonstrated that they are also involved in the cell wall integrity (CWI) signalling pathway as shown by protein-protein interactions (PPIs) with, for example Slt2, the MAP kinase of the CWI pathway. The following databases: SGD, BioGRID and Hit Predict, which collate PPIs from various research papers, have been scrutinized for evidence of PPIs between Prs1-Prs5 and components of the CWI pathway. The level of certainty in PPIs was verified by interaction scores available in the Hit Predict database revealing that well-documented interactions correspond with higher interaction scores and can be graded as high confidence interactions based on a score > 0.28, an annotation score ≥ 0.5 and a method-based high confidence score level of ≥ 0.485. Each of the Prs1-Prs5 polypeptides shows some degree of interaction with the CWI pathway. However, Prs5 has a vital role in the expression of FKS2 and Rlm1, previously only documented by reporter assay studies. This report emphasizes the importance of investigating interactions using more than one approach since every method has its limitations and the use of different methods, as described herein, provides complementary experimental and statistical data, thereby corroborating PPIs. Since the experimental data described so far are consistent with a link between PRPP synthetase and the CWI pathway, our aim was to demonstrate that these data are also supported by high-throughput bioinformatic analyses promoting our hypothesis that two of the five PRS-encoding genes contain information required for the maintenance of CWI by combining data from our targeted approach with relevant, unbiased data from high-throughput analyses.

Exploring the common mechanism of vascular dementia and inflammatory bowel disease: a bioinformatics-based study.

Objective: Emerging evidence has shown that gut diseases can regulate the development and function of the immune, metabolic, and nervous systems through dynamic bidirectional communication on the brain-gut axis. However, the specific mechanism of intestinal diseases and vascular dementia (VD) remains unclear. We designed this study especially, to further clarify the connection between VD and inflammatory bowel disease (IBD) from bioinformatics analyses. Methods: We downloaded Gene expression profiles for VD (GSE122063) and IBD (GSE47908, GSE179285) from the Gene Expression Omnibus (GEO) database. Then individual Gene Set Enrichment Analysis (GSEA) was used to confirm the connection between the two diseases respectively. The common differentially expressed genes (coDEGs) were identified, and the STRING database together with Cytoscape software were used to construct protein-protein interaction (PPI) network and core functional modules. We identified the hub genes by using the Cytohubba plugin. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify pathways of coDEGs and hub genes. Subsequently, receiver operating characteristic (ROC) analysis was used to identify the diagnostic ability of these hub genes, and a training dataset was used to verify the expression levels of the hub genes. An alternative single-sample gene set enrichment (ssGSEA) algorithm was used to analyze immune cell infiltration between coDEGs and immune cells. Finally, the correlation between hub genes and immune cells was analyzed. Results: We screened 167 coDEGs. The main articles of coDEGs enrichment analysis focused on immune function. 8 shared hub genes were identified, including PTPRC, ITGB2, CYBB, IL1B, TLR2, CASP1, IL10RA, and BTK. The functional categories of hub genes enrichment analysis were mainly involved in the regulation of immune function and neuroinflammatory response. Compared to the healthy controls, abnormal infiltration of immune cells was found in VD and IBD. We also found the correlation between 8 shared hub genes and immune cells. Conclusions: This study suggests that IBD may be a new risk factor for VD. The 8 hub genes may predict the IBD complicated with VD. Immune-related coDEGS may be related to their association, which requires further research to prove.

Ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap mass spectrometry and molecular network analysis alongside network pharmacology to elucidate the active constituents and mechanism of action of Huahong tablet in treating pelvic inflammatory disease.

RATIONALE: Huahong tablet, a commonly used clinical Chinese patent medicine, shows good efficacy in treating pelvic inflammation and other gynaecological infectious diseases. However, the specific composition of Huahong tablets, which are complex herbal formulations, remains unclear. Therefore, this study aims to identify the active compounds and targets of Huahong tablets and investigate their mechanism of action in pelvic inflammatory diseases. METHODS: We utilised ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap mass spectrometry and the relevant literature to identify the chemical components of Huahong tablets. The GNPS database was employed to further analyse and speculate on the components. Potential molecular targets of the active ingredients were predicted using the SwissTargetPrediction website. Protein-protein interaction analysis was conducted using the STRING database, with visualisation in Cytoscape 3.9.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. Additionally, a traditional Chinese medicine-ingredient-target-pathway network was constructed using Cytoscape 3.10.1. Molecular docking validation was carried out to investigate the interaction between core target and specific active ingredient. RESULTS: A total of 66 chemical components were identified, and 41 compounds were selected as potential active components based on the literature and the TCMSP database. Moreover, 38 core targets were identified as key targets in the treatment of pelvic inflammatory diseases with Huahong tablets. GO and KEGG enrichment analysis revealed 986 different biological functions and 167 signalling pathways. CONCLUSION: The active ingredients in Huahong tablets exert therapeutic effects on pelvic inflammatory diseases by acting on multiple targets and utilising different pathways. Molecular docking confirmed the high affinity between the specific active ingredients and disease targets.

Identification of potential biomarkers of leprosy: A study based on GEO datasets.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.