Differential expression of circRNAs during osteogenic/odontogenic differentiation of stem cells from apical papilla promoted by blue light-emitting diode.

BACKGROUND: Circular RNA (circRNA) is a key player in regulating the multidirectional differentiation of stem cells. Previous research by our group found that the blue light-emitting diode (LED) had a promoting effect on the osteogenic/odontogenic differentiation of human stem cells from apical papilla (SCAPs). This research aimed to investigate the differential expression of circRNAs during the osteogenic/odontogenic differentiation of SCAPs regulated by blue LED. MATERIALS AND METHODS: SCAPs were divided into the irradiation group (4 J/cm2) and the control group (0 J/cm2), and cultivated in an osteogenic/odontogenic environment. The differentially expressed circRNAs during osteogenic/odontogenic differentiation of SCAPs promoted by blue LED were detected by high-throughput sequencing, and preliminarily verified by qRT-PCR. Functional prediction of these circRNAs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the circRNA-miRNA-mRNA networks were also constructed. RESULTS: It showed 301 circRNAs were differentially expressed. GO and KEGG analyses suggested that these circRNAs were associated with some signaling pathways related to osteogenic/odontogenic differentiation. And the circRNA-miRNA-mRNA networks were also successfully constructed. CONCLUSION: CircRNAs were involved in the osteogenic/odontogenic differentiation of SCAPs promoted by blue LED. In this biological process, circRNA-miRNA-mRNA networks served an important purpose, and circRNAs regulated this process through certain signaling pathways.

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.

DeepSS2GO: protein function prediction from secondary structure.

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

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.

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.

Identification of circRNAs expression profiles and functional networks in parotid gland of type 2 diabetes mouse.

BACKGROUND: Circular RNAs (circRNAs) are a novel kind of non-coding RNAs proved to play crucial roles in the development of multiple diabetic complications. However, their expression and function in diabetes mellitus (DM)-impaired salivary glands are unknown. RESULTS: By using microarray technology, 663 upregulated and 999 downregulated circRNAs companied with 813 upregulated and 525 downregulated mRNAs were identified in the parotid glands (PGs) of type2 DM mice under a 2-fold change and P < 0.05 cutoff criteria. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis of upregulated mRNAs showed enrichments in immune system process and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Infiltration of inflammatory cells and increased inflammatory cytokines were observed in diabetic PGs. Seven differently expressed circRNAs validated by qRT-PCR were selected for coding-non-coding gene co-expression (CNC) and competing endogenous RNA (ceRNA) networks analysis. PPAR signaling pathway was primarily enriched through analysis of circRNA-mRNA networks. Moreover, the circRNA-miRNA-mRNA networks highlighted an enrichment in the regulation of actin cytoskeleton. CONCLUSION: The inflammatory response is elevated in diabetic PGs. The selected seven distinct circRNAs may attribute to the injury of diabetic PG by modulating inflammatory response through PPAR signaling pathway and actin cytoskeleton in diabetic PGs.

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.

Multi-omics integration highlights the role of ubiquitination in endometriosis fibrosis.

BACKGROUND: Endometriosis, characterized by the presence of active endometrial-like tissues outside the uterus, causes symptoms like dysmenorrhea and infertility due to the fibrosis of endometrial cells, which involves excessive deposition of extracellular matrix (ECM) proteins. Ubiquitination, an important post-transcriptional modification, regulates various biological processes in human diseases. However, its role in the fibrosis process in endometriosis remains unclear. METHODS: We employed multi-omics approaches on two cohorts of endometriosis patients with 39 samples. GO terms and KEGG pathways enrichment analyses were used to investigate the functional changes involved in endometriosis. Pearson's correlation coefficient analysis was conducted to explore the relationship between global proteome and ubiquitylome in endometriosis. The protein expression levels of ubiquitin-, fibrosis-related proteins, and E3 ubiquitin-protein ligase TRIM33 were validated via Western blot. Transfecting human endometrial stroma cells (hESCs) with TRIM33 small interfering RNA (siRNA) in vitro to explore how TRIM33 affects fibrosis-related proteins. RESULTS: Integration of proteomics and transcriptomics showed genes with concurrent change of both mRNA and protein level which involved in ECM production in ectopic endometria. Ubiquitylomics distinguished 1647 and 1698 ubiquitinated lysine sites in the ectopic (EC) group compared to the normal (NC) and eutopic (EU) groups, respectively. Further multi-omics integration highlighted the essential role of ubiquitination in key fibrosis regulators in endometriosis. Correlation analysis between proteome and ubiquitylome showed correlation coefficients of 0.32 and 0.36 for ubiquitinated fibrosis proteins in EC/NC and EC/EU groups, respectively, indicating positive regulation of fibrosis-related protein expression by ubiquitination in ectopic lesions. We identified ubiquitination in 41 pivotal proteins within the fibrosis-related pathway of endometriosis. Finally, the elevated expression of TGFBR1/α-SMA/FAP/FN1/Collagen1 proteins in EC tissues were validated across independent samples. More importantly, we demonstrated that both the mRNA and protein levels of TRIM33 were reduced in endometriotic tissues. Knockdown of TRIM33 promoted TGFBR1/p-SMAD2/α-SMA/FN1 protein expressions in hESCs but did not significantly affect Collagen1/FAP levels, suggesting its inhibitory effect on fibrosis in vitro. CONCLUSIONS: This study, employing multi-omics approaches, provides novel insights into endometriosis ubiquitination profiles and reveals aberrant expression of the E3 ubiquitin ligase TRIM33 in endometriotic tissues, emphasizing their critical involvement in fibrosis pathogenesis and potential therapeutic targets.

Construction of ferroptosis-related prediction model for pathogenesis, diagnosis and treatment of ruptured abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease, which often brings great psychological burden and economic pressure to patients. If AAA rupture occurs, it is a serious threat to patients' lives. Therefore, it is of clinical value to actively explore the pathogenesis of ruptured AAA and prevent its occurrence. Ferroptosis is a new type of cell death dependent on lipid peroxidation, which plays an important role in many cardiovascular diseases. In this study, we used online data and analysis of ferroptosis-related genes to uncover the formation of ruptured AAA and potential therapeutic targets. We obtained ferroptosis-related differentially expressed genes (Fe-DEGs) from GSE98278 dataset and 259 known ferroptosis-related genes from FerrDb website. Enrichment analysis of differentially expressed genes (DEGs) was performed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Receiver Operating characteristic (ROC) curve was employed to evaluate the diagnostic abilities of Fe-DEGs. Transcription factors and miRNAs of Fe-DEGs were identified through PASTAA and miRDB, miRWalk, TargetScan respectively. Single-sample gene set enrichment analysis (ssGSEA) was used to observe immune infiltration between the stable group and the rupture group. DGIdb database was performed to find potential targeted drugs of DEGs. GO and KEGG enrichment analysis found that DEGs mainly enriched in "cellular divalent inorganic cation homeostasis," "cellular zinc ion homeostasis," "divalent inorganic cation homeostasis," "Mineral absorption," "Cytokine - cytokine receptor interaction," "Coronavirus disease - COVID-19." Two up-regulated Fe-DEGs MT1G and DDIT4 were found to further analysis. Both single and combined applications of MT1G and DDIT4 showed good diagnostic efficacy (AUC = 0.8254, 0.8548, 0.8577, respectively). Transcription factors STAT1 and PU1 of MT1G and ARNT and MAX of DDIT4 were identified. Meanwhile, has_miR-548p-MT1G pairs, has_miR-53-3p/has_miR-181b-5p/ has_miR-664a-3p-DDIT4 pairs were found. B cells, NK cells, Th2 cells were high expression in the rupture group compared with the stable group, while DCs, Th1 cells were low expression in the rupture group. Targeted drugs against immunity, GEMCITABINE and INDOMETHACIN were discovered. We preliminarily explored the clinical significance of Fe-DEGs MT1G and DDIT4 in the diagnosis of ruptured AAA, and proposed possible upstream regulatory transcription factors and miRNAs. In addition, we also analyzed the immune infiltration of stable and rupture groups, and found possible targeted drugs for immunotherapy.

Co-expression network analysis and identification of core genes in the interaction between wheat and Puccinia striiformis f. sp. tritici.

The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns.

Mutual annotation-based prediction of protein domain functions with Domain2GO.

Identifying unknown functional properties of proteins is essential for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.

Potential mechanism of Chai Gui Zexie Decoction for NSCLC treatment assessed using network pharmacology, bioinformatics, and molecular docking: An observational study.

To explore the potential mechanism of Chai Gui Zexie Decoction for non-small cell lung cancer (NSCLC) treatment using network pharmacology, bioinformatics, and molecular docking. The active ingredients of Chai Gui Zexie Decoction and the associated predicted targets were screened using the TCMSP database. NSCLC-related targets were obtained from GeneCards and OMIM. Potential action targets, which are intersecting drug-predicted targets and disease targets, were obtained from Venny 2.1. The protein-protein interaction network was constructed by importing potential action targets into the STRING database, and the core action targets and core ingredients were obtained via topological analysis. The core action targets were entered into the Metascape database, and Gene Ontology annotation analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Differentially expressed genes were screened using the Gene Expression Omnibus, and the key targets were obtained by validating the core action targets. The key targets were input into The Tumor IMmune Estimation Resource for immune cell infiltration analysis. Finally, the molecular docking of key targets and core ingredients was performed. We obtained 60 active ingredients, 251 drug prediction targets, and 2133 NSCLC-related targets. Meanwhile, 147 potential action targets were obtained, and 47 core action targets and 40 core ingredients were obtained via topological analysis. We detected 175 pathways related to NSCLC pharmaceutical therapy. In total, 1249 Gene Ontology items were evaluated. Additionally, 3102 differential genes were screened, and tumor protein P53, Jun proto-oncogene, interleukin-6, and mitogen-activated protein kinase 3 were identified as the key targets. The expression of these key targets in NSCLC was correlated with macrophage, CD4+ T, CD8+ T, dendritic cell, and neutrophil infiltration. The molecular docking results revealed that the core ingredients have a potent affinity for the key targets. Chai Gui Zexie Decoction might exert its therapeutic effect on NSCLC through multiple ingredients, targets, and signaling pathways.

Exploration of the molecular linkage between endometriosis and Crohn disease by bioinformatics methods.

BACKGROUND: Endometriosis (EMT) is a common disease in reproductive-age woman and Crohn disease (CD) is a chronic inflammatory disorder in gastrointestinal tract. Previous studies reported that patients with EMT had an increased risk of CD. However, the linkage between EMT and CD remains unclear. In this study, we aimed to investigate the potential molecular mechanism of EMT and CD. METHODS: The microarray data of EMT and CD were downloaded from Gene Expression Omnibus. Common genes of EMT and CD were obtained to perform the Gene Ontology and Kyoto Encyclopedia of Gene Genomes enrichments. The protein-protein interaction network was constructed by Cytoscape software and the hub genes were identified by CytoHubba plug-in. Finally we predicted the transcription factors (TFs) of hub genes and constructed a TFs-hub genes regulation network. RESULTS: A total of 50 common genes were identified. Kyoto Encyclopedia of Gene Genomes enrichment showed that the common genes mainly enriched in MAPK pathway, VEGF pathway, Wnt pathway, TGF-beta pathway, and Ras pathway. Fifteen hub genes were collected from the protein-protein interaction network, including FMOD, FRZB, CPE, SST, ISG15, EFEMP1, KDR, ADRA2A, FZD7, AQP1, IGFBP5, NAMPT, PLUA, FGF9, and FHL2. Among them, FGF9, FZD7, IGFBP5, KDR, and NAMPT were both validated in the other 2 datasets. Finally TFs-hub genes regulation network were constructed. CONCLUSION: Our findings firstly revealed the linkage between EMT and CD, including inflammation, angiogenesis, immune regulation, and cell behaviors, which may lead to the risk of CD in EMT. FGF9, FZD7, IGFBP5, KDR, and NAMPT may closely relate to the linkage.

Transcriptome analysis reveals the molecular mechanisms underlying the enhancement of salt-tolerance in Melia azedarach under salinity stress.

Melia azedarach demonstrates strong salt tolerance and thrives in harsh saline soil conditions, but the underlying mechanisms are poorly understood. In this study, we analyzed gene expression under low, medium, and high salinity conditions to gain a deeper understanding of adaptation mechanisms of M. azedarach under salt stress. The GO (gene ontology) analysis unveiled a prominent trend: as salt stress intensified, a greater number of differentially expressed genes (DEGs) became enriched in categories related to metabolic processes, catalytic activities, and membrane components. Through the analysis of the category GO:0009651 (response to salt stress), we identified four key candidate genes (CBL7, SAPK10, EDL3, and AKT1) that play a pivotal role in salt stress responses. Furthermore, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis revealed that DEGs were significantly enriched in the plant hormone signaling pathways and starch and sucrose metabolism under both medium and high salt exposure in comparison to low salt conditions. Notably, genes involved in JAZ and MYC2 in the jasmonic acid (JA) metabolic pathway were markedly upregulated in response to high salt stress. This study offers valuable insights into the molecular mechanisms underlying M. azedarach salt tolerance and identifies potential candidate genes for enhancing salt tolerance in M. azedarach.

Identification of Lipotoxicity-Related Biomarkers in Diabetic Nephropathy Based on Bioinformatic Analysis.

Objective: This study is aimed at investigating diagnostic biomarkers associated with lipotoxicity and the molecular mechanisms underlying diabetic nephropathy (DN). Methods: The GSE96804 dataset from the Gene Expression Omnibus (GEO) database was utilized to identify differentially expressed genes (DEGs) in DN patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the DEGs. A protein-protein interaction (PPI) network was established to identify key genes linked to lipotoxicity in DN. Immune infiltration analysis was employed to identify immune cells with differential expression in DN and to assess the correlation between these immune cells and lipotoxicity-related hub genes. The findings were validated using the external dataset GSE104954. ROC analysis was performed to assess the diagnostic performance of the hub genes. The Gene set enrichment analysis (GSEA) enrichment method was utilized to analyze the key genes associated with lipotoxicity as mentioned above. Result: In this study, a total of 544 DEGs were identified. Among them, extracellular matrix (ECM), fatty acid metabolism, AGE-RAGE, and PI3K-Akt signaling pathways were significantly enriched. Combining the PPI network and lipotoxicity-related genes (LRGS), LUM and ALB were identified as lipotoxicity-related diagnostic biomarkers for DN. ROC analysis showed that the AUC values for LUM and ALB were 0.882 and 0.885, respectively. The AUC values for LUM and ALB validated in external datasets were 0.98 and 0.82, respectively. Immune infiltration analysis revealed significant changes in various immune cells during disease progression. Macrophages M2, mast cells activated, and neutrophils were significantly associated with all lipotoxicity-related hub genes. These key genes were enriched in fatty acid metabolism and extracellular matrix-related pathways. Conclusion: The identified lipotoxicity-related hub genes provide a deeper understanding of the development mechanisms of DN, potentially offering new theoretical foundations for the development of diagnostic biomarkers and therapeutic targets related to lipotoxicity in DN.

The shared circulating diagnostic biomarkers and molecular mechanisms of systemic lupus erythematosus and inflammatory bowel disease.

Background: Systemic lupus erythematosus (SLE) is a multi-organ chronic autoimmune disease. Inflammatory bowel disease (IBD) is a common chronic inflammatory disease of the gastrointestinal tract. Previous studies have shown that SLE and IBD share common pathogenic pathways and genetic susceptibility, but the specific pathogenic mechanisms remain unclear. Methods: The datasets of SLE and IBD were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package. Weighted gene coexpression network analysis (WGCNA) was used to determine co-expression modules related to SLE and IBD. Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for co-driver genes. Using the Least AbsoluteShrinkage and Selection Operator (Lasso) regressionand Support Vector Machine-Recursive Feature Elimination (SVM-RFE), common diagnostic markers for both diseases were further evaluated. Then, we utilizedthe CIBERSORT method to assess the abundance of immune cell infiltration. Finally,we used the single-cell analysis to obtain the location of common diagnostic markers. Results: 71 common driver genes were identified in the SLE and IBD cohorts based on the DEGs and module genes. KEGG and GO enrichment results showed that these genes were closely associated with positive regulation of programmed cell death and inflammatory responses. By using LASSO regression and SVM, five hub genes (KLRF1, GZMK, KLRB1, CD40LG, and IL-7R) were ultimately determined as common diagnostic markers for SLE and IBD. ROC curve analysis also showed good diagnostic performance. The outcomes of immune cell infiltration demonstrated that SLE and IBD shared almost identical immune infiltration patterns. Furthermore, the majority of the hub genes were commonly expressed in NK cells by single-cell analysis. Conclusion: This study demonstrates that SLE and IBD share common diagnostic markers and pathogenic pathways. In addition, SLE and IBD show similar immune cellinfiltration microenvironments which provides newperspectives for future treatment.

Calcium-sensing Receptor, a Potential Biomarker Revealed by Large-scale Public Databases and Experimental Verification in Metastatic Breast Cancer.

INTRODUCTION: Breast cancer (BC) is a common cancer characterized by a high molecular heterogeneity. Therefore, understanding its biological properties and developing effective treatments for patients with different molecular features is imperative. Calcium-sensing receptor (CaSR) has been implicated in several regulatory functions in various types of human cancers. However, its underlying pathological mechanism in BC progression remains elusive. METHODS: We utilized The Cancer Genome Atlas and Gene Expression Omnibus databases to explore the function of CaSR in the metastasis of BC. Gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, and Gene Set Enrichment Analysis of biological processes and cell signaling pathways revealed that CaSR could be activated or inhibited. Importantly, quantitative reverse transcriptase-polymerase chain reaction and western blotting were used to verify the gene expression of the CaSR. Wound healing and transwell assays were conducted to assess the effect of CaSR on the migration of BC cells. RESULTS: We demonstrated that CaSR expression in metastatic BC was higher than that in non-metastatic BC. It is the first time that database information has been used to reveal the biological process and molecular mechanism of CaSR in BC. Moreover, the CaSR expression in normal breast epithelial cells was notably less compared to that in BC cells. The activation of CaSR by Cinacalcet (a CaSR agonist) significantly enhanced the migration of BC cells, whereas NPS-2143 (a CaSR antagonist) treatment dramatically inhibited these effects. CONCLUSION AND FUTURE PERSPECTIVE: Bioinformatics techniques and experiments demonstrated the involvement of CaSR in BC metastasis. Our findings shed new light on the receptor therapy and molecular pathogenesis of BC, and emphasize the crucial function of CaSR, facilitating the metastasis of BC.

Exploring the Regulators of Keratinization: Role of BMP-2 in Oral Mucosa.

The oral mucosa functions as a physico-chemical and immune barrier to external stimuli, and an adequate width of the keratinized mucosa around the teeth or implants is crucial to maintaining them in a healthy and stable condition. In this study, for the first time, bulk RNA-seq analysis was performed to explore the gene expression of laser microdissected epithelium and lamina propria from mice, aiming to investigate the differences between keratinized and non-keratinized oral mucosa. Based on the differentially expressed genes (DEGs) and Gene Ontology (GO) Enrichment Analysis, bone morphogenetic protein 2 (BMP-2) was identified to be a potential regulator of oral mucosal keratinization. Monoculture and epithelial-mesenchymal cell co-culture models in the air-liquid interface (ALI) indicated that BMP-2 has direct and positive effects on epithelial keratinization and proliferation. We further performed bulk RNA-seq of the ALI monoculture stimulated with BMP-2 in an attempt to identify the downstream factors promoting epithelial keratinization and proliferation. Analysis of the DEGs identified, among others, IGF2, ID1, LTBP1, LOX, SERPINE1, IL24, and MMP1 as key factors. In summary, these results revealed the involvement of a well-known growth factor responsible for bone development, BMP-2, in the mechanism of oral mucosal keratinization and proliferation, and pointed out the possible downstream genes involved in this mechanism.

Screening of key immune-related gene in Parkinson's disease based on WGCNA and machine learning. / 基于WGCNAå’Œæœºå™¨å­¦ä¹ ç­›é€‰å¸•é‡‘æ£®ç— å ç–«ç›¸å ³å ³é”®åŸºå› .

OBJECTIVES: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning. METHODS: This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn. RESULTS: A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells, NK cells, CD8+ T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation. CONCLUSIONS: This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.

Bioinformation Analysis of Differential Expression Proteins in Different Processes of COVID-19.

COVID-19 is a highly infectious respiratory disease whose progression has been associated with multiple factors. From SARS-CoV-2 infection to death, biomarkers capable of predicting different disease processes are needed to help us further understand the molecular progression of COVID-19 disease. The aim is to find differentially expressed proteins that are associated with the progression of COVID-19 disease or can be potential biomarkers, and to provide a reference for further understanding of the molecular mechanisms of COVID-19 occurrence, progression, and treatment. Data-independent Acquisition (DIA) proteomics to obtain sample protein expression data, using R language screening differentially expressed proteins. Gene Ontology and Kyoto Encyclopedia for Genes and Genomes analysis was performed on differential proteins and protein-protein interaction (PPI) network was constructed to screen key proteins. A total of 47 differentially expressed proteins were obtained from COVID-19 incubation patients and healthy population (L/H), mainly enriched in platelet-related functions, and complement and coagulation cascade reaction pathways, such as platelet degranulation and platelet aggregation. A total of 42 differential proteins were obtained in clinical and latent phase patients (C/L), also mainly enriched in platelet-related functions and in complement and coagulation cascade reactions, platelet activation pathways. A total of 10 differential proteins were screened in recovery and clinical phase patients (R/C), mostly immune-related proteins. The differentially expressed proteins in different stages of COVID-19 are mostly closely associated with coagulation, and key differential proteins, such as FGA, FGB, FGG, ACTB, PFN1, VCL, SERPZNCL, APOC3, LTF, and DEFA1, have the potential to be used as early diagnostic markers.