The amidase activities are expressed as improvement factor (IF) referred to CaLB wild type activity: IF = Amidase activity of mutant/Amidase activity of CaLB wild.CaLB mutants used for the validation of the BioGPS-UPCA model and taken from ref 20 and ref 39.

An extensible and customizable gene annotation portal that emphasizes community extensibility and user customizability. It is a complete resource for learning about gene and protein function. Community extensibility reflects a belief that any BioGPS user should be able to add new content to BioGPS using the simple plugin interface, completely independently of the core developer team. User customizability recognizes that not all users are interested in the same set of gene annotation data, so the gene report layouts enable each user to define the information that is most relevant to them. Currently, BioGPS supports eight species: Human (Homo sapiens), Mouse (Mus musculus), Rat (Rattus norvegicus), Fruitfly (Drosophila melanogaster), Nematode (Caenorhabditis elegans), Zebrafish (Danio rerio), Thale-cress (Arabidopsis thaliana), Frog (Xenopus tropicalis), and Pig (Sus scrofa). BioGPS presents data in an ortholog-centric format, which allows users to display mouse plugins next to human ones. Our data for defining orthologs comes from NCBI's HomoloGene database.

Context

Selective inhibition of CD4+ T-cell cytokine production and autoimmunity by BET protein and c-Myc inhibitors,by Jason Greenbaum jgbaum@liai.org,

Bromodomain-containing proteins bind acetylated lysine residues on histone tails and are involved in the recruitment of additional factors that mediate histone modifications and enable transcription. A compound, I-BET-762, that inhibits binding of an acetylated histone peptide to BRD4 and other proteins of the BET (bromodomain and extra-terminal domain) family, was previously shown to suppress the production of pro-inflammatory proteins by macrophages and block acute inflammation in mice. RNA from resting or activated CD4+ T cells grown in the presence of a control substance (DMSO or Control-768) or two different concentrations of I-BET-762, was hybridized to the chip. There are 3 biological replicates for a total of 2 (cell states) x 4 (conditions) x 3 (replicates) = 24 samples. http://biogps.org/#goto=genereport&id=12566&show_dataset=E-GEOD-39886 http://biogps.org/dataset/E-GEOD-39886/ Photo by United Nations Covid-19 Response

Content

24 Samples. Three biological replicates for a total of 2 (cell states) x 4 (conditions) x 3 (replicates) .

Acknowledgements

http://biogps.org/#goto=genereport&id=12566&show_dataset=E-GEOD-39886

Photo by United Nations Covid-19 Response

Inspiration

Patients infected with COVID-19 that may die due to an excessive response of their immune system, characterized by the abnormal release of circulating cytokines, termed cytokine release syndrome (CRS). This phenomenon of a plethora of cytokines is often vividly referred to as “cytokine storm.”

Context

Raw data for Expression data from mutant SOD1(G93A) transgenic mice.

http://biogps.org/#goto=genereport&id=12566&show_dataset=E-GEOD-18597

Content

Expression profiling of spinal cord from SOD1(G93A) mice and age matched controls at ages 28, 42, 56, 70, 98, 112, and 126 days of age. We used microarrays to determine differential gene expression throughout disease progression in the spinal cord of mutant SOD1(G93A) model of ALS. Samples were collected from male B6SJL SOD1(G93A) and age matched controls. 3 samples were collected representing each genotype and age group for RNA extraction and hybridization on Affymetrix microarrays.

Acknowledgements

http://biogps.org/#goto=genereport&id=12566&show_dataset=E-GEOD-18597

Bruce Lerman , Eric Hoffman.

Photo by National Cancer Institute on Unsplash

Inspiration

ALS disease.

Heart-enriched (fold change) values are derived from the BioGPS website (http://biogps.org/) [28] and embedded GeneAtlas MOE430 gcrma gene expression activity chart [29], which were used to interrogate cardiac expression of genes of interest. Statistical values are from CircaDB and the JTK_Cycle algorithm, Mouse 1.OST Heart (Affymetrix) microarrays [19]–[22].

Data were compiled from public databases (Http://www.ncbi.nlm.nih.gov; http://www.informatics.jax.org) February 25, 2011 and http://biogps.gnf.org/#goto=welcome, February 25, 2011). NCBI/MGD: YES – expression of a gene was observed; NO – expression of a gene was not observed; NT – not tested. BioGPS: Majority of data were obtained using Gene Atlas MOE430, *Gene Atlas GNF1M, **Gene Atlas U133A. M = median value across all samples for a single probe set. NT – not tested.

Database platform of an integrated view of eight databases (mouse gene expression resources: EMAGE, GXD, GENSAT, BioGPS, ABA, EUREXPRESS; human gene expression databases: HUDSEN, BioGPS and Human Protein Atlas) that allows the experimentalist to retrieve relevant statistical information relating gene expression, anatomical structure (space) and developmental stage (time). Moreover, general biological information from databases such as KEGG, OMIM and MTB is integrated too. It can be queried using gene and anatomical structure. Output information is presented in a friendly format, allowing the user to display expression maps and correlation matrices for a gene or structure during development. An in-depth study of a specific developmental stage is also possible using heatmaps that relate gene expression with anatomical components. This is a powerful tool in the gene expression field that makes easy the access to information related to the anatomical pattern of gene expression in human and mouse, so that it can complement many functional genomics studies. The platform allows the integration of gene expression data with spatial-temporal anatomic data by means of an intuitive and user friendly display., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Supplemental Figure 1. Image obtained from protein atlas databases. Sections of a testes obtained from a patient were stained with Sec23ip antibody. Patient data and cell types with Sec23ip expression are indicated. Supplemental Table 1. A list of proteins from the 14-3-3γ interactome in cAMP-treated MA-10 cells at times 0, 30, 60 and 120. The proteins are listed based on progressive increase in interactions with 14-3-3γ. Supplemental Table 2. A selected list of 14-3-3γ interactome containing proteins that showed consistent interactions at all time points across n= 3. Supplemental Table 3. Relative gene expression for Sec23ip in steroidogenic tissues in human and mice. This data was obtained from biogps database using the probeset 209175-at for human and probeset 1433627-at for mice. Supplemental Table 4. The sequences of 3 siRNA used for Sec23ip knock down.

Supplementary Table S1. X-chromosomal genes that are more than 2 fold higher in testis compared to other normal tissues in BioGPS database.

Context

Acute venous hypertension induces local release of inflammatory cytokines and endothelial activation in humans (Dataset)

Interleukin-6 (IL-6), Plasma endothelin-1 (ET-1), , vascular cell adhesion molecule-1 (VCAM-1) and chemokine (C-X-C motif) ligand 2 (CXCL2) were significantly increased in the congested arm. 5,332 probe sets were differentially expressed in venous ECs before vs. after testing. Among the 143 probe sets that exhibited a significant absolute fold change >2, we identified several inflammatory mediators including ET-1, VCAM-1, and CXCL2. Conclusions: Acute experimental venous hypertension is sufficient to cause local increase in circulating inflammatory mediators and to activate venous ECs in healthy human subjects. Additional work is needed to determine the effect of venous hypertension in patients with established HF. 24 samples were analyzed from 12 patients. Each patient contributed 2 samples (1 prior to intervention and 1 after intervention). The pre-intervention sample serves as the control. http://biogps.org/#goto=genereport&id=1017&show_dataset=E-GEOD-38783

Content

Venous hypertension is often present in advanced and in acute decompensated heart failure (HF). However, it is unclear whether high intravenous pressure can cause alterations in homeostasis by promoting inflammation and endothelial cell (EC) activation. We used an experimental model of acute, local venous hypertension to study the changes in circulating inflammatory mediators and EC phenotype that occur in response to biomechanical stress. Methods and Results: Twenty-four healthy subjects (14 men, age 35±2 years) were studied. Venous arm pressure was increased to ~30 mmHg above baseline level by inflating a tourniquet cuff around the dominant arm (test arm). Blood and endothelial cells (ECs) were sampled from test and control arm (lacking an inflated cuff) before and after 75 minutes of venous hypertension, using angiocatheters and endovascular wires. Magnetic beads coated with EC specific antibodies were used for EC separation; amplified mRNA was analyzed by Affymetrix HG-U133 2.0 Microarray.

Acknowledgements

http://biogps.org/#goto=genereport&id=1017&show_dataset=E-GEOD-38783

Photo by K. Mitch Hodge on Unsplash

Inspiration

The evidence that circulating IL-6 (Interleukin-6) levels are closely linked to the severity of COVID-19 infection. An increase in IL-6 levels has previously been observed in patients with respiratory dysfunction .

BackgroundChronic atrophic gastritis (CAG) is the first step of gastric precancerous lesions, and the study of the pathogenesis of CAG is helpful for the prevention and treatment of gastric cancer(GC). The purpose of this study is to explore the potential biomarkers and therapeutic drugs of CAG through bioinformatics analysis.MethodsThe GSE11632 dataset was downloaded from Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were obtained by using GEO2R online tool. We searched GeneCard and DisGeNET databases for genes related to CAG and used the overlapping genes as final DEGs for further functional enrichment analysis and Protein-protein Interaction (PPI) network analysis. Tissue-specific expressed genes were identified by BioGPS database. Cytoscape software was used to identify key hub genes and validated them in GSE27411 data sets. The upstream miRNAs of hub gene was predicted by TargetScan, miRDB and miRWalk. Finally, run the Connectivity Map (CMap) to identify new potential drugs for the treatment of CAG.ResultsA total of 430 differentially expressed mRNA were identified in this study, including 315 up-regulated genes and 115 down-regulated genes. After intersecting with CAG-related genes in GeneCard and DisGeNET databases, 42 DEGs were obtained. 24 DEGs were identified as tissue-specific expressed genes, most of which were expressed in stomach. GO and KEGG pathway analysis showed that DGEs was mainly enriched in digestion, IL-1 production, gastric acid secretion and so on. A total of 6 hub genes were generated by cytoHubba plug-in, among which ATP4A, CFTR and EPCAM had high diagnostic value. A total of 13 overlapping miRNA were predicted by 6 hub genes.ConclusionATP4A, CFTR and EPCAM may be potential biomarkers of CAG. hsa-miR-185-5p-CFTR, hsa-miR-4644-CFTR and hsa-miR-4505-CFTR are potential RNA regulatory pathways to control the progression of CAG disease. Finally, amonafide, etoposide, mycophenolate-mofetil, cycloheximide and Emetine may be potential therapeutic drugs for CAG.

a-, no expression; −/+, very weak expression; +, weak to moderate expression; ++, strong expression; +++, very strong expression.bMicroarray data were obtained from the Mouse MOE430 Gene Atlas database at BioGPS portal server in order to compare with results of RT-PCR.cND, not determined.

Background: Herbal medicines traditionally target organs for treatment based on medicinal properties, and this theory is widely used for prescriptions. However, the scientific evidence explaining how herbs act on specific organs by biological methods has been still limited. This study used bioinformatic tools to identify the target organ locations of Radix Achyranthis Bidentatae (RAB), a blood-activating herb that nourishes the liver and kidney, strengthens bones, and directs prescription to the lower body.Methods: RAB’s active compounds and targets were collected and predicted using databases such as TCMSP, HIT2.0, and BATMAN-TCM. Next, the RAB’s target list was analyzed based on two approaches to obtain target organ locations. DAVID and Gene ORGANizer enrichment-based approaches were used to enrich an entire gene list, and the BioGPS and HPA gene expression-based approaches were used to analyze the expression of core genes.Results: RAB’s targets were found to be involved in whole blood, blood components, and lymphatic organs across all four tools. Each tool indicated a particular aspect of RAB’s target organ locations: DAVID-enriched genes showed a predominance in blood, liver, and kidneys; Gene ORGANizer showed the effect on low body parts as well as bones and joints; BioGPS and HPA showed high gene expression in bone marrow, lymphoid tissue, and smooth muscle.Conclusion: Our bioinformatics-based target organ location prediction can serve as a modern interpretation tool for the target organ location theory of traditional medicine. Future studies should predict therapeutic target organ locations in complex prescriptions rather than single herbs and conduct experiments to verify predictions.

Objective To analyze the correlation between the expression of topoisomerase Ⅱα(TOP2A) gene and the number of CD4+T cells in epithelial ovarian cancer (EOC) and its immune mechanism.Methods The expression of TOP2A mRNA in normal ovarian tissue and EOC tissue, as well as its prognostic significance for EOC patients, were analyzed using the Gene Annotation Resource Database (BioGPS), gene expression profile interaction analysis (GEPIA), and Kaplan-Meier Plotter databases. The Human Protein Atlas database was utilized to analyze the expression of TOP2A protein in ovarian cancer and normal ovarian tissues. Co-expressed genes of TOP2A, along with their gene ontology (GO) and pathway enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG), were examined using GENE and Metascape databases. Furthermore, the relationship between the TOP2A gene and CD4+T cells, cell subsets, various immune cells was investigated using TISIDB, ImmuCellAI, and TIMER databases. Additionally, the impact of CD4+T cell distribution on survival and prognosis of patients with EOC was assessed.Results The expression of TOP2A mRNA and protein was not significant in normal ovarian tissue and CD4+T cells, but was significantly high in EOC tissue, which was not conducive to the survival prognosis of patients. The GO function of TOP2A co-expressed genes is mainly concentrated in the mitotic cell cycle G0 and early G1 division phase, PID-E2F pathway and transcriptional regulation of TP53, while KEGG is mainly concentrated in metabolic regulation, positive/negative feedback regulation of biological processes, stimulus response, cell cycle and cell growth and development. The expression of TOP2A gene was significantly correlated with the purity of tumor cells and the number of CD4+T cells and multiple immune cells. A variety of immune cells have certain copy number variation in EOC, among which deep deletion is the most significant. However, only the number of CD8+T cells infiltrated by tumor microenvironment had a significant impact on survival and prognosis of EOC patients.Conclusion The expression of TOP2A mRNA in EOC tissues is positively correlated with the number of CD4+T cells, and the low distribution of tumor-infiltrating CD8+T cells is not conducive to the survival prognosis of EOC patients.

BackgroundSystemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs.MethodsIn this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA–miRNA–mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes.ResultsThe pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified.ConclusionsThis study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

BackgroundRoburic acid (ROB) is a newly discovered tetracyclic triterpene acid extracted from oak galls, which has anti-inflammatory effects, but the mechanism of its anticancer effect is not clear. Our study focuses on exploring the potential mechanism of action of ROB in the treatment of lung cancer using a combination of network pharmacological prediction, molecular docking technique and experimental validation.MethodsA network pharmacology approach was used to screen the protein targets of ROB and lung cancer, and PPI network analysis and enrichment analysis were performed on the intersecting genes. The tissue and organ distribution of the targets was also evaluated based on the BioGPS database. To ensure the reliability of the network pharmacology prediction results, we proceeded to use molecular docking technique to determine the relationship between drugs and targets. Finally, in vitro experiments with cell lines were performed to further reveal the potential mechanism of ROB for the treatment of lung cancer.ResultsA total of 83 potential targets of ROB in lung cancer were collected and further screened by using Cytoscape software, and 7 targets of PTGS2, CYP19A1, PTGS1, AR, CYP17A1, PTGES and SRD5A1 were obtained as hub genes and 7 hub targets had good binding energy with ROB. GO and KEGG analysis showed that ROB treatment of lung cancer mainly involves Arachidonic acid metabolism, Notch signaling pathway, cancer pathway and PPAR signaling pathway. The results of in vitro experiments indicated that ROB may inhibit the proliferation and metastasis of lung cancer cells and activate the PPARγ signaling pathway, as well as induce cellular autophagy.ConclusionsThe results of this study comprehensively elucidated the potential targets and molecular mechanisms of ROB for the treatment of lung cancer, providing new ideas for further lung cancer therapy.

I uploaded this dataset from the following link: http://biogps.org/dataset/tag/brca1/

The dataset contains information on breast cancer patients, including their Patient_ID, Age, Gender, and expression levels of four proteins (Protein1, Protein2, Protein3, Protein4). The dataset also includes the Breast cancer stage of the patient (Tumor_Stage), Histology (type of cancer), ER, PR, and HER2 status, Surgery_type, Date of Surgery, Date of Last Visit, and Patient Status (Alive/Dead).

This information can be used to analyze the relationship between protein expression levels, cancer stage, and patient outcomes. It can also be used to understand the impact of different types of surgeries on patient survival and to identify potential risk factors for breast cancer progression.

Genetic factors play an important role in determining the risk of multiple sclerosis (MS). The strongest genetic association in MS is located within the major histocompatibility complex class II region (MHC), but more than 50 MS loci of modest effect located outside the MHC have now been identified. However, the relative candidate genes that underlie these associations and their functions are largely unknown. We conducted a protein-protein interaction (PPI) analysis of gene products coded in loci recently reported to be MS associated at the genome-wide significance level and in loci suggestive of MS association. Our aim was to identify which suggestive regions are more likely to be truly associated, which genes are mostly implicated in the PPI network and their expression profile. From three recent independent association studies, SNPs were considered and divided into significant and suggestive depending on the strength of the statistical association. Using the Disease Association Protein-Protein Link Evaluator tool we found that direct interactions among genetic products were significantly higher than expected by chance when considering both significant regions alone (p<0.0002) and significant plus suggestive (p<0.007). The number of genes involved in the network was 43. Of these, 23 were located within suggestive regions and many of them directly interacted with proteins coded within significant regions. These included genes such as SYK, IL-6, CSF2RB, FCLR3, EIF4EBP2 and CHST12. Using the gene portal BioGPS, we tested the expression of these genes in 24 different tissues and found the highest values among immune-related cells as compared to non-immune tissues (p<0.001). A gene ontology analysis confirmed the immune-related functions of these genes. In conclusion, loci currently suggestive of MS association interact with and have similar expression profiles and function as those significantly associated, highlighting the fact that more common variants remain to be found to be associated to MS.

Context

Transcription profiling of human blood from children with autism spectrum disorder

Content

Gene expression in blood of children with autism spectrum disorder

Acknowledgements

biogps.org

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

BackgroundChronic pressure overload triggers pathological cardiac hypertrophy that eventually leads to heart failure. Effective biomarkers and therapeutic targets for heart failure remain to be defined. The aim of this study is to identify key genes associated with pathological cardiac hypertrophy by combining bioinformatics analyses with molecular biology experiments.MethodsComprehensive bioinformatics tools were used to screen genes related to pressure overload-induced cardiac hypertrophy. We identified differentially expressed genes (DEGs) by overlapping three Gene Expression Omnibus (GEO) datasets (GSE5500, GSE1621, and GSE36074). Correlation analysis and BioGPS online tool were used to detect the genes of interest. A mouse model of cardiac remodeling induced by transverse aortic constriction (TAC) was established to verify the expression of the interest gene during cardiac remodeling by RT-PCR and western blot. By using RNA interference technology, the effect of transcription elongation factor A3 (Tcea3) silencing on PE-induced hypertrophy of neonatal rat ventricular myocytes (NRVMs) was detected. Next, gene set enrichment analysis (GSEA) and the online tool ARCHS4 were used to predict the possible signaling pathways, and the fatty acid oxidation relevant pathways were enriched and then verified in NRVMs. Furthermore, the changes of long-chain fatty acid respiration in NRVMs were detected using the Seahorse XFe24 Analyzer. Finally, MitoSOX staining was used to detect the effect of Tcea3 on mitochondrial oxidative stress, and the contents of NADP(H) and GSH/GSSG were detected by relevant kits.ResultsA total of 95 DEGs were identified and Tcea3 was negatively correlated with Nppa, Nppb and Myh7. The expression level of Tcea3 was downregulated during cardiac remodeling both in vivo and in vitro. Knockdown of Tcea3 aggravated cardiomyocyte hypertrophy induced by PE in NRVMs. GSEA and online tool ARCHS4 predict Tcea3 involved in fatty acid oxidation (FAO). Subsequently, RT-PCR results showed that knockdown of Tcea3 up-regulated Ces1d and Pla2g5 mRNA expression levels. In PE induced cardiomyocyte hypertrophy, Tcea3 silencing results in decreased fatty acid utilization, decreased ATP synthesis and increased mitochondrial oxidative stress.ConclusionOur study identifies Tcea3 as a novel anti-cardiac remodeling target by regulating FAO and governing mitochondrial oxidative stress.