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.

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.

Overview of macrophage markers. Based on this list and tissue-specific gene expression from GeneAtlas.

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.

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.

*All genes listed are associated with tissue-specific expression >30 MoMs in BioGPS.†Gene functions obtained from public databases (Entrez Gene and UniProt KB), descriptions modified due to space constraints.

Distance in kb from start of transcription for CIS other than Wnt or Fgf genes.*expression in normal mammary tissue (data from BioGPS).+number of BALB and C3H tumors with this CIS.#data from Oncomine database. Abbreviations: HBC, human breast cancer; LBC, lobular breast carcinoma; IBC, invasive breast carcinoma; ILC, invasive lobular carcinoma; DBC ductal carcinoma.

Recurrent implantation failure (RIF) represents a new challenge in the field of assisted reproductive technology (ART). Considering the known effects of immune cell regulation on embryo implantation process, as well as our gene set variation analysis (GSVA) results that suggested the association between RIF and pathways of oxidative stress and immune responses, we hypothesized that oxidative stress- related genes (OSGs) associated with aberrant immunological factor may represent novel biomarkers for unexplained RIF. We therefore screened out the immune cell coexpressed OSGs by performing CIBERSORT, LM22 matrix and Pearson correlation, followed by constructing an OSG signature by least absolute shrinkage and selection operator (LASSO) regression. Three OSGs (AXL, SLC7A11 and UBQLN1) were then identified to establish a RIF risk signature, which showed high ability to discriminating RIF from fertile control. A nomogram was established, with a free online calculator for easier clinical application. Finally, Chilibot, protein-protein interaction analysis and BioGPS were sequentially applied for the investigation of functional relationships of these three genes with RIF and other OSGs, as well as their expression abundance across different human tissues. In conclusion, we identified an OSG signature that are relevant novel markers for the occurrence of unexplained RIF.

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.

BackgroundYTH N6-methyladenosine RNA binding protein 1 (YTHDF1) has been indicated proven to participate in the cross-presentation of tumor antigens in dendritic cells and the cross-priming of CD8+ T cells. However, the role of YTHDF1 in prognosis and immunology in human cancers remains largely unknown.MethodsAll original data were downloaded from TCGA and GEO databases and integrated via R 3.2.2. YTHDF1 expression was explored with the Oncomine, TIMER, GEPIA, and BioGPS databases. The effect of YTHDF1 on prognosis was analyzed via GEPIA, Kaplan-Meier plotter, and the PrognoScan database. The TISIDB database was used to determine YTHDF1 expression in different immune and molecular subtypes of human cancers. The correlations between YTHDF1 expression and immune checkpoints (ICP), tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens in human cancers were analyzed via the SangerBox database. The relationships between YTHDF1 expression and tumor-infiltrated immune cells were analyzed via the TIMER and GEPIA databases. The relationships between YTHDF1 and marker genes of tumor-infiltrated immune cells in urogenital cancers were analyzed for confirmation. The genomic alterations of YTHDF1 were investigated with the c-BioPortal database. The differential expression of YTHDF1 in urogenital cancers with different clinical characteristics was analyzed with the UALCAN database. YTHDF1 coexpression networks were studied by the LinkedOmics database.ResultsIn general, YTHDF1 expression was higher in tumors than in paired normal tissue in human cancers. YTHDF1 expression had strong relationships with prognosis, ICP, TMB, MSI, and neoantigens. YTHDF1 plays an essential role in the tumor microenvironment (TME) and participates in immune regulation. Furthermore, significant strong correlations between YTHDF1 expression and tumor immune-infiltrated cells (TILs) existed in human cancers, and marker genes of TILs were significantly related to YTHDF expression in urogenital cancers. TYHDF1 coexpression networks mostly participated in the regulation of immune response and antigen processing and presentation.ConclusionYTHDF1 may serve as a potential prognostic and immunological pan-cancer biomarker. Moreover, YTHDF1 could be a novel target for tumor immunotherapy.