Image1_Transcription factors direct epigenetic reprogramming at specific loci in human cancers.JPEG

The characterization of epigenetic changes during cancer development and progression led to notable insights regarding the roles of cancer-specific epigenetic reprogramming. Recent studies showed that transcription factors (TFs) are capable to regulate epigenetic reprogramming at specific loci in different cancer types through their DNA-binding activities. However, the causal association of dynamic histone modification change mediated by TFs is still not well elucidated. Here we evaluated the impacts of 636 transcription factor binding activities on histone modification in 24 cancer types. We performed Instrumental Variables analysis by using genetic lesions of TFs as our instrumental proxies, which previously discovered to be associated with histone mark activities. As a result, we showed a total of 6 EpiTFs as strong directors of epigenetic reprogramming of histone modification in cancers, which alters the molecular and clinical phenotypes of cancer. Together our findings highlight a causal mechanism driven by the TFs and genome-wide histone modification, which is relevant to multiple status of oncogenesis.</p

Image2_Transcription factors direct epigenetic reprogramming at specific loci in human cancers.JPEG

The characterization of epigenetic changes during cancer development and progression led to notable insights regarding the roles of cancer-specific epigenetic reprogramming. Recent studies showed that transcription factors (TFs) are capable to regulate epigenetic reprogramming at specific loci in different cancer types through their DNA-binding activities. However, the causal association of dynamic histone modification change mediated by TFs is still not well elucidated. Here we evaluated the impacts of 636 transcription factor binding activities on histone modification in 24 cancer types. We performed Instrumental Variables analysis by using genetic lesions of TFs as our instrumental proxies, which previously discovered to be associated with histone mark activities. As a result, we showed a total of 6 EpiTFs as strong directors of epigenetic reprogramming of histone modification in cancers, which alters the molecular and clinical phenotypes of cancer. Together our findings highlight a causal mechanism driven by the TFs and genome-wide histone modification, which is relevant to multiple status of oncogenesis.</p

Results of B-ELISA, I-ELISA and SNT with reference sera.

<p>Note: Coincidence rates between different methods were calculated using Microsoft Excel's CORREL function. The results showed that the blocking ELISA and the SNT have a positive coincidence with coincidence rate of 70.6%. The coincidence rate between indirect ELISA and blocking ELISA was 0.35, and the coincidence rate between indirect ELISA and SNT was 0.44.</p

Specificity of the b-ELISA to anti-DTMUV serum.

<p>Seven antisera against different viruses were investigated. The PI value of anti-DTMUV serum reached a maximum of 69.13%, while the other antisera against H5N1 AIV, H9N2 AIV, NDV, DHV-1, DPV, RV, and JEV ranged from −2.7% to 2.3%.</p

DataSheet1_Identification of the diagnostic genes and immune cell infiltration characteristics of gastric cancer using bioinformatics analysis and machine learning.ZIP

Background: Finding reliable diagnostic markers for gastric cancer (GC) is important. This work uses machine learning (ML) to identify GC diagnostic genes and investigate their connection with immune cell infiltration.Methods: We downloaded eight GC-related datasets from GEO, TCGA, and GTEx. GSE13911, GSE15459, GSE19826, GSE54129, and GSE79973 were used as the training set, GSE66229 as the validation set A, and TCGA & GTEx as the validation set B. First, the training set screened differentially expressed genes (DEGs), and gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG), disease Ontology (DO), and gene set enrichment analysis (GSEA) analyses were performed. Then, the candidate diagnostic genes were screened by LASSO and SVM-RFE algorithms, and receiver operating characteristic (ROC) curves evaluated the diagnostic efficacy. Then, the infiltration characteristics of immune cells in GC samples were analyzed by CIBERSORT, and correlation analysis was performed. Finally, mutation and survival analyses were performed for diagnostic genes.Results: We found 207 up-regulated genes and 349 down-regulated genes among 556 DEGs. gene ontology analysis significantly enriched 413 functional annotations, including 310 biological processes, 23 cellular components, and 80 molecular functions. Six of these biological processes are closely related to immunity. KEGG analysis significantly enriched 11 signaling pathways. 244 diseases were closely related to Ontology analysis. Multiple entries of the gene set enrichment analysis analysis were closely related to immunity. Machine learning screened eight candidate diagnostic genes and further validated them to identify ABCA8, COL4A1, FAP, LY6E, MAMDC2, and TMEM100 as diagnostic genes. Six diagnostic genes were mutated to some extent in GC. ABCA8, COL4A1, LY6E, MAMDC2, TMEM100 had prognostic value.Conclusion: We screened six diagnostic genes for gastric cancer through bioinformatic analysis and machine learning, which are intimately related to immune cell infiltration and have a definite prognostic value.</p

Results of testing field –origin duck sera in blocking ELISA and SNT.

a<p>Percent inhibition(PI) ≥18.4 was considered positive (indicated in brackets).</p>b<p>SNT titers ≥5 were considered positive (indicated in brackets).</p

Specificity of the b-ELISA to anti-DTMUV serum.

<p>To test the abilities of mAb to bind specifically to domain III of E protein, western blot was conducted with purified fusion protein including both the domain III (12 kDa) of E protein and TF tag protein (52 kDa) (line 1), and purified TF tag protein (52 kDa) (line 2) expressed by pCold plasmids. The mAb 1F5 was able to bind specifically to the 64-kDa fusion protein, but could not bind to purified TF tag protein (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053026#pone-0053026-g002" target="_blank">Fig 2</a>).</p

Characterization of monoclonal antibody 1F5 by immunofluorescence assay.

<p>Monoclonal antibody 1F5 was used to perform indirect immunofluorescence assay on DF-1 cells infected with DTMUV FX2010 and 293T cells transfected with pCAGGS-E plasmids. A) DF-1 cells infected with DTMUV FX2010, B) control DF-1 cells, C) 293T cells transfected with recombinant plasmid pCAGGS-E, and D) control 293T cells fixed with 4% paraformaldehyde, and then incubated with mAb 1F5 and FITC-conjugated goat anti-mouse IgG, in turn. Cells were mounted with 10 mM p-phenylenediamine (PPD) in glycerol-PBS and observed under a fluorescent microscope.</p

Comparison of percent inhibition obtained in blocking ELISA and SNT using a serially diluted duck anti-DTMUV serum.

<p>Note: “+” positive and “−” negative.</p

Characteristics of the studies included in meta-analysis.

<p>BMI: body mass index; ASA: American Society of Anesthesiology; TNM: tumor, node, metastasis; NR: not reported; POD: postoperative day; TG: total gastrectomy; DG: distal gastrectomy; PG: proximal gastrectomy; SG: subtotal gastrectomy;</p>a<p>: Age grouped by <65/≥65;</p>b<p>: BMI grouped by <25 kg/m²/≥25 kg/m²;</p>c<p>: median;</p>d<p>: ASA score grouped by 0/(1 and 2).</p><p>Characteristics of the studies included in meta-analysis.</p