Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: S100A10 protein overexpressed in PDAC compared to PanIN lesions, nonductal stroma, and normal tissue. (A) imagej IHC profiler plugin was used to quantify S100A10 protein expression (see in methods). Briefly, images were color deconvoluted to isolate the brown DAB stain from non‐DAB image. An area of interest (PDAC shown) was manually highlighted and quantified based on pixel intensity and the percentage contribution of each pixel subcategory (0–60, 61–120, 121–180, 181–255; see H ‐scoring in methods). (B) The graph shows the H ‐score the S100A10 protein expression quantified by imagej in six different regions: PanINs stroma, PDAC stroma, normal adjacent to PanINs, normal adjacent to PDAC, PanINs, and PDAC lesions. Each H ‐score was divided by the mean H ‐score of all measurements to yield a mean‐normalized H ‐score ± SEM. Significance was determined using one‐way ANOVA of unmatched samples (nonpaired). Scale bars, 100 μm.

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: Expressing, Staining

Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: S100A10 mRNA expression is predictive of overall and RFS in four PDAC patient cohorts. Kaplan–Meier (KM) plots of OS (A,C–E) and RFS ( n = 139; B) of PDAC patients based on their S100A10 mRNA expression. Patients in (A,B) are from the TCGA provisional cohort. Patients in (C,D,E) are derived from Chen et al . ( , http://www.ncbi.nlm.nih.gov/protein/GSE57495 ), Moffitt et al . ( , http://www.ncbi.nlm.nih.gov/protein/GSE71729 ), and ICGC. The ternary cutoff was applied to classify the high‐positive, low‐positive, and weak/negative subgroups. P ‐values were adjusted to the Bonferroni‐corrected threshold. Adjusted P ‐value is P ‐value/ K = 0.017 where K = 3 and represents the number of comparisons made (Table ).

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: Expressing, Derivative Assay

Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: Differentially methylated CpG sites negatively correlate with S100A10 mRNA expression and serve as predictors of survival. (A) Schematic illustration of the human S100A10 gene based on UCSC Ref‐Seq. The genomic distance is approximate but is not drawn to scale. T c SS, transcription start site; T L SS, translation start site; TSS1500, region between 200 bp and 1500 bp upstream of T c SS; TSS200, region 200 bp upstream of T c SS; 5′UTR, 5′ untranslated region. The S100A10 gene is encoded on the negative strand (−), four probes mapped to the opposite positive (+) strand. Five probes were mapped to TSS1500, three to TSS200, and seven probes to the 5′UTR. (B) For normal vs. tumor comparisons, the raw data were extracted from MethHC ( http://methhc.mbc.nctu.edu.tw/php/index.php ), described by Huang et al . . The β‐values of each probe were assessed in 85 PDAC tumors and nine normal tissues (first and third columns). For mRNA vs. methylation correlations, raw β‐values of individual probes were extracted from Maplab Wanderer ( http://maplab.imppc.org/wanderer/ ) (Díez‐Villanueva et al ., ) and plotted against RNA seq V2 (RSEM) expression values of S100A10 in matched patients. Pearson's correlation was used to generate correlation graphs of β‐values and S100A10 mRNA expression (second and fourth columns). β‐Values for the probe cg06786599 were absent for normal samples, and no significant correlation ( P ‐value = 0.1023) between S100A10 tumor mRNA and cg06786599 β‐values was found. Cg06786599 was then excluded from further analysis. Significance was determined using unpaired Tukey test. Data are represented as mean ± SD. Kaplan–Meier (KM) plots of OS ( n = 178; C,F) and RFS ( n = 139; D,G) based on β‐values of the cg13249591 and cg13445177 probes. Overall survival was also assessed in the ICGC cohort was assessed based on the β‐values of both probes (E,H). P ‐values were adjusted to the Bonferroni‐corrected threshold. Adjusted P ‐value is P ‐value/ K = 0.017 where K = 3 and represents the number of comparisons made (Table ).

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: Methylation, Expressing, RNA Sequencing

Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: S100A10 mRNA and protein expression negatively correlated with promoter methylation in PDAC cell lines. (A) The relationship between S100A10 methylation and mRNA expression in 831 CCLE cell lines. mRNA expression (RNA seq V2 RSEM) and methylation (RRBS β‐values) were extracted from the broad institute CCLE portal ( https://portals.broadinstitute.org/ccle ). S100A10 mRNA (RT‐qPCR; B) and protein expression (C) in three PDAC representative cell lines: Panc 10.05, Panc‐1, and AsPC‐1. (D) S100A10 promoter construct for bisulfite and pyrosequencing covering 24 CpG dinucleotides. (E) Global methylation of the 24 CpGs in the S100A10 promoter. The graph represents the averages of percentages of all 24 sites in each cell line. Significance was determined using one‐way ANOVA. Data are represented as mean ± SD.

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: Expressing, Methylation, RNA Sequencing, Quantitative RT-PCR, Construct

Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: S100A10 mRNA expression is regulated by differential CpG site methylation. S100A10 mRNA (A,B,C) and protein (D,E,F) changes in Panc 10.05 (A,D), Panc‐1 (B,E), and AsPC‐1 (C,F) in response to 10 μ m decitabine (DAC) for 72 h. Global and CpG‐specific methylation of the 24 CpGs in the S100A10 promoter in Panc 10.05 (G,J), Panc‐1 (H,K), and AsPC‐1 (I,L). Graphs G–I represent the averages of percentages of all 24 sites in each cell line. Graphs J–L represent the percentage methylated of cytosines of a specific CpG site within each sample. Significance was determined using unpaired t ‐tests. Data are represented as mean ± SD.

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: Expressing, Methylation

Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: S100A10 modulates plasminogen activation and cellular invasiveness in vitro and is regulated by KRAS signaling. (A) Western blot of scramble control and S100A10‐depleted (S100A10 shRNA1) Panc‐1 cells. (B) Cells were equally seeded into a 96‐well plate and cell viability (MTS assay) was measured every day for three consecutive days. The absorbance of the MTS reagent at 490 nm is plotted for each time point. (C) Cells were incubated with 0.5 μ m plasminogen, and plasmin activity was measured as the absorbance of the chromogenic plasmin substrate (S2251) at a wavelength of 405 nm. 5 × 10 3 cells of scramble control and S100A10 shRNA1 Panc‐1 cells were seeded into 96‐well plates. Plasminogen activation (per 1 × 10 5 cells) was then calculated under the following conditions: no plasminogen, with plasminogen, with the lysine analog ACA (100 m m ) and the serine protease Ap (2.2 μ m ). ACA is a lysine analog that prevents plasminogen interaction with the carboxyl terminus. Ap is a serine protease pan‐inhibitor which quenches the generated plasmin confirming the ability of these cells to generate plasmin. (D) The matrigel Boyden chamber invasion assay assesses the ability of cells to invade through a Matrigel barrier (substitute for ECM) in response to a chemoattractant (10% FBS). Invasion assay of scramble control and S100A10 shRNA 1 Panc‐1 cells in the presence/absence of Pg. The results are represented as the number of invading cells per one field of view at 20× magnification. (E) Western blots of S100A10, active RAS, and β‐actin in Panc‐1 (a) and BxPC‐3 (c) treated with 10 μ m of the farnesyltransferase inhibitor Zarnestra for 48 h. A Raf pulldown was performed to measure RAS activity. (F) Quantification of S100A10 protein expression normalized to β‐actin in DMSO‐ and Zarnestra‐treated Panc‐1 and BxPC‐3. (G) Genomic construct setup of the mouse iKRAS pancreatic cancer cells. rtTA is a reverse tetracycline transactivator and is required for doxycycline‐inducible expression of KRAS G12D . Western blot (H) and quantification (I) of S100A10 protein in iKRAS cells in the absence (−Doxy) or presence (+Doxy) of 1 μg·mL −1 doxycycline and Zarnestra (10 μ m ) for 4 days. (J) Plasminogen activation assay of IKRAS cells treated with doxycycline and Zarnestra). (K) Western blot analysis of iKRAS cells treated with doxycycline in the presence/absence of 10 μ m decitabine for 72 h.

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: Activation Assay, In Vitro, Western Blot, Control, MTS Assay, Incubation, Activity Assay, Generated, Invasion Assay, shRNA, Expressing, Construct

Journal: Molecular Oncology

Article Title: S100A10, a novel biomarker in pancreatic ductal adenocarcinoma

doi: 10.1002/1878-0261.12356

Figure Lengend Snippet: S100A10 depletion in Panc‐1 tumors reduces primary tumor size in vivo . 5 × 10 6 scramble control and S100A10 shRNA 1 Panc‐1 cells were injected intraperitoneally into NOD/SCID mice. Representative images (A) and weight (B) of endpoint tumors (50 days postinjection). RT‐qPCR (C,D) and western blot (E,F) quantification of CCND1 (C,E) and VEGF (D,F).

Article Snippet: We also examined S100A10 mRNA levels (microarray z ‐scores) across all 930 human cancer cell lines listed in the CCLE from the Broad Institute ( http://www.ncbi.nlm.nih.gov/protein/GSE36133 ) (Barretina et al ., ).

Techniques: In Vivo, Control, shRNA, Injection, Quantitative RT-PCR, Western Blot

Journal: Frontiers in Genetics

Article Title: Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

doi: 10.3389/fgene.2014.00191

Figure Lengend Snippet: World map showing genomic records for cyanobacteria generated using an interface powered by google maps, available on the Genomes Online Database (GOLD) website ( http://genomesonline.org/cgi-bin/GOLD/index.cgi ) . Red labels indicate the original location of a specifically sequenced strain. Labels direct the user to information on the organism, genome characteristics (i.e., GC content, size), sequencing method used, specific coordinates of the origin of the strain, as well as links to external databases (as shown for Prochlorococcus marinus NATL1A). The GOLD also describes the status of each record in tabular format.

Article Snippet: In the study of marine cyanobacteria, genome-level microarray analyses have frequently focused on limiting growth factors in oceans, such as iron (Thompson et al., ), nickel (Dupont et al., ), copper (Stuart et al., ), phosphate (Tetu et al., ; Ostrowski et al., ), and nitrogen (Su et al., ; Tolonen et al., ).

Techniques: Generated, Sequencing

Journal: Frontiers in Genetics

Article Title: Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

doi: 10.3389/fgene.2014.00191

Figure Lengend Snippet: Publically available full genome sequences for cyanobacteria in various repositories as at April 2014 .

Article Snippet: In the study of marine cyanobacteria, genome-level microarray analyses have frequently focused on limiting growth factors in oceans, such as iron (Thompson et al., ), nickel (Dupont et al., ), copper (Stuart et al., ), phosphate (Tetu et al., ; Ostrowski et al., ), and nitrogen (Su et al., ; Tolonen et al., ).

Techniques:

Journal: Frontiers in Genetics

Article Title: Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

doi: 10.3389/fgene.2014.00191

Figure Lengend Snippet: Pie chart showing records of expression data for different cyanobacteria that are currently available in the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ) . The bias toward Synechocystis 6803 can be clearly seen in this species breakdown of transcriptomic data. Data from April 2014.

Article Snippet: In the study of marine cyanobacteria, genome-level microarray analyses have frequently focused on limiting growth factors in oceans, such as iron (Thompson et al., ), nickel (Dupont et al., ), copper (Stuart et al., ), phosphate (Tetu et al., ; Ostrowski et al., ), and nitrogen (Su et al., ; Tolonen et al., ).

Techniques: Expressing, Gene Expression

Journal: Frontiers in Genetics

Article Title: Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

doi: 10.3389/fgene.2014.00191

Figure Lengend Snippet: Web-based analytical tools with a specific focus on cyanobacteria as at April 2014 .

Article Snippet: In the study of marine cyanobacteria, genome-level microarray analyses have frequently focused on limiting growth factors in oceans, such as iron (Thompson et al., ), nickel (Dupont et al., ), copper (Stuart et al., ), phosphate (Tetu et al., ; Ostrowski et al., ), and nitrogen (Su et al., ; Tolonen et al., ).

Techniques: Gene Expression