Journal: bioRxiv

Article Title: RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia

doi: 10.1101/2022.04.03.486880

Figure Lengend Snippet: Density of UG nucleotide sequences 100bp upstream and downstream of m6A modifications identified by cross-linking induced mutation sites (CIMS; A ) or cross-linking induced truncation sites (CITS; B ) in relation to random sequences (red line). Grey shading represents 95% confidence regions. ( C ) Schematic of HaloTag immunoprecipitation and dot blot procedure. ( D ) Dot blot for total RNA (detected by methylene blue) or m6A-modified RNA (detected by anti-m6A antibody) isolated by immunoaffinity purification of HaloTag-labeled proteins in HEK293T cells overexpressing HaloTag, TDP43-HaloTag or YTHDF2-HaloTag from 3 biological replicates. ( E ) Diagram illustrating insertion of the HaloTag open reading frame into the endogenous TARDBP locus immediately 5’ to the TDP43 start codon, resulting in a fusion of HaloTag to the N-terminus of TDP43. ( F ) Halo-TDP43 HEK293T cells labeled live with JF646 Halo dye (red), then fixed, permeabilized, and immunostained with anti-TDP43 antibody (green) prior to imaging. DAPI (blue) marks the nucleus of each cell. Scale bar = 10µm. ( G ) Dot blot for total RNA (detected by methylene blue) or m6A-modified RNA (detected by anti-m6A antibody) isolated by immunoaffinity purification of endogenous HaloTag-TDP43 or exogenous HaloTag. Additional replicates shown in Sup. Fig. 1.

Article Snippet: Human spinal cord samples were homogenized in Trizol and RNA was extracted using phenol-chloroform extraction for the m6A mRNA&lncRNA Epitranscriptomic microarray (8×60K, Arraystar, Rockville, MD, USA).

Techniques: Mutagenesis, Immunoprecipitation, Dot Blot, Modification, Isolation, Immunoaffinity Purification, Labeling, Imaging

Journal: bioRxiv

Article Title: RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia

doi: 10.1101/2022.04.03.486880

Figure Lengend Snippet: ( A ) HaloTag-TDP43 immunoprecipitation was followed by DART-seq to delineate m6A sites within TDP43 target RNAs. HaloTag-TDP43 HEK293T cells were transfected with APOBEC1-YTH or APOBEC1-YTHmut and crosslinked before immunoaffinity purification of HaloTag-labeled proteins. Immunoprecipitated RNAs were then sequenced and C-T transitions were identified in the context of DRACH motifs (red shaded box, D=A/G/T, R=A/G, H=A/C/T). Absolute counts ( B ) and relative frequency ( C ) of base pair transitions observed by RNA-seq in each condition. Shaded boxes represent transition types expected from APOBEC1 activity. ( D ) Example m6A sites identified by DART-seq in RPL10A . C-T transitions are highlighted in red, and DRACH motifs in pink. Green arrow, transcription start site; red hexagon, transcription stop site; thick blue bars, coding exons; thin blue bars, untranslated region. ( E ) Absolute count and relative distribution ( F ) of DART-seq reads in cells expressing APOBEC1-YTH and APOBEC1-YTHmut. UTR, untranslated region; CDS, coding sequence. ( G ) Scatter plot of TDP43 targets, determined by fold enrichment in precipitated RNA from HaloTag-TDP43 cells (expressing APOBEC1-YTH and APOBEC10YTHmut) compared to cells transfected with HaloTag. Red dots signify transcripts showing > 2-fold enrichment in both APOBEC1-YTH and APOBEC1-YTHmut expressing cells. TARDBP , yellow dot, identified as high confidence target. ( H ) Stacked bar graph showing percentage of m6A modified RNA in TDP43 targets (red) and non-targets (black). ( I ) Cumulative distribution of RNA methylation in TDP43 targets (red) and non-targets (black). p = 1.87×10 −55 by Kolmogorov Smirnov test. ( J ) Euler diagram depicting overlap between TDP43 targets identified in this study, and those identified by TDP43 cross linking and immunoprecipitation followed by RNA-sequencing (CLIP-seq) in HEK293T cells (Hallegger et al ., 2021) . **p=1.5×10 −117 , hypergeometric test. ( K ) Pie charts demonstrating the percentage of methylated RNA among TDP43 targets (pink) and non-targets (grey). **p<1×10 −5 chi-square test.

Article Snippet: Human spinal cord samples were homogenized in Trizol and RNA was extracted using phenol-chloroform extraction for the m6A mRNA&lncRNA Epitranscriptomic microarray (8×60K, Arraystar, Rockville, MD, USA).

Techniques: Immunoprecipitation, Transfection, Immunoaffinity Purification, Labeling, RNA Sequencing, Activity Assay, Expressing, Sequencing, Modification, Methylation

Journal: bioRxiv

Article Title: RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia

doi: 10.1101/2022.04.03.486880

Figure Lengend Snippet: ( A ) TARDBP gene map, illustrating TDP43 binding region (TBR), the location of the DRACH motif (pink square), and the C-T transition (red box) identified by DART-seq within this domain, representing an m6A site. ( B ) Schematic of the TARDBP minigene reporter, consisting of the mCherry ORF upstream of TARDBP exon 6 and 3.4 Kb of the TARDBP 3’ UTR. The A residue adjacent to the detected C-T transition via DART-seq in the WT reporter (mCherry-TBR) was mutated to a G, precluding methylation the mutant reporter (mCherry-mTBR). Red, methylated residue; blue line, DRACH motif; dagger, C-T transition from DART-seq. ( C ) HaloTag-TDP43 was isolated by immunoaffinity purification from HaloTag-TDP43 HEK293T cells expressing mCherry-TBR or mCherry-mTBR, and reporter RNA detected in elution fractions by qRT-PCR. ( D ) Outline of TDP43 autoregulation assay. Excess TDP43 binds to the reporter, triggering reporter splicing, destabilization, and reduced mCherry fluorescence. ( E ) Primary rodent neurons were transfected with WT (mCherry-TBR) or mutant (mCherry-mTBR) reporters, together with EGFP or TDP43-EGFP. After 7d, mCherry expression was assessed by fluorescence microscopy. Scale bar= 20 µm. Normalized RFP (mCherry) intensity in primary neurons expressing WT mCherry-TBR reporter ( F ) or mutant mCherry-mTBR ( G ) reporter together with EGFP or TDP43(WT)-EGFP. Cherry-TBR+GFP n= 160, Cherry-TBR+TDP43(WT)-GFP n= 58, Cherry-mTBR+GFP n= 105, Cherry-mTBR+TDP43(WT)-GFP n= 44. Data in C plotted as mean ± SD, collected from 3 biological replicates. ns= not significant, *p< 0.05, **p< 0.01; one-way ANOVA with Tukey’s test. Data in F and G plotted as mean ± SD, color coded by biological replicate. ns = not significant, *p < 0.05; Welch’s t-test.

Article Snippet: Human spinal cord samples were homogenized in Trizol and RNA was extracted using phenol-chloroform extraction for the m6A mRNA&lncRNA Epitranscriptomic microarray (8×60K, Arraystar, Rockville, MD, USA).

Techniques: Binding Assay, Residue, Methylation, Mutagenesis, Isolation, Immunoaffinity Purification, Expressing, Quantitative RT-PCR, Fluorescence, Transfection, Microscopy

Journal: bioRxiv

Article Title: RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia

doi: 10.1101/2022.04.03.486880

Figure Lengend Snippet: ( A ) Genome-wide analysis of RNA methylation via epitranscriptomic array. RNA was extracted from control (n= 3) and sporadic ALS (sALS) patient (n= 4) spinal cord samples, prior to m6A RNA immunoprecipitation. The resulting samples were separated into methylated and non-methylated RNA, then labeled with distinct fluorescent dyes (red and green stars) prior to hybridization, allowing relative quantification of methylation at each annotated locus. ( B ) Principal component analysis (PCA) plot comparing methylation levels of control (grey) and ALS (red) patient samples. ( C ) Hierarchical clustering of mRNA methylation profiles from control and ALS mRNA samples. ( D ) Volcano plot depicting fold change in mRNA methylation levels in ALS compared to control spinal cord. ( E ) Hierarchical clustering of lncRNA methylation profiles from control ALS lncRNA samples. ( F ) Volcano plot showing fold change in lncRNA methylation levels in ALS compared to control spinal cord. In D and F , grey horizontal vertical lines represent p= 0.05 and fold change (FC)= 2. ( G ) Euler diagram demonstrating overlap (n= 322, p= 5.09×10 −119 , hypergeometric test) among TDP43 substrates and methylated transcripts identified in HEK293T cells, in additional to hypermethylated transcripts determined via m6A array in sALS spinal cord. Comparisons were limited to the subset of transcripts expressed in both HEK293T cells and human spinal cord (nTPM>2). ( H ) Based on comparisons with the GEO transcription factor loss-of-function database via Enrichr , there was strong enrichment for TDP43-regulated genes not only among the set of 2034 transcripts hypermethylated in sALS spinal cord, but also among the 322 TDP43 targets that were also hypermethylated in sALS (A1 in G ). Combined score = (log 10 p * Z-score). ( I ) Immunohistochemical staining for m6A in control and sALS spinal cord sections. Scale bars= 50 µm. ( J ) Quantification of m6A antibody reactivity in spinal cord neurons from control (n= 110 neurons) and sALS (n= 277 neurons) sections. Plot shows mean +/- SD, color coded by patient. ****p< 0.0001 via Mann-Whitney test.

Article Snippet: Human spinal cord samples were homogenized in Trizol and RNA was extracted using phenol-chloroform extraction for the m6A mRNA&lncRNA Epitranscriptomic microarray (8×60K, Arraystar, Rockville, MD, USA).

Techniques: Genome Wide, Methylation, Control, RNA Immunoprecipitation, Labeling, Hybridization, Quantitative Proteomics, Immunohistochemical staining, Staining, MANN-WHITNEY

Journal: bioRxiv

Article Title: RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia

doi: 10.1101/2022.04.03.486880

Figure Lengend Snippet: ( A ) Representative images of rodent primary neurons transfected with plasmids expressing Cas9-2A-EGFP and sgRNA targeting the neuronal protein NeuN or negative control (LacZ). 5d after transfection, neurons were fixed and immunostained for NeuN (red). White dashed circles indicate nucleus stained with Hoechst (blue). ( B ) NeuN antibody reactivity measured in EGFP-positive neurons expressing sgLacZ (n= 565) or sgNeuN (n= 654), ****p < 0.0001 by Mann-Whitney. ( C ) Schematic depicting m6A writers (green), erasers (red), and readers (orange) targeted by CRISPR/Cas9. ( D ) Primary neurons expressing EGFP and TDP43-mApple were assessed at regular 24h intervals by fluorescence microscopy, and their survival assessed by automated image analysis. Individual neurons are assigned unique identifiers (yellow number) and tracked until their time of death (red), indicated by cellular dissolution, blebbing, or neurite retraction. Scale bar= 20µm. ( E ) Cumulative hazard plot depicting risk of death for neurons expressing TDP43(WT) + non-targeting (NT) (red line), mApple + NT (grey line), or TDP43(WT) + Atxn2 sgRNA (purple line). †p<2.0 ×10 −16 , Hazard ratio (HR)= 3.45; ***p= 5.81 ×10 −4 , HR= 0.80). ( F ) Forest plot showing HR for TDP43-overexpressing neurons upon knockdown of m6A writers (green), erasers (dark red), and readers (orange), in comparison to nontargeting (NT) control. Dashed line indicates HR= 1, representing the survival of the reference condition, neurons expressing TDP43-mApple and NT sgRNA. Values >1 indicate increased toxicity, whereas values <1 denote relative protection. Error bars represent 95% CI. ( G ) Alkbh5 knockout significantly increases TDP43 associated toxicity. †p=3.11 ×10 −5 , HR= 1.59; ***p= 2.65×10 −11 , HR= 2.03. ( H ) Ythdf2 knockout significantly extends survival in TDP43-expressing neurons. ***p <2.0 ×10 −16 , HR= 1.69; †p= 6.2 ×10 −6 , HR= 0.71. ( I ) YTHDF2 overexpression is toxic to neurons. ***p= 3.07×10 −5 , HR= 1.30. ( J ) METTL3/14 overexpression enhances TDP43-dependent toxicity in neurons. †p = 5.53 ×10 −4 , HR= 1.32; ***p =4.16 ×10 −6 , HR= 1.31. p values in E, G-J determined via Cox proportional hazards analysis, with a minimum 3 of biological replicates.

Article Snippet: Human spinal cord samples were homogenized in Trizol and RNA was extracted using phenol-chloroform extraction for the m6A mRNA&lncRNA Epitranscriptomic microarray (8×60K, Arraystar, Rockville, MD, USA).

Techniques: Transfection, Expressing, Negative Control, Staining, MANN-WHITNEY, CRISPR, Fluorescence, Microscopy, Dissolution, Knockdown, Comparison, Control, Knock-Out, Over Expression

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: Oncotarget

Article Title: Pancreatic cancer-derived exosomes transfer miRNAs to dendritic cells and inhibit RFXAP expression via miR-212-3p

doi:

Figure Lengend Snippet: The miRNA target prediction between 9 PC derived exosomal miRNAs and 208 down-regulated mRNA in dendritic cells. miR-203, miR-101-3p, miR-212-3p and miR-139-5p are the potential regulators that can inhibite RFXAP expression.

Article Snippet: Human genome-wide mRNA microarray (A10201-1-40-56, ribobio, Guangzhou, China) was used for mRNA profiling of iDC and exo-iDC which contains approximately 40 000 transcripts selected from the National Center for Biotechnology Information (NCBI) RefSeq database (Release 56).

Techniques: Derivative Assay, Expressing

Journal: Oncotarget

Article Title: Pancreatic cancer-derived exosomes transfer miRNAs to dendritic cells and inhibit RFXAP expression via miR-212-3p

doi:

Figure Lengend Snippet: A. qRT-PCR indicated that RFXAP mRNA declined by 84.6% after stimulation by PANC-1 derived exosomes. B. Western blot showed RFXAP and MHC II expression were inhibited in exo-iDC. C. There were 5.5 folds changes between iDC and exo-iDC, indicating miR-212-3p was transferred into iDC by exosome.

Article Snippet: Human genome-wide mRNA microarray (A10201-1-40-56, ribobio, Guangzhou, China) was used for mRNA profiling of iDC and exo-iDC which contains approximately 40 000 transcripts selected from the National Center for Biotechnology Information (NCBI) RefSeq database (Release 56).

Techniques: Quantitative RT-PCR, Derivative Assay, Western Blot, Expressing

Journal: Oncotarget

Article Title: Pancreatic cancer-derived exosomes transfer miRNAs to dendritic cells and inhibit RFXAP expression via miR-212-3p

doi:

Figure Lengend Snippet: A. qRT-PCR analysis of relative miR-212-3p expression in PDAC cell lines and gastric cancer cell lines. B. miR-212-3p expression in tumor cells derived exosome. C. qRT-PCR analysis of RFXAP mRNA expression in exosome stimulated iDC. D. Western blot analysis of RFXAP and MHC II expression in tumor exosome stimulated iDC. The expression of RFXAP and MHC II were significantly inhibited by SW1990 and BxPC-3 derived exosome, while SGC-7901 exosome did not. E. Transfection of miR-212-3p inhibitors and mimics to SW1990, BxPC-3 and SGC-7901 exo-iDCs reversed the expression of RFXAP and MHC II.

Article Snippet: Human genome-wide mRNA microarray (A10201-1-40-56, ribobio, Guangzhou, China) was used for mRNA profiling of iDC and exo-iDC which contains approximately 40 000 transcripts selected from the National Center for Biotechnology Information (NCBI) RefSeq database (Release 56).

Techniques: Quantitative RT-PCR, Expressing, Derivative Assay, Western Blot, Transfection