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Supplemental Methods R-Scripts Used for Microarray Image Analysis And

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SUPPORTING INFORMATION Supplemental Methods R-Scripts used for microarray image analysis and analysis of gene transcription profiles of salmon juveniles from enriched and unenriched hatchery rearing envrionments. Microarray data acquisition (scripts adapted from Booman et al. 2011) 1. Images exported from the ScanArray Gx Plus scanner and ScanExpress v4.0 software were visualized using Imagene. In Imagene, manual flagging was used to exclude spots of poor quality. 2. Further processing of the data was done in R: # Script based on 'script 110327.R' # Script written and executed over multiple days January 24-25 2013 # Load marray package # Read in array info to make object of class marrayInfo targets_fam_trmt_melissa <- read.marrayInfo(fname="targets_fam_trmt.txt",info.id=NULL, labels=NULL, notes="targets_fam_trmt_melissa", sep="\t", skip=0, quote="\"") # Read in gal file info indicating Name and ID columns and columns containing layout info to make object of class marrayInfo galinfoAgilent <- read.Galfile("025055_D_20090817_AID.gal", path=".", info.id=c("Name","Annotation ID","ID","ControlType"), layout.id=c(Block="Block", Row="Row", Column="Column"), labels="Name", notes="", sep="\t", skip=NULL, ncolumns=1) # Create vector of array names arrayNames <- targets_fam_trmt_melissa@maLabels # Read in, summarize and display intensity data for all arrays mrawMedian <- read.marrayRaw(fnames=arrayNames, path=".", name.Gf="Signal Median 2", name.Gb="Background Median 2", name.Rf="Signal Median 1", name.Rb="Background Median 1", name.W="Flag", layout=galinfoAgilent$layout, gnames=galinfoAgilent$gnames, targets=targets_fam_trmt_melissa, notes=NULL, skip=NULL, sep="\t", quote="\"", DEBUG=FALSE) summary(mrawMedian) # Check if order of intensities and target are the same checkTargetInfo(mrawMedian) # Remove control spots 1 mrawMedianNoControl <- subset(mrawMedian,!(mrawMedian@maGnames@maInfo$ControlType %in% c("ignore","pos","neg"))) # Calculate summary stats and cutoff levels # Create separate object for this, and remove manual flagged spots (flag 1) # Manually flagged spots are removed to prevent blowing up the background SD because of dust etc. mrawMedianStats <- mrawMedianNoControl for (i in 1:32){ mrawMedianStats@maGf[,i][mrawMedianStats@maW[,i]==1]<-NA mrawMedianStats@maGb[,i][mrawMedianStats@maW[,i]==1]<-NA mrawMedianStats@maRf[,i][mrawMedianStats@maW[,i]==1]<-NA mrawMedianStats@maRb[,i][mrawMedianStats@maW[,i]==1]<-NA } # Calculate average (avg), standard deviation (sd), signal/background ratio (sbr) and cutoffs avgGf <- colMeans(mrawMedianStats@maGf, na.rm=TRUE) avgGb <- colMeans(mrawMedianStats@maGb, na.rm=TRUE) avgRf <- colMeans(mrawMedianStats@maRf, na.rm=TRUE) avgRb <- colMeans(mrawMedianStats@maRb, na.rm=TRUE) sdGf <- apply(mrawMedianStats@maGf,2,sd, na.rm=TRUE) sdGb <- apply(mrawMedianStats@maGb,2,sd, na.rm=TRUE) sdRf <- apply(mrawMedianStats@maRf,2,sd, na.rm=TRUE) sdRb <- apply(mrawMedianStats@maRb,2,sd, na.rm=TRUE) sbrG <- avgGf/avgGb sbrR <- avgRf/avgRb cutoffs <- matrix(0,32,2) for (i in 1:32){ cutoffs[i,1] <- avgGb[i] + (2*sdGb[i]) cutoffs[i,2] <- avgRb[i] + (2*sdRb[i]) } # To export cutoffs into text file, add column and row names cutoffsExport <- cbind(arrayNames,cutoffs) cutoffsExport <- rbind(c('ArrayID','Green cutoff','Red cutoff'),cutoffsExport) write.table(cutoffsExport,"cutoff levels.txt",quote=FALSE,sep="\t") # To export summary statistics to text file, add column and row names statsExport <- cbind(avgGf,sdGf,avgRf,sdRf,avgGb,sdGb,avgRb,sdRb,sbrG,sbrR) write.table(statsExport,"summary statistics.txt",quote=FALSE,sep="\t") # Continue with normalizing dataset, using the mrawMedianNoControl object # (which has only control spots removed) # Normalize using printtip Loess 2 mnormMedianNoControl <- maNormMain(mrawMedianNoControl,echo=TRUE) summary(mnormMedianNoControl) # Plot boxplots to check normalization for (i in 1:32){ pdf(paste("RawMedianBoxPlots",targets_fam_trmt_melissa@maInfo[i,2],".pdf", sep=""), paper="letter") boxplot(mrawMedianNoControl[, i], xvar = "maPrintTip", yvar = "maM") dev.off() pdf(paste("NormMedianBoxPlots",targets_fam_trmt_melissa@maInfo[i,2],".pdf", sep=""), paper="letter") boxplot(mnormMedianNoControl[, i], xvar = "maPrintTip", yvar = "maM") dev.off() } pdf("RawMedianBoxPlotsAllArrays.pdf", paper="letter") boxplot(mrawMedianNoControl, yvar = "maM") dev.off() pdf("NormMedianBoxPlotsAllArrays.pdf", paper="letter") boxplot(mnormMedianNoControl, yvar = "maM") dev.off() # Plot scatterplots; example MA plots with LowessLines for (i in 1:32){ pdf(paste("RawMedianScatterPlot",targets_fam_trmt_melissa@maInfo[i,2],".pdf", sep=""), paper="letter") defs <- maDefaultPar(mrawMedianNoControl[, i], x = "maA", y = "maM", z = "maPrintTip") legend.func <- do.call("maLegendLines", defs$def.legend) lines.func <- do.call("maLowessLines", c(list(TRUE, f = 0.3), defs$def.lines)) plot(mrawMedianNoControl[, i], xvar = "maA", yvar = "maM", zvar = "maPrintTip", lines.func, text.func = maText(), legend.func) dev.off() pdf(paste("NormMedianScatterPlot",targets_fam_trmt_melissa@maInfo[i,2],".pdf", sep=""), paper="letter") defs <- maDefaultPar(mnormMedianNoControl[, i], x = "maA", y = "maM", z = "maPrintTip") legend.func <- do.call("maLegendLines", defs$def.legend) lines.func <- do.call("maLowessLines", c(list(TRUE, f = 0.3), defs$def.lines)) plot(mnormMedianNoControl[, i], xvar = "maA", yvar = "maM", zvar = "maPrintTip", lines.func, text.func = maText(), legend.func) dev.off() } # Create array data where values below specific cutoff are replaced by NA after normalization # Create different datasets # And one with only cutoff for Green used (i.e. to prevent loss of genes that are switched off in a subgroup of samples) 3 mnormCutoffBoth <- mnormMedianNoControl for (i in 1:91){ removeG <- mrawMedianNoControl@maGf[,i]<cutoffs[i,1] removeR <- mrawMedianNoControl@maRf[,i]<cutoffs[i,2] mnormCutoffBoth@maM[,i][removeG]<-NA mnormCutoffBoth@maM[,i][removeR]<-NA mnormCutoffBoth@maA[,i][is.na(mnormCutoffBoth@maM[,i])]<-NA } mnormCutoffGreen <- mnormMedianNoControl for (i in 1:32){ removeG <- mrawMedianNoControl@maGf[,i]<cutoffs[i,1] mnormCutoffGreen@maM[,i][removeG]<-NA mnormCutoffGreen@maA[,i][is.na(mnormCutoffGreen@maM[,i])]<-NA } # Make a count of the Imagene quality flags remaining after removal of controls # (since spots below cutoff are not removed but rather replaced by NA, a count after cutoff levels is useless) flags <- mrawMedianNoControl@maW flags1 <- flags == 1 flags2 <- flags == 2 flags3 <- flags == 3 flagcounts <- matrix(0,32,3) flagcounts[,1] <- apply(flags1,2,sum) flagcounts[,2] <- apply(flags2,2,sum) flagcounts[,3] <- apply(flags3,2,sum) write.table(flagcounts,"flagcounts after control removal.txt",quote=FALSE,sep="\t") # Remove all flagged spots mnormCutoffBothrmFlagAll <- mnormCutoffBoth for (i in 1:1){ mnormCutoffBothrmFlagAll@maM[,i][mnormCutoffBothrmFlagAll@maW[,i]==1]<-NA [mnormCutoffBothrmFlagAll@maW[,i]==2]<-NA mnormCutoffBothrmFlagAll@maA[,i][is.na(mnormCutoffBothrmFlagAll@maM[,i])]<-NA } mnormCutoffBothrmFlagAll.table <- cbind(mnormCutoffBothrmFlagAll@maGnames@maInfo$ID,mnormCutoffBothrmFlagAll@maM) write.table(mnormCutoffBothrmFlagAll.table,"mnormCutoffBothrmFlagAll_ratios.txt",quote=FALSE,sep ="\t") mnormCutoffGreenrmFlagAll <- mnormCutoffGreen for (i in 1:32){ mnormCutoffGreenrmFlagAll@maM[,i][mnormCutoffGreenrmFlagAll@maW[,i]==2]<-NA mnormCutoffGreenrmFlagAll@maA[,i][is.na(mnormCutoffGreenrmFlagAll@maM[,i])]<-NA } for (i in 1:32){ 4 mnormCutoffGreenrmFlagAll@maM[,i][mnormCutoffGreenrmFlagAll@maW[,i]==1]<-NA mnormCutoffGreenrmFlagAll@maA[,i][is.na(mnormCutoffGreenrmFlagAll@maM[,i])]<-NA } mnormCutoffGreenrmFlagAll.table <- cbind(mnormCutoffGreenrmFlagAll@maGnames@maInfo$ID,mnormCutoffGreenrmFlagAll@maM) write.table(mnormCutoffGreenrmFlagAll.table,"mnormCutoffGreenrmFlagAll_ratios.txt",quote=FALSE,s ep="\t") # Make new tables removing spots with >=25% NA (32 arrays, 8 NA, so 8 and smaller allowed) mnormCutoffGreenrmFlagAllNACount <- rowSums(is.na(mnormCutoffGreenrmFlagAll.table)) mnormCutoffGreen.NA <- cbind(mnormCutoffGreenrmFlagAll.table,mnormCutoffGreenrmFlagAllNACount) write.table(mnormCutoffGreen.NA,file="mnormCutoffGreenNACount.txt",quote=FALSE,sep="\t") mnormCutoffGreen.full <- read.table("mnormCutoffGreenNACount.txt",header=TRUE,sep="\t",row.names=1,fill=TRUE) mnormCutoffGreen.NA25 <- subset(mnormCutoffGreen.full,mnormCutoffGreen.full[,34]<9) mnormCutoffGreen.NA25 <- mnormCutoffGreen.NA25[,1:33] write.table(mnormCutoffGreen.NA25,"Green_avg_NACutoff_25percent.txt",quote=FALSE,sep="\t") # Impute missing data using EMarray from LSimpute applet # Adapt LSimpute commands to new files and folders # Do not forget to change text files first to insert a tab at the beginning and remove './' and '.txt' 5 Siggenes analysis (scripts adapted from Booman et al. 2011) 1. As part of the previous script, all probes for which the log2 ratio was missing (NA) in more than 25% of arrays were removed from the normalized and thresholded log transcription data. This resulted in a final dataset comprised of 21,117 probes. 2. Missing data in the new dataset were imputed using the EM_array algorithm from the LSimpute package as described by Bø et al., Nucleic Acids Research 2004, 32:e34. 3. Imputed data were read into R and two-class comparison analysis was performed with the package ‘siggenes’: #Read in imputed data table.melissa.avg.25.imputed.full <- read.table("Green_avg_NACutoff_25percent_imputed_EMarray_repl_rem.txt",header=TRUE,sep="\t",r ow.names=1,fill=TRUE) table.25imputed_all_families_NG_vs_G <- cbind(table.melissa.avg.25.imputed.full[,1:5],table.melissa.avg.25.imputed.full[,11:15],table.melissa.avg
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    Kelley et al. BMC Cell Biology 2010, 11:63 http://www.biomedcentral.com/1471-2121/11/63 RESEARCH ARTICLE Open Access Karyopherin a7 (KPNA7), a divergent member of the importin a family of nuclear import receptors Joshua B Kelley1, Ashley M Talley1, Adam Spencer1, Daniel Gioeli2, Bryce M Paschal1,3* Abstract Background: Classical nuclear localization signal (NLS) dependent nuclear import is carried out by a heterodimer of importin a and importin b. NLS cargo is recognized by importin a, which is bound by importin b. Importin b mediates translocation of the complex through the central channel of the nuclear pore, and upon reaching the nucleus, RanGTP binding to importin b triggers disassembly of the complex. To date, six importin a family members, encoded by separate genes, have been described in humans. Results: We sequenced and characterized a seventh member of the importin a family of transport factors, karyopherin a 7 (KPNA7), which is most closely related to KPNA2. The domain of KPNA7 that binds Importin b (IBB) is divergent, and shows stronger binding to importin b than the IBB domains from of other importin a family members. With regard to NLS recognition, KPNA7 binds to the retinoblastoma (RB) NLS to a similar degree as KPNA2, but it fails to bind the SV40-NLS and the human nucleoplasmin (NPM) NLS. KPNA7 shows a predominantly nuclear distribution under steady state conditions, which contrasts with KPNA2 which is primarily cytoplasmic. Conclusion: KPNA7 is a novel importin a family member in humans that belongs to the importin a2 subfamily. KPNA7 shows different subcellular localization and NLS binding characteristics compared to other members of the importin a family.
  • Compromised Glutamate Transport in Human Glioma Cells: Reduction

    Compromised Glutamate Transport in Human Glioma Cells: Reduction

    The Journal of Neuroscience, December 15, 1999, 19(24):10767–10777 Compromised Glutamate Transport in Human Glioma Cells: Reduction–Mislocalization of Sodium-Dependent Glutamate Transporters and Enhanced Activity of Cystine–Glutamate Exchange Zu-Cheng Ye,1 Jeffrey D. Rothstein,2 and Harald Sontheimer1 1Department of Neurobiology, The University of Alabama at Birmingham, Birmingham, Alabama 35294, and 2Department of Neurology, Johns Hopkins University, Baltimore, Maryland 21287 1 Elevated levels of extracellular glutamate ([Glu]o ) can induce 50% of glutamate transport was Na -independent and medi- 2 seizures and cause excitotoxic neuronal cell death. This is ated by a cystine–glutamate exchanger (system xc ). Extracel- normally prevented by astrocytic glutamate uptake. Neoplastic lular L-cystine dose-dependently induced glutamate release transformation of human astrocytes causes malignant gliomas, from glioma cells. Glutamate release was enhanced by extra- which are often associated with seizures and neuronal necrosis. cellular glutamine and inhibited by (S)-4-carboxyphenylglycine, Here, we show that Na 1-dependent glutamate uptake in gli- which blocked cystine–glutamate exchange. These data sug- oma cell lines derived from human tumors (STTG-1, D-54MG, gest that the unusual release of glutamate from glioma cells is D-65MG, U-373MG, U-251MG, U-138MG, and CH-235MG) is caused by reduction–mislocalization of Na 1-dependent gluta- up to 100-fold lower than in astrocytes. Immunohistochemistry mate transporters in conjunction with upregulation of cystine– and subcellular fractionation show very low expression levels of glutamate exchange. The resulting glutamate release from gli- the astrocytic glutamate transporter GLT-1 but normal expres- oma cells may contribute to tumor-associated necrosis and sion levels of another glial glutamate transporter, GLAST.
  • B Number Gene Name Mrna Intensity Mrna

    B Number Gene Name Mrna Intensity Mrna

    sample) total list predicted B number Gene name assignment mRNA present mRNA intensity Gene description Protein detected - Membrane protein membrane sample detected (total list) Proteins detected - Functional category # of tryptic peptides # of tryptic peptides # of tryptic peptides detected (membrane b0002 thrA 13624 P 39 P 18 P(m) 2 aspartokinase I, homoserine dehydrogenase I Metabolism of small molecules b0003 thrB 6781 P 9 P 3 0 homoserine kinase Metabolism of small molecules b0004 thrC 15039 P 18 P 10 0 threonine synthase Metabolism of small molecules b0008 talB 20561 P 20 P 13 0 transaldolase B Metabolism of small molecules chaperone Hsp70; DNA biosynthesis; autoregulated heat shock b0014 dnaK 13283 P 32 P 23 0 proteins Cell processes b0015 dnaJ 4492 P 13 P 4 P(m) 1 chaperone with DnaK; heat shock protein Cell processes b0029 lytB 1331 P 16 P 2 0 control of stringent response; involved in penicillin tolerance Global functions b0032 carA 9312 P 14 P 8 0 carbamoyl-phosphate synthetase, glutamine (small) subunit Metabolism of small molecules b0033 carB 7656 P 48 P 17 0 carbamoyl-phosphate synthase large subunit Metabolism of small molecules b0048 folA 1588 P 7 P 1 0 dihydrofolate reductase type I; trimethoprim resistance Metabolism of small molecules peptidyl-prolyl cis-trans isomerase (PPIase), involved in maturation of b0053 surA 3825 P 19 P 4 P(m) 1 GenProt outer membrane proteins (1st module) Cell processes b0054 imp 2737 P 42 P 5 P(m) 5 GenProt organic solvent tolerance Cell processes b0071 leuD 4770 P 10 P 9 0 isopropylmalate