User:Matthew Whiteside/Notebook/Malaria Microarray/2009/01/29

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Malaria Task 3.4 Looking in-depth for publicly available human malarial datasets in pubmed, specifically cerebral malaria. DEFINITIVE LIST of papers to use in meta-analysis Chakravorty SJ, Carret C, Nash GB, Ivens A, Szestak T, and Craig AG. Altered phenotype and gene transcription in endothelial cells, induced by Plasmodium falciparum-infected red blood cells: pathogenic or protective?. Int J Parasitol. 2007 Jul;37(8-9):975-87. DOI:10.1016/j.ijpara.2007.02.006 | Dataset: Transcription profiling of human endothelials treated with Plasmodium falciparum infected RBC (PRBCs) and/or TNF-alpha. (AE ID: E-SGRP-3) Study: Plasmodium falciparum infected RBC (PRBCs) co-cultered with human umbilical vein endothelials cells. Study simulates of PRBC sequestration to brain microvascular sites. Studies role of endothelium. Chip: (GPL570) Affymetrix GeneChip Human Genome U133 Plus 2.0 Samples: 28 samples; combinations of HUVEC endothelials, TNF-alpha, and infected/uninfected RBCs. Normalized: normalized data available. Protocol: Bioconductor affy package RMA normalization. Griffiths MJ, Shafi MJ, Popper SJ, Hemingway CA, Kortok MM, Wathen A, Rockett KA, Mott R, Levin M, Newton CR, Marsh K, Relman DA, and Kwiatkowski DP. Genomewide analysis of the host response to malaria in Kenyan children. J Infect Dis. 2005 May 15;191(10):1599-611. DOI:10.1086/429297 | Dataset: Transcription profiling of human childrens host response to malaria (AE ID: E-SMDB-2669) Study: Whole-blood from Kenyan children with malaria and possibly other infections. Compare gene expression before and after treatment and confirmed infection resolution. Chip: (GPL2614) SMD Homo sapiens Lymphochip Array LC-36 Samples: 28 samples; Bacterial & viral infections only, acute malaria, combinations of malaria and other infections, covalescent malaria (baseline). Normalized: normalized data available. Protocol: Array features with a signal:background ratio of <2.5 (in either sample or reference channel) and a regression correlation coefficient between sample and reference signal of <0.6 were excluded. Fluorescence signals from each array were scaled on the basis of the geometric mean of the sample:reference signal ratio from all array features after local background subtraction. Features representing the same GenBank accession number were collapsed to an arithmetic mean. Gene features with consistent signal quality across the 28 arrays were identified by selecting features that were present on 25 of the arrays. Signal intensity for identical gene features replicated across the arrays were median centered. This refined data set comprised 9869 gene features. Boldt AB, Luty A, Grobusch MP, Dietz K, Dzeing A, Kombila M, Kremsner PG, and Kun JF. Association of a new mannose-binding lectin variant with severe malaria in Gabonese children. Genes Immun. 2006 Jul;7(5):393-400. DOI:10.1038/sj.gene.6364312 | Dataset: Transcription profiling of whole blood cells from healthy African children and those with uncomplicated malaria or severe malarial anemia (AE ID: E-GEOD-1124, GEO: GEO GSE1124, GDS1971). Study: Compare gene expression between uncomplicated and severe malaria anemia. Chip: (GPL96) Affymetrix GeneChip Human Genome HG-U133A Samples: 15 microarrays with pooled RNA. 4 Control (C), 20 with severe (S) and 20 with uncomplicated (U). Normalized: normalized data is available. Protocol: We standardized for sample loading and variations in staining by scaling the signals on all arrays to constant target intensity (TGT 150). Ockenhouse CF, Hu WC, Kester KE, Cummings JF, Stewart A, Heppner DG, Jedlicka AE, Scott AL, Wolfe ND, Vahey M, and Burke DS. Common and divergent immune response signaling pathways discovered in peripheral blood mononuclear cell gene expression patterns in presymptomatic and clinically apparent malaria. Infect Immun. 2006 Oct;74(10):5561-73. DOI:10.1128/IAI.00408-06 | Dataset: Presymptomatic and symptomatic malaria: peripheral blood mononuclear cells (GEO: GDS2362) Study: Comparison of peripheral blood mononuclear cells of subjects with early, presymptomatic, experimentally acquired malaria to those with acute, uncomplicated, naturally acquired malaria. Results provide insight into the immune response to malaria in these two stages of infection. Chip: (GPL96) Affymetrix GeneChip Human Genome U133 Array Set HG-U133A Samples: 71 samples. RNA extracted from Peripherial Blood Mononuclear Cells (PBMCs). 22 malaria-naive US volunteers (used as baseline). Infected with malaria and comparison Infected sample taken. Group 2: 15 Cameroonian with acute malaria infection, and samples from 12 of them after treatment with chloroguine. Normalization: The scanned images were analyzed using Affymetrix MAS 5.0 to generate CEL files (fluorescence intensity files), which were normalized at the probe level using the robust multichip average method (19), with the average fluorescence intensity of each probe expressed as log2. The data sets from all groups (22 data sets from experimentally infected U.S. volunteers, 22 data sets from healthy U.S. volunteers, and 15 data sets from naturally infected Cameroonian volunteers) were normalized together in order to permit direct comparisons of gene expression patterns in the two groups relative to the same baseline. Muehlenbachs A, Fried M, Lachowitzer J, Mutabingwa TK, and Duffy PE. Genome-wide expression analysis of placental malaria reveals features of lymphoid neogenesis during chronic infection. J Immunol. 2007 Jul 1;179(1):557-65. DOI:10.4049/jimmunol.179.1.557 | Dataset: Placental malaria(GEO: GDS2822) Study: Analysis of inflamed placentas from patients with chronic placental malaria (PM) Chip: (GPL570) Affymetrix GeneChip Human Genome U133 Plus 2.0 Array Samples: Compare gene expression of RNA extracted from placentas from first-time Tanzanian mothers. 10 with active placental malaria, 10 PM negative (however, some with evidence of past PM). Normalized: Transcription profiles were defined by GeneChip operating system (GCOS) absolute expression analysis. Data were normalized by the GeneChip robust multiarray analysis (GC-RMA) algorithm and then analyzed by t test and hierarchical clustering with Acuity 4.0 (Axon). Tripathi AK, Sullivan DJ, and Stins MF. Plasmodium falciparum-infected erythrocytes increase intercellular adhesion molecule 1 expression on brain endothelium through NF-kappaB. Infect Immun. 2006 Jun;74(6):3262-70. DOI:10.1128/IAI.01625-05 | Dataset: Effect of Plasmodium falciparum infected erythrocytes on primary human brain microvascular endothelial cell (GEO: GSE9861) Study: investigated the global transcriptional gene response of primary human brain endothelial cells after incubation with high numbers of infected erythrocytes. Chip: (GPL571) Affymetrix GeneChip Human Genome U133A 2.0 Array Samples: Total of 8 samples (4 control and 4 treated) were analyzed. 4 control samples included two normal RBC control and two medium controls. 4 treated samples includes 2 exposed to low binding Pf-IRBC and 2 exposed to high binding Pf-IRBC (Pf-IRBC-P). Medium and RBC controls were finally used as four replicates of control and all four Pf-IRBC or Pf-IRBC-P exposed endothelial cells were used as 4 separate treated controls. Normalized: Signal intensity was calculated by GCOS 1.4 software - but affy values are NOT NORMALIZED!! Karsten performed normalization - email: I have placed files with normalized values into /tmp on koch: khokamp@koch:/tmp> l GSE9861_* -rw-r--r-- 1 khokamp wg-users 3257047 2009-05-13 13:56 GSE9861_clean_gcrma.txt -rw-r--r-- 1 khokamp wg-users 3256320 2009-05-13 13:56 GSE9861_gcrma.txt But if you are going to analyse them with limma, you could easily re-do this yourself in R: 1. Change into the directory containing the (compressed) CEL files, e.g. cd /tmp/GSE9861_cleanCEL 2. start R 3. read data and normalize: library(gcrma) data = ReadAffy() norm_data = gcrma(data) norm_data is an expressionSet that should be readily usable with limma The last step that I applied for extracting the data was write.exprs(norm_data, file='GSE9861_clean_gcrma.txt') It might be best to run both the original and the clean data set through limma to see if the resulting gene lists differ greatly. Explanation of 'clean' version: this experiment has been loaded into ArrayWiki...http://arraywiki.bme.gatech.edu/index.php/GSE9861 As you can see, the first slide has some fairly strong artifacts and gets the lowest quality score (88.63%). They provide a 'clean' version of the original data, where the spots with high variance have been replaced with the median value of this probe from other chips in the dataset (see here for more details: http://arraywiki.bme.gatech.edu/index.php/BioPNG_format) Others pubmed=18174328 Stretch - microarray of ~300 immunity genes in 3 different human populations - data is available in GEO. Focuses on Treg response, and protective alleles/phenotype in Fulani pop. John CC. Cerebral malaria pathogenesis: what can we learn from microarray analysis?. Am J Pathol. 2007 Dec;171(6):1729-32. DOI:10.2353/ajpath.2007.070917 | Commentary on current knowledge of CM using ma. No references to human microarray dataset, strictly murine. Singh AP, Buscaglia CA, Wang Q, Levay A, Nussenzweig DR, Walker JR, Winzeler EA, Fujii H, Fontoura BM, and Nussenzweig V. Plasmodium circumsporozoite protein promotes the development of the liver stages of the parasite. Cell. 2007 Nov 2;131(3):492-504. DOI:10.1016/j.cell.2007.09.013 | Microarray of liver celling expression plasmodium protein. Is monkey model studies interesting/useful?