Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

Proceedings of the National Academy of Sciences of the United States of America

Volumn 113, Issue 17, 2016, Pages E2393-2402

  Vandenbon, Alexis ^a   Dinh, Viet H ^a   Mikami, Norihisa ^a   Kitagawa, Yohko ^a   Teraguchi, Shunsuke ^a   Ohkura, Naganari ^a,b   Sakaguchi, Shimon ^a  

^a OSAKA UNIVERSITY   (Japan)

^b OSAKA UNIVERSITY GRADUATE SCHOOL OF MEDICINE   (Japan)

Author keywords

Database; Gene expression; Immune system; Network inference; Regulatory T cells

Indexed keywords

BETA INTEGRIN; BETA8 INTEGRIN; BULLOUS PEMPHIGOID ANTIGEN 1; CCAAT ENHANCER BINDING PROTEIN BETA; IKAROS TRANSCRIPTION FACTOR; IMMUNOGLOBULIN ENHANCER BINDING PROTEIN; INTERLEUKIN 2 RECEPTOR ALPHA; LYMPHOCYTE FUNCTION ASSOCIATED ANTIGEN 1; PHOSPHOPROTEIN PHOSPHATASE 1; STAT1 PROTEIN; STAT3 PROTEIN; T LYMPHOCYTE RECEPTOR; TRANSCRIPTION FACTOR FOXP3; UNCLASSIFIED DRUG; ZINC FINGER PROTEIN; FORKHEAD TRANSCRIPTION FACTOR; FOXP3 PROTEIN, MOUSE;

ANIMAL CELL; ARTICLE; CD4+ T LYMPHOCYTE; CELL SPECIFICITY; CELLS BY BODY ANATOMY; DENDRITIC CELL; EPIGENETICS; GENE EXPRESSION; HIGH THROUGHPUT SEQUENCING; IMMUNE SYSTEM; IMMUNOCOMPETENT CELL; IMMUNOREGULATION; MACROPHAGE; MOUSE; NONHUMAN; POSITION WEIGHT MATRIX; PRIORITY JOURNAL; PROMOTER REGION; PROTEIN EXPRESSION; REGULATORY T LYMPHOCYTE; SIGNAL TRANSDUCTION; STEM CELL; ANIMAL; CYTOLOGY; FACTUAL DATABASE; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HEMATOPOIESIS; PROCEDURES; REGULATOR GENE;

ANIMALS; DATABASES, FACTUAL; FORKHEAD TRANSCRIPTION FACTORS; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; GENES, REGULATOR; GENOME-WIDE ASSOCIATION STUDY; HEMATOPOIESIS; IMMUNE SYSTEM; MICE; T-LYMPHOCYTES, REGULATORY;

EID: 84964721009     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1604351113     Document Type: Article

References (45)

1. Kolesnikov N, et al. (2015) ArrayExpress update-simplifying data submissions. Nucleic Acids Res 43(Database issue):D1113-D1116.
2. Barrett T, et al. (2013) NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res 41(Database issue):D991-D995.
3. Marbach D, et al.; DREAM5 Consortium (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796-804.
4. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717-729.
5. Obayashi T, et al. (2014) ATTED-II in 2014: Evaluation of gene coexpression in agriculturally important plants. Plant Cell Physiol 55(1):e6.
6. Okamura Y, et al. (2015) COXPRESdb in 2015: Coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucleic Acids Res 43(Database issue):D82-D86.
7. Michalopoulos I, et al. (2012) Human gene correlation analysis (HGCA): A tool for the identification of transcriptionally co-expressed genes. BMC Res Notes 5(1):265.
8. Jupiter D, Chen H, VanBuren V (2009) STARNET 2: A web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data. BMC Bioinformatics 10:332.
9. Usadel B, et al. (2009) Co-expression tools for plant biology: Opportunities for hypothesis generation and caveats. Plant Cell Environ 32(12):1633-1651.
10. Kondo M (2010) Lymphoid and myeloid lineage commitment in multipotent hematopoietic progenitors. Immunol Rev 238(1):37-46.
11. Iwasaki H, Akashi K (2007) Myeloid lineage commitment from the hematopoietic stem cell. Immunity 26(6):726-740.
12. Piro RM, et al. (2011) An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction. Eur J Hum Genet 19(11):1173-1180.
13. Jojic V, et al.; Immunological Genome Project Consortium (2013) Identification of transcriptional regulators in the mouse immune system. Nat Immunol 14(6):633-643.
14. Gorenshteyn D, et al. (2015) Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity 43(3):605-614.
15. Leek JT, et al. (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11(10):733-739.
16. Leek JT (2014) svaseq: Removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res 42(21):1-9.
17. Gilad Y, Mizrahi-Man O (2015) A reanalysis of mouse ENCODE comparative gene expression data. F1000 Res 4(121):121.
18. Scherer A (2009) Batch Effects and Noise in Microarray Experiments: Sources and Solutions, ed Scherer A (John Wiley and Sons, Chichester, UK).
19. Irizarry RA, et al. (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2(5):345-350.
20. Sugimoto N, et al. (2006) Foxp3-dependent and-independent molecules specific for CD25+CD4+ natural regulatory T cells revealed by DNA microarray analysis. Int Immunol 18(8):1197-1209.
21. Gavin MA, et al. (2007) Foxp3-dependent programme of regulatory T-cell differentiation. Nature 445(7129):771-775.
22. Hill JA, et al. (2007) Foxp3 transcription-factor-dependent and-independent regulation of the regulatory T cell transcriptional signature. Immunity 27(5):786-800.
23. Morikawa H, et al.; FANTOM Consortium (2014) Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation. Proc Natl Acad Sci USA 111(14):5289-5294.
24. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118-127.
25. De Santa F, et al. (2007) The histone H3 lysine-27 demethylase Jmjd3 links inflammation to inhibition of polycomb-mediated gene silencing. Cell 130(6): 1083-1094.
26. De Santa F, et al. (2009) Jmjd3 contributes to the control of gene expression in LPSactivated macrophages. EMBO J 28(21):3341-3352.
27. Hori S, Nomura T, Sakaguchi S (2003) Control of Regulatory T Cell Development by the Transcription Factor Foxp3. Science 299(5609):1057-1061.
28. Josefowicz SZ, Lu L-F, Rudensky AY (2012) Regulatory T cells: Mechanisms of differentiation and function. Annu Rev Immunol 30:531-564.
29. Pallandre J-R, et al. (2007) Role of STAT3 in CD4+CD25+FOXP3+ regulatory lymphocyte generation: Implications in graft-versus-host disease and antitumor immunity. J Immunol 179(11):7593-7604.
30. Taniguchi T, Ogasawara K, Takaoka A, Tanaka N (2001) IRF family of transcription factors as regulators of host defense. Annu Rev Immunol 19:623-655.
31. Subramanian A, et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545-15550.
32. Ohkura N, et al. (2012) T cell receptor stimulation-induced epigenetic changes and Foxp3 Expression are independent and complementary events required for Treg cell development. Immunity 37(5):785-799.
33. Feuerer M, Hill JA, Mathis D, Benoist C (2009) Foxp3+ regulatory T cells: Differentiation, specification, subphenotypes. Nat Immunol 10(7):689-695.
34. Fu W, et al. (2012) A multiply redundant genetic switch 'locks in' the transcriptional signature of regulatory T cells. Nat Immunol 13(10):972-980.
35. Delgoffe GM, et al. (2013) Stability and function of regulatory T cells is maintained by a neuropilin-1-semaphorin-4a axis. Nature 501(7466):252-256.
36. Edwards JP, Thornton AM, Shevach EM (2014) Release of active TGF-?1 from the latent TGF-?1/GARP complex on T regulatory cells is mediated by integrin ?8. J Immunol 193(6):2843-2849.
37. Worthington JJ, et al. (2015) Integrin ?v?8-Mediated TGF-? Activation by Effector Regulatory T Cells Is Essential for Suppression of T-Cell-Mediated Inflammation. Immunity 42(5):903-915.
38. Isaacs JD, et al. (2008) The cellular prion protein is preferentially expressed by CD4+ CD25+ Foxp3+ regulatory T cells. Immunology 125(3):313-319.
39. Grönholm M, et al. (2011) TCR-induced activation of LFA-1 involves signaling through Tiam1. J Immunol 187(7):3613-3619.
40. Baron U, et al. (2007) DNA demethylation in the human FOXP3 locus discriminates regulatory T cells from activated FOXP3(+) conventional T cells. Eur J Immunol 37(9): 2378-2389.
41. Hicks SC, Teng M, Irizarry RA (2015) On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv, 10.1101/025528.
42. Irizarry RA, et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249-264.
43. Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) affy: Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307-315.
44. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9):1724-1735.
45. Ashburner M, et al.; The Gene Ontology Consortium (2000) Gene ontology: Tool for the unification of biology. Nat Genet 25(1):25-29.