Supplementary Materials

Supplementary Material for:

A validated gene regulatory network and GWAS identifies early regulators of T cell–associated diseases

Mika Gustafsson,* Danuta R. Gawel, Lars Alfredsson, Sergio Baranzini, Janne Björkander, Robert Blomgran, Sandra Hellberg, Daniel Eklund, Jan Ernerudh, Ingrid Kockum, Aelita Konstantinell, Riita Lahesmaa, Antonio Lentini, H. Robert I. Liljenström, Lina Mattson, Andreas Matussek, Johan Mellergård, Melissa Mendez, Tomas Olsson, Miguel A. Pujana, Omid Rasool, Jordi Serra-Musach, Margaretha Stenmarker, Subhash Tripathi, Miro Viitala, Hui Wang, Huan Zhang, Colm E. Nestor, Mikael Benson*

*Corresponding author. E-mail: mika.gustafsson{at}liu.se (M.G.); mikael.benson{at}liu.se (M.B.)

Published 11 November 2015, Sci. Transl. Med. 7, 313ra178 (2015)
DOI: 10.1126/scitranslmed.aad2722

This PDF file includes:

  • Materials and Methods
  • Fig. S1. Expression of signature genes in T cell subsets.
  • Fig. S2. Comparison of GWAS enrichment for gene sets defined from alternative strategies defined by the T cell differentiation microarray data.
  • Fig. S3. GWAS SNPs in different populations.
  • Fig. S4. Methylation probe levels of selected housekeeping and tissue-specific genes.
  • Fig. S5. Enrichment in GWAS SNPs in GATA3, MAF, and MYB in T cell diseases, infectious diseases, and malignancies.
  • Fig. S6. Overlap between different methods to define GATA3 bindings.
  • Fig. S7. GRN-predicted targets of GATA3, MAF, and MYB are differentially expressed in six T cell–related diseases.
  • Fig. S8. Validation of the differentially expressed splice variants in SAR.
  • Fig. S9. Time series analyses of GATA3, MAF, and MYB splice variant expression during TH1 and TH2 differentiation.
  • Fig. S10. Predicted effects of alternative splicing on the protein structures of GATA3, MAF, and MYB.
  • Fig. S11. Heat maps of the top 50 most differentially expressed genes in siRNA-mediated MAF knockdown using four different vectors.
  • Fig. S12. Effect of MAF knockdown on TH1, TH2, TH17, and Treg signature genes.
  • Fig. S13. MAF siRNA splice–specific targets.
  • Fig. S14. Expression profiles of SAR patients and controls show a consisting overlap even for the most significantly differentially expressed genes.
  • Fig. S15. Expression profiles of MS patients and controls show a consisting overlap even for the most significantly differentially expressed genes.
  • Fig. S16. Expression overlap between patients and controls in many T cell–associated diseases.
  • Fig. S17. Expression overlap between patients and controls in many T cell–associated diseases.
  • Fig. S18. GRN validation.
  • Legends for tables S1 to S3
  • Table S4. Statistics of all eight T cell diseases.
  • Legends for tables S5 to S7
  • Table S8. Splice variant qPCR details (reagents and company).
  • Legend for table S9
  • Table S10. MAF (NM_001031804 and NM_005360) knockdown efficiency.
  • Legend for table S11
  • Table S12. GRN predictions compared with TFBS using ChIP-seq peak counts.
  • Table S13. Source data values for Fig. 5A.
  • Table S14. Source data values for Fig. 5B.
  • Table S15. Source data values for Fig. 5C.
  • Table S16. Source data values for Fig. 5D.
  • Legends for data S1 and S2
  • References (2947)

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Other Supplementary Material for this manuscript includes the following:

  • Table S1 (Microsoft Excel format). Predicted regulatory SNPs for GATA3, MAF, and MYB using rSNPBase.
  • Table S2 (Microsoft Excel format). All pathways for the targets of GATA3, MAF, and MYB, respectively, reported by Ingenuity Pathway Analysis.
  • Table S3 (Microsoft Excel format). All pathways for the early predicted targets of all early TFs.
  • Table S5 (Microsoft Excel format). SNPs that might affect splicing processes were identified by mapping all disease-associated SNPs (GWAS) to the splice regulatory sites of MAF, MYB, and GATA3.
  • Table S6 (Microsoft Excel format). Gene ontology enrichment analysis of biological processes of the MAF splice variant 2–specific targets.
  • Table S7 (Microsoft Excel format). Results for the naïve questions “how many protein-protein interactions (PPIs) have the TH differentially expressed genes, globally or just early,” and “would the TFs be identified through this PPI analysis.”
  • Table S9 (Microsoft Excel format). The list of all genes with at least one disease-associated SNP identified by GWAS.
  • Table S11 (Microsoft Excel format). Results of the patient control classification using GATA3, MAF, and/or MYB or their targets expression values.
  • Data S1 (.txt format). Five methylation arrays covering all known enhancers based on previous publications using DNase I hypersensitive sites sequencing.
  • Data S2 (.cys format). A validated network, Cytoscape file.

[Download Supplementary Tables S1 to S3, S5 to S7, S9 and S11]