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Commun Biol. 2022 Jan 11;5(1):31. doi: 10.1038/s42003-021-02991-5.

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.

Communications biology

Weiyang Tao, Timothy R D J Radstake, Aridaman Pandit

Affiliations

  1. Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. [email protected].
  2. Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. [email protected].
  3. Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  4. Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  5. Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. [email protected].
  6. Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. [email protected].

PMID: 35017649 DOI: 10.1038/s42003-021-02991-5

Abstract

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

© 2022. The Author(s).

References

  1. Linnarsson, S. & Teichmann, S. A. Single-cell genomics: Coming of age. Genome Biol. 17, 97 (2016). - PubMed
  2. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016). - PubMed
  3. Clark, N. R. et al. The characteristic direction: a geometrical approach to identify differentially expressed genes. BMC Bioinforma. 15, 1–16 (2014). - PubMed
  4. Nguyen, N. T., Lindsey, M. L. & Jin, Y.-F. Systems analysis of gene ontology and biological pathways involved in post-myocardial infarction responses. BMC Genomics 16, S18 (2015). - PubMed
  5. Walley, J. W. et al. Integration of omic networks in a developmental atlas of maize. Sci. (80-.) 353, 814–818 (2016). - PubMed
  6. Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet 12, 56–68 (2011). - PubMed
  7. Bhattacharyya, M. & Chakrabarti, S. Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malar. J. 14, 70 (2015). - PubMed
  8. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). - PubMed
  9. Almalki, S. G. & Agrawal, D. K. Key transcription factors in the differentiation of mesenchymal stem cells. Differentiation 92, 41–51 (2016). - PubMed
  10. Lesage, K. M. et al. Cooperative binding of ApiAP2 transcription factors is crucial for the expression of virulence genes in Toxoplasma gondii. Nucleic Acids Res. 46, 6057–6068 (2018). - PubMed
  11. Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010). - PubMed
  12. Garcia-Alonso, L. et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 78, 769–780 (2018). - PubMed
  13. Keenan, A. B. et al. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Res. 47, W212–W224 (2019). - PubMed
  14. Wang, Z. et al. BART: a transcription factor prediction tool with query gene sets or epigenomic profiles. Bioinformatics 34, 2867–2869 (2018). - PubMed
  15. Puente-Santamaria, L., Wasserman, W. W. & Del Peso, L. TFEA. ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets. Bioinformatics 35, 5339–5340 (2019). - PubMed
  16. Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010). - PubMed
  17. Lefebvre, C. et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol. Syst. Biol. 6, 377 (2010). - PubMed
  18. Qi, D., Wu, B., Tong, D., Pan, Y. & Chen, W. Identification of key transcription factors in caerulein-induced pancreatitis through expression profiling data. Mol. Med. Rep. 12, 2570–2576 (2015). - PubMed
  19. Peter, I. S. & Davidson, E. H. Evolution of gene regulatory networks controlling body plan development. Cell 144, 970–985 (2011). - PubMed
  20. Voordeckers, K., Pougach, K. & Verstrepen, K. J. How do regulatory networks evolve and expand throughout evolution? Curr. Opin. Biotechnol. 34, 180–188 (2015). - PubMed
  21. Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS One 5, e12776 (2010). - PubMed
  22. Margolin, A. A. et al. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinforma. 7, S7 (2006). - PubMed
  23. Lachmann, A., Giorgi, F. M., Lopez, G. & Califano, A. ARACNe-AP: Gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32, 2233–2235 (2016). - PubMed
  24. Ahsen, M. E. et al. NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers. in Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics 1–13 (2020). - PubMed
  25. Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016). - PubMed
  26. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830 (2018). - PubMed
  27. Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Disco. 9, 1102–1123 (2019). - PubMed
  28. Chari, A. et al. Oral selinexor–dexamethasone for triple-class refractory multiple myeloma. N. Engl. J. Med 381, 727–738 (2019). - PubMed
  29. Califano, A. & Alvarez, M. J. The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat. Rev. Cancer 17, 116–130 (2017). - PubMed
  30. Guzzi, P. H., Mercatelli, D., Ceraolo, C. & Giorgi, F. M. Master regulator analysis of the SARS-CoV-2/human interactome. J. Clin. Med. 9, 982 (2020). - PubMed
  31. Gargouri, M. et al. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii. J. Exp. Bot. 66, 4551–4566 (2015). - PubMed
  32. Zhuang, D. Y., Jiang, L. I., He, Q. Q., Zhou, P. & Yue, T. Identification of hub subnetwork based on topological features of genes in breast cancer. Int. J. Mol. Med 35, 664–674 (2015). - PubMed
  33. Wasserman, S., Faust, K. & others. Social network analysis: Methods and applications. vol. 8 (Cambridge university press, 1994). - PubMed
  34. Bouquet, J. et al. Longitudinal transcriptome analysis reveals a sustained differential gene expression signature in patients treated for acute Lyme disease. MBio 7, (2016). - PubMed
  35. Barbieri, E. et al. Histone chaperone CHAF1A inhibits differentiation and promotes aggressive neuroblastoma. Cancer Res. 74, 765–774 (2014). - PubMed
  36. Basso, K. et al. Integrated biochemical and computational approach identifies BCL6 direct target genes controlling multiple pathways in normal germinal center B cells. Blood, J. Am. Soc. Hematol. 115, 975–984 (2010). - PubMed
  37. Schneider, W. M., Chevillotte, M. D. & Rice, C. M. Interferon-Stimulated Genes: A Complex Web of Host Defenses. Annu. Rev. Immunol. 32, 513–545 (2014). - PubMed
  38. Schoggins, J. W. Interferon-Stimulated Genes: What Do They All Do? Annu. Rev. Virol. 6, 567–584 (2019). - PubMed
  39. Jefferies, C. A. Regulating IRFs in IFN driven disease. Front. Immunol. 10, 325 (2019). - PubMed
  40. Seifert, L. L. et al. The ETS transcription factor ELF1 regulates a broadly antiviral program distinct from the type I interferon response. PLoS Pathog. 15, e1007634 (2019). - PubMed
  41. Froggatt, H. M., Harding, A. T., Heaton, B. E. & Heaton, N. S. ETV7 limits antiviral gene expression and control of SARS-CoV-2 and influenza viruses. bioRxiv 851543 (2020). - PubMed
  42. Bruchez, A. et al. MHC class II transactivator CIITA induces cell resistance to Ebola virus and SARS-like coronaviruses. Sci. (80-.) 370, 241–247 (2020). - PubMed
  43. Langfelder, P., Mischel, P. S. & Horvath, S. When is hub gene selection better than standard meta-analysis? PLoS One 8, e61505 (2013). - PubMed
  44. He, X. & Zhang, J. Why do hubs tend to be essential in protein networks? PLoS Genet 2, e88 (2006). - PubMed
  45. Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes, Brain Behav. 13, 13–24 (2014). - PubMed
  46. Meyer, P. E., Lafitte, F. & Bontempi, G. Minet: A r/bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinforma. 9, 461 (2008). - PubMed
  47. He, J., Zhou, Z., Reed, M. & Califano, A. Accelerated parallel algorithm for gene network reverse engineering. BMC Syst. Biol. 11, 83 (2017). - PubMed
  48. Glass, K., Huttenhower, C., Quackenbush, J. & Yuan, G.-C. Passing messages between biological networks to refine predicted interactions. PLoS ONE 8, e64832 (2013). - PubMed
  49. Silva-Cardoso, S. C. et al. CXCL4 links inflammation and fibrosis by reprogramming monocyte-derived dendritic cells in vitro. Front. Immunol. 11, 2149 (2020). - PubMed
  50. Mijnheer, G. et al. Conserved human effector Treg signature is reflected in transcriptomic and epigenetic landscape. bioRxiv (2020). - PubMed
  51. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). - PubMed
  52. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). - PubMed
  53. Wang, J. et al. Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information. Nucleic Acids Res. 41, e97 (2013). - PubMed
  54. Greenham, K. & Robertson McClung, C. Time to build on good design: Resolving the temporal dynamics of gene regulatory networks. Proc. Natl. Acad. Sci. USA. 115, 6325–6327 (2018). - PubMed
  55. Chen, X., Li, M., Zheng, R., Wu, F. X. & Wang, J. D3GRN: A data driven dynamic network construction method to infer gene regulatory networks. BMC Genomics 20, 1–8 (2019). - PubMed
  56. Zhu, H., Shyama Prasad Rao, R., Zeng, T. & Chen, L. Reconstructing dynamic gene regulatory networks from sample-based transcriptional data. Nucleic Acids Res. 40, 10657–10667 (2012). - PubMed
  57. Chai, L. E. et al. A review on the computational approaches for gene regulatory network construction. Computers Biol. Med. 48, 55–65 (2014). - PubMed
  58. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 9, 559 (2008). - PubMed
  59. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012). - PubMed
  60. Huynh-Thu, V. A. & Sanguinetti, G. Gene Regulatory Network Inference: An Introductory Survey. in Methods in Molecular Biology vol. 1883 1–23 (Humana Press Inc., 2019). - PubMed
  61. Han, H. et al. TRRUST: a reference database of human transcriptional regulatory interactions. Sci. Rep. 5, 11432 (2015). - PubMed
  62. Liu, Z.-P., Wu, C., Miao, H. & Wu, H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database 2015, bav095 (2015). - PubMed
  63. Marbach, D. et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat. Methods 13, 366 (2016). - PubMed
  64. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). - PubMed
  65. Phan, R. T., Saito, M., Basso, K., Niu, H. & Dalla-Favera, R. BCL6 interacts with the transcription factor Miz-1 to suppress the cyclin-dependent kinase inhibitor p21 and cell cycle arrest in germinal center B cells. Nat. Immunol. 6, 1054–1060 (2005). - PubMed
  66. Christian, S. L. et al. Suppression of IFN-induced transcription underlies IFN defects generated by activated Ras/MEK in human cancer cells. PLoS One 7, e44267 (2012). - PubMed
  67. Thomas, E. et al. HCV infection induces a unique hepatic innate immune response associated with robust production of type III interferons. Gastroenterology 142, 978–988 (2012). - PubMed
  68. Duncan, C. J. A. et al. Human IFNAR2 deficiency: Lessons for antiviral immunity. Sci. Transl. Med. 7, 307ra154 (2015). - PubMed
  69. Tsoi, L. C. et al. Hypersensitive IFN Responses in Lupus Keratinocytes Reveal Key Mechanistic Determinants in Cutaneous Lupus. J. Immunol. 202, 2121–2130 (2019). - PubMed
  70. Sirois, M. et al. TRAF6 and IRF7 control HIV replication in macrophages. PLoS One 6, e28125 (2011). - PubMed
  71. Hernandez, N. et al. Life-threatening influenza pneumonitis in a child with inherited IRF9 deficiency. J. Exp. Med 215, 2567–2585 (2018). - PubMed
  72. Steiger, J. et al. Imatinib Triggers Phagolysosome Acidification and Antimicrobial Activity against Mycobacterium bovis Bacille Calmette-Guérin in Glucocorticoid-Treated Human Macrophages. J. Immunol. 197, 222–232 (2016). - PubMed
  73. Mehraj, V. et al. Monocyte responses in the context of Q fever: from a static polarized model to a kinetic model of activation. J. Infect. Dis. 208, 942–951 (2013). - PubMed
  74. Nograles, K. E. et al. Th17 cytokines interleukin (IL)-17 and IL-22 modulate distinct inflammatory and keratinocyte-response pathways. Br. J. Dermatol 159, 1092–1102 (2008). - PubMed
  75. Smith, J. A. et al. Gene expression analysis of macrophages derived from ankylosing spondylitis patients reveals interferon-gamma dysregulation. Arthritis Rheum. 58, 1640–1649 (2008). - PubMed
  76. Kang, K. et al. IFN-γ selectively suppresses a subset of TLR4-activated genes and enhancers to potentiate macrophage activation. Nat. Commun. 10, 1–14 (2019). - PubMed

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