TASL is the SLC15A4-associated adaptor for IRF5 activation by TLR7–9

  • 1.

    Blasius, A. L. & Beutler, B. Intracellular Toll-like receptors. Immunity 32, 305–315 (2010).

  • 2.

    Pelka, K., Shibata, T., Miyake, K. & Latz, E. Nucleic acid-sensing TLRs and autoimmunity: novel insights from structural and cell biology. Immunol. Rev. 269, 60–75 (2016).

  • 3.

    Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. Immunol. 11, 373–384 (2010).

  • 4.

    Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).

  • 5.

    Odhams, C. A. et al. Interferon inducible X-linked gene CXorf21 may contribute to sexual dimorphism in systemic lupus erythematosus. Nat. Commun. 10, 2164 (2019).

  • 6.

    Han, J. W. et al. Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus. Nat. Genet. 41, 1234–1237 (2009).

  • 7.

    Blasius, A. L. et al. Slc15a4, AP-3, and Hermansky–Pudlak syndrome proteins are required for Toll-like receptor signaling in plasmacytoid dendritic cells. Proc. Natl Acad. Sci. USA 107, 19973–19978 (2010).

  • 8.

    Sasawatari, S. et al. The solute carrier family 15A4 regulates TLR9 and NOD1 functions in the innate immune system and promotes colitis in mice. Gastroenterology 140, 1513–1525 (2011).

  • 9.

    Kobayashi, T. et al. The histidine transporter SLC15A4 coordinates mTOR-dependent inflammatory responses and pathogenic antibody production. Immunity 41, 375–388 (2014).

  • 10.

    Liu, S. et al. Phosphorylation of innate immune adaptor proteins MAVS, STING, and TRIF induces IRF3 activation. Science 347, aaa2630 (2015).

  • 11.

    Zhao, B. et al. Structural basis for concerted recruitment and activation of IRF-3 by innate immune adaptor proteins. Proc. Natl Acad. Sci. USA 113, E3403–E3412 (2016).

  • 12.

    Tsokos, G. C. Systemic lupus erythematosus. N. Engl. J. Med. 365, 2110–2121 (2011).

  • 13.

    Moulton, V. R. et al. Pathogenesis of human systemic lupus erythematosus: a cellular perspective. Trends Mol. Med. 23, 615–635 (2017).

  • 14.

    Tsokos, G. C., Lo, M. S., Costa Reis, P. & Sullivan, K. E. New insights into the immunopathogenesis of systemic lupus erythematosus. Nat. Rev. Rheumatol. 12, 716–730 (2016).

  • 15.

    Kieser, K. J. & Kagan, J. C. Multi-receptor detection of individual bacterial products by the innate immune system. Nat. Rev. Immunol. 17, 376–390 (2017).

  • 16.

    Janeway, C. A. Jr & Medzhitov, R. Innate immune recognition. Annu. Rev. Immunol. 20, 197–216 (2002).

  • 17.

    O’Neill, L. A., Golenbock, D. & Bowie, A. G. The history of Toll-like receptors – redefining innate immunity. Nat. Rev. Immunol. 13, 453–460 (2013).

  • 18.

    Blasius, A. L., Krebs, P., Sullivan, B. M., Oldstone, M. B. & Popkin, D. L. Slc15a4, a gene required for pDC sensing of TLR ligands, is required to control persistent viral infection. PLoS Pathog. 8, e1002915 (2012).

  • 19.

    Baccala, R. et al. Essential requirement for IRF8 and SLC15A4 implicates plasmacytoid dendritic cells in the pathogenesis of lupus. Proc. Natl Acad. Sci. USA 110, 2940–2945 (2013).

  • 20.

    Dosenovic, P. et al. Slc15a4 function is required for intact class switch recombination to IgG2c in response to TLR9 stimulation. Immunol. Cell Biol. 93, 136–146 (2015).

  • 21.

    Griffith, A. D. et al. A requirement for Slc15a4 in imiquimod-induced systemic inflammation and psoriasiform inflammation in mice. Sci. Rep. 8, 14451 (2018).

  • 22.

    Langefeld, C. D. et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 8, 16021 (2017).

  • 23.

    Pollard, K. M. et al. Induction of systemic autoimmunity by a xenobiotic requires endosomal TLR trafficking and signaling from the late endosome and endolysosome but not type I IFN. J. Immunol. 199, 3739–3747 (2017).

  • 24.

    Rebsamen, M. et al. SLC38A9 is a component of the lysosomal amino acid sensing machinery that controls mTORC1. Nature 519, 477–481 (2015).

  • 25.

    Pichlmair, A. et al. Viral immune modulators perturb the human molecular network by common and unique strategies. Nature 487, 486–490 (2012).

  • 26.

    Nakamura, N. et al. Endosomes are specialized platforms for bacterial sensing and NOD2 signalling. Nature 509, 240–244 (2014).

  • 27.

    Harris, V. M., Harley, I. T. W., Kurien, B. T., Koelsch, K. A. & Scofield, R. H. Lysosomal pH is regulated in a sex dependent manner in immune cells expressing CXorf21. Front. Immunol. 10, 578 (2019).

  • 28.

    Libert, C., Dejager, L. & Pinheiro, I. The X chromosome in immune functions: when a chromosome makes the difference. Nat. Rev. Immunol. 10, 594–604 (2010).

  • 29.

    Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).

  • 30.

    FANTOM Consortium and the RIKEN PMI and CLST. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  • 31.

    Arazi, A. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 902–914 (2019).

  • 32.

    Muskardin, T. L. W. & Niewold, T. B. Type I interferon in rheumatic diseases. Nat. Rev. Rheumatol. 14, 214–228 (2018).

  • 33.

    Hu, Y., Song, F., Jiang, H., Nuñez, G. & Smith, D. E. SLC15A2 and SLC15A4 mediate the transport of bacterially derived di/tripeptides to enhance the nucleotide-binding oligomerization domain-dependent immune response in mouse bone marrow-derived macrophages. J. Immunol. 201, 652–662 (2018).

  • 34.

    Lee, J. et al. pH-dependent internalization of muramyl peptides from early endosomes enables Nod1 and Nod2 signaling. J. Biol. Chem. 284, 23818–23829 (2009).

  • 35.

    Chow, K. T. et al. Differential and overlapping immune programs regulated by IRF3 and IRF5 in plasmacytoid dendritic cells. J. Immunol. 201, 3036–3050 (2018).

  • 36.

    Graham, R. R. et al. A common haplotype of interferon regulatory factor 5 (IRF5) regulates splicing and expression and is associated with increased risk of systemic lupus erythematosus. Nat. Genet. 38, 550–555 (2006).

  • 37.

    Schoenemeyer, A. et al. The interferon regulatory factor, IRF5, is a central mediator of Toll-like receptor 7 signaling. J. Biol. Chem. 280, 17005–17012 (2005).

  • 38.

    Takaoka, A. et al. Integral role of IRF-5 in the gene induction programme activated by Toll-like receptors. Nature 434, 243–249 (2005).

  • 39.

    Eames, H. L., Corbin, A. L. & Udalova, I. A. Interferon regulatory factor 5 in human autoimmunity and murine models of autoimmune disease. Transl. Res. 167, 167–182 (2016).

  • 40.

    Maeda, T. et al. A novel plasmacytoid dendritic cell line, CAL-1, established from a patient with blastic natural killer cell lymphoma. Int. J. Hematol. 81, 148–154 (2005).

  • 41.

    Steinhagen, F. et al. IRF-5 and NF-κB p50 co-regulate IFN-β and IL-6 expression in TLR9-stimulated human plasmacytoid dendritic cells. Eur. J. Immunol. 43, 1896–1906 (2013).

  • 42.

    Newstead, S. Recent advances in understanding proton coupled peptide transport via the POT family. Curr. Opin. Struct. Biol. 45, 17–24 (2017).

  • 43.

    Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protocols 10, 845–858 (2015).

  • 44.

    Chen, W. et al. Insights into interferon regulatory factor activation from the crystal structure of dimeric IRF5. Nat. Struct. Mol. Biol. 15, 1213–1220 (2008).

  • 45.

    Lopez-Pelaez, M. et al. Protein kinase IKKβ-catalyzed phosphorylation of IRF5 at Ser462 induces its dimerization and nuclear translocation in myeloid cells. Proc. Natl Acad. Sci. USA 111, 17432–17437 (2014).

  • 46.

    Ren, J., Chen, X. & Chen, Z. J. IKKβ is an IRF5 kinase that instigates inflammation. Proc. Natl Acad. Sci. USA 111, 17438–17443 (2014).

  • 47.

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

  • 48.

    Bigenzahn, J. W. et al. LZTR1 is a regulator of RAS ubiquitination and signaling. Science 362, 1171–1177 (2018).

  • 49.

    Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

  • 50.

    Troegeler, A. et al. An efficient siRNA-mediated gene silencing in primary human monocytes, dendritic cells and macrophages. Immunol. Cell Biol. 92, 699–708 (2014).

  • 51.

    Rudashevskaya, E. L. et al. A method to resolve the composition of heterogeneous affinity-purified protein complexes assembled around a common protein by chemical cross-linking, gel electrophoresis and mass spectrometry. Nat. Protocols 8, 75–97 (2013).

  • 52.

    Varjosalo, M. et al. Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. Nat. Methods 10, 307–314 (2013).

  • 53.

    Olsen, J. V. et al. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010–2021 (2005).

  • 54.

    Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

  • 55.

    Apweiler, R. et al. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 32, D115–D119 (2004).

  • 56.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

  • 57.

    Wright, J. C. & Choudhary, J. S. DecoyPyrat: fast non-redundant hybrid decoy sequence generation for large scale proteomics. J. Proteomics Bioinform. 9, 176–180 (2016).

  • 58.

    Kim, S. & Pevzner, P. A. M. S.-G. F. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 5, 5277 (2014).

  • 59.

    Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

  • 60.

    The, M., MacCoss, M. J., Noble, W. S. & Käll, L. Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0. J. Am. Soc. Mass Spectrom. 27, 1719–1727 (2016).

  • 61.

    Choi, H. et al. Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT. Curr. Protoc. Bioinformatics 39, 8.15.1–8.15.23 (2012).

  • 62.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

  • 63.

    Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat. Methods 10, 730–736 (2013).

  • 64.

    UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47 (D1), D506–D515 (2019).

  • 65.

    Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).

  • 66.

    Drozdetskiy, A., Cole, C., Procter, J. & Barton, G. J. JPred4: a protein secondary structure prediction server. Nucleic Acids Res. 43, W389–W394 (2015).

  • 67.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  • 68.

    Derr, A. et al. End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data. Genome Res. 26, 1397–1410 (2016).

  • 69.

    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).

  • 70.

    Marbach, D. et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat. Methods 13, 366–370 (2016).

  • 71.

    Hochberg, Y. & Benjamini, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).

  • 72.

    Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4, 44–57 (2009).

  • 73.

    Blaszczyk, M., Ciemny, M. P., Kolinski, A., Kurcinski, M. & Kmiecik, S. Protein-peptide docking using CABS-dock and contact information. Brief. Bioinform. 20, 2299–2305 (2019).

  • 74.

    London, N., Raveh, B., Cohen, E., Fathi, G. & Schueler-Furman, O. Rosetta FlexPepDock web server—high resolution modeling of peptide–protein interactions. Nucleic Acids Res. 39, W249–W253 (2011).

  • 75.

    Colas, C. et al. An improved flow cytometry assay to monitor phagosome acidification. J. Immunol. Methods 412, 1–13 (2014).

  • 76.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

  • 77.

    Deutsch, E. W. et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 45, D1100–D1106 (2017).

  • 78.

    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

  • Products You May Like

    Articles You May Like

    SpaceX overcame parachute, thruster problems in Crew Dragon development
    Rehabilitating the Vandals, the bearded ladies of geology, and how to get a job in academia: Books in brief
    Cryogenic temperature sensors: installation techniques for success – Physics World
    Monkey studies encouraging for coronavirus vaccine; virus travels further on breezy days
    How coronavirus lockdowns stopped flu in its tracks

    Leave a Reply

    Your email address will not be published. Required fields are marked *