Studying Plant MIF/D-DT-Like Genes and Proteins (MDLs)

  • Dzmitry Sinitski
  • Katrin Gruner
  • Jürgen BernhagenEmail author
  • Ralph PanstrugaEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2080)


Human macrophage migration inhibitory factor (MIF) is an inflammatory cytokine with chemokine-like characteristics and an upstream regulator of host innate immunity. It is a critical mediator of a variety of human diseases, such as acute and chronic inflammatory diseases, autoimmunity, atherosclerosis, and cancer. MIF is an atypical chemokine that not only signals through its cognate receptor CD74, but also interacts with the classical chemokine receptors CXCR2 and CXCR4. MIF and its homolog D-dopachrome tautomerase (D-DT)/MIF-2 are structurally unique proteins that are conserved across kingdoms and that share a remarkable homology with bacterial tautomerases/isomerases, albeit the relevance of the tautomerase activity in mammalian systems has remained unclear. Intriguingly, in silico analysis also predicts MIF orthologs in plants such as in the model plant Arabidopsis thaliana. There are three predicted MIF orthologs in A. thaliana, which have been termed A. thaliana MIF/D-DT-like proteins (AtMDLs). Anticipating that there will be a future research interest in studying AtMDLs or other plant MDLs, here we describe methods how to clone, recombinantly express and purify AtMDL proteins, taking into account codon usage differences between plant and mammalian cell systems.

Key words

Arabidopsis thaliana Arabidopsis thaliana MIF/D-DT-like protein (AtMDL) Chemotaxis Cross-kingdom biology Macrophage migration inhibitory factor (MIF) Innate immunity Inflammation 



This work was supported by the Deutsche Forschungsgemeinschaft (DFG)-Agence Nationale Recherche (ANR) co-funded project “X-KINGDOM-MIF - Cross-kingdom analysis of macrophage migration inhibitory factor (MIF) functions.” Respective DFG grants are BE 1977/10-1 to J.B. and PA 861/15-1 to R.P. Additional funding was provided by the DFG under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology EXC 2145 SyNergy [grant number 390857198].


  1. 1.
    Calandra T, Roger T (2003) Macrophage migration inhibitory factor: a regulator of innate immunity. Nat Rev Immunol 3(10):791–800CrossRefGoogle Scholar
  2. 2.
    Tillmann S, Bernhagen J, Noels H (2013) Arrest functions of the mif ligand/receptor axes in atherogenesis. Front Immunol 4:115CrossRefGoogle Scholar
  3. 3.
    Morand EF, Leech M, Bernhagen J (2006) MIF: a new cytokine link between rheumatoid arthritis and atherosclerosis. Nat Rev Drug Discov 5(5):399–410CrossRefGoogle Scholar
  4. 4.
    Zernecke A, Bernhagen J, Weber C (2008) Macrophage migration inhibitory factor in cardiovascular disease. Circulation 117(12):1594–1602CrossRefGoogle Scholar
  5. 5.
    Sinitski D et al (2019) Macrophage Migration inhibitory factor (MIF)-based therapeutic concepts in atherosclerosis and inflammation. Thromb Haemost. Scholar
  6. 6.
    Leng L et al (2003) MIF signal transduction initiated by binding to CD74. J Exp Med 197(11):1467–1476CrossRefGoogle Scholar
  7. 7.
    Bernhagen J et al (2007) MIF is a noncognate ligand of CXC chemokine receptors in inflammatory and atherogenic cell recruitment. Nat Med 13(5):587–596CrossRefGoogle Scholar
  8. 8.
    David JR (1966) Delayed hypersensitivity in vitro: its mediation by cell-free substances formed by lymphoid cell-antigen interaction. Proc Natl Acad Sci U S A 56(1):72–77CrossRefGoogle Scholar
  9. 9.
    Bernhagen J et al (1993) MIF is a pituitary-derived cytokine that potentiates lethal endotoxaemia. Nature 365(6448):756–759CrossRefGoogle Scholar
  10. 10.
    Merk M et al (2012) D-dopachrome tautomerase (D-DT or MIF-2): doubling the MIF cytokine family. Cytokine. Scholar
  11. 11.
    Bloom J, Sun S, Al-Abed Y (2016) MIF, a controversial cytokine: a review of structural features, challenges, and opportunities for drug development. Expert Opin Ther Targets 20(12):1463–1475CrossRefGoogle Scholar
  12. 12.
    Sun HW et al (1996) Crystal structure at 2.6-A resolution of human macrophage migration inhibitory factor. Proc Natl Acad Sci U S A 93(11):5191–5196CrossRefGoogle Scholar
  13. 13.
    Kapurniotu A, Gokce O, Bernhagen J (2019) The multitasking potential of alarmins and atypical chemokines. Front Med (Lausanne) 6:3CrossRefGoogle Scholar
  14. 14.
    Lolis E, Bucala R (2003) Macrophage migration inhibitory factor. Expert Opin Ther Targets 7(2):153–164CrossRefGoogle Scholar
  15. 15.
    Stamps SL, Fitzgerald MC, Whitman CP (1998) Characterization of the role of the amino-terminal proline in the enzymatic activity catalyzed by macrophage migration inhibitory factor. Biochemistry 37(28):10195–10202CrossRefGoogle Scholar
  16. 16.
    Taylor AB et al (1999) Crystal structure of macrophage migration inhibitory factor complexed with (E)-2-fluoro-p-hydroxycinnamate at 1.8 A resolution: implications for enzymatic catalysis and inhibition. Biochemistry 38(23):7444–7452CrossRefGoogle Scholar
  17. 17.
    Merk M et al (2011) The D-dopachrome tautomerase (DDT) gene product is a cytokine and functional homolog of macrophage migration inhibitory factor (MIF). Proc Natl Acad Sci U S A 108(34):E577–E585CrossRefGoogle Scholar
  18. 18.
    Sparkes A et al (2017) The non-mammalian MIF superfamily. Immunobiology 222(3):473–482CrossRefGoogle Scholar
  19. 19.
    Esumi N et al (1998) Conserved gene structure and genomic linkage for D-dopachrome tautomerase (DDT) and MIF. Mamm Genome 9(9):753–757CrossRefGoogle Scholar
  20. 20.
    Miska KB et al (2007) Characterisation of macrophage migration inhibitory factor from Eimeria species infectious to chickens. Mol Biochem Parasitol 151(2):173–183CrossRefGoogle Scholar
  21. 21.
    Panstruga R, Baumgarten K, Bernhagen J (2015) Phylogeny and evolution of plant macrophage migration inhibitory factor/D-dopachrome tautomerase-like proteins. BMC Evol Biol 15:64CrossRefGoogle Scholar
  22. 22.
    Angov E (2011) Codon usage: nature’s roadmap to expression and folding of proteins. Biotechnol J 6(6):650–659CrossRefGoogle Scholar
  23. 23.
    Gustafsson C, Govindarajan S, Minshull J (2004) Codon bias and heterologous protein expression. Trends Biotechnol 22(7):346–353CrossRefGoogle Scholar
  24. 24.
    Murray EE, Lotzer J, Eberle M (1989) Codon usage in plant genes. Nucleic Acids Res 17(2):477–498CrossRefGoogle Scholar
  25. 25.
    Elena C et al (2014) Expression of codon optimized genes in microbial systems: current industrial applications and perspectives. Front Microbiol 5:21CrossRefGoogle Scholar
  26. 26.
    Lee SF, Li YJ, Halperin SA (2009) Overcoming codon-usage bias in heterologous protein expression in Streptococcus gordonii. Microbiology 155(Pt 11):3581–3588CrossRefGoogle Scholar
  27. 27.
    Chin JX, Chung BK, Lee DY (2014) Codon Optimization OnLine (COOL): a web-based multi-objective optimization platform for synthetic gene design. Bioinformatics 30(15):2210–2212CrossRefGoogle Scholar
  28. 28.
    Puigbo P et al (2007) OPTIMIZER: a web server for optimizing the codon usage of DNA sequences. Nucleic Acids Res 35(Web Server issue):W126–W131CrossRefGoogle Scholar
  29. 29.
    Grote A et al (2005) JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 33(Web Server issue):W526–W531CrossRefGoogle Scholar
  30. 30.
    Burgess-Brown NA et al (2008) Codon optimization can improve expression of human genes in Escherichia coli: A multi-gene study. Protein Expr Purif 59(1):94–102CrossRefGoogle Scholar
  31. 31.
    Xue F et al (2016) Expression of codon-optimized plant glycosyltransferase UGT72B14 in Escherichia coli enhances salidroside production. Biomed Res Int 2016:9845927PubMedPubMedCentralGoogle Scholar
  32. 32.
    Zhou Z et al (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci U S A 113(41):E6117–E6125CrossRefGoogle Scholar
  33. 33.
    Boel G et al (2016) Codon influence on protein expression in E. coli correlates with mRNA levels. Nature 529(7586):358–363CrossRefGoogle Scholar
  34. 34.
    Zama M (1990) Codon usage and secondary structure of mRNA. Nucleic Acids Symp Ser 22:93–94Google Scholar
  35. 35.
    Bentele K et al (2013) Efficient translation initiation dictates codon usage at gene start. Mol Syst Biol 9:675CrossRefGoogle Scholar
  36. 36.
    Buhr F et al (2016) Synonymous codons direct cotranslational folding toward different protein conformations. Mol Cell 61(3):341–351CrossRefGoogle Scholar
  37. 37.
    Tian J et al (2017) Predicting synonymous codon usage and optimizing the heterologous gene for expression in E. coli. Sci Rep 7(1):9926CrossRefGoogle Scholar
  38. 38.
    Nieuwkoop T, Claassens NJ, van der Oost J (2019) Improved protein production and codon optimization analyses in Escherichia coli by bicistronic design. Microb Biotechnol 12(1):173–179CrossRefGoogle Scholar
  39. 39.
    Horton RM et al (1993) Gene splicing by overlap extension. Methods Enzymol 217:270–279CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Vascular Biology, Institute for Stroke and Dementia ResearchKlinikum der Universitaet Muenchen, Ludwig-Maximilians-University (LMU) MunichMunichGermany
  2. 2.Institute for Biology I, Unit of Plant Molecular Cell BiologyRWTH Aachen UniversityAachenGermany
  3. 3.Vascular Biology, Institute for Stroke and Dementia Research (ISD)Klinikum der Universitaet Muenchen, Ludwig-Maximilians-University (LMU) MunichMunichGermany
  4. 4.Munich Heart AllianceMunichGermany
  5. 5.Munich Cluster for Systems Neurology (SyNergy)MunichGermany
  6. 6.Institute for Biology I, Unit of Plant Molecular Cell BiologyRWTH Aachen UniversityAachenGermany

Personalised recommendations