Pisapia, L., Hamilton, R.S., D’Agostino, V, Pasqualea, B., Strazzullo, M., Provenzano, A., Gianfrani, C. & Del Pozzo, G. (2018) Tristetraprolin/ZFP36 regulates the turnover of autoimmune-associated HLA-DQ mRNAs. DOI:https://doi.org/10.1101/337907
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Tristetraprolin/ZFP36 regulates the turnover of autoimmune-associated HLA-DQ mRNAs

Pisapia Lauraa,‡, Hamilton S. Russellb,‡, D’Agostino Vitoc, Barba Pasqualea, Strazzullo Mariaa, Provenzano Alessandroc, Gianfrani Carmend and Del Pozzo Giovannaa,§

a Institute of Genetics and Biophysics “Adriano Buzzati Traverso” CNR, Via Pietro Castellino, 111, 80131, Naples, Italy
b Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Site, Cambridge, CB2 3DY
d Institute of Protein Biochemistry-CNR, Via Pietro Castellino, 111, 80131, Naples, Italy
Equal contribution
§ Corresponding author


Pisapia, L., Hamilton, R.S., D’Agostino, V, Pasqualea, B., Strazzullo, M., Provenzano, A., Gianfrani, C. & Del Pozzo, G. (2018) Tristetraprolin/ZFP36 regulates the turnover of autoimmune-associated HLA-DQ mRNAs bioRxiv


We have previously demonstrated that the expression of HLA class II transcripts is regulated by the binding of a ribonucleoprotein complex that affects mRNA processing. We identified protein components of a complex binding transcripts encoding the HLA-DR molecule. Here we aimed to verify if the same RNA binding proteins interact with 3’UTR of messengers encoding the HLA-DQ isotype Specifically, we focused on the HLA-DQ2.5 molecule, expressed on the surface of antigen presenting cells, and representing the main susceptibility factor for celiac disease. This molecule, encoded by DQA105 and DQB102 alleles, presents the antigenic gluten peptides to CD4+ T lymphocytes, activating the autoimmune response. Here, we identified an additional component of the RNP complex, Tristetraprolin (TTP) or ZFP36, a zinc- finger protein, widely described as a factor modulating mRNA stability. TTP shows high affinity binding to 3’UTR of CD-associated DQA105 and DQB102 alleles, in contrast to lower affinity binding to DQA101 and DQB105 non-CD associated alleles. Our in silico analysis, confirmed by molecular depletion of protein and HLA mRNA quantification, demonstrates that TTP specifically modulates the stability of the transcripts associated with celiac disease. Our work demonstrates, for the first time, that proteins of the RNP complex, affecting the processing of transcripts, interact with allele-specific transcripts.

Figure 2

RNAVienna (Lorenz et al, 2011) dot bracket notation structures were rendered into 2D images using FORNA (Kerpedjiev et al, 2015). Additional motifs were added from FOLDALIGN (Havgaard et al, 2007) and the AURichness.pl script described below.

Figure 3

To calculate the AU rich motifs in the riboprobes a stand alone Perl script takes the sequences and produces frequency tables. These tables are then inputted into the R script to produce the plots as seen in Figure 3.

The following commands run from the same directory will produce frequency tables and plots from them using R.

Generate frequency tables:

perl AURichness.pl

Example frequency table:

Sequence Dinucleotide Obs_freq Exp_freq
DQB102 AA 0.0144230769230769 0.0313408575810993
DQB102 AC 0.0673076923076923 0.0592935143426204
DQB102 AG 0.0576923076923077 0.0372702090153614

Example density table:

position DQA101_di DQA101_tri DQA101_quad DQA101_penta DQA101_hexa DQA105_di DQA105_tri DQA105_quad DQA105_penta DQA105_hexa
0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 1 0
2 0 0 0 0 0 0 0 0 0 0

Create plots from frequency tables:

Rscript AURichAnalysis.R

Example AU Rich Motif Observed Vs Expected Frequency plot


Examples AU Rich Motif Position plot



Havgaard JH, Torarinsson E, Gorodkin J. Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix. PLoS Comput Biol. 2007 Oct;3(10):1896-908. doi: 10.1371/journal.pcbi.0030193. PubMed PMID: 17937495; PubMed Central PMCID: PMCPMC2014794

Kerpedjiev P, Hammer S, Hofacker IL. Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams. Bioinformatics. 2015 Oct 15;31(20):3377-9. doi: 10.1093/bioinformatics/btv372. PubMed PMID: 26099263; PubMed Central PMCID: PMCPMC4595900

Lorenz R, Bernhart SH, Honer Zu Siederdissen C, et al. ViennaRNA Package 2.0. Algorithms Mol Biol. 2011 Nov 24;6:26. doi: 10.1186/1748-7188-6-26. PubMed PMID: 22115189; PubMed Central PMCID: PMCPMC3319429


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