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, Italyb 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
HLA class II genes encode highly polymorphic heterodimeric proteins with the function to present antigens to T cells and to stimulate a specific immune response. Many HLA genes are strongly associated to autoimmune diseases as they stimulate self-antigen specific CD4+ T cells that drives pathogenic response against proper tissues or organs. Recent evidences have demonstrated high expression of HLA class II risk genes associated to autoimmune disease influencing the strength of CD4+ T mediated autoimmune response. The expression of HLA class II genes is regulated at both transcriptional and post-transcriptional levels. We have previously identified some proteins included in a RNP complex binding the 3’UTR and affecting mRNA processing . In this work, we studied the regulation of HLA-DQ2.5 risk genes, the main susceptibility genetic factor for celiac disease (CD). The DQ2.5 molecule, encoded by HLA-DQA105 and HLA-DQB102 alleles, presents the antigenic gluten peptides to CD4+ T lymphocytes, activating the autoimmune response. Through molecular approaches we identified, the zinc-finger protein Tristetraprolin (TTP) or ZFP36, widely described as a factor modulating mRNA stability, among the components of the RNP complex. Since the 3’UTR of CD-associated HLA-DQA105 and HLA-DQB102 mRNA does not contain the canonical TTP binding consensus sequences, we performed a computational study focusing on mRNA secondary structure accessibility and stability. This in silico approach uncovered key structural differences specific to the CD-associated mRNAs allowing them to strongly interact with TTP through their 3’UTR, conferring a rapid turnover, in contrast to lower affinity binding to HLA non-CD associated mRNA.
Structures predicted using RNAVienna package (v2.4.12) (Lorenz et al, 2011) with the following command RNAfold -T 37 < <FASTA FILE>. The -T 37 flag indicated the folding temperature should be 37°C (i.e. human body temperature). Bracket notation secondary structure from RNAfold were rendered into images with selected motifs mapped onto them using FORNA (Kerpedjiev et al, 2015). Additional motifs were added from FOLDALIGN (Havgaard et al, 2007) and the AURichness.pl script as described below.
Table: download sequences and structure predictions
| Allele | Sequence | RNAFold 2D Structures |
|---|---|---|
| DQA101 | [fasta] | [bracket] [stockholm] |
| DQA105 | [fasta] | [bracket] [stockholm] |
| DQB102 | [fasta] | [bracket] [stockholm] |
| DQB105 | [fasta] | [bracket] [stockholm] |
FOLDALIGN
All pairwise combinations of alleles were run through FOLDALIGN:
| Comparison | Command | FOLDALIGN Results File |
|---|---|---|
| 3DQA101 vs 3DQA105 | foldalign 3DQA101.fasta 3DQA105.fasta > 3DQA101_3DQA105.foldalign.txt |
3DQA101_3DQA105.foldalign.txt |
| 3DQA101 vs 3DQB102 | foldalign 3DQA101.fasta 3DQB102.fasta > 3DQA101_3DQB102.foldalign.txt |
3DQA101_3DQB102.foldalign.txt |
| 3DQA101 vs 3DQB105 | foldalign 3DQA101.fasta 3DQB105.fasta > 3DQA101_3DQB105.foldalign.txt |
3DQA101_3DQB105.foldalign.txt |
| 3DQA105 vs 3DQB102 | foldalign 3DQA105.fasta 3DQB102.fasta > 3DQA105_3DQB102.foldalign.txt |
3DQA105_3DQB102.foldalign.txt |
| 3DQA105 vs 3DQB105 | foldalign 3DQA105.fasta 3DQB105.fasta > 3DQA105_3DQB105.foldalign.txt |
3DQA105_3DQB105.foldalign.txt |
| 3DQB102 vs 3DQB105 | foldalign 3DQB102.fasta 3DQB105.fasta > 3DQB102_3DQB105.foldalign.txt |
3DQB102_3DQB105.foldalign.txt |
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:
Script available (AURichness.pl)
perl AURichness.pl
Download frequency tables:
- [mononuc.table.txt]
- [dinuc.table.txt]
- [trinuc.table.txt]
- [quadnuc.table.txt]
- [pentanuc.table.txt]
- [hexanuc.table.txt]
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 |
Download Density Tables
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 |
Structure Accessibility
Calculated for each sequence and added to the AU frequency plots using RNAplfold (Lorenz et al, 2011) with a temperature of 37°C, window size of 75 and mean structure score for fragments of 10 nt (RNAplfold -T 37 -W 75 -u 10).
Plots
Create plots from frequency tables using the provided R script (AURichAnalysis_V2.R) (R v3.4.4):
Rscript AURichAnalysis_V2.R
Example AU Rich Motif Observed Vs Expected Frequency plot
Examples AU Rich Motif Position plot
Sfold was run via the web server sfold.wadsworth.org (Ding et al, 2004). Additional motifs were added from FOLDALIGN (Havgaard et al, 2007)
Pairwise sequence alignments against the mouse sequences were performed using the Needlman-Wunch global alignment algorithm EMBL-EBI API (Needleman & Wunsch, 1970; Madeira et al, 2019). Each DQ allele to mouse alignment was then manually appended and edited to optimise the alignment.
Table: download sequences and alignments
| Alleles | Sequences | Alignments |
|---|---|---|
| DQA | [fasta] | [clustal] |
| DQB | [fasta] | [clustal] |
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