Innovate therapeutic targets for autoimmune diseases: insights from proteome-wide mendelian randomization and Bayesian colocalization

Abstract

Background

Despite growing knowledge regarding the pathogenesis of autoimmune diseases (ADs) onset, the current treatment remains unsatisfactory. This study aimed to identify innovative therapeutic targets for ADs through various analytical approaches.

Research design and methods

Utilizing Mendelian randomization, Bayesian co-localization, phenotype scanning, and protein–protein interaction network, we explored potential therapeutic targets for 14 ADs and externally validated our preliminary findings.

Results

This study identified 12 circulating proteins as potential therapeutic targets for six ADs. Specifically, IL12B was judged to be a risk factor for ankylosing spondylitis (p = 1.61E − 07). TYMP (p = 6.28E − 06) was identified as a protective factor for ulcerative colitis. For Crohn’s disease, ERAP2 (p = 4.47E − 14), HP (p = 2.08E − 05), and RSPO3 (p = 6.52E − 07), were identified as facilitators, whereas FLRT3 (p = 3.42E − 07) had a protective effect. In rheumatoid arthritis, SWAP70 (p = 3.26E − 10), SIGLEC6 (p = 2.47E − 05), ISG15 (p = 3.69E − 05), and FCRL3 (p = 1.10E − 10) were identified as risk factors. B4GALT1 (p = 6.59E − 05) was associated with a lower risk of Type 1 diabetes (T1D). Interestingly, CTSH was identified as a protective factor for narcolepsy (p = 1.58E − 09) but a risk factor for T1D (p = 7.36E − 11), respectively. External validation supported the associations of eight of these proteins with three ADs.

Conclusions

Our integrated study identified 12 potential therapeutic targets for ADs and provided novel insights into future drug development for ADs.

Keywords:

• Autoimmune diseases
• plasma proteome
• therapeutic target
• Mendelian randomization
• Bayesian co-localization analysis

Acknowledgments

We would like to appreciate the participants and researchers of the IEU OpenGWAS project and FinnGen study. In addition, we want to acknowledge Figdraw (https://www.figdraw.com) for their assistance in Figure 1.

Author’s contributions

PS and QBJ designed the study. QBJ and FHR performed the computations and manuscript writing. All authors contributed to the article and approved the submitted version. QBJ and FHR contributed equally to this work and share first authorship.

Ethics approval and consent to participate

The GWAS data employed in the present research are publicly available on the database website. The specific ethical approval was expressed in the original GWAS article, so further ethical approval is not required.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Summary statistics of ADs can be downloaded from IEU consortium (https://gwas.mrcieu.ac.uk/) and the GWAS ID was presented in Supplementary Table 1. The pQTL statistics of plasma proteome are publicly available on the Decode database (https://download.decode.is/form/folder/proteomics).

Further inquiries can be directed to the corresponding author.

Additional information

Funding

This manuscript was funded by National Natural Science Foundation of China (82074448).