RAG genomic variation causes autoimmune diseases through specific structure-based mechanisms of enzyme dysregulation

Neshatul Haque; Tomoki Kawai; Brian D Ratnasinghe; Jessica B Wagenknecht; Raul Urrutia; Luigi D Notarangelo; Michael T Zimmermann

doi:10.1016/j.isci.2023.108040

RAG genomic variation causes autoimmune diseases through specific structure-based mechanisms of enzyme dysregulation

iScience. 2023 Sep 27;26(10):108040. doi: 10.1016/j.isci.2023.108040. eCollection 2023 Oct 20.

Authors

Neshatul Haque¹, Tomoki Kawai², Brian D Ratnasinghe¹, Jessica B Wagenknecht¹, Raul Urrutia^{1

3

4}, Luigi D Notarangelo², Michael T Zimmermann^{1

4

5}

Affiliations

¹ Bioinformatics Research and Development Laboratory, Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
² Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Disease, National Institutes of Health, Bethesda, MD 20817, USA.
³ Department of Surgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
⁴ Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
⁵ Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Abstract

Interpreting genetic changes observed in individual patients is a critical challenge. The array of immune deficiency syndromes is typically caused by genetic variation unique to individuals. Therefore, new approaches are needed to interpret functional variation and accelerate genomics interpretation. We constructed the first full-length structural model of human RAG recombinase across four functional states of the recombination process. We functionally tested 182 clinically observed RAG missense mutations. These experiments revealed dysfunction due to recombinase dysfunction and altered chromatin interactions. Structural modeling identified mechanical and energetic roles for each mutation. We built regression models for RAG1 (R² = 0.91) and RAG2 (R² = 0.97) to predict RAG activity changes. We applied our model to 711 additional RAG variants observed in population studies and identified a subset that may impair RAG function. Thus, we demonstrated a fundamental advance in the mechanistic interpretation of human genetic variations spanning from rare and undiagnosed diseases to population health.

Keywords: Genetics; Molecular modeling; Molecular structure.