Functional characterization of 105 factor H variants associated with aHUS: lessons for variant classification

Héctor Martín Merinero(Consejo Superior de Investigaciones Científicas), Yuzhou Zhang(University of Iowa), Emilia Arjona(Consejo Superior de Investigaciones Científicas), Guillermo del Angel(Alexion Pharmaceuticals (United States)), Renee X. Goodfellow(University of Iowa), Elena Gómez-Rubio(Consejo Superior de Investigaciones Científicas), Rui‐Ru Ji(Alexion Pharmaceuticals (United States)), Malkoa Michelena(Consejo Superior de Investigaciones Científicas), Richard J. Smith(University of Iowa), Santiago Rodrı́guez de Córdoba(Consejo Superior de Investigaciones Científicas)
Blood
June 30, 2021
Cited by 57Open Access
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Abstract

Atypical hemolytic uremic syndrome (aHUS) is a life-threatening thrombotic microangiopathy that can progress, when untreated, to end-stage renal disease. Most frequently, aHUS is caused by complement dysregulation due to pathogenic variants in genes that encode complement components and regulators. Among these genes, the factor H (FH) gene, CFH, presents with the highest frequency (15% to 20%) of variants and is associated with the poorest prognosis. Correct classification of CFH variants as pathogenic or benign is essential to clinical care but remains challenging owing to the dearth of functional studies. As a result, significant numbers of variants are reported as variants of uncertain significance. To address this knowledge gap, we expressed and functionally characterized 105 aHUS-associated FH variants. All FH variants were categorized as pathogenic or benign and, for each, we fully documented the nature of the pathogenicity. Twenty-six previously characterized FH variants were used as controls to validate and confirm the robustness of the functional assays used. Of the remaining 79 uncharacterized variants, only 29 (36.7%) alter FH expression or function in vitro and, therefore, are proposed to be pathogenic. We show that rarity in control databases is not informative for variant classification, and we identify important limitations in applying prediction algorithms to FH variants. Based on structural and functional data, we suggest ways to circumvent these difficulties and, thereby, improve variant classification. Our work highlights the need for functional assays to interpret FH variants accurately if clinical care of patients with aHUS is to be individualized and optimized.


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