Journal of Zhejiang University. Science. B · 2026
Selenocystine-based fluorescence assay combined with structural prediction for functional analysis of cystine transporter variants in cystinuria.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
He Xiaobai, Qian Xinyi, Zheng Xiaoguang, Zhang Hong, Shao Jinbang, Chen Xiaopan, Ruan Qi, Lyu Jianxin, Yang Leixiang, Chen Linjie
Аннотация
OBJECTIVES: Cystine stones account for 1%‒2% of adult and up to 10% of pediatric kidney stones. They result from cystinuria, an autosomal recessive disorder caused by mutations in solute carrier family 3 member 1 (SLC3A1) and SLC7A9, which encode the renal cystine transporter subunits. These mutations impair cystine reabsorption, raising urinary cystine levels and driving stone formation. Current diagnostic options remain limited in terms of detecting molecular dysfunctions. Thus, we aimed to develop a nonradioactive, cell-based method for the functional assessment of cystine transporters and mutation-specific pathologies.
METHODS: Using human embryonic kidney 293 (HEK293) cells transiently co-expressing wild-type or mutant SLC3A1 and SLC7A9, we developed an integrated approach that combined a selenocystine-based fluorescence uptake assay with AlphaFold3-based structural predictions to rapidly and accurately assess cystine transporter function and the molecular impact of genetic mutations.
RESULTS: The affinity of the SLC3A1/SLC7A9 complex was comparably apparent for selenocystine (Michaelis constant Km=(156.3±24.2) μmol/L) and cystine (literature Km approximately 200 μmol/L). Using operational thresholds (mild >60%, moderate 20%‒60%, severe <20% residual activity), the assay differentiated the functional impacts of eight clinically characterized variants, including SLC7A9 A70V, A182T, G105R, R333W, V170M, A354T, and P482L, and SLC3A1 M467T, with categorical assignments consistent with previously published radioisotope-based functional data. AlphaFold3 modeling, combined with molecular docking, provides mechanistic interpretations of the dysfunction observed in the P482L and A354T mutants.
CONCLUSIONS: The integrated approach employed in this work, which combines a sensitive selenocystine fluorescence assay with artificial intelligence (AI)-powered structural analysis, enables the rapid, precise diagnosis of cystinuria variants. This platform is compatible with standard microplate-reader infrastructure and offers potential utility in variant-interpretation pipelines and future genotype-guided therapeutic decision-making, pending prospective clinical validation.
METHODS: Using human embryonic kidney 293 (HEK293) cells transiently co-expressing wild-type or mutant SLC3A1 and SLC7A9, we developed an integrated approach that combined a selenocystine-based fluorescence uptake assay with AlphaFold3-based structural predictions to rapidly and accurately assess cystine transporter function and the molecular impact of genetic mutations.
RESULTS: The affinity of the SLC3A1/SLC7A9 complex was comparably apparent for selenocystine (Michaelis constant Km=(156.3±24.2) μmol/L) and cystine (literature Km approximately 200 μmol/L). Using operational thresholds (mild >60%, moderate 20%‒60%, severe <20% residual activity), the assay differentiated the functional impacts of eight clinically characterized variants, including SLC7A9 A70V, A182T, G105R, R333W, V170M, A354T, and P482L, and SLC3A1 M467T, with categorical assignments consistent with previously published radioisotope-based functional data. AlphaFold3 modeling, combined with molecular docking, provides mechanistic interpretations of the dysfunction observed in the P482L and A354T mutants.
CONCLUSIONS: The integrated approach employed in this work, which combines a sensitive selenocystine fluorescence assay with artificial intelligence (AI)-powered structural analysis, enables the rapid, precise diagnosis of cystinuria variants. This platform is compatible with standard microplate-reader infrastructure and offers potential utility in variant-interpretation pipelines and future genotype-guided therapeutic decision-making, pending prospective clinical validation.