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. 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. 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. 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. 目的: 胱氨酸结石约占成人肾结石的1%–2%，在儿童中可达10%，其病因为胱氨酸尿症——一种由SLC3A1和SLC7A9基因突变所致的常染色体隐性遗传病。上述突变导致胱氨酸重吸收障碍，尿中胱氨酸浓度升高，进而促进结石形成。目前临床检测手段难以精确评估不同突变对转运蛋白功能的影响。为此，本研究旨在建立一种无放射性、基于细胞的功能评估体系，以实现对胱氨酸转运蛋白活性及突变致病变异的系统鉴定。方法: 将野生型或突变型SLC3A1与SLC7A9质粒共转染至HEK293细胞，瞬时表达胱氨酸转运复合体。方法学上整合两条技术路径:其一，基于硒代胱氨酸的荧光摄取实验，利用其作为胱氨酸的功能性类似物，借助固有荧光特性实现转运活性的实时定量检测，规避传统放射性底物法的操作繁琐与辐射安全问题;其二，联合AlphaFold3进行蛋白质结构预测及分子对接，从分子层面解析突变导致功能丧失的机制。结果: SLC3A1/SLC7A9复合体对硒代胱氨酸的表观米氏常数（Km=(156.3±24.2) μmol/L)与文献报道的天然底物胱氨酸（约200 μmol/L）高度接近，验证了替代底物的可行性。依据残余活性阈值（轻度>60%;中度20%–60%;重度<20%），对8种临床相关变异体（SLC7A9:A70V、A182T、G105R、R333W、V170M、A354T、P482L;SLC3A1:M467T）进行功能分级，结果与既往放射性同位素法数据一致。AlphaFold3建模显示，P482L可能破坏跨膜区螺旋堆积，A354T则可能干扰氢键网络，为功能丧失提供了结构解释。结论: 本研究建立的集成平台结合了灵敏的荧光功能检测与AI驱动的结构分析，可实现胱氨酸尿症变异体的快速精准评估。该法无需放射性同位素，兼容常规微孔板读板设备，可嵌入现有变异解读流程，未来有潜力指导基因型个体化治疗，但尚需前瞻性临床验证。.
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