CD4⁺ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune-related diseases. CD4⁺ T cells’ metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naïve, Th1, Th2, and Th17 CD4⁺ T-cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validation, together with literature-based evidence, showed that modulation of fifty percent of identified drug targets suppressed CD4⁺ T cells, further increasing their potential impact as therapeutic interventions. Our approach can be generalized in the context of other diseases, and the metabolic models can be further used to dissect CD4⁺ T-cell metabolism.

CD4 ⁺ T细胞提供针对病原体和异常细胞的适应性免疫，它们还与各种免疫相关疾病有关。CD4 ⁺ T 细胞的代谢在这些病理中失调，代表了药物发现和开发的机会。基因组规模的代谢建模通过在模型疾病的背景下提供有关可能目标空间的高质量信息，为加速药物发现提供了机会。在这里，我们开发了 naïve、Th1、Th2 和 Th17 CD4 ^{+的基因组规模模型}用于绘制类风湿性关节炎、多发性硬化症和原发性胆汁性胆管炎中代谢紊乱的 T 细胞亚型。我们对这些模型进行计算机模拟，以对现有 FDA 批准的药物和化合物进行药物反应分析。将疾病特异性差异表达基因与对代谢扰动的反应改变进行整合，确定了三种自身免疫性疾病的 68 个药物靶点。体外实验验证以及基于文献的证据表明，调节 50% 的已识别药物靶点可抑制 CD4 ⁺ T 细胞，进一步增加其作为治疗干预的潜在影响。我们的方法可以推广到其他疾病的背景下，代谢模型可以进一步用于剖析 CD4 ⁺T细胞代谢。