Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio 48160, Spain.
Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg.
Sci Adv. 2021 Feb 3;7(6). doi: 10.1126/sciadv.abe5735. Print 2021 Feb.
Dysregulations in the inflammatory response of the body to pathogens could progress toward a hyperinflammatory condition amplified by positive feedback loops and associated with increased severity and mortality. Hence, there is a need for identifying therapeutic targets to modulate this pathological immune response. Here, we propose a single cell-based computational methodology for predicting proteins to modulate the dysregulated inflammatory response based on the reconstruction and analysis of functional cell-cell communication networks of physiological and pathological conditions. We validated the proposed method in 12 human disease datasets and performed an in-depth study of patients with mild and severe symptomatology of the coronavirus disease 2019 for predicting novel therapeutic targets. As a result, we identified the extracellular matrix protein versican and Toll-like receptor 2 as potential targets for modulating the inflammatory response. In summary, the proposed method can be of great utility in systematically identifying therapeutic targets for modulating pathological immune responses.
机体对病原体的炎症反应失调可能会发展为一种过度炎症状态,这种状态会被正反馈回路放大,并与严重程度和死亡率的增加相关。因此,有必要确定治疗靶点来调节这种病理性免疫反应。在这里,我们提出了一种基于生理和病理条件下功能细胞间通讯网络的重建和分析来预测调节失调的炎症反应的蛋白质的单细胞计算方法。我们在 12 个人类疾病数据集上验证了所提出的方法,并对 2019 年冠状病毒病轻度和重度症状的患者进行了深入研究,以预测新的治疗靶点。结果表明,细胞外基质蛋白 versican 和 Toll 样受体 2 可能是调节炎症反应的潜在靶点。总之,该方法可用于系统地识别调节病理性免疫反应的治疗靶点。