Understanding of cellular ligand discrimination as a stochastic information processing system and its application for the control of immunological self/non-self discrimination

2021 Fiscal Year Final Research Report

• Project/Area Number: 18K18147
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 62010:Life, health and medical informatics-related
• Research Institution: University of Fukui (2019-2021); The University of Tokyo (2018)
• Principal Investigator: Kajita Masashi 福井大学, 学術研究院工学系部門, 助教 (40804191)
• Project Period (FY): 2018-04-01 – 2022-03-31
• Keywords: システム生物学 / 数理生物学 / 確率モデル / 化学反応ネットワーク / T細胞 / 分子認識 / 分子識別

Outline of Final Research Achievements

Immune T cells recognize antigenic peptides using receptor molecules on the cell surface, and discriminate between target and non-target ligands even though the difference is only a single amino acid substitution. This ligand recognition is conducted by a reaction system composed of a relatively small number of receptor molecules. However, little is known about the mechanism for accurate ligand discrimination under the stochastic noise due to the small number effect. In this study, we mathematically modeled and analyzed immune T cells' antigen recognition reaction systems. We clarified the reaction network structure that non-linearly amplifies the difference between the target and non-target ligands by utilizing noise. We also analyzed parameters that can control ligand discrimination accuracy and threshold of target and non-target ligands.

Free Research Field

理論免疫学

Academic Significance and Societal Importance of the Research Achievements

免疫T細胞は類似する非標的分子が多数混在するなかで、標的分子を正確に識別している。さらに抗原識別反応は比較的少数の分子からなる確率的な反応系で構成されており、確率ノイズ下で正確な分子識別を可能にするメカニズムは不明である。本研究ではT細胞抗原認識過程を数理モデル化し、T細胞が直感に反してノイズと混在する非標的分子を活用して識別精度を高めるメカニズムがありうることを理論的に明らかにした。さらに上記以外のT細胞抗原識別モデルの構築と解析についても広く行なった。本成果はT細胞を利用した治療法開発における理論的土台に発展することが期待される。