PDF(Pubmed)
Abstract：
The innate immune system is the body\'s first line of defense against infection. Natural killer (NK) cells, a vital part of the innate immune system, help to control infection and eliminate cancer. Studies have identified a vast array of receptors that NK cells use to discriminate between healthy and unhealthy cells. However, at present, it is difficult to explain how NK cells will respond to novel stimuli in different environments. In addition, the expression of different receptors on individual NK cells is highly stochastic, but the reason for these variegated expression patterns is unclear. Here, we studied the recognition of unhealthy target cells as an inference problem, where NK cells must distinguish between healthy targets with normal variability in ligand expression and ones that are clear \"outliers.\" Our mathematical model fits well with experimental data, including NK cells\' adaptation to changing environments and responses to different target cells. Furthermore, we find that stochastic, \"sparse\" receptor expression profiles are best able to detect a variety of possible threats, in agreement with experimental studies of the NK cell repertoire. While our study was specifically motivated by NK cells, our model is general and could also apply more broadly to explain principles of target recognition for other immune cell types.
摘要：
先天免疫系统是机体抵御感染的第一道防线。自然杀伤（NK）细胞，先天免疫系统的重要组成部分，有助于控制感染和消除癌症。研究已经确定了NK细胞用来区分健康和不健康细胞的大量受体。然而,目前,很难解释NK细胞在不同环境中对新刺激的反应。此外,不同受体在单个NK细胞上的表达是高度随机的，但这些多样化表达模式的原因尚不清楚。这里,我们研究了识别不健康的靶细胞作为一个推理问题，其中NK细胞必须区分具有正常配体表达变异性的健康靶标和明显的“异常值”。“我们的数学模型与实验数据吻合得很好，包括NK细胞适应变化的环境和对不同靶细胞的反应。此外，我们发现随机的，“稀疏”受体表达谱最能够检测各种可能的威胁，与NK细胞库的实验研究一致。虽然我们的研究是由NK细胞引起的，我们的模型是通用的，也可以更广泛地应用于解释其他免疫细胞类型的靶标识别原理。
