hdWGCNA module hub genes.

Neutrophils, an essential innate immune cell type with a short lifespan, rely on continuous replenishment from bone marrow (BM) precursors. Although it is established that neutrophils are derived from the granulocyte-macrophage progenitor (GMP), the molecular regulators involved in the differentiation process remain poorly understood. Here we developed a random forest-based machine-learning pipeline, NeuRGI (Neutrophil Regulatory Gene Identifier), which utilized Positive-Unlabeled Learning (PU-learning) and neural network-based in silico gene knockout to identify neutrophil regulators. We interrogated features including gene expression dynamics, physiological characteristics, pathological relatedness, and gene conservation for the model training. Our identified pipeline leads to identifying Mitogen-Activated Protein Kinase-4 (MAP4K4) as a novel neutrophil differentiation regulator. The loss of MAP4K4 in hematopoietic stem cells and progenitors in mice induced neutropenia and impeded the differentiation of neutrophils in the bone marrow. By modulating the phosphorylation level of proteins involved in cell apoptosis, such as STAT5A, MAP4K4 delicately regulates cell apoptosis during the process of neutrophil differentiation. Our work presents a novel regulatory mechanism in neutrophil differentiation and provides a robust prediction model that can be applied to other cellular differentiation processes.

中性粒细胞是一类寿命较短的核心天然免疫细胞，依赖骨髓（BM）前体细胞持续补充。尽管已有研究证实中性粒细胞源自粒-巨噬细胞祖细胞（GMP），但其分化过程中的分子调控机制仍有待阐明。本研究构建了一种基于随机森林的机器学习分析流程NeuRGI（中性粒细胞调控基因识别器，Neutrophil Regulatory Gene Identifier），该流程结合正样本未标注学习（PU学习）与基于神经网络的计算机模拟基因敲除技术，用于筛选中性粒细胞调控基因。模型训练阶段，我们整合了基因表达动态、生理特征、病理相关性及基因保守性等多维度特征。通过该分析流程，我们鉴定出丝裂原活化蛋白激酶4（MAP4K4）为全新的中性粒细胞分化调控因子。小鼠造血干细胞及祖细胞中MAP4K4的缺失会引发中性粒细胞减少症，并阻滞骨髓内中性粒细胞的分化进程。MAP4K4可通过调控信号转导与转录激活因子5A（STAT5A）等细胞凋亡相关蛋白的磷酸化水平，精准调控中性粒细胞分化过程中的细胞凋亡。本研究揭示了中性粒细胞分化过程中一种全新的调控机制，并构建了可推广至其他细胞分化过程的高性能预测模型。