Journal Article

Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins

Lin, Jing-Wen, Zhou, Zhen, Liu, Defu, Mao, Bin, Linghu, Kepan, Xu, Zijie, Li, Ruihan, Zhang, Dan, Yang, Qingxin, Chen, Li, Hu, Xiao, Tang, Chao, Wang, Guihua, Lin, Yishan, Zhang, Yating, He, Zhifeng, Wang, Huiying, Chen, Lu, Song, Junwei

2025

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Animals / Apoptosis / Apoptosis - genetics / Apoptosis - physiology / Apoptotic proteins / Archives & records / Biology and Life Sciences / Bone marrow / Cell differentiation / Cell Differentiation - genetics / Cell Differentiation - physiology / Cell research / Computational Biology / Differentiation (biology) / Enzymes / Gene expression / Genomes / Hematopoietic stem cells / Hematopoietic Stem Cells - metabolism / Hemopoiesis / Humans / Immune system / Intracellular Signaling Peptides and Proteins - genetics / Intracellular Signaling Peptides and Proteins - metabolism / Kinases / Learning algorithms / Leukocytes (granulocytic) / Leukocytes (neutrophilic) / Life span / Machine Learning / Macrophages / MAP kinase / Medicine and Health Sciences / Mice / Mice, Inbred C57BL / Mice, Knockout / Mitogen-activated protein kinases / Neural networks / Neural stem cells / Neutropenia / Neutropenia - metabolism / Neutrophils / Neutrophils - cytology / Neutrophils - metabolism / Osteoprogenitor cells / Phosphorylation / Physiological aspects / Physiology / Prediction models / Progenitor cells / Protein Serine-Threonine Kinases - genetics / Protein Serine-Threonine Kinases - metabolism / Proteins / R&D / Regulatory mechanisms (biology) / Research & development / Stem cells / Umbilical cord