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

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 differentiati...

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Main Authors: Guihua Wang, Dan Zhang, Zhifeng He, Bin Mao, Xiao Hu, Li Chen, Qingxin Yang, Zhen Zhou, Yating Zhang, Kepan Linghu, Chao Tang, Zijie Xu, Defu Liu, Junwei Song, Huiying Wang, Yishan Lin, Ruihan Li, Jing-Wen Lin, Lu Chen
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-03-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1012877
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author Guihua Wang
Dan Zhang
Zhifeng He
Bin Mao
Xiao Hu
Li Chen
Qingxin Yang
Zhen Zhou
Yating Zhang
Kepan Linghu
Chao Tang
Zijie Xu
Defu Liu
Junwei Song
Huiying Wang
Yishan Lin
Ruihan Li
Jing-Wen Lin
Lu Chen
author_facet Guihua Wang
Dan Zhang
Zhifeng He
Bin Mao
Xiao Hu
Li Chen
Qingxin Yang
Zhen Zhou
Yating Zhang
Kepan Linghu
Chao Tang
Zijie Xu
Defu Liu
Junwei Song
Huiying Wang
Yishan Lin
Ruihan Li
Jing-Wen Lin
Lu Chen
author_sort Guihua Wang
collection DOAJ
description 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.
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language English
publishDate 2025-03-01
publisher Public Library of Science (PLoS)
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spelling doaj-art-24fe1ecdbaee43e7a3f6ebca39f7eb3f2025-08-20T01:55:22ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-03-01213e101287710.1371/journal.pcbi.1012877Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.Guihua WangDan ZhangZhifeng HeBin MaoXiao HuLi ChenQingxin YangZhen ZhouYating ZhangKepan LinghuChao TangZijie XuDefu LiuJunwei SongHuiying WangYishan LinRuihan LiJing-Wen LinLu ChenNeutrophils, 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.https://doi.org/10.1371/journal.pcbi.1012877
spellingShingle Guihua Wang
Dan Zhang
Zhifeng He
Bin Mao
Xiao Hu
Li Chen
Qingxin Yang
Zhen Zhou
Yating Zhang
Kepan Linghu
Chao Tang
Zijie Xu
Defu Liu
Junwei Song
Huiying Wang
Yishan Lin
Ruihan Li
Jing-Wen Lin
Lu Chen
Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.
PLoS Computational Biology
title Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.
title_full Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.
title_fullStr Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.
title_full_unstemmed Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.
title_short Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.
title_sort machine learning based prediction reveals kinase map4k4 regulates neutrophil differentiation through phosphorylating apoptosis related proteins
url https://doi.org/10.1371/journal.pcbi.1012877
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