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Targeting Enterobacter cloacae attenuates osteolysis by reducing ammonium in multiple myeloma

Blood 2025年第17期145卷 1876-1889页

作者： Yang, Qin Zhu, Yinghong Jian, Xingxing Qiu, Yi Zhu, Yan Zhao, Lia He, Yanjuan An, Gang Qiu, Lugui Guo, Jiaojiao He, Nihan Abudumijiti, Huerxidan Hu, Cong Chen, Xun Huang, Siqing Feng, Xiangling Li, Xin Liu, Jing Xu, Yajing Zhou, Wen State Key Laboratory of Experimental Hematology Bioinformatics Center National Clinical Research Center for Geriatric Disorders Key Laboratory for Carcinogenesis and Invasion Chinese Ministry of Education Key Laboratory of Carcinogenesis Chinese Ministry of Health Furong Laboratory Department of Hematology Xiangya Hospital Central South University Hunan Changsha China Cancer Research Institute School of Basic Medical Sciences Central South University Hunan Changsha China State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology and Blood Diseases Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Tianjin China Center for Preventive Medicine Experiment Xiangya School of Public Health Central South University Hunan Changsha China Department of Hematology the Third Xiangya Hospital Central South University Hunan Changsha China

Multiple myeloma (MM)-induced bone disease affects not only patients’ quality of life but also their overall survival. Our previous work demonstrated that the gut microbiome plays a crucial role in MM progression and drug resistance. However, the role of altered gut microbiota in MM bone disease remains unclear. In this study, we show that intestinal Enterobacter cloacae is significantly enriched in patients with MM with osteolysis. Through fecal microbial transplantation and single bacterial colonization experiments in a 5TGM1 MM mouse model, we found that intestinal colonization of E cloacae promotes osteolysis by increasing circulating ammonium levels. Elevated ammonium promotes osteoclastogenesis by increasing Trap protein levels in osteoclast precursors and by acetylating and stabilizing chemokine ligand 3 protein in MM cells. Inhibition of ammonium synthesis, using E cloacae with a deleted dcd gene, along with probiotic supplementation, alleviated osteolysis in MM. Overall, our work suggests that E cloacae promotes osteolysis in MM by synthesizing ammonium. This establishes a novel mechanism and potential intervention strategy for managing MM with osteolysis. © 2025 American Society of Hematology

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Cancer type and survival prediction based on transcriptomic feature map

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Computers in Biology and Medicine 2025年第PT A期192卷 110267-110267页

作者： Yan, Ming Dong, Zirou Zhu, Zhaopo Qiao, Chengliang Wang, Meizhi Teng, Zhixia Xing, Yongqiang Liu, Guojun Liu, Guoqing Cai, Lu Meng, Hu Inner Mongolia Key Laboratory of Life Health and Bioinformatics College of Life Science and Technology Inner Mongolia University of Science and Technology Baotou014010 China College of Information and Computer Engineering Northeast Forestry University Harbin China Center for Medical Genetics & Hunan Key Laboratory School of Life Sciences Central South University Huna Changsha410008 China

This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network and gated convolutional modules yielded a pan-cancer classification accuracy of 91.8 %. Additionally, by extracting 31 differential genes from different cancer feature maps, an interaction network diagram was drawn, identifying two key genes, ANXA5 and ACTB. These genes are potential biomarkers related to cancer progression, angiogenesis, metastasis, and treatment resistance. Survival prediction analysis on 10 cancer types, combined with feature maps and data amplification, cancer survival prediction accuracy reached from 0.75 to 0.91. This transcriptomic feature map provides a novel approach for cancer omics analysis, to facilitate personalized treatments and reflecting individual differences. © 2025 The Authors

关键词： Deep learning

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MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 469-480页

作者： Ming Chen Yunhan Pan Xiujuan Lei Chunyan Ji Yinglong Dai Yi Pan Department of Artificial Intelligence Hunan Normal University Changsha Hunan China School of Computer Science Shaanxi Normal University Xi’an China Computer Science Department BNU-HKBU United International College Zhuhai China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations bioinformatics Sodium Market research Graph convolutional networks Computer science Computational modeling

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Ten computational challenges in human virome studies

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Virologica Sinica 2024年第6期39卷 845-850页

作者： Yifan Wu Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China

In recent years,substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and *** this progress,the field of human virome research remains nascent,primarily hindered by the lack of effective methods,particularly in the domain of computational *** perspective systematically outlines ten computational challenges spanning various types of virome *** challenges arise due to the vast diversity of viromes,the absence of a universal marker gene in viral genomes,the low abundance of virus populations,the remote or minimal homology of viral proteins to known proteins,and the highly dynamic and heterogeneous nature of *** each computational challenge,we discuss the underlying reasons,current research progress,and potential *** resolution of these challenges necessitates ongoing collaboration among computational scientists,virologists,and multidisciplinary *** essence,this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.

关键词： Human virome Computational biology Computational viromics Artificial intelligence

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Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

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Virologica Sinica 2022年第3期37卷 437-444页

作者： Huiting Chen Zhaozhong Zhu Ye Qiu Xingyi Ge Heping Zheng Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha410082China

The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune ***,there is still a lack of effective tools for determining the cleavage sites of the 3CL *** study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and *** residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in *** RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus *** achieved an AUC of 0.95 and predicted correctly 80%of the cleavage ***,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF *** proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and ***,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF ***,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.

关键词： Coronavirus 3C-like protease Cleavage sites Machine-learning algorithms 3CLP webserver

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Computational Viromics: Applications of the Computational Biology in Viromics Studies

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Virologica Sinica 2021年第5期36卷 1256-1260页

作者： Congyu Lu Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China

Viruses are a kind of biological entities which rely on host cells for *** on the genetic materials and replication mode,they can be grouped into double-stranded DNA(dsDNA),single-stranded DNA(ssDNA),doublestranded RNA(dsRNA),positive-sense single-stranded RNA(+ssRNA),negative-sense single-stranded RNA(-ssRNA),ssRNA reverse transcriptase viruses(ssRNART)and dsDNA reverse transcriptase viruses(dsDNA-RT)(Walker et al.2020).Viruses can infect most kinds of biological entities,including viruses,bacteria,archaea and eukaryote(La Scola et al.2008;Fermin,2018).

关键词： al. dsDNA stranded

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Curation,nomenclature,and topological classification of receptor-like kinases from 528 plant species for novel domain discovery and functional inference

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Molecular Plant 2024年第4期17卷 658-671页

作者： Qian Liu Qiong Fu Yujie Yan Qian Jiang Longfei Mao Long Wang Feng Yu Heping Zheng State Key Laboratory of Chemo/Biosensing and Chemometrics Hunan University College of BiologyChangshaHunan 410082China Bioinformatics Center Hunan University College of BiologyChangshaHunan 410082China

Receptor-like kinases(RLKs)are the most numerous signal transduction components in plants and play important roles in determining how different plants adapt to their ecological *** on RLKs has focused mainly on a small number of typical RLK members in a few model *** is an urgent need to study the composition,distribution,and evolution of RLKs at the holistic level to increase our understanding of how RLKs assist in the ecological adaptations of different plant *** this study,we collected the genome assemblies of 528 plant species and constructed an RLK *** this dataset,we identified and characterized 524948 RLK family *** member underwent systematic topological classification and was assigned a gene ID based on a unified nomenclature ***,we identified two novel extracellular domains in some RLKs,designated Xiao and *** analysis of the RLK family revealed that the RLCK-XVII and RLCK-XII-2 classes were present exclusively in dicots,suggesting that diversification of RLKs between monocots and dicots may have led to differences in downstream cytoplasmic *** also used an interaction proteome to help empower data mining for inference of new RLK functions from a global perspective,with the ultimate goal of understanding how RLKs shape the adaptation of different plants to the environments/*** assembled RLK dataset,together with annotations and analytical tools,forms an integrated foundation of multiomics data that is publicly accessible via the metaRLK web portal(http://***).

关键词： RLK RLCK domain signaling pathway receptor-protein interactions

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Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023)

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Virologica Sinica 2025年第2期40卷 288-291页

作者： Yousong Peng Lei Yang Weijuan Huang Mi Liu Xiao Ding Xiangjun Du Yuelong Shu Taijiao Jiang Dayan Wang Bioinformatics Center College of Biology Hunan Provincial Key Laboratory of Medical Virology Hunan University Changsha 410082 China. National Institute for Viral Disease Control and Prevention China CDC Beijing 102206 China. Jiangsu Institute of Clinical Immunology The First Affiliated Hospital of Soochow University Suzhou 215006 China. Suzhou Institute of Systems Medicine Suzhou 215123 China. Department of Biostatistics and Systems Biology School of Public Health (Shenzhen) Sun Yat-sen University Shenzhen Guangdong 510275 China. Key Laboratory of Pathogen Infection Prevention and Control (MOE) State Key Laboratory of Respiratory Health and Multimorbidity National Institute of Pathogen Biology Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 102629 China. Electronic address: yshu@***. Guangzhou National Laboratory Guangzhou 510005 China. Electronic address: jiang_taijiao@***. National Institute for Viral Disease Control and Prevention China CDC Beijing 102206 China. Electronic address: dayanwang@***.

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EVIHVR: A platform for analysis of expression, variation and identification of human virus receptors

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Infectious Medicine 2022年第1期1卷 59-62页

作者： Zheng Zhang Zena Cai Longfei Mao Xing-Yi Ge Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangshaChina

Motivation:Virus receptors are presented on the cell surfaces of a host and are key for viral infection of host ***,no unified resource for the study of viral receptors is currently ***:To address this problem,we built EVIHVR,a platform for analyzing the expression and variation,and for the identification of human virus *** provides three functions:(1)Receptor expression function for browsing and analyzing the expression of human virus receptors in various human tissues/cells;(2)Receptor gene polymorphism function for analyzing the genetic polymorphism of human virus receptors in different human populations and human tissues;and(3)Predict receptor function for identifying potential virus receptors based on differential expression *** can become a useful tool for the analysis and identification of human virus receptors.

关键词： Virus receptor Genetic polymorphism Receptor identification Human-infecting virus Web services

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keyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary keyword Sub-Prompt

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Big Data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce keyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,keyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that keyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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