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Che J, Wang Y, Feng L, Gragnoli C, Griffin C, Wu R. High-order interaction modeling of tumor-microenvironment crosstalk for tumor growth. Phys Life Rev 2025; 54:11-23. [PMID: 40412053 DOI: 10.1016/j.plrev.2025.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2025] [Accepted: 05/16/2025] [Indexed: 05/27/2025]
Abstract
Signaling interactions between cancer cells and nonmalignant cells in the tumor microenvironment (TME) are believed to influence tumor progression and drug resistance. However, the genomic machineries mediating such an influence remain elusive, making it difficult to determine therapeutic targets on the tumor and its microenvironment. Here, we argue that a computational model, derived from the integration of evolutionary game theory and ecosystem theory through allometric scaling law, can chart the genomic atlas of high-order interaction networks involving tumor cells, TME, and tumor mass. We assess the application of this model to identify the causal influence of gene-induced tumor-TME crosstalk on tumor growth. The findings demonstrate that cooperation and competition between tumor cells and their infiltrating microenvironment promote or inhibit tumor growth in diverse ways. We identify specific genes that govern this promotion or inhibition, which can be used as genetic targets to alter tumor growth. This model opens up a new avenue to precisely infer the genomic underpinnings of tumor-TME interactions and their impact on tumor progression from any omics data.
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Affiliation(s)
- Jincan Che
- Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, PR China; Center for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, PR China
| | - Yu Wang
- Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, PR China
| | - Li Feng
- Center for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, PR China
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Christopher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai 200433, PR China.
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Wu S, Pan W, Dong A. idopNetwork Analysis of Salt-Responsive Transcriptomes Reveals Hub Regulatory Modules and Genes in Populus euphratica. Int J Mol Sci 2025; 26:4091. [PMID: 40362331 PMCID: PMC12071587 DOI: 10.3390/ijms26094091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2025] [Revised: 04/18/2025] [Accepted: 04/22/2025] [Indexed: 05/15/2025] Open
Abstract
Euphrates poplar (Populus euphratica) is known as a system model to study the genomic mechanisms underlying the salt resistance of woody species. To characterize how dynamic gene regulatory networks (GRNs) drive the defense response of this species to salt stress, we performed mRNA sequencing of P. euphratica roots under short-term (ST) and long-term (LT) salt stress treatments across multiple time points. Comparisons of these transcriptomes revealed the diverged gene expression patterns between the ST and LT treated samples. Based on the informative, dynamic, omnidirectional, and personalized networks model (idopNetwork), inter- and intra-module networks were constructed across different time points for both the ST and LT groups. Through the analysis of the inter-module network, we identified module 4 as the hub, containing the largest number of genes. Further analysis of the gene network within module 4 revealed that gene XM_011048240.1 had the most prominent interactions with other genes. Under short-term salt stress, gene interactions within the network were predominantly promoted, whereas under long-term stress, these interactions shifted towards inhibition. As for the gene ontology (GO) annotation of differentially expressed genes, the results suggest that P. euphratica may employ distinct response mechanisms during the early and late stages of salt stress. Taking together, these results offer valuable insights into the regulatory mechanism involved in P. euphratica's stress response, advancing our understanding of complex biological processes.
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Affiliation(s)
- Shuang Wu
- Center for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, China; (S.W.); (W.P.)
- Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Wenqi Pan
- Center for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, China; (S.W.); (W.P.)
- Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Ang Dong
- Center for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, China; (S.W.); (W.P.)
- Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
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Cui X, Song J, Li Q, Ren J. Identification of biomarkers and target drugs for melanoma: a topological and deep learning approach. Front Genet 2025; 16:1471037. [PMID: 40098976 PMCID: PMC11911340 DOI: 10.3389/fgene.2025.1471037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Accepted: 02/04/2025] [Indexed: 03/19/2025] Open
Abstract
Introduction Melanoma, a highly aggressive malignancy characterized by rapid metastasis and elevated mortality rates, predominantly originates in cutaneous tissues. While surgical interventions, immunotherapy, and targeted therapies have advanced, the prognosis for advanced-stage melanoma remains dismal. Globally, melanoma incidence continues to rise, with the United States alone reporting over 100,000 new cases and 7,000 deaths annually. Despite the exponential growth of tumor data facilitated by next-generation sequencing (NGS), current analytical approaches predominantly emphasize single-gene analyses, neglecting critical insights into complex gene interaction networks. This study aims to address this gap by systematically exploring immune gene regulatory dynamics in melanoma progression. Methods We developed a bidirectional, weighted, signed, and directed topological immune gene regulatory network to compare transcriptional landscapes between benign melanocytic nevi and cutaneous melanoma. Advanced network analysis tools were employed to identify structural disparities and functional module shifts. Key driver genes were validated through topological centrality metrics. Additionally, deep learning models were implemented to predict drug-target interactions, leveraging molecular features derived from network analyses. Results Significant topological divergences emerged between nevi and melanoma networks, with dominant functional modules transitioning from cell cycle regulation in benign lesions to DNA repair and cell migration pathways in malignant tumors. A group of genes, including AURKA, CCNE1, APEX2, and EXOC8, were identified as potential orchestrators of immune microenvironment remodeling during malignant transformation. The deep learning framework successfully predicted 23 clinically actionable drug candidates targeting these molecular drivers. Discussion The observed module shift from cell cycle to invasion-related pathways provides mechanistic insights into melanoma progression, suggesting early therapeutic targeting of DNA repair machinery might mitigate metastatic potential. The identified hub genes, particularly AURKA and DDX19B, represent novel candidates for immunomodulatory interventions. Our computational drug prediction strategy bridges molecular network analysis with clinical translation, offering a paradigm for precision oncology in melanoma. Future studies should validate these targets in preclinical models and explore network-based biomarkers for early detection.
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Affiliation(s)
- Xiwei Cui
- Research Center of Plastic Surgery Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- Key Laboratory of External Tissue and Organ Regeneration, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jipeng Song
- Comprehensive Ward of Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qingfeng Li
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jieyi Ren
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Cui Y. GLMY Homology Theory Meets idopNetwork: Dissecting Soil Microbiota Resilience Under Forest Thinning and Climate Change: Comment on "Topological change of soil microbiota networks for forest resilience under global warming" by Gong et al. Phys Life Rev 2025; 52:44-45. [PMID: 39616896 DOI: 10.1016/j.plrev.2024.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2024] [Accepted: 11/27/2024] [Indexed: 03/01/2025]
Affiliation(s)
- Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, United States.
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Sun Z, Ye J, Sun W, Jiang L, Shan B, Zhang M, Xu J, Li W, Liu J, Jing H, Zhang T, Hou M, Xie C, Wu R, Pan H, Yuan J. Cooperation of TRADD- and RIPK1-dependent cell death pathways in maintaining intestinal homeostasis. Nat Commun 2025; 16:1890. [PMID: 39987261 PMCID: PMC11846980 DOI: 10.1038/s41467-025-57211-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Accepted: 02/12/2025] [Indexed: 02/24/2025] Open
Abstract
Dysfunctional NF-κB signaling is critically involved in inflammatory bowel disease (IBD). We investigated the mechanism by which RIPK1 and TRADD, two key mediators of NF-κB signaling, in mediating intestinal pathology using TAK1 IEC deficient model. We show that phosphorylation of TRADD by TAK1 modulates RIPK1-dependent apoptosis. TRADD and RIPK1 act cooperatively to mediate cell death regulated by TNF and TLR signaling. We demonstrate the pathological evolution from RIPK1-dependent ileitis to RIPK1- and TRADD-co-dependent colitis in TAK1 IEC deficient condition. Combined RIPK1 inhibition and TRADD knockout completely protect against intestinal pathology and lethality in TAK1 IEC KO mice. Furthermore, we identify distinctive microbiota dysbiosis biomarkers for RIPK1-dependent ileitis and TRADD-dependent colitis. These findings reveal the cooperation between RIPK1 and TRADD in mediating cell death and inflammation in IBD with NF-κB deficiency and suggest the possibility of combined inhibition of RIPK1 kinase and TRADD as a new therapeutic strategy for IBD.
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Affiliation(s)
- Ziyu Sun
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, China
| | - Jianyu Ye
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, China
| | - Weimin Sun
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Libo Jiang
- School of Life Sciences and Medicine, Shandong University of Technology, Zibo, Shandong, 255000, China
| | - Bing Shan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Mengmeng Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Jingyi Xu
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, P.R. China
| | - Wanjin Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Jianping Liu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Hongyang Jing
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Tian Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Meiling Hou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Cen Xie
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, P.R. China
| | - Rongling Wu
- Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408, China
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, 100084, China
- Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai, 200433, China
| | - Heling Pan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, China
| | - Junying Yuan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 201203, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, China.
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Che J, Jin Y, Gragnoli C, Yau ST, Wu R. IdopNetwork as a genomic predictor of drug response. Drug Discov Today 2025; 30:104252. [PMID: 39603519 DOI: 10.1016/j.drudis.2024.104252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 11/13/2024] [Accepted: 11/21/2024] [Indexed: 11/29/2024]
Abstract
Despite being challenging, elucidating the systematic control mechanisms of multifactorial drug responses is crucial for pharmacogenomic research. We describe a new form of statistical mechanics to reconstruct informative, dynamic, omnidirectional, and personalized networks (idopNetworks), which cover all pharmacogenomic factors and their interconnections, interdependence, and mechanistic roles. IdopNetworks can characterize how cell-cell crosstalk is mediated by genes and proteins to shape body-drug interactions and identify key roadmaps of information flow and propagation for determining drug efficacy and toxicity. We argue that idopNetworks could potentially provide insight into the genomic machinery of drug responses and provide scientific guidance for the design of drugs whose potency is maximized at lower doses.
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Affiliation(s)
- Jincan Che
- School of Grassland Science, Beijing Forestry University, Beijing 100083, China; Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Yuebo Jin
- Department of Mathematics, Brandeis University, Waltham, MA 02453, USA
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Shing-Tung Yau
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China; Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai 200433, China
| | - Rongling Wu
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China; Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai 200433, China.
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Berg A. Causal inference from high-dimensional static data in soil microbiota networks: Comment on "Topological change of soil microbiota networks for forest resilience under global warming" by Gong et al. Phys Life Rev 2024; 51:281-282. [PMID: 39476548 DOI: 10.1016/j.plrev.2024.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 10/23/2024] [Indexed: 12/06/2024]
Affiliation(s)
- Arthur Berg
- Division of Biostatistics & Bioinformatics, Pennsylvania State University, Hershey, PA 17033, USA.
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8
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Hsiao YC, Dutta A. Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1211-1230. [PMID: 38498762 DOI: 10.1109/tcbb.2024.3378155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.
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Gong H, Wang H, Wang Y, Zhang S, Liu X, Che J, Wu S, Wu J, Sun X, Zhang S, Yau ST, Wu R. Topological change of soil microbiota networks for forest resilience under global warming. Phys Life Rev 2024; 50:228-251. [PMID: 39178631 DOI: 10.1016/j.plrev.2024.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/31/2024] [Accepted: 08/02/2024] [Indexed: 08/26/2024]
Abstract
Forest management by thinning can mitigate the detrimental impact of increasing drought caused by global warming. Growing evidence shows that the soil microbiota can coordinate the dynamic relationship between forest functions and drought intensity, but how they function as a cohesive whole remains elusive. We outline a statistical topology model to chart the roadmap of how each microbe acts and interacts with every other microbe to shape the dynamic changes of microbial communities under forest management. To demonstrate its utility, we analyze a soil microbiota data collected from a two-way longitudinal factorial experiment involving three stand densities and three levels of rainfall over a growing season in artificial plantations of a forest tree - larix (Larix kaempferi). We reconstruct the most sophisticated soil microbiota networks that code maximally informative microbial interactions and trace their dynamic trajectories across time, space, and environmental signals. By integrating GLMY homology theory, we dissect the topological architecture of these so-called omnidirectional networks and identify key microbial interaction pathways that play a pivotal role in mediating the structure and function of soil microbial communities. The statistical topological model described provides a systems tool for studying how microbial community assembly alters its structure, function and evolution under climate change.
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Affiliation(s)
- Huiying Gong
- School of Grassland Science, Beijing Forestry University, Beijing 100083, China; Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Hongxing Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yu Wang
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Shen Zhang
- Qiuzhen College, Tsinghua University, Beijing 100084, China
| | - Xiang Liu
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Jincan Che
- School of Grassland Science, Beijing Forestry University, Beijing 100083, China; Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Shuang Wu
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Jie Wu
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Xiaomei Sun
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China.
| | - Shougong Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Shing-Tung Yau
- Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; Qiuzhen College, Tsinghua University, Beijing 100084, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
| | - Rongling Wu
- School of Grassland Science, Beijing Forestry University, Beijing 100083, China; Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; Qiuzhen College, Tsinghua University, Beijing 100084, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
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Ding Y, Bajpai AK, Wu F, Lu W, Xu L, Mao J, Li Q, Pan Q, Lu L, Wang X. 5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia. Aging (Albany NY) 2024; 16:2340-2361. [PMID: 38277218 PMCID: PMC10911375 DOI: 10.18632/aging.205484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024]
Abstract
Acute myeloid leukemia (AML) is a highly heterogeneous malignant disease of the blood cell. The current therapies for AML are unsatisfactory and the molecular mechanisms underlying AML are unclear. 5-methylcytosine (m5C) is an important posttranscriptional modification of mRNA, and is involved in the regulation of mRNA stability, translation, and other aspects of RNA metabolism. However, based on our knowledge of published literature, the role of the m5C regulators has not been explored in AML till date. In this study, we clarified the expression and gene variants of m5C regulators in AML and found that most m5C regulators were differentially expressed and correlated with disease prognosis. We also found that the methylation status of certain m5C regulators (e.g., DNMT3A, DNMT3B) affects the survival of AML patients. Two m5C modification subtypes, and high- and low-risk subgroups identified based on the expression of m5C regulators showed significant differences in the prognosis as well as immune cell infiltration. In addition, most of the m5C regulators were found to be correlated with miRNA expression in AML, as well as IC50 values of many drugs. The miRNA and GSVA analysis were used to identify the different miRNAs and KEGG or hallmark pathways between high- and low-risk subgroups. We also built a prognostic model based on m5C regulators, which was validated by two GSE databases. To verify the reliability of our analysis and conclusions, qPCR was used to identify the expressions of m5C regulators between normal and AML. In summary, we comprehensively explored the molecular characteristics of m5C regulators and built a prognostic model in AML. We proposed new mechanistic insights into the role of m5C in multiple databases and clinical data, which may pave novel ways for the development of therapeutic strategies.
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Affiliation(s)
- Yuhong Ding
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Akhilesh K. Bajpai
- Department of Genetics, Genomics and Informatics University of Tennessee Health Science Cente, Memphis, TN 38163, USA
| | - Fengxia Wu
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Weihua Lu
- Department of Hematology and Oncology, The Branch Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Lin Xu
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Jiawei Mao
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Qiang Li
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Qi Pan
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
| | - Lu Lu
- Department of Genetics, Genomics and Informatics University of Tennessee Health Science Cente, Memphis, TN 38163, USA
| | - Xinfeng Wang
- Department of Hematology, The Affiliated Hospital of Nantong University, Jiangsu 226000, China
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11
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Wang Y, Sang M, Feng L, Gragnoli C, Griffin C, Wu R. A pleiotropic-epistatic entangelement model of drug response. Drug Discov Today 2023; 28:103790. [PMID: 37758020 DOI: 10.1016/j.drudis.2023.103790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/10/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.
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Affiliation(s)
- Yu Wang
- Center for Computational Biology, Beijing Forestry University, Beijing 100083, China
| | - Mengmeng Sang
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, Jiangsu 226019, China
| | - Li Feng
- Fisheries Engineering Institute, Chinese Academy of Fishery Sciences, Beijing 1000141, China
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Christopher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Center for Computational Biology, Beijing Forestry University, Beijing 100083, China; Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
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12
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Wu S, Liu X, Dong A, Gragnoli C, Griffin C, Wu J, Yau ST, Wu R. The metabolomic physics of complex diseases. Proc Natl Acad Sci U S A 2023; 120:e2308496120. [PMID: 37812720 PMCID: PMC10589719 DOI: 10.1073/pnas.2308496120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/15/2023] [Indexed: 10/11/2023] Open
Abstract
Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.
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Affiliation(s)
- Shuang Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing100083, China
| | - Xiang Liu
- Chern Institute of Mathematics, Nankai University, Tianjin300071, China
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing100083, China
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033
- Department of Medicine, Creighton University School of Medicine, Omaha, NE68124
- Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome00197, Italy
| | - Christopher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA16802
| | - Jie Wu
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
| | - Shing-Tung Yau
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
- Yau Mathematical Sciences Center, Tsinghua University, Beijing100084, China
| | - Rongling Wu
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing101408, China
- Yau Mathematical Sciences Center, Tsinghua University, Beijing100084, China
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13
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Chen C, Shen B, Zhang L, Yu T, Wang M, Wu R. A Cartographic Tool to Predict Disease Risk-associated Pseudo-Dynamic Networks from Tissue-specific Gene Expression. Bio Protoc 2023; 13:e4583. [PMID: 36789091 PMCID: PMC9901473 DOI: 10.21769/bioprotoc.4583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/05/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023] Open
Abstract
Understanding how genes are differentially expressed across tissues is key to reveal the etiology of human diseases. Genes are never expressed in isolation, but rather co-expressed in a community; thus, they co-act through intricate but well-orchestrated networks. However, existing approaches cannot coalesce the full properties of gene-gene communication and interactions into networks. In particular, the unavailability of dynamic gene expression data might impair the application of existing network models to unleash the complexity of human diseases. To address this limitation, we developed a statistical pipeline named DRDNetPro to visualize and trace how genes dynamically interact with each other across diverse tissues, to ascertain health risk from static expression data. This protocol contains detailed tutorials designed to learn a series of networks, with the illustration example from the Genotype-Tissue Expression (GTEx) project. The proposed toolbox relies on the method developed in our published paper ( Chen et al., 2022 ), coding all genes into bidirectional, signed, weighted, and feedback looped networks, which will provide profound genomic information enabling medical doctors to design precise medicine. Graphical abstract Flowchart illustrating the use of DRDNetPro. The left panel contains the summarized pipeline of DRDNetPro and the right panel contains one pseudo-illustrative example. See the Equipment and Procedure sections for detailed explanations.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, University of Maryland School of Medicine, Baltimore, MD, USA.,Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Biyi Shen
- Bristol Myers Squibb, Lawrenceville, NJ, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Tonghui Yu
- School of Mathematics, Hefei University of Technology, Anhui, China
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rongling Wu
- Division of Biostatistics and Bioinformatics, College of Medicine, Penn State College of Medicine, Hershey, PA, USA
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14
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Wang Q, Dong A, Zhao J, Wang C, Griffin C, Gragnoli C, Xue F, Wu R. Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis. Front Microbiol 2022; 13:998813. [PMID: 36338093 PMCID: PMC9631484 DOI: 10.3389/fmicb.2022.998813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/09/2022] [Indexed: 09/07/2024] Open
Abstract
Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.
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Affiliation(s)
- Qian Wang
- Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Jinshuai Zhao
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Chen Wang
- Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China
| | - Christipher Griffin
- Applied Research Laboratory, The Pennsylvania State University, State College, PA, United States
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States
- Division of Endocrinology, Department of Medicine, Creighton University School of Medicine, Omaha, NE, United States
- Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome, Italy
| | - Fengxia Xue
- Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China
| | - Rongling Wu
- Center for Statistical Genetics, Department of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA, United States
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15
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Chen C, Shen B, Ma T, Wang M, Wu R. A statistical framework for recovering pseudo-dynamic networks from static data. Bioinformatics 2022; 38:2481-2487. [PMID: 35218338 PMCID: PMC9991900 DOI: 10.1093/bioinformatics/btac038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. RESULTS We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations. AVAILABILITY AND IMPLEMENTATION DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, University of Maryland School of Medicine, Baltimore, MD 21201, USA.,Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Biyi Shen
- Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA
| | - Ming Wang
- Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
| | - Rongling Wu
- Division of Biostatistics and Bioinformatics, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
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16
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A graph model of combination therapies. Drug Discov Today 2022; 27:1210-1217. [PMID: 35143962 DOI: 10.1016/j.drudis.2022.02.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 12/31/2021] [Accepted: 02/02/2022] [Indexed: 11/24/2022]
Abstract
The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.
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17
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A Single-Cell Omics Network Model of Cell Crosstalk during the Formation of Primordial Follicles. Cells 2022; 11:cells11030332. [PMID: 35159142 PMCID: PMC8834074 DOI: 10.3390/cells11030332] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/04/2022] [Accepted: 01/11/2022] [Indexed: 12/27/2022] Open
Abstract
The fate of fetal germ cells (FGCs) in primordial follicles is largely determined by how they interact with the surrounding granulosa cells. However, the molecular mechanisms underlying this interactive process remain poorly understood. Here, we develop a computational model to characterize how individual genes program and rewire cellular crosstalk across FGCs and somas, how gene regulatory networks mediate signaling pathways that functionally link these two cell types, and how different FGCs diversify and evolve through cooperation and competition during embryo development. We analyze single-cell RNA-seq data of human female embryos using the new model, identifying previously uncharacterized mechanisms behind follicle development. The majority of genes (70%) promote FGC–soma synergism, only with a small portion (4%) that incur antagonism; hub genes function reciprocally between the FGC network and soma network; and germ cells tend to cooperate between different stages of development but compete in the same stage within a developmental embryo. Our network model could serve as a powerful tool to unravel the genomic signatures that mediate folliculogenesis from single-cell omics data.
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18
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Cao X, Dong A, Kang G, Wang X, Duan L, Hou H, Zhao T, Wu S, Liu X, Huang H, Wu R. Modeling spatial interaction networks of the gut microbiota. Gut Microbes 2022; 14:2106103. [PMID: 35921525 PMCID: PMC9351588 DOI: 10.1080/19490976.2022.2106103] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 06/03/2022] [Accepted: 07/21/2022] [Indexed: 02/04/2023] Open
Abstract
How the gut microbiota is organized across space is postulated to influence microbial succession and its mutualistic relationships with the host. The lack of dynamic or perturbed abundance data poses considerable challenges for characterizing the spatial pattern of microbial interactions. We integrate allometric scaling theory, evolutionary game theory, and prey-predator theory into a unified framework under which quasi-dynamic microbial networks can be inferred from static abundance data. We illustrate that such networks can capture the full properties of microbial interactions, including causality, the sign of the causality, strength, and feedback loop, and are dynamically adaptive along spatial gradients, and context-specific, characterizing variability between individuals and within the same individual across time and space. We design and conduct a gut microbiota study to validate the model, characterizing key spatial determinants of the microbial differences between ulcerative colitis and healthy controls. Our model provides a sophisticated means of unraveling a complete atlas of how microbial interactions vary across space and quantifying causal relationships between such spatial variability and change in health state.
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Affiliation(s)
- Xiaocang Cao
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Guangbo Kang
- School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China
| | - Xiaoli Wang
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Liyun Duan
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Huixing Hou
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Tianming Zhao
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Shuang Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Xinjuan Liu
- Department of Gastroenterology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - He Huang
- School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China
| | - Rongling Wu
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA, USA
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19
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The Genomic Physics of COVID-19 Pathogenesis and Spread. Cells 2021; 11:cells11010080. [PMID: 35011641 PMCID: PMC8750765 DOI: 10.3390/cells11010080] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/19/2021] [Accepted: 12/23/2021] [Indexed: 12/11/2022] Open
Abstract
Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.
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20
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Feng L, Jiang P, Li C, Zhao J, Dong A, Yang D, Wu R. Genetic dissection of growth trajectories in forest trees: From FunMap to FunGraph. FORESTRY RESEARCH 2021; 1:19. [PMID: 39524511 PMCID: PMC11524299 DOI: 10.48130/fr-2021-0019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2024]
Abstract
Growth is the developmental process involving important genetic components. Functional mapping (FunMap) has been used as an approach to map quantitative trait loci (QTLs) governing growth trajectories by incorporating growth equations. FunMap is based on reductionism thinking, with a power to identify a small set of significant QTLs from the whole pool of genome-wide markers. Yet, increasing evidence shows that a complex trait is controlled by all genes the organism may possibly carry. Here, we describe and demonstrate a different mapping approach that encapsulates all markers into genetic interaction networks. This approach, symbolized as FunGraph, combines functional mapping, evolutionary game theory, and prey-predator theory into mathematical graphs, allowing the observed genetic effect of a locus to be decomposed into its independent component (resulting from this locus' intrinsic capacity) and dependent component (due to extrinsic regulation by other loci). Using FunGraph, we can visualize and trace the roadmap of how each locus interact with every other locus to impact growth. In a population-based association study of Euphrates poplar, we use FunGraph to identify the previously neglected genetic interaction effects that contribute to the genetic architecture of juvenile stem growth. FunGraph could open up a novel gateway to comprehend the global genetic control mechanisms of complex traits.
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Affiliation(s)
- Li Feng
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Peng Jiang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Caifeng Li
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jinshuai Zhao
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Dengcheng Yang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA
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21
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Wang H, Ye M, Fu Y, Dong A, Zhang M, Feng L, Zhu X, Bo W, Jiang L, Griffin CH, Liang D, Wu R. Modeling genome-wide by environment interactions through omnigenic interactome networks. Cell Rep 2021; 35:109114. [PMID: 33979624 DOI: 10.1016/j.celrep.2021.109114] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/11/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022] Open
Abstract
How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.
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Affiliation(s)
- Haojie Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yaru Fu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Miaomiao Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wenhao Bo
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dan Liang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
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22
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Sun L, Jiang L, Grant CN, Wang HG, Gragnoli C, Liu Z, Wu R. Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk. Cancers (Basel) 2020; 12:cancers12082086. [PMID: 32731407 PMCID: PMC7465094 DOI: 10.3390/cancers12082086] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/24/2020] [Accepted: 07/25/2020] [Indexed: 01/03/2023] Open
Abstract
Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-risk patients by which to reconstruct large patient-specific idopNetworks. By converting these networks into risk-specific representations, we found that the shift in patients from a low to high risk or from a high to low risk might be due to the reciprocal change of hub regulators. By altering the directions of regulation exerted by these hubs, it may be possible to reduce a high risk to a low risk. Results from a holistic, systems-oriented paradigm through idopNetworks can potentially enable oncologists to experimentally identify the biomarkers of neuroblastoma and other cancers.
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Affiliation(s)
- Lidan Sun
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China; (L.S.); (L.J.)
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China; (L.S.); (L.J.)
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christa N. Grant
- Division of Pediatric Surgery, Department of Surgery, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Hong-Gang Wang
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA;
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA
- Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, 00197 Rome, Italy
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA;
- Correspondence: (Z.L.); (R.W.); Tel.: +1-717-531-0003 (Z.L.); +1-717-531-2037 (R.W.)
| | - Rongling Wu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Correspondence: (Z.L.); (R.W.); Tel.: +1-717-531-0003 (Z.L.); +1-717-531-2037 (R.W.)
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