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Gao R, Li P, Ni Y, Peng X, Ren J, Chen L. mNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes. Gut Microbes 2024; 16:2327349. [PMID: 38512768 PMCID: PMC10962612 DOI: 10.1080/19490976.2024.2327349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/04/2024] [Indexed: 03/23/2024] Open
Abstract
In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.
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Affiliation(s)
- Rong Gao
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China
- Big Data Institute, Central South university, Changsha, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China
- Longmen Laboratory, Luoyang, Henan, China
| | - Yueqiong Ni
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Xueqing Peng
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China
| | - Jing Ren
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
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Peng X, Gao R, Ren J, Lu J, Ma X, Li P. Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome. Brief Bioinform 2023; 25:bbad465. [PMID: 38189541 PMCID: PMC10772986 DOI: 10.1093/bib/bbad465] [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: 08/15/2023] [Revised: 11/15/2023] [Accepted: 11/23/2023] [Indexed: 01/09/2024] Open
Abstract
There generally exists a critical state or tipping point from a stable state to another in the development of colorectal cancer (CRC) beyond which a significant qualitative transition occurs. Gut microbiome sequencing data can be collected non-invasively from fecal samples, making it more convenient to obtain. Furthermore, intestinal microbiome sequencing data contain phylogenetic information at various levels, which can be used to reliably identify critical states, thereby providing early warning signals more accurately and effectively. Yet, pinpointing the critical states using gut microbiome data presents a formidable challenge due to the high dimension and strong noise of gut microbiome data. To address this challenge, we introduce a novel approach termed the specific network information gain (SNIG) method to detect CRC's critical states at various taxonomic levels via gut microbiome data. The numerical simulation indicates that the SNIG method is robust under different noise levels and that it is also superior to the existing methods on detecting the critical states. Moreover, utilizing SNIG on two real CRC datasets enabled us to discern the critical states preceding deterioration and to successfully identify their associated dynamic network biomarkers at different taxonomic levels. Notably, we discovered certain 'dark species' and pathways intimately linked to CRC progression. In addition, we accurately detected the tipping points on an individual dataset of type I diabetes.
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Affiliation(s)
- Xueqing Peng
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China
| | - Rong Gao
- Big Data Institute, Central South University, Changsha, China
| | - Jing Ren
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China
| | - Jianbo Lu
- National Human Genetics Resource Center, National Research Institute for Family Planning, Beijing 100081, China
| | - Xu Ma
- National Human Genetics Resource Center, National Research Institute for Family Planning, Beijing 100081, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
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Akagi K, Koizumi K, Kadowaki M, Kitajima I, Saito S. New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory. Cells 2023; 12:2297. [PMID: 37759519 PMCID: PMC10528308 DOI: 10.3390/cells12182297] [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: 08/20/2023] [Revised: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.
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Affiliation(s)
- Kazutaka Akagi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Isao Kitajima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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