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Zhang H, Chong H, Yu Q, Zha Y, Cheng M, Ning K. Tracing human life trajectory using gut microbial communities by context-aware deep learning. Brief Bioinform 2023; 24:6984796. [PMID: 36631408 DOI: 10.1093/bib/bbac629] [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: 10/25/2022] [Revised: 12/15/2022] [Accepted: 12/25/2022] [Indexed: 01/13/2023] Open
Abstract
The gut microbial communities are highly plastic throughout life, and the human gut microbial communities show spatial-temporal dynamic patterns at different life stages. However, the underlying association between gut microbial communities and time-related factors remains unclear. The lack of context-awareness, insufficient data, and the existence of batch effect are the three major issues, making the life trajection of the host based on gut microbial communities problematic. Here, we used a novel computational approach (microDELTA, microbial-based deep life trajectory) to track longitudinal human gut microbial communities' alterations, which employs transfer learning for context-aware mining of gut microbial community dynamics at different life stages. Using an infant cohort, we demonstrated that microDELTA outperformed Neural Network for accurately predicting the age of infant with different delivery mode, especially for newborn infants of vaginal delivery with the area under the receiver operating characteristic curve of microDELTA and Neural Network at 0.811 and 0.436, respectively. In this context, we have discovered the influence of delivery mode on infant gut microbial communities. Along the human lifespan, we also applied microDELTA to a Chinese traveler cohort, a Hadza hunter-gatherer cohort and an elderly cohort. Results revealed the association between long-term dietary shifts during travel and adult gut microbial communities, the seasonal cycling of gut microbial communities for the Hadza hunter-gatherers, and the distinctive microbial pattern of elderly gut microbial communities. In summary, microDELTA can largely solve the issues in tracing the life trajectory of the human microbial communities and generate accurate and flexible models for a broad spectrum of microbial-based longitudinal researches.
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Affiliation(s)
- Haohong Zhang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Hui Chong
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Qingyang Yu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yuguo Zha
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Mingyue Cheng
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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Zha Y, Chong H, Yang P, Ning K. Microbial Dark Matter: from Discovery to Applications. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:867-881. [PMID: 35477055 PMCID: PMC10025686 DOI: 10.1016/j.gpb.2022.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/28/2021] [Accepted: 03/22/2022] [Indexed: 01/12/2023]
Abstract
With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.
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Affiliation(s)
- Yuguo Zha
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Chong
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Pengshuo Yang
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Kang Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
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Chong H, Zha Y, Yu Q, Cheng M, Xiong G, Wang N, Huang X, Huang S, Sun C, Wu S, Chen WH, Coelho LP, Ning K. EXPERT: transfer learning-enabled context-aware microbial community classification. Brief Bioinform 2022; 23:6702669. [PMID: 36124759 PMCID: PMC9677468 DOI: 10.1093/bib/bbac396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/08/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022] Open
Abstract
Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.
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Affiliation(s)
| | | | | | - Mingyue Cheng
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Guangzhou Xiong
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Nan Wang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Xinhe Huang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Shijuan Huang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Chuqing Sun
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Sicheng Wu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Kang Ning
- Corresponding author: Kang Ning, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China. E-mail:
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