Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls
The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial en...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2019-07-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/7315.pdf |
| _version_ | 1797419496608104448 |
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| author | Dongmei Ai Hongfei Pan Xiaoxin Li Min Wu Li C. Xia |
| author_facet | Dongmei Ai Hongfei Pan Xiaoxin Li Min Wu Li C. Xia |
| author_sort | Dongmei Ai |
| collection | DOAJ |
| description | The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research. |
| first_indexed | 2024-03-09T06:49:10Z |
| format | Article |
| id | doaj.art-4fa3f4bf9a8a4a358a462abfa4838f16 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:49:10Z |
| publishDate | 2019-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-4fa3f4bf9a8a4a358a462abfa4838f162023-12-03T10:31:00ZengPeerJ Inc.PeerJ2167-83592019-07-017e731510.7717/peerj.7315Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controlsDongmei Ai0Hongfei Pan1Xiaoxin Li2Min Wu3Li C. Xia4Basic Experimental Center for Natural Science, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, ChinaDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USAThe human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.https://peerj.com/articles/7315.pdfHuman gut microbiomeColorectal cancerTopological analysisHUMAnN2Enzymatic |
| spellingShingle | Dongmei Ai Hongfei Pan Xiaoxin Li Min Wu Li C. Xia Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls PeerJ Human gut microbiome Colorectal cancer Topological analysis HUMAnN2 Enzymatic |
| title | Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls |
| title_full | Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls |
| title_fullStr | Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls |
| title_full_unstemmed | Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls |
| title_short | Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls |
| title_sort | association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls |
| topic | Human gut microbiome Colorectal cancer Topological analysis HUMAnN2 Enzymatic |
| url | https://peerj.com/articles/7315.pdf |
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