101
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Ruotsalainen AL, Tejesvi MV, Vänni P, Suokas M, Tossavainen P, Pirttilä AM, Talvensaari-Mattila A, Nissi R. Child type 1 diabetes associated with mother vaginal bacteriome and mycobiome. Med Microbiol Immunol 2022; 211:185-194. [PMID: 35701558 PMCID: PMC9304052 DOI: 10.1007/s00430-022-00741-w] [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: 03/10/2022] [Accepted: 05/18/2022] [Indexed: 10/27/2022]
Abstract
Mother vaginal microbes contribute to microbiome of vaginally delivered neonates. Child microbiome can be associated with autoimmune diseases, such as type 1 diabetes (T1D). We collected vaginal DNA samples from 25 mothers with a vaginally delivered child diagnosed with T1D and samples from 24 control mothers who had vaginally delivered a healthy child and analyzed bacteriome and mycobiome of the samples. The total DNA of the samples was extracted, and ribosomal DNA regions (16S for bacteria, ITS2 for fungi) were amplified, followed by next-generation sequencing and machine learning. We found that alpha-diversity of bacteriome was increased (P < 0.002), whereas alpha-diversity of mycobiome was decreased (P < 0.001) in mothers with a diabetic child compared to the control mothers. Beta-diversity analysis suggested differences in mycobiomes between the mother groups (P = 0.001). Random forest models were able to effectively predict diabetes and control status of unknown samples (bacteria: 0.86 AUC, fungi: 0.96 AUC). Our data indicate several fungal genera and bacterial metabolic pathways of mother vaginal microbiome to be associated with child T1D. We suggest that early onset of T1D in a child has a relationship with altered mother vaginal microbiome and that both bacteriome and mycobiome contribute to this shift.
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Affiliation(s)
- A L Ruotsalainen
- Department of Ecology and Genetics, University of Oulu, POB 3000, 90014, Oulu, Finland.
| | - M V Tejesvi
- Department of Ecology and Genetics, University of Oulu, POB 3000, 90014, Oulu, Finland.,Genobiomics LLC, Oulu, Finland
| | - P Vänni
- Genobiomics LLC, Oulu, Finland
| | - M Suokas
- Department of Ecology and Genetics, University of Oulu, POB 3000, 90014, Oulu, Finland.,Biocenter Oulu Sequencing Center, University of Oulu, POB 8000, 90014, Oulu, Finland
| | - P Tossavainen
- Department of Pediatrics, PEDEGO Research Unit and Medical Research Center, University of Oulu and Oulu University Hospital, PO Box 23, 90029 OYS, Oulu, Finland
| | - A M Pirttilä
- Department of Ecology and Genetics, University of Oulu, POB 3000, 90014, Oulu, Finland
| | - A Talvensaari-Mattila
- Department of Obstetrics and Gynecology, University of Oulu, PL 23, FI90029, Oulu, Finland
| | - R Nissi
- Department of Obstetrics and Gynecology, University of Oulu, PL 23, FI90029, Oulu, Finland
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102
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Wani AK, Roy P, Kumar V, Mir TUG. Metagenomics and artificial intelligence in the context of human health. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2022; 100:105267. [PMID: 35278679 DOI: 10.1016/j.meegid.2022.105267] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 12/12/2022]
Abstract
Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.
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Affiliation(s)
- Atif Khurshid Wani
- Department of Biotechnology, School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
| | - Priyanka Roy
- Department of Basic and Applied Sciences, National Institute of Food Technology Entrepreneurship and Management, Sonipat 131 028, Haryana, India
| | - Vijay Kumar
- Department of Basic and Applied Sciences, National Institute of Food Technology Entrepreneurship and Management, Sonipat 131 028, Haryana, India.
| | - Tahir Ul Gani Mir
- Department of Biotechnology, School of Bioengineering and Biosciences, Lovely Professional University, Punjab 144411, India
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103
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Segura Munoz RR, Mantz S, Martínez I, Li F, Schmaltz RJ, Pudlo NA, Urs K, Martens EC, Walter J, Ramer-Tait AE. Experimental evaluation of ecological principles to understand and modulate the outcome of bacterial strain competition in gut microbiomes. THE ISME JOURNAL 2022; 16:1594-1604. [PMID: 35210551 PMCID: PMC9122919 DOI: 10.1038/s41396-022-01208-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 12/03/2021] [Accepted: 02/01/2022] [Indexed: 01/07/2023]
Abstract
It is unclear if coexistence theory can be applied to gut microbiomes to understand their characteristics and modulate their composition. Through experiments in gnotobiotic mice with complex microbiomes, we demonstrated that strains of Akkermansia muciniphila and Bacteroides vulgatus could only be established if microbiomes were devoid of these species. Strains of A. muciniphila showed strict competitive exclusion, while B. vulgatus strains coexisted but populations were still influenced by competitive interactions. These differences in competitive behavior were reflective of genomic variation within the two species, indicating considerable niche overlap for A. muciniphila strains and a broader niche space for B. vulgatus strains. Priority effects were detected for both species as strains’ competitive fitness increased when colonizing first, which resulted in stable persistence of the A. muciniphila strain colonizing first and competitive exclusion of the strain arriving second. Based on these observations, we devised a subtractive strategy for A. muciniphila using antibiotics and showed that a strain from an assembled community can be stably replaced by another strain. By demonstrating that competitive outcomes in gut ecosystems depend on niche differences and are historically contingent, our study provides novel information to explain the ecological characteristics of gut microbiomes and a basis for their modulation.
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Affiliation(s)
- Rafael R Segura Munoz
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.,Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Sara Mantz
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Ines Martínez
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - Fuyong Li
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada.,Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Robert J Schmaltz
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Nicholas A Pudlo
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Karthik Urs
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Eric C Martens
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Jens Walter
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada. .,Department of Biological Sciences, University of Alberta, Edmonton, Canada. .,APC Microbiome Ireland, School of Microbiology, and Department of Medicine, University College Cork, Cork, Ireland.
| | - Amanda E Ramer-Tait
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA. .,Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
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104
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Li B, Zhong D, Qiao J, Jiang X. GNPI: Graph normalization to integrate phylogenetic information for metagenomic host phenotype prediction. Methods 2022; 205:11-17. [PMID: 35636652 DOI: 10.1016/j.ymeth.2022.05.007] [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: 03/31/2022] [Revised: 05/17/2022] [Accepted: 05/26/2022] [Indexed: 11/24/2022] Open
Abstract
Microorganisms play important roles in our lives especially on metabolism and diseases. Determining the probability of human suffering from specific diseases and the severity of the disease based on microbial genes is the crucial research for understanding the relationship between microbes and diseases. Previous could extract the topological information of phylogenetic trees and integrate them to metagenomic datasets, thus enable classifiers to learn more information in limited datasets and thus improve the performance of the models. In this paper, we proposed a GNPI model to better learn the structure of phylogenetic trees. GNPI maintained the original vector format of metagenomic datasets, while previous research had to change the input form to matrices. The vector-like form of the input data can be easily adopted in the baseline machine learning models and is available for deep learning models. The datasets processed with GNPI help enhance the accuracy of machine learning and deep learning models in three different datasets. GNPI is an interpretable data processing method for host phenotype prediction and other bioinformatics tasks.
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Affiliation(s)
- Bojing Li
- Hubei Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China; School of Computer, Central China Normal University, Wuhan, China
| | - Duo Zhong
- Hubei Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China; School of Computer, Central China Normal University, Wuhan, China
| | - Jimei Qiao
- Mathematics and Science College, Shanghai Normal University, Shanghai, China
| | - Xingpeng Jiang
- Hubei Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China; School of Computer, Central China Normal University, Wuhan, China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, China.
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105
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Kumar P, Rani A, Singh S, Kumar A. Recent advances on
DNA
and omics‐based technology in Food testing and authentication: A review. J Food Saf 2022. [DOI: 10.1111/jfs.12986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Pramod Kumar
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
| | - Alka Rani
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
| | - Shalini Singh
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
| | - Anuj Kumar
- National Institute of Cancer Prevention and Research Indian Council for Medical Research (ICMR‐NICPR) Noida India
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106
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Mreyoud Y, Song M, Lim J, Ahn TH. MegaD: Deep Learning for Rapid and Accurate Disease Status Prediction of Metagenomic Samples. Life (Basel) 2022; 12:life12050669. [PMID: 35629336 PMCID: PMC9143510 DOI: 10.3390/life12050669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 12/23/2022] Open
Abstract
The diversity within different microbiome communities that drive biogeochemical processes influences many different phenotypes. Analyses of these communities and their diversity by countless microbiome projects have revealed an important role of metagenomics in understanding the complex relation between microbes and their environments. This relationship can be understood in the context of microbiome composition of specific known environments. These compositions can then be used as a template for predicting the status of similar environments. Machine learning has been applied as a key component to this predictive task. Several analysis tools have already been published utilizing machine learning methods for metagenomic analysis. Despite the previously proposed machine learning models, the performance of deep neural networks is still under-researched. Given the nature of metagenomic data, deep neural networks could provide a strong boost to growth in the prediction accuracy in metagenomic analysis applications. To meet this urgent demand, we present a deep learning based tool that utilizes a deep neural network implementation for phenotypic prediction of unknown metagenomic samples. (1) First, our tool takes as input taxonomic profiles from 16S or WGS sequencing data. (2) Second, given the samples, our tool builds a model based on a deep neural network by computing multi-level classification. (3) Lastly, given the model, our tool classifies an unknown sample with its unlabeled taxonomic profile. In the benchmark experiments, we deduced that an analysis method facilitating a deep neural network such as our tool can show promising results in increasing the prediction accuracy on several samples compared to other machine learning models.
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Affiliation(s)
- Yassin Mreyoud
- Program in Bioinformatics and Computational Biology, Saint Louis University, Saint Louis, MO 63104, USA;
| | - Myoungkyu Song
- Department of Computer Science, University of Nebraska Omaha, Omaha, NE 68182, USA;
| | - Jihun Lim
- Saint Paul Preparatory, Seoul 06593, Korea;
| | - Tae-Hyuk Ahn
- Program in Bioinformatics and Computational Biology, Saint Louis University, Saint Louis, MO 63104, USA;
- Department of Computer Science, Saint Louis University, Saint Louis, MO 63104, USA
- Correspondence:
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107
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Poulsen CS, Ekstrøm CT, Aarestrup FM, Pamp SJ. Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes. Microbiol Spectr 2022; 10:e0009022. [PMID: 35289669 PMCID: PMC9045301 DOI: 10.1128/spectrum.00090-22] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 02/22/2022] [Indexed: 11/20/2022] Open
Abstract
Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.
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Affiliation(s)
- Casper S. Poulsen
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Claus T. Ekstrøm
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Frank M. Aarestrup
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sünje J. Pamp
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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108
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Bakir-Gungor B, Hacılar H, Jabeer A, Nalbantoglu OU, Aran O, Yousef M. Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods. PeerJ 2022; 10:e13205. [PMID: 35497193 PMCID: PMC9048649 DOI: 10.7717/peerj.13205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/10/2022] [Indexed: 01/12/2023] Open
Abstract
The tremendous boost in next generation sequencing and in the "omics" technologies makes it possible to characterize the human gut microbiome-the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn's disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.
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Affiliation(s)
- Burcu Bakir-Gungor
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Hilal Hacılar
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Amhar Jabeer
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | | | - Oya Aran
- TETAM, Bogazici University, Istanbul, Turkey
| | - Malik Yousef
- Zefat Academic College, Zefat, Israel,Galilee Digital Health Research Center, Zefat Academic College, Zefat, Israel
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109
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Chen X, Zhu Z, Zhang W, Wang Y, Wang F, Yang J, Wong KC. Human disease prediction from microbiome data by multiple feature fusion and deep learning. iScience 2022; 25:104081. [PMID: 35372808 PMCID: PMC8971930 DOI: 10.1016/j.isci.2022.104081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 09/16/2021] [Accepted: 03/13/2022] [Indexed: 10/29/2022] Open
Abstract
Human disease prediction from microbiome data has broad implications in metagenomics. It is rare for the existing methods to consider abundance profiles from both known and unknown microbial organisms, or capture the taxonomic relationships among microbial taxa, leading to significant information loss. On the other hand, deep learning has shown unprecedented advantages in classification tasks for its feature-learning ability. However, it encounters the opposite situation in metagenome-based disease prediction since high-dimensional low-sample-size metagenomic datasets can lead to severe overfitting; and black-box model fails in providing biological explanations. To circumvent the related problems, we developed MetaDR, a comprehensive machine learning-based framework that integrates various information and deep learning to predict human diseases. Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time, and effectively discovers the informative features with biological insights.
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Affiliation(s)
- Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Zifan Zhu
- Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Weitong Zhang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Yuchen Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.,Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
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110
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Cronin P, Murphy CL, Barrett M, Ghosh TS, Pellanda P, O'Connor EM, Zulquernain SA, Kileen S, McCourt M, Andrews E, O'Riordain MG, Shanahan F, O'Toole PW. Colorectal microbiota after removal of colorectal cancer. NAR Cancer 2022; 4:zcac011. [PMID: 35399186 PMCID: PMC8991967 DOI: 10.1093/narcan/zcac011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 03/08/2022] [Accepted: 04/04/2022] [Indexed: 01/01/2023] Open
Abstract
The colonic microbiome has been implicated in the pathogenesis of colorectal cancer (CRC) and intestinal microbiome alterations are not confined to the tumour. Since data on whether the microbiome normalises or remains altered after resection of CRC are conflicting, we studied the colonic microbiota of patients after resection of CRC. We profiled the microbiota using 16S rRNA gene amplicon sequencing in colonic biopsies from patients after resection of CRC (n = 63) in comparison with controls (n = 52), subjects with newly diagnosed CRC (n = 93) and polyps (i = 28). The colonic microbiota after surgical resection remained significantly different from that of controls in 65% of patients. Genus-level profiling and beta-diversity confirmed two distinct groups of patients after resection of CRC: one with an abnormal microbiota similar to that of patients with newly diagnosed CRC and another similar to non-CRC controls. Consumption levels of several dietary ingredients and cardiovascular drugs co-varied with differences in microbiota composition suggesting lifestyle factors may modulate differential microbiome trajectories after surgical resection. This study supports investigation of the colonic microbiota as a marker of risk for development of CRC.
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Affiliation(s)
- Peter Cronin
- Department of Biological Science, University of Limerick, Limerick, V94 T9PX, Ireland
| | - Clodagh L Murphy
- APC Microbiome Ireland, University College Cork, Cork, T12 YT20, Ireland
| | - Maurice Barrett
- APC Microbiome Ireland, University College Cork, Cork, T12 YT20, Ireland
| | | | - Paola Pellanda
- APC Microbiome Ireland, University College Cork, Cork, T12 YT20, Ireland
| | - Eibhlis M O'Connor
- Department of Biological Science, University of Limerick, Limerick, V94 T9PX, Ireland
| | | | - Shane Kileen
- Cork University Hospital, Cork, T12 DC4A, Ireland
| | | | | | | | - Fergus Shanahan
- APC Microbiome Ireland, University College Cork, Cork, T12 YT20, Ireland
| | - Paul W O'Toole
- APC Microbiome Ireland, University College Cork, Cork, T12 YT20, Ireland
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111
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Vilne B, Ķibilds J, Siksna I, Lazda I, Valciņa O, Krūmiņa A. Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease. Front Microbiol 2022; 13:627892. [PMID: 35479632 PMCID: PMC9036178 DOI: 10.3389/fmicb.2022.627892] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 02/24/2022] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.
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Affiliation(s)
- Baiba Vilne
- Bioinformatics Lab, Riga Stradins University, Riga, Latvia
- COST Action CA18131 - Statistical and Machine Learning Techniques in Human Microbiome Studies, Brussels, Belgium
- *Correspondence: Baiba Vilne
| | - Juris Ķibilds
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Inese Siksna
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Ilva Lazda
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Olga Valciņa
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Angelika Krūmiņa
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
- Department of Infectology and Dermatology, Riga Stradins University, Riga, Latvia
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Grazioli F, Siarheyeu R, Alqassem I, Henschel A, Pileggi G, Meiser A. Microbiome-based disease prediction with multimodal variational information bottlenecks. PLoS Comput Biol 2022; 18:e1010050. [PMID: 35404958 PMCID: PMC9022840 DOI: 10.1371/journal.pcbi.1010050] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 04/21/2022] [Accepted: 03/22/2022] [Indexed: 01/12/2023] Open
Abstract
Scientific research is shedding light on the interaction of the gut microbiome with the human host and on its role in human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. Most of them leverage shotgun metagenomic sequencing to extract gut microbial species-relative abundances or strain-level markers. Each of these gut microbial profiling modalities showed diagnostic potential when tested separately; however, no existing approach combines them in a single predictive framework. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model capable of learning a joint representation of multiple heterogeneous data modalities. MVIB achieves competitive classification performance while being faster than existing methods. Additionally, MVIB offers interpretable results. Our model adopts an information theoretic interpretation of deep neural networks and computes a joint stochastic encoding of different input data modalities. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundances and strain-level markers. MVIB is evaluated on human gut metagenomic samples from 11 publicly available disease cohorts covering 6 different diseases. We achieve high performance (0.80 < ROC AUC < 0.95) on 5 cohorts and at least medium performance on the remaining ones. We adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to the model’s predictions. We also perform cross-study generalisation experiments, where we train and test MVIB on different cohorts of the same disease, and overall we achieve comparable results to the baseline approach, i.e. the Random Forest. Further, we evaluate our model by adding metabolomic data derived from mass spectrometry as a third input modality. Our method is scalable with respect to input data modalities and has an average training time of < 1.4 seconds. The source code and the datasets used in this work are publicly available. The gut microbiome can be an indicator of various diseases due to its interaction with the human system. Our main objective is to improve on the current state of the art in microbiome classification for diagnostic purposes. A rich body of literature evidences the clinical value of microbiome predictive models. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model for microbiome-based disease prediction. MVIB learns a joint stochastic encoding of different input data modalities to predict the output class. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundance and strain-level marker profiles. Both of these gut microbial features showed diagnostic potential when tested separately in previous studies; however, no research has combined them in a single predictive tool. We evaluate MVIB on various human gut metagenomic samples from 11 publicly available disease cohorts. MVIB achieves competitive performance compared to state-of-the-art methods. Additionally, we evaluate our model by adding metabolomic data as a third input modality and we show that MVIB is scalable with respect to input feature modalities. Further, we adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to our model predictions.
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Affiliation(s)
| | | | | | - Andreas Henschel
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE
- Research and Data Intelligence Support Center, Khalifa University, Abu Dhabi, UAE
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Ghaffari P, Shoaie S, Nielsen LK. Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions. J Transl Med 2022; 20:173. [PMID: 35410233 PMCID: PMC9004034 DOI: 10.1186/s12967-022-03365-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/26/2022] [Indexed: 02/08/2023] Open
Abstract
AbstractThe human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient’s lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.
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114
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Mathieu A, Leclercq M, Sanabria M, Perin O, Droit A. Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation. Front Microbiol 2022; 13:811495. [PMID: 35359727 PMCID: PMC8964132 DOI: 10.3389/fmicb.2022.811495] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/02/2022] [Indexed: 12/12/2022] Open
Abstract
Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using de novo binning strategies, but these genomes, not yet fully characterized, are not used in classic approaches, whereas machine and deep learning methods can use them as models. In this article, we attempted to review the different methods and their efficiency to improve the annotation of metagenomic sequences. Deep learning models have reached the performance of the widely used k-mer alignment-based tools, with better accuracy in certain cases; however, they still must demonstrate their robustness across the variety of environmental samples and across the rapid expansion of accessible genomes in databases.
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Affiliation(s)
- Alban Mathieu
- Computational Biology Laboratory, CHU de Québec - Université Laval Research Centre, Québec City, QC, Canada
| | - Mickael Leclercq
- Computational Biology Laboratory, CHU de Québec - Université Laval Research Centre, Québec City, QC, Canada
| | | | - Olivier Perin
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-Bois, France
| | - Arnaud Droit
- Computational Biology Laboratory, CHU de Québec - Université Laval Research Centre, Québec City, QC, Canada
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115
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Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa. PLoS Comput Biol 2022; 18:e1010066. [PMID: 35446845 PMCID: PMC9064115 DOI: 10.1371/journal.pcbi.1010066] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/03/2022] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies. The composition of the human microbiome has been linked to a large number of different diseases. In this context, classification methodologies based on machine learning approaches have represented a promising tool for diagnostic purposes from metagenomics data. The link between microbial population composition and host phenotypes has been usually performed by considering taxonomic profiles represented by relative abundances of microbial species. In this study, we show that it is more the presence rather than the relative abundance of microbial taxa to be relevant to maximize classification accuracy. This is accomplished by conducting a meta-analysis on more than 4,000 shotgun metagenomes coming from 25 case-control studies and in which original relative abundance data are degraded to presence/absence profiles. Findings are also extended to 16S rRNA data and advance the research field in building prediction models directly from human microbiome data.
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116
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Liu B, Sträuber H, Saraiva J, Harms H, Silva SG, Kasmanas JC, Kleinsteuber S, Nunes da Rocha U. Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture. MICROBIOME 2022; 10:48. [PMID: 35331330 PMCID: PMC8952268 DOI: 10.1186/s40168-021-01219-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/17/2021] [Indexed: 05/10/2023]
Abstract
BACKGROUND The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities. RESULTS By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production. CONCLUSIONS Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.
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Affiliation(s)
- Bin Liu
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
| | - Heike Sträuber
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
| | - João Saraiva
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
| | - Hauke Harms
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
| | - Sandra Godinho Silva
- Institute for Bioengineering and Biosciences, Department of Bioengineering, Instituto Superior Técnico Universidade de Lisboa, Lisbon, Portugal
| | - Jonas Coelho Kasmanas
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Computer Science and Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Sabine Kleinsteuber
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
| | - Ulisses Nunes da Rocha
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
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117
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Kishikawa T, Tomofuji Y, Inohara H, Okada Y. OMARU: a robust and multifaceted pipeline for metagenome-wide association study. NAR Genom Bioinform 2022; 4:lqac019. [PMID: 35265838 PMCID: PMC8900191 DOI: 10.1093/nargab/lqac019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/04/2022] [Accepted: 02/18/2022] [Indexed: 12/11/2022] Open
Abstract
Microbiome is an essential omics layer to elucidate disease pathophysiology. However, we face a challenge of low reproducibility in microbiome studies, partly due to a lack of standard analytical pipelines. Here, we developed OMARU (Omnibus metagenome-wide association study with robustness), a new end-to-end analysis workflow that covers a wide range of microbiome analysis from phylogenetic and functional profiling to case–control metagenome-wide association studies (MWAS). OMARU rigorously controls the statistical significance of the analysis results, including correction of hidden confounding factors and application of multiple testing comparisons. Furthermore, OMARU can evaluate pathway-level links between the metagenome and the germline genome-wide association study (i.e. MWAS-GWAS pathway interaction), as well as links between taxa and genes in the metagenome. OMARU is publicly available (https://github.com/toshi-kishikawa/OMARU), with a flexible workflow that can be customized by users. We applied OMARU to publicly available type 2 diabetes (T2D) and schizophrenia (SCZ) metagenomic data (n = 171 and 344, respectively), identifying disease biomarkers through comprehensive, multilateral, and unbiased case–control comparisons of metagenome (e.g. increased Streptococcus vestibularis in SCZ and disrupted diversity in T2D). OMARU improves accessibility and reproducibility in the microbiome research community. Robust and multifaceted results of OMARU reflect the dynamics of the microbiome authentically relevant to disease pathophysiology.
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Affiliation(s)
- Toshihiro Kishikawa
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Head and Neck Surgery, Aichi Cancer Center Hospital, Nagoya 464-8681, Japan
| | - Yoshihiko Tomofuji
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
| | - Hidenori Inohara
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
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118
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Lee KA, Thomas AM, Bolte LA, Björk JR, de Ruijter LK, Armanini F, Asnicar F, Blanco-Miguez A, Board R, Calbet-Llopart N, Derosa L, Dhomen N, Brooks K, Harland M, Harries M, Leeming ER, Lorigan P, Manghi P, Marais R, Newton-Bishop J, Nezi L, Pinto F, Potrony M, Puig S, Serra-Bellver P, Shaw HM, Tamburini S, Valpione S, Vijay A, Waldron L, Zitvogel L, Zolfo M, de Vries EGE, Nathan P, Fehrmann RSN, Bataille V, Hospers GAP, Spector TD, Weersma RK, Segata N. Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat Med 2022; 28:535-544. [PMID: 35228751 PMCID: PMC8938272 DOI: 10.1038/s41591-022-01695-5] [Citation(s) in RCA: 163] [Impact Index Per Article: 81.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 01/13/2022] [Indexed: 12/13/2022]
Abstract
The composition of the gut microbiome has been associated with clinical responses to immune checkpoint inhibitor (ICI) treatment, but there is limited consensus on the specific microbiome characteristics linked to the clinical benefits of ICIs. We performed shotgun metagenomic sequencing of stool samples collected before ICI initiation from five observational cohorts recruiting ICI-naive patients with advanced cutaneous melanoma (n = 165). Integrating the dataset with 147 metagenomic samples from previously published studies, we found that the gut microbiome has a relevant, but cohort-dependent, association with the response to ICIs. A machine learning analysis confirmed the link between the microbiome and overall response rates (ORRs) and progression-free survival (PFS) with ICIs but also revealed limited reproducibility of microbiome-based signatures across cohorts. Accordingly, a panel of species, including Bifidobacterium pseudocatenulatum, Roseburia spp. and Akkermansia muciniphila, associated with responders was identified, but no single species could be regarded as a fully consistent biomarker across studies. Overall, the role of the human gut microbiome in ICI response appears more complex than previously thought, extending beyond differing microbial species simply present or absent in responders and nonresponders. Future studies should adopt larger sample sizes and take into account the complex interplay of clinical factors with the gut microbiome over the treatment course.
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Affiliation(s)
- Karla A Lee
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Laura A Bolte
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Johannes R Björk
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Laura Kist de Ruijter
- Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | | | | | | | - Ruth Board
- Department of Oncology, Lancashire Teaching Hospitals NHS Trust, Preston, UK
| | - Neus Calbet-Llopart
- Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Lisa Derosa
- U1015 INSERM, University Paris Saclay, Gustave Roussy Cancer Center and Oncobiome Network, Villejuif-Grand-Paris, France
| | - Nathalie Dhomen
- Molecular Oncology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
| | - Kelly Brooks
- Molecular Oncology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
| | - Mark Harland
- Division of Haematology and Immunology, Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Mark Harries
- Biochemical and Molecular Genetics Department, Hospital Clínic de Barcelona, IDIBAPS and University of Barcelona, Barcelona, Spain
- Department of Medical Oncology, Guys Cancer Centre, Guys and St Thomas's NHS Trust, London, UK
| | - Emily R Leeming
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Paul Lorigan
- The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | - Richard Marais
- Molecular Oncology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
| | - Julia Newton-Bishop
- Division of Haematology and Immunology, Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Luigi Nezi
- European Institute of Oncology (Istituto Europeo di Oncologia, IRCSS), Milan, Italy
| | | | - Miriam Potrony
- Centro de Investigación Biomédica en Red en Enfermedades Raras, Instituto de Salud Carlos III, Barcelona, Spain
- Biochemical and Molecular Genetics Department, Hospital Clínic de Barcelona, IDIBAPS and University of Barcelona, Barcelona, Spain
| | - Susana Puig
- Centro de Investigación Biomédica en Red en Enfermedades Raras, Instituto de Salud Carlos III, Barcelona, Spain
- Biochemical and Molecular Genetics Department, Hospital Clínic de Barcelona, IDIBAPS and University of Barcelona, Barcelona, Spain
| | | | - Heather M Shaw
- Department of Medical Oncology, Mount Vernon Cancer Centre, Northwood, UK
| | - Sabrina Tamburini
- European Institute of Oncology (Istituto Europeo di Oncologia, IRCSS), Milan, Italy
| | - Sara Valpione
- Molecular Oncology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
- The Christie NHS Foundation Trust, Manchester, UK
| | - Amrita Vijay
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Rheumatology & Orthopaedics Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - Levi Waldron
- Department CIBIO, University of Trento, Trento, Italy
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Laurence Zitvogel
- U1015 INSERM, University Paris Saclay, Gustave Roussy Cancer Center and Oncobiome Network, Villejuif-Grand-Paris, France
| | - Moreno Zolfo
- Department CIBIO, University of Trento, Trento, Italy
| | - Elisabeth G E de Vries
- Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Paul Nathan
- Biochemical and Molecular Genetics Department, Hospital Clínic de Barcelona, IDIBAPS and University of Barcelona, Barcelona, Spain
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Véronique Bataille
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Dermatology, Mount Vernon Cancer Centre, Northwood, UK
| | - Geke A P Hospers
- Department of Medical Oncology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands.
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy.
- European Institute of Oncology (Istituto Europeo di Oncologia, IRCSS), Milan, Italy.
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119
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Chen Y, Wang H, Lu W, Wu T, Yuan W, Zhu J, Lee YK, Zhao J, Zhang H, Chen W. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut Microbes 2022; 14:2025016. [PMID: 35040752 PMCID: PMC8773134 DOI: 10.1080/19490976.2021.2025016] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The human gut microbiome is a complex ecosystem that is closely related to the aging process. However, there is currently no reliable method to make full use of the metagenomics data of the gut microbiome to determine the age of the host. In this study, we considered the influence of geographical factors on the gut microbiome, and a total of 2604 filtered metagenomics data from the gut microbiome were used to construct an age prediction model. Then, we developed an ensemble model with multiple heterogeneous algorithms and combined species and pathway profiles for multi-view learning. By integrating gut microbiome metagenomics data and adjusting host confounding factors, the model showed high accuracy (R2 = 0.599, mean absolute error = 8.33 years). Besides, we further interpreted the model and identify potential biomarkers for the aging process. Among these identified biomarkers, we found that Finegoldia magna, Bifidobacterium dentium, and Clostridium clostridioforme had increased abundance in the elderly. Moreover, the utilization of amino acids by the gut microbiome undergoes substantial changes with increasing age which have been reported as the risk factors for age-associated malnutrition and inflammation. This model will be helpful for the comprehensive utilization of multiple omics data, and will allow greater understanding of the interaction between microorganisms and age to realize the targeted intervention of aging.
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Affiliation(s)
- Yutao Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Hongchao Wang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China,CONTACT Hongchao Wang School of Food Science and Technology
| | - Wenwei Lu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China,Wenwei Lu School of Food Science and Technology
| | - Tong Wu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Weiwei Yuan
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Yuan Kun Lee
- Department of Microbiology & Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore,International Joint Research Laboratory for Pharmabiotics & Antibiotic Resistance, Jiangnan University, Wuxi, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, China,Wuxi Translational Medicine Research Center and Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
| | - Wei Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, P. R China,School of Food Science and Technology, Jiangnan University, Wuxi, China,Department of Microbiology & Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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120
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Thomas SC, Xu F, Pushalkar S, Lin Z, Thakor N, Vardhan M, Flaminio Z, Khodadadi-Jamayran A, Vasconcelos R, Akapo A, Queiroz E, Bederoff M, Janal MN, Guo Y, Aguallo D, Gordon T, Corby PM, Kamer AR, Li X, Saxena D. Electronic Cigarette Use Promotes a Unique Periodontal Microbiome. mBio 2022; 13:e0007522. [PMID: 35189698 PMCID: PMC8903898 DOI: 10.1128/mbio.00075-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 01/28/2022] [Indexed: 12/15/2022] Open
Abstract
Electronic cigarettes (e-cigs) have become prevalent as an alternative to conventional cigarette smoking, particularly in youth. E-cig aerosols contain unique chemicals which alter the oral microbiome and promote dysbiosis in ways we are just beginning to investigate. We conducted a 6-month longitudinal study involving 84 subjects who were either e-cig users, conventional smokers, or nonsmokers. Periodontal condition, cytokine levels, and subgingival microbial community composition were assessed, with periodontal, clinical, and cytokine measures reflecting cohort habit and positively correlating with pathogenic taxa (e.g., Treponema, Saccharibacteria, and Porphyromonas). α-Diversity increased similarly across cohorts longitudinally, yet each cohort maintained a unique microbiome. The e-cig microbiome shared many characteristics with the microbiome of conventional smokers and some with nonsmokers, yet it maintained a unique subgingival microbial community enriched in Fusobacterium and Bacteroidales (G-2). Our data suggest that e-cig use promotes a unique periodontal microbiome, existing as a stable heterogeneous state between those of conventional smokers and nonsmokers and presenting unique oral health challenges. IMPORTANCE Electronic cigarette (e-cig) use is gaining in popularity and is often perceived as a healthier alternative to conventional smoking. Yet there is little evidence of the effects of long-term use of e-cigs on oral health. Conventional cigarette smoking is a prominent risk factor for the development of periodontitis, an oral disease affecting nearly half of adults over 30 years of age in the United States. Periodontitis is initiated through a disturbance in the microbial biofilm communities inhabiting the unique space between teeth and gingival tissues. This disturbance instigates host inflammatory and immune responses and, if left untreated, leads to tooth and bone loss and systemic diseases. We found that the e-cig user's periodontal microbiome is unique, eliciting unique host responses. Yet some similarities to the microbiomes of both conventional smokers and nonsmokers exist, with strikingly more in common with that of cigarette smokers, suggesting that there is a unique periodontal risk associated with e-cig use.
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Affiliation(s)
- Scott C. Thomas
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Fangxi Xu
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Smruti Pushalkar
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Ziyan Lin
- Applied Bioinformatics Labs, New York University School of Medicine, New York, New York, USA
| | - Nirali Thakor
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Mridula Vardhan
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Zia Flaminio
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | | | - Rebeca Vasconcelos
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Adenike Akapo
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Erica Queiroz
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Maria Bederoff
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Malvin N. Janal
- Department of Epidemiology & Health Promotion, New York University College of Dentistry, New York, New York, USA
| | - Yuqi Guo
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Deanna Aguallo
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Terry Gordon
- Department of Environmental Medicine, New York University School of Medicine, New York, New York, USA
| | - Patricia M. Corby
- Department of Oral Medicine, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Angela R. Kamer
- Department of Periodontology and Implant Dentistry, New York University College of Dentistry, New York, New York, USA
| | - Xin Li
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
| | - Deepak Saxena
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, New York, USA
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Naseri M, Palizban F, Yadegar A, Khodarahmi M, Asadzadeh Aghdaei H, Houri H, Zahiri J. Investigation and characterization of human gut phageome in advanced liver cirrhosis of defined etiologies. Gut Pathog 2022; 14:9. [PMID: 35168645 PMCID: PMC8845349 DOI: 10.1186/s13099-022-00482-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 02/03/2022] [Indexed: 11/22/2022] Open
Abstract
Background Liver cirrhosis is a major public health problem, accounting for high rates of morbidity and mortality worldwide. The cirrhosis etiology is a broad and essential step in planning a treatment strategy. Many recent studies have documented that gut microbiome alterations play a vital role in the development and progression of cirrhosis and its complications. Nevertheless, there is insufficient data on the correlation between liver cirrhosis and gut phageome alterations in patients with advanced liver diseases. This study aimed to analyze the taxonomic structure and functional attributes of the gut phageome in six different etiologies of advanced liver cirrhosis. Methods We first retrieved metagenomic sequencing data from three datasets of fecal samples taken from cirrhotic patients with various etiologies. Subsequently, several bioinformatics pipelines were used to analyze bacteriophage composition and determine their functionality. Results A gene catalog of 479,425 non-redundant genes was developed as a reference to measure gene prevalence. The results of the analysis revealed a few significant differences among the cohorts at the phage species level. However, the alternations were more evident as there were more in-depth analyses of the functional resolution of the gut phageome. Conclusions Our findings suggest that the functional analysis of the gut phageome would provide meaningful markers to predict the progression of liver cirrhosis and facilitate the development of novel treatment approaches. Supplementary Information The online version contains supplementary material available at 10.1186/s13099-022-00482-4.
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Affiliation(s)
- Mohadeseh Naseri
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fahimeh Palizban
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Abbas Yadegar
- Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Shahid Arabi Ave., Yemen St., Velenjak, Tehran, Iran
| | - Mohsen Khodarahmi
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.,Medical Imaging Center, Karaj, Alborz, Iran
| | - Hamid Asadzadeh Aghdaei
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamidreza Houri
- Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Shahid Arabi Ave., Yemen St., Velenjak, Tehran, Iran.
| | - Javad Zahiri
- Department of Neuroscience, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0662, USA.
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Abstract
The evolutionary dynamics of gut microbiota are still being explored. In this issue of Cell Host & Microbe, Dapa et al. conduct an experimental evolution study in mice to track the rapid adaptation of the gut microbiome based on host diet.
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Affiliation(s)
- Nandita Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
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Pickett BE, Connor R, Berhanu-Denka T, Bhalla S, Brover V, Chambers MJ, Chaudhary K, Cissé OH, Dillman A, Elmassry MM, Feldgarden M, Holloway E, Huang X, Klimke W, Inês Mendes C, Norred SE, Parkinson J, Sevilla S, Garcia Solache M, Surujon D, Torian U, Zalunin V, Busby B. Lessons learned in virulence factor identification and data management from a hackathon on microbial virulence. F1000Res 2022. [DOI: 10.12688/f1000research.26452.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Virulence is a complex mix of microbial traits and host susceptibility that could ultimately lead to disease. The increased prevalence of multidrug resistant infections complicates treatment options, augmenting the need for developing robust computational methods and pipelines that enable researchers and clinicians to rapidly identify the underlying mechanism(s) of virulence in any given sample/isolate. Consequently, the National Center for Biotechnology and Information at the National Institutes of Health hosted an in-person hackathon in Bethesda, Maryland during July 2019 to assist with developing cloud-based methods to reduce reliance on local computational infrastructure. Groups of attendees were assigned tasks that are relevant to identifying relevant tools, constructing pipelines capable of identifying microbial virulence factors, and managing the associated data and metadata. Specifically, the assigned tasks consisted of the following: data indexing, metabolic functions, virulence factors, antimicrobial resistance, mobile elements in enterococci, and metatranscriptomics. The cloud-based framework established by this hackathon can be augmented and built upon by the research community to aid in the rapid identification of microbial virulence factors.
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Briscoe L, Balliu B, Sankararaman S, Halperin E, Garud NR. Evaluating supervised and unsupervised background noise correction in human gut microbiome data. PLoS Comput Biol 2022; 18:e1009838. [PMID: 35130266 PMCID: PMC8853548 DOI: 10.1371/journal.pcbi.1009838] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 02/17/2022] [Accepted: 01/15/2022] [Indexed: 12/13/2022] Open
Abstract
The ability to predict human phenotypes and identify biomarkers of disease from metagenomic data is crucial for the development of therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Supervised methods to correct for background noise, originally designed for gene expression and RNA-seq data, are commonly applied to microbiome data but may be limited because they cannot account for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach that is presently used in other domains but has not been applied to microbiome data to date. We find that the unsupervised principal component correction approach has comparable ability in reducing false discovery of biomarkers as the supervised approaches, with the added benefit of not needing to know the sources of variation apriori. However, in prediction tasks, it appears to only improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses. The human gut microbiome is known to play a major role in health and is associated with many diseases including colorectal cancer, obesity, and diabetes. The prediction of host phenotypes and identification of biomarkers of disease is essential for harnessing the therapeutic potential of the microbiome. However, many metagenomic datasets are affected by technical variables that introduce unwanted variation that can confound the ability to predict phenotypes and identify biomarkers. Currently, supervised methods originally designed for gene expression and RNA-seq data are commonly applied to microbiome data for correction of background noise, but they are limited in that they cannot correct for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach and find that all correction approaches reduce false positives for biomarker discovery. In the task of predicting phenotypes, different approaches have varying success where the unsupervised correction can improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.
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Affiliation(s)
- Leah Briscoe
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (LB); (EH); (NRG)
| | - Brunilda Balliu
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Sriram Sankararaman
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Institute of Precision Health, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (LB); (EH); (NRG)
| | - Nandita R. Garud
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (LB); (EH); (NRG)
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125
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Petrick JL, Wilkinson JE, Michaud DS, Cai Q, Gerlovin H, Signorello LB, Wolpin BM, Ruiz-Narváez EA, Long J, Yang Y, Johnson WE, Shu XO, Huttenhower C, Palmer JR. The oral microbiome in relation to pancreatic cancer risk in African Americans. Br J Cancer 2022; 126:287-296. [PMID: 34718358 PMCID: PMC8770575 DOI: 10.1038/s41416-021-01578-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 09/14/2021] [Accepted: 10/01/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND African Americans have the highest pancreatic cancer incidence of any racial/ethnic group in the United States. The oral microbiome was associated with pancreatic cancer risk in a recent study, but no such studies have been conducted in African Americans. Poor oral health, which can be a cause or effect of microbial populations, was associated with an increased risk of pancreatic cancer in a single study of African Americans. METHODS We prospectively investigated the oral microbiome in relation to pancreatic cancer risk among 122 African-American pancreatic cancer cases and 354 controls. DNA was extracted from oral wash samples for metagenomic shotgun sequencing. Alpha and beta diversity of the microbial profiles were calculated. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between microbes and pancreatic cancer risk. RESULTS No associations were observed with alpha or beta diversity, and no individual microbial taxa were differentially abundant between cases and control, after accounting for multiple comparisons. Among never smokers, there were elevated ORs for known oral pathogens: Porphyromonas gingivalis (OR = 1.69, 95% CI: 0.80-3.56), Prevotella intermedia (OR = 1.40, 95% CI: 0.69-2.85), and Tannerella forsythia (OR = 1.36, 95% CI: 0.66-2.77). CONCLUSIONS Previously reported associations between oral taxa and pancreatic cancer were not present in this African-American population overall.
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Affiliation(s)
| | - Jeremy E Wilkinson
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Dominique S Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hanna Gerlovin
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Lisa B Signorello
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Edward A Ruiz-Narváez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - W Evan Johnson
- Department of Medicine, Division of Computational Biomedicine, Boston University, Boston, MA, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA.
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126
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Bertin PN, Crognale S, Plewniak F, Battaglia-Brunet F, Rossetti S, Mench M. Water and soil contaminated by arsenic: the use of microorganisms and plants in bioremediation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:9462-9489. [PMID: 34859349 PMCID: PMC8783877 DOI: 10.1007/s11356-021-17817-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 11/23/2021] [Indexed: 04/16/2023]
Abstract
Owing to their roles in the arsenic (As) biogeochemical cycle, microorganisms and plants offer significant potential for developing innovative biotechnological applications able to remediate As pollutions. This possible use in bioremediation processes and phytomanagement is based on their ability to catalyse various biotransformation reactions leading to, e.g. the precipitation, dissolution, and sequestration of As, stabilisation in the root zone and shoot As removal. On the one hand, genomic studies of microorganisms and their communities are useful in understanding their metabolic activities and their interaction with As. On the other hand, our knowledge of molecular mechanisms and fate of As in plants has been improved by laboratory and field experiments. Such studies pave new avenues for developing environmentally friendly bioprocessing options targeting As, which worldwide represents a major risk to many ecosystems and human health.
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Affiliation(s)
- Philippe N Bertin
- Génétique Moléculaire, Génomique et Microbiologie, UMR7156 CNRS - Université de Strasbourg, Strasbourg, France.
| | - Simona Crognale
- Water Research Institute, National Research Council of Italy (IRSA - CNR), Rome, Italy
| | - Frédéric Plewniak
- Génétique Moléculaire, Génomique et Microbiologie, UMR7156 CNRS - Université de Strasbourg, Strasbourg, France
| | | | - Simona Rossetti
- Water Research Institute, National Research Council of Italy (IRSA - CNR), Rome, Italy
| | - Michel Mench
- Univ. Bordeaux, INRAE, BIOGECO, F-33615, Pessac, France
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127
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Youngblut ND, de la Cuesta-Zuluaga J, Ley RE. Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomes. Environ Microbiol 2022; 24:3966-3984. [PMID: 35049120 DOI: 10.1111/1462-2920.15910] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/15/2022] [Indexed: 11/29/2022]
Abstract
Tree-based diversity measures incorporate phylogenetic or functional relatedness into comparisons of microbial communities. This can improve the identification of explanatory factors compared to tree-agnostic diversity measures. However, applying tree-based diversity measures to metagenome data is more challenging than for single-locus sequencing (e.g., 16S rRNA gene). Utilizing the Genome Taxonomy Database (GTDB) for species-level metagenome profiling allows for functional diversity measures based on genomic content or traits inferred from it. Still, it is unclear how metagenome-based assessments of microbiome diversity benefit from incorporating phylogeny or function into measures of diversity. We assessed this by measuring phylogeny-based, function-based, and tree-agnostic diversity measures from a large, global collection of human gut metagenomes composed of 30 studies and 2943 samples. We found tree-based measures to explain phenotypic variation (e.g., westernization, disease status, and gender) better or equivalent to tree-agnostic measures. Ecophylogenetic and functional diversity measures provided unique insight into how microbiome diversity was partitioned by phenotype. Tree-based measures greatly improved machine learning model performance for predicting westernization, disease status, and gender, relative to models trained solely on tree-agnostic measures. Our findings illustrate the usefulness of tree- and function-based measures for metagenomic assessments of microbial diversity, which is a fundamental component of microbiome science. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nicholas D Youngblut
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Max Planck Ring 5, 72076, Tübingen, Germany
| | - Jacobo de la Cuesta-Zuluaga
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Max Planck Ring 5, 72076, Tübingen, Germany
| | - Ruth E Ley
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Max Planck Ring 5, 72076, Tübingen, Germany
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Giulia A, Anna S, Antonia B, Dario P, Maurizio C. Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications. FRONTIERS IN BIOINFORMATICS 2022; 1:794547. [PMID: 36303759 PMCID: PMC9580939 DOI: 10.3389/fbinf.2021.794547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/07/2021] [Indexed: 11/24/2022] Open
Abstract
Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.
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Affiliation(s)
- Agostinetto Giulia
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
- *Correspondence: Agostinetto Giulia,
| | | | - Bruno Antonia
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Pescini Dario
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Casiraghi Maurizio
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
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Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer. Sci Rep 2022; 12:450. [PMID: 35013454 PMCID: PMC8748837 DOI: 10.1038/s41598-021-04182-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.
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130
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Current Status and Future Therapeutic Options for Fecal Microbiota Transplantation. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58010084. [PMID: 35056392 PMCID: PMC8780626 DOI: 10.3390/medicina58010084] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/23/2021] [Accepted: 01/04/2022] [Indexed: 12/12/2022]
Abstract
The intestinal microbiota plays an important role in maintaining human health, and its alteration is now associated with the development of various gastrointestinal (ulcerative colitis, irritable bowel syndrome, constipation, etc.) and extraintestinal diseases, such as cancer, metabolic syndrome, neuropsychiatric diseases. In this context, it is not surprising that gut microbiota modification methods may constitute a therapy whose potential has not yet been fully investigated. In this regard, the most interesting method is thought to be fecal microbiota transplantation, which consists of the simultaneous replacement of the intestinal microbiota of a sick recipient with fecal material from a healthy donor. This review summarizes the most interesting findings on the application of fecal microbiota transplantation in gastrointestinal and extraintestinal pathologies.
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131
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Zhu C, Wang X, Li J, Jiang R, Chen H, Chen T, Yang Y. Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors. BMC Microbiol 2022; 22:4. [PMID: 34979898 PMCID: PMC8722223 DOI: 10.1186/s12866-021-02414-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/06/2021] [Indexed: 02/08/2023] Open
Abstract
Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model's performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models' performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.
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Affiliation(s)
- Congmin Zhu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
- Institute for Artificial Intelligence and Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Xin Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China
| | - Jianchu Li
- Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China
| | - Rui Jiang
- Bioinformatics Division and Center for Synthetic & Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Ting Chen
- Institute for Artificial Intelligence and Department of Computer Science and Technology, Tsinghua University, Beijing, China.
| | - Yuqing Yang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
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132
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Bi C, Xiao G, Liu C, Yan J, Chen J, Si W, Zhang J, Liu Z. Molecular Immune Mechanism of Intestinal Microbiota and Their Metabolites in the Occurrence and Development of Liver Cancer. Front Cell Dev Biol 2021; 9:702414. [PMID: 34957088 PMCID: PMC8693382 DOI: 10.3389/fcell.2021.702414] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 11/11/2021] [Indexed: 12/12/2022] Open
Abstract
Intestinal microorganisms are closely associated with immunity, metabolism, and inflammation, and play an important role in health and diseases such as inflammatory bowel disease, diabetes, cardiovascular disease, Parkinson’s disease, and cancer. Liver cancer is one of the most fatal cancers in humans. Most of liver cancers are slowly transformed from viral hepatitis, alcoholic liver disease, and non-alcoholic fatty liver disease. However, the relationship between intestinal microbiota and their metabolites, including short-chain fatty acids, bile acids, indoles, and ethanol, and liver cancer remains unclear. Here, we summarize the molecular immune mechanism of intestinal microbiota and their metabolites in the occurrence and development of liver cancer and reveal the important role of the microbiota-gut-liver axis in liver cancer. In addition, we describe how the intestinal flora can be balanced by antibiotics, probiotics, postbiotics, and fecal bacteria transplantation to improve the treatment of liver cancer. This review describes the immunomolecular mechanism of intestinal microbiota and their metabolites in the occurrence and development of hepatic cancer and provides theoretical evidence support for future clinical practice.
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Affiliation(s)
- Chenchen Bi
- Department of Pharmacology, Medical College of Shaoxing University, Shaoxing, China
| | - Geqiong Xiao
- Department of Oncology, Affiliated Hospital of Shaoxing University, Shaoxing, China
| | - Chunyan Liu
- Department of Clinical Medicine, Shaoxing People's Hospital, Shaoxing, China
| | - Junwei Yan
- Department of Pharmacology, Medical College of Shaoxing University, Shaoxing, China
| | - Jiaqi Chen
- Department of Pharmacology, Medical College of Shaoxing University, Shaoxing, China
| | - Wenzhang Si
- Department of General Surgery, Affiliated Hospital of Shaoxing University, Shaoxing, China
| | - Jian Zhang
- Department of Pharmacology, Medical College of Shaoxing University, Shaoxing, China
| | - Zheng Liu
- Department of Pharmacology, Medical College of Shaoxing University, Shaoxing, China
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133
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Curry KD, Nute MG, Treangen TJ. It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. Emerg Top Life Sci 2021; 5:815-827. [PMID: 34779841 PMCID: PMC8786294 DOI: 10.1042/etls20210213] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/29/2021] [Accepted: 10/06/2021] [Indexed: 02/01/2023]
Abstract
Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.
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Affiliation(s)
- Kristen D. Curry
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Michael G. Nute
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Todd J. Treangen
- Department of Computer Science, Rice University, Houston, TX 77005, USA
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134
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Decoding gut microbiota by imaging analysis of fecal samples. iScience 2021; 24:103481. [PMID: 34927025 PMCID: PMC8652011 DOI: 10.1016/j.isci.2021.103481] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 09/21/2021] [Accepted: 11/19/2021] [Indexed: 01/09/2023] Open
Abstract
The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis. A deep convolution network classifies gut microbiota based on fecal sample images Image-based quantitative prediction of gut microbiota composition is demonstrated This result provides a simple and inexpensive method of gut microbiota analysis
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135
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Narayana JK, Mac Aogáin M, Goh WWB, Xia K, Tsaneva-Atanasova K, Chotirmall SH. Mathematical-based microbiome analytics for clinical translation. Comput Struct Biotechnol J 2021; 19:6272-6281. [PMID: 34900137 PMCID: PMC8637001 DOI: 10.1016/j.csbj.2021.11.029] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 12/20/2022] Open
Abstract
Traditionally, human microbiology has been strongly built on the laboratory focused culture of microbes isolated from human specimens in patients with acute or chronic infection. These approaches primarily view human disease through the lens of a single species and its relevant clinical setting however such approaches fail to account for the surrounding environment and wide microbial diversity that exists in vivo. Given the emergence of next generation sequencing technologies and advancing bioinformatic pipelines, researchers now have unprecedented capabilities to characterise the human microbiome in terms of its taxonomy, function, antibiotic resistance and even bacteriophages. Despite this, an analysis of microbial communities has largely been restricted to ordination, ecological measures, and discriminant taxa analysis. This is predominantly due to a lack of suitable computational tools to facilitate microbiome analytics. In this review, we first evaluate the key concerns related to the inherent structure of microbiome datasets which include its compositionality and batch effects. We describe the available and emerging analytical techniques including integrative analysis, machine learning, microbial association networks, topological data analysis (TDA) and mathematical modelling. We also present how these methods may translate to clinical settings including tools for implementation. Mathematical based analytics for microbiome analysis represents a promising avenue for clinical translation across a range of acute and chronic disease states.
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Affiliation(s)
- Jayanth Kumar Narayana
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Micheál Mac Aogáin
- Biochemical Genetics Laboratory, Department of Biochemistry, St. James’s Hospital, Dublin, Ireland
- Clinical Biochemistry Unit, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics & Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
| | - Sanjay H. Chotirmall
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore
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136
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Barone M, D'Amico F, Fabbrini M, Rampelli S, Brigidi P, Turroni S. Over-feeding the gut microbiome: A scoping review on health implications and therapeutic perspectives. World J Gastroenterol 2021; 27:7041-7064. [PMID: 34887627 PMCID: PMC8613651 DOI: 10.3748/wjg.v27.i41.7041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/02/2021] [Accepted: 10/14/2021] [Indexed: 02/06/2023] Open
Abstract
The human gut microbiome has gained increasing attention over the past two decades. Several findings have shown that this complex and dynamic microbial ecosystem can contribute to the maintenance of host health or, when subject to imbalances, to the pathogenesis of various enteric and non-enteric diseases. This scoping review summarizes the current knowledge on how the gut microbiota and microbially-derived compounds affect host metabolism, especially in the context of obesity and related disorders. Examples of microbiome-based targeted intervention strategies that aim to restore and maintain an eubiotic layout are then discussed. Adjuvant therapeutic interventions to alleviate obesity and associated comorbidities are traditionally based on diet modulation and the supplementation of prebiotics, probiotics and synbiotics. However, these approaches have shown only moderate ability to induce sustained changes in the gut microbial ecosystem, making the development of innovative and tailored microbiome-based intervention strategies of utmost importance in clinical practice. In this regard, the administration of next-generation probiotics and engineered microbiomes has shown promising results, together with more radical intervention strategies based on the replacement of the dysbiotic ecosystem by means of fecal microbiota transplantation from healthy donors or with the introduction of synthetic communities specifically designed to achieve the desired therapeutic outcome. Finally, we provide a perspective for future translational investigations through the implementation of bioinformatics approaches, including machine and deep learning, to predict health risks and therapeutic outcomes.
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Affiliation(s)
- Monica Barone
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Federica D'Amico
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Marco Fabbrini
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Simone Rampelli
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Patrizia Brigidi
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
| | - Silvia Turroni
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
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137
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Ma Y, Wang J, Wu J, Tong C, Zhang T. Meta-analysis of cellular toxicity for graphene via data-mining the literature and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148532. [PMID: 34328986 DOI: 10.1016/j.scitotenv.2021.148532] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/13/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
Since graphene is currently incorporated into various consumer products and used in a variety of applications, determining the relationships between the physicochemical properties of graphene and its toxicity is critical for conducting environmental and health risk analyses. Data from the literature suggest that exposure to graphene may result in cytotoxicity. However, existing graphene toxicity data are complex and heterogeneous, making it difficult to conduct risk assessments. Here, we conducted a meta-analysis of published data on the cytotoxicity of graphene based on 792 publications, including 986 cell viability data points, 762 half maximal inhibitory concentration (IC50) data points, and 100 lactate dehydrogenase (LDH) release data points. Models to predict graphene cytotoxicity were then developed based on cell viability, IC50, and LDH release as toxicity endpoints using random forests learning algorithms. The most influential attributes influencing graphene cytotoxicity were revealed to be exposure dose and detection method for cell viability, diameter and surface modification for IC50, and detection method and organ source for LDH release. The meta-analysis produced three sets of key attributes for the three abovementioned toxicity endpoints that can be used in future studies of graphene toxicity. The findings indicate that rigorous data mining protocols can be combined with suitable machine learning tools to develop models with good predictive power and accuracy. The results also provide guidance for the design of safe graphene materials.
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Affiliation(s)
- Ying Ma
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jianli Wang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jingying Wu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Chuxuan Tong
- School of Information Technology and Electrical Engineering, The University of Queensland Brisbane, QLD 4072, Australia
| | - Ting Zhang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
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138
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Predicting the Role of the Human Gut Microbiome in Constipation Using Machine-Learning Methods: A Meta-Analysis. Microorganisms 2021; 9:microorganisms9102149. [PMID: 34683469 PMCID: PMC8539211 DOI: 10.3390/microorganisms9102149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 12/12/2022] Open
Abstract
(1) Background: Constipation is a common condition that affects the health and the quality of life of patients. Recent studies have suggested that the gut microbiome is associated with constipation, but these studies were mainly focused on a single research cohort. Thus, we aimed to construct a classification model based on fecal bacterial and identify the potential gut microbes’ biomarkers. (2) Methods: We collected 3056 fecal amplicon sequence data from five research cohorts. The data were subjected to a series of analyses, including alpha- and beta-diversity analyses, phylogenetic profiling analyses, and systematic machine learning to obtain a comprehensive understanding of the association between constipation and the gut microbiome. (3) Results: The alpha diversity of the bacterial community composition was higher in patients with constipation. Beta diversity analysis evidenced significant partitions between the two groups on the base of gut microbiota composition. Further, machine learning based on feature selection was performed to evaluate the utility of the gut microbiome as the potential biomarker for constipation. The Gradient Boosted Regression Trees after chi2 feature selection was the best model, exhibiting a validation performance of 70.7%. (4) Conclusions: We constructed an accurate constipation discriminant model and identified 15 key genera, including Serratia, Dorea, and Aeromonas, as possible biomarkers for constipation.
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139
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Specific gut microbiome signatures and the associated pro-inflamatory functions are linked to pediatric allergy and acquisition of immune tolerance. Nat Commun 2021; 12:5958. [PMID: 34645820 PMCID: PMC8514477 DOI: 10.1038/s41467-021-26266-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/23/2021] [Indexed: 12/16/2022] Open
Abstract
Understanding the functional potential of the gut microbiome is of primary importance for the design of innovative strategies for allergy treatment and prevention. Here we report the gut microbiome features of 90 children affected by food (FA) or respiratory (RA) allergies and 30 age-matched, healthy controls (CT). We identify specific microbial signatures in the gut microbiome of allergic children, such as higher abundance of Ruminococcus gnavus and Faecalibacterium prausnitzii, and a depletion of Bifidobacterium longum, Bacteroides dorei, B. vulgatus and fiber-degrading taxa. The metagenome of allergic children shows a pro-inflammatory potential, with an enrichment of genes involved in the production of bacterial lipo-polysaccharides and urease. We demonstrate that specific gut microbiome signatures at baseline can be predictable of immune tolerance acquisition. Finally, a strain-level selection occurring in the gut microbiome of allergic subjects is identified. R. gnavus strains enriched in FA and RA showed lower ability to degrade fiber, and genes involved in the production of a pro-inflammatory polysaccharide. We demonstrate that a gut microbiome dysbiosis occurs in allergic children, with R. gnavus emerging as a main player in pediatric allergy. These findings may open new strategies in the development of innovative preventive and therapeutic approaches. Trial: NCT04750980. Here, the authors profile the taxonomic composition and genetic potential of the gut microbiome of children with food or respiratory allergies and find that the gut metagenome of these patients is characterized by higher proinflammatory potential and reduced capacity of degrading complex polysaccharides, with Ruminococcus gnavus playing a central role.
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140
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Muller E, Algavi YM, Borenstein E. A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations. MICROBIOME 2021; 9:203. [PMID: 34641974 PMCID: PMC8507343 DOI: 10.1186/s40168-021-01149-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 08/18/2021] [Indexed: 05/15/2023]
Abstract
BACKGROUND Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. RESULTS In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. CONCLUSIONS Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.
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Affiliation(s)
- Efrat Muller
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Yadid M. Algavi
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, NM USA
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141
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Gawlik A, Salonen A, Jian C, Yanover C, Antosz A, Shmoish M, Wasniewska M, Bereket A, Wudy SA, Hartmann MF, Thivel D, Matusik P, Weghuber D, Hochberg Z. Personalized approach to childhood obesity: Lessons from gut microbiota and omics studies. Narrative review and insights from the 29th European childhood obesity congress. Pediatr Obes 2021; 16:e12835. [PMID: 34296826 DOI: 10.1111/ijpo.12835] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 06/20/2021] [Accepted: 07/05/2021] [Indexed: 12/19/2022]
Abstract
The traditional approach to childhood obesity prevention and treatment should fit most patients, but misdiagnosis and treatment failure could be observed in some cases that lie away from average as part of individual variation or misclassification. Here, we reflect on the contributions that high-throughput technologies such as next-generation sequencing, mass spectrometry-based metabolomics and microbiome analysis make towards a personalized medicine approach to childhood obesity. We hypothesize that diagnosing a child as someone with obesity captures only part of the phenotype; and that metabolomics, genomics, transcriptomics and analyses of the gut microbiome, could add precision to the term "obese," providing novel corresponding biomarkers. Identifying a cluster -omic signature in a given child can thus facilitate the development of personalized prognostic, diagnostic, and therapeutic approaches. It can also be applied to the monitoring of symptoms/signs evolution, treatment choices and efficacy, predisposition to drug-related side effects and potential relapse. This article is a narrative review of the literature and summary of the main observations, conclusions and perspectives raised during the annual meeting of the European Childhood Obesity Group. Authors discuss some recent advances and future perspectives on utilizing a systems approach to understanding and managing childhood obesity in the context of the existing omics data.
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Affiliation(s)
- Aneta Gawlik
- Department of Paediatrics and Paediatric Endocrinology, Faculty of Medical Sciences, Medical University of Silesia, Katowice, Poland
| | - Anne Salonen
- Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ching Jian
- Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Chen Yanover
- Healthcare Informatics, IBM Research-Haifa, Haifa, Israel
| | - Aleksandra Antosz
- Department of Paediatrics and Paediatric Endocrinology, Faculty of Medical Sciences, Medical University of Silesia, Katowice, Poland
| | - Michael Shmoish
- Bioinformatics Knowledge Unit, The Lokey Centre, Technion - Israel Institute of Technology, Haifa, Israel
| | - Malgorzata Wasniewska
- Department of Human Pathology in Adulthood and Childhood, University of Messina, Messina, Italy
| | - Abdullah Bereket
- School of Medicine, Department of Paediatric Endocrinology, Marmara University, Istanbul, Turkey
| | - Stefan A Wudy
- Steroid Research & Mass Spectrometry Unit, Laboratory for Translational Hormone Analytics, Division of Paediatric Endocrinology & Diabetology, Center of Child and Adolescent Medicine, Justus-Liebig-University, Giessen, Germany
| | - Michaela F Hartmann
- Steroid Research & Mass Spectrometry Unit, Laboratory for Translational Hormone Analytics, Division of Paediatric Endocrinology & Diabetology, Center of Child and Adolescent Medicine, Justus-Liebig-University, Giessen, Germany
| | - David Thivel
- University Clermont Auvergne, UFR Medicine, Clermont-Ferrand, France
| | - Pawel Matusik
- Department of Paediatrics and Paediatric Endocrinology, Faculty of Medical Sciences, Medical University of Silesia, Katowice, Poland
| | - Daniel Weghuber
- Department of Paediatrics, Paracelsus Medical University, Salzburg, Austria
| | - Ze'ev Hochberg
- Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
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142
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He Y, Lu M, Che J, Chu Q, Zhang P, Chen Y. Biomarkers and Future Perspectives for Hepatocellular Carcinoma Immunotherapy. Front Oncol 2021; 11:716844. [PMID: 34552872 PMCID: PMC8450565 DOI: 10.3389/fonc.2021.716844] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 08/18/2021] [Indexed: 12/11/2022] Open
Abstract
Hepatocellular cancer is the sixth most frequently diagnosed malignant disease worldwide, and was responsible for tens of millions of deaths in 2020; however, treatment options for patients with advanced hepatocellular carcinoma remain limited. Immunotherapy has undergone rapid development over recent years, especially in the field of immune checkpoint inhibitors (ICIs). These drugs aim to activate and enhance antitumor immunity and represent a new prospect for the treatment of patients with advanced cancer. Nevertheless, only a small proportion of liver cancer patients currently benefit from ICI-based treatment, highlighting the need to better understand how ICIs and tumors interact, as well as identify predictive biomarkers for immunotherapeutic responses. In this review, we highlight clinical trials and basic research in hepatocellular carcinoma, with a particular focus on predictive biomarkers for the therapeutic efficacy of ICIs. Predictive biomarkers for immune-related adverse events are also discussed.
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Affiliation(s)
- Yuqing He
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengyao Lu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Che
- College of Life Sciences, Wuhan University, Wuhan, China
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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143
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Majidova K, Handfield J, Kafi K, Martin RD, Kubinski R. Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review. Genes (Basel) 2021; 12:1465. [PMID: 34680860 PMCID: PMC8535572 DOI: 10.3390/genes12101465] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Inflammatory bowel diseases (IBD), subdivided into Crohn's disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial intelligence (AI) has increased. The purpose of this scoping review is to explore this growing field of research to summarize the role of DH and AI in the diagnosis, treatment, monitoring and prognosis of IBD. A review of 21 articles revealed the impact of both AI algorithms and DH technologies; AI algorithms can improve diagnostic accuracy, assess disease activity, and predict treatment response based on data modalities such as endoscopic imaging and genetic data. In terms of DH, patients utilizing DH platforms experienced improvements in quality of life, disease literacy, treatment adherence, and medication management. In addition, DH methods can reduce the need for in-person appointments, decreasing the use of healthcare resources without compromising the standard of care. These articles demonstrate preliminary evidence of the potential of DH and AI for improving the management of IBD. However, the majority of these studies were performed in a regulated clinical environment. Therefore, further validation of these results in a real-world environment is required to assess the efficacy of these methods in the general IBD population.
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Affiliation(s)
| | | | | | | | - Ryszard Kubinski
- Phyla Technologies Inc., Montréal, QC H3C 4J9, Canada; (K.M.); (J.H.); (K.K.); (R.D.M.)
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Human Milk Oligosaccharide-Stimulated Bifidobacterium Species Contribute to Prevent Later Respiratory Tract Infections. Microorganisms 2021; 9:microorganisms9091939. [PMID: 34576834 PMCID: PMC8465161 DOI: 10.3390/microorganisms9091939] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022] Open
Abstract
(1) Background: Human milk oligosaccharides (HMOs) may support immune protection, partly via their action on the early-life gut microbiota. Exploratory findings of a randomized placebo-controlled trial associated 2′fucosyllactose (2′FL) and lacto-N-neotetraose (LNnT) formula feeding with reduced risk for reported bronchitis and lower respiratory tract illnesses (LRTI), as well as changes in gut microbiota composition. We sought to identify putative gut microbial mechanisms linked with these clinical observations. (2) Methods: We used stool microbiota composition, metabolites including organic acids and gut health markers in several machine-learning-based classification tools related prospectively to experiencing reported bronchitis or LRTI, as compared to no reported respiratory illness. We performed preclinical epithelial barrier function modelling to add mechanistic insight to these clinical observations. (3) Results: Among the main features discriminant for infants who did not experience any reported bronchitis (n = 80/106) or LRTI (n = 70/103) were the 2-HMO formula containing 2′FL and LNnT, higher acetate, fucosylated glycans and Bifidobacterium, as well as lower succinate, butyrate, propionate and 5-aminovalerate, along with Carnobacteriaceae members and Escherichia. Acetate correlated with several Bifidobacterium species. By univariate analysis, infants experiencing no bronchitis or LRTI, compared with those who did, showed higher acetate (p < 0.007) and B. longum subsp. infantis (p ≤ 0.03). In vitro experiments demonstrate that 2′FL, LNnT and lacto-N-tetraose (LNT) stimulated B. longum subsp. infantis (ATCC15697) metabolic activity. Metabolites in spent culture media, primarily due to acetate, supported epithelial barrier protection. (4) Conclusions: An early-life gut ecology characterized by Bifidobacterium-species-driven metabolic changes partly explains the observed clinical outcomes of reduced risk for bronchitis and LRTI in infants fed a formula with HMOs. (Trial registry number NCT01715246.).
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Vänni P, Tejesvi MV, Ainonen S, Renko M, Korpela K, Salo J, Paalanne N, Tapiainen T. Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome. Sci Rep 2021; 11:17483. [PMID: 34471207 PMCID: PMC8410856 DOI: 10.1038/s41598-021-97007-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Delivery mode and perinatal antibiotics influence gut microbiome composition in children. Most microbiome studies have used the sequencing of the bacterial 16S marker gene but have not reported the metabolic function of the gut microbiome, which may mediate biological effects on the host. Here, we used the PICRUSt2 bioinformatics tool to predict the functional profiles of the gut microbiome based on 16S sequencing in two child cohorts. Both Caesarean section and perinatal antibiotics markedly influenced the functional profiles of the gut microbiome at the age of 1 year. In machine learning analysis, bacterial fatty acid, phospholipid, and biotin biosynthesis were the most important pathways that differed according to delivery mode. Proteinogenic amino acid biosynthesis, carbohydrate degradation, pyrimidine deoxyribonucleotide and biotin biosynthesis were the most important pathways differing according to antibiotic exposure. Our study shows that both Caesarean section and perinatal antibiotics markedly influence the predicted metabolic profiles of the gut microbiome at the age of 1 year.
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Affiliation(s)
- Petri Vänni
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland.
| | - Mysore V Tejesvi
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Ecology and Genetics, Faculty of Science, University of Oulu, Oulu, Finland
| | - Sofia Ainonen
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Marjo Renko
- Department of Paediatrics, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Katja Korpela
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Jarmo Salo
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
| | - Niko Paalanne
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
| | - Terhi Tapiainen
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
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Abstract
Quantitative comparison among microbiomes can link microbial beta-diversity to environmental features, thus enabling prediction of ecosystem properties or dissection of host-microbiome interaction. However, to compute beta-diversity, current methods mainly employ the entire community profiles of taxa or functions, which can miss the subtle differences caused by low-abundance community members that may play crucial roles in the properties of interest. In this work, I review the distance metrics and search engines that we developed to match microbiomes at a large scale based on whole-community-level similarities, as well as their limitations in tackling the microbiome changes caused by less abundant community features. Then I propose the concept of microbiome "local alignment," including an algorithm to measure microbiome similarity on specific fractions of biodiversity and an indexing strategy for rapidly fetching microbiome local-alignment matches from the data repository.
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Affiliation(s)
- Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, China
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
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147
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Abstract
Microbes serve as sensitive indicators of ecosystem change due to their vast diversity and tendency to change in abundance in response to environmental conditions. Although we most frequently observe these changes to study the microbial community itself, it is increasingly common to use them to understand the surrounding environment. In this way microbial communities can be thought of as powerful sensors capable of reporting shifts in chemical or physical conditions with high fidelity. In this commentary, I further explore this idea by drawing a comparison to the olfactory system, where populations of sensory neurons respond to the presence of specific odorants. The possible combinations of sensory neurons that can transduce a signal are virtually limitless. Yet, the brain can deconvolute the signal into recognizable and actionable data. The further development of machine learning techniques and its application hold great promise for our ability to interpret microbes to detect environmental change.
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Arukha AP, Freguia CF, Mishra M, Jha JK, Kariyawasam S, Fanger NA, Zimmermann EM, Fanger GR, Sahay B. Lactococcus lactis Delivery of Surface Layer Protein A Protects Mice from Colitis by Re-Setting Host Immune Repertoire. Biomedicines 2021; 9:1098. [PMID: 34572293 PMCID: PMC8470720 DOI: 10.3390/biomedicines9091098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 12/13/2022] Open
Abstract
Inflammatory bowel disease (IBD) is characterized by gastrointestinal inflammation comprised of Crohn's disease and ulcerative colitis. Centers for Disease Control and Prevention report that 1.3% of the population of the United States (approximately 3 million people) were affected by the disease in 2015, and the number keeps increasing over time. IBD has a multifactorial etiology, from genetic to environmental factors. Most of the IBD treatments revolve around disease management, by reducing the inflammatory signals. We previously identified the surface layer protein A (SlpA) of Lactobacillus acidophilus that possesses anti-inflammatory properties to mitigate murine colitis. Herein, we expressed SlpA in a clinically relevant, food-grade Lactococcus lactis to further investigate and characterize the protective mechanisms of the actions of SlpA. Oral administration of SlpA-expressing L. lactis (R110) mitigated the symptoms of murine colitis. Oral delivery of R110 resulted in a higher expression of IL-27 by myeloid cells, with a synchronous increase in IL-10 and cMAF in T cells. Consistent with murine studies, human dendritic cells exposed to R110 showed exquisite differential gene regulation, including IL-27 transcription, suggesting a shared mechanism between the two species, hence positioning R110 as potentially effective at treating colitis in humans.
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Affiliation(s)
- Ananta Prasad Arukha
- Department of Infectious Diseases and Immunology, University of Florida, Gainesville, FL 32608, USA; (A.P.A.); (M.M.)
- Comparative, Diagnostic and Population Medicine, University of Florida, Gainesville, FL 32608, USA;
| | | | - Meerambika Mishra
- Department of Infectious Diseases and Immunology, University of Florida, Gainesville, FL 32608, USA; (A.P.A.); (M.M.)
| | - Jyoti K. Jha
- Rise Therapeutics, Rockville, MD 20850, USA; (C.F.F.); (J.K.J.); (G.R.F.)
| | - Subhashinie Kariyawasam
- Comparative, Diagnostic and Population Medicine, University of Florida, Gainesville, FL 32608, USA;
| | | | - Ellen M. Zimmermann
- Division of Gastroenterology, University of Florida College of Medicine, Gainesville, FL 32608, USA;
| | - Gary R. Fanger
- Rise Therapeutics, Rockville, MD 20850, USA; (C.F.F.); (J.K.J.); (G.R.F.)
| | - Bikash Sahay
- Department of Infectious Diseases and Immunology, University of Florida, Gainesville, FL 32608, USA; (A.P.A.); (M.M.)
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149
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Nguyen QP, Karagas MR, Madan JC, Dade E, Palys TJ, Morrison HG, Pathmasiri WW, McRitche S, Sumner SJ, Frost HR, Hoen AG. Associations between the gut microbiome and metabolome in early life. BMC Microbiol 2021; 21:238. [PMID: 34454437 PMCID: PMC8400760 DOI: 10.1186/s12866-021-02282-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 07/14/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life. RESULTS Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. CONCLUSIONS Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.
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Affiliation(s)
- Quang P. Nguyen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Margaret R. Karagas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Children’s Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH USA
| | - Juliette C. Madan
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Children’s Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH USA
- Department of Pediatrics, Children’s Hospital at Dartmouth, Hanover, NH USA
| | - Erika Dade
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Thomas J. Palys
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Hilary G. Morrison
- Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA USA
| | - Wimal W. Pathmasiri
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Susan McRitche
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Susan J. Sumner
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Anne G. Hoen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Children’s Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH USA
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150
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Bakir-Gungor B, Bulut O, Jabeer A, Nalbantoglu OU, Yousef M. Discovering Potential Taxonomic Biomarkers of Type 2 Diabetes From Human Gut Microbiota via Different Feature Selection Methods. Front Microbiol 2021; 12:628426. [PMID: 34512559 PMCID: PMC8424122 DOI: 10.3389/fmicb.2021.628426] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 05/03/2021] [Indexed: 12/24/2022] Open
Abstract
Human gut microbiota is a complex community of organisms including trillions of bacteria. While these microorganisms are considered as essential regulators of our immune system, some of them can cause several diseases. In recent years, next-generation sequencing technologies accelerated the discovery of human gut microbiota. In this respect, the use of machine learning techniques became popular to analyze disease-associated metagenomics datasets. Type 2 diabetes (T2D) is a chronic disease and affects millions of people around the world. Since the early diagnosis in T2D is important for effective treatment, there is an utmost need to develop a classification technique that can accelerate T2D diagnosis. In this study, using T2D-associated metagenomics data, we aim to develop a classification model to facilitate T2D diagnosis and to discover T2D-associated biomarkers. The sequencing data of T2D patients and healthy individuals were taken from a metagenome-wide association study and categorized into disease states. The sequencing reads were assigned to taxa, and the identified species are used to train and test our model. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization, Maximum Relevance and Minimum Redundancy, Correlation Based Feature Selection, and select K best approach. To test the performance of the classification based on the features that are selected by different methods, we used random forest classifier with 100-fold Monte Carlo cross-validation. In our experiments, we observed that 15 commonly selected features have a considerable effect in terms of minimizing the microbiota used for the diagnosis of T2D and thus reducing the time and cost. When we perform biological validation of these identified species, we found that some of them are known as related to T2D development mechanisms and we identified additional species as potential biomarkers. Additionally, we attempted to find the subgroups of T2D patients using k-means clustering. In summary, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of T2D, investigates potential biomarkers of T2D, and finds out which subset of microbiota is more informative than other taxa by applying state-of-the art feature selection methods.
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Affiliation(s)
- Burcu Bakir-Gungor
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gül University, Kayseri, Turkey
| | - Osman Bulut
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gül University, Kayseri, Turkey
| | - Amhar Jabeer
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gül University, Kayseri, Turkey
| | - O. Ufuk Nalbantoglu
- Department of Computer Engineering, Genome and Stem Cell Center, Erciyes University, Kayseri, Turkey
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center, Zefat Academic College, Zefat, Israel
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