1
|
Creus-Martí I, Moya A, Santonja FJ. Methodology for microbiome data analysis: An overview. Comput Biol Med 2025; 192:110157. [PMID: 40279974 DOI: 10.1016/j.compbiomed.2025.110157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 03/07/2025] [Accepted: 04/04/2025] [Indexed: 04/29/2025]
Abstract
It is known that microbiome and health are related, in addition, recent research has found that microbiome has potential clinical uses. These facts highlight the importance of the microbiome in actual science. However, microbiome data has some characteristics that makes its statistical study challenging. In recent years, longitudinal and non-longitudinal methods have been designed to analyze the microbiota and knowing more about the bacterial behavior. In this article in the form of a review we summarize the characteristics of microbiome data and the statistical methods most widespread to analyze it. We have taken into account if the strategies are longitudinal or not. We also classify the methods based on their specific analytical objectives and based on their mathematical characteristics. The methods are structured according to their biological goals and mathematical features, ensuring that the insights provided are both relevant and accessible to professionals in biology and statistics. We present this review as a reference for the most widely used methods in microbiome data analysis and as a foundation for identifying potential areas for future research. We want to point out that this review can be particularly useful to remark the importance of the methodology designed in order to study microbiome longitudinal datasets.
Collapse
Affiliation(s)
- Irene Creus-Martí
- Department of Applied Mathematics, Universitat Politècnica de València, Valencia, Spain.
| | - Andrés Moya
- Institute for Integrative Systems Biology (I2Sysbio), Universitat de València and CSIC, València, Spain; The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain; CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Francisco J Santonja
- Department of Statistics and Operation Research, Universitat de València, Valencia, Spain
| |
Collapse
|
2
|
Chen S, Wang Y, Da Zhou, Hu J. Bayesian Inference of Phenotypic Plasticity of Cancer Cells Based on Dynamic Model for Temporal Cell Proportion Data. Biom J 2025; 67:e70055. [PMID: 40298362 DOI: 10.1002/bimj.70055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 09/18/2024] [Accepted: 10/31/2024] [Indexed: 04/30/2025]
Abstract
Mounting evidence underscores the prevalent hierarchical organization of cancer tissues. At the foundation of this hierarchy reside cancer stem cells, a subset of cells endowed with the pivotal role of engendering the entire cancer tissue through cell differentiation. In recent times, substantial attention has been directed toward the phenomenon of cancer cell plasticity, where the dynamic interconversion between cancer stem cells and nonstem cancer cells has garnered significant interest. Since the task of detecting cancer cell plasticity from empirical data remains a formidable challenge, we propose a Bayesian statistical framework designed to infer phenotypic plasticity within cancer cells, utilizing temporal data on cancer stem cell proportions. Our approach is grounded in a stochastic model, adept at capturing the dynamic behaviors of cells. Leveraging Bayesian analysis, we scrutinize the moment equation governing cancer stem cell proportions, derived from the Kolmogorov forward equation of our stochastic model. Our methodology introduces an improved Euler method for parameter estimation within nonlinear ordinary differential equation models, also extending insights to compositional data. Extensive simulations robustly validate the efficacy of our proposed method. To further corroborate our findings, we apply our approach to analyze published data from SW620 colon cancer cell lines. Our results harmonize with in situ experiments, thereby reinforcing the utility of our method in discerning and quantifying phenotypic plasticity within cancer cells.
Collapse
Affiliation(s)
- Shuli Chen
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong, China
- School of Mathematical Science, Xiamen University, Xiamen, Fujian, China
| | - Yuman Wang
- School of Mathematical Science, Xiamen University, Xiamen, Fujian, China
| | - Da Zhou
- School of Mathematical Science, Xiamen University, Xiamen, Fujian, China
| | - Jie Hu
- School of Mathematical Science, Xiamen University, Xiamen, Fujian, China
| |
Collapse
|
3
|
Hammond J, Smith VA. Bayesian networks for network inference in biology. J R Soc Interface 2025; 22:20240893. [PMID: 40328299 PMCID: PMC12055290 DOI: 10.1098/rsif.2024.0893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 02/14/2025] [Accepted: 02/20/2025] [Indexed: 05/08/2025] Open
Abstract
Bayesian networks (BNs) have been used for reconstructing interactions from biological data, in disciplines ranging from molecular biology to ecology and neuroscience. BNs learn conditional dependencies between variables, which best 'explain' the data, represented as a directed graph which approximates the relationships between variables. In the 2000s, BNs were a popular method that promised an approach capable of inferring biological networks from data. Here, we review the use of BNs applied to biological data over the past two decades and evaluate their efficacy. We find that BNs are successful in inferring biological networks, frequently identifying novel interactions or network components missed by previous analyses. We suggest that as false positive results are underreported, it is difficult to assess the accuracy of BNs in inferring biological networks. BN learning appears most successful for small numbers of variables with high-quality datasets that either discretize the data into few states or include perturbative data. We suggest that BNs have failed to live up to the promise of the 2000s but that this is most likely due to experimental constraints on datasets, and the success of BNs at inferring networks in a variety of biological contexts suggests they are a powerful tool for biologists.
Collapse
Affiliation(s)
- James Hammond
- Department of Biology, University of Oxford, Oxford, UK
- School of Biology, University of St Andrews, St Andrews, UK
| | - V. Anne Smith
- School of Biology, University of St Andrews, St Andrews, UK
| |
Collapse
|
4
|
Špiljak B, Ozretić P, Andabak Rogulj A, Lončar Brzak B, Brailo V, Škerlj M, Vidović Juras D. Oral Microbiome Research in Biopsy Samples of Oral Potentially Malignant Disorders and Oral Squamous Cell Carcinoma and Its Challenges. APPLIED SCIENCES 2024; 14:11405. [DOI: 10.3390/app142311405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
This study aims to evaluate the potential benefits and challenges of integrating oral microbiome research into the clinical management of oral potentially malignant disorders (OPMD) and oral squamous cell carcinoma (OSCC). The oral microbiome has gained significant attention for its role in the pathogenesis and progression of these conditions, with emerging evidence suggesting its value as a diagnostic and prognostic tool. By critically analyzing current evidence and methodological considerations, this manuscript examines whether microbiome analysis in biopsy samples can aid in the early detection, prognosis, and management of OPMD and OSCC. The complexity and dynamic nature of the oral microbiome require a multifaceted approach to fully understand its clinical utility. Based on this review, we conclude that studying the oral microbiome in this context holds significant promise but also faces notable challenges, including methodological variability and the need for standardization. Ultimately, this manuscript addresses the question, “Should such research be undertaken, given the intricate interactions of various factors and the inherent obstacles involved?”, and also emphasizes the importance of further research to optimize clinical applications and improve patient outcomes.
Collapse
Affiliation(s)
- Bruno Špiljak
- Department of Oral Medicine, University of Zagreb School of Dental Medicine, 10000 Zagreb, Croatia
| | - Petar Ozretić
- Laboratory for Hereditary Cancer, Division of Molecular Medicine, Ruđer Bošković Institute, 10000 Zagreb, Croatia
| | - Ana Andabak Rogulj
- Department of Oral Medicine, University of Zagreb School of Dental Medicine, 10000 Zagreb, Croatia
- Clinical Department of Oral Diseases, Dental Clinic, University Hospital Centre (UHC) Zagreb, 10000 Zagreb, Croatia
| | - Božana Lončar Brzak
- Department of Oral Medicine, University of Zagreb School of Dental Medicine, 10000 Zagreb, Croatia
| | - Vlaho Brailo
- Department of Oral Medicine, University of Zagreb School of Dental Medicine, 10000 Zagreb, Croatia
- Clinical Department of Oral Diseases, Dental Clinic, University Hospital Centre (UHC) Zagreb, 10000 Zagreb, Croatia
| | - Marija Škerlj
- Oncological Cytology Department, Ljudevit Jurak Clinical Department of Pathology and Cytology, Sestre Milosrdnice University Hospital Center Zagreb, 10000 Zagreb, Croatia
| | - Danica Vidović Juras
- Department of Oral Medicine, University of Zagreb School of Dental Medicine, 10000 Zagreb, Croatia
- Clinical Department of Oral Diseases, Dental Clinic, University Hospital Centre (UHC) Zagreb, 10000 Zagreb, Croatia
| |
Collapse
|
5
|
Oh VKS, Li RW. Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400458. [PMID: 39535493 DOI: 10.1002/advs.202400458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 09/16/2024] [Indexed: 11/16/2024]
Abstract
Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.
Collapse
Affiliation(s)
- Vera-Khlara S Oh
- Big Biomedical Data Integration and Statistical Analysis (DIANA) Research Center, Department of Data Science, College of Natural Sciences, Jeju National University, Jeju City, Jeju Do, 63243, South Korea
| | - Robert W Li
- United States Department of Agriculture, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705, USA
| |
Collapse
|
6
|
Melkikh AV. Why does a cell function? New arguments in favor of quantum effects. Biosystems 2024; 245:105311. [PMID: 39173899 DOI: 10.1016/j.biosystems.2024.105311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 08/18/2024] [Accepted: 08/19/2024] [Indexed: 08/24/2024]
Abstract
In this study, the complexities of intracellular processes have been analyzed, including DNA folding, alternative splicing, mitochondrial function, and enzyme transport in lysosomes. Based on a previously proposed hypothesis (Levinthal's generalized paradox), a conclusion is made that all abovementioned processes cannot be realized with sufficient accuracy and in a realistic timeframe within the framework of classical physics. It is unclear why the cell functions at all. For the cell to function, its internal environment must be highly structured. In this regard, the cell shares similarities with computational devices (computers). In this study, quantum models of interactions between biologically important molecules were constructed, taking into account the long-range effects. One significant aspect of these models is the special role of the phase of the wavefunction, which serves as a controlling parameter. Experiments have been proposed that may confirm or refute these models.
Collapse
Affiliation(s)
- A V Melkikh
- Ural Federal University, Yekaterinburg, Russia.
| |
Collapse
|
7
|
Ruiz-Perez D, Gimon I, Sazal M, Mathee K, Narasimhan G. Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA. mSystems 2024; 9:e0130323. [PMID: 39240096 PMCID: PMC11494969 DOI: 10.1128/msystems.01303-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 07/10/2024] [Indexed: 09/07/2024] Open
Abstract
A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases. IMPORTANCE We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
Collapse
Affiliation(s)
- Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
| | - Isabella Gimon
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
| | - Musfiqur Sazal
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
| | - Kalai Mathee
- Florida International University, Miami, Florida, USA
- Biomolecular Sciences Institute, Florida International University, Miami, Florida, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
- Biomolecular Sciences Institute, Florida International University, Miami, Florida, USA
| |
Collapse
|
8
|
Peleg O, Borenstein E. Interpolation of microbiome composition in longitudinal data sets. mBio 2024; 15:e0115024. [PMID: 39162569 PMCID: PMC11389371 DOI: 10.1128/mbio.01150-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 07/11/2024] [Indexed: 08/21/2024] Open
Abstract
The human gut microbiome significantly impacts health, prompting a rise in longitudinal studies that capture microbiome samples at multiple time points. Such studies allow researchers to characterize microbiome changes over time, but importantly, also present major analytical challenges due to incomplete or irregular sampling. To address this challenge, longitudinal microbiome studies often employ various interpolation methods, aiming to infer missing microbiome data. However, to date, a comprehensive assessment of such microbiome interpolation techniques, as well as best practice guidelines for interpolating microbiome data, is still lacking. This work aims to fill this gap, rigorously implementing and systematically evaluating a large array of interpolation methods, spanning several different categories, for longitudinal microbiome interpolation. To assess each method and its ability to accurately infer microbiome composition at missing time points, we used three longitudinal microbiome data sets that follow individuals over a long period of time and a leave-one-out approach. Overall, our analysis demonstrated that the K-nearest neighbors algorithm consistently outperforms other methods in interpolation accuracy, yet, accuracy varied widely across data sets, individuals, and time. Factors such as microbiome stability, sample size, and the time gap between interpolated and adjacent samples significantly influenced accuracy, allowing us to develop a model for predicting the expected interpolation accuracy at a missing time point. Our findings, combined, suggest that accurate interpolation in longitudinal microbiome data is feasible, especially in dense cohorts. Furthermore, using our predictive model, future studies can interpolate data only in time points where the expected interpolation accuracy is high. IMPORTANCE Since missing samples are common in longitudinal microbiome dataset due to inconsistent collection practices, it is important to evaluate and benchmark different interpolation methods for predicting microbiome composition in such samples and facilitate downstream analysis. Our study rigorously evaluated several such methods and identified the K-nearest neighbors approach as particularly effective for this task. The study also notes significant variability in interpolation accuracy among individuals, influenced by factors such as age, sample size, and sampling frequency. Furthermore, we developed a predictive model for estimating interpolation accuracy at a specific time point, enhancing the reliability of such analyses in future studies. Combined, our study, thus, provides critical insights and tools that enhance the accuracy and reliability of data interpolation methods in the growing field of longitudinal microbiome research.
Collapse
Affiliation(s)
- Omri Peleg
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, New Mexico, USA
| |
Collapse
|
9
|
Deek RA, Ma S, Lewis J, Li H. Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data. eLife 2024; 13:e88956. [PMID: 38832759 PMCID: PMC11149933 DOI: 10.7554/elife.88956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/10/2024] [Indexed: 06/05/2024] Open
Abstract
Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.
Collapse
Affiliation(s)
- Rebecca A Deek
- Department of Biostatistics, University of PittsburghPittsburghUnited States
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt School of MedicineNashvilleUnited States
| | - James Lewis
- Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| |
Collapse
|
10
|
Xiao L, Zhou T, Zuo Z, Sun N, Zhao F. Spatiotemporal patterns of the pregnancy microbiome and links to reproductive disorders. Sci Bull (Beijing) 2024; 69:1275-1285. [PMID: 38388298 DOI: 10.1016/j.scib.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/04/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024]
Abstract
The microbiome of females undergoes extensive remodeling during pregnancy, which is likely to have an impact on the health of both mothers and offspring. Nevertheless, large-scale integrated investigations characterizing microbiome dynamics across key body habitats are lacking. Here, we performed an extensive meta-analysis that compiles and analyzes microbiome profiles from >10,000 samples across the gut, vagina, and oral cavity of pregnant women from diverse geographical regions. We have unveiled unexpected variations in the taxonomic, functional, and ecological characteristics of microbial communities throughout the course of pregnancy. The gut microbiota showed distinct trajectories between Western and non-Western populations. The vagina microbiota exhibited fluctuating transitions at the genus level across gestation, while the oral microbiota remained relatively stable. We also identified distinctive microbial signatures associated with prevalent pregnancy-related disorders, including opposite variations in the oral and gut microbiota of patients with gestational diabetes and disrupted microbial networks in preterm birth. This study establishes a comprehensive atlas of the pregnancy microbiome by integrating multidimensional datasets and offers foundational insights into the intricate interplay between microbes and host factors that underlie reproductive health.
Collapse
Affiliation(s)
- Liwen Xiao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Tian Zhou
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenqiang Zuo
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Ningxia Sun
- Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China; Department of Reproductive Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
11
|
Sizemore N, Oliphant K, Zheng R, Martin CR, Claud EC, Chattopadhyay I. A digital twin of the infant microbiome to predict neurodevelopmental deficits. SCIENCE ADVANCES 2024; 10:eadj0400. [PMID: 38598636 PMCID: PMC11006218 DOI: 10.1126/sciadv.adj0400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 03/06/2024] [Indexed: 04/12/2024]
Abstract
Despite the recognized gut-brain axis link, natural variations in microbial profiles between patients hinder definition of normal abundance ranges, confounding the impact of dysbiosis on infant neurodevelopment. We infer a digital twin of the infant microbiome, forecasting ecosystem trajectories from a few initial observations. Using 16S ribosomal RNA profiles from 88 preterm infants (398 fecal samples and 32,942 abundance estimates for 91 microbial classes), the model (Q-net) predicts abundance dynamics with R2 = 0.69. Contrasting the fit to Q-nets of typical versus suboptimal development, we can reliably estimate individual deficit risk (Mδ) and identify infants achieving poor future head circumference growth with ≈76% area under the receiver operator characteristic curve, 95% ± 1.8% positive predictive value at 98% specificity at 30 weeks postmenstrual age. We find that early transplantation might mitigate risk for ≈45.2% of the cohort, with potentially negative effects from incorrect supplementation. Q-nets are generative artificial intelligence models for ecosystem dynamics, with broad potential applications.
Collapse
Affiliation(s)
- Nicholas Sizemore
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Kaitlyn Oliphant
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, USA
| | - Ruolin Zheng
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Camilia R. Martin
- Division of Neonatology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Erika C. Claud
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, USA
- Neonatology Research, University of Chicago, Chicago, IL 60637, USA
| | - Ishanu Chattopadhyay
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
- Committee on Quantitative Methods in Social, Behavioral, and Health Sciences, University of Chicago, Chicago, IL 60637, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
- Center for Health Statistics, University of Chicago, Chicago, IL 60637, USA
| |
Collapse
|
12
|
Luo M, Zhu J, Jia J, Zhang H, Zhao J. Progress on network modeling and analysis of gut microecology: a review. Appl Environ Microbiol 2024; 90:e0009224. [PMID: 38415584 PMCID: PMC11207142 DOI: 10.1128/aem.00092-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Abstract
The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.
Collapse
Affiliation(s)
- Meng Luo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jiajia Jia
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| |
Collapse
|
13
|
Tsamir-Rimon M, Borenstein E. A manifold-based framework for studying the dynamics of the vaginal microbiome. NPJ Biofilms Microbiomes 2023; 9:102. [PMID: 38102172 PMCID: PMC10724123 DOI: 10.1038/s41522-023-00471-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
The vaginal microbiome plays a crucial role in our health. The composition of this community can be classified into five community state types (CSTs), four of which are primarily consisted of Lactobacillus species and considered healthy, while the fifth features non-Lactobacillus populations and signifies a disease state termed Bacterial vaginosis (BV), which is associated with various symptoms and increased susceptibility to diseases. Importantly, however, the exact mechanisms and dynamics underlying BV development are not yet fully understood, including specifically possible routes from a healthy to a BV state. To address this gap, this study set out to characterize the progression from healthy- to BV-associated compositions by analyzing 8026 vaginal samples and using a manifold-detection framework. This approach, inspired by single-cell analysis, aims to identify low-dimensional trajectories in the high-dimensional composition space. It further orders samples along these trajectories and assigns a score (pseudo-time) to each analyzed or new sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV states for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. Several BV indicators can also be successfully predicted based on pseudo-time scores, and key taxa involved in BV development can be identified using this approach. Taken together, these findings demonstrate how manifold detection can be used to successfully characterize the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.
Collapse
Affiliation(s)
| | - Elhanan Borenstein
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
- Santa Fe Institute, Santa Fe, NM, USA.
| |
Collapse
|
14
|
Ruiz-Perez D, Gimon I, Sazal M, Mathee K, Narasimhan G. Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571384. [PMID: 38168315 PMCID: PMC10760167 DOI: 10.1101/2023.12.12.571384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
Collapse
Affiliation(s)
- Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Isabella Gimon
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Musfiqur Sazal
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Kalai Mathee
- Florida International University, Miami, FL 33199, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
| |
Collapse
|
15
|
Tsamir-Rimon M, Borenstein E. A Manifold-Based Framework for Studying the Dynamics of the Vaginal Microbiome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556518. [PMID: 37732273 PMCID: PMC10508760 DOI: 10.1101/2023.09.06.556518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) - a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsel's test, and several Amsel's criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.
Collapse
Affiliation(s)
| | - Elhanan Borenstein
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, NM, USA
| |
Collapse
|
16
|
Muralitharan RR, Snelson M, Meric G, Coughlan MT, Marques FZ. Guidelines for microbiome studies in renal physiology. Am J Physiol Renal Physiol 2023; 325:F345-F362. [PMID: 37440367 DOI: 10.1152/ajprenal.00072.2023] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/28/2023] [Accepted: 07/07/2023] [Indexed: 07/15/2023] Open
Abstract
Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.
Collapse
Affiliation(s)
- Rikeish R Muralitharan
- Hypertension Research Laboratory, School of Biological Sciences, Faculty of Science, Monash University, Melbourne, Victoria, Australia
- Institute for Medical Research, Ministry of Health Malaysia, Kuala Lumpur, Malaysia
| | - Matthew Snelson
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Guillaume Meric
- Cambridge-Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
| | - Melinda T Coughlan
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Parkville, Victoria, Australia
| | - Francine Z Marques
- Hypertension Research Laboratory, School of Biological Sciences, Faculty of Science, Monash University, Melbourne, Victoria, Australia
- Heart Failure Research Group, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
17
|
Lyu X, Du Y, Liu G, Mai T, Li Y, Zhang Z, Bei C. Prevalence and influencing factors of hyperuricemia in middle-aged and older adults in the Yao minority area of China: a cross-sectional study. Sci Rep 2023; 13:10185. [PMID: 37349536 PMCID: PMC10287663 DOI: 10.1038/s41598-023-37274-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/19/2023] [Indexed: 06/24/2023] Open
Abstract
Hyperuricemia (HUA) endangers human health, and its prevalence has increased rapidly in recent decades. The current study investigated HUA's prevalence and influencing factors in Gongcheng, southern China. A cross-sectional investigation was conducted; 2128 participants aged 30-93 years were included from 2018 to 2019. Univariate and multivariate logistic regression models were used to screen HUA variables. A Bayesian network model was constructed using the PC algorithm to evaluate the association between influencing factors and HUA. The prevalence of HUA was 15.6% (23.2% in men, 10.7% in women). After screening the variables using a logistic regression analysis model, fatty liver disease (FLD), dyslipidemia, abdominal obesity, creatinine (CREA), somatotype, bone mass, drinking, and physical activity level at work were included in the Bayesian network model. The model results showed that dyslipidemia, somatotype, CREA, and drinking were directly related to HUA. Bone mass and FLD were indirectly associated with HUA by affecting the somatotype. The prevalence of HUA in Gongcheng was high in China. The prevalence of HUA was related to somatotype, drinking, bone mass, physical activity level at work, and other metabolic diseases. A good diet and moderate exercise are recommended to maintain a healthy somatotype and reduce the prevalence rate of HUA.
Collapse
Affiliation(s)
- Xiao Lyu
- Department of Epidemiology and Health Statistics, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China
| | - Yuanxiao Du
- Department of Epidemiology and Health Statistics, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China
| | - Guoyu Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China
| | - Tingyu Mai
- Department of Environmental and Occupational Health, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China
| | - You Li
- Department of Environmental and Occupational Health, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China
| | - Zhiyong Zhang
- Department of Environmental and Occupational Health, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China.
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, School of Public Health, Guilin Medical University, Guilin, China.
| | - Chunhua Bei
- Department of Epidemiology and Health Statistics, School of Public Health, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin, 541004, Guangxi, China.
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guangxi Health Commission Key Laboratory of Entire Lifecycle Health and Care, School of Public Health, Guilin Medical University, Guilin, China.
| |
Collapse
|
18
|
Valdes C, Stebliankin V, Ruiz-Perez D, Park JI, Lee H, Narasimhan G. Microbiome maps: Hilbert curve visualizations of metagenomic profiles. FRONTIERS IN BIOINFORMATICS 2023; 3:1154588. [PMID: 37405310 PMCID: PMC10317073 DOI: 10.3389/fbinf.2023.1154588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/22/2023] [Indexed: 07/06/2023] Open
Abstract
Abundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. We created Jasper, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a "Microbiome Map", where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order taxa in multiple ways, and the resulting microbiome maps can highlight "hot spots" of microbes that are dominant in taxonomic clades or biological conditions. We use Jasper to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes.
Collapse
Affiliation(s)
- Camilo Valdes
- Lawrence Livermore National Laboratory, Physical and Life Sciences Directorate, Livermore, CA, United States
| | - Vitalii Stebliankin
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, United States
| | - Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, United States
| | - Ji In Park
- Department of Medicine, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Gangwon-do, Republic of Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, United States
- Biomolecular Sciences Institute, Florida International University, Miami, FL, United States
| |
Collapse
|
19
|
Yousefi B, Melograna F, Galazzo G, van Best N, Mommers M, Penders J, Schwikowski B, Van Steen K. Capturing the dynamics of microbial interactions through individual-specific networks. Front Microbiol 2023; 14:1170391. [PMID: 37256048 PMCID: PMC10225591 DOI: 10.3389/fmicb.2023.1170391] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
Collapse
Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
- École Doctorale Complexite du vivant, Sorbonne University, Paris, France
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Niels van Best
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Institute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, Germany
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands
| | - John Penders
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, Belgium
| |
Collapse
|
20
|
Shen K, Din AU, Sinha B, Zhou Y, Qian F, Shen B. Translational informatics for human microbiota: data resources, models and applications. Brief Bioinform 2023; 24:7152256. [PMID: 37141135 DOI: 10.1093/bib/bbad168] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 05/05/2023] Open
Abstract
With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.
Collapse
Affiliation(s)
- Ke Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Ahmad Ud Din
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Baivab Sinha
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Yi Zhou
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Fuliang Qian
- Center for Systems Biology, Suzhou Medical College of Soochow University, Suzhou 215123, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou 215123, China
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| |
Collapse
|
21
|
Aldirawi H, Morales FG. Univariate and Multivariate Statistical Analysis of Microbiome Data: An Overview. Appl Microbiol 2023. [DOI: 10.3390/applmicrobiol3020023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Microbiome data is high dimensional, sparse, compositional, and over-dispersed. Therefore, modeling microbiome data is very challenging and it is an active research area. Microbiome analysis has become a progressing area of research as microorganisms constitute a large part of life. Since many methods of microbiome data analysis have been presented, this review summarizes the challenges, methods used, and the advantages and disadvantages of those methods, to serve as an updated guide for those in the field. This review also compared different methods of analysis to progress the development of newer methods.
Collapse
|
22
|
Yang L, Chen J. Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. Brief Bioinform 2023; 24:bbac607. [PMID: 36617187 PMCID: PMC9851339 DOI: 10.1093/bib/bbac607] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/16/2022] [Accepted: 12/10/2022] [Indexed: 01/09/2023] Open
Abstract
Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.
Collapse
Affiliation(s)
- Lu Yang
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55901, USA
| | - Jun Chen
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55901, USA
| |
Collapse
|
23
|
Stebliankin V, Sazal M, Valdes C, Mathee K, Narasimhan G. A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis. Microb Genom 2022; 8:mgen000899. [PMID: 36748547 PMCID: PMC9837561 DOI: 10.1099/mgen.0.000899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 09/11/2022] [Indexed: 12/24/2022] Open
Abstract
The use of whole metagenomic data to infer the relative abundance of all its microbes is well established. The same data can be used to determine the replication rate of all eubacterial taxa with circular chromosomes. Despite their availability, the replication rate profiles (metareplicome) have not been fully exploited in microbiome analyses. Another relatively new approach is the application of causal inferencing to analyse microbiome data that goes beyond correlational studies. A novel scalable pipeline called MeRRCI (Metagenome, metaResistome, and metaReplicome for Causal Inferencing) was developed. MeRRCI combines efficient computation of the metagenome (bacterial relative abundance), metaresistome (antimicrobial gene abundance) and metareplicome (replication rates), and integrates environmental variables (metadata) for causality analysis using Bayesian networks. MeRRCI was applied to an infant gut microbiome data set to investigate the microbial community's response to antibiotics. Our analysis suggests that the current treatment stratagem contributes to preterm infant gut dysbiosis, allowing a proliferation of pathobionts. The study highlights the specific antibacterial resistance genes that may contribute to exponential cell division in the presence of antibiotics for various pathogens, namely Klebsiella pneumoniae, Citrobacter freundii, Staphylococcus epidermidis, Veilonella parvula and Clostridium perfringens. These organisms often contribute to the harmful long-term sequelae seen in these young infants.
Collapse
Affiliation(s)
- Vitalii Stebliankin
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA
| | - Musfiqur Sazal
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA
- Present address: Microsoft Corporation, GA, Atlanta, USA
| | - Camilo Valdes
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA
- Present address: Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, USA
| | - Kalai Mathee
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL, USA
| |
Collapse
|
24
|
Saha S, Perrin L, Röder L, Brun C, Spinelli L. Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests. Nucleic Acids Res 2022; 50:e114. [PMID: 36107776 PMCID: PMC9639209 DOI: 10.1093/nar/gkac715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/29/2022] [Accepted: 09/12/2022] [Indexed: 12/04/2022] Open
Abstract
Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.
Collapse
Affiliation(s)
- Saswati Saha
- Aix Marseille Univ, INSERM, TAGC (UMR1090), Turing Centre for Living systems, Marseille, France
| | - Laurent Perrin
- Aix Marseille Univ, INSERM, TAGC (UMR1090), Turing Centre for Living systems, Marseille, France
- CNRS, Marseille, France
| | - Laurence Röder
- Aix Marseille Univ, INSERM, TAGC (UMR1090), Turing Centre for Living systems, Marseille, France
| | - Christine Brun
- Aix Marseille Univ, INSERM, TAGC (UMR1090), Turing Centre for Living systems, Marseille, France
- CNRS, Marseille, France
| | - Lionel Spinelli
- Aix Marseille Univ, INSERM, TAGC (UMR1090), Turing Centre for Living systems, Marseille, France
| |
Collapse
|
25
|
Yuan Y, Cosme C, Adams TS, Schupp J, Sakamoto K, Xylourgidis N, Ruffalo M, Li J, Kaminski N, Bar-Joseph Z. CINS: Cell Interaction Network inference from Single cell expression data. PLoS Comput Biol 2022; 18:e1010468. [PMID: 36095011 PMCID: PMC9499239 DOI: 10.1371/journal.pcbi.1010468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 09/22/2022] [Accepted: 08/03/2022] [Indexed: 12/05/2022] Open
Abstract
Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.
Collapse
Affiliation(s)
- Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Carlos Cosme
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Taylor Sterling Adams
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Jonas Schupp
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Koji Sakamoto
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Nikos Xylourgidis
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Matthew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Jiachen Li
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Ziv Bar-Joseph
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
| |
Collapse
|
26
|
Kim H, Jeon J, Lee KK, Lee YH. Longitudinal transmission of bacterial and fungal communities from seed to seed in rice. Commun Biol 2022; 5:772. [PMID: 35915150 PMCID: PMC9343636 DOI: 10.1038/s42003-022-03726-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/14/2022] [Indexed: 12/22/2022] Open
Abstract
Vertical transmission of microbes is crucial for the persistence of host-associated microbial communities. Although vertical transmission of seed microbes has been reported from diverse plants, ecological mechanisms and dynamics of microbial communities from parent to progeny remain scarce. Here we reveal the veiled ecological mechanism governing transmission of bacterial and fungal communities in rice across two consecutive seasons. We identify 29 bacterial and 34 fungal members transmitted across generations. Abundance-based regression models allow to classify colonization types of the microbes. We find that they are late colonizers dominating each community at the ripening stage. Ecological models further show that the observed temporal colonization patterns are affected by niche change and neutrality. Source-sink modeling reveals that parental seeds and stem endosphere are major origins of progeny seed microbial communities. This study gives empirical evidence for ecological mechanism and dynamics of bacterial and fungal communities as an ecological continuum during seed-to-seed transmission.
Collapse
Affiliation(s)
- Hyun Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jongbum Jeon
- Interdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul, 08826, Republic of Korea.,Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea
| | - Kiseok Kieth Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea.,Department of Ecology and Evolution, The University of Chicago, 1101 East 57th Street, Chicago, IL, 60637, USA
| | - Yong-Hwan Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea. .,Interdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul, 08826, Republic of Korea. .,Center for Plant Microbiome Research, Seoul National University, Seoul, 08826, Republic of Korea. .,Plant Immunity Research Center, Seoul National University, Seoul, 08826, Republic of Korea. .,Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
| |
Collapse
|
27
|
Kodikara S, Ellul S, Lê Cao KA. Statistical challenges in longitudinal microbiome data analysis. Brief Bioinform 2022; 23:bbac273. [PMID: 35830875 PMCID: PMC9294433 DOI: 10.1093/bib/bbac273] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/28/2022] [Accepted: 06/12/2022] [Indexed: 11/13/2022] Open
Abstract
The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
Collapse
Affiliation(s)
- Saritha Kodikara
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia
| | - Susan Ellul
- Murdoch Children’s Research Institute and Department of Paediatrics, University of Melbourne, Bouverie Street, 3052, Victoria, Australia
| | - Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia
| |
Collapse
|
28
|
Armoni R, Borenstein E. Temporal Alignment of Longitudinal Microbiome Data. Front Microbiol 2022; 13:909313. [PMID: 35814702 PMCID: PMC9257075 DOI: 10.3389/fmicb.2022.909313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/18/2022] [Indexed: 11/24/2022] Open
Abstract
A major challenge in working with longitudinal data when studying some temporal process is the fact that differences in pace and dynamics might overshadow similarities between processes. In the case of longitudinal microbiome data, this may hinder efforts to characterize common temporal trends across individuals or to harness temporal information to better understand the link between the microbiome and the host. One possible solution to this challenge lies in the field of “temporal alignment” – an approach for optimally aligning longitudinal samples obtained from processes that may vary in pace. In this work we investigate the use of alignment-based analysis in the microbiome domain, focusing on microbiome data from infants in their first years of life. Our analyses center around two main use-cases: First, using the overall alignment score as a measure of the similarity between microbiome developmental trajectories, and showing that this measure can capture biological differences between individuals. Second, using the specific matching obtained between pairs of samples in the alignment to highlight changes in pace and temporal dynamics, showing that it can be utilized to predict the age of infants based on their microbiome and to uncover developmental delays. Combined, our findings serve as a proof-of-concept for the use of temporal alignment as an important and beneficial tool in future longitudinal microbiome studies.
Collapse
Affiliation(s)
- Ran Armoni
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, NM, United States
- *Correspondence: Elhanan Borenstein,
| |
Collapse
|
29
|
Explainable Machine Learning for Longitudinal Multi-Omic Microbiome. MATHEMATICS 2022. [DOI: 10.3390/math10121994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.
Collapse
|
30
|
Sunkavalli A, McClure R, Genco C. Molecular Regulatory Mechanisms Drive Emergent Pathogenetic Properties of Neisseria gonorrhoeae. Microorganisms 2022; 10:922. [PMID: 35630366 PMCID: PMC9147433 DOI: 10.3390/microorganisms10050922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 12/05/2022] Open
Abstract
Neisseria gonorrhoeae is the causative agent of the sexually transmitted infection (STI) gonorrhea, with an estimated 87 million annual cases worldwide. N. gonorrhoeae predominantly colonizes the male and female genital tract (FGT). In the FGT, N. gonorrhoeae confronts fluctuating levels of nutrients and oxidative and non-oxidative antimicrobial defenses of the immune system, as well as the resident microbiome. One mechanism utilized by N. gonorrhoeae to adapt to this dynamic FGT niche is to modulate gene expression primarily through DNA-binding transcriptional regulators. Here, we describe the major N. gonorrhoeae transcriptional regulators, genes under their control, and how these regulatory processes lead to pathogenic properties of N. gonorrhoeae during natural infection. We also discuss the current knowledge of the structure, function, and diversity of the FGT microbiome and its influence on gonococcal survival and transcriptional responses orchestrated by its DNA-binding regulators. We conclude with recent multi-omics data and modeling tools and their application to FGT microbiome dynamics. Understanding the strategies utilized by N. gonorrhoeae to regulate gene expression and their impact on the emergent characteristics of this pathogen during infection has the potential to identify new effective strategies to both treat and prevent gonorrhea.
Collapse
Affiliation(s)
- Ashwini Sunkavalli
- Department of Immunology, Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA;
| | - Ryan McClure
- Pacific Northwest National Laboratory, Richland, WA 99354, USA;
| | - Caroline Genco
- Department of Immunology, Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA;
| |
Collapse
|
31
|
Kaiser T, Jahansouz C, Staley C. Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data. Future Microbiol 2022; 17:621-631. [PMID: 35360922 DOI: 10.2217/fmb-2021-0219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.
Collapse
Affiliation(s)
- Thomas Kaiser
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.,Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
| | - Cyrus Jahansouz
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Christopher Staley
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.,Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
| |
Collapse
|
32
|
Li D, Velazquez JJ, Ding J, Hislop J, Ebrahimkhani MR, Bar-Joseph Z. TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data. Genome Biol 2022; 23:73. [PMID: 35255944 PMCID: PMC8900372 DOI: 10.1186/s13059-022-02629-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/09/2022] [Indexed: 02/08/2023] Open
Abstract
A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids.Software https://github.com/doraadong/TraSig .
Collapse
Affiliation(s)
- Dongshunyi Li
- Computational Biology Department, School of Computer Science, Carnegie Mellon Universit, Pittsburgh, 15213, PA, USA
| | - Jeremy J Velazquez
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, 15261, PA, USA
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, Quebec, Canada
| | - Joshua Hislop
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, 15261, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, 15261, PA, USA
| | - Mo R Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, 15261, PA, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, 15261, PA, USA.
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, 15219, PA, USA.
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon Universit, Pittsburgh, 15213, PA, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA
| |
Collapse
|
33
|
He L, Wang C, Hu J, Gao Z, Falcone E, Holland SM, Blaser MJ, Li H. ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study. Front Genet 2022; 13:777877. [PMID: 35281829 PMCID: PMC8914110 DOI: 10.3389/fgene.2022.777877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.
Collapse
Affiliation(s)
- Linchen He
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
| | - Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States
| | - Jiyuan Hu
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States
| | - Zhan Gao
- Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, United States
| | - Emilia Falcone
- Division of Intramural Research, Immunopathogenesis Section, NIAID, NIH, Bethesda, MD, United States
| | - Steven M. Holland
- Division of Intramural Research, Immunopathogenesis Section, NIAID, NIH, Bethesda, MD, United States
| | - Martin J. Blaser
- Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, United States
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States
- *Correspondence: Huilin Li,
| |
Collapse
|
34
|
Kodera SM, Das P, Gilbert JA, Lutz HL. Conceptual strategies for characterizing interactions in microbial communities. iScience 2022; 25:103775. [PMID: 35146390 PMCID: PMC8819398 DOI: 10.1016/j.isci.2022.103775] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.
Collapse
Affiliation(s)
- Sho M Kodera
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
| | - Promi Das
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Jack A Gilbert
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Holly L Lutz
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA.,Negaunee Integrative Collections Center, Field Museum of Natural History, Chicago, IL 60605, USA
| |
Collapse
|
35
|
Lugo-Martinez J, Xu S, Levesque J, Gallagher D, Parker LA, Neu J, Stewart CJ, Berrington JE, Embleton ND, Young G, Gregory KE, Good M, Tandon A, Genetti D, Warren T, Bar-Joseph Z. Integrating longitudinal clinical and microbiome data to predict growth faltering in preterm infants. J Biomed Inform 2022; 128:104031. [DOI: 10.1016/j.jbi.2022.104031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 12/20/2021] [Accepted: 02/14/2022] [Indexed: 10/19/2022]
|
36
|
Wang C, Hu J, Blaser MJ, Li H. Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study. BMC Genomics 2021; 22:667. [PMID: 34525957 PMCID: PMC8442444 DOI: 10.1186/s12864-021-07948-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/25/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time. RESULTS We propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice. CONCLUSIONS The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.
Collapse
Affiliation(s)
- Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016 NY USA
| | - Jiyuan Hu
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016 NY USA
| | - Martin J. Blaser
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, 08854-8021 NJ USA
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016 NY USA
| |
Collapse
|
37
|
Sharma D, Xu W. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Bioinformatics 2021; 37:3707-3714. [PMID: 34213529 DOI: 10.1093/bioinformatics/btab482] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/24/2021] [Accepted: 06/30/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Research shows that human microbiome is highly dynamic on longitudinal timescales, changing dynamically with diet, or due to medical interventions. In this paper, we propose a novel deep learning framework "phyLoSTM", using a combination of Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) for feature extraction and analysis of temporal dependency in longitudinal microbiome sequencing data along with host's environmental factors for disease prediction. Additional novelty in terms of handling variable timepoints in subjects through LSTMs, as well as, weight balancing between imbalanced cases and controls is proposed. RESULTS We simulated 100 datasets across multiple time points for model testing. To demonstrate the model's effectiveness, we also implemented this novel method into two real longitudinal human microbiome studies: (i) DIABIMMUNE three country cohort with food allergy outcomes (Milk, Egg, Peanut and Overall) (ii) DiGiulio study with preterm delivery as outcome. Extensive analysis and comparison of our approach yields encouraging performance with an AUC of 0.897 (increased by 5%) on simulated studies and AUCs of 0.762 (increased by 19%) and 0.713 (increased by 8%) on the two real longitudinal microbiome studies respectively, as compared to the next best performing method, Random Forest. The proposed methodology improves predictive accuracy on longitudinal human microbiome studies containing spatially correlated data, and evaluates the change of microbiome composition contributing to outcome prediction. AVAILABILITY AND IMPLEMENTATION https://github.com/divya031090/phyLoSTM.
Collapse
Affiliation(s)
- Divya Sharma
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Wei Xu
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
38
|
Matchado MS, Lauber M, Reitmeier S, Kacprowski T, Baumbach J, Haller D, List M. Network analysis methods for studying microbial communities: A mini review. Comput Struct Biotechnol J 2021; 19:2687-2698. [PMID: 34093985 PMCID: PMC8131268 DOI: 10.1016/j.csbj.2021.05.001] [Citation(s) in RCA: 147] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/01/2021] [Accepted: 05/01/2021] [Indexed: 12/20/2022] Open
Abstract
Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.
Collapse
Affiliation(s)
- Monica Steffi Matchado
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Michael Lauber
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Sandra Reitmeier
- ZIEL - Institute for Food & Health, Technical University of Munich, 85354 Freising, Germany
- Chair of Nutrition and Immunology, Technical University of Munich, 85354 Freising, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Brunswick, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Brunswick, Germany
| | - Jan Baumbach
- Institute of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Dirk Haller
- ZIEL - Institute for Food & Health, Technical University of Munich, 85354 Freising, Germany
- Chair of Nutrition and Immunology, Technical University of Munich, 85354 Freising, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| |
Collapse
|
39
|
Abstract
A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.
Collapse
|
40
|
Zhang H, Chen J, Feng Y, Wang C, Li H, Liu L. Mediation effect selection in high-dimensional and compositional microbiome data. Stat Med 2021; 40:885-896. [PMID: 33205470 PMCID: PMC7855955 DOI: 10.1002/sim.8808] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 08/31/2020] [Accepted: 10/16/2020] [Indexed: 01/08/2023]
Abstract
The microbiome plays an important role in human health by mediating the path from environmental exposures to health outcomes. The relative abundances of the high-dimensional microbiome data have an unit-sum restriction, rendering standard statistical methods in the Euclidean space invalid. To address this problem, we use the isometric log-ratio transformations of the relative abundances as the mediator variables. To select significant mediators, we consider a closed testing-based selection procedure with desirable confidence. Simulations are provided to verify the effectiveness of our method. As an illustrative example, we apply the proposed method to study the mediation effects of murine gut microbiome between subtherapeutic antibiotic treatment and body weight gain, and identify Coprobacillus and Adlercreutzia as two significant mediators.
Collapse
Affiliation(s)
- Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA
| | - Yang Feng
- Department of Biostatistics, College of Global Public Health, New York University, New York, NY 10003, USA
| | - Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO 63110, USA
| |
Collapse
|
41
|
Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lopes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Microbiol 2021; 12:634511. [PMID: 33737920 PMCID: PMC7962872 DOI: 10.3389/fmicb.2021.634511] [Citation(s) in RCA: 160] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/01/2021] [Indexed: 12/19/2022] Open
Abstract
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
Collapse
Affiliation(s)
- Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | | | | | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland
| | - Vladimir Trajkovik
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | - Oliver Aasmets
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Magali Berland
- Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Jasminka Hasic
- University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Mikhail Kolev
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal
- Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO)Barcelona, Spain
- Colorectal Cancer Group, Institut de Recerca Biomedica de Bellvitge (IDIBELL), Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Irina Naskinova
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Elin Org
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
| | - Inês Paciência
- EPIUnit – Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | | | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Group for Microbiology and Microbial Biotechnology, Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | | | | | - Marcus J. Claesson
- School of Microbiology & APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Isabel Moreno-Indias
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Clínico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| |
Collapse
|
42
|
Zaura E, Pappalardo VY, Buijs MJ, Volgenant CMC, Brandt BW. Optimizing the quality of clinical studies on oral microbiome: A practical guide for planning, performing, and reporting. Periodontol 2000 2021; 85:210-236. [PMID: 33226702 PMCID: PMC7756869 DOI: 10.1111/prd.12359] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
With this review, we aim to increase the quality standards for clinical studies with microbiome as an output parameter. We critically address the existing body of evidence for good quality practices in oral microbiome studies based on 16S rRNA gene amplicon sequencing. First, we discuss the usefulness of microbiome profile analyses. Is a microbiome study actually the best approach for answering the research question? This is followed by addressing the criteria for the most appropriate study design, sample size, and the necessary data (study metadata) that should be collected. Next, we evaluate the available evidence for best practices in sample collection, transport, storage, and DNA isolation. Finally, an overview of possible sequencing options (eg, 16S rRNA gene hypervariable regions, sequencing platforms), processing and data interpretation approaches, as well as requirements for meaningful data storage, sharing, and reporting are provided.
Collapse
Affiliation(s)
- Egija Zaura
- Department of Preventive DentistryAcademic Centre for Dentistry Amsterdam (ACTA)Vrije Universiteit Amsterdam and University of AmsterdamAmsterdamthe Netherlands
| | - Vincent Y. Pappalardo
- Department of Preventive DentistryAcademic Centre for Dentistry Amsterdam (ACTA)Vrije Universiteit Amsterdam and University of AmsterdamAmsterdamthe Netherlands
| | - Mark J. Buijs
- Department of Preventive DentistryAcademic Centre for Dentistry Amsterdam (ACTA)Vrije Universiteit Amsterdam and University of AmsterdamAmsterdamthe Netherlands
| | - Catherine M. C. Volgenant
- Department of Preventive DentistryAcademic Centre for Dentistry Amsterdam (ACTA)Vrije Universiteit Amsterdam and University of AmsterdamAmsterdamthe Netherlands
| | - Bernd W. Brandt
- Department of Preventive DentistryAcademic Centre for Dentistry Amsterdam (ACTA)Vrije Universiteit Amsterdam and University of AmsterdamAmsterdamthe Netherlands
| |
Collapse
|
43
|
Sazal M, Mathee K, Ruiz-Perez D, Cickovski T, Narasimhan G. Inferring directional relationships in microbial communities using signed Bayesian networks. BMC Genomics 2020; 21:663. [PMID: 33349235 PMCID: PMC7751116 DOI: 10.1186/s12864-020-07065-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. RESULTS In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. CONCLUSIONS BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.
Collapse
Affiliation(s)
- Musfiqur Sazal
- Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, 33199 FL USA
| | - Kalai Mathee
- Herbert Wertheim College of Medicine, Florida International University, Miami, 33199 FL USA
- Biomolecular Sciences Institute (BSI), Florida International University, Miami, 33199 FL USA
| | - Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, 33199 FL USA
| | - Trevor Cickovski
- Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, 33199 FL USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, 33199 FL USA
- Biomolecular Sciences Institute (BSI), Florida International University, Miami, 33199 FL USA
| |
Collapse
|
44
|
Lee CY, Cheu RK, Lemke MM, Gustin AT, France MT, Hampel B, Thurman AR, Doncel GF, Ravel J, Klatt NR, Arnold KB. Quantitative modeling predicts mechanistic links between pre-treatment microbiome composition and metronidazole efficacy in bacterial vaginosis. Nat Commun 2020; 11:6147. [PMID: 33262350 PMCID: PMC7708644 DOI: 10.1038/s41467-020-19880-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Bacterial vaginosis is a condition associated with adverse reproductive outcomes and characterized by a shift from a Lactobacillus-dominant vaginal microbiota to a polymicrobial microbiota, consistently colonized by strains of Gardnerella vaginalis. Metronidazole is the first-line treatment; however, treatment failure and recurrence rates remain high. To understand complex interactions between Gardnerella vaginalis and Lactobacillus involved in efficacy, here we develop an ordinary differential equation model that predicts bacterial growth as a function of metronidazole uptake, sensitivity, and metabolism. The model shows that a critical factor in efficacy is Lactobacillus sequestration of metronidazole, and efficacy decreases when the relative abundance of Lactobacillus is higher pre-treatment. We validate results in Gardnerella and Lactobacillus co-cultures, and in two clinical cohorts, finding women with recurrence have significantly higher pre-treatment levels of Lactobacillus relative to bacterial vaginosis-associated bacteria. Overall results provide mechanistic insight into how personalized differences in microbial communities influence vaginal antibiotic efficacy.
Collapse
Affiliation(s)
- Christina Y Lee
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ryan K Cheu
- University of Miami Department of Pediatrics, University of Miami, Miami, FL, USA
- Department of Pharmaceutics, University of Washington, Seattle, WA, USA
| | - Melissa M Lemke
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Andrew T Gustin
- University of Miami Department of Pediatrics, University of Miami, Miami, FL, USA
- Department of Pharmaceutics, University of Washington, Seattle, WA, USA
| | - Michael T France
- Institute for Genome Sciences and Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Benjamin Hampel
- Division of Infectious Diseases and Hospital Epidemiology, University of Zurich, Zürich, Switzerland
| | | | | | - Jacques Ravel
- Institute for Genome Sciences and Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nichole R Klatt
- University of Miami Department of Pediatrics, University of Miami, Miami, FL, USA.
- Department of Pharmaceutics, University of Washington, Seattle, WA, USA.
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
| | - Kelly B Arnold
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
45
|
Park SY, Ufondu A, Lee K, Jayaraman A. Emerging computational tools and models for studying gut microbiota composition and function. Curr Opin Biotechnol 2020; 66:301-311. [PMID: 33248408 PMCID: PMC7744364 DOI: 10.1016/j.copbio.2020.10.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.
Collapse
Affiliation(s)
- Seo-Young Park
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Arinzechukwu Ufondu
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - Kyongbum Lee
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA.
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA; Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
| |
Collapse
|
46
|
Cosma-Grigorov A, Meixner H, Mrochen A, Wirtz S, Winkler J, Marxreiter F. Changes in Gastrointestinal Microbiome Composition in PD: A Pivotal Role of Covariates. Front Neurol 2020; 11:1041. [PMID: 33071933 PMCID: PMC7538808 DOI: 10.3389/fneur.2020.01041] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022] Open
Abstract
Altered gut microbiota may trigger or accelerate alpha-synuclein aggregation in the enteric nervous system in Parkinson's disease (PD). While several previous studies observed gut microbiota alterations in PD, findings like diversity indices, and altered bacterial taxa itself show a considerable heterogeneity across studies. We recruited 179 participants, of whom 101 fulfilled stringent inclusion criteria. Subsequently, the composition of the gut microbiota in 71 PD patients and 30 healthy controls was analyzed, sequencing V3–V4 regions of the bacterial 16S ribosomal RNA gene in fecal samples. Our goal was (1) to evaluate whether gut microbiota are altered in a southern German PD cohort, (2) to delineate the influence of disease duration, stage, and motor impairment, and (3) to investigate the influence of PD associated covariates like constipation and coffee consumption. Aiming to control for a large variety of covariates, strict inclusion criteria were applied. Finally, propensity score matching was performed to correct for, and to delineate the effect of remaining covariates (non-motor symptom (NMS) burden, constipation, and coffee consumption) on microbiota composition. Prior to matching altered abundances of distinct bacterial classes, orders, families, and genera were observed. Both, disease duration, and stage influenced microbiome composition. Interestingly, levodopa equivalent dose influenced the correlation of taxa with disease duration, while motor impairment did not. Applying different statistical tests, and after propensity score matching to control for NMS burden, constipation and coffee consumption, Faecalibacterium and Ruminococcus were most consistently reduced in PD compared to controls. Taken together, similar to previous studies, alterations of several taxa were observed in PD. Yet, further controlling for PD associated covariates such as constipation and coffee consumption revealed a pivotal role of these covariates. Our data highlight the impact of these PD associated covariates on microbiota composition in PD. This suggests that altered microbiota may mediate the protective effect of i.e., coffee consumption and the negative effect of constipation in PD.
Collapse
Affiliation(s)
- Alexandra Cosma-Grigorov
- Department of Molecular Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany.,Department of Neurology and Neuroscience, Albert-Ludwigs-University Medical Center Freiburg, Freiburg, Germany
| | - Holger Meixner
- Department of Molecular Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Anne Mrochen
- Department of Neurology, FAU, University Hospital Erlangen, Erlangen, Germany
| | - Stefan Wirtz
- Department of Medicine 1, FAU, University Hospital Erlangen, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| |
Collapse
|
47
|
A network approach to elucidate and prioritize microbial dark matter in microbial communities. ISME JOURNAL 2020; 15:228-244. [PMID: 32963345 PMCID: PMC7852563 DOI: 10.1038/s41396-020-00777-x] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/18/2020] [Accepted: 09/10/2020] [Indexed: 01/13/2023]
Abstract
Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.
Collapse
|
48
|
Xia Y. Correlation and association analyses in microbiome study integrating multiomics in health and disease. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 171:309-491. [PMID: 32475527 DOI: 10.1016/bs.pmbts.2020.04.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.
Collapse
Affiliation(s)
- Yinglin Xia
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States.
| |
Collapse
|
49
|
Jang BS, Chang JH, Chie EK, Kim K, Park JW, Kim MJ, Song EJ, Nam YD, Kang SW, Jeong SY, Kim HJ. Gut Microbiome Composition Is Associated with a Pathologic Response After Preoperative Chemoradiation in Patients with Rectal Cancer. Int J Radiat Oncol Biol Phys 2020; 107:736-746. [PMID: 32315676 DOI: 10.1016/j.ijrobp.2020.04.015] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 04/01/2020] [Accepted: 04/08/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE There are ongoing investigations to find promising biomarkers for predicting a complete response (CR) after concurrent chemoradiation (CCRT) in rectal cancer. We aimed to find the predictive value in the gut microbiome in terms of response after preoperative CCRT. METHODS AND MATERIALS We collected a total of 45 fecal samples from patients with rectal cancer before CCRT. Tumor response after CCRT was assessed according to the American Joint Committee on Cancer tumor regression grading system. Analysis of linear discriminant analysis effect size and MetaCyc pathway abundance predictions were performed to compare composition and metabolic function of microbiome between patients with and without CR. We also established a Bayesian network model to identify microbial networks and species to be related with CCRT response. RESULTS Seven patients (15.6%) demonstrated pathologically CR, and 38 patients (84.4%) showed non-CR after preoperative CCRT. Between CR and non-CR patients, there was a significant difference in terms of β-diversity (P = .028), but no difference in α-diversity was found. Bacteroidales (Bacteroidaceae, Rikenellaceae, Bacteroides) were relatively more abundant in patients with non-CR than those with CR. Pathways related to anabolic function predominated in CR patients. According to Bayesian network analysis, Duodenibacillus massiliensis was linked with the improved CR rate. CONCLUSIONS From the fecal microbiome using samples obtained before preoperative CCRT, differences in microbial community composition and functions were observed between patients with and without CR in rectal cancer. However, the finding that a specific taxon may be linked with the improved therapeutic response should be verified in a prospective setting.
Collapse
Affiliation(s)
- Bum-Sup Jang
- Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ji Hyun Chang
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea
| | - Kyubo Kim
- Department of Radiation Oncology, Ewha Womans University College of Medicine, Seoul, Korea
| | - Ji Won Park
- Department of Surgery, Seoul National University Hospital, Seoul, Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Min Jung Kim
- Department of Surgery, Seoul National University Hospital, Seoul, Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Eun-Ji Song
- Department of Food Biotechnology, Korea University of Science and Technology, Daejeon, Korea; Research Group of Healthcare, Korea Food Research Institute, Wanju, Korea
| | - Young-Do Nam
- Department of Food Biotechnology, Korea University of Science and Technology, Daejeon, Korea; Research Group of Healthcare, Korea Food Research Institute, Wanju, Korea
| | - Seung Wan Kang
- Department of Nursing, Seoul National University College of Nursing, Seoul, Korea
| | - Seung-Yong Jeong
- Department of Surgery, Seoul National University Hospital, Seoul, Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Hak Jae Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
| |
Collapse
|
50
|
Peñalver Bernabé B, Maki PM, Dowty SM, Salas M, Cralle L, Shah Z, Gilbert JA. Precision medicine in perinatal depression in light of the human microbiome. Psychopharmacology (Berl) 2020; 237:915-941. [PMID: 32065252 DOI: 10.1007/s00213-019-05436-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 12/11/2019] [Indexed: 12/17/2022]
Abstract
Perinatal depression is the most common complication of pregnancy and affects the mother, fetus, and infant. Recent preclinical studies and a limited number of clinical studies have suggested an influence of the gut microbiome on the onset and course of mental health disorders. In this review, we examine the current state of knowledge regarding genetics, epigenetics, heritability, and neuro-immuno-endocrine systems biology in perinatal mood disorders, with a particular focus on the interaction between these factors and the gut microbiome, which is mediated via the gut-brain axis. We also provide an overview of experimental and analytical methods that are currently available to researchers interested in elucidating the influence of the gut microbiome on mental health disorders during pregnancy and postpartum.
Collapse
Affiliation(s)
- Beatriz Peñalver Bernabé
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States.
| | - Pauline M Maki
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Department of Psychology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Obstetrics and Gynecology, University of Illinois at Chicago, Chicago, IL, USA
| | - Shannon M Dowty
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Mariana Salas
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
| | - Lauren Cralle
- University of Massachusetts Medical School, Worcester, MA, USA
| | - Zainab Shah
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Jack A Gilbert
- Scripts Oceanographic Institute, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| |
Collapse
|