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Meng Q, Zhou Q, Shi S, Xiao J, Ma Q, Yu J, Chen J, Kang Y. VTwins: inferring causative microbial features from metagenomic data of limited samples. Sci Bull (Beijing) 2023; 68:2806-2816. [PMID: 37919157 DOI: 10.1016/j.scib.2023.10.024] [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: 05/25/2023] [Revised: 07/19/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
It is difficult to infer causality from high-dimension metagenomic data due to interference from numerous confounders. By imitating the twin studies in genetic research, we develop a straightforward method-virtual twins (VTwins)-to eliminate the confounder effects by transforming the original cohort into a paired cohort of "Twin" samples with distinct phenotypes but matched taxonomic profiles. The results show that VTwins outperforms the conventional approach in the sensitivity of identifying causative features and only requires a 10-fold reduced sample size for recalling disease-associated microbes or pathways, as tested by simulated and empirical data. Benchmark test with other 16 kinds of software further validates the power and applicability of VTwins for handling high-dimension compositional datasets and mining causalities in metagenomic research. In conclusion, VTwins is straightforward and effective in handling high-diversity, high-dimension compositional data, promising applications in mining causalities for metagenomic and potentially other omics data. VTwins is open access and available at https://github.com/mengqingren/VTwins.
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Affiliation(s)
- Qingren Meng
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518100, China
| | - Qian Zhou
- International Cancer Center, Shenzhen University Medical School, Shenzhen 518055, China
| | - Shuo Shi
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jingfa Xiao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus OH 43210, USA
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Jun Chen
- National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518100, China
| | - Yu Kang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China.
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2
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Sun Y, Zhang X, Jin C, Yue K, Sheng D, Zhang T, Dou X, Liu J, Jing H, Zhang L, Yue J. Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy. J Transl Med 2023; 21:221. [PMID: 36967379 PMCID: PMC10041716 DOI: 10.1186/s12967-023-04054-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/10/2023] [Indexed: 03/28/2023] Open
Abstract
BACKGROUND Neoadjuvant concurrent chemoradiotherapy (nCCRT) is a standard treatment for locally advanced rectal cancer (LARC). The gut microbiome may be reshaped by radiotherapy through its effects on microbial composition, mucosal immunity, and the systemic immune system. We sought to clarify dynamic, longitudinal changes in the gut microbiome and blood immunomodulators throughout nCCRT and to explore the relationship of such changes with outcomes after nCCRT. METHODS A total of 39 patients with LARC were recruited for this study. Fecal samples and peripheral blood samples were collected from all 39 patients before nCCRT, during nCCRT (at week 3), and after nCCRT (at week 5). The gut microbiota and the microbial community structure were analyzed by 16S rRNA sequencing of the V3-V4 region. Levels of blood immunomodulatory proteins were measured with a Millipore HCKPMAG-11 K kit and Luminex 200 platform (Luminex, USA). RESULTS Cross-sectional and longitudinal analyses revealed that the gut microbiome profile and enterotype exhibited characteristic variations that could distinguish patients with good response (AJCC TRG classification 0-1) vs poor response (TRG 2-3) to nCCRT. Sparse partial least squares regression and canonical correspondence analyses showed multivariate associations between specific microbial taxa, host immunomodulatory proteins, immune cells, and outcomes after nCCRT. An integrated model consisting of baseline Clostridium sensu stricto 1 levels, fold changes in Intestinimonas, blood levels of the herpesvirus entry mediator (HVEM/CD270), and lymphocyte counts could predict good vs poor outcome after nCCRT [area under the receiver-operating characteristics curve (AUC)= 0.821; area under the precision-recall curve [AUPR] = 0.911]. CONCLUSIONS Our results showed that longitudinal variations in specific gut taxa, associated host immune cells, and immunomodulatory proteins before and during nCCRT could be useful for early predictions of the efficacy of nCCRT, which could guide the choice of individualized treatment for patients with LARC.
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Affiliation(s)
- Yi Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiang Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chuandi Jin
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Microbiome-X, National Institute of Health Data Science of China & Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Kaile Yue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Microbiome-X, National Institute of Health Data Science of China & Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dashuang Sheng
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Microbiome-X, National Institute of Health Data Science of China & Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xue Dou
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jing Liu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hongbiao Jing
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Lei Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Microbiome-X, National Institute of Health Data Science of China & Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China.
| | - Jinbo Yue
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
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3
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Park DJ, Plantinga AM. Impact of Data and Study Characteristics on Microbiome Volatility Estimates. Genes (Basel) 2023; 14:genes14010218. [PMID: 36672959 PMCID: PMC9859452 DOI: 10.3390/genes14010218] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility.
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Affiliation(s)
| | - Anna M. Plantinga
- Department of Mathematics and Statistics, Williams College, Williamstown, MA 01267, USA
- Correspondence:
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4
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Hu YJ, Satten GA. A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome. Bioinformatics 2022; 38:3689-3697. [PMID: 35723568 PMCID: PMC9991891 DOI: 10.1093/bioinformatics/btac399] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/09/2022] [Accepted: 06/16/2022] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis. RESULTS Here, we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F-statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants. AVAILABILITY AND IMPLEMENTATION We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi-Juan Hu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Glen A Satten
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA 30322, USA
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5
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Huang C, Callahan BJ, Wu MC, Holloway ST, Brochu H, Lu W, Peng X, Tzeng JY. Phylogeny-guided microbiome OTU-specific association test (POST). MICROBIOME 2022; 10:86. [PMID: 35668471 PMCID: PMC9171974 DOI: 10.1186/s40168-022-01266-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effects of microbes, and phylogenetic information should be incorporated in a data-supervised fashion. RESULTS In this work, we propose a local collapsing test called phylogeny-guided microbiome OTU-specific association test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenetic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community level to OTU level. Using simulation studies, we show that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, in real data applications on bacterial vaginosis and on preterm birth, we find that POST can identify similar or more outcome-associated OTUs that are of biological relevance compared to existing methods. CONCLUSIONS Using POST, we show that adaptively leveraging the phylogenetic information can enhance the selection performance of associated microbiome features by improving the overall true-positive and false-positive detection. We developed a user friendly R package POSTm which is freely available on CRAN ( https://CRAN.R-project.org/package=POSTm ). Video Abstract.
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Affiliation(s)
- Caizhi Huang
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA
| | - Benjamin J Callahan
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, 27607, USA
| | - Michael C Wu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, 98109, USA
| | - Shannon T Holloway
- Department of Statistics, North Carolina State University, Raleigh, 27606, USA
| | - Hayden Brochu
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, 27607, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, 27606, USA
| | - Xinxia Peng
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, 27607, USA
| | - Jung-Ying Tzeng
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA.
- Department of Statistics, North Carolina State University, Raleigh, 27606, USA.
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6
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Sun H, Huang X, Huo B, Tan Y, He T, Jiang X. Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations. Brief Bioinform 2022; 23:6585623. [PMID: 35561307 DOI: 10.1093/bib/bbac149] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/11/2022] [Accepted: 04/02/2022] [Indexed: 12/18/2022] Open
Abstract
The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome-phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.
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Affiliation(s)
- Han Sun
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China.,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
| | - Xiaoyun Huang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.,Collaborative & Innovative Center for Educational Technology, Central China Normal University, Wuhan 430079, China
| | - Ban Huo
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.,School of Computer, Central China Normal University, Wuhan 430079, China
| | - Yuting Tan
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China.,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
| | - Tingting He
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.,School of Computer, Central China Normal University, Wuhan 430079, China.,National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan 430079, China
| | - Xingpeng Jiang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.,School of Computer, Central China Normal University, Wuhan 430079, China.,National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan 430079, China
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7
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He J, Chu Y, Li J, Meng Q, Liu Y, Jin J, Wang Y, Wang J, Huang B, Shi L, Shi X, Tian J, Zhufeng Y, Feng R, Xiao W, Gan Y, Guo J, Shao C, Su Y, Hu F, Sun X, Yu J, Kang Y, Li Z. Intestinal butyrate-metabolizing species contribute to autoantibody production and bone erosion in rheumatoid arthritis. SCIENCE ADVANCES 2022; 8:eabm1511. [PMID: 35148177 PMCID: PMC11093108 DOI: 10.1126/sciadv.abm1511] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
The imbalance between pathogenic and beneficial species of the intestinal microbiome and metabolism in rheumatoid arthritis (RA) remains unclarified. Here, using shotgun-based metagenome sequencing for a treatment-naïve patient cohort and a "quasi-paired cohort" method, we observed a deficiency of butyrate-producing species and an overwhelming number of butyrate consumers in RA patients. These outcomes mainly occurred in patients with positive ACPA, with a mean AUC of 0.94. This panel was also validated in established RA with an AUC of 0.986 in those with joint deformity. In addition, we showed that butyrate promoted Tregs, while suppressing Tconvs and osteoclasts, due to potentiation of the reduction in HDAC expression and down-regulation of proinflammatory cytokine genes. Dietary butyrate supplementation conferred anti-inflammatory benefits in a mouse model by rebalancing TFH cells and Tregs, as well as reducing antibody production. These findings reveal the critical role of butyrate-metabolizing species and suggest the potential of butyrate-based therapies for RA patients.
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Affiliation(s)
- Jing He
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Yanan Chu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformatics, Beijing 100101, China
| | - Jing Li
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Qingren Meng
- School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yudong Liu
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing 100044, China
| | - Jiayang Jin
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Yifan Wang
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Jian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformatics, Beijing 100101, China
| | - Bo Huang
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Lianjie Shi
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Xing Shi
- Department of Respiratory and Critical Care Medicine, Shenzhen Institute of Respiratory Diseases, The First Affiliated Hospital (Shenzhen People’s Hospital), Southern University of Science and Technology, Shenzhen 518055, China
| | - Jiayi Tian
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Yunzhi Zhufeng
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Ruiling Feng
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Wenjing Xiao
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Yuzhou Gan
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Jianping Guo
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Changjun Shao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformatics, Beijing 100101, China
| | - Yin Su
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Fanlei Hu
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Xiaolin Sun
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
| | - Jun Yu
- University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yu Kang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformatics, Beijing 100101, China
| | - Zhanguo Li
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing 100044, China
- Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing 100044, China
- Peking-Tsinghua Center for Life Sciences, Beijing 100091, China
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8
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Stanislawski MA, Frank DN, Borengasser SJ, Ostendorf DM, Ir D, Jambal P, Bing K, Wayland L, Siebert JC, Bessesen DH, MacLean PS, Melanson EL, Catenacci VA. The Gut Microbiota during a Behavioral Weight Loss Intervention. Nutrients 2021; 13:3248. [PMID: 34579125 PMCID: PMC8471894 DOI: 10.3390/nu13093248] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022] Open
Abstract
Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.
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Affiliation(s)
- Maggie A. Stanislawski
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Daniel N. Frank
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Sarah J. Borengasser
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Danielle M. Ostendorf
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Diana Ir
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Purevsuren Jambal
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Kristen Bing
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Liza Wayland
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Janet C. Siebert
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Daniel H. Bessesen
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Paul S. MacLean
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
| | - Edward L. Melanson
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
- Eastern Colorado Veterans Affairs Geriatric Research, Education and Clinical Center, Denver, CO 80045, USA
| | - Victoria A. Catenacci
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.N.F.); (S.J.B.); (D.M.O.); (D.I.); (P.J.); (K.B.); (L.W.); (J.C.S.); (D.H.B.); (P.S.M.); (E.L.M.); (V.A.C.)
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9
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MITCHELL CM, Nanxun, MITCHELL AJ, WU MC, VALINT DJ, PROLL S, REED SD, GUTHRIE KA, LACROIX AZ, LARSON JC, PEPIN R, RAFTERY D, FREDRICKS DN, SRINIVASAN S. Association between postmenopausal vulvovaginal discomfort, vaginal microbiota, and mucosal inflammation. Am J Obstet Gynecol 2021; 225:159.e1-159.e15. [PMID: 33675793 PMCID: PMC8328873 DOI: 10.1016/j.ajog.2021.02.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 01/31/2021] [Accepted: 02/28/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Half of all postmenopausal women report symptoms of vulvar, vaginal, or urinary discomfort with substantial impact on sexual function and quality of life; underlying mechanisms leading to symptoms are poorly understood. OBJECTIVE To examine the possibility that the vaginal microbiota and/or mucosal immune response contributes to the severity of bothersome vaginal symptoms, we conducted a substudy of samples from a randomized trial of vaginal treatment for genitourinary syndrome of menopause to compare these features between women whose symptoms improved and women whose symptoms did not improve. STUDY DESIGN This is a secondary analysis of samples collected in a 12-week randomized trial of treatment with vaginal estradiol or moisturizer vs placebo for moderate-severe postmenopausal symptoms of vaginal discomfort. We randomly selected 20 women in each arm with ≥2-point decrease in most bothersome symptom severity (responders) and 20 matched controls with ≤1-point decrease (nonresponders). At 0, 4, and 12 weeks, we characterized vaginal microbiota (16S ribosomal RNA gene sequencing), vaginal fluid metabolites (broad-based metabolomic profiling), vaginal fluid-soluble immune markers (Meso Scale Discovery), pH, and vaginal maturation index. We compared responders with nonresponders at baseline and across all visits using linear mixed models to evaluate associations with microbiota, metabolites, and immune markers, incorporating visit and participant-specific random effects while controlling for treatment arm. RESULTS Here, the mean age of women was 61 years (n=120), and most women (92%) were White. At enrollment, no significant differences were observed between responders and nonresponders in age, most bothersome symptom type or severity, microbiota composition or diversity, Lactobacillus dominance, metabolome, or immune markers. There was a significant decrease in diversity of the vaginal microbiota in both responders and nonresponders (P<.001) over 12 weeks. Although this change did not differ by responder status, diversity was associated with treatment arm: more women in the estradiol arm (63%) had Lactobacillus-dominant, lower diversity bacterial communities than women in the moisturizer (35%) or dual placebo (23%) arms (P=.001) at 12 weeks. The metabolome, vaginal maturation index, and measured immune markers were not associated with responder status over the 12 weeks but varied by treatment arm. CONCLUSION Postmenopausal vaginal symptom severity was not significantly associated with vaginal microbiota or mucosal inflammatory markers in this small study. Women receiving vaginal estradiol experienced greater abundance of lactobacilli and lower vaginal pH at end of treatment.
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Affiliation(s)
- Caroline M. MITCHELL
- Vincent Center for Reproductive Biology, Massachusetts General Hospital, Boston, MA
| | - Nanxun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Alissa J. MITCHELL
- Vincent Center for Reproductive Biology, Massachusetts General Hospital, Boston, MA
| | - Michael C. WU
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - DJ VALINT
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Sean PROLL
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Susan D. REED
- Department of Obstetrics and Gynecology, University of Washington, Seattle WA
| | - Katherine A. GUTHRIE
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Andrea Z. LACROIX
- Herbert Wertheim School of Public Health, University of California, San Diego
| | - Joseph C LARSON
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Robert PEPIN
- Department of Anesthesia & Pain Medicine, University of Washington, Seattle WA
| | - Daniel RAFTERY
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA,Department of Anesthesia & Pain Medicine, University of Washington, Seattle WA
| | - David N. FREDRICKS
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA,Department of Medicine, University of Washington, Seattle WA
| | - Sujatha SRINIVASAN
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA
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10
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Yang Z, Xu F, Li H, He Y. Beyond samples: A metric revealing more connections of gut microbiota between individuals. Comput Struct Biotechnol J 2021; 19:3930-3937. [PMID: 34377361 PMCID: PMC8319210 DOI: 10.1016/j.csbj.2021.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/03/2021] [Accepted: 07/07/2021] [Indexed: 10/31/2022] Open
Abstract
Studies of gut microbiota explore their complicated connections between individuals of different characteristics by applying different metrics to abundance data obtained from fecal samples. Although classic metrics are capable to quantify differences between samples, the microbiome of fecal sample is not a good surrogate for the gut microbiome of individuals because the microbial populations of the distal colon does not adequately represent that of the entire gastrointestinal tract. To overcome the deficiency of classic metrics in which the differences can be measured between the samples analyzed, but not the corresponding populations, we propose a metric for representing composition differences in the gut microbiota of individuals. Our investigation shows this metric outperforms traditional measures for multiple scenarios. For gut microbiota in diverse geographic populations, this metric presents more explainable data variance than others, not only in regular variance analysis but also in principle component analysis and partition analysis of biologic characteristics. With time-series data, the metric further presents a strong correlation with the time interval of serial sampling. Our findings suggest that the metric is robust and powerfully detects the intrinsic variations in gut microbiota. The metric holds promise for revealing more relations between gut microbiota and human health.
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Affiliation(s)
- Zhen Yang
- Shanghai Fifth People's Hospital, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Feng Xu
- Shanghai Fifth People's Hospital, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Hongdou Li
- Obstetrics Gynecology Hospital, The Institute of Reproduction and Developmental Biology, Fudan University, Shanghai, China
| | - Yungang He
- Shanghai Fifth People's Hospital, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
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11
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Kamp KJ, Plantinga AM, Cain KC, Burr RL, Barney P, Jarrett M, Luna RA, Savidge T, Shulman R, Heitkemper MM. A Comprehensive Self-Management Program With Diet Education Does Not Alter Microbiome Characteristics in Women With Irritable Bowel Syndrome. Biol Res Nurs 2021; 23:471-480. [PMID: 33412896 DOI: 10.1177/1099800420984543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Changes in diet and lifestyle factors are frequently recommended for persons with irritable bowel syndrome (IBS). It is unknown whether these recommendations alter the gut microbiome and/or whether baseline microbiome predicts improvement in symptoms and quality of life following treatment. Therefore, the purpose of this study was to explore if baseline gut microbiome composition predicted response to a Comprehensive Self-Management (CSM) intervention and if the intervention resulted in a different gut microbiome composition compared to usual care. METHODS Individuals aged 18-70 years with IBS symptoms ≥6 months were recruited using convenience sampling. Individuals were excluded if medication use or comorbidities would influence symptoms or microbiome. Participants completed a baseline assessment and were randomized into the eight-session CSM intervention which included dietary education and cognitive behavioral therapy versus usual care. Questionnaires included demographics, quality of life, and symptom diaries. Fecal samples were collected at baseline and 3-month post-randomization for 16S rRNA-based microbiome analysis. RESULTS Within the CSM intervention group (n = 30), Shannon diversity, richness, and beta diversity measures at baseline did not predict benefit from the CSM intervention at 3 months, as measured by change in abdominal pain and quality of life. Based on both alpha and beta diversity, the change from baseline to follow-up microbiome bacterial taxa did not differ between CSM (n = 25) and usual care (n = 25). CONCLUSIONS AND INFERENCES Baseline microbiome does not predict symptom improvement with CSM intervention. We do not find evidence that the CSM intervention influences gut microbiome diversity or composition over the course of 3 months.
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Affiliation(s)
| | | | | | | | | | | | | | - Tor Savidge
- 3989Baylor College of Medicine, Houston, TX, USA
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12
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Baek MG, Woo SJ, Kim NE, Baek C, Won S, Kim Y, Lee JJ, Yi H, Hong JY. Respiratory microbiome profiles differ by recent hospitalization and nursing home residence in patients on mechanical ventilation. J Transl Med 2020; 18:464. [PMID: 33287847 PMCID: PMC7720271 DOI: 10.1186/s12967-020-02642-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/27/2020] [Indexed: 02/08/2023] Open
Abstract
Background Healthcare-associated pneumonia (HCAP) is a heterogeneous disease. We redefined nursing-home- and hospital-associated infections (NHAI) group by revising existing HCAP risk factors. The NHAI group comprised nursing home residents with a poor functional status, or recent (past 90 days) hospitalization or recent (past 180 days) antibiotic therapy. Our aim was to determine whether respiratory microbiota profiles are related to newly defined NHAI group in critically ill patients on mechanical ventilation. Methods The 180 endotracheal aspirates (ETAs) from 60 mechanically ventilated ICU patients (NHAI group, n = 24; non-NHAI group, n = 36) were prospectively collected on days 1, 3 and 7 in a university hospital. The bacterial community profiles of the ETAs were explored by 16S rRNA gene sequencing. A phylogenetic-tree-based microbiome association test (TMAT), generalized linear mixed models (GLMMs), the Wilcoxon test and the reference frame method were used to analyze the association between microbiome abundance and disease phenotype. Results The relative abundance of the genus Corynebacterium was significantly higher in the pneumonia than in the non-pneumonia group. The microbiome analysis revealed significantly lower α-diversity in the NHAI group than in the non-NHAI group. In the analysis of β-diversity, the structure of the microbiome also differed significantly between the two groups (weighted UniFrac distance, Adonis, p < 0.001). The abundance of Corynebacterium was significantly higher, and the relative abundances of Granulicatella, Staphylococcus, Streptococcus and Veillonella were significantly lower, in the NHAI group than in the non-NHAI group. Conclusions The microbiota signature of the ETAs distinguished between patients with and without risk factors for NHAI. The lung microbiome may serve as a therapeutic target for NHAI group.
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Affiliation(s)
- Min-Gyung Baek
- Department of Public Health, Korea University, Seoul, Republic of Korea
| | - Seong Ji Woo
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Nam Eun Kim
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Chaeyun Baek
- Department of Public Health, Korea University, Seoul, Republic of Korea
| | - Sungho Won
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.,Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Youngmi Kim
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Hana Yi
- School of Biosystems and Biomedical Sciences, College of Health Science, Korea University, Seoul, Republic of Korea.
| | - Ji Young Hong
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea. .,Division of Pulmonary and Critical Care Medicine, Department of Medicine, Chuncheon Sacred Heart Hospital, Hallym University Medical Center, 77, Sakju-ro, Chuncheon-si, Gangwon-do, 200-704, Republic of Korea.
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13
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Zhang M, Chu Y, Meng Q, Ding R, Shi X, Wang Z, He Y, Zhang J, Liu J, Zhang J, Yu J, Kang Y, Wang J. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. SCIENCE ADVANCES 2020; 6:eaba3760. [PMID: 33087359 PMCID: PMC7577716 DOI: 10.1126/sciadv.aba3760] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 09/02/2020] [Indexed: 06/11/2023]
Abstract
Growing evidence suggests that autism spectrum disorder (ASD) is strongly associated with dysbiosis in the gut microbiome, with the exact mechanisms still unclear. We have proposed a novel analytic strategy-quasi-paired cohort-and applied it to a metagenomic study of the ASD microbiome. By comparing paired samples of ASD and neurotypical subjects, we have identified significant deficiencies in ASD children in detoxifying enzymes and pathways, which show a strong correlation with biomarkers of mitochondrial dysfunction. Diagnostic models based on these detoxifying enzymes accurately distinguished ASD individuals from controls, and the dysfunction score inferred from the model increased with the clinical rating scores of ASD. In summary, our results suggest a previously undiscovered potential role of impaired intestinal microbial detoxification in toxin accumulation and mitochondrial dysfunction, a core component of ASD pathogenesis. These findings pave the way for designing future therapeutic strategies to restore microbial detoxification capabilities for patients with ASD.
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Affiliation(s)
- Mengxiang Zhang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Yanan Chu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
| | - Qingren Meng
- School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China
| | - Rui Ding
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
- Autism Research Center of Peking University Health Science Center, Beijing 100191, China
| | - Xing Shi
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Zuqun Wang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
- Autism Research Center of Peking University Health Science Center, Beijing 100191, China
| | - Yi He
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
- Autism Research Center of Peking University Health Science Center, Beijing 100191, China
| | - Juan Zhang
- Department of Pediatrics, Peking University Third Hospital, Beijing 100191, China
| | - Jing Liu
- Key Laboratory of Mental Health, Ministry of Health, Peking University Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
| | - Jie Zhang
- Children Health Care Center, Xi'an Children's Hospital, Xi'an 710003, China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
| | - Yu Kang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China.
| | - Juan Wang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China.
- Autism Research Center of Peking University Health Science Center, Beijing 100191, China
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14
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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: 9.3] [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.
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Affiliation(s)
- Yinglin Xia
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States.
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15
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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: 2.8] [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.
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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
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