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Williams A, Porter J, Kingsley K, Howard KM. Higher Prevalence of the Periodontal Pathogen Selenomonas noxia among Pediatric and Adult Patients May Be Associated with Overweight and Obesity. Pathogens 2024; 13:338. [PMID: 38668293 PMCID: PMC11053746 DOI: 10.3390/pathogens13040338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
New evidence has suggested that oral and gut microflora may have significant impacts on the predisposition, development, and stability of obesity in adults over time-although less is known about this phenomenon in children. Compared with healthy-weight controls, overweight and obese adult patients are now known to harbor specific pathogens, such as Selenomonas noxia (S. noxia), that are capable of digesting normally non-digestible cellulose and fibers that significantly increase caloric extraction from normal dietary intake. To evaluate this phenomenon, clinical saliva samples (N = 122) from subjects with a normal BMI (18-25) and a BMI over 25 (overweight, obese) from an existing biorepository were screened using qPCR. The prevalence of S. noxia in samples from normal-BMI participants were lower (21.4%) than in overweight-BMI (25-29; 46.1%) and obese-BMI (30 and above; 36.8%) samples-a strong, positive correlation that was not significantly affected by age or race and ethnicity. These data strongly suggest that S. noxia may be intricately associated with overweight and obesity among patients, and more research will be needed to determine the positive and negative feedback mechanisms that may be responsible for these observations as well as the interventions needed to remove or reduce the potential effects of this oral pathogen.
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Affiliation(s)
- Austin Williams
- Department of Advanced Education in Pediatric Dentistry, School of Dental Medicine, University of Nevada, Las Vegas, 1700 West Charleston Blvd, Las Vegas, NV 89106, USA
| | - Jace Porter
- Department of Clinical Sciences, School of Dental Medicine, University of Nevada, Las Vegas, 1700 West Charleston Blvd, Las Vegas, NV 89106, USA
| | - Karl Kingsley
- Department of Biomedical Sciences, School of Dental Medicine, University of Nevada, Las Vegas, 1001 Shadow Lane, Las Vegas, NV 89106, USA;
| | - Katherine M. Howard
- Department of Biomedical Sciences, School of Dental Medicine, University of Nevada, Las Vegas, 1001 Shadow Lane, Las Vegas, NV 89106, USA;
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Guo J, Tang B, Fu J, Zhu X, Xie W, Wang N, Ding Z, Song Z, Yang Y, Xu G, Xiao X. High-plex spatial transcriptomic profiling reveals distinct immune components and the HLA class I/DNMT3A/CD8 modulatory axis in mismatch repair-deficient endometrial cancer. Cell Oncol (Dordr) 2024; 47:573-585. [PMID: 37847338 PMCID: PMC11090934 DOI: 10.1007/s13402-023-00885-8] [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] [Accepted: 09/23/2023] [Indexed: 10/18/2023] Open
Abstract
PURPOSE Tumors bearing mismatch repair deficiency (MMRd) are characterized by a high load of neoantigens and are believed to trigger immunogenic reactions upon immune checkpoint blockade treatment such as anti-PD-1/PD-L1 therapy. However, the mechanisms are still ill-defined, as multiple cancers with MMRd exhibit variable responses to immune checkpoint inhibitors (ICIs). In endometrial cancer (EC), a distinct tumor microenvironment (TME) exists that may correspond to treatment-related efficacies. We aimed to characterize EC patients with aberrant MMR pathways to identify molecular subtypes predisposed to respond to ICI therapies. METHODS We applied digital spatial profiling, a high-plex spatial transcriptomic approach covering over 1,800 genes, to obtain a highly resolved TME landscape in 45 MMRd-EC patients. We cross-validated multiple biomarkers identified using immunohistochemistry and multiplexed immunofluorescence using in-study and independent cohorts totaling 123 MMRd-EC patients and validated our findings using external TCGA data from microsatellite instability endometrial cancer (MSI-EC) patients. RESULTS High-plex spatial profiling identified a 14-gene signature in the MMRd tumor-enriched regions stratifying tumors into "hot", "intermediate" and "cold" groups according to their distinct immune profiles, a finding highly consistent with the corresponding CD8 + T-cell infiltration status. Our validation studies further corroborated an existing coregulatory network involving HLA class I and DNMT3A potentially bridged through dynamic crosstalk incorporating CCL5. CONCLUSION Our study confirmed the heterogeneous TME status within MMRd-ECs and showed that these ECs can be stratified based on potential biomarkers such as HLA class I, DNMT3A and CD8 in pathological settings for improved ICI therapeutic efficacy in this subset of patients.
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Affiliation(s)
- Jingjing Guo
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- School of Medical and Life Sciences, Chengdu University of TCM, Chengdu, China
| | - Baijie Tang
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Fu
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xuan Zhu
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- School of Medical and Life Sciences, Chengdu University of TCM, Chengdu, China
| | - Wenlong Xie
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- School of Medical and Life Sciences, Chengdu University of TCM, Chengdu, China
| | - Nan Wang
- Mills Institute for Personalized Cancer Care, Jinan, China
| | - Zhiyong Ding
- Mills Institute for Personalized Cancer Care, Jinan, China
| | - Zhentao Song
- Mills Institute for Personalized Cancer Care, Jinan, China
| | - Yue Yang
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Xu
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xue Xiao
- Department of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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Chetty A, Blekhman R. Multi-omic approaches for host-microbiome data integration. Gut Microbes 2024; 16:2297860. [PMID: 38166610 PMCID: PMC10766395 DOI: 10.1080/19490976.2023.2297860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.
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Affiliation(s)
- Ashwin Chetty
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL, USA
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
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Warembourg C, Anguita-Ruiz A, Siroux V, Slama R, Vrijheid M, Richiardi L, Basagaña X. Statistical Approaches to Study Exposome-Health Associations in the Context of Repeated Exposure Data: A Simulation Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:16232-16243. [PMID: 37844068 PMCID: PMC10621661 DOI: 10.1021/acs.est.3c04805] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/17/2023] [Accepted: 09/18/2023] [Indexed: 10/18/2023]
Abstract
The exposome concept aims to consider all environmental stressors simultaneously. The dimension of the data and the correlation that may exist between exposures lead to various statistical challenges. Some methodological studies have provided insight regarding the efficiency of specific modeling approaches in the context of exposome data assessed once for each subject. However, few studies have considered the situation in which environmental exposures are assessed repeatedly. Here, we conduct a simulation study to compare the performance of statistical approaches to assess exposome-health associations in the context of multiple exposure variables. Different scenarios were tested, assuming different types and numbers of exposure-outcome causal relationships. An application study using real data collected within the INMA mother-child cohort (Spain) is also presented. In the simulation experiment, assessed methods showed varying performance across scenarios, making it challenging to recommend a one-size-fits-all strategy. Generally, methods such as sparse partial least-squares and the deletion-substitution-addition algorithm tended to outperform the other tested methods (ExWAS, Elastic-Net, DLNM, or sNPLS). Notably, as the number of true predictors increased, the performance of all methods declined. The absence of a clearly superior approach underscores the additional challenges posed by repeated exposome data, such as the presence of more complex correlation structures and interdependencies between variables, and highlights that careful consideration is essential when selecting the appropriate statistical method. In this regard, we provide recommendations based on the expected scenario. Given the heightened risk of reporting false positive or negative associations when applying these techniques to repeated exposome data, we advise interpreting the results with caution, particularly in compromised contexts such as those with a limited sample size.
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Affiliation(s)
- Charline Warembourg
- Univ
Rennes, Inserm, EHESP, Irset (Institut de recherche en santé,
environnement et travail)—UMR_S 1085, F-35000 Rennes, France
| | - Augusto Anguita-Ruiz
- ISGlobal, 08003 Barcelona, Spain
- CIBEROBN
(CIBER Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Valérie Siroux
- Team
of Environmental Epidemiology Applied to Development and Respiratory
Health, Institute for Advanced Biosciences, Université Grenoble Alpes, INSERM, CNRS, 38700 La Tronche, France
| | - Rémy Slama
- Team
of Environmental Epidemiology Applied to Development and Respiratory
Health, Institute for Advanced Biosciences, Université Grenoble Alpes, INSERM, CNRS, 38700 La Tronche, France
| | - Martine Vrijheid
- ISGlobal, 08003 Barcelona, Spain
- Spanish
Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid 28029, Spain
- Universitat
Pompeu Fabra (UPF), 08003 Barcelona, Spain
| | - Lorenzo Richiardi
- Department
of Medical Sciences, University of Turin
and CPO-Piemonte, 10124 Turin, Italy
| | - Xavier Basagaña
- ISGlobal, 08003 Barcelona, Spain
- Spanish
Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid 28029, Spain
- Universitat
Pompeu Fabra (UPF), 08003 Barcelona, Spain
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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: 17] [Impact Index Per Article: 8.5] [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.
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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
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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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Interactions between microbiome and lungs: Paving new paths for microbiome based bio-engineered drug delivery systems in chronic respiratory diseases. Chem Biol Interact 2019; 310:108732. [DOI: 10.1016/j.cbi.2019.108732] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/18/2019] [Accepted: 07/01/2019] [Indexed: 12/18/2022]
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