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Liu Q, Chang X, Lian R, Chen Q, Wang J, Fu S. Evaluation of bi-directional causal association between obstructive sleep apnoea syndrome and diabetic microangiopathy: a Mendelian randomization study. Front Cardiovasc Med 2024; 11:1340602. [PMID: 38784169 PMCID: PMC11112003 DOI: 10.3389/fcvm.2024.1340602] [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: 11/18/2023] [Accepted: 04/01/2024] [Indexed: 05/25/2024] Open
Abstract
Background The relationship between obstructive sleep apnea syndrome (OSAS) and diabetic microangiopathy remains controversial. Objective This study aimed to use bidirectional two-sample Mendelian Randomization (MR) to assess the causal relationship between OSAS and diabetic microangiopathy. Methods First, we used the Linkage Disequilibrium Score Regression(LDSC) analysis to assess the genetic correlation. Then, the bidirectional two-sample MR study was conducted in two stages: OSAS and lung function-related indicators (forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)) were investigated as exposures, with diabetic microangiopathy as the outcome in the first stage, and genetic tools were used as proxy variables for OSAS and lung function-related measures in the second step. Genome-wide association study data came from the open GWAS database. We used Inverse-Variance Weighted (IVW), MR-Egger regression, Weighted median, Simple mode, and Weighted mode for effect estimation and pleiotropy testing. We also performed sensitivity analyses to test the robustness of the results. Furthermore, we performed multivariate and mediation MR analyses. Results In the LDSC analysis, We found a genetic correlation between OSAS, FVC, FEV 1, and diabetic microangiopathy. In the MR analysis, based on IVW analysis, genetically predicted OSAS was positively correlated with the incidence of diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). In the subgroup analysis of DR, there was a significant causal relationship between OSAS and background diabetic retinopathy (BDR) and proliferative diabetic retinopathy (PDR). The reverse MR did not show a correlation between the incidence of diabetic microangiopathy and OSAS. Reduced FVC had a potential causal relationship with increased incidence of DR and PDR. Reduced FEV1 had a potential causal relationship with the increased incidence of BDR, PDR, and DKD. Multivariate MR analysis showed that the association between OSAS and diabetic microangiopathy remained significant after adjusting for confounding factors. However, we did not find the significant mediating factors. Conclusion Our results suggest that OSAS may be a cause of the development of diabetic microangiopathy, and OSAS may also be associated with a high risk of diabetic microangiopathy, providing a reference for a better understanding of the prevention of diabetic microangiopathy.
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Affiliation(s)
- Qianqian Liu
- Department of Endocrinology, First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Gansu Provincial Endocrine Disease Clinical MedicineResearch Center, Lanzhou, Gansu, China
| | - Xingyu Chang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Rongna Lian
- Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Qi Chen
- Department of Endocrinology, First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Gansu Provincial Endocrine Disease Clinical MedicineResearch Center, Lanzhou, Gansu, China
| | - Jialei Wang
- Department of Endocrinology, First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Gansu Provincial Endocrine Disease Clinical MedicineResearch Center, Lanzhou, Gansu, China
| | - Songbo Fu
- Department of Endocrinology, First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Gansu Provincial Endocrine Disease Clinical MedicineResearch Center, Lanzhou, Gansu, China
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2
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Espinoza JL, Phillips A, Prentice MB, Tan GS, Kamath PL, Lloyd KG, Dupont CL. Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.583560. [PMID: 38559265 PMCID: PMC10979853 DOI: 10.1101/2024.03.08.583560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible platform that bridges the gap between genomics and biotechnological solutions.
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Affiliation(s)
- Josh L. Espinoza
- Department of Environment and Sustainability, J. Craig Venter Institute, La Jolla, CA 92037, USA
- Department of Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Allan Phillips
- Department of Environment and Sustainability, J. Craig Venter Institute, La Jolla, CA 92037, USA
- Department of Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | | | - Gene S. Tan
- Department of Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Pauline L. Kamath
- School of Food and Agriculture, University of Maine, Orono, ME 04469, USA
| | - Karen G. Lloyd
- Microbiology Department, University of Tennessee, Knoxville, TN 37917, USA
| | - Chris L. Dupont
- Department of Environment and Sustainability, J. Craig Venter Institute, La Jolla, CA 92037, USA
- Department of Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA 92037, USA
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3
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Brewer RC, Lanz TV, Hale CR, Sepich-Poore GD, Martino C, Swafford AD, Carroll TS, Kongpachith S, Blum LK, Elliott SE, Blachere NE, Parveen S, Fak J, Yao V, Troyanskaya O, Frank MO, Bloom MS, Jahanbani S, Gomez AM, Iyer R, Ramadoss NS, Sharpe O, Chandrasekaran S, Kelmenson LB, Wang Q, Wong H, Torres HL, Wiesen M, Graves DT, Deane KD, Holers VM, Knight R, Darnell RB, Robinson WH, Orange DE. Oral mucosal breaks trigger anti-citrullinated bacterial and human protein antibody responses in rheumatoid arthritis. Sci Transl Med 2023; 15:eabq8476. [PMID: 36812347 PMCID: PMC10496947 DOI: 10.1126/scitranslmed.abq8476] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 02/02/2023] [Indexed: 02/24/2023]
Abstract
Periodontal disease is more common in individuals with rheumatoid arthritis (RA) who have detectable anti-citrullinated protein antibodies (ACPAs), implicating oral mucosal inflammation in RA pathogenesis. Here, we performed paired analysis of human and bacterial transcriptomics in longitudinal blood samples from RA patients. We found that patients with RA and periodontal disease experienced repeated oral bacteremias associated with transcriptional signatures of ISG15+HLADRhi and CD48highS100A2pos monocytes, recently identified in inflamed RA synovia and blood of those with RA flares. The oral bacteria observed transiently in blood were broadly citrullinated in the mouth, and their in situ citrullinated epitopes were targeted by extensively somatically hypermutated ACPAs encoded by RA blood plasmablasts. Together, these results suggest that (i) periodontal disease results in repeated breaches of the oral mucosa that release citrullinated oral bacteria into circulation, which (ii) activate inflammatory monocyte subsets that are observed in inflamed RA synovia and blood of RA patients with flares and (iii) activate ACPA B cells, thereby promoting affinity maturation and epitope spreading to citrullinated human antigens.
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Affiliation(s)
- R. Camille Brewer
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Tobias V. Lanz
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
- Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68167, Germany
| | - Caryn R. Hale
- Rockefeller University, New York City, NY 10065, USA
| | | | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin D. Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Thomas S. Carroll
- Bioinformatics Resource Center, Rockefeller University, 1230 York Ave., New York, NY 10065, USA
| | - Sarah Kongpachith
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Lisa K. Blum
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Serra E. Elliott
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Nathalie E. Blachere
- Rockefeller University, New York City, NY 10065, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | - John Fak
- Rockefeller University, New York City, NY 10065, USA
| | - Vicky Yao
- Department of Computer Science, Rice University, Houston, TX 77005, USA
- Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA
| | - Olga Troyanskaya
- Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
- Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Mayu O. Frank
- Rockefeller University, New York City, NY 10065, USA
| | - Michelle S. Bloom
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Shaghayegh Jahanbani
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Alejandro M. Gomez
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Radhika Iyer
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Nitya S. Ramadoss
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Orr Sharpe
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | | | - Lindsay B. Kelmenson
- Division of Rheumatology, University of Colorado - Denver, Aurora, CO, 80045, USA
| | - Qian Wang
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Heidi Wong
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | | | - Mark Wiesen
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Dana T. Graves
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kevin D. Deane
- Division of Rheumatology, University of Colorado - Denver, Aurora, CO, 80045, USA
| | - V. Michael Holers
- Division of Rheumatology, University of Colorado - Denver, Aurora, CO, 80045, USA
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Robert B. Darnell
- Rockefeller University, New York City, NY 10065, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - William H. Robinson
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Dana E. Orange
- Rockefeller University, New York City, NY 10065, USA
- Hospital for Special Surgery, New York City, NY 10075, USA
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4
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Acharjee A, Singh U, Choudhury SP, Gkoutos GV. The diagnostic potential and barriers of microbiome based therapeutics. Diagnosis (Berl) 2022; 9:411-420. [PMID: 36000189 DOI: 10.1515/dx-2022-0052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/03/2022] [Indexed: 02/07/2023]
Abstract
High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.
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Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University of Birmingham, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, UK.,MRC Health Data Research UK (HDR UK), Birmingham, UK
| | - Utpreksha Singh
- Department of Health and Life Sciences, Coventry University, Coventry, UK
| | | | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University of Birmingham, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, UK.,MRC Health Data Research UK (HDR UK), Birmingham, UK.,NIHR Experimental Cancer Medicine Centre, Birmingham, UK
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5
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Boyraz A, Pawlowsky-Glahn V, Egozcue JJ, Acar AC. Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data. Brief Bioinform 2022; 23:6675749. [PMID: 36007229 DOI: 10.1093/bib/bbac328] [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: 02/01/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Statistical and machine learning techniques based on relative abundances have been used to predict health conditions and to identify microbial biomarkers. However, high dimensionality, sparsity and the compositional nature of microbiome data represent statistical challenges. On the other hand, the taxon grouping allows summarizing microbiome abundance with a coarser resolution in a lower dimension, but it presents new challenges when correlating taxa with a disease. In this work, we present a novel approach that groups Operational Taxonomical Units (OTUs) based only on relative abundances as an alternative to taxon grouping. The proposed procedure acknowledges the compositional data making use of principal balances. The identified groups are called Principal Microbial Groups (PMGs). The procedure reduces the need for user-defined aggregation of $\textrm{OTU}$s and offers the possibility of working with coarse group of $\textrm{OTU}$s, which are not present in a phylogenetic tree. PMGs can be used for two different goals: (1) as a dimensionality reduction method for compositional data, (2) as an aggregation procedure that provides an alternative to taxon grouping for construction of microbial balances afterward used for disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs provide a coherent data analysis for the search of biomarkers in human microbiota. The source code and demo data for PMGs are available at: https://github.com/asliboyraz/PMGs.
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Affiliation(s)
- Aslı Boyraz
- Department of Computer Programming, Recep Tayyip Erdoğan University, Ardeşen Vocational School, Rize, 53400, Turkey
| | - Vera Pawlowsky-Glahn
- Department of Computer Sciences, Applied Mathematics and Statistics, University of Girona, Campus Montilivi, 17003 Girona, Spain
| | - Juan José Egozcue
- Department of Civil and Environmental Engineering, Universitat Politécnica de Catalunya, Barcelona, 08034, Spain
| | - Aybar Can Acar
- Department of Medical Informatics, Middle East Technical University, Ankara Turkey
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6
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Coenders G, Greenacre M. Three approaches to supervised learning for compositional data with pairwise logratios. J Appl Stat 2022; 50:3272-3293. [PMID: 37969895 PMCID: PMC10637191 DOI: 10.1080/02664763.2022.2108007] [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: 02/02/2022] [Accepted: 07/25/2022] [Indexed: 10/15/2022]
Abstract
Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K-1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn's disease.
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Affiliation(s)
- Germà Coenders
- Department of Economics, Universitat de Girona, Girona, Spain
| | - Michael Greenacre
- Department of Economics and Business and Barcelona School of Management, Universitat Pompeu Fabra, Barcelona, Spain
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7
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Principal Amalgamation Analysis for Microbiome Data. Genes (Basel) 2022; 13:genes13071139. [PMID: 35885922 PMCID: PMC9318429 DOI: 10.3390/genes13071139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/14/2022] [Accepted: 06/21/2022] [Indexed: 12/02/2022] Open
Abstract
In recent years microbiome studies have become increasingly prevalent and large-scale. Through high-throughput sequencing technologies and well-established analytical pipelines, relative abundance data of operational taxonomic units and their associated taxonomic structures are routinely produced. Since such data can be extremely sparse and high dimensional, there is often a genuine need for dimension reduction to facilitate data visualization and downstream statistical analysis. We propose Principal Amalgamation Analysis (PAA), a novel amalgamation-based and taxonomy-guided dimension reduction paradigm for microbiome data. Our approach aims to aggregate the compositions into a smaller number of principal compositions, guided by the available taxonomic structure, by minimizing a properly measured loss of information. The choice of the loss function is flexible and can be based on familiar diversity indices for preserving either within-sample or between-sample diversity in the data. To enable scalable computation, we develop a hierarchical PAA algorithm to trace the entire trajectory of successive simple amalgamations. Visualization tools including dendrogram, scree plot, and ordination plot are developed. The effectiveness of PAA is demonstrated using gut microbiome data from a preterm infant study and an HIV infection study.
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8
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Sperlea T, Schenk JP, Dreßler H, Beisser D, Hattab G, Boenigk J, Heider D. The relationship between land cover and microbial community composition in European lakes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 825:153732. [PMID: 35157872 DOI: 10.1016/j.scitotenv.2022.153732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/19/2022] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
Microbes are essential for element cycling and ecosystem functioning. However, many questions central to understanding the role of microbes in ecology are still open. Here, we analyze the relationship between lake microbiomes and the lakes' land cover. By applying machine learning methods, we quantify the covariance between land cover categories and the microbial community composition recorded in the largest amplicon sequencing dataset of European lakes available to date. Our results show that the aggregation of environmental features or microbial taxa before analysis can obscure ecologically relevant patterns. We observe a comparatively high covariation of the lakes' microbial community with herbaceous and open spaces surrounding the lake; nevertheless, the microbial covariation with land cover categories is generally lower than the covariation with physico-chemical parameters. Combining land cover and physico-chemical bioindicators identified from the same amplicon sequencing dataset, we develop analytical data structures that facilitate insights into the ecology of the lake microbiome. Among these, a list of the environmental parameters sorted by the number of microbial bioindicators we have identified for them points towards apparent environmental drivers of the lake microbial community composition, such as the altitude, conductivity, and area covered herbaceous vegetation surrounding the lake. Furthermore, the response map, a similarity matrix calculated from the Jaccard similarity of the environmental parameters' lists of bioindicators, allows us to study the ecosystem's structure from the standpoint of the microbiome. More specifically, we identify multiple clusters of highly similar and possibly functionally linked ecological parameters, including one that highlights the importance of the calcium-bicarbonate equilibrium for lake ecology. Taken together, we demonstrate the use of machine learning approaches in studying the interplay between microbial diversity and environmental factors and introduce novel approaches to integrate environmental molecular diversity into monitoring and water quality assessments.
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Affiliation(s)
- Theodor Sperlea
- Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Lahn, Germany; Biological Oceanography, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
| | - Jan Philip Schenk
- Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Lahn, Germany
| | - Hagen Dreßler
- Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Lahn, Germany
| | - Daniela Beisser
- Department of Biodiversity, Center for Water and Environmental Research, University of Duisburg-Essen, D-45141 Essen, Germany
| | - Georges Hattab
- Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Lahn, Germany
| | - Jens Boenigk
- Department of Biodiversity, Center for Water and Environmental Research, University of Duisburg-Essen, D-45141 Essen, Germany
| | - Dominik Heider
- Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Lahn, Germany.
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9
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Approximation of a Microbiome Composition Shift by a Change in a Single Balance Between Two Groups of Taxa. mSystems 2022; 7:e0015522. [PMID: 35532211 PMCID: PMC9239069 DOI: 10.1128/msystems.00155-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The method proposed here extends the range of compositional methods providing interpretation of classical statistical tools applied to data converted to the ILR coordinates. It provides a strictly optimal solution in several special cases. The approach is universally applicable to compositional data of any nature, including microbiome data sets.
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10
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Pawlowski J, Bruce K, Panksep K, Aguirre FI, Amalfitano S, Apothéloz-Perret-Gentil L, Baussant T, Bouchez A, Carugati L, Cermakova K, Cordier T, Corinaldesi C, Costa FO, Danovaro R, Dell'Anno A, Duarte S, Eisendle U, Ferrari BJD, Frontalini F, Frühe L, Haegerbaeumer A, Kisand V, Krolicka A, Lanzén A, Leese F, Lejzerowicz F, Lyautey E, Maček I, Sagova-Marečková M, Pearman JK, Pochon X, Stoeck T, Vivien R, Weigand A, Fazi S. Environmental DNA metabarcoding for benthic monitoring: A review of sediment sampling and DNA extraction methods. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 818:151783. [PMID: 34801504 DOI: 10.1016/j.scitotenv.2021.151783] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 11/06/2021] [Accepted: 11/14/2021] [Indexed: 06/13/2023]
Abstract
Environmental DNA (eDNA) metabarcoding (parallel sequencing of DNA/RNA for identification of whole communities within a targeted group) is revolutionizing the field of aquatic biomonitoring. To date, most metabarcoding studies aiming to assess the ecological status of aquatic ecosystems have focused on water eDNA and macroinvertebrate bulk samples. However, the eDNA metabarcoding has also been applied to soft sediment samples, mainly for assessing microbial or meiofaunal biota. Compared to classical methodologies based on manual sorting and morphological identification of benthic taxa, eDNA metabarcoding offers potentially important advantages for assessing the environmental quality of sediments. The methods and protocols utilized for sediment eDNA metabarcoding can vary considerably among studies, and standardization efforts are needed to improve their robustness, comparability and use within regulatory frameworks. Here, we review the available information on eDNA metabarcoding applied to sediment samples, with a focus on sampling, preservation, and DNA extraction steps. We discuss challenges specific to sediment eDNA analysis, including the variety of different sources and states of eDNA and its persistence in the sediment. This paper aims to identify good-practice strategies and facilitate method harmonization for routine use of sediment eDNA in future benthic monitoring.
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Affiliation(s)
- J Pawlowski
- Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland; Institute of Oceanology, Polish Academy of Sciences, 81-712 Sopot, Poland; ID-Gene Ecodiagnostics, 1202 Geneva, Switzerland
| | - K Bruce
- NatureMetrics Ltd, CABI Site, Bakeham Lane, Egham TW20 9TY, UK
| | - K Panksep
- Institute of Technology, University of Tartu, Tartu 50411, Estonia; Chair of Hydrobiology and Fishery, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia; Chair of Aquaculture, Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Estonia
| | - F I Aguirre
- Water Research Institute, National Research Council of Italy (IRSA-CNR), Monterotondo, Rome, Italy
| | - S Amalfitano
- Water Research Institute, National Research Council of Italy (IRSA-CNR), Monterotondo, Rome, Italy
| | - L Apothéloz-Perret-Gentil
- Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland; ID-Gene Ecodiagnostics, 1202 Geneva, Switzerland
| | - T Baussant
- Norwegian Research Center AS, NORCE Environment, Marine Ecology Group, Mekjarvik 12, 4070 Randaberg, Norway
| | - A Bouchez
- INRAE, CARRTEL, 74200 Thonon-les-Bains, France
| | - L Carugati
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona 60131, Italy
| | - K Cermakova
- ID-Gene Ecodiagnostics, 1202 Geneva, Switzerland
| | - T Cordier
- Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland; NORCE Climate, NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, Norway
| | - C Corinaldesi
- Department of Materials, Environmental Sciences and Urban Planning, Polytechnic University of Marche, Via Brecce Bianche, Ancona 60131, Italy
| | - F O Costa
- Centre of Molecular and Environmental Biology (CBMA), Department of Biology, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal; Institute of Science and Innovation for Bio-Sustainability (IB-S), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - R Danovaro
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona 60131, Italy
| | - A Dell'Anno
- Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona 60131, Italy
| | - S Duarte
- Centre of Molecular and Environmental Biology (CBMA), Department of Biology, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal; Institute of Science and Innovation for Bio-Sustainability (IB-S), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - U Eisendle
- University of Salzburg, Dept. of Biosciences, 5020 Salzburg, Austria
| | - B J D Ferrari
- Swiss Centre for Applied Ecotoxicology (Ecotox Centre), EPFL ENAC IIE-GE, 1015 Lausanne, Switzerland
| | - F Frontalini
- Department of Pure and Applied Sciences, Urbino University, Urbino, Italy
| | - L Frühe
- Technische Universität Kaiserslautern, Ecology Group, D-67663 Kaiserslautern, Germany
| | - A Haegerbaeumer
- Bielefeld University, Animal Ecology, 33615 Bielefeld, Germany
| | - V Kisand
- Institute of Technology, University of Tartu, Tartu 50411, Estonia
| | - A Krolicka
- Norwegian Research Center AS, NORCE Environment, Marine Ecology Group, Mekjarvik 12, 4070 Randaberg, Norway
| | - A Lanzén
- AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Pasaia, Gipuzkoa, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Bizkaia, Spain
| | - F Leese
- University of Duisburg-Essen, Faculty of Biology, Aquatic Ecosystem Research, Germany
| | - F Lejzerowicz
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - E Lyautey
- Univ. Savoie Mont Blanc, INRAE, CARRTEL, 74200 Thonon-les-Bains, France
| | - I Maček
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia; Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT), University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
| | - M Sagova-Marečková
- Czech University of Life Sciences, Dept. of Microbiology, Nutrition and Dietetics, Prague, Czech Republic
| | - J K Pearman
- Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson 7042, New Zealand
| | - X Pochon
- Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson 7042, New Zealand; Institute of Marine Science, University of Auckland, Warkworth 0941, New Zealand
| | - T Stoeck
- Technische Universität Kaiserslautern, Ecology Group, D-67663 Kaiserslautern, Germany
| | - R Vivien
- Swiss Centre for Applied Ecotoxicology (Ecotox Centre), EPFL ENAC IIE-GE, 1015 Lausanne, Switzerland
| | - A Weigand
- National Museum of Natural History Luxembourg, 25 Rue Münster, L-2160 Luxembourg, Luxembourg
| | - S Fazi
- Water Research Institute, National Research Council of Italy (IRSA-CNR), Monterotondo, Rome, Italy.
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11
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Oluwagbemigun K, Schnermann ME, Schmid M, Cryan JF, Nöthlings U. A prospective investigation into the association between the gut microbiome composition and cognitive performance among healthy young adults. Gut Pathog 2022; 14:15. [PMID: 35440044 PMCID: PMC9019932 DOI: 10.1186/s13099-022-00487-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/17/2022] [Indexed: 12/14/2022] Open
Abstract
Background There is emerging evidence that the gut microbiome composition is associated with several human health outcomes, which include cognitive performance. However, only a few prospective epidemiological studies exist and none among young adults. Here we address the gap in the literature by investigating whether the gut microbiome composition is prospectively linked to fluid intelligence among healthy young adults. Methods Forty individuals (65% females, 26 years) from the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study provided a fecal sample for gut microbiome composition and subsequently (average of 166 days) completed a cognitive functioning test using the Cattell’s Culture Fair Intelligence Test, revised German version (CFT 20-R). The assessment of the gut microbiome at the genera level was by 16S rRNA V3-V4 Illumina sequencing. The relative abundance of 158 genera was summarized into bacterial communities using a novel data-driven dimension reduction, amalgamation. The fluid intelligence score was regressed on the relative abundance of the bacterial communities and adjusted for selected covariates. Results The 158 genera were amalgamated into 12 amalgams (bacterial communities), which were composed of 18, 6, 10, 14, 8, 10, 16, 13, 12, 12, 3, and 11 genera. Only the 14-genera bacterial community, named the “Ruminococcaceae- and Coriobacteriaceae-dominant community” was positively associated with fluid intelligence score (β = 7.8; 95% CI: 0.62, 15.65, P = 0.04). Conclusion Among healthy young adults, the abundance of a gut bacterial community was associated with fluid intelligence score. This study suggests that cognitive performance may potentially benefit from gut microbiome-based intervention.
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Affiliation(s)
- Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany.
| | - Maike E Schnermann
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - John F Cryan
- APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
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12
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Gordon-Rodriguez E, Quinn TP, Cunningham JP. Learning sparse log-ratios for high-throughput sequencing data. Bioinformatics 2021; 38:157-163. [PMID: 34498030 PMCID: PMC8696089 DOI: 10.1093/bioinformatics/btab645] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/09/2021] [Accepted: 09/03/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION The automatic discovery of sparse biomarkers that are associated with an outcome of interest is a central goal of bioinformatics. In the context of high-throughput sequencing (HTS) data, and compositional data (CoDa) more generally, an important class of biomarkers are the log-ratios between the input variables. However, identifying predictive log-ratio biomarkers from HTS data is a combinatorial optimization problem, which is computationally challenging. Existing methods are slow to run and scale poorly with the dimension of the input, which has limited their application to low- and moderate-dimensional metagenomic datasets. RESULTS Building on recent advances from the field of deep learning, we present CoDaCoRe, a novel learning algorithm that identifies sparse, interpretable and predictive log-ratio biomarkers. Our algorithm exploits a continuous relaxation to approximate the underlying combinatorial optimization problem. This relaxation can then be optimized efficiently using the modern ML toolbox, in particular, gradient descent. As a result, CoDaCoRe runs several orders of magnitude faster than competing methods, all while achieving state-of-the-art performance in terms of predictive accuracy and sparsity. We verify the outperformance of CoDaCoRe across a wide range of microbiome, metabolite and microRNA benchmark datasets, as well as a particularly high-dimensional dataset that is outright computationally intractable for existing sparse log-ratio selection methods. AVAILABILITY AND IMPLEMENTATION The CoDaCoRe package is available at https://github.com/egr95/R-codacore. Code and instructions for reproducing our results are available at https://github.com/cunningham-lab/codacore. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC 3126, Australia
| | - John P Cunningham
- Department of Statistics, Columbia University, New York, NY 10025, USA
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13
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Bastiaanssen TFS, Cryan JF. Dairy alters the microbiome, are we but skimming the surface? EBioMedicine 2021; 68:103417. [PMID: 34091415 PMCID: PMC8185236 DOI: 10.1016/j.ebiom.2021.103417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Thomaz F S Bastiaanssen
- Department of Anatomy & Neuroscience, School of Medicine, College of Medicine & Health, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - John F Cryan
- Department of Anatomy & Neuroscience, School of Medicine, College of Medicine & Health, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland.
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14
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Yang F, Zou Q. DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data. Brief Bioinform 2021; 22:6217721. [PMID: 33834198 DOI: 10.1093/bib/bbab094] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/22/2021] [Accepted: 03/03/2021] [Indexed: 12/23/2022] Open
Abstract
How best to utilize the microbial taxonomic abundances in regard to the prediction and explanation of human diseases remains appealing and challenging, and the relative nature of microbiome data necessitates a proper feature selection method to resolve the compositional problem. In this study, we developed an all-in-one platform to address a series of issues in microbiome-based human disease prediction and taxonomic biomarkers discovery. We prioritize the interpretation, runtime and classification accuracy of the distal discriminative balances analysis (DBA-distal) method in selecting a set of distal discriminative balances, and develop DisBalance, a comprehensive platform, to integrate and streamline the workflows of disease model building, disease risk prediction and disease-related biomarker discovery for microbiome-based binary classifications. DisBalance allows the de novo model-building and disease risk prediction in a very fast and convenient way. To facilitate the model-driven and knowledge-driven discoveries, DisBalance dedicates multiple strategies for the mining of microbial biomarkers. The independent validation of the models constructed by the DisBalance pipeline is performed on seven microbiome datasets from the original article of DBA-distal. The implementation of the DisBalance platform is demonstrated by a complete analysis of a shotgun metagenomic dataset of Ulcerative Colitis (UC). As a free and open-source, DisBlance can be accessed at http://lab.malab.cn/soft/DisBalance. The source code and demo data for Disbalance are available at https://github.com/yangfenglong/DisBalance.
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Affiliation(s)
- Fenglong Yang
- University of Electronic Science and Technology of China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
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15
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Erb I, Gloor GB, Quinn TP. Editorial: Compositional data analysis and related methods applied to genomics-a first special issue from NAR Genomics and Bioinformatics. NAR Genom Bioinform 2020; 2:lqaa103. [PMID: 33575646 PMCID: PMC7724639 DOI: 10.1093/nargab/lqaa103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Ionas Erb
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
| | - Gregory B Gloor
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 5C1, Canada
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute, Deakin University, Waurn Ponds, VIC 3216, Australia
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