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Petrov VA, Sharapov SZ, Shagam L, Nostaeva AV, Pezer M, Li D, Hanić M, McGovern D, Louis E, Rahmouni S, Lauc G, Georges M, Aulchenko YS. Association Between Human Gut Microbiome and N-Glycan Composition of Total Plasma Proteome. Front Microbiol 2022; 13:811922. [PMID: 35572712 PMCID: PMC9100934 DOI: 10.3389/fmicb.2022.811922] [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: 11/09/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Being one of the most dynamic entities in the human body, glycosylation of proteins fine-tunes the activity of the organismal machinery, including the immune system, and mediates the interaction with the human microbial consortium, typically represented by the gut microbiome. Using data from 194 healthy individuals, we conducted an associational study to uncover potential relations between the gut microbiome and the blood plasma N-glycome, including N-glycome of immunoglobulin G. While lacking strong linkages on the multivariate level, we were able to identify associations between alpha and beta microbiome diversity and the blood plasma N-glycome profile. Moreover, for two bacterial genera, namely, Bilophila and Clostridium innocuum, significant associations with specific glycans were also shown. The study’s results suggest a non-trivial, possibly weak link between the total plasma N-glycome and the gut microbiome, predominantly involving glycans related to the immune system proteins, including immunoglobulin G. Further studies of glycans linked to microbiome-related proteins in well-selected patient groups are required to conclusively establish specific associations.
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Affiliation(s)
- Vyacheslav A. Petrov
- Unit of Animal Genomics, Grappe Interdisciplinaire de Génoprotéomique Appliquée-Institute, University of Liège, Liège, Belgium
- Central Research Laboratory, Ministry of Healthcare of Russian Federation, Federal State Budget Educational Institution of Higher Education, Siberian State Medical University, Tomsk, Russia
| | - Sodbo Zh. Sharapov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia
| | - Lev Shagam
- Central Research Laboratory, Ministry of Healthcare of Russian Federation, Federal State Budget Educational Institution of Higher Education, Siberian State Medical University, Tomsk, Russia
| | - Arina V. Nostaeva
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia
| | | | - Dalin Li
- Cedars-Sinai Medical Center, The F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, CA, United States
| | - Maja Hanić
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
| | - Dermot McGovern
- Cedars-Sinai Medical Center, The F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, CA, United States
| | - Edouard Louis
- Laboratory of Translational Gastroenterology, Grappe Interdisciplinaire de Génoprotéomique Appliquée-Institute, University of Liège, Liège, Belgium
| | - Souad Rahmouni
- Central Research Laboratory, Ministry of Healthcare of Russian Federation, Federal State Budget Educational Institution of Higher Education, Siberian State Medical University, Tomsk, Russia
| | - Gordan Lauc
- Glycoscience Research Laboratory, Genos Ltd., Zagreb, Croatia
- Department of Biochemistry and Molecular Biology, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Michel Georges
- Central Research Laboratory, Ministry of Healthcare of Russian Federation, Federal State Budget Educational Institution of Higher Education, Siberian State Medical University, Tomsk, Russia
| | - Yurii S. Aulchenko
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia
- *Correspondence: Yurii S. Aulchenko,
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Uh HW, Klarić L, Ugrina I, Lauc G, Smilde AK, Houwing-Duistermaat JJ. Choosing proper normalization is essential for discovery of sparse glycan biomarkers. Mol Omics 2021; 16:231-242. [PMID: 32211690 DOI: 10.1039/c9mo00174c] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Rapid progress in high-throughput glycomics analysis enables the researchers to conduct large sample studies. Typically, the between-subject differences in total abundance of raw glycomics data are very large, and it is necessary to reduce the differences, making measurements comparable across samples. Essentially there are two ways to approach this issue: row-wise and column-wise normalization. In glycomics, the differences per subject are usually forced to be exactly zero, by scaling each sample having the sum of all glycan intensities equal to 100%. This total area (row-wise) normalization (TA) results in so-called compositional data, rendering many standard multivariate statistical methods inappropriate or inapplicable. Ignoring the compositional nature of the data, moreover, may lead to spurious results. Alternatively, a log-transformation to the raw data can be performed prior to column-wise normalization and implementing standard statistical tools. Until now, there is no clear consensus on the appropriate normalization method applied to glycomics data. Nor is systematic investigation of impact of TA on downstream analysis available to justify the choice of TA. Our motivation lies in efficient variable selection to identify glycan biomarkers with regard to accurate prediction as well as interpretability of the model chosen. Via extensive simulations we investigate how different normalization methods affect the performance of variable selection, and compare their performance. We also address the effect of various types of measurement error in glycans: additive, multiplicative and two-component error. We show that when sample-wise differences are not large row-wise normalization (like TA) can have deleterious effects on variable selection and prediction.
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Affiliation(s)
- Hae-Won Uh
- Department of Biostatistics and Research Support, University Medical Center Utrecht, Utrecht, Netherlands.
| | - Lucija Klarić
- Genos Glycoscience Research Laboratory, Zagreb, Croatia and MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ivo Ugrina
- Genos Glycoscience Research Laboratory, Zagreb, Croatia and University of Split, Faculty of Science, Split, Croatia and Intellomics Ltd, Croatia
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia and Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Age K Smilde
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeanine J Houwing-Duistermaat
- Department of Biostatistics and Research Support, University Medical Center Utrecht, Utrecht, Netherlands. and Department of Statistics, University of Leeds, Leeds, UK
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McAuliffe E, Galligan M, Revill P, Kamwendo F, Sidat M, Masanja H, de Pinho H, Araujo E. Factors influencing job preferences of health workers providing obstetric care: results from discrete choice experiments in Malawi, Mozambique and Tanzania. Global Health 2016; 12:86. [PMID: 27998288 PMCID: PMC5175394 DOI: 10.1186/s12992-016-0222-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 11/23/2016] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Task shifting from established health professionals to mid-level providers (MLPs) (professionals who undergo shorter training in specific procedures) is one key strategy for reducing maternal and neonatal deaths. This has resulted in a growth in cadre types providing obstetric care in low and middle-income countries. Little is known about the relative importance of the different factors in determining motivation and retention amongst these cadres. METHODS This paper presents findings from large sample (1972 respondents) discrete choice experiments to examine the employment preferences of obstetric care workers across three east African countries. RESULTS The strongest predictors of job choice were access to continuing professional development and the presence of functioning human resources management (transparent, accountable and consistent systems for staff support, supervision and appraisal). Consistent with similar works we find pay and allowances significantly positively related to utility, but financial rewards are not as fundamental a factor underlying employment preferences as many may have previously believed. Location (urban vs rural) had the smallest average effect on utility for job choice in all three countries. CONCLUSIONS These findings are important in the context where efforts to address the human resources crisis have focused primarily on increasing salaries and incentives, as well as providing allowances to work in rural areas.
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Affiliation(s)
- Eilish McAuliffe
- School of Nursing, Midwifery & Health Systems, University College Dublin, Dublin, Ireland
| | - Marie Galligan
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Paul Revill
- Centre for Health Economics, University of York, York, UK
| | - Francis Kamwendo
- Centre for Reproductive Health, College of Medicine, University of Malawi, Zomba, Malawi
| | - Mohsin Sidat
- Department of Community Health, Faculty of Medicine, Eduardo Mondlane University, Maputo, Mozambique
| | | | - Helen de Pinho
- Averting Maternal Death and Disability Program (AMDD), Heilbrunn Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, USA
| | - Edson Araujo
- Health, Nutrition and Population Programme, World Bank, Washington DC, USA
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