[en] Being one of the most dynamic entities in the human body, glycosylation of proteins fine-tunes the activity of organismal
machinery, including the immune system, and mediates interaction with the human microbial consortium, typically represented by
the gut microbiome. Using data from 194 healthy people, we conducted an associational study to uncover potential relations
between gut microbiome and 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 blood plasma
N-glycome profile. Moreover, for two bacterial genera, Bilophila and Clostridium innocuum group, significant associations with
specific glycans were also shown. Our results suggest a non-trivial, possibly weak link between total plasma N-glycome and gut
microbiome, predominantly involving glycans related to the immune system proteins, including immunoglobulin G. Lager studies of
glycans linked to microbiome-related proteins in well-selected patient groups are required to conclusively establish specific
associations.
Disciplines :
Genetics & genetic processes
Author, co-author :
Petrov, Viacheslav ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques ; Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Unit of Animal Genomics
Sharapov, Sodbo
Shagam, Lev ; Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Unit of Animal Genomics ; Université de Liège - ULiège > Département de gestion vétérinaire des Ressources Animales (DRA) > GIGA-R : Génomique animale
Nostaeva, Arina
Pezer, Marija
Li, Dalin
Hanić, Maja
McGovern, Dermot
LOUIS, Edouard ; Université de Liège - ULiège > GIGA > GIGA I3 - Translational gastroenterology ; Centre Hospitalier Universitaire de Liège - CHU > > Service de gastroentérologie, hépatologie, onco. digestive ; Université de Liège - ULiège > Département des sciences cliniques > Hépato-gastroentérologie
Rahmouni, Souad ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques ; Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Unit of Animal Genomics
Lauc, Gordan
Georges, Michel ; Université de Liège - ULiège > GIGA ; Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Unit of Animal Genomics
Akmačić I. T. Ugrina I. Štambuk J. Gudelj I. Vuèković F. Lauc G. et al. (2015). High-throughput glycomics: optimization of sample preparation. Biochemistry 80 934–942. 10.1134/S0006297915070123 26542006
Bates D. Mächler M. Bolker B. Walker S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67 1–48. 10.18637/jss.v067.i01
Bokulich N. A. Kaehler B. D. Rideout J. R. Dillon M. Bolyen E. Knight R. et al. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6:90. 10.1186/s40168-018-0470-z 29773078
Bolyen E. Rideout J. R. Dillon M. R. Bokulich N. A. Abnet C. C. Al-Ghalith G. A. et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37 852–857. 10.1038/s41587-019-0209-9 31341288
Bougeard S. Dray S. (2018). Supervised multiblock analysis in R with the ade4 package. J. Stat. Softw. 86 1–17. 10.18637/jss.v086.i01
Bushnell B. (2014). BBTools. Available Online at: https://sourceforge.net/projects/bbmap/ (accessed August 3, 2020).
Bycroft C. Freeman C. Petkova D. Band G. Elliott L. T. Sharp K. et al. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature 562 203–209. 10.1038/s41586-018-0579-z 30305743
Callahan B. J. McMurdie P. J. Rosen M. J. Han A. W. Johnson A. J. A. Holmes S. P. (2016). DADA2: high resolution sample inference from Illumina amplicon data. Nat. Methods 13 581–583. 10.1038/nmeth.3869 27214047
Cambay F. Forest-Nault C. Dumoulin L. Seguin A. Henry O. Durocher Y. et al. (2020). Glycosylation of Fcγ receptors influences their interaction with various IgG1 glycoforms. Mol. Immunol. 121 144–158. 10.1016/j.molimm.2020.03.010 32222585
Chang C. C. Chow C. C. Tellier L. C. Vattikuti S. Purcell S. M. Lee J. J. (2015). Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7. 10.1186/s13742-015-0047-8 25722852
Chia J.-H. Wu T.-S. Wu T.-L. Chen C.-L. Chuang C.-H. Su L.-H. et al. (2018). Clostridium innocuum is a vancomycin-resistant pathogen that may cause antibiotic-associated diarrhoea. Clin. Microbiol. Infect. 24 1195–1199. 10.1016/j.cmi.2018.02.015 29458157
Cinar O. Viechtbauer W. (2020). poolr: Package for Pooling the Results from (Dependent) Tests. Available Online at: https://ozancinar.github.io/poolr/ (accessed October 12, 2021).
Clerc F. Reiding K. R. Jansen B. C. Kammeijer G. S. M. Bondt A. Wuhrer M. (2016). Human plasma protein N-glycosylation. Glycoconj. J. 33 309–343. 10.1007/s10719-015-9626-2 26555091
Crum-Cianflone N. (2009). Clostridium innocuum bacteremia in an AIDS patient: case report and review of the literature. Am. J. Med. Sci. 337 480–482. 10.1097/MAJ.0b013e31819f1e95 19525665
de Goffau M. C. Lager S. Salter S. J. Wagner J. Kronbichler A. Charnock-Jones D. S. et al. (2018). Recognizing the reagent microbiome. Nat. Microbiol. 3 851–853. 10.1038/s41564-018-0202-y 30046175
Dotz V. Wuhrer M. (2019). N-glycome signatures in human plasma: associations with physiology and major diseases. FEBS Lett. 593 2966–2976. 10.1002/1873-3468.13598 31509238
Eisenhofer R. Minich J. J. Marotz C. Cooper A. Knight R. Weyrich L. S. (2019). Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 27 105–117. 10.1016/j.tim.2018.11.003 30497919
Galligan M. C. Saldova R. Campbell M. P. Rudd P. M. Murphy T. B. (2013). Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution. BMC Bioinformatics 14:155. 10.1186/1471-2105-14-155 23651459
Galwey N. W. (2009). A new measure of the effective number of tests, a practical tool for comparing families of non-independent significance tests. Genet. Epidemiol. 33 559–568. 10.1002/gepi.20408 19217024
Greenacre M. (2018). Compositional Data Analysis in Practice, 1st Edn. Boca Raton, FL: Chapman and Hall.
Harvey D. J. Merry A. H. Royle L. Campbell M. P. Dwek R. A. Rudd P. M. (2009). Proposal for a standard system for drawing structural diagrams of N- and O-linked carbohydrates and related compounds. Proteomics 9 3796–3801. 10.1002/pmic.200900096 19670245
Hirano A. Umeno J. Okamoto Y. Shibata H. Ogura Y. Moriyama T. et al. (2018). Comparison of the microbial community structure between inflamed and non-inflamed sites in patients with ulcerative colitis. J. Gastroenterol. Hepatol. [Online ahead of print]. 10.1111/jgh.14129 29462845
Jervis-Bardy J. Leong L. E. X. Marri S. Smith R. J. Choo J. M. Smith-Vaughan H. C. et al. (2015). Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome 3:19. 10.1186/s40168-015-0083-8 25969736
Klarić L. Tsepilov Y. A. Stanton C. M. Mangino M. Sikka T. T. Esko T. et al. (2020). Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases. Sci. Adv. 6:eaax0301. 10.1126/sciadv.aax0301 32128391
Köster J. Rahmann S. (2012). Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28 2520–2522. 10.1093/bioinformatics/bts480 22908215
Kudelka M. R. Stowell S. R. Cummings R. D. Neish A. S. (2020). Intestinal epithelial glycosylation in homeostasis and gut microbiota interactions in IBD. Nat. Rev. Gastroenterol. Hepatol. 17 597–617. 10.1038/s41575-020-0331-7 32710014
Lauc G. Pezer M. Rudan I. Campbell H. (2016). Mechanisms of disease: the human N-glycome. Biochim. Biophys. Acta 1860 1574–1582. 10.1016/j.bbagen.2015.10.016 26500099
Lloyd-Jones L. R. Zeng J. Sidorenko J. Yengo L. Moser G. Kemper K. E. et al. (2019). Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10:5086. 10.1038/s41467-019-12653-0 31704910
McHardy I. H. Goudarzi M. Tong M. Ruegger P. M. Schwager E. Weger J. R. et al. (2013). Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome 1:17. 10.1186/2049-2618-1-17 24450808
Momozawa Y. Dmitrieva J. Théâtre E. Deffontaine V. Rahmouni S. Charloteaux B. et al. (2018). IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nat. Commun. 9:2427. 10.1038/s41467-018-04365-8 29930244
Pučić M. Knežević A. Vidič J. Adamczyk B. Novokmet M. Polašek O. et al. (2011). High throughput isolation and glycosylation analysis of IgG–variability and heritability of the IgG glycome in three isolated human populations. Mol. Cell. Proteomics 10:M111.010090. 10.1074/mcp.M111.010090 21653738
Quast C. Pruesse E. Yilmaz P. Gerken J. Schweer T. Yarza P. et al. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41 D590–D596. 10.1093/nar/gks1219 23193283
R Core Team (2019). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
Richardson T. G. Harrison S. Hemani G. Davey Smith G. (2019). An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. eLife 8:e43657. 10.7554/eLife.43657 30835202
Salter S. J. Cox M. J. Turek E. M. Calus S. T. Cookson W. O. Moffatt M. F. et al. (2014). Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12:87. 10.1186/s12915-014-0087-z 25387460
Shannon C. E. (1948). A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423. 10.1002/j.1538-7305.1948.tb01338.x
Sharapov S. Z. Shadrina A. S. Tsepilov Y. A. Elgaeva E. E. Tiys E. S. Feoktistova S. G. et al. (2021). Replication of 15 loci involved in human plasma protein N-glycosylation in 4802 samples from four cohorts. Glycobiology 31 82–88. 10.1093/glycob/cwaa053 32521004
Sharapov S. Z. Tsepilov Y. A. Klaric L. Mangino M. Thareja G. Shadrina A. S. et al. (2019). Defining the genetic control of human blood plasma N-glycome using genome-wide association study. Hum. Mol. Genet. 28 2062–2077. 10.1093/hmg/ddz054 31163085
Suhre K. Trbojević-Akmaèić I. Ugrina I. Mook-Kanamori D. O. Spector T. Graumann J. et al. (2019). Fine-mapping of the human blood plasma N-glycome onto its proteome. Metabolites 9:122. 10.3390/metabo9070122 31247951
Trbojević Akmačić I. Ventham N. T. Theodoratou E. Vuèković F. Kennedy N. A. Krištić J. et al. (2015). Inflammatory bowel disease associates with proinflammatory potential of the immunoglobulin G glycome. Inflamm. Bowel Dis. 21 1237–1247. 10.1097/MIB.0000000000000372 25895110
Trbojević-Akmačić I. Ugrina I. Lauc G. (2017). Comparative analysis and validation of different steps in glycomics studies. Methods Enzymol. 586 37–55. 10.1016/bs.mie.2016.09.027 28137572
van den Boogaart K. G. Tolosana-Delgado R. (2008). “compositions”: a unified R package to analyze compositional data. Comput. Geosci. 34 320–338. 10.1016/j.cageo.2006.11.017
Vučković F. Theodoratou E. Thaçi K. Timofeeva M. Vojta A. Štambuk J. et al. (2016). IgG glycome in colorectal cancer. Clin. Cancer Res. 22 3078–3086. 10.1158/1078-0432.ccr-15-1867 26831718
Wickham H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer-Verlag. 10.1007/978-0-387-98141-3
Wolfert M. A. Boons G.-J. (2013). Adaptive immune activation: glycosylation does matter. Nat. Chem. Biol. 9 776–784. 10.1038/nchembio.1403 24231619
Zaytseva O. O. Freidin M. B. Keser T. Štambuk J. Ugrina I. Šimurina M. et al. (2020). Heritability of human plasma N-glycome. J. Proteome Res. 19 85–91. 10.1021/acs.jproteome.9b00348 31747749
