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Microbial Genomics logo Microbial Genomics

Volume 10, Issue 12

Secretor status is a modifier of vaginal microbiota-associated preterm birth risk Open Access

Abstract

Mutations in the gene that result in a lack of expression of histo-blood group antigens on secreted glycoproteins may shape the vaginal microbiota with consequences for birth outcome. To test this, we analysed the relationship between secretor status, vaginal microbiota and gestational length in an ethnically diverse cohort of 302 pregnant women, including 82 who delivered preterm. and were found to have distinct co-occurrence patterns with other microbial taxa in non-secretors. Moreover, non-secretors with spp. depleted high diversity vaginal microbiota in early pregnancy had significantly shorter gestational length than spp. dominated non-secretors (mean of 241.54 days (=47.14) versus 266.21 (23.61); -value=0.0251). Similar gestational length differences were observed between non-secretors with high vaginal diversity and secretors with spp. dominance (mean of 262.52 days (SD=27.73); -=0.0439) or depletion (mean of 266.05 days (SD=20.81); -=0.0312). Our data highlight secretor status and blood-group antigen expression as being important mediators of vaginal microbiota–host interactions in the context of preterm birth risk.

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  • Accepted:
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Keyword(s): blood-type antigens , Lactobacillus , preterm birth , secretor status and vaginal microbiome
Funding
This study was supported by the:
  • March of Dimes Foundation
    • Principal Award Recipient: DavidA MacIntyre
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2025-12-05

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References

  1. Audfray A, Varrot A, Imberty A. Bacteria love our sugars: interaction between soluble lectins and human fucosylated glycans, structures, thermodynamics and design of competing glycocompounds. Comptes Rendus Chimie 2013; 16:482–490 [View Article]
     [Google Scholar]
  2. Marcobal A, Southwick AM, Earle KA, Sonnenburg JL. A refined palate: bacterial consumption of host glycans in the gut. Glycobiology 2013; 23:1038–1046 [View Article] [PubMed]
     [Google Scholar]
  3. McGuckin MA, Lindén SK, Sutton P, Florin TH. Mucin dynamics and enteric pathogens. Nat Rev Microbiol 2011; 9:265–278 [View Article] [PubMed]
     [Google Scholar]
  4. Kelly RJ, Rouquier S, Giorgi D, Lennon GG, Lowe JB. Sequence and expression of a candidate for the human secretor blood group α(1,2)fucosyltransferase gene (FUT2). J Biol Chem 1995; 270:4640–4649 [View Article]
     [Google Scholar]
  5. Soejima M, Pang H, Koda Y. Genetic variation of FUT2 in a Ghanaian population: identification of four novel mutations and inference of balancing selection. Ann Hematol 2007; 86:199–204 [View Article] [PubMed]
     [Google Scholar]
  6. ENCODE Project Consortium Birney E, Stamatoyannopoulos JA, Dutta A, Guigó R et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007; 447:799–816 [View Article] [PubMed]
     [Google Scholar]
  7. Ferrer-Admetlla A, Sikora M, Laayouni H, Esteve A, Roubinet F et al. A natural history of FUT2 polymorphism in humans. Mol Biol Evol 2009; 26:1993–2003 [View Article] [PubMed]
     [Google Scholar]
  8. Carlsson B, Kindberg E, Buesa J, Rydell GE, Lidón MF et al. The G428A nonsense mutation in FUT2 provides strong but not absolute protection against symptomatic GII.4 norovirus infection. PLoS One 2009; 4:e5593 [View Article]
     [Google Scholar]
  9. Blackwell CC, Jónsdóttir K, Hanson M, Todd WTA, Chaudhuri AKR et al. Non-secretion of ABO antigens predisposing to infection by Neisseria meningitidis and Streptococcus pneumoniae. Lancet 1986; 328:284–285 [View Article]
     [Google Scholar]
  10. Blackwell CC, Jonsdottir K, Hanson MF, Weir DM. Non-secretion of ABO blood group antigens predisposing to infection by Haemophilus influenzae. Lancet 1986; 328:687 [View Article]
     [Google Scholar]
  11. Wacklin P, Tuimala J, Nikkilä J, Tims S, Mäkivuokko H et al. Faecal microbiota composition in adults is associated with the FUT2 gene determining the secretor status. PLoS One 2014; 9:e94863 [View Article] [PubMed]
     [Google Scholar]
  12. Rausch P, Rehman A, Künzel S, Häsler R, Ott SJ et al. Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype. Proc Natl Acad Sci U S A 2011; 108:19030–19035 [View Article] [PubMed]
     [Google Scholar]
  13. Wacklin P, Mäkivuokko H, Alakulppi N, Nikkilä J, Tenkanen H et al. Secretor genotype (FUT2 gene) is strongly associated with the composition of Bifidobacteria in the human intestine. PLoS One 2011; 6:e20113 [View Article] [PubMed]
     [Google Scholar]
  14. Lewis ZT, Totten SM, Smilowitz JT, Popovic M, Parker E et al. Maternal fucosyltransferase 2 status affects the gut bifidobacterial communities of breastfed infants. Microbiome 2015; 3:13 [View Article] [PubMed]
     [Google Scholar]
  15. Qin Y, Havulinna AS, Liu Y, Jousilahti P, Ritchie SC et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Nat Genet 2022; 54:134–142 [View Article] [PubMed]
     [Google Scholar]
  16. Bayar E, Bennett PR, Chan D, Sykes L, MacIntyre DA. The pregnancy microbiome and preterm birth. Semin Immunopathol 2020; 42:487–499 [View Article] [PubMed]
     [Google Scholar]
  17. Bennett PR, Brown RG, MacIntyre DA. Vaginal microbiome in preterm rupture of membranes. Obstet Gynecol Clin North Am 2020; 47:503–521 [View Article] [PubMed]
     [Google Scholar]
  18. Al‐Memar M, Bobdiwala S, Fourie H, Mannino R, Lee Y et al. The association between vaginal bacterial composition and miscarriage: a nested case–control study. BJOG 2020; 127:264–274 [View Article]
     [Google Scholar]
  19. Brown RG, Marchesi JR, Lee YS, Smith A, Lehne B et al. Vaginal dysbiosis increases risk of preterm fetal membrane rupture, neonatal sepsis and is exacerbated by erythromycin. BMC Med 2018; 16:1–15 [View Article]
     [Google Scholar]
  20. Callahan BJ, DiGiulio DB, Goltsman DSA, Sun CL, Costello EK et al. Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc Natl Acad Sci USA 2017; 114:9966–9971 [View Article]
     [Google Scholar]
  21. Pike KC, Lucas JSA. Respiratory consequences of late preterm birth. Paediatr Respir Rev 2015; 16:182–188 [View Article] [PubMed]
     [Google Scholar]
  22. Lax ID, Duerden EG, Lin SY, Mallar Chakravarty M, Donner EJ et al. Neuroanatomical consequences of very preterm birth in middle childhood. Brain Struct Funct 2013; 218:575–585 [View Article] [PubMed]
     [Google Scholar]
  23. Chehade H, Simeoni U, Guignard JP, Boubred F. Preterm birth: long term cardiovascular and renal consequences. Curr Pediatr Rev 2018; 14:219–226 [View Article] [PubMed]
     [Google Scholar]
  24. Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008; 371:75–84 [View Article]
     [Google Scholar]
  25. Kyrgiou M, Mitra A, Arbyn M, Stasinou SM, Martin-Hirsch P et al. Fertility and early pregnancy outcomes after treatment for cervical intraepithelial neoplasia: systematic review and meta-analysis. BMJ 2014; 349:g6192 [View Article] [PubMed]
     [Google Scholar]
  26. Mitra A, MacIntyre DA, Lee YS, Smith A, Marchesi JR et al. Cervical intraepithelial neoplasia disease progression is associated with increased vaginal microbiome diversity. Sci Rep 2015; 5:16865 [View Article]
     [Google Scholar]
  27. Mitra A, MacIntyre DA, Ntritsos G, Smith A, Tsilidis KK et al. The vaginal microbiota associates with the regression of untreated cervical intraepithelial neoplasia 2 lesions. Nat Commun 2020; 11:1999 [View Article] [PubMed]
     [Google Scholar]
  28. Caldwell J, Matson A, Mosha M, Hagadorn JI, Moore J et al. Maternal H-antigen secretor status is an early biomarker for potential preterm delivery. J Perinatol 2021; 41:2147–2155 [View Article] [PubMed]
     [Google Scholar]
  29. Pausan M-R, Kolovetsiou-Kreiner V, Richter GL, Madl T, Giselbrecht E et al. Human milk oligosaccharides modulate the risk for preterm birth in a microbiome-dependent and -independent manner. mSystems 2020; 5:e00334-20 [View Article] [PubMed]
     [Google Scholar]
  30. MacIntyre DA, Chandiramani M, Lee YS, Kindinger L, Smith A et al. The vaginal microbiome during pregnancy and the postpartum period in a European population. Sci Rep 2015; 5:8988 [View Article] [PubMed]
     [Google Scholar]
  31. Silva LM, Carvalho AS, Guillon P, Seixas S, Azevedo M et al. Infection-associated FUT2 (Fucosyltransferase 2) genetic variation and impact on functionality assessed by in vivo studies. Glycoconj J 2010; 27:61–68 [View Article] [PubMed]
     [Google Scholar]
  32. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13:581–583 [View Article] [PubMed]
     [Google Scholar]
  33. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics | Oxford Academic; https://academic.oup.com/bioinformatics/article/34/18/3094/4994778 accessed 22 February 2024
  34. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint 2012
     [Google Scholar]
  35. Patterson M, Marschall T, Pisanti N, van Iersel L, Stougie L et al. WhatsHap: weighted haplotype assembly for future-generation sequencing reads. J Comput Biol 2015; 22:498–509 [View Article] [PubMed]
     [Google Scholar]
  36. Henry S, Mollicone R, Fernandez P, Samuelsson B, Oriol R et al. Molecular basis for erythrocyte Le(A+ b+) and salivary ABH partial-secretor phenotypes: expression of A FUT2 secretor allele with an A-->T mutation at nucleotide 385 correlates with reduced alpha(1,2) fucosyltransferase activity. Glycoconj J 1996; 13:985–993 [View Article] [PubMed]
     [Google Scholar]
  37. Fumagalli M, Cagliani R, Pozzoli U, Riva S, Comi GP et al. Widespread balancing selection and pathogen-driven selection at blood group antigen genes. Genome Res 2009; 19:199–212 [View Article] [PubMed]
     [Google Scholar]
  38. Frank JA, Reich CI, Sharma S, Weisbaum JS, Wilson BA et al. Critical evaluation of two primers commonly used for amplification of bacterial 16S rRNA genes. Appl Environ Microbiol 2008; 74:2461–2470 [View Article] [PubMed]
     [Google Scholar]
  39. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011; 17:10 [View Article]
     [Google Scholar]
  40. Andrew S. FastQC: a quality control tool for high throughput sequence data; 2010
  41. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019; 37:852–857 [View Article]
     [Google Scholar]
  42. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 2018; 6:90 [View Article]
     [Google Scholar]
  43. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013; 41:D590–6 [View Article] [PubMed]
     [Google Scholar]
  44. Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW et al. Modeling zero-inflated count data with glmmTMB. bioRxiv 2017132753 [View Article]
     [Google Scholar]
  45. Hartig F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models; 2020
  46. Lenth RV, Bolker B, Buerkner P, Giné-Vázquez I, Herve M et al. emmeans: estimated marginal means, aka least-squares means; 2024 https://cran.r-project.org/web/packages/emmeans/ accessed 22 February 2024
  47. Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One 2013; 8:e67019 [View Article] [PubMed]
     [Google Scholar]
  48. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2014; 2:15 [View Article] [PubMed]
     [Google Scholar]
  49. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol 2017; 8:2224 [View Article] [PubMed]
     [Google Scholar]
  50. Palarea-Albaladejo J, Martín-Fernández JA. zCompositions — R package for multivariate imputation of left-censored data under a compositional approach. Chemom Intell Lab Syst 2015; 143:85–96 [View Article]
     [Google Scholar]
  51. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P et al. vegan: community ecology package; 2022 https://cran.r-project.org/web/packages/vegan/index.html accessed 22 February 2024
  52. Schwager E, Mallick H, Ventz S, Huttenhower C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput Biol 2017; 13:e1005852 [View Article] [PubMed]
     [Google Scholar]
  53. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships in psychometric data. J Stat Soft 2012; 48: [View Article]
     [Google Scholar]
  54. Pedersen TL. RStudio. ggraph: an implementation of grammar of graphics for graphs and networks; 2022 https://cran.r-project.org/web/packages/ggraph/index.html accessed 22 February 2024
  55. France MT, Ma B, Gajer P, Brown S, Humphrys MS et al. Valencia: a nearest centroid classification method for vaginal microbial communities based on composition. Microbiome 2020; 8:166 [View Article] [PubMed]
     [Google Scholar]
  56. Fettweis JM, Serrano MG, Brooks JP, Edwards DJ, Girerd PH et al. The vaginal microbiome and preterm birth. Nat Med 2019; 25:1012–1021 [View Article] [PubMed]
     [Google Scholar]
  57. Ma B, Wang Y, Ye S, Liu S, Stirling E et al. Earth microbial co-occurrence network reveals interconnection pattern across microbiomes. Microbiome 2020; 8:82 [View Article] [PubMed]
     [Google Scholar]
  58. van de Wijgert JHHM, Verwijs MC, Gill AC, Borgdorff H, van der Veer C et al. Pathobionts in the vaginal microbiota: individual participant data meta-analysis of three sequencing studies. Front Cell Infect Microbiol 2020; 10:129 [View Article] [PubMed]
     [Google Scholar]
  59. Robinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol 2016; 125:747–757 [View Article] [PubMed]
     [Google Scholar]
  60. van der Veer C, Hertzberger RY, Bruisten SM, Tytgat HLP, Swanenburg J et al. Comparative genomics of human Lactobacillus crispatus isolates reveals genes for glycosylation and glycogen degradation: implications for in vivo dominance of the vaginal microbiota. Microbiome 2019; 7:49 [View Article] [PubMed]
     [Google Scholar]
  61. Moreno I, Codoñer FM, Vilella F, Valbuena D, Martinez-Blanch JF et al. Evidence that the endometrial microbiota has an effect on implantation success or failure. Am J Obstet Gynecol 2016; 215:684–703 [View Article] [PubMed]
     [Google Scholar]
  62. Fu M, Zhang X, Liang Y, Lin S, Qian W et al. Alterations in vaginal microbiota and associated metabolome in women with recurrent implantation failure. mBio 2020; 11:e03242–19 [View Article]
     [Google Scholar]
  63. Benedetto C, Tibaldi C, Marozio L, Marini S, Masuelli G et al. Cervicovaginal infections during pregnancy: epidemiological and microbiological aspects. J Maternal-Fetal Neonat Med 2004; 16:9–12 [View Article]
     [Google Scholar]
  64. Lurie S, Ben-Aroya Z, Eldar S, Sadan O. Association of Lewis blood group phenotype with preterm premature rupture of membranes. J Soc Gynecol Investig 2003; 10:291–293 [View Article] [PubMed]
     [Google Scholar]
  65. Tamrakar R, Yamada T, Furuta I, Cho K, Morikawa M et al. Association between Lactobacillus species and bacterial vaginosis-related bacteria, and bacterial vaginosis scores in pregnant Japanese women. BMC Infect Dis 2007; 7:128 [View Article] [PubMed]
     [Google Scholar]
  66. MacIntyre DA, Sykes L, Bennett PR. The human female urogenital microbiome: complexity in normality. Emerg Top Life Sci 2017; 1:363–372 [View Article] [PubMed]
     [Google Scholar]
  67. Brown RG, Al-Memar M, Marchesi JR, Lee YS, Smith A et al. Establishment of vaginal microbiota composition in early pregnancy and its association with subsequent preterm prelabor rupture of the fetal membranes. Translatl Res 2019; 207:30–43 [View Article]
     [Google Scholar]
  68. Huang C, Gin C, Fettweis J, Foxman B, Gelaye B et al. Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth. BMC Biol 2023; 21:199 [View Article] [PubMed]
     [Google Scholar]
  69. Gudnadottir U, Debelius JW, Du J, Hugerth LW, Danielsson H et al. The vaginal microbiome and the risk of preterm birth: a systematic review and network meta-analysis. Sci Rep 2022; 12:7926 [View Article] [PubMed]
     [Google Scholar]
  70. Elovitz MA, Gajer P, Riis V, Brown AG, Humphrys MS et al. Cervicovaginal microbiota and local immune response modulate the risk of spontaneous preterm delivery. Nat Commun 2019; 10:1305 [View Article] [PubMed]
     [Google Scholar]
  71. Golob JL, Oskotsky TT, Tang AS, Roldan A, Chung V et al. Microbiome preterm birth DREAM challenge: crowd sourcing machine learning approaches to advance preterm birth research. Cell Rep Med 2024; 5:101350 [View Article] [PubMed]
     [Google Scholar]
  72. Lomberg H, Jodal U, Leffler H, De Man P, Svanborg C. Blood group non-secretors have an increased inflammatory response to urinary tract infection. Scand J Infect Dis 1992; 24:77–83 [View Article] [PubMed]
     [Google Scholar]
  73. Stout MJ, Zhou Y, Wylie KM, Tarr PI, Macones GA et al. Early pregnancy vaginal microbiome trends and preterm birth. Am J Obstet Gynecol 2017; 217:356 [View Article] [PubMed]
     [Google Scholar]
  74. Tabatabaei N, Eren A, Barreiro L, Yotova V, Dumaine A et al. Vaginal microbiome in early pregnancy and subsequent risk of spontaneous preterm birth: a case–control study. BJOG 2019; 126:349–358 [View Article]
     [Google Scholar]
  75. Grewal K, Lee YS, Smith A, Brosens JJ, Bourne T et al. Chromosomally normal miscarriage is associated with vaginal dysbiosis and local inflammation. BMC Med 2022; 20:38 [View Article] [PubMed]
     [Google Scholar]
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Secretor status is a modifier of vaginal microbiota-associated preterm birth risk
M Gen 10, 001323 (2024); https://doi.org/10.1099/mgen.0.001323
/content/journal/mgen/10.1099/mgen.0.001323
/content/journal/mgen/10.1099/mgen.0.001323
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