Cohort profile: molecular signature in pregnancy (MSP): longitudinal high-frequency sampling to characterise cross-omic trajectories in pregnancy in a resource-constrained setting

Tobias Brummaier, Basirudeen Syed Ahamed Kabeer, Pornpimon Wilaisrisak, Mupawjay Pimanpanarak, Aye Kyi Win, Sasithon Pukrittayakamee, Alexandra K Marr, Tomoshige Kino, Souhaila Al Khodor, Annalisa Terranegra, Verena I Carrara, Francois Nosten, Jürg Utzinger, Damien Chaussabel, Daniel H Paris, Rose McGready, Tobias Brummaier, Basirudeen Syed Ahamed Kabeer, Pornpimon Wilaisrisak, Mupawjay Pimanpanarak, Aye Kyi Win, Sasithon Pukrittayakamee, Alexandra K Marr, Tomoshige Kino, Souhaila Al Khodor, Annalisa Terranegra, Verena I Carrara, Francois Nosten, Jürg Utzinger, Damien Chaussabel, Daniel H Paris, Rose McGready

Abstract

Purpose: A successful pregnancy relies on the interplay of various biological systems. Deviations from the norm within a system or intersystemic interactions may result in pregnancy-associated complications and adverse pregnancy outcomes. Systems biology approaches provide an avenue of unbiased, in-depth phenotyping in health and disease. The molecular signature in pregnancy (MSP) cohort was established to characterise longitudinal, cross-omic trajectories in pregnant women from a resource constrained setting. Downstream analysis will focus on characterising physiological perturbations in uneventful pregnancies, pregnancy-associated complications and adverse outcomes.

Participants: First trimester pregnant women of Karen or Burman ethnicity were followed prospectively throughout pregnancy, at delivery and until 3 months post partum. Serial high-frequency sampling to assess whole blood transcriptomics and microbiome composition of the gut, vagina and oral cavity, in conjunction with assessment of gene expression and microbial colonisation of gestational tissue, was done for all cohort participants.

Findings to date: 381 women with live born singletons averaged 16 (IQR 15-18) antenatal visits (13 094 biological samples were collected). At 5% (19/381) the preterm birth rate was low. Other adverse events such as maternal febrile illness 7.1% (27/381), gestational diabetes 13.1% (50/381), maternal anaemia 16.3% (62/381), maternal underweight 19.2% (73/381) and a neonate born small for gestational age 20.2% (77/381) were more often observed than preterm birth.

Future plans: Results from the MSP cohort will enable in-depth characterisation of cross-omic molecular trajectories in pregnancies from a population in a resource-constrained setting. Moreover, pregnancy-associated complications and unfavourable pregnancy outcomes will be investigated at the same granular level, with a particular focus on population relevant needs such as effect of tropical infections on pregnancy. More detailed knowledge on multiomic perturbations will ideally result in the development of diagnostic tools and ultimately lead to targeted interventions that may disproportionally benefit pregnant women from this resource-limited population.

Trial registration number: NCT02797327.

Keywords: clinical physiology; epidemiology; fetal medicine; maternal medicine; reproductive medicine.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Setting and location of recruitment clinics.
Figure 2
Figure 2
Screening, enrolment and outcome flow chart. ANC, antenatal care; EGA, estimated gestational age; FHB, fetal heartbeat; SMRU, Shoklo Malaria Research Unit.
Figure 3
Figure 3
Number of samples over the course of pregnancy by week of gestation. EGA, estimated gestational age.
Figure 4
Figure 4
Principal component (PC) analysis of whole blood gene expression data (RNAseq) of 19 uneventful, term pregnancies of the MSP cohort compared between first trimester (early pregnancy), third trimester (late pregnancy) and 3- month post partum (non-pregnant). MSP, molecular signature in pregnancy.

References

    1. Soma-Pillay P, Nelson-Piercy C, Tolppanen H, et al. . Physiological changes in pregnancy. Cardiovasc J Afr 2016;27:89–94. 10.5830/CVJA-2016-021
    1. Mor G, Aldo P, Alvero AB. The unique immunological and microbial aspects of pregnancy. Nat Rev Immunol 2017;17:469–82. 10.1038/nri.2017.64
    1. Peterson LS, Stelzer IA, Tsai AS, et al. . Multiomic immune clockworks of pregnancy. Semin Immunopathol 2017.
    1. Koren O, Goodrich JK, Cullender TC, et al. . Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 2012;150:470–80. 10.1016/j.cell.2012.07.008
    1. MacIntyre DA, Chandiramani M, Lee YS, et al. . The vaginal microbiome during pregnancy and the postpartum period in a European population. Sci Rep 2015;5:8988. 10.1038/srep08988
    1. Aghaeepour N, Ganio EA, Mcilwain D, et al. . An immune clock of human pregnancy. Sci Immunol 2017;2. 10.1126/sciimmunol.aan2946. [Epub ahead of print: 01 Sep 2017].
    1. Ursell LK, Metcalf JL, Parfrey LW, et al. . Defining the human microbiome. Nutr Rev 2012;70:S38–44. 10.1111/j.1753-4887.2012.00493.x
    1. Morgan TK. Role of the placenta in preterm birth: a review. Am J Perinatol 2016;33:258–66. 10.1055/s-0035-1570379
    1. Acharya KP, Pathak S. Applied research in low-income countries: why and how? Front Res Metr Anal 2019;4 10.3389/frma.2019.00003
    1. Spielman RS, Bastone LA, Burdick JT, et al. . Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet 2007;39:226–31. 10.1038/ng1955
    1. Nilsson R, Björkegren J, Tegnér J. On reliable discovery of molecular signatures. BMC Bioinformatics 2009;10:38. 10.1186/1471-2105-10-38
    1. Chaussabel D, Pascual V, Banchereau J. Assessing the human immune system through blood transcriptomics. BMC Biol 2010;8:84. 10.1186/1741-7007-8-84
    1. Brummaier T, Syed Ahamed Kabeer B, Lindow S, et al. . A prospective cohort for the investigation of alteration in temporal transcriptional and microbiome trajectories preceding preterm birth: a study protocol. BMJ Open 2019;9:e023417. 10.1136/bmjopen-2018-023417
    1. Fellmeth G, Plugge EH, Carrara V, et al. . Migrant perinatal depression study: a prospective cohort study of perinatal depression on the Thai-Myanmar border. BMJ Open 2018;8:e017129. 10.1136/bmjopen-2017-017129
    1. Pudpong N, Durier N, Julchoo S, et al. . Assessment of a voluntary Non-Profit health insurance scheme for migrants along the Thai–Myanmar border: a case study of the migrant fund in Thailand. Int J Environ Res Public Health 2019;16:2581 10.3390/ijerph16142581
    1. Han SM, Rahman MM, Rahman MS, et al. . Progress towards universal health coverage in Myanmar: a national and subnational assessment. Lancet Glob Health 2018;6:e989–97. 10.1016/S2214-109X(18)30318-8
    1. McLean E, Cogswell M, Egli I, et al. . Worldwide prevalence of anaemia, who vitamin and mineral nutrition information system, 1993-2005. Public Health Nutr 2009;12:444–54. 10.1017/S1368980008002401
    1. Gilder ME, Simpson JA, Bancone G, et al. . Evaluation of a treatment protocol for anaemia in pregnancy nested in routine antenatal care in a limited-resource setting. Glob Health Action 2019;12:1621589. 10.1080/16549716.2019.1621589
    1. Hashmi AH, Solomon N, Lee SJ, et al. . Nutrition in transition: historical cohort analysis summarising trends in under- and over-nutrition among pregnant women in a marginalised population along the Thailand-Myanmar border from 1986 to 2016. Br J Nutr 2019;121:1413–23. 10.1017/S0007114519000758
    1. Cheah PY, Lwin KM, Phaiphun L, et al. . Community engagement on the Thai-Burmese border: rationale, experience and lessons learnt. Int Health 2010;2:123–9. 10.1016/j.inhe.2010.02.001
    1. Chawanpaiboon S, Vogel JP, Moller A-B, et al. . Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health 2019;7:e37–46. 10.1016/S2214-109X(18)30451-0
    1. Umesawa M, Kobashi G. Epidemiology of hypertensive disorders in pregnancy: prevalence, risk factors, predictors and prognosis. Hypertens Res 2017;40:213–20. 10.1038/hr.2016.126
    1. Gilder ME, Zin TW, Wai NS, et al. . Gestational diabetes mellitus prevalence in Maela refugee cAMP on the Thai-Myanmar border: a clinical report. Glob Health Action 2014;7:23887. 10.3402/gha.v7.23887
    1. Chiefari E, Arcidiacono B, Foti D, et al. . Gestational diabetes mellitus: an updated overview. J Endocrinol Invest 2017;40:899–909. 10.1007/s40618-016-0607-5
    1. Villar J, Cheikh Ismail L, Victora CG, et al. . International standards for newborn weight, length, and head circumference by gestational age and sex: the newborn cross-sectional study of the INTERGROWTH-21st project. Lancet 2014;384:857–68. 10.1016/S0140-6736(14)60932-6
    1. Tomei S, Mattei V. A protocol for extraction of total RNA from finger stick whole blood samples preserved with TempusTM solution. F1000Research 2018;7:1739.
    1. Speake C, Whalen E, Gersuk VH, et al. . Longitudinal monitoring of gene expression in ultra-low-volume blood samples self-collected at home. Clin Exp Immunol 2017;188:226–33. 10.1111/cei.12916
    1. Ngo TTM, Moufarrej MN, Rasmussen M-LH, et al. . Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 2018;360:1133–6. 10.1126/science.aar3819
    1. Vangay P, Johnson AJ, Ward TL, et al. . US immigration Westernizes the human gut microbiome. Cell 2018;175:962–72. 10.1016/j.cell.2018.10.029

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