Mapping the Cord Blood Transcriptome of Pregnancies Affected by Early Maternal Anemia to Identify Signatures of Fetal Programming

Gad Hatem, Line Hjort, Olof Asplund, Daniel T R Minja, Omari Abdul Msemo, Sofie Lykke Møller, Thomas Lavstsen, Louise Groth-Grunnet, John P A Lusingu, Ola Hansson, Dirk Lund Christensen, Allan A Vaag, Isabella Artner, Thor Theander, Leif Groop, Christentze Schmiegelow, Ib Christian Bygbjerg, Rashmi B Prasad, Gad Hatem, Line Hjort, Olof Asplund, Daniel T R Minja, Omari Abdul Msemo, Sofie Lykke Møller, Thomas Lavstsen, Louise Groth-Grunnet, John P A Lusingu, Ola Hansson, Dirk Lund Christensen, Allan A Vaag, Isabella Artner, Thor Theander, Leif Groop, Christentze Schmiegelow, Ib Christian Bygbjerg, Rashmi B Prasad

Abstract

Context: Anemia during early pregnancy (EP) is common in developing countries and is associated with adverse health consequences for both mothers and children. Offspring of women with EP anemia often have low birth weight, which increases risk for cardiometabolic diseases, including type 2 diabetes (T2D), later in life.

Objective: We aimed to elucidate mechanisms underlying developmental programming of adult cardiometabolic disease, including epigenetic and transcriptional alterations potentially detectable in umbilical cord blood (UCB) at time of birth.

Methods: We leveraged global transcriptome- and accompanying epigenome-wide changes in 48 UCB from newborns of EP anemic Tanzanian mothers and 50 controls to identify differentially expressed genes (DEGs) in UCB exposed to maternal EP anemia. DEGs were assessed for association with neonatal anthropometry and cord insulin levels. These genes were further studied in expression data from human fetal pancreas and adult islets to understand their role in beta-cell development and/or function.

Results: The expression of 137 genes was altered in UCB of newborns exposed to maternal EP anemia. These putative signatures of fetal programming, which included the birth weight locus LCORL, were potentially mediated by epigenetic changes in 27 genes and associated with neonatal anthropometry. Among the DEGs were P2RX7, PIK3C2B, and NUMBL, which potentially influence beta-cell development. Insulin levels were lower in EP anemia-exposed UCB, supporting the notion of developmental programming of pancreatic beta-cell dysfunction and subsequently increased risk of T2D in offspring of mothers with EP anemia.

Conclusions: Our data provide proof-of-concept on distinct transcriptional and epigenetic changes detectable in UCB from newborns exposed to maternal EP anemia.

Trial registration: ClinicalTrials.gov NCT02191683.

Keywords: beta-cell development; beta-cell function; developmental programming; epigenetic programming; maternal early pregnancy anemia; type 2 diabetes.

© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.

Figures

Figure 1.
Figure 1.
Overview of study design and findings.
Figure 2.
Figure 2.
(A) Volcano plot showing differentially expressed genes (DEGs) between cord blood from mothers with early pregnancy anemia compared to controls. All genes showing FDR  1.5 are presented in orange. DEGs with FDR  1 are presented in red while those with FDR  1 are labeled. (B) The expression of LCORL, NMD3 and NCBP1 genes was upregulated in umbilical cord blood from offspring of mothers with early pregnancy anemia compared to controls. (C) cg12884187 and (D) cp06549407 DNAm negatively correlated with LCORL expression (E) cg12884187 DNAm correlated with maternal HB levels in early pregnancy (F) cg06549407 DNAm correlated positively with delivery length (G) LCORL expression correlated with delivery length.
Figure 3.
Figure 3.
RNAseq from human pancreatic islets (n = 188). COBLL1 (A), SSTR5-AS1 (B), and ELN (C) were differentially expressed between diabetic donor islets compared to controls. The expression of COBLL1 (D) was negatively whereas those of SSTR5-AS1 (E) and ELN (F) were positively correlated with INS expression. ZDHHC14 (G) expression was upregulated whereas LCORL (H), EZH1 (I) and DBF4B (J) expression was downregulated in T2D compared to non-T2D donor islets, ZDDHC14 (K) expression correlated positively with insulin expression whereas EZHI (M) and DBF4B (N) correlated negatively. LCORL (L) showed no correlation. Normoglycemic islets (n = 31) were exposed to normal (5.5 mmol/L) and high (18.9 mmol/L) for 24 hours. ZDHHC14(O) expression was significantly upregulated whereas that of LCORL (P), EZH1 (Q) and DBF4B (R) was downregulated upon high glucose stimulation.
Figure 4.
Figure 4.
(A) Expression of genes whose expression in altered in cord blood from mothers with early anemia in fetal vs adult pancreas. (B) Expression of selected genes in sorted fetal beta, alpha and adult beta-cells (C) Immunohistochemical staining of 8-week fetal pancreas was performed for P2XR7 (red), NUMBL (red), PIK3C2B (red), INS (green), and GCG (white). Scale bar indicates 50 µm, pictures were taken with a 20× objective. Arrows denote insulin positive cells. Note: False positive green staining outside pancreatic epithelium is due to auto-fluorescence from red blood cells.

References

    1. McLean E, Cogswell M, Egli I, Wojdyla D, de Benoist B. Worldwide prevalence of anaemia, WHO Vitamin and Mineral Nutrition Information System, 1993-2005. Public Health Nutr. 2009;12(4):444-454.
    1. Ozturk M, Ozturk O, Ulubay M, et al. . Anemia prevalence at the time of pregnancy detection. Turk J Obstet Gynecol. 2017;14(3):176-180.
    1. Statistics NNB. Tanzania demographic and health survey and malaria indicator survey 2015-2016.2016. Accessed June 21, 2020.
    1. Tolentino K, Friedman JF. An update on anemia in less developed countries. Am J Trop Med Hyg. 2007;77(1):44-51.
    1. Sliwa K. The multifactorial burden of anaemia in Africa. S Afr Med J. 2009;99(12):864-865.
    1. Tine RC, Ndiaye M, Hansson HH, et al. . The association between malaria parasitaemia, erythrocyte polymorphisms, malnutrition and anaemia in children less than 10 years in Senegal: a case control study. BMC Res Notes. 2012;5:565.
    1. McClure EM, Goldenberg RL, Dent AE, Meshnick SR. A systematic review of the impact of malaria prevention in pregnancy on low birth weight and maternal anemia. Int J Gynaecol Obstet. 2013;121(2):103-109.
    1. Stephen G, Mgongo M, Hussein Hashim T, Katanga J, Stray-Pedersen B, Msuya SE. Anaemia in pregnancy: prevalence, risk factors, and adverse perinatal outcomes in Northern Tanzania. Anemia. 2018;2018:1846280.
    1. Wirth JP, Woodruff BA, Engle-Stone R, et al. . Predictors of anemia in women of reproductive age: biomarkers reflecting inflammation and nutritional determinants of anemia (BRINDA) project. Am J Clin Nutr. 2017;106(Suppl 1):416S-427S.
    1. Kozuki N, Lee AC, Katz J, Child Health Epidemiology Reference Group. . Moderate to severe, but not mild, maternal anemia is associated with increased risk of small-for-gestational-age outcomes. J Nutr. 2012;142(2):358-362.
    1. Mahajan S, Aalinkeel R, Shah P, Singh S, Gupta N, Kochupillai N. Nutritional anaemia dysregulates endocrine control of fetal growth. Br J Nutr. 2008;100(2):408-417.
    1. Dewey KG, Oaks BM. U-shaped curve for risk associated with maternal hemoglobin, iron status, or iron supplementation. Am J Clin Nutr. 2017;106(Suppl 6):1694S-1702S.
    1. Hamalainen H, Hakkarainen K, Heinonen S. Anaemia in the first but not in the second or third trimester is a risk factor for low birth weight. Clin Nutr. 2003;22(3):271-275.
    1. Scanlon KS, Yip R, Schieve LA, Cogswell ME. High and low hemoglobin levels during pregnancy: differential risks for preterm birth and small for gestational age. Obstet Gynecol. 2000;96(5 Pt 1):741-748.
    1. Hales CN. Fetal and infant growth and impaired glucose tolerance in adulthood: the “thrifty phenotype” hypothesis revisited. Acta Paediatr Suppl. 1997;422(S422):73-77.
    1. Vaag AA, Grunnet LG, Arora GP, Brons C. The thrifty phenotype hypothesis revisited. Diabetologia. 2012;55(8):2085-2088.
    1. Wang C, Lin L, Su R, et al. . Hemoglobin levels during the first trimester of pregnancy are associated with the risk of gestational diabetes mellitus, pre-eclampsia and preterm birth in Chinese women: a retrospective study. BMC Pregnancy Childbirth. 2018;18(1):263.
    1. Azulay CE, Pariente G, Shoham-Vardi I, Kessous R, Sergienko R, Sheiner E. Maternal anemia during pregnancy and subsequent risk for cardiovascular disease. J Matern Fetal Neonatal Med. 2015;28(15):1762-1765.
    1. Vielwerth SE, Jensen RB, Larsen T, et al. . The effect of birthweight upon insulin resistance and associated cardiovascular risk factors in adolescence is not explained by fetal growth velocity in the third trimester as measured by repeated ultrasound fetometry. Diabetologia. 2008;51(8):1483-1492.
    1. Kalage R, Blomstedt Y, Preet R, Hoffman K, Bangha M, Kinsman J. INDEPTH training and research centres of excellence (INTREC) -Tanzania country report.2012.
    1. Preet R. INDEPTH training and research centres of excellence (INTREC): building research capacity in social determinants of health in low- and middle-income countries.2015.
    1. Hjort L, Lykke Moller S, Minja D, et al. . FOETAL for NCD-FOetal exposure and epidemiological transitions: the role of Anaemia in early Life for non-communicable diseases in later life: a prospective preconception study in rural Tanzania. BMJ Open. 2019;9(5):e024861.
    1. Prasad RB, Hatem G, Hjort L, et al. . Data for: Mapping the cord blood transcriptome of pregnancies affected by early maternal anemia to identify signatures of fetal programming. LUDC repository. Deposited December 3, 2021. ; Direct data link . doi:10.6084/m9.figshare.17129543
    1. Dobin A, Davis CA, Schlesinger F, et al. . STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.
    1. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22(10):2008-2017.
    1. Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166-169.
    1. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140.
    1. Houseman EA, Accomando WP, Koestler DC, et al. . DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinf. 2012;13:86.
    1. Kim S. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Commun Stat Appl Methods. 2015;22(6):665-674.
    1. Teschendorff AE, Menon U, Gentry-Maharaj A, et al. . An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS One. 2009;4(12):e8274.
    1. Teschendorff AE, Marabita F, Lechner M, et al. . A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189-196.
    1. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118-127.
    1. Houseman EA, Kim S, Kelsey KT, Wiencke JK. DNA methylation in whole blood: uses and challenges. Curr Environ Health Rep. 2015;2(2):145-154.
    1. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547-1548.
    1. Du P, Zhang X, Huang CC, et al. . Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinf. 2010;11:587.
    1. Carithers LJ, Moore HM. The Genotype-Tissue Expression (GTEx) Project. Biopreserv Biobank 2015;13(5):307-308.
    1. DeLuca DS, Levin JZ, Sivachenko A, et al. . RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012;28(11):1530-1532.
    1. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28(10):1353-1358.
    1. Matsuoka TA, Zhao L, Artner I, et al. . Members of the large Maf transcription family regulate insulin gene transcription in islet beta cells. Mol Cell Biol. 2003;23(17):6049-6062.
    1. Subramanian A, Tamayo P, Mootha VK, et al. . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545-15550.
    1. Mootha VK, Lindgren CM, Eriksson KF, et al. . PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267-273.
    1. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019;47(D1):D419-D426.
    1. Rahmati S, Delpishe A, Azami M, Hafezi Ahmadi MR, Sayehmiri K. Maternal Anemia during pregnancy and infant low birth weight: A systematic review and Meta-analysis. Int J Reprod Biomed (Yazd). 2017;15(3):125-134.
    1. Sukrat B, Wilasrusmee C, Siribumrungwong B, et al. . Hemoglobin concentration and pregnancy outcomes: a systematic review and meta-analysis. Biomed Res Int. 2013;2013:769057.
    1. Fowden AL. The role of insulin in fetal growth. Early Hum Dev. 1992;29(1-3):177-181.
    1. Ottosson-Laakso E, Krus U, Storm P, et al. . Glucose-induced changes in gene expression in human pancreatic islets: causes or consequences of chronic hyperglycemia. Diabetes. 2017;66(12):3013-3028.
    1. Prasad RB, Groop L. Genetics of type 2 diabetes-pitfalls and possibilities. Genes (Basel). 2015;6(1):87-123.
    1. Lyssenko V, Groop L, Prasad RB. Genetics of type 2 diabetes: it matters from which parent we inherit the risk. Rev Diabet Stud. 2015;12(3-4):233-242.
    1. Blodgett DM, Nowosielska A, Afik S, et al. . Novel observations from next-generation RNA sequencing of highly purified human adult and fetal islet cell subsets. Diabetes. 2015;64(9):3172-3181.
    1. Popescu DM, Botting RA, Stephenson E, et al. . Decoding human fetal liver haematopoiesis. Nature. 2019;574(7778):365-371.
    1. Xiong X, Buekens P, Alexander S, Demianczuk N, Wollast E. Anemia during pregnancy and birth outcome: a meta-analysis. Am J Perinatol. 2000;17(3):137-146.
    1. Ren A, Wang J, Ye RW, Li S, Liu JM, Li Z. Low first-trimester hemoglobin and low birth weight, preterm birth and small for gestational age newborns. Int J Gynaecol Obstet. 2007;98(2):124-128.
    1. Gicquel C, El-Osta A, Le Bouc Y. Epigenetic regulation and fetal programming. Best Pract Res Clin Endocrinol Metab. 2008;22(1):1-16.
    1. Haider BA, Olofin I, Wang M, Spiegelman D, Ezzati M, Fawzi WW; on behalf of Nutrition Impact Model Study Group (anaemia). Anaemia, prenatal iron use, and risk of adverse pregnancy outcomes: systematic review and meta-analysis. BMJ. 2013;346:f3443.
    1. Figueiredo A, Gomes-Filho IS, Silva RB, et al. . Maternal anemia and low birth weight: a systematic review and meta-analysis. Nutrients. 2018;10(5):601.
    1. Conway E, Jerman E, Healy E, et al. . A family of vertebrate-specific polycombs encoded by the LCOR/LCORL genes balance PRC2 subtype activities. Mol Cell. 2018;70(3):408-421.e8.
    1. Gudbjartsson DF, Walters GB, Thorleifsson G, et al. . Many sequence variants affecting diversity of adult human height. Nat Genet. 2008;40(5):609-615.
    1. Salavati N, Smies M, Ganzevoort W, et al. . The possible role of placental morphometry in the detection of fetal growth restriction. Front Physiol. 2018;9:1884.
    1. Roland MC, Friis CM, Voldner N, et al. . Fetal growth versus birthweight: the role of placenta versus other determinants. PLoS One. 2012;7(6):e39324.
    1. Salavati N, Sovio U, Mayo RP, Charnock-Jones DS, Smith GC. The relationship between human placental morphometry and ultrasonic measurements of utero-placental blood flow and fetal growth. Placenta. 2016;38:41-48.
    1. Chin KT, Xu HT, Ching YP, Jin DY. Differential subcellular localization and activity of kelch repeat proteins KLHDC1 and KLHDC2. Mol Cell Biochem. 2007;296(1-2):109-119.
    1. Knop MR, Geng TT, Gorny AW, et al. . Birth weight and risk of type 2 diabetes mellitus, cardiovascular disease, and hypertension in adults: a meta-analysis of 7 646 267 participants from 135 studies. J Am Heart Assoc. 2018;7(23):e008870.
    1. Butler AE, Janson J, Bonner-Weir S, Ritzel R, Rizza RA, Butler PC. Beta-cell deficit and increased beta-cell apoptosis in humans with type 2 diabetes. Diabetes. 2003;52(1):102-110.
    1. Meier JJ, Bonadonna RC. Role of reduced beta-cell mass versus impaired beta-cell function in the pathogenesis of type 2 diabetes. Diabetes Care. 2013;36(Suppl 2):S113-S119.
    1. Meier JJ. Linking the genetics of type 2 diabetes with low birth weight: a role for prenatal islet maldevelopment? Diabetes. 2009;58(6):1255-1256.
    1. Mahajan A, Taliun D, Thurner M, et al. . Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50(11):1505-1513.
    1. Prokopenko I, Poon W, Magi R, et al. . A central role for GRB10 in regulation of islet function in man. PLoS Genet. 2014;10(4):e1004235.
    1. Petersen PH, Zou K, Hwang JK, Jan YN, Zhong W. Progenitor cell maintenance requires numb and numblike during mouse neurogenesis. Nature. 2002;419(6910):929-934.
    1. Maffucci T, Cooke FT, Foster FM, Traer CJ, Fry MJ, Falasca M. Class II phosphoinositide 3-kinase defines a novel signaling pathway in cell migration. J Cell Biol. 2005;169(5):789-799.
    1. Toblli JE, Cao G, Oliveri L, Angerosa M. Effects of iron deficiency anemia and its treatment with iron polymaltose complex in pregnant rats, their fetuses and placentas: oxidative stress markers and pregnancy outcome. Placenta. 2012;33(2):81-87.
    1. Mascio CE, Olison AK, Ralphe JC, Tomanek RJ, Scholz TD, Segar JL. Myocardial vascular and metabolic adaptations in chronically anemic fetal sheep. Am J Physiol Regul Integr Comp Physiol. 2005;289(6):R1736-R1745.
    1. Song KH, Kim JH, Lee YH, et al. . Mitochondrial reprogramming via ATP5H loss promotes multimodal cancer therapy resistance. J Clin Invest. 2018;128(9):4098-4114.
    1. Torii S, Okamura N, Suzuki Y, Ishizawa T, Yasumoto K, Sogawa K. Cyclic AMP represses the hypoxic induction of hypoxia-inducible factors in PC12 cells. J Biochem. 2009;146(6):839-844.
    1. Lai KP, Wang SY, Li JW, et al. . Hypoxia causes transgenerational impairment of ovarian development and hatching success in fish. Environ Sci Technol. 2019;53(7):3917-3928.
    1. Kresovich JK, Zheng Y, Cardenas A, et al. . Cord blood DNA methylation and adiposity measures in early and mid-childhood. Clin Epigenetics. 2017;9:86.
    1. Andrews SV, Ellis SE, Bakulski KM, et al. . Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder. Nat Commun. 2017;8(1):1011.
    1. Breen MS, Wingo AP, Koen N, et al. . Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes. Brain Behav Immun. 2018;73:320-330.
    1. Siddharthan T, Ramaiya K, Yonga G, et al. . Noncommunicable diseases in East Africa: assessing the gaps in care and identifying opportunities for improvement. Health Aff (Millwood). 2015;34(9):1506-1513.

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera