Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Nutrition and Health (including climate and ecological aspects)

Placental expression of RNU44, RNU48 and miR-16-5p: stability and relations with fetoplacental growth



The current study aimed to identify suitable reference miRNA for placental miRNA expression analysis in a set of well-characterized and fetal-sex balanced small- (SGA) and appropriate- (AGA) for gestational age full-term singleton pregnancies.


In this retrospective study, placental samples (n = 106) from 35 SGA (19 male and 16 female) and 71 AGA (30 male and 41 female) full-term singleton pregnancies were utilized. Placental transcript abundance of three widely used reference miRNAs [miR-16-5p and Small nucleolar RNAs (snoRNAs) RNU44 and RNU48] were assessed by real-time quantitative PCR. Raw cycle threshold (Ct) analysis and RefFinder tool analysis were conducted for evaluating stability of expression of these miRNAs.


Raw Ct values of miR-16-5p were similar between SGA and AGA births (P = 0.140) and between male and female births within SGA (P = 0.159) and AGA (P = 0.060) births while that of RNU44 and RNU48 were higher in SGA births (P = 0.008 and 0.006 respectively) and in male births within the SGA group (P = 0.005) for RNU44 and in female births within the AGA group (P = 0.048) for RNU48. Across all 106 samples tested using the RefFinder tool, miR-16-5p and RNU44 were equally stable reference miRNAs.


We recommend miR-16-5p and RNU44 as suitable reference miRNAs for placental samples from settings similar to our study.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Distribution of RNU44, RNU48 and miR-16-5p expression [cycle threshold (Ct) values] in placental tissue from 35 SGA and 71AGA births.
Fig. 2: Stability of the reference genes (RNU44, RNU48 and miR-16-5p) using multiple algorithms.


  1. 1.

    Morales-Prieto DM, Chaiwangyen W, Ospina-Prieto S, Schneider U, Herrmann J, Gruhn B, et al. MicroRNA expression profiles of trophoblastic cells. Placenta 2012;33:725–34.

    CAS  Article  Google Scholar 

  2. 2.

    Kontomanolis EN, Kalagasidou S, Fasoulakis Z. MicroRNAs as potential serum biomarkers for early detection of ectopic pregnancy. Cureus 2018;10:e2344.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lee ACC, Katz J, Blencowe H, Cousens S, Kozuki N, Vogel JP, et al. National and regional estimates of term and preterm babies born small for gestational age in 138 low-income and middle-income countries in 2010. Lancet Glob Heal 2013;1:e26–36.

    Article  Google Scholar 

  4. 4.

    Higashijima A, Miura K, Mishima H, Kinoshita A, Jo O, Abe S, et al. Characterization of placenta-specific microRNAs in fetal growth restriction pregnancy. Prenat Diagn. 2013;33:214–22.

    CAS  Article  Google Scholar 

  5. 5.

    Tang Q, Wu W, Xu X, Huang L, Gao Q, Chen H, et al. miR-141 contributes to fetal growth restriction by regulating PLAG1 expression. PLoS ONE. 2013;8:e58737.

    CAS  Article  Google Scholar 

  6. 6.

    Huang L, Shen Z, Xu Q, Huang X, Chen Q, Li D. Increased levels of microRNA-424 are associated with the pathogenesis of fetal growth restriction. Placenta 2013;34:624–7.

    CAS  Article  Google Scholar 

  7. 7.

    Hromadnikova I, Kotlabova K, Ondrackova M, Pirkova P, Kestlerova A, Novotna V, et al. Expression profile of C19MC microRNAs in placental tissue in pregnancy-related complications. DNA Cell Biol. 2015;34:437–57.

    CAS  Article  Google Scholar 

  8. 8.

    Higuchi R, Fockler C, Dollinger G, Watson R. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Bio/Technol. 1993;11:1026–30.

    CAS  Google Scholar 

  9. 9.

    Heid CA, Stevens J, Livak KJ, Williams PM. Real time quantitative PCR. Genome Res. 1996;6:986–94.

    CAS  Article  Google Scholar 

  10. 10.

    Bustin SA, Nolan T. Pitfalls of quantitative real- time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004;15:155–66.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6:279–84.

    CAS  Article  Google Scholar 

  12. 12.

    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 2001;25:402–8.

    CAS  Article  Google Scholar 

  13. 13.

    Corral-Vazquez C, Blanco J, Salas-Huetos A, Vidal F, Anton E. Normalization matters: tracking the best strategy for sperm miRNA quantification. Mol Hum Reprod. 2017;23:45–53.

    CAS  Article  Google Scholar 

  14. 14.

    Ling D, Salvaterra PM. Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. Lin B, editor. PLoS One. 2011;6:e17762.

    CAS  Article  Google Scholar 

  15. 15.

    Maccani MA, Padbury JF, Marsit CJ. miR-16 and miR-21 expression in the placenta is associated with fetal growth. PLoS ONE. 2011;6:e21210.

    CAS  Article  Google Scholar 

  16. 16.

    Cindrova-Davies T, Herrera EA, Niu Y, Kingdom J, Giussani DA, Burton GJ. Reduced cystathionine γ-lyase and increased miR-21 expression are associated with increased vascular resistance in growth-restricted pregnancies: hydrogen sulfide as a placental vasodilator. Am J Pathol. 2013;182:1448–58.

    CAS  Article  Google Scholar 

  17. 17.

    Thamotharan S, Chu A, Kempf K, Janzen C, Grogan T, Elashoff DA, et al. Differential microRNA expression in human placentas of term intra-uterine growth restriction that regulates target genes mediating angiogenesis and amino acid transport. PLoS ONE. 2017;12:e0176493.

    Article  Google Scholar 

  18. 18.

    Bratkovič T, Bozič J, Rogelj B. Functional diversity of small nucleolar RNAs. Nucleic Acids Res 2020;48:1627–51.

    Article  Google Scholar 

  19. 19.

    Mcmahon M, Contreras A, Ruggero D. Small RNAs with big implications: New insights into H/ACA snoRNA function and their role in human disease. Wiley Interdiscip Rev RNA. 2015;6:173–89.

    CAS  Article  Google Scholar 

  20. 20.

    Solayman MHM, Langaee T, Patel A, El-Wakeel L, El-Hamamsy M, Badary O, et al. Identification of suitable endogenous normalizers for qRT-PCR analysis of plasma microRNA expression in essential hypertension. Mol Biotechnol. 2016;58:179–87.

    CAS  Article  Google Scholar 

  21. 21.

    Bryzgunova OE, Zaripov MM, Skvortsova TE, Lekchnov EA, Grigor’eva AE, Zaporozhchenko IA, et al. Comparative study of extracellular vesicles from the urine of healthy individuals and prostate cancer patients. Carter DRF, editor. PLoS ONE.2016;11:e0157566.

  22. 22.

    Lange T, Stracke S, Rettig R, Lendeckel U, Kuhn J, Schlüter R, et al. Identification of miR-16 as an endogenous reference gene for the normalization of urinary exosomal miRNA expression data from CKD patients. Ray RB, editor. PLoS ONE. 2017;12:e0183435.

  23. 23.

    Mukhopadhyay A, Thomas T, Bosch RJ, Dwarkanath P, Thomas A, Duggan CP, et al. Fetal sex modifies the effect of maternal macronutrient intake on the incidence of small-for-gestational-age births: a prospective observational cohort study. Am J Clin Nutr. 2018;108:814–20.

    CAS  Article  Google Scholar 

  24. 24.

    WHO. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee.World Health Organization, Geneva, Switzerland. WHO Tech Rep. Ser. 1995;854:1–452.

    Google Scholar 

  25. 25.

    Mukhopadhyay A, Ravikumar G, Dwarkanath P, Meraaj H, Thomas A, Crasta J, et al. Placental expression of the insulin receptor binding protein GRB10: Relation to human fetoplacental growth and fetal gender. Placenta 2015;36:1225–30.

    CAS  Article  Google Scholar 

  26. 26.

    Wang Y, Lumbers ER, Arthurs AL, Corbisier de Meaultsart C, Mathe A, Avery-Kiejda KA, et al. Regulation of the human placental (pro)renin receptor-prorenin-angiotensin system by microRNAs. Mol Hum Reprod. 2018;24:453–64.

    CAS  PubMed  Google Scholar 

  27. 27.

    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:1–12.

    Article  Google Scholar 

  28. 28.

    Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper - Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26:509–15.

    CAS  Article  Google Scholar 

  29. 29.

    Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64:5245–50.

    CAS  Article  Google Scholar 

  30. 30.

    Silver N, Best S, Jiang J, Thein SL. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol. 2006;7:33.

    Article  Google Scholar 

  31. 31.

    De Spiegelaere W, Dern-Wieloch J, Weigel R, Schumacher V, Schorle H, Nettersheim D, et al. Reference gene validation for RT-qPCR, a note on different available software packages. Cotterill S, editor. PLoS ONE. 2015;10:e0122515.

  32. 32.

    Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, et al. Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999;75:291–5.

    CAS  Article  Google Scholar 

  33. 33.

    Mukhopadhyay A, Ravikumar G, Meraaj H, Dwarkanath P, Thomas A, Crasta J, et al. Placental expression of DNA methyltransferase 1 (DNMT1): gender-specific relation with human placental growth. Placenta 2016;48:119–25.

    CAS  Article  Google Scholar 

  34. 34.

    Xie F, Xiao P, Chen D, Xu L, Zhang B. miRDeepFinder: A miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol Biol. 2012;80:75–84.

    CAS  Article  Google Scholar 

  35. 35.

    Wang D, Na Q, Song WW, Song GY. Altered expression of miR-518b and miR-519a in the placenta is associated with low fetal birth weight. Am J Perinatol. 2014;31:729–34.

    Article  Google Scholar 

  36. 36.

    Tryggestad JB, Vishwanath A, Jiang S, Mallappa A, Teague AM, Takahashi Y, et al. Influence of gestational diabetes mellitus on human umbilical vein endothelial cell miRNA. Clin Sci. 2016;130:1955–67.

    CAS  Article  Google Scholar 

  37. 37.

    Lasabová Z, Vazan M, Zibolenova J, Svecova I. Overexpression of miR-21 and miR-122 in preeclamptic placentas. Neuroendocrinol Lett. 2015;36:695–9.

    PubMed  Google Scholar 

  38. 38.

    McDermott AM, Kerin MJ, Miller N. Identification and validation of miRNAs as endogenous controls for RQ-PCR in blood specimens for breast cancer studies. Samant R, editor. PLoS ONE. 2013;8:e83718.

    Article  Google Scholar 

  39. 39.

    Gee HE, Buffa FM, Camps C, Ramachandran A, Leek R, Taylor M, et al. The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis. Br J Cancer. 2011;104:1168–77.

    CAS  Article  Google Scholar 

  40. 40.

    Lee DC, Romero R, Kim JS, Tarca AL, Montenegro D, Pineles BL, et al. MiR-210 targets iron-sulfur cluster scaffold homologue in human trophoblast cell lines: siderosis of interstitial trophoblasts as a novel pathology of preterm preeclampsia and small-for-gestational-age pregnancies. Am J Pathol. 2011;179:590–602.

    CAS  Article  Google Scholar 

Download references


We thank the pregnant women who participated in the study and doctors and nurses who made this study possible. The contribution of the research assistants Ms. Nancy N, Ms. Roopashree C, Ms. Aruna BS and Ms. Arogya M who collected the samples and data and of histopath technician Ms. Mahalakshmi S assisted the pathologists in placental grossing experiments is acknowledged. This work was supported by the Women Scientist Scheme, Department of Science & Technology, Government of India to PK (Reference no. SR/WOS-A/LS-669/2016) and the Department of Biotechnology, Government of India grants to AM and AVK (Grant sanction nos. BT/PR22326/MED/97/349/2016 and BT/PR30276/MED/97/399/2018).

Author information




AM, AVK and PK designed the study. AM and PK contributed to the planning of the experiments. PD, GR, AT and JC provided samples and clinical information for the study. PK performed all experiments. AM and TT conducted the statistical analyses. AM and PK wrote the manuscript. AM, AVK, TT and PD contributed to the interpretation of the results and manuscript writing. All authors discussed the results, have seen and approved the final version of the manuscript.

Corresponding author

Correspondence to A. Mukhopadhyay.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kochhar, P., Dwarkanath, P., Ravikumar, G. et al. Placental expression of RNU44, RNU48 and miR-16-5p: stability and relations with fetoplacental growth. Eur J Clin Nutr (2021).

Download citation


Quick links