Metabolism-related microRNAs in maternal breast milk are influenced by premature delivery

Molly C Carney, Andrij Tarasiuk, Susan L DiAngelo, Patricia Silveyra, Abigail Podany, Leann L Birch, Ian M Paul, Shannon Kelleher, Steven D Hicks, Molly C Carney, Andrij Tarasiuk, Susan L DiAngelo, Patricia Silveyra, Abigail Podany, Leann L Birch, Ian M Paul, Shannon Kelleher, Steven D Hicks

Abstract

BackgroundMaternal breast milk (MBM) is enriched in microRNAs, factors that regulate protein translation throughout the human body. MBM from mothers of term and preterm infants differs in nutrient, hormone, and bioactive-factor composition, but the microRNA differences between these groups have not been compared. We hypothesized that gestational age at delivery influences microRNA in MBM, particularly microRNAs involved in immunologic and metabolic regulation.MethodsMBM from mothers of premature infants (pMBM) obtained 3-4 weeks post delivery was compared with MBM from mothers of term infants obtained at birth (tColostrum) and 3-4 weeks post delivery (tMBM). The microRNA profile in lipid and skim fractions of each sample was evaluated with high-throughput sequencing.ResultsThe expression profiles of nine microRNAs in lipid and skim pMBM differed from those in tMBM. Gene targets of these microRNAs were functionally related to elemental metabolism and lipid biosynthesis. The microRNA profile of tColostrum was also distinct from that of pMBM, but it clustered closely with tMBM. Twenty-one microRNAs correlated with gestational age demonstrated limited relationships with method of delivery, but not other maternal-infant factors.ConclusionPremature delivery results in a unique MBM microRNA profile with metabolic targets. This suggests that preterm milk may have adaptive functions for growth in premature infants.

Conflict of interest statement

Disclosure statement: Dr. Hicks holds a preliminary patent for microRNA biomarkers in autism spectrum disorder and serves as a consultant for Motion Intelligence, Inc. Dr. Kelleher serves as president and CEO of LactoGenix, Inc. These conflicts of interest are unrelated to the current manuscript and are under an active management plan by the Penn State College of Medicine.

Figures

Figure 1
Figure 1
Partial Least Squares Discriminant Analysis. A PLS DA of the total microRNA profile for pMBM (class 2, +, n=31), tMBM (class 3, ×, n=23) and tColostrum (class 0, ∆, n=10) achieved partial separation using two dimensions and accounted for 23.6% of the variance between samples.
Figure 2
Figure 2
Hierarchical clustering (HC) analysis. HC of the 20 miRNAs with the most significant changes across the three groups showed clustering of pMBM (class 2; n=31) from tMBM (class 3; n=23) and tColostrum (class 0; n=13). Note that tMBM fore milk (n=10) and hind milk (n=13) samples (denoted a and b respectively), as well as tMBM and tColostrum samples (taken from the same mother one month apart) were also spatially clustered. Gray scale values indicate average Z score of normalized abundance for each miRNA.
Figure 3
Figure 3
Pearson correlations. Heatmap visualization of Pearson’s correlation for the 26 miRNAs altered in lipid or skim pMBM demonstrates correlations between gestational age (as a continuous variable) and 21 miRNAs. Six of these miRNAs were correlated with delivery method, but no correlation was found between miRNAs and other variables. Color represents Pearson R values.

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Source: PubMed

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