Plasma miRNA Profiles in Pregnant Women Predict Infant Outcomes following Prenatal Alcohol Exposure

<div><p>Fetal alcohol spectrum disorders (FASD) are difficult to diagnose since many heavily exposed infants, at risk for intellectual disability, do not exhibit craniofacial dysmorphology or growth deficits. Consequently, there is a need for biomarkers that predict disability. In both animal models and human studies, alcohol exposure during pregnancy resulted in significant alterations in circulating microRNAs (miRNAs) in maternal blood. In the current study, we asked if changes in plasma miRNAs in alcohol-exposed pregnant mothers, either alone or in conjunction with other clinical variables, could predict infant outcomes. Sixty-eight pregnant women at two perinatal care clinics in western Ukraine were recruited into the study. Detailed health and alcohol consumption histories, and 2^nd and 3^rd trimester blood samples were obtained. Birth cohort infants were assessed by a geneticist and classified as unexposed (UE), heavily prenatally exposed and affected (HEa) or heavily exposed but apparently unaffected (HEua). MiRNAs were assessed in plasma samples using qRT-PCR arrays. ANOVA models identified 11 miRNAs that were all significantly elevated in maternal plasma from the HEa group relative to HEua and UE groups. In a random forest analysis classification model, a combination of high variance miRNAs, smoking history and socioeconomic status classified membership in HEa and UE groups, with a misclassification rate of 13%. The RFA model also classified 17% of the HEua group as UE-like, whereas 83% were HEa-like, at least at one stage of pregnancy. Collectively our data indicate that maternal plasma miRNAs predict infant outcomes, and may be useful to classify difficult-to-diagnose FASD subpopulations.</p></div

Random Forest Analysis (RFA) classifies HEa and UE maternal samples into distinct groups.

<p>(a) RFA analysis comparing HEa and UE groups at mid (MP) and late (LP) pregnancy resulted in an overall classification error rate of 13% (18.2% for the HEa group and 8.7% for the UE group). miRNAs constituted 7 out of the top 10 variables that contributed to classification accuracy. Graph depicts Mean Decrease Accuracy (the effect of permuting a variable on prediction after training) on the X-axis and contributory variables in order of decreasing importance on the Y-axis. Astrisks indicate miRNA variables that contributed to prediction accuracy at mid- and late-pregnancy. (b) RFA analysis with difference in miRNA expression (ΔΔCT) between mid and late pregnancy. The overall misclassification rate increased to 24.4%. However, a plot of ‘Mean Decrease Accuracy’ (Y-axis) against variables (X-axis) showed that miRNAs constituted 6 out of the top 10 predictive variables. miRNAs in red text represent variables present in both model 1 and 2. For additional details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165081#pone.0165081.s006" target="_blank">S4 Fig</a>. Smokstat, sescat, parity and momage are as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165081#pone.0165081.t001" target="_blank">Table 1</a>, CSEX is as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165081#pone.0165081.t002" target="_blank">Table 2</a>.</p

Pathway overrepresentation analysis to assess functions of predictive plasma miRNAs.

<p>Pathway overrepresentation analysis was performed using IPA software on targets of the eleven miRNAs that exceeded the FDR-corrected ANOVA criterion of P<0.05 (HEa>(HEua ≅ UE), and the five unique miRNAs among the top 10 contributory variables by random forest analysis ((HEa ≅ HEua)≠UE). (a) The -log₁₀ p-values of significantly enriched pathways (P < 0.05) for both the ANOVA model and the RFA model were plotted against each other with transformed significance values for pathways exclusively enriched among the ANOVA model depicted in blue, the RFA model in green, and pathways enriched in both the RFA and ANOVA models in red. (b) A heat map was constructed of the top 25 significantly enriched pathways among the RFA and ANOVA models. (c) The 17 pathways enriched among both the RFA and ANOVA model share a high degree of interconnectedness. Proteins outlined in red indicate overlapping targeting by miRNAs in both the RFA and ANOVA models.</p

Maternal characteristics of the sample, Ukraine, 2006–2011.

<p>Maternal characteristics of the sample, Ukraine, 2006–2011.</p

Placental Lactogen (hPL) and total RNA content in maternal plasma samples.

<p>(a) Plasma placental lactogen content in late pregnancy was not significantly different among HEa, HEua and UE groups. (b) Analysis of total plasma RNA content indicated that there was a statistically significant, ~15% decrease in total RNA recovery in the alcohol exposure groups (HEa and HEua) compared to UE controls.</p

Infant characteristics of the sample, Ukraine, 2006–2011.

<p>Infant characteristics of the sample, Ukraine, 2006–2011.</p

ANOVA model identifies maternal plasma miRNAs elevated specifically in the HEa group.

<p>(a) List of miRNAs that pass the FDR-corrected criterion of P < 0.05. Color scale ranges from 10^th (green) to 90^th (red) percentile of expression. miRNA expression in the HEa group at both mid and late pregnancy was generally elevated compared to expression in all other groups (for additional detail, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165081#pone.0165081.s004" target="_blank">S2 Fig</a>). (b) Average expression of miRNAs that exceed P < 0.05 and P < 0.1 BH-corrected criteria. (c) Cluster analysis (with Euclidean distance and average linkage) of miRNAs that exceed the BH-corrected P < 0.1 criterion indicates that HEua and UE groups cluster together and are different from HEa groups at mid and late pregnancy. MP, mid-pregnancy; LP, late pregnancy; MIMAT#######, miRNA unique ID as in mirbase.org.</p