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Maternal thinness leads to metabolic challenges in the offspring, but it is unclear whether reduced maternal fat mass or muscle mass drives these metabolic changes. Recently, it has been shown that low maternal muscle mass—as measured by arm muscle area (AMA)—is associated with depressed nutrient transport to the fetus. To determine the role of maternal muscle mass on placental function, we analyzed the gene expression profiles of 30 human placentas over the range of AMA (25.2-90.8 cm2) from uncomplicated term pregnancies from the Southampton Women’s Survey cohort. Eighteen percent of the ~60 genes that were highly expressed in less muscular women were related to immune system processes and the interferon-γ (IFNG) signaling pathway in particular. Those transcripts related to the IFNG pathway included IRF1, IFI27, IFI30, and GBP6. Placentas from women with low muscularity are, perhaps, more sensitive to the effects of inflammatory cytokines than those from more muscular women.
Adult muscle mass derives from muscle growth during fetal and postnatal life. In men, adult fat-free mass increases 2.2 kg for every 1 kg increase in birth weight.1 Barker et al proposed that in low-birth-weight babies, muscle development in utero is sacrificed in preference to brain growth in the “brain-sparing” process.1 Accordingly, people undernourished in the womb will have a relatively low muscle mass for life.2 This finding has been validated by Baker et al using data from the National Health and Nutrition Examination Survey.3 They showed that the rate of growth of muscle mass—as measured by arm muscle area (AMA)—remains stunted in people born small, while brain growth is conserved well into childhood. Low muscularity in a pregnant woman may have important implications for her amino acid turnover and glucose metabolism, two processes that are critically important for fetal growth.2,4 There is evidence that maternal thinness leads to metabolic challenges in the offspring. For example, low maternal body mass index (BMI) is associated with higher fasting insulin levels in adult progeny independent of birth weight and placental weight.5,6 Maternal influences on placental structure and function could explain relationships between maternal body composition and disease in later life. Placental function is sensitive to maternal BMI as reflected in changes in placental gene expression of inflammatory markers, metabolic pathways, and nutrient transporters that relate to maternal BMI.7–10 However, maternal muscle mass, a component of BMI that may also affect placental and fetal growth and function, has received little attention. While BMI is usually used as an indicator of relative fat mass in pregnant women, a high BMI can reflect a large muscle mass-to-height ratio as well. Our lack of understanding of the separate role of muscle mass in regulating placental growth and function leaves an important gap in our knowledge because muscle mass is a key regulator of maternal metabolic capacity1 and is thus a potential regulator of placental function. To shed light on that issue, Lewis et al recently reported that placental system A amino acid transport activity is reduced in mothers with low AMA,11 suggesting that the placenta is sensitive to maternal muscle mass.
We hypothesized that the quantity of muscle in a mother’s body would lead to a unique molecular signature in her term placenta. We sought to determine whether specific groups of genes or functional pathways in the placenta would be differentially regulated according to the levels of maternal muscle mass. To test this hypothesis, we analyzed the gene expression profiles of 30 human placentas from women in three different quintiles of AMA from a cohort of uncomplicated term pregnancies collected during the Southampton Women’s Survey.
Fifty women enrolled in the Southampton Women’s Survey with healthy term pregnancies were ranked by AMA. For this study, 10 women were selected using random numbers from the highest, middle, and lowest quintiles across the AMA spectrum.12 Women from the fifth, third, and first quintiles were chosen for further study in order to maximize the ability to detect the expression differences between placentas by focusing on clusters of 10 within the middle of the AMA range and the extremes. Their AMA had been measured before pregnancy using the mid-upper arm circumference and triceps skinfold thicknesses according to the established formulae13 [(arm circumference − Π × triceps skinfold thickness)2/4Π] − 6.5. The trend in maternal characteristics across the AMA groups was assessed using linear regression in Stata version 11.0 (Statacorp, Texas).
The placenta was collected within 30 minutes of delivery. The umbilical cord and blood clots were removed, membranes were trimmed and placentas weighed. Five villous tissue samples were collected from each placenta using a stratified random method. Villous tissue was snap frozen in liquid nitrogen and stored at −80°C until further analysis. Prior to analysis, samples were pulverized in a frozen press and mixed to generate a representative sample. RNeasy fibrous tissue RNA isolation mini kit (Qiagen, UK) was used to extract total RNA per manufacturer’s instructions.
Microarray assays were performed using Affymetrix HG-U133 Plus 2.0 oligonucleotide arrays designed for analysis of 47 000 transcripts. Hybridizations were done in the Affymetrix Microarray Core of the Oregon Health and Science University (OHSU) Gene Microarray Shared Resource and amplified and labeled target complementary RNA (cRNA) was generated following methods described in the Affymetrix GeneChip Expression Analysis Technical Manual (www.affymetrix.com/support/technical/manual/expression_manual.affx). Messenger RNA (mRNA) was amplified and labeled from 2 μg of total RNA using the Affymetrix IVT amplification/labeling protocol (Affymetrix, Inc, Santa Clara, CA) for gene expression profiling with GeneChip 3’IVT arrays. Target yield was measured by UV260 absorbance. Before hybridization, quality assessment of cRNA was performed on the RNA 6000 LabChip using the 2100 Bioanalyzer (Agilent, Palo Alto, California).
Raw Affymetrix data files (.CEL files) were normalized together using Robust Multiarray Average. It was noted that anomalies in the expression distributions remained after normalization. One sample in the low AMA group had a noticeably distinct interquartile range and was excluded from further analysis. A linear model was fit to each transcript to test for putative differential expression. All P values were false discovery rate (FDR) adjusted per Benjamini and Hochberg.14 Based on FDR adjusted P < .10, there were 61 significant transcripts used for high versus low AMA comparison, 123 for high versus mid-AMA comparison, and 15 for mid versus low AMA.
Differentially expressed transcript data (FDR 10%) was imported into GeneSifter software (GeoSpiza, Inc, Seattle, Washington) for annotation and further analysis. Unsupervised hierarchical clustering of data was performed using Euclidean distance. To identify genes whose expression differed between maternal AMA groups, samples were partitioned using K-medoid clustering. Gene ontology (GO) functional terms were used to aid in the interpretation of the biological significance of the gene clusters. GO stat software15 was used to find statistically overrepresented GO terms within gene clusters. P values for GO enrichment were corrected for multiple testing and dependencies.16 Functional gene networks were identified by uploading the gene lists to the Ingenuity pathway analysis software version 8.8 (Ingenuity Systems; Redwood City, CA http://ingenuity.com).
Polymerase chain reaction (PCR) was performed using the Stratagene Mx3005P Thermocycler (Stratagene, Santa Clara, CA). Gene-specific primers were designed using primer designer software (Clone Manager ProfessionalSuite ver.8). β-Actin, a housekeeping gene that is not altered by maternal AMA based on the microarray data and confirmed by real-time PCR analysis, was used as a control. The PCR amplicons were detected by fluorescent detection of SYBR Green (Power SYBR Green Master Mix; Applied Biosystems, Carlsbad, CA). For each primer pair, standard curve samples, no template controls, and unknowns were run in triplicate. Following cycling, the melt curve of the resulting amplicon was analyzed to ensure that a single product was detected. Quantification of mRNA was achieved using the respective standard curves with manufacturer’s software (MxPro QPCR; Stratagene). Values were expressed as a ratio of the gene of interest:control in each sample. Significant differences between maternal AMA groups were assessed by 1-way analysis of variance (ANOVA) with post hoc differences determined by Tukey multiple comparison tests. Significance was defined as P < .05. Data are expressed as mean ± standard error of the mean.
Fixed placental tissue was paraffin embedded and sectioned for histologic examination. The chorionic villi and chorionic plate were scored for histologic evidence of acute or chronic inflammation using routine hematoxylin and eosin-stained histologic sections by a board-certified placental pathologist (T.K.M.) blinded to all identifiers.
Samples of 6 randomly selected placentas from women in each of the 3 AMA groups were studied by immunohistochemistry: a human superoxide dismutase (SOD) 2 antibody (The Binding Site; SanDiego, California; Cat. # PC096), CD3 (Dako; Carpinteria, California; Cat.# A0452; Polyclonal Rabbit Anti-Human), CD20 (Dako; Cat.# M0755; Monoclonal Mouse Anti-Human), CD64 (AbD Serotec; Raleigh, North Carolina; Cat.# MCA756GT; Monoclonal Mouse Anti-Human), and CD68 (Dako; Cat.# M0814; Monoclonal Mouse Anti-Human). Staining intensity of SOD2 was scored blindly on a scale of 1 to 3 as weak focal (1) to diffuse strong (3) using at least 4 representative ×10 objective fields with the most staining per case. For CD3, CD20, CD64, and CD68, the number of positive cells counted per area of tissue analyzed in each objective field was averaged over ×10 objective fields per case. Scores were averaged for cases within each group and compared by 1-way ANOVA with post hoc differences determined by Tukey multiple comparison tests. Statistical significance was defined as P < .05.
The physical characteristics of the mothers, babies, and placentas are shown in Table 1. There was a trend across AMA groups by AMA and BMI which was not surprising, and a suggestion of a trend with height, but apart from these variables, there was no apparent difference between the groups in parameters relating to the pregnancy or birth.
Of 47 000 genes queried on the HG-U133 Plus 2.0 oligonucleotide array, we observed a statistically significant change in expression (FDR 10%) of 188 probe sets, corresponding to 184 genes, between placentas of women with high, mid, and low AMA. To examine the relationship between groups (H, M, and L), hierarchical clustering of the groups was performed. Placentas from the mid- and low-AMA groups were most similar to each other. K-medoids (Euclidean) were used to supervise the clustering of probe sets into 2 groups. Cluster 1 and cluster 2 represent probe sets that show low (Figure 1A) or high (Figure 1B) levels of gene expression in the lower maternal AMA groups (M and L). The 2 clusters were further analyzed to determine GO terms associated with these different gene expression patterns. We found that the GO significantly overrepresented in cluster 2 (genes with high expression in the lower maternal AMA groups) are involved in immune response (P = 10−7) and immune system processes (P = 10−5). No GO terms were statistically significantly overrepresented in cluster 1 (transcripts with low expression in the lower maternal AMA placentas).
Heat maps of differentially expressed placental transcripts (limited to those with >0.5 log2 fold change for display purposes) from clusters 1 and 2 are shown in Figure 2. Placentas from women with high AMA are the most distinct from the other 2 groups, indicated by the highest branch point. Tables 2 and and33 list placental transcripts with significantly lower and higher expression, respectively, in less muscular women (mid-AMA and low-AMA) as compared with the high muscle mass group.
Real-time PCR analysis was used to confirm the expression patterns of selected genes from clusters 1 (Figure 3C) and 2 (Figure 3A and andB).B). We were able to confirm the microarray expression patterns in 12 of 17 genes analyzed. Superoxide dismutase 2 mRNA (P < .05 by ANOVA, Tukey post hoc test) and protein expression (Figure 4) were low in placentas of women with high AMA. Interferon-inducible 27 (IFI27) was higher in women with low AMA (P < .05). Placental Ras homolog enriched in brain (RHEB) was lowest in women with mid or low AMA.
To further examine the biological significance of placental genes which are differentially expressed between maternal AMA groups, we grouped the transcripts by function using GO annotation. Cluster 1 contained 125 transcripts representing 121 unique genes, 78 of which were annotated. Cluster 2 contained 63 transcripts representing 63 unique genes, 33 of which were annotated. Table 4 shows the distribution of gene ontologies of interest among placental transcripts which are lower or highly expressed in the less muscular women. The only GO categories that significantly differ between the 2 clusters were in the “inflammation and immune response” category. They include the GO categories “immune response” and “immune system process” (Table 5). These GO terms were overrepresented among transcripts that were more highly expressed in cluster 1 than in cluster 2. To investigate the potential relationships between differentially expressed immune response/inflammatory transcripts, we used Ingenuity pathway analysis software (Figure 5) and found that many of these genes are associated with the interferon-γ (IFNG) signaling pathway.
To further explore the observed differences in immune response gene expression, a pathologist blinded to groupings reviewed histologic sections of placentas from each AMA group. Histological analysis did not reveal any significant difference in the frequency of chronic inflammation (chronic villitis) or acute inflammation (acute chorioamnionitis) between groups. To increase the sensitivity to detect differences in immune cells, placentas were also immunostained for lineage-specific markers of macrophages (CD68), neutrophils (CD64), T cells (CD3), and B cells (CD20). No differences were identified between groups (Figure 6).
This study compared placental transcriptomes across the range of muscularity in 3 quintile groups of mothers. We found that levels of placental immune response gene transcripts were higher in women in the middle and lowest quintiles of prepregnant AMA compared with the upper quintile. Of the ~100 genes with lower expression within the lower AMA groups, no particular functional pathway or gene ontology was over- or underrepresented. In contrast, 18% of the ~60 genes that were highly expressed in less muscular women were involved in immune system processes, and many were related to the IFNG-signaling pathway. While the biological meaning of this finding is uncertain, we speculate that placentas from the lower muscle mass women would be more sensitive to the actions of cytokines and especially to those that stimulate inflammatory processes.
Muscle mass is a key regulator of metabolism,1 and as maternal metabolic capacity has an important influence on placental function, fetal growth, and development,17–19 we hypothesized that women with high and low muscle mass would have unique placental gene expression profiles. One key finding of this study was that placental immune response pathways were stimulated among women with lower muscle mass. Excessive activation of inflammatory molecules within the placenta has been associated with preeclampsia, fetal growth restriction, preterm labor, and fetal death.7,19–23 Additionally, maternal obesity and diabetes (conditions of altered metabolic capacity) are associated with placental inflammation.7,19
We found increased expression levels in a number of genes that are regulated by IFNG in placentas of less muscular women. The IFNG is a proinflammatory cytokine that plays an important role in activating cellular immunity and apoptosis. It is important for normal reproduction including successful implantation and establishment of pregnancy in mice.24 We would not characterize the placentas from the lower muscle mass women as “inflamed” because we did not find histologic evidence of acute or chronic inflammation or immune cell infiltration in placentas from any group. We did not expect to find obvious signs of inflammation in these term placentas from uncomplicated pregnancies. Roberts et al found high inflammatory gene expression within placentas of nonpreeclamptic, nondiabetic obese women, without detecting an influx of leukocytes in the chorionic villi.25 We suggest that the high expression of IFNG-regulated genes in placentas of less muscular women indicates the activation of inflammatory pathways that may have significant downstream effects on other placental pathways. At first glance, our observations in placentas of lean, less muscular women seem to contradict previous findings that women with the highest BMI have the highest levels of placental inflammation.7,19,25 However, placental inflammatory signatures differ between obese women with and without gestational diabetes,19 between lean women with and without preeclampsia20 and between lean and severely obese women,7,25 suggesting along with our data that different maternal phenotypes and pregnancy conditions are associated with unique placental gene expression patterns. Although recent studies have focused on the poor outcomes associated with maternal obesity, the negative effects associated with maternal thinness, which are supported by our current findings, have also been reported.6,26
Several of the immune response genes that were highly expressed in low AMA placentas are known to be regulated by IFNG including IFI27 (interferon-α inducible protein 27), IFI35 (interferon-induced protein 35), GBP6 (guanylate-binding protein family member 6), IFITM2 (interferon-induced transmembrane protein 2), and IRF1 (interferon regulatory factor 1). The latter is a transcription factor, activated by IFNG signaling through the JAK/STAT1 pathway,27 that stimulates the transcription of major histocompatibility complex (MHC) class 1a and II molecules as well as GBP6. 24 ,27 Furthermore, we found that expression of the MHC class II β chain gene (HLA-DRB1) and proteasome subunit beta type 8 and 9 (PSMB8 and PSMB9), located in the class II region of MHC, were highest in placentas of women with lower muscle mass. Although MHC class II genes are not expressed by the trophoblast, they are constitutively expressed in leukocytes28; either maternal or fetal immune cells are likely sources of HLA-DRB1 mRNA. The trophoblast mimics immune cell behavior by monitoring the surrounding environment and communicating with local leukocytes to protect the fetus from infection.24,29 Although not associated with an influx of immune cells, the high levels of IFNG-responsive genes detected in placentas of lower muscle mass women may signify an increased sensitivity of maternal or fetal leukocytes, trophoblast, or endothelial cells to inflammatory cytokines.
Three immune response genes were lower in placentas of lower AMA women compared with those with high AMA: the NF-kappaB subunit RELA (transcription factor p65),30 IRF5 (interferon responsive factor 5)31 a protein known to influence inflammatory macrophage polarization,32 and CD4033 required for activation of antigen-presenting cells. Expression of all 3 of these genes is responsive to IFNG. Despite seeing numerous changes in IFNG-responsive genes, we did not detect differences in the expression of the IFNG gene itself in the placentas of women with lower AMA groups compared with the high-AMA group. We speculate that many of the highly expressed IFNG-related genes in the lower muscle mass women have high expression in response to circulating IFNG or the stimuli from unknown maternal chemical cues related to low maternal muscularity.11 Alternatively, the placenta, maternal, or fetal immune cells may be sensitive to altered metabolism (glucose homeostasis) in women with lower muscle mass leading to the stimulation of IFNG-related genes, as found in other tissues.34 Although altered glucose homeostasis is typically studied in the setting of obesity, several studies have found an association between altered glucose metabolism, thinness, and low muscle mass.1,6,26,35
Lewis et al found that maternal muscle mass was positively correlated with placental system A amino acid transport activity.11 The authors proposed that placentas of muscular women “sense” a metabolic or endocrine factor released by maternal skeletal muscle that leads to enhanced amino acid transport. Amino acid transport activity is known to be regulated by mammalian target of rapamycin (mTOR).36 The mTOR signaling pathway is responsive to glucose, insulin, and amino acid levels and is important in regulating nutrient uptake and transport.36–38 mTOR is potently activated by the binding of the guanosine triphosphatase (GTPase) RHEB.39 High levels of RHEB stimulate the mTOR pathway.39 We found that placental RHEB mRNA expression was 20-fold higher in women with high muscle mass as compared with less muscular women. Thus, increases in RHEB may underlie the positive relationship between placental system A amino acid transport activity and high muscle mass in pregnant women and vice versa.11
We did not detect differences in system A amino acid transporter (SNATs) expression between maternal AMA groups. This is consistent with the findings of Lewis et al that placental SNAT mRNA expression did not mirror activity levels.11 Their findings suggest that the placental transport activity for system A amino acids is sensitive to maternal muscularity; this may be driven by changes in the mTOR signaling pathway, independent of concomitant alterations in SNAT gene expression.
Lower levels of the antioxidant SOD-2 were found in placentas of women with high AMA as compared with less muscular women. This dismutase binds the superoxide anion, a natural product of oxidative phosphorylation, and catalyzes the conversion to hydrogen peroxide and diatomic oxygen which protects the cell from damage from reactive oxygen species.40 The interpretation of this finding is unclear. While decreased antioxidant levels may indicate a response to chronic oxidative stress reviewed by Myatt and Cui,41 others have shown that genes for antioxidants, such as SOD2, are upregulated in response to increases in pro-oxidant molecules and that low levels indicate a low demand for antioxidant action.42 Further experiments are needed to determine the relationship between maternal muscularity and placental oxidant/antioxidant status.
Arm muscle area is a validated measure of muscle mass,13 but we cannot exclude the possibility that other maternal factors which are also different among AMA groups could be contributory. Based on anthropometric measures, it is not possible to cleanly separate different aspects of maternal body composition, and future studies may confirm these findings through dual energy x-ray absorptiometry in women before or after pregnancy. Groups of 10 women with the highest, lowest, and mid quintiles of AMA were chosen for analysis. Information may have been lost by grouping the data in this manner rather than analyzing AMA as a continuous variable against gene expression. Although the differences in SOD2 expression were confirmed using immunohistochemical techniques, protein levels of the remaining differentially expressed genes were not investigated in this study.
We have shown that placentas from women with high and average/low prepregnancy maternal muscle masses are distinguished by their unique gene expression signatures. High maternal muscularity is associated with placental gene expression profiles that promote amino acid transport. Our findings suggest that placentas of less muscular women have gene expression patterns that suggest cytokine stimulation and that these placentas would be more vulnerable to the effects of proinflammatory cytokines, such as IFNG, compared with mothers with high muscle mass.
The authors would like to thank Kristina Vartanian, Michelle Garred, and Chris Harrington of the OHSU Gene Microarray Shared Resource for valuable advice regarding microarrays. In addition, we thank Cara Poage and Carolyn Gendron of OHSU Histopathology Shared Resource for technical assistance in preparation of samples for histologic analysis.
Author’s Note: KT, KG, RL, DB, GB, and MH designed and initiated the study. PFO, TKM, and SM were involved in data analysis. KG, CC, HMI, and the Southampton Women’s Survey study group designed and/or implemented aspects of the Southampton Women’s Survey from which the tissues were collected and prepregnancy measurements were made. RML collected the tissues and performed the RNA extraction. All authors were involved in the writing of the manuscript.
Declaration of Conflicting Interests: The authors declared no conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: PFO is supported by the National Institute of Child Health & Development (1K99HD062841-01). KG is supported by the National Institute for Health Research through the Southampton NIHR Nutrition, Diet & Lifestyle Biomedical Research Unit. SKM is supported by the National Institutes of Health, National Center for Research Resources (5UL1RR024140) and National Institutes of Health, National Cancer Institute (5 P30 CA069533-13). TKM is supported in part by the Office of Research on Women’s Health and the National Institute of Child Health and Human Development, Oregon (BIRCWH HD043488-08). This work was also supported by the National Heart Lung and Blood Institute (HL048546 [GB]), National Cancer Institute (CA138237 [GB]), Department of Veterans Affairs (GB), Edwards Endowment (KLT), British Heart Foundation (MH), Medical Research Council (CC, HI, KG) and Arthritis Research UK (CC).