Abstract

Background

Preterm birth (PTB), defined as the delivery of an infant before 37weeks of completed gestation, is a worldwide health problem and remains the leading cause of global perinatal morbidity and mortality [1–3]. PTB is a complex, multifactorial syndrome comprised of multiple clinical subtypes that can be defined as either idiopathic or ‘medically indicated’ (by caesarean or labor induction). Idiopathic or spontaneous PTB (sPTB) accounts for 70% of total PTB, while medically indicated PTB usually represents 30% of total PTB [4, 5]. Within the sPTB group, 45% of cases may occur without preterm rupture of membranes, while the remaining 25% are the consequence of the preterm premature rupture of membranes (PPROM) [5–8]. PTB has also been stratified according to gestational age (GA); neonates born between 24 and 33weeks (severe PTB) are at higher risk of death and diseases later in life than moderate PTB (GA between 34 and 36weeks). It has been speculated that PTB from different GA groups has diverse causes and/or pathological mechanisms [8]. Regardless of the PTB subtype, current therapies are not successful in prolonging time to birth once labor has been initiated [3].

PTB occurs as a result of the interaction of both genetic and environmental components, and it constitutes a complex multifactorial syndrome [3, 9]. The genetic architecture of pregnancy and PTB has proven challenging not only because of the multiple causes of PTB but also because of the numerous maternal and fetal gestational tissues that must interact to facilitate parturition [3, 10]. These tissues include the decidua, myometrium, cervix, maternal blood originating from the mother and villous placenta, fetal membranes (chorion and amnion), umbilical cord, and fetal blood originating from the fetus [5].

The main etiological factors related to PTB are inflammation, hemorrhage, activation of the maternal or fetal hypothalamic-pituitary axis, immune dysregulation, distension of the myometrium and cervical insufficiency [1, 3, 9, 11]. All these processes have diverse and distinctive ways of initiating labor but may share a common pathway that ends in the release of mediators that stimulate myometrial contraction, degradation of extracellular matrix components, inflammation and apoptosis. Consequently, these processes promote membrane rupture, cervical ripening, and uterine emptying, resulting in PTB [1, 11]. Studies of the transcriptomes of these tissues can help with the development of a molecular landscape of preterm labor and improve understanding of the physiology and pathology of term and preterm parturition. Specifically, study of the transcriptome of the membranes at the site of rupture in PTB may indicate shared genes of those pathways.

RNA sequencing (RNA-Seq) is a potent technology for transcriptome analysis that allows for a comprehensive characterization of gene expression [12]. The published RNA-Seq studies on human labor have been restrained to normal term pregnancies and confined to the placenta at different GAs [13–15]. More placental gene expression data are available from experiments based on microarrays [16], but most of these are concentrated on preeclampsia [13].

As mentioned, GA determines the diverse pathological mechanisms of PTB [8]. Genetic background seems to play a more relevant role in severe PTB neonates than in moderate PTB [17]. Modifications to chorioamniotic expression that end in severe PTB should be more drastic than those ending in term delivery or even moderate PTB. Therefore, we decided to focus on the transcriptome of severe PTB chorioamniotic tissues in an attempt to find DE genes with more biological significance. To validate the principal pathways found in this study, our results were compared with data previously reported in the literature.

Based on our previous work in the field [17–20] and the current status of knowledge of term and preterm labor [21–26], we anticipate modifications in inflammatory pathways in the preterm transcriptome compared to those in the term transcriptome. Nevertheless, the ultimate goal of the study is to obtain a PTB expression signature.

Methods

Results

Demographic and clinical characteristics of the term and severe preterm groups are presented in Table ​Table1.1. There were significant differences between cases and controls for gestational age, birth weight and the number of prenatal medical appointments, while the remaining assessed variables did not present significant differences between groups (Table ​(Table1).1). We selected 8 of the 24 chorioamniotic membranes to pursue RNA-Seq analysis (Additional file 2). The 8 tissues were selected and paired to match cases and controls based on the mother’s demographics, the newborn’s sex and socioeconomic status (see Additional file 2).

Table 1

Demographic and clinical characteristics of term and severe preterm patients

	Term birth	Severe preterm birth
	Total (n = 15)	Total (n = 9)	p valuea
Mothers age, years ± SE	24.1 ± 1.6	20.6 ± 1.0	0.146
Gestational age, weeks ± SE	39.3 ± 0.3	30.7 ± 0.9	1.9 × 10−10
Education: elementary school or less, n (%)	9 (60.0)	3 (33.3)	0.399
Marital status, % single mother	13.3	22.2	0.472
PROM (%)	4 (26.7)	2 (22.2)	1
Preeclampsia (%)	0	1 (11.1)	0.238
IUGR (%)	0	1 (11.1)	0.238
Parity, n (%)			0.335
Nulliparous	5 (33.3)	4 (44.4)
≥2	5 (33.3)	2 (22.2)
Birth weight (g), mean ± SE	3492 ± 120	1696 ± 205	1,6 × 10−07
Birth sex, % female	47	55	0.472
Prenatal appointments ± SE	8.8 ± 0.8	4.3 ± 0.9	0.002

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^aSignificance was assessed with Student’s t-test or Chi-square test, as applicable

PROM: Preterm rupture of membranes, IUGR: Intrauterine restriction growth, SE: Standard error

Exploration and evaluation of overall gene expression

Genome-wide gene expression in chorioamnion tissue samples was evaluated using RNA-Seq technology for 4 term and 4 severe preterm samples. A summary of the sequencing read alignments to the reference genome is presented in Additional file 3. Illumina sequencing effectively produced large numbers of high-quality reads from all samples. On average, 93.7% of the total reads were mapped successfully. Among the aligned reads, 91.5% were mapped to unique genomic regions.

A PCA was performed on the gene expression rlog transformed values (Fig. ​(Fig.1a).1a). A high portion of the overall variance was accounted for by the first two principal components of this model (PC1, 64%; PC2, 22%). The PCA showed term samples as a clearly packed group, while severe preterm samples showed greater variability.

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Fig. 1

Overall gene expression of fetal membrane transcriptomes. a Principal component analysis (PCA) plot, denoting preterm and PROM conditions. b Heatmap of regularized log (rlog) normalized values of expression levels and hierarchical clustering for the 270 DE genes (FDR < 0.05 and absolute logarithm fold change >2). The hierarchical clustering dendrogram and the condition of each sample are denoted at the top. Rlog values are coded on the red-to-green scale (high expression: red; low expression: green). Blue dots on the gene’s dendrogram nodes represent 3 major groups according to gene expression patterns. In sample names, the prefix P is used for preterms and C for controls

A total of 270 genes were DE simultaneously according to the 3 methods employed (FDR < 0.05 and absolute logarithm fold change ≥2) (Additional file 4). Among the DE genes, 252 were upregulated and 18 were downregulated in severe preterm births relative to term births. The full list of DE genes detected (FDR < 0.05 and absolute log fold change >2) can be found in Additional file 5a. Additional file 5 also includes references to previous studies that associated PTB with the DE genes found in this work (Additional file 5d). Approximately 50% of DE genes were previously reported to be associated with preterm labor.

When visualizing the rlog transformed read counts of the 270 significantly DE genes, control samples had a homogenous expression pattern (Fig. ​(Fig.1b).1b). Notably, several of the DE genes were associated with immune-related functions such as interleukin 24, CXC motif chemokine ligands and tumor necrosis factor signaling pathways. A hierarchical clustering analysis clustered genes in 3 groups. Remarkably, one of the clusters was a group of 18 genes that had a notably discordant expression pattern for term and severe preterm samples. This group consisted of the aforementioned 18 downregulated genes (Fig. ​(Fig.2),2), which potentially characterize a severe preterm transcriptome pattern and therefore are candidate genes for understanding the syndrome. These genes included potassium voltage-gated channel subfamily members, genes involved in cell adhesion functions, placental alkaline phosphatase and a Wnt family member. This gene cluster was related to GO terms involved in the nervous system, organ morphogenesis, and ion channel complexes, but no term remained as enriched with statistical significance in the group after BH p-value correction.

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Fig. 2

Gene expression levels of the 18 downregulated genes. Heatmap of regularized log (rlog) normalized values of expression levels and hierarchical clustering for the 18 downregulated DE genes. Gene symbols are indicated to the right. Rlog values are coded on the red-to-green scale (high expression: red; low expression: green). In sample names, the suffix P is used for preterms and C for controls

The remaining two groups consisted of 147 and 105 genes. Mainly, these genes were related to chemokines, TNF family members, coagulation factors, and transmembrane proteins. A GO term enrichment search revealed 508 and 301 terms, respectively that were statistically significant (BH corrected p-value <0.05). These terms were mostly related to immune and inflammatory responses and the immune system.

We found some overlap between the 270 DE genes and genes related to labor that were previously identified by RNA microarrays [49–52] . In total, 58 of the 270 DE genes found in this study were identified as DE between labor and no labor births (Additional file 5, b).

When analyzing whether PROM has an effect on gene expression, we found only 14 genes differentially expressed between groups with and without PROM (FDR < 0.05 and absolute log fold change >2) (Additional file 5, c). Only 2 of those genes were also detected as DE with respect to preterm condition: KCNJ16 and TRPV6.

Gene set enrichment analysis using gene ontology

In an effort to identify processes and pathways that could be regulated differentially between the severe preterm and term groups, we performed a Gene Set Enrichment Analysis implemented in the Goseq R package. Genes that showed an FDR < 0.05 were tested against the background set of all expressed genes with ENSEMBL annotations. In this sense, for the enrichment analysis comparing gene expression between severe preterm and term births, 270 significant genes were tested against 18,675 background genes. The significant enrichment of GO terms was tested using the Wallenius approximation and by applying multiple hypothesis testing correction (BH). Only terms with 3 or more statistically significant genes were considered.

A total of 1886 GO terms were enriched among the DE genes (FDR < 0.05), corresponding to Biological Processes (562 terms), Cellular Component (49) and Molecular Function (35) (Fig. ​(Fig.3a).3a). Within the Biological Processes category, the terms immune response, immune system process and defense response were the most significantly enriched for DE genes. Likewise, the Cellular Component category subdivided annotated sequences into plasma membrane, cell periphery, and cytoplasmic vesicle part, which were the most highly represented terms. Within the category Molecular Function, the three principal groups were signaling receptor activity, receptor activity, and transducer activity.

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Fig. 3

Enriched GO categories (a) and KEGG pathways (b) of genes in severe preterm birth. Enrichment analysis of Biological Process GO categories (a) and KEGG pathways (b) of DE genes in severe preterm/term comparison. The top 10 most significant terms or pathways are shown. The x-axis represents the enrichment value as the 278 logarithm of the adjusted p-value (FDR). The number of genes identified in each pathway is reported inside parentheses. All represented GO terms are significantly enriched, with an adjusted p-value <0.01

When analyzing KEGG pathways, the most significant upregulated pathways were Chemokine signaling pathway and Hematopoietic cell lineage (both with FDR = 7.038 × 10⁻⁰⁹; Fig. ​Fig.4).4). Several relevant processes for PTB were upregulated, such as those involved in intracellular signaling: Toll-like, NOD-like, T cell and Jak-STAT signaling pathway (Fig. ​(Fig.3b).3b). No statistically significant downregulated pathways were found.

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Fig. 4

KEGG diagram of chemokine signaling pathways. Genes overexpressed in preterm birth fetal membranes are red, and downregulated genes are green

Discussion

Although PTB is responsible for most neonatal deaths and morbidity [58], there has been a shortage of studies that permit the creation of a distinctive expression footprint to describe its pathology and to consequently achieve improvements in its prevention, diagnosis, and treatment. To overcome this gap in knowledge, we analyzed the gene expression of the chorioamnion membranes from severe PTB and term newborns by RNA-seq.

In this study, we present a distinct transcriptome pattern of severe PTB. Our study revealed 270 DE genes between both conditions. A number of this magnitude may partially explain the lack of previous molecular signatures for PTB as well as the difficulty in identifying biomarkers and potential therapeutic interventions [4, 39]. Several pathways and genes are involved in labor, and most of them are either turned on or off during pathological conditions [4, 16, 37, 40].

It has been proposed than pregnancy is maintained by the downregulation of chemokines at the maternal-fetal interface [43]. Removal of this downregulation results in term birth, while in the case of PTB, the activation of multiple pathways of the immune system overrides this downregulation [3, 43, 55, 59–62]. In concordance with this idea, we found that upregulated genes were mostly related to the immune response (e.g., IL1B, interleukin 24, CXC motif chemokine ligands or TNF). Moreover, we found that severe preterm labor was significantly enriched for immune processes in comparison to term labor. Gene ontology and KEGG pathway enrichment analyses indicated that inflammatory and immune pathways were enriched in severe preterm samples (Fig. ​(Fig.3).3). These findings are in agreement with previously mentioned studies, demonstrating that preterm labor is associated with extensive immune response activation in several gestational tissues. In fact, the incidence of PTB in association with inflammation is higher earlier in pregnancy [63], so the overexpression of inflammatory and immune pathways in severe PTB is consistent with the results reported in the literature.

IL1B was upregulated in severe preterm births in our data. IL1B is a central mediator in the pathological process of inflammation since it can stimulate the expression and release of other labor mediators [21, 23, 64–66]. Recent evidence indicates that IL1B may be involved in labor through activation of the inflammasome [22]. The inflammasome is a multi-protein complex located in the cytoplasm of cells whose activation induces the transformation of pro-IL1B (immature form) into the active forms of IL1B via the actions of active caspase-1 (CASP1) [67–70]. NLR family pyrin domain-containing protein 3 (NLRP3) is an inflammasome recruiting pattern recognition receptor [68, 71]. The amniotic fluid concentrations of some inflammasome mediators (CASP1, IL1B, NLRP3, and IL18) are greater in women who have spontaneous preterm or term labor with intraamniotic infection/inflammation than in those without this clinical condition [22, 72–77]. We found that some inflammasome genes, including CARD domain containing 4 (NLRC4), caspase 5 (CASP5), absent in melanoma 2 (AIM2) and the already mentioned NLRP3, were upregulated in our data. Moreover, the inflammasome complex GO term (GO:0061702) was significantly enriched in this group. Taking all data together, inflammasome activation may be implicated in the induction of preterm labor. If the hypothesis that inflammasome activation contributes to PTB is true, it may have medical implications. Components of the inflammasome may be the targets of drugs that could potentially abort PTB once it is clinically suspected and before the full immunological response begins.

As can be observed in Additional file 5, several genes identified as DE in this work were previously associated with PTB. Interestingly, two of the overexpressed genes found in this study were previously proposed as biomarkers of PTB because their concentration in amniotic fluid was higher in PTB than in term labor: C-X-C motif chemokine ligand 10 (CXCL10) gene and matrix metallopeptidase 8 (MMP8) [46, 78–80]. Consequently, these biomarkers can be analyzed in amniotic fluid and provide information to prevent PTB. CXCL10 is related to maternal anti-fetal rejection in the same way that it is related to allograft rejection [46]. In this context, CXCL10 overexpression in PTB may lead to premature labor by triggering a fetal inflammatory response. MMP8 is known to decrease the integrity of the cervical and fetal membrane extracellular matrix [81]. MMP-8 may alter the membrane extracellular matrix and permit the passage of bacteria into the endometrium, leading to bacteria-induced PTB [82].

A small group of 18 genes clustered together discriminated between term and severe preterm cases (Fig. ​(Fig.2).2). All of the genes were downregulated. These genes potentially characterize a severe preterm transcriptome pattern and therefore are candidate genes for understanding the syndrome. None of these downregulated genes were previously proposed in the literature as preterm birth biomarkers. However, the placental alkaline phosphatase (ALPP) and pappalysin 1 (PAPPA) genes were included in a group of nine placental genes that together could predict GA in a recent pilot study [45]. ALPP and Pappalysin 2 (PAPPA2), a closely related gene to PAPPA, were among the 18 downregulated genes found here. Additionally, PAPPA2, a type of metalloproteinase with typically high gene expression in the placenta [83], was found to be DE in placenta tissue between spontaneous PTB and term birth [84].

The low number of downregulated genes made it difficult to establish statistically significant pathways: only two of the 18 clustered genes were included in the same KEGG pathway. The Wnt family member 1 (WNT1) and distal-less homeobox 5 (DLX5) genes belong to the pathway: “Signaling pathways regulating pluripotency of stem cells” (hsa04550). In term births, expression of this pathway increases at the end of pregnancy [85]. The WNT1 gene belongs to the WNT signaling pathway, which is involved in cell proliferation, development, and tumor progression. WNT signaling pathway inhibition could contribute to miscarriages and PTB by decreasing trophoblast proliferation and invasion [86]. Notably, the WNT2 gene, which is typically overexpressed in placenta, was not DE in our data. DLX5 is a member of a homeobox transcription factor gene family. Homeobox genes regulate embryonic as well as placental development [87]. They are widely expressed in the human placenta. A potential role for DLX5 may be related to neurobehavioral development and to the regulation of stem cell function, both of which are important for normal placental development [87]. Additionally, DLX5 is upregulated in preeclamptic placentas, presumably due to altered methylation at the DLX5 locus in preeclampsia that results in loss of imprinting [88]. A decrease in the expression of this gene later in gestation was found in human placenta as a response to an increase in gene body methylation over gestational age [87]. Our results indicate that WNT1 and DLX5 expression are lower in severe PTB fetal membranes than in term membranes. In this context, we speculate that downregulated genes may act as maturation genes that set the gestation timing.

These putative maturation genes may be very difficult to discriminate from those leading to preterm birth. A major issue in the study of PTB transcriptomes in humans is the inability to collect healthy control tissue at the same gestational age (GA) to compare with pathologic preterm tissue. Consequently, DE genes between PTB and term fetuses reflect differences in both GA and the pathological events of preterm labor. We are aware that, as a consequence of the design of our study, genes responsible for PTB and GA overlap. By comparing human sPTB and term transcriptomes with GA-matched control transcriptomes from a closely related species (macaques), Eidem et al. [89] identified a low number of distinctive, promising sPTB-specific candidate genes or genes potentially related to GA effects. The authors found similar functions for PTB and GA genes, so they speculated that the effects of sPTB and GA do not correspond to biologically distinct processes [89].

In an attempt to narrow the gene signature of PTB, DE genes were compared with labor-related genes described in previous works [49–52]. Fifty-eight of the DE genes found in this study were identified as labor-related genes, making them candidates for labor independent of GA. In the remaining group, it is probable that PTB genes overlap with those related to GA. However, whether PTB is labor that occurs too soon or represents a pathological event with a new group of expressed genes is still an open question.

The present transcriptome of chorioamnion membranes does not resolve the controversy, but it adds information that will help to complete the PTB expression scenario. On one hand, our results confirmed that immune response pathways are upregulated in PTB. On the other hand, this RNA-Seq approach allowed the identification of upregulated genes and pathways that were not previously reported to be associated with PTB (hematopoietic cell lineage pathway, osteoclast differentiation). Additionally, we found that some of the downregulated genes are involved in the nervous system, morphogenesis (WNT1, DLX5, PAPPA2) and ion channel complexes (potassium voltage-gated channel subfamily J member 16 (KCNJ16), potassium voltage-gated channel subfamily B member 1 (KCNB1)). It is difficult to speculate on the importance of these genes and pathways in PTB; for example, ion channels are involved in a wide spectrum of different functions that eventually lead to labor or PTB, including implantation [90], Ca⁺⁺ signaling [91], smooth muscle contraction [92] and inflammation [93]. Regarding the possibility that the hematopoietic cell lineage pathway is connected to PTB, it is known that the placenta produces hematopoietic stem cells (HSCs) [94, 95]. The placenta can generate HSCs and cause their expansion but not their differentiation. The placental HSC pool first appears early in gestation and then decreases while the HSC reservoir develops in the liver [95, 96]. Subsequent studies demonstrated that mid-gestation placenta also harbors a large pool of pluripotent hematopoietic stem cells that have self-renewal capacity [94, 97]. Moreover, hematopoietic progenitors were found in full-term placentas within a low percentage of lineage-committed cells [98]. Since the hematopoietic monocyte-macrophage lineage produces osteoclasts, the hematopoietic cell lineage and osteoclast differentiation pathways are connected. Although both pathways appear far removed from term labor or PTB a priori, they may represent interesting unexplored paths to preterm labor. Nevertheless, all of the mentioned genes and biological processes open new lines of research, making them good candidates as biomarkers of PTB.

A literature search to validate these genes revealed that, surprisingly, there are few high-throughput studies analyzing chorioamniotic tissues and none using RNA-Seq technology. To our knowledge, this is the first RNA-Seq study of chorioamniotic tissues in preterm and term births. When comparing the results from Bukowski et al. [43], only 7 genes are shared with our study. All of these genes may be related to PTB in one way or another: C-C motif chemokine receptor-like 2 (CCRL2) and C-X-C motif chemokine ligand 2 (CXCL2) belong to inflammatory pathways [99, 100], and G protein-coupled receptor 84 (GPR84) is a receptor of fatty acids that is also related to inflammation [101]. BCL2 related protein A1 (BCL2A1) and chitinase 3 like 1 (CHI3L1) were recently associated with chorioamnionitis in monkeys and humans [102]; solute carrier family 16 member 10 (SLC16A10) is upregulated in the mid-gestational placenta [103]; and SAM domain, SH3 domain and nuclear localization signals 1 (SAMSN1) is a poorly characterized gene that may be involved in the differentiation of lymphocytes B [104]. The fact that these subsets of genes overlap in both studies, which used different experimental techniques, stresses that they should be targets of further research into PTB.

One interesting finding is the homogeneity for gene expression in the control samples. After a linear reduction of the variability for more than 30,000 expressed genes, the control samples cluster together in a small group. The dispersion of the samples in the PCA analysis is lower compared to other tissue expression profiles using the same exploratory analysis [105]. In contrast, PTB chorioamniotic expression analysis revealed a more disperse group, further stressing the heterogeneity of this condition. This may be a consequence of the complexity of the syndrome characterization, as it has been previously reported in the expression of other tissues related to PTB like the myometrium [106]. In concordance, Ngo et al. [45] developed a gene expression model based on GA that predicted time until delivery for full-term but that failed for preterm deliveries. This suggests that to predict PTB the classifiers must incorporate at least part of the several outlier physiological events that may lead to PTB [45], e.g. PROM.

Premature and preterm rupture of membranes are frequent complications of birth and, more importantly, preterm birth [107]. The transcriptome profile of the chorioamnion may differ with and without PROM. To overcome this issue, we performed statistical analyses including PROM as a co-variable.

Moreover, when analyzing the differential gene expression of PROM, we did not find substantial differences between samples from newborns with and without PROM. We only detected 14 genes with DE among groups. Additionally, the PCA did not show any clustering based on this condition (Fig. ​(Fig.1).1). This low number of DE genes related to PROM is likely the consequence of our experimental design, which focused on sampling groups discriminated by the severe preterm condition while matching for the remaining variables. Additionally, when analyzing gene expression between PROM and no PROM groups, the group that presents rupture of membranes is limited in the number of samples, meaning that the results must be confirmed with a larger sample size.

Finally, this study was performed in an admixed population where PTB is frequent (approx. 10%), indicating that it is a serious health public problem. Population-specific studies are relevant, as genetic background may differently impact different populations, e.g., it is known that African American ancestry increases the risk of PTB [108–110]. Generally, there is an information gap regarding specific populations, except those coming from North America and Europe. With our literature search, we recovered only three out of 101 transcriptomic analyses based on populations from Latin American countries [46, 111, 112], stressing the fact that most studies do not include susceptible populations from this part of the continent.

All of the Latin American studies were performed with microarray analysis, meaning that the data shown in this work are the first RNA-Seq data available to contrast against future studies. Specifically, the Uruguayan population is admixed, with a predominant European contribution and Native American and African contributions of 10.4 and 5.6%, respectively [113, 114]. Common genetic variation affects expression variability [115], as well as the admixed genome structure [116, 117], stressing the necessity of population-specific transcriptome analyses or, alternatively, including diversity in high-throughput population analyses.

Conclusions

The use of state-of-the-art RNA-Seq analytics identified previously reported genes and pathways as well as novel candidates involved in labor and PTB. The combination of up- and downregulated genes permitted the development of a multi-gene disease classifier. These markers were generated in an admixed population in which PTB has a high incidence. Further studies including the testing of these classifiers using a second and larger independent sample are needed to validate the results. Moreover, these markers should be compared to other population studies to obtain a global landscape of severe PTB.

Additional files

Acknowledgements

Abbreviations

Authors’ contributions

SP, BB and RS conceived and designed the experiments. CS assisted in sample collection. SP performed the experiments and analyzed the data. All authors discussed results. SP, BB and RS wrote the manuscript. All authors read and approved the final manuscript.

Notes

References