https://orcid.org/0000-0003-4669-7029
Figures
Abstract
High throughput sequencing has previously identified differentially expressed genes (DEGs) and enriched signalling networks in human myometrium for term (≥37 weeks) gestation labour, when defined as a singular state of activity at comparison to the non-labouring state. However, transcriptome changes that occur during transition from early to established labour (defined as ≤3 and >3 cm cervical dilatation, respectively) and potentially altered by fetal membrane rupture (ROM), when adapting from onset to completion of childbirth, remained to be defined. In the present study, we assessed whether differences for these two clinically observable factors of labour are associated with different myometrial transcriptome profiles. Analysis of our tissue (‘bulk’) RNA-seq data (NCBI Gene Expression Omnibus: GSE80172) with classification of labour into four groups, each compared to the same non-labour group, identified more DEGs for early than established labour; ROM was the strongest up-regulator of DEGs. We propose that lower DEGs frequency for early labour and/or ROM negative myometrium was attributed to bulk RNA-seq limitations associated with tissue heterogeneity, as well as the possibility that processes other than gene transcription are of more importance at labour onset. Integrative analysis with future data from additional samples, which have at least equivalent refined clinical classification for labour status, and alternative omics approaches will help to explain what truly contributes to transcriptomic changes that are critical for labour onset. Lastly, we identified five DEGs common to all labour groupings; two of which (AREG and PER3) were validated by qPCR and not differentially expressed in placenta and choriodecidua.
Citation: Lai PF, Lei K, Zhan X, Sooranna G, Li JKH, Georgiou EX, et al. (2021) Labour classified by cervical dilatation & fetal membrane rupture demonstrates differential impact on RNA-seq data for human myometrium tissues. PLoS ONE 16(11): e0260119. https://doi.org/10.1371/journal.pone.0260119
Editor: Luca Giannella, Azienda Ospedaliero Universitaria Ospedali Riuniti di Ancona Umberto I G M Lancisi G Salesi, ITALY
Received: May 30, 2021; Accepted: November 2, 2021; Published: November 19, 2021
Copyright: © 2021 Lai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw RNA-seq dataset is available in the NCBI Gene Expression Omnibus (GEO) repository and is identified by accession number GSE80172. All other relevant data are within the paper and its Supporting information files.
Funding: This work was financially supported by Action Medical Research (registered charity 208701, www.action.org.uk; funding to PFL via project grant GN2395), Robert McAlpine Foundation (registered charity 226646, www.robertmcalpinefoundation.org; funding to JKHL for studentship support with Borne), Westminster Medical School Research Trust (registered charity 247808, www.register-ofcharities.charitycommission.gov.uk/charity-search/-/charity-details/247808/charity-overview; funding to EXG via project grant JRC FG 002 03/14-15) and Borne (No. 1167073, www.borne.org.uk; funding to PFL, KL, GS, JKHL, AD, NS and RMT). Technical support and resources for RNA-seq were provided by the Imperial BRC Genomics Facility, which is supported by National Institute for Health Research (NIHR, www.nihr.ac.uk) funding to the Imperial Biomedical Research Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Understanding how parturition (i.e. the process of birth) is initiated, specifically how the myometrium (uterine smooth muscle) is activated to generate the contractions of labour, is essential if we are to reduce rates of adverse maternal and fetal/neonatal outcomes associated with aberrant timing of birth, such as preterm birth and prolonged pregnancy. Preterm birth (i.e. birth prior to 37 weeks of gestation) affects 10–15% of pregnancies worldwide and is the leading cause of death for children under the age of 5 years (including neonates and infants) [1]. The majority (~70%) of preterm births occur spontaneously without clear etiology or cause [2]. Prolonged pregnancy (>41 weeks of gestation; late and post term) is also problematic as it increases the risk of stillbirth and significant neonatal morbidity [3]. Current clinical strategies to prevent preterm labour, stop preterm labour after it has started, or induce labour at late/post term pregnancy are relatively unsuccessful for improving maternal and neonatal outcomes [4, 5]; this is mostly due to our incomplete understanding of the physiological process of term labour and the pathological mechanisms that result in its mistiming.
Human labour can be clinically divided into two distinct phases: (i) ‘early’ phase, which is characterised by cervical effacement, with increasing frequency/intensity of uterine contractions and ≤3 cm cervical dilatation, and (ii) ‘established’ phase, which is characterised by regular, strong uterine contractions and >3 cm cervical dilatation. Rupture of fetal (amniotic and chorionic) membranes (ROM), which decreases intrauterine pressure with release of (cytokines-containing) amniotic fluids, occurs after contractions have initiated for most pregnancies [6]. Transcriptional changes responsible for initiating uterine contractions to start parturition are more likely to be detected in myometrium samples obtained during early labour, whereas consequential changes are expected to dominate observations for established labour; whether ROM can alter the myometrial transcriptome irrespective of labour status defined by cervical dilatation has not been previously determined. In fact, most published myometrium transcriptome studies [7–17] have compared samples without clear differential analysis for these factors of labour and, in some cases, samples were obtained from women after clinical interventions to artificially augment the process; both shortfalls have potential to obscure the identities of true (i.e. endogenous) labour initiators [18, 19].
Previously, we used whole tissue (‘bulk’) RNA-seq to obtain a transcriptomics dataset for myometrial biopsies from singleton pregnant women at term gestation, who were clinically classified as (i) not in labour (TNL), (ii) in early phase labour (TEaL) or (iii) in established phase labour (TEsL), which has already been used for an integrated analysis with those from two other myometrium-based studies (one also RNA-seq [20] and another that used microarrays [15]). Although this identified parturition-related signalling networks from a combined total of 33 non-labouring and 38 labouring women [21], the data from our TEaL and TEsL samples were combined as one labour group so that the impact of cervical dilatation, as well as ROM, were not differentiated. Thus, it remained unclear which genes and signalling networks were most likely to be responsible for starting the process of labour.
In the present study, we applied a different bioinformatics approach to only our dataset to assess transcriptomic changes that occur in the myometrium at different stages of physiological labour. We specifically investigated whether refining classification of term labour by cervical dilatation or ROM status would result in different profiles of DEGs and enriched gene ontology (GO) terms. Our observations highlight factors of myometrial tissue heterogeneity, which will be discussed for the intention of improving design of future omics-based studies. Additionally, we used a new (‘second’) sample cohort to validate five DEGs, which were identified from our RNA-seq cohort during the present study to be common to all our labour classifications of interest. We also determined to what extent these labour-associated DEGs of interest were distinct to myometrium, when compared to their expression patterns in cohort-matched placenta and choriodecidua.
Materials and methods
Ethical approval
Myometrium, placenta and choriodecidua biopsies from women undergoing Caesarean section with singleton pregnancies at term (37+3–41+1 weeks) gestation were obtained with written consent in accordance with the Declaration of Helsinki guidelines, and with approval from the Brompton and Harefield Research Ethics Committee (London, UK; Ethics No. 10/H0801/45).
Study setting and participant selection
Setting.
The study was carried out in Chelsea & Westminster Hospital NHS Foundation Trust, a tertiary referral teaching hospital in London, England, UK.
Participants.
All women in the study underwent Caesarean section following medical advice provided by their clinical care team at Chelsea & Westminster Hospital. Following this, recruitment to the study and related documentation of clinical data were conducted by obstetricians (clinical research fellows and consultants) and a postdoctoral scientist within the research term.
Subject categorisation.
Participants were firstly categorised as TNL, TEaL or TEsL, as described previously for the RNA-seq cohort [21], for all 38 women in the present study. Briefly, labour was defined by the presence of both regular palpable uterine contractions (≥1–2 per 10 minutes; assessed using cardiotocography) and progressive cervical dilatation (assessed using digital examination) [22]; TNL women presented with no palpable uterine contractions and a closed cervix. Labouring women were further categorised as either TEaL (≤3 cm dilated at cervix; both cohorts) or TEsL (>3 cm dilated at cervix; RNA-seq cohort only) immediately prior to Caesarean section. Additionally, ROM status of labouring women was determined during speculum examination of the vaginal cavity prior to Caesarean section. For the present study, women were categorised as labouring without ROM (TL-ROM) if ROM was documented as present for ≤1 hour (i.e. ROM negative or ROM occurred in the operating theatre), or labouring in the presence of ROM (TL+ROM) if ROM occurred >1 hour, prior to fetal delivery.
Exclusion criteria.
For both cohorts, women with diabetes (gestational, type I and type II), preeclampsia, obstetric cholestasis, signs of an infection, who were administered drugs for labour induction or augmentation (i.e. prostaglandins and oxytocin), or who were having Caesarean section for failure to progress were excluded.
Tissue samples collection
Myometrium biopsies from the upper edge of Caesarean section incision, made to the lower uterine segment, were excised prior to completion of suturing after fetal and placental delivery; all participating pregnancies resulted in live births. Biopsies of placenta (maternal side; region adjacent to umbilical cord insertion) and chorionic membrane (after manual separation from amniotic membrane and without removing decidua; i.e. choriodecidua) were collected after their routine clinical checks were completed outside of the operating theatre. Myometrium and placenta biopsies were immediately washed with ice-cold sterile Dulbecco’s phosphate-buffered saline (Sigma-Aldrich, Dorset, UK), dissected into ~3 mm3 pieces, treated with RNAlater (Sigma-Aldrich) overnight at 2–4°C and transferred into -80°C storage prior to RNA extraction, as described (for myometrium) previously [21]; the same process was undertaken for choriodecidua except dissected tissues were flash frozen in liquid nitrogen instead of using RNAlater.
RNA extraction, library preparation, sequencing and data processing for RNA-seq
RNAlater-treated myometrial tissues were used for RNA extraction as described previously [21], along with RNA quality assessment, preparation of cDNA libraries and strand-specific RNA-seq; the latter utilised a HiSeq 2000 instrument (Illumina, San Diego, USA) to generate an average of 42 million DNA fragments per sample (100 base pair paired-end reads; strand-specific) and FastQC software (version 0.11.2; Babraham Institute, Cambridge, UK) was used for quality control, all undertaken at the Imperial BRC Genomics Facility (Imperial College London, UK). The raw dataset has been deposited into NCBI’s Gene Expression Omnibus (GEO) with series accession number GSE80172 [21].
RNA-seq reads were aligned to the GRCh38 Homo sapiens reference genome provided by the Ensembl project (release 84) [23] using HISAT2 (version 2.1.0) [24, 25] with parameters of–dta-cufflinks–fr–phred33 –p 4 –q. Index was built with the information about single nucleotide polymorphisms (SNPs) and annotated transcripts. Ensembl annotated a total of 58825 genes, which included 20465 protein-coding genes. A transcript merging procedure was implemented to produce gene level models for expression analysis. Specifically, exons labelled as ‘retained_intron’ were first excluded, then overlapping interval exons of each gene were merged and a final gene level model was produced in general feature format (GFF). Only uniquely mapped reads, where a NH:i:1 tag was present, were used to produce gene read counts. An average of 53 million reads were obtained from each sample. More than 94.92% of total reads were successfully aligned to the GRCh38 reference human genome and unique concordant pair ratio was greater than 88.57%. In total, 37082 genes were mapped with the following criteria: (i) at least one RNA-seq read was assigned to a gene, and (ii) a read was only assigned to a gene when >90% of this read matched the exon regions of the gene. A gene expression matrix and design of the experiment were provided to DESeqDataSetFromMatrix function from DESeq2 (version 1.6.3) [26]; expression values presented in transcripts per million (TPM) units at S1 and S2 Datasets.
Feature normalization was conducted by rlog function to transform the matrix to log2 scale and principal component analysis (PCA) was performed by principal function to produce the top ten principal components. DEGs between sample groups were identified using DESeq2, edgeR (version 3.8.6) [27] and baySeq (version 2.4.1) [28] differential expression analysis packages. Raw p values were adjusted by false discovery rate (FDR) to produce q values (i.e p values corrected to account for multiple comparisons between sample groups); a q value of 0.05 was chosen as the cut-off for statistical significance in DESeq2, edgeR and baySeq. Fold change (FC) ≥1.5 in median TPM, whereby the larger TPM was always divided by the smaller TPM, between two sample groups of interest were used for GO enrichment and Venn diagrams; the expression FC was calculated as a ratio of median, rather than mean, TPM to minimise impact from large variance between samples.
Enrichment Analysis for Customised Organism (EACO) package [29] was used for its computational pipeline to undertake statistical analysis of GO enrichment; genes with a median TPM≥1.5 in at least one of the two sample groups of paired comparison were used as background. Fisher’s exact test was used to identify whether DEGs (foreground genes) were enriched in a specific GO category when compared to background genes; p values were adjusted for multiple testing using the Benjamini-Hochberg procedure for FDR [30]. Venn diagrams were drawn using the jvenn online tool [31] to visualise FC≥1.5 DEGs lists from all pairs of comparisons made to TNL; their accompanying gene lists were interpreted using the Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) Gene BioMart online tool [32].
qPCR
Total mRNA was extracted and purified from all tissues using the TRIzol Plus RNA Purification kit (Life Technologies, Paisley, UK). After Nanodrop quantification, 1.0 μg RNA was reverse transcribed using the QuantiTect Reverse Transcription kit (Qiagen, Manchester, UK). SYBR Green (Life Technologies) was used for qPCR with a Rotor-Gene Q thermocycler (Qiagen); DNA denaturation, annealing and extension steps were as described previously [33], and qPCR standards (prepared from pooled second cohort cDNA samples) were defined by copy number. Nucleotide sequences for qPCR primers are listed in Table 1, which were designed using NCBI Primer-BLAST [34] and purchased from Life Technologies. Data for cohort-matched samples were acquired during the same set of qPCR cycles for each pair of primers so that relative expression patterns were comparable between tissue types. Each DEG was normalised to two housekeeping genes, β2-microglobulin (B2M) and ribosomal protein L19 (RPL19); the geometric mean of these normalised copy numbers was calculated for each sample and all subsequently log10 transformed for data presentation.
Statistical analyses
Prism 8.0 (GraphPad, San Diego, USA) was used for statistical analyses of patient demographics and qPCR data; their fit to normal distribution was assessed using the Shapiro-Wilk test. For patient demographics, data were analysed using non-parametric two-tailed Mann-Whitney or Kruskal-Wallis (Dunn’s post hoc) test. For qPCR data, all geometric mean values were log10 transformed to permit parametric analysis (of lognormal populations) using a two-tailed Welch’s t test or Brown-Forsythe & Welch (Dunnett’s T3 post hoc) ANOVA. Statistical significance was identified as p≤0.05 and p values from comparisons between ≥3 groups were adjusted for multiplicity of pairings.
Results
Participant demographics and clinical characteristics
Demographic and clinical characteristics details for all participants are summarised in Tables 2 and 3 (RNA-seq cohort [21]; n = 22) and Table 4 (second cohort; n = 16), where reasons for Caesarean section are presented.
DEGs associated with labour classified by status of cervical dilatation
To evaluate the overall impact of cervical dilatation (Table 2) on myometrial transcriptome profiles, samples from women within the RNA-seq cohort were grouped as TNL (n = 8), TEaL (n = 8) and TEsL (n = 6). Median (and range) of RNA integrity numbers (RINs) were 7.9 (TNL; 7.2–8.3), 7.6 (TEaL; 6.7–7.9) and 8.0 (TEsL; 7.6–8.2). PCA visualisation of variance [35] at whole transcriptome level (Fig 1) showed TNL was the group with biological replicates that shared greatest similarity to each other. In contrast, biological replicates for TEaL broadly formed two clusters, whereby half mostly overlapped with TNL and the other half were more similar to TEsL samples; TEsL whole transcriptome profiles showed relatively less overlap with TNL than TEaL samples.
Summary by principal component analysis (PCA) of overall differences in transcriptome profiles from human myometrium biopsies obtained from term gestation singleton pregnant women, which were grouped by status of cervical (Cx) dilatation or fetal membrane rupture (ROM; >1 hour prior to fetal delivery) at time of Caesarean section; sample identification numbers are shown next to their respective data points. For Cx dilatation, labouring women were grouped as either in early (≤3 cm; TEaL, n = 8) or established (>3 cm; TEsL, n = 6) labour. For ROM, women were grouped as either labouring in the absence (TL-ROM, n = 8) or presence (TL+ROM, n = 6) of ROM. The non-labouring (TNL, n = 8) group was the same for both sets of comparisons.
DESeq2, edgeR and baySeq analysis were combined to identify statistically significant DEGs, which were consistently identified as differentially expressed despite high variance between overall transcriptome profiles. DEGs with a q value (i.e. FDR-adjusted p value) ≤0.05 in at least two of these methods was defined as a ‘shared’ potential DEG. To remove background noise derived from genes with a low expression level but a high FC for each two-group comparison, these shared DEGs were filtered according to the following rationale: if their median TPM was <1 in any one of the two sample groups, they were only designated as a ‘robust’ DEG for further analysis if FC was still ≥1.5 when this median TPM value was artificially set to 1 [36]. Total numbers of DEGs, both before and after this FC≥1.5 filtering, are shown in Table 5 (full lists of filtered DEGs in S1 Dataset). TEsL vs TNL was associated with 5.9 (before FC≥1.5 filtering) and 7.6 (after FC≥1.5 filtering) fold more DEGs than TEaL vs TNL; thus, the further the progress of cervical dilatation, the more differential gene expression occurred in the myometrium at labour (albeit a small proportion of all genes that contribute to the total myometrial transcriptome).
DEGs associated with labour classified by status of ROM
To assess the overall impact of ROM on myometrial transcriptomes, using the same analyses methods applied to labour grouped by cervical dilatation, the same RNA-seq cohort of samples from labouring women were grouped as TL-ROM (n = 8) and TL+ROM (n = 6) instead to generate a separate output of DEGs (Table 3). Median ROM duration was ~9 hours (~4–26 hours range) for TL+ROM, and only the artificial ROM case (sample #66; Fig 1) was >24 hours ROM prior to Caesarean section. Median cervical dilatation was 3 cm for both TL-ROM (1–8 cm range) and TL+ROM (2–6 cm range), which showed no statistically discernible difference (p = 0.87) unlike cervical dilatation status at TEaL (2 cm; 1–3 cm) vs TEsL (4 cm, 4–8 cm) where p = 0.0003. Median and range of RINs were 7.7 (TL-ROM; 6.7–8.2) and 7.8 (TL+ROM; 7.4–8.2).
PCA plot annotation for ROM status (Fig 1) showed TL-ROM samples were relatively less distinct than TL+ROM when compared to TNL. One TL+ROM sample (labelled ‘#51’ at Fig 1) showed no distinction in its overall transcriptomic profile from TNL samples despite being associated with a ROM duration of ~9 hours. DEGs identification and FC≥1.5 filtering for ROM groupings were undertaken as described for cervical dilatation groupings; total DEG numbers and full lists of filtered DEGs are provided in Table 5 and S2 Dataset, respectively. TL+ROM vs TNL had 15.6 (before FC≥1.5 filtering) and 21.3 (after FC≥1.5 filtering) fold more DEGs than TL-ROM vs TNL; thus, more myometrial genes were differentially expressed at labour when ROM was present than absent, and ROM was associated with more DEGs than cervical dilatation.
Differences between labour classifications at enriched GO terms
GO enrichment was undertaken with consideration of both the number of DEGs assigned to each GO term and their expression FC values; terms for biological process (BP), molecular function (MF) and cellular component (CC) GO classes were identified. For labour grouped by cervical dilatation, GO terms enriched from TEaL vs TNL and TEsL vs TNL for all three classes are presented in S3 Dataset, and the top five BP terms are listed in Table 6. Immune/inflammation-related BP terms dominated the up-regulated DEGs at both TEaL and TEsL; whereas for down-regulated DEGs, ‘rhythmic process’ was the only enriched BP term for TEaL and the top five BP terms for TEsL were all related to the regulation of muscle contractions. For labour grouped by ROM status, all GO terms for BP, MF and CC classes enriched from TL-ROM vs TNL and TL+ROM vs TNL are presented in S4 Dataset, and the top five BP terms are also listed in Table 6. As with cervical dilatation groupings, immune/inflammation-related processes dominated the top five BP terms for up-regulated DEGs at both TL-ROM and TL+ROM, some of which were the same to those enriched by TEaL and TEsL. Contrastingly, no BP terms were enriched by DEGs down-regulated at TL-ROM, whereas TL+ROM down-regulated DEGs enriched ‘rhythmic process’ and ‘circadian rhythm’ terms.
Unique and shared DEGs across all labour classifications
Venn diagrams (Fig 2) show the numbers of DEGs for TEaL, TEsL, TL-ROM and TL+ROM, each relative to TNL, that were unique or shared amongst all four labour groups. For up-regulated transcription, TL+ROM had the most and TL-ROM had the least unique DEGs. For down-regulated transcription, TEsL had the most unique DEGs and none were found for both TEaL and TL-ROM. Four up-regulated DEGs were shared between all labour classifications; namely AREG, LIF, LILRA5 and NAMPT (S5 Dataset). Only one down-regulated DEG, PER3, was shared between the same labour groups (S6 Dataset). Median FC and q values from differential expression analysis for these five DEGs are listed in Table 7 (summarised from S1 and S2 Datasets).
Venn diagrams generated from lists of labour-associated up-regulated (↑) and down-regulated (↓) differentially expressed genes (DEGs) in myometrium biopsies, which were obtained from term gestation singleton pregnant women. Values shown only represent number of DEGs that demonstrated median fold change (FC) ≥1.5, when compared to the non-labouring state (TNL, n = 8), in transcript abundance for each of type of labour, which was classified by status of cervical dilatation or fetal membrane rupture (ROM; >1 hour prior to fetal delivery) at time of Caesarean section. For cervical dilatation, labouring women were grouped as either in early (≤3 cm; TEaL, n = 8) or established (>3 cm; TEsL, n = 6) labour. For ROM, women were grouped as either labouring in the absence (TL-ROM, n = 8) or presence (TL+ROM, n = 6) of ROM.
Myometrial DEGs of all labour classifications in placenta and choriodecidua
Myometrial AREG, LIF, LILRA5, NAMPT and PER3 mRNA abundance was assessed by qPCR to further determine their status as common DEGs for all four classifications of labour (Fig 2) using RNA-seq samples and those from a second cohort of women; the latter were grouped as TNL (n = 8) and TEaL (n = 8, median 1.5 cm (1–3 cm range) cervical dilatation; comprised of n = 5 for TL-ROM and n = 3 for TL+ROM) (Table 4). For the RNA-seq cohort, labour-associated changes to all except LILRA5 were consistent to what was observed from transcriptome analysis albeit to different extents of ANOVA-based significance (Fig 3). For the second cohort, where only the TEaL vs TNL comparison was considered, myometrial AREG and PER3 expression patterns were the most consistent to those identified from RNA-seq samples (Fig 4). PER3 and NAMPT are regulated by circadian rhythm [37] but differences in times of fetal delivery for each labour group vs TNL were not statistically significant for both cohorts (Tables 2–4; S1 Fig).
Log10 transformed qPCR data (mean with standard deviation; n = 6–8) for myometrial mRNA levels of genes identified by RNA-seq (Fig 2) to be differentially expressed (DEGs) for all four classifications of labour, when compared to the non-labouring state (TNL), in the same biopsies. For cervical dilatation, labour was classified as early (≤3 cm; TEaL) or established (>3 cm; TEsL) at time of Caesarean section. These TEaL and TEsL samples were alternatively classified by fetal membrane rupture (ROM) status, whereby either ROM was absent (TL-ROM) or present (TL+ROM) for >1 hour prior to fetal delivery, to assess the effect of ROM irrespective of cervical dilatation. All data for DEGs of interest were normalised to both β2-microglobulin (B2M) and ribosomal protein L19 (RPL19). Brown-Forsythe & Welch ANOVA (Dunnett’s T3 post-hoc) was used for statistical analyses of TEaL / TEsL vs TNL (a p≤0.05, aa p≤0.01) and TL-ROM / TL+ROM vs TNL (b p≤0.05, bb p≤0.01).
Log10 transformed qPCR data (mean with standard deviation; n = 7–8) for myometrial mRNA levels of genes identified by RNA-seq (Fig 2) to be differentially expressed (DEGs) during early phase labour (defined as ≤3 cm cervical dilatation; TEaL), when compared to the non-labouring state (TNL), in biopsies obtained from a cohort of term gestation singleton pregnant women separate to those in the RNA-seq cohort. All data for DEGs of interest were normalised to both β2-microglobulin (B2M) and ribosomal protein L19 (RPL19). Two-tailed Welch’s t-test was used for statistical analysis of TEaL vs TNL (** p≤0.01, **** p≤0.0001).
RNA extracts from placenta biopsies obtained from the same two cohorts of women, along with choriodecidua available for only the second cohort, were also analysed by qPCR for their expression of these five genes to determine whether they follow the same patterns as their patient-matched myometrium. From this, placental mRNA levels for these five genes were found to not be different for all labour groups relative to TNL (Fig 5); the same was observed for choriodecidual mRNA abundance at TEaL vs TNL comparison for the second sample cohort.
Log10 transformed qPCR data (mean with standard deviation) for mRNA expression levels in placenta and choriodecidua biopsies of genes identified from myometrium RNA-seq data (Fig 2) to be differentially expressed (DEGs) for four classifications of labour, when compared to the non-labouring state (TNL), in biopsies from the RNA-seq (‘#1’; n = 5–8) and second (‘#2’; n = 7–8) cohorts of term gestation singleton pregnant women. For cervical dilatation, labour was classified as early (≤3 cm; TEaL) or established (>3 cm; TEsL) at time of Caesarean section. These TEaL and TEsL samples were alternatively classified by fetal membrane rupture (ROM) status, whereby either ROM was absent (TL-ROM) or present (TL+ROM) for >1 hour prior to fetal delivery, to assess the effect of ROM irrespective of cervical dilatation. Data for all DEGs of interest were normalised to both β2-microglobulin (B2M) and ribosomal protein L19 (RPL19). For statistical analyses of cohort #1 data, Brown-Forsythe & Welch ANOVA (Dunnett’s T3 post-hoc) was used for TEaL / TEsL vs TNL and TL-ROM / TL+ROM vs TNL; all p>0.05. For statistical analysis of cohort #2 data, Welch’s t-test was used for TEaL vs TNL; all p>0.05.
Discussion
We used our RNA-seq dataset to show the extent to which transcriptomic analysis of myometrium biopsies from healthy term labouring women can produce different outcomes for different states of cervical dilatation and ROM. Thus, our present work demonstrates that this physiologically dynamic process [22] cannot be represented by a single state of the myometrial transcriptome from initiation to completion. To identify signalling networks specifically involved in ensuring that labour starts at the right time, which will be the most promising therapeutic targets for preventing its mistiming, it is important for samples of interest to represent the beginning (rather than the middle or end) of labour. This is an important consideration for use of transcriptomics, when it is common practice for individual (non-integrated) RNA-seq studies to comprise of 2–6 samples per group as a reasonable compromise between economic cost and variance minimisation [38, 39]; the latter is particularly difficult for human samples and makes stringent clinical phenotyping necessary [40, 41].
PCA, which was used to visualise overall (unfiltered) differences between transcriptome profiles, showed a high degree of variance for myometrium biopsies from labouring women. Relatively tighter clustering for TNL samples suggests that labour-associated variance was caused by biological, rather than technical, factors. Upon filtering at differential expression analysis, the absence of FC≥1.5 DEGs from comparison of labour groups to each other (i.e. TEsL vs TEaL and TL+ROM vs TL-ROM) emphasised the impact of variance specifically for samples from labouring women. It is likely that no FC≥1.5 DEGs were identified because neither labour group in each paired analysis contained sufficiently similar TPM values between biological replicates for FCs to be consistent. Similarities between labouring samples for status of ROM (at TEaL vs TEsL) or cervical dilatation (at TL+ROM vs TL-ROM) may have minimised differences at PCA. High variance may also be explained by human myometrial tissue heterogeneity, which arises from its physiological state, specifically distribution of functional output (i.e. contractile activity) at organ level, and/or cell type diversity during labour.
Functional heterogeneity at the myometrium has been evidenced by studies that measured in vivo labour contractions, which showed that they occur at discreet and randomly localised regions of the uterus, rather being evenly distributed in force and frequency across the entire organ, especially during its onset [42, 43]. Such unpredictable distribution of myometrial contractility, especially at the beginning of labour, makes it difficult to confirm whether the 60–100 mg tissue samples used for RNA-seq were all representative of uterine regions that were activated or quiescent to the same extent for each set of biological replicates [44]; their overall transcriptome differences may thus reflect their difference in activation status. Cell type heterogeneity also exists in the myometrium, which consists of mostly myometrial smooth muscle cells but also contains leukocytes, Cajal-like interstitial cells, fibroblasts and vascular smooth muscle cells [45–47]. Bulk RNA-seq is not designed to delineate the transcriptomes of different cell types. Thus, our findings support the use of single cell (sc)RNA-seq [48–50] and “computational deconvolution” [51, 52], ideally in an integrative manner [53], to determine whether it is tissue function and/or cell population heterogeneity that impacts on human myometrial transcriptome profiling the most at labour.
Myometrial DEGs identification showed labour mostly enhances (rather than suppresses) transcriptional activity and ROM is its biggest driver. GO analysis was only used for robust DEGs that were most consistent in their expression patterns despite apparent sample heterogeneity. Enrichment of immune/inflammation-related GO terms by up-regulated DEGs for all labour groups was expected because parturition is generally accepted as an inflammatory process [46, 54–56]. Cells of the immune system, along with those of the reproductive tissues, can release pro-inflammatory mediators [57, 58] that potentially increase the expression of myometrial contraction-associated genes [59]. However, other GO terms that were also enriched should not be ignored because immunology-related GO terms are the most well-annotated in knowledgebases and thus can be overrepresented by GO analyses [60, 61]. For down-regulated FC≥1.5 DEGs, their low frequency for all labour groups (relative to TNL) resulted in less, or none in the case of TL-ROM, enriched GO terms when compared to up-regulated DEGs. Technical bias caused by insufficient GO annotation within knowledgebases may have also resulted in lack of GO enrichment for down-regulated DEGs [62]. Afterall, there were more enriched GO terms for up-regulated TL-ROM than down-regulated TL+ROM DEGs, despite the former having 3.1-fold less FC≥1.5 DEGs.
Down-regulated FC≥1.5 DEGs did not enrich immune/inflammation-related GO terms. Instead, rhythmic process/circadian rhythm GO terms were enriched for TEaL and TL+ROM. Roles of circadian rhythm genes in labouring human myometrium have so far not been directly assessed but there is rationale from related research to support focus on their contribution. Rhesus macaques in vivo spontaneous uterine contractures follow a maternal circadian rhythm [63, 64]. A nocturnal peak in circulating oxytocin concentration has been observed in pregnant rhesus macaques [65] and women [66]. Chronodisruption during human pregnancy [67] has been associated with increased rates of adverse outcomes [68–71]. For TEsL, we observed a down-regulation of GO terms associated with muscle contractions. This would appear to suggest enhanced cervical dilatation coincides with reduced myometrial contractility, at least in the lower uterine segment where relaxation near the cervix may aid fetal expulsion. Alternatively, these transcriptomic changes may indicate negative feedback in response to sufficient accumulation of proteins required to maintain contractility for the rest of the labouring process. Proteomics and physiology-based comparisons of TEsL and TEaL will be needed to determine whether either interpretation is true.
Only AREG, LIF, LILRA5, NAMPT and PER3 were identified as differentially expressed throughout labour. Additional analysis using qPCR helped to demonstrate whether these five DEGs are worthy of further investigation, especially for instances when high sensitivity techniques like RNA-seq are not readily available. LILRA5 was not observed as a DEG using qPCR and, unlike the other four DEGs, is known only to be expressed in leukocytes and other hematopoietic cells [72]. Only AREG and PER3 were identified by qPCR as myometrial DEGs for two different cohorts of women, which made them the most convincing DEGs for labour. Furthermore, labour-related AREG and PER3 expression was specific to myometrium, at least when compared to in utero adjacent choriodecidua and placenta. Lack of differential expression in placenta, which is more vascularised than myometrium, suggests changes in myometrial AREG and PER3 expression at labour is unlikely due to dominance of transcriptional activity by surrounding leukocytes.
In the end, only two myometrial DEGs were confidently identified to be relevant to onset at labour out of a possibility of 20465 protein-coding genes in the Ensembl knowledgebase; namely AREG and PER3. A key limitation of the present study was that cervical dilatation and ROM status groupings had to be analysed independently of each other (using the same pool of 15 samples) due to a small cohort. A larger sample cohort that would allow each TEaL and TEsL group to be sub-divided by ROM status (rather than each being a mix of TL-ROM and TL+ROM) could potentially reveal more distinct labour-related PCA clustering, as well as provide more certainty to the identification of DEGs associated specifically with the start of labour. Nevertheless, there is evidence from previous research that supports further assessment of AREG and PER3 as novel labour-related DEGs, which may make their signalling pathways the most promising therapeutic targets for reducing the risk of labour mistiming. AREG encodes amphiregulin, a ligand of epidermal growth factor receptors (EGFRs), which are expressed in human uterine tissues at labour [73] and EGFR signalling was identified as relevant to human parturition by previous integrative analysis [21]. PER3 is a circadian rhythm gene, which encodes a transcriptional repressor that controls the circadian clock system in peripheral tissues [74] and, incidentally, a genetic polymorphism within its ‘rs228669’ coding region [74] has been linked to high risk spontaneous preterm births [75].
Conclusions
Refined sample groupings for our bulk RNA-seq dataset showed ROM, which typically occurs after uterine contractions have initiated, has substantial effects on the myometrial transcriptome. Thus, ROM, along with cervical dilatation, status needs to be defined when profiling myometrium biopsies for investigating mechanisms of labour onset. Our findings highlight the need to consider labour as a dynamic process that should not be represented by a single profile of changes at the uterus. Molecular events at different stages of labour need to be differentiated for its full characterisation from start to finish, which will increase the chance of discovering novel therapeutic targets with the highest potential in improving obstetric outcomes that are dependent on the timing of labour. Moving forward with the use of transcriptomics, alternative methodologies (with integrative approach) and additional sample cohorts are needed to determine whether our observation of low DEG numbers for the beginning of labour (represented mostly by TEaL and TL-ROM) was due to (i) cell type-specific localisation of labour-inducing transcriptomic changes, or (ii) changes at the proteome level or other aspect of myometrial activity playing a more vital role at labour onset than gene transcription. AREG and PER3 were validated by qPCR out of the five DEGs shared between all four of our labour classifications, both of which are supported for further investigation in the context of labour onset by rationale from the findings of previous research; it will be interesting to see whether AREG or PER3 remain as candidate DEGs after further studies.
Supporting information
S1 Dataset. Lists of up- and down-regulated FC≥1.5 DEGs for TEaL vs TNL and TEsL vs TNL comparisons.
https://doi.org/10.1371/journal.pone.0260119.s001
(XLSX)
S2 Dataset. Lists of up- and down-regulated FC≥1.5 DEGs for TL-ROM vs TNL and TL+ROM vs TNL comparisons.
https://doi.org/10.1371/journal.pone.0260119.s002
(XLSX)
S3 Dataset. Lists of enriched GO terms for up- and down-regulated FC≥1.5 DEGs at TEaL vs TNL and TEsL vs TNL comparisons.
https://doi.org/10.1371/journal.pone.0260119.s003
(XLSX)
S4 Dataset. Lists of enriched GO terms for up- and down-regulated FC≥1.5 DEGs at TL-ROM vs TNL and TL+ROM vs TNL comparisons.
https://doi.org/10.1371/journal.pone.0260119.s004
(XLSX)
S5 Dataset. Lists of shared and unique up-regulated FC≥1.5 DEGs (relative to TNL) for all labour classifications of interest.
https://doi.org/10.1371/journal.pone.0260119.s005
(XLSX)
S6 Dataset. Lists of shared and unique down-regulated FC≥1.5 DEGs (relative to TNL) for all labour classifications of interest.
https://doi.org/10.1371/journal.pone.0260119.s006
(XLSX)
S1 Fig. Times of birth for Caesarean section deliveries associated with biopsies from RNA-seq & second cohorts.
Fetal delivery times for both cohorts of women from whom biopsies were obtained at term gestation singleton pregnancy for RNA-seq and/or qPCR; frequency of fetal deliveries pooled into 2-hour intervals are shown for a full day (24-hour) period. For the RNA-seq cohort, samples were obtained from non-labouring (TNL, n = 8), early labouring (≤3 cm cervical dilatation; TEaL, n = 8) or established labouring (>3 cm cervical dilatation; TEsL, n = 6) women; these TEaL and TEsL samples were alternatively grouped according to whether labour occurred in the absence (TL-ROM, n = 8) or presence (TL+ROM, n = 6) of fetal membrane rupture for >1 h prior to fetal delivery. Only TNL and TEaL groupings were used for samples from the second cohort of women. Statistical analysis was undertaken using Kruskal-Wallis (Dunn’s post-hoc; RNA-seq cohort) or Mann-Whitney (second cohort) tests to compare each labour group to their cohort-matched TNL group; all p>0.05.
https://doi.org/10.1371/journal.pone.0260119.s007
(PDF)
Acknowledgments
We are immensely thankful to all maternity unit patients and staff at Chelsea & Westminster Hospital (London, UK) who contributed to acquisition of biopsies for this study. The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), National Institute for Health Research (NIHR) or Department of Health & Social Care UK.
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