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RESEARCH ARTICLE OPEN ACCESS

VOLUME 5 | ISSUE 1 | DOI: HTTPS://DOI.ORG/10.33696/GYNAECOLOGY.5.059

BACTERIAL DIVERSITY IN PLACENTAS FROM COMPLICATED PREGNANCIES USING 16S RRNA
GENE SEQUENCING

KAITLIN ELIZABETH SPRONG1, COLLEEN ANNE WRIGHT2,3, SURESHNEE PILLAY4, JAMES
EMMANUEL SAN4, EDUAN WILKINSON4,5, SHARLENE GOVENDER1,*

 * 1Department of Biochemistry & Microbiology, Nelson Mandela University, Port
   Elizabeth, South Africa
 * 2Division of Anatomical Pathology, University of Stellenbosch, Cape Town,
   South Africa
 * 3Pathology, National Health Laboratory Services, Gqeberha
 * 4KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R
   Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
 * 5Centre for Epidemic Response and Innovation (CERI), School of Data Science
   and Computational Thinking, University of Stellenbosch, Stellenbosch, South
   Africa
   

+ Affiliations - Affiliations


*Corresponding Author

Sharlene Govender, sharlene.govender@mandela.ac.za

Received Date: March 24, 2024

Accepted Date: April 16, 2024

Citation

Sprong KE, Wright CA, Pillay S, San JE, Wilkinson E, Govender S. Bacterial
Diversity in Placentas from Complicated Pregnancies Using 16s rRNA Gene
Sequencing. Arch Obstet Gynecol. 2024;5(1):18-32.


Copyright
© 2024 Sprong KE, 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.


ABSTRACT

Introduction: The ‘sterile womb paradigm’ is currently under debate and the
advent of next generation 16S rRNA gene sequencing is driving the
characterization of microbes associated with the amniotic cavity during
pregnancy.

Objective: To characterize the bacterial diversity in placentas from preterm and
term births using next generation 16S rRNA gene sequencing in association with
adverse pregnancy outcomes and histopathology studies.

Methods: In this prospective study, placentas were collected consecutively from
patients attending a public tertiary referral hospital in South Africa,
delivering preterm (n=42; 28-34 weeks gestational age) and term (n=20; >37 weeks
gestational age). Placentas underwent histopathology tests and next generation
16S rRNA gene sequencing.

Results: Analysis of microbial diversity in the placenta showed a significantly
higher alpha diversity in term placentas compared to preterm placentas
(P=0.000075), in placentas with acute chorioamnionitis compared to placentas
without acute chorioamnionitis (P=0.0061), between HIV negative term births and
both HIV negative and HIV positive preterm births (P=0.0118 and P=0.0008),
respectively. Beta diversity was significantly different between preterm and
term births (unweighted UniFrac distance, P=0.003996; Jaccard distance,
P=0.03696) and Escherichia/Shigella, Shuttleworthia, Anaeroglobus and
Megasphaera were differentially expressed.

Conclusion: This is the first South African study to characterize the bacterial
diversity in placentas from complicated pregnancies using next generation 16S
rRNA gene sequencing in conjunction with full placental histology. Microbial
diversity differs between preterm and term placentas where HIV may act as a
cofactor associated with decreased bacterial alpha diversity in placentas from
preterm birth. 


KEYWORDS

Next generation sequencing, 16S rRNA, Placenta, Preterm birth, HIV, Acute
chorioamnionitis

INTRODUCTION

Intra-amniotic infection due to microbial invasion of the amniotic cavity has
been implicated in various pregnancy complications; spontaneous preterm labor,
preterm premature rupture of membranes, chorioamnionitis and adverse maternal
and neonatal outcomes [1-4]. Infection may result from multiple routes of
invasion including ascending infection from the lower genital tract,
hematogenous dissemination through the placenta, retrograde seeding through the
fallopian tube and iatrogenic infection [1,5]. Bacterial colonization of the
placenta has been reported in association with adverse pregnancy outcomes
including preterm birth and chorioamnionitis [4,6]. A study demonstrated the
presence of bacteria in the basal plate of the placenta, at the maternal-fetal
interface, in association with early preterm birth and in the absence of
chorioamnionitis [7].

The ‘sterile womb paradigm’ is currently under debate and the advent of next
generation 16S rRNA gene sequencing is driving the characterization of microbes
associated with the amniotic cavity during pregnancy [8-10]. In 2014, the
sterile womb hypothesis was challenged by Aagaard et al. who reported the
detection of bacterial DNA in placental samples [11]. However, a complication
for these studies is the presence of low levels of microbial DNA in laboratory
dust and commercial reagents which may produce signals from the highly sensitive
molecular methods used. A further complication is that it is possible to
generate germ-free neonates by sterile caesarean delivery which makes the
sterile womb hypothesis appealing as pathogens can cross the placenta [12]. This
contemporary area of research has led to the recent proliferation of microbiome
studies of the amniotic cavity to increase understanding on this topic.

Major findings of this research include detection and identification of
bacterial species from predominantly amniotic fluid and placenta samples and
association of the microbiome with specific outcomes or pathology in study
cohorts [6,11,13-15]. However, some placenta-based studies failed to detect
microbial DNA [16-18]. This may be due to the low biomass of microbes reported
in placental tissue as well as the difficulty in isolating genomic DNA from
tissue rather than amniotic fluid [11]. Recent studies oppose the idea that
there is a microbiome associated with the placenta, citing contamination or
background signals [16,17,19,20]. In contrast, some studies offer insight into a
uterine microbiome which is often described as unique and associated with
specific factors such as birth weight, chorioamnionitis and pathology
[5,6,11,14]. Cohort selection criteria also plays an important role in
microbiome studies such as Kuperman et al. (2019) where placentas were delivered
from women with preeclampsia, which is not typically associated with microbial
infection, and the lack of detection of microbial DNA is expected [16].

However, the different regions of the placenta must also be considered when
comparing subsets of placental microbiome studies. Variations have been
described in microbial composition between the fetal amniotic membranes, the
placental villi, and the basal plate, and these three regions of the placenta
differ by fetal or maternal origin, structure, function, and barrier capacities
as well as timing of infection [6,15,21]. In this current study, the placental
samples submitted for next generation 16S rRNA gene sequencing comprised the
placental parenchyma, including the chorionic plate, the intervillous space, and
basal plate.

The clinical relevance of using DNA-based molecular detection of microbes has
been questioned as it is known that DNA is able to persist for weeks following
antibiotic usage and cell death and perhaps longer in a stable environment such
as the uterine cavity [22,23]. The inability to differentiate between viable and
non-viable cells is a major limitation in microbiome studies which characterize
dynamic systems [31]. However, additional investigations such as quantification
of microbes using qPCR or correlation of the presence of microbial DNA with
clinical indications of infection or inflammation may provide a more detailed
understanding of the specific microbiome being studied.

Other studies have employed a similar approach to this current study by using
NGS of the 16S rRNA [11,14,17,19]. Zheng et al. detected microorganisms in the
placenta in association with neonatal low (LBW) or normal (NBW) birth weight
[14]. The relative abundance of Lactobacillus, Clostridium, Cyanobacteria,
Ruminococcus and Lawsonia were significantly lower in LBW group and conversely
Megasphaera, Faecalibacterium, Jeotgalicoccus, Pediococcus, Sneathia, and
Sphingobacterium were significantly higher in LBW group, compared with the NBW
group. Prince et al. found a higher abundance of Ureaplasma parvum,
Streptococcus agalactiae and Fusobacterium nucleatum from fetal chorion and/or
villous placental membranes in association with preterm birth with severe
chorioamnionitis [6].

Despite recent advances in metagenomics, and perhaps because of the opposing
results of microbiome studies, it is apparent that the microbial census of the
amniotic cavity remains incomplete. Gaining understanding of the microbial
diversity associated with pregnancy may propel research regarding the source of
infection, pathogenicity, synergism of pathogens and adverse clinical outcomes.
Microbiome data combined with clinical outcomes may significantly enhance the
knowledge of the role of placental colonization and the effect on adverse
pregnancy outcomes. The objective of this study was to characterize the
microbial diversity in placentas from complicated pregnancies using next
generation sequencing of the 16S rRNA gene in association with adverse pregnancy
outcomes and histopathology studies.

MATERIALS AND METHODS

Ethical clearance, study population and design

This prospective hospital-based study included patients delivering in the labor
ward at a tertiary referral hospital in the Eastern Cape, South Africa from
March 2016 to November 2017. This hospital services a large area and only
patients who present with complicated deliveries are eligible for admission to
the maternity unit. Ethics approval was granted from the Nelson Mandela
University Research Ethics Committee (Human) (reference: H15-SCI-BCM-001).
Permission to conduct the study was obtained from the Eastern Cape Department of
Health (EC_2015RP8_78) and from the acting Clinical Governance Manager at the
hospital respectively. Patients were recruited and informed consent was obtained
including permission for the collection of their placenta after delivery and for
their medical records to be accessed for maternal and neonatal chart review.
Patients received unique and anonymous study numbers for entry into a
de-identified database. Placentas from preterm deliveries (n=42; 28 to 34 weeks
gestational age) were collected as the test cohort, while placentas from term
deliveries (n=20; >37 weeks gestational age) were collected as the control
cohort. Placentas were collected consecutively according to confirmation of
inclusion criteria of gestational age and if the mother was able and willing to
give informed consent after delivery. The gestational age range was selected as,
according to the World Health Organization, viability of preterm neonates
reaches a 50% chance of survival at 34 weeks gestational age in low to middle
income countries, compared to 24 weeks in high income countries [25].

Placentas

Following delivery, the placenta was immediately transferred to a sterile
container and processed within 1 hour. The placenta was placed maternal side
down on a sterile work table in a biosafety cabinet. The fetal surface was
decontaminated using 70% ethanol to minimize contamination by maternal skin and
vaginal flora depending on mode of delivery. Using sterile surgical implements,
an incision was made in the amnion, at a point approximately halfway between
cord insertion and placental edge, where a single full-thickness biopsy
(approximately 1 cm3) was removed from below the chorioamnion including the
chorionic villi, syncytiotrophoblast and decidua and was stored in RNAlater
(Life Technologies, Canada) in a cryogenic vial at -80°C. The remainder of each
of the placentas underwent routine macroscopic examination, followed by fixation
in 10% buffered formalin and histology at National Health Laboratory Services
(NHLS), Gqeberha, Eastern Cape. All cases were evaluated histologically by a
single pathologist using a standardized placental macroscopic evaluation
protocol and histology template approved and validated by the NHLS laboratory.
Prof CA Wright, anatomical pathologist, a specialist in placental histology,
reviewed all the placenta specimens for this study and histopathology data was
published in 2023 [27]. The pathologist was blinded to all information except
gestational age and whether live-born or stillbirth. Based on the macroscopic
and microscopic data, a histological diagnosis was made according to the
Amsterdam Consensus Classification System [26,27].

Maternal and neonatal chart review

A maternal chart review was performed to gather demographic and obstetric
characteristics such as maternal race, age, parity, smoking status, diabetes,
preeclampsia, Human Immunodeficiency Virus (HIV) status and the mode of delivery
(Caesarean section [CS] or normal vertex delivery [NVD]) (Table 1). Certain
fetal/neonatal characteristics that were available shortly after delivery were
recorded in the maternal chart before a neonatal chart was opened (Table 2).
Demographic information was required to investigate patterns around different
disease conditions in the study population group [28]. A neonatal chart review
was performed for each neonate from preterm and term delivery. General
characteristics such as live birth/ still birth, neonatal gender, weight, and
APGAR score were recorded at birth. Neonatal charts were later reviewed for
outcomes, i.e., respiratory distress syndrome, sepsis, pneumonia, neonatal
jaundice, and necrotizing enterocolitis. Data on histopathology is available
(Tables 1 and 2).

Table 1. The distribution of placental lesions from preterm and term births.

Placental pathology*

Preterm, n (%) (n=42)

Term, n (%) (n=20)

ACAM

8 (19)

11 (55)

MVM

20 (48)

8 (40)

AP

18 (43)

7 (35)

FVM

4 (10)

0 (0)

VUE

4 (10)

0 (0)

ACAM with FIR

4 10)

0 (0)

Chorangiosis, chorangioma

1 (2)

0 (0)

Intravillous haemorrhage

2 (5)

0 (0)

TTS

1 (2)

0 (0)

RPH

2 (5)

0 (0)

Other

7 (17)

3 (14)

ACAM: Acute Chorioamnionitis; MVM: Maternal Vascular Malperfusion; AP: Abruptio
Placenta; FVM: Fetal Vascular Malperfusion; VUE: Villitis of Unknown Etiology;
FIR: Fetal Inflammatory Response; RPH: Retroplacental Hemorrhage; TTS: Twin
Transfusion Syndrome.

*Placentas may have more than one lesion.

 

Table 2. Maternal and obstetric characteristics of the preterm study and term
control groups.

 

Preterm (n=42)

Term (n=20

Numerical

Mean (SD)

Mean (SD)

Maternal age, years

28.88 (6.46)

27.35 (4.84)

Parity

2.05 (1.11)

0.90 (0.91)

Categorical

Total, n (%)

Total, n (%)

Ethnicity

African

29 (69)

14 (70)

 Mixed

13 (31)

6 (30)

Birth Mode

NVD

19 (45)

10 (50)

CS

23 (55)

10 (50)

Substance abuse

Smoking

9 (21)

15 (75)

Alcohol

6 (14)

10 (50)

HIV

18 (43)

4 (20)

Diabetes

1 (2)

3 (15)

Pre-eclampsia

14 (33)

1 (5)

AEDF

2 (5)

1 (5)

HELLP

4 (10)

0 (0)

PROM

9 (21)

2 (10)

SD: Standard Deviation; NVD: Normal Vertex Delivery; CS: Caesarean Section;
AEDF: Absent End Diastolic Flow; HELLP: Haemolysis Elevated Liver Enzymes and
Low Platelets Syndrome; PROM: Premature Rupture of Membranes.

DNA extraction and library preparation

The 16S rRNA gene is made up of nine hypervariable regions (designated V1-V9)
[29]. The choice of which hypervariable region to target for microbiota studies
is complex as the phylogenetic resolution and abundance estimate achieved with
each region will differ for the different taxa within the community. While the
V3-V4 regions have been used to examine cervical and vaginal communities, there
is no agreement on the optimal region for cervical and vaginal microbiota
studies [30]. The V3-V4 region of the 16S rRNA gene was targeted to characterize
the bacterial community within preterm and term placenta samples. Next
generation sequencing was performed by KRISP (Kwazulu-Natal Research Innovation
and Sequencing Platform) Laboratories, Nelson R Mandela School of Medicine at
the University of KwaZulu-Natal (http://www.krisp.org.za). Tissue samples from
42 preterm and 20 term placentas (50 mg) were excised from the original tissue
biopsy of the chorionic villi, syncytiotrophoblast and decidua within a
biological safety cabinet. Tissues were lysed using the Qiagen Tissue Lyser at
30 pulses for 45 seconds. Total nucleic acid was extracted using the PerkinElmer
Chemagic 360 Automated system and DNA was quantified using the Qubit 3.0
instrument using the Qubit dsDNA Assay Kit (Thermo Fisher Scientific, South
Africa) and normalized to 5 ng/μl in 10 mM Tris, pH 8.5. Negative kit controls
were included for extraction, library preparation, and amplification.

The V3 and V4 regions of the bacterial 16S rRNA gene were amplified by PCR using
Platinum Taq DNA Polymerase Kit (Invitrogen, California, USA) with Forward
primer: 5’-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG-3’
and Reverse primer 5’-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV
GGG TAT CTA ATCC-3’ which result in a single amplicon of approximately ~460 bp
(Illumina 16S Metagenomic Sequencing Library Preparation Guide, California, USA)
[31]. Sequencing included negative controls. PCR assays were performed in 12.5
μL reactions containing 5 μL of extracted DNA, 0.1 μL of DNA Polymerase, 1.2 μL
buffer, 0.5 μL MgCl2, 0.3 μL dNTP, 2 μL of each primer (1 μM) and sterile
DNase/RNase-free dH2O. The Nextera XT Index primers (Illumina, California, USA)
were used to provide a unique barcode to each sample. Agencourt AMPure XP beads
(Beckman Coulter, California, USA) were used to remove unbound adapters. DNA was
quantified using the Qubit 3.0 instrument and Qubit dsDNA Assay Kit. A fragment
size of 550 bp was verified on a 1% agarose gel. The libraries were normalized
to equimolar 4 nM prior to sequencing.

Bioinformatics approach and statistical analysis

Statistical analyses and data visualization were performed using ‘R’ programming
language and environment (R, 2018, https://www.r- project.org version 3.5.0).
Raw reads were evaluated for sequence quality and trimming parameters using
Fastqc [(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)] and MultiQC
[32]. The Divisive Amplicon Denoising Algorithm (DADA) 2 [33] was then used to
filter for quality, trim, and infer amplicon sequence variants (ASVs were
taxonomically classified to genus or higher levels using a Naïve Bayes
classification approach [33] and SILVA ribosomal RNA database [34]. A maximum
likelihood phylogeny was inferred using the R package Phangorn. The resulting
phylogenetic tree, ASV table, taxonomy table, and sample metadata were combined
into a Phyloseq object using the R package phyloseq [35] to perform community
compositional analyses and ordination. Prior to the compositional analyses,
non-bacterial sequences, taxa that are not classified beyond the Phylum level,
outliers and taxa which were not prevalent in at least 1% of the samples were
removed. Rarefaction curves were generated using the R package vegan [35] to
determine if sequencing depth was sufficient. These curves show the number of
species as a function of number of samples. Information regarding sequencing
depth and other descriptors are indicated in Figures S1 and S2, and Table S1.
The range was 108 – 23190. Samples with read counts less than 100 were
considered too low and therefore dropped from subsequent analyses.

Community compositional analyses

Alpha diversity (within-group diversity) estimates for richness and evenness
were calculated using Shannon indexes while presence-absence was performed using
the Simpson indexes [36]. The effect of various parameters on alpha diversity
was determined using the Kruskal Wallis and Wilcoxon rank sum tests after
confirming that the data was not normally distributed using the Shapiro test.
Interactions were modelled using the R base function, “interaction”. The dunn
test was used post-hoc analysis to determine the effect of interacting groups.
These parameters assessed include histopathology, maternal characteristics, and
neonatal outcomes.

Bacterial community dissimilarity or beta diversity was determined using; the
Bray-Curtis index which accounts for ASV relative abundance between samples, the
Jaccard index which accounts for ASV presence/absence between samples, the
UniFrac which takes into consideration the phylogenetic relatedness of ASVs
detected. The weighted UniFrac considers ASV relative abundance between samples
while the unweighted UniFrac which considers ASV presence/absence between
samples. Principal Coordinate Analysis (PCoA) ordinations were used to visualize
sample relationships and statistical testing to confirm observed relatedness.
Permutational multivariate analysis of variance (PERMANOVA) testing for
statistical significance was performed using the adonis function from the Vegan
R package (https://github.com/vegandevs/vegan),
(https://cran.r-project.org/web/packages/vegan/index.html) [37].

Evaluating differential abundance

DESeq2 [38] was used to determine taxa that are differentially abundant between
term and preterm deliveries. This implements the FDR/Benjamini-Hochberg method
which ranks the genes by p-value, then multiplies each ranked p-value by m/rank.
Prior to testing for differential abundance, an independent filter was used to
exclude ASVs absent in at least 1% of the samples. ASVs were considered
significantly differentially abundant between classes if their adjusted P value
was <0.05 and if the estimated fold change was >1.5 or <1/1.5. The sequence data
bioproject accession for the requested raw read files have been successfully
submitted to NCBI SRA: PRJNA1047970.

RESULTS

Library preparation and data assessment

DNA was isolated from the chorioamnion of placentas from preterm (n=42) and term
(n=20) births and subjected to next generation sequencing. Obtained sequences
were quality filtered and ASVs were evaluated. Following DNA extraction and
quality assurance, one sample had a very low read count (<100 reads) and was
thus eliminated. The remaining 61 samples (41 preterm and 20 term) comprised the
phyloseq object containing 12029 taxa prior to filtering. Supervised prevalence
filtering removed non-microbial sequences and taxa prevalent below 1%, before
outlier detection. ASV outliers were removed, therefore leaving 38 preterm and
19 term samples.

Alpha diversity was higher among term births

Overall, there was a higher alpha diversity in term (n=19) samples than preterm
(n=38) samples with a statistically significant measure of the Shannon diversity
index (Figure 1A; Kruskal-Wallis, P=0.000075). Term samples had higher abundance
and richness of ASVs than preterm samples as observed by the higher Shannon
diversity index.

Alpha diversity differed significantly between placentas with chorioamnionitis
and those without (Figure 1B; P=0.0061) and placentas from HIV positive vs HIV
negative mothers (Figure 1C; P=0.0089). Statistical significance was absent for
mode of delivery (Figure 1D; P=0.75) and placentas with histological maternal
vascular malperfusion (P=0.056), abruptio placenta (P=0.99), diabetes (P=0.39),
smoking (P=0.44), preeclampsia (P=0.59), race (P=0.43), and live-birth vs
stillbirth (P=0.16).



Figure 1. Alpha diversity measures using the Shannon index. A. Box plot of alpha
diversity for preterm and term samples (Kruskal-Wallis, P=0.000075). B-D. Mean
Standard Deviation violin plot of alpha diversity measures for; B. placentas
with chorioamnionitis (CAM) vs placentas with no CAM (Wilcoxon rank sum,
P=0.0061), C. maternal HIV negative vs HIV positive status (Wilcoxon rank sum,
P=0.0089), D. mode of delivery as caesarean section (CS) vs. normal vertex
delivery (NVD) (Wilcoxon rank sum, P=0.75).

There was a statistically significant difference between HIV negative term
births and both HIV negative and HIV positive preterm births, P=0.0118 and
P=0.0008, respectively (Table 3). This indicates that there is a difference in
Shannon diversity of preterm placentas in association with HIV status. In
contrast, for term placentas there was no significant difference for HIV
positive versus HIV negative status (P=0.2858). Therefore, the effect of HIV
cannot be ruled out in preterm birth in this study where HIV is a cofactor and
its presence is associated with decreased microbial alpha diversity in preterm
birth.

Table 3. Kruskal-Wallis rank sum test cross tabulation of alpha diversity
measured as the Shannon diversity index between preterm and term birth for HIV
status.

Col Mean-

Row Mean

HIV negative Preterm

HIV negative Term

HIV positive Preterm

HIV negative Term

-2.263258

0.0118*

 

 

HIV positive Preterm

1.160623

0.01229*

3.169693

0.0008*

 

HIV positive Term

-0.808623

0.2094

0.565647

0.2858

-1.468421

0.0710

Bold*: P-values

Beta diversity revealed for between group clustering

Principal coordinate plots (Figures 2A-2D) showed differences in clustering
between preterm and term beta diversity measures. Significant differences in
beta diversity were seen between preterm and term samples in the Jaccard PCoA
plot (PERMANOVA, P=0.03696) and unweighted UniFrac PCoA plot (PERMANOVA,
P=0.003996) indicating that overall microbial community composition of placental
samples was significantly different for preterm compared to term births at ASV
level, but not for abundance (Bray Curtis PERMANOVA, P=0.1289 and W Unifrac
PERMANOVA, P=0.1149).



Figure 2. Beta diversity Principal Coordinate Analysis (PCoA) plots. A. weighted
(w) UniFrac, B. unweighted UniFrac, C. Bray Curtis and, D. Jaccard Distances
demonstrating the beta diversity of the placental microbial community for
preterm birth (blue) and term birth (yellow). The values in parentheses show the
percentages of total community variation explained.

Differential abundance altered for gestational age

A heat map (Figure 3A) of abundant genera between preterm and term pregnancies,
with estimated fold change and P values (Figure 3B). Clustering of ASV’s between
preterm and term samples is visually difficult to interpret from the heat map
generated, and therefore significance in differential abundance was determined
with Log2 fold change and associated P values. Statistical significance was
observed for ASV 8 (Shuttleworthia, P=0.04), ASV 12 (Megasphaera, P=0.01), ASV
16 (Anaeroglobus, P=0.02) and ASV 21 (Escherichia/Shigella, P=0.03).

Based on the heat map generated (Figure 3A), the distribution of abundant genera
by preterm and term identified that Lactobacillus were uniformly distributed in
all preterm and term placentas (P=1.00). From visual inspection of the heat map
and fold change values Gardnerella, Prevotella, Peptostreptococcus, Atopobium,
Anaerococcus and Bifidobacterium were abundantly present in both preterm and
term placentas. Non-bacterial sequences, unidentified bacteria, outliers, and
taxa which were not prevalent in at least 1% of the samples were removed from
subsequent analyses.



Figure 3. Bacterial profile of placental samples. A. Heat map of mean abundance
per genus illustrating differences in Log2 Fold Change of prominent amplicon
sequence variants (ASVs) among placental samples of preterm (n=38) and term
(n=19) births. B. Most prominent ASVs listed at genus level, with corresponding
Log2 fold change and adjusted P values.

Bacterial genera significantly differentially expressed are represented in
orange in the Volcano plot of the DESeq results (Figure 4). Bacterial genera
above the horizontal line remain significant after multiple testing correction
(False Discovery Rate). Those on the left of the first vertical dotted line are
more abundant in the preterm birth while those on the right of the second
vertical line are less abundant in preterm birth. ASV 21 Escherichia/Shigella
(P=0.03) were more abundant in preterm placentas, while ASV 8 Shuttleworthia
(P=0.04), ASV12 Megasphaera (P=0.01) and ASV 16 Anaeroglobus (P=0.02) were less
abundant in preterm placentas.



Figure 4. Volcano plot of DESeq results indicating the positive Log2 fold change
- Term of differentially expressed bacteria. Escherichia/Shigella, Anaeroglobus,
Shuttleworthia, and Megasphaera were considered statistically significant.

DISCUSSION

This study uniquely characterizes the bacterial diversity in placentas from
complicated pregnancies in South Africa using next generation 16S rRNA gene
sequencing in conjunction with full placental histology. Next generation
sequencing was performed on 16S rRNA gene amplicons derived from placentas from
preterm and term births. A significantly higher alpha diversity was found in
term than preterm placenta samples which correlated with the higher incidence of
chorioamnionitis in term placentas and a lower alpha diversity in placentas from
women who were HIV positive. There was also a significant difference in beta
diversity between term and preterm placentas with Escherichia/ Shigella,
Shuttleworthia, Anaeroglobus and Megasphaera differentially abundant.

It is well established that microbial infection of the amniotic cavity has been
implicated in adverse pregnancy outcomes including preterm birth [39]. However,
the origin and characterization of these microbes is less clear, as well as
their clinical significance, in pregnancy outcomes.

The placental microbiome may vary with factors such as infant birthweight at
term, maternal gestational weight gain, gestational diabetes, and preeclampsia
[14,15,40,41]. Communities of bacteria were found to be spatially distinct
within the placenta, specifically the fetal amniotic membranes, placental villi,
and maternal basal plate [15]. Clinical vaginosis may be associated with changes
in the placental microbiota [40]. Studies have shown that specific bacterial
communities within the placental membranes were associated with preterm birth
and chorioamnionitis and were independent of mode of delivery [6,42]. The
growing body of evidence supporting the presence of a low biomass placental
community or pathobiome in association with preterm birth risk may assist with
clinical prediction of adverse pregnancy outcomes [43].

After quality filtering, 38 preterm and 19 term samples remained and were
analyzed for alpha and beta diversity significance. Many published studies have
used fewer samples for similar analyses; n=8 [5]; n=20 [43]; n=24 [14]; n=28
[16]; n=29 [17] and n=48 [11]. Together, these highlight the sufficiency of
power within this study to detect differential abundance and to characterize the
bacterial communities represented in the study groups. On the contrary, it
highlights the need for larger study cohorts in this and similar settings to
further validate these findings.

In this study, there was a significantly lower alpha diversity in preterm than
term placenta samples as evidenced by the Shannon diversity index
(Kruskal-Wallis, P=0.000075), which is in contrast with previous placental and
vaginal microbiome studies [6,44]. This indicates that preterm placentas have
fewer microbial constituents within their microbiome. However, the higher alpha
diversity in term placentas compared to preterm placentas, and higher alpha
diversity in placentas with chorioamnionitis compared to those without CAM
(P=0.0061) correlates with the higher incidence of CAM in term placentas
compared to preterm placentas (P=0.002) [27]. Prince et al. also found that
chorioamnionitis was associated with lower alpha diversity in the placental
membrane microbiome, although associated with preterm birth in their study [6].

The effect of various histopathology, maternal characteristics and neonatal
outcomes on alpha diversity was determined with the Shannon index and
statistical significance assigned with the Wilcoxon rank sum test. There was a
mean decrease in alpha diversity for patients who were HIV positive in
comparison to HIV negative (P=0.0089). This agrees with previous gut microbiome
studies which indicate that alpha diversity is decreased in association with
recent HIV infection [45,46]. In microbiome studies of the placenta, vagina or
amniotic fluid, HIV infection is often part of the exclusion criteria [44,40,51]
or is not evaluated as a factor contributing to microbiome variances
[17,18,49,50].

In a review by Gootenberg et al. which addresses changes in the enteric
microbial community in association with HIV infection, two key points were made
which may be applicable to this study [45]. The first is that there is
HIV-driven destruction of gastrointestinal CD4+ T cells which may disturb the
balance between the microbiota and mucosal immune system, leading to disruption
of the stable gut microbiome and systemic inflammation. This same mechanism may
occur in placental tissue as HIV is known to cross the placenta leading to
infection of the neonate in utero, which may also disrupt the microbial balance.
The second key point is that most HIV-enteric microbiome studies have occurred
in developed countries, which may not be representative of countries with an
increased HIV disease burden and potentially hindering the application of these
outcomes to the populations of greatest need. The same can also be applied to
microbiome studies of the amniotic cavity in developed countries [17,52]. A
large-scale study by Doyle et al. reported HIV infection in 13.3% (n=1097) of
included participants, compared to 37.5% HIV positive women in this study,
however, they did not evaluate HIV infection as a factor contributing to the
microbial diversity of the placenta [42]. In countries with a high HIV
prevalence as well as high incidence of PTB, such as South Africa, it is of
vital importance to further characterize the microbial diversity of the placenta
in relation to HIV infection, PTB and associated outcomes. This study is the
first to characterize the placental microbiome in South Africa, a country noted
for its high incidence of HIV infection. In this study, Shannon diversity was
significantly different between HIV positive versus negative groups in preterm
birth, P=0.0008 and P=0.0118 respectively, implying that HIV may be a cofactor
associated with decreased bacterial alpha diversity in placentas from preterm
birth.

No significant difference in alpha diversity was seen for mode of delivery
(Wilcoxon rank sum, P=0.75), suggesting that the diversity of microbes in the
placenta is not associated with vaginal delivery and therefore not a consequence
of contamination of the placenta by vaginal microbiota during birth. This is
further confirmed by the presence of Lactobacillus, a common vaginal commensal,
in placentas from both vaginal and caesarean delivery (P=1.00). Other studies
also reported no significant difference in diversity by mode of delivery
[6,11,42]. In contrast, some studies have found bacterial signals in association
with a vaginal mode of delivery, which highlights the importance of sterile
sample collection technique when assessing microbial diversity [19,51]. Studies
investigating the vaginal microbiome in association with PTB often have
disparate results, which emphasizes the need for further investigation of niche
specific microbiomes during pregnancy and in PTB [52,53]. Previous studies on
gut and oral microbiomes demonstrate that certain niche bacterial taxa may be
implicated in intrauterine infection [53-58]. The hematogenous spread of common
oral commensals, such as Fusobacterium spp. and Streptococcus spp., to the
placenta in association with chorioamnionitis may account for the similarities
observed between placental and oral or niche specific microbiomes [56].

In this study, there was a mean increase in alpha diversity for placentas with
histological chorioamnionitis compared to those without chorioamnionitis
(P=0.0061). In contrast, Prince et al. found a decrease in species diversity
(Shannon index) in preterm subjects with severe chorioamnionitis and Doyle et
al. reported a distinct difference in bacteria, a higher bacterial load and
lower alpha diversity (species richness) in association with chorioamnionitis
[6,57]. Various factors may have contributed to the different results obtained
in this study, including differences in the maternal cohort. Alpha diversity has
been reported as consistently higher in women of African ancestry than other
ancestries in the vaginal microbiome, which may impact the placental microbiome
if infection is due to ascending infection [50]. Although infection in the
chorion and amnion is not always correlated with infection of the amniotic
fluid, chorioamnionitis can be associated with hematogenous or ascending
microbial infection of the amniotic cavity, which is often polymicrobial in
nature and may influence species diversity in the preterm group [58]. In this
study setting, patients in preterm labor are given prophylactic antibiotic
treatment with erythromycin (Zithromax 500 mg daily) and the use of antibiotics
for prophylaxis, suspected infection or clinically diagnosed chorioamnionitis
may also have influenced the alpha diversity in this cohort of high-risk
pregnancies. This is a limitation of the study as the information on duration of
antibiotic prophylaxis administered to certain patients in preterm labor was not
available. Prince et al. found that antibiotic use made no significant
alteration to prevalent taxa at genus level in preterm and term placentas and
suggested that variations in the placental membrane microbiome may be associated
with inflammation rather than infection [6].

The graphical representation of beta diversity in a PCoA plot indicates the
similarity of microbiomes at the genus level between individual samples.
Subjects can be characterized by categorical variables (e.g., preterm or term
birth) and then visualized where each group clusters in relation to principal
components (PC) on the axes. Most programs indicate the percentage of the
difference between samples for each principal component. The higher the
percentage, the more one can conclude that clusters are because of specific
taxa, or lower percentages indicate that differences are based on multiple taxa
which implies that no specific taxa are correlated with the outcome of preterm
birth [15].

Upon analyzing beta diversity, a significant difference in clustering was found
by PERMANOVA for the unweighted UniFrac distance (P=0.003996) and Jaccard
distance (P=0.03696) on the PCoA plots, which indicate the richness and
diversity of microbes significantly differs at the ASV level between preterm and
term placentas. The large percentage for PC1 (44.3%) on the weighted UniFrac
PCoA plot indicates that clustering may be a result of specific bacterial taxa
shaping beta diversity between preterm and term placentas. These observations
are consistent with previous evidence suggesting that the placental microbiome
is altered in placentas from preterm birth [6,11,59].

Genera that were differentially abundant and were considered statistically
significant were Escherichia/ Shigella, Shuttleworthia, Anaeroglobus and
Megasphaera. Escherichia were found more abundantly in preterm placentas while
Shuttleworthia, Anaeroglobus and Megasphaera were more abundant in term
placentas. Escherichia spp. are considered as both human commensals and
opportunistic pathogens and have been found in the urogenital and reproductive
tracts. Escherichia was reported as one of the principal members of the
placental microbiota and also in low abundance [6,11,13,14,17].

A study by Aagaard et al. also detected a higher abundance of Escherichia in the
placenta and proposed that the presence of Escherichia in meconium, which is
strongly associated with early onset neonatal sepsis, may be consistent with
intrauterine seeding with the placenta acting as a reservoir [11]. They also
suggest that the presence of oral bacteria in the amniotic cavity, such as
Fusobacterium nucleatum, may facilitate hematogenous transmission of other
bacteria during placentation by altering the mucosal permeability of the
vascular endothelium [11,60]. Anaeroglobus is a relatively newly classified
pathogen belonging to the family Veillonellaceae and is closely related to
Megasphaera [60]. Megasphaera and Shuttleworthia have been strongly associated
with bacterial vaginosis, which is linked to preterm birth [62-64]. The known
association with bacterial vaginosis suggests that the presence of these
bacteria within the placenta may arise because of ascending infection. However,
in this study Megasphaera and Shuttleworthia were found more abundantly in term
placentas from high-risk pregnancies which suggests their role may be associated
with other adverse outcomes, though this association should be investigated
further.

In bacterial vaginosis there is a shift in the Lactobacillus dominated
microbiota to overgrowth of Gram negative or anaerobic bacteria such as
Megasphaera spp., Gardnerella vaginalis, Atopobium spp., Prevotella, Ureaplasma
spp., or M. hominis. Megasphaera has previously been associated with an
increased risk of spontaneous preterm birth [63,65]. Lactobacillus are prevalent
in the intestinal and vaginal flora, which may suggest that their presence in
the placenta arises as a result of ascending transmission [15]. The ubiquitous
presence of Lactobacillus in the placentas of preterm and term birth in this
study may indicate that they are commensals, not associated with pathology of
preterm birth, and are not influenced by mode of delivery as they were present
in placentas delivered by caesarean section and therefore not exposed to vaginal
flora during delivery. An overall increased bacterial diversity, together with a
depletion of Lactobacillus, has been described as a universal marker for
diagnosis of bacterial vaginosis [66].

This study used primers that targeted the V3-V4 region to amplify the 16S rRNA
gene for next generation sequencing. Other studies have used V3–V4, as well as
16S regions V1–V3, and V6-8 [11,14,67]. Parnell et al. sequenced all nine
variable regions of the 16S rRNA gene and compared results according to total
amplification and amplification of negative controls [15]. They reported that
targeting the areas flanking the V4 region successfully amplified DNA in most
samples, had sufficient reads for analysis and the lowest incidence of reads in
negative controls compared to other variable regions. Studies of samples with
low microbial biomass, such as the placenta, are particularly vulnerable to
contamination, either ‘kitome’ or ‘splashome’, during processing and should
consider additional measures for absolute abundance of total 16S rRNA gene
copies, i.e., qPCR, to validate sequence analysis and avoid reporting false
positive signals [15,18,42]. This is a potential limitation of this study, as
qPCR was not performed for placental samples and the two methods of PCR
(conventional and next generation sequencing of 16S rRNA) already underscored
discrepancies in bacterial detection. This may lead to under-reporting of
bacterial species in studies where only the next generation sequencing of 16S
rRNA genes is used for detection.

The following limitations should be acknowledged; the term control group was
from high-risk pregnancies delivered at a tertiary hospital which specializes in
complicated cases, and so does not represent a true ‘healthy’ term pregnancy
cohort. The use of antibiotics was not routinely monitored by researchers, and
therefore antibiotic use may have influenced the microbiota present in the
placenta, although Prince et al. report minimal influence of antibiotics on the
placental microbiome in their study [6]. There was also a delay from sample
collection to the NGS experiment of approximately 11 months and at least two
freeze-thaw cycles before tissues were excised for DNA extraction, which may
have influenced the integrity of the samples, although stored in RNAlater at
-80°C. The concern of contamination is currently a hot topic in studies of
low-biomass samples [18]. Contamination may occur during the automated DNA
extraction process as well as during the sequence library build process if
samples are not randomized for sequencing at a minimum. However, the results of
this study show significant differences in alpha diversity between placentas
from preterm vs term birth and in association with chorioamnionitis and maternal
HIV status, as well as beta diversity with differentially expressed bacterial
genera. The total study cohort is also relatively homogenous with regards to
maternal race, therefore this factor could not be used for microbial diversity
analysis. It is known that racioethnicity influences the vaginal microbiome
during pregnancy [50] and as such, this study offers a unique cohort of
predominantly African women who are considered ‘high risk’ for preterm birth and
certainly adds to the body of knowledge on microbiomes of a specific cohort in
the South African context.

A South African study by Lennard et al. found that the vaginal microbiome varies
by geographical location between two cities within the same country [68]. They
suggested that clinical diagnostic and therapeutic approaches be tailored
accordingly as women from different cities but from similar low-income high
population density communities, and otherwise analogous demographics, had
significantly different vaginal microbiomes. This emphasizes the effect of
cohort selection on microbial diversity analysis where very few studies from low
to middle income countries have been reported.

CONCLUSION

This is the first South African study to characterize the bacterial diversity in
placentas from complicated pregnancies using next generation 16S rRNA gene
sequencing in conjunction with full placental histology. Microbial diversity
differs between preterm and term placentas where HIV may act as a cofactor
associated with decreased bacterial alpha diversity in placentas from preterm
birth. Characterizing the bacteria in the placenta has contributed to further
understanding of the pathology associated with preterm birth. This has the
potential to predict clinical pathology in the fetus, thereby reducing the risk
of preterm birth and its associated adverse outcomes.

FUNDING STATEMENT

The National Research Foundation (NRF) development grant provided funding
support for this study.

AUTHOR CONTRIBUTIONS

KES collected and analyzed samples. SP, JES, and EW were involved in sample
sequencing and data analysis. KES and SG drafted manuscript. SG edited the
manuscript. SG and CAW designed and supervised the study which was part of the
PhD thesis of KES, and all authors approved the final manuscript.

CONSENT TO PARTICIPATE

Written informed consent was obtained from patients prior to sample acquisition.

AVAILABILITY OF DATA AND MATERIAL

The datasets generated during and/or analyzed during the current study are
available from the corresponding author on reasonable request.

DECLARATION OF COMPETING INTEREST 

None.

ACKNOWLEDGMENTS

The authors acknowledge the NRF development grant for funding support and thank
the patients and staff for their participation at the tertiary hospital where
the study occurred. The opinions, findings, and conclusions or recommendations
expressed by the NRF supported research is that of the authors alone, and the
NRF accepts no liability whatsoever in this regard.


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