Abstract
Background
Multiple sclerosis (MS) is a neuroinflammatory disease in which
pregnancy leads to a temporary amelioration in disease activity as
indicated by the profound decrease in relapses rate during the 3rd
trimester of pregnancy. CD4^+ and CD8^+ T cells are implicated in MS
pathogenesis as being key regulators of inflammation and brain lesion
formation. Although Tcells are prime candidates for the
pregnancy-associated improvement of MS, the precise mechanisms are yet
unclear, and in particular, a deep characterization of the epigenetic
and transcriptomic events that occur in peripheral T cells during
pregnancy in MS is lacking.
Methods
Women with MS and healthy controls were longitudinally sampled before,
during (1st, 2nd and 3rd trimesters) and after pregnancy. DNA
methylation array and RNA sequencing were performed on paired CD4^+ and
CD8^+ T cells samples. Differential analysis and network-based
approaches were used to analyze the global dynamics of epigenetic and
transcriptomic changes.
Results
Both DNA methylation and RNA sequencing revealed a prominent
regulation, mostly peaking in the 3rd trimester and reversing
post-partum, thus mirroring the clinical course with improvement
followed by a worsening in disease activity. This rebound pattern was
found to represent a general adaptation of the maternal immune system,
with only minor differences between MS and controls. By using a
network-based approach, we highlighted several genes at the core of
this pregnancy-induced regulation, which were found to be enriched for
genes and pathways previously reported to be involved in MS. Moreover,
these pathways were enriched for in vitro stimulated genes and
pregnancy hormones targets.
Conclusion
This study represents, to our knowledge, the first in-depth
investigation of the methylation and expression changes in peripheral
CD4^+ and CD8^+ T cells during pregnancy in MS. Our findings indicate
that pregnancy induces profound changes in peripheral T cells, in both
MS and healthy controls, which are associated with the modulation of
inflammation and MS activity.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12974-023-02781-2.
Keywords: Multiple sclerosis, Pregnancy, CD4^+, CD8^+, T cells,
Methylation, Transcriptomics, Networks, Modules
Introduction
Multiple sclerosis (MS) is an autoimmune disease of the central nervous
system (CNS), to a large degree driven by T cell-mediated inflammation
[[53]1]. Despite recent advances in immunomodulatory treatments, many
people with MS continue to deteriorate. To identify biomarkers for
personalized treatment and to develop better treatment options, we need
a better understanding of the disease-promoting and -alleviating immune
mechanisms MS. Interestingly, women with MS (wwMS) show a marked
decrease in disease activity during pregnancy, while this improvement
is disrupted at delivery and followed by a transient worsening after
pregnancy [[54]2]. Thus, pregnancy and the period after pregnancy
(post-partum) provides a useful model to assess the dynamics of immune
regulation in MS. Of note, pregnancy is one of the most profound
suppressors of disease activity, with a 70–80% reduction in relapse
rate in the 3rd trimester [[55]2, [56]3], thereby exceeding the effects
of many currently available treatments. Indeed, pregnancy induces
profound and timely tuned immune adaptations in the maternal immune
system to ensure tolerance of the semi-foreign fetus [[57]4]. Thereby
these changes, which involve both innate and adaptive immune responses,
help to avoid inflammation-mediated pregnancy complications [[58]4,
[59]5]. The phenomenon of pregnancy-associated immunomodulation is
plausibly a result of immune-endocrine changes involving increase in
levels of pregnancy hormones, such as progesterone and estrogen, which
gradually rise and peak in the 3rd trimester of pregnancy [[60]6].
Notably, the period following delivery is associated with a temporary
worsening of disease activity, coinciding with a rapid decline in
pregnancy hormone levels [[61]7]. A better understanding of how the
modulation of the maternal immune system affects MS could provide
insights into central disease mechanisms as well as facilitate the
discovery of new treatment strategies.
T cells are key regulators of inflammation, and in accordance [[62]8],
they are central in the regulation of tolerance during pregnancy
[[63]9] as well as in the regulation of MS pathology [[64]1]. Extensive
evidence supports a crucial role for peripherally activated
autoreactive T cells in MS, where CD4^+ and CD8^+ T cells are
implicated in both initiating and propagating the disease [[65]10,
[66]11]. Also, we recently observed that pregnancy can affect
autoimmune disease-associated methylation changes in T cells [[67]12],
which further supports a pregnancy-specific modulation of T cells of
relevance for MS. Several studies have previously shown the involvement
of T cells in MS during pregnancy [[68]13–[69]18], which has shed light
on the pregnancy-associated modulation, although the precise effects of
pregnancy on CD4^+ and CD8^+ T cells remain relatively unknown.
Further, it is unclear if the changes induced during pregnancy are
MS-specific or a general consequence of pregnancy.
High-throughput approaches are well-suited tools to characterize the
molecular events associated with the dynamic immune changes occurring
during pregnancy. Analysis of pregnancy-induced transcriptomic [[70]19,
[71]20] and epigenetic changes [[72]21] in the 3rd trimester compared
to post-partum (PP) in wwMS and healthy controls (HC), has led to the
identification of molecular signatures of potential immune-regulatory
markers. However, no studies have specifically utilized omics
approaches in CD4^+ and CD8^+ T cells to study dynamic changes during
pregnancy. In particular, combining mRNA expression profiling of T
cells, which provides a snapshot of the current state of the cells,
together with the epigenetic regulation could provide a more
comprehensive view of the underlying molecular events of relevance for
the T cell modulation in MS during pregnancy.
In the present study, in-depth characterization using genome-wide
methylation status and mRNA expression of paired samples from
circulating CD4^+ and CD8^+ T cells, longitudinally collected
throughout and after pregnancy, showed that pregnancy induced large
epigenetic and transcriptomic changes in both wwMS and HC.
Interestingly, the most prominent changes were observed in the 3rd
trimester, which rebounded post-partum, thus mirroring the effect of
pregnancy on the disease activity in MS. Using a network-based approach
to capture the most highly interconnected genes, we identified CD4^+
and CD8^+ rebound pregnancy modules that were overlapping between the
two omics and disclosed central genes and pathways involved in T cell
regulation and differentiation. Our findings highlight a systemic
pregnancy-induced modulation of T cells, coinciding with the temporary
improvement and worsening of MS, which could be associated with the
modulation of inflammation and MS disease activity. The enrichment of
MS-associated genes and MS-relevant pathways further supports the
importance of T cell regulation in MS and during pregnancy.
Results
Pregnancy induces genome-wide epigenetic and transcriptomic changes
In this explorative study, wwMS and HC were longitudinally blood
sampled during and after pregnancy to capture the dynamic effects of
pregnancy on MS (Table [73]1, Figs. [74]1, [75]2). An overview and the
general design of the study is presented in Fig. [76]1 and Additional
file [77]1: Figs. S1, S2. Resting and in vitro activated isolated CD4^+
and CD8^+ T cells were analyzed by RNA sequencing (RNA-seq) and DNA
methylation array (only resting cells) to delineate the transcriptomic
and epigenetic changes underlying the disease modulation that occurs
during pregnancy (see Additional file [78]1: Fig. S3 and Additional
file [79]2: Table S1 for a detailed description of the samples). To get
an initial global understanding of the changes induced by pregnancy, we
investigated the changes across all genes (CD4^+ n = 13,440 genes;
CD8^+ n = 13,448 genes) and CpGs (n = 740,552 for both cell types). The
changes occurring throughout pregnancy correlated well in MS and HC
(Pearson’s r 0.48–0.77, all correlations had p < 2.2 × 10^–16), at both
expression and methylation level (Additional file [80]1: Fig. S4). We
thus concluded that pregnancy induces consistent genome-wide changes in
both groups. Conversely, a significant negative correlation was noted
between the changes induced during pregnancy and after pregnancy, which
was generally more pronounced around the 3rd trimester, as compared to
the 2nd (Fig. [81]3A, B and Additional file [82]1: Fig. S5). This
pattern was further investigated by differential analysis. However, the
number of features (i.e., genes or CpGs) with a false discovery rate
(FDR)-corrected p-value ≤ 0.05 varied drastically depending on cell
type, disease and omic (Additional file [83]3: Table S2, Additional
file [84]4: Table S3, Additional file [85]5: Table S5). Therefore, to
investigate the effect of pregnancy on all groups in a consistent
manner, the less stringent requirement of nominal statistical
significance (p ≤ 0.05) was chosen as criterion to identify
differentially expressed genes (DEGs) and differentially methylated
probes (DMPs). During pregnancy, the highest number of DEGs/DMPs was
generally observed in the 3rd trimester (3rd-1st, Additional file
[86]1: Fig. S6). Further, these changes generally showed the highest
overlap with the DEGs/DMPs post-partum (PP-3rd; Additional file [87]1:
Fig. S6), again highlighting the 3rd trimester as a central time point
for maximal regulation during pregnancy. Taken together, we observed
the most marked changes during the 3rd trimester and post-partum, in
agreement with the effect of pregnancy on the disease activity in MS
[[88]2]. We therefore decided to focus the subsequent analyses on these
two timelines, i.e., changes in 1st versus 3rd trimesters and 3rd
trimester versus post-partum.
Table 1.
Study cohort characteristics
MS (n = 11) HC (n = 7)
Samples
Pre-pregnancy, n 6 N/A
1st trimester, n; gw median (range) 11; 10.0 (8.0–13.0) 7; 11.9
(10.7–12.0)
2nd trimester, n; gw median (range) 11; 25.0 (23.1–26.3) 7; 25.0
(24.6–26.3)
3rd trimester, n; gw median (range) 11; 35.0 (33.0–36.1) 7; 35.0
(33.7–35.3)
Post-partum, n; week median (range) 11; 6.0 (3.7–7.0) 7; 6.9 (4.3–12.9)
Subject characteristics
Age (years), mean ± SD 31.8 ± 2.1 27.7 ± 3.4
BMI, mean ± SD 23.9 ± 3.8 22.7 ± 3.1
Current pregnancy
Gw at delivery, mean ± SD 40.1 ± 0.9 39.3 ± 1.2
Fetal sex 5 males/ 6 females 3 males/ 4 females
Mode of delivery
Vaginal delivery, n 10 7
Cesarean section, n 1 0
Pregnancy history (mean ± SD)
Parity 0.6 ± 0.8 0.4 ± 0.8
Previous miscarriages 0.3 ± 0.9 0.1 ± 0.4
Previous live births 0.6 ± 0.8 0.4 ± 0.8
Disease parameters
Disease duration, mean ± SD 7.4 ± 6.2 N/A
Disease severity
EDSS at inclusion, median (range) 0.5 (0–2.5)* N/A
EDSS post-partum, median (range) 0.5 (0–2.0) + N/A
Treatment
Treatment washout (weeks)
From pre-P sampling, median (range) 8.0 (0–20.9) N/A
From 1st trimester sampling, median (range) 14.9 (2.0–48.3) N/A
[89]Open in a new tab
BMI body mass index kg/m^2, EDSS Expanded Disability Status Score, gw
gestational week, HC healthy controls, MS multiple sclerosis, SD
standard deviation
*Data on EDSS were missing for 1 individual, + data on EDSS were
missing for 5 individuals
Fig. 1.
[90]Fig. 1
[91]Open in a new tab
Overview of the study. CD4^+ and CD8^+ T cells were isolated from women
with MS and healthy controls (HC) that were longitudinally sampled
throughout and after pregnancy (1st, 2nd, 3rd trimester and
post-partum). For MS, samples were also included before pregnancy. RNA
and DNA were extracted from resting and in vitro activated cells.
RNA-sequencing was performed to investigate transcriptomic changes and
genome-wide profiling of DNA methylation was performed using the
Illumina Infinium DNA Methylation EPIC Array. Differentially expressed
genes (DEGs) and differentially methylated genes (DMGs) were used to
construct modules using the module inference tool DIAMOnD. BP before
pregnancy, HC healthy controls, MS multiple sclerosis, PP post-partum
Fig. 2.
[92]Fig. 2
[93]Open in a new tab
Study cohort characteristics. Schematic overview of the pregnant women
with MS (n=11) and healthy controls (HC, n=7) included in the study.
Shown in the figure as black circles is the sampling point (in
gestational weeks) during the 1st, 2nd, and 3rd trimesters of
pregnancy, and the sampling point post-partum or before pregnancy (in
weeks, the latter available only for some of the women with MS). The
gestational week of delivery (white rhombus), and, for the women with
MS, the time since the latest treatment (grey line) and the type of
treatment are also depicted. Women with MS were recruited at Karolinska
University Hospital, Stockholm, Sweden (n=7) and Linköping University
Hospital, Linköping, Sweden (n=4). All healthy pregnant controls (n=7)
were recruited at Kalmar County Hospital, Kalmar, Sweden. DMF; dimethyl
fumarate (n=1), GA; glatiramer acetate (n=2), IFNbeta-1a; interferon
beta-1a (n=2), NTZ; natalizumab (n=1), PON; ponesimod (n=1), RTX;
rituximab (n=3), untreated (n=1)
Fig. 3.
[94]Fig. 3
[95]Open in a new tab
Pregnancy induces genome-wide epigenetic and transcriptomic changes in
CD4^+ and CD8^+ T cells. DNA and RNA extracted from CD4^+ and CD8^+ T
cells from women with MS and healthy controls (HC) were analyzed during
pregnancy by RNA-seq and Illumina Infinium DNA Methylation EPIC Array
for DNA methylation. The Pearson correlation coefficients between gene
counts (for RNA-seq) and the beta values of all detected CpGs (for
methylation) for the comparisons 3rd-1st trimester and PP-3rd trimester
for resting A CD4^+ cells and B CD8^+ cells in women with MS (upper
panel) and HC (lower panel) are shown. Pearson’s correlation r is shown
in the individual graphs for each comparison. All correlations had a
p < 2.2 x 10^−16. HC healthy controls, MS multiple sclerosis, PP
post-partum
The changes in CD4^+ and CD8^+ T cells during pregnancy rebound post-partum
In both MS and HC, pregnancy was dominated by hypermethylation in both
CD4^+ and CD8^+ T cells, whereas post-partum was characterized by
hypomethylation (Fig. [96]4A, B). On the other hand, at the
transcriptomic level, there was a more equal distribution of up- and
down-regulated genes (Fig. [97]4A, B). In line with the genome-wide
negative correlations between the changes induced during and after
pregnancy, differential analysis confirmed that many of the changes
observed in the 3rd trimester were reversed post-partum (Fig. [98]4A,
B). Indeed, there was a significant overlap between the DEGs/DMPs in
the 3rd trimester and post-partum (Additional file [99]1: Fig. S7).
Strikingly, for all these overlapping DEGs/DMPs, expression and
methylation changed direction post-partum, i.e., upregulated genes
(respectively, hypomethylated CpGs) in the 3rd trimester became
downregulated (respectively, hypermethylated) after pregnancy and,
similarly, downregulated genes (hypermethylated CpGs) became
upregulated (hypomethylated); these DEGs/DMPs will be hereon referred
to as rebound DEGs/DMPs. The presence of a rebound in DNA methylation
and gene expression was further confirmed by considering the samples
collected before pregnancy (BP; only available for wwMS) as the
baseline. Indeed, differential analysis identified few DMPs and DEGs
between BP and PP, relative to the comparisons between BP and the 1st,
2nd and 3rd trimesters (Additional file [100]1: Fig. S8). However, it
should be noted that for most of the probes (> 97%) and genes (> 90%)
the measurements at post-partum did not reach the original levels
before pregnancy (Additional file [101]1: Fig. S8), indicating that the
observed rebound is not complete (i.e., the values at post-partum are
not equal to those before pregnancy).
Fig. 4.
[102]Fig. 4
[103]Open in a new tab
Differential analysis in CD4^+ and CD8^+ T cells reveals a post-partum
rebound. Alluvial plots for A CD4^+ and B CD8^+ cells in both pregnant
women with MS and pregnant healthy controls (HC) showing all CpGs
(methylation) and all genes (RNA-seq) that were either differentially
expressed (nominal p≤0.05) or differentially methylated (nominal p≤0.05
and |Δβ|>0.05) during (3rd-1st) or after pregnancy (PP-3rd). The genes
and CpGs are grouped based on their direction during and after
pregnancy (red=hypomethylation or upregulated, blue= hypermethylation
or downregulated). The number of genes and CpGs in each corresponding
region are depicted in the figure. HC healthy controls, MS multiple
sclerosis
Pregnancy induces similar changes irrespective of disease or not
Given that the temporary improvement of disease activity is most
pronounced during the 3rd trimester, we hypothesized that the
identified rebound genes and CpGs could be involved in mediating these
pregnancy-induced effects. Interestingly, a significant overlap was
found between the rebound DEGs and DMPs in MS and HC (CD4^+ OR = 7.9
(methylation), 43.0 (RNA-seq) and p = 0.03, < 2.2 × 10^–16; CD8^+
OR = 15.6, 22.9 and p < 2.2 × 10^–16, 2 × 10^–10). Moreover, by
analyzing the directionality in the rebound DEGs/DMPs, we observed that
most of them changed in the same direction in both MS and HC
(Fig. [104]5A, B). This was particularly evident for the CD8^+ DMPs,
where > 90% showed the same pattern in both MS and HC (Fig. [105]5B).
Further, not only did MS and HC resemble each other when considering
DNA methylation and gene expression separately, but also the regulatory
patterns between the two omics were highly similar. More precisely, the
regulatory role of each CpG was measured as the Spearman correlation
coefficient with the corresponding gene and a significant overlap was
observed between CpG–gene pairs characterized by similar correlations
in MS and HC, for both CD4^+ and CD8^+ T cells (Additional file [106]1:
Fig. S9).
Fig. 5.
[107]Fig. 5
[108]Open in a new tab
Pregnancy induces similar changes irrespective of disease. Comparison
of the differentially methylated CpGs and differentially expressed
genes during (3rd-1st) and after (PP-3rd) pregnancy in women with MS
and healthy controls (HC) in A CD4^+ and B CD8^+. The axes indicate the
change of methylation and expression during and after pregnancy as
measured by the log[2] of the limma coefficients β[d,3rd] - β[d,1st]
and β[d,PP] - β[d,3rd], respectively, where the disease group d (MS or
HC) is specified in the axis labels. The percentages of the dots in
each area are shown. The colors are based on the density of the dots
where darker color represents a higher density. HC healthy controls, MS
multiple sclerosis
Since the changes of most rebound DEGs/DMPs shared the same
directionality in MS and HC, ranging from 60 to 98% in overlap, we
continued our analysis on these genes/probes, hereon referred to as
shared rebound DEGs/DMPs, comprising a total of 160 and 114 DEGs for
CD4^+ and CD8^+, respectively, and 126 and 2161 DMPs. In particular,
the shared rebound DMPs were associated to 80 and 551 genes in CD4^+
and CD8^+, respectively, which we denoted as shared rebound DMGs.
The implication of the shared rebound DEGs/DMGs in MS was investigated
by considering a list of MS-associated genes obtained by combining the
database DisGeNET [[109]22] and the latest GWAS in MS [[110]23]. Of
note, the rebound DEGs/DMGs that were MS-specific, i.e., only rebounded
in MS, were not significantly enriched in MS-associated genes (data not
shown), whereas the shared rebound DEGs/DMGs were significantly
enriched (OR = 1.6–2.5, p ≤ 0.05 as determined by Fisher’s exact test,
Additional file [111]1: Fig. S10), highlighting that the changes
relevant for MS are affected and modulated by pregnancy itself, and not
specific to the disease. Interestingly, the shared rebound DEGs/DMGs
were also significantly enriched for genes affected during our in vitro
T cell activation (CD4^+ OR = 3.2, p = 2.1 × 10^–13, CD8^+ OR = 2.5,
p = 6.7 × 10^–6), suggesting their potential importance in T cell
regulation (Additional file [112]1: Fig. S10). Surprisingly, even
though for example the JAK–STAT signaling pathway was significantly
enriched in CD4^+ T cells in both omics (Additional file [113]1: Fig.
S10), there was little to no overlap between the DEGs and DMGs in
either CD4^+ or CD8^+ T cells; more precisely, the overlap was only 2
out of 106/98 (DEGs/DMGs) in CD4^+ HC, 48 out of 158/8,850 in CD8^+ HC,
0 out of 84/45 in CD4^+ MS, and 0 out of 49/258 in CD8^+ MS.
Using a network-based modular approach reveals common immune-mediated
multi-omics processes during pregnancy
The observation that some pathways appeared in both cell types (CD4^+
and CD8^+ T cells) and omics (methylation and transcription) suggested
that the shared rebound DEGs/DMGs, despite the modest overlap between
the actual genes, could be involved in similar biological processes. To
further explore this possibility, we employed a network-based modular
approach to identify more functionally related genes. In particular, we
applied DIAMOnD [[114]24] from the module inference package MODifieR
[[115]25] on the shared rebound DEGs/DMGs. DIAMOnD is based on the
observation that genes involved in complex diseases are characterized
by a high level of connectivity in the protein–protein interaction
(PPI) network, compared to random proteins, thereby forming modules
consisting of highly interconnected genes [[116]24]. Using DIAMOnD, we
identified four different pregnancy modules, i.e., one per cell type
and omic, ranging from 259 to 590 genes per module (Additional file
[117]7: Table S6). The pregnancy modules showed an overall enrichment
for immune-related pathways associated with T cell signaling and
differentiation, such as T cell receptor signaling and JAK–STAT
signaling (Fig. [118]6A, Additional file [119]8: Table S7), emphasizing
T cell regulation and differentiation as central processes that are
affected during pregnancy. Furthermore, the pregnancy modules were all
highly enriched in MS-associated genes and genes associated with our
experimentally induced T cell activation (Fig. [120]6B, C). We indeed
found a significant overlap between the shared rebound methylation and
RNA-seq modules for both cell types (CD4^+ OR = 42.7, CD8^+ OR = 40.9,
p < 2.2 × 10^–16), highlighting a functional relationship between the
shared rebound DEGs and DMGs. This resulted in 74 common genes for
CD4^+ and 118 common genes for CD8^+ (Additional file [121]7: Table
S6), hereon referred to as CD4^+ and CD8^+ rebound pregnancy modules,
respectively (Fig. [122]7A, B). These module genes were also enriched
for pathways related to T cell differentiation and signaling
(Fig. [123]6A) and were even more highly enriched for MS-associated
genes as compared to the separate pregnancy modules (CD4^+ OR = 19.6,
p < 2.2 × 10^–16; CD8^+ OR = 11.7, p < 2.2 × 10^–16; Fig. [124]6B). To
further validate the biological relevance of the identified modules, we
used the data from a previous published independent study, where we
identified a set of 1992 genes that were significantly affected by
progesterone (P4) in CD4^+ T cells [[125]26]. P4 is one of the major
pregnancy hormones, with pronounced anti-inflammatory properties, whose
levels during and after gestation coincide with the temporary
improvement and worsening of disease activity in MS. It has therefore
been suggested to be one of the main drivers of the pregnancy-induced
modulation of MS [[126]6]. Indeed, we found that the rebound genes, and
even more markedly the rebound pregnancy modules, were significantly
enriched for these P4-associated genes (CD4^+ OR = 3.3, p < 5 × 10^–4;
CD8^+ OR = 3.2, p < 7 × 10^–4; Additional file [127]1: Fig. S11). In
summary, by using a network-based approach we identified a set of genes
that were shared across both omics (and cell types) and reflected the
dynamics of disease-associated changes as well as central T
cell-related processes.
Fig. 6.
[128]Fig. 6
[129]Open in a new tab
A network-based modular approach reveals common immune-mediated
multi-omics processes during pregnancy. Overlapping differentially
expressed genes (DEGs) and differentially methylated probes (DMPs)
between the 3rd trimester and post-partum were characterized by changes
in expression and methylation of opposite direction in these two time
points in both women with MS and healthy controls (HC) and were thus
termed shared rebound DEGs/DMPs. Modules were inferred from these
shared rebound DEGs/DMPs using DIAMOnD and the PPI network (threshold
>700). A KEGG pathway analysis of the module genes derived from
RNA-seq, methylation data or the overlap between the modules derived
from the two omics. Shown are the top 5 pathways, in terms of adjusted
p-value, for at least one of the two groups (CD4^+ and CD8^+). All the
pathways have an adjusted p-value ≤ 0.05; n is the total number of
genes contained in the pathways of each column and gene ratio indicates
the number of genes in a pathway divided by the total number of module
genes. B Enrichment of the module genes for MS-associated genes
retrieved from GWAS-derived MS genes and DisGeNET (all comparisons had
p < 2.2e−16) in transcriptomics and methylomics of CD4^+ and CD8^+ T
cells. The number of overlapping genes is shown above each bar. C The
overlap between the genes derived from shared genes derived from the
RNA-seq and methylation modules, termed rebound module, for each cell
type, respectively, and the genes associated to the activated cells.
Enrichment and overlaps were computed using Fisher’s exact test, p ≤
0.05 was considered statistically significant.
Fig. 7.
[130]Fig. 7
[131]Open in a new tab
CD4^+ and CD8^+ rebound pregnancy modules. A, B Graphical illustration
of the rebound pregnancy modules for CD4^+ and CD8^+ T cells,
respectively. The rebound pregnancy modules were derived by overlapping
the module genes from the RNA-seq and methylation module from each cell
type. The module inference method DIAMOnD was used to construct the
original modules for each omic and cell type separately. Nodes
represent genes and the connecting edges show the protein–protein
interactions. The networks were created based on the protein–protein
interaction networks from STRINGdb (threshold > 950). Functional
clustering of the genes was performed by KEGG pathway analysis. For
illustrative purposes, each gene was assigned to only one pathway. Some
genes were annotated to several similar pathways and the complete list
of pathways, and their annotated genes can be found in Additional file
[132]8: Table S7. MS multiple sclerosis
Discussion
Despite the evidence that T cells play a central role in MS [[133]10],
few studies have investigated how these cells are affected by the
course of pregnancy, a potent suppressor of inflammation and disease
activity. Using longitudinally collected blood samples during and after
pregnancy, we examined the genome-wide DNA methylation and gene
expression patterns in peripheral CD4^+ and CD8^+ T cells from wwMS and
HC. Pregnancy induced prominent dynamic changes in both methylation and
gene expression patterns, converging during the 3rd trimester, where
most changes were common between MS and HC. The majority of
pregnancy-induced changes rebounded post-partum, in accordance with the
pregnancy-induced modulation of disease activity. By using a
network-based modular approach, we identified pregnancy modules based
on the rebound genes and their most highly interconnected genes. These
modules were significantly enriched in MS-associated genes and
disclosed central genes and pathways involved in T cell regulation such
as JAK–STAT and T cell receptor signaling. Interestingly, the modules
overlapped significantly between the omics for each cell type,
respectively, highlighting changes that are regulated both at
methylation and expression level.
Studies on systemic effects induced by pregnancy emphasize that the
changes from pre-pregnancy to the 3rd trimester are reversed
post-partum [[134]13, [135]19, [136]27, [137]28], coinciding with the
temporary improvement during the 3rd trimester and worsening of the
disease post-partum [[138]2]. The notion of the 3rd trimester
representing a pivotal time point in immune regulation during pregnancy
is further emphasized by the increased risk of certain infections at
this time point [[139]29]. In accordance, we found that the 3rd
trimester and the post-partum period were characterized by the largest
changes in the differential analysis, mostly revealing that the induced
changes during the 3rd trimester were reversed post-partum. In
particular, we found that the progression of pregnancy was associated
with an increasingly hypermethylated state, in agreement with previous
findings [[140]12]. On the other hand, we noticed a more equal
distribution of up- and downregulated genes at the transcriptomic
level, which could partly be explained by the dual demands on the
maternal immune system during pregnancy, preserving effective immunity
while maintaining fetal tolerance [[141]4].
Although T cells are central in driving inflammatory responses in MS,
previous studies on pregnancy have reported diverging results on how T
cells are modulated during this time of transient tolerance, which
might reflect differences in study design and methodology [[142]14,
[143]15, [144]21]. We found dynamic alterations in the epigenetic and
transcriptomic profiles of both CD4^+ and CD8^+ T cells during the
course of pregnancy, alterations that were subsequently reversed
post-partum. Importantly, we found that the observed changes rather
represent a general adaption of the maternal immune system during
pregnancy, where only a small number of genes were influenced
specifically in the presence of MS. Strikingly, the small number of
genes that were altered in MS but not in HC were not enriched in
MS-associated genes. These small differences are in agreement with
other studies that have investigated changes in the immune system
during pregnancy in MS [[145]13, [146]14, [147]28], which is also in
line with similar observations in RA [[148]30] where pregnancy was
shown to have a stronger impact than the presence of disease. Taken
together, our results suggest that the decrease in inflammatory
activity in MS observed during pregnancy is likely due to the general
immune adaptation that takes place during this time, thus not an
MS-specific phenomenon but rather a bonus effect of general
pregnancy-induced changes.
T cell responses have generally been suggested to shift towards a more
anti-inflammatory status during pregnancy to protect the semi-allogenic
fetus, which could explain the temporary modulation of T cell-mediated
diseases, although the precise mechanisms remain unclear [[149]31,
[150]32]. Single-cell transcriptomic analysis of peripheral blood
mononuclear cells (PBMCs) throughout healthy pregnancy showed that
pathways related to T cell activation and regulation were dampened
during pregnancy [[151]33], highlighting the need to suppress
potentially alloreactive T cells. Our modular approach revealed central
genes and pathways involved in T cell regulation and differentiation
such as JAK–STAT signaling, T cell receptor signaling and Th17 cell
differentiation, which were shared across both omics. We found that
pregnancy affected many MS-associated genes, including for example
CXCL10, IL17A, STAT3 and the T cell activation marker CD69.
Simultaneously, the rebound modules were also significantly enriched in
genes associated with our in vitro T cell activation, which is
consistent with previous studies showing that MS is associated with a
dysregulation in response to activation in CD4^+ T cells [[152]34,
[153]35]. Taken together, pregnancy seemingly influences genes and
pathways that are highly relevant in driving inflammatory processes in
MS, which could play a role in the pregnancy-induced modulation of MS
by potentially affecting autoreactive T cells [[154]13].
The pregnancy hormones P4 and estrogen have been considered main
drivers behind the modulation of the maternal immune system during
pregnancy [[155]6]. The changes in hormonal concentrations during and
after pregnancy, coinciding with the temporary improvement and
worsening of MS, highlight a potential role of sex hormones in the
pregnancy-induced alterations of MS. We and others have previously
shown that P4 significantly dampens T cell activation [[156]26,
[157]36–[158]39], whereas estrogens seem to exert both
immune-activating and immune-dampening effects [[159]37, [160]40,
[161]41]. Indeed, estrogen treatment in humans has not shown convincing
results [[162]6, [163]42] while clinical trials investigating
progesterone/progestins are largely missing. Interestingly, we found
that the pregnancy modules were significantly enriched for genes known
to be affected by P4 [[164]26], indicating important circuits of
interactions between P4, T cells and inflammatory activity in MS.
Further studies looking into the potential effect of P4 and progestins
as add-on treatment in MS are highly needed.
While the relatively small sample size of our study is a potential
limitation, our longitudinal study design with multiple samples taken
from the same individual helps to reduce the variance in the data,
thereby increasing the signal-to-noise ratio. Still, the huge number of
genes (> 13,000) and methylation sites (850,000 CpGs) could explain the
lack of probes and genes surviving FDR correction in the initial
analyses. It was thus important to find ways to strengthen the
biological signal and limit the number of false positives. Therefore,
we applied a twofold analysis approach. Firstly, we focused solely on
nominally significant changes that were corroborated by multiple time
points. The fact that these DEGs and DMPs showed a high degree of
similarity in MS and HC, both in terms of overlap and direction, was
taken as an indication that they represented a robust set of core
changes related to pregnancy. Secondly, module inference was used to
enhance the signal of the input genes and aid our understanding of
multi-omics data. In fact, although there was little overlap at the
gene level between the omics, the original DEGs and DMGs pointed to
similar pathways. By using our network-based approach, we were able to
identify core sets of genes that were shown to significantly overlap
between RNA-seq and methylation data.
Another potential limitation is that we have only investigated changes
related to CD4 and CD8 T cells. T cells are implicated as main drivers
in MS [[165]10, [166]23], supported both by animal studies and
genome-wide association studies, and are highly relevant in the immune
regulation during pregnancy [[167]9]. However, there are other cell
types, such as B cells and monocytes, involved in the MS pathogenesis
that would be highly relevant to study in the context of
disease-modulation during pregnancy that should be considered in future
studies.
This study represents, to our knowledge, the first global methylation
and expression analysis that evaluates the temporal changes induced in
peripheral CD4^+ and CD8^+ T cells during pregnancy in MS. Our findings
emphasize pregnancy as a potent modulator of central immune cells in
the MS pathogenesis and underscore the need for further studies
investigating treatment strategies that can mimic the pregnancy milieu.
Materials and methods
An overview of the study is shown in Fig. [168]1 and Additional file
[169]1: Figs. S1 and S2.
Study population
The present study utilized blood samples from 11 pregnant wwMS and 7
pregnant HC, who were enrolled in the Pregnancy-MS study, a prospective
longitudinal cohort study performed at four centers in Sweden. In the
study, women with relapsing–remitting MS planning to get pregnant
(before pregnancy (BP)) or wwMS and HC in the first trimester of
pregnancy were included and followed longitudinally during pregnancy
(1st, 2nd, and 3rd trimesters) and 6 weeks post-partum. Blood samples
were collected from a total of 54 wwMS and 30 HC with singleton
pregnancies. wwMS were recruited at Karolinska University Hospital,
Solna, Linköping University Hospital, Linköping, and Ryhov County
Hospital, Jönköping, and controls were recruited at Region Kalmar
County, Kalmar. All sites followed an identical sampling procedure.
Eligible for the study were women of Caucasian ethnicity, aged
18–45 years. Women with immune-associated or other severe diseases (in
addition to MS in the MS group), as well as women pregnant after/by
assisted reproductive technique/in vitro fertilization (IVF) or a
history of previous obstetric complications were not considered for the
study. The study was performed in accordance with the Helsinki
Declaration’s ethical principles for medical research and was approved
by the Regional ethical review board in Linköping (2012/402-31). All
participants signed informed consent.
Out of the 54 wwMS enrolled in the study, three failed to get pregnant,
one had a miscarriage in gestational week (gw) 13, one had an elective
abortion in gw 14, and 11 dropped out of the study for personal reasons
after the first sampling occasion. From the remaining 38 wwMS, the
selection of the 11 who were finally included in the present study, was
based on the availability of complete sample sets, the treatment
washout period before pregnancy (the ones with the longest washout were
prioritized), and the absence of relapses and/or MS-related treatments
during pregnancy. Nevertheless, 4 of the included women experienced a
relapse post-partum and in addition, there was one case of
hypothyroidism. Out of the 30 HC recruited in the Pregnancy-MS study,
one woman had a miscarriage in gw 12, ten dropped out of the study
after the initial sampling, and eight had one or more missing samples
during pregnancy and/or post-partum resulting in 18 women being
eligible to be included in the present study. From them, the seven
women were randomly chosen among the ones with no pregnancy
complications, however there was one case of depression (not
pharmacologically treated). There was a significant difference in the
age between the groups (MS: (mean ± SD) 31.8 ± 2.1, HC: 27.7 ± 3.4, p:
0.0054, unpaired t test). No difference was noted for body mass index
(BMI). Further, there were no differences in fetal sex, mode of
delivery, previous miscarriages and previous live births between MS and
HC (determined by Fisher’s exact test). The sampling was performed in
the 1st trimester (for MS: gestational week (gw) median (range) 10.0
(8.0–13.0), HC: gw 11.9 (10.7–12.0)), in the 2nd trimester (MS: gw 25.0
(23.1–26.3), HC: gw 25.0 (24.6–26.3)), in the 3rd trimester (MS: gw
35.0 (33.0–36.1), HC: gw 35.0 (33.7–35.3)), and after delivery (MS:
week 6.0 (3.7–7.0), HC: week 6.9 (4.3–12.9)). The sampling time
differed significantly between MS and HC for the 1st trimester (p:
0.0046, unpaired t test). The characteristics of the cohort are shown
in Table [170]1 and Fig. [171]2.
Isolation of peripheral blood mononuclear cells
Venous blood was collected in Vacutainer® CPT™ with sodium citrate (BD
Bioscience, Franklin Lakes, NJ, USA) and centrifuged for 15 min,
1500 × g at room temperature (RT). After removal of the plasma, the
cell suspension was washed twice in Dulbecco’s phosphate-buffered
saline (DPBS; Thermo Fisher Scientific, Waltham, MA, USA). The cells
were resuspended in 20% dimethyl sulfoxide (Sigma-Aldrich, Saint
Louise, MO, USA) with 80% heat-inactivated fetal bovine serum (FBS,
Sigma-Aldrich) and placed in a CoolCell® (Corning®; Corning, NY, USA)
for at least 4 h before being transferred and stored in liquid nitrogen
until further use.
Positive selection of CD4^+ and CD8^+ T cells
The PBMCs were transferred from liquid nitrogen and thawed in a 37 °C
water bath and washed twice for 10 min, 400 × g in RT in pre-heated (at
37 °C) Iscove’s modified Dulbecco’s medium (IMDM; Invitrogen, Carlsbad,
CA, USA) supplemented with l-glutamine (292 mg/mL; Sigma-Aldrich), MEM
non-essential amino acids 100X (10 ml/L; Gibco®, Thermo Fisher
Scientific), penicillin–streptomycin (5000 U/mL; Lonza™ BioWhittaker™,
Thermo Fisher Scientific), sodium bicarbonate (3.024 g/L,
Sigma-Aldrich) and 10% FBS (HyClone™; Thermo Fisher Scientific). The
cells were resuspended in Hank’s Balanced Salt Solution (HBSS; Thermo
Fisher Scientific) and filtered through a pre-separation filter (30 µm;
Miltenyi Bio tec, Bergisch Gladbach, North Rhine-Westphalia,
Germany).
The cells were counted in a Bürker chamber (Hecht Assistant®, Sondheim
vor der Rhön, Germany) and the viability was assessed by Trypan Blue
(Thermo Fisher Scientific) showing an average viability of 86%.
CD8^+ and CD4^+ T cells were separated by immunomagnetic positive
selection according to the instructions provided by the manufacturer
using MS columns and miniMACS separators (Miltenyi Biotec). The CD8^+
cells were isolated first and the CD4^+ cells were subsequently
isolated from the CD8^− fraction. A small portion of the cells was
processed for flow cytometry. The remainder of cells were either (1)
resuspended in RLT Plus Buffer (Qiagen, Hilden, Germany) using syringe
and needle to lyse and homogenize the cells and transferred to − 70 °C
until RNA/DNA extraction or (2) processed for in vitro stimulation (see
below). The average viability before culture was 87% for CD4^+ and 88%
for CD8^+ cells and the purity was > 88% in CD4^+ (mean 96%) and > 86%
(mean 94%) in CD8^+, based on the flow cytometry analysis (for gating
strategy see Additional file [172]1: Figs. S12 and S13).
In vitro stimulation
Twenty-four well plates (Corning) were pre-coated overnight at 4 °C
with 0.25 µg/mL anti-CD3 (low endotoxin clone UCHT1) and anti-CD28 (low
endotoxin clone YTH913.12) antibodies (both from Bio-Rad AbD Serotec
Limited, Hercules, CA, USA). The plates were washed three times in PBS
(Medicago, Uppsala, Sweden). The CD8^+ and CD4^+ T cells were cultured
in IMDM + 5%FBS (Thermo Fisher Scientific) for 24 h at 37 °C and 5%
CO[2] at a density of 2 million cells/mL. After culture, a proportion
of the cells were processed for flow cytometry and the rest were
homogenized and lysed in RLT Plus Buffer (Qiagen) and stored at − 70 °C
until extraction. The proportion of live cells (evaluated by LIVE/DEAD™
Fixable Aqua Dead Cell Stain, see details below) was > 68% for the
activated CD4^+ cells and > 72% for the activated CD8^+ cells. The
average expression of CD69 after activation in all samples (as
determined by flow cytometry) was for CD4^+ MS: 5.4% (standard
deviation ± 1.8) and HC: 9.5% (± 4.6); CD8^+ MS: 15.4% (± 7.2) and HC:
25.6% (± 6.5) (Additional file [173]1: Fig. S14).
Flow cytometry
Before and after the in vitro stimulation, the cells were analyzed by
flow cytometry. The CD8^+ and CD4^+ T cells were resuspended in
LIVE/DEAD™ Fixable Aqua Dead Cell Stain (Invitrogen), diluted according
to the instructions provided by the company, and labeled with mouse
anti-human CD3-PE (UCHT-1), CD4-PeCy7 (SK3) or CD8-PeCy7 (SK1),
CD45RA-V450 (HI100) and CD69-APCCy7 (FN50; all purchased from BD
Biosciences, San Jose, CA, USA) for 15 min in the dark at RT. The cells
were resuspended in PBS + 0.1% FBS prior to analysis. Ten thousand
cells were collected and analyzed using FACS Canto II (BD Biosciences)
and Kaluza flow cytometry software version 2.1 (Beckman Coulter,
Fullerton, CA, USA). For information regarding the gating strategy, see
Additional file [174]1: Figs. S12 and S13.
Extraction of DNA and RNA
Total DNA and RNA were isolated using the Quick-DNA/RNA™ Microprep Plus
Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s
instructions. The RNA and DNA concentrations were determined using a
Qubit™ 3.0 fluorometer (Thermo Fisher Scientific) with the Qubit™ RNA
BR Assay kit for RNA and Qubit™ dsDNA BR Assay Kit for DNA (both from
Thermo Fisher Scientific). The integrity of the extracted RNA was
evaluated using the Agilent RNA 6000 Nano Kit (Agilent Technologies,
Santa Clara, CA, USA) on an Agilent 2100 Bioanalyzer (Agilent
Technologies). The median RNA Integrity (RIN) was 8.8 (range 7.3–10).
For two samples, RIN values were not measurable but since there was a
sufficient amount of RNA, the samples were included and processed for
library preparation and subsequent sequencing.
DNA methylation
DNA from 154 samples (from 78 resting CD4^+ and 76 resting CD8^+ T
cells, Additional file [175]2: Table S1 and Additional file [176]1:
Fig. S3) was sent to the SNP&SEQ-technology platform at SciLifeLab
(Uppsala University, Uppsala, Sweden). Only 6 samples were available BP
in MS and due to insufficient amount of material, samples were also
missing for two time points in CD8^+ in MS (Additional file [177]2:
Table S1 and Additional file [178]1: Fig. S3). Bisulfite conversion was
performed using the EZ DNA Methylation™ Kit (Zymo Research) with 250 ng
of DNA per sample as input. The bisulfite converted DNA was eluted in
15 μl Elution Buffer according to the manufacturer´s protocol,
evaporated to a volume of < 4 μl, and used for methylation analysis
using Infinium Human MethylationEPIC BeadChip array (Illumina) covering
850,000 methylation sites across the genome.
RNA sequencing
Samples (resting and activated cells, n = 233) were sent to the
National Genomics Infrastructure in Stockholm (SciLifeLab) for RNA
sequencing. Library preparations were carried out on an Agilent NGS
Bravo workstation (Agilent Technologies) in 96-well plates following
the instructions provided for the Illumina TruSeq Stranded mRNA kit
from Illumina (Illumina). Briefly, mRNA was purified from 200 ng of
total RNA through selective binding on poly dT-coated beads and
fragmented using divalent cations under elevated temperature. cDNA was
synthesized from the resulting fragments by adding SuperScript II
Reverse Transcriptase (Thermo Fisher Scientific). This step was
followed by bead clean-up with the AMPure XP solution (Thermo Fisher
Scientific) to selectively retain fragments of desired lengths. The
cDNA was then subjected to 3’ adenylation, followed by adapter ligation
to the 3’ adenylated end of the fragment. The fragments with ligated
adapters were cleaned on AMPure XP beads to remove non-ligated adapters
and were amplified by PCR. The PCR products were purified by binding to
AMPure XP beads, washed with 80% ethanol, and eluted in elution buffer
(Qiagen, Hilden, Germany). The quality of the adapter-ligated libraries
was checked on the BioAnalyzer or LabChip® GX/HT DNA High Sensitivity
Kit (PerkinElmer, Waltham, MA, USA), and their concentration was
determined by Quant-iT (Thermo Fisher Scientific). Because of technical
errors at the sequencing facility, 34 samples failed library
preparation. The libraries with concentrations above 20 nM were
normalized and pooled, and the concentration of the final pools was
estimated by qPCR. The pool was sequenced on the NovaSeq S6000
(Illumina) on 3 lanes of the S4-300 (v1.5) flowcell. After sequencing,
2 samples were excluded due to insufficient sequencing depth. A total
of 197 samples were successfully sequenced, passing FASTQC and with a
high enough sequencing depth (average 67 × 10^6 million reads per
sample) and were included in subsequent analyses, which resulted in
samples from 65 resting CD4^+ cells, 51 activated CD4^+ cells, 61
resting CD8^+ cells and 20 activated CD8^+ cells (Additional file
[179]2: Table S1 and Additional file [180]1: Fig. S3). The difference
in sample availability resulted in an uneven distribution of some
samples across time points and groups, particularly for the activated
samples where limited material was available for performing the T-cell
activation assay (see Additional file [181]2: Table S1 and Additional
file [182]1: Fig. S3 for a detailed description of sequenced samples).
The samples were demultiplexed and pre-processing was carried out using
the nf-core/rnaseq3.0 pipeline
([183]https://github.com/nf-core/rnaseq). Briefly, quality control was
performed using FASTQC (version 0.11.9; Babraham Institute
[184]https://bioinformatics.babraham.ac.uk) and trimming of low-quality
reads and adapter contamination was done using TrimGalore! (version
0.6.6; Babraham Institute). Paired-end reads were aligned and mapped to
the Ensemble human reference genome GRCh38 (Genome Reference Consortium
Human Build 38) using STAR [[185]43] (version 2.6.1d). Gene count was
quantified using Salmon [[186]44] (version 1.4.0).
Bioinformatics analysis
Data obtained from the DNA methylation and RNA sequencing were analyzed
using the programming language R (version. 4.2.1); the code is
available at [187]https://github.com/albertozenere/GraMS.
Pre-processing of methylation and RNA sequencing data
Pre-processing of the data generated by Infinium Human MethylationEPIC
BeadChip (Illumina) was performed using the package ChAMP [[188]45]
(version 2.22.0). It included filtering out probes: (i) with a
detection p-value < 0.01, (ii) with a bead count less than 3, (iii)
with no GC start, (iv) close to a SNP, following the list in [[189]46].
The number of probes that survived the filtering was 740,552. The data
were normalized using the Beta-Mixture Quantile dilation method
[[190]47] and batch correction was carried out using ComBat [[191]48],
where the slide effect was removed while the biological signal coming
from cell type, time and disease was preserved. When applicable, CpGs
were mapped to genes using the annotation provided by Illumina. For the
RNA-seq, batch correction was performed with ComBat-seq [[192]49],
contained in the package sva (version 3.44.0), where the library batch
effect was corrected and the signal associated to cell type, time,
disease and our in vitro activation was retained. Normalization was
carried out with edgeR (version 3.38.1) using the TMM method [[193]50].
Genes with low counts, i.e., with count-per-million below 10 in 70% of
the samples, were filtered out. In total, 13,440 genes for CD4^+ and
13,448 genes for CD8^+ cells were retained (for both resting and
activated samples).
Differential analysis
Methylation and RNA-seq data were modeled using the R package limma
[[194]51] (version 3.52.1). Regarding the methylation data,
differential expression analysis was performed on the M-values, which
have been shown to be more appropriate for differential analysis
[[195]52]. The measurements of each CpG and gene were fitted to account
for the effect of disease group (MS or HC), time (BP, 1st, 2nd, 3rd
trimester and post-partum), age, the proportion of memory cells, and
cell viability. The data were fitted by the linear model:
[MATH:
y=β0+βd,t<
/mrow>∗s+βa∗a+βm∗m+βc∗c, :MATH]
where y indicates the measurements of a CpG or gene,
[MATH: β0 :MATH]
is the intercept,
[MATH: d :MATH]
is the disease group,
[MATH: t :MATH]
denotes time,
[MATH: s :MATH]
is a term that includes both the disease group and time, a represents
the age, m the proportion of memory cells and c the cell viability.
[MATH:
βd,t,βa,β<
mi>m,βc
:MATH]
are the coefficients associated with the disease group and time, age,
the proportion of memory cells and cell viability, respectively.
Although there were no statistically significant differences in the
proportion of naïve and memory cells across time or between groups
(Additional file [196]1: Fig. S15), the observed differences in
methylation patterns between naïve and memory T cells [[197]53,
[198]54] are a potential confounder, and therefore the proportion of
memory cells was also included as a covariate in the model for both the
methylation and RNA-seq analysis. The effects of disease and time were
grouped under the same term to capture disease-specific changes over
time. The individual effect (samples from the same donor tend to be
correlated) was modeled as a random effect using the function
duplicateCorrelation. The heteroscedasticity of RNA-seq data was
accommodated using the function voom [[199]55]. For each disease group,
the coefficients
[MATH:
βd,t
:MATH]
were used as a measure of the effect of time on the data. Moderated
t-statistics and corresponding p-values were computed using the limma
function eBayes. Differentially methylated probes (DMPs) were defined
as CpGs with a nominally significant p-value (p ≤ 0.05) and an average
absolute change in beta value greater than 0.05, where CpGs with a
positive value were considered hypermethylated and negative as
hypomethylated. Each gene associated to at least one DMP was considered
differentially methylated (DMG). Genes were defined as differentially
expressed if the corresponding p-value was nominally significant
(p ≤ 0.05). Since there were very few differences between BP and the
1st trimester during pregnancy in MS (< 2% of DEGs/DMPs; Additional
file [200]1: Fig. S8), and since BP samples were not available in the
HC group, the BP samples were omitted in the remaining analyses and the
1st trimester samples were used as the baseline to evaluate the changes
associated with pregnancy in both groups. DMPs and DEGs in the 3rd
trimester and post-partum were visualized using the package ggalluvial
(version 0.12.3).
Genes affected by in vitro T cell activation
A separate differential analysis was performed to identify genes that
were statistically affected by activation at the mRNA level, as
measured by the proportion of cells expressing the activation marker
CD69 as determined by flow cytometry. Notably, the samples collected
from HC were characterized by higher levels of activation compared to
their MS counterparts (Additional file [201]1: Fig. S14), with
statistically significant differences around the 2nd trimester and
post-partum in CD4^+ cells (Additional file [202]1: Fig. S14). For this
reason, the level of activation was added as covariate in the model to
ensure it would not confound the results. Differential analysis was
performed to identify genes that were significantly (FDR-adjusted
p-values ≤ 0.05) affected by the state of activation (i.e., resting or
activated).
Module inference
The protein–protein interaction (PPI) network was downloaded from the
STRING database [[203]56] (version 11) and filtered to contain only
interactions with a high confidence defined by a combined score of at
least 700. To identify a set of functionally related genes affected by
pregnancy, the common DMGs and DEGs between the 3rd trimester and
post-partum, referred to as rebound DEGs/DMGs, were used as input (also
denoted as seed genes) for module inference, which was performed with
DIAMoND [[204]24], using the R package MODifieR [[205]25] (version
0.1.3). For each omic and cell type, a maximum number of 200 genes were
added to the initial set of genes to form the CD4^+ and CD8^+ modules
for RNA-seq and methylation. As the RNA-seq and methylation pregnancy
modules of each cell type showed a significant degree of overlap, we
selected the common genes to form CD4^+ and CD8^+ rebound pregnancy
modules, which were visualized using Cytoscape [[206]57] (version
3.8.2). For visualization, a threshold of score ≥ 950 for the PPI was
used.
Enrichment and pathway analysis
The biological importance of both seed and module genes was tested in
multiple ways. Pathway enrichment analysis, based on KEGG terms, was
performed using the function compareCluster from clusterProfiler
[[207]58] (version 3.16.1). The results were corrected for the
background (i.e., all measured genes with the inclusion of the PPI
network genes when testing module genes), and the pathways with an
adjusted p-value ≤ 0.05 were considered significant. To assess the
enrichment of MS-associated genes we combined DisGeNET [[208]22]
(n = 1117 genes) with a list of GWAS-derived genes. For the
GWAS-associated genes, we used 26,033 MS-associated SNPs derived from
the latest GWAS in MS [[209]23] (p < 10^–6) and mapped them to the
closest genes (tssRegion -3000 to 3000) using ChIPseeker [[210]59]
(version 1.31.4), which resulted in a total of 573 genes. Combined
genes from both GWAS and DisGeNET resulted in a list of a total of 1550
MS-associated genes. Fisher’s exact test was used to assess the
enrichment of MS-associated genes in seed and module genes. Lastly, we
evaluated the enrichment for a recently reported list of P4-associated
genes [[211]26] using Fisher’s exact test.
Supplementary Information
[212]12974_2023_2781_MOESM1_ESM.docx^ (3MB, docx)
Additional file 1: Figure S1. Overview of the experimental set up.
Figure S2. Overview of the analysis workflow from initial raw data to
differential analysis and module inference. Rebound DEGs/DMPs were
defined as the overlap between the differentially expressed
genes/differentially methylated probes identified in the 3rd
trimesterand post-partumsimultaneously, calculated for MS and HC
separately. Genes associated with at least one rebound DMP were denoted
rebound DMGs. Rebound DEGs/DMGs in common between MS and HC were termed
shared rebound DEGs/DMGs. These genes served as input for creating
RNA-seq and methylation modules for each cell type separately. The
RNA-seq and methylation modules were overlapped to create one CD4^+
rebound pregnancy module and one CD8^+ rebound pregnancy module. DEGs
and DMPs with a nominal p-value ≤0.05were included for analysis. DEG,
differentially expressed gene; DMG, differentially methylated gene;
DMP, differentially methylated probe; MMD, mean methylation difference.
Figure S3. Overview of the samples used for RNA sequencing and DNA
methylation from women with MS and healthy controls. A number of
samples were excluded for the RNA-sequencing due to technical issues at
the sequencing facility. Two samples were also excluded after
sequencing due to insufficient sequencing depth. The number of samples
is stated as the number of resting/activatedor resting cells alonein
the two lower panels. HC, healthy controls; MS, Multiple sclerosis.
Figure S4. DNA and RNA extracted from CD4^+ and CD8^+ T cells from
women with MS and healthy controlswere analyzed by RNA-seq and Infinium
Methylation EPIC 850 K for DNA methylation. Shown are the correlation
between gene countsand the beta values of all detected CpGsfor the
comparisons 3rd-2nd trimester and 2nd -1st trimester for resting CD4^+
cells and CD8^+ cells in women with MS and HC. Pearson’s correlation r
is shown in the individual graphs for each comparison. All correlations
had a p < 2.2 x 10^-16. HC, healthy controls; MS, multiple sclerosis.
Figure S5. DNA and RNA extracted from CD4^+ and CD8^+ T cells from
women with MS and healthy controlswere analyzed by RNA-seq and Infinium
MethylationEPIC 850K for DNA methylation. The correlation between gene
countsand the beta values of all detected CpGsbetween PP-2nd trimester
and 2nd-1st trimester for resting CD4^+ cells and CD8^+ cells in women
with MS and HC. Pearson’s correlation r is shown in the individual
graphs for each comparison. All correlations had a p < 2.2 x 10^-16.
HC, healthy controls; MS, multiple sclerosis, PP; post-partum. Figure
S6. Number of nominally differentially expressed genesand
differentially methylated CpGsduring pregnancy and post-partum
comparing 2nd-1st, 3rd-2nd , 3rd-1st, PP-3rd and PP-2nd.Number of
overlapping genes and CpGs during pregnancy and after pregnancy, i.e.,
PP-2nd compared to 2nd-1st and PP-3rd compared to 3rd-2nd and 3rd-1st
trimesters. DEGs; differentially expressed genes, DMPs; differentially
methylated probes, HC; healthy controls, MS; multiple sclerosis, PP;
post-partum. Figure S7. Venn diagrams showing the overlap between the
nominally differentially expressed genes or differentially methylated
CpGsduringand after pregnancyin women with MS and healthy controls.
Fisher’s exact test was used to calculate the enrichment of the
overlaps. Odds ratios are shown, and all overlaps had a p < 10^-16. HC;
healthy controls, MS; multiple sclerosis, OR; odds ratio, PP;
post-partum. Figure S8. Differential analysis between the samples
collected before pregnancy and the remaining time points.Number of DMPs
and DEGsfor each comparison.Number of probes and genes that are
hypermethylated/hypomethylatedin each comparison. BP, before pregnancy;
PP, post-partum; DEG, differentially expressed gene; DMP,
differentially methylated probe. Figure S9. Regulatory patterns between
DNA methylation and gene expression are conserved between MS and HC.
The regulation exerted by each CpG was measured by the Spearman
correlation coefficient with the respective gene, as annotated by
Illumina. Correlations were computed independently in CD4^+ and CD8^+,
for MS and HC. The correlations obtained in each of the four groups
were divided into deciles based on their absolute values. The overlap
between CpG–gene pairs that belong to the same decile in both MS and HC
was carried out using Fisher’s exact test. **p < 0.01, ****p < 0.0001,
Δ p < 10^-16. Figure S10. The CD4^+ and CD8^+ rebound genes were
derived by overlappingthe differentially expressed genesfrom 3rd-1st
trimester and PP-3rd in both women with MS and healthy
controlsandGenesfor the same comparisons. These genes were later used
to infer modules.Enrichment of MS-associated genes based on
GWAS-derived MS genes and DisGeNET; p denotes p-value. Overlap between
the genes significantly affected by activation, performed only on the
genes derived from the RNA-seq analysis.KEGG pathway enrichment. Shown
are the top 5 pathwaysof both groups. Number of genesis shown above.
Enrichment was calculated using Fisher’s exact test and p ≤ 0.05 was
considered statistically significant. DEGs; differentially expressed
genes, DMPs; differentially methylated probes, MS; multiple sclerosis.
Shown are the top 5 pathwaysof both groups, with adjusted p-value ≤
0.05. Figure S11. Enrichment of P4-associated genes. The rebound seed
genes, the resulting module genes for each omic and the shared genes
derived from combining the modules for both RNA-seq and methylation for
each cell typewere tested for enrichment of P4-associated genes using
Fisher’s exact test. The P4-associated geneswere derived from Hellberg
et al., Front Immunol. ns; non-significant, P4; progesterone. *p <
0.05, ****p < 0.0001. Figure S12. Flow cytometry characterization of
resting and activated CD4^+ T cells. Flow cytometry gating strategies
to assess purity, viability, and activation status in resting and
activated CD4^+ T cells. Viable cells were identified as Aqua- and
further gated based on forwardand sidescatter to further characterize
CD4^+ cells. The cut-off value for CD69 expression was based on the
expression in the resting cells. Definition of naïve and memory T cells
was based on the contour of the CD45RA+and CD45RA-populations.
Viability, purity of the isolated cells, activation leveland proportion
of naïve and memory CD4^+ T cells were evaluated on resting cells on
day 0. Activated cells were analyzed for viability, CD69 expressionand
proportion of naïve and memory cells. The figure shows one
representative sample. Figure S13. Flow cytometry characterization of
resting and activated CD8^+ T cells. Flow cytometry gating strategies
to assess purity, viability, and activation status in resting and
activated CD8^+ T cells. Viable cells were identified as Aqua- and
further gated based on forwardand sidescatter and to characterize CD8+
cells. The cut-off value for CD69 expression was based on the
expression in the resting cells. Definition of naïve and memory was
based on the contour of the CD45RA^+ and CD45RA^– populations.
Viability, purity of the isolated cells, activation leveland proportion
of naïve and memory cells was evaluated on resting cells on D0.
Activated cells were analyzed for viability, CD69 expressionand
proportion of naïve and memory cells. The figure shows one
representative sample. Figure S14. CD69 expression in resting and
activated CD4^+ and CD8^+ T cells.Proportion of CD69^+ cells among
CD4^+ or CD8^+ resting and activated T cells combining all time points
within women with MS and healthy controls. CD69 expression in activated
CD4^+ and CD8^+ cells before pregnancy, 1st, 2nd and 3rd trimester and
post-partum. No activated cells were available before pregnancy for the
healthy controls. The number of activated samples differ from the
number of resting samples as not all samples had enough material to
perform the T-cell activation assay, resulting in fewer activated
samples. Statistical differences were determined using an unpaired t
test between MS and HC within each time point, respectively. BP, before
pregnancy; HC, healthy controls; MS, Multiple sclerosis; PP,
post-partum; trim, trimester. **p < 0.01, ***p < 0.001, ****p < 0.0001.
Figure S15. Proportions of naïveand memoryin resting CD4^+ and CD8^+
cells from women with MS and healthy controlsbefore, during and after
pregnancy. MS and HC were also combined to compare differences over
time irrespective of disease. There were no statistical differences
within each group over time, between groups over time or at the same
time point. Statistical differences were determined using one-way
ANOVA. No statistically significant differences were found. BP; before
pregnancy, HC, healthy controls; MS, Multiple sclerosis, PP;
post-partum.
[213]12974_2023_2781_MOESM2_ESM.xlsx^ (22.1KB, xlsx)
Additional file 2: Table S1. Overview of samples used for RNA
sequencing and DNA methylation.
[214]12974_2023_2781_MOESM3_ESM.xlsx^ (74.6MB, xlsx)
Additional file 3: Table. S2. Results of differential analysis on
methylation data from healthy controls.
[215]12974_2023_2781_MOESM4_ESM.xlsx^ (56.4MB, xlsx)
Additional file 4: Table. S3. Results of differential analysis on
methylation data from multiple sclerosispatients.
[216]12974_2023_2781_MOESM5_ESM.xlsx^ (333.7KB, xlsx)
Additional file 5: Table S4. Results of differential analysis on
RNA-seq data from multiple sclerosispatients.
[217]12974_2023_2781_MOESM6_ESM.xlsx^ (212.2KB, xlsx)
Additional file 6: Table. S5. Results of differential analysis on
RNA-seq data from multiple sclerosispatients.
[218]12974_2023_2781_MOESM7_ESM.xlsx^ (35.8KB, xlsx)
Additional file 7: Table S6. Genes identified by DIAMOnD from the
module inference tool MODIfieR.
[219]12974_2023_2781_MOESM8_ESM.xlsx^ (110.3KB, xlsx)
Additional file 8: Table S7. KEGG pathway enrichment analysis of seed
and module genes.
Acknowledgements