Background
The in utero period and the first years of human life are crucial for the development and maturation of organs [
1]. Insults during these periods may result in later adverse health consequences, which might persist during the whole lifespan. This is known as the Developmental Origins of Health and Disease (DOHaD) concept [
2].
Maternal smoking during pregnancy represents one of the most important avoidable risk factors, and its short- and long-term adverse effects on offspring, including prematurity, lower birth weight, increased risk of asthma and obesity, and impaired neurodevelopment, have been widely reported [
3,
4]. In 15 European countries, the prevalence of maternal smoking at any time during pregnancy ranged between 4.2 and 18.9% in 2011–2012 [
5]. Secondhand smoke (SHS) is one of the main contributors to the indoor air pollution, with 40% of children exposed worldwide in 2004 [
6]. In Europe between 1999 and 2008, among never-smoking adolescents, around 50%, 70%, and 45% were exposed to SHS inside home, outside home, and both, respectively [
7]. SHS has been related to increased risk of asthma, lower respiratory infections, and sudden infant death syndrome [
4,
6].
The molecular alterations resulting from tobacco smoke exposure are only partially understood. Their study can facilitate the development of biomarkers of exposure that surpass the limitations of existing ones, such as questionnaires and urinary cotinine, which only informs about recent exposure [
8,
9]. For instance, the first epigenetic biomarker of maternal smoking allowed the discrimination between exposed and unexposed children with an accuracy > 90% [
10]. They may also provide knowledge on the molecular mechanisms that could mediate the effects of tobacco smoking on health. For instance, it has been described that epigenetic deregulation of
JNK2 gene by maternal smoking during pregnancy is associated with impaired lung function in early childhood [
11]. Also, methylation levels of maternal smoking-related CpGs in adolescents/adults have been causally linked through Mendelian randomization to inflammatory bowel disease and schizophrenia [
12]. Moreover, molecular responses might be more sensitive and earlier markers of a biological effect than clinical outcomes.
In this line, several studies have shown that maternal smoking in pregnancy is associated with altered patterns of DNA methylation at birth, in placenta [
13] and in cord blood [
11,
14‐
16]. Interestingly, some of the maternal smoking-related blood loci show persistent dysregulation until childhood [
15,
16], adolescence [
16,
17], or even adulthood [
18]. However, not much is known about the transcriptional consequences of these persistent DNA methylation changes [
15]. Furthermore, while the alterations at multiple molecular layers, from epigenetics to metabolomics, have been investigated in relation to current smoking in adults [
19‐
22], there is lack of information about the multi-layer molecular changes associated with in utero exposure or with the exposure to SHS in children. Regarding adult SHS exposure, only DNA methylation candidate studies are available [
23].
Here, we aimed to identify multi-layer molecular signatures associated with exposure to tobacco smoke in two early life susceptibility windows, in utero due to maternal smoking and in childhood through exposure to SHS. For this, we used molecular data from 1203 children of the Human Early Life Exposome (HELIX) study, including child blood DNA methylation and transcription, plasma proteins, and sera and urinary metabolites.
Discussion
Despite the efforts of public health campaigns, maternal smoking in pregnancy and childhood SHS are still main adverse avoidable risk factors for child health. This study is the first to examine the association of exposure to tobacco smoking at different windows of exposure, in utero and in childhood, with multi-layer molecular phenotypes.
Exposure to maternal smoking in pregnancy was associated with DNA methylation of 41 CpG sites located in 18 different loci. All loci had previously been related to maternal smoking during pregnancy [
14‐
16] or current smoking [
21]. However, none of the previous studies had incorporated substantial transcriptomics data from the same subjects to interpret the functional consequences of these epigenetic changes. Furthermore, few studies investigated duration and intensity of maternal smoking in pregnancy, which might be relevant for public health advice [
14,
16].
As in cord blood [
14,
16], sustained maternal smoking in pregnancy produced larger effects on childhood blood DNA methylation than any maternal smoking, with the latter group including sustained smoker mothers as well as mothers that usually only smoked during the 1st trimester. Moreover, when considering duration and intensity of maternal smoking in pregnancy, some CpGs showed a dose-response trend, whereas others got saturated with any maternal smoking in pregnancy or did not have any meaningful response. These heterogeneous patterns, even in the same locus, can be explained by CpG-specific responses, but also by less accuracy in the measurement of some CpGs (low biological response to high technical noise) [
66]. In any case, the persistence and the linear trend response of CpGs in
MYO1G,
GNG12,
AHRR,
FRMD4A,
RADIL, and 7q11.22 make them interesting candidates for the development of an epigenetic biomarker for in utero exposure to smoking [
10]. In general, maternal SHS during pregnancy had mostly negligible effects on offspring blood DNA methylation at the 41 significant CpGs, or at least their effects were diluted over time and not detected in childhood with the actual sample size.
We also observed that DNA methylation in 5 of these 18 loci was related to gene expression of nearby genes, but usually not of the closest annotated gene. However, these effects were weak as we did not detect significant associations between maternal smoking in pregnancy and expression of these genes. In other words, DNA methylation response to maternal smoking in pregnancy was not mirrored at the transcriptional level of nearby genes. Similarly, previous studies in former smokers have shown that smoking has a longer-lasting influence on the methylome compared to the transcriptome [
67]. The reversal rate of gene expression at 1 year after smoking cessation has been calculated in > 50% and reaches > 85% after 10 years, whereas for methylation, it ranges from 17 to 33% with some effects still visible 40 years after smoking cessation. The different reversal rates between methylation and transcription could be explained by the complex transcriptional regulation that involves mechanisms other than DNA methylation. Whether persistent epigenetic marks act as a memory of the cell to previous exposures, to trigger rapid or amplified transcriptional activation in certain contexts (i.e., after a second exposure event), is unknown. Also, the weak association between exposure to tobacco smoke and transcription, in comparison to methylation, might be explained by the highest instability of the RNA compared to DNA, which might have introduced noise into the transcriptional data.
AHRR (
Aryl-Hydrocarbon Receptor Repressor), which is involved in xenobiotic detoxification, cell growth, and differentiation, has widely been reported in relation to smoking. In particular, cg05575921 has been found to be hypo-methylated in cord blood [
15], adult blood [
21], placenta [
13], and adipose tissue [
67].
AHRR is an interesting example to discuss the complexity of epigenetic regulation. First, in our study,
AHRR exhibited both hyper-methylation (intron 1) and hypo-methylation (other introns) in response to smoking. Through the eQTM analyses, we found that both hyper- and hypo-methylation were related to increased expression of the gene in blood. This finding evidences that epigenetic regulation of transcription is gene context-specific (i.e., intron 1 behaves different from other introns) and that methylation-expression correlations are fundamental to understand final transcriptional consequences. Second, besides
AHRR gene, methylation at CpGs of this locus was also associated with the expression of two other nearby genes:
PDCD6 and
EXOC3.
PDCD6 (Programmed Cell Death 6) is a calcium sensor involved in endoplasmic reticulum (ER)-Golgi vesicular transport, endosomal biogenesis, or membrane repair, and
EXOC3 (Exocyst Complex Component 3) is a component of the exocyst complex involved in the docking of exocytic vesicles with fusion sites on the plasma membrane. Further research might clarify the potential role of these genes, if any, in relation to tobacco smoking.
Two metabolites (lactate and alanine), known to be increased with glycemic dysregulation [
68], were found at higher levels in urine of children born from sustained smoker mothers compared with non-smokers. Although there are some studies reporting an association between maternal smoking and type 2 diabetes and metabolic syndrome, the evidences are still inconclusive according to a recent meta-analysis [
69].
In contrast to in utero exposure, exposure to childhood SHS, assessed through either questionnaire or cotinine, was associated with child serum metabolites and plasma proteins. In particular, we found that SHS, defined as urinary cotinine above the LOD, increased plasma PAI1 (plasminogen activator inhibitor-1
SERPINE1 gene) protein levels. PAI1 is the principal inhibitor of tissue plasminogen activator (tPA) and urokinase (uPA), enzymes that convert plasminogen into plasmin (fibrinolysis) (Additional file
3: Fig. S15). Therefore, higher levels of PAI1 are indicative of a thrombotic state, and they have been found in active smokers [
70,
71]. Although the increase of plasma PAI1 levels in SHS-exposed children was substantially smaller than the increase detected in active smokers [
70], our findings evidence that SHS was sufficient to produce a pro-thrombotic state in children. The long-term consequences of this pro-thrombotic state in children, if prolonged over time, are unknown, but in adults, it is linked to age-related subclinical (i.e., inflammation or insulin resistance) or clinical (i.e., myocardial infarction, obesity) conditions [
72].
We also found several serum metabolites altered in SHS-exposed children. Reduced levels of diacyl (aa)- and acyl-alkyl (ae)-phosphatidylcholines and of sphingomyelin (OH) C22:2 were in agreement with findings in active adult smokers [
19]. Is it worth noting that these diacyl (aa)- and acyl-alkyl (ae)-phosphatidylcholines overlap with those positively associated with adherence to Mediterranean diet and with protective risk factors for cardiovascular disease [
73]. We considered the reported location of childhood SHS exposure (inside home, outside, and in both places) as a surrogate of intensity of exposure to smoking. While PAI1 plasma protein levels and carnitine C9 were higher in children exposed in both locations, no clear dose-response patterns were observed for other metabolites.
Given that SHS effects might be subtler than those of active smoking, we also examined whether molecular features (CpG methylation or gene/miRNA transcription) described in current smokers overlapped with our findings of postnatal SHS with a less stringent
p value cutoff. We did not detect any enrichment, suggesting that if postnatal SHS has an effect on these molecular layers, our sample size is too limited to detect it. Conversely, as an indication of the long-term and strong effects of active smoking, we did find enrichment for child CpGs associated with maternal smoking in pregnancy among CpGs described for current smoking in other studies. Indeed, it has been described a remarkable overlap between the blood methylation signatures detected in adult smokers and in newborns of smoker mothers [
74].
Globally, our findings together with previous literature suggest that offspring blood DNA methylation captures strong and permanent effects associated with active maternal smoking during pregnancy. In our study, the associations between maternal smoking during pregnancy and DNA methylation were not attenuated after adjustment for childhood SHS, likely due the weaker effects of passive compared to active smoking. In contrast, the potential biological effects of SHS were best captured by dynamic molecules with fast responses, such as metabolites and proteins. Time-course studies will be needed to dissect this acute response in more detail. Adjustment for maternal smoking during pregnancy attenuated the effects of childhood SHS on these markers, highlighting the importance of mutually adjusted models in order to identify period-specific effects.
Findings should be considered within the context of the study’s limitations. First, exposure assessment to tobacco smoking, through either questionnaire or cotinine, has some intrinsic limitations. Maternal smoking in pregnancy and child exposure to SHS were self- or parental-reported, and they can be subject to misreporting [
75]. Urinary cotinine, although more objective, only provides information about the most recent exposure (half-life in urine ~ 20 h) [
8]. In our study, cotinine detection correlated strongly with the childhood SHS classification though, giving us reasonable confidence in the questionnaire reports. Second, we aimed to dissect pregnancy from childhood exposure associations using mutually adjusted models. However, misclassification and weaker effects of SHS compared to effects of maternal smoking in pregnancy (i.e., PAI1 or
AHRR [
23]), as well as the high level of overlap between maternal and childhood smoking exposure, might have limited our ability to distinguish between these two time periods. Larger samples of children exposed to SHS, without in utero exposure, might be needed to investigate SHS, especially for DNA methylation. Third, although the statistical models were adjusted for an exhaustive list of confounders, including child zBMI, we cannot completely rule out residual confounding. For example, plasma PAI1, which can be released by fat cells [
76], is related to BMI and percent body fat [
77]. Fourth, although the study has been designed as a comprehensive screening using high-throughput omics platforms, these platforms do not have complete coverage of the molecular layers, and consequently, we might have missed some biological signals. Also, cell type-specific responses might have been diluted within the context of whole blood analyses. Fifth, some of the smoking sensitive CpGs detected in our study are known to be regulated by mQTLs. The role of genetic variation in modifying the effects of the exposure to smoking deserves future research. Finally, our study predominantly consisted of European ancestry children, and thus, additional studies involving diverse ethnic backgrounds are needed in order to improve the generalizability of the findings. Potential confounding by ancestry was controlled by adjusting the models for self-reported ethnic origin. For the top signals, we also performed a sensitivity analysis restricted to European ancestry children and results did not change substantially.
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