Prenatal cannabis exposure is associated with alterations in offspring DNA methylation at genes involved in neurodevelopment, across the life course
https://ror.org/052gg0110grid.4991.50000 0004 1936 8948Translational Gastroenterology Unit, Nuffield Department of Experimental Medicine, University of Oxford, Oxford, UK
https://ror.org/052gg0110grid.4991.50000 0004 1936 8948Biomedical Research Centre, University of Oxford, Oxford, UK
https://ror.org/01jmxt844grid.29980.3a0000 0004 1936 7830Christchurch Health and Development Study, Department of Psychological Medicine, University of Otago Christchurch, Christchurch, New Zealand
https://ror.org/03y7q9t39grid.21006.350000 0001 2179 4063School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
Abstract
Prenatal cannabis exposure (PCE) is of increasing concern globally, due to the potential impact on offspring neurodevelopment, and its association with childhood and adolescent brain development and cognitive function. However, there is currently a lack of research addressing the molecular impact of PCE, that may help to clarify the association between PCE and neurodevelopment. To address this knowledge gap, here we present epigenome-wide association study data across multiple time points, examining the effect of PCE and co-exposure with tobacco using two longitudinal studies, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Christchurch Health and Development Study (CHDS) at birth (0 y), 7 y and 15–17 y (ALSPAC), and ~27 y (CHDS). Our findings reveal genome-wide significant DNA methylation differences in offspring at 0 y, 7 y, 15–17 y, and 27 y associated with PCE alone, and co-exposure with tobacco. Importantly, we identified significantly differentially methylated CpG sites within the genes LZTS2, NPSR1, NT5E, CRIP2, DOCK8, COQ5, and LRP5 that are shared between different time points throughout development in offspring. Notably, functional pathway analysis showed enrichment for differential DNA methylation in neurodevelopment, neurotransmission, and neuronal structure pathways, and this was consistent across all timepoints in both cohorts. Given the increasing volume of epidemiological evidence that suggests a link between PCE and adverse neurodevelopmental outcomes in exposed offspring, this work highlights the need for further investigation into PCE, particularly in larger cohorts.
Introduction
The use of cannabis during pregnancy has increased in recent years [1–4], becoming the most commonly used drug, excluding alcohol and tobacco, among pregnant women in the US [5, 6]; recent estimates suggest an increase from 3.4% in 2017 to 7.0% in 2022 [4, 7] with these figures still rising post COVID-19 pandemic [8]. The exponential increase in frequency is likely to be a consequence of legalisation and decriminalisation of cannabis [9–12], which may have decreased the public perception of any risk associated with maternal cannabis consumption during pregnancy [13–15]. However, the limited empirical research on prenatal cannabis exposure (PCE) has resulted in a lack of clarity and understanding regarding any potential risks and long-term harm to exposed individuals [16–18].
Recent epidemiological studies indicate an impact of PCE on neonatal health and neurodevelopment [18]. Specifically, PCE is associated with physical neonatal health metrics such as birth weight, birth timing and infant growth restriction [2, 19–21] but also traits related to childhood and adolescent neurodevelopment and brain function, for example, higher-order executive function [22, 23], and psychosocial and neurobehavioural traits such as psychopathy [24] and behavioural development [25]. Furthermore, PCE has observed impacts on Conduct Problem (CP) phenotypes such as externalising behaviours [26], learning disorders, and autism [27], and may increase the risk of neurodevelopmental and psychiatric disorders such as depression, anxiety, attention deficit and psychosis/schizophrenia in adulthood [28]. While these studies are associative, they warrant further investigation to clarify the risk profile of PCE. This is because PCE has the potential to directly impact offspring neurodevelopment; cannabis can cross the placenta [29], and can therefore impact the developing neonatal endocannabinoid system (ECS) [22]. The ECS is integral for correct prenatal neurodevelopment, with roles in neurite outgrowth, migration, and axon growth and guidance [30], as well as synaptic plasticity [31, 32], neuronal specification [33] and immune regulation [34]. Thus, given the central role of the ECS in brain development [22], any interference with this system via PCE may disrupt ECS signalling at critical stages of development, potentially altering structural and functional neurodevelopment [35], brain growth [36], and nervous system functioning in later life [22]. Consequently, because neurodevelopmental disorders and diseases have a complex aetiology, molecular evidence is required to be able to determine whether PCE is implicated in neurodevelopmental outcomes.
Neurodevelopmental phenotypes are often mediated at the molecular level by environmentally-induced genetic and epigenetic change [37–41], such as changes to DNA methylation (an epigenetic modification that is sometimes associated with gene expression levels [42–45]). Indeed, targeted studies from animal models indicate that PCE can impact DNA methylation in the genomes of exposed offspring, e.g. refs. [46–49]. Further studies show an impact of cannabis on the methylome of adult cannabis users, at genes involved in neuronal signalling [50, 51]. Additionally, cannabis exposure is associated with differential methylation at genes enriched for cardiogenesis and neurodevelopment in sperm [52, 53]. Overall, large-scale studies of PCE on human populations, while starting to emerge (e.g. refs. [23, 54]) are still scant, and thus the true nature of the relationship between PCE and neurodevelopment is still not fully understood. Consequently, the scientific and medical communities are in agreement that there is an urgent need for more evidence and research into the impacts of prenatal cannabis exposure on infant neurodevelopment.
Thus, to investigate the molecular impact of PCE, here we assessed DNA from two independent longitudinal studies: i) the Avon Longitudinal Study of Parents and Children (ALSPAC, United Kingdom), and; ii) the Christchurch Health and Development Study (CHDS, New Zealand). Using DNA methylation arrays (Illumina Infinium Human Methylation 450 K BeadChip [ALSPAC] and Illumina Infinium MethylationEPIC BeadChip [CHDS]) over the lifecourse (birth, 7 y and 15 y [ALSPAC] and 27 y [CHDS]) we performed epigenome-wide association studies (EWAS) to investigate the effect of PCE on genome-wide DNA methylation patterns, to explore the hypothesis that PCE will be associated with differential DNA methylation at genes and in pathways that have roles in neurodevelopment and neurodevelopmental disease. The findings from this study will contribute to our knowledge around the potential for PCE to influence neurodevelopmental outcomes.
Methods
Avon Longitudinal Study of Parents and Children cohort characteristics
Pregnant women resident in Avon, UK with expected dates of delivery between 1st April 1991 and 31st December 1992 were invited to take part in the study [55, 56]. 20,248 pregnancies were identified as being eligible and the initial number of pregnancies enroled was 14,541. Data is available for 13,988 children who were alive at 1 year of age. A subset of individuals from the Accessible Resource for Integrated Epigenomics Studies (ARIES [57]) at age 0 (N = 858), 7 y (N = 924), and 15 y (N = 922) were assessed for PCE (Table 1). As a longitudinal study, each time point represents the same group of patients, re-sampled at three different times. PCE was characterised based on the self-reported answer to smoked cannabis during pregnancy. Exposure was defined as mothers that responded with ‘every day’ use, 2–4 times per week, <once per week, and once per week. Non-exposed was defined as mothers that responded with ‘not at all’. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool (http://www.bristol.ac.uk/alspac/researchers/our-data/). Consent for biological samples has been collected in accordance with the Human Tissue Act (2004).Cohort Age (y) N Sample Female (%) PCE PCTE PTE Tobacco smoking Cannabis user ALSPAC Timepoint 1 0 858 Cord blood 448 (52.21%) 10 (1.51%) 30 (3.14%) 168 (19.58%) Timepoint 2 7 924 Whole blood 473 (51.19%) 9 (1.08%) 22 (3.46%) 184 (19.91%) Timepoint 3 15-17 922 Whole blood 484 (52.49%) 11 (1.19%) 21 (3.57%) 182 (19.73%) CHDS Timepoint 1 ~27 98 Whole blood 27 (27.5%) 4 (4.08%) 13 (13.2%) 55 (56.1%) 28 (28.5%) 37 (37.5%)
Christchurch Health and Development Study cohort characteristics
The CHDS includes individuals who have been studied on 24 occasions from birth to age of 40 (N = 904 at age 30), with blood collected at approximately age 27. A subset of N = 98 individuals for whom blood samples were available are included in the current study (Table 1). Of these 98 participants, N = 13 were prenatally exposed to cannabis, either with or without co-exposure to tobacco, with N = 85 serving as unexposed controls (neither prenatal cannabis nor prenatal tobacco exposure). Usage data was derived from self-reports from mothers at their birth interviews (undertaken in 1977). Tobacco consumption was measured via four questions: How many cigarettes per day did you smoke a) before you were pregnant; b) during the first three months of pregnancy; c) during the second three months of pregnancy; and d) during the last three months of pregnancy. The answers for each trimester were summed to create a measure of the average number of cigarettes smoked during each trimester. Cannabis exposure was assessed via the following self-report question: During pregnancy, did you take any of the following drugs? (y/n response option), with option a) cannabis. Mode of cannabis consumption in this cohort was via smoking, for all participants. All aspects of the study were approved by the Southern Health and Disability Ethics Committee, under application number CTB/04/11/234/AM10 “Collection of DNA in the Christchurch Health and Development Study”, and the CHDS ethics approval covering the collection of cannabis use: “16/STH/188/AM03 The Christchurch Health and Development Study 40 Year Follow-up”.
DNA extraction and genome-wide methylation profiling for CHDS
DNA was extracted from whole blood samples using the Kingfisher Flex System (Thermo Scientific, Waltham, MA USA), as per the published protocols. DNA was quantified via NanoDropTM (Thermo Scientific, Waltham, MA USA) and standardised to 100 ng/μl. Equimolar amounts were shipped to the Australian Genomics Research Facility (AGRF, Melbourne, VIC, Australia) for processing via the Infinium® Methylation EPIC BeadChip (Illumina, San Diego, CA USA). The arrays were conducted in groups over four batches (2016, 2017, 2020 and 2022). Analysis was carried out in R statistical software (Version 3.5.2) [58].
Data analysis for CHDS
Quality control checks were performed on the raw data: firstly, sex chromosomes and a total of 90 failed probes (detection P value of <0.01 in at least 50% of samples) were excluded from the analysis. Additionally, CpG sites with adjacent single nucleotide variants or that did not map to a single location in the genome were also excluded [59], removing a total of 195,354 probes from the final CHDS analysis. Functional normalisation was performed and was inspected using beta density distribution plots and multi-dimensional scaling of the 5000 most variable CpG sites. Both slide and array positions were corrected for using ComBat (as implemented by ChAMP [60]) to correct for the batch effects between sampling (four batches). Cell proportions were estimated using the Housman algorithm [61].
DNA extractions and analysis for ALSPAC
DNA samples were extracted from cord blood on delivery, and from peripheral blood samples at two time points in childhood (7 y and 15–17 y) following established methods [62], and methylation detection was undertaken using the Illumina Infinium Human Methylation450 BeadChip. Data was available post-normalisation and with estimated cell proportions with the Houseman algorithm [61] for the peripheral blood and the cord blood cell type reference for cord blood samples [63].
EWAS analysis
To partition the data for epigenome-wide analysis, data were separated into several analysis cohorts, by cohort origin (ALSPAC or CHDS), and by age. Specifically, for ALSPAC data, we undertook EWAS at three time points (Table 1); birth, where N = 30 case participants were identified, comprised of N = 10 exposed to cannabis prenatally, “PCE”, plus N = 20 exposed to cannabis and tobacco prenatally, “PCTE”, vs N = 654 controls (neither prenatal cannabis nor tobacco exposure); at 7 y old, where N = 31 case participants were identified, comprised of N = 9 PCE and N = 22 PCTE, vs. N = 700 controls, and at 15 y where N = 32 case participants were identified, comprised of N = 11 PCE and N = 21 PCTE, vs. N = 701 controls. Overall there was missing data on PCE status at each of the time points (N = 30, cord blood; N = 31, 7 y; N = 32, 15 y) and these individuals were removed from further analysis. For CHDS data, we undertook EWAS using N = 13 individuals exposed to either cannabis only (“PCE”) or cannabis and tobacco (“PCTE”) prenatally vs. N = 85 controls (Table 1).
ALSPAC EWAS analyses were controlled for prenatal tobacco exposure, but this was not possible for CHDS due to small sample sizes. Therefore data for ALSPAC are presented as PCE, corrected for prenatal tobacco exposure, while CHDS data are presented as prenatal cannabis and tobacco exposure, PCTE. To overcome the limitation of a small number of PCE individuals in CHDS, which may prohibit direct comparison between ALSPAC and CHDS datasets, we also present data from EWAS of individuals prenatally exposed to tobacco only (“PTE”, N = 55, Table 1) for CHDS, in order to distinguish between cannabis-specific vs. tobacco-associated differential methylation in the CHDS cohort.
The effects of PCE (ALSAPC) and PCTE (CHDS) exposure on methylation were analysed by linear regression with an empirical Bayes correction (limma) for: prenatal tobacco exposure (for PCE/ALSPAC only) (bivariate), adult tobacco status (bivariate), adult cannabis use (bivariate), sex (bivariate), the first principal component of the estimated cell proportions (CD8+, CD4+, granulocytes, B cells, monocytes, natural killer) as covariates. A linear regression model was then fitted to the data with the Q-Q plots of the residuals used to generate lambda values to assess for over-inflation. A similar linear regression model was used to assess prenatal tobacco exposure (PTE, CHDS only), however, the variable prenatal cannabis exposure (bivariate) was corrected for, along with adult tobacco smoking status, sex, and the first principal component of the cell type proportions. Top tables of differentially methylated CpG sites, which were corrected for multiple testing using Benjamini-Hochberg (BH) false discovery rate (FDR), were generated for all models. Differentially methylated CpG sites that were intergenic were matched to the nearest neighbouring genes in Hg19 using Granges [64]. The package ggplot2 (Version 3.3.2) was used to construct all graphs [65].
Gene ontology
We performed gene ontology enrichment analyses to determine which pathways (biological process, BP; molecular function, MF; cellular component, CC) were significantly enriched in EWAS data, at 0 y, 7 y, and 15–17 y (ALSPAC) and at 27 y (CHDS). The top 5000 differentially methylated loci in response to PCE (ALSPAC) or PCTE (CHDS; nominal P < 0.01) were selected and submitted to the EWAS Toolkit [66]. ALSPAC samples were run against a 450 K array background, and CHDS samples were run against the EPIC/850 K array background.
Results
Genome wide methylation analysis of prenatal cannabis exposure in ALSPAC cohort at 0 y, 7 y, and 15–17 y
Individual EWAS were performed for all three-time points in the ALSPAC cohort for PCE. With Benjamini–Hochberg (BH) false discovery rate (FDR) correction for multiple testing, 104 CpG sites were significantly differentially methylated (P adj < 0.05) at 0 y (cord blood, Table 2a, Supplementary Table 1 and Supplementary Fig. 1), 49 of which were hypomethylated, compared to 55 that were hypermethylated. 36 CpG sites were significantly differentially methylated (P adj < 0.05) at 7 y (whole blood, Table 2b, Supplementary Table 2 and Supplementary Fig. 2), 17 of which were hypomethylated, compared to 19 that were hypermethylated. 552 CpG sites were significantly differentially methylated (P adj < 0.05) at 15–17 y (whole blood, Table 2c, Supplementary Table 3 and Supplementary Fig. 3), 198 of which were hypomethylated, compared to 354 that were hypermethylated. The most significantly differentially methylated CpG sites with an associated gene at each timepoint were in the genes TUBB2B (0 y, P adj = 0.0160), LZTS2 (7 y, P adj = 0.0004) and WAC (15–17 y, P adj = 0.00005). All EWAS were assessed for genomic inflation, with lambda values calculated as: 0 y, 0.768; 7 y, 1.301; 15–17 y, 0.794 (Supplementary Fig. 4a–c).Rank IlmnID Gene Name CHR Beta difference logFC P value adj. P val a) 1 cg22272277 7 0.0003 0.0046 1.42E−08 0.0067 2 cg23801012 TUBB2B 6 −0.0005 0.0113 6.94E−08 0.0160 3 cg18488855 NOVA1 14 0.0098 0.0067 2.23E−07 0.0160 4 cg23837191 18 −0.0030 0.0226 2.28E−07 0.0160 5 cg25533519 21 −0.0057 −0.0152 2.31E−07 0.0160 6 cg11818867 OGFR 20 −0.0010 0.0071 3.57E−07 0.0160 7 cg27551657 TAF13 1 0.0001 0.0085 3.65E−07 0.0160 8 cg17463149 PKP1 1 0.0063 0.0103 3.69E−07 0.0160 9 cg21201659 SEC23IP 10 0.0002 0.0029 4.23E−07 0.0160 10 cg09048530 FZD10 12 0.0015 0.0068 4.43E−07 0.0160 11 cg16109817 FLJ37453 1 −0.0027 0.0140 4.64E−07 0.0160 12 cg14528525 C19orf48 19 −0.0007 0.0114 5.31E−07 0.0160 13 cg14932794 TOM1L1 17 0.0003 0.0018 5.78E−07 0.0160 14 cg22256604 STARD3 17 0.0007 0.0081 6.00E−07 0.0160 15 cg08930904 17 −0.0083 −0.0120 6.02E−07 0.0160 16 cg01911440 RPTOR 17 −0.0115 −0.0175 6.05E−07 0.0160 17 cg19141861 COQ5 12 −0.0003 0.0039 6.11E−07 0.0160 18 cg04802236 RPL23 17 0.0002 0.0057 6.71E−07 0.0162 19 cg02742186 CRYL1 13 0.0004 0.0067 7.29E−07 0.0162 20 cg08479688 TARBP1 1 0.0000 0.0063 7.74E−07 0.0162 b) 1 cg10170214 LZTS2 10 −0.0009 −0.0092 9.12E-10 0.0004 2 cg25208479 15 0.0017 0.0115 1.09E−07 0.0254 3 cg16028064 BPTF 17 −0.0002 0.0020 2.39E−07 0.0254 4 cg02850468 NPSR1 7 −0.0403 −0.0222 2.53E−07 0.0254 5 cg23992470 GAK 4 −0.0033 −0.0104 5.15E−07 0.0354 6 cg10872815 PDE7B 6 −0.0069 −0.0109 5.39E−07 0.0354 7 cg00731404 MTIF3 13 0.0000 0.0032 6.65E−07 0.0354 8 cg11681126 ZNF32 10 0.0006 0.0114 7.27E−07 0.0354 9 cg23938542 CRIP2 14 0.0007 0.0035 7.58E−07 0.0354 10 cg00646883 COX18 4 0.0019 0.0111 8.82E−07 0.0354 11 cg17721710 SLC30A10 1 0.0003 0.0024 9.32E−07 0.0354 12 cg14344315 15 0.0034 0.0027 1.08E−06 0.0354 13 cg23762037 TUBGCP6 22 0.0001 0.0016 1.18E−06 0.0354 14 cg19906737 C8orf41 8 −0.0001 0.0048 1.37E−06 0.0354 15 cg13770088 PRRG4 11 −0.0001 0.0021 1.53E−06 0.0354 16 cg18654873 MAP3K7 6 0.0002 0.0038 1.53E−06 0.0354 17 cg18262051 MEPCE 7 0.0046 0.0041 1.65E−06 0.0354 18 cg04010471 13 0.0001 0.0020 1.65E−06 0.0354 19 cg20514239 RBP2 3 −0.0174 −0.0245 1.67E−06 0.0354 20 cg18112005 C14orf80 14 0.0003 0.0043 1.68E−06 0.0354 c) 1 cg13799287 WAC 10 0.0001 0.0063 2.20E-10 5.16E−05 2 cg20249919 PCSK6 15 0.0024 0.0089 2.84E-10 5.16E−05 3 cg14316565 CAT 11 0.0023 0.0077 3.28E-10 5.16E−05 4 cg14703784 RASA3 13 0.0002 −0.0101 6.50E-10 7.68E−05 5 cg05165940 2 0.0049 0.0220 8.29E-10 7.83E−05 6 cg01450600 FAM160B1 10 −0.0069 −0.0183 5.36E−09 0.0004 7 cg13692446 13 0.0009 0.0070 2.16E−08 0.0013 8 cg21108767 PILRB 7 −0.0010 0.0092 3.32E−08 0.0017 9 cg10888941 2 0.0048 0.0068 3.99E−08 0.0019 10 cg14283922 ACBD3 1 0.0017 0.0088 5.44E−08 0.0022 11 cg15994321 ATF6 1 0.0002 0.0033 5.73E−08 0.0022 12 cg03886242 NFE2L3 7 0.0005 0.0017 5.96E−08 0.0022 13 cg16528511 RIPPLY2 6 0.0032 0.0048 7.49E−08 0.0025 14 cg25559490 WRNIP1 6 0.0004 0.0066 9.74E−08 0.0029 15 cg10416994 FBXO21 12 −0.0012 0.0056 1.04E−07 0.0029 16 cg11443159 IRGQ 19 0.0061 0.0068 1.09E−07 0.0029 17 cg16174609 R3HDML 20 0.0005 −0.0145 1.10E−07 0.0029 18 cg23494140 EDNRB 13 0.0021 0.0068 1.27E−07 0.0032 19 cg24635468 NT5E 6 −0.0003 0.0020 1.36E−07 0.0032 20 cg07479092 PLK2 5 0.0037 0.0078 1.46E−07 0.0033 d) 1 cg01483824 GRIN2D 19 −0.1303 −0.1404 1.48E-10 5.63E−05 2 cg00007036 ZNF362 1 −0.1265 −0.1352 2.79E-10 5.63E−05 3 cg24534173 SPATA22 17 −0.2623 −0.2776 2.87E-10 5.63E−05 4 cg16034787 SLC9A3 5 −0.3313 −0.3447 3.75E-10 5.63E−05 5 cg16326123 HDAC4 2 −0.1098 −0.1162 4.79E-10 5.63E−05 6 cg01311063 2 −0.1439 −0.1556 6.12E-10 5.63E−05 7 cg02144266 C20orf135 20 −0.1248 −0.1355 6.44E-10 5.63E−05 8 cg05174710 RUNX1 21 −0.2468 −0.2585 6.44E-10 5.63E−05 9 cg02698990* 6 −0.6245 −0.6284 3.40E−09 0.0003 10 cg19541688 DMWD 19 −0.1395 −0.1461 4.18E−09 0.0003 11 cg10086072* LPAR1 9 −0.2381 −0.2064 1.56E−08 0.0009 12 cg16628641 VASN 16 −0.0697 −0.0784 1.62E−08 0.0009 13 cg01666550 LRP5 11 −0.3310 −0.3487 2.02E−08 0.0011 14 cg26306476 PRR22 19 −0.2331 −0.2449 2.23E−08 0.0011 15 cg16324015* SLC38A3 3 −0.6517 −0.6592 2.88E−08 0.0013 16 cg12525596 GOLGA3 12 −0.6627 −0.6689 4.09E−08 0.0018 17 cg20227471 ADCY3 2 −0.4592 −0.4973 5.56E−08 0.0023 18 cg23367339 ARHGAP23 17 −0.5325 −0.5396 6.34E−08 0.0025 19 cg05262724 PDZD2 5 −0.4150 −0.4301 7.25E−08 0.0027 20 cg15159625 MAN1A2 1 −0.2121 −0.2092 1.34E−07 0.0047 21 cg13470557 C20orf20 20 −0.1508 −0.1654 1.53E−07 0.0051 22 cg18924102* SHANK2 11 −0.6460 −0.6504 7.48E−07 0.0238 23 cg24641214* MOGAT3 7 −0.6265 −0.6210 1.08E−06 0.0325 24 cg14080585 TAF4 20 −0.3175 −0.3323 1.11E−06 0.0325
Overlap of CpG sites between 0 y, 7 y, and 15-17 y associated with prenatal cannabis exposure
EWAS data was investigated for overlap of significantly differentially CpG methylated sites between all time points in the ALSPAC cohort (Fig. 1 and Table 3). Between 0 y and 7 y, one CpG site was shared (cg19141861 COQ5). A total of four CpG sites were shared between 7 y and 15-17 y (cg10170214 LZTS2, cg02850468 NPSR1, cg24635468 NT5E, and cg18576588 CRIP2) and a further two CpG sites shared between 15–17 y and 0 y (cg19201719 DOCK8, cg21836627).IlmnID Gene Name logFC 0 y adj.P.Val 0 y logFC 7 y adj.P.Val 7 y logFC 15-17 y adj.P.Val 15-17 y cg19141861 COQ5 0.0039 0.0160 0.0031 0.0457 cg10170214 LZTS2 −0.0092 0.0004 −0.0072 0.0086 cg02850468 NPRS1 −0.0222 0.0254 −0.0174 0.0379 cg24635468 NT5E 0.0035 0.0362 0.0020 0.0032 cg18576588 CRIP2 0.0040 0.0427 0.0026 0.0045 cg19201719 DOCK8 0.0033 0.0267 0.0060 0.0116 cg21836627 0.0018 0.0282 0.0065 0.0102
Genome-wide DNA methylation analysis of prenatal cannabis and co-exposure with tobacco in the CHDS cohort at 27 y
EWAS was undertaken with the CHDS cohort. With Benjamini-Hochberg (BH) false discovery rate (FDR) correction for multiple testing, 24 CpG sites were significantly associated with either prenatal cannabis (PCE) and cannabis and tobacco (PCTE) exposure (whole blood, Table 2d and Supplementary Fig. 5). All differentially methylated CpG sites were hypomethylated. The most significantly differentially methylated CpG site was in the gene GRIN2D (P adj = 0.00006). The EWAS was assessed for genomic inflation, with lambda calculated as 1.714 (Supplementary Fig. 4d).
Genome-wide DNA methylation of prenatal tobacco only exposure (PTE) in the CHDS cohort at 27 y
To assess the effect of PCE only on DNA methylation, vs. any confounding factors that may be present in co-exposure with tobacco, we performed an additional EWAS on PCE and PCTE individuals, compared to non-exposed controls, with the analysis instead based on prenatal tobacco exposure (PTE), that is, corrected for prenatal cannabis exposure and adult cannabis and tobacco use. A total of two CpG sites both within the gene FRMD4A were significantly differentially methylated (P adj < 0.05), with one additional differentially methylated CpG site in the same not reaching the FDR cut-off for genome-wide significance (Supplementary Table 4 and Supplementary Fig. 6). The EWAS was assessed for genomic inflation, with lambda calculated as 0.838 (Supplementary Fig. 4e)
Overlap of significantly differentially methylated CpG sites between ALSPAC and CHDS
Significant differential methylation at one CpG site (cg01666550) within the gene body of LRP5 (chr11:68181086) was shared between the ALSPAC cohort at age 15–17 y (rank 448 of 552, log fold change −0.0066, P adj = 0.0255) and CHDS cohort at age 27 y (rank 13 of 24, log fold change −0.3487, P adj = 0.0011) in whole blood. Five out of the 24 most significantly differentially methylated CpG sites in the CHDS cohort were probes that were only present on the 850 K array, and were absent from the 450 K array (probes ranked 9, 11, 15, 22 and 22, Table 2d).
Pathway analyses
At 0 y, 7 y, 15 y, and 27 y, gene set enrichment indicated that differential DNA methylation was enriched at loci that are annotated to multiple gene ontology pathways associated with neurodevelopment, neurotransmission, regulation of neural pathways, and neuronal structure, as well as molecular pathways that have associations with neurodevelopmental diseases and disorders (Table 4).GO Description type DMG background p FDR ALSPAC PCE 0 y GO:0048665 neuron fate specification BP 18 34 9.44E−05 1 GO:0048663 neuron fate commitment BP 26 65 1.34E−03 1 GO:0030594 neurotransmitter receptor activity MF 31 115 1.48E−03 1 GO:0001708 cell fate specification BP 34 99 1.76E−03 1 GO:0099566 regulation of postsynaptic cytosolic calcium ion concentration BP 8 12 1.84E−03 1 GO:0004505 phenylalanine 4-monooxygenase activity MF 3 3 2.57E−03 1 GO:0061304 retinal blood vessel morphogenesis BP 5 6 4.14E−03 1 GO:0016616 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor MF 28 118 4.54E−03 1 GO:0004970 ionotropic glutamate receptor activity MF 10 19 4.60E−03 1 GO:0007379 segment specification BP 8 17 4.95E−03 1 ALSPAC PCE 7 y GO:0051899 membrane depolarisation BP 31 91 0.0003 1 GO:0017156 calcium ion regulated exocytosis BP 46 152 0.0007 1 GO:0043196 varicosity CC 6 8 0.0010 1 GO:0016079 synaptic vesicle exocytosis BP 38 118 0.0010 1 GO:0005854 nascent polypeptide-associated complex CC 4 5 0.0010 1 GO:0098797 plasma membrane protein complex CC 134 563 0.0012 1 GO:0003714 transcription corepressor activity MF 62 233 0.0014 1 GO:0032892 positive regulation of organic acid transport BP 12 33 0.0015 1 GO:0046717 acid secretion BP 35 124 0.0015 1 GO:0098742 cell-cell adhesion via plasma-membrane adhesion molecules BP 69 271 0.0016 1 ALSPAC PCE 15-17 y GO:1901533 negative regulation of hematopoietic progenitor cell differentiation BP 6 8 0.0009 1 GO:0050905 neuromuscular process BP 36 107 0.0012 1 GO:0032885 regulation of polysaccharide biosynthetic process BP 17 37 0.0016 1 GO:0035254 glutamate receptor binding MF 20 46 0.0024 1 GO:0032881 regulation of polysaccharide metabolic process BP 18 42 0.0025 1 GO:0005979 regulation of glycogen biosynthetic process BP 14 29 0.0025 1 GO:0010962 regulation of glucan biosynthetic process BP 14 29 0.0025 1 GO:0031646 positive regulation of neurological system process BP 25 63 0.0028 1 GO:0060364 frontal suture morphogenesis BP 4 4 0.0032 1 GO:0006750 glutathione biosynthetic process BP 9 17 0.0035 1 CHDS PCTE 27 y GO:0022038 corpus callosum development BP 10 16 0.0006 1 GO:0035927 RNA import into mitochondrion BP 4 5 0.0009 1 GO:0010842 retina layer formation BP 12 22 0.0013 1 GO:0060563 neuroepithelial cell differentiation BP 21 53 0.0016 1 GO:1900086 positive regulation of peptidyl-tyrosine autophosphorylation BP 4 4 0.0016 1 GO:1902285 semaphorin-plexin signalling pathway involved in neuron projection guidance BP 9 13 0.0018 1 GO:0045747 positive regulation of Notch signalling pathway BP 21 52 0.0018 1 GO:0021532 neural tube patterning BP 17 40 0.0018 1 GO:0061478 response to platelet aggregation inhibitor BP 8 13 0.0029 1 GO:0072300 positive regulation of metanephric glomerulus development BP 4 5 0.0033 1
Discussion
To date, limited research has explored molecular changes in offspring associated with prenatal cannabis exposure (PCE). In this study, we performed epigenome-wide association studies of PCE, and prenatal cannabis and tobacco exposure (PCTE), in two independent cohorts at four time points; 0 y, 7 y, 15-17 y, and ~27 y. Our findings show that both PCE and PCTE are associated with genome-wide significant DNA methylation differences at all time points, in both cohorts, at genes, and in pathways associated with neurodevelopment. These results provide molecular evidence in support of the epidemiological associations between PCE and impacts on neurodevelopment. Further, they provide justification for larger studies in this area and support further conversations around the need for guidelines to discuss a reduction in prenatal cannabis exposure.
Prenatal cannabis exposure and differential DNA methylation in the ALSPAC cohort
To assess the impact of PCE on DNA methylation we undertook EWAS at three time points, including birth (0 y), early childhood (7 y), and adolescence (15–17 y), in the ALSPAC cohort. The top PCE-associated differentially methylated CpG site at 0 y was in the gene TUBB2B. This gene is associated with malformations of cortical development [67], and mutations in TUBB2B are associated with developmental delays [68]. The top CpG site displaying differential methylation at 7 y was in the gene LZTS2. Differential methylation of LZTS2 is associated with cross-generational effects of THC exposure in rat nucleus accumbens [48]. Further, LZTS2 is associated with major depressive disorder [69]. Lastly, the top differentially methylated CpG site at age 15 y was in the gene WAC, which has been linked to severe intellectual disability [70].
We detected varying abundances of differentially methylated loci between early childhood and adolescence in these data, which was not unexpected; it is well established that genome-wide levels of DNA methylation change over time and that stochastic methylation changes occur during the ageing process [71], and this pattern reflected in these data. However, the varying methylation abundance with age also means that identifying consistent changes between childhood, adolescence, and adulthood, where differential methylation is expected to be more prominent [71], based on each individual’s environmental exposures, and related to exposures during development, is challenging. Despite this challenge, in these data, we identified shared differential DNA methylation at individual CpG sites between different time points examined in the ALSPAC cohort. Specifically, between individuals at 7 y and 15 y, differential DNA methylation was shared at 4 CpG sites. These sites were in or near the genes LZTS2 (discussed above), NPSR1, NT5E, and CRIP2. NPSR1 is associated with educational attainment [72] and amyloid beta measurement [73]. NT5E may have a role in excitatory neurotransmission [74]. CRIP2 currently has no known roles in neurodevelopment or neurodevelopmental disorders, however, is associated with heart development [75, 76] which is intriguing as growing evidence suggests that PCE is associated with impacts on heart development [77], and further, adult cannabis use is known to impact DNA methylation in biological pathways associated with cardiomyopathy [78]. Next, we detected significant differential DNA methylation shared between individuals at 0 y and 15 y at 2 CpG sites, in response to PCE. These sites were in the gene DOCK8, and an un-annotated CpG, with probe ID cg21836627. DOCK8 is associated with anxiety [79], opioid dependence [80], and autism spectrum disorder (ASD) [81]. The probe cg21836627 is not within or directly associated with a gene (chr6:30,069,797). However, this CpG site is 877 bp upstream of TRIM31 (chr6:30,070,674-30,080,867), which is associated with cognitive performance/intelligence [82, 83], memory [83], unipolar depression [83, 84], ASD and schizophrenia [85], all of which are epidemiologically associated with PCE. Differential methylation at one site was shared between 0 y and 7 y, and this site was in the gene COQ5. COQ5 is a methyltransferase enzyme [86] that is located in the mitochondria. COQ5 has associations with educational attainment [87], global developmental delay [88], and intellectual disability [89]. Taken together, these findings imply an impact of PCE on DNA methylation at genes with roles in neurodevelopment, across childhood and adolescence.
Given these data indicate an impact on genes involved in neurodevelopment, we undertook gene ontology enrichment analyses to determine whether this pattern was supported at the molecular pathway level. Our data indicated that differential methylation is significantly enriched at pathways associated with neurodevelopment, but also neurotransmission, regulation of neural pathways, and neuronal structure, at 0 y, 7 y, and 15-17 y. The signature of enrichment, in response to prenatal exposure, supports the principle that many neurodevelopmental disorders and diseases have developmental origins, and is also supportive of the epidemiological associations between PCE and neurodevelopment. For example, PCE is associated with consequences for neurodevelopment [25], potentially via disruption of the developing central nervous system by cannabinoid exposure [36, 90], which could impact neuron development and growth [91]. This potential impact on the developing nervous system may be linked to the association between PCE and an increase in psychosis and psychopathology in middle childhood [24, 92], and the greater risk of psychiatric disorders into adolescence [93], including affective symptoms and ADHD [94]. Further, PCE is associated with impacts on memory and behaviour in children and adolescents, and this impact persists into adulthood [26]. Thus, along with the frequent and wide-ranging reviews on the association between PCE and neurodevelopment and behaviour across the lifespan [39, 95], and recent evidence of an impact of PCE on DNA methylation at genes involved in autism in animal models [49], taken together, these data support a role for DNA methylation in the biological response to PCE.
Prenatal cannabis and tobacco exposure and differential DNA methylation in the CHDS cohort
In the CHDS cohort, we detected 24 significantly differentially methylated CpG sites, in individuals exposed to cannabis and tobacco during development, compared to controls. The most significantly differentially methylated sites were in the genes: GRIN2D, which is a glutamate receptor gene that functions in synaptic transmission and is involved in long-term potentiation/learning and memory [96, 97]; ZNF362, which has associations with individual schizophrenia symptom severity [98]; SPATA2, which is involved in fertilisation, but also intelligence [99], attention deficit hyperactivity disorder (ADHD) and ASD [100]; SLC9A3, an Na+/H+ exchanger, classes of which have an emerging role in developmental brain disorders [101], and finally; HDAC4, which has been associated with ADHD, uni- and bipolar depression, ASD and schizophrenia [102], educational attainment [87], as well as a prominent role in Brachydactyly Mental Retardation Syndrome, symptoms of which commonly include cognitive delay [103, 104] and autistic-like behaviours [105].
Gene ontology enrichment analysis for CHDS confirmed that differential methylation of genes involved in neurodevelopment and neurodevelopmental disorders were supported at the pathway level. We identified enrichment for the terms neuroepithelial cell differentiation, neural tube patterning, and neuron projection guidance, indicating enrichment for genes associated with neurogenesis and neural connections. Additionally, differential methylation is enriched at genes associated with corpus collosum development. The corpus collosum is consistently found to be reduced in size in patients with ASD [106] and schizophrenia [107], and is thought to have a role in neurodevelopmental disorders such as ASD and ADHD [108]. Further enriched is the GO term ‘positive regulation of peptidyl-tyrosine autophosphorylation’. This GO term is enriched in studies of ASD [109] and also neurodevelopmental disorders/neuromotor development [110]. Finally, loci within the Notch signalling pathway are enriched in these data. Notch is integral in nervous system development and influences plasticity and learning in the adult central nervous system [111]. Altered Notch signalling has been associated with ASD [112], schizophrenia and bipolar disorder [113]. Thus, taken together, pathway enrichment analyses support the finding that the most significantly differentially methylated loci function in neurodevelopment and neurodevelopmental disorders, as well as provide molecular evidence in support the epidemiological associations of a link between PCE and these disorders.
To understand whether an analysis of cannabis and tobacco exposure (PCTE), in combination with cannabis-only exposure (PCE), was skewing these data in favour of tobacco exposure-driven differential methylation, we assessed the same DNA methylation data and asked the extent of differential DNA methylation in response to prenatal tobacco exposure alone (PTE), corrected for PCE and adult cannabis and tobacco use. This EWAS detected genome-wide significant DNA methylation at two CpG sites, both within the gene Ferm domain containing 4 A (FRMDA4). Previous studies have found FRMDA4 to be differentially methylated in response to prenatal tobacco exposure, with the strongest effects observed in sustained prenatal tobacco smoking [114]. Thus, while we were unable to control for PTE in our CHDS analysis, our EWAS observations of both PCTE and PTE support a distinct pattern of differential DNA methylation that is driven by prenatal exposure to cannabis, in adult offspring.
Overlap in differentially methylated CpG sites between the ALSPAC and CHDS cohorts
One CpG site in the gene LRP5 was differentially methylated in CHDS adults and in the eldest time point (15–17 y) in the ALSPAC cohort. LRP5 has not yet been associated with PCE, and therefore may represent a novel target for PCE. The LRP5 gene codes for low-density lipoprotein receptor-related protein 5, and differential methylation of CpG sites within this gene have previously been associated with passive smoking exposure [115], and implicated in the proposed link between cigarette smoking habit and metabolic syndrome [116]. Moreover, it has associations with individual educational attainment [72] and mathematical ability [87], which supports further investigation of this gene for its role in the molecular link between PCE and offspring neurodevelopment.
Strengths and challenges associated with cross-cohort DNA methylation analyses
It is important to acknowledge the challenges associated with our study. Firstly, while our study identifies DNA methylation changes at loci and pathways that support the epidemiological associations of PCE, at all time points, which reflects the strength of our hypothesis, our study consists of a relatively small number of individuals with prenatal exposure to cannabis, and as such, we must emphasise the need for replication in a more highly powered study cohort. Secondly, using independent data from multiple cohorts, as we have done here, can pose additional challenges, including those associated with attempting to validate differential methylation between samples at different ages, and differences between workflows, including normalisation and quality control steps, where raw data is not available. Further, combining and validating across age points and independent datasets is even more challenging when one considers confounding factors such as tobacco, alcohol, and other drug use; these common confounders lead to heterogeneity in results cross-cohort, and along with sociodemographic factors, makes uniformity across studies hard to achieve [117]. This is pertinent when we consider the genomic inflation values for the 15–17 y time point; the lambda value here indicates a slight inflation. We predict that this is due to the available variables from the ALSPAC study; clinical data was only collected at 7 y, meaning we were unable to correct for personal tobacco smoking status, which is relevant at this time point. We suggest that this might be a possible explanation for the inflation value and the increase in the number of differentially methylated probes observed at this time point, however, without access to more clinical data, we are unable to correct for this in our EWAS model. Therefore, while identifying independent cohorts that reliably and consistently measure all possible confounding variables is not possible or feasible, it nevertheless remains a limitation of studies such as this. We also acknowledge the limitations that surround the necessary use of DNA from blood samples in this study, which we are aware may or may not reflect changes in the brain. Furthermore, due to the aforementioned limitations around study size, it was necessary to analyse PCE in combination with tobacco (PCTE) for one cohort, and we acknowledge that, despite controlling for tobacco exposure, we cannot ignore this as a potential confounder in these data. Nevertheless, while both cannabis and tobacco can result in different changes to the methylome, and while there is a large proportion of PCE individuals who have co-exposure with tobacco, we demonstrated that many significant CpG sites are unique to prenatal cannabis exposure in the CHDS. Moreover, we demonstrate that each dataset, whether PCE or PCTE, is enriched for similar/shared biological pathways, giving us confidence that our data are supportive of a biological role for DNA methylation in the association between PCE and neurodevelopment, and that further investigation in larger cohorts is required.
Conclusion
These data provide hypothesis-building insights into molecular alterations associated with prenatal cannabis exposure. The association between DNA methylation and genes involved in neurodevelopment and neurodevelopmental disorders in the present study may suggest a role for DNA methylation in the link between PCE and neurodevelopmental outcomes in exposed children, as well as provide a suite of reference genes for future investigations into the molecular mechanism of the impact of PCE on individuals. While both of our two independent cohorts contain modest sample numbers of exposed individuals, finding suitable cohorts with defined and comprehensive data on PCE is a difficult task, and the biological relevance of these data to the phenotypes being studied provides justification that PCE should be investigated strategically and specifically, in much larger sample sizes. Given the increasing prevalence of PCE, further research is required to identify the underlying mechanisms that may underlie the effects of PCE on neurodevelopment, in order to start to establish whether PCE is causative for neurodevelopmental impacts. Moreover, these data will help to fill current gaps in our knowledge around how drug exposures during development may lead to lifelong molecular disturbances that predispose an individual to poor neurocognitive outcomes in early childhood, adolescence, and beyond [39].
Supplementary information
Supplementary information
The online version contains supplementary material available at 10.1038/s41380-024-02752-w.
Acknowledgements
The authors are grateful to Matthew Walters for assistance with visual design. CHDS This work was supported by a Major Project Grant from the Canterbury Medical Research Foundation (MPG2021-Osborne, awarded to AJO and AJN). The Christchurch Health and Development Study is supported by the Health Research Council of New Zealand (HRC, Programme Grant 16/600). ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. This publication is the work of the authors and Amy Osborne and Alexandra Noble who will serve as guarantors for the contents of this paper. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grant funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf); In addition to the above, this research was specifically funded by The Wellcome Trust and MRC (Core) grant 076467/Z/05/Z. Methylation data was funded as per the below: Child: Data: ARIES; Funder: BBSRC; PI: Caroline Relton; Grant references: BBI025751/1 and BB/I025263/1. Funder: IEU grant codes; PI: George Davey Smith; Grant references: MC_UU_12013/1 & MC_UU_12013/2 & MC_UU_12013/8. Funder: National Institute of Child and Human Development grant; PI: Ted Barker; Grant references: R01HDO68437; Funder: NIH; PI: Hongmei Zhang; Grant references: 5RO1AI121226-02. Funder: CONTAMED EU collaborative Project; PI: Andreas Kortenkamp; Grant references: 212502
Funding
Open Access funding enabled and organized by CAUL and its Member Institutions.
Data availability
CHDS – Data available on request.
Code availability
The computer codes used to generate these results may be requested via the corresponding author.
Competing interests
A.J.O, A.J.N, A.T.A, and J.M.B reported no biomedical financial interests or potential conflicts of interest. J.S. is a Director of the UK Inflammatory Bowel Disease Registry.