Three Years Follow-Up Study Showed Prenatal Methamphetamine Exposure May Cause Chronic Abnormalities In Transcriptomic Pattern

doi:10.21203/rs.3.rs-668566/v1

Background: The high rate of methamphetamine use disorder among young adults and women of childbearing age makes it imperative to clarify the long-term effects of Methamphetamine exposure on the offspring. Behavioral and cognitive problems had been reported in children with parental Methamphetamine exposure (PME). The present study aimed to assess the acute and chronic effects of PME in molecular regulations and gene expression profiles of children during their first years of life.

Methods: All subjects were recruited before birth, and sampling was conducted from the first ten days of birth, twelve months, twenty months, and thirty-six months of age. Finally, 2658 children with PME and 3573 normal children had been finished the follow-up. RNA extraction was operated from blood samples and gene expression profiling was conducted by using the Affymetrix GeneChip Human Genome U133 plus 2.0 Array Platform. Gene expression data were confirmed by Real-time PCR.

Results: Gene expression profiling during thirty-six months showed several constant mRNA level alterations in children with PME compared with normal. These genes are involved in several gene ontologies and pathways involved with the immune system, neuronal functions, and bioenergetic metabolism. It seems that Methamphetamine use disorder before and during the pregnancy period may affect the expression profile of children, and these changes could remain years after birth. Affected genes have some similarities with the gene expression patterns of addiction, psychiatric disorders, neurodevelopmental disabilities, and immune deficiencies.

Conclusion: Findings may shed light on the molecular effects of prenatal methamphetamine exposure and may lead to new psychological and somatic caring protocols for these children based on their potential abnormalities.

General Microbiology

prenatal methamphetamine exposure

gene expression

microarray

follow up

Methamphetamine (MA) is a psychostimulant, neurotoxic drug that causes chronically psychological dependence in the use disorder. Methamphetamine (MA) use disorder is a global problem, and its use disorders are more than cocaine and opiates combined (1). Gender analysis showed that female first-time users of methamphetamine are more than male first-time users (2). On the other hand, only less than fifty percent of those women are seeking treatment. In the United States, about seven percent of MA dependents that were referred to clinics for treatment were pregnant women (3). In Iran, Methamphetamine use is not longer than three decades, but unfortunately, the prevalence of Methamphetamine use disorder is fast growing. Also, while the total percentage of women addicts from all Iranian addicts are about 10 percent, the percentage of women with Methamphetamine use disorder from all Methamphetamine users are about 40 percent. Taken together, prenatal methamphetamine exposure could be a major health concern in Iran's future.

Previous studies about the effects of prenatal methamphetamine exposure on child development showed an increase in gestational size at birth and decreased length through three years. Deficiency in movement and arousal along with increased stress signs in the newborn period and poor grasping ability at one to three years old children were reported(4). Further studies in the age of three to five years have been shed light on attentional issues such as inhibitory control, aggressive behavior, and adjustment issues that could be related to abnormalities in the development of executive function in prenatal methamphetamine exposure children(5, 6).

Acute and chronic methamphetamine use disorder and dependents show severe behavioral problems, including addictive behaviors (such as craving, salience) and psychotic symptoms (such as aggressiveness, hallucination, and dilution). Also, individuals with methamphetamine dependence have a higher risk of schizophrenia (7). The workgroup recommendations for DSM-5 revisions suggested combining use disorder and dependence criteria into a single substance use disorder (2). While it is accepted that methamphetamine is a highly addictive substance with widespread and constant effects in the central nervous system (CNS), its molecular mechanisms on adults and fetuses are not clarified. Methamphetamine increased blood-brain barrier (BBB) permeability by rearrangement of F-actin cytoskeleton and reduction of tight junction (TJ) proteins (8). Mouse model experiments were reported that increased total DNA methylation in the mPFC could be associated with mPFC malfunction and long-term cognitive decline (9).

While there is a lack of information about the molecular mechanisms of methamphetamine and its effects on the development of CNS, neuroimaging studies provide valuable evidence about the role of prenatal methamphetamine exposure in functional and structural changes in the brain (10). Methamphetamine use disorder in adult persons could decrease the metabolism in the insula and frontal cortex and increase in basal ganglia (11). However, diffusion tensor imaging (DTI) studies have reported that prenatal exposure to methamphetamine is more associated with white matter microstructure alterations (12). Alteration such as diffusion measures in white matter connections and reduction of fractional anisotropy in several connections between the striatum and midbrain (12, 13).

Based on some studies, mood difficulties, cognitive deficiencies, and chronic behavioral abnormalities are increased in PME children in childhood and maybe adolescence (13). Early detection of vulnerable children and psychological caring may prevent severe social and psychiatric problems in the next generation. That is why a general pattern of all alterations caused by prenatal methamphetamine exposure in molecular, neuroanatomical, and behavioral levels is highly needed (13).

The present study aimed to comprehensively evaluate the acute (or early) and chronically (or long-standing) effects of prenatal methamphetamine exposure in gene expression profile, behavior, and neuronal activities in the brain.

We followed up a large sample size of children with prenatal methamphetamine exposure from date of birth to thirty-six months of age by using a functional genome-wide profiling approach.

Subject selection

Participants were recruited from a national cohort project of methamphetamine addiction (NCPM) in Iran, with over twenty thousand methamphetamine-dependent subjects. Infants were referred from female subjects of NCPM to PME group based on voluntary maternal self-report of methamphetamine use disorder during pregnancy, confirmed by positive meconium screen, and gas chromatography or mass spectroscopy confirmation. PME infants and mothers were matched to a normal group for maternal race, age of mother and father in pregnancy time, birth-weight category (<1500 g, 1500–2500 g, >2500 g), health care insurance situation, and education in comparison of infant-mother pairs.

Sociodemographic and prenatal substance use details obtained from interview with mothers (4). Study assessments and sampling were conducted five times when infants were 1, 12, 24, 30, and 36 months of age. Mothers with less than twenty years of age, with any records of opioid, or any other use disorder during the pregnancy, any records of severe emotional disorders or cognitive dysfunctioning, or psychotic abnormalities, or antipsychotic usage were excluded from the study. Children with exclusion criteria including critical illness, multiple gestations, life-threatening congenital anomaly, chromosomal abnormality, overt clinical evidence of intrauterine infection, or a sibling previously enrolled in the study. The occurrence of any severe somatic or psychological disease or changes in lifestyle such as immigration, adaptation, or parents' divorce during the thirty-six months of follow-up were lead to the exclusion of subjects from the study. Maternal use of alcohol and tobacco and smoking during pregnancy was considered as background variables in both PME and normal comparison groups. Heavy methamphetamine exposure was defined as maternal use at least three days per week across pregnancy. The pattern of use, according to trimester and was showed in Table 1. Indicated overall decline and quitting methamphetamine use throughout the pregnancy. The level of exposure to other drugs of use disorder was calculated as cigarettes per day, ounces of absolute alcohol per day.

Written consent form filled by all mothers after explaining the aim of study for each subject in private. The study was approved by the local ethical committee based on Helsinki declaration obligations.

Blood sampling and Microarray Analysis

Blood samples (5 ml, PAXgene vacutainer blood RNA-tubes; PreAnalytiX, Hombrechtikon, Switzerland) were collected between 10.00 and 11.00 AM. Total RNA was extracted from peripheral blood samples immediately after sampling by column Purification kit (GeneJET™ RNA Purification Kit#K0732, Thermo scientific, Latvia). RNA quality and integrity were examined by Agilent 2100 Bioanalyzer (Agilent Technologies) before the beginning of the microarray process. Gene expression profiling analyses were conducted by using the Affymetrix GeneChip Human Genome U133 plus 2.0 Array Platform containing probes representing 39,000 genes. Preparation of labeled and fragmented aRNA targets, hybridization, and scanning was carried out according to the manufacturer's protocol (Affymetrix Santa Clara, CA). A hundred nanograms of total RNA for each sample were processed using the GeneChip 3′ IVT Express Kit. RNA was reverse transcribed and converted to double-stranded cDNA before biotin labeling during in vitro transcription. In next step, fifty micrograms of labeled aRNA were fragmented, and quality control (QC) was carried out using the Agilent Bioanalyzer. Fragmented aRNA was hybridized on GeneChip Human Genome U133 Plus 2.0 arrays for sixteen hours at 45°C. Finally, arrays were washed and stained by using the GeneChip Hybridization, Wash, and Stain Kit on the GeneChip Fluidics Station 450. Then chips were scanned using the Affymetrix GeneChip Scanner 3000, and all arrays passed the QC criteria examination (14).

The GeneChip analysis was performed in Genesis 2.0 (Gene Logic Inc.) and with Microarray Analysis Suite (MAS) 5.0, Data Mining Tool 2.0, and Microarray Database software (available at http://www.affymetrix.com) (14). All of the genes represented on the GeneChip were globally normalized and scaled to a signal intensity of a hundred. Genes that passed MAS 5.0 criteria for false-positive results reduce included to further assessment. Expression data were analyzed using Genesis 2.0 (GeneLogic Inc., Gaithersburg, MD, USA) and DAVID software (Strand Genomics, Redwood City, CA, USA). Gene expression values were floored to 1, and then log2 transformed. Analysis of variance test (ANOVA) was performed for each gene to identify significant gene expression alteration. Genes with statistical significance were subjected to a post hoc t-test for evaluation of contrasts groups one by one for identification of differences between groups. Two criteria were used in all subjects to determine whether a gene was differentially expressed; a Fold change greater than 2 and a P value less than 0.05. Then P values adjusted for multiple testing correction.

Pathways and gene ontology analysis

Functional enrichment was conducted on differentially expressed data used for all comparisons. For functional annotation, gene ontology analysis was performed for differentially expressed genes with enrichment algorithms integrated into the online Database for Annotation, Visualization, and Integrated Discovery (DAVID 6.8) (15, 16). To enrichment analysis of the differentially expressed genes (DEGs) and calculated the enrichment factor (EF) for each term or pathway, Kyoto encyclopedia of genes and genomic (KEGG) pathway onloine data base were used. The P values were calculated via hypergeometric tests and go with a correction (17). Only those GO terms or KEGG pathways with enrichment of corrected P < 0.05 and EF > 2.0were included.

Biomarker assessment based on the k-nearest neighbor algorithm

Detection of most reliable and potential predictor biomarkers and footprint of prenatal methamphetamine exposure effects that could be potentially used for clinical and prognostic aspects are highly desired. Microarray data had analyzed by using Bioconductor and GeneFilter package from R programming language for the removal of low-intensity features. The GeneFilter package was used to filter and select genes from the microarray dataset according to a variety of different filtering mechanisms. Then the k-nearest neighbor (KNN) classification was performed using leave-one-out cross-validation. Commonly, the KNN algorithm assumes that similar things exist in close proximity (18). This is a supervised machine learning algorithm that recently draws attention to use for classification and regression models in molecular biology. First, to the exclusion of any false positive and false negative results DEGs that were meet the criteria (Fold change > 2, multiple comparisons corrected P-value < 0.05, and intensity threshold > 2000) in all four-time points were selected. The function returns the predicted classifications as its returned value. To achieve the most corrected predictive model, the K range between 6 to 12 and Area Under the curve (AUC) performance between 0.6 to 1.00 in receiver operating characteristic (ROC) assumed as selective criteria. only one model meets these criteria which included 17 genes from all 26 genes with DEGs criteria mentioned above. These 17 genes were considered for confirmation with qPCR. classification conducted based on previous studies (16).

Confirmation by quantitative Real-Time PCR

To testing the reproducibility of the predictive PME biomarkers model, and also for the regular confirmation of array data's reliability, mRNA levels of 17 selected genes (fold change>2 and p<0.05) were quantified by quantitative RT-PCR in all subjects. Blood sampling, RNA extraction, and cDNA synthesis were repeated with the same method used for DNA microarray. Primers for all selected genes were designed by "oligo7" software and blasted on the NCBI website to check the specificity. Quantitative PCR was performed by using SYBR green (Thermo Scientific Maxima SYBR Green/ROX qPCR Master Mix (2X) #K0221, Fermentas, Latvia). Triplicate method performed for Quantitative Real-time PCR by using the 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). GAPDH is used for normalization as an endogenous reference gene.

Statistical analysis

Statistical analysis analyzed by SPSS, version 26. Descriptive data are expressed as mean ± SD (range), and the level of statistical significance was set at P < 0.01. Compliance with normal distribution for continuous variables was assessed via the Kolmogorov-Smirnov test. One-way ANOVA analysis was used for statistical differences in multiple group comparisons. Pearson correlation test was used for the evaluation of relations between gene expression data and clinical results. Pearson correlation analysis was conducted to determine the relationship between variables. Bonferroni correction was used for multiple comparison corrections. Body mass index (BMI), RNA quality and concentration, cDNA synthesis quality, plates/runs of qPCR, and primer efficiency were added as covariates, and persistence of the significant difference in the main effect between groups was assessed by ANCOVA to control any potential confusion.

Maternal and neonatal characteristics were examined for significant differences between the PME group and the normal group (Table 1). No difference was found in the baseline and demographic characteristics between groups. At the beginning of the study, 3560 PME infants, including 2217 boys and 1343 girls, and 4120 normal infants, including 2252 boys and 1868 girls, were recruited. At the end of the 36 months follow-up in the PME group, only 2658 children, including 1287 boys and 930 girls, and in the normal children group, 3573 children including 2064 boys, and 1509 girls remained in the project and were participated in all terms of sampling and testing. Only these 2658 children in the PME group (1287 boys / 930 girls) and 3573 normal children (2064 boys / 1509 girls) were analyzed for the study, and other children were excluded from the project.

DNA microarray results

Subjects of each time point were considered separately and DEGs were selected based on at least 2fold change differentiation and corrected p-value less than 0.05. Initial filtering with MAS 5.0 based on significance, fold change, and the effect size was showed 1234 genes differently expressed (626 up-regulated genes,608 down-regulated genes) in PME infants vs. normal infants. In twelve months sampling 1103 genes (572 up-regulated genes,531down-regulated genes), in twenty-four months sampling 976 genes (561 up-regulated genes,415 down-regulated genes), and thirty-six months sampling 1006 genes (628 up-regulated genes,378 down-regulated genes) were differently expressed in PME children vs. normal children. Final results were calculated after FDR correction (p<0.01), the ANOVA, and post hoc analysis (p<0.01) along with Bonferroni multiple-comparison correction. Details list of differentially expressed genes in microarray in all comparisons between PME groups and normal subjects have been presented at supplementary data. There were 826 genes whit altered expression during the whole 36 months of life in PMEs. Gene lists and venn plots of DEGs in each sampling during the follow-up are shown in supplementary file table S1 and part A of figure 1.

DAVID and KEGG analysis

To reveal the molecular and cellular functional characteristics and pathways affected by PME, all of the DEGs were subjected to enriched gene ontology calculations, pathway analysis by DAVID and KEGG pathways. Most of the constant DEGs during the follow-up period were involved in eighteen gene ontologies and three pathways including immunodeficiency, synapse formation, and respiratory chain complexes. DAVID gene ontology findings were presented in supplementary file table S2 and part B of figure 1. KEGG pathways findings were presented in supplementary file table S3 and part B and C of figure 1 and also figure 2.

Potential biomarker prediction analysis

QPCR confirmation was conducted on 17 DEGs to validate changes in gene expression from the microarray analysis. Genes were selected based on fold changes, p-value, intensity threshold, and KNN classification algorithm. Real-time PCR confirmations in these significantly altered genes were showed the same alterations in the same directions as those observed in the microarray. All genes in this list were showed the same direction in expression in all follow-up samplings. The comparison of the fold change obtained from qPCR and microarray determined a significant correlation between techniques (P < 0.0001,r = 0.874) that shows confirmations by qPCR indicated a strong reproducibility of gene expression results. Results of biomarker assessments showed a shortlist of candidate genes with high sustained during the follow-up, which was determined in supplementary file table S4 and figure 3. Also, ROC analysis of 17 biomarkers was presented in supplementary file table S5. Dopamine receptors, dopamine degradations, and FOXP2 as human accelerator genes were showed the most sensitivity and specificity during the follow-up samplings. Primers of analyzed genes in qPCR are presented in table 2.

The present study aimed to shed a light on the big picture of molecular changes in prenatal methamphetamine exposure during the first three years of life. Findings showed that three molecular pathways, including Primary immunodeficiency, Apoptosis, and Parkinson's disease are mostly affected by PME. Also, abnormalities in the bioenergetic, dopaminergic, and synaptic processes were detected at all time points. Based on expression profiling data a gene list of biomarkers was selected by machine learning to use as a potential model to diagnosis the PMEs.

Recently studies have focused on the effects of prenatal exposure to CNS stimulant drugs (19). Although there are several reports about the molecular, cognitive, and brain structure of methamphetamine use disorder, there is a great lack of information about PMEs. Few animal studies reported the PME effects on molecular pathways in the brain (20, 21, 22). In addition, some psychological studies evaluated the cognitive effects of PME in children at the age of six or seven or adolescence (23, 24).

Genome expression profiling on methamphetamine-treated animal model studies showed changes in critical genes for brain functions, including neuronal plasticity, mitochondrial energy metabolism, and immune response (25). METH treatment induction on mitochondrial energy metabolism and associated glutamate receptor alterations as a neurotoxic response in the brain (including the amygdala, prefrontal cortex, hippocampus, and striatum) of rats accompanied with a significant behavioral sensitization had reported (26, 27).

The present study may suggest that PME may induce chronic bioenergetic deregulations in children that, in turn, could increase the possibility of severe psychiatric disorders caused by mitochondrial dysfunction. Mitochondrial dysfunction especially in complex I of the respiratory chain are biomarkers for depressive mode (28,29) as well as psychosis and several cognitive dysfunctions in schizophrenia and bipolar disorder (30).

Neuron projection, which is the most affected cellular component during the follow up is any process extending from a neural cell, such as axons or dendrites, and is related to addiction tendency and stress problems, and changes in the brain circuitry (31, 32). Synaptic transmission and transmission of nerve impulses are two affected biological processes that are essential for normal cognitive functions such as learning and memory (33). Dysfunction in synaptic transmission and transmission of nerve impulses had been reported in several psychiatric disorders such as schizophrenia and autism (34, 35). Findings may explain the learning and cognitive problem (36, 37) and even anatomical changes in the brain of PMEs, such as reduced caudate and thalamus volume (38).

Probably the major and severe primary immunodeficiency is the most worrying effect of PME observed in the present study. METH treatment may cause an inflammatory response that plays a potential role in METH-induced neuronal injury and deregulation of cellular immune responses (39, 40). METH also could increase blood-brain barrier (BBB) permeability that may explain similarities between PMEs and methamphetamine use disorders (8), but taking together with primary immunodeficiency it could be considered as a risk factor for CNS infections (41). Immunodeficiency caused by methamphetamine can get escalated when accompanied by mitochondrial damage and increased levels of oxidative stress (42). By the way, primary immunodeficiency diseases (PIDs) are heterogeneous disorders, and many PIDs patients are diagnosed late. Due to poor prognosis, many cases suffer from complications by the burden of chronic infections, irretrievable end-organ damage, or even death before the definitive diagnosis (43, 44). Results revealed that PMEs are born at risk of lethal immunodeficiency disorders, but the bright side is that the targeted prognosis of PMEs may lead to timely diagnosis and appropriate treatment of those PMEs who may affect by PIDs.

Recent epidemiological cohort studies showed that methamphetamine use disorder increases the risk for developing Parkinson's disease (PD) and there are similar neurodegenerative processes in PDs and Meth use disorders that target the nigrostriatal system (45,46). Like addiction tendency, anxiety, and low-stress resilience, Parkinson's disease pathway also is mostly involved with dopaminergic and GABAergic pathways (47). Constant and severe deregulation of Parkinson-related genes in PMEs provide evidence that prenatal methamphetamine exposure can cause long-lasting disabilities or degeneration of dopaminergic cell bodies, and it may increase the risk of PD in PMEs as well as stress disorders and addictive behaviors. Also, these potential risks for PD can be detected in neonates.

The number of deregulations caused by methamphetamine, including reactive oxygen species activation and mitochondrial dysfunctions may lead to death receptor and ER pathways of apoptosis (48, 49). Methamphetamine-induced apoptosis pathways are mostly mitochondria-mediated (50), but also the footprints of the mTOR signaling pathway were detected (51). Several genes from both mitochondrial and mTOR were differentially expressed in PMEs. mTOR pathway is also associated with autistic-like behaviors and locomotor activity problems (52).

Based on our assessment to find predictive biomarkers for PME, we short-listed a gene list of seventeen genes. five genes of the biomarker list are involved in the dopamine pathway, dopamine receptors (D1, D2, and D3), brain-derived neurotrophic factor (BDNF), and MAOA. The similarity of expression pattern in PME and methamphetamine use disorder in adults may support the evidence that methamphetamine can pass through the blood-brain barrier of infants. MAOA down-regulation that observed almost constantly during all-time points may suggest that PME children could be at high risk for psychiatric disorders, also may explain the stress and violence problem of PMEs (4,5,53,54).

Genes related to the immune system that meet the criteria of biomarkers are all involved in neurodevelopmental disorders, especially ASD. Cytokines participate in normal neural development and function, and abnormal cytokine activity caused by meth neurotoxic effects may lead to several neurological dysfunctions. It may explain some of the similar symptoms of ASDs and PMEs, such as language impairments and lack of communication (4, 5,55).

Synaptogenesis dysfunctions are shared between PMEs and ASDs. Lack of function in SHANK2 may cause ASD-like behaviors such as reduced social interaction and social communication and repetitive behaviors (56, 57,58).

Transcription factors in the PMEs biomarker list, CREB1, DLG4, MECP2, EGR2, and FOXP2 are essential in memory formation, language function, and cognition. Findings may suggest that PMEs are vulnerable to affect by psychiatric disorders as well as syndromic and heterogeneous disabilities in executive functions such as memory abnormalities and language impairments (59, 60).

CYP2E1 is an essential protein for the cytochrome P450 mixed-unction oxidase system that is involved in the metabolism and degradation of drugs, toxic environmental chemicals, and carcinogens along with several basic metabolic reactions such as fatty acid oxidations, ethanol, and glucose metabolism (61). It seems that abnormal activity of the immune system in PMEs could cause constant effects on xenobiotic metabolism and several digestive pathways related to glucose and lipids. The expression level of interlukins (IL1B and IL8), CDK4, and TGFB1 were previously reported as modulators of humoral immunity via transcriptional programs and suppressed cellular energetics of both glycolysis and oxidative phosphorylation (62). Deregulation of these genes may lead to a number of autoimmune disorders.

These 17 DEGs may use as biomarkers to predict PME-associated disease in children. Constant up-regulation of dopamine receptors and down-regulation of dopamine degradation gene (MAOA) strongly suggest that PMEs could be high risk for psychotic disorders such as schizophrenia and bipolar disorder. Several transcription factors and synaptogenesis genes related to neurodevelopmental disorders could provide evidence that PME could be a risk factor for language impairments, ADHD, and Autistic like behaviors. It seems that the most PME associated potential diseases would be severe psychiatric disorders and autoimmune diseases.

Lack of accessibility to the expression profile of brain tissue may reduce the accuracy of pathway interpretations. Also, neuroimaging and psychological assessments are missing in this study and we need to continue the follow-up to add these evaluations to the study.

Findings suggest that PMEs may face psychological and somatic problems during their lifetime. The severity of these problems could be different subject to subject and could be related to time and dosage of exposure. Finding out the nature of these health concerns along with early detection of affected pathways may lead to better and targeted preventive health behaviors. Further functional and epigenetic studies may explain some of the complexity that was detected in the expression pattern during our follow-up. It seems that future follow-up studies along with psychological testings in different ages, including children at the school age, after the synaptic pruning period, adolescence, and even adulthood may help to better understanding PME effects on brain functions. It is planned to continue the follow-up until adolescence age and synaptic pruning that may shed light on the long-term effects of PME in the young generation.

PME: Parental methamphetamine exposure.

CNS: Central nervous system.

METH: Methamphetamine.

ASD: Autism spectrum disorder.

NCPM: National cohort project of methamphetamine addiction.

QC: Quality control.

MAS: Microarray analysis suite.

KEGG: Kyoto encyclopedia of genes and genomic.

DEGs: Differentially expressed genes.

EF: Enrichment factor.

KNN: k-nearest neighbor.

BMI: Body mass index.

Ethics approval and consent to participate:

All processes of the study were performed based on Helsinki declaration obligations. Written consent form filled by all mothers after explaining the aim of the study. The study was approved by the central ethical committee of Islamic Azad University.

Consent for publication:

All authors are informed and consent about the submission of manuscript and order of authors.

Availability of data and materials

All data are fully available without restriction in the Arvin Gene Company database. The datasets generated and/or analysed during the current study are available in the "https://github.com/gaolongs/arvin" repository, and "GSE255077" accession in GEO database.

Competing interests:

The authors declare that they have no competing interests.

Funding :

The study was funded by neuroimaging genetic laboratory of Arvin Gene Company, Tehran, Iran.

Authors' contributions:

AH is the head of the research team and he participated in study design, laboratory procedure, data analysis, and manuscript writing. SS and PR participated in laboratory procedure and data analysis and preparation of parts of the manuscript. NR and RG participated in a laboratory procedure.

Acknowledgments:

The present study was funded by Arvin Gene Company Ltd, Tehran, Iran. We would like to thanks all participants of the study and their families, health care staff, and addiction treatment centers who helped us during the follow-up period. we would like to thanks Zeinab Tabrizi, Fatemeh Mohammadpour and Parsa Seyrafi, Arvin Gene company staff, for their valuable participation in the laboratory process. Also, I would like to thanks from deceased Hassan Tehranimoghadam for all of his spiritual inspirations.

Ma J, Li XD, Wang TY, Li SX, Meng SQ, Blow FC, Ilgen M, Degenhardt L, Lappin J, Wu P, Shi J. Relationship between the duration of methamphetamine use and psychotic symptoms: A two-year prospective cohort study. Drug and alcohol dependence. 2018 Jun 1;187:363-9.
Hasin DS, O’Brien CP, Auriacombe M, Borges G, Bucholz K, Budney A, Compton WM, Crowley T, Ling W, Petry NM, Schuckit M. DSM-5 criteria for substance use disorders: recommendations and rationale. American Journal of Psychiatry. 2013 Aug;170(8):834–51.
Gfroerer J, Bose J, Trunzo D, Strashny A, Batts K, Pemberton M. Estimating substance abuse treatment: A comparison of data from a household survey, a facility survey, and an administrative data set. Rockville, MD. 2014.
LaGasse LL, Derauf C, Smith LM, Newman E, Shah R, Neal C, Arria A, Huestis MA, Della Grotta S, Lin H, Dansereau LM. Prenatal methamphetamine exposure and childhood behavior problems at 3 and 5 years of age. Pediatrics. 2012 Apr 1;129(4):681-8.
Smith LM, Diaz S, LaGasse LL, Wouldes T, Derauf C, Newman E, Arria A, Huestis MA, Haning W, Strauss A, Della Grotta S. Developmental and behavioral consequences of prenatal methamphetamine exposure: a review of the infant development, environment, and lifestyle (IDEAL) study. Neurotoxicology and Teratology. 2015 Sep 1;51:35–44.
Derauf C, LaGasse LL, Smith LM, Newman E, Shah R, Neal CR, Arria AM, Huestis MA, DellaGrotta S, Dansereau LM, Lin H. Prenatal methamphetamine exposure and inhibitory control among young school-age children. The Journal of pediatrics. 2012 Sep 1;161(3):452-9.
Callaghan RC, Cunningham JK, Allebeck P, Arenovich T, Sajeev G, Remington G, Boileau I, Kish SJ. Methamphetamine use and schizophrenia: a population-based cohort study in California. American Journal of Psychiatry. 2012 Apr;169(4):389–96.
Xue Y, He JT, Zhang KK, Chen LJ, Wang Q, Xie XL. Methamphetamine reduces expressions of tight junction proteins, rearranges F-actin cytoskeleton and increases the blood-brain barrier permeability via the RhoA/ROCK-dependent pathway. Biochemical and biophysical research communications. 2019 Feb 5;509(2):395–401.
Gonzalez B, Jayanthi S, Gomez N, Torres OV, Sosa MH, Bernardi A, Urbano FJ, Garcia-Rill E, Cadet JL, Bisagno V. Repeated methamphetamine and modafinil induce differential cognitive effects and specific histone acetylation and DNA methylation profiles in the mouse medial prefrontal cortex. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2018 Mar 2;82:1–1.
Warton FL, Taylor PA, Warton CM, Molteno CD, Wintermark P, Lindinger NM, Zöllei L, van der Kouwe A, Jacobson JL, Jacobson SW, Meintjes EM. Prenatal methamphetamine exposure is associated with corticostriatal white matter changes in neonates. Metabolic brain disease. 2018 Apr 1;33(2):507 – 22.
Vuletic D, Dupont P, Robertson F, Warwick J, Zeevaart JR, Stein DJ. Methamphetamine dependence with and without psychotic symptoms: A multi-modal brain imaging study. NeuroImage: Clinical. 2018 Jan 1;20:1157–62.
Brinker MJ, Cohen JG, Sharrette JA, Hall TA. Neurocognitive and neurodevelopmental impact of prenatal methamphetamine exposure: A comparison study of prenatally exposed children with nonexposed ADHD peers. Applied Neuropsychology: Child. 2019 Apr 3;8(2):132-9.
Roos A, Fouche JP, du Toit S, du Plessis S, Stein DJ, Donald KA. Structural brain network development in children following prenatal methamphetamine exposure. Journal of Comparative Neurology. 2020 Jul 15;528(11):1856–63.
Sequeira A, Klempan T, Canetti L, Benkelfat C, Rouleau GA, Turecki G. Patterns of gene expression in the limbic system of suicides with and without major depression. Molecular psychiatry. 2007 Jul;12(7):640–55.
Kauffmann A, Gentleman R, Huber W. arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009 Feb 1;25(3):415-6.
Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols. 2009 Jan;4(1):44.
Zhu L, Li J, Dong N, Guan F, Liu Y, Ma D, Goh EL, Chen T. mRNA changes in nucleus accumbens related to methamphetamine addiction in mice. Scientific reports. 2016 Nov 21;6(1):1–3.
Ayyad SM, Saleh AI, Labib LM. Gene expression cancer classification using modified K-Nearest Neighbors technique. Biosystems. 2019 Feb 1;176:41–51.
Plessinger MA. Prenatal exposure to amphetamines: risks and adverse outcomes in pregnancy. Obstetrics and gynecology clinics of North America. 1998 Mar 1;25(1):119–38.
Zhu L, Zhu J, Liu Y, Chen Y, Li Y, Chen S, Li T, Dang Y, Chen T. Chronic methamphetamine regulates the expression of MicroRNAs and putative target genes in the nucleus accumbens of mice. Journal of neuroscience research. 2015 Oct;93(10):1600–10.
Zhu L, Zhu J, Liu Y, Chen Y, Li Y, Huang L, Chen S, Li T, Dang Y, Chen T. Methamphetamine induces alterations in the long non-coding RNAs expression profile in the nucleus accumbens of the mouse. BMC neuroscience. 2015 Dec;16(1):1–3.
Itzhak Y, Ergui I, Young JI. Long-term parental methamphetamine exposure of mice influences behavior and hippocampal DNA methylation of the offspring. Molecular psychiatry. 2015 Feb;20(2):232–9.
Roos A, Stein DJ, Donald KA. Assessing cognition in children with prenatal methamphetamine exposure in South Africa. Comprehensive psychiatry. 2019 Nov;95:152112.
Kwiatkowski MA, Donald KA, Stein DJ, Ipser J, Thomas KG, Roos A. Cognitive outcomes in prenatal methamphetamine exposed children aged six to seven years. Comprehensive psychiatry. 2018 Jan 1;80:24–33.
Zhu L, Li J, Dong N, Guan F, Liu Y, Ma D, Goh EL, Chen T. mRNA changes in nucleus accumbens related to methamphetamine addiction in mice. Scientific reports. 2016 Nov 21;6:36993.
Feier G, Valvassori SS, Lopes-Borges J, Varela RB, Bavaresco DV, Scaini G, Morais MO, Andersen ML, Streck EL, Quevedo J. Behavioral changes and brain energy metabolism dysfunction in rats treated with methamphetamine or dextroamphetamine. Neuroscience letters. 2012 Nov 14;530(1):75–9.
Bu Q, Lv L, Yan G, Deng P, Wang Y, Zhou J, Yang Y, Li Y, Cen X. NMR-based metabonomic in hippocampus, nucleus accumbens and prefrontal cortex of methamphetamine-sensitized rats. Neurotoxicology. 2013 May 1;36:17–23.
Clay HB, Sillivan S, Konradi C. Mitochondrial dysfunction and pathology in bipolar disorder and schizophrenia. International Journal of Developmental Neuroscience. 2011 May 1;29(3):311 – 24.
Rezin GT, Amboni G, Zugno AI, Quevedo J, Streck EL. Mitochondrial dysfunction and psychiatric disorders. Neurochemical research. 2009 Jun;34(6):1021–9.
Haghighatfard A, Andalib S, Amini Faskhodi M, Sadeghi S, Ghaderi AH, Moradkhani S, Rostampour J, Tabrizi Z, Mahmoodi A, Karimi T, Ghadimi Z. Gene expression study of mitochondrial complex I in schizophrenia and paranoid personality disorder. The World Journal of Biological Psychiatry. 2018 Nov 15;19(sup3):S133-46.
Ungless MA, Argilli E, Bonci A. Effects of stress and aversion on dopamine neurons: implications for addiction. Neuroscience & Biobehavioral Reviews. 2010 Nov 1;35(2):151-6.
Brenhouse HC, Sonntag KC, Andersen SL. Transient D1 dopamine receptor expression on prefrontal cortex projection neurons: relationship to enhanced motivational salience of drug cues in adolescence. Journal of Neuroscience. 2008 Mar 5;28(10):2375–82.
González-Burgos I. From synaptic transmission to cognition: an intermediary role for dendritic spines. Brain and cognition. 2012 Oct 1;80(1):177 – 83.
Fischbach GD. NRG1 and synaptic function in the CNS. Neuron. 2007 May 24;54(4):495–7.
Abbasy S, Shahraki F, Haghighatfard A, Qazvini MG, Rafiei ST, Noshadirad E, Farhadi M, alsadat Shiryazdi A, Ghamari R, Tabrizi Z, Mehrfard R. Neuregulin1 types mRNA level changes in autism spectrum disorder, and is associated with deficit in executive functions. EBioMedicine. 2018 Nov 1;37:483-8.
Diaz SD, Smith LM, LaGasse LL, Derauf C, Newman E, Shah R, Arria A, Huestis MA, Della Grotta S, Dansereau LM, Neal C. Effects of prenatal methamphetamine exposure on behavioral and cognitive findings at 7.5 years of age. The Journal of pediatrics. 2014 Jun 1;164(6):1333-8.
Luo YL, Bian JW, Zheng ZJ, Zhao L, Han S, Sun XH, Li JF, Ni GX. Effects of methamphetamine use disorder on spatial cognitive function. Scientific reports. 2018 Apr 3;8(1):1–0.
Warton FL, Meintjes EM, Warton CM, Molteno CD, Lindinger NM, Carter RC, Zöllei L, Wintermark P, Jacobson JL, van der Kouwe A, Jacobson SW. Prenatal methamphetamine exposure is associated with reduced subcortical volumes in neonates. Neurotoxicology and teratology. 2018 Jan 1;65:51 – 9.
Beardsley PM, Hauser KF. Glial modulators as potential treatments of psychostimulant abuse. Advances in pharmacology. 2014 Jan 1;69:1–69.
Loftis JM, Choi D, Hoffman W, Huckans MS. Methamphetamine causes persistent immune dysregulation: a cross-species, translational report. Neurotoxicity research. 2011 Jul;20(1):59–68.
Krasnova IN, Justinova Z, Cadet JL. Methamphetamine addiction: involvement of CREB and neuroinflammatory signaling pathways. Psychopharmacology. 2016 May 1;233(10):1945-62.
Langford D, Grigorian A, Hurford R, Adame A, Crews L, Masliah E. The role of mitochondrial alterations in the combined toxic effects of human immunodeficiency virus Tat protein and methamphetamine on calbindin positive-neurons. Journal of neurovirology. 2004 Nov 1;10(6):327 – 37.
Rezaei N, Bonilla FA, Seppänen M, De Vries E, Bousfiha AA, Puck J, Orange J. Introduction on primary immunodeficiency diseases. InPrimary immunodeficiency diseases 2017 (pp. 1–81). Springer, Berlin, Heidelberg.
Picard C, Gaspar HB, Al-Herz W, Bousfiha A, Casanova JL, Chatila T, Crow YJ, Cunningham-Rundles C, Etzioni A, Franco JL, Holland SM. International union of immunological societies: 2017 primary immunodeficiency diseases committee report on inborn errors of immunity. Journal of clinical immunology. 2018 Jan 1;38(1):96–128.
Callaghan RC, Cunningham JK, Sajeev G, Kish SJ. Incidence of Parkinson's disease among hospital patients with methamphetamine-use disorders. Movement Disorders. 2010 Oct 30;25(14):2333–9.).
Thrash B, Thiruchelvan K, Ahuja M, Suppiramaniam V, Dhanasekaran M. Methamphetamine-induced neurotoxicity: the road to Parkinson’s disease. Pharmacological reports. 2009 Nov 1;61(6):966 – 77.
Azadmarzabadi E, Haghighatfard A, Mohammadi A. Low resilience to stress is associated with candidate gene expression alterations in the dopaminergic signalling pathway. Psychogeriatrics. 2018 May;18(3):190–201.
Redza-Dutordoir M, Averill-Bates DA. Activation of apoptosis signalling pathways by reactive oxygen species. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research. 2016 Dec 1;1863(12):2977-92.
Sharikova AV, Quaye E, Park JY, Maloney MC, Desta H, Thiyagarajan R, Seldeen KL, Parikh NU, Sandhu P, Khmaladze A, Troen BR. Methamphetamine induces apoptosis of microglia via the intrinsic mitochondrial-dependent pathway. Journal of Neuroimmune Pharmacology. 2018 Sep 1;13(3):396–411.
Park JH, Seo YH, Jang JH, Jeong CH, Lee S, Park B. Asiatic acid attenuates methamphetamine-induced neuroinflammation and neurotoxicity through blocking of NF-kB/STAT3/ERK and mitochondria-mediated apoptosis pathway. Journal of neuroinflammation. 2017 Dec 1;14(1):240.
Liu HQ, An YW, Hu AZ, Li MH, Wu JL, Liu L, Shi Y, Cui GH, Chen Y. Critical roles of the PI3K-Akt-mTOR signaling pathway in apoptosis and autophagy of astrocytes induced by methamphetamine. Open Chemistry. 2019 Jan 1;17(1):96–104.
Winden KD, Ebrahimi-Fakhari D, Sahin M. Abnormal mTOR activation in autism. Annual review of neuroscience. 2018 Jul 8;41:1–23.
Dong E, Dzitoyeva SG, Matrisciano F, Tueting P, Grayson DR, Guidotti A. Brain-derived neurotrophic factor epigenetic modifications associated with schizophrenia-like phenotype induced by prenatal stress in mice. Biological psychiatry. 2015 Mar 15;77(6):589–96.
Goines PE, Ashwood P. Cytokine dysregulation in autism spectrum disorders (ASD): possible role of the environment. Neurotoxicology and teratology. 2013 Mar 1;36:67–81.
Won H, Lee HR, Gee HY, Mah W, Kim JI, Lee J, Ha S, Chung C, Jung ES, Cho YS, Park SG. Autistic-like social behaviour in Shank2-mutant mice improved by restoring NMDA receptor function. Nature. 2012 Jun;486(7402):261.
Schmeisser MJ, Ey E, Wegener S, Bockmann J, Stempel AV, Kuebler A, Janssen AL, Udvardi PT, Shiban E, Spilker C, Balschun D. Autistic-like behaviours and hyperactivity in mice lacking ProSAP1/Shank2. Nature. 2012 Jun;486(7402):256–60.
Berkel S, Marshall CR, Weiss B, Howe J, Roeth R, Moog U, Endris V, Roberts W, Szatmari P, Pinto D, Bonin M. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nature genetics. 2010 Jun;42(6):489.
Enard W, Przeworski M, Fisher SE, Lai CS, Wiebe V, Kitano T, Monaco AP, Pääbo S. Molecular evolution of FOXP2, a gene involved in speech and language. Nature. 2002 Aug;418(6900):869–72.
Kandel ER. The molecular biology of memory: cAMP, PKA, CRE, CREB-1, CREB-2, and CPEB. Molecular brain. 2012 Dec;5(1):14.
Curie A, Lesca G, Bussy G, Manificat S, Arnaud V, Gonzalez S, Revol O, Calender A, Gérard D, des Portes V. Asperger syndrome and early-onset schizophrenia associated with a novel MECP2 deleterious missense variant. Psychiatric genetics. 2017 Jun 1;27(3):105-9.
Rendic S, Carlo FJ. In Human cytochrome P450 enzymes: a status report summarizing their reactions, substrates, inducers, and inhibitors. Drug metabolism reviews. 1997 Jan 1;29(1–2):413–580.
Komai T, Inoue M, Okamura T, Morita K, Iwasaki Y, Sumitomo S, Shoda H, Yamamoto K, Fujio K. Transforming growth factor-β and interleukin-10 synergistically regulate humoral immunity via modulating metabolic signals. Frontiers in immunology. 2018 Jun 14;9:1364.

Table 1. groups' comparison for socio-demographic differences of mothers and children (at the time of birth)

Characteristics	Prenatal methamphetamine exposure children	Normal children	p-value
Maternal/demographic
Public insurance	2498(93.98)	3466(97.0)	0.17
No partner	1776 (66.81)	2112(60.9)	0.13
Education (who didn't complete 12 years of education)	1262(47.5)	1511(42.3)	0.14
Maternal age	22.56±1.3	23.34±1.2	0.18
Prenatal marijuana use	422(15.9)	403(11.3)	0.08
Joints/d across pregnancy	0.066± 0.24	0.052 ±0.16	0.12
Prenatal alcohol use	(15.6)	23 (8.4)	0.09
Absolute alcohol/day (oz) across pregnancy	0.016± 0.29	0.011 ±0.08	0.05
Prenatal tobacco exposure	2068 (77.8)	2443(68.4)	0.18
Cigarettes/d across pregnancy	2.94± 6.3	1.86 ±4.8	0.06
Neonatal/demographic
Birth weight, g	3366±522	3596±447	0.16
Low birth weight	760(28.6)	468(13.1)	0.04
Length, cm	50.46±4.4	51.06±3.7	0.12
Head circumference, cm	31.23±2.1	34.06±1.9	0.03
Gestational age, wk	38.1±2.2	39.2±1.2	0.22

Data are presented as n (%) or mean ± SD.

Table 2. Primer sequences of 17 differentially genes and GAPDH gene for Real-time PCR

Gene	Forward Primer	Reverse Primer
DRD1	5'GGCTCTCGAAAGGAAGCCAA3'	5'ACTAGACCCCGGCTAAGGG3'
DRD2	5' CATGGACCACTCACACCCC 3'	5' GAACGAACAAACACACACGGG 3'
DRD3	5' GAACCCCACGAATGTTTCAGG 3'	5' GAGTTGTCAGGTCCCCATCAG 3'
BDNF	5'AAGCTCCGTAGTGCAGGAAG3'	5' ACATCCAGTTGTCCTTCGGG 3'
FOXP2	5'CCTAGGACTCCGTTTCAAGGT3'	5'TCTGTCGCAGATTCCTGCAT3'
TGFB1	5'ACCTCATCCAGGAAGTCCCC3'	5'CAGAGGAACGTCAATGCAGG3'
IL1B	5'CCAAACCTCTTCGAGGCACA3'	5'TTCAGACACCTAGTTGTAAGGAAGA3'
IL8	5'AGCACTCCATAAGGCACAAACT3'	5'TGGTTCCTTCCGGTGGTTTC3'
CDK4	5'GGAGTCTGTGATTGTAGGGTCTC3'	5'TAGGCACCGACACCAATTTCA3'
UPF3B	5'CAGGTCGATTCTGGTGGCAA3'	5' CCTTCCTGGAGAGGGTACACA3'
EGR2	5'AATAACACTACACCAGCAACTCC3'	5'CCCAACTCCCTCGCTACTC3'
CREB1	5'GCTTGAAATTCTGTGACTCTTCCC3'	5'TGGACTTGAACTGTCTGCCC3'
CYP2E1	5'TTCCGATGTTGAATTTTCCTTCTGG3'	5'GGTGACTCCGAGGGCAGA3'
MECP2	5'TATAGTTCCCATCAGGAGCCGT3'	5'CACATCAAAGCAGGAACTGGTG3'
MAOA	5'TAGAAGGGTCCTTCCCACCC3'	5'CACCTCCGATCACGACTACG3'
DLG4	5'CCATCCCCAGAAATACCGCT3'	5'CTCGGTCCCGTTCACATATCC3'
SHANK2	5'TTGTACCCCTTGTGCCAACC3'	5'TCCAAGTTGCAAGACTGGGC3'
GAPDH	5'CACTAGGCGCTCACTGTTCTC3'	5'AAATCCGTTGACTCCGACCT3'

No competing interests reported.

SUPPLEMENTARYFILEPMEtable12345.xlsx

Three Years Follow-Up Study Showed Prenatal Methamphetamine Exposure May Cause Chronic Abnormalities In Transcriptomic Pattern

Status:

Version 1

Abstract

Figures

Background

Material And Methods

Results

Discussion

Limitations

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1