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Marleen M. Miller, Gerhard A. Zielhuis, Lolkje T. A particular birth defect may have its origins through multiple mechanisms and possible exposures, including medications. A specific pathogenic process may result in different outcomes depending upon factors such as embryonic age at which a drug is administered, duration and dose of exposure and genetic susceptibility. This review focuses on the teratogenic mechanisms associated with a number of medications. Food and Drug Administration class D or X. Mechanisms were included only if they are associated with major structural birth defects and medications that are used relatively frequently by women of reproductive age. We identified six teratogenic mechanisms associated with medication use: folate antagonism, neural crest cell disruption, endocrine disruption, oxidative stress, vascular disruption and specific receptor- or enzyme-mediated teratogenesis. Many medications classified as class X are associated with at least one of these mechanisms. Identifying teratogenic mechanisms may not only be relevant for etiologic and post-marketing research, but may also have implications for drug development and prescribing behavior for women of reproductive age, especially since combinations of seemingly unrelated prescription and over the counter medications may utilize similar teratogenic mechanisms with a resultant increased risk of birth defects. Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:. Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account. Choose this option to get remote access when outside your institution. 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To purchase short-term access, please sign in to your personal account above. Don't already have a personal account? Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Advertisement intended for healthcare professionals. Sign in through your institution. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Folate Antagonism. Neural Crest Cell Disruption. Endocrine Disruption: Sex Hormones. Oxidative Stress. Vascular Disruption. Specific Receptor- or Enzyme-mediated Teratogenesis. Authors' Roles. Journal Article. Teratogenic mechanisms of medical drugs. Oxford Academic. Google Scholar. Iris A. Richard K. Gerhard A. Lolkje T. Nel Roeleveld. Revision received:. Cite Cite Marleen M. Select Format Select format. Permissions Icon Permissions. 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Objective : Amphetamine-type stimulant ATS and opioid dependencies are chronic inflammatory diseases with similar symptoms and common genomics. However, their coexpressive genes have not been thoroughly investigated. We aimed to identify and verify the coexpressive hub genes and pathway involved in the pathogenesis of ATS and opioid dependencies. Functional annotation and protein—protein interaction network detected the potential functions. The hub genes were screened using the CytoHubba and MCODE plugin with different algorithms, and further validated by receiver operating characteristic analysis in the GSE database. The top 10 hub genes were mainly enriched in apoptotic process CD44, Dusp1, Sgk1, and Hspa1b , neuron differentiation, migration, and proliferation Nr4a2 and Ddit4 , response to external stimulation Fos and Cdkn1a , and transcriptional regulation Nr4a2 and Npas4. Conclusions: The identification of hub genes was associated with ATS and opioid dependencies, which were involved in apoptosis, neuron differentiation, migration, and proliferation. Ellis et al. Chronic and repeated administration could also cause tolerance and dependence, recurrent encephalopathy e. Polysubstance dependence, especially opioid and ATS dependence, is evolving into an epidemic drug use pattern worldwide Jones et al. There are similar target genes e. However, the potential coexpressive genes and pathogenesis of ATS and opioid dependence have not been thoroughly investigated. We hypothesized that coexpressive target genes and signaling pathways may provide further insight into the common pathophysiological process of ATS and opioid dependencies. Following the development of bioinformatic technology, RNA sequencing RNAseq and high-throughput microarray had been widely used to explore and detect the biomarkers, functional annotation, and molecular mechanism of a variety of diseases in vivo and in vitro in the past decades Kang et al. All the databases could be downloaded and reanalyzed freely. As the most economic and effective technique, bioinformatic analysis was performed to identify candidate hub genes. Previous microarrays mainly focused on a single drug, such as nicotine Jung et al. They mainly focused on comparatively small samples, a single timepoint, or individual reward brain regions Piechota et al. Therefore, we conducted the integrated bioinformatic analysis with all conditions and timepoints in all GEO databases. The study was approved by the Ethics Committee of Sichuan University. The exclusion criteria were the following: 1 The mice were intervened by other drugs. In addition, for overlapping databases, only the maximum samples were included. The workflow of the study is shown in Figure 1. If a gene had multiple probes on the same chip, the average value of all probes would be taken as the gene expression value. If the genes lacked probes, they were removed. Gene Ontology GO Ashburner et al. The minimum required interaction score of more than 0. The Cytoscape software version 3. Four most effective algorithms in the CytoHubba plugin in Cytoscape software were performed to identify hub genes, including the maximal clique centrality MCC , density of maximum neighborhood component DMNC , maximum neighborhood component MNC , and degree. A high score indicated that the target was closely related to the disease and was a possible hub gene. The TF-targeted top 20 hub genes of the PPI network were predicted with the iRegulon plugin in the Cytoscape software, which integrated information from the larger modify and track collections. The BV2 cells were seeded in six-well plates at a density of 2. Then BV2 cells were treated with different concentrations of METH or heroin for different periods to mimic the drug injury in vitro , respectively. The concentration of drugs did not induce significant cell damage, even in a time course experiment Figures 2C, D. The concentrations were consistent with previous studies Lai et al. It indicated that the METH and heroin effectively established inflammatory models in vitro. The qPCR was performed to examine the difference in transcriptional levels of the hub genes and key factors of signaling pathway in BV2 cells after heroin and METH treatment. The primers and amplicon sizes of hub genes are shown in Supplementary Table S1. All samples were performed in triplicate. Otherwise, the Kruskal—Wallis test was used. AUC was used to evaluate the sensitivity and specificity of each gene. The genes with an AUC of more than 0. According to the inclusion criteria, GSE Piechota et al. The main administration methods were intraperitoneal ip injection five studies of opioids and four studies of ATS. The duration of administration was 3 to 14 days for all opioid models and three ATS models. All the tissues were involved in brain reward regions, including the striatum, hippocampal, cerebral cortex, and nucleus acumbens NAc. As shown in Supplementary Figures S1 and S2 , the midline of ATS and opioid treatments were matched by boxplot analysis, and the gene expression profiles were comfortable for further study. The number of DEGs obtained in each microarray varied widely, ranging from 0 to more than A total of DEGs were identified from opioid treatment databases, of which genes were upregulated and genes were downregulated. TABLE 2. GO biological analysis is demonstrated in Figures 4A, B. For DEGs of opioid treatment, there were about 57 terms in the biological process BP category, 26 terms in the molecular function MF category, and 11 terms in the cellular component CC category. Similarly, 1, nodes and 5, edges were screened in the 1, DEGs of ATS treatment, and the average node degree was The hub genes screened by the CytoHubba plugin and predicted by receiver operating characteristic ROC curve. Red indicates the hub genes. Pink indicates the other DEGs. Blue indicates TF-target genes. Finally, eight interesting modules for ATS treatment and two interesting modules for opioid treatment were selected, respectively Table 3 and Supplementary Figure S3. Pathway enrichment analysis of the interesting module demonstrated that each module was functionally correlated Supplementary Table S3. TABLE 3. TABLE 4. Based on the MCODE score, we selected the top three targets in each interesting module as the hub genes. Furthermore, most hub genes screened by the CytoHubba plugin were also enriched in the interesting modules. TABLE 5. The results were consistent with those of the bioinformatic analysis and GSE database. The bioinformatic analysis was conducted to better understand the hub genes and molecular mechanisms of substance dependence. The relative mRNA levels of the aforementioned hub genes were significantly different between cases and controls in the GES database. It indicated that the hub genes could be accurately predicted. These results were consistent with those of the bioinformatic analysis and GSE database. The number of DEGs obtained in each microarray ranged from 0 to more than It might be caused by the differences in detection platform and model parameters including drug types, doses, frequency, and duration of drug administration. BP was the main enriched term of those DEGs, including transcriptional regulation, apoptotic process, phosphorylation, cell proliferation and adhesion, and nervous system development in ATS treatment, and nervous system development, transcriptional regulation, cell differentiation and adhesion, and ion transport in opioid treatment. It indicated that the common biological processes involved in ATS and opioid dependencies were apoptosis, nervous system development, cell differentiation, and proliferation Piechota et al. The results illustrated that ATS and opioid dependencies shared common target genes and molecular mechanism, which were consistent with the previous studies and whole-genome microarray profiling Piechota et al. Qiao et al. The Akt phosphorylation in the NAc was related to heroin-seeking behavior Zhu et al. Sgk1, Ddit4, and Cdkn1a were located in 6q The hub genes were widely distributed in different tissues, and were involved in multiple physiological functions and pathophysiological conditions, such as hypoxia, ionizing radiation, heat shock, oxidative stress, hormone release, cell proliferation and apoptosis, autophagy, fibrosis disease, ischemia sequelae, neuronal survival, neuroexcitability, and neurodegeneration Ellisen et al. The GSE database was the only female model, the difference might be associated with gender Dumeige et al. Further study is needed to reveal the exact reason. The results were consistent with the bioinformatic analysis and other studies on liver injury, traumatic brain injury, anterior cruciate ligament transection, and lung injury Lang et al. Furthermore, function annotation demonstrated that Sgk1, Ddit4, and Cdkn1a were mainly involved in apoptotic process and cell proliferation. It suggested that Sgk1, Ddit4, and Cdkn1a played key roles in ATS and opioid dependencies through neuronal autophagy and apoptosis, and may be potential target genes of drug dependence. With Fos and Dusp1, as the immediate early genes Wojcieszak et al. The results were in line with other studies \[e. Fos and Dusp1 locus resided on 14q21—q31 and 5q Fos and Dusp1 played vital roles in regulating signaling transduction, cell proliferation, and differentiation Duric et al. Some studies demonstrated that Fos and Dusp1 were not only deemed as biomarkers of neuronal activity Hughes and Dragunow but also a vital initial step in regulating neuroplasticity caused by drugs Harlan and Garcia Kuroda et al. However, Beauvais et al. It may be that Fos and Dusp1 were the immediate early genes. It might be associated with drug species. Nr4a2 was predominantly expressed in the midbrain, substantia nigra, and ventral tegmental Zetterstrom et al. In the study, Nr4a2 was mainly involved in neuron differentiation and migration, response to external stimulation, and transcriptional regulation Horvath et al. Combined with previous studies, we speculated that the Nr4a2 was a potential target gene for substance dependence. There were some limitations in the present study. First, due to database limitations, the microarrays with different model parameters were all included. It might result in biases of selected hub genes. So, the hub genes might not really be involved in the pathogenesis of substance dependence. Further studies are needed to verify the pathophysiological mechanism of these hub genes and pathways that participated in the substance-dependent animal model and humans. Second, the study was based on animal databases and BV2 cells. The results might not be suitable to extrapolate to substance dependence of other animal models and humans. Despite the interspecies differences, animal studies contributed significantly to addiction research and are still of great assistance for future research with a more relevant model of compulsive drug use in humans. Third, the mechanisms of ATS and opioid dependencies might involve both genetic and epigenetic aspects. Epigenetic regulation consequences were beyond alterations in steady-state levels of expressed RNAs, which were far beyond the coverage of microarrays. The next-generation sequencing technologies e. That work would be the focus of our future studies. The findings may be helpful for better understanding of the shared pathogenesis and molecular mechanisms of ATS and opioid dependencies, and consequently detecting the new detection and potential therapeutic targets for drug dependence. WZ contributed to conceiving and designing the experiments, analyzing the data, and drafting the manuscript. HL and MY were responsible for the statistical analyses and cell culture. YM and LZ contributed to interpreting the data and revising the manuscript. YL and FH contributed to conceiving and designing the experiments, evaluating and guiding the full text of the manuscript, and providing economic support. All authors read and approved the final manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Anacker, C. Ashburner, M. Gene Ontology: Tool for the Unification of Biology. Bader, G. BMC Bioinformatics 4, 2. Bandettini, W. Barrett, T. Nucleic Acids Res. Beauvais, G. Brain Res. 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Moratalla, R. Nangraj, A. Palmer, A. Genome 16 5 , — Paolone, G. Neuropsychopharmacol 32 12 , — Park, J. Piechota, M. Genes, Brain Behav. Qi, B. Qiao, X. Affective Disord. Renthal, W. Neuron 62 3 , — Rubio, F. Sabrini, S. Neurotoxicology 77, 20— Saijo, K. Cell 1 , 47— Sanchis-Segura, C. Neuropsychopharmacol 34 13 , — Shannon, P. Genome Res. Shoshani, T. Silva, C. Singh, M. Neuropharmacology 49 8 , — Sinha, D. FASEB j. Skupio, U. Smith, M. PLoS One 15 5 , e Steiner, J. Alcohol Alcohol. Su, W. Sun, F. Markers , 1— Szklarczyk, D. Takaki, M. Torii, T. Gene 2 , — Visvader, J. Wang, Z. Wojcieszak, J. Wolff, N. Cancer Res. Wong, Y. Ijms 17 2 , Wu, M. S2, — Xu, H. Chen, X. ACS Chem. Chen, Y. Zhang, W. Zhou, S. Zhu, L. Bioinformatics 33 8 , — Zhu, H. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. Top bar navigation. About us About us. Sections Sections. About journal About journal. Article types Author guidelines Editor guidelines Publishing fees Submission checklist Contact editorial office. Flow diagram of the study design. TABLE 1. Basic information of the selected databases in the study.

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