Abstract Idiopathic autism, caused by genetic susceptibility interacting with unknown environmental triggers, has increased dramatically in the past 25 years. Identifying environmental triggers has been difficult due to poorly understood pathophysiology and subjective definitions of autism. The use of antidepressants by pregnant women has been associated with autism. These and other unmetabolized psychoactive pharmaceuticals (UPPs) have also been found in drinking water from surface sources, providing another possible exposure route and raising questions about human health consequences. Here, we examined gene expression patterns of fathead minnows treated with a mixture of three psychoactive pharmaceuticals (fluoxetine, venlafaxine & carbamazepine) in dosages intended to be similar to the highest observed conservative estimates of environmental concentrations. We conducted microarray experiments examining brain tissue of fish exposed to individual pharmaceuticals and a mixture of all three. We used gene-class analysis to test for enrichment of gene sets involved with ten human neurological disorders. Only sets associated with idiopathic autism were unambiguously enriched. We found that UPPs induce autism-like gene expression patterns in fish. Our findings suggest a new potential trigger for idiopathic autism in genetically susceptible individuals involving an overlooked source of environmental contamination.

Citation: Thomas MA, Klaper RD (2012) Psychoactive Pharmaceuticals Induce Fish Gene Expression Profiles Associated with Human Idiopathic Autism. PLoS ONE 7(6): e32917. https://doi.org/10.1371/journal.pone.0032917 Editor: Efthimios M. C. Skoulakis, Alexander Flemming Biomedical Sciences Research Center, Greece Received: September 2, 2011; Accepted: February 6, 2012; Published: June 6, 2012 Copyright: © 2012 Thomas, Klaper. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: MAT was supported by a PhRMA Foundation Sabbatical Fellowship grant, National Institutes of Health Grant Number P20 RR016454 from the INBRE Program of the National Center for Research Resources, and grant number URC-FY2010-05 from the University Research Committee of Idaho State University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Discussion We found enrichment of gene sets associated with idiopathic ASD but not of sets involving autism diagnoses secondary to other disorders (Rett and fragile X syndromes) known to be caused by specific mutations. This is significant, because it indicates that enrichment in our treatments involve only idiopathic forms of ASD. There was no enrichment of other neurological disorders except MS (in one of two sets) and Parkinson’s. This is significant because it indicates that enrichment of the idiopathic ASD set was not simply associated with general neurological processes, pathways or systems generally common to neurological disorders. The MS set has a low NES (<1.40), and a second MS set (see Table 4) is not enriched; therefore, we are not exceedingly confident in describing that set as enriched. The other enriched non-ASD set, Parkinson’s, is more interesting, with a convincing NES and an intriguing potential connection to ASD involving similar phenomenology involving brain dysfunction [41]. A number of the genes contributing to enrichment of ASD gene sets have been implicated in other recent studies not included in our analysis. For example, Suda et al. [42] found that relative expression levels of EFNB3, PLXNA4 and ROBO2 were significantly different in individuals with autism than in neurotypical individuals; protein levels of PLXNA4 and ROBO2, but not for EFNB3, were significantly reduced in brains of individuals with autism compared to control brains. In the present study, we found 3 plexin genes (PLXNB1, PLXND1 & PLXNA3; PLXNA4 was not on the array) and ROBO2 to be strongly down-regulated in response to the MIX treatment, while EFNB3 and five related genes (EFNB1, EFNA1, EFNA2, EFNA3 & EFNA5) were up-regulated. These and other genes contributing to gene set enrichment are associated with the formation of synapses, perturbation of which may indicate an altered and imprecise synaptic connections or a failure to form mature neural circuits. The results presented here are consistent with several recent lines of inquiry: First, the hypothesis that hyperserotonemia plays a role in autism, affecting the developing fetus and potentially involving SSRIs [27]. In that work, Hadjikhani explored a potential role of elevated serotonin levels perturbing brain development during pregnancy (in which he assumes that maternal serotonin ultimately passes the fetal blood brain barrier). The author speculated that elevated levels could be increased by maternal use of serotonin elevating pharmaceuticals (like SSRIs) or consumption of serotonin-rich foods. In the present study, serotonin levels were not measured. However, all six serotonin receptor genes on the array (HTR1A, HTR1B, HTR2C, HTR4, HTR7& SLC6A4) were strongly down-regulated in response to the MIX treatment. If this implies a consequential elevation of serotonin levels, our results would seem to be consistent with the Hadjikhani hypothesis [27] and with other recent experimental work using model organisms [13]. Second, recent evidence supports an association of antidepressants, including SSRIs, with autism [21]. In that study, Croen and colleagues found a 2-fold increase in ASD risk associated with SSRIs, with the strongest effect occurring in the first trimester. The results of the present study are consistent with this finding. However, maternal SSRI use is not sufficient to explain the increase in prevalence of ASD. Third, there is evidence for an unambiguous environmental component involved in the etiology of autism [7]. In that study, Hallmayer and colleagues provide robust evidence that, while having a moderate genetic component, ASD also clearly involves an environmental trigger. The results of the present study are consistent with this finding, as is the assumption that the environmental trigger acts in concert with genetic susceptibility. Fourth, there is evidence of demographic changes that may have increased the proportion of genetically susceptible individuals in contemporary populations [43]. In that study, Baron-Cohen proposed that assortative mating among genetically susceptible individuals has increased the proportion of susceptible individuals in human populations since the 1970s. Especially when coupled with increased levels of an environmental trigger, this would create circumstances in which one would expect an increase in ASD prevalence. Given that SSRIs were introduced in the mid-1980s and SNRIs in the mid-1990s, coincident with increases in ASD prevalence [10], the assortative mating hypothesis provides a framework for understanding why such a trigger is able to induce such a large effect. The results of the present study provide a potential source of exposure to psychoactive pharmaceuticals that does not involve maternal clinical usage of SSRIs. Given the conserved nature (i.e., sequence and function) of the genes involved in the observed expression profiles, and given that the genes on the Fathead array are homologous to highly conserved human genes, it is reasonable to expect induction of humans gene expression profiles similar to the Fathead profiles. This sort of approach has been effectively used for other models of human disorders [44] and in previous investigations involving the Fathead microarray platform [37]. Here, many of the enriched sets involve genes associated with neuronal development and growth [38], which is consistent with systems and pathways known to be perturbed in the developing brain of individuals with autism [40], [45]. The concentrations used in this study were higher than observed environmental concentrations in order to account for conservative concentration estimates and the presence of related formulations and active metabolites [29], [34], [46], [47]. Future work needs to be conducted to measure the concentrations of all UPP constituents present in aquatic systems and drinking water (with appropriate temporal and geographic sampling) in order to accurately assess human exposure and health consequences. Conclusions These results provide a new perspective on the etiology of idiopathic ASD and suggest new directions for research into autism’s environmental “exposome” [48]. The results of the gene expression study indicate that a mixture of UPPs can induce an ASD-like gene expression profile in a model organism. Using a low-cost model system like fathead minnow, researchers can rapidly screen potential teratogens for their ability to induce ASD-like gene expression patterns in developing brains. In order to clearly determine if UPPs are associated with idiopathic ASD in humans, future work needs to examine a wider palette of UPPs (and other potential teratogens) and results need to be validated by demonstrating treatment response in another model systems. This could involve using a mouse model, with which one could measure fetal brain expression patterns, UPP concentration in fetal blood, and concentrations of fetal neurohypophyseal hormones, following maternal treatment. Further, epidemiological studies at the individual patient level should be conducted to confirm and specify the relationship between environmental contaminants and ASDs. The mimicry of ASD-like gene expression profiles in fish, described above, does not conclusively indicate UPP induction of ASD in humans. It does, however, serve as the basis for new hypotheses regarding the etiology of idiopathic ASD.

Materials and Methods Ethics Statement All fish handling and treatments were performed at the Great Lakes WATER Institute (School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin) using appropriate UWM Institutional Animal Care and Use Committee (IACUC) approved protocols (approval number 0708#14). Fish Treatments Full details of the fish treatments are described in a previous report [38]. Briefly, three 2-gallon tanks were used for each pharmaceutical treatment along with three tanks for a mixture treatment (containing all three pharmaceuticals in the concentrations listed in Table 1) and three tanks for control (containing no pharmaceuticals). Each tank housed five juvenile fathead minnows. Dosages of pharmaceuticals were re-administered with each change of the tank water (every 2 days). Fish were exposed to treatments for eighteen days. Gene Set Enrichment Analysis Fish mRNA was pooled within a tank for microarray work (for 3 replicates per treatment) but not for qPCR validation (for 15 replicates per treatment). Details of microarray experiments, including validation by qPCR analysis of 9 genes with high rank correlation and all data files, are described in a previous report [38]. Microarray experiments conformed to MIAME guidelines and results were deposited in GEO (GSE22261). The previous study [38] also described an altered phenotype associated with pharmaceutical treatment that involved measuring fish behavior in response to a startle stimulus modeled after predator avoidance behavior used elsewhere [49]. We found that fish behavior was indicative of a neurologically relevant phenotype: following a “startle,” the distance traveled and number of direction changes both significantly increased for treated fish [38]. Here, two groups of gene sets gene-class analyses were conducted: ND (“neurological disorder”) and ASD (“autism spectrum disorder”). The gene sets in the ND collection, known to be associated with a variety of human neurological disorders, are described in Table 2. The gene sets in the ASD collection, associated with enriched gene expression in autism, are described in Table 3. Both gene sets are provided in Supporting Information (Table S1, “ND gene list,” and Table S2, “ASD gene list”). Each group consisted of a collection of gene sets, with each set tested against the ranked list of genes reflecting signal-to-noise ratio of MIX treatment (combining FLX, VNX & CBZ) relative to control. Additional comparisons between the control and treatments consisting of the three pharmaceuticals considered individually were included for comparison (Tables 6, 7). Gene-class analyses used GSEA release 2.06 and MSigDB release 2.5. Weighted enrichment scores were calculated using gene expression lists ranked by signal-to-noise ratio. The genes on the array were ranked by correlation between the MIX and CTL treatments (those genes with the strongest up-regulation in treatment relative to control were ranked highest; those with strongest down-regulation were ranked lowest). (See Table S3, “Ranked gene list,” for these data.) The maximum gene set size was set to 500 genes; the minimum gene set size was set to 10 genes; the number of permutations was set to 1000. Permutations were conducted by gene set (rather than by phenotype). For details of GSEA parameter usage, see Subramanian et al. [36]. Gene sets were examined to ensure they contained only GSEA-recognized primary HUGO symbols, rather than aliases or unapproved symbols. This was accomplished through the use of a custom script that compared each gene in a given set to the GENE_SYMBOLS.chip file (from GSEA) containing a list of HUGO symbols with accepted aliases. Gene set components listed as aliases in this file were replaced with the appropriate HUGO symbol. For additional details of the annotation and GSEA implementation using the EcoArray 15k Fathead Minnow arrays, see Thomas et al. [37].

Acknowledgments D. Arndt and J. Crago provided support and expertise on lab techniques and fish handling. C. Ryan and E. O’Leary-Jepsen provided critical expertise in support of the qPCR analysis. R. Salmore, P. Hallock, L. Yang, S. St. Hillaire, and G. Kaushik provided feedback on an early draft of the manuscript.

Author Contributions Conceived and designed the experiments: MAT RDK. Performed the experiments: MAT. Analyzed the data: MAT. Contributed reagents/materials/analysis tools: MAT RDK. Wrote the paper: MAT RDK.