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Keywords:

  • autism spectrum disorder;
  • superoxide dismutase;
  • genetic variants;
  • oxidative stress;
  • ROS

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

Oxidative stress is suspected to be one of the several contributing factors in the etiology of autism spectrum disorder (ASD). We analyzed genes of the superoxide dismutase family (SOD1, SOD2, and SOD3) that are part of a major antioxidative stress system in human in order to detect the genetic variants contributing to the development of ASD. Using the optimized high-resolution melting (HRM) analysis, we identified two rare single nucleotide polymorphisms (SNPs) associated with the etiology of ASD. Both are located in the superoxide dismutase 1 (SOD1) gene and have a minor allele frequency in healthy population ∼5%. The SNP c.239 + 34A>C (rs2234694) and SNP g.3341C>G (rs36233090) were detected with an odds ratio of 2.65 and P < 0.01. Both are located in the noncoding potentially regulatory regions of the SOD1 gene. This adds to the importance of rare SNPs in the etiology of complex diseases as well as to the importance of noncoding genetic variants analysis with a potential influence on the regulation of gene expression. Autism Res 2014, 7: 138–144. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

Oxidative stress is a state of disturbed equilibrium between antioxidative mechanisms of the cell and production of reactive oxygen species (ROS) [Migdal & Serres, 2011 ]. Prolonged exposure to elevated levels of ROS leads to the development of various pathological processes that influence human health [Vaya, 2013 ]. Increased level of oxidative stress is linked to atherosclerosis [Drummond, Selemidis, Griendling, & Sobey, 2011 ], cancer, aging, complications in diabetes [Vaya, 2013 ], autoimmune [Weiskopf et al., 2010 ], and neurological disorders [Hayashi, Miyata, & Tanuma, 2012 ]. Reaction and interaction network of the reactive oxygen species is wide and diverse and includes reactions with amino acids [Frustaci et al., 2012 ], lipids [Montuschi, Barnes, & Roberts, 2004 ], nucleotide bases [Cooke, Evans, Dizdaroglu, & Lunec, 2003 ], small molecules like nitric oxide, carbon dioxide [Veselá & Wilhelm, 2002 ], and others [Ghanizadeh, 2012 ] influencing the variety of physiological processes [Vaya, 2013 ]. Being a part of an antioxidative system of the cell, the family of the superoxide dismutase enzymes (SOD1, SOD2, and SOD3) is crucial for the cell defense against the ROS, specifically against the superoxide anion (inline image), which is converted into hydrogen peroxide in the reaction catalyzed by superoxide dismutase (SOD) enzymes [Deuel, 2002 ]. Elevated levels of ROS and a consequentially increased oxidative stress are considered to be involved in the etiology of autism spectrum disorder (ASD) [Chauhan & Chauhan, 2006; Frustaci et al., 2012; James et al., 2004 ] presumably through interference with biochemical processes in neural networks of the central nervous system (CNS) [Halliwell, 2006 ]. The ROS are proven to influence levels of gene expression and protein–protein interactions, consequently influencing body homeostasis [Ghanizadeh, 2012; Hayashi et al., 2001 ].

The ASD is a pervasive developmental disorder, which manifests itself in different levels of communication and social impairment, restricted interests, and repetitive behavior [Deuel, 2002 ]. The ASD is subdivided according to the differences in phenotypes (mostly speech and social interaction capabilities) into autistic disorder (AD), atypical autism (pervasive developmental disorder not otherwise specified [PDD-NOS]), and Asperger syndrome [American Psychiatric Association, 2000 ].

The progress in the ASD etiology research indicates wide variety of cellular processes that are potentially involved into the development of the disease. There are indications of impaired synaptic connectivity due to the genetic variants of synapses components [Bourgeron, 2009 ], decreased ROS protection [Chauhan & Chauhan, 2006; Main, Angley, O'Doherty, Thomas, & Fenech, 2012 ], and other cellular processes [Anney et al., 2011 ]. On the other hand, the search for common genetic variants in ASD showed wide heterogeneity and relatively low penetrance of the genetic markers in the ASD population [Anney et al., 2012; Folstein & Rosen-Sheidley, 2001; Muhle, Trentacoste, & Rapin, 2004 ].

Reports on the involvement of the SOD enzymes in participants with the ASD are rather contradicting, with several results indicating increased activity [Zoroglu et al., 2004 ], other results showing decreased activity [Yorbik, Sayal, Akay, Akbiyik, & Sohmen, 2002 ], and some with no significant change [Tórsdóttir, Hreidarsson, Kristinsson, Snaedal, & Jóhannesson, 2005 ] in the activity of the SOD enzymes [Frustaci et al., 2012 ]. The possible reason for this discrepancy may lie in a relatively small number of participants involved in those studies, possible differences in the diagnostic criteria of the participants with ASD or a real lack of association of superoxide dismutase enzymes with ASD etiology. We hypothesized that the connection between the SOD enzymes and the etiology of ASD may be in the SOD genetic variants influencing enzyme activity or expression levels.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

Participants

One hundred and thirty-four participants diagnosed with ASD by the national team with the expertise in the field were recruited to participate in the study. One hundred and one participants were diagnosed with autism, 28 with Asperger syndrome, and five with PDD-NOS. The median age of participants was 8.16 years. There were 115 males and 19 females (6:1 ratio). Samples from the 350 apparently healthy individuals of Slovenian ethnicity were included in the study as healthy controls with male to female ratio 3:1. All participants or their legal guardians signed an informed consent to participate in the study. The protocol was approved by the Slovenian National Medical Ethics Committee (No: 24/10/09).

DNA Isolation

Up to 5 mL of whole blood was taken, and DNA was isolated according to the established laboratory protocols using the FlexiGene isolation kit (Qiagen, Germany). The quality and the concentration of the isolated DNA were assessed with a spectrophotometer, and consequently DNA samples were diluted to a concentration of 5 ng/uL.

PCR Primers Selection

The polymerase chain reaction (PCR) primers (Supporting Information Table S1) were designed according to the established laboratory protocol. The reference sequence for each amplicon was acquired from ENSEMBL database (http://www.ensembl.org) [Flicek et al., 2011 ]. Optimal primers were selected by the Primer3 online tool (http://bioinfo.ut.ee/primer3/) [Rozen & Skaletsky, 2000 ] and analyzed for possible polymorphisms with the SNPcheck tool (https://secure.ngrl.org.uk/SNPCheck) [Yu & Zhang, 2011 ]. The suitability of the constructed primers for PCR was tested by the MOPS online tools (Eurofins MWG Operon, Germany).

High-Resolution Melting Analysis (HRM)

High-resolution melting analysis of the PCR amplicons was performed on the 7500 Fast RT-PCR System (Applied Biosystems, USA) using the Type-IT HRM master mix (Qiagen, Germany). The HRM was performed for each coding and 5′UTR region of the SOD genes, with amplicon size ranging between 195 and 488 base pairs reaching into intron regions for up to 40 base pairs to cover possible splicing and/or regulatory variants. Ten nanogram of DNA was used together with 5 μL of Type-IT HRM master mix and with selected forward and reverse primers per sample for each HRM reaction. The temperature protocol was a modified version of the suggested protocols provided by the supplier of the HRM master mix. The melting curves were recorded between 70°C and 98°C with 1% temperature ramp, aligned and normalized with the HRM v2.0.1 software (Applied Biosystems, USA), followed by a clustering analysis performed by the open source Rapid-Miner v5.2 software [Mierswa, Wurst, Klinkenberg, Scholz, & Euler, 2006 ]. The results of the clustering analysis were visualized by the open source Tableau Public v7.0 software (http://www.tableausoftware.com).

Clustering Analysis of HRM Results

Specific analytical algorithm to analyze the results of the HRM experiments was prepared following the described procedure by Reja and colleagues [Reja et al., 2010 ]. The principal component analysis was performed on the resulting differential curves. The resulting first three principal components were submitted to the unsupervised clustering analysis with density-based spatial clustering of applications with noise (DBSCAN) and expectation maximization (EM) clustering algorithm. The numerical measure type for the DBSCAN algorithm was set to use Manhattan distances, and the value of the distance ε was set to 0.055. While the samples belonging to the specific clusters usually carry a variant of single nucleotide polymorphism (SNP), the outliers (detected by DBSCAN clustering algorithm) usually indicated the presence of sporadic mutations. The outliers as well as three representative samples from each generated cluster, visualized on the scatter-plot matrix, were submitted to DNA sequencing to determine all genetic variants of our population.

DNA Sequencing

HRM samples chosen for sequencing were diluted in the ratio 1:4 with distilled water, and 2.5 μL of this solution was used for further sequence analysis. The primers from the HRM reaction were removed in the first step with the use of the ExoSap-IT enzyme mix (Affymetrix, USA), followed by sequence reaction with the BigDye Terminator sequencing master mix (Applied Biosystems, USA) according to the established laboratory protocol. The sequencing was performed on the ABI Prism 310 Genetic Analyser (Applied Biosystems, USA). Sequences were analyzed with the Sequencing Analysis Software v5.2.0 (Applied Biosystems, USA). The results were aligned to reference sequence using a Clustal X v2.0.12 [Larkin et al., 2007 ].

Genotyping PCR Analysis

We prepared reference samples from the carriers of specific genetic variants, detected with the HRM analysis in our ASD population. These references were used in the HRM experiments for healthy controls to quickly assign genetic variants in healthy population. The TaqMan (Applied Biosystems, USA) or KASPAR (KBioscience, UK) genotyping assays were used for genetic variants that showed statistically and significantly different enrichment between the ASD population and healthy controls in the HRM experiments to confirm the results of the HRM experiments with a genotyping PCR.

All genotyping experiments were performed following standard protocols provided by the suppliers and run on the 7500 Fast RT-PCR system. The results of genotyping experiments were analyzed with the 7500 Software v2.0.1 (Applied Biosystems, USA).

Statistics

The statistical significance of the results (P), the odds ratio (OR), and the 95% confidence interval (95% CI) were calculated using Fisher's exact statistical test. The Fisher's exact test was performed with open source RStudio software based on statistical programming language R (http://www.rstudio.com). The post hoc statistical power (1–β) for α = 0.05 of our analysis was calculated with the use of an exact test for the independent proportions on a G*Power calculator v3.1.3 for each statistically significant SNP separately [Faul, Erdfelder, Lang, & Buchner, 2007 ]. The P-value of < 0.05 was considered to be statistically significant.

The allele counts were presented in Table 1 as minor allele frequency (MAF) values for each detected SNP.

Table 1. The List of All Genetic Variants of the Autism Spectrum Disorder (ASD) Population Detected in Superoxide Dismutase Genes
GeneVariantSNP/mutRS#AA changeFunctionFrequency (European population, MAF)Frequency (case/control, MAF)FSTP-value (D.M.)
  1. Note. The first column (Gene) indicates the superoxide dismutase gene where variants were detected, followed by the nomenclature description of those variants (Variant), the type of variant (SNP/mut), the assigned RS number and the amino acid change. The predicted influence on the protein function or gene expression is listed in the function column, followed by the minor allele frequency (MAF) in European population according to data from ENSEMBL database, MAF in our case/control population, FST value, and a calculated statistical P-value for the dominant model of inheritance in the last column.

SOD1g.3341C>GSNPrs36233090NAPromotor region, regulatory feature0.060.0878/0.03500.0204P < 0,001
 c.169 + 51_169 + 56delACAGTASNPnewNAIntronic variant, unknown functionNA0.0038/0.006NAP > 0,05
 c.239 + 34A>CSNPrs2234694NAIntronic variant, regulatory feature0.060.0878/0.03500.0204P < 0,001
 c.315C>Tmutationnewp.I105ISilent mutationNA0.0038/0.00NANA
SOD2c.47C>TSNPrs4880p.A16VMissense variant0.460.522/0.4700.0018P > 0,05
SOD3c.−104C>TSNPnewNAUnknown functionNA0.022/0.01NAP > 0,05
 c.172A>GSNPrs2536512p.A58TMissense variant0.370.368/0.3800.0002P > 0,05
 c.211C>TSNPrs8192291p.L71LSynonymous SNP0.200.276/0.2410.332P > 0,05

To assess and evaluate the effect of population stratification, we calculated the FST factor for all detected genetic variants with the rs numbers [Holsinger & Weir, 2009 ].

Linkage Disequilibrium Analysis

The linkage disequilibrium (LD) was analyzed to assess the full impact of a detected genetic variants using tools available on the ENSEMBL webpage (http://www.ensembl.org). Data from the ENSEMBL were used to calculate the LD and determine the location of those variants relative to the SOD genes. The data for five proximal variants with r2 = 1 was extracted and analyzed for presence of regulatory regions that could influence the expression of SOD genes.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

Eight genetic variants of SOD genes (Table 1) were present in our autistic population. Two variants identified in the SOD1 gene, c.239 + 34A>C and g.3341C>G, were previously described and have rs numbers rs2234694 and rs36233090, respectively, while two other variants identified in the SOD1 gene, c.169 + 51_169 + 56delACAGTA and c.315C>T, were novel. A single known genetic variant in the SOD2 gene c.47C>T (rs4880) was identified. We also identified three variants, c.−104C>T, c.172A>G, and c.211C>T in the SOD3 gene. The SOD3 c.−104C>T variant was novel, while the SOD3 c.172A>G and the SOD3 c.211C>T have rs numbers rs2536512 and rs8192291, respectively. Four of the identified variants were located in the coding regions and four in the noncoding (intronic regions) of the SOD genes. The SNP rs2234694 was present in 6.1% of the analyzed healthy population and in 16.8% of the ASD participants. Statistical analysis showed that the intronic SNP rs2234694 with possible regulatory effect according to the ENSEMBL analysis had significant correlation with ASD (OR(allel C) = 2.65, 95% CI = 1.401–5.007, P = 0.00137, (1–β)α=0.05 > 0.85). Using a dominant model of inheritance (D.M.) for variant association, the odds ratio for carriers of the rs2234694 SNP is even higher (ORD.M. = 3.86, 95% CID.M. = 1.552–6.156, pD.M. = 0.0006, (1–β)α=0.05 > 0.9). The analysis of the rs2234694 linkage disequilibrium revealed an additional SNP (rs36233090) with possible regulatory effect, located 1660bp from the beginning of 5′UTR site of SOD1 gene with r2 = 1. Using a custom TaqMan genotyping probe, we indirectly confirmed a high level of linkage disequilibrium between the rs2234694 and the rs36233090 in our samples, where statistical P-value, OR, and 95% CI values for rs36233090 (OR(allel G) = 2.65, 95% CI = 1.398–4.995, P = 0.00216, (1–β)α=0.05 > 0.85; ORD.M. = 3.08, 95% CID.M. = 1.548–6.135, pD.M. = 0.0006, (1–β)α=0.05 > 0.9) were very close to those for rs2234694. A determined zygosity linkage between both SNPs was 99.58%.

The SOD1 c.169 + 51_169 + 56delACAGTA intronic variant located in the second intron of the SOD1 is a hexamere deletion with a MAF of 0.6% in healthy population. A novel silent mutation in the SOD1 gene, c.315C>T, was also identified and present in only one subject with the ASD. We detected a previously unknown variant in the SOD3, c.−104C>T with the MAF 1.1% in healthy population and confirmed its presence with DNA sequencing and KASPAR genotyping assay. The in silico analysis of the novel genetic variant effects with the ENSEMBL online tools (http://www.ensembl.org/tools.html) did not reveal any additional clues on how those genetic changes could influence the function of the SOD genes. Additionally, we detected the SNP c.172A>G (rs2536512) and c.211C>T (rs8192291) in the SOD3 with MAF 19.64% and 27.33%, respectively. The association between those genetic variants and the etiology of ASD was not statistically significant. The analysis of detected variants prevalence in each ASD subclass separately did not reveal any statistically significant association.

The average value of the calculated Fst factors (Table 1) for five detected SNPs with the rs numbers was 0.075, ranging from 0.0002 (rs2536512) to 0.332 (rs8192291).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

It was recently indicated that the rare SNPs play significantly more important role in the etiology of the complex diseases as believed previously [Gorlov, Gorlova, Sunyaev, Spitz, & Amos, 2008 ]. The exome sequencing studies of ASD patients indicated the importance of rare variants in ASD, especially the occurrence of sporadic mutations in genes involved in neural development [O'Roak et al., 2011, 2012; Sanders et al., 2012 ], while the ENCODE project demonstrated the importance of noncoding variants, many of which have regulatory features [Dunham et al., 2012 ].

This study revealed statistically significant association between the noncoding genetic variants in the SOD1 gene and ASD etiology. The SNP rs2234694 is located in the intronic region of the SOD1 gene, which according to the ENSEMBL and the ENCODE data, is predicted as a DNase1 hypersensitive site that interacts with the histone H2K36me3. The rs2234694 effect prediction with the ENSEMBL tools classified this genetic variant with a regulatory effect and a sequence ontology term regulatory region variant (SO:0001566). The histone H3K36me3 is associated with the regulation of gene expression [Lee & Shilatifard, 2007; Wagner & Carpenter, 2012 ]. The analysis of the linkage disequilibrium for the rs2234694 polymorphism revealed additional variants with high LD-value (r2 ≈ 1) using the data of 1000 genomes project for the CEU population (results for five proximal variants with r2 = 1 are in the Table 2). The analysis of those results indicated the variant rs36233090 to be located in a possible regulatory region of the SOD1 gene associated with a cisRED motif (craHsap46119). The cisRED is a database of conserved regulatory elements identified and evaluated by a genome-scale computational methodology [Robertson et al., 2006 ]. The variant rs36233090 is located near the highly conserved sequence of guanosine hexamere, changing the sequence from GCGGGGGG [RIGHTWARDS ARROW] GGGGGGGG. Although the Fst values of detected SNPs are relatively low, it is important to emphasize that the effect of potential population stratification may inflate the observed association between the ASD and SNPs rs2234694 and rs36233090 due to the low number of genetic variants included into the calculation of Fst. Another important factor that might contribute to the inflation of the detected association is the gender ratio discrepancy between the ASD and the control group. The impact of the identified variants on the ASD etiology needs to be validated with the gene expression experiments and genetic studies with greater number of case/control participants to minimize the potential for effects due to the population stratification and gender ratio discrepancy. It is possible that the interaction of SNPs rs2234694 and rs36233090 contributes to the ASD etiology. Alternatively, only one variant could have an impact on the etiology of ASD through the influence on the tissue-specific expression profile of the SOD1 gene in specific brain regions. The Multifactor Dimensionality Reduction (MDR) analysis could be performed to partially answer the question of genetic variant's interaction using open source MDR software (http://www.multifactordimensionalityreduction.org); however, complete linkage disequilibrium (r2 = 1) between genetic variants rs2234694 and rs36233090 makes the correct interpretation of the MDR analysis results difficult [Moore et al., 2006 ].

Table 2. The Analysis of the Linkage Disequilibrium for rs2234694
RS#Function
  1. Note. The analysis was performed with the ENSEMBL online tool and the results for 5 proximal genetic variants with r2 = 1 are listed. We also analyzed the possible impact of each of those 5 genetic variants on the SOD1 gene. Only the genetic variant with the rs36233090 proved to be located in the region with the predicted SOD1 promoter and enhancer site.

rs73199602Unknown
rs36233090Predicted SOD1 promoter site associated
rs17881274Unknown
rs6650814Unknown
CS983706Unknown

High metabolic rate of the neural cells that supplies their energy needs is linked to high production of ROS. It consequently makes them prone to the oxidative stress damage if the ROS equilibrium is disrupted due to the environmental or genetic factors. The influence of SNPs rs2234694 and rs36233090 on the tissue-specific expression of SOD1 in the neural cells may be dose dependent, where a drop in the level of the available SOD1 enzyme would negatively influence the ability of the neural cells to remove excessive ROS. Consequently the levels of ROS and their cytotoxic by-products would increase and disrupt redox and cell homeostasis crucial for the differentiation and the development of neural cells [Kennedy, Sandiford, Skerjanc, & Li, 2011 ] and would also influence the neurodevelopment [Celotto, Liu, Vandemark, & Palladino, 2012 ]. A disrupted neurodevelopmental process is one of the main features of the ASD etiology [Deuel, 2002 ].

Additional research of the SOD1 expression and the tissue-specific activity of the superoxide dismutase 1 is required to explain precise biochemical mechanism involved in the development of the ASD. Nevertheless, the present results demonstrated the potential importance of the genetic variants located outside of the genes' coding regions, as well as the importance of the future research on the rare genetic variants and their influence on the etiology of complex diseases such as the ASD.

The identified SOD2 variant was c.47C>T (rs4880), and according to the literature and the HGMDB database, this variant was also associated with the increased risk of complications in diabetes mellitus type 1 patients [Hovnik, Dolzan, Bratina, Podkrajsek, & Battelino, 2009 ], but there was no association with the ASD.

The use of HRM analysis resulted in the discovery of three new genetic variants and effective detection of rare single nucleotide polymorphisms that could be involved in the ASD etiology. Enhancing the HRM analysis with the principal component analysis and the unsupervised clustering algorithms, especially the DBSCAN algorithm, proved to be a crucial step toward the improvement of the detection rate, speed, and accuracy of the HRM experiments, while at the same time it allowed us to reduce the reaction volume and consequently optimize and improve the efficiency of the experiment. Coupled with the selective DNA sequencing and the PCR genotyping, the HRM analysis provided a fast, reliable, and a cost-effective workflow for the identification of genetic variants for a relatively big number of samples simultaneously.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

Our results indicate that the genetic variants rs2234694 and rs36233090 in the noncoding region of the SOD1 confer an increased risk in the development of the ASD. The association between the genetic variants in genes of the antioxidative stress mechanisms and the ASD can serve as an additional piece of the puzzle in the understanding of the ROS role in the ASD etiology. Additional research is required to confirm a connection between the discovered genetic variants, the levels of the tissue-specific oxidative stress markers, and the etiology of the ASD.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information

This study was supported by the Slovenian National Research Agency grants J3-2412 and P3-0343. No potential conflicts of interest relevant to this article were reported.

We would like to express our gratitude to Jurka Ferran and Eva Ðalić for their expert technical assistance and to Dr. Maruša Debeljak, Dr. Simona Murko, and Dr. Tinka Hovnik for their constructive suggestions.

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  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
aur1345-sup-0001-si.docx13KTable S1. The list of the PCR primers used for the HRM analysis.

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