The genetic variants in 3’ untranslated region of voltage-gated sodium channel alpha 1 subunit gene affect the mRNA-microRNA interactions and associate with epilepsy
- Tian Li2, 3Email authorView ORCID ID profile,
- Yaoyun Kuang1 and
- Bin Li1
https://doi.org/10.1186/s12863-016-0417-y
© The Author(s). 2016
Received: 3 April 2016
Accepted: 15 July 2016
Published: 29 July 2016
Abstract
Background
mRNA expression in a cell or subcellular organelle is precisely regulated for the purpose of gene function regulation. The 3’ untranslated region (3’UTR) of mRNA is the binding target of microRNA and RNA binding proteins. Their interactions regulate mRNA level in specific subcellular regions and determine the intensity of gene repression. The mutations in the coding region of voltage-gated sodium channel alpha 1 subunit gene, SCN1A, were identified in epileptic patients and confirmed as causative factors of epilepsy. We investigated if there were genetic variants in 3’UTR of SCN1A, affecting the microRNA-mRNA 3’UTR interaction and SCN1A gene repression, potentially associated with epilepsy.
Results
In this case–control study, we identified twelve variants, NM_001202435.1:n.6277A > G, n.6568_6571del, n.6761C > T, n.6874A > T, n.6907 T > C, n.6978A > G, n.7065_7066insG, n.7282 T > C, n.7338_7344del, n.7385 T > A, n.7996C > T, and n.8212C > T in 3’UTR of SCN1A gene. We found that the variant of n.6978A > G in all our samples was completely mutated (G/G). In male group, T allele in n.7282 T > C was associated with epilepsy, while C allele was significantly less frequent in epileptic patients than in normal males (OR 0.424). Consequently, the haplotype “CTTACATGACGA” / “CTCTA” was significantly less frequent in male epileptic patients (0.173) than in normal males (0.305). The frequency of haplotype block found in females, "TTTAACA", "TTCAACA", and "CTTAACA" was 0.499, 0.254 and 0.234 respectively. Within STarMir model analysis, the “CTCTA” haplotype showed significantly higher site accessibility to microRNA targeting and higher downstream sequence accessibility for nonconserved binding than that of other haplotypes. Overall, the male genotypes have the higher accessibility of the downstream 30nt block of nonconserved site than the female genotypes.
Conclusions
NM_001202435.1:n.7282 T > C is the genetic variant associated with epilepsy in males, and the related haplotype “CTTACATGACGA” / “CTCTA” in the region of chr2: 165991297–165989081, which has high site accessibility for microRNA binding, is the genetic protective factor against epilepsy in males. In female subset, the frequencies of haplotype block "TTTAACA", "TTCAACA", and "CTTAACA" were 0.499,0.254 and 0.234 respectively. Alleles and haplotypes distribution did not differ in female cases in comparison to female controls.
Keywords
Background
mRNA stability, transport, and local translation are critical for gene function regulation. The mRNA intrinsic sequence of a particular gene and other intracellular factors determines the half-life of the mRNA. MicroRNA (miRNA) binds to a site or a part of sequences in three prime untranslated region (3’UTR) of mRNA, destabilizes mRNA, and represses the targeted gene translation [1]. The variant sequences in 3’ UTR alter the binding feature of miRNA, and influence gene repression process. For example, some SNPs in the responsible genes (IL23R, LCE3D, et al.) destroyed or created miRNA binding sites and were associated with the clinical psoriasis phenotypes [2]. In a group of schizophrenia patients, rs3219151 (C > T, GABRA6) was identified and related to the decreased risk for schizophrenia [3]. miRNA works in a particular way for activity-dependent regulation of mRNA stability and translation [4, 5]. The variants in miRNA coding genes altered miRNA expression, processing, function, and then associated with diseases. Studies showed that rs353291 in miR-145 associated with breast cancer [6], rs11614913 in miR-196a2 associated with bladder cancer [7]. The local concentrations of miRNA and RNA binding proteins determine the binding site occupancies, which in turn regulate mRNA stability and localization, protein production [8].
To analyze the mRNA-miRNA interactions, STarMir is a helpful resource for miRNA studies [9]. It describes many detailed features of predicted sites based on the logistic prediction models [10]. Those features could represent the mRNA-microRNA binding with the probability related to mRNA structure and microRNA binding location. The predicted sites could be distinguished and analyzed with the binding energy, the availability of binding and adjacent sequences, AU percentage in binding sites, and relative starting location of predicted sites. For example, would particular mRNA sequence have higher site availability than others and would the high concentrations of mRNA and microRNA facilitate the mRNA-microRNA binding or induce the competition? STarMir model analysis hypothesizes that miRNA-binding has two steps, nucleation, and hybrid elongation, and both of them requires energy [11]. The power of distinct binding sites displays the difference of each mRNA-miRNA interaction, due to the variant sequence in mRNA binding sites. It is novel to analyze the whole 3’UTR sequences and its binding ability to microRNA, with the usage of STarMir parameters. The imitation of microRNA binding to 3’UTR could provide the information that could be investigated for the potential effects of 3’UTR genetic variants on gene repression.
The abnormality in the gene coding voltage-gated sodium channel alpha1 subunit (SCN1A) is a causative factor in febrile seizure related epilepsy syndromes, such as Dravet syndrome, and Genetic Epilepsy with Febrile seizure plus [12]. The mutations of the protein-coding region (or exon) of SCN1A gene are also pathogenic factors to neuronal hyperexcitability [13]. Another factor that regulates the SCN1A expression is also the potentially epileptogenic factor, such as 5’ untranslated region of SCN1A gene [14, 15]. We hypothesize that the variant sequence in 3’UTR is also a critical regulatory mechanism through miRNA-binding 3’UTR interactions [16].
Methods
Study subjects
We enrolled 101 epileptic patients and 126 healthy individuals in 2004 through 2010 in The Second Affiliated Hospital of Guangzhou Medical University. The clinical diagnosis of epilepsy or epilepsy syndrome was based on the criteria of the Commission on Classification and Terminology of the International League Against Epilepsy (ILAE) (1981, 1989). All individuals enrolled in this study were not related with one another by the family relationship or consanguinity.
Genotyping and allele analysis of variants
The genomic DNA was extracted from peripheral white blood cells of participants with guanidine/SDS method (see Additional file 1: Supplemental material 1). We applied the final genomic DNA collection directly in PCR experiment for target fragments replication (3’UTR in SCN1A gene). The four pairs of primers for genomic DNA replication were listed below, wp615-5’-TGATCTGACCATGTCCACTGC, wp616-5’-CCCTCATGCAAACCACGAC (680 bp); wp617-5’-TTTTGTAAACGAAGTTTCTGTTGAG, wp618-5’-GAAACCAGATACAGCAGCATGG (732 bp); wp619-5’-TGTAGAGTGCAAGCTTTACACAGG, wp620-5’-GAATCGTGAACCTATTTTGCTCC (601 bp); wp621-5’-CACAATCACTTTTCTTACTTTCTGTCC, wp622-5’-CCTTCTCCCCCAATTTGTAATG (660 bp). We then sent the PCR products to BGI Guangzhou Office (ABI 3730xl sequencer) for sequencing. If the sequencing files indicated the deletion or insertion mutations, we would use molecular cloning method to amplify the single genomic DNA chain within cloning vectors for re-evaluation. The variants identified by genotyping were summarized in male and female group respectively. The number of “aa”, “ab”, and “bb” genotypes were summed into chi-square test table for genotype-associated study. At the same time we calculated the frequency and number of two alleles (a or b) with the formula (a = 2*aa + ab; b = 2*bb + ab) and summarized them in another chi-square test table for allele-associated study. The p-value less than 0.05 was the criteria for the statistically significant difference.
Haplotype analysis
The findings of genetic variants from male and female individuals were summarized into two tables (Additional file 2: Table S1 and Table S2) with Haploview program [17] Linkage Format (*.ped). The form or file listed the variables of family ID (enrollment ID into the study), Individual ID (case ID in Epilepsy Center or in Healthy Center), Father ID (zero, independent without family relationship between subjects), Mother ID (zero), gender (1 means male, 2 means female), affection status (1 means unaffected, 2 means affected or with epilepsy), and the alleles labeled with two numbers from zero to four (1 = A, 2 = C, 3 = G, 4 = T, 0 = missing data or deletion). By choosing “Four Gamete Rule” to define blocks, the haplotype data were displayed with frequency and chi-square value, the association p-value.
STarMir input, output, and data analysis
miRNA data from Landgraf et al. [18] were downloaded and the approximate 50 miRNAs mostly expressed in each part of CNS (hippocampus, frontal cortex, midbrain, and cerebellum) were input into the miRbase database (http://www.mirbase.org//cgi-bin/starmirtest2.pl/) for mature 22 nt-miRNA form. In the STarMir web (http://sfold.wadsworth.org/cgi-bin/starmirtest2.pl), we uploaded one 50-miRNA set miRNA files into “microRNA sequence(s), manual sequence entry”. The 3’UTR sequence files were modified with background genetic variant, n.6978A > G (G/G), and each of seven haplotypes, two deletions, one insertion, or one wild-type (reference sequence). The modified sequence files were input into the option of “single target sequence, manual sequence entry”. By choosing “V-CLIP based model (human)”, “Human (homo sapiens)”, and “3’UTR”[10], the following parameters [19] would be displayed in the output window and ready for further analysis: “LogitProb”, the probability of the site being a miRNA-binding site as predicted by STarMir logistic model. “site_position”, start to end position of the predicted miRNA target region in mRNA; “seed_position”, start to end position of the target sub-region in mRNA complementary to the miRNA seed (corresponding to positions 2-7/8 of the miRNA); “seed_type”, 6mer, offsite 6mer, 7mer-A1, 7mer-m8, and 8mer seed sites described in Bartel 2009 [20]; “site_access”, the structural accessibility as calculated by the average probability of a nucleotide in mRNA being single-stranded for binding the nucleotides in microRNA; “seed_access”, the structural accessibility as calculated by the average probabilities being single-stranded of the nucleotides in the target sub-region of mRNA complementary to the miRNA seed; “upstream_access (#nt)”, the structural accessibility described by the average probabilities of single-stranded nucleotides in upstream block of the predicted binding site (# is the block size); “dwstream_access (#nt)”, the structural accessibility displayed by the average of single-stranded probabilities for the downstream block of the predicted site; “upstream_AU (#nt)”, the percentage of AU for the upstream block of the binding site (# is the block size); “dwstream_AU (#nt)”, the percentage of AU for the downstream block of the binding site (# is the block size); “site_location”, the location of the predicted site relative to the start and end along the entire length of sequence (for 3’UTR, zero represents the 5’ end and one accounts for the 3’ end); ΔGhybrid, the stability of miRNA:target hybrid as calculated by RNAhybrid [21] in Rehmsmeier et al. 2004; ΔGnucl, the potential nucleation for miRNA:target hybridization [10]; ΔGtotal, the total energy change during the entire process of hybridization [11]. We filtered the output data with criteria of LogitProb of 0.5 or higher [10] and ΔGhybrid of −15 kcal/mol or lower [22]. We compared the means of those parameters in each genotype group with one-way ANOVA or Kruskal-Wallis test. We also added the miRNA copy number [18] and mRNA expression amount (reads per kilobase transcript per million reads, RPKM, http://www.gtexportal.org/home) [23] into the data pool for the correlation and regression analysis with STarMir parameters.
Statistical analysis
Manual chi-square table was applied to describe the distribution of common genetic variants in case/control study, to calculate and compare the genotype frequency and allele frequency in case group and control group. Fisher’s exact test was used to analyze the distribution of rare genetic variants in case and control groups. Haploview software 4.2 was applied to calculate the haplotype frequency in groups and the chi-square value, p-value in haplotype association test. Using IBM SPSS Statistics 23 (IBM Corporation, Armonk, NY, USA), we compared the means, standard deviation (SD), standard errors of means (SEM) of STarMir parameters in “genotype” groups (11 genotypes) with one-way ANOVA, and “Dunnett T3” in Post Hoc. If the data of some STarMir parameters did not pass the “homogeneity for variants” test (p < 0.05) the non-parameter test, “independent samples Kruskal-Wallis test” was used to compare the mean ranks of multiple genotype groups. “Bivariate Correlation” and “Spearman” were applied in the correlation analysis (p < 0.05 and r ≠ 0) with miRNA expression weight or mRNA expression quantities as “x” variable, and other STarMir parameters as “y” variable. In linear regression analysis, “miRNA expression weight” or “mRNA expression” was “Independent(s)” and other significantly-correlated STarMir parameters were “Dependent”, p < 0.05 in “ANOVA” table to define the significant regression. We used the B values of constant and mRNA_expr to fill the linear vector form (y = ax + b).
Results
Study subjects
We collected SCN1A 3’UTR genotyping data from 101 epileptic patients (52 males and 43 females) and 126 controls (59 males and 68 females). All patients, when enrolled in this study, were older than two years old. In the male subgroup, 15 (29 %) patients had a positive history of febrile seizures. 28 (53.8 %) patients were at the age of 2–9 years, 11 (21.1 %) patients were at the age of 10–19 years, and 7 (13.5 %) patients were at the age of 20–29 years. In the female subgroup, eight (18.6 %) patients had a positive history of febrile seizures. 18 (41.9 %) patients were at the age of 2–9 years. 13 (30.2 %) patients were at the age of 10–19 years. And five (11.7 %) patients were at the age of 20–29 years. In the male healthy control group, 29 (49.2 %) males were at the age of 20–29 years. Among female controls, three (4.9 %) females were at the age of 10–19 years and 45 (73.8 %) females were at the age of 20–29 years. All participants were Chinese Han, and most of them lived in southern China.
SCN1A-3’UTR Genotyping and association analysis of single variants
The distribution of n.7282 T > C, n.7996C > T, n.8212C > T in males and females case/control groups
n.7282 T > C | CC+CT | TT | C | T | C frequency | ||||
Female patients | 24 | 22 | 25 | 67 | 0.272 | ||||
Female controls | 28 | 40 | 34 | 102 | 0.250 | ||||
p > 0.05 | p > 0.05 | ||||||||
Male patients | 21 | 34 | 21 | 89 | 0.191 | ||||
Male controls | 35 | 24 | 36 | 82 | 0.305 | ||||
p <0.05 | p <0.05 | ||||||||
n.7996C > T | CC+CT | TT | C | T | |||||
Female patients | 18 | 28 | 18 | 74 | 0.196 | ||||
Female controls | 26 | 42 | 29 | 107 | 0.213 | ||||
p > 0.05 | p > 0.05 | ||||||||
Male patients | 21 | 34 | 22 | 88 | 0.200 | ||||
Male controls | 24 | 34 | 25 | 91 | 0.216 | ||||
p > 0.05 | p > 0.05 | ||||||||
n.8212C > T | CC | CT+TT | C | T | T frequency | ||||
Female patients | 38 | 8 | 84 | 8 | 0.087 | ||||
Female controls | 55 | 13 | 122 | 14 | 0.103 | ||||
p > 0.05 | p > 0.05 | ||||||||
Male patients | 40 | 15 | 94 | 16 | 0.145 | ||||
Male controls | 45 | 13 | 102 | 14 | 0.121 | ||||
p > 0.05 | p > 0.05 |
Haplotype analysis in case/control study
The Haploview association analysis of blocks in the male and female group. *marked the data point with statistically significant difference in chi-square test (p<0.05)
Full block | Abbre. | Freq | Case, control counts | Case, control ratio | Chi square | p value | |
---|---|---|---|---|---|---|---|
Male | |||||||
CTTATATGACGA | CTTTA | 0.445 | 51:53,47:69 | 0.490,0.361 | 1.612 | 0.2042 | |
CTTACATGACGA | CTCTA | 0.241 | 18:86,35:81 | 0.173,0.305 | 4.963 | 0.0259* | |
CCTATATGACGA | CCTTA | 0.186 | 22:82,19:97 | 0.212,0.206 | 0.824 | 0.3639 | |
TTTATATGACGA | TTTTA | 0.114 | 13:91,12:104 | 0.125,0.103 | 0.253 | 0.6151 | |
Female | |||||||
TTATATGACGA | TTTAACA | 0.499 | 44:42,67.7:70.3 | 0.512,0.490 | 0.095 | 0.7578 | |
TTACATGACGA | TTCAACA | 0.254 | 24:62,33:105 | 0.279,0.239 | 0.445 | 0.5045 | |
CTATATGACGA | CTTAACA | 0.234 | 18:68,34.3:103.7 | 0.209,0.249 | 0.459 | 0.498 |
STarMir model analysis of SCN1A-3’UTR haplotype and miRNA interaction
miRNA expression profile in CNS and the means of STarMir parameters in genotype groups. a According to Landgraf P et al., 2007 [18], the four parts of CNS, hippocampus, frontal cortex, cerebellum, and midbrain, have distinctive miRNA expression profile. The approximate 50 miRNAs most expressed in each part were described in a color-coded histogram with copyright permission (Additional file 1: Table S4). b the means of LogitProb in genotype groups were significantly different. * represents that the mean of LogitProb in the genotype group was significantly different from that of wild type group. c the means of ΔGtotal in genotype groups were significantly different. * represents that the mean of ΔGtotal in the genotype group was significantly different from that of wild type group. d the means of site_access in genotype groups were significantly different. * represents that the mean of site_access in the genotype group was significantly different from that of wild type group. e the means of Dwstream_access_30 nt in genotype groups were significantly different. * represents that the mean of Dwstream_access_30 nt in the genotype group was significantly different from that of wild type group
- 1.
STarMir parameter comparison among genotypes
The mean ± SD (standard deviation) of STarmir parameters in 11 genotype groups
LogitProb | ΔGhybrid | ΔGnucl | ΔGtotal | site_access | Upstream_Access_10nt_nonconserved | Dwstream_Access_30nt_nonconserved | Upstream_AU_30nt | Dwstream_AU_30nt | Site_Location | Seed_Access | Upstream_Access_15nt_conserved | Dwstream_Access_10nt_conserved | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wild type | 0.632 ± 0.079 | −17.378 ± 2.116 | −1.866 ± 1.802 | −4.143 ± 4.935 | 0.406 ± 0.117 | 0.407 ± 0.163 | 0.426 ± 0.099 | 0.663 ± 0.116 | 0.670 ± 0.101 | 0.457 ± 0.289 | 0.385 ± 0.169 | 0.419 ± 0.113 | 0.382 ± 0.122 |
CTTTA | 0.626 ± 0.079 | −17.397 ± 2.129 | −1.789 ± 1.774 | −3.757 ± 5.011 | 0.396 ± 0.117 | 0.411 ± 0.166 | 0.427 ± 0.103 | 0.661 ± 0.116 | 0.669 ± 0.102 | 0.450 ± 0.288 | 0.350 ± 0.165 | 0.462 ± 0.162 | 0.393 ± 0.142 |
CTCTA | 0.633 ± 0.081 | −17.371 ± 2.107 | −1.870 ± 1.894 | −4.172 ± 5.175 | 0.408 ± 0.128 | 0.412 ± 0.175 | 0.431 ± 0.107 | 0.660 ± 0.116 | 0.669 ± 0.102 | 0.448 ± 0.287 | 0.369 ± 0.191 | 0.445 ± 0.148 | 0.383 ± 0.137 |
CCTTA | 0.625 ± 0.079 | −17.409 ± 2.135 | −1.829 ± 1.719 | −3.815 ± 4.867 | 0.396 ± 0.110 | 0.405 ± 0.155 | 0.422 ± 0.096 | 0.662 ± 0.116 | 0.669 ± 0.102 | 0.458 ± 0.289 | 0.359 ± 0.165 | 0.448 ± 0.130 | 0.387 ± 0.135 |
TTTTA | 0.627 ± 0.079 | −17.400 ± 2.124 | −1.824 ± 1.790 | −3.814 ± 5.027 | 0.394 ± 0.119 | 0.408 ± 0.166 | 0.426 ± 0.107 | 0.662 ± 0.116 | 0.669 ± 0.102 | 0.453 ± 0.289 | 0.335 ± 0.169 | 0.458 ± 0.149 | 0.392 ± 0.131 |
CTTAACA | 0.626 ± 0.078 | −17.407 ± 2.135 | −1.823 ± 1.711 | −3.841 ± 4.851 | 0.397 ± 0.110 | 0.406 ± 0.155 | 0.422 ± 0.096 | 0.663 ± 0.116 | 0.669 ± 0.102 | 0.459 ± 0.290 | 0.362 ± 0.137 | 0.446 ± 0.125 | 0.393 ± 0.122 |
TTCAACA | 0.626 ± 0.080 | −17.394 ± 2.126 | −1.824 ± 1.782 | −3.839 ± 5.007 | 0.398 ± 0.120 | 0.410 ± 0.167 | 0.428 ± 0.104 | 0.659 ± 0.117 | 0.668 ± 0.102 | 0.450 ± 0.287 | 0.346 ± 0.156 | 0.459 ± 0.142 | 0.392 ± 0.131 |
TTTAACA | 0.625 ± 0.078 | −17.397 ± 2.129 | −1.782 ± 1.752 | −3.724 ± 4.940 | 0.394 ± 0.115 | 0.410 ± 0.166 | 0.428 ± 0.103 | 0.661 ± 0.116 | 0.668 ± 0.102 | 0.450 ± 0.287 | 0.357 ± 0.167 | 0.461 ± 0.157 | 0.396 ± 0.138 |
6568_6571del | 0.628 ± 0.078 | −17.401 ± 2.130 | −1.841 ± 1.788 | −3.910 ± 4.956 | 0.398 ± 0.116 | 0.408 ± 0.162 | 0.418 ± 0.099 | 0.663 ± 0.116 | 0.668 ± 0.103 | 0.460 ± 0.290 | 0.347 ± 0.147 | 0.433 ± 0.132 | 0.376 ± 0.135 |
7338_7344del | 0.627 ± 0.079 | −17.408 ± 2.131 | −1.844 ± 1.681 | −3.969 ± 4.686 | 0.397 ± 0.106 | 0.406 ± 0.149 | 0.421 ± 0.095 | 0.663 ± 0.116 | 0.669 ± 0.103 | 0.459 ± 0.290 | 0.369 ± 0.127 | 0.457 ± 0.119 | 0.393 ± 0.109 |
7065_7066insG | 0.629 ± 0.079 | −17.394 ± 2.124 | −1.843 ± 1.741 | −4.048 ± 4.858 | 0.402 ± 0.112 | 0.406 ± 0.158 | 0.423 ± 0.097 | 0.662 ± 0.116 | 0.669 ± 0.102 | 0.458 ± 0.290 | 0.373 ± 0.164 | 0.431 ± 0.125 | 0.382 ± 0.127 |
- 2.
The description and comparison of conserved sites and non-conserved sites
STarMir parameter comparison between conserved/ nonconserved sites and gender groups. a the free energy change in conserved and nonconserved binding. * represents that the mean of the free energy parameter in nonconserved sites was significantly different from that of conserved sites. b the comparison of STarMir parameters commonly used in both conserved and nonconserved sites. * represents that the mean of the marked STarMir parameter in nonconserved sites was significantly different from that in conserved sites. c the accessibility of downstream 30 nt block of the nonconserved site in male and female groups. * represents that the mean of the Dwstream_access_30nt_nonconserved of male genotypes was significantly different from that of female genotypes
- 3.
The comparable means of StarMir parameters from gender-deviated genotypes
- 4.
miRNA and mRNA expression level affects miRNA-mRNA (3’UTR) interaction
the correlation and linear regression analysis of mRNA and microRNA
y= | Vector form | Correlation coefficient | Regression R square linear | Regression p | |
---|---|---|---|---|---|
x = microRNA expression copy | LogitProb | y = −0.000047x + 0.628775 | −0.024 | 0.000888 | p < 0.001 |
ΔGnucl | y = −0.00029x-1.823338 | 0.023 | 0.000832 | p < 0.001 | |
ΔGhybrid | y = 0.001226x-17.425679 | −0.046 | 0.000067 | p = 0.025 | |
upstream_AU_30nt | y = −0.000032x + 0.662505 | −0.021 | 0.000191 | p < 0.001 | |
dwstream_AU_30nt | y = 0.000025x + 0.668299 | 0.008 | 0.000146 | p = 0.001 | |
seed_access | y = −0.000427x + 0.369282 | −0.117 | 0.01815 | p < 0n001 | |
x = mRNA expression level | dwstream_access_30nt_nonconserved | y = −0.000327x + 0.426487 | −0.008 | 0.00012 | p = 0.003 |
site_location | y = 0.000632x + 0.451115 | 0.008 | 0.000054 | p = 0.044 | |
seed_access | y = −0.004605x + 0.382857 | −0.068 | 0.008944 | p = 0.002 | |
dwstream_access_10nt_conserved | y = 0.003130x + 0.372415 | 0.072 | 0.006371 | p = 0.01 |
The correlation and regression analysis of miRNA expression or mRNA baseline expression with the seed accessibility of miRNA conserved binding sites. a the expression copies of microRNAs binding the predicted conserved sites of 3’UTR in SCN1A gene in all four parts of CNS. b the correlation and linear regression illustration of seed accessibility with miRNA expression copies. The linear regression constant and vector form were shown on the upper right part of the figure. c the SCN1A mRNA expression profile in the human brain, according to the GTEx Consortium 2015 [23]. The mRNA expression RPKM of male group, female group, and overall average were revealed in four parts of the human brain. d the negative correlation and regression relationship of seed accessibility with baseline mRNA expression level. The regression constant and vector form were shown in the upper right part of the figure
Discussion
Gene mutations in a coding region caused amino acid replacement, deletion, abnormal protein structure, or truncated/incomplete protein sequence. In non-coding regions of DNA sequence, the variants may exist in order to regulate the gene product quantity or preserve characteristic information [4]. In the genotyping results, n.6978A > G was fully deviated from the reference (A/A). It may indicate that our subjects’ SCN1A gene was originated from a narrow genetic source with distinctive inherent background (racial factor), or our subjects are under the influence of the small range of residency area or of similar social factors. The allele of n.7282 T > C had significantly lower frequency of C in male patients than that in normal males. Consequently, the frequency of haplotype “CTCTA” was significantly lower in patients group. It indicated that C allele in n.7282 T > C should be a protective factor against epilepsy (OR 0.424). The miRNA-mRNA (3’UTR) interaction STarMir prediction data supported this finding: the structural accessibility of the predicted site in “CTCTA” 3’UTR (0.408 ± 0.128) was as high as that in wild-type (0.406 ± 0.117), while site_access of other genotypes was significantly lower than that of wild-type. Additionally, “CTCTA” genotype had also the higher accessibility of downstream sequence of non-conserved binding sites (0.431 ± 0.107) than wild-type genotype (0.426 ± 0.099). “CTCTA” should be friendly accessible for miRNA binding and gene repression process, which should not be halted or handicapped by the mutated 3’UTR sequences. Instead, the 3’UTR sequences of other genotypes could influence the miRNA-related gene repression negatively. In the female subset, although we identified several novel mutations and recognized the major alternation in STarMir parameters of two deletions and one insertion mutation, due to few mutated case (only one for each mutation) we could not reach the statistically significant difference between female patients and controls. Other mutations were distributed very even in female patients and female controls. On the contrary, in males, there were no severe mutations that changed the microRNA-mRNA (3’UTR) interaction, but the common variants were distributed differently in male patients and male controls, which contributed to the statistically significant and comparative results. In comparison to other studies, the authors analyzed the genetic variants in 3’UTR with other mechanisms on gene regulation, such as AU-rich sequence [24], GAPDH-binding site [25]. Our study could have the limitation of the chi-square test and the fundamental theory, the microRNA-mRNA (3UTR’) interaction, to analyze the genetic variants in 3’UTR.
Overall, the miRNA expression negatively influenced microRNA-mRNA (3’UTR) interaction according to the correlation and regression results on STarMir parameters. The predominantly negative effects of higher miRNA expression were the lower probability of being the site predicted, the lower stability of microRNA:target, the lower percentage of AU in upstream block, and the lower structural accessibility of microRNA-complementary sequence. We might have interpreted that more microRNA would induce the competition among the possible binding sites, the excessive possibility of microRNA:target binding could interfere the existing microRNA:target hybridization, more microRNA would impair the accessibility of microRNA-complementary sequences, and the more microRNA could shift the binding sites to Less AU upstream sites. On the contrary, the mRNA quantities could positively influence the microRNA-mRNA (3’UTR) interaction by promoting 3’end-proximal site binding, and higher downstream sequence of conserved site accessibility. Based on the negative effects of microRNA and mRNA quantity on seed_access, we could reasonably assume that on the physiological baseline, the microRNA expressed in CNS and mRNA of SCN1A are relatively excessive to conserved binding of microRNA-mRNA (3’UTR) interactions, which might be an interesting study direction in the future.
STarMir principally uses two free energy values to predict and calculate the microRNA-mRNA binding features [11]. It is believed that STarMir has less putative targets with additional specific threshold condition [26]. It required the input mRNA sequence less than 5000 nt. However, our study subject, SCN1A gene, has the long mRNA sequence (NM_001202425.1), 8342 bp. Although STarMir provides the optimal option for mRNA input, the full length of mRNA for complex structural assessment, we were able to input 3’UTR sequence of SCN1A gene, a part of mRNA sequence, for binding site prediction. For the purpose of the description of binding sites, STarMir had better performance on our task, providing multiple parameters for the site comparison and analysis. Other microRNA predictive tools, such as PITA [26], and RNAhybrid [21] could not provide those binding features for our purposes.
The diseases of multifactorial inheritance have complex etiology with the function of multiple genes and the complex epigenetic mechanisms are involved. Epilepsy is one of the diseases of multifactorial inheritance. The causative factors include the dysfunctional gene products of SCN1A, GABRA1 (alpha subunit of GABA receptor), and CHRNA4 (subunits of nicotinic AChr receptor) gene [27]. Many factors are contributing to the gender difference of human brains, such as sex hormone physiology, the fine tune of neuroendocrine system functioning [28, 29], and the environmental and educational interferences. Consistently, the males and females have different mRNA expression baseline of SCN1A gene in the brain [23], which further supports our finding and could also be the outcome of gene regulation based on microRNA-mRNA interaction. Generally speaking, our study presented the gender-different data probably resulting from epigenetic mechanism (actively involving environmental factors), and dynamic miRNA-mRNA (3’UTR) interaction. On the other side, it could also be the outcome of human gender-different adaption and selection in the genetic evolution. How the sex factors fine-tune the gene expression and repression or the function of voltage-gated sodium channels would be an attractive topic for clinical scientists and neurobiologists in the future.
Conclusions
Using case/control association study and STarMir model, we efficiently analyzed the genetic variants in 3’UTR of SCN1A gene in epileptic patients and small-sized controls. The male epileptic patients had significantly lower frequency of C allele in n.7282 T > C than normal males, and the OR value was 0.424. The related haplotype “CTCTA” also had the significantly lower frequency in male epileptic patients. The frequencies of haplotype block "TTTAACA", "TTCAACA", and "CTTAACA" in female subset were 0.499,0.254 and 0.234. Their frequencies were not significantly different between case and controls. The STarMir analysis displayed that male haplotype “CTCTA” had high site accessibility and other favorable features for miRNA binding, compared with other genotypes and haplotypes. The 3’UTR or related miRNAs could be the potential targets of therapeutic strategies in the future study of epilepsy.
Abbreviations
3’UTR, 3’ untranslated region; miRNA, microRNA; SCN1A, voltage-gated sodium channel α 1 subunit; SNP, single nucleotide polymorphism; IL23R, interleukin 23 receptor; LCE3D, late cornified envelope 3D; GABRA6, gamma-aminobutyric acid (GABA) A receptor, subunit α 6; GABRA1, gamma-aminobutyric acid (GABA) A receptor, subunit alpha 1; CHRNA4, subunits of nicotinic acetylcholine receptor.
Declarations
Acknowledgement
The authors thank Yuesheng Long, for PCR primers design; Lidong Hua, Guangzhou Women and Children’s Medical Center and Na He, Department of Neurology of the Second Affiliated Hospital of Guangzhou Medical University, for the assistance in the recruitment of study subjects, and lab technique support. The authors appreciate the advice of genetic variant statistical analysis from Jin Guo, School of Public Health, Capital Medical University, and Su Liu, Chinese Center for Disease Control and Prevention. We greatly appreciate the advice on miRNA and mRNA data collection from Rui Song, MCB department, University of California at Berkeley. We greatly appreciate the financial support from Postdoctoral Startup Research Foundation of Guangzhou Human Resource Department (310109–011).
Availability of data and materials
Supplement Material M1 was describing the details of the genotyping method (guanidine/SDS method for genomic DNA collection).
Authors’ contribution
TL designed the experiment protocol and analysis strategy, contributed the genomic DNA collection, PCR replication, statistical analysis, results interpretation, and manuscript writing. YK and BL analyzed the sequencing files, statistical analysis, and helped with 3’UTR fragment cloning. All authors have read and approved the manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Within the research consent, the participants were informed that their bio-sample data would be used anonymously for scientific data analysis and potential publication. With the signed consent, every participant or the guardians agreed to give the permission of their data usage for scientific publication.
Ethics approval and consent to participate
The study was based on one part of a comprehensive genetic study protocol to screen mutations in SCN1A, SCN2A, and SCN1B genes of epileptic patients. The protocol was approved by the institutional ethic committee and board of the Second Affiliated Hospital of Guangzhou Medical University. And we confirmed that the study subjects or their guardians (if the patient was younger than 18 years old) voluntarily participated in the study with signed research consent.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
- Liu B, Li J, Cairns MJ. Identifying miRNAs, targets and functions. Brief Bioinform. 2014;15:1–19.View ArticlePubMedGoogle Scholar
- Pivarcsi A, Ståhle M, Sonkoly E. Genetic polymorphisms altering microRNA activity in psoriasis--a key to solve the puzzle of missing heritability? Exp Dermatol. 2014;23:620–4.View ArticlePubMedGoogle Scholar
- Gong Y, Wu CN, Xu J, Feng G, Xing QH, Fu W, et al. Polymorphisms in microRNA target sites influence susceptibility to schizophrenia by altering the binding of miRNAs to their targets. Eur Neuropsychopharmacol. 2013;23:1182–9.View ArticlePubMedGoogle Scholar
- Cohen JE, Lee PR, Fields RD. Systematic identification of 3'-UTR regulatory elements in activity-dependent mRNA stability in hippocampal neurons. Philos Trans R Soc Lond B Biol Sci. 2014;369:1652.View ArticleGoogle Scholar
- Malmevik J, Petri R, Klussendorf T, Knauff P, Åkerblom M, Johansson J, et al. Identification of the miRNA targetome in hippocampal neurons using RIP-seq. Sci Rep. 2015;5:12609.View ArticlePubMedPubMed CentralGoogle Scholar
- Chacon-Cortes D, Smith RA, Haupt LM, Lea RA, Youl PH, Griffiths LR. Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. BMC Med Genet. 2015;16:107.View ArticlePubMedPubMed CentralGoogle Scholar
- Deng S, Wang W, Li X, Zhang P. Common genetic polymorphisms in pre-microRNAs and risk of bladder cancer. World J Surg Oncol. 2015;13:297.View ArticlePubMedPubMed CentralGoogle Scholar
- Jens M, Rajewsky N. Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat Rev Genet. 2015;16:113–26.View ArticlePubMedGoogle Scholar
- Ullah AZ, Sahoo S, Steinhöfel K, Albrecht AA. Derivative scores from site accessibility and ranking of miRNA target predictions. Int J Bioinform Res Appl. 2012;8:171–91. doi:10.1504/IJBRA.2012.048966.View ArticlePubMedGoogle Scholar
- Rennie W, Liu C, Carmack CS, Wolenc A, Kanoria S, Lu J, et al. STarMir: a web server for prediction of microRNA binding sites. Nucleic Acids Res. 2014;42:W114–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Long D, Lee R, Williams P, Chan CY, Ambros V, Ding Y. Potent effect of target structure on microRNA function. Nat Struct Mol Biol. 2007;14:287–94.View ArticlePubMedGoogle Scholar
- Catterall WA, Kalume F, Oakley JC. NaV1.1 channels and epilepsy. J Physiol. 2010;588(Pt 11):1849–59.View ArticlePubMedPubMed CentralGoogle Scholar
- Schutte RJ, Schutte SS, Algara J, Barragan EV, Gilligan J, Staber C, et al. Knock-in model of Dravet syndrome reveals a constitutive and conditional reduction in sodium current. J Neurophysiol. 2014;112:903–12.View ArticlePubMedPubMed CentralGoogle Scholar
- Long YS, Zhao QH, Su T, Cai YL, Zeng Y, Shi YW, et al. Identification of the promoter region and the 5'-untranslated exons of the human voltage-gated sodium channel Nav1.1 gene (SCN1A) and enhancement of gene expression by the 5'-untranslated exons. J Neurosci Res. 2008;86:3375–81.View ArticlePubMedGoogle Scholar
- Dong ZF, Tang LJ, Deng GF, Zeng T, Liu SJ, Wan RP, et al. Transcription of the human sodium channel SCN1A gene is repressed by a scaffolding protein RACK1. Mol Neurobiol. 2014;50:438–48.View ArticlePubMedGoogle Scholar
- Gardiner AS, Twiss JL, Perrone-Bizzozero NI. Competing interactions of RNA-binding proteins, MicroRNAs, and their targets control neuronal development and function. Biomolecules. 2015;5:2903–18.View ArticlePubMedPubMed CentralGoogle Scholar
- Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.View ArticlePubMedGoogle Scholar
- Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007;129:1401–14.View ArticlePubMedPubMed CentralGoogle Scholar
- STarMir Manual. In: Software for statistical folding of nucleic acids and studies of regulatory RNAs. Ding RNA Bioinformatics Lab. 2007. http://sfold.wadsworth.org/data/STarMir_manual.pdf. Accessed 15 Oct 2015.
- Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136:215–33.View ArticlePubMedPubMed CentralGoogle Scholar
- Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10:1507–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34(Web Server issue):W451–4.View ArticlePubMedPubMed CentralGoogle Scholar
- The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.View ArticleGoogle Scholar
- Küspert M, Murakawa Y, Schäffler K, Vanselow JT, Wolf E, Juranek S, Fischer U. LARP4B is an AU-rich sequence associated factor that promotes mRNA accumulation and translation. RNA. 2015;21(7):1294–305. doi:10.1261/rna.051441.115. Epub 2015 May 22.
- Zeng T, Dong ZF, Liu SJ, Wan RP, Tang LJ, Liu T, et al. A novel variant in the 3' UTR of human SCN1A gene from a patient with Dravet syndrome decreases mRNA stability mediated by GAPDH's binding. Hum Genet. 2014;133(6):801–11. doi:10.1007/s00439-014-1422-8. Epub 2014 Jan 25.
- Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278–48. Google Scholar
- Spillane J, Kullmann DM, Hanna MG. Genetic neurological channelopathies: molecular genetics and clinical phenotypes. J Neurol Neurosurg Psychiatry. 2015. doi:10.1136/jnnp-2015-311233. [Epub ahead of print].
- Scharfman HE, MacLusky NJ. Sex differences in the neurobiology of epilepsy: a preclinical perspective. Neurobiol Dis. 2014;72 Pt B:180–92. Google Scholar
- Barth C, Villringer A, Sacher J. Sex hormones affect neurotransmitters and shape the adult female brain during hormonal transition periods. Front Neurosci. 2015;9:37.View ArticlePubMedPubMed CentralGoogle Scholar