Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Functional Coding Variants in SLC6A15, a Possible Risk Gene for Major Depression

Functional Coding Variants in SLC6A15, a Possible Risk Gene for Major Depression

  • Carina Quast, 
  • Serena Cuboni, 
  • Daniel Bader, 
  • André Altmann, 
  • Peter Weber, 
  • Janine Arloth, 
  • Simone Röh, 
  • Tanja Brückl, 
  • Marcus Ising, 
  • Anna Kopczak


SLC6A15 is a neuron-specific neutral amino acid transporter that belongs to the solute carrier 6 gene family. This gene family is responsible for presynaptic re-uptake of the majority of neurotransmitters. Convergent data from human studies, animal models and pharmacological investigations suggest a possible role of SLC6A15 in major depressive disorder. In this work, we explored potential functional variants in this gene that could influence the activity of the amino acid transporter and thus downstream neuronal function and possibly the risk for stress-related psychiatric disorders. DNA from 400 depressed patients and 400 controls was screened for genetic variants using a pooled targeted re-sequencing approach. Results were verified by individual re-genotyping and validated non-synonymous coding variants were tested in an independent sample (N = 1934). Nine variants altering the amino acid sequence were then assessed for their functional effects by measuring SLC6A15 transporter activity in a cellular uptake assay. In total, we identified 405 genetic variants, including twelve non-synonymous variants. While none of the non-synonymous coding variants showed significant differences in case-control associations, two rare non-synonymous variants were associated with a significantly increased maximal 3H proline uptake as compared to the wildtype sequence. Our data suggest that genetic variants in the SLC6A15 locus change the activity of the amino acid transporter and might thus influence its neuronal function and the risk for stress-related psychiatric disorders. As statistically significant association for rare variants might only be achieved in extremely large samples (N >70,000) functional exploration may shed light on putatively disease-relevant variants.


The SLC6A15 gene encodes a sodium-dependent neutral amino acid transporter, belonging to the solute carrier 6 (SLC6) gene family that also includes the transporters for monoamines and gamma-amino butyric acid (GABA) [1]. This transporter is predominantly expressed in neurons with high levels in many regions of the brain including the hippocampus [2]. Proline is the amino acid with the highest affinity for SLC6A15 and may serve as precursor for the synthesis of the neurotransmitter glutamate [1] and thus this transporter might be involved in the regulation of glutamate transmission [3]. Convergent data from human genetics, animal models and pharmacological studies suggest that SLC6A15 may be involved in the pathophysiology of major depressive disorder (MDD).

In 2011, a genome-wide association study (GWAS) identified SLC6A15 as a novel susceptibility gene for MDD [4]. The authors identified a single nucleotide polymorphism (SNP), rs1545843 about 690 kb downstream of SLC6A15 on chr12q21.31, that was associated with unipolar depression at genome-wide significance (p = 1.41e-09) in a meta-analysis across seven samples. Expression quantitative trait locus data from lymphoblastoid cell lines as well as hippocampus showed that rs1545843 risk allele genotype status was associated with a decreased SLC6A15 gene expression in both tissues. Furthermore the same common polymorphism showed an association with reduced hippocampal volume in patients with depression and reduced hippocampal neuronal integrity in healthy controls. The hippocampus is an important brain region modulating the hypothalamic-pituitary-adrenocortical (HPA) axis, which is dysregulated in depressed patients [5].

Schumacher et al. could show that rs1545843 risk allele genotype carriers have an enhanced adrenocorticotropic hormone (ACTH) and cortisol response in the combined dexamethasone/corticotrophin-releasing hormone (Dex/CRH) test. Furthermore, they observed an impaired memory and attention performance in risk genotype carriers also supporting a hippocampal dysfunction associated with this genetic variant [6].

Data from animal models also suggest that this gene plays an important role in stress-susceptibility. Behavioral phenotyping of SLC6A15 knockout mice showed that these mice had increased levels of anxiety in the open field and dark box test immediately after they were subjected to a forced swim stress test as compared to wildtype mice, although this result could not be replicated in subsequent experiments [7]. The possibility of SLC6A15 as a stress response gene was further supported by data from a chronic social stress model in outbred animals, were a reduced SLC6A15 gene expression was observed in the hippocampus of stress-susceptible mice compared to stress-resilient mice [4].

Further indication for the possible involvement of SLC6A15 in the pathophysiology of MDD originates from pharmacological investigations. The crystal structure of the bacterial leucine transporter (LeuT) derived from Aquifex aeolicus has been determined [8]. This transporter is a homolog of the human SLC6A15 transporter sharing 20–25% sequence identity and 40–45% sequence similarity. It has been shown that the LeuT transporter binds tricyclic antidepressant drugs at a site not overlapping with the leucine-binding site. This non-competitive binding prevents conformational changes of the protein and closes the molecular gate for leucine which inhibits the re-uptake of the substrate. As both the antidepressant- binding site and its inhibition mechanism of leucine uptake are probably conserved in humans one could assume that tricyclic antidepressant drugs also bind to the human transporter.

Given this convergent evidence for a possible role of SLC6A15 in MDD, we aimed to further explore potential functional variants that could impact the amino acid transport of this protein and by extension neuronal function and possibly the risk for stress-related psychiatric disorders. To this end the SLC6A15 gene locus was screened in depressed patients and controls for genetic variants using a pooled next-generation sequencing (NGS) approach. Following discovery, variants altering the amino acid sequence were then tested for their functional effects by measuring SLC6A15 transporter activity in a cellular uptake assay.

Materials and Methods

Ethics Statement

The current study was approved by the local ethics committee of the Ludwig-Maximilians-University (LMU) in Munich. Written informed consent was obtained from all individuals.

Sample Characterization

Discovery sample.

400 selected patients from the Munich Antidepressant Response Signature (MARS) project at the Max Planck Institute of Psychiatry (MPIP) in Munich were included in the study. Of the included patients, 88.0% suffered from recurrent depressive disorder, 12.0% presented a first depressive episode (see table 1 for demographic and clinical characteristics). Patients were recruited as described in Hennings et al. and Ising et al. [9], [10]. Severity of depression and anxiety was measured using the Hamilton Depression Scale (HAM-D) and the Hamilton Anxiety Scale (HAM-A) [11], [12]. Patients fulfilling the criteria of a HAM-D score ≥18 for recurrent depressive disorder or ≥20 for single depression episode at in-patient admission and an age at onset ≤55 were included in the study. Ethnicity was recorded using a self-report questionnaire for nationality, mother language and ethnicity of the subject itself and all four grandparents. All included patients were Caucasian, 78.0% of German origin.

Table 1. Demographic and clinical characteristics of the discovery and replication case-control sample.

400 controls from the general population were selected randomly and screened for the absence of psychiatric disorders as described in Erhardt et al. and Kohli et al, [13], [4]. The controls were matched to the patient sample for age, gender and ethnicity. All controls were Caucasian, 91.8% of German origin.

Replication sample.

905 patients were recruited at the MPIP and psychiatric hospitals in Augsburg and Ingolstadt. All patients suffered from recurrent major depression (Table 1). For further details regarding patient recruitment see Lucae et al. and Muglia et al. [14], [15]. All included patients were Caucasian, 89.5% of German origin.

1029 controls, matched for age, gender and ethnicity to the patient sample, were selected randomly from a Munich-based community sample and screened for the absence of anxiety and affective disorders [14], [15]. All controls were Caucasian, 93.0% of German origin.

DNA Amplification and Pooling Strategy

DNA was isolated from whole blood using a standardized extraction procedure (Puregene whole blood DNA-extraction kit, Gentra Systems Inc) and quantified using picogreen based fluorometry. Genomic DNA was combined in eight pools consisting of equimolar amounts of DNA from 50 depressed patients and eight pools consisting of equimolar amounts of DNA from 50 controls. In order to amplify the SLC6A15 locus on chromosome 12, approximately 53 kb in length, eleven oligonucleotide primer pairs covering target regions between 2 and 11 kb were designed. Primers are listed in table S1. With exception of a 3.5 kb intronic region for which a working oligonucleotide primer could not be designed, the whole gene including the 5′ promoter region and 10 kb 3′ of the SLC6A15 locus were covered. Individual Long Range PCR reactions were performed for each amplicon and each pool using LongAmp Taq DNA Polymerase (New England Biolabs (NEB)). For further information see Methods S1. Finally, four pools of PCR products consisting of equal amounts of amplified DNA from 100 patients and four pools of PCR products consisting of equal amounts of amplified DNA from 100 controls were used for NGS.

Next-generation Sequencing

All eight DNA pools were prepared for the sequencing run performed on the Life Technologies SOLiD 4 sequencer following the manufacturer’s instructions for barcoded standard fragment library preparation. Bead production and enrichment was carried out using the EZ bead system. Each approximately 476 million beads were deposited on two full slides. The SOLiD sequencing run was performed using SOLiD ToP Fragment Barcoding Sequencing chemistry for a single F3 Tag with a read length of 50 bp.

Data analysis of this NGS run showed that the coverage of two amplicons was low in all eight sequenced libraries. Thus these two amplicons were re-sequenced in a second run. Barcoded fragment library and bead preparation were prepared as for the first run. Each approximately 139 million beads were deposited on two lanes of a flow chip for sequencing on the 5500xl SOLiD Sequencer using the SOLiD FWD SR sequencing chemistry for a single F3 Tag with a read length of 75 bp. The raw data of this experiment are deposited under the GenBank ID: SRP022550.

Data Analysis of NGS Experiment: Variant Detection and Annotation

The raw reads of both SOLiD runs were subjected to the quality control (QC) procedure as described in Altmann et al. [16]. For further information regarding read numbers see table S2. The reads surviving the QC were aligned using BWA version 0.5.7 [17] and SHRiMP version 2.2.0 (Short Read Mapping Package) [18] to chromosome 12 of the human genome (NCBI36/hg18 for the first run and GRCh37/hg19 for the second run) allowing a maximum of four mismatches. Single nucleotide variant (SNV) calling and annotation steps were performed as described in Quast et al. [19] using vipR [16] and ANNOVAR [20]. Prerequisite for the inclusion into the variant calling procedure was that the coverage at a given base position in each pool had to be at least 5,000. Thus in the first run 81.4% of the whole sequence and 96.3% of the protein coding sequence were included into the SNV calling procedure. In the second run 74.3% of the whole sequence and 99.2% of the protein coding region had a mean coverage above 5,000. SNV calling was performed with QC filtered reads which were mapped using the BWA aligner and in a second approach using the SHRiMP aligner. Only genetic variants which were called in both approaches were included for further analysis while SNVs that were only detected in one alignment approach were excluded. We employed this mapping protocol in order to minimize the false discovery rate of rare SNVs.

SNV Genotyping

Validation of 69 variants detected in the re-sequencing experiments was performed using MALDI-TOF (matrix-assisted laser desorption ionization time of flight) mass spectrometry on the Sequenom platform (San Diego, USA). We selected all coding variants and 53 variants across the whole minor allele frequency (MAF) range for technical validation. Correlations between the MAFs estimated by the Sequenom re-genotyping and the MAFs obtained by the NGS experiment were analysed using SPSS version 18.0.

Non-synonymous variants, which could be validated in the discovery sample, were re-genotyped in the replication sample using the same method as described above. Additionally, 22 non-synonymous SNVs from the Exome Sequencing Project (ESP) database (Exome Variant Server,, which currently incorporates 6,503 individuals with different ethnic background, were re-genotyped in the replication sample (accessed in October 2012).

Power calculations were performed using Quanto version 1.2.3 ( [21].

Case-control Analysis

PLINK was used to test for case-control associations of common variants with a MAF >5% [22]. The cohort allelic sum test (CAST) was used to test the hypothesis that a combination of several rare variants is associated with a complex disease [23]. Specifically, we investigated the differences in the presence of minor alleles (PRA) and the sum of minor alleles (SMA) from a specific SNV set between depressed patients and controls. Our SNV set contained all discovered non-synonymous variants in the NGS experiment (N = 9). Statistical significance was assessed using independent samples t-test for the SRA and contingency tables for the PRA in SPSS version 18.0. P-values were not corrected for the multiple comparisons. The level of significance was set to 0.05.

Experimental Functional Analysis

Site-directed mutagenesis.

The cDNA of the long isoform of the human SLC6A15 gene was inserted into the pEGFP-C1 vector (Clonetech). Variants of eGFP-hSLC6A15 containing nine different non-synonymous SNVs were generated using site-directed mutagenesis using the primers listed in table S3. After amplification 1 µl of the restriction endonuclease DpnI (NEB) was added and the samples were incubated for 1 h at 37°C. 5 µl of each PCR product were transformed into E.Coli DH5α cells performing a heat shock for 1 minute at 42°C. Success of site-directed mutagenesis was verified by Sanger sequencing of all mutated plasmids.

SLC6A15 uptake assay.

EGPF-hSLC6A15 encoding plasmids (wildtype and nine mutants) were transfected into HEK293 cells using Lipofectamine (Invitrogen). HEK293 cells were cultured in Dulbecco’s Modified Eagle medium (DMEM, Gibco) containing 10% of fetal calf serum (FCS) and 5% Penicillin/Streptomycin at 37°C in a humidified incubator (5% CO2). The following day, transfected cells were detached, counted and plated in 96 well plates for the uptake measurement. The uptake of 20 nM 3H proline (Perkin Elmer) was measured in dependence of the concentration of the non-labeled amino acid L-proline (3 µM, 12 µM, 48 µM, 195 µM, 781 µM, 3.1 mM, 12.5 mM, 50.0 mM) using the Wallac MicroBeta luminescence counter (Perkin Elmer). The maximal uptake of 3H proline which occurs in the absence of antagonists and the IC50 were assessed using Sigma Plot. Differences in the mean 3H proline uptake between wildtype and mutant were assessed using general linear models in SPSS version 18.0. Transfection efficiency was included as covariate into the analysis. An alpha level of 0.05 after correction for multiple testing using the Bonferroni method was considered statistically significant.

Fluorescence imaging.

HEK293 cells were plated on cover slips precoated with poli D-Lysine (PDL). After one day the medium was removed and cells were fixed with 4% paraformardehyde (PFA). Excess of PFA was removed performing two washing steps with PBS. The cover slips were mounted on slides using 4 µl mounting medium containing DAPI which stains cell nuclei. After few hours samples were analysed at the confocal microscope.


Variant Identification Using NGS

In total, 405 genetic variants were detected in the sequencing runs. Of these, 218 (53.8%) have not been reported in the dbSNP137 database (accessed in April 2013). Furthermore, 225 (55.6%) variants have not been identified in the 1,000 Genome Project database (April 2012 release). As expected, more than 50% (N = 225) of the detected SNVs were rare with a MAF <0.5%, i.e., less than four occurrences among the 800 screened individuals.

16 variants (4.0%) were in the protein coding regions of the gene. Twelve of those 16 variants were non-synonymous and of those, seven have been previously reported in dbSNP137 and eight have been identified in the ESP database (accessed in April 2013). Three variants (0.7%) were in the 5′ untranslated region (UTR), 44 (10.9%) in the 3′ UTR and 257 (63.4%) in intronic sequences. 85 variants (21.0%) were 5′ or 3′ of the gene locus.

Variant Validation Using Sequenom Re-genotyping

Using Sequenom re-genotyping as an independent method, 71.2% of the successfully re-genotyped variants (N = 66) could be confirmed as polymorphic (Figure S1), including nine non-synonymous coding SNVs, three of them only present in the short isoform of the SLC6A15 locus (Table 2). For the validated variants, the correlation between the MAFs estimated from NGS and verified by re-genotyping was excellent with r2 = 0.992.

Table 2. Overview of all validated non-synonymous variants in the re-genotyping experiment combined with potential functional effects assessed by in silico analysis.

All nine non-synonymous SNVs validated in the discovery sample were re-genotyped in the replication sample and supplemented by 22 additional non-synonymous SNVs from the ESP that had not been detected in the discovery sample (Table S4). Only three of the ESP variants were polymorphic, two of them were only present in a single individual in the replication cohort (Table 2). For the validated non-synonymous variants (N = 12) which were also present in the ESP database (N = 11), the correlation between the MAFs denoted in the ESP for the European American population and obtained from the re-genotyping experiment in either the discovery, the replication or the combined sample was r2 = 1.000. For the non-synonymous variants genotyped in both cohorts, no consistent direction for over-representation in cases versus controls could be observed (Table S4).

Annotation of Non-coding Variants

Mapping all 405 detected variants to ENCODE transcription factor binding sites (TFBSs) which were determined by Chip-Seq (, we could identify 15 intronic and three intergenic variants in predicted TFBSs of different tissues including neuroblastoma cell lines (Table S5). In addition, these 18 variants were identified to be located in ENCODE/Duke DNaseI hypersensitivity sites in brain including cerebellum, frontal cerebrum and frontal cortex ( [24]. Out of these variants with a potential influence on gene transcription the four variants with the highest/lowest OR were re-genotyped. Two variants which were previously reported in dbSNP137 could be validated in the discovery sample. Variants disrupting putative miRNA target sites in the 3′UTR of genes, predicted by TargetScanHuman 5.1 were not observed. Using PhastCons, we were able to identify four non-coding variants in conserved regions of the genome. These were re-genotyped in the discovery sample and one variant upstream and one variant in intron 1 of the gene could be validated, both already reported in dbSNP137. As the ORs of the above variants were around 1, they were not re-genotyped in the replication cohort.

Case-control Analysis

None of the common variants (N = 62) showed a significant association with case-control status. Performing the CAST no significant differences in SRA and PRA between depressed patients (N = 1305) and controls (N = 1429) of the combined sample could be observed.

Translation of the Detected Non-synonymous Variants into Function

Computational functional annotation.

The potential functional effects of twelve non-synonymous coding variants were first investigated performing in silico analysis using SIFT [25], PolyPhen2 [26] and Panther [27] (Table 2). These three tools showed consistent predictions for only two non-synonymous variants to have a deleterious effect on protein function. Further evidence for possible functional consequences came from the evolutionary nucleotide conservation prediction tools PhastCons [28] and PhyloP [29] which identified five non-synonymous variants to be located in evolutionary conserved regions of the genome. Splicing analysis using FastSNP [30] predicted seven non-synonymous variants to create new exonic splicing enhancer (ESE) or silencer (ESS) motifs, or to disrupt already existing splicing motifs (Table 2).

Experimental functional analysis.

The functional effects of all nine non-synonymous variants in the long human SLC6A15 isoform (Table 3) were tested in a proline uptake assay. The IC50 values for 3H proline uptake did not differ between HEK cells transfected with plasmids containing the wildtype SLC6A15 sequence and cells transfected with plasmids harbouring a point mutation in the SLC6A15 gene (Figure 1).

Figure 1. Inhibition of 3H proline transport by the non-radioactive labeled amino acid L-proline.

Concentration of cold L-proline is plotted on the x-axis, 3H proline uptake as counts per minute (cpm) on the y-axis. Each datapoint represents the mean transport activity of triplicate samples.

Table 3. Overview of the mutant plasmids created by site-directed mutagenesis.

In contrast, the maximal uptake of 3H proline (Bmax) showed large differences, ranging from approximately 8600 to 12400 cpm (Figure 2). In order to verify these findings, the three mutants with the largest differences in 3H proline uptake compared to wildtype (T49A, A400V and L421P mutants) were selected and subjected to a second independent experiment. The results obtained in the first Bmax measurement could be replicated for all tested mutants (Figure 3). Across all concentrations of cold L-proline, significant differences in 3H proline uptake could be observed (p = 1.8e-7, in a two way ANOVA including mutant and cold proline concentration as the two predictors and transfection efficiency as a covariate (F = 18.9, df = 2). Mutant T49A and mutant A400V showed a significantly increased maximal 3H proline uptake compared to the wildtype, withstanding correction for multiple testing using the Bonferroni method (p = 8.4e-9 and p = 0.001 respectively). Mutant L421P showed a decrease in maximal uptake as in the first experiment, but this was not significant after adjustment for multiple comparisons (p nominal = 0.016, p corrected = 0.158).

Figure 2. Maximal 3H proline uptake of wildtype (WT) and all tested mutants.

The maximum in uptake was measured in the presence of 3 µM cold L-proline. Data are expressed as means ± standard deviation (SD) obtained from triplicate samples. Mutants with a circle were tested in a second independent experiment.

Figure 3. Repeated Bmax measurement of mutants that showed large differences in 3H proline uptake compared to WT.

The uptake was measured under four different experimental conditions. Each bar represents the 3H proline uptake (mean ± SD) obtained from triplicates for the buffer solution, six samples for the 3 µM and 12 µM L-proline solution respectively and nine samples for the 780 µM L-proline solution. Corrected p-values given in brackets are based on the difference in mean 3H proline uptake between WT and tested mutant.

The cellular localization of the SLC6A15 transporter to the cell membrane was not changed in any of the mutants as indicated by fluorescence microscopy (Figure 4). In addition, these imaging experiments did not show any alterations in transporter levels at the cell membrane, indicating similar SLC6A15 expression levels in wildtype and mutants.

Figure 4. Cellular localization of the SLC6A15 protein.

Both the wildtype cells (left) and the T49A mutant cells (right) showed a similar expression of SLC6A15 in the cell membrane. The localization of the eGFP-hSLC6A15 fusion product is indicated in green. Cell nuclei were stained with DAPI (blue).


In this study we combined the detection of common and rare genetic variants in the SLC6A15 gene using NGS with the functional characterization of these variants on the amino acid transporter activity using site-directed mutagenesis and cellular expression systems. While we could not confirm any significant differences in allele frequencies for any of the tested variants, we were able to identify two non-synonymous rare variants that lead to an increased transport of proline without changing the cellular localization of the SLC6A15 protein. This suggests that these rare coding variants may influence the biochemical function of this transporter and therefore the risk for major depression. However, extremely large case/control samples (over 70,000 for T49A and over 22,000 for A400V) would be required to detect the estimated ORs for these variants at an alpha level of 0.05 with a power of 0.8.

Since the establishment of high throughput sequencing methods, the identification of genetic variants, even those with a private character, has become a standard method in human genetics. While the detection of variants has become increasingly easy, their functional annotation remains a challenging task. Variants in protein coding regions of the genome resulting in amino acid substitutions, premature stop sites or deleted parts of a gene are the most obvious candidates for alterations of gene function. Indeed, such variants are heavily enriched among disease causing variation in rare/Mendelian disorders [31] while variants in regulatory regions seem to predominate as risk factors for common disorders [32]. However, it is likely that both common and rare variants contribute to the risk of common disorders [33], so that the exploration of both aspects will be important for a complete picture of disease risk. In this manuscript, we have focused on the functional characterization of non-synonymous coding variants in SLC6A15, a gene for which common risk regulatory variants have been identified.

The functional annotation of coding variants is of paramount importance as every individual carries 20,000–24,000 of such variants including 10,000–11,000 non-synonymous coding variants that could be deleterious for gene function, but in most cases are not [34]. Computational approaches may provide a fast and easy assessment of the functional relevance of such coding variants. A variety of tools has been developed based on the principle of sequence homology between organisms [35]. It is assumed that disease-causing variants are more likely at positions of the genome that are conserved and have not been removed by natural selection [36], [29]. One major disadvantage of these prediction methods is that direct comparisons of results obtained from different tools are problematic as each tool uses different algorithms and sequence databases as reference for the deleteriousness estimation. Some tools additionally include information about the structure of the protein into their predictions [37], [38]. Other tools incorporate biochemical data such as positions of active sites and disulfide bridges, charge of amino acids or protein-protein interactions [39]. The integration of structural and biochemical information to comparative sequence analysis could significantly improve predictions of deleteriousness [40], [41]. In this study, we used the three protein-sequence based prediction tools SIFT, PolyPhen2 and Panther and two nucleotide-sequence based prediction tools PhyloP and PhastCons. From our twelve detected non-synonymous variants only one was predicted to be functionally relevant in all tools (see table 2).

Although computational functional analyses are convenient, their inconsistent results indicate the need for experimental analysis. The prerequisite for experimental analyses is that the function of the gene product is known. While this point sounds trivial, a large number of human proteins are lacking sufficient functional annotation to design an experimental assay. In our case, it is known that SLC6A15 transports neutral amino acids into the cell, predominantly in neurons [1]. Proline is one substrate of the transporter and its uptake is a measurable property that associates with function. The two rare variants in the SLC6A15 protein T49A and A400V (Figure 3) were associated with a significant increase in maximal proline uptake in HEK cells. High levels of proline have been shown to be neurotoxic and have been associated with CNS symptoms such as seizures and mental retardation [42]. Furthermore, SLC6A15 is not only a transporter for proline but also transports other neutral amino acids like methionine and leucine. Methionine is a precursor of S-adenosylmethionine (SAM). This major methyl group donor in humans transfers methyl groups to different substrates including DNA nucleotides and histones. SAM metabolism has been associated with different diseases including psychiatric disorders such as MDD [43]. Leucine is a major donor of nitrogen for the synthesis of glutamate and GABA [44]. Therefore an alteration in the maximal amino acid uptake could have in impact on glutamatergic transmission which is connected to psychiatric disorders [45]. This hypothesis is further supported by a previously published study showing that SLC6A15 is expressed in glutamatergic and GABAergic neurons [46].

The observed alterations in amino acid uptake related to the coding variants might be explained by a number of mechanisms including altered maximal transporter velocity, but also altered expression, protein stability or membrane localization. The fact that fluorescence imaging showed similar protein levels in the plasma membrane, however, supports that functional rather than quantitative changes of the transporter may underly the observed genetic effects and that these rare non-synonymous variants indeed influence levels of amino acids in the cell and thus amino acid metabolism.

While the experimental analysis identified T49A and A400V to influence the levels of proline in HEK cells, for T49A only Panther and PhyloP and for A400V only PhastCons did predict an influence on protein function. This discrepancy is not surprising as the Encyclopedia of DNA Elements (ENCODE) project found that the correlation between estimates obtained from evolutionary annotations and estimates derived from experiments is only modest [24], [47]. One reason for this modest correlation might be that genetic variants that are biochemically functional do not necessarily have to be biologically relevant and may not affect the phenotype of interest [24], [48]. Indeed, even though the investigated variants in SLC6A15 alter proline uptake it can not automatically be concluded that the altered amino acid levels are associated with an altered risk for the investigated phenotype MDD. While addressing the question of biological relevance for psychiatric disorders by genetic association studies may be difficult due to the extremely large samples sizes required (see above), additional experiments in neuronal cells lacking endogenous SLC6A15 or humanized transgenic animals may shed more light on the putative relevance of these biochemical differences on biological measures and behavioral phenotypes. In addition, for stress-related psychiatric disorders such as MDD, the risk conveyed by a specific variant may only be unmasked with exposure to stress or trauma, so that a strict case/control design may be insufficient.

Newer data from exome re-sequencing projects indicated that rare variants might not contribute to disease risk with much higher ORs than common variants [unpublished data], so that the samples sizes needed to detect the significant association for any one of them may be prohibitive. One possible solution could be the use of burden testing, which assesses whether a combination of rare variants is non-randomly distributed between different groups. This approach may enhance the power to detect associations of multiple rare variants in specific candidate genes or pathways [49], but will still require large sample sizes comprising at least several thousand cases and controls to achieve genome-wide significance [50]. A drawback of burden testing is that functional relevant variants should be identified and analyzed together. This is typically done using in silico annotation, without proof of true biological relevance and variants with opposite effects can even combined into the same group. Experimental functional annotation would thus likely increase the power of such burden tests [51].

In conclusion, our study suggests that rare variants in SLC6A15 may have an influence on the biochemical function of this amino acid transporter. To further evaluate the impact of these variants on neurobiological phenotypes and ultimately MDD, additional in vitro and in vivo experiments as well as very large samples sizes for case-control association or gene × environment interaction studies will be required. Our data also highlight that if possible, experimental validations should be performed to assess the functionality of coding variants as computational tools only give insufficient information.

Supporting Information

Figure S1.

Overview of the SNV validation performing Sequenom re-genotyping. Denoted MAF was estimated from NGS.


Table S1.

Oligonucleotides used for amplification of the SLC6A15 locus via Long Range PCR.


Table S2.

Number of reads obtained in the two sequencing runs. Reads were mapped using the BWA aligner. All reads are given in millions.


Table S3.

Oligonucleotide primers used for site-directed mutagenesis. The sequence encoding for the substituted amino acid is underlined. The changed nucleotide is bold.


Table S4.

Summary of the re-genotyping of all non-synonymous variants in the discovery sample, replication sample and combined sample.


Table S5.

Overview of all ENCODE TFBSs, identified using Chip-Seq (chromatin immunoprecipitation with antibodies against the transcription factor and sequencing of the precipitated DNA). TFBSs can overlap so that a variant can be located in two or more sites. All 18 variants were identified to be located in ENCODE/Duke DNaseI hypersensitivity sites in brain including cerebellum, frontal cerebrum and frontal cortex (



The authors would like to thank Maik Ködel, Anne Löschner and Monika Rex-Haffner for excellent technical assistance.

Author Contributions

Conceived and designed the experiments: EBB CQ. Performed the experiments: CQ SC PW. Analyzed the data: DB AA CQ SR JA. Contributed reagents/materials/analysis tools: SC FH. Wrote the paper: CQ EBB. Recruitment of patient and control samples: TB AK AE Coordination of recruitment: MI SL Critical revision of the manuscript: EBB PW AA.


  1. 1. Broer A, Tietze N, Kowalczuk S, Chubb S, Munzinger M, et al. (2006) The orphan transporter v7–3 (slc6a15) is a Na+-dependent neutral amino acid transporter (B(0)AT2). Biochemical Journal 393: 421–430.
  2. 2. Farmer MK, Robbins MJ, Medhurst AD, Campbell DA, Ellington K, et al. (2000) Cloning and characterization of human NTT5 and v7–3: Two orphan transporters of the Na+/Cl–dependent neurotransmitter transporter gene family. Genomics 70: 241–252.
  3. 3. Tapiero H, Mathe G, Couvreur P, Tew KD (2002) Dossier: Free amino acids in human health and pathologies - II. Glutamine and glutamate. Biomedicine & Pharmacotherapy 56: 446–457.
  4. 4. Kohli MA, Lucae S, Saemann PG, Schmidt MV, Demirkan A, et al. (2011) The Neuronal Transporter Gene SLC6A15 Confers Risk to Major Depression. Neuron 70: 252–265.
  5. 5. Holsboer F (2000) The corticosteroid receptor hypothesis of depression. Neuropsychopharmacology 23: 477–501.
  6. 6. Schuhmacher A, Lennertz L, Wagner M, Hoefels S, Pfeiffer U, et al. (2013) A variant of the neuronal amino acid transporter SLC6A15 is associated with ACTH and cortisol responses and cognitive performance in unipolar depression. International Journal of Neuropsychopharmacology 16: 83–90.
  7. 7. Drgonova J, Liu Q-R, Hall FS, Krieger RM, Uhl GR (2007) Deletion of v7–3 (SLC6A15) transporter allows assessment of its roles in synaptosomal proline uptake, leucine uptake and behaviors. Brain Research 1183: 10–20.
  8. 8. Zhou Z, Zhen J, Karpowich NK, Goetz RM, Law CJ, et al. (2007) LeuT-desipramine structure reveals how antidepressants block neurotransmitter reuptake. Science 317: 1390–1393.
  9. 9. Hennings JM, Owashi T, Binder EB, Horstmann S, Menke A, et al. (2008) Clinical characteristics and treatment outcome in a representative sample of depressed inpatients - Findings from the Munich Antidepressant Response Signature (MARS) project. Journal of Psychiatric Research 43: 215–229.
  10. 10. Ising M, Lucae S, Binder EB, Bettecken T, Uhr M, et al. (2009) A Genomewide Association Study Points to Multiple Loci That Predict Antidepressant Drug Treatment Outcome in Depression. Archives of General Psychiatry 66: 966–975.
  11. 11. Hamilton M (1959) The assessment of anxiety-states by rating. British Journal of Medical Psychology 32: 50–55.
  12. 12. Hamilton M (1960) A rating scale for depression. Journal of Neurology Neurosurgery and Psychiatry 23: 56–62.
  13. 13. Erhardt A, Czibere L, Roeske D, Lucae S, Unschuld PG, et al. (2011) TMEM132D, a new candidate for anxiety phenotypes: evidence from human and mouse studies. Molecular Psychiatry 16: 647–663.
  14. 14. Lucae S, Salyakina D, Barden N, Harvey M, Gagne B, et al. (2006) P2RX7, a gene coding for a purinergic ligand-gated ion channel, is associated with major depressive disorder. Human Molecular Genetics 15: 2438–2445.
  15. 15. Muglia P, Tozzi F, Galwey NW, Francks C, Upmanyu R, et al. (2010) Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts. Molecular Psychiatry 15: 589–601.
  16. 16. Altmann A, Weber P, Quast C, Rex-Haffner M, Binder EB, et al. (2011) vipR: variant identification in pooled DNA using R. Bioinformatics. 27: I77–I84.
  17. 17. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760.
  18. 18. David M, Dzamba M, Lister D, Ilie L, Brudno M (2011) SHRiMP2: Sensitive yet Practical Short Read Mapping. Bioinformatics 27: 1011–1012.
  19. 19. Quast C, Altmann A, Weber P, Arloth J, Bader D, et al. (2012) Rare Variants in TMEM132D in a Case-Control Sample for Panic Disorder. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics 159B: 896–907.
  20. 20. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research 38.
  21. 21. Gauderman W, Morrison J (2006) QUANTO 1.1: A computer program for power and sample size calculations for genetic-epidemiology studies.
  22. 22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, et al. (2007) PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81: 559–575.
  23. 23. Morgenthaler S, Thilly WG (2007) A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: A cohort allelic sums test (CAST). Mutation Research-Fundamental and Molecular Mechanisms of Mutagenesis 615.
  24. 24. Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447: 799–816.
  25. 25. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protocols 4: 1073–1082.
  26. 26. Thomas PD, Campbell MJ, Kejariwal A, Mi HY, Karlak B, et al. (2003) PANTHER: A library of protein families and subfamilies indexed by function. Genome Research 13: 2129–2141.
  27. 27. Adzhubei I, Jordan DM, Sunyaev SR (2013) Predicting functional effect of human missense mutations using PolyPhen-2. Current protocols in human genetics/editorial board, Jonathan L Haines [et al] Chapter 7: Unit7.20–Unit27.20.
  28. 28. Thomas JW, Touchman JW, Blakesley RW, Bouffard GG, Beckstrom-Sternberg SM, et al. (2003) Comparative analyses of multi-species sequences from targeted genomic regions. Nature 424: 788–793.
  29. 29. Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, et al. (2005) Distribution and intensity of constraint in mammalian genomic sequence. Genome Research 15: 901–913.
  30. 30. Yuan H-Y, Chiou J-J, Tseng W-H, Liu C-H, Liu C-K, et al. (2006) FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization. Nucleic Acids Research 34: W635–W641.
  31. 31. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nature Genetics 33: 228–237.
  32. 32. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, et al. (2012) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA. Science 337: 1190–1195.
  33. 33. Gibson G (2012) Rare and common variants: twenty arguments. Nature Reviews Genetics 13: 135–145.
  34. 34. Altshuler DL, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073.
  35. 35. Cooper GM, Shendure J (2011) Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Reviews Genetics 12: 628–640.
  36. 36. Miller MP, Kumar S (2001) Understanding human disease mutations through the use of interspecific genetic variation. Human Molecular Genetics 10: 2319–2328.
  37. 37. Sunyaev S, Ramensky V, Bork P (2000) Towards a structural basis of human non-synonymous single nucleotide polymorphisms. Trends in Genetics 16: 198–200.
  38. 38. Chasman D, Adams RM (2001) Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: Structure-based assessment of amino acid variation. Journal of Molecular Biology 307: 683–706.
  39. 39. Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, et al. (2005) The universal protein resource (UniProt). Nucleic Acids Research 33: D154–D159.
  40. 40. Bao L, Cui Y (2005) Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics 21: 2185–2190.
  41. 41. Dobson RJ, Munroe PB, Caulfield MJ, Saqi MAS (2006) Predicting deleterious nsSNPs: an analysis of sequence and structural attributes. Bmc Bioinformatics 7.
  42. 42. Wyse ATS, Netto CA (2011) Behavioral and neurochemical effects of proline. Metabolic Brain Disease 26: 159–172.
  43. 43. Papakostas GI (2009) Evidence for S-Adenosyl-L-Methionine (SAM-e) for the Treatment of Major Depressive Disorder. Journal of Clinical Psychiatry 70: 18–22.
  44. 44. Yudkoff M, Daikhin Y, Grunstein L, Nissim I, Stern J, et al. (1996) Astrocyte leucine metabolism: Significance of branched-chain amino acid transamination. Journal of Neurochemistry 66: 378–385.
  45. 45. Javitt DC (2004) Glutamate as a therapeutic target in psychiatric disorders. Molecular Psychiatry 9: 984–997.
  46. 46. Hägglund MGA, Roshanbin S, Löfqvist E, Hellsten SV, Nilsson VCO, et al. (2013) B0AT2 (SLC6A15) Is Localized to Neurons and Astrocytes, and Is Involved in Mediating the Effect of Leucine in the Brain. PLoS ONE 8: e58651.
  47. 47. Margulies EH, Cooper GM, Asimenos G, Thomas DJ, Dewey CN, et al. (2007) Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome. Genome Research 17: 760–774.
  48. 48. Cooper GM, Brown CD (2008) Qualifying the relationship between sequence conservation and molecular function. Genome Research 18: 201–205.
  49. 49. Bansal V, Libiger O, Torkamani A, Schork NJ (2010) Statistical analysis strategies for association studies involving rare variants. Nature Reviews Genetics 11: 773–785.
  50. 50. Zoellner S (2012) Sampling strategies for rare variant tests in case-control studies. European Journal of Human Genetics 20: 1085–1091.
  51. 51. Raychaudhuri S (2011) Mapping Rare and Common Causal Alleles for Complex Human Diseases. Cell 147: 57–69.