Serine hydroxymethyltransferase (SHMT) catalyzes the transfer of the β-carbon of serine to tetrahydrofolate (THF) to form glycine and 5,10-methylene-THF (Fig. 1). Glycine is an inhibitory neurotransmitter in the central nervous system, and both glycine and serine are NMDA receptor modulators (Schell 2004). NMDA receptor agonists, including serine and glycine, have been reported to be decreased in both plasma and cerebrospinal fluid of patients with schizophrenia (Hashimoto et al. 2003, 2005; Sumiyoshi et al. 2004; Neeman et al. 2005; Bendikov et al. 2007). In addition to the neurological implications of the SHMT substrate and product, serine and glycine, 5,10-methylene-THF is involved in serotonin synthesis, and low circulating folate levels have been associated with major depressive disorder (Gilbody et al. 2007; Miller 2008). Finally, 5,10-methylene-THF is a source of the one-carbon units that are required for neurotransmitter synthesis and metabolism (Miller 2008). However, even though SHMT1 and SHMT2 catalyze the same reaction, they play different biological roles.
SHMT2 maps to 12q13 (Garrow et al. 1993) and is expressed predominately in the mitochondrian, but it has also been reported to be present in the cytoplasm and nucleus (Anderson and Stover 2009). The Chinese hamster ovary cell line, which lacks SHMT2 activity, exhibits glycine auxotrophy (Pfendner and Pizer 1980). This phenotype can be rescued by SHMT2 transfection, suggesting that SHMT2 is essential for the formation of glycine in these cells in vivo (Stover et al. 1997; Anderson and Stover 2009). SHMT1 maps to 17p11.2 (Garrow et al. 1993) and is expressed in the cytoplasm, but it can be transported to the nucleus during S-phase (Anderson and Stover 2009). SHMT1 knockout mice are viable (MacFarlane et al. 2008) but, when they are crossed with a neural tube defect mouse model in which Pax3 is inactivated, the incidence of neural tube defects in the offspring is increased when pregnant mice are fed a low folate diet (Beaudin et al. 2011). Sequence variation in these genes, specifically the SHMT1 Leu474Phe variant allozyme, has been associated with decreased human red blood cell folate levels (Heil et al. 2001; Relton et al. 2004).
Although SHMT1 and SHMT2 encode important enzymes, no comprehensive attempt has been made to identify and functionally characterize common genetic variants in these genes. Therefore, we resequenced both SHMT1 and SHMT2 using DNA extracted from the Coriell Institute ‘Human Variation Panel’ of lymphoblastoid cell lines (LCLs) obtained from 288 healthy subjects of three ethnicities. Functional genomic studies were then performed to determine whether sequence variation in these genes had an effect on transcription or, for non-synonymous (ns) single nucleotide polymorphisms (SNPs), protein quantity or enzyme activity of variant allozymes. Finally, we compared the relative expression of SHMT1 and SHMT2 messenger RNA (mRNA) expression in the ‘Human Variation Panel’ LCLs used to resequence these two genes. We also compared hepatic SHMT1/2 protein levels with previously published data for the protein expression of other Folate and Methionine Cycle proteins as well as SNP genotypes in those same liver samples. In summary, this study provides the first comprehensive overview of common genetic variation and the functional consequences of that variation for SHMT1 and SHMT2. We also obtained preliminary data which suggested the possible co-regulation of SHMT1 and SHMT2 with other Folate and Methionine Cycle genes.
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The Folate Cycle plays an important role in neurological development, as demonstrated by the link between folate intake and risk for neural tube defects. SHMT1 and SHMT2 represent an important component of the Folate Cycle (see Fig. 1). Substrates and products for these enzymes are neurotransmitter receptor modulators (glycine and serine), inhibitory neurotransmitters (glycine), and precursors for monoamine neurotransmitter biosynthesis (5,10-methylene-THF). The SHMT enzymes also provide single carbon units that can be used for homocysteine remethylation (Miller 2008). Genetic variation in SHMT1 and SHMT2 has been associated with a wide variety of human phenotypes, including risk for neural tube defects (Heil et al. 2001; Relton et al. 2004), childhood acute leukemia (Vijayakrishnan and Houlston 2010), rectal carcinoma (Komlosi et al. 2010), and prostate cancer (Collin et al. 2009). Most of those studies focused on the SHMT1 Leu474Phe variant allozyme. However, we failed to detect significant differences in either enzyme activity or protein quantity, as compared with WT, for any of the nine nsSNPs in SHMT2 or the four nsSNPs in SHMT1, including Leu474Phe (Fig. 3). Therefore, our results suggest that these 13 nsSNPs, including that encoding Leu474Phe, might not themselves be biologically relevant, a conclusion that agrees, in part, with previous substrate kinetic studies (Fu et al. 2005). Obviously, we cannot rule out the possibility that these variants might have a significant effect on other activities catalyzed by SHMT1 that we did not measure (Schirch and Szebenyi 2005).
Although none of the nsSNPs that we observed was associated with significant functional consequences, we did identify very strong relationships between SHMT1 mRNA expression in LCLs and SNP genotypes (Fig. 4). The strongest associations were observed for rs669340 in intron 1 (p = 2.2E−14) and rs7207306 in intron 5 (p = 5.4E−13). It might be useful to point out that rs1979277, which encodes the Leu474Phe variant allozyme, was also strongly associated mRNA expression in the LCLs (p-value = 6.29E−06), and that it is in LD with rs7207306, which might explain, in part, the clinical associations reported previously for this SNP (Heil et al. 2001; Relton et al. 2004; Collin et al. 2009; Komlosi et al. 2010; Vijayakrishnan and Houlston 2010). However, rs669340 and rs7207306 in introns 1 and 5, respectively, were not in LD with one another (r2 = 0.03–0.20), suggesting there might be two or more SNPs that regulate SHMT1 expression. In an attempt to validate and extend associations that we observed in LCLs at the mRNA level, with a clear understanding that transcription regulation is tissue-specific, we also assayed variation in SHMT1 protein concentrations in cytosolic preparations obtained from human liver biopsy samples. Of the SNPs that were genotyped, rs669340 in intron 1 showed the strongest association with protein quantity (p = 1.18E−05) (Figs 4 and 5b). That association remained significant even after correcting for multiple comparisons. However, the intron 5 SNP (rs7207306) was not significantly associated with hepatic cytosol SHMT1 protein levels – serving to emphasize the tissue-specific nature of transcription regulation.
In an attempt to identify functional candidates that might regulate transcription and/or translation, we considered all SNPs with strong associations to either SHMT1 mRNA expression in LCLs and/or protein expression in human hepatic tissue as possible candidates for further functional follow-up. Although rs7207306 and rs669340 had the lowest p-values, 14 additional SNPs had significant associations with SHMT1 mRNA expression (p < 1E−10) that could potentially be functional (Fig. 4). Therefore, we studied the function of rs7207306 and rs669340, the SNPs with the lowest p-values, as well as two promoter SNPs, rs638416 and rs643333. The rs638416 SNP is located (−119) bp from the site of transcription initiation and is in strong LD with rs669340, while rs643333 is located (−283) bp from the transcription start site and is in LD with rs7207306. These two SNPs, as well as approximately 1 kb of 5′-flanking sequence, a region that we had resequenced, were cloned into luciferase reporter gene constructs and expressed in HepG2 cells. Both rs638416 and rs643333 map to a region of SHMT1 that contains many experimentally characterized transcription factor binding sites based on the ‘ENCODE Integrated Regulation’ track on the UCSC genome browser (http://genome.ucsc.edu). For example, the rs638416 variant allele disrupts a Wilms tumor 1 transcription factor binding site, while the variant allele for rs643333 introduces a putative serum response factor transcription factor binding site (http://gene-regulation.com/pub/programs/alibaba2/index.html). The results of our dual luciferase assays suggested that rs638416 might be functional (Fig. 6). However, rs643333 did not appear to be functional in HepG2 cells, although we cannot rule out the possibility that the effect of this SNP on SHMT1 transcription is specific to LCLs. In addition, the two most significant SNPs associated with mRNA expression, rs7207306 and rs669340, also failed to show a significant effect on transcriptional activity as determined by reporter gene assay (Figure S2), although we cannot exclude the possibility that these variants might be functional through other mechanisms (e.g. epigenetics).
Finally, in an attempt to take a broader approach that extended beyond SHMT1 and SHMT2, we compared previously published mRNA expression data for the same LCLs that were used in our studies as well as previously published hepatic protein expression data for the same hepatic biopsy samples that we studied for four other Folate and Methionine Cycle genes, COMT, BHMT, MAT2A and MAT2B (Fig. 1). Strong correlations were observed between mRNA expression levels among SHMT1, SHMT2, COMT, MAT2A and MAT2B in the LCLs (Table 1). BHMT was not expressed in these cells. At the protein level, SHMT1 was significantly correlated with both COMT enzyme activity and BHMT protein levels in cytosol preparations from the same set of hepatic biopsy samples (Table 1). These observations raise the possibility that genes encoding proteins within the Folate and Methinone Cycles might be regulated in a ‘coordinated fashion’. These associations will obviously require replication and the mechanism(s) responsible will require functional pursuit in the course of future experiments.
In conclusion, the results of the present study indicate that nsSNPs – SNPs that alter the encoded amino acid – in SHMT1 and SHMT2 may not have a major effect on the biological function of these enzymes, but multiple SNPs within SHMT1 are associated with SHMT1 mRNA expression, which could help explain some of the clinical association results that have been reported (Heil et al. 2001; Relton et al. 2004; , Collin et al. 2009; Komlosi et al. 2010; Vijayakrishnan and Houlston 2010). Finally, SHMT1 and SHMT2, as well as other Folate and Methionine Cycle genes, might be regulated in a coordinated but complex fashion. Therefore, the present study not only describes individual genetic variation that directly affects SHMT1 and SHMT2 activity, but may also provide insight into the overall regulation of the Folate and Methionine Cycles.
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Table S1. Primer sequences used for PCR amplifications, sequencing reactions and promoter cloning. Underlined nucleotide sequences represent restriction site for cloning. Primers labeled with “seq” represent internal sequencing primers.
Table S2.SHMT1 and SHMT2 polymorphisms. The table lists variant sequence locations, DNA sequence alterations, amino acid sequence alterations, rs numbers (when available) and MAF values for both the “Human Variation Panel” and “1000 Genomes” data.
Table S3. Estimated SHMT1 haplotypes and frequencies using EM algorithm implemented with Haplo.Stats. Nucleotides highlighted in light grey represent variant nucleotides and dark grey if an additional variant allele exists as compared to those present in the most common haplotype in the AA population (the WT). Designations for haplotypes in (A) and (B) were made on the basis of alterations in the encoded amino acid sequence (number) and allele frequency (letters) – starting with the most common haplotype in the AA population. Numbers for variant allozymes were assigned from the N- to the C-terminus of the protein. Letters refer to haplotypes encoding the same amino acid sequence, from most to least common, beginning with the most common haplotype in the AA population. “D” = deletion and “I” = insertion.
Table S4. Estimated SHMT2 haplotypes and frequencies using EM algorithm implemented with Haplo.Stats. Nucleotides highlighted in light grey represent variant nucleotides as compared to those present in the most common haplotype in the AA population (the WT). Designations for haplotypes in (A) and (B) were made on the basis of alterations in the encoded amino acid sequence (number) and allele frequency (letters) – starting with the most common haplotype in the AA population. Numbers for variant allozymes were assigned from the N- to the C-terminus of the protein. Letters refer to haplotypes encoding the same amino acid sequence, from most to least common, beginning with the most common haplotype in the AA population. “D” = deletion and “I” = insertion.
Figure S1. LD plots (r2), based on the gene resequencing, generated with Haploview. The variants graphed represent polymorphisms with ethnic specific MAFs ≥ 1% (EA, HCA, or AA) in SHMT1 (left) and SHMT2 (right). The figures at the top represent the genetic structures of SHMT1 and SHMT2, as defined by the UCSC Genome Browser.
Figure S2. Dual reporter assays for (A) rs669340 and (B) rs7207306. Error bars indicate SEM.
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