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你可能喜欢fusiondb: a database for in-depth analysis of prokaryotic gene fusion events　
【关键词】& fusiondb
information génomique and structurale, cnrs-upr 2589, 31 chemin joseph aiguier, 13402 marseille cedex 20, france
*to whom correspondence should be addressed. tel: +33 4 91 16 46 04; fax: +33 4 91 16 45 49; email:
fusiondb () constitutes a resource dedicated to in-depth analysis of bacterial and archaeal gene fusion events. such events can provide the ‘rosetta stone’ in the search for potential protein protein interactions, as well as metabolic and regulatory networks. however, the false positive rate of this approach may be quite high, prompting a detailed scrutiny of putative gene fusion events. fusiondb readily provides much of the information required for that task. moreover, fusiondb extends the notion of gene fusion from that of a single gene to that of a family of genes by assembling pairs of genes from different genomes that belong to the same cluster of orthogonal groups (cog). multiple sequence alignments and phylogenetic tree reconstruction for the n- and c-terminal parts of these ‘cog fusion’ events are provided to distinguish single and multiple fusion events from cases of gene fission, pseudogenes and other false positives. finally, gene fusion events with matches to known structures of heterodimers in the protein data bank (pdb) are identified and may be visualized. fusiondb is fully searchable with access to sequence and alignment data at all levels. a number of different scores are provided to easily differentiate ‘real’ from ‘questionable’ cases, especially when larger database searches are performed. fusiondb is cross-linked with the ‘phylogenomic display of bacterial genes’ (phydbac) online web server. together, these servers provide the complete set of information required for in-depth analysis of non-homology-based gene function attribution.
introduction
gene fusion events have been proposed to represent valuable ‘rosetta stone’ information for the identification of potential protein protein interactions and metabolic or regulatory networks (1,2). more generally, information on gene fusion events can be combined with other non-homology-based approaches, such as phylogenomic profiling and identification of conserved chromosomal localization, to provide hypotheses for the characterization of proteins of unknown function (3 5). a number of web-based databases, such as allfuse (5), string (6) and predictome (7), implement this idea already. however, most of the available databases limit the definition of a gene fusion event to simple non-overlapping side-by-side blast (8) matches of two genes from a reference genome to a single open reading frame (orf) in a target genome, but without providing much information for further in-depth analysis. searches based on these databases give good starting points for hypothesis building, but the false positive rate may be quite high (in particular in cases where genes evolved through gene duplication and where the identification of gene orthology is hence difficult). the user is then left with the task of assembling the data required for more extensive case analysis.
here we present a database that is based on a more strict definition of a gene fusion event, applying a mutual best match criteria . it drastically reduces the number of false positives, at the expense of a potentially similarly high number of false negatives. to recover from this drawback, gene fusion events between genes from different genomes that belong to the same cluster of orthologous groups (cog) (10) are pulled together in what we call ‘cog fusion events’. analysis of these cog fusion events then allows for the investigation of gene fusion in its phylogenomic context, using multiple alignments and phylogenetic tree reconstruction. questions on the history of individual gene fusion events, such as whether a particular event occurred only once or many times during evolution, or whether more complex processes such as horizontal gene transfer, gene fission and gene decay are involved may be addressed using the information provided by fusiondb. the extension to ‘cog fusion events’ also provides information on general gene fusion tendencies in a whole bacterial genomic context to address questions such as ‘which type of genes are most likely to fuse?’ fusiondb thereby complements our phylogenetic profiling web server phydbac () (11), which is based on the same philosophy: providing detailed non-homology-based information for in-depth analysis of potential protein protein interactions. fusiondb is thus complementary to the databases cited above (5 7).
figure 1. criteria for a putative gene fusion event based on a mutual best match criteria (see text for details).
sources of genomic data and methods
all available 89 fully sequenced non-redundant bacterial and archaeal genomes (see
for a full list) were downloaded from ncbi refseq. those genomes for which a cog annotation of their genes was available (51 genomes) were checked for putative gene fusion (pfe) events in all 89 genomes as follows: a pfe between two genes from a given reference genome in a given target genome is subject to three criteria (fig. 1):
(i) each of the two reference genes must match the same orf in the target genome as their highest scoring blast hit. the overlap between the blast hits of both genes must not exceed 10% of the size of the smaller of the two target genes.
(ii) when split between the two blast hits, the two halves of the target orf must match back to the original two reference genes as their best blast hit to the reference genome.
(iii) the reference genes must not be homologous to each other.
note that the search for pfes is done on the basis of the annotated genes from a given reference genome, but against all possible orfs in the target genome (including overlapping orfs). this increases the chances of finding a gene fusion event that might have been discarded by a human annotator. every pfe is then subjected to a scoring scheme based on different evaluations of its pairwise and multiple (triple) alignments by calculating the following five scores.
(i) the separation index (sep) is a measurement of the mix between the domains from the two reference genes when they are placed in a triple alignment with the target orf. this index varies between 0 (total mix) and 1 (complete separation).
(ii) the fusion index (fus) is the fraction of residues in the concatenated reference genes that have similar properties to their aligned counterparts in the target orf. this index may vary between 0 (virtually no homology between the reference genes and the target orf) and 1 (strong homology).
(iii) the gene coverage (cov) is the fraction of the two reference genes that is alignable with the target orf in a triple alignment. this index varies between 0 (no relationship at all between the reference genes and the target orf) and 1 (all domains of the reference genes have a counterpart in the target orf).
(iv) the size ratio (ratio) between the size of the reference genes and the target orf indicates possible domain gain or loss after the gene fusion event has occurred.
(v) the ‘baditude’ (bad) is the fraction of residues that are aligned between the reference genes when placed in a triple alignment with the target orf. this index varies between 0 (both reference genes are evolutionarily unrelated) and 1 (both reference genes are homologues). a high ‘baditude’ is an indicator of genes with paralogous domains.
querying the database
fusiondb may be searched by gene name, gene annotation, gene function, cog identifier or simply by entering an amino acid sequence in fasta format. queries may be confined to specific reference and target genomes, and limits on the different scores can be imposed. output in full-page mode contains visualization of the different alignments that were used for scoring, and in the case of gene pairs that both belong to a cog a special cog-analysis page is provided. this cog-analysis page contains different types of multiple alignments and related phylogenetic trees, as well as information on related cog fusion events (networks) (fig. 2). extension of the research results, e.g. to all hits to a given fusion orf is possible. in cases where a gene fusion event has a match to a heterodimer in the protein data bank (pdb), a special pdb analysis page is available, providing a scored multiple alignment between the reference genes, the fusion gene and the sequences of the heterodimer in the pdb file. output in tabulated mode or limitation to only the best hit for each gene pair may be requested if a large number of hits is expected. on each page a cross-link to phydbac gives direct access to the phylogenetic profiles and eventual conserved chromosomal proximity of the two fusion genes.
figure 2. screenshot of fusiondb full-page output for a query to cog2080 and examples of some related information that can be obtained through this page. phydbac () is the ‘phylogenomic display of bacterial genes’ online web tool. in the top of the ‘cog fusion alignment’, n- and c- terminal genes are presented in red and green, respectively, fusion orfs are in black. the alignment of the merged genes with the fusion genes is presented below. a colour scale ranging from green over yellow to red represents the emboss plotcon score for this ‘merged alignment’. the ‘phylogenetic trees’ are based on the n- and the c-terminal ‘cog fusion alignments’, respectively. genomes in which fusion events occurred are highlighted in red in the trees. the ‘alignment to the pdb’ is a representation of the t-coffee alignment core index of the reference genes (top row), the fusion orf (middle row) and the sequence of the heterodimer (bottom row), warmer colours indicating a higher confidence in the alignment quality. pdbsum () is a database of the known 3d structures of proteins and nucleic acids.
by default, all queries are limited to a separation index (sep) of 0.6. this is found to be the most robust indicator of a ‘true’ gene fusion event (k. suhre et al., see also fusiondb/results/). note that the fusion index (fus) is dependent on the evolutionary distance between the reference and the target genome. values of the gene coverage (cov) and the size ratio (ratio) that differ significantly from 1 are indicators of domains that have been lost or added in the process of evolution. such cases should be inspected carefully. in some cases this can give rise to a high ‘baditude’ (bad) score when the added domains are homologous. if for a given query gene no fusion event is found, the user may try to extend the search to the cog family to which this gene belongs (or use the sequence search option, note also that genes with a high degree of paralogy in most genomes may not be identified as a fusion event). in situations where both genes of a pfe are associated with a cog and where several fusion events are identified by fusiondb, coherence between the phylogenetic trees of the n- and c-terminal genes as well as the history of the gene fusion can be used as indicators of ‘real’ fusion events and true functional orthology between the implicated genes in the different genomes. this kind of key information is not readily available on other existing database servers.
concluding remarks and future plans
fusiondb presents significant additions to other gene-fusion-related databases. the extension of the concept of a gene fusion to a ‘cog fusion’ event and the application of a mutual best match criteria not only reduces the number of false positives, but also makes the use of gene fusion events as ‘rosetta stones’ applicable at a genome-independent level, where the common gene pool of all prokaryotes is viewed as the sum of all identified (and still to be discovered) cogs. the wealth of pre-calculated multiple alignments and phylogenetic trees will be welcomed by many biological analysts and annotators, as fusiondb currently covers 20 000 potentially ‘real’ gene fusion events (having a separation index > 0.6), which correspond to 1355 different fused cog pairs. a more detailed analysis of these cases is underway (k. suhre et al., in preparation). fusiondb will be updated regularly as the number of publicly available fully sequenced genomes increases, and lower eukaryotes should be added in a future version. this will be particularly beneficial for obtaining more complete phylogenetic trees, which is still the best way to evaluate the ‘reality’ of the gene fusion events. ultimately, fusiondb is designated to prioritize and record the experimental validity of the molecular or functional interaction of the genes involved in gene fusion events.
acknowledgements
sequence data was downloaded from ncbi refseq (). multiple alignments were computed using t-coffee () (12). phylogenetic tree reconstruction was done using phylip ( phylip.html). multiple alignments were scored with emboss plotcon (). mode data for protein structures are from the pdb () (13).
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sali,a. (1999) functional links between proteins. nature, 402, 23 26.
marcotte,e.m. (2000) computational genetics: finding protein function by nonhomology methods. curr. opin. struct. biol., 10, 359 365.
marcotte,e.m., pellegrini,m., thompson,m.j., yeates,t.o. and eisenberg,d. (1999) a combined algorithm for genome-wide prediction of protein function. nature, 402, 83 86.
enright,a.j. and ouzounis,c.a. (2001) functional associations of proteins in entire genomes via exhaustive detection of gene fusion. genome biol., 2, 341 347
von mering,c., huynen,m., jaeggi,d., schmidt,s., bork,p. and snel,b. (2003) string: a database of predicted functional associations between proteins. nucleic acids res., 31, 258 261.
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tatusov,r.l., natale,d.a., garkavtsev,i.v., tatusova,t.a., shankavaram,u.t., rao,b.s., kiryutin,b., galperin,m.y., fedorova,n.d. and koonin,e.v. (2001) the cog database: new developments in phylogenetic classification of proteins from complete genomes. nucleic acids res., 29, 22 28.
enault,f., suhre,k., poirot,o., abergel,c. and claverie,j.m. (2003) phydbac (phylogenomic display of bacterial genes): an interactive resource for the annotation of bacterial genomes. nucleic acids res., 31, .
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Research article
Folate network genetic variation, plasma homocysteine, and global genomic methylation content: a genetic association study
Susan M Wernimont, Andrew G Clark, Patrick J Stover, Martin T Wells, Augusto A Litonjua, Scott T Weiss, J Michael Gaziano, Katherine L Tucker, Andrea Baccarelli, Joel Schwartz, Valentina Bollati and Patricia A Cassano*
Corresponding author:
Patricia A Cassano
Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA
Department of Molecular Biology & Genetics, Cornell University, Ithaca, NY, USA
Department of Biological Statistics & Computational Biology, Cornell, Ithaca, NY, USA
Channing Laboratory, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
Veterans Administration (VA) Normative Aging Study, VA Boston Healthcare System, and Division of Aging, Brigham & Women's Hospital, Boston, MA, USA
Department of Health Sciences, Northeastern University, Boston, MA, USA
Departments of Environmental Health and Epidemiology, Harvard University, Boston, MA, USA
Center of Molecular and Genetic Epidemiology, Department of Environmental and Occupational Health, Università degli Studi di Milano and IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
209 Savage Hall, Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA
For all author emails, please .
BMC Medical Genetics 2011, 12:150&
doi:10.50-12-150
The electronic version of this article is the complete one and can be found online at:
Received:27 May 2011
Accepted:21 November 2011
Published:21 November 2011
& 2011 W licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Sequence variants in genes functioning in folate-mediated one-carbon metabolism are
hypothesized to lead to changes in levels of homocysteine and DNA methylation, which,
in turn, are associated with risk of cardiovascular disease.
330 SNPs in 52 genes were studied in relation to plasma homocysteine and global genomic
DNA methylation. SNPs were selected based on functional effects and gene coverage,
and assays were completed on the Illumina Goldengate platform. Age-, smoking-, and
nutrient-adjusted genotype--phenotype associations were estimated in regression models.
Using a nominal P ≤ 0.005 threshold for statistical significance, 20 SNPs were associated
with plasma homocysteine, 8 with Alu methylation, and 1 with LINE-1 methylation. Using
a more stringent false discovery rate threshold, SNPs in FTCD, SLC19A1, and SLC19A3 genes remained associated with plasma homocysteine. Gene by vitamin B-6 interactions
were identified for both Alu and LINE-1 methylation, and epistatic interactions with
the MTHFR rs1801133 SNP were identified for the plasma homocysteine phenotype. Pleiotropy involving
the MTHFD1L and SARDH genes for both plasma homocysteine and Alu methylation phenotypes was identified.
Conclusions
No single gene was associated with all three phenotypes, and the set of the most statistically
significant SNPs predictive of homocysteine or Alu or LINE-1 methylation was unique
to each phenotype. Genetic variation in folate-mediated one-carbon metabolism, other
than the well-known effects of the MTHFR c.665C&T (known as c.677 C&T, rs1801133, p.Ala222Val), is predictive of cardiovascular
disease biomarkers.
Background
Folate and other B vitamins play key roles in biologic processes important to health,
including DNA synthesis and the generation of cellular methylation potential. Folate
status is influenced by both dietary intake and variation in genes encoding folate-related
enzymes, and altered folate status due to nutritional or genetic perturbations is
associated with adverse outcomes, including birth defects, cardiovascular disease
(CVD), and cancer [].
Elevated plasma homocysteine, a sulfur-containing amino acid by-product of folate
metabolism, is a marker of disturbed folate-mediated one-carbon metabolism, and is
associated with an increased risk of CVD [-]. Homocysteine levels are modulated by nutrition, particularly folate and vitamin
B-12 [], and by genetic variants, including a well-studied SNP in the methylenetetrahydrofolate
reductase gene MTHFR c.665C&T (known as c.677 C&T, rs1801133, p.Ala222Val)[].
The association of homocysteine with CVD is hypothesized to be mediated, in part,
by changes in DNA methylation []. Folate-mediated one-carbon metabolism is linked to DNA methylation status through
regulation of S-adenosylmethionine, the universal methyl donor, and through the activity
of enzymes involved in methylation reactions [,].
LINE-1 and Alu elements are abundant, transposable elements whose methylation status
has been shown to be highly correlated with genome-wide DNA methylation in some studies
[,]. Atherosclerosis is characterized by global DNA hypomethylation and transposable
element methylation levels are associated with heart disease, stroke,
reduced LINE-1 methylation was associated with an increased incidence of ischemic
heart disease and stroke in the Normative Aging Study (NAS) []. These findings contribute to interest in global genomic DNA methylation as a potential
biomarker of CVD risk.
Most previous work investigating variation in genes contributing to folate-mediated
one-carbon metabolism in relation to homocysteine and genomic methylation phenotypes
focused on a small numbe however, other enzymes and genes may
thus this study represents both first report and replication efforts.
To investigate the genetic and nutritional predictors of homocysteine and methylation
phenotypes, this candidate gene study examined variation across the network of genes
representing folate-mediated one-carbon metabolism in relation to homocysteine and
methylation outcomes. 330 single nucleotide polymorphisms (SNPs) in 52 genes with
a role in folate-mediated one-carbon metabolism were studied. The set of genes, the
SNP markers, and the nutrients examined in this study were selected to represent the
full functional variation of the folate-mediated one carbon metabolic pathway.
Study population
The Veterans' Administration (VA) established the NAS in
men aged 21-81
years (mean age of 42 y at study entry) were enrolled in the study on the basis of
details have been described elsewhere [,]. The analyses described herein focus on non-Hispanic white males using data from
the subset of men (~ 700) with measurements of homocysteine and global genomic DNA
methylation (Alu and LINE-1). This study complied with the Helsinki Declaration and
was approved by the following: Brigham and Women's Hospital Human Subjects committee,
VA R&D committee, Harvard School of Public Health, Cornell University Committee on
Human Subjects.
DNA extraction, SNP selection and genotyping
Genomic DNA was extracted from stored frozen buffy coat of 7 ml whole blood using
the QIAamp DNA Blood Kit (QIAGEN, Valencia, CA). The REPLI-g whole genome amplification
kit (QIAGEN) was used to amplify genomic DNA when quantity was insufficient for genotyping.
52 genes that contribute to folate-mediated one-carbon metabolism were identified
(Additional file ). SNP selection encompassed 2 kb on either side of the gene to include promoter and/or
regula a total of 384 SNPs were selected. 384 SNPs were submitted
to the Center for Inherited Disease Research at the Johns Hopkins University for genotyping
via an Illumina GoldenGate custom genotyping panel. Genotype frequencies in controls
were compared with those expected in Hardy-Weinberg equilibrium (HWE). Of the 384
SNPs originally submitted, 54 were ultimately excluded, leaving 330 SNPs available
for analysis (Additional file ).
Additional file 1. 52 genes in the folate-mediated one-carbon pathway.
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Additional file 2. 330 folate-related SNPs assayed in men in the Normative Aging Study.
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Extensive previously collected data on study participants includes physical measurements,
lifestyle factors, and blood assays. Plasma folate, vitamin B-6 (as pyridoxal-5'-
PLP) and vitamin B-12 were assayed as previously described []. Plasma total homocysteine was assayed in the same unselected subset of stored blood
samples as plasma folate, vitamin B-6, and vitamin B-12 []. The analysis of transposon DNA methylation was reported in prior publications [,].
Restricted maximum likelihood and ordinary least squares regression models evaluated
the relation between SNPs and the plasma homocysteine and global DNA me
maximum likelihood regression was used to evaluate epistatic interactions with the
dummy-coded MTHFR SNP. Previous work in this cohort demonstrated no population substructure [], thus no adjustments were made. All regression models were adjusted for age, smoking
status, and nutrient residuals (variation in nutrient not predicted by SNP), and an
extended model also adjusted for the MTHFR rs1801133 variant (coded as recessive to account for the pattern of association using
the fewest model terms). For the homocysteine phenotype, further models tested the
interaction of each genotype with the rs1801133 SNP. For all phenotypes, further models
tested the interaction of each genotype with the nutrients.
For main effects, regression coefficients with a nominal P ≤ 0.005 were reported,
and a False Discovery Rate (FDR) multiple testing correction [] was applied, with an FDR-adjusted P value significance threshold of 0.05; final models
were conditional on a first step that selected the best genetic model for each SNP,
thus the FDR is conditional on this first step. For interactions, a less stringent
FDR-adjusted P value significance threshold of 0.20 was used. For gene-nutrient interactions,
regression coefficients with a nominal P ≤ 0.02 were reported, given few results reached
the FDR threshold.
To assess effect modification, product terms between the SNP and the nutrient biomarker
residual were included in models. Interactions were captured in
significance of the interaction was assessed by the P value for the interaction term.
Interactions with MTHFR rs1801133, which was dummy-coded, were assessed with the likelihood ratio test (LRT).
All statistical analyses were conducted with SAS v. 9.2 (SAS, Cary, NC).
Additional details on methodology are provided in online materials (Additional file
Additional file 3. Supplemental methods.
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Measurements of the homocysteine phenotype, the Alu element methylation phenotype,
and the LINE-1 methylation phenotype were available for 760, 628 and 621 participants,
respectively. All had genotype data, 533 men had data on
analysis included the maximum number possible. The phenotype groups had similar frequencies
for the MTHFR rs1801133 TT genotype, but differed by age and hence differed slightly on age-related variables
(Table ). The MTHFR rs1801133 TT genotype prevalence in the largest group, the plasma homocysteine group, was 12.2%,
similar to the frequency reported in a large North American sample [].
Characteristics of Normative Aging Study participants, , with measurements
on three phenotypes.
Age and current smoking status were associated with homocysteine (P ≤ 0.001), age
was associated with Alu (P ≤ 0.005), and current smoking was associated with LINE-1
(P = 0.055). Folate, vitamin B-6, and vitamin B-12 were associated with homocysteine
(P ≤ 0.005), vitamin B-6 was associated with Alu (P ≤ 0.05), and these biomarkers
had little or no association with LINE-1. Models exploring the SNP--phenotype association
were adjusted for age, smoking, and nutrient residuals. Adjusting for age and smoking
made little difference to the coefficients for each SNP. The set of SNPs comprising
the most significant associations was nearly identical with or without adjusting for
nutrient residuals. Further adjustment for the MTHFR rs1801133 variant made little or no difference to the SNP regression coefficients.
The most statistically significant SNPs for each phenotype were relatively common
(MAF ≥13%), and the set of most significant SNPs was unique to each phenotype (Tables
and Figure ).
The most statistically significant associations (P ≤ 0.005) between single nucleotide
polymorphisms and the plasma homocysteine phenotype a, e
The most statistically significant associations (P ≤ 0.005) between single nucleotide
polymorphisms and the Alu methylation phenotype a, b, d, f
The most statistically significant association (P ≤ 0.005) between single nucleotide
polymorphisms and the LINE-1 methylation phenotype a, b, d, e, f
Manhattan plot. Folate-related SNPs as predictors of plasma total homocysteine and global genomic
DNA methylation phenotypes. Models adjusted for age, smoking status, and folate, vitamin
B-6, and vitamin B-12 residuals. Horizontal lines represent nominal P values of 0.05
(lower dashed line), 0.02 (center solid line) and 0.005 (upper dashed line). Boxes
indicate SNPs that reached False Discovery Rate significance.
Total plasma homocysteine phenotype
Of the 20 SNPs with a nominal P ≤ 0.005, five were also significant at the FDR threshold
(P ≤ 0.05) (Table ). These 5 SNPs comprise 3 genes: formiminotransferase cyclodeaminase (FTCD; 1 SNP, intronic), solute carrier family 19 (folate transporter), member 1 (SLC19A1, 3 SNPs, representing coding nonsynonymous, 5' region, and intronic variants), and
solute carrier family 19, member 3 (SLC19A3, 1 SNP, intronic). Genetic variation in all 5 SNPs was positively associated with
plasma homocysteine levels, and effects were similar in direction and magnitude (variant
genotypes associated with a 4.9-7.2% higher plasma total homocysteine vs. the referent
genotype). In each case, the association of the genotype with homocysteine was partially
m when plasma folate and vitamin B-6 or B-12 biomarkers were
added to the models, the regression coefficients were reduced by 29% for FTCD rs2277820, by 43% for SLC19A1 rs1051266, rs1131596, and rs4819130, and by 34% for SLC19A3 rs (data not shown). A model containing a nonredundant set of 3 of the top
5 FDR-significant SNPs (FTCD rs2277820, SLC19A3 rs, SLC19A1 rs1051266) explained 3.6% of the variation in plasma homocysteine beyond that explained
by age, smoking, and folate, B-6, and B-12 residuals (data not shown); the set of
3 SNPs was statistically significant (LRT = 17.6, P = 0.0005, 3 degrees of freedom,
df), and the coefficients for each SNP were similar to coefficients from single SNP
models. Considering the MTHFR genotype in more detail, the TT genotype group (vs. CC) had elevated homocysteine (nominal P = 0.0052), but the CT genotype had no association with homocysteine (nominal P = 0.8107); thus, the MTHFR genotype did not pass preset FDR thresholds.
In models investigating interactions between each SNP and MTHFR rs interaction terms were below the FDR threshold (FDR-adjusted P value
≤ 0.2) for the homocysteine phenotype (Additional file ). No SNP--nutrient (folate, B-6, or B-12) interaction coefficients reached FDR-significance
(FDR-adjusted P value ≤ 0.2; Additional file ). The MTHFR--folate interaction did not reach preset statistical thresholds (pnominal = 0.0578), but the pattern of interaction supported a greater association of MTHFR TT genotype with homocysteine conditional on lower folate status.
Additional file 4. Epistatic interactions with the MTHFR rs1801133 SNP and plasma homocysteine. The most statistically significant associations (FDR-adjusted Likelihood Ratio Test
P ≤ 0.2) for SNP by MTHFR rs1801133 interactions in relation to the plasma homocysteine phenotype for men in
the Normative Aging Study.
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Additional file 5. Gene-nutrient interactions and plasma homocysteine. The most statistically significant associations (P ≤ 0.02) for SNP by nutrient interactions
in relation to the plasma homocysteine phenotype for men in the Normative Aging Study.
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Global genomic DNA methylation phenotype: Alu elements
In analyses of the Alu element methylation phenotype, 8 SNPs were statistically significant
with a nominal P ≤ 0.005; however, none were statistically significant at the FDR
threshold (FDR-adjusted P value ≤ 0.05) (Table ). There was little or no mediation of the association by nutrients or plasma homocysteine
levels (data not shown). There were no SNP--nutrient interactions with folate or B-12
that reached FDR thresholds for statistical significance (FDR-adjusted P ≤ 0.2) (Additional
file ). Three SNPs had an FDR-significant interaction with plasma vitamin B-6 (Additional
file ); these interactions involved 3 intronic SNPs in 2 genes, aminomethyltransferase
(AMT, rs1464567 and rs1464566) and DNA (cytosine-5-)-methyltransferase 3 beta (DNMT3B, rs1883729). Comparing men with the AMT rs1464567 CC/CG genotype to the GG genotype, the mean Alu element methylation was 0.4 SD higher at low B-6, 0.1 SD higher
at median B-6, and 0.4 SD lower at high B-6. Comparing men with the AMT rs1464566 GG/GA genotype to the AA genotype, the mean Alu element methylation was 0.4 SD higher at low B-6, 0.1 SD higher
at median B-6, and 0.3 SD lower at high B-6. Comparing men with the DNMT3B rs1883729 AA genotype to the AG/GG genotype, the mean Alu element methylation was 0.1 SD lower at low B-6, 0.3 SD higher
at median B-6, and 0.8 SD higher at high B-6.
Additional file 6. Gene-nutrient interactions and Alu element methylation. The most statistically significant associations (P ≤ 0.02) for SNP by nutrient interactions
in relation to the global genomic DNA methylation phenotype (Alu elements) for men
in the Normative Aging Study.
Format: DOC
Size: 86KB This file can be viewed with:
Global genomic DNA methylation phenotype: LINE-1 elements
No SNP main effect associations reached the FDR-significance threshold for LINE-1
methylation (FDR-adjusted P ≤ 0.05; Table ). There were no SNP--nutrient interactions for folate or B-12 that reached FDR-significance
levels (FDR-adjusted P ≤ 0.2) (Additional file ). An interaction of plasma B-6 with 1 SNP was significant at the FDR threshold of
P ≤ 0.2 (rs, an intronic SNP in methylenetetrahydrofolate dehydrogenase (NADP+
dependent) 1-like, MTHFD1L) (Additional file ), suggesting that the relation of the SNP to LINE-1 methylation varied according
to plasma levels of vitamin B-6. Comparing participants with the MTHFD1L rs CA genotype to the CC/AA genotype, mean LINE-1 element methylation was 0.6 SD higher at low B-6, 0.2 SD higher
at median B-6, and 0.4 SD lower at high B-6.
Additional file 7. Gene-nutrient interactions and LINE-1 element methylation. The most statistically significant associations (P ≤ 0.02) for SNP by nutrient interactions
in relation to the global genomic DNA methylation phenotype (LINE-1 elements) for
men in the Normative Aging Study
Format: DOC
Size: 58KB This file can be viewed with:
Discussion
We investigated sequence variation in a network of candidate genes involved in one-carbon
metabolism in relation to plasma total homocysteine and two measures of global genomic
DNA methylation (Alu, LINE-1).
Genes involved in absorption and transport had the most statistically significant
associations with the ho about 30-40% of the association was
mediated through plasma folate and vitamin B-6 and B-12 levels. For the Alu-element
methylation phenotype, the top hits were in genes involved in mitochondrial metabolism,
nuclear metabolism, and methylation/homocysteine metabolism. For the LINE-1 methylation
phenotype, the top SNP was in a gene in the methylation/homocysteine pathway. There
was no evidence that nutrient biomarkers mediated the association of SNPs with the
methylation phenotypes.
The set of genes represented in the top hits was unique to each phenotype, although
pleiotropy was identified for plasma homocysteine and Alu element methylation involving
the MTHFD1L and sarcosine dehydrogenase (SARDH) genes.
Plasma total homocysteine phenotype
SLC19A1. There were FDR-significant associations between 3 SNPs in the SLC19A1 gene and plasm the direction and magnitude of association were
similar. Thus, each copy of the coding nonsynonymous rs1051266 A allele, the 5'region rs1131596 C allele, and the intronic rs4819130 C allele was associated with about a 5.0% increase in plasma homocysteine. HapMap plots
indicate high LD across the SLC19A1 gene, thus the three SNPs may represent a single effect. The SLC19A1 gene encodes a transporter involved in folate and thiamine uptake and may play a role
in intracellular folate distribution []. Transporter expression may be regulated by folate status []. About half of the association of these three SLC19A1 SNPs with homocysteine was mediated by plasma folate and vitamins B-6/B-12. The nonsynonymous
SLC19A1 rs1051266 SNP was previously associated with blood folate levels [,], and risk of intracranial aneurysm [], but not with homocysteine [,] or abdominal aortic aneurysm []. The 5' region SLC19A1 rs1131596 SNP was associated with reduced RBC folate levels in coronary artery disease
patients and decreased SLC19A1 protein expression [,]. Genetic variation in SLC19A1 may influence homocysteine levels, mediated by changes in nutrient biomarkers.
FTCD. The intronic FTCD rs2277820 SNP was associated with plasma total homocysteine. The CT genotype group was 7.2% higher on plasma total homocysteine vs. the CC/TT group. FTCD encodes a Golgi-associated enzyme involved in the production of 5,10-methenyl-tetrahydrofolate
(THF) []. Based on HapMap LD patterns the association with the intronic rs2277820 SNP may
proxy variation elsewhere in the gene. Mutations in FTCD are associated with inherited disorders of folate metabolism []. 29% of the association between rs2277820 and homocysteine was mediated through plasma
folate and vitamins B-6/B-12.
SLC19A3. An FDR-significant association was identified between the intronic rs SNP
in SLC19A3 and plasma total homocysteine. The CT genotype group was 6.9% higher on plasma total homocysteine vs. the CC/TT group. The SLC19A3 gene belongs to the folate transporter family and encodes a thiamine transporter []. Although SLC19A3 is not known to transport folate or vitamins B-6/B-12, 34% of the
SNP--homocysteine association was mediated by these nutrients. No prior reports link
SLC19A3 to biochemical or disease phenotypes, and a biological basis for the link to thiamine
metabolism could not be identified.
The variability in homocysteine explained by the model containing the set of the 3
most significant nonredundant SNP hits was 3.6%, a small proportion of the estimated
& 50% heritability in homocysteine [,], and similar to the proportion explained by age and smoking together.
There were four FDR-significant interactions between studied SNPs and MTHFR rs1801133 (Additional file ); the most statistically significant was for the ALDH1L1 rs2305230 SNP. In participants with the ALDH1L1 rs2305230 AA genotype, men with 1 copy of the MTHFR rs1801133 T allele had plasma homocysteine 64% higher than men with no copies. However, among
participants with the ALDH1L1 rs2305230 AC/CC genotype, men with 1 copy of the MTHFR rs1801133 T allele had plasma homocysteine 2.1% lower than men with no copies.
There were no FDR-significant interactions between studied SNPs and plasma folate,
vitamin B-6, or vitamin B-12 for the plasma homocysteine phenotype. The null results
may be due to an overly conservative FDR significance threshold, network compensation
for genetic and nutritional stresses, or inadequate power to evaluate interactions
involving low MAF SNPs; also, the folate status for men in the NAS was relatively
high in comparison to national averages as reported in Pfeiffer et al [], and SNP--nutrient interactions may be attenuated in this range of folate status.
The MTHFR rs1801133 SNP, which is expected to interact with folate in predicting the homocysteine
phenotype, had a nonsignificant interaction in these data (nominal Pinteraction = 0.0578), but the association of MTHFR with homocysteine was stronger at lower concentrations of plasma folate (data not
A cluster of SNP--vitamin B-6 interactions was noted for variants in the CBS gene, but the P values for these interaction terms were about 0.1 and did not reach
thresholds set prior to the analysis. These findings suggest that interactions between
vitamin B-6 and genetic variants in the SHMT1 and CBS genes may only be evident with very low vitamin B-6 status, which is consistent with
previous work [,]. A systematic review of literature published prior to August, 2009 revealed only
one report of a statistically significant interaction between genetic variation in
SHMT1 (rs1979277) and B-6 [].
Global genomic DNA methylation phenotype (Alu elements)
There were no FDR-significant main effect associations for the Alu element methylation
outcome. None of the SNP--folate or SNP--vitamin B-12 interaction terms reached FDR
significance thresholds. Given that the Alu phenotype was measured after the introduction
of mandatory folate fortification in the U.S., findings may be limited. Three FDR-significant
SNP--vitamin B-6 interactions were identified, including two intronic SNPs in the
AMT gene (rs1464567 and rs1464566) and one intronic SNP in the DNMT3B gene (rs1883729). The AMT gene encodes an enzyme that functions in the vitamin B-6-dependent mitochondrial glycine
cleavage system []. B-6 interactions involving SNPs in GLDC were among the top nominally significant hits for the homocysteine and Alu methylation
phenotypes, but did not reach FDR-significance. The DNMT3B gene encodes a DNA methyltransferase enzyme that is localized to the nucleus, developmentally
regulated, and functions to establish de novo methylation patterns [,]; DNMT3B expression is associated with cancer [-]. Although cell culture studies have not supported Alu elements as DNMT3B targets [,] in both in vitro and in vivo models, DNMT3b protein levels were down-regulated by B vitamin deficiency (deficiency
of folate, B-6, and B-12 together), de novo methylation was suppressed both in vitro and in vivo under conditions of B vitamin deficiency[], and S-adenosylmethionine levels were markedly decreased in response to lowered B-6
concentrations in culture medium [] consistent with the direction of association observed here.
Global genomic DNA methylation phenotype (LINE-1 elements)
There were no FDR-significant associations observed for the LINE-1 methylation phenotype.
There were no FDR-significant interactions between SNPs and folate or vitamin B-12;
the measurement of LINE-1 in Normative Aging Study men took place after the introduction
of mandatory folate fortification in the U.S., and limited variation may have limited
findings. A single SNP--vitamin B-6 interaction was significant at the FDR threshold
for the intronic rs in the MTHFD1L gene. The MTHFD1L gene product functions downstream from the vitamin B-6-dependent glycine cleavage
system [] and intronic variation in MTHFD1L was previously associated with CVD [].
Conclusions
Strengths of the present study include investigation of a large cohort with homocysteine
data collected prior to the introduction of mandatory folate-fortification in the
U.S. Also, SNP selection for the genotyping assay reflected functional, LD, and physical
coverage of genes. Using a systematic approach, we identified the best genetic models
for each SNP, then tested single SNPs, the interaction of each SNP with MTHFR rs1801133, and the interaction of each SNP with folate, vitamin B-6 and vitamin B-12.
Findings were corrected for multiple comparisons and those surpassing the FDR threshold
were discussed in more detail. Weaknesses of the study include the fact that methylation
(but not homocysteine) measures were collected after the introduction of mandatory
folate fortification in the U.S., which may have limited variation in B-vitamin status.
Information on additional nutrients such as choline would have allowed a more complete
evaluation of gene-nutrient interactions. S-adenosylhomocysteine and/or the ratio
of S-adenosylmethionine to S-adenosylhomocysteine are likely to be more sensitive
indicators of vascular disease risk than homocysteine [,], but were not measured. The methylation phenotypes studied here are believed to be
an adequate proxy of genome-wide DNA methylation. Gene-specific methylation data was
furthermore, because the folate-mediated one-carbon network functions
to generate cellular methylation potential and thus contributes to numerous methylation
reactions, it may be more appropriate to evaluate folate network genetic variation
in relation to global measures of methylation. Finally, due to genotyping failure,
some key variants could not be analyzed, for example rs6922269 in MTHFD1L [], although proxies were selected purposefully to address this limitation.
The most significant hits for the homocysteine and methylation outcomes reflected
genes involved in the generation of one-carbon units, including SLC19A1 and FTCD. Because a unique set of genes was identified for each phenotype, and because the
top hits could not be predicted on the basis of hypothesized impact on cellular methylation
potential, this work suggests that not all folate effects are mediated through the
ratio of S-adenosylmethionine to S-adenosylhomocysteine. Thus, beyond the well-described
MTHFR rs1801133 SNP, polymorphisms in other genes make important contributions to homocysteine
and global genomic DNA methylation phenotypes. Furthermore, some associations are
sensitive to nutritional status of B vitamins. Future work should continue to include
a broad evaluation of one-carbon network genetic and nutritional variation in unfortified
or pre-fortification populations and extend these findings for CVD biomarkers to an
investigation of CVD phenotypes.
Abbreviations
% 5-meC: percentage of
3': 3' 5': 5' A: A
AHCY: Ad AHCYL1: Adenosylhomocysteinase-like 1; AHCYL2: Adenosylhomocysteinase-like 2, KIAA0828; ALDH1L1: Aldehyde dehydrogenase 1 family: member L1; AMT: Am ATIC: 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP
BHMT: Betaine-homocysteine S- CBS: Cystathionine-beta- CTH: Cystathionase (cystathionine gamma-lyase); CELF1: CUGBP: Elav-like family member 1; CEPH: Centre d'Etude du Polymorphisme H CV: coefficient
CN: C CS: C CVD: cardiovascular
df: DHFR: Dih DMGDH: Dimethylg DNMT1: DNA (cytosine-5-)-methyltransferase 1; DNMT3A: DNA (cytosine-5-)-methyltransferase 3 DNMT3B: DNA (cytosine-5-)-methyltransferase 3 D: D FDR: False Discovery R
FOLH1: Folate hydrolase (prostate-specific membrane antigen) 1; FOLR1: Folate receptor 1 (adult); FOLR2: Folate receptor 2 (fetal); FOLR3: Folate receptor 3 (gamma); FPGS: Folylpo FTCD: Formiminotransf FTH1: Ferritin: heavy polypeptide 1; GART: Phosphoribosylglycinamide formyltransferase: phosphoribosylglycinamide synthetase:
phosphoribosylamino GCSH: Glycine cleavage system protein H (aminomethyl carrier); GGH: Gamma-glutamyl hydrolase (conjugase: folylpolygammaglutamyl hydrolase); GLDC: Glycine dehydrogenase (decarboxylating); GNMT: Glycine N- HSPA8: Heat shock 70 kDa protein 8; HWE: Hardy-W I: I LRT:
l LD: li MAF: mi MARS: Methionyl-tRNA MAT1A: Methionine adenosyltransferase I: MAT2A: Methionine adenosyltransferase II: MAT2B: Methionine adenosyltransferase II: M.E.: M MTHFD1: Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1: methenyltetrahydrofolate
cyclohydrolase: formyltetrahy MTHFD1L: Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1- MTHFD2: Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2: methenyltetrahydrofolate
MTHFR: Methylenetetrahydrofolate reductase (NADPH); MTHFS: 5,10-methenyltetrahydrofolate synthetase (5-formyltetrahydrofolate cyclo-ligase);
MTR: 5-methyltetrahydrofolate-homocystei MTRR: 5-methyltetrahydrofolate-homocysteine methylt NAS: Normative
Aging S O: O PLP: pyridoxal-5'- R: R SARDH: Sar SHMT1: Serine hydroxymethyltransferase 1 (soluble); SHMT2: Serine hydroxymethyltransferase 2 (mitochondrial); SLC19A1: Solute carrier family 19 (folate transporter): member 1; SLC19A2: Solute carrier family 19 (thiamine transporter): member 2; SLC19A3: Solute carrier family 19: member 3; SLC25A32: Solute carrier family 25: member 32; SLC46A1: Solute carrier family 46 (folate transporter): member 1; SNP: single nucleotide
TCN1: Transcobalamin I (vitamin B-12 binding protein: R binder family); TCN2: Transcobalamin II; THF: TYMS: Th UBE2I: Ubiquitin-conjugating enzyme E2I (UBC9 homolog: yeast); UBE2N: Ubiquitin-conjugating enzyme E2N (UBC13 homolog: yeast); VA: Veterans' Administration.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SMW, PAC, AAL, STW, JMG, KLT, AB, and JS SMW, PJS, AGC, MTW, VB
and PAC SMW and PAC SMW, PAC, AGC, PJS, and MTW
wrote the paper, and PAC had primary responsibility for all work and final content.
All authors read and approved the final manuscript.
Acknowledgements
The authors wish to thank Ms. Ellen M. Smith, Cornell '09, for her contributions to
preliminary analyses for this work. The project was supported in part by the Center
for Vertebrate Genomics (PAC), the Presidents Council for Cornell Women (PAC), and
the Bronfenbrenner Life Course Center (PAC), all at Cornell U by U.S. Department
of Agriculture Cooperative State Research, Education, and Extension Service grant
-13135 (subproject to PAC); The Veterans Administration (VA) Normative Aging
Study is supported by the Department of Veterans Affairs Cooperative Studies Program/ERIC
and is a research component of the Massachusetts Veterans Epidemiology and Information
Center (MAVERIC); by National Institute of Environmental Health Sciences grant ES014663
(JS); and by T32DK007158 (SMW), National Institute of Diabetes, Digestive and Kidney
Diseases. Dr. Baccarelli receives salary support from New Investigator funding from
the Harvard School of Public Health-National Institute of Environmental Health Sciences
Center for Environmental Health (ES000002). Dr. Schwartz receives support through
National Institute of Environmental Health Sciences grant R01ES015172. Genotyping
services were provided (to PAC) by the Johns Hopkins University under federal contract
number (N01-HV-48195) from the National Heart, Lung, and Blood Institute. The content
is solely the responsibility of the authors and does not necessarily represent the
official views of the National Institute of Diabetes And Digestive And Kidney Diseases,
the National Heart, Lung, and Blood Institute, or the National Institutes of Health.
The funding bodies played no role in study design, the collection, analysis, and interpretation
of data, the writing of the manuscript, or in the decision to submit the manuscript
for publication.
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