AJCN Tufts Nutrition Symposium, Boston & Online Sept 2009
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American Journal of Clinical Nutrition, Vol. 87, No. 1, 181-189, January 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Supplementation of healthy volunteers with nutritionally relevant amounts of selenium increases the expression of lymphocyte protein biosynthesis genes1,2,3

Vasileios Pagmantidis, Catherine Méplan, Evert M van Schothorst, Jaap Keijer and John E Hesketh

1 From the Institute for Cell and Molecular Biosciences, Newcastle University Medical School, Newcastle-upon-Tyne, United Kingdom (VP, CM, and JEH), and the Institute of Food Safety (RIKILT), Wageningen University and Research Centre, Wageningen, Netherlands (VP, EMvS, and JK)

2 VP, CM, and EMvS contributed equally to this work.

3 Supported by the UK Food Standards Agency (N05041) and the European Nutrigenomics Organisation.

4 Reprints not available. Address correspondence to JE Hesketh, Institute of Cell and Molecular Biosciences, Newcastle University Medical School, Framlington Place, Newcastle-upon-Tyne, NE2 4HH, United Kingdom. E-mail: j.e.hesketh{at}ncl.ac.uk; or to J Keijer, RIKILT-Institute of Food Safety, Wageningen University and Research Centre, PO Box 230, Wageningen 6700 AE, Netherlands. E-mail: jaap.keijer{at}wur.nl.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Selenium is incorporated into 25 selenoproteins in humans. Low dietary selenium has deleterious effects on health and may result in cancer, cardiovascular disease, and immune dysfunction. The underlying mechanisms are not fully understood. Lymphocytes are a target tissue; they can be assessed in healthy persons, and their response has not been explored by using global gene expression profiling techniques.

Objectives: The objectives of the study were to assess the overall effect of selenium supplementation within a normal physiological range on the pattern of lymphocyte gene expression and to identify downstream processes affected by selenium intake.

Design: Gene expression was assessed in lymphocytes isolated from 39 healthy persons before and after a 6-wk supplementation with 100 µg Se/d as sodium selenite. Presupplementation and postsupplementation RNA samples from 16 subjects were chosen at random for microarray analysis. Differential gene expression was analyzed by using individual labeling and hybridization with human whole-genome microarrays. Array data were validated by quantitative real-time reverse transcriptase–polymerase chain reaction.

Results: The study subjects had an average 19% increase in plasma selenium concentration, which was within a normal range. Fold changes in gene expression were small, but data analysis using biological process identification showed that selenium predominantly affected the genes that encode proteins functioning in protein biosynthesis. Gene expression changes were confirmed by quantitative polymerase chain reaction for 3 representative target genes (RPL37A, RPL30, and EEF1E1).

Conclusions: Ribosomal protein and translation factor genes were up-regulated in response to increased selenium intake. We hypothesize that this up-regulation is linked to increased selenoprotein production and enhanced lymphocyte function.

Key Words: Transcriptomics • lymphocytes • ribosomal protein • selenoprotein • micronutrient


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Selenium is a trace element that is essential for human health. Severe selenium deficiency is found in the Keshan area of China, where the soil is extremely low in selenium. This results in an endemic cardiomyopathy caused by a combination of selenium deficiency and coxsackie B virus infection (1, 2). Such severe deficiency is rare, but suboptimal selenium intake, as observed in most European countries (2, 3), is common and has been linked to greater cancer susceptibility and other clinical symptoms, including cardiovascular disease, progression of virus infection, and greater mortality in HIV-infected patients (3-5). Selenium status has been diminishing in many European countries in recent decades and now is below that in the United States (6, 7). For example, the status of plasma selenium in the UK population has fallen, and the average selenium intake in the United Kingdom currently is {approx}50% of the recommended intake (8), which raises the question of supplementation. In the United States, selenium supplementation (200 µg/d) has been reported to reduce cancer mortality (4). Thus, although European and US selenium intakes are not low enough to cause overt deficiency, they may not be sufficient for optimal health.

Significant amounts of selenium are found in immune tissues such as the spleen and the lymph nodes (6). Numerous studies suggest that low selenium intake is accompanied by an impaired immune function (9). Both cellular and humoral immune responses can be affected, which can lead to a general immunosuppression (10, 11). Impaired immune function may explain the association of low selenium with progression of hepatitis B or C, influenza, coxsackie virus, and HIV infection (12, 13). Animal and cell culture experiments have shown that selenium supplementation leads to increases in antibody response, T cell proliferation, and killing by macrophages (10, 11). In humans, selenium supplementation has been reported to lead to increases in lymphocyte proliferation, expression of interleukin (IL)-2 receptor (14), and IL-2 production in patients with chronic hepatitis (15) and also to an augmented cellular immune response to live attenuated polio vaccine virus and a greater clearance of the virus (16).

Selenium is incorporated into selenoproteins as the amino acid selenocysteine during translation. Twenty-five selenoproteins have been identified in humans (17); for {approx}10 of those selenoproteins, there is functional information suggesting roles in antioxidant protection, redox regulation of transcription factors, and thyroid hormone metabolism (18). When the selenium supply is limited, the concentration of selenoproteins is lower, which in some cases is also reflected in alteration of the concentrations of selenoprotein mRNAs (19, 20). There is evidence that selenium supplementation increases selenoprotein activity, but the links between selenium supplementation, selenoprotein activity, and physiological and clinical effects are not well defined (3, 21).

Because the role of selenium in the immune system is not well understood, the aims of this study were to investigate the overall effect of selenium supplementation on the pattern of lymphocyte gene expression and to identify downstream selenium target genes. Our nutrigenomics approach was to use microarray analysis of lymphocyte RNA from persons in the United Kingdom before and after supplementation with selenium (100 µg/d for 6 wk) that improved their selenium status to within the normal range and increased intake to levels associated with improved immune function (9, 16).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Selenium supplementation and collection of blood samples
The present study involved 39 participants who were part of the larger SELGEN selenium supplementation trial, which was described in detail elsewhere (22). Participants already taking selenium, multivitamins, or vitamin E supplements; those with excessive alcohol consumption (>30 units/wk); and those with any cardiovascular, hepatic, gastrointestinal, or thyroid disorder or chronic intake of antiinflammatory drugs were excluded from the study. At the start of the study, peripheral blood samples were drawn; they were processed on the same day. Participants took a daily supplement of 100 µg sodium selenite (Cardinal Health, Swindon, United Kingdom) for 6 wk. At the end of the supplementation period, another blood sample was taken. Overall compliance of the volunteers with selenium supplementation was estimated by counting the returned capsules at the end of the supplementation and by assessing plasma selenium concentrations. All blood samples were collected between 0830 and 1100. Volunteers did not fast before sampling. Lymphocytes and plasma were prepared as described (22), and total plasma selenium was measured by using inductively coupled plasma mass spectrometry (ICP-MS) as previously described (16).

Written informed consent was provided by all SELGEN study participants. The SELGEN study was approved by the Sunderland Research Ethical Committee (United Kingdom).

RNA was isolated from lymphocytes prepared from the blood drawn from all SELGEN study participants before and after selenium supplementation. RNA of sufficient quality for quantitative real-time reverse transcriptase–polymerase chain reaction (qPCR) or microarray analysis (both before and after supplementation) was obtained from 39 subjects, and these samples were analyzed by qPCR. From this group, 16 subjects were selected at random for microarray analysis.

RNA extraction
Total RNA was isolated from lymphocytes with the use of Trizol (Invitrogen, Paisley, United Kingdom) according to the manufacturer's instructions, with the use of an additional phenol/chloroform/isoamylalcohol (25:24:1, vol:vol:vol) extraction step, which followed by a second chloroform purification. Total RNA was further purified by using RNeasy columns (Qiagen, Venlo, Netherlands). The integrity and quality of RNA samples were checked on 0.5% TBE agarose gels or Experion automated electrophoresis systems (BioRad, Veenendaal, Netherlands) after incubation for 1 h at 37 °C to increase the detection of potential degradation or impurity. Concentrations were determined by using a Nanodrop ND-1000 spectrophotometer (Isogen Life Sciences, Maarssen, Netherlands). All RNA samples had a 260:280 absorbance ratio between 1.9 and 2.1.

Microarray analysis
A pooled reference design in which all 32 test samples (16 subjects, before and after supplementation) were labeled individually was used to analyze differential gene expression in human lymphocyte RNA before and after selenium supplementation. For the cDNA synthesis and subsequent cRNA amplification and labeling, we used the protocol for the Agilent Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies, Palo Alto, CA) with some minor modifications, as described previously (23). Briefly, all cDNA samples, synthesized from 500 ng total RNA, were split into 2 aliquots: 1 aliquot was amplified and labeled with Cy5 (sample) and the other with Cy3 (reference). The Cy3-labeled cRNA samples were pooled on an equal concentration basis and served as a reference pool. Termination reaction, hybridization, and SSPE washing (Saline Sodium Phosphate EDTA buffer; Sigma-Aldrich, Zwijndrecht, Netherlands) of the Human 1A (V2) oligo array containing 22 575 60-mer oligo spots (including 1502 control spots) (Agilent Technologies) were performed according to the manufacturer's protocol by using 1 µg Cy5-labeled cRNA and 1 µg Cy3-labeled cRNA for hybridization.

Microarrays were scanned with a Scanarray Express HT scanner (Perkin Elmer/NEN Life Sciences, Boston, MA). Signal intensities for each spot were quantified using ARRAYVISION (version 7.0; Imaging Research, Ste Catherine's, Canada). Saturated spots, flagged spots, and spots with signal intensity <2 times the background intensity were discarded, which left 14 362 transcripts for normalization and analysis of differential expression. Quality checks on raw unprocessed data were performed for each microarray by using GENEMATHS XT software (version 1.5; Applied Maths, St Martens-Latem, Belgium), R statistical software [version 2.4.1 (24)], and Microsoft EXCEL software (version 2003; Microsoft Corporation, Redmond, WA). All arrays, based on MA-plot [scatterplot of the intensity log-ratio M = log2 (Cy5/Cy3) versus the mean log intensity A = log2(Cy5xCy3)], normal probability plot, and signal intensity distribution (25, 26), were within the accepted quality limits necessary for further analysis. Data normalization over all arrays was performed according to a method of Pellis et al (27) with the use of GENEMATHS XT 1.5. An initial screening for differential expressed genes was performed on the basis of a paired Student t test (before supplementation compared with after supplementation; n = 16). No correction for multiple testing was made, because we aimed at selecting a subset of genes for further analysis of coordinate changes, and we reasoned that this would not be markedly influenced by random false positives. Ratios of after supplementation to before supplementation (after:before supplementation) were calculated for each gene per person and averaged over all 16 subjects.

Because the gene expression changes were small, further analysis focused on the identification of coordinate gene expression changes—ie, on genes that occur in the same pathway or interaction network. Both network interaction analysis and pathway analysis were performed with METACORE software (version 4.3; GeneGo Inc, St Joseph, MI). Network interaction analysis was performed on the basis of "shortest paths." The observed P value is generated from a hypergeometric distribution, in which the P value essentially represents the probability that a particular mapping would arise by chance, given the number of genes in the set of all genes on maps or networks, genes on a particular map or network, and genes in the experiment.

The array data and corresponding sample information (ie, volunteer number, sex, and age), were made available in a microarray experiment (MIAME)–compliant (28) format by submission to the ArrayExpress database [(29) Internet: http://www.ebi.ac.uk/arrayexpress] with ArrayExpress accession number E-MEXP-1046.

Quantitative real-time reverse transcriptase–polymerase chain reaction
Differential expression for individual genes was assessed by qPCR. cDNA was synthesized from 500 ng of total RNA for each sample by using the iScript cDNA Synthesis kit (BioRad). Exon-spanning primers (Biolegio, Nijmegen, Netherlands) were designed for SYBR Green probes with BEACON DESIGNER software (version 4.0; Premier Biosoft International, Palo Alto, CA). The primers used are shown in Table 1Go. PCR amplification and detection were performed with the iQ SYBR Green Supermix and the MyIQ single-color real-time polymerase chain reaction (PCR) detection system (both: BioRad); after 3 min at 95 °C, a 2-step procedure consisting of 45 cycles of 10 s at 95 °C and 45 s at 59.5 °C was performed; that was followed by a temperature increase (0.5 °C/10 s, starting at 55 °C) to generate a melting curve. A standard curve for all genes, including reference genes, was generated by using serial dilutions from a pool prepared from all cDNA samples. Acceptable limits for the standard curve included a PCR efficiency of 100 ± 10% and a correlation coefficient (R2) > 0.99. All samples (diluted 1-in-100) were analyzed within-run and in duplicate, and they were averaged: differences between the 2 values were <5%. Five reference genes were selected on the basis of least variation in the microarray analysis and a signal-to-background ratio > 10 for all microarrays (data not shown). Analysis with GENORM software [version 3.4; downloadable from http://medgen.ugent.be/~jvdesomp/genorm (30)] identified 2 of these 5 gene as the most stable expressed reference genes: actin-related protein 2/3 complex subunit 5 16-kDa (ARPC5) and hydroxyacylglutathione hydrolase (HAGH). The expression level of each target gene was normalized against the mean of the 2 reference genes, and the after:before supplementation ratio was calculated. Changes in expression were statistically analyzed by using a paired t test. Significance was defined as P < 0.05, and data are shown as means ± SEMs unless stated otherwise.


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  		TABLE 1. Primer sequences used for the quantitative real-time reverse transcriptase–polymerase chain reaction

 
Statistical analysis
All statistical analyses were performed with SPSS software (version 14.0; SPSS Institute, Chicago, IL) unless stated otherwise.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Selenium intervention and study population
To assess the effects of selenium supplementation, gene expression was analyzed in lymphocytes of 39 healthy volunteers before and after daily supplementation with 100 µg Se in the form of sodium selenite for 6 wk [as part of the SELGEN study (22)]. Compliance in taking the supplement was 98%. Of these 39 participants (21 F, 18 M), 16 (10 F, 6 M) were randomly chosen for the primary microarray analysis. In the group of 39, plasma selenium increased after 6 wk of supplementation from 1.19 ± 0.022 to 1.41 ± 0.025 µmol/L (mean after:before supplementation = 1.19 ± 0.018; P < 0.001), whereas, in the group of 16 volunteers, there was also a significant increase in plasma selenium (mean after:before supplementation = 1.23 ± 0.09; P < 0.001). In addition, selenium supplementation led to greater lymphocyte glutathione peroxidase activity (22).

Microarray analysis of differentially expressed genes in response to selenium supplementation
Sixteen pairs of lymphocyte RNA samples were analyzed by hybridization to 22K Agilent microarrays. After initial quality control and normalization (see Subjects and Methods), a total of 14 362 transcripts showed expression levels twice the background levels, and they were used for subsequent data analyses. Initially, we calculated mean gene expression after:before supplementation ratios. These ratios generally were small (0.86–1.30), which indicated that the gene expression changes induced by selenium supplementation were rather subtle.

Selenocysteine incorporation occurs during translation (17, 18), and therefore selenium intake influences selenoprotein synthesis at the translational rather than the transcriptional level. As a result, selenium intake would not necessarily be expected to lead to changes in the abundances of selenoprotein mRNAs. However, changes in selenium supply have been reported to affect expression levels of some selenoprotein mRNAs, possibly as a result of changes in mRNA stability (18, 20, 31-35). The after:before supplementation gene expression ratios for the selenoproteins and glutathione-related genes (P < 0.05) that were present on the array are shown in Table 2Go. Those ratios showing significantly (P < 0.01) greater expression were those for selenophosphate synthetase 1 (SEPHS1) and microsomal glutathione S-transferase 1 (MGST1), whereas expression of selenoprotein K (SELK), 15-kDa selenoprotein (SEP15), and glutathione S-transferase kappa 1 (GSTK1) also increased, but with lower significance (P < 0.05).


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  		TABLE 2. Significantly regulated selenium and glutathione metabolism-associated genes1

 
Because the observed ratios in gene expression were small, further data analysis was carried out by using network interaction and pathway analysis. Network analysis shows genes that interact as part of the same process —eg, by physical interaction—whereas pathway analysis shows genes encoding proteins that are part of the same biochemical pathway, but that do not necessarily interact directly. Both analyses take interaction into account and are therefore more powerful in identifying meaningful changes, especially in nutritional expression sets, where changes in gene expression usually are small (36). To select genes for subsequent interaction network and pathway analysis, we used a paired t test. This test identified 250 genes differing significantly (P < 0.01) between before and after supplementation, whereas 1256 genes were identified at P < 0.05. (See Table S1 under "Supplemental data" in the current online issue at www.ajcn.org.) When the subset of 250 genes was loaded onto METACORE software, 189 genes were mapped on networks. Of the 1256 somewhat less significant genes, 929 could be mapped on networks. We decided to use the larger gene set to allow a wider range of genes and, thus, of processes to be taken into account. Interaction network analysis based on GeneGO processes identified the protein biosynthesis network (translation-translation initiation) distinctively on top (P = 4.70e–19), followed by translation-elongation termination (P = 1.35e–08) and immune T-cell receptor signaling (P = 6.39e–05) (all: METACORE). The protein biosynthesis network was composed of 177 genes, 61 of which were differentially expressed genes (Table 3Go and Table 4Go) with significance of P < 0.05, including 15 genes with significance of P < 0.01. The protein biosynthesis network (translation-translation initiation) was also the most significant network when the smaller subset was analyzed (P = 7.47e–05); the next most significant networks were development-skeletal muscle development (P = 1.71e–03) and translation-elongation termination (P = 2.67e–02). When the differentially expressed genes were analyzed with pathway analysis, cytoskeleton remodeling appeared as the most prominently affected process (24 of 176 genes). However, because it was identified with only a very low significance (P = 4.33e–04, as opposed to 4.70e–19 for protein biosynthesis by network interaction; METACORE), it was discarded from downstream confirmational analysis.


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  		TABLE 3. Network interaction analysis of significant regulated genes1

 

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  		TABLE 4. Functional categories of protein biosynthesis network1

 
The protein biosynthesis network is mainly composed of genes coding for ribosomal proteins, but it also includes translation elongation and translation initiation factors. Eukaryotic ribosomes, the multimeric complexes that catalyze protein synthesis, consist of a small 40S subunit and a large 60S subunit. These subunits are composed of 4 RNA species together with {approx}80 structurally distinct proteins (37). As shown in Table 3Go, 51 of 61 of these genes are differentially expressed using a cutoff of mean after:before supplementation ratio of 100 ± 10%; together, they make up a very strong overrepresentation. More important, all of the ribosomal genes change in the same direction—ie, they increase in expression. This orchestrated gene expression pattern identifies protein biosynthesis—and, in particular, ribosomal gene expression—as being increased by selenium supplementation.

Confirmation of array data by quantitative real-time polymerase chain reaction
Among the genes changed in expression on microarray analysis were the large ribosomal subunit protein–encoding genes RPL30 and RPL37A, the small ribosomal subunit protein–encoding gene RPS3A, and the elongation factor–encoding gene EEF1E1. On the basis of the expression after:before supplementation ratio and the statistical significance, these genes were selected for confirmation of differential expression. To substantiate the biological importance of the identified gene changes, analysis of expression of these genes was carried out by using an independent technique (qPCR), and it was extended to a total of 39 pairs (before and after supplementation) of RNA samples, including the original 16 pairs. When only qPCR data that conformed to stringent quality-control criteria [PCR efficiency of 100 ± 10%, and a correlation coefficient (R2) > 0.99] were included in the statistical analysis, as shown in Figure 1Go, 3 of the 4 target genes (RPL30, RPL37A, and EEF1E1) showed a significant increase in expression after selenium supplementation (P < 0.05). The fourth target gene, RPS3A, also showed an increase in expression, but it was not significant. For each of the 4 genes, the mRNA expression after:before supplementation ratio was calculated; the values were 1.5 ± 0.15 for RPL30 (P = 0.025), 1.17 ± 0.07 for RPL37A (P = 0.042), 1.08 ± 0.07 for RPS3A (P = 0.52), and 1.36 ± 0.16 for EEF1E1 (P = 0.02). Overall, the qPCR analyses show differential expression of RPL30, RPL37A, and EEF1E1, which validates the microarray data.


Figure 1
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  		FIGURE 1.. Quantitative real-time reverse transcriptase–polymerase chain reaction analysis of RPL30, RPL37A, RPS3A, and EEF1E1 expression in lymphocytes from healthy individuals before ({square}) and after ({blacksquare}) supplementation. The gene expression values have been normalized against the mean of 2 reference genes (ARPC5 and HAGH), with presupplementation values set at 100%, and the values shown are means ± SEM from analysis of 24 (RPL30 and RPS3A) or 8 (RPL37A and EEF1E1) pairs of RNA samples. The number of data points analyzed for the different transcripts varied because data were subjected to stringent quality control, in which data were restricted to those from subjects (before and after supplementation) in whom the polymerase chain reaction efficiency was 100 ± 10% and the correlation coefficient (R2) for the standard curve was >0.99. Statistical analysis was performed with a paired t test.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Alterations in selenium intake have been reported to affect many tissues including the immune system (9), but the mechanisms responsible are not well understood. The aim of the present work was to use a microarray approach to analyze global gene expression patterns in a selenium target organ, namely lymphocytes, from normal healthy volunteers given a selenium supplement. The Recommended Nutrient Intake (United Kingdom) for selenium currently is 55–75 µg/d and supplementation was performed with 100 µg/d. The supplementation improved the nutrient status, as judged by plasma selenium concentration, within the normal physiological range; it reached a level comparable to that achieved by altered dietary intake or readily available selenium supplements. This increase ranges in general from concentrations typical of European selenium concentrations (2, 3) to those found in the United States (38). Previously, supplementation with 100 µg sodium selenite/d has been found to significantly improve selenium status to a plasma selenium concentration that is not further increased by supplementation for 15 wk (16). Microarrayanalysis was able to identify downstream targets of selenium supplementation and network interaction analysis identified significant changes in the expression of genes encoding ribosomal components that resulted from this subtle, dietary relevant increase in selenium status.

Several studies (39-42) used microarrays or macroarrays to investigate the effects of selenium supplementation in patients, rodent models, or cell lines, but at concentrations higher than those used in the present study. Common findings of these studies included a cell cycle arrest at the G1 phase and the induction of several apoptotic genes (40). The present study is distinct in that it does not present a comparison of severe deficiency and optimal selenium supply or an analysis of ill patients, but, rather, it studies the effects of a modest selenium supplementation in healthy human volunteers (and is the first study to do so).

Unlike studies focusing on disease development, toxins, or pharmaceuticals, which usually perturb the system beyond the normal physiological state and consequently result in large differences in gene expression, nutrition studies result mostly in changes within the normal physiological range, and the organism responds with relatively minor changes in homeostasis. As a result changes in gene expression in nutritional studies are expected to be smaller than those seen in disease or after pharmaceutical exposure (43). Indeed, such a range of responses in gene expression was elegantly shown for the peroxisome proliferator–activated receptor-{alpha} response after 3 challenges of diminishing strength: a pharmaceutical ligand, a 24-h fast, and an oral lipid load (36). The degree of change in the expression of individual genes observed in this study is small but significant, which is consistent with the earlier findings that nutritional studies result in changes of gene expression within the range of physiological homeostasis.

Analysis of gene expression response at the process level, rather than at the individual gene level, has been shown to be a valid approach to the identification of changes in sets of genes (44). Even when a physiological response could not be identified by analyzing individual genes, a more integrated approach using gene set enrichment analysis identified gene overrepresentation in this case. Because gene expression products do not operate in isolation but perform their function as part of a biochemical pathway or functional complex, an integrated response analysis gives greater resolution. In the present work, despite the inherent variation in lymphocyte gene expression (45), this approach allowed us to identify changes in expression after dietary selenium supplementation.

The major striking change observed was the up-regulation of genes encoding products that function in protein biosynthesis. In addition to genes encoding proteins that are constituents of the large (60S) and small (40S) cytoplasmic ribosomal subunits, other components of the translation machinery, such as nuclear-encoded mitochondrial ribosomal proteins, translation initiation factors, and translation elongation factors (Table 3Go), were upregulated. Thus, the data suggest that selenium supplementation leads to a coherent and integrated response that promotes lymphocyte protein synthesis. We hypothesize that this may partly reflect increased immune cell activity and, thus, a greater immune capacity after selenium supplementation. The observed changes also may be linked to the mechanism of selenium incorporation. In particular, the greater expression of RPL30 may reflect its recently identified role in selenium incorporation (46). Selenium incorporation into selenoproteins as the amino acid selenocysteine requires recognition of the stop-codon UGA as a codon for selenocysteine, a specific tRNA charged with selenocysteine (tRNASec) and a stem loop structure termed selenocysteine insertion sequence (SECIS). In eukaryotes, the SECIS is found in the 3-untranslated region (18, 46). SECIS-binding or -associated proteins required for selenocysteine incorporation include a selenocysteine-specific elongation factor (eEFSec) and the 2 key proteins SECIS-binding protein 2 (SBP2) and RPL30 (47, 48). RPL30 has been reported to bind to both SECIS (it competes with SBP2) and to selenoprotein mRNAs in vivo, as well as enhancing UGA recoding (49-51). The essential role of RPL30 in selenocysteine incorporation provides a functional link between the gene expression changes observed in this study and an effect of selenium supplementation on protein synthesis. It is interesting that the microarray analysis of esophageal mucosa from Chinese persons with squamous dysplasia who had been supplemented with selenomethionine showed a greater expression of the gene coding for ribosomal protein S24 (RPS24), which is compatible with the present observation that ribosomal protein genes respond to selenium supplementation (52).

In the present study, selenium supplementation resulted in changes in expression of a small number of the selenoprotein-related genes—namely, SEPHS1, SELK, and SEP15. In mammals, 2 selenophosphate synthetases, SPS1 and SPS2, catalyze the conversion of selenite into selenophosphate, a critical step in selenocysteyl-tRNA synthesis (53, 54). It is interesting that SPS1 encoded by the SEPHS1 gene is not a selenoprotein, but it contributes to the synthesis or recycling of selenocysteine (55). It is believed that SPS1 catalyzes the synthesis of selenocysteine required for the synthesis of the selenoprotein SPS2, and, in turn, SPS2 would allow the production of selenocysteine for other selenoprotein synthesis (55). Taken together, up-regulation of ribosomal protein genes, the protein synthesis machinery, and SEPHS1 gene expression could act synergistically to increase the synthesis of selenoproteins. The observation that lymphocyte glutathione peroxidase-1 protein concentration and activity were found to increase significantly in response to this selenium supplementation (22), despite the lack of a significant change in the concentration of its mRNA, supports this hypothesis. The limited changes observed in selenoprotein mRNA expression may reflect the major control of these genes at the posttranscriptional level together with the possibility that either the synthesis of their corresponding proteins may already be saturated in lymphocytes (3) or the amount of selenium necessary is not limiting at the lower levels of intake in this study.

In conclusion, the present study has shown that it is feasible to use microarrays, combined with biological process identification based on protein interaction, to identify differential gene expression patterns in humans after changes in dietary intake that lead to changes in status within the physiological range of a nutrient—in this case, selenium. These findings suggest that, in humans, selenium supplementation leads to up-regulation of several genes involved in the protein biosynthesis machinery. This possibility is consistent with data suggesting a key role for certain ribosomal proteins in selenoprotein synthesis (46-51). Our hypothesis is that this up-regulation reflects greater selenoprotein synthesis and greater lymphocyte activity. Further studies are required to determine how these observed changes are linked to improved immune function and to the reported benefits of selenium supplementation for human health.


   ACKNOWLEDGMENTS
 
We thank Brian Burtle and Wendy Bal (Newcastle University, Newcastle, United Kingdom) for help with the sampling; Vincent de Boer [Institute of Food Safety (RIKILT), Wageningen, Netherlands] for helpful discussions; and Franck McArdle (University of Liverpool, Liverpool, United Kingdom) for measurement of plasma selenium by inductively coupled plasma mass spectrometry. We also thank all volunteers who took part in the SELGEN study.

The authors' responsibilities were as follows—VP and EMvS: in the microarray experiment; CM: sampling, the quantitative polymerase chain reaction confirmation experiment (including EMvS), and the writing of the manuscript; JEH and JK: the design of the experiment and the writing of the manuscript; and VP, CM, and EMvS: equal contributions to the study and to the writing of the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication July 18, 2007. Accepted for publication September 10, 2007.




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