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© 2004 The American Society for Nutritional Sciences J. Nutr. 134:2965-2974, November 2004

Nutrient-Gene Interactions

Hepatic Genes Altered in Expression by Food Restriction Are Not Influenced by the Low Plasma Glucose Level in Young Male GLUT4 Transgenic Mice1,2

Chunxiao Fu*, Liang Xi*, Yimin Wu{dagger}, Roger McCarter{dagger}, Arlan Richardson**,{ddagger}, Morgen Hickey* and Eun-Soo Han*,3

* Department of Biological Science, University of Tulsa, 600 S. College Avenue, Tulsa, OK 74104; {dagger} Department of Physiology and ** Department of Cellular and Structural Biology, The University of Texas Health Science Center, 7703 Floyd Curl Drive, San Antonio, TX 78229; and {ddagger} South Texas Veterans Health Care System at San Antonio, San Antonio, TX 78229

3To whom correspondence should be addressed. E-mail: eun-han{at}utulsa.edu.


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 
Because food restriction (FR) has a profound effect on most tissues, it is plausible that the modulation of aging by FR occurs through cellular processes such as gene expression. The effect of FR in lowering plasma glucose levels has been demonstrated in mice, rats, and nonhuman primates. The consistency of this finding suggests that decreased plasma glucose may be an important consequence of FR. Indeed, lowering plasma glucose in the absence of FR would be expected to change the expression of some of the same genes as seen with FR. GLUT4 transgenic (TG) mice were particularly suited to this examination because they have low plasma glucose levels like FR mice. We investigated altered gene expression by FR and the effect of low plasma glucose levels caused by genetic manipulation by measuring mRNA expression in liver tissues of 4- to 6-mo-old mice with 2.5–4.5 mo of FR using microarrays and 4 groups: GLUT4 TG (C57BL/6 background) consumed food ad libitum (AL), GLUT4 TG FR, wild-type littermates AL, and wild-type littermates FR. The 3 statistical analysis methods commonly indicated that FR altered the expression of 1277 genes; however, none of these genes was altered by additional GLUT4 expression. In fact, the low plasma glucose level in GLUT4 TG mice did not affect gene expression. Some results were confirmed by real-time quantitative RT-PCR. We conclude that a low plasma glucose level does not contribute to or coincide with the effect of FR on gene expression in the liver.

KEY WORDS: • gene expression • food restriction • GLUT4 transgenic mouse • GeneChip array

Food restriction (FR)4 extends life span and retards many age-related cellular and molecular changes in laboratory rats (1–4). However, its underlying mechanisms are not fully understood (5). Changes in gene expression occur with age and can markedly affect the physiological status of an organism. Consequently, altered gene expression undoubtedly plays an important role in directing the anti-aging mechanisms of FR. Richardson (6) hypothesized that FR retarded the age-related decline in gene expression and showed (7) that FR altered the transcription of a specific gene. Our earlier study (8) revealed that FR partially reversed age-related changes in gene expression.

The effect of FR in lowering plasma glucose levels was demonstrated in mice, rats, and nonhuman primates of different ages using a variety of FR regimens (9–12). The consistency of this finding suggests that decreased plasma glucose may be mechanistically involved in the retardation of aging by FR. Such a role is suggested by current knowledge of both FR and aging: FR exerts protective effects on cellular homeostasis by enabling appropriate rates of glucose utilization to occur at lower circulating levels. Therefore we hypothesized that decreased plasma glucose is an important factor in the anti-aging action of FR. If this is the case, then lowering plasma glucose in the absence of restricted food intake would be expected to exert the same or related beneficial effects as FR and to similarly retard aging.

To test our hypothesis, we used GLUT4 transgenic (TG) mice. The GLUT4 TG mouse overexpresses the GLUT4 glucose transporter protein in skeletal muscle, cardiac muscle, and adipose tissue (13). The result of this tissue-delimited expression is lifelong reduction in the level of plasma glucose similar to that of non-TG (NTG) FR mice (~20% reduction). However, TG mice have the same food intake and body weight as NTG mice that consume food ad libitum (AL). Restricting the food intake of TG mice by 40% results in a further lowering of plasma glucose (about 25% below the 20% reduced glucose levels of GLUT4 TG mice). The combination of genetic and nutritional manipulation thus produces mice with 3 different levels of plasma glucose but with comparable body weight and food intake (3,14). We investigated whether the reduced plasma glucose altered the pattern of gene expression in TG mice similarly to that of the FR mice using Affymetrix oligonucleotide arrays.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 LITERATURE CITED
 
    Animals. The GLUT4 TG mice were obtained from Dr. Michael Gibbs through an agreement with Pfizer (13). A breeding colony of the GLUT4 (hemizygous) TG mice was established at the Animal Core of the Nathan Shock Center at the University of Texas Health Science at San Antonio and was maintained by breeding GLUT4 (hemizygous) TG males (C57BL/6 background) to NTG C57BL/6 females. For the experiments, young (4–6 mo) male GLUT4 TG (n = 36) and their young male nontransgenic littermates (n = 36) were obtained from the Animal Core (total of 72 mice). The NTG parental mice were purchased from The Jackson Laboratories. All mice consumed ad libitum Harlan Teklad LM-485 mouse/rat sterilizable diet No. 79125 until 6 wk of age. At 6 wk, half of the mice from each group (18 from NTG group and 18 from TG group) were allowed to continue to eat this diet (groups NTG AL and TG AL) until killed. The remaining 36 mice (18 from NTG group and 18 from TG group) were restricted to 60% of the mean food intake of group AL until killed (groups NTG FR and TG FR). FR mice were given their food allotment 1 h before the start of the dark phase of the light cycle. Mice were kept on a cycle of 12 h of darkness, 12 h of light (lights on at 0600 h).

Sentinel mice were tested monthly by a veterinary pathologist in Laboratory Animal Resources at the University of Texas Health Science Center at San Antonio. Every 6 mo, the presence of murine virus antibodies was monitored with serum samples from sentinel mice by BioReliance. All tests were negative. All procedures involving the use of mice were approved by the Institutional Animal Care and Use Committee of the University of Texas Health Science Center and the Subcommittee for Animal Studies at the Audie L. Murphy Memorial Veterans Hospital.

    Tissue collection and RNA preparation. Livers from 18 each of TG AL, TG FR, NTG AL, and NTG FR 4- to 6-mo-old male mice were collected between 1000 and 1200 h. The tissues were quickly frozen in liquid nitrogen and stored at –80°C until RNA extraction. Total RNA was extracted from each liver as previously described (15).

    Screening of mRNA by Affymetrix GeneChip arrays. A total of 18 mice per treatment group was used; however, RNAs from 3 mice were pooled together (the same amount of RNA from each mouse) to generate 6 pooled samples for each treatment group (i.e., NTG AL 1–6, NTG FR 1–6, TG AL 1–6, and TG FR 1–6, n = 6 arrays per treatment group).

Murine Genome U74A Version 2 GeneChips containing oligonucleotide probes for ~12,000 genes were purchased from Affymetrix. We followed the vendor’s protocols for the GeneChip hybridization and scanning (16).

    Statistical analysis of microarray data. Affymetrix Microarray Suite version 5.0 (MAS 5.0, Affymetrix) was used to quantitate each GeneChip (17). The summary intensities for each probe (24 CEL files) were loaded into DNA-Chip Analyzer (dChip), version 1.3 (18), for normalization, generation of expression measures, and analysis. For normalization, we used dChip’s method of invariant set normalization in which the chip with the median intensity value (TG FR 4) was used as the baseline against the remaining 23 chips. Model-based expression values were then calculated using dChip’s Perfect-Match-only model. Following dChip’s standards for defining array outliers (chips with >5% of probe sets flagged as array outliers), we removed 2 chips from further consideration, TG AL 3 (5.5%) and TG FR 3 (16.3%). The remaining 22 arrays were renormalized in dChip as above. For this subset, NTG FR 6 had the median intensity value and was used as the baseline against which the remaining 21 chips were normalized. Measurement expression values were again computed in dChip as above. In this set of samples, single outliers accounted for from 0.02 to 1.01% of genes (median = 0.26%) and were treated as missing values in subsequent analyses. These data (i.e., outlier arrays and single outlier values deleted) were log base 2 transformed and the analyses as detailed below were assayed on these values.

We analyzed 6 ANOVA-based group comparisons: 1) AL vs. FR, 2) NTG AL vs. NTG FR, 3) TG AL vs. TG FR, 4) NTG vs. TG, 5) NTG AL vs. TG AL, and 6) NTG FR vs. TG FR. In addition, we ran a SAS (statistical analysis system) mixed model with Scheffé adjustment for multiple comparisons to assess the presence of an interaction between genotype and diet. For the 6 group comparisons, rather than choosing a method for generating multiple comparison adjusted P values, we chose to use 3 statistical methods and focus on results in common to the 3 methods. As an additional "correction," we estimated the false discovery rate (FDR) within each method as a quality indication for the subset selection. Comparisons were conducted in dChip, in Significance Analysis of Microarrays (SAM) (19), and in SAS, version 8.02 for Windows/PCs. Our final results were composed of those genes found to differ significantly in all 3 methods of analysis at a P value of <0.001 or, in the case of SAM, at the minimum FDR, not to exceed 5%.

    Real-time quantitative RT (QRT)-PCR. The same sources of RNA used for the GeneChip array study were used for the real-time QRT-PCR. Primers were designed using the OligoPerfect Designer (Invitrogen) and purchased from Invitrogen. Supplemental Table 1 includes the list of genes, their primer sequences, and the annealing temperatures. The 18S rRNA was used as an internal control for PCR quantitation. DNase I (Invitrogen) digestion, reverse transcription reaction with SuperScript II RT (Invitrogen), and PCR reaction using a Smart Cycler thermal cycler (Cepheid) were carried out as previously described (20). The specificity of the reaction is monitored by determining the product melting curve to avoid nonspecific signals.

Relative quantitation of gene expression was performed using the method published by Marino et al. (20). Briefly, using a Visual Basic Excel macro, the Smart Cycler optics data were converted to a logarithmic format, and then slopes, intercepts, and their respective standard errors for the samples in each experimental group were determined. If the slopes between 2 comparison groups are not different (P > 0.10), the amplification efficiency of a specific amplicon in these samples is comparable. Finally, fold changes and 95% confidence intervals were calculated.


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 LITERATURE CITED
 
    FR effect on the gene expression profiles of young male mice. Altered gene expression by short-term FR was measured using the liver tissues of young male mice and Affymetrix oligonucleotide arrays. The results generated from comparisons of AL vs. FR, NTG AL vs. NTG FR, and TG AL vs. TG FR groups are summarized (Table 1). For the AL and FR comparison, 6 NTG AL and 5 TG AL GeneChips were combined as the AL group and 6 NTG FR and 5 TG FR GeneChips were combined as the FR group. The total numbers of genes identified as significantly differentially expressed in the 3 comparisons of AL vs. FR, NTG AL vs. NTG FR, and TG AL vs. TG FR by the 3 analysis methods, dChip, SAM, and SAS, are listed (Table 1). Supplemental Table 2 contains the complete list of genes. The numbers of genes upregulated and downregulated by FR are indicated, as well as the numbers of genes selected by all 3 analysis methods in each comparison.


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  		TABLE 1 The numbers of genes identified as significantly differentially expressed by dChip, SAM, and SAS in the comparisons among control and FR young, male GLUT4 transgenic and nontransgenic mice1

 
The distribution of the numbers of genes selected by all 3 methods of analysis among the 3 comparisons is illustrated (Fig. 1). There were 1277, 333, and 207 genes in common to the 3 analysis methods in the comparisons of AL vs. R, NTG AL vs. TG FR, and TG AL vs. G FR, respectively. Only 115 genes robustly showed differential expression throughout all 3 of the comparisons. Among these 115 genes, 62 were upregulated and 53 were downregulated by FR. Twenty each of the most significantly up- and downregulated genes of the 115 genes with detectable expression levels (present call as calculated in MAS 5.0) in one or both treatment groups are shown (Table 2). Flavin-containing monooxygenase 3 (FMO3) was most upregulated by FR. Several genes from the cytochrome P450 (cyp) family 2 as well as regulator of G-protein signaling 16 were upregulated by FR. In the case of G-protein signaling 16, two different probe sets, each with a different GenBank accession number (U94828 or AV349152), indicated upregulation of the gene by FR. Two genes from the glutathione S-transferase family were among the genes upregulated by FR. The gene most downregulated by FR was 3ß-hydroxysteroid dehydrogenase V (HSD3b5). Several genes from the cyp family (family 7, 4, and 2) were also among the genes downregulated by FR. There are 2 probe sets, each with a different GenBank accession number (U36993, AV141027), for the cyp, family 7, subfamily b, polypeptide 1 (cyp7B1) gene. Both probe sets were detected as significantly downregulated by FR.



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  		FIGURE 1 Venn diagrams showing the number of significantly differentially expressed genes in common to the 3 analysis methods dChip, SAM, and SAS and to the 3 comparisons of AL vs. FR, NTG AL vs. NTG FR, and TG AL vs. TG FR mice. For the AL and FR comparison, 6 NTG AL and 5 TG AL GeneChips were combined as the AL group and 6 NTG FR and 5 TG FR GeneChips were combined as the FR group ({uparrow}, upregulated by FR; {downarrow}, downregulated by FR).

 

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  		TABLE 2 Selected significantly up- and downregulated genes in the FR mice1

 
    Effect of low plasma glucose level by genetic manipulation rather than by FR on gene expression profiles. The results from comparisons of the NTG vs. TG, NTG AL vs. TG AL, and NTG FR vs. TG FR groups are shown (Table 3). For the NTG vs. TG comparison, 6 NTG AL and 6 NTG FR GeneChips were combined as the NTG group and 5 TG AL and 5 TG FR GeneChips were combined as the TG group. The total numbers of genes identified as significantly differentially expressed from the 3 comparisons, NTG vs. TG, NTG AL vs. TG AL, and NTG FR vs. TG FR by the 3 analysis methods, dChip, SAM, and SAS, are listed (Table 3). Supplemental Table 3 contains the complete list of genes. The numbers of upregulated and downregulated genes in GLUT4 transgenic mice are also indicated. The data in Table 3 also show that there were no genes in common to the 3 analysis methods, dChip, SAM, and SAS, and to the 3 comparisons, NTG vs. TG, NTG AL vs. TG AL, and NTG FR vs. TG FR. Although there are 17 genes selected as significantly differentially expressed by 3 analysis methods in the NTG vs. TG comparison, given the high proportions of true null as reported in the SAM and SAS results, the validity and reliability of these 17 genes are suspect. None of these 17 genes was selected as significantly differentially expressed in the FR comparisons. The results from multiple comparison analysis indicated that there were no interactions between genotype and diet, and the proportion of significant tests for this analysis was estimated to be 0%.


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  		TABLE 3 The numbers of genes identified as significantly differentially expressed by dChip, SAM, and SAS in the comparisons among control and FR young, male GLUT4 transgenic and nontransgenic mice1

 
    Gene ontology grouping for genes significantly differentially expressed by FR. Genes selected for their differential expression by FR using 3 analysis methods and in common to the 3 comparisons of AL vs. FR, NTG AL vs. NTG FR, and TG AL vs. TG FR (115 genes) were grouped by their biological process using the Database for Annotation, Visualization, and Integrated Discoverys Gene Ontology (GO) Charts (21). The top 6 GO biological processes and those associated genes either up- or downregulated by FR are shown (Table 4). The major biological process affected by FR is the regulation of transcription by both up- (6 genes) and downregulation (3 genes). The cell cycle was also affected by FR; 4 genes involved in cell cycle were upregulated by FR, and 2 genes were downregulated. Three genes involved in intracellular transport were upregulated while 1 gene was downregulated by FR. There were 4 genes altered in the main pathways of carbohydrate metabolism by FR and all of them were upregulated. Four genes involved in the protein amino acid phosphorylation were altered by FR and 1 of them was downregulated while others were upregulated. The G-protein coupled receptor protein signaling pathway was also affected by FR; 1 gene was up- and 2 genes were downregulated by FR. Thus, the data in Table 4 demonstrate that, in most cases, FR both up- and downregulates the expression of genes in the same biological processes.


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  		TABLE 4 Top 6 gene ontology biological process groupings for differentially expressed genes in FR mice1

 
    Confirmation of some GeneChip array results by real time QRT-PCR. To validate the microarray data, real-time QRT-PCR was carried out on 11 randomly chosen genes with fold differences that ranged from ~30% to 30-fold. Just as for the array hybridization, 6 samples per treatment group, each with RNA from the liver tissues of 3 mice, were used for the QRT-PCR. In Table 5, the array and the real-time QRT-PCR results are given as point estimates of the ratios of 2 comparison groups and the 95% confidence intervals are indicated in parentheses. Genes detected as significantly differentially expressed from 30% to 5-fold by FR using the array also showed similar changes in the real-time QRT-PCR measurements. Results confirmed that 16 of 21 array and QRT-PCR ratio pairs were equivalent, as indicated by the fact that their 95% confidence intervals overlapped. After 40 cycles of PCR, if desirable amplified gene products (melting peaks in the melt curves) were detected in samples of 1 treatment group and not detected in the other treatment group, we considered that gene to be turned off by the latter treatment or turned on by the former treatment. Real-time QRT-PCR results indicated 3 genes [FMO3; cyp, family 2, subfamily b, polypeptide 9 (cyp2B9); and cyp, family 2, subfamily b, polypeptide 13 (cyp2B13)] that have female specific expression in the liver (22,23) were expressed (turned on) in the liver tissues of our male FR mice and not expressed in the liver tissues of the male AL mice. One gene (HSD3b5) that is male specific in liver tissues (24) was not expressed (turned off) in FR samples (i.e., expressed in AL samples).


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  		TABLE 5 The validation of some of the microarray results by real-time QRT-PCR

 

   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
LITERATURE CITED
 
Using Affymetrix oligonucleotide arrays, we identified differentially expressed genes by FR in the liver of young (4- to 6-mo-old) male C57BL/6 wild-type and GLUT4 TG mice. In order to minimize the weaknesses and capitalize on differing strengths of various analysis methods, we analyzed our data using 3 methods (dChip, SAM, and SAS). In dChip, gene-specific t tests for gene selection are sensitive to variance across measures, but rely on the rarely met assumption of normally distributed errors in the data. The SAM t test modifies and stabilizes the variance so genes with small fold changes will not be selected as significant (19). In SAS, gene selection was based on running an analysis of variance using a mixed effects model. Such models allow for multiple sources of variation and account for correlation among the observations that arise as a consequence of different layers of variation. Because any 1 analysis method may give biased results, we reported those results in common to the 3 methods to produce a "better" list of genes that represent true change. To further confirm our findings, 11 randomly chosen genes with a range of 30% to 30-fold difference in the comparisons of AL vs. FR, NTG AL vs. NTG FR, and TG AL vs. TG FR were subjected to real-time QRT-PCR. Of the 7 genes changed up to 5-fold in these 3 comparisons, 16 of the 21 (76%) array ratios were confirmed by QRT-PCR as evidenced by the fact that the 95% confidence intervals overlapped. For the remaining 4 genes, the QRT-PCR results indicated that the expression in 1 of the 2 comparison groups (either AL or FR) was either turned on or turned off by FR.

Three genes that are normally not expressed in the liver tissue of male mice were noted for being expressed by FR in this tissue: FMO3, cyp2B9, and cyp2B13. Flavin-containing monooxygenases are microsomal enzymes that catalyze the flavin adenine dinucleotide and NADPH- and O2-dependent oxidation of many nitrogen-, sulfur-, selenium-, and phosphorus-containing compounds (25). FMO3 expression was shown to be female specific in the mouse liver and this sex dependence appears to be due to repression of FMO3 expression by testosterone (26). Plasma testosterone is decreased by FR (27). Therefore, the reduced levels of testosterone may no longer repress FMO3 expression in male FR mice. The other 2 genes, cyp2B9 and cyp2B13, which were turned on by FR, are also female specific in the mouse liver (23). Since cyp2B9 is involved in testosterone metabolism (28), its induction may be partly responsible for the decreased testosterone in FR mice. We also found 1 gene that was turned off by FR. HSD3b5 belongs to the 3ß-hydroxysteroid dehydrogenase family (24). HDS3b5 is a NADPH-dependent 3-ketosteroid reductase and does not biosynthesize active steroid hormones, but rather converts an active androgen, dihydrotestosterone, into an inactive androgen, 5{alpha}-androgen-3ß,17ß-diol. The expression of HSD3b5 is specific in the male mouse liver, and Wong and Gill (28) reported that FR significantly suppressed HSD3b5 expression. Consistent with these findings, we observed that FR turned off the expression of HSD3b5 in the male mouse liver. Glucocorticoid downregulates HSD3b5 expression (28), and our early study indicated that FR is associated with an enhanced diurnal elevation of glucocorticoid (29). Thus, the increased plasma glucocorticoid by FR may suppress HSD3b5 expression in the male FR mouse liver.

Another noticeable observation from our current study was the regulation of cyp family genes by FR. Several genes from cyp family were either up- or downregulated by FR. Cyps act as monooxygenases, with functions ranging from the synthesis and degradation of endogenous steroid hormones, vitamins, and fatty acid derivatives to the metabolism of foreign compounds such as drugs, environmental pollutants, and carcinogens (30). Wong and Gill (28) reported that when mice were food restricted with corn oil at 60 to 70% of the amount normally eaten by control mice, the gene expression of cyp2B9 was upregulated while that of cyp7B1 was downregulated. Our data also indicated that cyp2B9 as well as cyp2B13, cyp2A4 and cyp2B10 were upregulated and that cyp7B1 as well as cyp4A12, cyp2D9, and cyp2F2 were downregulated by FR (supplemental Table 2).

When we grouped the genes by GO Chart categories, we noticed that FR altered the mRNA expression of many transcription factors. D site albumin promoter binding protein, RAB1, 2 nuclear receptor subfamilies (1D2 and 1H4), metal response element binding transcription factor 2, and thyrotroph embryonic factor were upregulated transcription factors by FR. RAB1 is a small GTP binding protein and is a key regulator of the intracellular vesicle transport (31). Nuclear receptor subfamily 1D2 is a possible clock-regulated gene (32). Metal response element binding transcription factor 2 may be involved in the activation of metallothionein genes in response to heavy-metal ions (33). Transcription factors downregulated by FR were interleukin 3 nuclear factor, interferon regulatory factor 6, and nuclear receptor subfamily 5A1. Interleukin 3 nuclear factor regulated the anti-apoptotic process (34). The differential expressions of the genes we detected could be the secondary effects of these altered transcription factor expressions as well as the primary effects of FR. We speculate that FR exerts its effect by altering the expression of transcription factors, which may give rise to the cascade effect to other genes. Other major biological processes influenced by FR were cell cycles, intracellular transport, carbohydrate metabolism, protein amino acid phosphorylation, and G protein–coupled receptor protein signaling pathway.

We compared genes we identified in our study with genes identified as differentially expressed by FR and/or aging from previously published gene expression array studies (35–44) (Table 6). We found that 47 genes (the first 47 genes listed in Table 6) altered by FR in our study corresponded with results from previously published data. Of those 47, 7 genes (the 41st through the 47th listed in Table 6) showed age-related change in their expression and FR reversed the effect in previously published studies. The comparison also indicated that our study identified an additional 20 genes (the 48th through the 67th listed in Table 6) in which FR reversed age-related gene expression.


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  		TABLE 6 Genes identified commonly in our study and in other gene expression array studies on FR and/or aging

 
One of the goals of the present work was to identify genes whose expressions are altered by GLUT4 overexpression and then to identify which, if any, of those genes correspond to ones affected by FR in NTG mice. Genes that had altered expression in the comparison of NTG AL vs. NTG FR mice were not altered at all in the comparison of NTG AL vs. TG AL mice even though the NTG FR and TG AL mice had similar levels of plasma glucose. We observed the same phenomenon in that genes that altered their expression in the comparison of TG AL vs. TG FR mice were not altered in the comparison of NTG FR vs. TG FR mice. Finally, when we compared the magnitudes of alteration in gene expression in the NTG AL vs. NTG FR comparison to those in the comparison of NTG AL vs. TG FR mice, we found no significant differences even though TG FR mice had lower plasma glucose levels than NTG FR mice. Thus, our data indicate that lowered plasma glucose levels have little effect on the alteration of gene expression observed after 2.5 to 4.5 mo of FR. The effects of GLUT4 overexpression on insulin and IGF-1 levels were not clear. Liu et al. (45) reported that the insulin levels were about 50% lower in the GLUT4 mice compared to the wild-type mice. However, our measurements indicated that insulin levels tended to be 5% lower (P = 0.1). Furthermore, our recent survival studies with GLUT4 TG mice indicated their longevity was not increased like that of FR mice (data not shown). It seems that the mechanism of FR on anti-aging does not relate to the consequent effects of a lowered plasma glucose level.

Whether the changes in gene expression that have been identified in this study represent changes in expression per se or changes in the distribution of cell types within the liver cannot be assessed by these studies. Also, we do not know whether these changes result in corresponding changes in protein expression/activity. Nevertheless, this study identified genes affected by short-term FR in young male mice. These findings are novel and likely to provide impetus to many investigators with expertise in the functions of the identified genes to pursue more mechanistic studies to understand the physiological significance of these changes in the anti-aging actions of FR.


   ACKNOWLEDGMENTS
 
The authors thank Ms. Vivian Diaz for excellent breeding and care of the mice, Susan Hilsenbeck for assistance with statistical analyses, Kenton Miller for his expertise on the real-time QRT-PCR, and Glen Collier and Kenton Miller for their critical reading and useful comments on the manuscript.


   FOOTNOTES
 
1 Supported by National Institute of Health Grants AG-00746 and AG-14674–04S1 to E.-S.H. Back

2 Supplemental files are available with the online posting of this paper at www.nutrition.org. Back

4 Abbreviations used: AL, consumed food ad libitum; cyp, cytochrome P450; cyp2B9, cytochrome P450, family 2, subfamily b, polypeptide 9; cyp2B13, cytochrome P450, family 2, subfamily b, polypeptide 13; cyp7B1, cytochrome P450, family 7, subfamily b, polypeptide 1; dChip, DNA-chip analyzer; FDR, false discovery rate; FMO3, flavin-containing monooxygenase 3; FR, food restriction; GO, gene ontology; HSD3b5, 3ß-hydroxysteroid dehydrogenase V; MAS, microarray suite; NTG, nontransgenic; PM, perfect match; QRT-PCR, quantitative RT-PCR; SAM, significance analysis of microarrays; SAS, statistical analysis system; TG, transgenic. Back

5 The proximate composition of Harlan Teklad LM-485 mouse/rat sterilizable diet 7912 is 19.92% protein, 5.67% fat, 4.37% fiber, 6.48% ash, 53.66% nitrogen-free extract, and 2.90% linoleic acid. Back

Manuscript received 6 May 2004. Initial review completed 28 June 2004. Revision accepted 3 August 2004.


   LITERATURE CITED
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 

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