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Nutrient-Gene Interactions

Gene Expression Profiling in Human Preadipocytes and Adipocytes by Microarray Analysis^1,2

Sumithra Urs^*, Colton Smith, Brett Campbell^*, Arnold M. Saxton^**, James Taylor, Bing Zhang, Jay Snoddy^,, Brynn Jones Voy^*,, and Naima Moustaid-Moussa^*,,3

^* Department of Nutrition, Web Services, and ^** Animal Science, The University of Tennessee, Knoxville, TN 37996-1920; Plastic Surgery, The University of Tennessee Medical Center, Knoxville, TN; University of Tennessee-Oak Ridge National Laboratory Graduate School in Genome Science and Technology, Oak Ridge, TN; and Oak Ridge National Laboratory, Oak Ridge, TN

³To whom correspondence should be addressed. E-mail: moustaid{at}utk.edu.

	ABSTRACT

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Uncontrolled expansion of adipose tissue leads to obesity, a public health epidemic affecting >30% of adult Americans. Adipose mass increases in part through the recruitment and differentiation of an existing pool of preadipocytes (PA) into adipocytes (AD). Most studies investigating adipogenesis used primarily murine cell lines; much less is known about the relevant processes that occur in humans. Therefore, characterization of genes associated with adipocyte development is key to understanding the pathogenesis of obesity and developing treatments for this disorder. To address this issue, we performed large-scale analyses of human adipose gene expression using microarray technology. Differential gene expression between PA and AD was analyzed in 6 female patients using human cDNA microarray slides and data analyzed using the Stanford Microarray Database. Statistical analysis for the gene expression was performed using the SAS mixed models. Compared with PA, several genes involved in lipid metabolism were overexpressed in AD, including fatty acid binding protein, adipose differentiation-related protein, lipoprotein lipase, perilipin, and adipose most abundant transcript 1. Novel genes expressed in adipocytes included E2F5 transcriptional factor and SMARC (SWI/SNF-related, matrix associated, actin-dependent regulator of chromatin). PA predominantly expressed genes encoding extracellular matrix components such as fibronectin, matrix metalloprotein, and novel proteins such as lysyl oxidase. Despite the high differential expression of some of these genes, many did not differ significantly likely due to high variability and limited statistical power. A comprehensive list of differential gene expression is presented according to cellular function. In conclusion, these studies offer an overview of the gene expression profiles in PA and AD and identify new genes with potentially important functions in adipose tissue development and obesity that merit further investigation.

KEY WORDS: • microarray • adipose tissue • gene profiling • reverse transcriptase-polymerase chain reaction

Adipose tissue is now recognized as an important tissue not only for energy storage but also for its endocrine function. This latter role has emerged in recent years with the increasing identification of adipocyte-secreted proteins and their broad effects on whole-body homeostasis (1,2). Abnormal adiposity leads to metabolic disorders. Expansion of adipose tissue in obesity is accompanied by increased secretion of hormones such as cytokines, resistin, and adiponectin, which affect insulin sensitivity, and angiotensin, which regulates blood pressure (1–5). The direct link of alterations in adiposity to morbidity, mortality, and many disorders unquestionably calls for an in-depth understanding of adipocyte (AD)⁴ development.

Adipocytes arise from a pluripotent mesenchymal stem cell (MSC) population that is also capable of osteogenic, myogenic, or chondrogenic differentiation (6). These cells become adipoblasts when unidentified mechanisms trigger a commitment to the AD lineage. Growth arrest signals the progression to preadipocytes (PA), which can then differentiate directly into mature AD under appropriate conditions (7). This multistep process occurs throughout life in mammals. The hormonal factors that trigger AD differentiation have been well described, particularly for rodent cell lines. This physiologic conversion requires upregulation of numerous genes, mediated in large part by increased expression of the transcription factors peroxisome proliferator-activated receptor- (PPAR-), CCAAT/enhancer binding protein- (C/EBP), and ADD1 sterol regulatory element binding protein ADD1/SREBP1, and downregulation of other genes such as Pref-1 (8–15). Although most studies on AD development were conducted using murine cell lines, limited studies have used primary cultures of human adipose tissue to study adipose differentiation and metabolism (16–18).

Microarrays are a relatively new high throughput method to identify changes in gene expression. Limited microarray studies related to AD gene expression have been published to date using hamsters (17), mice (19–23) and rats (24), as model systems. To our knowledge, only a few studies on genome analysis in whole human adipose tissue using DNA array have been reported to date (18). Recent use of microarrays included the identification of genes that were differentially expressed in murine PA and differentiated mature AD (24). Interestingly, in addition to identifying many genes that were up- or downregulated in mature fat cells compared with PA, the authors also concluded that these sets of genes differed significantly between primary cells and cell lines. In fact, two regulatory pathways active in primary cells appeared to be completely absent in 3T3-L1 cells. This report highlights the fact that although AD cell lines are excellent models, they may not completely represent the normal pathways that are important in vivo.

Human adipose tissue are not as well studied as rodent adipose tissue, in part due to lack (until recently) of human PA cell lines and limited access to human adipose tissue (25). Although rodents are accepted as very appropriate models of human metabolism, differences in basic physiology that may affect gene expression do exist. In addition, even human cell lines reflect a single genetic makeup rather than the spectrum of genetic background found in humans. Therefore, in the present study, we used the cDNA microarray technique to identify global changes in gene expression profiles occurring during human adipogenesis. We present here an overview of the differences in gene expression between PA and AD among 6 patients, regardless of their characteristics and variability, in support of our hypothesis that genes with consistent patterns of differential expression between PA and AD are true adipogenic markers, regardless of the patients’ genetic or metabolic status. These markers are expected to include lipid metabolizing genes, transcription factors, and secreted proteins. Overall, such studies provide further insight into the adipogenic process and identify AD-specific genes that merit further investigation, and that may serve as markers of human AD differentiation and/or form the basis for developing new therapeutic approaches to obesity.

	MATERIALS AND METHODS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
    Human subjects and human adipose tissue. Subcutaneous abdominal fat was acquired from 6 women undergoing elective surgeries or liposuctions. The mean patient age was 51.33 ± 7.31 y and mean BMI was 29.03 ± 8.76 kg/m². All protocols were approved by the Institutional Review Board (IRB) of The University of Tennessee.

    Adipocyte isolation and processing. Tissues were processed immediately upon procurement to avoid contamination and cell lysis. Adipose tissue was separated from the skin and vascular tissue and processed as described previously (26). The floating AD were transferred to separate tubes, washed, and sonicated for cell disruption with guanidium thiocyanide and mercaptoethanol, followed by centrifugation at 1000 x g at 4°C for 20 min to separate the soluble fraction from the lipid and cell debris. The soluble fraction is then used for RNA isolation

    Preadipocyte isolation, culture and differentiation. Preadipocytes contained in the stromal vascular fraction of tissue digests were processed and cultured as described previously (16). Media were changed on alternate days until the cells reached confluence, at which time cells were harvested for isolation of total RNA. The PA were subcultured for amplification to obtain larger quantities of RNA and also to allow differentiation of PA to AD to assess their adipogenic potential. To induce differentiation, confluent PA were washed and subjected to differentiation following the previously described protocol (25).

    RNA isolation. Total RNA was isolated by cesium chloride density gradient centrifugation for 20 h at 20°C at 203,000 x g as described previously (3). RNA was then purified by phenol-chloroform extraction, precipitated with ethanol, and quantified spectrophotometrically; the integrity of the RNA was confirmed on a 1% denaturing agarose gel.

    Microarray analysis. Human cDNA microarray slides spotted with 19K human genes and expressed sequence tags (EST) were obtained from University Health Network Microarray Center, Toronto, Canada. The 19K human cDNA gene array consisted of 2 slides (A and B) containing 9366 and 8754 spots, respectively. Total RNA from PA and AD (10 µg) for each patient was used per slide. The PA and AD RNA were labeled with Cy3 and Cy5 dUTP dyes, respectively, by incorporating the dye during first-strand synthesis using RT. Hybridizations were performed following the protocols developed by The Institute of Genomic Research (27). After washing, the slides were scanned immediately with a GSI 4000 scanner from GSI Lumonics (Perkin Elmer). The Cy3 and Cy5 images were stored as tiff files for further analyses. The scanned images were merged by overlaying the two images using ScanAlyze software (developed at Stanford University) for spot identification, local background determination, and extracting fluorescence intensities for each spot. Data files were uploaded to the Stanford microarray database (SMD) (28) installed at The University of Tennessee for analysis. Intensity values for each spot were normalized according to the default method in SMD, by dividing the intensity and background values for the red channel by a constant calculated from the red:green ratio of spots passing quality criteria for signal intensity and uniformity. The normalized data for each spot were combined into a ratio of AD:PA and transformed logarithmically (base 2) and replicate spots for each gene combined. Data were analyzed on the basis of red:green (R:G normalized mean) values for expression levels in AD and green:red (G:R normalized mean) for PA. In all of the experiments presented here, PA and AD were labeled with Cy 3 and Cy5, respectively. However, because of the differences in Cy 3 and Cy 5 incorporation into nucleic acids, we also performed some dye-swapping experiments between the PA and AD, which gave essentially the same pattern of gene expression (data not shown). For the first level of analysis, genes were defined as differentially expressed if they displayed a mean change of twofold or greater in AD vs. PA.

We also performed the functional classification of genes that were significantly upregulated in PA or AD (P < 0.05). This was generated using the Gene Ontology Tree Machine [GOTM, developed at Oak Ridge National Laboratory; (29) and unpublished data]. It was based on the 4th annotation level under biological process. The observed vs. the expected number of genes in the gene ontology (GO) categories using all of the genes on the array as a reference was computed and shown as bar graphs. In addition, the directed acyclic graph (DAG) representing the categories of genes differentially expressed and their relative location in the GOTree are provided in the online posting of this paper.⁵

    Statistics. Statistical analyses were conducted separately from SMD, using SAS software (30) on log base 2 values, which have reduced skew and desirable variance properties. Log intensity data within a gene were examined for outliers, robustly defined as observations >10 times the median absolute deviation away from the median. Approximately 0.2% of the data was discarded as outliers; the remaining data had within-gene CV ranging from 3 to 168%. Normalization was done with a loess smoothing parameter of 0.3. Normalized and corrected data were analyzed with SAS mixed models (31), including the random effect of array and fixed effect of dye (equivalent to cell type). The array x dye interaction was the error term for tests of differential expression. Differences in means on the log scale are equivalent to ratios of gene expression, and the latter from SMD are reported.

    RT-PCR confirmation. Total RNA was prepared from AD and PA using the method described above. Adipocyte-specific genes that were shown previously to be differentially expressed during AD differentiation were detected by RT-PCR following a two-step/two-enzyme reaction protocol using the GeneAmp PCR kit from Perkin Elmer Cetus. RT was performed using oligo (dT) at 70°C for 10 min, 42°C for 60 min and terminated by increasing the temperature to 95°C for 10 min. The cDNA product was amplified by the addition of Taq DNA polymerase. Specific sense and antisense primer sets were designed for gene sequences using the Primer 3 online program (32) and synthesized by Sigma-Genosys. The sense and antisense sequences for the various primers were as follows: glycerol 3 phosphate dehydrogenase (GPD1), 5'GATTGGAGCCCCACACTCTA 3' and 5'CCTCAAAGGCCTTATCACCA 3'; PPAR-, 5'CCTGCTACAAGCCCTGGA 3' and 5'CTGCACGTGTTCCGTGAC 3'; fatty acid binding protein (FABP), 5'GTGGGCTTTGCCACCA 3' and 5'CCTGGCCCAGTATGAA 3'; fatty acid synthase (FAS), 5'ACCTACCCACCCGTGTCA 3' and 5'ACCACCACGTCAGCCACT 3'; lipoprotein lipase (LPL), 5'GCCGCCCTGTACAAGAGA 3' and 5'CCTGTCCCACCAGTTTGG 3'; and 18s mRNA 5'AGTCCCTGCCCTTTGTACACA 3' and 5'GATCCGAGGGCCTCACTAAAC 3'. The fragments were amplified under the following cycle conditions: 1 x (95°C, 5 min); 30 x (95°C, 1 min; 55°C, 1 min; 74°C, 2 min); 1 x (74°C, 6 min).

	RESULTS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
The microarray experiments among all 6 patients showed consistent patterns of gene expression for both PA and AD. It is worth noting that many of the elements on the arrays corresponded to EST or unknown genes and are not reported here. The spot intensities, background, and hybridization efficiencies did not differ. Data were analyzed in two different ways due to the inherent variability in samples from different patients. In the first, a cut-off value of twofold or more change between cell types (AD vs. PA) regardless of statistical significance was used. We report in Table 1 the list of known genes that were overexpressed by twofold or more in 5 of 6 patients. Due to patient-to-patient variability, many of these were not significant; this is further addressed in the Discussion section.

View larger version (49K): [in this window] [in a new window]   FIGURE 1 Functional classification of genes significantly upregulated in human AD. This list was generated by GOTM. The white bars represent gene numbers expected in the GO categories using all of the genes on the array as a reference. The black bars represent the numbers of significantly upregulated genes in AD in the GO categories. Four GO categories were significantly enriched at this level based on the hypergeometric test implemented in GOTM. They are "cell growth," "biosynthesis," "oxygen and reactive oxygen species metabolism," and "response to oxidative stress." GOTM identified a total of 19 categories that were significantly enriched.

We also performed the functional classification of genes significantly upregulated in PA (P < 0.05). None of the GO categories was significantly enriched at this level. However, GOTM identified a total of 14 categories that were significantly enriched (data not shown).

A similar list of genes that were overexpressed by twofold or more in PA vs. AD was also generated (Table 2). Among the genes overexpressed in PA, lysyl oxidase (LOX), PPAR-, C/EBP-, and protein tyrosine phosphatase receptor N (PTPRN) were the prominent metabolism-associated genes. Fibronectin (FN1), collagens (COL3A1, 5A1, 6A3), thrombospondin 1, 2, and 4 (THBS1, THBS2 and THBS4), matrix metalloprotein (MMP9), osteonectin (SPARC), and decorin (DCN) were associated with the cytoskeletal and extracellular matrix (ECM) and expressed in PA. A limited number of genes showed a significant upregulation in PA vs. AD including PPARD, E2F4, PTPRN, LOX, FN1, interleukin receptor (IL)18BP, lumican (LUM), pregnancy-specific glycoprotein (PSG)9, and pregnancy-induced growth inhibitor (OKL)38.

View larger version (16K): [in this window] [in a new window]   FIGURE 2 Genes differentially expressed in PA or AD on the bases of fold change and the number of patients: genes that are increased by twofold (panel A) or fourfold (panel B) based on the number of patients studied (4 or 5). The fold difference was calculated on the basis of the log2 ratios of the normalized intensity values of the red and green channels.

It is apparent that AD express genes predominantly associated with fatty acid metabolism, whereas PA express genes associated with the cytoskeletal and cell matrix. Finally, differential gene expression in PA and AD was confirmed by RT-PCR for selected AD genes, some of which (such as the FAS gene) were not represented in the array. All of these genes (LPL, GPD1 and FAS, FABP, and PPAR) were expressed at very high levels in AD compared with PA (Fig. 3). 18s RNA expression levels were used as a reference, and the band intensities in both PA and AD were comparable. These results confirm the microarray data and provide additional confirmation for our analysis.

View larger version (87K): [in this window] [in a new window]   FIGURE 3 Analysis of expression of selected genes in PA and AD by RT-PCR. Human adipose tissue was digested with collagenase to separate AD from the stroma vascular fraction, which was then cultured to propagate the PA. RNA was isolated form PA and AD and analyzed by RT-PCR using specific primers (sequence provided in the methods section) for 18s RNA, LPL, FABP, FAS, PPAR, and GPD1. Amplified fragments were then visualized on agarose gel.

	DISCUSSION

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

Given that stromal vascular cells and mature AD represent different cell characteristics from morphological and biochemical perspectives, our studies provide additional evidence of significant differences in the expression patterns between committed PA cells and mature, differentiated AD. Adipose tissue as a whole contains mature AD and nonadipose cells including endothelial cells, blood cells, fibroblasts, and PA at different stages of differentiation. The existence of such preadipose cells explains why adipose mass can increase throughout life given favorable environmental conditions by modifications in cell size, cell number, or both. Primary cell cultures of the stromal vascular fraction are comprised primarily of PA cell populations, and culturing of the cells to subconfluence likely eliminated the other cell types and facilitated selective isolation of committed PA (33). Further, to determine the adipogenic potential of the PA from the stromal vascular fraction, we induced them to differentiate into AD and demonstrated that a majority (>80%) of the PA differentiated into AD (not shown). The detection of PPAR- and C/EBP-, the early markers of adipogenesis in PA, indicated that these cells from the stromal vascular fraction of human adipose tissue have reached the committed phase toward adipogenesis.

Adipocytes respond to insulin by expressing lipid-metabolizing genes such as those coding for adipocyte FABP (34), LIPE (35), LPL (36), and GPD1 (37). The elevated expression of these genes is predictable in AD and conforms to the predominant metabolic pathways in AD (8–13). FABP4/aP2 and FABP5 are AD genes involved in fatty acid uptake, transport, and metabolism in AD and bind both long-chain fatty acid and retinoic acid. FABP4 in particular is implicated in obesity and related disorders including insulin resistance, type II diabetes, and atherosclerosis (34,38). LPL is expressed as a homodimer in adipose tissue, and functions as triglyceride hydrolase and ligand/bridging factor for receptor-mediated lipoprotein uptake, whereas GPD1 is responsible for reesterification of fatty acids to form triacylglycerol. LIPE is responsible for lipolysis and is distributed throughout the cytoplasm of unstimulated cells; it moves to the surface of lipid droplets upon lipolytic stimulation (35). The elevated expression of LOX in PA is substantiated by the fact that it is an amine oxidase expressed and secreted by fibrogenic cells. LOX has not been reported previously in PA, but it plays a critical role in the formation and repair of the ECM by oxidizing lysine residues in elastin and collagen (39). From this study, we propose LOX as a distinct early differentiation or committed PA marker of adipogenesis especially because LOX expression is dramatically reduced in AD (0.053–0.5 log range among the patients).

Overexpression of genes encoding proteins for energy and AD metabolism including enzymes such as ALDH, arachidonate lipoxygenase, and phosphofructokinase, lipid-associated proteins such as PLIN (40) and ADFP (41), and secreted proteins such as vitronectin (VN) (42), APM1 (2,43), and AGT (3) in AD strengthens the hypothesis that AD possess endocrine functions and that AD themselves are physiologically active cells contributing to whole-body metabolism. In the present study, PLIN was expressed in AD at very high levels (up to a log₂ value of 17.76) and to a much lower extent in PA, signifying its role in lipid storage in the AD. Expression of PLIN simultaneously signals the commitment of the PA toward differentiation because the first step in conversion begins with lipid storage. APM1 is expressed exclusively in adipose tissue and secreted into the bloodstream; it is involved in the control of fat metabolism and insulin sensitivity, both of which are regulated in a depot-specific manner (43,44). We identified VN as a novel protein secreted from adipose tissue (42). Although this protein has been studied extensively for its role in clotting and cardiovascular disease, no research has addressed its role in AD metabolism or obesity. Therefore, further investigation is warranted to determine its potential endocrine and paracrine roles in fat cells. Earlier reports confirmed the expression of some of these proteins from adipose tissue and this study confirms the presence of their respective mRNA in the AD.

Complementing the detection of these genes and proteins involved in fatty acid metabolism is the upregulation of transcription factors, the crucial players in adipogenesis. PPAR and PPAR are upregulated during adipose conversion at different time points (14). PPAR is detectable in growing PA and is upregulated at confluence to reach a maximal expression during the postconfluent proliferation. The isoform is induced at the end of clonal expansion and is maximally expressed in terminally differentiated cells (9,13). In the present study, PPAR mRNA was expressed in PA, whereas PPAR was expressed in AD. Interestingly, PPAR binding protein (PPARBP) expression was twofold higher in PA than AD. The other major transcription factor, C/EBP, is expressed at the onset of the adipose differentiation program and is downregulated during terminal differentiation similarly to PPAR (7,14). The RXRs (A and B) form heterodimers with PPARs and regulate transcription of various genes. They bind retinoic acid, the biologically active form of vitamin A, which mediates cellular signaling in embryonic morphogenesis, cell growth, and differentiation (45). The E2F family of transcription factors plays a crucial role in the control of cell cycle and regulates AD differentiation (46). Members of this family contain several evolutionarily conserved domains including a DNA binding domain and a dimerization domain that determines interaction with the differentiation-regulated transcription factor proteins. Depletion of E2F4 induces adipogenesis, whereas E2F1 induces PPAR transcription during clonal expansion and represents the link among proliferative signaling pathways, triggering clonal expansion, and terminal AD differentiation through regulation of PPAR expression (47). The role of E2F5 in AD differentiation is not yet clear, but a role has been established in pocket protein-mediated G1 control (46).

It is interesting to note the increased expression of the gene encoding SMARCB1 in AD. Although the function of this protein is relatively unknown, members of this family (SWI/SNF family) possess helicase and ATPase activities and are thought to regulate the transcription of certain genes by altering the chromatin structure around those genes (48). These results concur with the fact that adipogenesis is a very defined and complex process involving a series of changes including activation and inactivation of several genes that are coordinately regulated. Most of the proteins secreted from adipose tissue operate in an autocrine/paracrine manner to regulate AD metabolism and, upon secretion into the bloodstream, act as endocrine signals at multiple distant sites to regulate energy homeostasis. Examples include resistin, angiotensin II, adiponectin and others (1–3). The differences in gene expression between the PA and AD correspond to these theories and hypotheses with upregulation of genes associated with fatty acid metabolism and other endocrine factors in AD. Among these proteins, AGT, PLIN, APM, ADFP, and uncoupling protein 4 were prominently upregulated.

Among the genes involved in the ECM, expression of fibronectin, collagen, and MMPs in adipose tissue was documented (49–51), and these are the dominant genes upregulated in PA in this study. In the differentiation of preadipose cells into adipose cells, there is active synthesis of collagens during the preadipose state (52); FN, an adhesive ECM protein, is strongly expressed in PA and decreases during adipose conversion. Associated with FN is THBS, also an adhesive glycoprotein, which mediates cell-to-cell and cell-to-matrix interactions, and can bind to fibrinogen, FN, laminin, and type V collagen (53). The increased expression of these cytoskeletal genes is therefore expected and validated. The MMP family is involved in the breakdown of ECM in normal physiologic processes. Dermatopontin is an ECM protein that functions in cell-matrix interactions and matrix assembly, and mediates adhesion by cell surface integrin binding; it serves as a communication link between the dermal fibroblast cell surface and its ECM environment. SPARC, on the other hand, is a matrix-associated protein that elicits changes in cell shape, inhibits cell-cycle progression, influences the synthesis of ECM, and was recently reported to be associated with obesity (54,55). Decorin plays a functional role during growth and differentiation of AD by contributing to the morphological changes occurring in the cell proteoglycan. Interestingly, SPARC, THBS, and decorin are expressed primarily in PA at high levels (more than fourfold), adding to the list of PA markers. Finally, it is worth noting that although several genes showed consistently high expression levels in either PA or AD among all patients, they were not significantly different. This is likely due to their low expression level and the variability among patients, thus requiring a large number of samples to be analyzed to increase the statistical power. Indeed, when we performed power calculations to determine how many patients would be required to have an 80% chance of detecting a twofold difference with 95% confidence, the genes that differed significantly in the analysis reported here required <6 patients (5 for FABP and 4 for GLUL). By contrast, some genes known to be overexpressed in AD vs. PA but that were not significantly overexpressed in our study include SCD (P < 0.058), PLIN (P < 0.06), APM1 (P < 0.1), and AGT (P < 0.09). On the basis of power calculations, these genes would require 8, 9, 12, 15, and 35 individual specimens, respectively, to be significantly overexpressed in AD vs. PA at P < 0.05. Because our main aim was to identify genes that are either up- or downregulated during the process of AD differentiation, we listed the genes involved in AD differentiation and fatty acid metabolism that were detected in the microarray slides used (excluding EST and unknown genes).

In conclusion, this is the first attempt to explore differential gene expression in PA and AD derived from adipose tissue of the same patient. The results confirm some of the previously reported genes such as FABP, GPD1, PPAR-, LIPE, AGT, and RXR as adipose tissue markers. We also identified several genes that are upregulated in AD such as APM1, PLIN, VN, LPL, SMARC, E2F5, dual specificity phosphatase 1, and DPT, making them selective potential AD markers. Similarly, upregulated genes in the PA such as LOX; E2F4; FN 1; COL 3A1, 5A1, and 6A1; THBS; DCN; and SPARC are the potential markers of committed PA. In this study, we also present a new bioinformatic tool (GOTM) for microarray analysis by gene function and ontology.

	ACKNOWLEDGMENTS

 
The authors thank the Center for Environmental Biotechnology (CEB) for providing access to the scanner and the Web Services Department for SMD installation at The University of Tennessee, with special thanks to Robert Hillhouse, John Rose, and Bhanu Rekapalli for their assistance with SMD applications.

	FOOTNOTES

 
¹ Supported by research awards from The Physicians Medical Education and Research Foundation at Knoxville, TN, a gift fund from the plastic surgery division of The University of Tennessee Medical Center, the Functional Plant and Animal Genomics Group at The University of Tennessee, the Center of Excellence for Genomics and Bioinformatics at the University of Tennessee Health Sciences Center, a Professional Development Award from the Office of the Provost, The University of Tennessee, and The Tennessee Agricultural Experiment Station, The University of Tennessee Institute of Agriculture.

² The gene nomenclature used is based on gene identity given by the University Health Network Microarray Center (UHNMC), Toronto, Canada for the human slides.

⁴ Abbreviations used: AD, adipocytes; ADD1/SREBP, ADD1 adipocyte differentiation & determination factor, sterol regulatory element binding protein; ADFP, adipose differentiation related protein; ADORA, adenosine receptor; AGT, angiotensinogen; ALDH, aldehyde dehydrogenase; APM, adipose most abundant transcript; C/EBP, CCAAT/enhancer binding protein; CHST, carbohydrate sulfotransferase; COL, collagen; CRYAB, crystalline B; CTSG, cathepsin G; CYB, cytochrome; DAG, directed acyclic graph; DCN, decorin; DPT, dermatopontin; ECM, extracellular matrix protein; EST, expressed sequence tags; FABP, fatty acid binding protein; FACL, fatty acid co-enzyme A ligase; FAS, fatty acid synthase; FN, fibronectin; FXYD, FXYD domain containing transport regulator; GLUL, glutamate ammonia ligase; GO, gene ontology; GOTM, GO Tree Machine; GPD1, glycerol 3-phosphate dehydrogenase; GPX, glutathione peroxidase; IL, interleukin receptor; LIPE, lipase, hormone sensitive; LOX, lysyl oxidase; LPL, lipoprotein lipase; LRP, LDL receptor; LUM, lumican; MMP, matrix metalloprotein; MSC, mesenchymal stem cells; OKL, pregnancy-induced growth inhibitor; PA, preadipocytes; PAP, phosphatidylinositol phosphate phosphatase adaptor; PLEK, pleckstrin; PLIN, perilipin; PPAR, peroxisome proliferator-activated receptor; PSG, pregnancy-specific glycoprotein; PTPRN, protein tyrosine phosphatase receptor N; RXR retinoid X receptor; SMARC, SWI/SNF-related, matrix associated, actin-dependent regulator of chromatin; SMD, Stanford Microarray Database; SNN, stannin; SPARC (osteonectin), Secreted Protein Acidic and Rich in Cysteine; SPTBN, spectrin; THBS, thrombospondin; VN, vitronectin.

⁵ Provided as supplemental data in the online posting of this paper at www.nutrition.org.

Manuscript received 24 June 2003. Initial review completed 29 August 2003. Revision accepted 8 January 2004.

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TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 

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