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At least 19 recordsLinked to original sources

PubMed1995

Identification of differentially expressed genes by restriction endonuclease-based gene expression fingerprinting.

A novel method for identification of differentially expressed genes has been developed. It is based on the consecutive restriction digestions of 3' terminal cDNA fragments to produce a fingerprint of gene expression. cDNA molecules are synthesized using a biotinylated oligo(dT) primer, digested with a frequently cutting restriction endonuclease and the 3'-terminal restriction fragments are isolated using streptavidin microbeads. After amplification by PCR, cDNA fragments are immobilized again on streptavidin beads, radiolabeled and treated sequentially with a set of restriction endonucleases. The products of individual enzymatic reactions from two or more different RNA populations are resolved by polyacrylamide gel electrophoresis and compared to reveal differentially expressed genes. This strategy enabled us to identify and clone the fragments of five genes expressed differentially in murine thymus and spleen. One of the genes was found to encode terminal deoxynucleotidyl transferase; others are apparently previously unknown genes.

Animals↗

PubMed2005

Correlation between gene expression levels and limitations of the empirical bayes methodology for finding differentially expressed genes.

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely related methods employing a two-component mixture model, represent typical examples. It is frequently assumed that dependence between gene expressions (or associated test statistics) is sufficiently weak to justify the application of such methods for selecting differentially expressed genes. By applying resampling techniques to simulated and real biological data sets, we have studied a potential impact of the correlation between gene expression levels on the statistical inference based on the empirical Bayes methodology. We report evidence from these analyses that this impact may be quite strong, leading to a high variance of the number of differentially expressed genes. This study also pinpoints specific components of the empirical Bayes method where the reported effect manifests itself.

Journal Article↗

PubMed2003

[Indentification of differentially expressed genes in proliferating and involuting hemangiomas by cDAN microarray analysis of gene-expression profiles].

OBJECTIVE: To investigate the differentially expressed genes of proliferating and involuting hemangiomas by cDNA microarray analysis of gene-expression profiles in an effort to identify the key disease-related genes. METHODS: Samples were processed from total RNA and purified to mRNA, which was reverse-transcripted and hybridized onto Biodoor Genechip expression microarrays. Analyses were performed to determine the consensus pattern of gene expression in the proliferating and involuting stages of the same hemangioma and the changes in the expression level. RESULTS: In proliferating hemangioma, 79 genes were overexpressed, and 115 genes were underexpressed in comparison with the involuting hemangioma. Some cytokines and growth factors such as neurotensin, Nov, CYR6, keratinocyte growth factor, interleukin-10 were overexpressed in proliferative hemangioma. In involuting hemangioma, apoptotic factors such as bcl-2 binding component, cytochrome C were overexpressed. The overexpression of Nov, CYR6, c-myc implied that angiogenesis and oncogenes might participate in the pathogenesis of hemangiomas. Mitochondria activated apoptotic passage (cytokines, bcl-2, cytochrome C) and Wnt/beta-catenin passage(Frizzled, beta-catenin, c-myc) were involved. CONCLUSION: The development of hemangiomas may be the results of imbalance of cell proliferation and apoptosis.

Apoptosis↗

PubMed2004

Differentially expressed genes in pancreatic ductal adenocarcinomas identified through serial analysis of gene expression.

Serial analysis of gene expression (SAGE) is a powerful tool for the discovery of novel tumor markers. The publicly available online SAGE libraries of normal and neoplastic tissues (http://www.ncbi.nlm.nih.gov/SAGE/) have recently been expanded; in addition, a more complete annotation of the human genome and better biocomputational techniques have substantially improved the assignment of differentially expressed SAGE "tags" to human genes. These improvements have provided us with an opportunity to re-evaluate global gene expression in pancreatic cancer using existing SAGE libraries. SAGE libraries generated from six pancreatic cancers were compared to SAGE libraries generated from 11 non-neoplastic tissues. Compared to normal tissue libraries, we identified 453 SAGE tags as differentially expressed in pancreatic cancer, including 395 that mapped to known genes and 58 "uncharacterized" tags. Of the 395 SAGE tags assigned to known genes, 223 were overexpressed in pancreatic cancer, and 172 were underexpressed. In order to map the 58 uncharacterized differentially expressed SAGE tags to genes, we used a newly developed resource called TAGmapper (http://tagmapper.ibioinformatics.org), to identify 16 additional differentially expressed genes. The differential expression of seven genes, involved in multiple cellular processes such as signal transduction (MIC-1), differentiation (DMBT1 and Neugrin), immune response (CD74), inflammation (CXCL2), cell cycle (CEB1) and enzymatic activity (Kallikrein 6), was confirmed by either immunohistochemical labeling of tissue microarrays (Kallikrein 6, CD74 and DMBT1) or by RT-PCR (CEB1, Neugrin, MIC1 and CXCL2). Of note, Neugrin was one of the genes whose previously uncharacterized SAGE tag was correctly assigned using TAGmapper, validating the utility of this program. Novel differentially expressed genes in a cancer type can be identified by revisiting updated and expanded SAGE databases. TAGmapper should prove to be a powerful tool for the discovery of novel tumor markers through assignment of uncharacterized SAGE tags.

Adenocarcinoma↗

PubMed2006

Gene expression profiling separates chromophobe renal cell carcinoma from oncocytoma and identifies vesicular transport and cell junction proteins as differentially expressed genes.

PURPOSE: To compare gene expression profiles of chromophobe renal cell carcinoma (RCC) and benign oncocytoma, aiming at identifying differentially expressed genes. EXPERIMENTAL DESIGN: Nine cases each of chromophobe RCC and oncocytoma were analyzed by oligonucleotide microarray. Candidate genes that showed consistent differential expression were validated by reverse transcription-PCR using 25 fresh-frozen and 15 formalin-fixed, paraffin-embedded tumor samples. Immunohistochemical analysis was also done for two selected gene products, claudin 8 and MAL2. RESULTS: Unsupervised hierarchical clustering separated the chromophobe RCC and oncocytoma into two distinct groups. By a combination of data analysis approaches, we identified 11 candidate genes showing consistent differential expression between chromophobe RCC and oncocytoma. Five of these genes, AP1M2, MAL2, PROM2, PRSS8, and FLJ20171, were shown to effectively separate these two tumor groups by quantitative reverse transcription-PCR using fresh tissue samples, with similar trends seen on formalin-fixed tissues. Immunohistochemical analysis revealed selective expression of MAL2 and claudin 8 in distal renal tubules, with MAL2 antibody showing differential expression between chromophobe RCC and oncocytoma. Functional analyses suggest that genes encoding tight junction proteins and vesicular membrane trafficking proteins, normally expressed in distal nephrons, are retained in chromophobe RCC and lost or consistently down-regulated in oncocytoma, indicating that these two tumor types, believed to be both derived from distal tubules, are likely distinctive in their histogenesis. CONCLUSIONS: We showed that chromophobe RCC and oncocytoma are distinguishable by mRNA expression profiles and a panel of gene products potentially useful as diagnostic markers were identified.

Adaptor Protein Complex 1↗

PubMed2006

On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data.

It has been shown that the generalized F-statistics can give satisfactory performances in identifying differentially expressed genes with microarray data. However, for some complex diseases, it is still possible to identify a high proportion of false positives because of the modest differential expressions of disease related genes and the systematic noises of microarrays. The main purpose of this study is to develop statistical methods for Affymetrix microarray gene expression data so that the impact on false positives from non-expressed genes can be reduced. I proposed two novel generalized F-statistics for identifying differentially expressed genes and a novel approach for estimating adjusting factors. The proposed statistical methods systematically combine filtering of non-expressed genes and identification of differentially expressed genes. For comparison, the discussed statistical methods were applied to an experimental data set for a type 2 diabetes study. In both two- and three-sample analyses, the proposed statistics showed improvement on the control of false positives.

Computer Simulation↗

PubMed2002

Relationships and differentially expressed genes among pancreatic cancers examined by large-scale serial analysis of gene expression.

Pancreatic adenocarcinoma is among the most fatal of cancers, in part because of late diagnosis and a lack of effective therapies. Comprehensive studies are needed to better understand and address the cellular mechanisms and pathways of tumorigenesis. Serial analysis of gene expression was used to analyze gene expression profiles of pancreatic cancer cell lines, short-term cultures of normal pancreatic ductal epithelium, and primary pancreatic cancer tissue. A total of 294,920 tags representing 77,746 genes in 10 serial analysis of gene expression libraries were analyzed. A pancreatic cancer cell line (Hs766T) that exhibited a "normoid" profile of gene expression was identified. Several genes that may be involved in the fundamental nature of malignant changes in pancreatic ductal epithelium were suggested from those differentially and highly expressed in pancreatic cancer cells as compared with normal epithelium. Some overexpressed genes, such as S100A4, prostate stem cell antigen, carcinoembryonic antigen-related cell adhesion molecule 6, and mesothelin, suggest potential use as diagnostic markers. Others suggest potential novel therapeutic targets.

Carcinoma, Pancreatic Ductal↗

PubMed2001

Identification of differentially expressed genes by serial analysis of gene expression in human prostate cancer.

Prostate cancer is the leading cause of cancer death in American males. To better understand the genetic bases of this disease, we have generated a comprehensive molecular profile of human prostate. The gene expression pattern in normal and prostate cancer tissues was analyzed by serial analysis of gene expression (SAGE). A total of 133,217 transcripts were analyzed, and 35,185 distinct SAGE tags were identified representing 19,287 genes. Comparison of the transcripts in normal and tumor tissue revealed 156 differentially expressed genes (P < 0.05), of which 88 genes were up-regulated and 68 genes were down-regulated in the tumor tissue. Based on SAGE data, we estimate that the transcriptome for human prostate is approximately 37,000. Several differentially expressed genes identified by SAGE were selected for confirmation using immunohistochemistry. Some genes (e.g., E2F4) were overexpressed in tumor epithelial cells and some (e.g., Daxx) were increased in tumor stroma. Further characterization of the role of E2F4 and Daxx as well as other differentially expressed genes may provide useful insights into the mechanism of prostate cancer development.

Dihydrotestosterone↗

PubMed2001

Identification of differentially expressed genes in organ-confined prostate cancer by gene expression array.

BACKGROUND: To understand the molecular mechanisms underlying prostate cancer, we have utilized the gene expression array to search for genes whose expression is altered in this disease. METHODS: RNA quality from manual microdissected tissue was compared with that from microselected tissue by electrophoresis. For array analysis, malignant and normal prostate epithelium was enriched using microselection technique from prostate cancer and the peripheral zone of a normal prostate. Identical array membrane was hybridized to labeled cancer and normal cDNA, respectively. The differentially expressed gene was further evaluated by RT-PCR. RESULTS: Microdissection, but not microselection, causes visible degradation to RNA. Of the 588 genes on the membrane, 87 genes yielded significant signals. Based on a three fold difference relative to normal prostate tissue, 1 gene was overexpressed and 12 genes underexpressed in prostate cancer. Of them, five showed statistically significant reduction in mRNA levels in six prostate cancer specimens compared with seven normal prostate specimens. These five genes are glutathione S-transferase M1 (GSTM1), monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor-alpha receptor-1 (TNFR-1), transforming growth factor beta3 (TGF-beta3), and inhibitor of DNA binding-1 (ID-1). CONCLUSIONS: GST-based metabolism, cytokine MCP-1 and TNFR-1, and TGF-beta3 signaling pathways, and some helix-loop-helix nuclear proteins could be potentially important in organ-confined prostate cancer and deserve further investigation.

Adult↗

PubMed1999

Specific gene expression in pancreatic beta-cells: cloning and characterization of differentially expressed genes.

Identification and characterization of genes expressed preferentially in pancreatic beta-cells will clarify the mechanisms involved in the specialized properties of these cells, as well as providing new markers of the development of type 1 diabetes. Despite major efforts, relatively few beta-cell-specific genes have been characterized. We applied representational difference analysis to identify genes expressed selectively in the pancreatic beta-cell line betaTC1 compared with the pancreatic alpha-cell line alphaTC1 and isolated 26 clones expressed at higher levels in the beta-cells than in the alpha-cells. DNA sequencing revealed that 14 corresponded to known genes (that is, present in GenBank). Only four of those genes had been shown previously to be expressed at higher levels in beta-cells (insulin, islet amyloid polypeptide, neuronatin, and protein kinase A regulatory subunit [RIalpha]). The known genes include transcription factors (STAT6) and mediators of signal transduction (guanylate cyclase). The remaining 12 genes are absent from the GenBank database or are present as expressed sequence tag (EST) sequences (4 clones). Some of the genes are expressed in a highly specific pattern-expression in betaTC1 and islet cells and in relatively few of the non-beta-cell types examined; others are expressed in most cell types tested. The identification of these differentially expressed genes may aid in attaining a clearer understanding of the mechanisms involved in beta-cell function and of the possible immunogens involved in development of type 1 diabetes.

Amyloid↗

PubMed1989

Codon usage and gene expression level in Dictyostelium discoideum: highly expressed genes do 'prefer' optimal codons.

Codon usage patterns in the slime mould Dictyostelium discoideum have been re-examined (a total of 58 genes have been analysed). Considering the extreme A + T-richness of this genome (G + C = 22%), there is a surprising degree of codon usage variation among genes. For example, G + C content at silent sites varies from less than 10% to greater than 30%. It was previously suggested [Warrick, H.M. and Spudich, J.A. (1988) Nucleic Acids Res. 16: 6617-6635] that highly expressed genes contain fewer 'optimal' codons than genes expressed at lower levels. However, it appears that the optimal codons were misidentified. Multivariate statistical analysis shows that the greatest variation among genes is in relative usage of a particular subset of codons (about one per amino acid), many of which are C-ending. We have identified these as optimal codons, since (i) their frequency is positively correlated with gene expression level, and (ii) there is a strong mutation bias in this genome towards A and T nucleotides. Thus, codon usage in D. discoideum can be explained by a balance between the forces of mutational bias and translational selection.

Codon↗

PubMed2004

The Arabidopsis root transcriptome by serial analysis of gene expression. Gene identification using the genome sequence.

Large-scale identification of genes expressed in roots of the model plant Arabidopsis was performed by serial analysis of gene expression (SAGE), on a total of 144,083 sequenced tags, representing at least 15,964 different mRNAs. For tag to gene assignment, we developed a computational approach based on 26,620 genes annotated from the complete sequence of the genome. The procedure selected warrants the identification of the genes corresponding to the majority of the tags found experimentally, with a high level of reliability, and provides a reference database for SAGE studies in Arabidopsis. This new resource allowed us to characterize the expression of more than 3,000 genes, for which there is no expressed sequence tag (EST) or cDNA in the databases. Moreover, 85% of the tags were specific for one gene. To illustrate this advantage of SAGE for functional genomics, we show that our data allow an unambiguous analysis of most of the individual genes belonging to 12 different ion transporter multigene families. These results indicate that, compared with EST-based tag to gene assignment, the use of the annotated genome sequence greatly improves gene identification in SAGE studies. However, more than 6,000 different tags remained with no gene match, suggesting that a significant proportion of transcripts present in the roots originate from yet unknown or wrongly annotated genes. The root transcriptome characterized in this study markedly differs from those obtained in other organs, and provides a unique resource for investigating the functional specificities of the root system. As an example of the use of SAGE for transcript profiling in Arabidopsis, we report here the identification of 270 genes differentially expressed between roots of plants grown either with NO3- or NH4NO3 as N source.

Arabidopsis↗

PubMed2006

Distribution-insensitive cluster analysis in SAS on real-time PCR gene expression data of steadily expressed genes.

Cluster analysis is a tool often employed in the micro-array techniques but used less in the real-time PCR. Herein we present core SAS code that instead of the Euclidian distances takes correlation coefficient as a dissimilarity measure. The dissimilarity measure is made robust using a rank-order correlation coefficient rather than a parametric one. There is no need for an overall probability adjustment like in scoring methods based on repeated pair-wise comparisons. The rank-order correlation matrix gives a good base for the clustering procedure of gene expression data obtained by real-time RT-PCR as it disregards the different expression levels. Associated with each cluster is a linear combination of the variables in the cluster, which is the first principal component. Large set of variables can then be replaced by the set of cluster components with little loss of information. In this way, distinct clusters containing unregulated housekeeping genes along with other steadily expressed genes can be disclosed and utilized for standardization purposes. Simulated data in parallel with the data from a biological experiment were taken to validate the SAS macro. For both cases, good intuitive results were obtained.

Cluster Analysis↗

PubMed2006

Identification of differentially expressed genes in human bladder cancer through genome-wide gene expression profiling.

Large-scale gene expression profiling is an effective strategy for understanding the progression of bladder cancer (BC). The aim of this study was to identify genes that are expressed differently in the course of BC progression and to establish new biomarkers for BC. Specimens from 21 patients with pathologically confirmed superficial (n = 10) or invasive (n = 11) BC and 4 normal bladder samples were studied; samples from 14 of the 21 BC samples were subjected to microarray analysis. The validity of the microarray results was verified by real-time RT-PCR. Of the 136 up-regulated genes we detected, 21 were present in all 14 BCs examined (100%), 44 in 13 (92.9%), and the other 71 in 12 BCs (85.7%). Of 69 down-regulated genes, 25 were found in all 14 BCs (100%), 22 in 13 (92.9%), and the other 22 in 12 BCs (85.7%). Functional annotation revealed that of the up-regulated genes, 36% were involved in metabolism and 14% in transcription and processing; 25% of the down-regulated genes were linked to cell adhesion/surface and 21% to cytoskeleton/cell membrane. Real-time RT-PCR confirmed the microarray results obtained for the 6 most highly up- and the 2 most highly down-regulated genes. Among the 6 most highly up-regulated genes, CKS2 was the only gene with a significantly greater level of up-regulation in invasive than in superficial BC (p = 0.04). To confirm this result, we subjected all 21 BC samples to real-time PCR assay for CKS2. We found a considerable difference between superficial and invasive BC (p = 0.001). Interestingly, there was a considerable difference between the normal bladder and invasive BC (p = 0.001) and less difference between the normal bladder and superficial BC (p = 0.005). We identified several genes as promising candidates for diagnostic biomarkers of human BC and the CKS2 gene not only as a potential biomarker for diagnosing, but also for staging human BC. This is the first report demonstrating that CKS2 expression is strongly correlated with the progression of human BC.

Aged↗

PubMed2003

Gene expression profiling of differentially expressed genes in granulosa cells of bovine dominant follicles using suppression subtractive hybridization.

Development of antral follicles beyond 3 to 4 mm in cattle appears as a wave pattern that occurs two to three times during the estrous cycle. Each wave presents a cyclic recruitment of multiple follicles at the 3- to 4-mm stage, followed by the selection of a single follicle that becomes the dominant follicle (DF). The molecular determinants involved in the follicular dominance process remain poorly understood. The objective of the current study was to compare gene expression in granulosa cells (GCs) between growing dominant follicles from Day 5 of the estrous cycle and nonselected small follicles (<or=4 mm) using the suppression subtractive hybridization (SSH) approach to identify candidate genes differentially expressed in GCs of the DF. Small follicle cDNAs were subtracted from DF cDNAs (DF-SF) and used to establish a DF GC-subtracted cDNA library. A total of 42 nonredundant cDNAs were identified. Detection of previously identified genes such as CX43, CYP19, INHBA, and SERPINE2 supported the validity of our experimental model and the use of SSH as the method of analysis. For selected genes such as ApoER2, CPD, CSPG2, 14-3-3 epsilon, NR5A2/SF2, RGN/SMP30, and SERPINE2, gene expression profiles were compared by virtual Northern blot or reverse transcriptase-polymerase chain reaction, and results confirmed an increase or induction of their mRNA in GCs of dominant follicles compared with that of small follicles. We conclude that we have identified novel genes (known and unknown) that are up-regulated in bovine GCs that may affect follicular growth, dominance, or both.

Animals↗

PubMedDate not supplied

Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes.

The ordinary-, penalized-, and bootstrap t-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), i.e. the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-t-test yielded up to 80% lower FDRs than the alternative statistics, and its FDR was always as good as or better than any of the alternatives. Generally, the predicted FDR from the bootstrapped P-values agreed well with their empirical estimates, except when the number of mRNA samples is smaller than 16. In a cancer data set, the bootstrap-t-test discovered 200 differentially regulated genes at a FDR of 2.6%, and in a knock-out gene expression experiment 10 genes were discovered at a FDR of 3.2%. It is argued that, in the case of microarray data, control of the FDR takes sufficient account of the multiple testing, whilst being less stringent than Bonferoni-type multiple testing corrections. Extensions of the bootstrap simulations to more complicated test-statistics are discussed.

Analysis of Variance↗

PubMed2005

Gene expression profile studies of human keratoconus cornea for NEIBank: a novel cornea-expressed gene and the absence of transcripts for aquaporin 5.

PURPOSE: To increase the database of genes expressed in human cornea and to gain insights into the molecular basis of keratoconus (KC). METHODS: A cDNA library was constructed from KC corneas harvested at keratoplasty and used for expressed sequence tag (EST) analysis. Data were analyzed using grouping and identification of sequence tags (GRIST). Expression of selected clones was examined by RT-PCR. RESULTS: A total of 7680 clones was sequenced from the 5' end. After bioinformatics analysis, 4090 clusters of clones, each potentially representing individual genes, were identified. Of these, 887 genes were represented by more than one clone. The five most abundant transcripts, represented by >60 clones each, were for keratin-12, TGFBI (BIGH3), decorin, ALDH3, and enolase 1, all known markers for cornea. Many other markers for epithelial, stromal, and endothelial genes were also present. One cluster of six clones came from an apparently novel gene (designated KC6) located on chromosome 18 at p12.3. RT-PCR of RNA from several human tissues detected KC6 transcripts only in cornea. In addition, no clones were observed for the usually prominent corneal epithelial cell marker aquaporin 5 (AQP5), a water channel protein. Semiquantitative RT-PCR confirmed that expression of AQP5 is much lower in KC cornea than in non-KC cornea. CONCLUSIONS: This analysis increases the database of genes expressed in the human cornea and provides insights into KC. KC6 is a novel gene of unknown function that shows cornea-preferred expression, whereas the suppression of transcripts for AQP5 provides the first clear evidence of a molecular defect identified in KC.

Amino Acid Sequence↗

PubMed1989

Differentiation-induced gene expression in 3T3-L1 preadipocytes. A second differentially expressed gene encoding stearoyl-CoA desaturase.

Previously we isolated and characterized a differentially expressed gene from mouse 3T3-L1 preadipocytes that encodes stearoyl-CoA desaturase (SCD1; Ntambi, J. M., Buhrow, S. A., Kaestner, K. H., Christy, R. J., Sibley, E., Kelly, T. J., Jr., and Lane, M. D. (1988) J. Biol. Chem. 263, 17291-17300). Genomic Southern blot analysis indicated the existence of another closely related gene. Here we report the isolation and characterization of this gene and the corresponding cDNA which encode a second stearoyl-CoA desaturase, SCD2, 3T3-L1 adipocytes. SCD2 cDNA is 5 kilobase pairs in length and encodes a protein of 358 amino acids with greater than 87% amino acid sequence identity to SCD1. RNase protection analysis reveals a 10-fold increase in the expression of SCD2 mRNA during 3T3-L1 preadipocyte differentiation. SCD2 mRNA is expressed constitutively at a high level in brain, is not expressed in liver, and its expression in kidney, adipose, and lung tissue is increased greatly by shifting mice from a diet containing unsaturated fatty acids to a diet devoid of fat. The tissue distribution and the dietary alteration of SCD1 mRNA expression differs markedly from that of SCD2 mRNA being absent from brain, constitutive in adipose tissue, and subject to negative control in liver by feeding a diet containing unsaturated fatty acids. The SCD2 gene spans approximately 15 kilobase pairs and consists of six exons and five introns, with intron/exon junctions similar to those of SCD1. As determined by primer extension analysis the start site of transcription maps 300 nucleotides upstream of the initiator methionine codon. Unlike the SCD1 gene, SCD2 lacks a typical "TATA" box in the 5'-flanking region, but has two "CCAAT" boxes at positions -90 and -135 relative to the transcription initiation site. The SCD2 promoter contains a 140-base pair sequence (located between nucleotides -54 and -201) which possesses 77% sequence identity to a region (located between nucleotides -472 and -325) in the SCD1 promoter. There is a GC-rich sequence in the SCD2 promoter (at nucleotide -175) similar to the binding site for the nuclear transcription factor Sp1 as well as an element with homology to the core consensus sequence for the glucocorticoid regulatory element position -500 and a potential CCAAT box/enhance binding protein sequence at position -540. The SCD gene family provides a new model system for the study of differentiation-induced as well as tissue-specific metabolite controlled gene expression.

Adipose Tissue↗