Differential Gene Expression Induced by Insulin and Insulin-like Growth Factor-II through the Insulin Receptor Isoform A
2003; Elsevier BV; Volume: 278; Issue: 43 Linguagem: Inglês
10.1074/jbc.m304980200
ISSN1083-351X
AutoresGiuseppe Pandini, Enzo Médico, Enrico Conte, Laura Sciacca, Riccardo Vigneri, Antonino Belfiore,
Tópico(s)Cancer, Hypoxia, and Metabolism
ResumoThe human insulin receptor (IR) exists in two isoforms (IR-A and IR-B). IR-A is a short isoform, generated by the skipping of exon 11, a small exon encoding for 12 amino acid residues at the carboxyl terminus of the IR α-subunit. Recently, we found that IR-A is the predominant isoform in fetal tissues and malignant cells and binds with a high affinity not only insulin but also insulin-like growth factor-II (IGF-II). To investigate whether the activation of IR-A by the two ligands differentially activate post-receptor molecular mechanisms, we studied gene expression in response to IR-A activation by either insulin or IGF-II, using microarray technology. To avoid the interfering effect of the IGF-IR, IGF-II binding to the IR-A was studied in IGF-IR-deficient murine fibroblasts (R- cells) transfected with the human IR-A cDNA (R-/IR-A cells). Gene expression was studied at 0.5, 3, and 8 h. We found that 214 transcripts were similarly regulated by insulin and IGF-II, whereas 45 genes were differentially transcribed. Eighteen of these differentially regulated genes were responsive to only one of the two ligands (12 to insulin and 6 to IGF-II). Twenty-seven transcripts were regulated by both insulin and IGF-II, but a significant difference between the two ligands was present at least in one time point. Interestingly, IGF-II was a more potent and/or persistent regulator than insulin for these genes. Results were validated by measuring the expression of 12 genes by quantitative real-time reverse transcriptase-PCR. In conclusion, we show that insulin and IGF-II, acting via the same receptor, may differentially affect gene expression in cells. These studies provide a molecular basis for understanding some of the biological differences between the two ligands and may help to clarify the biological role of IR-A in embryonic/fetal growth and the selective biological advantage that malignant cells producing IGF-II may acquire via IR-A overexpression. The human insulin receptor (IR) exists in two isoforms (IR-A and IR-B). IR-A is a short isoform, generated by the skipping of exon 11, a small exon encoding for 12 amino acid residues at the carboxyl terminus of the IR α-subunit. Recently, we found that IR-A is the predominant isoform in fetal tissues and malignant cells and binds with a high affinity not only insulin but also insulin-like growth factor-II (IGF-II). To investigate whether the activation of IR-A by the two ligands differentially activate post-receptor molecular mechanisms, we studied gene expression in response to IR-A activation by either insulin or IGF-II, using microarray technology. To avoid the interfering effect of the IGF-IR, IGF-II binding to the IR-A was studied in IGF-IR-deficient murine fibroblasts (R- cells) transfected with the human IR-A cDNA (R-/IR-A cells). Gene expression was studied at 0.5, 3, and 8 h. We found that 214 transcripts were similarly regulated by insulin and IGF-II, whereas 45 genes were differentially transcribed. Eighteen of these differentially regulated genes were responsive to only one of the two ligands (12 to insulin and 6 to IGF-II). Twenty-seven transcripts were regulated by both insulin and IGF-II, but a significant difference between the two ligands was present at least in one time point. Interestingly, IGF-II was a more potent and/or persistent regulator than insulin for these genes. Results were validated by measuring the expression of 12 genes by quantitative real-time reverse transcriptase-PCR. In conclusion, we show that insulin and IGF-II, acting via the same receptor, may differentially affect gene expression in cells. These studies provide a molecular basis for understanding some of the biological differences between the two ligands and may help to clarify the biological role of IR-A in embryonic/fetal growth and the selective biological advantage that malignant cells producing IGF-II may acquire via IR-A overexpression. The human insulin receptor (IR) 1The abbreviations used are: IR, insulin receptor; IGF, insulin-like growth factor; TGF, tumor necrosis growth factor; EST, expressed sequence tag; ERK, extracellular signal-regulated kinase; r.m.s.s.d., root mean square standard deviation; dChip, DNA chip analyzer. exists in two isoforms (IR-A and IR-B). IR-A is a short isoform, generated by the skipping of exon 11, a small exon encoding for 12 amino acid residues at the carboxyl terminus of the IR α-subunit. The relative abundance of the two IR isoforms is regulated by tissue-specific factors, stage of development, and cell differentiation (1Mosthaf L. Grako K. Dull T.J. Coussens L. Ullrich A. McClain D.A. EMBO J. 1990; 9: 2409-2413Crossref PubMed Scopus (290) Google Scholar, 2Moller D.E. Yokota A. Caro J.F. Flier J.S. Mol. Endocrinol. 1989; 3: 1263-1269Crossref PubMed Scopus (264) Google Scholar, 3Giddings S.J. Carnaghi L.R. Mol. Endocrinol. 1992; 6: 1665-1672PubMed Google Scholar). Genetic studies carried out in transgenic mice have shown that fetal growth in response to IGF-II is partially mediated by the IR (4Louvi A. Accili D. Efstratiadis A. Dev. Biol. 1997; 189: 33-48Crossref PubMed Scopus (327) Google Scholar, 5Liu J.P. Baker J. Perkins A.S. Robertson E.J. Efstratiadis A. Cell. 1993; 75: 59-72Abstract Full Text PDF PubMed Scopus (2597) Google Scholar, 6DeChiara T.M. Efstratiadis A. Robertson E.J. Nature. 1990; 345: 78-80Crossref PubMed Scopus (1410) Google Scholar), and we have recently demonstrated that IR-A is the predominant isoform in fetal tissues and binds IGF-II with high affinity (7Frasca F. Pandini G. Scalia P. Sciacca L. Mineo R. Costantino A. Goldfine I.D. Belfiore A. Vigneri R. Mol. Cell. Biol. 1999; 19: 3278-3288Crossref PubMed Scopus (730) Google Scholar). We also demonstrated that malignant transformation is associated with both IR overexpression and an increased relative abundance of IR-A, both in epithelial and in mesenchymal tumors (8Papa V. Pezzino V. Costantino A. Belfiore A. Giuffrida D. Frittitta L. Vannelli G.B. Brand R. Goldfine I.D. Vigneri R. J. Clin. Invest. 1990; 86: 1503-1510Crossref PubMed Scopus (275) Google Scholar, 9Frittitta L. Sciacca L. Catalfamo R. Ippolito A. Gangemi P. Pezzino V. Filetti S. Vigneri R. Cancer. 1999; 85: 492-498Crossref PubMed Scopus (43) Google Scholar, 10Pandini G. Vigneri R. Costantino A. Frasca F. Ippolito A. Fujita-Yamaguchi Y. Siddle K. Goldfine I.D. Belfiore A. Clin. Cancer Res. 1999; 5: 1935-1944PubMed Google Scholar, 11Sciacca L. Costantino A. Pandini G. Mineo R. Frasca F. Scalia P. Sbraccia P. Goldfine I.D. Vigneri R. Belfiore A. Oncogene. 1999; 18: 2471-2479Crossref PubMed Scopus (241) Google Scholar, 12Vella V. Pandini G. Sciacca L. Mineo R. Vigneri R. Pezzino V. Belfiore A. J. Clin. Endocrinol. Metab. 2002; 87: 245-254Crossref PubMed Scopus (159) Google Scholar, 13Sciacca L. Mineo R. Pandini G. Murabito A. Vigneri R. Belfiore A. Oncogene. 2002; 21: 8240-8250Crossref PubMed Scopus (137) Google Scholar), and that IR-A relative abundance may further increase with cells dedifferentiation, as observed in thyroid cancer (12Vella V. Pandini G. Sciacca L. Mineo R. Vigneri R. Pezzino V. Belfiore A. J. Clin. Endocrinol. Metab. 2002; 87: 245-254Crossref PubMed Scopus (159) Google Scholar, 14Vella V. Sciacca L. Pandini G. Mineo R. Squatrito S. Vigneri R. Belfiore A. Mol. Pathol. 2001; 54: 121-124Crossref PubMed Scopus (154) Google Scholar). Accumulating evidence also indicates that IR-A overexpression may play a significant role in growth promotion and apoptosis protection of malignant cells when tumors produce IGF-II (13Sciacca L. Mineo R. Pandini G. Murabito A. Vigneri R. Belfiore A. Oncogene. 2002; 21: 8240-8250Crossref PubMed Scopus (137) Google Scholar, 15Kalli K.R. Falowo O.I. Bale L.K. Zschunke M.A. Roche P.C. Conover C.A. Endocrinology. 2002; 143: 3259-3267Crossref PubMed Scopus (127) Google Scholar). In contrast, IR-B is the predominant IR isoform in normal adult tissues that are major targets for the metabolic effects of insulin (adipose tissue, liver, and muscle) (1Mosthaf L. Grako K. Dull T.J. Coussens L. Ullrich A. McClain D.A. EMBO J. 1990; 9: 2409-2413Crossref PubMed Scopus (290) Google Scholar, 2Moller D.E. Yokota A. Caro J.F. Flier J.S. Mol. Endocrinol. 1989; 3: 1263-1269Crossref PubMed Scopus (264) Google Scholar, 16Kosaki A. Webster N.J. J. Biol. Chem. 1993; 268: 21990-21996Abstract Full Text PDF PubMed Google Scholar). The binding characteristics of insulin and IGF-II to IR-A and the biological effects of IR-A stimulation by IGF-II have been studied previously in a variety of models (7Frasca F. Pandini G. Scalia P. Sciacca L. Mineo R. Costantino A. Goldfine I.D. Belfiore A. Vigneri R. Mol. Cell. Biol. 1999; 19: 3278-3288Crossref PubMed Scopus (730) Google Scholar). In particular, we studied IGF-II binding to the IR-A in IGF-IR-deficient murine fibroblasts (R- cells) transfected with the human IR-A cDNA (R-/IR-A cells). This study revealed that IGF-II displaces labeled insulin from IR-A with a lower affinity than insulin (ED50 = 2.5 versus 0.9, respectively). However, unexpectedly, IGF-II was a more efficacious mitogen than insulin in these cells. In contrast, insulin was more potent than IGF-II in stimulating glucose uptake (7Frasca F. Pandini G. Scalia P. Sciacca L. Mineo R. Costantino A. Goldfine I.D. Belfiore A. Vigneri R. Mol. Cell. Biol. 1999; 19: 3278-3288Crossref PubMed Scopus (730) Google Scholar). In accordance with our findings, it was independently shown that IGF-II is stronger than insulin in inducing growth in IR-transfected R- cells (17Morrione A. Valentinis B. Xu S.Q. Yumet G. Louvi A. Efstratiadis A. Baserga R. Proc. Natl. Acad. Sci. U. S. A. 1997; 94: 3777-3782Crossref PubMed Scopus (193) Google Scholar). These findings were confirmed and extended in SKUT-1 human rabdomyosarcoma cells, which lack functional IGF-IR and express almost only IR-A. In SKUT-1 cells, IGF-II was significantly more potent than insulin in stimulating the Shc/ERK pathway, whereas insulin was more potent than IGF-II in stimulating IR autophosphorylation and the IRS-1/phosphatidylinositol 3-kinase/Akt pathway. As a result, IGF-II was more potent than insulin in inducing cell chemoinvasion, whereas insulin was slightly more effective in apoptosis protection (13Sciacca L. Mineo R. Pandini G. Murabito A. Vigneri R. Belfiore A. Oncogene. 2002; 21: 8240-8250Crossref PubMed Scopus (137) Google Scholar). Taken together, these studies indicate that insulin and IGF-II, by binding to the same receptor, may induce the preferential activation of different intracellular pathways. These differences may result in significant differences in the biological effects between the two ligands. To gain further insights on the molecular mechanisms differentially activated by either IGF-II or insulin in R-/IR-A cells, we investigated gene expression in response to either ligand using microarray technology. Microarray techniques have emerged as a new potent approach for the global analysis of gene transcription. We used Affymetrix MG-U74A Gene-Chips to measure changes in mRNA levels for ∼6,000 functionally characterized murine genes and ∼6,000 expressed sequence tags (ESTs). We found that 45 genes are differentially transcribed in response to either insulin or IGF-II in R-/IR-A cells. We also validated these results by evaluating the expression profile of 12 genes by quantitative real-time reverse transcriptase-PCR. These findings provide a molecular basis for understanding the biological differences between insulin and IGF-II after binding to the same receptor. Materials—The pNTK2 expression vector containing the cDNA for the A (Ex11-) isoform of the human IR was kindly provided by Dr. Axel Ullrich (Martinsried, Germany). Fetal calf serum, glutamine, LipofectAMINE, DNAase I were from Invitrogen; RPMI 1640 medium, Dulbecco's modified Eagle's medium, bovine serum albumin (BSA, radioimmunoassay grade), bacitracin, phenylmethylsulfonyl fluoride, puromycin, porcine insulin were from Sigma; IGF-II was obtained from Calbiochem Laboratories. TRIzol reagent and Superscript Choice system were purchased from Invitrogen; Oligotex mRNA kit and RNeasy Mini kit were obtained from Qiagen; BioArray HighYield RNA transcript labeling kit (ENZO Bioarray kit) was obtained from Affymetrix. Cells—R- mouse fibroblasts (mouse 3T3-like cells derived from animals with a targeted disruption of the IGF-IR gene, expressing ∼5 × 103 native insulin receptors/cell) were kindly provided by Dr. R. Baserga (Philadelphia, PA) and were routinely grown in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum. R- cells grown in 35-mm plates until 60-70% confluent were co-transfected with 2 μg of pNTK2 expression vector containing the cDNA encoding for the A (Ex11-) isoform of the human IR (18Ullrich A. Gray A. Tam A.W. Yang-Feng T. Tsubokawa M. Collins C. Henzel W. Le Bon T. Kathuria S. Chen E. EMBO J. 1986; 5: 2503-2512Crossref PubMed Scopus (1511) Google Scholar) and with the pPDV6+ plasmid encoding for the puromicyn resistance gene. Cells were subsequently subjected to antibiotic selection in medium supplemented with 2.4 μg/ml puromicin for 3 weeks. Stably transfected cells were then cloned, and a cell clone with ∼5 × 105 receptors/cell was obtained, as described previously (7Frasca F. Pandini G. Scalia P. Sciacca L. Mineo R. Costantino A. Goldfine I.D. Belfiore A. Vigneri R. Mol. Cell. Biol. 1999; 19: 3278-3288Crossref PubMed Scopus (730) Google Scholar). Receptor content was evaluated in selected cell clones by enzyme-linked immunosorbent assay (10Pandini G. Vigneri R. Costantino A. Frasca F. Ippolito A. Fujita-Yamaguchi Y. Siddle K. Goldfine I.D. Belfiore A. Clin. Cancer Res. 1999; 5: 1935-1944PubMed Google Scholar). cRNA Preparation—R-/IR-A cells were grown until 80% confluent and serum-starved for 24 h. Cells were then stimulated with 10 nm of either insulin or IGF-II for 30 min or 3 or 8 h. Total RNA was isolated by TRIzol reagent, and mRNA was purified from total RNA using Oligotex mRNA kit, according to the protocol recommended by Affymetrix. mRNA (2 μg) was then used to synthesize double-stranded cDNA by Superscript Choice system with T7-(dT)24 as a primer. Biotin-labeled cRNAs were in vitro transcribed using the ENZO BioArray kit and fragmented to produce a distribution of RNA fragments with size ranging from ∼35 to 200 bases. Samples of fragmented cRNA (15 μg) were hybridized for 16 h at 45 °C to MG-U74A mouse arrays (Affymetrix). Analysis of the scanned chips was carried out using Affymetrix Microarray Suite version 5.0 (MAS5). Data Treatment—Raw data from GeneChip microarrays were converted with the MAS5 software into a single, tab-delimited text file reporting, for each probe set, the "signal" and "detection" values from all experimental points. This file was subsequently processed with Microsoft Excel as follows. For each probe set, average signal was calculated across all experimental points. The average signal column was used to sort rows by increasing signal and to normalize individual microarray columns using a moving average (window of 200 probe sets) of increasing signal. Such normalization corrected signal non-linearity and allowed comparison of any experimental point with any other. Stimulation points were compared with the controls or between each other through pairwise log2 ratio calculation and averaging. Standard deviations (S.D.s) of these average log2 ratios were also calculated. To obtain a more reliable estimate of variability, for each probe set, we also calculated the root mean square standard deviation (r.m.s.s.d.), encompassing all S.D.s of the average log2 ratios. In fact, although the S.D. of a single duplicate comparison can easily be aberrantly high or low by chance, the r.m.s.s.d. from many duplicate comparisons is a more stable and reliable parameter. An additional test was performed on these data, based on the "detection" call (present, absent, marginal). In synthesis, if a gene is induced in a certain experimental point, it must be called expressed in that point (not necessarily in the control). Otherwise, if it is suppressed, it must be called expressed in the control. At the end of this process, the following data were obtained for each gene: 1) normalized expression levels for all individual control and stimulated points; 2) average log2 ratio for each experimental condition with respect to the control or to another experimental condition of choice; 3) S.D. for each average log2 ratio, and r.m.s.s.d.; 4) call compatibility for each comparison. The first filter was the call compatibility, after which the other parameters were included and "tuned" in a statistical test aimed at identifying significantly regulated genes. The test requires that after subtraction of m *S.D. or of m *r.m.s.s.d., the average log2 ratio is still higher than a threshold value of T. The tunable values in this test are m, the S.D./r.m.s.s.d. multiplier, and T, the threshold-fold change. To optimize test tuning, we systematically evaluated the false discovery rate, that is, the percentage of the sequences that could have passed the test by chance. False discovery can be estimated by generation, through data permutation, of mixed couples of microarray data that are not expected to display significant gene regulation. Existing microarray analysis tools such as significance analysis of microarrays (19Tusher V.G. Tibshirani R. Chu G. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 5116-5121Crossref PubMed Scopus (9802) Google Scholar) support data permutation. Differently from significance analysis of microarrays, our modified test weights overall variability of each probe set across all duplicates, which allows more reliable detection of tiny differences in gene expression. 2E. Medico, M. Riba, L. D'Alessandro, J. Aach, G. M. Church, and P. M. Comoglio, manuscript in preparation. We also implemented a permutation strategy and estimated the false discovery rate of our analysis based on 1,260 permutations. The test tuning parameters showing the best performance with the present data were T = 0.4 and m = 1.5, with which we could detect 259 regulated genes with an false discovery rate below 10%. Test tuning for identifying genes differentially regulated by insulin and IGF-II was slightly different, with T = 0.4 and m = 1. The false discovery rate above 10% indicated the necessity for real-time PCR validation of these data. As a control of data robustness, we also used dChip (20Li C. Wong W.H. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 31-36Crossref PubMed Scopus (2713) Google Scholar) to normalize the data. We saw a lower coefficient of variation in dChip-normalized triplicates and could confirm >90% of the genes originally identified on MAS5-normalized data as regulated by insulin and/or IGF-II. Interestingly, dChip normalization rendered non-significant the regulation of a gene we had already validated by real-time PCR, which indicates that different normalization procedures may also yield non-overlapping false negatives. We therefore decided to make available for download the two spreadsheets containing, respectively, MAS5-normalized and dChip-normalized data. The analysis spreadsheets and the raw CEL files can be downloaded (www.ircc.it/∼emedico/FOG/data). The original data will also be submitted to the NCBI's Gene Expression Omnibus public data base (www.ncbi.nlm.nih.gov/geo) at a later date. Further information is available from the authors on request. Hierarchical Clustering—Hierarchical clustering of the selected genes was performed using the computer program Cluster (21Eisen M.B. Spellman P.T. Brown P.O. Botstein D. Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 14863-14868Crossref PubMed Scopus (13268) Google Scholar) and visualized using the program TreeView (available at rana.stanford.edu/software). Real-time Polymerase Chain Reaction—Primer Express software (PE Applied Biosystems, Foster City, CA) was used to design appropriate primer pairs and fluorescent probes. Primer pairs and probes with 5′-FAM reporter dye and 3′-TAMRA quencher dye were synthesized by MWG-Biotech (Ebersberg, Germany). Probe and primers for endogenous control (glyceraldehyde-3-phosphate dehydrogenase) were from predeveloped TaqMan assay reagents (Applied Biosystems). Quantitative real-time PCR was performed on Abi Prism 7700 (PE Applied Biosystems) using Sybr Green PCR Master Mix and Taqman Universal PCR Master Mix (PE Applied Biosystems) following manufacturer's instructions. To normalize gene expression, a parallel amplification (six replicates) of endogenous and target genes was performed with Sybr Green reagents. For Taqman analysis, all reactions (six replicates) were performed by co-amplifying in the same tube endogenous and target genes. To check reaction sensitivity, in preliminary experiments, serial dilutions of each cDNA (1, 1:10, 1:100, 1:1,000) were amplified for endogenous and target genes. The reaction efficiency resulted similar in simplex and duplex reactions (i.e. slope = 3.6 ≤ x ≥3; correlation coefficient ≥0.99). Relative quantitative evaluation (PE Applied Biosystems user bulletin number 2) of target gene levels was performed by comparing ΔCt, as described previously (22Ginzinger D.G. Exp. Hematol. 2002; 30: 503-512Abstract Full Text Full Text PDF PubMed Scopus (1009) Google Scholar). To analyze gene expression profiles following IR-A activation by either insulin or IGF-II, R-/IR-A cells were stimulated with either ligand (10 nm) for various time intervals (0.5, 3, and 8 h). Biotinylated cRNA probes were generated from the RNA extracted from control and ligand-stimulated cells and hybridized to microarray membranes containing the entire mouse genes, according to Affymetrix procedure. Using the analysis strategy described under "Experimental Procedures," we identified 259 genes (132 known genes and 127 ESTs) regulated by one or both hormones. Variations of gene expression, as compared with basal levels, ranged from +1.3 to +4.2 and from -1.3- to -7.9-fold changes. Two hundred and fourteen genes and ESTs were regulated with a similar pattern by both insulin and IGF-II (Fig. 1A). Sixty genes (Table I) and 52 ESTs were similarly up-regulated by the two hormones, whereas 48 genes (Table II) and 58 ESTs were similarly down-regulated. Three genes, JunB, IL-6, and zinc transporter 1, and one EST (GenBank™ accession number AI606257), are present in both tables because they are up-regulated and down-regulated at different time points: JunB and IL-6 were up-regulated at 30 min and 3 h, respectively, and then down-regulated at 8 h; zinc transporter 1 was down-regulated at 3 h and subsequently up-regulated at 8 h. Most of the genes regulated are considered regulators of apoptosis, cell cycle, proliferation, signal transduction, metabolism, and differentiation (Tables I and II).Fig. 1Cluster analysis of genes regulated by either insulin or IGF-II or both in R-/IR-A cells. Two hundred and fifty nine genes and ESTs demonstrated themselves to be either up-regulated or down-regulated at least at one time point (0.5, 3, or 8 h) by one or both ligands on the basis of microarray hybridization technique using Affymetrix MG-U74A GeneChips. These genes were subjected to three different hierarchical cluster analysis and represented: A, genes and ESTs similarly down- or up-regulated by the two ligands; B, genes and ESTs differentially expressed in response to either insulin or IGF-II. Genes regulated only by insulin are indicated in red; genes regulated only by IGF-II are indicated in blue. The scale of gene expression, as -fold changes, is shown.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Table IGenes similarly up-regulated by both insulin and IGF-IIGenBank accession No.DescriptionInsulinIGF-IITimehApoptosisU73478Acidic nuclear phosphoprotein 322.43.80.5/8AF064447Sex-determination protein homolog Fem1a2.73.13AB013819TIAP2.21.98U93583RAD51-associated protein 11.71.78Cell cycleD26091CDC471.81.98L26320Flap structure specific endonuclease 11.81.98AF098068CDC45-related protein1.81.98D26089Mini chromosome maintenance deficient 41.71.98D13803Rad51 homolog (Saccharomyces cerevisiae)1.61.78M38724Cell division cycle control protein 2a1.81.68J04620Primase p49 subunit (priA)1.81.68D26090CDC461.61.58Cytoskeletal functionsX99963rhoB gene1.81.70.5DNA mismatch repairU28724Postmeiotic segregation increased 21.61.78MetabolismU17132Zinc transporter 11.72.08AB000777Cryptochrome 1 (photolyase-like)1.61.73X13752δ-aminolevulinate dehydratase1.81.78AF043249Mitochondrial outer membrane protein (Tom40)1.61.68ProliferationM28845Early growth response 13.53.40.5L41352Amphiregulin2.82.73M59821Growth factor-inducible protein (pip92)2.72.60.5M14223Ribonucleotide reductase M2 subunit2.12.58AJ223087Cdc6-related protein2.32.48D87908Nuclear protein np952.22.38X60980Thymidine kinase2.62.38X67644Gly962.02.20.5M24377Early growth response 22.12.00.5M33960Mouse plasminogen activator inhibitor (PAI-1)2.12.03D86725MCM21.92.08M17298Nerve growth factor β2.41.93K02927Ribonucleotide reductase M11.81.88L07264Heparin binding EGF-likeaEGF, epidermal growth factor growth factor1.91.73M70642Fibroblast-inducible secreted protein1.71.63Proliferation/apoptosisU77844TRIP1.71.58Proliferation/cell transformationU20735Transcription factor junB2.11.80.5Proliferation/differentiationU51000Distal-less homeobox 12.42.68D30782Epiregulin2.62.53U03421Interleukin 112.21.73Signal transductionU88328Suppressor of cytokine signalling-31.92.00.5D16497Natriuretic peptide precursor type B1.91.63a EGF, epidermal growth factor Open table in a new tab Table IIGenes similarly down-regulated by both insulin and IGF-IIGenBank accession No.DescriptionInsulinIGF-IITimehApoptosisY13087Caspase-6-1.6-1.98L38822Max interacting protein 1-1.6-1.73M31418Interferon activated gene 202-1.7-1.73Cell cycleU60453Ezh1-2.2-2.03U00937GADD45-3.4-3.63Cell-to-matrix interactionAF022110Integrin β-5-1.6-1.58X06086Cathepsin L-1.6-1.48Z12604Matrix metalloproteinase 11-1.8-2.23D31951Osteoglycin-2.0-1.58Cytoskeletal functionsU05252SATB1-1.8-1.78DNA repairX916175-3 exonuclease-2.5-2.53/8MetabolismAF062071Zinc finger protein 216-1.5-1.43D50367KAP3B-1.8-1.53/8AF020039NADP-dependent isocitrate dehydrogenase (Idh)-1.6-1.58U17132Zinc transporter 1-1.4-1.43Proliferation/differentiationL10244Spermidine/spermine N1-acetyl transferase-2.0-1.98D16195Granulin-1.5-1.78AB012161KF-1-1.8-1.73X61940Growth factor-inducible immediate early gene-2.9-4.03/8AB019577UNC-51-like kinase (ULK) 2-1.7-1.53D78643Seizure-related-1.7-1.58AF037205RING zinc finger protein (Rzf)-1.6-2.08M36146Zinc finger protein 35-1.7-1.43/0.5U60593Ndr1-1.5-1.63Y07609Max binding protein-1.7-1.53U52073TDD5-1.7-1.83/8AB014485RA70-1.7-1.58U09504Thyroid hormone receptor α-1.6-1.73X89749TGIF-1.5-1.68CytokinesM64849Platelet derived growth factor B-2.1-1.68L07803Thrombospondin 2-2.1-1.88X54542Interleukin 6-3.5-1.68Signal transductionU90435Flotillin-1.5-1.78Transcription factorsY14296BTEB-1 transcription factor-1.5-1.98MiscellaneousM13945Proviral integration site 1-1.4-1.68AF020308HRS-1.5-1.53AF110520Major histocompatibility complex class II-1.5-1.80.5/3Z80112Lcr-1 gene-5.9-3.43/8X78445Cyp1-b-1-1.9-2.03X95761New-Rhobin-1.4-1.93 Open table in a new tab In some genes, up- or down-regulation was transient and only detected at one time point, whereas it was persistent in other cases, as indicated by cluster analysis (Fig. 1A). The number of genes and ESTs similarly up-regulated or down-regulated by the two ligands at the different time points is indicated in Fig. 2. Comparative analysis of the ∼6,000 genes and ∼6,000 ESTs on the cDNA microarrays revealed that 45 transcripts (27 genes and 18 ESTs) were differentially regulated by insulin and IGF-II. Transcripts Regulated by Only One Ligand—Eighteen of these differentially regulated genes (10 genes and 8 ESTs) were responsive to only one of the two ligands. Twelve transcripts (7 genes and 5 ESTs) responded only to insulin (3 up-regulated and 9 down-regulated, Table III), whereas 6 transcripts (3 genes and 3 ESTs) responded only to IGF-II (5 up-regulated and 1 down-regulated; see Table III).Table IIIGenes regulated by only one ligandIDDescriptionInsulin (-fold change)IGF-II (-fold change)ΔaΔ indicates the ratio of IGF-II stimulation/insulin stimulationTimeFunctionhA. Regulated only by insulinX16009Mrp / plf2.0—1.02.18Angiogenesis regulatorAI843384BLAST: BC019982 TK2—1.61.21.98DNA synthesis/repairAI853714BLAST: NM_007798 Cathepsin B—1.81.21.80.5MiscellaneousAI314706Unknown—1.41.31.78X07439Hox-3.11.91.21.60.5Angiogenesis regulatorD76446TAK1 (TGF-β-activated kinase)—1.51.11.60.5Signal transductionAI853375BLAST: BC050902 Mdm2—1.7—1.11.68Cell cycleAI851595Unknown—1.41.21.63MiscellaneousX74040Mesenchyme fork head-1—1.8—1.21.58Signal transductionD50418Mouse mRNA for AREC3—1.6—1.21.43MetabolismAJ009862Transforming growth factor-β 1—1.8—1.31.48CytokineK03235Proliferin1.61.11.18Angiogenesis regulatorB. Regulated only by IGF-IIAA667100BLAST: XM_128828 GATA-6—1.22.12.60.5TranscriptionD11091Protein kinase C θ1.3—1.51.98Signal transductionL76155Bat-4—1.21.41.88MetabolismAI845935BLAST: AB042855 GNB-1—1.11.51.78Signal transductionM26156Histocompatibility 2—1.21.41.60.5MiscellaneousAW259499BLAST: XM_194355 similar to hypothetical protein1.21.91.68a Δ indicates the ratio of IGF-II stimulation/insulin stimulation Open table in a new tab Three genes selectively up-regulated by insulin are genes involved in angiogenesis regulation and differentiation: mrp/ plf, proliferin, and Hox-3.1. Mrp/plf and proliferin are highly homolog proteins that belong to the superfami
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