Identification of Coexpressed Gene Clusters in a Comparative Analysis of Transcriptome and Proteome in Mouse Tissues
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Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues T. Mijalski*, A. Harder†, T. Halder†, M. Kersten†, M. Horsch*, T. M. Strom‡, H. V. Liebscher§, F. Lottspeich¶, M. Hrabeˇ de Angelis*, and J. Beckers*ʈ *Institute of Experimental Genetics, ‡Institute of Human Genetics, and §Institute of Biomathematics and Biometry, GSF–National Research Center GmbH, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; †TopLab GmbH, Fraunhoferstrasse 18, 82152 Martinsried, Germany; and ¶Max Planck Institute for Biochemistry, Am Klopferspitz 18a, 82152 Martinsried, Germany Edited by Rudolf Jaenisch, Massachusetts Institute of Technology, Cambridge, MA, and approved April 26, 2005 (received for review October 15, 2004) A major advantage of the mouse model lies in the increasing explore the general feasibility of such a comparative gene expres- information on its genome, transcriptome, and proteome, as well sion analysis. A comparison of RNA and protein expression profiles as in the availability of a fast growing number of targeted and from adult male mouse liver and kidney was made. The choice of induced mutant alleles. However, data from comparative transcrip- different tissues provided a large set of differentially expressed tome and proteome analyses in this model organism are very proteins and genes. We used this set of differential expression limited. We use DNA chip-based RNA expression profiling and 2D profiles as a tool to address three major questions. (i) Does protein gel electrophoresis, combined with peptide mass fingerprinting of expression correlate with transcriptional regulation for the most liver and kidney, to explore the feasibility of such comprehensive differential proteins? (ii) Do transcriptomics and proteomics ap- gene expression analyses. Although protein analyses mostly iden- proaches detect functional categories with different preferences? tify known metabolic enzymes and structural proteins, transcrip- (iii) Does coregulated gene expression correlate with colocalization tome analyses reveal the differential expression of functionally in the genome? diverse and not yet described genes. The comparative analysis suggests correlation between transcriptional and translational Materials and Methods expression for the majority of genes. Significant exceptions from Mouse Tissues. Breeding of wild-type C3HeB͞FeJ mice was under this correlation confirm the complementarities of both approaches. specified pathogen-free conditions. Left kidney and dorsal lobe of ϩ͞Ϫ Based on RNA expression data from the 200 most differentially the liver were collected at the age of 105 days ( 5 days) from expressed genes, we identify chromosomal colocalization of male mice, killed between 9:00 a.m. and 12:00 noon by CO2 known, as well as not yet described, gene clusters. The determi- asphyxiation. Organs were immediately frozen in liquid N2. nation of 29 such clusters may suggest that coexpression of colocalizing genes is probably rather common. Protein Isolation. For pH gradient 4–7, 50 mg of tissue was ground in liquid N2. Ten milligrams was dissolved in 200 l of lysis buffer ͞ ͞ ͞ coexpression and colocalization ͉ comparative expression profiles {7 M urea 2 M thiourea 2% DTT 4% CHAPS (3-[(3-cholami- dopropyl)dimethylammonio]-1-propanesulfonate)͞0.8% Pharma- lyte 3–10} and sonicated for 10 cycles for 1 s (60 W). The sample ost biochemical processes within and between cells are put was kept shaking for 30 min at 25°C, and centrifuged for 5 min at Minto effect by the interaction between proteins, or between 20,000 ϫ g. Protein concentration was determined by a modified proteins and their substrates (1–3). The proteome of a cell is the Bradford method. Two hundred fifty micrograms of protein was result of controlled biosynthesis and, therefore, is largely (but not loaded onto each 4–7 immobilized pH gradient strip. exclusively) regulated by gene expression (4). Vice versa, the For pH gradient 6–11, 15 mg of the tissue powder was suspended transcriptome can be regarded as a sensitive read-out of the in 200 l of 4°C trichloroacetic acid (TCA)͞acetone 20% (vol/vol)͞ proteome or the biochemical state of the cell. Thus, transcriptome 50% (vol/vol). After sonication for 15 min (30 W) in a 4°C water and proteome feed back to each other in a highly complex way. The bath, the suspension was diluted with 1.2 ml of TCA͞acetone 20% understanding of this functional regulation is generally limited to (vol/vol)͞50% (vol/vol), vortexed for 2 min, kept at 4°C for 16 h and distinct signaling or metabolic pathways. To begin to understand the centrifuged for 30 min at 20,000 ϫ g (4°C). The pellet was washed mutual regulatory interactions between transcriptome and pro- twicein200l of acetone, sonicated in a 4°C water bath for 20 min, teome, a comparative approach including the simultaneous mon- centrifuged for 30 min at 20,000 ϫ g (4°C), resuspended in 200 l GENETICS itoring of expression at the RNA and protein levels will be required. of lysis buffer, and sonicated 10 times for 1 s (60 W) on ice. Samples The basic technologies for genome-wide expression analyses at were kept shaking at room temperature for 45 min and spun down the mRNA (5–7) and protein levels (8–10) are available. Transcript for 5 min at 20,000 ϫ g. A modified Bradford protein determination profiling was used to assess normal variability in gene expression was done. Three hundred micrograms of protein was loaded onto levels of mouse liver, kidney, and testis (11) and to analyze changes 6–11 immobilized pH gradient strips. in expression patterns during embryonic and fetal liver develop- ment (12). So far, comparative transcriptome and proteome anal- 2D Gel Electrophoresis (2-DE). Isoelectric focusing (IEF) was done yses in complex organisms are very limited and have been per- with 18-cm immobilized pH gradient strips from Amersham formed in human platelets (13) and heart tissue (14), and in the Anopheles and Culex salivary glands (15, 16). In rodents, the proteome of mouse primary islet cells was correlated with RNA This paper was submitted directly (Track II) to the PNAS office. expression data of purified primary rat beta cells, suggesting a close Freely available online through the PNAS open access option. correlation between mRNA and protein expression (17). A parallel Abbreviation: PMF, peptide mass fingerprinting. analysis of transcripts and proteins at a genomic scale in identical Data deposition: The sequences reported in this paper have been deposited in the Gene mouse tissue samples has not been performed. Expression Omnibus database (accession nos. GSE1696 and GPL1413). We use DNA chip-based expression profiling, 2D gel electro- ʈTo whom correspondence should be addressed. E-mail: [email protected]. phoresis, and subsequent peptide mass fingerprinting (PMF) to © 2005 by The National Academy of Sciences of the USA www.pnas.org͞cgi͞doi͞10.1073͞pnas.0407672102 PNAS ͉ June 14, 2005 ͉ vol. 102 ͉ no. 24 ͉ 8621–8626 Downloaded by guest on September 27, 2021 Pharmacia Bioscience. For each sample, five gels with gradients Results pH 4–7 and 6–11 were made. After focusing to the steady state, Differential Transcriptome of Mouse Liver and Kidney. To analyze the the strips were loaded with SDS and equilibrated in DTT and differential transcriptome of mouse liver and kidney, RNA expres- iodacetamide (18). sion profiling was performed with cDNA microarrays (22) con- The second dimension was performed as SDS͞PAGE. T12% taining a sequence-verified 20,200 mouse clone set (19, 21). Sixteen and C2.8% SDS gels were run vertically in a Ho¨fer ISO-Dalt dual-color DNA chip hybridizations of cDNAs from age-matched chamber by using the Laemmli buffer system. The SDS͞PAGE was C3HeB͞FeJ male mice were made (Table 1, chips 1 a–f, 2 a–f, and stopped when the bromophenol blue front ran off the gel; 1,800– 3 a–d, which is published as supporting information on the PNAS 2,000 Vh were applied. Gels were stained with Sypro Ruby and web site). For each individual mouse, six or four replicate hybrid- scanned with a Fuji fluorescence scanner. Mastergels were made izations were done. Between 16,092 and 19,592 probes had detect- from five replicas of one tissue in each gradient and matched able signals in individual chips, and 9,042 probes had signals in all (PROTEOMWEAVER, Definiens AG, Munich). Statistical calcula- microarrays (Table 1). tions were performed allowing a standard deviation of Ͻ30% and We first analyzed the significance of genes with signals on all 16 a confidence level of 0.05 in the t test. Coomassie-stained micro- microarrays. Based on expectations from random permutations of preparative gels were run with 500 g of protein per gel. genes and expression ratios, the selection of the top 1,802 differ- entially expressed genes would include one or more reproducibly PMF MALDI-TOF. Proteins were identified by PMF MALDI-TOF. regulated (false positive) genes by chance with P Ͻ 0.01 (Table 1). Spots were picked from SDS gels, washed three times with 10 mM Of the 1,802 differentially expressed genes, 821 were more abun- NH4HCO3 and 30% acetonitril (ACN), incubated overnight in 5 l dant in liver than in kidney, and 981 genes were more abundant in 25 ng͞l trypsin (Roche Diagnostics)͞10 mM NH4HCO3 (pH 8) at kidney as compared with liver (fully listed in Table 2, which is 37°C, and sonicated for 20 min at 25°C, and the supernatant was published as supporting information on the PNAS web site). In concentrated in a SpeedVac. The solution was processed through addition, genes were ranked based on the lowest absolute signal a C18 reversed phase ZipTip column (Millipore) by using 0.1% intensity ratio in 16-chip hybridizations regardless of reproducibil- trifluoroacetic acid (TFA) and 80% ACN for elution.