Journal of Molecular and Cellular Cardiology 43 (2007) 93 – 106 www.elsevier.com/locate/yjmcc
Original article
Comprehensive identification and characterization of novel cardiac genes in mouse Inju Park, Seong-Eui Hong, Tae-Wan Kim, Jiae Lee, Jungsu Oh, Eunyoung Choi, Cecil Han, Hoyong Lee, Do Han Kim, Chunghee Cho ⁎ Department of Life Science, Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea Received 25 February 2007; received in revised form 14 May 2007; accepted 15 May 2007 Available online 24 May 2007
Abstract Comprehensive understanding of the molecular and physiological events occurring in cardiac muscle requires identification of unknown genes expressed in this tissue. We analyzed the mouse cardiac muscle UniGene library containing 827 gene-oriented transcript clusters, predicting that 19% of these genes are unknown. We systematically identified 15 authentic novel genes abundantly expressed in cardiac muscle. Northern blot analysis revealed transcriptional characteristics of the genes, such as transcript size and presence of isoforms. Transfection assays performed using various cell lines including mouse cardiac muscle cells provided information on the cellular characteristics of the novel proteins. Using correlation analysis, we identified co-regulated genes from previously reported microarray data sets. Our in silico and in vitro data suggest that a number of the novel genes are implicated in calcium metabolism, mitochondrial functions and gene transcription. In particular, we obtained new and direct evidence that one of the novel proteins is a calcium-binding protein. Taken together, we identified and characterized a number of novel cardiac genes by integrative approach. Our inclusive data establish a firm basis for future investigation into the cardiac gene network and functions of these genes. © 2007 Elsevier Inc. All rights reserved. Keywords: Cardiac muscle; Gene expression; Microarray; Systems biology; Transcript; UniGene
1. Introduction The heart is central to the cardiovascular system. This muscular organ beats about 100,000 times rhythmically and spontaneously each day throughout a person's life. Complicated gene networks regulate the mechanism of heart contraction [1], and subtle changes in genes expressed in cardiac muscle can cause serious heart disease. A comprehensive understanding of the molecular and physiological events occurring in cardiac muscle requires identification and characterization of genes expressed in this tissue. To date, a number of high-throughput studies have investigated gene expression profile in heart tissues. Most of them have focused on differential gene expression when normal and disease conditions were compared [2–12]. Although these ⁎ Corresponding author. Tel.: +82 62 970 2490; fax: +82 62 970 2484. E-mail address:
[email protected] (C. Cho). 0022-2828/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.yjmcc.2007.05.018
studies have provided important information on the expression profiles of a number of cardiac genes, the identities and characteristics of unknown cardiac genes with unassigned function are elusive. An inclusive approach to the discovery of novel genes expressed in a given cell or tissue is the analysis of a database containing cell-specific or tissue-specific transcriptomes. The UniGene database provides information on tissue-specific gene cluster expression. The expressed sequence tags (ESTs) in UniGene are organized into clusters and each cluster is composed of sequences overlapping with at least one other member of the same cluster, but not with members of any other cluster. Thus, each cluster is likely to contain sequences corresponding to a single gene. Currently, UniGene is a large and widely used EST database and contains a large amount of unanalyzed information [13]. Furthermore, the UniGene database, combined with other computational bioinformatics databases, provides a great deal of information to assist in an understanding of
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characteristics of genes. Such in silico gene identification and analysis is a powerful tool of modern molecular biology [14–16]. Of 24 mouse heart and muscle libraries deposited in the UniGene data base at the National Center for Biotechnology Information (NCBI), we used the adult mouse cardiac muscle library (Li.8901) with 827 UniGene entries. Our analysis showed that 671 entries are known genes. These genes had previously been named and potential functions had been assigned to the genes. The other 156 entries were potentially novel genes (unnamed genes with unknown or unassigned functions). We focused on these novel gene candidates. Initially, we examined whether these gene candidates are genuine novel genes. Through various expression analyses, we sought obvious, significant expression of these genes in mouse cardiac muscle. With authentic genes, we used various in vitro and in silico analyses to obtain comprehensive expression information at both the gene and protein levels. Our study, presented here, is unique in that we offer systematic identification of previously uncharacterized genes that show cardiac expression, providing a new basis for studies to uncover molecular mechanisms underlying cardiac function. 2. Materials and methods 2.1. Reverse transcription-polymerase chain reaction (RT-PCR) Total RNA samples were isolated from various tissues of adult mice and from hearts of mice of different ages. Our work conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH Publication No. 85-23, revised in 1996). Complementary DNAs (cDNAs) were synthesized by random hexamer and oligo(dT) (Promega), priming using Omniscript reverse transcriptase (QIAGEN). Gene-specific primers are listed in Table 1. For PCR, the following conditions were used. The initial treatment for 5 min at 94 °C was followed by 30 cycles of 94 °C for 20 s, 55 °C for 40 s and 72 °C for 1 min. The cDNA levels were calibrated by glyceraldehyde-3-phosphate dehydrogenase (Gapdh). 2.2. Northern blot analysis Total RNA and poly (A)+ RNA samples were incubated at 65 °C for 5 min and separated on 1.2% (w/v) agarose gels containing 1.8% (w/v) formaldehyde. Separated RNAs were transferred to Hybond-XL membrane (Amersham) using a capillary method. The RNA bands were crosslinked using the Stratalinker reagent (Stratagene, La Jolla, CA). The blots were pre-hybridized with Rapid-Hyb buffer (Amersham) for 30 min at 68 °C and then hybridized with fresh buffer containing 25 ng of probes, derived from gene-specific PCR products, at 68 °C for 2 h. Probe DNA was labeled with [α-32P]dCTP (Perkin Elmer) using NucTrap® Probe Purification Columns and the Prime-It random priming kit (Stratagene). The blot was rinsed twice with 2× SSC/0.05% (w/v) SDS at room temperature for
10 min and twice with 0.1× SSC/0.1% (w/v) SDS at 68 °C for 10 min. The blots were developed with Hyperfilm (Amersham), using intensifying screens, at − 80 °C. 2.3. Cell culture and transient transfection The atrially derived cardiac muscle cell line HL-1 (a generous gift from Dr. W. C. Claycomb, Louisiana State University Medical Center) was plated in gelatin/fibronectin-coated culture vessels and maintained in Claycomb medium (JRH Biosciences) supplemented with 10% (v/v) fetal bovine serum (JRH, Lot No. 3J0229, no heat inactivation), 0.1 mM norepinephrine, 2 mM L-glutamine, 100 U/ml penicillin and 100 U/ml streptomycin [17]. The C2C12 and COS-7 cell lines (ATCC, Manassas, VA) were routinely propagated in a growth medium consisting of Dulbecco's modified Eagle's medium (GIBCO) with 10% (v/v) fetal bovine serum (GIBCO), 100 U/ml of penicillin and 100 μg/ml of streptomycin. Cells were maintained in a 95% humidified chamber at 37 °C with 5% (v/v) CO2. Full-length cDNAs of the novel genes were inserted into the pEGFP-N2 vector (Clontech). The cloning was designed to generate a C-terminal green fluorescent protein (GFP) fusion in each fusion protein. Transient transfection of clones was achieved using the Lipofectamine™ 2000 and Lipofectamine™ LTX transfection reagents. As controls, cells were transfected with the cDNA-pEGFP construct of calnexin (NM_007597) or stained with a mitochondrion-selective probe, MitoTracker® (Molecular Probe™). After 18–36 h, cells on the coverslip were fixed with 4% (v/v) paraformaldehyde and mounted on slides. 2.4. Calcium overlay assay A PCR product corresponding to the entire coding region of the Mm.20818 gene was generated using gene-specific primers with a 5′ EcoHI site and a 3′ XhoI site. After restriction digestion, the amplified product was ligated into the pGEX-5X2 vector (Pahmarcia). The resulting construct was expressed in E. coli BL21. The calcium overlay technique was previously described [18]. In brief, proteins were transferred from gels to nitrocellulose membranes. The membranes were then washed three times in a buffer (60 mM KCl, 5 mM MgCl2, 10 mM imidazole–HCl, pH 6.8) for 30 min, followed by probing with 45 [Ca]Cl2 at concentrations of 0.2–2.0 μM for up to 1 h. After probing, the membranes were washed in 67% (v/v) ethanol, dried and subjected to autoradiography with X-ray film for 1–3 days at − 80 °C. 2.5. In silico analysis The cDNA sequences of novel genes were subjected to BLAST analysis using software available in the database of NCBI Mouse Genome Resources. The University of California Santa Cruz (UCSC) Genome Bioinformatics Center (available on the World Wide Web at genome.ucsc.edu/) provides working draft assemblies for a large collection of genomes. The data include chromosomal locations, exon–intron structures and analyses of comparative genomics. The programs Goblet (avail-
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Table 1 List of novel gene candidates and gene-specific primers designed for RT-PCR analysis No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
UniGene ID
Mm.261329 Mm.29831 Mm.274266 Mm.23928 Mm.222310 Mm.138832 Mm.44359 Mm.41877 Mm.38746 Mm.337184 Mm.333338 Mm.329858 Mm.3014 Mm.293696 Mm.290534 Mm.282614 Mm.27886 Mm.27469 Mm.269736 Mm.26377 Mm.251301 Mm.20818 Mm.41719 Mm.38813 Mm.359298 Mm.358759 Mm.357860 Mm.336238 Mm.329396 Mm.320571 Mm.30571 Mm.303430 Mm.301646 Mm.30113 Mm.29342 Mm.290953 Mm.275411 Mm.273584 Mm.272253 Mm.266515 Mm.262056 Mm.259910 Mm.25321 Mm.247453 Mm.23230 Mm.23049 Mm.23010 Mm.203866 Mm.19961 Mm.18936 Mm.169234 Mm.159956 Mm.159860 Mm.158769 Mm.133444 Mm.129840 Mm.12654 Mm.121122
GenBank ID
NM_026064 NM_175107 NM_026332 NM_172591 XM_619819 XM_887814 NM_001024952 NM_027460 NM_021502 NM_153530 XM_892747 XM_193941 XM_973090 NM_153803 NM_024480 NM_026641 NM_025551 NM_027533 NM_197979 NM_178404 NM_172851 NM_026441 NM_026443 NM_172458 XM_484909 NM_028883 XM_489068 NM_175446 NM_175164 XM_484402 NM_201407 XM_895138 NM_146119 XM_485037 NM_027900 NM_172116 NM_021434 NM_028732 NM_028260 NM_178916 NM_172943 XM_138271 NM_173748 NM_172511 NM_027293 XM_486240 NM_001031808 NM_175108 NM_008725 NM_183172 NM_026452 NM_025504 NM_026898 NM_027104 NM_026509 NM_178080 NM_175474 NM_026655
PCR primers
Product size (bp)
Forward
Reverse
AGGGCAAAGACCAAGACCAC TCAAGGCCAGGAAGACGATG GAGTCATGGCCAGCACAGTG CTGTGGAATGTCCTGATGCAG CAGTCTTCAGGAGGCCCATG AGACTGCCCGCAAATCCAC ACGAGAGAGCAGAGGTTGGAC ACGCGACTGCAGTCTTCGAG GCCAATATGGCGGTGTGCATC ACAAGTCCATGCGAGGGAAG CAGGAGGTCTTCAGTGTCTC CGATGCCGTGGTGATAGGAG ACTGGCTGTCACTGGCACAC CTAGAGGCCTTTATCCAGCTG GAGGAGCTGGAACATCTGAAC AGACATCCTGAACGCTCAGAC GTCATCTACACCACCGAG AGCCGGATCAGCCGTTATTG TGCTGTTCCGCAGAACTTC CAGGGCACGACAGAGAAGATG AGGAGCCACCTGTCATGATTC CAGTCAGTGGATGCCGATCAC TGCAGCTGTGGTGTGGTTGAG GAAGGCACGAACGACGTCATC GTGGCTGAGTGTGATCTGGTG TGAAGCCAACCGACAGGACAC CAGCCTTTTGCATCTGAGGCG CATATGTTCCGGTCCCACATG TGCTGGGTGTGATGGCTCATG AGAGGATTAGCCGGTACCAGG TCTATGAAGCGTTCCCGAGGG AGCATGGACAGTGCAGTCTC TGATGACAGAAGCGGAGCAG TGACCCACAAGCAGCTCTCAG CTCAAGTTGGTGCGCAGTCTG TGTCAGCCCAGTCCTTCGTC CATCACGTTTGCCAAGGGCTG TCTTGGCTCTCATCTCCAGGG CTGGCCGAGGAACTTCTGTTG CCGTCTGTGTTGGCAGAGAAG TGGATGAGGTCTCTTGGAGCC ACTCGATCTGGACGGGTATC CTCTACCGCAAGACCGACTTC GCACAGATCTTGTCGCTCTGC AGATCGAGCTGCTCAAGCTG ACGAGAAGCACAAGCACAC TGCACCTCCTGATGGTGAATG TTGCTCCTGAGCAGAGTCCTG GAAACCAGAGTGGGCAGAGAC TGTCCTGCCTGGAAGTACTC GAGAGTGAGGAGCAGCTACAG TGCACACTATGCACGTCCTG TCTGTCTCTGTAGCGTCGTG GACACTACTGCCAAGAGACCC GGACACGGATTGCCATAGGAC GCGGCGTTGGATCTTTCATA CTCCTCGTGACCATGAAGCTC AAAGCGCACAGCCATCTGGAG
TCATCCACCTCCTCGTCTG TGGAGACCTTTTGCTGCCTG GTCACTGCAGAGAACGTCAC CAAGCTTTGGTCTGGAAGCAG ACGTGGATGGGTCTGGTGAG GTATGCGGCGTGCTAGCTG ATGCAGTTCCAAGCTGAGCTG GAGGAGACGATCGGAGCGTC TTCTGAACGACTGAGGCAGGC GATGCCAGTTGCTGCTCGAG CAGACTCTGTGTGGATGCTG ACCGTTGGTCCAGCACCTTG CACCATGTTCCAGTAGAGGAC TCCAGTACTCCATCACCATC CTGAACTGGGCCTTGAGCTC CAAGCCCACATGCTTTGCTC GTAAGGTGTCGAAGGTGG CACAGCAAAGCACCATGCTG GTCGGTGAACGGCAACTTG AGTGAGGACAGCAGTCAGAG TGACATCTCCAGGCAGCTC AGCCGTCATGGTGACAAAGTC CAGTGCCATGGACCCCAAAC CTCAAGGACCCAGAACAAGTG CAGTGGATAGCGCTTGACTC GAGCTGAGGTAGATGACGGAC TCCAGCCTCCTTGCAGTACAC CTTGCTGTAGGTCTGTGGAG TTTCCTGAGGAGGACGAGGAG CTTGGAAGCCTTGAGGTCTCC CACTGCCAATGCAGAGGGAAG ACTGCTTGTCTGGGTCTCTC CAGAGCCTCCAAGATGGATG CTCTCATACCAATTGGCCGCG ACCTCTGTTCAGGGATCCTCG GAGCAGGTTCTGGACAGCAG CGCTCAGCTGTCACACAAAGG ACAACTTGTCTGGGTCCCCAG GACCTGTTGGCACGTAACTGC CCGTTATCCACCTTCACGGTG GGGAAAAGAGGCCCAAGATGG CACTGCTTGAGCGCCTGTAG GAGCTCTCGGTGTTGATCTTG CTGACTCTACCCTTCTGCCAC GATGGGATCGCTTACGGCAG CTTGCGCAGACTTTCCACTTC AGGTAGGAGTGGGCTTCATG ATGCAGAACCTGCATCTGGGG AGAGGGCAGATCTATCGGAGG CAGCTGCCATGACACGAAAC CTGCATCATCACCAGCTCTG AGCTCCAAGGACCTCACAGTG CTCTGGAGTTTCCACCCTG ATCCACTTGGGTAGGGAGCAG CAGGGAATGTCCTCCTGGAAG GGCACCTTACAAATGGCTTCA TCAGCAGAACCAGGATGCCAG CCTCGCAGCAAGTCCACTAAC
410 616 437 504 576 378 546 557 459 629 602 599 468 490 515 615 249 566 260 425 463 471 472 505 687 550 344 558 501 486 520 541 530 346 496 572 347 399 505 331 511 169 659 474 521 602 595 480 384 585 501 701 307 363 466 513 701 427
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able on the World Wide Web at goblet.molgen.mpg.de/cgi-bin/ GOblet.cgi) and AmiGO (available on the World Wide Web at godatabase.org/cgi-bin/go.cgi) were used to predict gene ontology. Amino acid sequences deduced from cDNA sequences of novel genes were analyzed using several computational bioinformatics tools. The SignalP and TMHMM (available on the World Wide Web at www.cbs.dtu.dk/services/) and InterProScan (available on the World Wide Web at www.ebi.ac.uk/InterProScan/) programs were used. 2.6. Preprocessing of heart-Related microarray data and identification of putative neighbor genes Gene Expression Omnibus (GEO) is a public repository for microarray studies, in which the novel genes are included in
microarray data from diverse organs and tissues. Data on 15 heart- and muscle-related microarray studies were obtained from GEO: GDS40 (cardiac development, maturation and aging), GDS41 (cardiac hypertrophy induced by pressure overload), GDS144 (cardiac hypertrophy, exercise induced), GDS246 (hypoxia), GDS251 (pulmonary fibrosis), GDS404 (circadian oscillations and cardiovascular function), GDS488 (myocardial infarction time course), GDS496 (heart failure in cardiac-specific TNF alpha transgenics), GDS627 (cardiac development in embryo), GDS638 (dystrophin-deficient mdx diaphragm muscle development time course), GDS639 (dystrophin-deficient mdx hindlimb muscle development time course), GDS641 (dystrophin-deficient mdx, mdx5cv and wild type skeletal muscle profiles), GDS1032 (coxsackievirus B3 infection effect on hearts: time course), GDS1303 (aldosterone effect
Fig. 1. Gene ontology profiles of genes expressed in cardiac muscle. Genes from the mouse cardiac muscle UniGene library (Lib.8901) were grouped into gene ontology categories belonging to the three main classes of molecular function (A), cellular component (B) and biological process (C). This classification is based on UinGene build 161 (January 2007).
I. Park et al. / Journal of Molecular and Cellular Cardiology 43 (2007) 93–106
on heart: time course) and GDS1306 (MAP kinase activation effect on heart: time course). The redundant expression profiles for any one gene were recalculated to obtain representative expression profiles. Then, UniGene identifiers were assigned using chip information from GEO. To obtain a correlation coefficient reflecting gene expression reliability across the 15 microarray data sets, a procedure merging the data sets into a single expression profile was followed. Here, we used a bypass to reject all genes having uneven distributions in the different microarray studies and we obtained accurate correlation coefficients as follows. Using the 15 heart-related microarray studies, the Pearson's correlation coefficient (PCC) between the expression levels of gene pairs was calculated to help identify novel cardiac genes on the basis of tissue-specific co-expression. The formula used was: n X
n X
n X Xi Yi i¼0 i¼0 i¼0 v ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ¼ u" #" # n n n n X X X u X t n Xi2 ðXi2 Þ n Yi2 ðYi2 Þ
nð
i¼0
Xi Yi Þ
i¼0
i¼0
i¼0
where r is PCC, X and Y are measures of gene expression and n represents the number of experiments from which data are taken. The PCC values were calculated in two different contexts. First, individual PCC values, from each microarray study, were calculated to reflect dynamic gene expression relationships under different heart-related conditions. Second, overall PCC values from integrated expression profiles across the 15 microarray studies were calculated to obtain static gene expression relationships in heart tissue. To validate the significance of PCC data, χ2 analyses were performed in comparison with randomly constructed expression profiles created by artificial, arbitrary selection of genes and experimental conditions in microarray data. P-values of b 10− 5 were considered significant. The final selection criteria for genes with related expression levels were as follows. First, the overall PCC, indicating a static functional gene relationship in the heart, was N 0.8. Second, a strong relationship, defined as a PCC N 0.9, was present in at least 25% of the microarray studies. Third, the minimum PCC (N0.7) identifying a functional relationship was satisfied in at least 75% of the microarray studies. Finally, at least 50% of the PCC values from each microarray study were N 0.8 to support the notion that valid functional relationships were under study. 3. Results 3.1. The cardiac muscle library and in silico analysis to select novel gene candidates Lib.8901, one of the UniGene libraries deposited in the UniGene database at NCBI (www.ncbi.nlm.nih.gov), is a collection of genes expressed in mouse cardiac muscle and contains 827 UniGene entries (as of January 2005). To gain an insight into the characteristics of the cardiac muscle library, we
97
analyzed the genes in the library based on gene ontology codes that provide information about cellular component, molecular function and biological process (Fig. 1). The overall gene ontology feature of these genes was similar to that of genome-wide genes in mouse [19], except for the notable expansion in the category of mitochondrial genes. This reflects the active aerobic metabolism required for muscle contraction and is consistent with the abundance (25%) of this organelle in the cardiac muscle cells. To identify and investigate novel genes expressed in cardiac muscle, we sorted genes in the library based on the following criteria. Genes previously named with known or potential functions were considered to be known genes, while unnamed genes with unassigned functions were regarded to indicate unknown or novel genes. This classification of the 827 gene entries disclosed that 671 (81%) are known genes, and 156 genes (19%) are unknown or novel (Tables 2 and 3). To select more principal and reliable genes, we further analyzed the 156 genes whether they have mRNAs with poly (A). As a result, we identified 110 gene entries with potential full-length transcript sequences. Gene ontology data and indications of putative protein domains and motifs were also criteria for more effective selection of novel genes. Using these processes, the potential 110 novel genes were narrowed down to 58 candidates. These genes further analyzed in vitro (Tables 1–3). 3.2. Cardiac expression of the novel genes To determine whether the candidates selected from the UniGene library are true novel genes with cardiac expression, we performed various expression analyses (Tables 1–3). RT-PCR analysis showed that 41 of the 58 candidates are expressed with the expected sizes in mouse heart. However, no PCR products were detected for the other 17 candidates in the heart. Thus, they were excluded from further analysis. It should be noted that PCR was designed to be similar among the candidates in primer property and product size, and reaction condition was the same for all the candidates. The 41 gene candidates were further examined for tissue distribution using mouse cDNAs from Table 2 Classification and selection of genes in the cardiac muscle UniGene library Genes
Number
Total entries Known Unknown Full-length genes Genes with GO and domain/motif Genes with cardiac expression (RT-PCR) Genes with similar or higher expression level in heart, compared to other tissue (RT-PCR) Genes with abundant expression (Northern blot)
827 671 156 110 58 41 26 15
Genes in the cardiac muscle UniGene library (as of January 2005) were classified into known and unknown genes. Of unknown genes, partial length genes were excluded. Then, genes with gene ontology (GO) data, i.e. information about the molecular function, biological process and cellular component of gene products, or with known domains or motifs, were selected. These genes were analyzed in vitro to select authentic genes with abundant cardiac expression.
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Table 3 List of unknown genes analyzed in this study
Table 3 (continued)
No.
ID
F
G/D/M
P1
P2
N
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
Mm.371632 Mm.261329 Mm.360052 Mm.335368 Mm.334198 Mm.29831 Mm.289485 Mm.274266 Mm.268307 Mm.25788 Mm.251655 Mm.23928 Mm.227983 Mm.222310 Mm.138832 Mm.138453 Mm.50840 Mm.49041 Mm.44359 Mm.41877 Mm.38746 Mm.372182 Mm.371763 Mm.371537 Mm.370610 Mm.370549 Mm.369924 Mm.369196 Mm.358815 Mm.354322 Mm.339945 Mm.337184 Mm.333338 Mm.329858 Mm.323315 Mm.318319 Mm.313930 Mm.311874 Mm.305990 Mm.3014 Mm.293696 Mm.290704 Mm.290534 Mm.288128 Mm.285021 Mm.282614 Mm.281668 Mm.28071 Mm.27886 Mm.27469 Mm.273196 Mm.269736 Mm.266485 Mm.26377 Mm.258 Mm.251301 Mm.229107 Mm.20818 Mm.205191 Mm.194305 Mm.41719 Mm.41583 Mm.38813 Mm.38269
+ +
+
+
+
+
+
+
+
+ + + + + + + + + + + + + +
+
+
+
+
+
+ +
+
+
+
+ + +
+ + +
+
+
+
+
+ + + + + + + + + + + +
+ + +
+
+
+ + +
+ + + + +
+
+
+ +
+ +
+ +
+
+ + +
+
+
+
+
+
+
+ +
+
+
+
+
+ + + +
+
+
+
+
+
+
+
+
+
No.
ID
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129
Mm.373432 Mm.372776 Mm.371996 Mm.370516 Mm.367149 Mm.362826 Mm.361745 Mm.361402 Mm.359298 Mm.358759 Mm.358702 Mm.35790 Mm.357860 Mm.352691 Mm.352188 Mm.351793 Mm.350314 Mm.348074 Mm.342680 Mm.342247 Mm.341267 Mm.340375 Mm.339493 Mm.336238 Mm.334678 Mm.330045 Mm.329396 Mm.328806 Mm.328361 Mm.320571 Mm.313975 Mm.309533 Mm.308627 Mm.30571 Mm.303430 Mm.301646 Mm.30113 Mm.297707 Mm.296878 Mm.294664 Mm.29342 Mm.290953 Mm.290810 Mm.290771 Mm.290116 Mm.28963 Mm.28869 Mm.27900 Mm.275411 Mm.273584 Mm.273536 Mm.272253 Mm.271774 Mm.27107 Mm.268027 Mm.266515 Mm.262056 Mm.259910 Mm.25321 Mm.250391 Mm.249115 Mm.247453 Mm.246412 Mm.244545 Mm.233440
F
G/D/M
P1
P2
+ + + + +
+ +
+
+
+
+
N
+ +
+ + + + + + + +
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+
+
+
+
+ + + +
+ +
+
+
+ +
+ +
+
+
+ +
+ +
+
+
+
+
+ + + +
+ +
+ +
+
+
+
+
+
I. Park et al. / Journal of Molecular and Cellular Cardiology 43 (2007) 93–106 Table 3 (continued) No.
ID
F
G/D/M
P1
130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 Total
Mm.23230 Mm.23049 Mm.23039 Mm.23010 Mm.228651 Mm.221303 Mm.216590 Mm.216313 Mm.208378 Mm.204831 Mm.203866 Mm.19961 Mm.19073 Mm.18936 Mm.182042 Mm.173826 Mm.170971 Mm.169234 Mm.159956 Mm.159860 Mm.159754 Mm.158769 Mm.133444 Mm.129840 Mm.128512 Mm.12654 Mm.121122
+ +
+ +
+ +
+ +
+
P2
N
+ +
+
+ + + + + + +
+ +
+ +
+
+
+ + + + +
+ + +
+ + +
+ + +
+ + +
+ +
+ +
+
+ + 110
+ + 58
+ 41
+ 26
+ 15
Abbreviations: F, gene with full-length transcript sequences; G/D/M, gene with gene ontology, domain or motif data; P1, genes with cardiac expression by PCR; P2, genes with expression level in heart similar to or higher than that in at least one other tissue; N, genes with signal by Northern blot analysis.
multiple tissues. Among these, we discarded 15 candidates with the lowest expression levels in cardiac tissue, compared to other tissues examined (data not shown). Expression data from the remaining 26 genes show the presence of at least one other tissue that shows a lower expression level than heart (Fig. 2). One gene candidate (Mm.19961) was expressed specifically in the heart and two candidates (Mm.158769 and Mm.133444) were either muscle-specific or were expressed predominantly in the heart. Expression of the other putative genes was widely distributed (Fig. 2A). To establish the expression patterns of the 26 novel genes during development, RT-PCR analysis was carried out using mouse heart cDNAs obtained at different time periods after birth. During development, the transcriptional levels of 12 genes did not change (group 1), and 14 genes showed increased transcriptional levels (group 2) (Fig. 2B). 3.3. Northern blot analysis To determine the transcript sizes of the 26 novel gene candidates, Northern blot analysis was performed. Fifteen of the 26 genes showed significant transcriptional signals in heart tissue (Fig. 3), whereas the analysis resulted in no signals in the other 11 genes. It should be noted that the relative expression levels in different tissues are not comparable between the RT-
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PCR and Northern blot results in some of the genes (Figs. 2A and 3). This may be due to a limitation in reliable quantification during the non-exponential phase of PCR reaction. Alternatively, our Northern blot assay may not be as accurate as the PCR assay. In this case, the Northern blot analysis should be regarded as an assay only for the qualitative evaluation of transcripts. Transcript sizes ranged from ∼ 0.5 kb (Mm.269736) to ∼ 5 kb (Mm.23928). For 12 genes, transcript sizes determined by Northern blotting were comparable to those estimated from the UniGene database, while for the other three genes, significant differences in transcript size (N 0.5 kb) were evident between Northern blots and UniGene database sequences (Fig. 3). Thus, the transcript sequences for the 12 genes can be regarded with confidence as full-length cDNAs or sequences containing the majority of entire cDNA sequences. Mm.261329 produces transcripts of more than a single length, both in heart and skeletal muscle. This suggests that multiple transcript isoforms of this gene may be formed by alternative splicing. Taken together, our in silico and in vitro analyses of the 156 potential genes identified 15 authentic genes abundantly transcribed in mouse heart (Tables 2 and 3). It should be noted that Mm.19961 (named as natriuretic precursor type A) has been reported during the course of our study [20]. Thus, 14 genes excluding Mm.19961 were analyzed further. 3.4. In silico analysis of genomic, transcript and protein characteristics To characterize genomic, transcript and protein natures of the 14 novel genes, we performed various database searches. Fig. 4 shows chromosomal locations, genomic structures, exon organization, transcript sizes, numbers of amino acids, specific domain/motif and gene ontology of the predicted proteins encoded by the novel genes. The novel genes are widely distributed on mouse chromosomes. The sizes of the genes range from 420 bp (Mm.269736) to 4812 bp (Mm.23928). The exon numbers in the genes are also variable, ranging from 1 (Mm.138832) to 26 (Mm.23928) exons. For the three genes (Mm.128832, Mm. 38813 and Mm. 158769) predicted to have additional transcript sequences, based on the Northern blot result (Fig. 3), gene size and exon number could be larger than the present estimation. The protein-coding region of each gene was defined by selecting the longest amino acid sequence terminating before a polyadenylation signal (if there is one present). Gene products were predicted to contain various domains and motifs and were found to have potential gene ontology codes (Fig. 4). It is noteworthy in relation to the in silico prediction of the characteristics of the novel genes that seven of the genes have been recently named in the UniGene database. They are Mm.23928 (FCH domain only 2), Mm.41877 (solute carrier family 25, member 33), Mm.27886 (NADH dehydrogenase 1 alpha subcomplex 12), Mm.20818 (penta-EF hand domain containing 1), Mm.30113 (NADH dehydrogenase Fe-S protein 3), Mm.29342 (R3H domain containing 2) and Mm.272253 (inner mitochondrial membrane peptidase-like). Nonetheless, in vitro expression and functional data for any of the novel genes have not been reported. Based
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Fig. 2. Tissue distribution and developmental expression patterns of 26 novel gene candidates. (A) Tissue distribution of the genes by RT-PCR analysis in various tissues of adult male mice. B, brain; H, heart; K, kidney; Li, liver; Lu, lung; Ov, ovary; Sk, skeletal muscle; Sp, spleen; T, testis. (B) Expression of the candidates in heart during development. Gene expression was analyzed by PCR using cDNAs prepared from postnatal hearts obtained at different days during mouse development. Fifteen of these 26 genes, found to show significant Northern signals (Fig. 3) and thus, analyzed further in this study, are indicated by vertical bars. To insure the use of the equivalent amounts of cDNA templates among different tissues (A) and different developmental stages (B), template amounts were normalized prior to PCR reaction of the novel genes, based on the expression levels of the glyceraldehyde-3-phosphate dehydrogenase (Gapdh) gene.
on the in silico information, proteins encoded by the novel genes might be implicated in calcium metabolism (Mm.261329 and Mm.20818), mitochondrial functions (Mm.41877, Mm.27866, Mm.269736, Mm.41719, Mm.30113 and Mm.272253), transcription and/or nuclear activity (Mm.138832, Mm.38813 and Mm.29342), cell structure (Mm.158769) and transmembrane transport (Mm.121122). 3.5. Expression and localization of the novel proteins To explore the characteristics of the novel genes at the protein and cellular levels [21], GFP-tagged full-length gene sequences were transiently transfected into various cell lines such as HL-1 (mouse cardiac muscle cells), C2C12 (mouse skeletal muscle myoblast cells) and COS-7 (monkey kidney cells) [17,22,23]. We observed GFP signals from most of the novel genes, except for Mm.29342 (Fig. 5). The cDNA se-
quence for Mm.29342 may not code for a protein, or expression of Mm.29342 could be highly transient, very low in amount or delayed. Proteins tagged with GFP were found to distribute to various subcellular organelles, such as nucleus (Mm.261329, Mm.138832, Mm.41877, Mm.27886, Mm.269736, Mm.20818, Mm.38813 and Mm.30113), cytoplasm (Mm.261329, Mm.23928, Mm.41877, Mm.27886, Mm.269736, Mm.20818, Mm.41719, Mm.38813, Mm.30113, Mm.272253, Mm.158769 and Mm.121122), mitochondria (Mm. 41719 and Mm.272253), cytoskeleton (Mm.30113) and endoplasmic reticulum (Mm.158769) (Fig. 5). It is important to note that localization patterns are highly consistent between cell types in the majority of the genes. Another notable feature in the distribution patterns is that several gene products are located in more than one compartment. We next compared bioinformatic information (Fig. 4) with the actual localizations of the gene products (Fig. 5). The in silico information and localization data were
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Fig. 3. Transcript analysis by Northern blot hybridization. Total RNA or poly (A) RNA from adult mouse heart (H), skeletal muscle (S) and liver (L) were hybridized with cDNA probes of the genes. Agarose gels were stained with ethidium bromide to visualize 28S and 18S RNAs as a control to ensure loading of the same amount of RNA in each lane. Transcript sizes from the UniGene database (DB) and transcripts with significant differences in size between the Northern blots and DB are indicated below the blots.
agreeable in the six of the 13 novel genes analyzed, such as Mm.261329, Mm.138832, Mm.20818, Mm.41719, Mm.38813 and Mm.121122. 3.6. Identification of genes correlated with the novel genes To extend our systematic investigation into the novel genes, we attempted to identify genes exhibiting transcriptional correlation with the novel genes in heart and muscle, using available cardiac or muscle microarray data from GEO. We used the Python script language, a valuable tool in the management of microarray and genome annotation data. Based on the PCC values, clustering algorithms were established to discover groups of genes which exhibited similar expression profiles across the data sets. Using the strict PCC values in 15 GEO data sets (see Materials and methods), we found correlated and coregulated genes for seven novel genes (Table 4). These were Mm.23928, Mm.41877, Mm.269736, Mm.20818, Mm.272253, Mm.158769 and Mm.121122. The numbers of the correlated genes varied in each novel gene and their gene ontology appeared also diverse. It is noteworthy that some of the genes correlated with Mm.269736 are involved in mitochondrial functions and Mm.269736 is also predicted to have the similar activity (Table 4 and Fig. 4). Taken together, these data of the correlated genes provide a basis for characterization of a gene network involving the novel genes.
3.7. Characterization of Mm.20818 As an initial step towards the functional characterization of the novel genes, we analyzed the protein characteristics of one of the novel genes, Mm.20818. The protein encoded by Mm. 20818 is predicted to have the EF hand motif (Fig. 4) and is found to be located in the nucleus and cytoplasm (Fig. 5). The EF hand is a helix–turn–helix structural motif with 30 amino acids. Two α-helices in the motif often bind to Ca2+ions by conformational changing of loop [24]. However, nearly onethird of all known EF-hand motifs do not bind to Ca2+. We performed 45Ca2+ overlay assay with the purified GST-Mm. 20818 protein. As shown in Fig. 6, the recombinant Mm.20818 protein was found to have a Ca2+-binding activity. The GST protein itself did not bind to Ca2+. A protein sample from sarcoplasmic reticulum from mouse heart, containing cardiac calsequestrin as the major protein, showed a calsequestrinspecific signal. Thus, the result provides the first evidence that Mm.20818 encode a protein with the Ca2+binding activity. 4. Discussion Cardiac muscle is specialized to pump blood throughout the body continuously and rhythmically. Cardiac muscle facilitates efficient blood pumping without control by the central nervous system. Unlike other tissues excited by membrane depolariza-
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Fig. 4. Genomic, transcript and protein characteristics of the novel genes in silico. The chromosomal locations of the novel genes, their structure and intron–exon organization were determined by genome database searches. Vertical bars and connecting horizontal lines show exons and introns, respectively. The directions of transcription are indicated by arrows. Coding regions were determined by selecting the longest open reading frames deduced from the cDNA sequences and the predicted coding regions are shaded. Putative domains or motifs are indicated and shaded. FCH, Fes/CIP4 (Cdc42-interacting protein 4) homology domain; Mito_carr, mitochondrial carrier protein; Complex1_17_2 kDa, NADH dehydrogenase; FRQ1, Frequenin 1; Q9CZX4, mitochondrial 18 kDa protein (MTP18); KRAB, Krueppel-associated box; Complex1_30 kDa, NADH dehydrogenase; Encore_like_R3H, the R3H domain containing an invariant arginine and a highly conserved histidine; Peptidase_S26, signal peptidase I; ABC transport, ATP-binding cassette.
tion, cardiac excitation and contraction coupling occurs upon polarization. When the pacemaker produces an excitation signal, the atria and ventricles contract sequentially. To maintain pumping at a constant rate, the heart provides its own energy. Many protein complexes are required for the physiological, energetic and mechanical cardiac functions. This suggests that many cardiac genes elaborately regulate cardiac behavior and function. To understand the heart in depth, identification of
systematic networks in cardiac regulation is required at both the gene and protein levels. Our inclusive approach to the discovery of novel genes from cardiac muscle uses databases and computational bioinformatics tools. UniGene provides information on gene-oriented clusters and also on tissue specificities of gene expression. UniGene is a powerful tool, offering an organized view of the transcriptosome, with frequent updates and convenient linkages
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Fig. 5. Subcellular localization of novel gene products in HL-1, C2C12 and COS-7 cell lines. The cells were transfected with novel cDNA-GFP fusions and GFP-tagged proteins were visualized under fluorescent light and protein locations determined. Regions stained with Hoechst 33258 dye (blue) indicate nucleus. Micrographs at the first column show cells expressing GFP alone. Micrographs at the last two columns represent positive controls for localization in endoplasmic reticulum and mitochondrion. Cells were transfected with the cDNA-GFP construct of calnexin known to be localized to endoplasmic reticulum or stained with a mitochondrionselective probe. N, nucleus; C, cytoplasm; M, mitochondrion; Cs, cytoskeleton; E, endoplasmic reticulum; Mit. probe, mitochondrion-selective probe. Scale bar = 10 μm.
to other bioinformatics tools. Lib.8901, selected from the UniGene database including 15 heart-related and 9 musclerelated mouse libraries, is the only cardiac muscle library. Our analysis of Lib.8901 revealed that the library has 827 UniGene entries and significant proportion (19%) of the genes are unknown or uncharacterized. Of these entries, many consist of only partial ESTs, and there is therefore a risk that the genes may be removed from the UniGene database or may be amalgamated with other gene entries. We thus selected 110 gene candidates with full-length sequences, and subsequently 58 candidates with available data on gene ontology and domain/
motif structure, since gene ontology and domain/motif data assist the direction of future work. Through in vitro expressional analyses, the 58 candidate genes were conclusively reduced to 15 authentic genes. The other 43 genes were excluded from consideration because their transcripts were not detected in the heart (17 candidates), observed at a low level, compared to other tissues (15 candidates) or not detected in the Northern blot analysis (11 candidates). Most of the 15 genes were widely expressed in various tissues. The developmental profile showed that a number of the novel genes are expressed even in the very early stage of
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Table 4 List of genes correlated with the novel genes in microarray data UniGene ID of novel genes
Genes with correlation UniGene ID
Gene ontology
Mm.23928
Mm.39472 Mm.74208 Mm.294821 Mm.18946 Mm.32889 Mm.390360 Mm.221758 Mm.27578 Mm.29867 Mm.275780 Mm.20841 Mm.21501 Mm.29683 Mm.34775 Mm.29939 Mm.248046 Mm.6635 Mm.158231 Mm.27499 Mm.141157 Mm.41926 Mm.425538 Mm.20914 Mm.305535 Mm.3759 Mm.28858 Mm.122725 Mm.925 Mm.276503 Mm.126534 Mm.4154 Mm.5390 Mm.24848 Mm.41583 Mm.49689 Mm.5189 Mm.30088 Mm.1963 Mm.329083 Mm.290628 Mm.285906 Mm.19992 Mm.119162 Mm.281086 Mm.34087 Mm.390367
Cholesterol biosynthesis Keratinization Vesicle transport Vesicle-mediated transport Translational initiation
Mm.41877 Mm.269736
Mm.20818
Mm.272253
Mm.158769 Mm.121122
Regulation from pol II Precursor metabolites Electron transport ATP synthesis Electron transport Tricarboxylic acid cycle Acetyl-CoA metabolism Sodium ion transport Protein folding Electron transport
Phosphorylation Arginine biosynthesis Negative regulation of proliferation Chemotaxis Metabolism S phase Regulation of translation Blood coagulation Electron transport
Phospholipid metabolism Protein folding Nuclear mRNA splicing Male gonad development Regulation of transcription Regulation of transcription
istics of the genes at the transcription level. New information about transcript size and isoform distribution was obtained. The data convincingly indicate that the putative genes are both authentic and significant. Mammalian cells are highly compartmentalized and a protein's localization is tightly related to its function. Despite rapid expansion of transcript databases, bioinformatic analyses alone are not sufficient enough to assign function to novel genes [21]. We examined the intracellular localization of the novel proteins, using the GFP fusion protein approach. In six of the novel genes, the localization data were matched to the information obtained from in silico analysis. In the other genes with the data contradicting the in silico predictions, the bioinformatic analysis may be unable to make accurate predictions for these genes. Alternatively, GFP may have abnormal effects on targeting signals of the proteins. Collectively, our results of transient transfection provided critical data on cellular localization of proteins encoded by the novel genes and provided a firm basis for further study on the genes. To gain an insight into gene network involving the novel genes and acquire detailed information on gene function in cells, we selected neighboring genes, defined as genes that are coregulated under a particular set of conditions. To define such genes, we used the GEO database, an online resource for gene expression data browsing, query and retrieval. Using GEO microarray data, and with the Python language, we detected several genes with similar expression patterns under various conditions. The Python script is a powerful tool in the systematic management of large amounts of data, such as may be found in the UniGene and GEO databases. From correlation analyses, we showed that several significant genes were related. These genes may function similarly in various complexes. Alternatively, the genes may share common upstream processes. We attempt to categorize the novel genes based on our in vitro and in silico findings. It should be noted that the in vitro
Expression profiles of the novel genes were compared to those of other genes, using a variety of heart- and muscle-related microarray data (see Materials and methods). Genes with transcriptional correlation, showing expression profiles similar to those of the novel genes, are listed.
development. As the heart develops, some genes increase in expression while others do not. In mice, the number of cardiomyocytes increases continuously for up to 3 weeks after birth. Although hyperplasia ends early, a long-lasting capacity for DNA replication causes binucleated cardiomyocytes to form and finally induces hypertrophy. Genes with differential expression during growth might have special functions at specific development stages. Northern blot analysis, critical but usually excluded in large-scale studies, revealed important character-
Fig. 6. Calcium-binding assay of the Mm.20818 protein. GST-fusion proteins were overexpressed in E. coli, resolved in by SDS–PAGE and stained with Coomassie brilliant blue (CBB). Autoradiogram of 45Ca2+overlay is shown at the right. A band indicated by an arrow corresponds to the expected size of the fusion protein (∼46 kDa). This GST-Mm.20818 protein was found to be insoluble, preventing affinity purification of the fusion protein. Thus, total proteins including the fusion protein were used for analysis. Lane M, size marker; Lane 1, proteins from sarcoplasmic reticulum of mouse heart; Lane 2, purified GST protein; Lane 3, total cell lysates with GST (only) expressed from the transfection vector (a control); Lane 4, total cell lysates from cells where the transfection vector carried a non-induced GST-Mm.20818 gene; Lane 5, as Lane 4, but with an induced GST-Mm.20818 gene.
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data confirm the bioinformatic predictions in some but not all of the novel genes and should extend the in silico data in future investigations. The first group (Mm.261329 and Mm.20818) consists of potential calcium-binding proteins with EF-hand motifs. Indeed, we performed a Ca2+overlay assay with the Mm.20818 protein and found for the first time that this novel protein binds to Ca2+. Ca2+, present in all cellular compartments, plays an essential role in the regulation of a wide variety of cellular functions in heart [25,26]. In muscle cells, the sarcoplasmic reticulum stores Ca2+, regulates intracellular Ca2+ levels and thus is central to excitation-contraction process [27]. The novel genes may be involved in cardiac contraction or signal transduction. Consistently, the human ortholog of Mm.261329 has been named as myosin regulatory light chain MRLC2. The second-group genes (Mm.41877, Mm.27886, Mm.269736, Mm.41719, Mm.30113 and Mm.272253) are likely to be related with mitochondrial regulation. Mitochondria have an important role in cardiac contraction; hence, these genes may be required for mitochondrial metabolism, perhaps as components of the electron transfer complex [28]. In fact, 4 of them (Mm.41877, Mm.27886, Mm.30113 and Mm.272253) have been recently named as components localized in mitochondria in the UniGene database (see Results). In addition, the human homolog of Mm.269736 has been given a gene name, ubiquinol–cytochrome c reductase complex 7.2kDa protein. However, they were found to be located in nucleus and/or cytoplasm in this study. Thus, to answer whether the in silico annotations are imprecise or the ability of the proteins to localize correctly is abolished in the GFP fusion proteins will require further investigation. The third-group genes (Mm. 138832, Mm.38813 and Mm29342) may have functions in the areas of gene transcriptional regulation or nuclear integrity. The other three novel genes should arguably not be clustered into a group, as the genes share no obvious expression or computational pattern. Two of them, Mm.158769 and Mm.121122, are predicted to be implicated in cell structure and transmembrane transport, respectively. In conclusion, identification of genes expressed in cardiac muscle is crucial to understanding of the molecular basis of cardiac function. Our systematic and integrative approaches explored the genomic, transcriptional and protein expression characteristics of these genes and correlated genes. Our fullscale study of novel cardiac genes should be a large resource and a firm basis for future investigation into functional characterization of the genes, leading to the elucidation of various mechanisms underlying cardiac function. Acknowledgments This work was supported by a grant from Korean Systems Biology Research grant, M10503010001-06N0301-00110, from Korea Ministry of Science and Technology. References [1] Schaub MC, Hefti MA, Zaugg M. Integration of calcium with the signaling network in cardiac myocytes. J Mol Cell Cardiol 2006;41:183–214.
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[2] Liew CC, Hwang DM, Wang RX, Ng SH, Dempsey A, Wen DH, et al. Construction of a human heart cDNA library and identification of cardiovascular based genes (CVBest). Mol Cell Biochem 1997;172: 81–7. [3] Bortoluzzi S, d'Alessi F, Danieli GA. A computational reconstruction of the adult human heart transcriptional profile. J Mol Cell Cardiol 2000;32:1931–8. [4] Plageman Jr TF, Yutzey KE. Microarray analysis of Tbx5-induced genes expressed in the developing heart. Dev Dyn 2006;235:2868–80. [5] Nanni L, Romualdi C, Maseri A, Lanfranchi G. Differential gene expression profiling in genetic and multifactorial cardiovascular diseases. J Mol Cell Cardiol 2006;41:934–48. [6] Roy S, Khanna S, Kuhn DE, Rink C, Williams WT, Zweier JL, et al. Transcriptome analysis of the ischemia-reperfused remodeling myocardium: temporal changes in inflammation and extracellular matrix. Physiol Genomics 2006;25:364–74. [7] Lamirault G, Gaborit N, Le Meur N, Chevalier C, Lande G, Demolombe S, et al. Gene expression profile associated with chronic atrial fibrillation and underlying valvular heart disease in man. J Mol Cell Cardiol 2006;40: 173–84. [8] Kittleson MM, Hare JM. Molecular signature analysis: using the myocardial transcriptome as a biomarker in cardiovascular disease. Trends Cardiovasc Med 2005;15:130–8. [9] Kittleson MM, Minhas KM, Irizarry RA, Ye SQ, Edness G, Breton E, et al. Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure. Physiol Genomics 2005;21:299–307. [10] Ueno S, Ohki R, Hashimoto T, Takizawa T, Takeuchi K, Yamashita Y, et al. DNA microarray analysis of in vivo progression mechanism of heart failure. Biochem Biophys Res Commun 2003;307:771–7. [11] Anisimov SV, Boheler KR. Aging-associated changes in cardiac gene expression: large scale transcriptome analysis. Adv Gerontol 2003;11: 67–75. [12] Zhao M, Chow A, Powers J, Fajardo G, Bernstein D. Microarray analysis of gene expression after transverse aortic constriction in mice. Physiol Genomics 2004;19:93–105. [13] Pontius JU, Wagner L, Schuler GD. National Center for Biotechnology Information. Bethesda, MA: National Library of Medicine, 2003. [14] Hong S, Choi I, Woo JM, Oh J, Kim T, Choi E, et al. Identification and integrative analysis of 28 novel genes specifically expressed and developmentally regulated in murine spermatogenic cells. J Biol Chem 2005;280:7685–93. [15] Oh J, Lee J, Woo JM, Choi E, Park I, Han C, et al. Systematic identification and integrative analysis of novel genes expressed specifically or predominantly in mouse epididymis. BMC Genomics 2006;7: 314. [16] Lopez C, Jorge V, Piegu B, Mba C, Cortes D, Restrepo S, et al. A unigene catalogue of 5700 expressed genes in cassava. Plant Mol Biol 2004;56:541–54. [17] Claycomb WC, Lanson Jr NA, Stallworth BS, Egeland DB, Delcarpio JB, Bahinski A, et al. HL-1 cells: a cardiac muscle cell line that contracts and retains phenotypic characteristics of the adult cardiomyocyte. Proc Natl Acad Sci U S A 1998;95:2979–84. [18] Maruyama K, Mikawa T, Ebashi S. Detection of calcium binding proteins by 45Ca autoradiography on nitrocellulose membrane after sodium dodecyl sulfate gel electrophoresis. J Biol Chem 1984;95:511–9. [19] Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, et al. Initial sequencing and comparative analysis of the mouse genome. Nature 2002;420:520–62. [20] Cea LB. Natriuretic peptide family: new aspects. Curr Med Chem 2005;3:87–98. [21] Simpson JC, Wellenreuther R, Poustka A, Pepperkok R, Wiemann S. Systematic subcellular localization of novel proteins identified by largescale cDNA sequencing. EMBO Rep 2000;1:287–92. [22] Sun L, Trausch-Azar JS, Ciechanover A, Schwartz AL. Ubiquitinproteasome-mediated degradation, intracellular localization, and protein synthesis of MyoD and Id1 during muscle differentiation. J Biol Chem 2005;280:26448–56.
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I. Park et al. / Journal of Molecular and Cellular Cardiology 43 (2007) 93–106
[23] Barbosa MS, Wettstein FO. Identification and characterization of the CRPV E7 protein expressed in COS-7 cells. Virology 1988;165:134–40. [24] Grabarek Z. Structural basis for diversity of the EF-hand calcium-binding proteins. J Mol Biol 2006;359:509–25. [25] Berridge MJ, Bootman MD, Roderick HL. Calcium signalling: dynamics, homeostasis and remodelling. Nat Rev, Mol Cell Biol 2003;4:517–29.
[26] Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat Rev, Mol Cell Biol 2000;1:11–21. [27] Bers DM. Cardiac excitation–contraction coupling. Nature 2002;415: 198–205. [28] Chan DC. Mitochondria: dynamic organelles in disease, aging, and development. Cell 2006;125:1241–52.
