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. 2008 Aug 7;14(1):1–21. doi: 10.1007/s12192-008-0060-2

Computational analysis of the human HSPH/HSPA/DNAJ family and cloning of a human HSPH/HSPA/DNAJ expression library

Jurre Hageman 1, Harm H Kampinga 2,
PMCID: PMC2673897  PMID: 18686016

Abstract

In this manuscript, we describe the generation of a gene library for the expression of HSP110/HSPH, HSP70/HSPA and HSP40/DNAJ members. First, the heat shock protein (HSP) genes were collected from the gene databases and the gene families were analyzed for expression patterns, heat inducibility, subcellular localization, and protein homology using several bioinformatics approaches. These results can be used as a working draft model until data are confirmed by experimental approaches. In addition, we describe the generation of a HSPA/DNAJ overexpression library and tested the effect of different fusion tags on HSPA and DNAJ members using different techniques for measuring chaperone activity. These results show that we have cloned a high-quality heat shock protein expression library containing most members from the HSPH, HSPA, DNAJA and DNAJB families which will be useful for the chaperone community to unravel the function of the highly diverse family of human molecular chaperones.

Keywords: HspH, HspA, DnaJ, Hsp110, Hsp70, Hsp40, Human chaperones, Bio-informatics

Introduction

All organisms, except some hyperthermophilic archaea, contain the family of HSP110/HSPH, HSP70/HSPA, and HSP40/DNAJ chaperones (Gribaldo et al. 1999). HSPA and DNAJ proteins function as molecular chaperones to assist in processes such as translation and transport of proteins across membranes.

The HSPA machine consists of the core HSPA protein along with a transient array of different co-factors such as DNAJ, HSPH, BAG-1, Hip, CHIP, and HSPBP1 (Kampinga 2006). The HSPH/HSPA and DNAJ families are protein families consisting of many members and as a whole, the HSPH/HSPA/DNAJ gene family is the largest chaperone gene family found in humans.

It is thought that many of its members are specialized (Sahi and Craig 2007). For instance, members of the HSPA, DNAJ, and HSPH family exist that are only expressed under stress conditions suggesting that these are specialized to function in the proteotoxic stress response (Albanese et al. 2006). Constitutively expressed members are found as well and such members are found in several cellular compartments such as the cytosol, mitochondria and the endoplasmic reticulum (ER) suggesting that cellular compartmentalization has driven some of the HSPA/DNAJ gene expansion (Brocchieri et al. 2008). In addition, some members have only been found at specific developmental stages or in specific cell types indicating the need for specialized members for specific substrates expressed only during specific developmental stages or in certain specialized cell types.

In contrast to the gene expansion as a result of compartmentalization, the gene expansion as a result of cellular specialization or organism development is poorly understood. It has been suggested that HSPA and DNAJ proteins bind small hydrophobic regions (Rudiger et al. 1997; Rudiger et al. 2001); yet there is great diversity and multiplicity within these families of which most members have not yet been studied in detail.

Although the various chaperone genes are now relatively well annotated, the molecular function for most of its members is currently unknown. For each of the families, only a single or restricted number of proteins has been studied in detail. In this paper, we used bioinformatics approaches to study the different HSPH, HSPA, and DNAJ members (Brocchieri et al. 2007). Thereafter, we describe the construction of a human HSPH/HSPA/DNAJ expression library.

Materials and methods

Bioinformatics

HSP gene retrieval HSPH, HSPA and DNAJ genes were collected from National Center for Biotechnology Information (NCBI) Gene (Maglott et al. 2007). Mouse orthologs were identified using NCBI Homologene (Wheeler et al. 2007). Protein molecular weights were calculated using the clone manager 7 suite (Sci-Ed Software).

EST count analysis Expression data based on tissue-specific and developmental stage specific expressed sequence tag (EST) numbers were collected from the NCBI UniGene database (Wheeler et al. 2007). EST numbers are displayed as counts per million.

Affymetrix gene array Investigation of genome-wide heat-induced transcriptional activation was described previously (Page et al. 2006). These experiments were performed in Hela cells using a 1.5-h heat shock at 43°C. Recovery times were 0.5, 2, and 4 h at 37°C. Affymetrix gene array data were downloaded (Page et al. 2006) and linear induction was calculated from the 2log fold change. Affymetrix uses different annotations for its probe sets. _at suffix designates a unique probe set, whereas the _s_at and _x_at suffixes designate probe sets that can cross hybridize with multiple genes. In the case of redundant probe sets, _at suffix were selected by default. In the case of no available _at suffix, the first probe set was selected routinely.

Subcellular localization analysis Predictions on subcellular localizations were performed using pSort, pTarget, CELLO, Multiloc, and Proteome analyst (Szafron et al. 2004; Yu et al. 2006; Guda 2006; Hoglund et al. 2006; Horton et al. 2007). Sequences from complete gene families were uploaded as fasta files. In each case, only the first rank localization is displayed. For all predictors, the default settings for mammalian or animal proteins were used. The presence of prenelation motifs was determined using the PrePS webserver (Maurer-Stroh et al. 2007).

Protein Alignments Primary amino acid alignments were performed in ClustalX2 using the neighbor-joining algorithm and Blosum matrixes at the default settings (Larkin et al. 2007). Bootstrap analysis was performed using 1,000 random number generator seeds and 1,000 bootstrap trials. Phylograms were made by importing the homology tree output of ClustalX in TreeView (Page 1996). The distance is depicted in the scale bar of Fig. 1 as 0.1 amino acids substitutions per position.

Fig. 1.

Fig. 1

Phylograms for the HSPH/A (A), DNAJA (B), DNAJB (C), and DNAJC (D) families. Primary amino acid alignments were performed using the Neighbor-joining algorithm using a Blosum scoring matrix in ClustalX (see “Methods” for details). Bootstrap values are indicated on the branch-points

Library cloning and validation

Gene Cloning

Detailed information about the plasmids used in this study can be found in Fig. 2. Briefly, tetracycline-inducible HSP expression plasmids were constructed as follows. First, the green fluorescent protein (GFP) and the v5 tag, harboring a Kozak consensus ATG initiation codon and lacking a stop codon, were cloned in the pCDNA5 FLP recombination target (FRT)/TO vector (Invitrogen). Subsequently, the coding sequence of the different chaperones was amplified using the primers listed in supplemental Table 8. As a template source, complementary DNA (cDNA) was made from total RNA as previously described (Hageman et al. 2005). As a source of total RNA, QPCR Human reference Total RNA (Stratagene) was used, which is a mixed source of RNA from the following cell line derivations: adenocarcinoma, mammary gland; hepatoblastoma, liver; adenocarcinoma, cervix; embryonal carcinoma, testis; glioblastoma, brain; melanoma, skin; liposarcoma, histiocytic lymphoma, macrophage, histocyte; lymphoblastic leukemia, T lymphoblast; plasmacytoma, myeloma, B lymphocyte. DNAJB4, DNAJB5, and DNAJB8 were amplified from cloned full-length cDNAs purchased from Open Biosystems (clone ID: DNAJB4: 4340658, DNAJB5: 4684829, and DNAJB8: 5296554). The fragments were cloned in pCDNA5 frt to GFP lacking a stop codon resulting in a N-GFP-cDNA-C protein. The presence of the correct gene was sequence verified. Protein expression was verified by Western blotting. Subsequently, fragments were subcloned to pCDNA5 frt to v5 and pCDNA5 frt to.

Fig. 2.

Fig. 2

Schematic overview of the library construction. (A) The pCDNA5/FRT/TO vector system together with the cloned fusion tags. (B) List of the cloned molecular chaperones. (C) Schematic representation of the FRT locus within the Flp-In T-REx HEK-293 cell line. (D) Primer sequences for the construction of the indicated fusion tags

Table 8.

Summary of the library cloning

Family Gene 5′-oligo 3′-oligo Site 1 Site 2
HSP110 HSPH1 CTCCCAGGGTTTCTTATCAG GATTTTAATCACAGCCCTCTTG NA NA
HSPH1* CACAGATATCACCATGTCGGTGGTGGGGTTG CGCGATCCTCGAGCTAGTCCAAGTCCATATTAACAG EcoRV XhoI
HSPH2 ACCCACTGGAAGGACTTAGG GAGCTCCTGCCATGTAAGTC NA NA
HSPH2* GACAGATATCACCATGTCGGTGGTGGGCATAGAC GACTGCGGCCGCGGAATCAATCAATGTCCATTTCAG EcoRV NotI
HSPH3 GCAATAGCCCAGAAGAGGAC GATGGACCCCGTGGTTACTTG NA NA
HSPH3* GACGGATATCACCATGTCTGTGGTTGGCATTGAC GATCGCGGCCGCAGACTTAGTCCACTTCCATCTC EcoRV NotI
HSP70 HSPA1A ACCAGAGGATCCACCATGGCCAAAGCCGCGGCGAT ATCACTGCGGCCGCCTAATCTACCTCCTCAATGG BamHI NotI
HSPA1L CACAGATATCACCATGGCTACTGCCAAGGGAAT GACTGCGGCCGCTTAATCTACTTCTTCAATTGTGGGGC EcoRV NotI
HSPA2 CACACAGGATCCACCATGTCTGCCCGTGGCCCGGCT GACTGCGGCCGCTTAGTCCACTTCTTCGATGGTGG BamHI NotI
HSPA6 GACAGATATCACCATGCAGGCCCCACGGGAGCT GACTGCGGCCGCTCAATCAACCTCCTCAATGA EcoRV No I
HSPA8 CACACAGGATCCACCATGTCCAAGGGACCTGCAGTTG GACTGCGGCCGCTTAATCAACCTCTTCAATGGTGGG BamHI NotI
HSPA14 CACACAGGATCCACCATGGCGGCCATCGGAGTTCA GACTGCGGCCGCCTAAGATGCTATCTCAATAGAGATTG BamHI NotI
DNAK ACCAATGGATCCACCATGGGTAAAATAATTGGTATC AATAATGCGGCCGCCCCGTGTCAGTATAATTACC BamHI NotI
SSA1 TACTAAGGATCCACCATGTCAAAAGCTGTCGGTATTG TAGTATGCGGCCGCTTAATCAACTTCTTCAACGGTTGGACC BamHI NotI
HSP40 DNAJA1 CACAATGGATCCACCATGGTGAAAGAAACAACTTACTACG GACTGCGGCCGCTTAAGAGGTCTGACACTGAAC BamHI NotI
DNAJA2 ATCCACGGATCCACCATGGCTAACGTGGCTGACACG GACTGCGGCCGCTTACTGATGGGCACACTGCAC BamHI NotI
DNAJA3 ATTCGAGGATCCACCATGGCTGCGCGGTGCTCCACA GACTGCGGCCGCGGCTGGGATATCATGAGGTA BamHI NotI
DNAJA4 ATAGCTGGATCCACCATGGTGAAGGAGACCCAGTA GACTGCGGCCGCTCATGCCGTCTGGCACTGCAC BamHI NotI
DNAJB1 CACAATGGATCCACCATGGGTAAAGACTACTACCAGACG GACTGCGGCCGCCTATATTGGAAGAACCTGCTCAAG BamHI NotI
DNAJB2a ATCGATGGATCCACCATGGCATCCTACTACGAGATC TACGATGCGGCCGCTCAGAACACATCTGCGGGTTTC BamHI NotI
DNAJB2b ATCGATGGATCCACCATGGCATCCTACTACGAGATC TACGATGCGGCCGCTCAGAGGATGAGGCAGCGAG BamHI NotI
DNAJB3 TACTACGGATCCACCATGGTGGACTACTACGAGGT TACTGTGCGGCCGCTTACTGAGTATTGATGCGAAGCAG BamHI NotI
DNAJB4 TGCAAAGGATCCACCATGGGGAAAGACTATTATTGC GACTGCGGCCGCCTATGAGGCAGGAAGATGTTTCC BamHI NotI
DNAJB5 GATCGCGGCCGCACCATGGGAAAAGATTATTACAAGATTCTTGGG GATATCGGGCCCCTAGGAACAGGGTAGGTGCTGC NotI ApaI
DNAJB6b GATATAGGATCCGGAACCATGGTGGATTACTATGAAGTTCT GATATTGCGGCCGCTTACTTGTTATCCAAGCGCAG BamHI NotI
DNAJB6a TATATAGGATCCACCATGGTGGATTACTATGAAGTTCT TATATAGCGGCCGCCTAGTGATTGCCTTTGGTCG BamHI NotI
DNAJB7 GATTACGATATCACCATGGTGGATTACTATGAAGT GATTACGCGGCCGCTTAACAATTCCTTTTGGTAGACTTC EcoRV NotI
DNAJB8 AAGTAAGGATCCACCATGGCTAACTACTACGAAGTG GATATAGCGGCCGCCTACTTGCTGTCCATCCATTTG BamHI NotI
DNAJB9 GATCGCGGCCGCACCATGGCTACTCCCCAGTCAAT GATATCGGGCCCCTACTGTCCTGAACAGTCAG NotI ApaI
DNAJB10 ATCGATGGATCCACCATGGCATCCTACTACGAGATTC TACGATGCGGCCGCTCAGAACACATCTGCTGGCTTC BamHI NotI

Luciferase refolding Assay Cell lysis and luciferase activity measurements were done as previously described (Michels et al. 1995). Luciferase activity was plotted relative to the percentage of activity in an unheated control. Error bars on plots represent standard deviations.

Filter trap assay To determine protein aggregates, the filter trap assay was performed as previously described (Carra et al. 2005). Briefly, 10, 2, and 0.4 µg of protein extracts were applied onto 0.2-µm pore cellulose acetate membrane pre-washed with FTA + 0.1% sodium dodecyl sulfate (SDS). Mild suction was applied and the membrane was washed three times. Aggregated proteins trapped in the membrane were probed with a mouse anti-GFP antibody JL-8 (Clontech) at a 1:5,000 dilution or a mouse anti-V5 antibody (Invitrogen) at a 1:5,000 dilution followed by horseradish peroxidase (HRP)-conjugated anti-mouse secondary antibody (Amersham) at 1:5,000 dilution. Visualisation was performed using enhanced chemiluminescence and Hyperfilm (ECL, Amersham).

Results

Bioinformatic analysis on the HSPH, HSPA, and DNAJ gene family

Collecting the HSPH, HSPA, and DNAJ gene family

In order to get a comprehensive overview of the gene family, we first extracted all human HSPH, HSPA, and DNAJ family members from the NCBI gene database. It should be noted that beside these protein encoding genes, we found many pseudogenes scattered throughout the human genome. Typically, pseudogenes show types of decay such as frame-shifts, stop-codons or gaps. For the HSPA family alone, already 30 pseudogenes have been documented (Brocchieri et al. 2008). For gene selection, we extracted the annotated non-pseudo genes from the NCBI gene bank (Maglott et al. 2007). We found 4 HSPH chaperones, 13 HSPA chaperones, and 49 DNAJ chaperones. The genes including the protein name, the old and alternative names, the NCBI human gene ID, the mouse ortholog gene ID, the human gene locus, the protein length, and the calculated molecular mass are listed in Table 1. Classically, human chaperones were ranked according to molecular mass. However, as can be seen from Table 1, many HSP genes deviate from this and contain only a HSP-like domain such as the HSP70 ATPase domain or the HSP40 DNAJ domain. For instance, while many HSP40/DNAJ proteins are around 40 kD, the sizes of proteins within this family range from 16 kD (DNAJC15) to 254 kD (DNAJC13). For this reason, a revised nomenclature was recently suggested (this issue of Cell Stress and Chaperones).

Table 1.

Overview of the human HSP70/HSP40 gene family

  Gene Name Protein Name Alternative Name Human GeneID Mouse ortholog ID Locus (human) Protein length (aa) Calculated Mass (kD)
HSPH HSPH1 HSPH1 HSP105 10808 15505 13q12.3 858 96.9
HSPH2 HSPA4 HSPA4, APG-2; HSP110 3308 15525 5q31.1–q31.2 840 94.3
HSPH3 HSPA4L HSPA4L, APG-1 22824 18415 4q28 839 94.5
HSPH4 HSPH4 HYOU1; GRP170 10525 12282 11q23.1 999 111.3
HSPA HSPA1A HSPA1A HSP70–1, HSP72, HSPA1 3303 193740 6p21.3 641 70.0
HSPA1B HSPA1B HSP70–2 3304 15511 6p21.3 641 70.0
HSPA1L HSPA1L hum70t, hum70t 3305 15482 6p21.3 641 70.4
HSPA2 HSPA2 Heat-shock 70kD protein-2 3306 15512 14q24.1 639 70.0
HSPA5 HSPA5 BIP, GRP78, MIF2 3309 14828 9q33-q34.1 654 71.0
HSPA6 HSPA6 heat shock 70kD protein 6 (HSP70B’) 3310 X 1q23 643 71.0
HSPA7 HSPA7 3311 X 1q23.3 ? ?
HSPA8 HSPA8 HSC70, HSC71, HSP71, HSP73 3312 15481 11q24.1 646/493 70.9/53.5
HSPA9 HSPA9 GRP75, HSPA9B, MOT, MOT2, PBP74, mot-2 3313 15526 5q31.1 679 73.7
HSPA12A HSPA12A FLJ13874, KIAA0417 259217 73442 10q26.12 1296 141.0
HSPA12B HSPA12B RP23–32L15.1, 2700081N06Rik 116835 72630 20p13 686 75.7
HSPA13 HSPA13 Stch 6782 110920 21q11 471 51.9
HSPA14 HSPA14 HSP70–4, HSP70L1, MGC131990 51182 50497 10p14 509 54.8
DNAJA DNAJA1 DNAJA1 DJ-2; DjA1; HDJ2; HSDJ; HSJ2; HSPF4; hDJ-2 3301 15502 9p13–p12 397 44.9
DNAJA2 DNAJA2 DNJ3; mDj3; DNAJ3; HIRIP4 10294 56445 16q11.1–q11.2 412 45.7
DNAJA3 DNAJA3 Tid-1; Tid1l 9093 83945 16p13.3 480 52.5
DNAJA4 DNAJA4 Dj4; Hsj4 55466 58233 15q24.1 397 44.7
DNAJB DNAJB1 DNAJB1 HSPF1; HSP40 3337 81489 19p13.2 340 38.2
DNAJB2 DNAJB2 HSJ1; HSPF3; DNAJB10 3300 56812 2q32–q34 324/277 35,6/30,6
DNAJB3 DNAJB3 Hsj3; Msj1; MSJ-1 414061 15504 1 D (Mm) 242 26.7
DNAJB4 DNAJB4 Hsc40 11080 67035 1p31.1 337 37.8
DNAJB5 DNAJB5 Hsc40; HSP40-3 25822 56323 9p13.2 348/241 39,1/26,9
DNAJB6 DNAJB6 Mrj; mDj4 10049 23950 7q36.3 326/241 36.1
DNAJB7 DNAJB7 Dj5; mDj5 150353 57755 22q13.2 309 35.4
DNAJB8 DNAJB8 mDj6 165721 56691 3q21.3 232 25.7
DNAJB9 DNAJB9 Mdg1; mDj7; ERdj4 4189 27362 7q31 223 25.5
DNAJB11 DNAJB11 Dj9; ABBP-2; ERdj3 51726 67838 3q28 358 40.5
DNAJB12 DNAJB12 Dj10; mDj10 54788 56709 10q22.2 375 41.9
DNAJB13 DNAJB13 Tsarg 374407 69387 11q13.4 316 36.1
DNAJB14 DNAJB14 EGNR9427; FLJ14281 79982 70604 4q23 379/294 42,5/33,5
DNAJC DNAJC1 DNAJC1 MTJ1; ERdj1; ERj1p; DNAJl1 64215 13418 0p12.33–p12.32 554 63.9
DNAJC2 DNAJC2 Zrf1; Zrf2; MIDA1 27000 22791 7q22 621 72.0
DNAJC3 DNAJC3 p58; mp58; Prkri; DNAJc3; p58IPK; DNAJc3b 5611 100037258 13q32 504 57.6
DNAJC4 DNAJC4 HSPf2; Mcg18 3338 57431 11q13 135 15.2
DNAJC5 DNAJC5 Csp 80331 13002 20q13.33 198 22.1
DNAJC5B DNAJC5B CSP-beta 85479 66326 8q12.3 199 22.5
DNAJC5G DNAJC5G MGC107182 285126 231098 2p23.3 189 21.4
DNAJC6 DNAJC6 mKIAA0473 9829 72685 1pter-q31.3 913 100.0
DNAJC7 DNAJC7 Ttc2; mDj11; mTpr2 7266 56354 17q11.2 484 55.5
DNAJC8 DNAJC8 AL024084; AU019262 22826 68598 1p35.2 264 31.0
DNAJC9 DNAJC9 AU020082 23234 108671 10q22.3 260 29.9
DNAJC10 DNAJC10 JPDI; ERdj5 54431 66861 2q32.1 793 91.1
DNAJC11 DNAJC11 FLJ10737 55735 230935 1p36.23 559 63.3
DNAJC12 DNAJC12 Jdp1; mJDP1 56521 30045 10q22.1 198/107 23,4/12,5
DNAJC13 DNAJC13 Rme8; RME-8; Gm1124 23317 235567 3q22.1 2243 254.4
DNAJC14 DNAJC14 HDJ3; LIP6; DRIP78 85406 74330 12q13.13 702 78.6
DNAJC15 DNAJC15 DNAJd1 29103 66148 13q14.1 150 16.4
DNAJC16 DNAJC16 mKIAA0962 23341 214063 1p36.1 782 90.6
DNAJC17 DNAJC17 C87112 55192 69408 15q15.1 304 34.7
DNAJC18 DNAJC18 MGC29463 202052 76594 5q31.2 358 41.5
DNAJC19 DNAJC19 TIM14; TIMM14 131118 67713 3q26.33 116 12.5
DNAJC20 DNAJC20 JAC1; HSC20 150274 100900 22q12.1 235 27.4
DNAJC21 DNAJC21 GS3; JJJ1; DNAJA5; 134218 78244 5p13.2 576/531 67,1/62,0
DNAJC22 DNAJC22 FLJ13236; Wurst 79962 72778 12q13.12 341 38.1
DNAJC23 DNAJC23 Sec63; AI649014 11231 140740 6q21 760 88.0
DNAJC24 DNAJC24 DPH4; zinc finger, CSL-type containing 3 120526 99349 11p13 149 17.1
DNAJC25 DNAJC25 bA16L21.2.1; DnaJ-like protein 548645 X 9q31.3 360 42.4
DNAJC26 DNAJC26 GAK; cyclin G associated kinase 2580 231580 4p16 1311 143.2
DNAJC27 DNAJC27 RBJ; Rabj 51277 217378 2p23.3 273 30.9
DNAJC28 DNAJC28 Orf28 open reading frame 28; C21orf55 54943 246738 1q25 454 51.1
DNAJC29 DNAJC29 SACS; Sacsin 26278 50720 13q12 4432 504.6
DNAJC30 DNAJC30 WBSCR18 84277 66114 7q11.23 226 26.0

HSPA6 and HSPA7 were found only in humans while and although HSPA7 contains an internal frame shift and might be a pseudogene; bypassing the frame shift will result in a protein with a full-length homology to HSPA1A. Thus, the HSPA7 protein with a full-length homology to HSPA1A might be produced, and this has recently been explained elsewhere (Brocchieri et al. 2008).

Patterns of tissue specific HSPH, HSPA, and DNAJ expression

Cellular specialization may require specialized chaperone proteins and therefore may be responsible for part of the chaperone gene expansion. The expression pattern of most chaperone genes is currently unknown. An estimation of the expression pattern can be made by assessing the relative number of EST per tissue using the Unigene database (Schuler 1997). However, some caution should be taken as the Unigene assignments of ESTs to individual genes is not necessarily accurate (i.e., poor sequence quality and related sequences lead to incorrect ‘binning’ of some ESTs).

The expression estimates are displayed in Table 2 (HSPH/HSPA), Table 3 (DNAJA/B), and Table 4 (DNAJC). The peak expression for each tissue is indicated in bold. As expected, HSPA8 shows a high expression in most tissues (Table 2). In contrast, HSPH3, HSPA1L, HSPA6, HSPA7, HSPA12A, and HSPA12B show very low levels in most tissues. HSPH3 and HSPA1L show the highest expression in the testis, which is in agreement with literature (Ito et al. 1998; Held et al. 2006). HSPA6 is absent under non-stress conditions and only expressed upon severe heat shock conditions (Noonan et al. 2007). The expression of HSPA1A is extremely variable, ranging from being absent in lymph (node), parathyroid, and umbilical cord to being expressed at very high levels in the spleen. As for the HSPH/HSPA family, the DNAJ family shows a highly variable expression profile (Tables 3 and 4). The highest expressed members throughout tissues are DNAJA1, DNAJB1, and DNAJB6 indicative of being “housekeeping” DNAJ chaperones although they all are lacking in a few tissues. As for the HSPA/HSPH families, the DNAJ family shows testis-specific members (DNAJB7, DNAJB8, DNAJC5B, and DNAJC5G). In general, with the exception of the testis, most HSP genes do not show an expression restricted to only a single tissue. In addition, peak levels per tissue are highly variable from gene to gene, providing no direct correlative clue for any specific partnerships between the diverse family members.

Table 2.

Expression levels of HspH and HspA genes in various human tissues

  HSPH1 HSPH2 HSPH3 HSPH4 HSPA1A HSPA1B HSPA1L HSPA2 HSPA5 HSPA6 HSPA7 HSPA8 HSPA9 HSPA12A HSPA12B HSPA13 HSPA14
Adipose tissue 152 76 0 76 684 608 0 0 608 76 0 989 76 76 0 0 76
Adrenal gland 180 120 0 210 1740 390 0 60 360 90 0 3780 510 90 0 90 30
Ascites 99 199 24 124 49 49 0 0 648 0 0 2720 923 0 0 24 24
Bladder 199 0 66 33 663 1161 0 0 199 99 33 1990 431 0 0 232 99
Blood 120 24 8 88 555 80 0 8 161 112 8 3069 386 0 0 40 40
Bone 125 153 0 97 153 0 0 0 501 13 27 1267 320 0 13 55 55
Bone marrow 142 61 0 447 40 81 0 40 610 20 0 1688 447 0 0 40 20
Brain 278 50 34 231 850 223 5 634 120 9 3 3672 338 87 8 130 67
Cervix 82 123 20 103 144 103 0 20 247 0 0 1257 453 0 20 103 82
Connective tissue 80 60 6 120 347 40 13 26 267 13 6 2145 227 26 0 60 26
Ear 183 61 0 0 734 0 0 122 122 0 0 367 61 61 0 0 0
Embryonic tissue 115 180 0 189 92 37 0 13 671 0 0 1315 278 13 0 83 92
Esophagus 246 98 49 689 2710 1724 0 246 1182 98 49 1921 542 0 0 49 197
Eye 61 109 23 85 180 56 14 42 185 14 14 1043 137 42 0 94 42
heart 55 33 11 55 1638 365 11 66 188 33 11 2170 232 22 66 33 22
Intestine 127 131 4 233 1028 165 0 72 416 16 8 3552 271 4 4 33 29
Kidney 159 65 37 301 889 221 9 192 122 9 4 4628 348 56 42 94 18
Larynx 81 0 0 81 245 81 0 0 613 0 0 695 81 0 0 0 81
Liver 62 48 9 302 316 72 14 4 326 0 0 1238 273 0 0 33 33
Lung 124 97 23 198 1475 162 8 17 210 76 2 1174 239 0 17 50 20
Lymph 112 90 0 112 0 0 0 0 45 0 0 1373 315 0 0 0 0
Lymph node 185 108 0 10 0 10 0 0 174 0 0 566 152 0 0 87 217
Mammary gland 71 155 6 1237 589 97 25 12 719 12 6 2280 563 6 0 71 58
Mouth 295 191 29 280 1372 132 0 162 132 0 14 1918 575 0 0 29 103
Muscle 147 83 27 166 258 55 0 27 110 9 0 2949 342 27 0 27 27
Nerve 316 63 0 253 2658 253 0 0 379 0 63 1202 189 379 0 0 63
Ovary 38 87 0 136 58 87 0 0 428 19 0 1558 87 29 19 48 38
Pancreas 69 78 0 130 380 157 0 4 390 18 0 808 125 0 0 27 27
Parathyroid 48 0 48 0 0 0 0 0 48 0 0 484 436 48 0 0 339
Pharynx 0 48 0 48 24 0 0 24 96 0 0 2144 144 0 0 24 24
Pituitary gland 597 59 0 59 119 0 59 59 537 59 0 1732 179 119 0 0 59
Placenta 88 84 10 119 165 17 0 225 306 0 56 908 221 14 56 77 70
Prostate 41 115 15 120 1131 193 5 20 366 10 5 1560 146 20 0 36 47
Salivary gland 0 147 0 49 0 0 0 147 49 0 0 394 98 0 0 49 49
Skin 127 189 0 146 222 56 0 146 174 18 4 2622 411 18 9 14 47
Spleen 36 18 0 55 6992 1405 18 92 110 73 0 5512 295 18 110 18 18
Stomach 205 432 0 72 586 154 0 41 483 30 10 2038 123 0 10 0 20
Testis 365 93 223 374 193 42 105 543 262 0 3 2079 322 21 9 172 78
Thymus 135 36 12 110 1821 406 0 12 36 159 0 4270 258 0 12 49 61
Thyroid 166 41 20 229 500 312 20 0 1835 0 0 1335 584 104 20 0 62
Tonsil 58 117 0 58 58 0 0 58 0 0 0 469 0 0 0 0 234
Trachea 381 19 152 991 2345 801 0 57 38 190 57 3318 209 0 19 247 38
Umbilical cord 0 0 0 798 0 0 0 726 145 0 0 1597 145 0 72 0 0
Uterus 89 115 17 200 722 273 8 111 585 8 4 2381 290 21 8 81 34
Vascular 173 0 19 924 962 57 0 19 173 0 0 8356 423 38 0 115 0

Number of transcripts per million are indicated.

Table 3.

Expression levels of DnaJA and DnaJB genes in various human tissues

  DNAJA1 DNAJA2 DNAJA3 DNAJA4 DNAJB1 DNAJB2 DNAJB4 DNAJB5 DNAJB6 DNAJB7 DNAJB8 DNAJB9 DNAJB11 DNAJB12 DNAJB13 DNAJB14
Adipose tissue 532 76 76 0 1065 76 152 0 152 0 0 76 0 152 0 76
Adrenal gland 180 60 30 0 210 0 30 60 180 0 0 0 0 120 0 30
Ascites 224 124 149 0 374 74 0 0 324 0 0 0 49 74 0 49
Bladder 165 66 66 99 199 66 99 0 132 0 0 33 0 33 0 0
Blood 169 96 88 32 128 24 32 8 161 0 0 32 56 32 0 32
Bone 125 194 69 0 125 83 27 27 125 0 0 27 41 27 0 0
Bone marrow 183 20 61 20 244 20 122 0 122 0 0 0 40 40 0 0
Brain 264 62 115 54 191 84 72 45 221 0 3 66 31 58 0 26
Cervix 268 20 82 82 164 20 20 0 82 0 0 41 82 20 0 0
Connective tissue 93 26 53 20 93 26 53 6 200 0 0 60 26 20 6 20
Ear 122 0 0 0 61 0 0 0 61 0 0 0 122 0 0 0
Embryonic tissue 305 46 55 0 120 41 32 37 236 0 0 18 74 27 0 50
Esophagus 98 98 246 49 246 49 344 0 344 0 0 0 0 147 0 0
Eye 208 52 23 42 227 94 23 42 170 4 0 33 66 33 0 37
Heart 143 44 33 88 265 22 166 55 199 0 0 99 121 66 0 0
Intestine 165 123 80 29 237 33 12 12 191 0 0 29 42 63 0 25
Kidney 202 23 47 14 197 42 117 32 225 0 0 117 18 84 0 18
Larynx 0 163 0 40 0 40 0 163 204 0 0 0 40 0 0 0
Liver 196 96 38 33 177 48 144 9 172 0 0 38 38 24 4 14
Lung 171 47 50 70 275 79 23 17 162 0 0 47 62 59 2 41
Lymph 90 22 90 0 67 90 22 0 157 0 0 0 112 22 0 0
Lymph node 76 43 32 54 76 32 0 10 76 10 0 10 32 65 0 152
Mammary gland 129 90 90 71 362 25 25 12 317 0 0 45 38 58 0 51
Mouth 29 88 118 88 88 44 14 0 354 0 0 29 14 29 0 14
Muscle 203 120 101 27 166 55 92 64 166 0 0 27 18 83 0 55
Nerve 189 0 126 189 696 189 189 0 63 0 0 63 63 126 0 0
Ovary 136 87 107 9 185 48 9 38 116 0 0 9 19 19 9 0
Pancreas 116 27 51 41 199 65 13 4 176 0 0 51 65 55 0 55
Parathyroid 0 0 0 48 145 0 0 48 48 0 0 0 48 96 0 0
Pharynx 578 48 48 24 120 0 0 0 506 0 0 24 24 72 0 96
Pituitary gland 478 59 0 119 298 119 59 59 179 0 0 59 0 0 0 59
Placenta 133 91 49 10 186 66 31 3 154 0 0 169 116 56 0 63
Prostate 99 62 52 26 235 52 10 57 115 0 0 47 104 68 10 20
Salivary gland 98 49 197 0 98 0 147 0 98 0 0 98 0 197 0 0
Skin 170 61 70 75 226 61 42 51 396 0 0 9 42 61 0 23
Spleen 129 92 55 18 591 18 0 18 295 0 0 55 55 55 0 36
Stomach 205 20 61 41 102 41 41 0 236 0 0 51 102 10 0 10
Testis 277 114 63 51 546 30 24 18 253 24 39 87 27 48 15 15
Thymus 12 49 49 24 184 36 73 0 196 0 0 73 24 24 0 12
Thyroid 208 146 62 20 104 125 41 20 104 20 0 20 146 20 0 0
Tonsil 880 0 58 0 58 117 0 0 0 0 0 0 58 176 0 0
Trachea 190 57 19 247 228 19 19 0 152 0 0 114 0 19 38 19
Umbilical cord 72 0 0 0 0 0 0 0 0 0 0 0 0 145 0 0
Uterus 286 111 25 42 324 38 47 34 252 0 0 21 34 55 12 25
Vascular 481 38 38 0 211 0 423 0 288 0 0 77 0 19 0 0

Number of transcripts per million are indicated

Table 4.

Expression levels of DnaJC genes in various human tissues

  DNAJC1 DNAJC2 DNAJC3 DNAJC4 DNAJC5 DNAJC5B DNAJC5G DNAJC6 DNAJC7 DNAJC8 DNAJC9 DNAJC10 DNAJC11 DNAJC12 DNAJC13 DNAJC14 DNAJC15 DNAJC16 DNAJC17 DNAJC18 DNAJC19 DNAJC20 DNAJC21 DNAJC22 DNAJC23 DNAJC24 DNAJC25 DNAJC26 DNAJC27 DNAJC28 DNAJC29 DNAJC30
Adipose tissue 0 0 0 76 0 0 0 0 152 152 0 0 228 0 0 0 0 0 0 0 0 0 0 0 304 0 0 152 0 0 76 0
Adrenal gland 60 0 30 0 0 0 0 0 210 210 30 30 90 0 30 60 30 0 0 0 60 0 30 0 30 30 60 60 30 0 0 0
Ascites 24 74 0 24 49 0 0 0 249 199 24 49 124 74 24 349 24 49 0 24 0 24 24 24 49 0 24 99 0 0 0 0
Bladder 0 33 33 0 33 0 0 0 66 132 66 0 0 0 99 99 0 0 33 0 33 0 0 0 0 33 166 66 0 0 0 0
Blood 24 40 56 0 80 0 0 0 64 499 16 8 32 0 8 161 0 16 0 8 0 8 40 0 32 24 64 96 0 8 16 40
Bone 111 27 0 27 69 0 0 0 69 181 27 97 55 41 55 222 41 0 0 0 0 13 13 0 83 13 69 55 0 13 13 13
Bone marrow 0 20 40 40 0 0 0 20 81 183 20 61 20 122 20 40 0 61 0 0 101 0 0 0 162 61 101 0 0 0 20 0
Brain 16 15 18 21 64 4 1 231 84 54 17 73 114 19 31 58 40 15 20 119 70 3 17 6 39 9 53 73 22 2 65 24
Cervix 0 82 0 0 20 0 0 0 61 164 20 0 123 82 61 82 20 0 20 0 0 0 20 0 103 20 41 41 0 0 0 41
Connective tissue 66 0 6 46 53 6 0 0 33 233 33 100 33 106 113 200 167 13 6 20 6 6 20 0 60 0 80 33 6 0 26 13
Ear 61 0 0 0 0 0 0 0 0 61 122 183 0 0 0 0 61 0 0 0 61 0 0 0 489 0 61 0 0 0 0 61
Embryonic tissue 37 41 9 23 50 0 4 4 88 189 32 157 78 0 27 115 9 13 13 23 37 4 41 0 74 27 32 64 9 4 74 4
Esophagus 0 0 197 0 0 0 0 0 147 49 49 197 0 0 0 0 0 0 0 0 49 0 0 0 0 0 0 0 0 0 0 0
Eye 28 14 4 75 137 0 0 23 109 128 23 33 47 9 28 132 52 47 9 28 28 9 14 0 66 0 37 42 71 0 28 23
Heart 22 22 0 44 33 0 0 11 44 177 33 33 55 0 66 55 121 11 0 0 55 22 33 0 55 0 11 22 33 0 0 66
Intestine 84 29 16 93 123 0 0 12 67 131 72 38 59 16 25 67 72 50 0 0 127 12 21 33 50 12 50 135 8 0 25 25
Kidney 61 37 28 112 18 0 0 18 61 94 37 61 122 18 28 65 94 32 4 23 75 32 14 32 70 14 127 61 37 0 32 32
Larynx 122 40 0 122 81 0 0 0 0 0 0 0 0 0 40 163 0 0 40 0 0 0 81 0 900 0 0 0 0 0 0 81
Liver 72 95 14 0 38 0 0 4 62 134 19 148 24 24 24 100 28 9 14 0 19 4 38 0 81 9 67 19 0 0 23 28
Lung 65 32 17 133 109 2 0 14 59 174 50 65 65 20 17 168 29 35 14 5 32 26 23 2 106 5 85 73 32 2 29 35
Lymph 22 22 45 22 90 0 0 45 337 157 0 22 878 45 22 22 22 0 0 0 0 45 0 0 0 0 22 22 0 0 22 0
Lymph node 0 32 21 130 119 0 0 0 21 76 43 272 32 0 87 43 87 152 21 0 43 0 10 0 43 0 32 141 0 0 32 32
Mammary gland 25 58 19 45 116 12 0 6 97 64 19 90 90 19 32 155 38 0 25 6 51 0 38 19 136 19 64 64 6 0 12 25
Mouth 14 0 14 59 0 0 0 0 29 44 0 88 162 0 147 147 29 14 44 0 14 0 0 0 74 0 74 59 0 0 0 0
Muscle 0 0 36 18 110 0 0 18 18 83 0 46 147 18 27 92 36 36 18 46 92 0 27 0 55 18 18 36 27 0 46 0
Nerve 0 0 0 0 63 0 0 253 0 0 63 63 0 0 0 63 0 63 0 0 0 0 0 0 63 0 63 126 0 0 0 0
Ovary 29 38 9 87 77 0 0 0 38 224 19 29 48 0 38 107 0 29 0 9 9 0 38 19 38 9 9 136 29 0 0 38
Pancreas 37 4 4 55 74 0 0 9 46 65 4 102 32 69 18 69 46 4 0 0 27 4 27 13 51 51 204 46 51 0 4 27
Parathyroid 193 0 0 290 0 0 0 0 48 581 0 48 0 387 145 0 96 0 0 0 48 0 0 0 48 0 48 242 0 0 0 96
Pharynx 0 0 24 0 0 0 0 0 24 963 0 24 24 0 48 192 48 48 0 0 0 0 24 0 0 24 72 0 0 24 24 0
Pituitary gland 0 0 0 59 59 0 0 59 0 0 0 59 0 0 119 0 119 0 0 59 179 0 0 0 0 0 59 179 0 0 0 0
Placenta 49 17 31 21 17 10 0 42 66 151 7 38 59 0 42 98 42 31 24 7 35 7 17 3 56 3 77 80 7 0 3 3
Prostate 41 26 10 94 94 0 0 0 115 89 15 52 78 10 41 89 73 15 5 5 335 10 41 0 146 20 83 52 26 0 0 26
Salivary gland 0 0 0 0 0 0 0 0 147 0 0 0 0 197 98 49 0 0 49 0 147 0 0 0 0 49 147 295 0 0 0 0
Skin 66 47 14 28 85 0 0 14 56 160 42 70 66 9 103 151 33 4 4 4 28 14 23 0 28 0 75 42 0 0 14 18
Spleen 36 73 18 36 55 0 0 0 92 73 0 110 203 0 0 18 55 18 0 0 0 0 0 0 36 0 18 36 0 0 18 36
Stomach 41 30 10 30 82 0 0 10 113 123 10 82 51 0 41 41 30 0 51 10 0 0 92 10 41 0 72 41 20 10 0 41
Testis 18 72 21 66 30 45 39 9 57 81 6 338 141 12 60 162 39 33 6 147 33 3 15 3 102 21 48 87 72 18 9 18
Thymus 73 36 0 12 0 0 0 0 36 12 12 123 110 0 24 159 12 12 0 24 12 0 36 0 36 0 0 123 12 0 0 0
Thyroid 62 20 0 41 20 0 0 0 125 20 0 83 62 0 41 83 0 41 104 0 41 62 41 0 125 0 41 62 20 0 0 0
Tonsil 58 0 0 58 0 0 0 0 58 117 0 58 293 0 0 293 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trachea 38 76 114 0 19 0 0 0 57 38 0 648 133 0 57 38 19 38 38 0 19 0 0 0 209 0 0 114 0 19 38 19
Umbilical cord 290 0 72 0 0 0 0 0 145 0 0 217 0 0 0 0 0 0 0 0 0 0 0 0 145 0 0 0 0 0 72 0
Uterus 34 55 8 4 55 0 0 0 111 149 12 205 85 4 42 175 29 8 4 29 21 4 12 8 76 12 89 34 4 0 76 17
Vascular 19 19 57 0 19 0 0 19 115 57 0 269 19 0 115 38 77 0 0 0 96 0 19 19 19 0 57 0 0 0 77 57

Number of transcripts per million are indicated

Patterns of developmental expression

We also used the Unigene database to look for developmental specific expression patterns (Table 5). The results show that HSPs are differentially expressed at different developmental stages. HSPA1A shows a large variation throughout different developmental stages. Interestingly, many DNAJC members are expressed during embryogenesis but are repressed in the neonate or infant (DNAC4-6 and DNAJC16-20). Only a minority of the genes were expressed the highest at adult stages.

Table 5.

Expression levels of Hsp genes at various developmental stages

  Embryoid body Blastocyst Fetus Neonate Infant Juvenile Adult
HSPH1 254 32 165 160 168 394 127
HSPH2 98 208 61 0 42 89 132
HSPH3 0 0 15 0 42 0 13
HSPH4 254 224 181 224 0 538 185
HSPA1A 84 80 713 448 168 896 274
HSPA1B 70 32 155 128 42 215 105
HSPA1L 0 0 14 0 0 0 1
HSPA2 0 32 24 320 0 35 67
HSPA5 819 529 174 64 126 269 438
HSPA6 0 0 7 64 0 0 17
HSPA7 0 0 1 0 0 0 12
HSPA8 1102 1283 2208 6509 8999 7371 2014
HSPA9 339 288 211 577 337 789 265
HSPA12A 14 32 22 32 253 107 28
HSPA12B 0 0 8 64 0 0 13
HSPA13 84 96 77 0 84 0 54
HSPA14 56 64 52 0 253 35 52
DNAJA1 197 304 140 64 844 358 158
DNAJA2 56 80 51 64 84 89 86
DNAJA3 56 80 40 0 42 125 52
DNAJA4 0 0 75 0 0 53 45
DNAJB1 127 96 155 320 211 430 236
DNAJB2 42 48 47 0 0 53 77
DNAJB4 56 16 38 256 42 107 36
DNAJB5 28 80 82 0 168 0 24
DNAJB6 127 336 147 416 253 251 207
DNAJB7 0 0 3 0 0 17 0
DNAJB8 0 0 5 0 0 0 2
DNAJB9 28 0 22 0 126 89 57
DNAJB11 42 128 51 32 0 17 66
DNAJB12 42 16 33 64 42 0 51
DNAJB13 0 0 0 0 0 0 2
DNAJB14 42 48 26 64 42 17 38
DNAJC1 70 0 61 128 0 107 44
DNAJC2 56 64 33 32 42 17 35
DNAJC3 28 0 22 32 0 17 17
DNAJC4 28 0 58 0 0 0 46
DNAJC5 84 80 31 0 0 35 79
DNAJC5B 0 0 0 0 0 0 5
DNAJC5G 0 16 0 0 0 0 2
DNAJC6 14 0 58 0 0 71 27
DNAJC7 70 64 63 128 0 107 90
DNAJC8 127 192 105 160 84 161 131
DNAJC9 42 32 36 32 126 17 23
DNAJC10 240 144 73 160 84 358 72
DNAJC11 56 128 72 64 42 35 52
DNAJC12 0 0 40 0 0 0 30
DNAJC13 28 16 77 0 42 0 47
DNAJC14 155 160 81 32 0 35 115
DNAJC15 14 16 59 0 0 0 50
DNAJC16 0 16 12 0 0 53 26
DNAJC17 0 32 5 0 0 0 14
DNAJC18 14 64 118 0 42 0 16
DNAJC19 14 64 84 0 0 17 68
DNAJC20 14 0 21 0 0 0 8
DNAJC21 70 48 36 32 0 0 35
DNAJC22 0 0 0 0 0 0 7
DNAJC23 70 80 81 64 0 143 94
DNAJC24 28 32 7 0 0 0 6
DNAJC25 56 16 56 64 0 0 67
DNAJC26 56 96 47 0 0 35 69
DNAJC27 0 16 51 0 0 0 13
DNAJC28 0 16 14 0 0 0 1
DNAJC29 70 112 75 64 0 17 14
DNAJC30 14 0 47 0 0 35 22

Number of transcripts per million

Heat-induced transcription

Although HSPs were originally identified as heat inducible proteins, most members are identified according to presence of typical domains such as the HSP70 ATPase domain or the HSP40 DNAJ domain. For most of these members, it is currently unknown whether they are induced by heat. To investigate this, we used Affymetrix gene array data (Page et al. 2006) and performed a biased search on the heat inducibility for HSPH, HSPA, and DNAJ members (Table 6). We used an arbitrary threshold of twofold induction to define heat inducibility. Using this threshold, we found that HSPH1, HSPA1A, HSPA1B, HSPA1L, HSPA6, DNAJB1, DNAJB2, DNAJB4, and DNAJB6 are the major heat-inducible genes in Hela cells. Thus, the majority of HSPs are not heat inducible. Of course, it must be noted that these patterns could be different for other cell lines and other heat conditions.

Table 6.

Heat-induced transcription of hsp genes

Family Gene Symbol 0.5 hour 2 hours 4 hours Probe Set ID
HSPH HSPH1 0.9 2.6 3.1 206976_s_at
HSPH2 0.9 1.5 1.3 208814_at
HSPH3 0.9 1.8 2.0 205543_at
HSPH4 1.0 1.0 1.2 200825_s_at
HSPA HSPA1A 1.1 2.3 2.0 200799_at
HSPA1A /// HSPA1B 1.2 4.4 3.3 200800_s_at
HSPA1L 0.9 2.6 1.1 210189_at
HSPA2 0.9 1.0 0.9 211538_s_at
HSPA5 0.9 1.2 1.4 211936_at
HSPA6 1.3 64.1 5.8 117_at
HSPA8 0.9 1.1 0.9 208687_x_at
HSPA9B 0.9 0.9 1.0 200690_at
HSPA12A 0.9 0.8 0.7 214434_at
HSPA13 0.9 0.8 0.8 202557_at
HSPA14 1.0 0.9 0.9 219212_at
DNAJA DNAJA1 1.0 1.5 1.5 200880_at
DNAJA2 0.9 0.9 0.8 209157_at
DNAJA3 0.9 0.9 0.7 205963_s_at
DNAJA4 1.1 1.9 1.2 220395_at
DNAJB DNAJB1 1.0 5.7 2.8 200664_s_at
DNAJB2 1.1 2.1 1.4 202500_at
DNAJB4 0.8 3.0 1.0 203810_at
DNAJB5 0.9 1.0 1.0 212817_at
DNAJB6 0.8 1.7 2.0 208810_at
DNAJB9 0.6 1.2 0.8 202843_at
DNAJB12 1.0 0.8 0.9 202865_at
DNAJB12 1.1 1.1 1.2 214338_at
DNAJB14 0.6 0.7 0.4 219237_s_at
DNAJC DNAJC1 1.3 1.0 1.1 218409_s_at
DNAJC3 1.0 0.8 1.0 208499_s_at
DNAJC4 1.0 1.0 1.1 206781_at
DNAJC6 0.8 0.7 0.7 204720_s_at
DNAJC7 1.0 1.2 1.4 202416_at
DNAJC8 0.8 0.8 0.9 212490_at
DNAJC9 0.9 0.9 1.0 213088_s_at
DNAJC10 0.9 0.8 0.8 221782_at
DNAJC11 1.0 0.9 1.0 215792_s_at
DNAJC12 1.0 0.8 0.8 218976_at
DNAJC13 1.0 0.9 0.8 212467_at
DNAJC15 1.0 1.0 1.0 218435_at
DNAJC16 0.7 0.8 0.5 212908_at
DNAJC17 1.1 1.0 1.2 219861_at
DNAJC22 1.0 1.0 1.0 216595_at
DNAJC23 0.8 0.8 0.7 201914_s_at
DNAJC26 1.1 1.0 1.1 202281_at
DNAJC28 0.9 1.1 1.0 220372_at
DNAJC29 1.0 0.6 0.7 213262_at

Affymetrix gene array data.

Data are shown as fold change compared to an unheated control

Subcellular localization

Determination of HSP localization is essential to understand its biochemical function. Unfortunately, high-throughput analysis of HSP localization without the use of possible interfering tags is currently impossible due to the lack of specific antibodies. As subcellular localization signals share common characteristics, computational methods have been developed to predict the subcellular localization of proteins (Sprenger et al. 2006). We selected several publicly available localization prediction methods, which accept large batches of protein sequences and which were able to predict all of the major subcellular localizations. The selected methods were Wolf PSORT (Horton et al. 2007), pTarget (Guda 2006), CELLO (Yu et al. 2006), Multiloc (Hoglund et al. 2006) and Proteome Analyst (Szafron et al. 2004). In addition, we searched the human protein database (Mishra et al. 2006) for experimentally verified HSP localizations. As can be seen from Table 7, there are large variations in the prediction using the various programs. Therefore, we first searched for the prediction method that showed the highest accuracy for biochemical verified HSP members such as HSPA1A/HSP70 (cytosol/nucleus), HSPA1B/HSP72 (cytosol/nucleus), HSPA8/Hsc70 (cytosol/nucleus), HSPA5/Bip (ER), HSPA9/Grp75 (mitochondria), DNAJA3/Tid1 (Mitochondria), DNAJB1/HSP40 (cytosol/nucleus), DNAJB9/ERdJ4 (ER) DNAJB11/Erdj3 (ER), DNAJC1/ERdJ1 (ER), DNAJC10/ERdj5 (ER), and DNAJC19/TIM14 (mitochondria). Out of these 12 known localized proteins, the following number of correct predictions was found: Wolf PSORT: 7; pTarget: 10; CELLO: 8; Multiloc: 7; and Proteome Analyst: 10. Thus, Proteome Analyst and CELLO showed the highest correct prediction. However, it should be noted that at this stage, all prediction are potentially unreliable and should be used carefully. The scoring of the most reliable prediction method does rely on a relatively low number of verified chaperone proteins and the most reliable prediction program could therefore change in the future once more proteins will be experimentally verified.

Table 7.

Prediction of Hsp subcellular localization

  Wolf Ptarget Cello Multiloc Prot. analyst Consensus Experimental Prenylation
HSPH HSPH1 c c c c c c No
HSPH2 c c n c c c g No
HSPH3 c c n n c c c No
HSPH4 e m c e e e e No
HSPA HSPA1A c c c p c c c No
HSPA1B c c c p c c c No
HSPA1L c c c p c c c No
HSPA2 c c c p c c n No
HSPA5 e c e e e e e No
HSPA6 c c c n c c n No
HSPA8 c c c p c c c No
HSPA9 m m m m m m m No
HSPA12A m m m m No
HSPA12B c n m p No
HSPA13 e e c e e e e No
HSPA14 x p c c c c No
DNAJA DNAJA1 c c n c e c c Yes FT
DNAJA2 c c n n e cn c No
DNAJA3 m m m m m m m No
DNAJA4 c c n c e c a Yes FT
DNAJB DNAJB1 n n c c c c c No
DNAJB2a n n c c cn c No
DNAJB2b n n o c n Yes FT GGT1
DNAJB4 c c c c e c No
DNAJB5 c n c c e c No
DNAJB6a n c x c c c No
DNAJB6b c c c c c c No
DNAJB7 n c o n n No
DNAJB8 c c c n c No
DNAJB9 x e c x c xc e No
DNAJB11 x e c e e e e No
DNAJB12 m n c n e n No
DNAJB13 c c c c c c No
DNAJB14a c c c n e c No
DNAJB14b c c x n c c No
DNAJC DNAJC1 a n n e n n e No
DNAJC2 n n n n n No
DNAJC3 x c c e c c c No
DNAJC4 c n n n c n No
DNAJC5 c c x x e cx v No
DNAJC5B c c x a e c No
DNAJC5G c n x c e c No
DNAJC6 n n n n c n n No
DNAJC7 n c n c c c c No
DNAJC8 n n n n e n l No
DNAJC9 c e c c c No
DNAJC10 a e c g e e e No
DNAJC11 c c n n cn No
DNAJC12a c c n n cn No
DNAJC12b c c n c c No
DNAJC13 a c c c c d No
DNAJC14 c n m n e n e No
DNAJC15 x c m n m m No
DNAJC16 e e m g c e No
DNAJC17 c c n c n c No
DNAJC18 c c n n e cn No
DNAJC19 x m m m m m m No
DNAJC20 m m n m m m m No
DNAJC21a n n n n n n No
DNAJC21b n c n n n n No
DNAJC22 a e a y ce ae no
DNAJC23 a n n e e ne e No
DNAJC24 x c n c c c No
DNAJC25 a c a e c ac a No
DNAJC26 a m n n c n c No
DNAJC27 c c c c g c No
DNAJC28 m m m m m No
DNAJC29 c c n n cn No
DNAJC30 m e m x c m e No

Legend: n nuclear, m mitochondrial, g Golgi, e er, p peroxisomes, x extracellular, a plasma membrane, o outer membrane, v cytoplasmic vesicle, l nucleolus, d endosome, k cytoskeleton, y lysosome, FT farnesyltransferase, GGT1 geranylgeranyltransferase 1

We used the PrePS webserver to predict farnesylation of chaperones. As shown in Table 7, DNAJA1 and DNAJA4 are predicted to be prenylated by farnesyltransferase, which is in agreement with the literature (Terada and Mori 2000). In addition, DNAJB2b was predicted to be prenylated by geranylgeranyltransferase I as shown in the literature (Chapple and Cheetham 2003).

Homology of HSPH, HSPA, and DNAJ paralogs

Next, we computed protein similarity trees based on the alignments of the HSP protein sequences using the Neighboring–joining clustering method (Gascuel and Steel 2006). Figure 1 shows the output of these alignments depicted as phylograms. Three subfamilies can be derived from Fig. 1A. As expected, the first contains all the HSPH/HSP110 members. The second subfamily contains the cytosolic predicted HSPA proteins HSPA1A, HSPA1B, HSPA1L, HSPA2, HSPA6, and HSPA8 and is flanked by the ER-localized HSPA5 and the mitochondrial-localized HSPA9 protein. The third subfamily consists of the distantly related HSPA12A and HSPA12B proteins. Thus, a high number of highly related HSPA proteins are localized in the cytosolic/nuclear compartment. To date, the reason for so many highly related cytosolic HSPA proteins is unknown.

DNAJ proteins can be divided in three subfamilies on the basis of the primary amino acid composition and are classified as type A, B and C proteins (Hennessy et al. 2005). Type A proteins are the closest human orthologs of the Escherichia coli DNAJ and contain, besides an extreme N-terminal J-domain, a glycine/phenylalanine-rich region, a cysteine rich region, and a variable C-terminal domain. Type B proteins contain an N-terminal J-domain, a glycine/phenylalanine-rich region but lack the cysteine rich region. Type C DNAJ proteins contain only the J domain that is not necessarily restricted at the N-terminus but can be positioned at any place within the protein (Hennessy et al. 2005). The DNAJA is a highly related family of proteins and DNAJA3 (the mitochondrial localized member) is the most distantly related member (Fig. 1B). For the DNAJB family, three major subfamilies are found (Fig. 1C). The first consists of the members DNAJB2, DNAJB6, DNAJB7, and DNAJB8, the second of the members DNAJB1, DNAJB4, DNAJB5, DNAJB9, DNAJB11, and DNAJB13 and the third of the members DNAJB12 and DNAJB14. Although different C-termini could be defined based on the primary amino acid level within the DNAJB family, at present, no clear biochemical function can be assigned to one of these subfamilies. The DNAJC family (Fig. 1D) shows the highest divergence of all families. Based on these results we decided to clone the HSPH, HSPA, DNAJA, and DNAJB family. As the DNAJC family is highly diverse, we omitted this family for library construction.

Cloning the HSPH, HSPA, and DNAJ gene families

Selection of an expression system

For cloning a human expression library to perform reverse genetic screens, we used a robust and versatile system with a high degree of flexibility: the Flp-In T-Rex tetracycline inducible expression system. The core promoter of this construct contains the full human cytomegalovirus (CMV) promoter followed by two tetracycline repressor binding sites. Thus, in cell systems engineered to express the tetracycline repressor, tetracycline can be used for regulated expression of the gene of interest, whereas full CMV strength promoter activity will be achieved in cell systems that do not contain the tetracycline repressor (Knopf et al. 2008). In addition to regulated expression, the vector contains an FRT recombination site for the Flp recombinase-mediated stable integration of the vector at a specific site in an engineered FRT site-harboring cell line (Garcia-Otin and Guillou 2006). The eukaryotic selection marker lacks a start codon, which selects for a site-specific integration in the target genome. We selected the Flp-In T-Rex 293 cell line, a modified human embryonic kidney (Hek-293) cell line that expresses the tetracycline repressor and harbors a single copy of the FRT site at an active site in the genome. The Hek-293 cell line has been used extensively as a model for protein-folding diseases and is widely known for its ease of manipulation (Graham et al. 1977). A summary of the Flp-In T-Rex system is depicted in Fig. 2.

Construction of vector fusion tags

Specific antibodies against most of the recently identified human heat shock proteins are not available. To verify the expression of the different proteins, we used a subset of frequently used protein tags. In some cases, protein tags interfere with the native function of the protein (Muller-Taubenberger 2006). Therefore, caution must be taken with the interpretation of the results obtained. In general, experiments using this library can always be confirmed using the non-tagged version. Although the protein expression cannot be confirmed with the untagged version, one can easily compare the biological effects detected in a particular assay.

As a first step toward a vector library for the expression of different heat shock proteins, we selected different protein tags harboring different biological properties. eGFP was selected for subcellular localization studies. As a second (smaller) tag, we used the V5 tag, consisting of only 14 amino acids for which high affinity antibodies are commercially available. In addition, we used a hexa-histidine tag for protein precipitation experiments (Fig. 2).

To reduce cloning efforts, an N-terminal fusion tag was preferred. In this setting, we could maintain the natural stop codon in the gene of interest, which allows for simple shuttling from tagged to non-tagged constructs. However, it should be mentioned that N-terminal fusion tags could interfere with the import in subcellular organelles such as the ER or mitochondria and non-tagged versions are in such cases preferred.

An overview of the fusion tag cloning primers and procedure is shown in Fig. 2. The polymerase chain reaction (PCR) product of the eGFP gene lacking a stop-codon was cloned in pCDNA5/FRT/TO. For V5 and His tags, the corresponding oligos were annealed and cloned directly in the pCDNA5/FRT/TO vector.

Cloning the chaperone library

The focus of our gene library is on the cytosolic and nuclear expressed chaperones. Therefore, we selected the HSP70/HSPA proteins, which are putatively expressed in the cytosol or the nucleus (Table 7). For the HSP40/DNAJ family of proteins, we selected the major part of the DNAJA and DNAJB subfamily, which are the closest orthologs to E. coli DNAJ. As a certain human cell type typically only expresses a subset of its genes, we used pooled RNA from 10 different human cell lines as a source for cDNA synthesis and gene amplification. No amplification products were obtained for DNAJB4, DNAJB5, and DNAJB8. Instead, these genes were amplified from commercially obtained cDNA plasmids (Open Biosystems, Huntsville, AL). In addition, the HSPA6 gene was not amplified from the pooled cDNA. As this gene did not contain any introns, we amplified it directly from human chromosomal DNA. The yeast HSP70 gene SSA1 and the prokaryotic HSP70 gene DNAK were amplified from genomic Saccharomyces cerevisiae and E. coli DNA, respectively. An overview of the cloning procedure can be found in Fig. 2 and the cloning details can be found in Table 8. We used a nested PCR approach for the HSPH gene family as the start and the end of the members in this gene family are similar. The PCR products were purified, digested, and cloned in the pCDNA5/FRT/TO GFP vector. The constructs were sequence verified for the presence of the correct insert. Thereafter, expression was verified by Western blot analysis (data not shown) and the genes were subcloned in the pCDNA5/FRT/TO V5, pCDNA5/FRT/TO HIS, and the pCDNA5/FRT/TO vector.

Validation of the library

To test the effect of the different protein tags, two different biochemical assays were used. First, the effect of the tag on the ability of HSPA1A to assist the refolding of heat-denatured luciferase was tested (Michels et al. 1997, 1999). Therefore, we used constructs containing the HSPA1A gene downstream and in frame with the GFP tag, the V5 tag, and the pCDNA5/FRT/TO vector lacking a tag and compared the efficacy in the stimulation of luciferase refolding. The GFP tag significantly reduced the activity of the HSPA1A protein (Fig. 3A), whereas the V5 tag showed little to no significant effect on HSPA1A activity. Yet, modulation of HSPA1A related refolding by the co-factor BAG-1 (Nollen et al. 2000) could be achieved with all tagged versions (Fig. 3B). Thus, HSPA1A N-terminally tagged with eGFP may be less active related to non-tagged versions but seems unaffected in its ability to cooperate with its cofactors.

Fig. 3.

Fig. 3

The effect of different fusion tags on HSPA1A (A) and (B) or DNAJB1 (C). (A) Luciferase refolding assay using GFP, V5, and untagged HSPA1A versions. Cells were transfected with different tagged versions of HSPA1A together with a plasmid encoding firefly luciferase. HSPA1A expression was induced using tetracycline. The day after transfection, the cells were heated at 37°C or 45°C for 30 min and reincubated for 1 h at 37°C to allow luciferase refolding. Thereafter, cells were lysed and measured for luciferase activity. The percentage of luciferase activity is plotted relative to the activity in unheated control cells (100%). (B) Modulation of tagged HSPA1A versions by BAG-1. Cells were treated as in (A) but also co-transfected with a BAG-1 encoding plasmid as indicated. (C) Filter trap assay showing aggregation of expanded Huntingtin. GFP, V5 and untagged versions of DNAJB1 were used as indicated. Cells were transfected with different tagged versions of DNAJB1 together with a plasmid encoding GFP-tagged Huntingtin containing either 23Q or 74Q. DNAJB1 was induced by tetracycline. Two days after transfection, cells were lysed and the lysates were loaded on to a cellulose acetate membrane. After transblotting, blots were immunostained for GFP to detect aggregated Huntingtin. GFP-tagged DNAJB1 alone did not show any signal on the membrane (not shown)

To test the effect of tagging DNAJ-like proteins, we used a filter trap assay to detect aggregation of polyglutamine proteins such as mutant Huntingtin. Aggregated Huntingtin is SDS-insoluble and retains trapped in a non-protein binding cellulose acetate membrane, and DNAJB1 is known to be able to inhibit this aggregation (Carra et al. 2005; Rujano et al. 2007). We used constructs containing the DNAJB1 gene downstream and in frame with the GFP tag, the V5 tag, and the pCDNA5/FRT/TO vector lacking a tag and compared the efficacy of DNAJB1 in the suppression of mutant Huntingtin aggregation. As shown in Fig. 3C, untagged DNAJB1 strongly suppresses mutant Huntingtin aggregation containing a polyQ tract of 74 residues. Both the V5- and the GFP-tagged showed an equal slight reduction on the aggregation suppression but yet retained substantial activity. Thus, N-terminal tagging sometimes does influence the maximal activity of the chaperones tested. This implies that after performing experiments with our tagged HSPs, confirmation with untagged versions is required.

Conclusion/Discussion

The HSPH/HSPA and DNAJ families are large gene families with many poorly studied individual members. We used bioinformatics approaches to study the expression, the localization, and the homology of these families. These approaches generated large datasets, which will be useful for the systematic biochemical analysis of these family members. It was found that HSPs are expressed at highly variable levels in different tissues. So far, no clear patterns were seen for paired expression of certain members within, e.g., the HSPA and DNAJ family in most tissues. Although it is valid to search for the highest expressing tissue for a particular transcript, it is difficult to compare the level of different transcripts for a particular tissue. This is thought to be partly because different messengers show a different half-life and partly because different transcripts show large differences in the window of bottom-to-peak expression making such a comparison difficult. Interestingly, we did find some pattern for the testis. A testis-specific HSPA transcript was found (HSPA1L) as well as testis-specific DNAJ members (DNAJB7, DNAJB8, DNAJC5B, and DNAJC5G). This could indicate that HSPA1L cooperates with one of these DNAJ members.

We also studied the expression levels of HSPs during various developmental stages. The results of the peak expression per transcript show that there is a wide variation in HSP expression throughout different developmental stages but many DNAJC members peak at the blastocyst and fetal stages, indicating that there is a need for specialized DNAJ members early in development.

Surprisingly, the heat inducibility of the different HSPs was restricted to only a couple of members within each family (HSPH1, HSPA1A/B, HSPA6, DNAJB1, DNAJB2, DNAJB4, and DNAJB6). This could mean that HSPA1A or HSPA6 cooperate with HSPH1 and one of these DNAJ members following stress conditions. Interestingly, no heat-inducible DNAJC member was found indicating that DNAJC members do not function in the stress response. It should be noted, however, that the array did not contain probes corresponding to all DNAJC members.

Analyzing the cellular distribution and homology of different HSP members showed that a very homologous subfamily of the HSPA family is predicted to be expressed in the cytosol (HSPA1A/B, HSPA1L, HSPA2, HSPA6, and HSPA8). This indicates that only a minority of the gene duplication occurred as a result of the compartmentalization. It is unclear at this stage if this homologous subgroup of HSPA chaperones is regulated by the same subset of co-factors and if they bind the same subset of client proteins. It will be highly interesting to answer these questions by using available biochemical approaches. For this purpose, we cloned a large collection of chaperone-encoded genes in a tetracycline-inducible vector system. Different tags with different properties were used in order to detect expression levels (V5), study subcellular localization in living or fixed cells (GFP/V5), or to enrich the expressed protein from crude cell lysates (His). In addition, non-tagged versions were made to verify obtained biological effects. This expression library will be useful to systematically study the biochemical and cell biological features of these poorly characterized HSPs and might help answer the intriguing question why we have so many HSPA and DNAJ chaperones.

Acknowledgements

We would like to thank Maria A.W.H. van Waarde for expert assistance on biochemical analysis. We thank Eefje Pelster, Alette H. Faber, and Reinier Bron for assistance on gene cloning. Lenja Bystrykh (Department of Cell Biology, Stem Cell Biology Section, University Medical Center Groningen, The Netherlands) is kindly acknowledged for help on the Unigene EST collection and Ron Dirks (section Biochemistry, Radbout University, Nijmegen, The Netherlands) for help on the Affimetrix gene array data. Russell S. Thomas (CIIT Centers for Health Research, NC, USA) is acknowledged for providing the array data sets. This work was supported by Innovatiegerichte Onderzoeksprogramma Genomics Grant IGE03018.

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