Coverage of whole proteome by structural genomics observed through protein homology modeling database

We have been developing FAMSBASE, a protein homology-modeling database of whole ORFs predicted from genome sequences. The latest update of FAMSBASE (http://daisy.nagahama-i-bio.ac.jp/Famsbase/), which is based on the protein three-dimensional (3D) structures released by November 2003, contains modeled 3D structures for 368,724 open reading frames (ORFs) derived from genomes of 276 species, namely 17 archaebacterial, 130 eubacterial, 18 eukaryotic and 111 phage genomes. Those 276 genomes are predicted to have 734,193 ORFs in total and the current FAMSBASE contains protein 3D structure of approximately 50% of the ORF products. However, cases that a modeled 3D structure covers the whole part of an ORF product are rare. When portion of an ORF with 3D structure is compared in three kingdoms of life, in archaebacteria and eubacteria, approximately 60% of the ORFs have modeled 3D structures covering almost the entire amino acid sequences, however, the percentage falls to about 30% in eukaryotes. When annual differences in the number of ORFs with modeled 3D structure are calculated, the fraction of modeled 3D structures of soluble protein for archaebacteria is increased by 5%, and that for eubacteria by 7% in the last 3 years. Assuming that this rate would be maintained and that determination of 3D structures for predicted disordered regions is unattainable, whole soluble protein model structures of prokaryotes without the putative disordered regions will be in hand within 15 years. For eukaryotic proteins, they will be in hand within 25 years. The 3D structures we will have at those times are not the 3D structure of the entire proteins encoded in single ORFs, but the 3D structures of separate structural domains. Measuring or predicting spatial arrangements of structural domains in an ORF will then be a coming issue of structural genomics.
Introduction
Genome sequencing projects provided a huge number of amino acid sequences without functional information (Stein 2001). To discover biological functions of those proteins, both computational predictions and biochemical experiments are necessary (Tsoka and Ouzounis 2000). Most of the proteins perform functions after forming specific 3D structures, and therefore protein 3D structure is one of the most valuable sources of information to predict protein function (Domingues et al. 2000; Xie and Bourne 2005). Protein function prediction based on 3D structures, especially protein surface structures, with evolutionary and/or physicochemical characteristics have been extensively studied (Lichtarge and Sowa 2002; Campbell et al. 2003; Kinoshita and Nakamura 2003; Laskowski et al. 2003; Ota et al. 2003; Pieper et al. 2006). However, determining protein structures of all the function-unknown proteins for applying these types of study is not practical.
Proteins are classified into a large number of ‘families’ based on the amino acid sequence similarity (Dayhoff 1972), and proteins with similar amino acid sequences are known to have similar 3D structures (Chothia and Lesk 1986), all because the proteins in a family are evolutionary related (Doolittle 1995). Once we have 3D structure of at least one of the proteins in a family, then 3D structures of other proteins in the same family can be computationally deduced by ‘homology modeling’ (Burley 2000; Baker and Sali 2001). Based on this logic, structural genomics (SG) projects, which are to determine protein 3D structures of representatives for each family have been proposed and launched (Vitkup et al. 2001; Brenner 2000; Burley and Bonnano 2002). In homology modeling, corresponding residues between an amino acid sequence of structure unknown protein (target) and that of 3D structure known protein (template) in the same family are determined by sequence alignment and every residue in a template protein is replaced by that in a target protein (Marti-Renoma et al. 2000).
SG projects have been providing new protein structures (Todd et al. 2005; Xie and Bourne 2005; Chandonia and Brenner 2006). Protein Data Bank (PDB) (Berman et al. 2000) now contains more than 390 3D structures for function unknown or hypothetical proteins (Stark et al. 2004). Protein function predictions based on 3D structures determined by SG projects are also in progress (Goldsmith-Fischman and Honig 2003; Liu et al. 2005; Petrey and Honig 2005). There are some projects that focus on a specific species and try to determine the 3D structures of whole proteins encoded in the genome of the species (Kim 2000; Yokoyama et al. 2000; Kim et al. 2003). Those projects provide a considerable number of 3D structures in a single protein family. This results in providing multiple templates for a single protein family and it can improve quality of homology modeling (Contreras-Moreira et al. 2003).
We have developed FAMSBASE; a database for homology modeling 3D structures of whole proteins predicted on whole genome sequences, since 2001 (Yamaguchi et al. 2003; http://daisy.nagahama-i-bio.ac.jp/Famsbase/). FAMSBASE contains results of homology modeling by FAMS, a full automatic modeling software (Ogata and Umeyama 2000). Sequence alignments between whole ORFs and proteins in PDB are based on GTOP (Kawabata et al. 2002).
We report here the update of the database including differences in the amount of structural data from the previous version, estimation of the time that whole ORFs predicted out of genome sequences are covered by homology modeling 3D structures and upcoming issues for utilizing those modeled structures.
Methods
Data update of FAMSBASE
Correspondence between ORFs derived from whole genome sequences and protein amino acid sequences whose 3D structures are known is provided by GTOP database (Kawabata et al. 2002). The update in May 2005 of FAMSBASE is based on February 2004 version of GTOP. Protein 3D structures in PDB by November 2003 are used for homology modeling templates. FAMS (Ogata and Umeyama 2000) is applied by Umeyama et al. to pair-wise alignments between a predicted ORF sequence and an amino acid sequence with known 3D structure, and a 3D structure is modeled. All the results are stored in FAMSBASE.
Assessing annual difference of data in FASBASE
Based on the amount of data in FAMSBASE in 2001 and the amount of increase in the following years, a due year for whole proteome 3D structure models is estimated. Estimation is done residue-wise, not ORF-wise, since modeled structures in FAMSBASE are often limited to structural domains. In this report, structural domains refer to SCOP domains (Andreeva et al. 2004). All ORFs predicted out of genome sequences are divided into soluble and membrane proteins. The division is carried out by SOSUI (Hirokawa et al. 1998), and a protein with one or more transmembrane regions is classified into a membrane protein. The number of residues of whole soluble proteins encoded in the genome sequence (G) of species i is denoted as S, and the number of residues of whole membrane proteins is denoted as M. The number of residues included in modeled 3D structures of soluble and membrane proteins are denoted as S3 and M3, respectively. For a certain genome Gi, the coverage of modeled 3D structures in whole soluble proteins is then S3/S
and the coverage for whole membrane proteins is M3/M. The coverage is summarized in different kingdoms of life as in the following equations;
Both figures are calculated based on the data at the different times of FAMSBASE update, gradients in figures are then calculated, and the figures are extrapolated up to the year that coverage reaches to 100.
It is getting to be known that not all ORFs assume stable 3D structures. Some parts of ORFs are considered to be natively disordered (Oldfield et al. 2005; Dyson and Wright 2005). Hence it is unlikely that coverage by homology modeling reaches to 100. We, therefore, estimate disordered regions in whole ORFs by DisEMBL (Linding et al. 2003) and omit these disordered regions from the calculation.
Non-overlap multiple model structures in single ORFs
Modeled 3D structures in FAMSBASE are often limited to structural domains. To find an ORF of which most of the entire 3D structure is modeled in pieces of structural domains, an ORF covered by non-overlapping three or more modeled 3D structures in eukaryotic genome is surveyed based on the following criteria; (1) 70% or more residues in the ORF are included in one of the modeled 3D structures, (2) the ORF contains three or more non-overlapping modeled structures, and (3) the sequence identity between a template protein and a target domain is no less than 25%. At the time of FAMSBASE building, five model structures are at most built for each ORF (Yamaguchi et al. 2003). Therefore, the expected number of modeled structures in the above criteria is between three and five.
Prediction of domain interfaces
The 3D structure in pieces for a single ORF needs to be assembled to model the entire 3D structure. For this procedure, a prediction of domain interfaces of each 3D structure is needed. A hydrophobicity index based on protein 3D structures is built for domain interface prediction. Hydrophobicity of amino acid residue is measured by buriedness of a residue inside the protein 3D structures. A representative 4,529 chains in PDB among which sequence identities are less than 30% were selected and solvent accessibility of each residue is calculated on a monomer state. For each amino acid residue type i (i = 1,...,20), the number of residue with accessibility no less than b (=0.0 − 1.0) is counted (Sb,i). Database derived hydrophobicity index (Ib,i) is obtained by;
b is set to 0.15 to maximize the difference of Ib,i among different residues. The index has good correlation with Kyte and Doolittle hydrophobicity index (Kyte and Doolittle 1982). The index I0.15,i is assigned to every residue on the surface (accessibility no less than 0.15) of a modeled 3D structure. The hydrophobicity of each residue on a surface of a protein is then obtained by averaging the assigned values of residues within 7.0 Å from the residue in concern. A hydrophobic patch on the surface of the modeled structure is found as a cluster of surface residues with the hydrophobicity no less than 0.0.
Results and discussion
Coverage of whole protein space by homology modeling
The latest update of FAMSBASE at May 2005 uses protein 3D structures deposited to PDB by the end of Nov. 2003 and ORFs predicted from genome sequences deposited by February 2004 (http://daisy.nagahama-i-bio.ac.jp/ Famsbase/). The latest FAMSBASE contains 1,396,272 modeled 3D structures of 368,724 ORFs derived from 17 archaebacterial, 130 eubacterial, 18 eukaryotic and 111 phage genomes; in total 276 genomes. Five models at maximum are built for each ORF in FAMSBASE. Those five models are the structure for the same or different regions in the ORF. When multiple models are built for the same region of ORF, we can evaluate the reliability of the model. When the model based on different templates have the similar 3D structures, then the 3D structure would be reliable. When the structures are different, the modeled structure would be less reliable. We further test the quality of modeled 3D structure by ProsaII (Sippl 1993) and find that about 72% of the modeled 3D structures are energetically ranked as number one and comparable to experimentally determined 3D structures. Some of the structures that fail the test are structures of a part of a large protein, mostly structural domains of large proteins. It is difficult to assess the quality of this type of domain structures, because interfaces of the domain for other parts of the protein are exposed in the modeled structures. Tendency of amino acid residue appearance in the interface is supposed to be different from that at the surface as we discuss down below.
In the genome of 276 species, 734,193 ORFs are predicted. Therefore, in FAMSBASE, 3D structure of 50% (368,724/734,193) of ORFs have been built and stored (Table 1). These are about 47% of ORFs in archaebacterial genomes, about 52% in eubacterial genomes and about 49% of eukaryotic genomes.Table 1Number of ORFs and those with modeled 3D structures in 276 genomesSpeciesORFModel%ArchaeaArchaeoglobus fulgidus DSM43042,4071,23351.2Aeropyrum pernix K12,69478929.3Halobacterium sp. NRC-12,6051,19545.9Methanosarcina acetivorans C2A4,5442,12446.7Methanocaldococcus jannaschii DSM26611,77087549.4Methanopyrus kandleri AV191,68778446.5Methanosarcina mazei Goe13,3711,63448.5Methanothermobacter thermautotrophicus1,86999853.4Nanoarchaeum equitans Kin4-M53626449.3Pyrococcus abyssi Orsay1,78494252.8Pyrobaculum aerophilum IM22,6051,04740.2Pyrococcus furiosus DSM 36382,0651,03550.1Pyrococcus horikoshii OT32,06187942.6Sulfolobus solfataricus P22,9941,36545.6Sulfolobus tokodaii 72,8261,22843.5Thermoplasma acidophilum DSM17281,47884457.1Thermoplasma volcaniumGSS11,52683955.0sum38,82218,07546.6EubacteriaAquifex aeolicus VF51,55392959.8Nostoc sp. PCC 71206,1322,76545.1Agrobacterium tumefaciens C585,3013,01756.9A. tumefaciens C58 (Dupont)5,4023,02856.1Bacillus anthracis str. Ames5,3112,46346.4Buchnera aphidicola Sg55241074.3B. aphidicola50738575.9Bordetella bronchiseptica RB504,9942,93458.8Borrelia burgdorferi1,63953532.6Bacillus cereus ATCC 145795,2552,53448.2Candidatus Blochmannia floridanus58344776.7Bacillus halodurans C-1254,0662,12752.3Bradyrhizobium japonicum8,3174,44953.5Bifidobacterium longum NCC27051,73198556.9Brucella melitensis 16M3,1981,80156.3Bordetella parapertussis4,1852,52560.3B. pertussis Tohama I3,4472,17963.2Bacillus subtilis 1684,1062,15352.4Brucella suis 13303,2641,67751.4Bacteroides thetaiotaomicron VPI-54824,8162,46251.1Buchnera sp. APS57443676.0Clostridium acetobutylicum ATCC8243,8482,05353.4Coxiella burnetii RSA 4932,04592545.2Chlamydophila caviae GPIC1,00550550.2Caulobacter crescentus3,7372,08455.8Corynebacterium diphtheriae NCTC131292,2721,16551.3Corynebacterium efficiens YS-3142,9981,51350.5Corynebacterium glutamicum ATCC 130323,0991,55450.1Campylobacter jejuni1,63489354.7Chlamydia muridarum Nigg91148353.0Clostridium perfringens 132,7231,47054.0Chlamydophila pneumoniae AR391,11649544.4Chlamydophila pneumoniae CWL0291,05249647.1Chlamydophila pneumoniae J1381,06950146.9Chlamydophila pneumoniae TW-1831,11350145.0Chlorobium tepidum TLS2,2521,16651.8Clostridium tetani E882,4321,30653.7Chlamydia trachomatis D/UW-3/CX89448554.3Chromobacterium violaceum ATCC 124724,3852,34353.4Deinococcus radiodurans R13,1021,57950.9Escherichia coli K-12 MG16554,2842,39856.0E. coli O157:H75,4472,60747.9E. coli O157:H7 EDL9335,4492,62948.2E. coli CFT0735,3792,55847.6Enterococcus faecalis V5833,2651,56848.0Fusobacterium nucleatum ATCC 255862,0671,01148.9Geobacter sulfurreducens PCA3,4451,90255.2Gloeobacter violaceus PCC 74214,4302,20849.8Haemophilus ducreyi 35000HP1,71786550.4Helicobacter hepaticus ATCC 514491,87590248.1Haemophilus influenzae Rd1,7091,03860.7Helicobacter pylori 266951,56674147.3Helicobacter pylori J991,49174750.1Listeria innocua Clip112623,0431,64153.9Leptospira interrogans serovar4,7251,71936.4Lactococcus lactis IL14032,2661,25455.3Listeria monocytogenes EGD-e2,8461,65358.1Lactobacillus plantarum WCFS13,0091,64754.7Mycobacterium bovis subsp.3,9202,01851.5Mycoplasma gallisepticum R72637151.1Mycoplasma genitalium G3748030563.5Mycobacterium leprae TN1,60591857.2Mesorhizobium loti MAFF3030997,2813,82952.6Mycoplasma penetrans1,03747245.5Mycoplasma pneumoniae M12968833348.4Mycoplasma pulmonis UAB CTIP78239850.9Mycobacterium tuberculosis H37Rv3,9182,03652.0Mycobacterium tuberculosis CDC15514,1871,99047.5Nitrosomonas europaea ATCC 197182,4611,36655.5Neisseria meningitidis MC582,0251,01650.2Neisseria meningitidis Z24912,0651,02549.6Oceanobacillus iheyensis HTE8313,4961,89254.1Phytoplasma asteris, OY strain75442356.1Pseudomonas aeruginosa PAO15,5663,20657.6Porphyromonas gingivalis W831,90994449.4Photorhabdus luminescens laumondii4,6832,28648.8Prochlorococcus marinus MED41,71293354.5Prochlorococcus marinus MIT93132,2651,12249.5Prochlorococcus marinus marinus1,88293949.9Pasteurella multocida PM702,0141,23761.4Pseudomonas putida KT24405,3502,96855.5Pseudomonas syringae pv. tomato str.5,6082,93852.4Pirellula sp. 17,3252,58835.3Rickettsia conorii Malish 71,37457241.6Rhodopseudomonas palustris4,8142,73956.9Rickettsia prowazekii Madrid E83449859.7Ralstonia solanacearum GMI10005,1162,69852.7Streptococcus agalactiae2,1241,15954.6Streptococcus agalactiae NEM3162,0941,17456.1Staphylococcus aureus Mu502,7481,45152.8Staphylococcus aureus N3152,6241,44755.1Staphylococcus aureus MW22,6591,41053.0Streptomyces avermitilis7,6714,00152.2Streptomyces coelicolor A3(2)8,1544,19551.4Staphylococcus epidermidis ATCC 122282,4851,30352.4Shigella flexneri 2a 3014,4522,30651.8Shigella flexneri 2a str. 2457T4,0682,15953.1Sinorhizobium meliloti 10216,2053,49956.4Streptococcus mutans UA1591,9601,13658.0Shewanella oneidensis MR-14,7782,29147.9Streptococcus pneumoniae R62,0941,10152.6Streptococcus pneumoniae TIGR42,0431,13555.6Streptococcus pyogenes SF3701,69695656.4Streptococcus pyogenes MGAS82321,84599654.0Streptococcus pyogenes MGAS3151,86598652.9Streptococcus pyogenes SSI-11,86197652.4Salmonella typhi CT184,7672,34749.2Salmonella typhimurium LT24,5542,45754.0Salmonella enterica subsp. enterica4,3232,26352.3Synechocystis sp. PCC 68033,1671,67953.0Synechococcus sp. WH 81022,5171,24349.4Thermosynechococcus elongatus BP-12,4751,30352.6Thermotoga maritima MSB81,8461,05156.9Treponema pallidum subsp.1,03151750.1Thermoanaerobacter tengcongensis MB4T2,5881,40354.2Tropheryma whipplei TW08/2778349463.1Tropheryma whipplei str. Twist80849961.8Ureaplasma urealyticum61130349.6Vibrio cholerae N169613,8281,97151.5Vibrio parahaemolyticus RIMD 22106334,8322,46150.9Vibrio vulnificus CMCP64,5372,46154.2Vibrio vulnificus YJ0165,0282,49949.7Wigglesworthia brevipalpis61144172.2Wolinella succinogenes DSMZ 17402,0441,20859.1Xanthomonas axonopodis pv. citri 3064,4272,37453.6Xanthomonas campestris pv. campestris4,1812,28754.7Xylella fastidiosa 9a5c2,8321,15840.9Xylella fastidiosa Temecula12,0361,06652.4Yersinia pestis CO924,0832,11651.8Yersinia pestis KIM4,2812,12349.6sum396,126206,31152.1EukaryotesArabidopsis thaliana28,72314,39450.1Caenorhabditis briggsae14,7137,06348.0Caenorhabditis elegans22,2208,84139.8Ciona intestinalis15,8657,99450.4Drosophila melanogaster18,3029,54152.1Danio rerio26,58716,44361.8Encephalitozoon cuniculi1,99688744.4Guillardia theta Nucleomorph63230748.6Homo sapiens (ENSEMBLE)28,06315,46755.1Leishmania major Friedlin1736235.8Mus musculus24,92814,38257.7Neurospora crassa10,0883,80037.7Oryza sativa16,7244,51727.0Plasmodium falciparum 3D75,2681,90536.2Rattus norvegicus28,68216,74058.4Saccharomyces cerevisiae5,8692,91349.6Schizosaccharomyces pombe5,2612,80753.4Takifugu rubripes rubripes37,45215,20240.6sum291,546143,26549.1Phages/Viruses18646817.444AHJD2114.844RR2.8t2525120.2933W80911.3A11872912.5A5111100.0Aeh13315115.4APSE-154611.1B11119.1B10317423.5Bcep7816158.2BF238112.5bIL1706423.1bIL2856258.1bIL28661711.5bIL30956610.7bIL31029413.8bIL31122627.3bIL31227311.1BK5-T6369.5Bxb1861214.0C23925.1Cp-12827.1ϕCTX4748.5D29791519.0D3941111.7Rb1549612.2ϕg1e49612.2GA-13538.6Gh-1421228.6H-19B22418.2HF2114119.6HK02257814.0HK62058610.3HK97611016.4HP14137.3HP23638.3K1394449.1KVP403815715.02,38957712.3L-413C40410.0L5851214.1λ661827.3A261813.1Mu53611.3N15601321.7Mycoplasma virus P11100.0Enterobacteria phage P11100.0P242511.9P2236925.0P2758915.5P33549612.2P412216.7P60801316.3PA0134514.7PaP369811.6ϕKZ306258.2ϕCh19899.2ϕYeO3-12591322.0ϕ10551815.7ϕC3155814.5ϕ3626501020.0ϕE125711216.9ϕETA66812.1ϕNIH1.155610.9ϕPV8365913.8ϕSLT621219.4ϕadh63812.7ϕBT155916.4ϕA1122501020.0P682229.1ϕKMV481122.9PM22214.5PRD122418.2ΨM23113.2ΨM10037410.8PY54671014.9PZA27414.8R1t50612.0RB692565621.9RB492724918.0Rd47612.8RM3781461711.6PVL62812.9Sfi112514.0V53713.2SIO134617.6Sk15411.9SP620630.0SP βc21853317.8SPP110676.6MM153611.3ST64B56814.3ST64T65913.8720146817.4DT147714.9O12055747.0Sfi1945613.3Sfi2150918.0Stx2165116.7T3441022.7T42785820.9T7581017.2TM48955.6TP901-156712.5Tuc200956712.5Ul365858.6VHML57814.0VpV2626746.0VT2-Sa821113.4Wϕ4449.1Sum7,699107313.9Total734,193368,72450.2
When a modeled 3D structure is counted based on the number of amino acid residues, not on the number of ORFs, a different aspect emerges. Figure 1 shows the percentage of amino acid residues per ORF included in the modeled structures. ORFs without a modeled structure are omitted. Of archaebacterial and eubacterial genomes, in 60% of ORFs, more than 80% of the residues are included in modeled 3D structures, however, of eukaryotic genomes, only in 30% of ORFs, more than 80% of the residues are included (red and blue sections in Fig. 1). The proportion of residues in modeled 3D structure can be measured by the number of residues in a typical structural domain as shown in SCOP (Andreeva et al. 2004). The average size of protein domain is around 100–150 residues (Copley et al. 2002). In ORFs with modeled structures, a continuous region of residues with one domain or more remains as structure unknown in only about 18% of ORFs of archaebacterial and eubacterial genomes, whereas in about 60% of ORFs of eukaryotic genomes, the regions with one domain or more remain as structure unknown.Fig. 1Percentage of amino acid residues included in modeled 3D structures in each ORF is classified by 10% bins and shown in pie charts. ORFs without a modeled structure are not included. A number of ORFs with modeled structures and an average length of the ORFs are shown at the center of each pie chart. Sections bordered by thick black lines indicate that the unmodeled region in the ORF is no less than the size of a domain (about 150 residues)
Annual difference of model structures
In FAMSBASE of 2001, 38% of amino acid residues in all ORFs of archaebacterial and 40% of eubacterial genomes were included in modeled 3D structures (Yamaguchi et al. 2003). In the current update of FAMSBASE based on data by around 2004, 42% of amino acid residues in all ORFs in archaebacterial and 46% of eubacterial genomes are included in modeled structures. In eukaryotic genomes, 24% of amino acid residues in 2003, and 26% in 2004 are included in modeled 3D structures. Those figures can be used to estimate the time when modeled 3D structures of whole proteins predicted from genomes are obtained. The estimation for the time obtaining the whole soluble and membrane proteins are treated separately, because the speed of structure determination for soluble and membrane proteins seems to differ. The assumption for the estimation is that the speed for structure determination would stay the same and no new protein family would appear.
For eubacterial genomes, 72.6% of residues in whole ORFs are predicted by SOSUI (Hirokawa et al. 1998) to encode soluble proteins and 27.4% to encode membrane proteins. This ratio is not so different from the previous prediction by Krogh et al. (2001). Of about 40% of whole eubacterial ORF that were with modeled 3D structures in 2001, approximately 90% were soluble proteins and 10% were membrane proteins. Therefore, about 50% (=0.40 × 0.90/0.726) of the whole soluble proteins were modeled. Of the whole membrane proteins in eubacterial genome, about 15% (=0.40 × 0.10/0.274) were modeled. In 2004, those figures are grown to 57% and 19%, respectively. In eubacterial whole ORFs, about 19.9% of amino acid residues are predicted to be included in disordered region by DisEMBL (Linding et al. 2003). Some of these regions are included in the modeled structures. These regions are either incorrectly predicted regions or incorrectly modeled regions. Assuming that the disordered regions without modeled 3D structures are correctly predicted, 10.8% of amino acid residues in soluble proteins were disordered and we would never obtain 3D structures of those regions. Then, by extrapolating the coverage of soluble proteins up to 89.2% (100–10.8) with the current growth rate, we can estimate that, by the year 2017, whole soluble proteins encoded in eubacterial genomes can be modeled (Fig. 2). Whole soluble proteins of archaebacterial genome can be modeled by 2021 and those of eukaryotic genomes, by 2031.Fig. 2Annual differences of modeled structures classified by kingdoms of life. The percentage is the number of amino acid residues included in modeled structures over the whole number of residues in predicted sequences for soluble and membrane proteins in each kingdom. (S) stands for soluble proteins and (M) stands for membrane proteins. Some of the residues are predicted to be in a disordered region. The percentage of residues in disordered regions is shown at the top
Orengo et al. (1999) showed percentage of ORFs with protein 3D structures as between 30 and 46% in 1999. The genome sequences known by 1999 were mostly derived from prokaryotic species and the known protein 3D structures were mostly soluble proteins. Therefore, the figures they presented in 1999 should correspond to the figures of archaebacterial and eubacterial soluble proteins. When we extrapolate the figures of archaebacterial and eubacterial soluble proteins to the past in Fig. 2, the figures are around 40% in 1999, indicating that their figures approximately lie on the extrapolated lines.
The current estimation indicates that we will obtain 3D structures of whole soluble proteins of eubacteria in 11 years and archaebacteria in 15 years. This estimation does not take into account the acceleration of structure determination speed by automation (McPherson 2004; DeLucas et al. 2005), which makes the due days closer to the present. For membrane proteins, speed of structure determination has been drastically accelerated by recent technical innovations (Kyogoku et al. 2003; Lundstrom 2004; Walian et al. 2004; Dobrovetsky et al. 2005), and therefore we will not linearly extrapolate the present status to estimate the due day for membrane proteins.
Frequency of template structure in use
When the template 3D structures used in FAMSBASE are classified by SCOP superfamily, which is a group of proteins that have low sequence identities but whose structural and functional features suggest that a common evolutionary origin is probable (Lo Conte et al. 2002), and frequencies of superfamilies in use are counted, ‘P-loop containing nucleoside triphosphate hydrolases’ superfamily is found to be the most frequent one; 7,532 times (about 12%) in whole archaebacterial model structures, 77,806 (about 10%) in eubacterial structures and 35,468 times (about 6%) in eukaryotic structures. The templates that follow in frequency in archaebacterial and eubacterial protein structures are ‘NAD(P)-binding Rossmann fold domains’, ‘4Fe–4S ferredxin’, and ‘PLP-dependent transferases’ superfamilies. In eukaryotic protein structures, ‘protein-kinase’, ‘immunoglobulin’ and ‘C2H2 and C2HC zinc fingers’ superfamilies, which appear specifically in eukaryotic genomes, follow the top.
Differences in distribution of frequency of templates in different kingdoms of life are evident, when frequencies in use of template are plotted in descending order (Fig. 3). In any kingdoms of life, the frequencies of the most and the second most used templates exceed those of the remaining templates. The frequencies of templates in use drops first in archaebacterial protein structures and then in eubacterial protein structures. The descending curve of eukaryotic template frequency is less steep compared with the others, indicating that one template can produce a large number of domain 3D structures in eukaryotic ORFs. In other words, a significant number of proteins encoded in eukaryotic genomes are originated by domain duplication, as Koonin et al. (2000) demonstrated. Superfamilies with the 3D structures and with many copies in eukaryotic genomes, but seldom in prokaryotic genomes are ‘protein kinase-like’, ‘immunoglobin’, ‘RNA-binding domain’, ‘C2H2 and C2HC zinc fingers’, ‘WD40-repeat’, ‘glucocorticoid receptor-like’, ‘homeodomain-like’, ‘PH domain-like’, ‘RING-box’, ‘L domain’, ‘ankyrin repeat’, ‘ARM repeat’, ‘cytochrome P-450’ and ‘EF-hand’ superfamilies. These superfamilies are transcription factors, protein–protein interaction mediators and response factor for toxic substances, mostly known to be unique to eukaryotes.Fig. 3Frequency of template usage in descending order. Horizontal axis is a template and the vertical axis is a frequency of templates in use. Red line is a template usage in archaebacteria, blue line is eubacteria and green line is eukaryotes
The ‘P-loop containing nucleoside triphosphate hydrolases’ superfamily outnumbering other superfamily in template frequency corresponds to the previous finding that the enzyme is highly frequently used in every kingdom of life (Leipe et al. 2003). When biological functions of these ORFs with the 3D structure of ‘P-loop containing nucleoside triphosphate hydrolases’ superfamily are classified, about half of the proteins are ABC transporters in archaebacterial and eubacterial proteomes, but numbers of G-proteins and motor proteins in eukaryotic proteomes are noticeable (Fig. 4).Fig. 4Protein family distribution of ‘P-loop containing nucleoside triphosphate hydrolases’ superfamily in each kingdom. In the three pie charts, the section with the same color is a category of the same family except for the white section
In the last two years, new protein structures were determined and contributed to an increase in the number of templates for homology modeling. A part of those template structures are listed in Table 2. A part of those top 15 templates contributed a lot for the growth of modeled 3D structure database. In Table 2, 3D structure derived from SG projects is rare. The ratio of SG products in Table 2 is the same as that in PDB (Editorial Board, Nature Structural & Molecular Biology 2004). As the SG projects in US and Europe have proceeded to phase 2 (Service 2005), SG products are expected to contribute to increase in the number of templates in the near future. The qualities of protein 3D structures, namely, size, resolution, R-factors and so forth, derived from SG projects were compared with those in PDB and no obvious compromise in quality of SG products were found (Todd et al. 2005). The quality of homology modeling based on products of SG projects in the future, therefore, will be expected to be no less than the current quality.Table 2Top 15 modeling templates in the newly determined 3D structures between 2002 and 2003PDBIDChainNumber of uses as a templateSGaProtein name1q12A7,031NMaltose/maltodextrin transport ATP-binding protein MalK1l2tA6,529NHypothetical ABC transporter ATP-binding protein Mj07961oxxK3,948NABC transporter ATP-binding protein GlcV1pf4A3,202NTransport ATP-Binding Protein MsbA1nr0A2,640YActin interacting protein 1 Aip11ixcA2,495NLysR-type regulatory protein CbnR1ld8A2,410NFarnesyltransferase α subunit1ji0A2,331YABC transporter1oywA2,251NATP-dependent DNA helicase; RecQ helicase1kt1A2,198NFk506-binding protein FKBP511mt0A1,961NHaemolysin secretion ATP-binding protein; ATP-binding domain1mdbA1,745N2,3-dihydroxybenzoate-AMP ligase DhbE1nnmA1,730NAcetyl-CoA synthetase1gxrA1,715NTransducin-like enhancer protein 1 Esg11uohA1,706N26S proteasome non-ATPase regulatory subunit 10a PDB entry seemingly derived from the SG projects judged by description in PDB file is tagged Y, and the remaining entry is tagged N
Whole structure and function of proteins from homology modeling of domain structures
Protein function prediction, especially studies on enzyme specificity, based on homology modeling structures is intensively carried out in the field of drug design and related fields (Goldsmith-Fischman and Honig 2003; Kopp and Schwede 2004). Those studies are mostly based on homology modeling of domain structures. As mentioned above, most of the eukaryotic protein structures in FAMSBASE are 3D structures of structural domains, not the entire coding regions (Fig. 1). Protein functional sites are often located at a cleft of domains (Laskowski et al. 1996), and therefore understanding relative location of domains will be a critical issue. Xie and Bourne (2005) and O’Toole et al. (2003) also pointed out this problem and mentioned, “even if all the domains of a multiple-domain query sequence have determined structures, the individual structures will not enable accurate modeling of how they associate together in the structure of the entire proteins (O’Toole et al. 2003).”
Figure 5 shows all eukaryotic ORFs whose 3D structures are mostly modeled in pieces. There are three types of enzymes and four types of cell surface receptors. A protein structure of ENSP00000264705 which is an ORF found in human genome can be modeled based on Escherichia coli carbamoylphosphate synthetase (CPS) and Pyrococcus abyssi asparatate transcarbamoylase (ATC). E. coli CPS is composed of a large subunit and a small subunit. CPS and ATC are the first and the second enzymes, respectively, in pyrimidine biosynthesis pathway. In mammalian genomes, those proteins are coded by a single gene and active in a hexamer form (Serre et al. 2004). Interactions between the large subunit domain and the small subunit domain of human CPS are conjectured to be the same as those between the large and the small subunits of E. coli CPS. N-terminal residues of the large subunit and the C-terminal residues of the small subunit are spatially located close in E. coli CPS, which permits the two chains to be chemically connected without disrupting subunit interfaces. To be active, human CPS should form a hexamer supramolecule and the interfaces for the supramolecule formation should be predicted from the modeled 3D structures. At the moment, the interfaces are unknown.Fig. 5Eukaryotic ORFs with multiple model structures covering more than 70% of entire protein. In each of the bar representation of proteins, a black box is a region with 3D structure. A name and PDB ID of a template structure and amino acid sequence identity between template and target domains are given below the black box. A yellow box is a putative signal peptide and green box is a putative transmembrane region. Template and modeled structures of ENSMUSP00000019416 were shown on the right side of the figure. Each domain is colored by hydrophobicity. A hydrophilic residue is in green and a hydrophobic residue is in red. A buried residue is in deep blue
ENSMUSP00000019416 is an ORF found in mouse genome and encodes a putative cell surface receptor. The protein is predicted to consist of six consecutive Ig-fold domains. There is a putative transmembrane helix at the C-terminal region of the protein. Two consecutive Ig-fold domains are modeled without overlap, and no pieces of information for relative orientation of three modeled structures have been found. Information of interaction sites of those domains is required to build the entire structure of the protein and to predict a target molecule of this receptor. Computational analyses of domain interfaces and of protein–protein interfaces have been targets for extensive study for a long time, and some general characteristics have been found. One of them is the hydrophobicity of the interfaces (Wodak and Janin 2002). Hydrophobic clusters on the surface of modeled structures of ENSMUSP00000019416 are shown in right side of Fig. 5. One of the template structures, Nkp46 ectodomain, has hydrophilic surface (green) around the C-terminal residues of the domain, however the modeled structure has a hydrophobic surface (orange) at the corresponding area. The other template structure, LIR-1 D1D2, has a hydrophilic surface around the N-terminal residues of the domain, however the modeled structure has a hydrophobic surface at the corresponding area. The surfaces uniquely turned into hydrophobic in modeled structures are close to the residues that are chemically bonded in the target protein, and therefore both of the areas likely form interfaces of the two domains. The modeled structure based on LIR-1 D1D2 domain has another hydrophobic surface around the C-terminal residues, which may interact with CD158j-like domain located at the C-terminal side of the domain.
Accuracy of homology modeling
There are at least three major issues that affect accuracy in homology modeling; the best template selection, accuracy of an amino acid sequence alignment between template and target protein sequences and the accuracy of structure building procedure itself (Contreras-Moreira et al. 2005). Accuracy of the alignment is high, when sequence identity of template and target proteins is higher than 30%, and alignment of proteins with identity less than 30% is known to be less reliable, thereby accuracy of homology modeling deteriorates (Kopp and Schwede 2004). FAMS has been shown to construct relatively accurate model structures, even with low sequence identity between template and target sequences in CAFASP2, the homology modeling competition (Iwadate et al. 2001; Yamaguchi et al. 2003). A distribution of sequence identity between amino acid sequences of template and target proteins in FAMSBASE is shown in Fig. 6. Half of the model structures in FAMSBASE rely on alignments of sequence identity less than 20%. Figure 6 suggests that the current 3D structure database does not contain good enough structures for high quality homology modeling. SG projects will eventually provide better template structures, and improvement in target selection, alignment and modeling methods are also in pursuit to overcome the difficulties in homology modeling (John and Sali 2003; Wallace et al. 2005).Fig. 6Distribution of sequence identity between template and target amino acid sequences in FAMSBASE
Conclusion
Construction of database of whole genome homology modeling clarified that protein 3D structures of about 50% of the protein coding regions in whole genome can now be modeled. Maintaining the current speed of 3D structure determination, it will take, at most, 11 years to have enough templates to cover whole soluble proteins of eubacterial genomes, and 25 years to cover those of eukaryotic genomes. The current advancement in technologies of protein structure determination is expected to make these due times closer to the present. What we obtain at those times are not the 3D structures of entire proteins, but domain structures in pieces. A homology modeled domain structure is now in use of predicting domain functions, but predicting spatial arrangement of domains in a protein will be an important issue for function prediction.

structural genomics
genome
homology modeling
p-loop
domain duplication
domain interactions


Hypothesis: hair cover can protect against invasive melanoma on the head and neck (Australia)

The anatomic distribution of cutaneous melanoma reflects people’s levels and patterns of sun exposure. While examining trends of incident invasive melanomas by site in recent decades in Australia we noted significant increases in incidence on the ears but not the face or any other site in women younger than 40 years, by 6% (95% confidence interval [CI] 2–10%) per year, and 40–59 years by 7% (95% CI 4–10%) per year. Men of the same age showed no corresponding changes in ear melanoma. However incidence rates of ear melanoma in general were fourfold higher in males than females in Australia. Further, using data from the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) Program, rates of invasive melanoma on the ear were found to be sevenfold higher in males than females in the US population in the same period. Higher rates of scalp and neck melanomas were also seen in men and women in both populations. We therefore speculated that the isolated rises of ear melanoma in younger women in Australia, and the higher overall rates of ear, scalp and neck melanoma in men compared with women, reflect differences in hair coverage. We tested the specific hypothesis that hair cover reduces sun exposure of the ears using experimental manikins and found that hair cover of the ear reduced solar ultraviolet-B exposure by 81% [SE ±8] compared with uncovered ears. We conclude that hair cover can protect against invasive melanoma on the ear and may similarly protect on the scalp and neck. When discretionary, hair may be an important additional factor to be considered for melanoma prevention.
Introduction
Sun exposure is the major environmental cause of melanoma [1] and so the anatomic distribution of melanoma in Caucasian populations largely reflects relative levels of sun exposure of different body sites [2]. Similarly in a given population, changes in site distribution of melanoma have long been known to offer clues about change in people’s sun-related behavior [3]. For example Magnus [4] analyzed melanoma incidence rates by age cohort and tumor site in Norway 1955–1977 and found that trunk and lower limb melanomas had increased much more than face and neck melanomas among younger generations, consistent with changes in clothing and sun-tanning habits in the first half of the twentieth century. Here we describe an isolated rise in the incidence of melanoma of the ear in women under 60 years of age in Australia. This distinct increase of invasive melanoma on a discrete site indicated some diminution in covering of the ear in young women in preceding decades. While a change in hat wear was one possible explanation, we speculated that a change in hair cover may also explain the observed rise, and that less hair coverage of the head among men in general may explain their higher relative incidence of melanoma on the ears and scalp compared with women [2]. When we searched for published data regarding the sun protection factor of hair we found none. Hence we set out to test the specific hypothesis that hair cover protects the ear from sun exposure using experimental manikins.
Recent trends in melanoma in Australia
Based on data for 109,062 invasive melanomas assembled by the Australian Institute of Health and Welfare from cancer registries in all Australian states and territories, trends in incidence of cutaneous melanoma in Australia 1982–1999 by sex and site were examined. Sites of invasive melanoma were pre-classified by cancer registries according to a standard protocol. For each site annual notifications were provided with age in 5-year bands. Population estimates by age and sex were obtained for each year from the Australian Bureau of Statistics. Annual sex-specific incidence rates of melanoma age-standardized to the WHO World Population were computed for each site and relative rates of increase were calculated for three age bands, less than 40 years, 40–59 years and 60 years and over, by regressing the logarithms of these rates on years.
The average relative change in melanoma incidence rates over all sites each year was estimated as 3.2% (99% CI 3.0–3.4%) in males and 1.5% (99% CI 1.3–1.8%) in females but increases varied by sex and age. Highest increases were seen in men 60 years and over (4.4% per year, 95% CI 4.2–4.6%), followed by women 60 and over and men 40–59 years (2.9% per year), and then by women aged 40–59 years (1.5% per year) (Table 1). In contrast persons under 40 years showed little to no relative increase in invasive melanoma in the last two decades: young men showed a 1.4% annual increase of borderline significance and women under 40 years showed virtually no change in melanoma incidence (Table 1).
Table 1Age-standardized incidence of melanoma on head and neck sites and all sites in Australia 1982–1986 and 1995–1999 by age and sexSiteAge (years)No. of melanomasIncidence rates per 100,000% Annual relative increase (95% CI)1982–19861995–1999MalesEar<401790.150.222.0 (−0.9–5.0)40–59 4260.951.31 1.9 (0.0– 3.8)≥60 1,1153.296.464.2 (3.0– 5.4)Face<40 3920.340.502.6 (−0.7–4.5)40–59 1,1252.483.85 3.2 (2.1–4.4)≥60 3,54611.0620.303.7 (3.0–4.4)All sites<40 10,7318.9111.981.4 (1.0–1.8)40–59 19,89843.3864.652.8 (2.5–3.1)≥60 27,95785.34166.534.4 (4.2–4.6)FemalesEar<40 1060.080.176.1 (2.4–9.9)40–59 1300.230.537.0 (3.6–10.4)≥60 2260.580.921.9 (−0.8–4.6)Face<404510.400.540.9 (−0.9–0.7)40–598402.482.340.0 (−1.3–1.4) ≥603,5089.7315.282.4 (1.8–3.1)All sites<4013,49413.4915.290.3 (0.1–0.7)40–5917,32844.2454.691.5 (1.2–1.8)≥6019,65455.7487.052.9 (2.6–3.2)
Recent site-specific trends in melanoma on the head and neck in Australia
Site-specific trends in melanoma for males and females within each age group reflected the above trends for all sites combined, such that men 60 and over generally had the highest relative increases on each anatomic site and women under 40, the least (data not shown for trunk and limbs). The striking exception was a significant 6.1% increase (95% CI 2.4–9.9%) in incidence rates of melanoma on the ears in women under 40 years based on 106 invasive tumors diagnosed in the study period. A similar rise of 7% (95% CI 3.6–10.4%) was seen for ear melanomas in women 40–59 years based on 130 invasive tumors (Table 1). The incidence rates of melanoma on the face in young women and those under 40–59 years showed little change (0.9% and 0.0% respectively), and similarly on the scalp and neck (data not shown).
In contrast, young men showed no significant relative increases of melanoma on the ear or any other sites on the head and neck in the same period. Men aged 40–59 years also showed no significant relative increases of melanoma on the ear but modest relative increases in melanoma on the face of around 3% per year (Table 1) and also on the scalp and neck.
Sex-specific differences in incidence rates of melanoma on the head and neck
Between 1982 and 1999 in Australia the overall age-standardized incidence rate of ear melanoma in males was 0.91 (99% CI 0.85–0.97) per 100,000, 4 times higher than the corresponding rate of 0.24 (99% CI 0.21–0.26) per 100,000 in females. The male excess was seen in all age groups though the difference in rates of ear melanoma between young men and young women was small by the end of the study period, compared with the sevenfold sex-specific difference seen in older men and women over 60. The incidence rate of scalp and neck melanoma (sites pre-combined) in males was 2 times higher than in females (2.18, 99% CI 2.09–2.27 and 1.08, 99% CI 1.02–1.14 per 100,000 respectively) though again the differences were narrowed in the youngest age group. Incidence rates of melanoma on the face were slightly higher overall in males than females (2.62, 99% CI 2.52–2.72 and 2.06, 99% CI 1.98–2.14 per 100,000 respectively) but were no different in men and women under 40 years (Table 1).
Differences in incidence rates of melanoma on the ear, scalp, face and neck were seen even more clearly in an earlier unrelated study of the Queensland data for 1987 [2] where site-specific incidence rates of melanoma had been calculated and further adjusted for relative body surface area. Incidence of invasive melanoma on the ear in Queensland males was 209 per unit area of skin per 100,000 per year compared with 50 per unit area of skin per 100,000 per year in females, again a fourfold difference. On the scalp there was a 20-fold greater incidence in males than females and on the neck a fourfold difference. Again rates on the face in males and females showed very little difference in Queensland in 1987 [2].
To assess whether comparable patterns were seen in other populations, age-standardized incidence rates of melanoma per 100,000 for 1982–1999 were calculated for sub-sites of the head and neck for the regions covered by the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) Program of population-based cancer registries [5]. In males compared with females there was a sevenfold higher incidence rate of ear melanoma and a threefold higher rate of scalp and neck melanoma compared with a less than twofold higher incidence rate of melanoma on the face.
Hypothesis
The systematically higher incidence of invasive melanoma on the ear, scalp and neck compared with the face in males and females is consistent with the general hypothesis that hair cover can offer long-term protection of the skin from sun exposure. We tested the specific hypothesis that hair cover of the ear protects it from solar ultraviolet (UV) radiation by conducting an experiment using manikin head-forms in which the difference in solar UV radiation to ears with and without hair cover was measured. We used four manikin head-forms: three wearing human hairpieces (blonde, brown and black) covering the ears and one uncovered (Fig. 1). These were set facing north on a concrete floor and exposed to natural sunlight in Brisbane (27°S) from 11 am to 1 pm Australian Eastern Standard Time daily for five consecutive days in May 2005. Cloud cover was zero and surface reflectivity less than 7%. Sun exposure was measured by UV-sensitive polysulfone dosimeters placed on the ears and nose of each manikin.
Fig. 1Measurement of solar ultraviolet radiation on ears in relation to hair cover using head-forms and polysulfone dosimeters
Ears covered by hair received on average 81% [SE ±8] less solar UV radiation than bare ears when exposed to the sun. As a control site on the face, the corresponding solar UV exposures to the noses of the manikins were monitored (Fig. 1) and showed a slight variation during the experiment in the reverse direction, namely a 5% [SE ±3.4] increase in UV radiation to the wigged manikins compared with the bare manikin.
Discussion
These experimental data support our hypothesis that hair cover offers substantial sun protection of the skin of the ear and by extrapolation, the skin of the scalp and back of the neck as well if hair is worn long. Taken together with the recently observed change in incidence of invasive melanoma on the ear in women in Australia and the consistent pattern in Australia and the United States of lower incidence of melanoma in females on the scalp, ear and neck – sites that are more frequently covered or screened by hair than in males, the evidence suggests that hair cover can indeed protect against invasive melanoma on these sites.
Other explanations for the observed site- and sex-specific patterns can be considered. Change in diagnostic trends was unlikely to explain the observed rise in ear melanomas in younger women since in situ melanomas were not included in the analyses. One possibility is that the distinct increase of ear melanomas in Australian women under 60 was due to the loss of some other form of physical protection. For example the rise in popularity of caps in the 1980s resulted in a decrease in cover of the ear but not the face in young women who wore brimmed hats in preceding decades [6]. If this were the sole explanation, some analogous rise in the rate of ear melanomas in men might have been expected as an aftermath as well, but has been observed neither in Australia nor the US. Another cause of increasing sun exposure of the ears in young women may have been the upsurge in women’s participation in outdoor sport that also occurred in the 1970s and 1980s [7] if caps rather than hats were worn. However even if change from hats to caps did explain the recent rise in incidence of ear melanomas in Australian women, difference in hat wear between the sexes does not explain the systematically higher rates of invasive melanoma in men than in women across the various sub-sites of the head and neck with less discrepant rates on the face. (Indeed facial hair cover in men with beards may mitigate the effect of overall higher outdoor exposure of men than women for facial melanomas). We also speculated that younger women’s hair styles may have changed over time and become shorter leading to greater exposure of the ears and neck, but there is little evidence available to address this question.
Finally in view of the known sex-specific differences in melanoma incidence on sites such as the scalp, it is surprising that the sun-protection factor of human hair has never been measured before. This absence of quantitative data contrasts with the extensive information available about the sun-protection provided by clothing and sunscreens [8, 9]. If our general hypothesis is confirmed, hair cover of the ears in particular would add another potential means of melanoma prevention, especially in high-risk populations, among individuals in whom amount of hair cover is discretionary.

hair
protection
melanoma
ear
solar ultraviolet radiation


Enhancement of stress tolerance in transgenic tobacco plants constitutively expressing AtIpk2β, an inositol polyphosphate 6-/3-kinase from Arabidopsis thaliana

Inositol phosphates (IPs) and their turnover products have been implicated to play important roles in stress signaling in eukaryotic cells. In higher plants genes encoding inositol polyphosphate kinases have been identified previously, but their physiological functions have not been fully resolved. Here we expressed Arabidopsis inositol polyphosphate 6-/3-kinase (AtIpk2β) in two heterologous systems, i.e. the yeast Saccharomycescerevisiae and in tobacco (Nicotiana tabacum), and tested the effect on abiotic stress tolerance. Expression of AtIpk2β rescued the salt-, osmotic- and temperature-sensitive growth defects of a yeast mutant strain (arg82Δ) that lacks inositol polyphosphate multikinase activity encoded by the ARG82/IPK2 gene. Transgenic tobacco plants constitutively expressing AtIpk2β under the control of the Cauliflower Mosaic Virus 35S promoter were generated and found to exhibit improved tolerance to diverse abiotic stresses when compared to wild type plants. Expression patterns of various stress responsive genes were enhanced, and the activities of anti-oxidative enzymes were elevated in transgenic plants, suggesting a possible involvement of AtIpk2β in plant stress responses.
Introduction
During their life plants are often challenged with various types of abiotic stresses. Proper physiological and biochemical responses to such stresses are controlled by an array of stress-dependent signal transduction pathways (Xiong et al. 2002). Several signaling molecules and second messengers including calcium ions (Ca2+; Knight et al. 1996; Knight et al. 1997) and inositol 1,4,5-trisphosphate (IP3; Takahashi et al. 2001) have been demonstrated to be important elements of signaling pathways that link the perception of stress to various types of downstream responses. As one of the central molecules in cell signaling, IP3 is subject to phosphorylation and dephosphorylation by specific inositol phosphate kinases and phosphatases, respectively, which helps to control intracellular IP3 concentration (Pattni and Banting 2004).
In higher plants, over-expression of an inositol polyphosphate 5-phosphatase (Ins5Pase), possibly leading to intracellular degradation of IP3, modulated stress and ABA signal transduction (Perera et al. 2002; Burnette et al. 2003). Likewise, mutation of an inositol polyphosphate 5-phosphatase gene (CVP2) results in increased sensitivity to ABA and vein patterning defects in cotyledons (Carland and Nelson 2004). IP3 can also be hydrolyzed by FRY1-like inositol polyphosphate 1-phosphatases (Ins1Pases; Xiong et al. 2002). The fry1 mutant of Arabidopsis accumulated around ten-fold more IP3 than the corresponding wild type, exhibited hypersensitivity towards ABA, and was compromised in tolerance to freezing, drought and salt stresses (Xiong et al. 2001). These data demonstrate the importance of inositol polyphosphate phosphatases in IP3 metabolism and IP3-mediated stress signal transduction. However, evidence for a role of inositol polyphosphate kinases in relation to stress tolerance is currently lacking in plants.
Inositol 1,4,5-trisphosphate 3-kinase (IP3K) and more generally inositol polyphosphate kinases play an essential role in cellular signal transduction and maintenance of Ca2+ homeostasis by phosphorylating inositol 1,4,5-trisphosphate (IP3) to inositol 1,3,4,5-tetrakisphosphate (IP4; Communi et al. 1995). Both IP3 and IP4 are second messengers responsible for Ca2+ mobilization from intracellular stores (Berridge 1997). IP4 itself is also able to regulate cytosolic Ca2+ concentration by promoting Ca2+ sequestration (Hill et al. 1988). In Arabidopsis thaliana, IP3K is encoded by two distinct genes, AtIpk2α and AtIpk2β, which are ubiquitously expressed in mature tissues (Stevenson-Paulik et al. 2002; Xia et al. 2003). Both of the Arabidopsis IP3Ks use IP3 as a substrate and display dual-specificity inositol polyphosphate 6-/3-kinase activities that successively phosphorylate IP3 to generate inositol 1,3,4,5,6-pentakisphosphate (IP5) predominantly via an inositol 1,4,5,6-tetrakisphosphate (IP4) intermediate (Stevenson-Paulik et al. 2002; Xia et al. 2003). Recently it was reported that AtIpk2α plays a role in pollen germination and root growth (Xu et al. 2005), while AtIpk2β functions in axillary shoot branching through the auxin signaling pathway (Zhang et al. 2007) and phytate synthesis (Stevenson-Paulik et al. 2005).
Although AtIpk2β has well been characterized at the biochemical level and has been shown to participate in plant development, it remained unclear whether it is also involved in stress signaling. In this study we demonstrate that AtIpk2β restores the salt-, osmotic- and temperature-sensitive growth defects of a yeast mutant strain (arg82Δ) deficient for the ARG82/IPK2 gene that encodes inositol polyphosphate multikinase. We also show that constitutive expression of AtIpk2β in transgenic tobacco enhances its tolerance towards various abiotic stresses. Thus, inositol polyphosphate kinase encoded by AtIpk2β very probably plays an important role in signaling pathways controlling the cellular response to abiotic stresses.
Materials and methods
Expression of AtIpk2β in yeast mutant arg82Δ
The S. cerevisiaearg82/Ipk2 deletion strain (derived from BY4741; Mat a; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0; YDR173c::kanMX4) was obtained from EUROSCARF (http://www.web.uni-frankfurt.de/fb15/mikro/euroscarf/). In this strain, the ARG82/IPK2 allele is disrupted through insertion of the kanamycin resistance gene, kanMX4. The AtIpk2β open reading frame was cloned into yeast expression plasmid pYX212. The resulting plasmid, pYX212-AtIpk2β, was used to transform the yeast mutant. The wild type strain BY4741 was used as control. Ten-fold serial dilutions (starting at OD600 = 1.0) of each strain were plated on YPD medium (1% yeast extract, 2% peptone, 2% glucose) supplemented with either NaCl (0.4 or 0.8 M) or mannitol (0.4 or 0.8 M), and incubated at 30°C for 48 h. For growth at 37°C, yeast cells were grown on media containing no extra NaCl or mannitol. Each experiment was repeated at least three times. For growth in liquid YPD medium, cells were cultured at 30°C for three days to saturation (OD600 = 12.0). Five μl of the cell culture were used to inoculate 3 ml of synthetic defined minimal medium supplemented with different concentrations of NaCl or mannitol. Cell density was measured at A600.
Quantitative real-time PCR analysis of AtIpk2β expression in Arabidopsis
Ten-day-old Arabidopsis seedlings grown on MS medium were used for different treatments as described previously (Shi and Zhu 2002). RNA was extracted with TRIZOL Reagent (Invitrogen) and reverse transcription of RNA was carried out using M-MLV RTase Synthesis Kit (TaKaRa). Quantitative real-time PCR was performed using the Rotor-Gene 3000 Series real time DNA amplification system under the following conditions: 95°C for 10 s; 40 cycles of 95°C for 5 s, 60°C for 20 s. AtIpk2β-specific primers were used (Table 1). Melting curve analysis was included to verify specificity of the DNA amplification. Expression of the reference gene Actin, which served as quantifying control, was monitored using gene-specific forward and reverse primers (Table 1). Data analysis was performed with Rotor-Gene software 6.0.
Table 1Gene-specific primers used in this studyPrimer namePrimer sequenceFor yeast expression vectorpYX-F5′-CTCCCATGGTAATGCTCAAAGTCCCTG-3′pYX-R5′-GATTGTCGACCTAGCGCCCGTTCTC-3′For real-time RT-PCRAtIPK2β-F 5′-CCACGGTTTCGTTGGGGTTC-3′AtIPK2β-R 5′-GTTACAACCGCCATAAACCTCTG-3′AtActin2-F 5′-CATCCTCCGTCTTGACCTTGC-3′AtActin2-R 5′-CAAACGAGGGCTGGAACAAG-3′For RT-PCRNtActin-F 5′-TTACGCCCTTCCTCATGCAATT-3′NtActin-R 5′-GGCGCCACCACCTTGATCTTC-3′AY562132-F5′-GTAGCATTGTTGGTGGTGGTGTG-3′AY562132-R5′- ACCGTGGAGCAGTCAATGGAAG-3′AY554169-F5′- ACAGTCGTGAGCATTCCCAAC-3′AY554169-R5′-CCAAACCTTCTGTGCTACCTC-3′AY554170-F5′-CCGGAGTGAAGGGGATGG-3′AY554170-R5′-CAAGCAATGTGAATGGTATGTGAG-3′NtERD10B-F5′-CAATTTAGTGCAGGCCAGGC-3′NtERD10B-R5′-GGTCCATGGTGGCCAGGAAG-3′NtERD10C-F5′-GGGTAGCGCAAACGTGGAG-3′NtERD10C-R5′-CTTTTCCCTCAGCCTCGTGC-3′
Vector construction and plant transformation
To create a Cauliflower Mosaic Virus (CaMV) 35S plant expression construct, the 903 bp long AtIpk2β cDNA containing the complete open reading frame was inserted via XbaI–SalI sites into the binary vector pBinAR-HPT. The resulting construct was introduced into Agrobacterium tumefaciens (strain LBA4404). Tobacco (Nicotiana tabacum cv. SR-1) leaf disks were infected with the transformed Agrobacteria. After two days of co-cultivation, the explants were transferred to regeneration medium containing 1-mg/l BAP, 0.1-mg/l NAA, 50-mg/l hygromycin and 200-mg/l ampicillin. Regenerated shoots were separated from the calli and transferred onto rooting medium containing MS salts, 0.1-mg/l NAA and 50-mg/l hygromycin. Rooted shoots were transplanted into soil. Seeds were harvested and homozygous plants were screened on a 12-h light/12-h dark photoperiod.
Salt stress tolerance tests
Wild type and homozygous T2 transgenic seeds were surface sterilized with a solution of 10% commercial bleach (0.525% sodium hypochlorite) for 10 min, and washed three times with sterile water. For germination assays, seeds were plated on MS medium supplemented with different concentrations of NaCl (0, 100 and 200 mM). For root growth measurements, 10-day-old seedlings cultured on solid MS medium were transferred to MS medium supplemented with different concentrations of NaCl. Plates were oriented vertically with seedlings kept upside down. Three replicates were performed for each experiment. Root length was recorded after seven days of treatment. To monitor stress effects on the growth of wild type and AtIpk2β expressing plants, 15-day-old seedlings were transferred to MS medium supplemented with 0, 100, 200 or 300 mM NaCl, and cultured at 25 ± 2°C with cool white fluorescent light (∼125 μmol m−2 s−1) under short day condition (12-h light/12-h dark). For salt stress treatments and ion measurements, wild type and transgenic seedlings were transferred to pots containing soil. Plants were watered bi-weekly with 1/8 concentrated MS salt solution supplemented with or without 200-mM NaCl and grown at 25 ± 2°C with cool white fluorescent light (∼125 μmol m−2 s−1), 70% humidity, under long-day conditions (16-h light/8-h dark) for 6 weeks. Mature plants treated in the same way were also used to determine cellular Na+ concentrations. The third leaf from the top of each plant was collected and dried at 90°C for 24 h, and the dry weight of each sample was measured. The dried leaf material was immersed in 0.1-M HAc, followed by water bath incubation at 90°C for 2.5 h, and analyzed for Na+ content using atomic absorption spectrophotometry. For hydroponic salt tolerance experiments, wild type plants and two independent transgenic lines (T2 generation) were grown hydroponically in Hoagland solution. Plants were grown at 25 ± 2°C with cool white fluorescent light (∼125 μmol m−2 s−1), 70% humidity, under long-day conditions (16-h light/8-h dark). The nutrient solution was replaced weekly.
Oxidative stress experiments
Malondialdehyde (MDA) concentration was determined as a biomarker for oxidative stress. To measure MDA concentration, leaf samples from plants grown at 200-mM NaCl were ground in 5 ml of 0.1% trichlor acetic acid (TCA) and mixed with 5 ml of 0.5% thiobarbituric acid. The samples were then boiled for 10 min, cooled down to room temperature and centrifuged at 1,200 × g. The cleared supernatant was analyzed by monitoring the difference in absorbance at A532 and A600. For oxidative damage experiments, leaf disks of 1.5-cm diameter were excised from healthy and fully expanded leaves of two-month-old tobacco plants. The disks were floated in a 10-ml solution with different concentrations of H2O2 or water (control) for 72 h. Chlorophyll a and b concentrations were measured as described previously (Lichtenthaler 1987). The treatment was carried out with continuous white light at 25 ± 2°C. The experiment was repeated at least three times.
Enzyme assays and protein determination
Enzyme assays were carried out spectrophotometrically using a nucleic acid and protein analyzer (Du 640, Beckman Coulter). One-month-old seedlings were treated with H2O or 1% H2O2 for 12 h, and ground in extraction buffer (1 mM ascorbate in 50-mM potassium phosphate buffer, pH 7.8) at a ratio of 0.1 g leaf material/1 ml ice-cold extraction buffer. The slurry was transferred to a centrifuge tube and centrifuged at 20,000 × g at 4°C for 20 min. The supernatant was used for enzyme activity assays and total protein determination as described previously (Badawi 2004; Bradford 1976). Ascorbate peroxidase (APX) activity was determined by measuring the rate of ascorbate oxidation at A290 in a 100-μl-reaction mixture (1-mM EDTA in 50-mM potassium phosphate buffer, pH 7.8, 0.2-mM ascorbate, 20-mM H2O2 and 5-μl soluble protein extract). APX activity is expressed as micromoles of ascorbate oxidized per minute per milligram of protein. Catalase (CAT) activity was measured by monitoring the decrease in absorbance at A240 caused by the disappearance of H2O2 in a 100-μl-reaction mixture (1-mM EDTA in 50-mM potassium phosphate buffer, pH 7.8, 20-mM H2O2 and 5-μl protein extract). The activity of superoxide dismutase (SOD) was measured at A560, in a 3-ml reaction mixture (13.05-mM dl-methionine, 0.1-μM EDTA, 0.075-mM Nitro Blue tetrazolium chloride and 2-μM riboflavin in 50-mM phosphate buffered saline, pH 7.8, and 100-μl protein extract).
Whole plant drought tolerance test
Wild type and transgenic plant seeds were surface sterilized as described above. Seedlings germinated on MS medium were transplanted to soil. After two months, healthy plants of the same size and age were pooled into two groups. For the first group (12 individual plants, each grown in a 6.5-inch pot), plants were irrigated with 60 g/l PEG-6000 to simulate drought stress. After 21 days, the plants were photographed. Simultaneously, plants of the second group were subjected to drought stress by omitting watering. After three weeks, the plants were re-hydrated and observed for recovery. Photos were taken 21 days after initiation of the stress treatment. The relative humidity was maintained at ∼60%. The experiments were repeated twice with three replicates in each experiment.
Freezing tolerance test
Three-week-old seedlings of wild type and transgenic plants grown on MS medium were cultured at 4°C for one day under long day condition (16-h light/8-h dark). After cold acclimation, the plants were left at −20°C for 1 or 2 h, transferred immediately to 4°C for another 12 h (overnight). Subsequently, plants were kept in the greenhouse at 25°C and observed further. Photographs were taken seven days after initiation of the recovery growth in the greenhouse.
Determination of proline concentration
Four-week-old seedlings grown on MS medium were transferred onto filter paper in a Petri dish saturated with 100- or 200-mM NaCl, incubated at 25 ± 2°C for 24 h under continuous white light. After the treatment, proline content was determined as described previously (Bates and Waldren 1973).
Western blot analysis
Leaves from either Arabidopsis or tobacco plants were ground in liquid N2 and suspended in 1.5 ml of 30 mM potassium phosphate buffer (pH 7.5) containing 40-mM NaCl and 3-mM dithiothreitol (Powell 1986). Homogenates were centrifuged for 5 min at 12,000 × g. Supernatants were transferred to new tubes, and protein concentrations were determined as described previously (Bradford 1976). A 10-μg sample of total protein was mixed with loading buffer, boiled for 3 min, and size-fractionated through electrophoresis in a 12.5% SDS polyacrylamide gel. Separated proteins were transferred to a PVDF membrane (Millipore Corporation) according to the manufacturer’s instructions. A polyclonal antibody against AtIpk2β protein purified from recombinant E. coli was raised in rabbits. Antiserum was purified by IgG affinity purification. The anti-AtIpk2β antiserum was used at a 1:5,000 dilution in TBST. Peroxidase-labeled affinity purified secondary antibody isolated from a pool of serum from goats immunized with purified rabbit IgG was used at a 1:1,000 dilution in TBST (KPL Corporation).
Stress gene expression analysis using reverse transcription PCR
Three-week-old tobacco seedlings grown on MS agar plates were treated with 300-mM NaCl for 5 h. Total RNA was isolated with TRIZOL Reagent (Invitrogen) and used for reverse transcription (RT) to generate first-strand cDNA. RT was performed on 2-μg total RNA per reaction using ReverTra Ace (TOYOBO). A portion of the resulting cDNA was then subjected to PCR amplification using gene specific primers (Table 1). Expression level of the Nicotiana tabacum ACTIN gene, which served as reference, was monitored with NtActin forward and reverse primers (Table 1).
Statistical analysis
For statistical analyses, the Student’s t-test was used to generate every P-value. The alpha level was 0.05. The tests were one-tailed. The data were normalized and all samples were normally distributed with homogeneity of variance.
Results
Yeast complementation with AtIpk2β
In S. cerevisiae, the ARG82/IPK2 gene encodes an inositol polyphosphate kinase (Odom et al. 2000). The S. cerevisiaearg82/Ipk2 (arg82Δ) mutant strain that lacks Ipk2 activity has slowed growth at 30°C and is unable to grow at 37°C (Odom et al. 2000). We and others have reported previously that AtIpk2β rescues its growth defects at 37°C (Stevenson-Paulik et al. 2002; Xia et al. 2003). This result led us to postulate that AtIpk2β may also be able to rescue its growth defects when other abiotic stresses were imposed. We observed that under adverse growth conditions such as high salt concentration or upon osmotic stress treatment, growth of the arg82Δ mutant was severely impaired when compared to that of the wild type (Fig. 1a). To test whether AtIpk2β restores the growth defect of the mutant strain under these conditions, we transformed it with the ArabidopsisAtIpk2β cDNA. Expression of AtIpk2β almost completely restored growth of the arg82Δ mutant under both kinds of stresses (Fig. 1a). It also restored growth at 37°C (Fig. 1a), as reported earlier (Stevenson-Paulik et al. 2002; Xia et al. 2003). We also performed experiments using liquid media and observed again, that AtIpk2β restores growth under conditions of abiotic stress (Fig. 1b).
Fig. 1Expression of AtIpk2β in S. cerevisiaearg82Δ. (a) AtIpk2β rescues growth defects of the yeast arg82Δ mutant under high salt or mannitol stress, and at 37°C. Ten-fold serial dilutions of the strain were plated and incubated at 30°C for 48 h on YPD medium supplemented with either 0.4- or 0.8-M NaCl, or with 0.4- or 0.8-M mannitol. Plates incubated at 37°C contained YPD medium without extra NaCl or mannitol. WT, wild type; arg82Δ, arg82Δ mutant; arg82Δ + AtIpk2ß, arg82Δ mutant harbouring plasmid pYX212-AtIpk2β. (b) Liquid yeast cultures. Cells were grown for three days to saturation in YPD medium (OD600 = 12.0). Five μl of cell culture were then used to inoculate 3 ml of defined synthetic minimal medium supplemented with different concentrations of NaCl or mannitol. Cell density was determined at various time points as absorbance at 600 nm. Cells were grown at 30°C. WT, wild type; arg82Δ, arg82Δ mutant; arg82Δ + AtIpk2ß, arg82Δ mutant harbouring pYX212-AtIpk2β; arg82Δ + vector, arg82Δ mutant harbouring pYX212
AtIpk2β is abiotic stress regulated in Arabidopsis
We were interested to know whether different kinds of stresses such as salinity, cold and drought, or treatment with ABA would affect AtIpk2β transcript levels in Arabidopsis plants. To this end we performed quantitative real-time PCR (qRT-PCR). In ten-day-old seedlings grown on MS medium, AtIpk2β transcript level increased slightly upon exposure to cold or drought stress, but decreased in response to salt or ABA treatment (Fig. S1A in Supplementary Material). These results showed that AtIpk2β is a stress responsive gene potentially involved in different abiotic stress- and ABA-triggered signaling pathways in plants. We further tested AtIpk2β protein level by Western blot analysis. Fourteen-day-old Arabidopsis seedlings grown on MS medium were subjected to different stress treatments for 24 h. In seedlings treated with 500-mM mannitol, AtIpk2β protein level increased slightly, however no significant changes in protein level were detected in seedlings subjected to cold or NaCl stress (Fig. S1B in Supplementary Material).
Constitutive expression of AtIpk2β in transgenic tobacco
To test whether constitutive expression of AtIpk2β affects abiotic stress tolerance in plants, we introduced its open reading frame (Fig. 2a) into the genome of tobacco (Nicotiana tabacum cv. SR-1) by Agrobacterium tumefaciens-mediated transformation. Expression of AtIpk2β in the transgenic plant was controlled by the Cauliflower Mosaic Virus (CaMV) 35S promoter. Forty-six independent transgenic plants (T0 generation) were obtained; six lines were grown to produce seeds. Transgenic plants homozygous for the 35S:AtIpk2β transgene were obtained and three lines (S13, S14 and S15) were chosen for further experiments. Constitutive expression of AtIpk2β did not affect overall plant morphology although the transgenic plants grew slightly faster than wild type plants under normal growth condition. Western blot analyses were performed to test for the presence of AtIpk2β protein. A 33-kDa band corresponding to AtIpk2β was identified in Arabidopsis plants; similarly, 33-kDa AtIpk2β protein was detected in the transgenic but not the wild type tobacco lines (Fig. 2b), indicating that it was successfully expressed in the genetically modified plants.
Fig. 2Construct used for tobacco transformation and Western blot analysis of AtIpk2β in wild type and transgenic tobacco plants. (a) Schematic map of the binary construct used for tobacco transformation. Expression of AtIpk2β is driven by the Cauliflower Mosaic Virus 35S promoter. Ocs, ocs terminator; RB and LB, right and left border of T-DNA, respectively. (b) Western blot analysis. Lane 1, wild type Arabidopsis; lane 2, wild type tobacco; lanes 3–7, transgenic AtIpk2β expressing tobacco lines S11, S13, S14, S15, and S18. Coomassie Bright Blue stained gel is shown below the Western blot to demonstrate equal loading of protein
Expression of AtIpk2β increases salt tolerance of transgenic tobacco plants
We first studied the salt tolerance of AtIpk2β expressing plants during germination and early stages of seedling development. Seeds of wild type and the three homozygous transgenic lines S13, S14 and S15 were germinated on MS medium containing different concentrations of NaCl. Germination rates of wild type and transgenic seeds were similar when cultured on normal MS medium (Fig. 3a). However, germination of wild type seeds was significantly impaired on MS medium supplemented with 100- or 200-mM NaCl, whereas germination of transgenic seeds was less strongly affected. After eight days on high salt MS medium (100-mM NaCl), only ∼28% of the wild type seeds, but more than 70% of the seeds obtained from AtIpk2β expressing plants germinated (Fig. 3b). When sown on MS medium supplemented with 200-mM NaCl, only ∼7% of the wild type seeds, but ∼30% of the transgenic seeds germinated after 18 days (Fig. 3c). At low salt concentration (50-mM NaCl), wild type and transgenic seeds displayed similar germination rates (data now shown). At 150-mM NaCl, both wild type and transgenic seeds germinated. However, development of wild type seedlings was impaired; plants produced smaller leaves and less root biomass than did transgenic seedlings (Fig. 3d). To further test the effect of salt on growth, 15-day-old wild type and transgenic seedlings were transferred to MS medium supplemented with NaCl at different concentrations. After two additional months, strong growth retardation was observed in wild type seedlings. Inhibition of growth was less evident in transgenic lines (Fig. 3e). A root-bending assay has previously been used to test the effect of elevated salt concentration on root growth (An et al. 2007; Zhu et al. 1998). At 50-mM NaCl on MS medium, root growth of both wild type and transgenic lines was accelerated, but it was inhibited at increasing NaCl concentrations. At 150-mM NaCl, inhibition of root growth was significantly more pronounced in wild type than in transgenic plants (Fig. 3f, g).
Fig. 3Germination rate and early seedling development of transgenic plants. (a–c) Percentage of germinating seeds of wild type (WT) and transgenic T2 plants (S13, S15) grown on MS medium supplemented with 0-, 100- or 200-mM NaCl. Results are presented as means and standard errors from three independent experiments (≥100 seeds of each line were sown for each experiment). (d) Wild type (WT) and transgenic seeds (S13, S15) geminated on MS medium supplemented with 50 or 150-mM NaCl. Photo was taken three weeks after seeds were sown. (e) Wild type (WT) and transgenic lines (S13, S14, S15) grown on MS medium supplemented with different concentrations of NaCl for 2 months. (f) Root bending assay of wild type (WT) and transgenic plants (S13, S15) on MS medium supplemented with 0 or 150-mM NaCl. Representative pictures are shown. (g) Primary root lengths of wild type (WT) and transgenic plants (S13, S15) in the presence of different salt concentrations. Root growth under control condition (0-mM NaCl) was set to 100%. The results are shown as means and standard errors from three independent experiments (≥15 seeds of each line were sown for each experiment). *** indicates significant differences in comparison to the wild type at P < 0.001 (Student’s t-test)
We also performed salt tolerance experiments with wild type and two transgenic lines grown in the greenhouse in soil. The plants were watered bi-weekly with 1/8 concentrated MS salt solution supplemented with or without 200-mM NaCl. After six weeks, distinct differences were observed between wild type and transgenic plants exposed to 200-mM NaCl (Fig. 4a). Although both sets of plants flowered under conditions of salt stress, wild type plants developed much shorter stems (Fig. 4b), produced fewer pods (Fig. 4c), and produced seeds that were not viable. We determined endogenous Na+ levels in leaves and roots of wild type and transgenic plants grown in the absence or presence of 200-mM NaCl. Both wild type and transgenic plants exposed to salt contained elevated levels of Na+ ions in leaves and roots (Fig. 4d), however, ion concentrations were not significantly different between wild type and transgenic plants (the same was true for plants grown under normal conditions). We also grew tobacco plants in hydroponic culture. In the absence of salt stress, no overt morphological difference was observed between wild type and transgenic plants. However, growth of wild type tobacco was severely inhibited in the presence of 300-mM NaCl (Fig. 4e), leading to a depression of shoot height by about one third, and of plant fresh weight by about 50% in comparison to AtIpk2β expressing tobacco plants after a two-month stress treatment (Fig. 4f). All these results indicated that AtIpk2β alleviated the negative effects imposed by salt stress on plant growth.
Fig. 4Salt tolerance test to compare wild type and AtIpk2β expressing tobacco plants. For each experiment, twelve plants of each line were used. (a) Photograph of representative wild type (WT) and transgenic plants (S13, S15) watered with 200-mM NaCl for six weeks. (b), (c) Shoot height and pod numbers of wild type (WT) and transgenic (S13, S15 ) plants treated with 200-mM NaCl. * and ** indicate significant differences in comparison to the wild type at P < 0.05 and P < 0.01, respectively (Student’s t-test). (d) Na+ content in leaves and roots of wild type (WT) and transgenic plants (S13, S15) grown in soil supplied with 0 or 200-mM NaCl solution. (e) Photograph of a representative wild type (WT) and transgenic line S15 hydroponically grown in nutrient solution supplemented with 300-mM NaCl for two months. (f) Shoot height and (g) fresh weight of wild type (WT) and transgenic lines S13 and S15 grown for two months under control (0-mM NaCl) or high salt (300-mM NaCl) conditions. Results are presented as means and standard errors from three independent experiments. ** indicates significant differences in comparison to the wild type at P < 0.01 (Student’s t-test)
Transgenic plants showed increased tolerance to osmotic, drought, freezing temperature and oxidative stress
In order to determine whether expression of AtIpk2β affects osmotic stress tolerance in plants, we watered wild type and transgenic tobacco plants (lines S13 and S15) with PEG-6000 (60 g/l) as described previously (Eltayeb et al. 2006). Under such experimental conditions, wild type plants were less vigorous, flowered earlier and produced smaller leaves than AtIpk2β expressing plants (Fig. 5a). We also tested the effect of water shortage on plant growth. After a 21-day drought period, wild type plants completely wilted, while transgenic plants were less severely affected (Fig. 5b). At the end of the drought tolerance test, the plants were re-watered and grown further to allow seed setting. Transgenic plants produced larger shoots than wild type plants (Fig. 5c).
Fig. 5Effect of drought and osmotic stress on plant performance. For each experiment, twelve plants of wild type (WT) and each transgenic line (S13, S15) were used. (a) Wild type and transgenic plants grown in soil were watered with 60 g/l PEG-6000. Photograph was taken after 21 days of treatment. (b) Water was withheld for 21 days to impose drought stress. (c) Drought stressed plants were re-watered and grown for seed setting. (d) Proline content of wild type and transgenic plants after treatment with 100 or 200 mM NaCl for 24 h. Results are presented as means and standard errors from three independent experiments. ** indicates significant differences in comparison to the wild type at P < 0.01 (Student’s t-test)
Proline accumulation in response to osmotic or salinity stress has been well documented in prokaryotic and eukaryotic organisms (Schobert 1997). Here, we compared the proline content of wild type and transgenic plants treated with 100- or 200-mM NaCl. Although proline accumulated in a dose-dependent manner in both wild type and transgenic plants, proline concentration was slightly higher in transgenic plants in the presence of 200-mM NaCl (Fig. 5d).
The results presented in Fig. 4 show that AtIpk2β expression increased the salt tolerance of transgenic tobacco plants. Since salt stress exerts osmotic stress to plant cells in addition to its ionic toxicity, we further determined if AtIpk2β expression confers elevated tolerance to oxidative stress on transgenic plants. Leaf disks from wild type and transgenic tobacco plants were exposed to increasing concentrations of H2O2 for 72 h. As shown in Fig. 6, transgenic plants exhibited a strong tolerance to oxidative stress. Leaf disks from wild type plants were almost completely bleached at the end of the stress treatment, whereas those from transgenic plants showed significant resistance to H2O2-caused damage (Fig. 6a).
Fig. 6Oxidative stress and short term freezing tolerance of wild type (WT) and transgenic tobacco plants (S13, S15). (a) Leaf disks were incubated in different concentration of H2O2 (1% and 2%, respectively) under continuous white light for 72 h. H2O2 induced leaf yellowing is delayed in the transgenic plants. (b) Three-week-old seedlings were stored at −20°C for 2 h, and then transferred to normal temperature for further growth. Photograph was taken seven days after the freezing treatment. (c) Quantification of the seedling survival rate (determined seven days after the freezing treatment). Results are presented as means and standard errors from three independent experiments. Fore each experiment, 40 seedlings of each line were used. *** indicates significant difference in comparison to the wild type at P < 0.001 (Student’s t-test). (d) Chlorophyll content was determined in leaf disks of wild type and T2 transgenic plants kept in H2O2 for 72 h. Disks floated in water served as control. Results are preans and standard errors from three independent experiments. ** and *** indicate significant differences in comparison to the wild type at P < 0.01 and P < 0.001, respectively (Student’s t-test)
To further examine the effect of constitutive AtIpk2β expression on plant performance, the tolerance towards freezing temperature was analyzed. To this end, three-week-old seedlings were kept at −20°C for 1 or 2 h, respectively, and subsequently allowed to recover for seven days in the greenhouse. Most transgenic plants resumed normal growth after the 2 h treatment at −20°C (Fig. 6B), but only very few wild type plants did so. When treated for 1 h at −20°C, the damage was less severe, but again transgenic plants grew better than wild type plants after transfer back to the greenhouse (data not shown). Transgenic seedlings showed a superior survival rate after short-term −20°C treatment (Fig. 6c). In the presence of oxidative stress, chlorophyll loss was significantly delayed in AtIpk2β expressing plants compared to that of the wild type (Fig. 6d).
Lipid hydroperoxidation is an effective indicator of cellular oxidative damage (Yoshimura et al. 2004). Changes in the rates of lipid hydroperoxide production induced by oxidative stress were measured by determining MDA content in leaf disks. At high salinity (200-mM NaCl), MDA concentration markedly increased in wild type plants, whereas only a marginal increase was observed in the two transgenic lines (Fig. 7a). Although superoxide dismutase (SOD) activity was largely unaffected in wild type and transgenic plants treated with 1% H2O2 (Fig. 7b), a slight elevation of APX activity was observed in transgenic plants (Fig. 7c). Also, catalase (CAT) activity increased in transgenic plants (Fig. 7d). These results indicate that constitutive expression of AtIpk2β enhanced tolerance to oxidative stress-induced membrane hydroperoxidation in transgenic tobacco plants.
Fig. 7MDA level, anti-oxidative enzyme activity, and expression level of stress regulated genes in transgenic plants. (a) MDA levels in wild type and transgenic lines S13, S15 after treatment with 200-mM NaCl. (b–d) One-month-old seedlings were incubated for 12 h in 1% H2O2 or water (control) under continuous white light. SOD, APX and CAT activities were determined. Results are presented as means and standard errors from three independent experiments. * and ** indicate significant differences in comparison to the wild type at P < 0.05 and P < 0.01, respectively (Student’s t-test). (e) Two-week-old seedlings of wild type and transgenic lines (S13, S15) were used for RNA extraction. For the salt stress experiments, seedlings were treated with 300-mM NaCl for 5 h before RNA isolation. The transcriptional levels of five stress genes were determined by RT-PCR analyses. The stress genes used for the tests are as follows: lipid transfer protein (AY562132); fructose-bisphosphate aldolase (AY554169); raffinose synthase family protein/seed imbibition protein (AY554170); group 2 LEA proteins NtERD 10B (AB049336) and NtERD 10C (AB049337)
Increased stress responsive gene expression in transgenic tobacco plants
Many genes responding to abiotic stress were cloned in recent years and used as molecular markers to monitor the activity of stress-signaling pathways in plants (Kasuga et al. 2004). The stress experiments described above suggested that AtIpk2β plays an important role in regulating stress tolerance in plants. To address whether or not AtIpk2β affects the expression of stress responsive genes, we investigated the transcriptional levels of five marker genes in wild type and transgenic tobacco plants by RT-PCR. The marker genes chosen are identical or homologous to those encoding lipid transfer protein, fructose-bisphosphate aldolase, raffinose synthase, chloroplast ATPase, and group 2 LEA proteins of tobacco. As shown in Fig. 7, transcript levels of these genes were higher in transgenic plants than in the wild type under both, normal and salt stress conditions (Fig. 7).
Discussion
A number of genes encoding Ins(1,4,5)P3 3-kinase or Ins(1,4,5)P3 dual specificity 6-/3-kinases were cloned and biochemically characterized in the past years. Examples include genes from yeast (Odom et al. 2000), animals (Bertsch et al. 1999), human (Dewaste et al. 2000), and higher plants (Stevenson-Paulik et al. 2002; Xia et al. 2003; Xu et al. 2005). It has recently been reported that in yeast (S. cerevisiae) inositol polyphosphate kinase (Kcs1p) is required for resistance to salt stress, cell wall integrity, and vacuolar morphogenesis (Dubois et al. 2002), and that overexpression of D-IP3K1, a Drosophila IP3-kinase gene, confers resistance of flies to H2O2-, but not to paraquat-induced oxidative stress (Monnier et al. 2002). Further genetic studies indicated that the protective effect conferred by elevated D-IP3K1 expression is mainly due to a lowered IP3 level and thus reduced Ca2+ ion release from internal stores, rather than an increased IP4 level.
Based on these observations, we were interested to know whether constitutive expression of AtIpk2β confers improved resistance to abiotic stresses on higher plants. Since the Arabidopsis AtIpk2β protein shares high amino acid sequence identity with the yeast IPK2 enzyme, which functions as a transcriptional regulator (also called Arg82 or ArgRIII) and displays dual-specificity InsP3-InsP4 kinase activity (Odom et al. 2000), we first produced transgenic Arabidopsis (ecotype Columbia-0) plants overexpressing AtIpk2β. Surprisingly, these plants did not exhibit a significant improvement to salt or osmotic stress in our preliminary studies (data not shown). This may be due to the possible overlapping/redundant functions of the two Arabidopsis inositol polyphosphate kinases (AtIpk2α and AtIpk2β) in vivo, as suggested earlier by the York group (Stevenson-Paulik et al. 2002). To examine this possibility, we further compared the growth of Arabidopsis wild type as well as transgenic AtIpk2β over-expresser (35S::AtIpk2β) and knock-out mutant (atipk2β, SALK_104995, ABRC) plants. All three types of plants had similar growth phenotypes when subjected to salt or osmotic stress (data not shown). Here we introduced AtIpk2β into tobacco plants and found that transgenic lines developed superior tolerance to various types of abiotic stresses.
Previously, AtNHX1, a vacuolar Na+/H+ antiporter gene from Arabidopsis, was over-expressed in tomato and canola to improve the salt tolerance of transgenic plants (Zhang and Blumwald 2001). However, a comparative analysis of Na+ and K+ accumulation in leaves and roots of transgenic AtNHX1 over-expressers and wild type plants grown under high-salt conditions was not performed because the high (200 mM) NaCl concentration used in these experiments was lethal to the wild type plants. In the study reported here, the relatively high salt tolerance of wild type tobacco plants made it possible to perform such an analysis. In the presence of 200-mM NaCl, despite a similar increase of Na+ concentration in wild type and transgenic plants, slightly more Na+ accumulated in leaves after long-term salt stress (Fig. 4d). This phenomenon is different from what has been seen in transgenic wheat overexpressing AtNHX1 (Xue et al. 2004). Salinity causes two major damages to plant cells, water deficit resulting from the relatively high solute concentrations in the soil, and ion toxicity due to altered K+/Na+ ratios as well as excessive Na+ and Cl− contents (Apse and Blumwald 2002). The Na+/H+ exchange activity of AtNHX1 is regulated in a Ca2+- and pH-dependent manner as reported earlier (Yamaguchi et al. 2005). The increased accumulation of salt in both leaves and roots suggests that unlike AtNHX1, which mainly works through sodium compartmentation, AtIpk2β may function through both sodium extrusion and compartmentation controlled by Ca2+-dependent signal transduction, although the precise mechanism of this still remains to be elucidated.
Proline contributes to osmotic adjustment (LeRudulier et al. 1989) and the protection of macromolecules during dehydration (Yancey et al. 1982), and as a hydroxyl radical scavenger (Hong et al. 2000). After treatment with 200-mM NaCl, but not 100-mM NaCl, proline accumulated to slightly higher levels in transgenic than in wild type tobacco plants (Fig. 5d). An increase in proline content was also observed in salt tolerant Arabidopsis with suppressed levels of proline degradation (Nanjo et al. 1999), transgenic salt tolerant tobacco plants with enhanced levels of proline biosynthesis (Kishor et al. 1995), and transgenic salt tolerant tomato plants overexpressing AtNHX1 (Zhang and Blumwald 2001). So, it is possible that the elevated concentration of proline in transgenic plants helps to protect anti-oxidative enzyme thus alleviating the negative effects imposed by salt on transgenic AtIpk2β plants.
Salt-stress-induced generation of reactive oxygen species (ROS) has been thought as one of the major causes of adverse effects of high salinity. ROS are products of chloroplast and mitochondrial metabolism produced during stress; they cause membrane damage resulting in electrolyte leakage (Apse and Blumwald 2002). At 200-mM NaCl, a relatively strong increase of MDA production was observed in wild type plants, but only a slight increase was seen in the two transgenic lines (Fig. 7a). Furthermore, catalase (CAT) activity increased significantly in transgenic plants after treatment with 1% H2O2 (Fig. 7d). These results led us to hypothesize that constitutive expression of AtIpk2β improves cell membrane integrity. Evidence supporting this hypothesis was obtained from stress tolerance experiments with transgenic Arabidopsis overexpressing aldehyde dehydrogenase, or transgenic tobacco overexpressing Chlamydomonas glutathione peroxidase (Yoshimura et al. 2004).
The induction of stress-related genes has been taken as a hallmark of stress adaptation in plants (Thomashow 1999). Earlier studies have shown that lipid transfer proteins are induced by osmotic stress (Smart et al. 2000). Genes encoding plastidic aldolase are induced by salt (Yamada et al. 2000). Transgenic plants overexpressing such enzymes exhibit improved tolerance to the respective stresses. Here, we observed that constitutive expression of AtIpk2β under the control of the CaMV 35S promoter triggered an increased expression of various stress responsive genes in transgenic tobacco. Already in the absence of salinity stress, transcript levels of genes encoding raffinose synthase family protein/seed imbibition protein (AY554170) and group 2 LEA protein NtERD 10C (AB049337) were found to be slightly elevated in the transgenic plants. At high salinity, the expression levels of genes encoding lipid transfer protein (AY562132) and raffinose synthase family protein/seed imbibition protein (AY554170) increased in the transgenic lines. These results suggest that AtIpk2β acts in one or more signal transduction pathway(s) thereby affecting the activity of stress-related genes and stress tolerance.
Many stresses evoke an elevation of the cytosolic calcium ion concentration, including salinity (Knight et al. 1997), drought (Knight et al. 1997), cold (Knight et al. 1996), and oxidative stress (Sanders et al. 2002). Previously, it has been shown that salinity and osmotic stress induce a rapid and transient increase of IP3 levels (DeWald et al. 2001;Takahashi et al. 2001), and the reported accumulation of IP3 occurs during a time frame similar to that observed for stress induced Ca2+ mobilization. Therefore, the possibility exists that enhanced abiotic stress tolerance of transgenic AtIpk2β plants results from changes of Ca2+ dependent signaling. Whether this is indeed the case has to be analyzed in future studies
Plant growth and productivity are severely affected by abiotic stress. Augmented resistance towards various environmental stresses has been a desirable goal for genetic improvement of plants. Stress tolerance can for example be improved by altering the expression of stress-responsive genes in transgenic plants. Representatives include genes encoding chitinase and glucanase (Hong and Hwuang 2006), betaine aldehyde dehydrogenase (Yang et al. 2005), tonoplast and plasma membrane Na+/H+ antiporters (Zhang and Blumwald 2001; Apse and Blumwald 2002), protein kinases (Xiong and Yang 2003), transcription factors (Waller et al. 2006), peroxidases (Yoshimura et al. 2004), DNA helicase (Sanan-Mishra et al. 2005), and DNA binding protein (polypeptide; Kalifa et al. 2004). Although the precise mode of action of AtIpk2β in plant responses to stress remains elusive, the results in this work provide direct evidence that alteration of AtIpk2β expression can significantly modify plant tolerance to various abiotic stresses, indicating a great potential of this gene for engineering stress tolerant crops.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Quantitative real-time RT-PCR and Western blot analyses of AtIpk2b in wild type Arabidopsis seedlings in response to different stresses. Fourteen-day old seedlings grown on MS agar plates were treated with 300-mM NaCl or 500-mM mannitol, or kept at 4°C for 24 h. (A) Quantitative real-time RT-PCR analysis of AtIpk2b transcript level. The experiments were repeated three times independently and the average calculated. Error bars represent standard error of the mean. (B) Western blot analysis of AtIpk2b expression level. Coomassie Bright Blue stained gel is shown below the Western blot to demonstrate equal protein loading. (TIF 457 kb)

stress tolerance
transgenic tobacco
atipk2β
arabidopsis thaliana
inositol phosphate
ip3