q>u6 fications - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/14649/13/13_publication.pdf · q>u6 fications Funct Integr Genomics DOl 10.1007/s10142-009-0119-x I SHORT COMMUNICATION

q>u6 fications

Funct Integr Genomics

DOl 10.1007/s10142-009-0119-x

I SHORT COMMUNICATION

. Histidine kinase and response regulator genes as they relate to salinity tolerance in rice

Ratna Karan • Sneh L. Singla-Pareek • Ashwani Pareek

Received: 5 November 2008 /Revised: 13 February 2009 I Accepted: 13 February 2009 © Springer-Verlag 2009

Abstract We have previously shown that Oryza sativa L. Pokkali maintains higher levels of transcripts under nonsaline conditions, wjlich are otherwise induced under salinity in the sensitive genotype-IR64. We wanted to test this hypothesis of differential gene regulation further, within the members of a given stress responsive gene family, which share significant structural and functional similarities. For this purpose, we chose to work on the twocomponent system (TCS family) which plays an important role in stress perception and signal transduction under hormonal, abiotic stress, light and developmental regulation. We present data to show that all members of TCS family, including sensory histidine kinases, phosphotransfer proteins and response regulators, are having differential transcript abundance (under both non-stress and salinity stress conditions) in contrasting rice genotypes. Further, under non-stress conditions, transcript abundance for all TCS members (except RR21) was found to be higher in the salt-tolerant genotype-Pokkali. TCS transcripts are otherwise induced by salinity stress to a relatively higher level in the sensitive cultivar IR64. A few of these members were also found to be localised within important salinity-related

Electronic supplementary material The online version of this article ( doi: I 0.1007 /s I 0142-009-0 119-x) contains supplementary material, which is available to authorized users.

R. Karan · A. Pareek ([81) Stress Physiology and Molecular Biology, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India e-mail: [email protected] URL: www.jnu.ac.in

S. L. Singla-Pareek Plant Molecular Biology, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India

Published online: 11 March 2009

quantitative trait loci identified earlier. Based on the above findings, we propose that the TCS members may have a significant role in salinity tolerance in rice and can serve as useful 'candidate genes' for raising salinity-tolerant crop plants.

Keywords Contrasting genotypes · Histidine kinase · Oryza sativa L. · Salinity· Two-component system · QTLs

Abbreviations TCS Two-component system HK Histidine kinase

Phosphotransfer protein Response regulator Quantitative trait loci

HPT RR QTL RFLP Restriction fragment length polymorphism

Introduction

The differential regulation of salinity-responsive genes among salt-tolerant (Pokkali) and salt-sensitive (IR64) genotypes of rice has been characterised by Kumari et a!. (2009a). Since these genes were picked up as a result of subtraction between RNA from control and salinity-stressed seedlings of rice, they were referred to as 'salinity-induced genes'. Similarly, differential regulation of genes under control and stress-induced conditions is one of the possible mechanisms responsible for the striking differences in stress response among contrasting genotypes in other genera as well (Wong et al. 2006; Kumar et al. 2009). However, differential regulation of genes which are either members of a family sharing high degree of structural conservation or which are key components of a given signal cascade is yet to be established.

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Earlier, we have compared the genome organisation of Arabidopsis and rice with respect to two-component system (TCS) members and found that the basic architecture of the TCS machinery remains conserved between these model dicot and monocot systems (Pareek et a!. 2006). In eukaryotic organisms, TCS essentially comprised members operating at three tiers, namely sensory histidine kinase (HK), phosphotransfer protein (HPT) and response regulator (RR; see Electronic supplementary material Fig. la; Hwang et a!. 2002; Lohrmann and Harter 2002; Oka et a!. 2002; Kakimoto 2003; Grefen and Harter 2004). Whole genome analysis of rice revealed a total of 55 TCS elements, including 14 HKs, five HPTs and 36 RR proteins. In the present study, we wanted to test the hypothesis that if the members of TCS pathway are playing a significant role in salinity response in rice, then (1) their transcription should be regulated under stress and should further show some correlation among the contrasting genotypes and (2) they should exhibit co-localisation with salinity-related quantitative trait loci (QTLs) in rice. For this purpose, we carried out transcript abundance analysis for TCS members in seedlings of Oryza sativa L. cv IR64, and its wild relative Pokkali, using quantitative polymerase chain reaction (qPCR). We show that the TCS members, although share a high degree of structural as well as functional similarities, are indeed differentially regulated in the contrasting cultivars of rice. Salt-tolerant Pokkali shows higher constitutive expression of most of the TCS pathway members which are further induced by salinity stress. Further, co-localisation of some of these differentially regulated TCS members with the QTLs responsible for salinity tolerance in rice indicates their potential as 'candidate genes' for raising salinity-tolerant transgenic plants.

Materials and methods

Plant material and stress treatments

Seeds for rice cultivars IR64 and Pokkali were germinated in a hydroponic system in half Yoshida medium as described earlier (Kumari et a!. 2009a). Stress treatment was given to 4-day-old seedlings (200 mM NaCl for 24 h).

Total RNA isolation, mRNA purification and eDNA synthesis

Leaves from the above 4-day-old seedlings of rice cultivars were used for total RNA and ploy A+ RNA isolation as described earlier (Kumari et a!. 2009a). First-strand eDNA synthesis was done using RevertAid™ eDNA synthesis kit (Fermentas Life Sciences, USA) as per manufacturer's instructions.

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Real-time quantitative RT-PCR analysis

Primers were designed using Primer Express 2.0 software (PE Applied Biosystems, USA) under default parameters. The uniqueness of each primer pair to amplify selective genes was confirmed by BLASTn using the KOME and NCBI databases. The sequences for these primers are listed in Table I. The rice actin gene (OsAct) was taken as the reference gene for normalisation of transcripts. The PCR mixture contained 5 ~-tl of eDNA (50 times diluted), 12.5 ~-tl

of 2x SYBR Green PCR Master Mix (Applied Biosystems, USA) and 200 nM of each gene-specific primer in a final volume of 25 ~-tl. The real-time PCRs were performed employing ABI Prism 7500 Sequence Detection System and software (PE Applied Biosystems). All the PCRs were performed under the following conditions: 2 min at 50°C, I 0 min at 95°C and 40 cycles of I 5 s at 95°C, I min at 60°C and 30 s at 72°C in 96-well optical reaction plates (Applied Biosystems). The specificity of the amplification was tested by dissociation curve analysis and agarose gel electrophoresis. Three technical replicates were analysed for each sample and the data analysis was performed using SDS 1.4 software (Applied Biosystems).

Heat map analysis

Absolute values for transcript abundance obtained from real-time PCR data were used for this purpose, and Heat map data analysis was performed using Mayday 2.0 software as described by Kumari et a!. (2009a).

Assigning of DNA markers on chromosomes

Microsatellite and restriction fragment length polymorphism (RFLP) genetic markers for salinity tolerance (Singh et a!. 2007) were assigned on different chromosomes using the Gramene database (http://www.gramene.org/markers/).

Results and discussion

Rice cultivars exhibit differential levels of TCS transcripts under non-stress and salinity stress conditions

Our quantitative reverse transcriptase PCR (qRT-PCR) analysis revealed that even under non-stress conditions, HK transcripts are accumulated to differential levels within a given genotype, e.g. HK6 and ERS2a are members which show very low expression, whilst the rest of the HK members show a relatively higher accumulation of transcripts (Electronic supplementary material Fig. I b). However, ERS2b shows an intermediate level of expression in the case of both the cultivars. It is also interesting to note


Table I List of primers and their sequences

Primers Nucleotide sequence ( 5' to 3 ')

HK3aRTF TACAGGCAACACACGAGGAG HK3aRTR CTGTACAGCTGCTCCCCTTC HK3bRTF ATTGGGCAGCCTTCTTCAC

. HK3bRTR CACAAGTAACTTCGGCAC HK4aRTF CACGAGGAATGCACAAAGTG HK4aRTR TGGGCCCAAGAACTTCTGTA HK4bRTF GGGATGCTGATTTTGTTGGT HK4bRTR CGAGATTTCAGAGAAGCCT HK5RTF GAGGGATGTATGGAATGTGGA HK5RTR TGTTGCATCAGCCTCTAGGA HK6RTF AGTGGGAAGGGCATTGATCT HK6RTR TGCAGTGATCCAGGCAATAA ERSIRTF TTGTCATCCAGCTTGGCAT ERSIRTR CACCGGACCAACAAAATC ERS2RTF TTCTATGGCCAGACCAGATG ERS2RTR CATGAGCAAACCTGAACAGC ERS3RTF GCCAGACTACTCTTGCTTTGC ERS3RTR GTCCTGGTTTTGGCAATCTG ETR2RTF AGCACCGATGACACCGTTC ETR2RTR GTTCATCTCCCAGTGCTGC ETR3RTF TTGTCATGGTGAACCTCGAA ETR3RTR GCTCCTGTACTGGCTGATCC ETR4RTF CTGATCGTCATTGCCGTC ETR4RTR CTGGACCAGCCCGTTGAT PHYARTF GAGCAGTCGGATGAAGGCA PHYARTR GACGCTGAGGATGAAGGTTG PHYBRTF CGCTGGACAACCCAAGAGG PHYBRTR AAGAAACTCCGCTCCGACTC PHYCRTF GCAACAAAGCACACACCAAC PHYCRTR GGCTCCAAGAGAGATGATGG AHPIRTF TGGATTTGGTGAGGACTGAA AHPJRTR ACAAGCTTGGATTTGCTGCT AHP2RTF TGGATTTGGTGAGGACTGAA AHP2RTR ACAAGCTTGGATTTGCTGCT PHPIRTF TGGATTTGGTGAGGACTGAA PHPJRTR ACAAGCTTGGATTTGCTGCT PHP2RTF CCGTCCTCAGGCAGAAGTG PHP2RTR TGAAAGCTGGGTACGAAGTG RRJRTF AGCGTCGAGCTGTCACATTA RRIRTR CAGCCGTTTGACCATCTGT RR3RTF GTGTCGCACTACTTCCAG RR3RTR CTGCCATTGGACCATCTGT RR4RTF GCAGCAGCAAGAGGAAGG RR4RTR CGCTGCTAGTGGAGGACAAT RR9RTF GCCCCACACAAAAGACCAA RR9RTR CCGATCAGACAGAAGCAAG RR13RTF AGGTGCGCTATCAGAGCAGA RR13RTR GCCTGATCTTCCTGTCCAGA RR21RTF GCGAGGGCAATTTGTTAGA

Table I (continued)

Primers

RR21RTR RR23RTF RR23RTR RR24RTF RR24RTR PRR12RTF PRR12RTR PRR37RTF PRR37RTR PRR59RTF PRR59RTR PRR95RTF PRR95RTR ACTINF ACTINR

Nucleotide sequence ( 5' to 3 ')

ACCATCTCTACCTCCCTG CATCCCGAAACTTCAGAGCG CACCCCAGGTCGTCGCTG ATTGATGGGTGGCGAGGT TCAGCTTGTCCCCCTACT GTGGTTGCCTTTGCCTCTT CTGGAGGAACGTTTGCTACG AAAAAGGTGCGGTACCAGAG ACCCTGCTGTTGTTGGTCTT TTCCGGATGAAAAGAAAAGAT TCACAAACTGGCCCTTAACC CCGGCTTAAGAGGAAAGACA GCCGAACAAACTGACCCTTA CAGCCACACTGTCCCCATCTA AGCAAGGTCGAGACGAAGGA

that at the time points studied here, the relative pattern of transcript accumulation for a given TCS member in the two cultivars was always similar. Further, Pokkali always exhibited higher expression of TCS members than IR64 under non-stress conditions (Electronic supplementary material Fig. 1 b-d).

Similar to sensory HKs, HPTs also exhibited a differential accumulation pattern, in the two rice cultivars, under nonstress conditions. The expression of PHPI was relatively lower in both the cultivars of rice as compared to AHPl, AHP2 and PHP2 (Electronic supplementary material Fig. lc). Again, we could clearly see that the constitutive expression of all the HPTs in Pokkali was relatively high~r than IR64 (Electronic supplementary material Fig. lc).

The expression pattern of the various RRs was again found to be differential within a genotype. However, relative abundance of various TCS members were similar in the two genotypes. Amongst the response regulators, expression ofPRR12 was noted to be the lowest in both the cultivars under non stress conditions, whilst RR9 was found to be the most abundant (Electronic supplementary material Fig. ld). Further, expression of RR13 was found to be almost the same in both cultivars, but the abundance of transcripts for all other RRs was more in Pokkali than IR64, except RR21. To further comment on the role of this TCS member showing the interesting pattern of regulation amongst the contrasting rice cultivars, we are now raising transgenic plants which are either expressing RR21 under the control of35S promoter or with a RNAi construct. However, we have searched the rice Tos 17 mutant database and found that the mutant line has narrow leaf and semi-dwarf phenotype with low fertility (http://tos.nias.affrc.go.jp ).

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It is interesting to note that under control conditions, the pattern of transcript accumulation for all TCS members in the case of the senstti\e cultivar was the same as that of the salt-tolerant cultivar. This observation indicates that evolution of TCS machinery amongst the two diverse genotypes has perhaps followed the same path. Also, the possible preparedness of the tolerant cultivar to cope with salinity stress is indicated by the overall higher levels of transcripts, as compared to the sensitive one.

Changes in the transcript levels of TCS members have been reported under various stresses such as salinity, drought, cold and abscisic acid (Jain et al. 2006, 2008), and OsRR6 is a member LlJ.at has been shown to be upregulated at least four times under salinity stress. With the objective of studying the salinity-regulated expression patterns for TCS members, we performed qRT-PCR after exposing them to 200 llL\1 NaCl for 24 h, the results of which are presented in Electronic supplementary material Fig. 2. IR64 is salt-senSitive, whilst Pokkali is salt-tolerant despite the fact that it takes up Na.. and is not a salt extruder. Thus. it must be balancing the osmoticum in the system and also maintainmg soilium homeostasis by some mechanism. Under salinity stress, differential accumulation of transcripts in all the TCS members could be seen in both the cultivars, but this induction is more in the case of salinity susceptible-IR64---as compared to its tolerant counterpart-Pokkali. The differential accumulation of other stress-related genes has also been reported recently in rice cultivars (Kumari et al. 2009a). Based on the above analysis, we propose that one of the mechanism by which Pokkali is able to tolerate salin:ty stress better than IR64 is by keeping the constitutive levels of Its TCS members relatively higher than its salt-sensitive cultivar IR64. ~erefore, the induction of TCS transcripts in Pokkali does not have to be a major event. This observation is in confirmation with previous stud1es reported m various other plant genera such as Arabidopsis (Wong et al. 2006), Brassica (Kumar et al. 2009) and rice (Walia et al. 2005; Kumari et al. 2009a), suggesting that maintaining high levels of important transcripts is an important survival strategy in plants. It is possible that a relatively small number of transcription factors are involved (li et al. 2008) or that epigenetic controls play a crucial role in this process.

The Heat map generated with the help of expression values of various TCS members in the two cultivars under control and salinity stress conditions indicated to some clear clusters (as shown in Electronic supplementary material Fig. 3). There are clearly a set of TCS members showing a relatively high expression, which is seen as cluster A for HKs, B for HPTs and C for RRs. In contrast, the rest of the members, which are represented as D for HKs, E for HPTs and F for RRs. did not show such high expression. Thus, we could see a similar pattern of gene

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Funct Integr Genornics

regulation among various TCS members in the case of contrasting genotypes irrespective of the fact that these TCS members are co-localised or distantly placed on a chromosome. Thus, we cannot rule out the possibility of the presence of a 'regulon ', operating amongst the various TCS members in rice, like it has been recently reported in Arabidopsis (Ma and Bohnert 2007; Mentz.en and Wurtele 2008). However, time course analysis with several environmental stresses needs to be carried out to prove this concept beyond doubt.

Some TCS members could be co-localised within the s."l!inity-related QTL>

The complex and multi genic nature of the salinity tolerance trait is considered to be a serious limitation towards generating salt-tolerant crop va.-rieties (Singh et a!. 2007). However, recent achievements like complete genome sequencing of crop plants, genome level expression studies along with refined molecular mapping techniques are being considered as stepping stones for the improvement of our understanding about the salinity response in crop plants. It has also been suggested that identification of molecular markers, which are tightly lir.ked to salinity-tolerant QTLs, as well as genes v.ithin, can sene as landmarks for the physical localisation of QTLs!genes helping in efficient marker-assisted selection. Recently, there have been several reports attempting to identifY and localise salt-related. QTLs in rice as well as to find candidate genes within them (Ismail et al. 2007; Leung 2008; ~\1.ohammadi-N"ejad et al. 2008).

We have mapped all the TCS members on the rice chromosomes using Lite accepted nomenclature as proposed earlier (Schaller et al. 2007). Eectronic supplementary material Fig. 4 illustrates this distribution of TCS members on various rice chromosomes. [nterestingly, although reported earlier (Pareek et al. 2006) that TCS members were found on all chromosomes, differences were noted in relation to their relative distnbution patterns. RRs were found on all chromosomes except number X, whilst HKs were found on all chromosomes except number VIII, IX, X and XII. Chromosome IL m and IV were found to contain maximum number of total members belonging to the TCS family. Since, we have observed differential regulation of various TCS members amongst the two contrasting rice cultivars, we wanted to see if we could localise some of these interesting salinity-induced genes within salinityrelated QTLs. For this purpose, we have combined the genetic map of rice for salinity-related QTLs (associated with its various traits) with physical location of TCS members based on the Grarnene marker database (httpJI www.gramene.org/markersf). The combined picture of the QTLs responsible for salinity trait, the flanking markers,


traits encoded by the QTLs and the location of various TCS members has been shown in Fig. 1. These QTLs and their linked markers (i.e. RFLP and microsatellites) have been picked up from a recent compilation (Singh et a!. 2007).

The Saito! QTL on chromosome number I, which has been recently identified and is believed to contribute more than 50% of the salinity trait, was not found to contain any TCS member within. However, we could localise at least 17 TCS

Fig. 1 a Chromosomal location of genetic markers for salinity

A IZMB related QTLs and TCS mem-bers. Genetic markers flanking

IIM2II

the salinity related QTLs were located on each rice chromo-some using gramene marker ~

'':! database (http://www.gramene. "' RMJ7ll2

~ N#"/K' c; g org/markers/). Location of TCS rorlo

i z genes is similar to the one AMI•s

11Mn9S a reported earlier (Pareek et a!. f 2006); however, nomenclature has been followed as suggested

N#"/K No",/1:" J (Schaller et a! 2007). Markers rgl/o rallo ..!_@

named RZ, R and G are RFLP l ..

markers, whilst RM are micro- ~ satellite markers. b Location of RM2.86 RZS96

i .. HK4 and RR41 between marker 12

Rl925 and RZ596 on chromo- :s r AMZBl J ~ & some III (upper panel) and ..

location of HK I and RR24 Clll.:J ILa/ "]!: S«ddlng bi<Nillng

~l AMZSS

RG162 between markers RG653 and 141Mwil ~ _,., RGI62 on chromosome VI. c:a6

.. 'OOtkngtt>

Note that these members may ~i A191$ AG6U ~ ~

represent a typical TCS signal ~ transduction cascade mediated II Ill IV v VI

via sensory HK, HPT and RR '2: "' J G24 _, s: .f

0

~

• Slloot /(" ~

~! AMl09

5l>cot !!...) wclohl ., AM206 ... i :ii IU&M

i Rl751 --CINlC IU6ll

l .. VII VIII IX X XI XII

B Rl925 R$1 HK4 Tchrlll $ ~ ~ 1653

i1 RR25 Rl162 $ Y ChrVI

~ m I Transmembrane domain 0 Rec:efver domain

c::J Transmitter domain D DNA binding domain

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members within the other salinity-related QTLs, and these included seven sensory HKs and ten RRs. Interestingly, we did not find any HPT localised within the salinity-related QTLs identified so far. On chromosome number III, we could locate a cluster of TCS members within the QTL responsible for causing 'leaf bronzing'. On the same chromosome, PHYB could also be localised in the QTL associated 'With N a+ K + ratio, which is an important physiological trait desired by plant breeders. Similarly, on chromosome IV, we could again find a cluster of RRs and ETR2 localised within two QTLs, one of which was responsible for Na +/K+ ratio and the other for maintaining Na+ concentration and K+ uptake (between markers RM225 and RZ69). The same chromosome had two other TCS members within another QTL responsible for Na+/K+ ratio between markers RM145 mtd R...\-1.3732. Chromosome VI has HKl and RR15 \\1thm the QTL responsible for seedling root length. whilst chromosome III has RR41 within the QTL for shoot K• concentration. At the moment it is difficult to comment that these TCS members localised with salinity-related QTLs have any direct contribution towards the salt tolerance trait. However, chromosomes III and IV present an interesti11g picture which needs to be analysed in detail since clUS!ers of TCS members could be localised within QTLs present on these chromosomes. We have carried out an exhausnve search for all the other genes present \\ithin the QTLs on chromosome III and VI. In fact, these regions were found to have a number of other important salt-stress-responsive genes. some of which have been characterised in details, whilst the rest are yet to be functionally characterised. Chromosome III has glutathione S transferase. serine-tr..reon::ne protein kinase, MYB52, CBS protein, MAPK, MAPKK, M"APKKK, DNA topoisomerase IV, salt tolerance protein and Ca ++-dependent protein kinase within that QTL, whilst chromosome VI contained peptidyl prolyl cr.s-trans isomerase. phosphoglycerate kinase, proline-rich protein. MYB transcription factor, bZJJl transcription factor, zinc finger protein, receptor-like protein kinase and salt-responsive proteins. Keeping in view the importance of these genes towards stress tolerance. we have already rrused transgenic plants which are overexpressing three of these genes. namely CBS protein. peptidyl prolyl cis-trans ISOmerase and phosphoglycerate kinase and the experiments for the same are at an advanced stage. Our recent work related to peptidyl prolyl cis-trans isomerase indicates its role in tmproving tolerance towards diverse abiotic stresses (Kuma.ri et al. 2009b ).

Based on the studies in thts paper, two possible gene models warrant detailed i."lvestigation. These are (1) HK4-RR41 on chromosome III and (2) HKI-RR15 on chromosome VI. Pfam domain 3I1Xysis of these proteins has indicated the presence of rno N domains in HK4, but

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Funct Tntegr Genomics

HKI does not contain a TM domain. Similarly, RR41 has one receiver domain, whilst RR24 has one receiver and one Myb-like DNA binding domain (Fig. lb).

Acknowledgements This 'II?Orlc was supported by research grants received from the International Atonuc Energy Agency (Vienna),. International Foundation for Science (Sweden), Department of Science and Technology, Government of India and Senior Research Fellowship (R. K.) from the t:niverslty Grants Commission, Government of India. Authors would also like to thank: Nausheen Tareen for helping in manuscript editing.

References

Grefen C, Harter K (2004) Plant tw~>-<:omponent systems: principles, functions, complexity and cross talk. Planta 219:733-742

Hwang I. Chen HC, Sheen J (2002) Tvo.-o-component signal transduction pathways m Arabuiopsis. Plant Physiol 129:500-515

Ismail AM, Heuer S, Thomson MJ, Wissuwa M (2007) Genetic and genom1c approoches to develop rice gennplasm for problem soils. Pl:mt Mol Bioi 65547-570

Jain M, Tyag1 AI<., Khurana JP (2006) Molecular characterization and differenual expression of C)tok':ll!D-responsive type-A response regulator m nee (Dr}-::a sattva). BMC Plant Bioi 6:1

Jain M, Tyag1 A.K, Khurana JP (2008) I>itfer=ual gene expresSIOn of rice two-componeill stgnruing elemen~ during reproductive development and reguhtion by abiotic stress. Fuoct Tntegr Genomics 8:175-180

Kakimoto T (20031 PerceptiOn cd signal transduction of cytokinins. Annu R1:11 Pl:mt Bioi 54:605-{)27

Kumar G, Purty RS, Sharma MP, Singla-Pareek SL, Pareek A (2009) Physiolog~cal responses among Brassica species under salinity stress show strong correlanon With transcript abundance for SOS pathway-related genes. J Pl:mt Physiol 166:507-520

Kumari S, Pan;ab1 Nee Sabhanl.·al ': Kushwaha HR. Sop01y SK, Singla-P=k SL, Pareek A (2009a) Transcriptome map for seedling stage speafic Slllmny stress response indicates a specific set of genes as =ilidate for salme tolerance m Ory::a saliva L Funct Integr Geootrucs 9·109-123

Kumari S, Singh P, Smgla-Pareek SL, Pareelc: A (2009b) Heterologous expression of a salinity ~ d.-..·elopmentally regulated rice cyclophilm gene (0sCyp2) m E. coli and S. cerevisiae confers tolerance towuds mutnple ab10uc stresses. Mol Biottthnol. doi:IO.I007 si2033-009-9153-0

Leung H (2008) Stressed genomics-bn"lging relief to rice fields. Curr Opin Plant Bioi 11.20 I 208

Lohrmann J, Hmter K (2002) Plant rv.·o-component signaling systems and the role of response regul.:ltors. Plant Physiol 128:363-369

Li WX, Oono Y. Zhu J, He XJ, Wu JM, Lida K., Lu XY, Cui X, Jin H, Zhu J-K (2008) 1be Arabu:iopsis ~FYA5 transcription factor is regulated transcripaonally and ~1-transcriptionally to promote drought reststance. Plant Cell 8.2238-225 I

Ma S, Bohnert ill (2007) Integraoon of A.rabuiopsis thaliana stressrelated profiles, promoter struc:ture:;, and cell specific expression. Genome Bwl 8:R49

Mentzen WI, Ww-tele ES (2008) Regulon orgai1i2ation of Arabidopsis. BMC Plant Blol8:99. do~:lO 1186/1471-2229-8-99

Mohammadi-Ne.Jad G. Arzani A, Rezai A...\.1, Singh RK, Gregorio GB (2008) Assessment of nee genotypes for salt tolerance using microsatellite m.:I!"kers asSOCJ:tted With the sallol QTL. Afr J Biotechnol 7:730-736


Oka A, Sakai H, Iwakoshi S (2002) His-Asp phosphorelay signal transduction in higher plants: receptors and response regulators for cytokinin signaling in Arabidopsis thaliana. Gene Genet Syst 77:383-391

Pareek A, Singh A, Kumar M, Kushwaha H, Singla-Pareek S (2006) Whole genome analysis of Oryza sativa reveals similar architecture of two component signaling machinery with Arabidopsis. Plant Physiol 142:380-397

Schaller GC, Doi K, Hwang I, Kieber JJ, Khurana JP, Kurata N, Mizuno T, Pareek A, Shiu SH, Wu P, Yip WK (2007) Nomenclature for two-component signaling elements of rice. Plant Physiol 143:555-557

Singh RK, Gregario GB, Jain RK (2007) QTL mapping for salinity tolerance in rice. Physiol Mol Bioi Plants 13:87-99

Walia H, Wilson C, Condamine P, Liu X, Ismail AM, Zeng L, Wanamaker SI, Mandai J, Xu J, Cui X, Close TJ (2005) Comparative transcriptional profiling of two contrasting rice genotypes under salinity stress during the vegetative growth stage. Plant Physiol 139:822-835

Wong CE, Li Y, Labbe A, Guevara D, Nuin P, Whitty B, Diaz C, Golding GB, Gray GR, Weretilnyk EA, Griffith M, Moffatt BA (2006) Transcriptional profiling implicates novel interactions between abiotic stress and hormonal responses in Thellungiella, a close relative of Arabidopsis. Plant Physiol 140:1437-1450

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Deciphering tools for gene

expression analysis

Deciphering tools for gene expression analysis Rohit Joshi, Ratna Karan, Sneh Lata Singla-Pareek and Ashwani Pareek*

Stress Physiology and Molecular Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi 100067, India. Fax: 91-11-26704504

*Author for correspondence Email: [email protected]

In Techniques of Biotechnology Ed. Anil Gupta


expression analysis

1. Introduction: Gene expression is a highly complex and tightly regulated process

allowing a cell to respond dynamically both to environmental stimuli and to its own changing needs. Each cell of an organism usually contain the same set of genes, however there are significant differences in their activation and control (Coe and Antler, 2004). Only a fraction of these genes are expressed at a particular time and confer unique properties to each cell type. Basic mechanism by which genes are utilized is same for all cells and involves transcription of a gene into mRNA before being translated into a protein. The expression of a single gene can be detected by Northern hybridization where, the total RNA of different samples are blotted on a nylon membrane and the expression of a gene of interest is detected by using probes of that particular gene of interest. Single gene expression can also be detected by dot blot technique where, the total RNA of different samples, upto 96 samples are immobilized on nylon membrane and expression of a particular gene in all the samples are detected by probe of that particular gene of interest. In the post genome sequencing era, a large number of genes sequences are known in different organisms hence, there is a need to understand the change that are occurring at transcriptome level ie change in the overall transcripts in a cell with the time or under different stress conditions. Study of transcriptome needs a suitable experimental design and high throughput instrument. Many techniques are available for the study of transcriptome such as reverse northern, SAGE (Serial Analysis of Gene Expression), subtractive hybridization and microarray. In this chapter, we are going to discuss about the principles and procedures of microarray. Microarray is also known as DNA chip, biochip and gene array where, thousands of different known genes are spotted on a suitable support and the spot sizes are typically less than 200 microns in diameter. DNA microarray is defined as an orderly arrangement of DNA fragments representing the genes of an organism (Southern, 2001 ). Thus, microarrays require specialized robotics and imaging equipment. Microarrays were originally designed to measure gene expression levels and now it has revolutionized functional genomic analysis.

2. How does it work? The basic principle of DNA microarray is that DNA molecules or

oligonucleotides corresponding to the genes whose expression has to be analyzed are used for making probes. A microarray is an analytical device that comprises an array of molecules (oligonucleotides, cDNAs, clones, PCR products, polypeptides, antibodies, and others) or tissue sections immobilized at discrete ordered or nonordered micrometer-to-millimeter-sized locations on the surface of a porous or nonporous insoluble solid support (Kricka and Fortina, 2001). A rnicroarray works by exploiting the ability of a given mRNA molecule hybridize specifically to the DNA template from which it originated. For global analysis of gene expression, i.e., for transcriptome analysis, high density microarrays have been developed. Newly annotated genes and novel genes can be analyzed by DNA microarray analysis .. Specific location is assigned to each

2 In Techniques of Biotechnology Ed. Anil Gupta


expression analysis

DNA fragment representing a gene on the array. Over 30,000 spots can be placed on one slide through the use of robotic spotters. The supports themselves are usually glass microscope slides, silicon chips or nylon membranes. The DNA is printed, spotted or synthesized directly onto the support. The spots themselves can be DNA, eDNA, or oligonucleotides. Fluorescently labeled DNA or RNA in the sample act as "mobile probes" will hybridize to the complementary spot on the array. The fluorescent tags are excited by the laser. Hybridized DNA will be identifiable as glowing spots on the array by exposing the microarray to a fluorescently labeled sample, while the spots that do not hybridized will not be visible under scanner. The microscope and camera work together to create a digital image of the array. The computer program then creates a table that contains the ratios of the intensity of red-to-green fluorescence for every spot on the array (Figure 1). 3. Making of Microarray

Probe samples are spotted (or printed) on a microscopic glass slide coated with polylysine with a microarrayer (or spotter). The polylysine coating is to enhance DNA/RNA binding to the plate through electrostatic interactions. Polylysine not fixed to DNA is blocked for slide preparation in order to avoid target binding. Apart from glass slides, special coated plastic films are also used for solid support (Bertucci et al. 1999; Eisen and Brown 1999). The spotting process is performed inside dust and vibration free chamber. Further evaporation of the samples can be avoided by maintaining humidity in the chamber during operation. After printing, slides are left at room temperature for 24 h, for efficient coupling of printed eDNA. Finally, dried slides are put in a beaker for washing with ethanol followed by air-drying and stored at room temperature till further use. Prior to hybridization, DNA is denatured to obtain a single strand DNA on the microarray. 4. Slide preparation: Microarray slide preparation requires a suitable support, so that the probes can be efficiently immobilized. The types, synthesis of probes and its methods of spotting i.e. immobilization on the support are described below. 4.1 Impermeable Supports

The support on which the probe has to be spotted should be impermeable to solvent so that it will not swell and shrink during microarray experiment. Nitrocellulose membrane provides an excellent support for the spotting of DNA which allows the probe to bind to the complementary target strand. The pores of the membrane provide a larger total surface for binding (Southern, 1975). It is possible to increase the area of dot blots, but to reduce the size of spots beyond certain limits, or to control their size and shape on a porous membrane is not possible. Glass or plastic supports have dimensional stability and rigidity, whereas permeable membranes swell in solvent and tend to shrink and distort when dried. Nucleic acids form a monolayer, saturating the surface, so the amount of attached DNA is consistent from one region of the array to another. Additionally as they are on the surface, the nucleic acids are favorably placed to take part in hybridization reactions. Interactions with the solution phase are



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much faster, because molecules do not have to diffuse into and out of the pores. It has been estimated that the amount of material deposited on the surface of the substrate forming monolayer is equivalent to 10pmoljmm2• Therefore, currently, over 50,000 eDNA probes can be spotted onto a 25X75mm slide by robot~c printing. Thousands of distinct spots of known oligos or cDNAs are printed on matrix platform located on silicon, nylon sheets, or glass slides, which i,s sufficient for the specific hybridization and detection.

4.2 Fabrication: Fabrication is the process of spotting the probe on a suitable support, where the presynthesized probe or in situ synthesis of probe is done by high throughput technology as discussed in this section. (A) Presynthesized Probes:

In this case, the presynthesized probes are covalently crosslinked to the glass slides containing poly-L-lysine by ultraviolet irradiation. The method of application of probes on the glass slide is a computer-controlled where, with a head canying a pin or pen device is used to to pick up small drops of solution containing presynthesized probes from the multiwell plates and spot them to the surface of glass slide (Figure 2A) (Guo et al., 1994). (B) In Situ Synthesis of Probes:

Here, the different types of probes are directly synthesized on the slide by coupling of nucleotides in a few steps. Probes can be synthesized by using inkJet fabrication, flow channels and cells or by light directed fabrication. For the synthesis of octanucleotide length probe individually the required steps will be 5,24,288 (8x48). These steps can be reduced to eight only using computer controlled programmable software in coupling methods which is a small multiple of length of probes. (a) Ink-Jet Fabrication:

This technique is based on the principle of ink-jet printers, where the firing solutions of nucleotide reagents are spotted onto the glass surface according to the information of probe sequences feeded in the computer (Blanchard et al., 1996). Acetonitrile is replaced by more viscous and less volatile solvent such as adiponitrile. Computer software is used for moving the pens and substrate and printing four colors to delivering precursors for four different bases. Thus, any set of oligonucleotides can be made and known sequences can be placed at any position in the array (Figure 2B) (Hughes et al., 2001). (b) Flow Channels and Cells:

The precursors for the four bases, A, C, G, T are introduced through channels to make 4 broad stripes of the mononucleotides on a square plate. A second set of four nucleotides are laid down in four narrower stripes within each of the monomers to create 16 stripes of dinucleotides. This process is repeated until the oligonucleotides have reached half of probes final length. At this point, the plate is turned 900 and the whole process is repeated. This method is particularly used for making arrays either to those comprising all oligonucleotides of a given length or those comprising all the complements of a



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target of known sequence. The dimensions of such arrays are determined by the width of the stripes (Southern et al., 1994). (c) Light-Directed Fabrication:

In this technique of fabrication, the oligonucleotides are synthesized to the specific positions on a glass surface by irradiation. Irradiation of glass surface is done by using light of wavelength 365nm. For the addition of bases, photolithographic masks are used for specifically synthesizing probes on a very small area of glass surface. Light is passed through the mask where nucleotide has to be added. When the light is passed through a specific areas on mask the protecting group on the 5' hydroxyl group of the previously added nucleotide gets activated . The surface is then flooded with the coupling agent for the next base and the process continued till the desired length of probe is synthesized .. This method has the advantage that any sequence can be synthesized at any position randomly and the size of the arrays is small (Pirrung et al., 1998). 4.3 Target preparation

Target is a sample for which the transcriptome analysis has to be performed. For the microarray experiment, target sample has to be labeled with fluorescent dye. mRNA of target sample is reverse transcribed using reverse transcriptase and dNTP mixture containing amino-allyl dCTP into eDNA. Amino-allyl nucleotide is very reactive for coupling with fluorescent dye cyanine (Cy). Labeling is performed by two dyes Cy3 (green dye) and Cy5 (red dye). One sample is labeled with Cy3 whereas other sample is labeled with Cy5 for the hybridization of probes on microarray slide in an experiment for the identification of differentially expressed genes (Eisen et al. 1998; Lockhart and Winzeler 2000). 4.4 Hybridisation:

Both green and red labelled eDNA (targets) are mixed together and put on the matrix of spotted single strand DNA (probes). The chip is then incubated overnight for hybridization at 60°C under highly humid conditions. At this temperature, DNA strands encounter the complementary strands of the probes on the slide and create double stranded DNA i.e., fluorescent DNA hybridize on the spotted ones. Double stranded DNA thus formed has one unlabelled and other labeled strand. To obtain high quality data effective hybridization of target is essential. Commercially available cover slips with raised Teflon edging for full contact with arrayed probes (Lifter Slips; Erie Scientific, Portsmouth, NH, USA), hybridization chambers for submerging microarray in water of set temperature (Corning, NY, USA), hybridization solution (Clontech, Palo Alto, CA, USA) etc. are used for effective hybridization (Evans et al., 2003). Precise temperature control is needed and the hybridization rate is increased if the hybridization solution is in motion over the surface of the array i.e., placing the array in a rotating cylinder.

RNA molecules fold as a result of intramolecular base pairing to form stable structures that interfere with the hybridization process. This problem is relieved by degrading the transcripts to fragments of a size comparable with that of the oligonucleotide probes. The problem is less severe in case of spotted

5 In Techniques of Biotechnology Ed. Ani I Gupta


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eDNA arrays because hybridization can be carried out at higher temperatures, which melt the intramolecular base pairing. 4.5 Slide scanning:

Microarrays are scanned at two different wavelengths corresponding to the absorbance of red and green dye after hybridization and washing. The signals are analyzed by passing a beam of laser through the microarray slide that excites each spot on the plate and the fluorescent emissions are gathered through a photo-multiplicator (PMT) coupled with a confocal microscope. The spot will: appear either red or green if the hybridization is stronger with one of the samples and the spot on the microarray will appear to be yellow, if the intensities of binding of two dyes to target samples are same. Axon GenePix 4000B scanner simultaneously exite a small region of glass surface(- 100Jlm2) by two lasers at a focal plane preset by the user (VVhite and Salamonsen, 2005). Microscopic detection and quantification of fluorescence images provide the basic array data, but variance is increased by background fluorescence, dust, and spot-to-spot and array-to-array differences in signal intensity. Due to this, complex normalization and correction routines must be applied to the resulting data (Yang et al., 2002). Microarray data is visually presented in a twodimensional table or "heat-map," each cell of which uses a simple colorcode to represent the relative transcript expression of a single gene under each of a defined set of experimental conditions. The vertical axis identifies each gene in the collection, whereas the horizontal axis displays each condition or time-point in a time series analysis (Eisen et al., 1998).

Radioactive detection is also used with wide dynamic range, even with a single exposure and the range can be extended by varying the exposure time. Quantitation can be very precise. It is easy to label targets to a high specific activity. Double labeling and high-resolution imaging can also be used for detection and separation. To align the specific grid of arrayed DNA spots and quantify the signal intensity at each location softwares are commercially available i.e., Imagene (Biodiscovery, CA, USA). 4.6 Data analysis:

Now there are two images from the same slide corresponding to the two dyes from which we have to calculate the number of D;.JA molecules in each experimental condition. For any spot on the slide, we measure the signal intensities in the green dye emission wavelength and the red dye emission wavelength. If the mR"\IA amount used for hybridization is proportional to the amount of fluorescent DNA fixed onto the plate, directly calculate the red/ green fluorescence ratio. Ratio greater than 1 (red on the image) shows that the gene expression is greater in the sample 2, while ratio smaller than 1 (green on the image) indicates that gene expression is greater in sample 1. These differences in expression can be visualized and interpret using commercially available softwares (i.e. Array plot). The signal intensities of the spots are correlated with the concentrations of target mRNA samples. Data mining is conducted by using statistical programs and algorithms to determine whether gene of interest is upregulated, down-regulated, or unchanged. Data is then organized using a



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database and gene annotation can be performed with GO (gene ontology) analysis, clustering analysis and analysis of pathways and networks of genes.

To adjust the data for systemic non biological effects arising from technical variation and measurement error normalization is essential in microarray. The aim is to remove the effect of noise from the data with maintaining ability to detect significantly differentially expressed genes. Still there is no universally accepted method of normalization, but the intensity dependent normalization i.e., local weighted regression is accepted most (Dudoit et al. 2002b, Yang et al. 2002, Park et al. 2003, Smyth and Speed 2003). 4.7 Expression profile clustering:

Clustering of expression profiles obtained through arrays can be done at the end of microarray analysis (Eisen et al., 1998). Genes sharing same expression profile, gradually form clusters during phylogenetic analysis. Other complex techniques such as principal component analysis or neuronal networks are also now being used for microarray analysis. Final data is presente<;I in the form of hierarchical clustering, where each column represents the microarray data from one experiment and each row a specific gene (Chuaqui et al. 2002). Rigorous experimental design and statistical analysis, together with adequate replication, is critical to draw broad conclusions about the biology of a system based on microarray data (Meyers et al., 2004). Mostly microarray experiments are designed with only one categorical factor, so paired t-test is used for statistical analysis, but for multiple categorical factors (time and genotype) require analysis of variance (ANOV A) based methods (Boise et al., 1993; Kerr and Churchill, 2001; Dudoit et al., 2002b). B-statistic is an estimate of the odds that the gene is differentially expressed (Lonnstedt and Speed, 2002). Commercially available software packages for normalization, statistical analysis and visualization are Cyber-T (Baldi and Long, 2001), SAM (Tusher et al., 2001), BRB-Array Tools (http:/ /linus.nci.nih.gov /BRB-ArrayTools), QVALUE (Storey and Tibshirani, 2003), Focus (Cole et al., 2003), statistical language R (Ihaka and Gentleman, 1996; Dudoit et al, 2002a) etc. 4.8 Microarray Data Management

As a single chip contains 30,000 spots of target DNA there is necessity of uniform system that manage and provide a disbursement point for microarray data. NCBI has done this to know the biological properties of control and sample DNA, experimental conditions and the results. As the proficiency in generating data is overcoming the capacity for storing and analyzing, this data requires standardization of storage and sharing. For public use and dissemination of gene expression data, NCBI launched the Gene Expression Omnibus (GEO). It has an expression data repository and online resource for the storage and retrieval of gene expression data from any organism or artificial source. GEO will aid in the study of functional genomics by storing vast amounts of data on gene expression profiles derived from multiple experiments using varied criteria and conditions. GEO facilitates cross-validation of data obtained using different techniques and technologies and helps to set standards for further gene expression studies.



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Microarray Markup Language (MAML) is developed by Microarray Gene Expression Database (MGED) and is a first attempt to provide a standard platform for submitting and analyzing the enormous amounts of microarray expression data generated around the world. The goal is to facilitate the adoption of standards for DNA-array experiment annotation and data representation, as well as the introduction of standard experimental controls and data normalization methods and also to facilitate the establishment of gene expression data repositories, the comparability of gene expression data from different sources, and data analysis software. MAML is independent experimental platform and provides a framework for describing experiments done on all types of DNA arrays. MAML provides a format which allows analysis of data obtained not only from any existing microarray platforms but also many of the possible future variants, including protein arrays. Additional data set from different experiments is available in the Barley Base repository for cereal Gene Chip data (http:/ /barleybase.org/), ArrayExpress (http:/ /www.ebi.ac.ukjarrayexpress/) and Microarray Gene Expression Data Society (http:/ jwww.mged.org/) providing an invaluable resource for the scientific community (Brazma et al., 2002; Eckardt, 2004). 5. Types of Microarray:

Main types of microarrays available commercially are oligonucleotide arrays (GeneChip™ by Affymetrix) and eDNA arrays (BD Biosciences). Other types of microarray are antibody array, protein chip array and tissue array. (A) Oligonucleotide Arrays:

In Oligonucleotide array (or DNA chip) small oligonucleotide (20-80-mer oligos) or peptide nucleic acid (PNA) probes are synthesized either in situ (onchip) or by conventional synthesis followed by on-chip immobilization (http:/ /www.affymetrix.com) (Yadav et al., 2006). Total 11 to 16 copies of this DNA are spotted for each gene on the array. These DNA fragments have little cross-reactivity with other genes for minimal non-specific hybridization. Still to combat any non-specific hybridization, a second probe identical to the first except for a mismatched base at its centre is placed next to the first. This is termed as Perfect Match/Mismatch (PM/MM) probe strategy. To obtain perfect hybridization any background hybridization with the MM probe is subtracted from the PM probe signal.

Combination of photolithography and combinatorial chemistry are used for the synthesis of diverse sequences of probes (Schena, 2003). Oligonucleotide array allows the simultaneous generation of thousands of probes relatively quickly on the chip surface of 5-inch square of quartz wafer which is an ideal substance on which chemicals adhere. This wafer is then washed with a blocking compound which is removed by exposure to light. A mask designed with 18-20 micron square windows is laid on top of array and allow ultra-violet light to pass through areas where a specific nucleotide is needed. Exposure of light removes the blocking compound from the probe. The wafer is then washed with a solution of the desired nucleotide that is linked to the same blocking compound and nucleotide attaches to the probes that were exposed to light,



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while the nucleotide-attached with blocking compound ensures that all the probes are protected again. A capping step is added so that probes that did not attach their appropriate nucleotide are not incorrectly synthesized. The process is repeated until all the probes are complete. (B) eDNA Arrays:

Principle is same in this array but the probes in this case are larger pieces of DNA that are complementary to the genes (Cheung et al., 1999). PCR using specific primers can be used to amplify specific genes from eDNA to generate the eDNA probes (500~5,000 bases long). A separate PCR reaction must be performed for each gene. eDNA probes can be mechanically spotted onto a glass slide (Harmer and Kay, 2000).

Two samples are prepared for hybridization to the array: a control sample and an experimental sample. Samples are prepared with mRNA being extracted from cells and reverse transcribed into eDNA, during the reverse transcription step a fluorescent dye is incorporated into the newly formed eDNA. Here a different dye employed to label the different samples e.g., the control sample can be labeled with a green-fluorescing dye Cy3 and the experimental sample labeled with a red-fluorescing dye Cy5. Since samples are labeled differently they can be combined and hybridized to the microarray together. Both samples will competitively bind to the probes on the array and the sample containing more gene expression for a particular probe will express more. If there is more of an mRNA transcript in the control sample than in the experimental sample (i.e. gene is down regulated) then more Cy3 will bind to the probe on the array and the spot will fluoresce green. If there is more experimental transcript, the reverse will happen and the spot will fluoresce red. When the two samples have the same amount of transcript, the dyes will cancel each other and the spot will fluoresce yellow. Black areas represent where neither the control nor sample DNA hybridized to the target. Thus comparing the intensities of hybridization signals for different mRNA samples allows the determination of changes in mRNA levels under the conditions tested for all the genes represented on the arrays (Eisen et al. 1998; Lockhart and Winzeler 2000). By using this technology one can display 409,000 spots in an area of 1.28 cm2 (Fodor, 1997). Hence, all 20,000-25,000 genes of Arabidopsis can be displayed on a single slide. Microarray technique is highly sensitive as it can detect mRNAs at level of 1/100,000 or 1/500,000.

There are few distinctions between oligonucleotide and eDNA arrays. First, eDNA arrays eliminate the need for the probe design required for oligonucleotide arrays, while also allowing the entire genome of an organism to be represented on the array easily, making eDNA arrays more useful for the analysis of gene expression on a global level. Oligonucleotide arrays have advantage for their greater hybridization specificity, due to PM/MM probe design resulting in more specific fluorescence detection (Clark et al., 2002; Hu et al., 2001). (C) Protein microarray:



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It is based on the principle of ligand-binding assay that relies on the formation of product with an immobilized capture molecule (target) present in the solution. Protein microarrays are now becoming very popular due to their use in study of antibody-protein, Enzyme-protein, DNA-protein and proteinprotein interactions (Figure 3). For analysis of protein interactions with other molecules, protein microarrays have various types of molecules immobilized on the slide surface using a contact spotter (MacBeath and Schreiber, 2000; Zhu et al., 2001) or a non-contact microarrayer (Delehanty, 2004; Delehanty and Ligler, 2003; Jones et al., 2006), to act as capture molecules. Aldehyde- and epoxyderivatized glass surfaces can be used for random attachment of proteins through amines (Kusnezow et al., 2003; MacBeath and Schreiber, 2000) and coating the glass surface with nitrocellulose, gel pads, or poly-L-lysine achieves a random orientation of the proteins (Angenendt et al., 2002; Charles et al., 2004; Kramer et al., 2004; Stillman and Tonkinson, 2000; Zhu et al., 2003) as the proteins are passively adsorbed onto the surface (Figure 3). Protein microarray is of three types (Hallet al., 2007):

Analytical microarrays are used to measure binding affinities, specificities, and protein expression levels of the proteins in the mixture. In this technique, a library of antibodies, aptamers, or affibodies is arrayed on a glass microscope shde and then probed with a protein solution. Antibody microarrays are the most common analytical microarray (Bertone and Snyder, 2005). Functional protein arrays are used to study the biochemical activities of an entire proteome in a single experiment i.e., protein-protein, protein-DNA, proteinRNA, protein-phospholipid, and protein-small molecule interactions (Hallet al., 2004; Zhu et al., 2001). In Reverse phase protein microarray (RP A), cells are isolated from various tissues of interest and are lysed and then arrayed onto a nitrocellulose slide using a contact pin microarrayer. Slides are then probed with antibodies against the target protein of interest, and the antibodies are detected with cherniluminescent, fluorescent, or colorimetric assays. (Speer et al., 2005). (D) Antibody Arrays:

Antibody microarray is a powerful chip-based technology, composed of hundreds of distinct monoclonal antibodies printed at high density on a glass microscope slide enabling to monitor the expression pattern of hundreds of proteins with a single experiment even in a pg/ml range. In this microarray glass slides or other chip types with monoclonal antibodies specific against proteins of interest attached to their surface. A single antibody microarray can have over 500 antibodies arrays on their surface. Instead of conducting many western blot analyses, one can simply use an antibody microarray to evaluate changes in protein expression levels. This technique does not measure absolute concentration, but provides a relative measure of protein abundance. (E) Tissue Microarray:

To analyze the expression of genes simultaneously in multiple individual tissue samples on one slide tissue microarrays are used. The array is composed of 0.6 - 3.0 mm cores of tissue from donor tissue paraffin blocks which are arrayed at a high density on a slide. Histochemical and molecular detection



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techniques can be used on the slides to allow tern to examine gene expression profiling in disease status across a variety of patients and disease conditions. It is a low cost and high throughput technique which could produce material for 500,000 assays and analysed per slide block with a wide-variety of automated analysis and data collection techniques. The tissue microarrays allow the entire cohort to be analyzed in one batch on a single slide with identical reagent concentrations, incubation time, temperature, wash conditions, and antigen retrieval and can be reused thousand of times with different reagents. The quantitative expression analysis can be done by H-score system, which is a product of intensity and area stained (Rimm et al., 2001). The software involved are TMA-Deconvoluter and Stain finder, Tissue Array Data Analysis, Tissue Array Database (TAD), TMAJ etc (Figure 4).

By using a microtome 4-5 micrometer tissue sections are produced. Specific areas of interest are selected from paraffin-embedded tissue blocks and are re-embedded into an arrayed blank recipient blocks. Tissue microarray is of different types i.e., cryo-tissue microarray, multi-tissue microarray, progression tissue microarray and prognosis tissue microarray. (F) Glycomics:

Glycans are termed as the compounds in which sugar residues are covalently attached with the proteins and biomolecules. An example of carbohydrate microarray is neoglycolipids, spotted onto nitrocellulose and PVD (Fukui et al., 2002). The neoglycolipids were probed with proteins of known carbohydrate binding specificity to confirm identification of predicted proteinoligosaccharide interactions. This array technology describes the link of oligosaccharides to the lipid derived from diverse sources; for example, glycoproteins, proteoglycans, glycolipids, whole cells, organs and synthetic oligosaccharides (Howbrook et al., 2003). 6. Applications:

DNA microarrays are better than other profiling methods (SAGE, SH, PCR) in that they are easier to use with high throughput results, can generate large amount of data in lesser time, do not require large scale sequencing and allow quantitation of genes for many samples. (A) Changes in gene expression level

In microarray technology whole genome can be used for expression analysis. Expression chip array is used in determining the level, or volume, at which a certain gene is expressed called as microarray expression analysis and examine changes in gene expression over a given time period i.e., within the cell cycle (Schena, et al., 1995; Raitio et al., 2001). In contrast to the analysis of a single nucleotide polymorphism, gene expression levels are best analyzed with relatively long probes. SNP has allowed polymorphisms to be more quickly assayed and also their relevance to disease to be easily determined. With long probes, it is possible to achieve good yields under stringent hybridization conditions. To detect mutations or polymorphisms in a gene sequence i.e., change or variation that can occur within a person's DNA sequence "mutation microarray" analysis is used. Genomic DNA derived from a normal sample is



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required for use in the hybridization mixture. In the same organism under two, different conditions or among two different organisms differential expression in the levels of gene(s) can be detected.

DNA microarrays are used to examine the gene expression changes under various diseases i.e., cancer. Tumor profiling using DNA microarrays allows the analysis of the development and the progression of such complex diseases. Microarrays are used to identify inheritable markers used in genotyping tool. Information about differences in gene expression between tissue types can help to uncover how our bodies develop sensitivity about which genes are harmful to target for disease therapy. By the microarray technique gene expression studies can be done for a subset of genes involved in various metabolic pathways.

With all the techniques to measure gene expression including northern blotting, differential display, serial analysis of gene expression and dot-blot analysis, main problem is that they are unsuitable for the parallel testing of multiple gene expression. Southern's method of rnicroarray contain multiple DNA sequences (probes) spotted or synthesized on a relatively small surface allowing simultaneous monitoring of the expression of thousands of genes, thus providing a functional aspect to sequence information, in a given sample (Eisen and Brown, 1999). This technique seem to be ideal for detection of complex phenotypes and identification of genes whose altered expression underlies complex traits that are located within genetic regions identified by quantitative traits loci (QTL) techniques (Doerge, 2002).

Microarrays, in combination with defined mutants, can be used to infer signaling pathways associated with an environmental response i.e., capability to identify common promoter regulatory elements shared by coreguJated genes in cell cycle (Cho et al., 1998; Spellman et al., 1998; Harmer and Kay, 2000). (B) Analysis of Sequence Variation

Microarray technique is used for mapping human genome using DNA polymorphisrns (Solomon and Bodmer, 1979) and analyzed to give enough analytical power to carry out genetic studies to find the genes associated with common diseases and inherited disease susceptibilities (Cargill et al., 1999). Sequence variation is best analyzed with the shortest oligonucleotides (<15-mer) that will give specific hybridization to the target site. Multiple genes can be analyzed simultaneously to get a snapshot of the whole transcriptome of a system at a given time point. Treating mRNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Cnlike classical quantitative traits, the genetic linkages associated with transcript abundance permits a more precise look at cellular biochemical processes (Tzouvelekis et al., 2004). Microarray provides powerful tools for the genome-wide correlation of gene transcript levels with physiological responses and alterations in physiological states. It have been applied almost exclusively to a few model species for which the abundant gene sequence data permit the fabrication of whole-genome microarrays. It can be successfully applied to nonmodel species to generate new



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insights of comparative and evolutionary significance into animal function (Graceyl and Cossins, 2003). (C) Toxicological research: Toxicogenomics

The goal of toxicogenomics is to find correlations between toxic responses to toxicants and changes in the genetic profiles of the objects exposed to such toxicants. New drug development protocols are including genomic and proteomic microarray data obtained during preclinical stages of investigation, but extrapolating this information to humans is not straightforward. However, such data can provide greater insight into and better prediction of the performance characteristics of the product as it moves into clinical phases of development (Petricoin et al., 2002). DNA microarray is an ideal tool for the identification of bacterial species in a mixed population giving information on both the abundance and identity of the bacteria in a particular environment (Straub and Chandler, 2003). (D) Drug discovery: Pharmacogenomics

The goal of pharmacogenomics is to find correlations between therapeutic responses to drugs and the genetic profiles of patients. Personalized drugs, molecular diagnostics, integration of diagnosis and therapeutics are the longterm promises of microarray technology. One can examine targets for drug discovery and potential diagnostic and prognostic biomarkers for many complex diseases. The patterns of correlated loss and increase of gene expression allowing gene interactions to be studied, or the use of microarray analysis in drug design (MacBeath and Schreiber 2000) and screening, with compounds that affect the expression of important genes being screened during drug screens (Crowther, 2002). (E) Diagnosis of disease

Expression chips are used in disease diagnosis, e.g., in the identification of new genes involved in environmentally triggered diseases, i.e., diseases affecting immune, nervous, and pulmonary/ respiratory systems. Microarrays have allowed the rapid identification of which genes are turned on and off in tumor development. With the help of microarray one can detect viruses and other pathogens from blood samples and thus can be used as a pathogen detection method. Insights into disease have been one of the most beneficial results of microarray .technology (Schena, 2003; Heller, 2002). Identification of genes that are lost during a disease are typically involved in a cellular function that directly or indirectly prevents the disease from occurring can be done with a microarray for targeted therapies. It is also used to diagnose diseases at very early stages, so that therapy could be commenced before the disease can cause any harm (Heller, 2002). Recently a strategy is proposed called SAM (significance analysis of microarrays), which allows the determination of significantly differentially expressed genes between groups of samples analyzed by expression arrays i.e., in early and late stages of cancer (Tusher et al., 2001). Microarray technology has been widely used in studies identifying new genes or molecular pathways involved in tumor classification, cancer progression, and chemotherapy resistance and sensitivity (Macgregor and Squire, 2002).



expression analysis

Mutations within DNA repair genes, which themselves act as frontline defence against mutation due to lost or broken chromosomes. Comparative Genomic Hybridization (CGH) is used to detect any change in the number of copies of a particular gene involved in a disease state. In this array each spot of target (large pieces of genomic DNA) in the array has a known chromosomal location and hybridization mixture contains fluorescently labeled genomic DNA taken from both control and diseased tissue.

Microanay technology can be used for the identify unexpected molecular participants and might help in the development of novel targets for improved treatment in idiopathic pulmonary fibrosis(Zuo et al., 2002a; Crystal et al., 2002) and asthma (Argyris et al, 2004). Microarray analysis can be a powerful tool for identifying mediators through a genomic-based strategy using animal models of asthma (Zuo et al., 2002b; Koike et al., 2004) and IPF (Kaminski et al., 2000; Katsuma et al., 2001) to provide a large scale of differentially expressed genes and led researchers to shed further light into transcriptional programs involved in cytokine signaling (Lee et al., 2001; Hakonarson et al., 2001; Syed et al., 1999) and apoptosis (femple et al., 2001; Brutsche et al., 2001; Kupfner et al., 2001) regulating emphysema, Chronic Obstructive Pulmonary Disease (Yamanaka et al., 2001) and pulmonary fibrosis. Furthermore, this technology is already being applied in respiratory clinical pharmacology of complex diseases such as asthma (Brutsche et al., 2002) and Chronic Obstructive Pulmonary Disease revealing modem approaches in therapeutic interventions in asthma (Laprise et al., 2004; Banerjee et al., 2002) as well as COPD (Yamanaka et al., 2001 ). Finally, it provided scientists with useful information to clarify physiological mechanisms underlying the actions of numerous drugs, elucidate the pathophysiological processes of complex diseases such as IPF (Zuo et al., 2002; Kaminski et al., 2000; Katsuma et al., 2001; Chambers et al., 2003; Liu et al., 2004), asthma (femple et al., 2001; Hakonarson et al., 2001; Laprise et al., 2004; Sayama et al., 2002; Nakajima et al., 2001; Syed et al., 1999), COPD (Koike et al., 2004; Yamanaka et al., 2001; Fuke et al., 2004; Vuillemenot et al., 2004; Hackett et al., 2003; Morris et al., 2003), lung fibrosis in acute lung injury (McDowell et al., 2000; Kupfner et al., 2001; Perkowski et al., 2003; Chinnaiyan et al., 2001) and pulmonary edema (Olman et al., 2004; Sabbadin et al., 2003; Perkowski et al., 2003; Tzouvelekis et al., 2004).

Tissue microarray technology is used for blood cell analysis of patients suffering from red cell disorder and microdissected discs can be used for PCRbased analysis (Rimm et al., 2001). (F) Comparative and Evolutionary Biology:

Currently, this method is perfectly suited for sub-classifying otherwise indistinguishable disease states using straightforward hierarchical clustering techniques and therefore well suited to situations where just a few genes underpin the condition and display pronounced changes in expression related to an imposed change in physiological status or perhaps in response to upstream events in a transduction pathway (Kato et al., 2001). Microarrays have also been used to identify genes that contribute to enhanced fitness (Ferea et al., 1999) and to assess the changes in gene expression following duplication and sequence



expression analysis

divergence of genes (Wagner, 2000). Transcript screening can also be used to find molecular basis of natural variation and genotype and phenotype interaction on an evolutionary scale (Streelman and Kocher, 2000). Whole genome microarrays point to the way in which environmental responses of organisms can be addressed on a global scale with differential expression of a common set of genes (Gasch et al., 2000; Causton et al., 2001). Transcript expression data across and between species shows that closely related species of the same genus may share the expression of many transcripts, whereas distantly related species will have more divergent profiles (Gracey1 and Cossins, 2003). A common gene ontology (GO) has been developed to provide order in a fragmented functional nomenclature by creating a single listing of attributes to describe objectively gene products in any organism (Ashburner et al., 2000). (G) Proteomics:

Microarray technology can be used efficiently to identify and quantify proteins and to study protein function from a global perspective (MacBeath, 2002; Templin, et al.2002). Protein microarrays are suitable for studying proteinprotein interactions (Zhu et al., 2001), enzyme-substrate, protein-DNA interactions (Hall et al., 2004), protein-lipid interactions (Zhu et al., 2001), protein-oligosaccharide, protein-drug interactions (Huang et al., 2004), proteinreceptor interactions (Jones et al., 2006), and antigen-antibody interactions (Michaud et al., 2003), kinase activities (Ptacek et al., 2005; Zhu et al., 2000) and for serum profiling (Zhu et al.,2006) simultaneously. Peptide microarray technology is used for proteome analyses to study molecular recognition events and the identification of biologically active peptides. DNA-protein interaction assays have proven useful in the characterization and identification of DNA binding proteins. This technique is also used to study protein-carbohydrate interactions in biological processes including normal tissue growth and repair, cell-cell adhesion and inflammation, cell growth, fertilization, viral replications, parasitic infection, tumor-cell motility and progression (Stoll et al., 2004). Reverse phase protein microarray allows the determination of the presence of altered proteins that may be the result of disease (Speer et al., 2005). Proteome chips have been used to screen human sera for the presence of autoantibodies (Hueber et al., 2005; Kattah et al., 2006) or viral specific antibodies (Zhu et al., 2006). Protein microarray is also used in the investigation of neurodegenerative disorders (Anderson et al., 2003), the correlation of cell phenotype with surface markers (Ko et al., 2005), identification of chromatin-related proteins (Coleman et al., 2003) as well as the mapping of WW domains (Hu et al., 2004). This technique is capable of detecting up to 10,000 proteins in parallel (Howbrook et al., 2003).

Acknowledgements: Authors would like to acknowledge the receipt of financial support received from Jawaharlal Nehru University, International Foundation of Science, Sweden, IAEA (Vienna), DST and DBT, Govt. of India. Ratna Karan acknowledges the award of Senior Research Fellowship from University Grants Commission, New Delhi, India.


Deciphering tools for gene expression analysis

Figure 1. Procedure of microarray Technology: Probes are amplified by PCR and printed on solid support. Total RNA samples extracted from treated and control cells/tissues (targets) are reverse transcribed and labelled with either Cy3 (green) or Cy5 (red). The samples are combined and competitively hybridised to the microarray.Following washing to remove nonhybridised target, laser excitation is applied and the emissions measured in each colour channel. Specialised software is used to measure intensity ratios to each spot, which are then exported for statistical analysis to identify differentially expressed genes.

Figure 2. Fabrication of Presynthesized Probes: (A) Fabrication on membrane support, (B) Fabrication by inkjet printer

Figure 3. Protein microarrays: proteins are immobilized on microscopic slide and the slide can be probed for various interactions such as for identification of Antibody, Enzymes, DNA as well as Protein. Cy3 is a fluorophore that has been used for labeling of target of Antibody, Enzymes, DNA and Protein.

Figure 4. Tissue microarrays: Section containing various tissue samples. The diameter of each tissue spot is 0.6 mm. The slide can be used for either histology or immunofluorescence or immunocytological studies and finally observed by microscope. Processing and analysis of data has been done bv various softwares .

.;



References:

A. Brazma, P. Hingamp, J. Quackenbush, G. Sherlock, P. Spellman, C. Stoeckert, J. Aach, W. Ansorge, C.A. Ball, H.C. Causton, T. Gaasterland, P. Glenisson, F.C.

Holstege, I.F. Kim, V. Markowitz, J.C. Matese, H. Parkinson, A. Robinson, U. Sarkans, S. Schulze-Kremer, J. Stewart, R. Taylor, J. Vilo and M. Vingron (2001) Minimum information about a microarray experiment (MIAME)- toward standards for microarray data. Nat Genet 29:365-371.

A. Kramer, T. Feilner, A. Possling, V. Radchuk, W. Weschke, L. Burkle and B. Kersten (2004) Identification of barley CK2alpha targets by using the protein microarray technology. Phytochemistry 65:1777-84.

A. Tzouvelekis, G. Patlakas and D. Bouros (2004) Application of microarray technology in pulmonary diseases. Respiratory Research 5:26-44.

A. Wagner (2000) Decoupled evolution of coding region and mRNA expression patterns after gene duplication: implications for the neutralist-selectionist debate. Proc. Natl. Acad. Sci. USA 97:6579-84.

A.L. Evans, A.S. Sharkey, S.A. Saidi, C.G. Print, R.D. Catalano, S.K. Smith and D.S. CharnockJanes (2003) Generation and use of a tailored gene array to investigate vascular biology. Angiogenesis 6: 93-104.

A.M. Chinnaiyan, M. Huber-Lang, C. Kumar-Sinha, T.R. Barrette, S. Shankar-Sinha, V,J. Sarma, V.A. Padgaonkar and P.A. Ward (2001) Molecular signatures of sepsis: multiorgan gene expression profiles of systemic inflammation. Am J Pathol 159:1199-1209.

A.P. Blanchard, R.J. Kaiser and L.E. Hood (1996) High density oligonucleotide arrays. Biosensors and Bioelectronics 11: 687-690.

A.P. Gasch, P.T. Spellman, C.M. Kao, 0. Carmel-Harel, M.B. Eisen, G. Storz, D. Botstein and P.O. Brown (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell11:4241-57

A.Y. Gracey1 and A.R. Cossins (2003) Application of microarray technology in environmental and comparative physiology. Annu Rev Physiol65:231-59 •

B. Coe and C. Antler (2004) Spot your genes- An overview of the microarray. The Science Creative Quaterly.http--www _scq_ubc_ca-wp-content-GeneChip_gif.mht.

B.A. Stillman and J.L. Tonkinson (2000) FAST slides: a novel surface for microarrays. Biotechniques 29:630-635.

B.C. Meyers, D.W. Galbraith, T. Nelson and V. Agrawal (2004) Methods for transcriptional profiling in plants. Be fruitful and replicate. Plant Physiol135: 637-652.

B.R. Vuillemenot, J.F. Rodriguez and G.W. Hoyle (2004) Lymphoid tissue and emphysema in the lungs of transgenic mice inducibly expressing tumor necrosis factor-alpha. Am J Respir Cell Mol Biol 30:438-48.

C. Laprise, R. Sladek, A. Ponton, M.C. Bernier, T.J. Hudson and M. Laviolette (2004) Functional classes of bronchial mucosa genes that are differentially expressed in asthma. BMC Genomics 5:21.



CA. White and L.A. Salamonsen (2005) A guide to issues in microarray analysis: application to endometrial biology. 130: 1-13.

D. Stoll, J. Bachmann, M.F. Templin and T.O. Joos (2004) Microarray technology: an increasing variety of screening tools for proteomic research. DDT: TARGETS 3: 24-31.

D.A. Hall, H. Zhu, X. Zhu, T. Royce, M. Gerstein and M. Snyder (2004) Regulation of gene expression by a metabolic enzyme. Science. 306:482-484.

D.A. Hall, H. Zhu, X. Zhu, T. Royce, M. Gerstein and M. Snyder (2004) Regulation of gene expression by a metabolic enzyme. Science 306:482-484.

D.A. Hall, J. Ptacek and M. Snyder (2007) Protein Microarray Technology. Mech Ageing Dev 128(1): 161-167.

D.G. Morris, X. Huang, ~. Kaminski, Y. Wang, S.D. Shapiro, G. Dolganov, A. Glick and D. Sheppard (2003) Loss of integrin alpha (v) beta6-mediated TGF-beta activation causes Mmp12-dependent emphysema. Nature 422:169-73.

D.J. Crowther (2002) Applications of Microarrays in the Pharmaceutical Industry. Curr Opin Pharmaco12(5): 551-4.

D.J. Lockhart and E.A. \Vinzeler (2000) Genomics, gene expression and DNA arrays. Nature 405 : 827-836

D.L. Rimm, R.I. Camp, L.A. Charette, D.A. Olsen and E. Provost (2001) Amplification of Tissue by construction of Tissue Microarrays. Exp. and Mol. Path. 70: 255-264

D.N. Howbrook, A.M. van der Valk, M.C. O'Shaughnessy, O.K. Sarker, S.C. Baker and A.W. Lloyd (2003) Developments in microarray technologies. DDT. 8: 642-651.

E. Koike, S. Hirano, A. Furuyama and T. Kobayashi (2004) eDNA microarray analysis of rat alveolar epithelial cells following exposure to organic extract of diesel exhaust particles. Toxicol Appl Pharmacol201:178-85.

E. Solomon and W. F. Bodmer (1979) Evolution of sickle variant gene (letter). Lancet 1: 923. E.F. Petricoin, J.L. Hackett, L.J. Lesko, R.K. Puri, S.I. Gutman, K. Chumakov, J. Woodcock,

D.W. Jr. Feigal, K.C. Zoon and F.D. Sistare (2002) Medical applications of microarray technologies: a regulatory science perspective. Nature Genetics Supplement 32: 474-479.

E.M. Southern (1975) Detection of specific sequences among 0;\l"A fragments separated by gel electrophoresis. J Mol Bioi 98: 503-517.

E.M. Southern (2001) DNA Microarrays. History and overview. Methods Mol Biol170: 1-15. E.M. Southern, S.C. Case-Green, J.K. Elder, M. Johnson, K.U. Mir, L. Wang and J.C. Williams

(1994) Arrays of complementary oligonucleotides for analyzing the hybridisation behaviour of nucleic acids. Nucleic Acids Res 22: 1368-1373.

F. Syed, S.J. Blakemore, D.M. Wallace, M.K. Trower, M. Johnson, A.F. Markham and J.F. Morrison (1999) CCR7 (EBil) receptor down-regulation in asthma: differential gene expression in human CD4+ T lymphocytes. QJM 92:463-n.

F. Zuo, N. Kaminski, E. Eugui, J. Allard, Z. Yakhini, A. Ben-Dor, L. Lollini, D. Morris, Y. Kim, B. DeLustro, D. Sheppard, A. Pardo, M. Selman and R.A. Heller (2002a) Gene expression analysis reveals matrilysin as a key regulator of pulmonary fibrosis in mice and humans. Proc Natl Acad Sci USA 99:6292-7.



F.C. Kafatos, C.W. Jones and A. Efstratiadis (1979) Determination of nucleic acid sequence homologies and relative concentrations by a dot hybridization procedure. Nucleic Acids Res 7: 1541-1552.

G. MacBeath (2002) Protein microarrays and proteomics. Nat Genet 32(Suppl): 526-532 G. MacBeath and S.L. Schreiber (2000) Printing proteins as microarrays for high-throughput

function determination. Science 289:1760-1763. G. MacBeath and S.L. Schreiber (2000) Printing proteins as microarrays for high-throughput

function determination. Science 289: 1760-1763 G.A. Michaud, M. Salcius, F. Zhou, R. Bangham, J. Bonin, H. Guo, M. Snyder, P.F. Predki and

B.I. Schweitzer (2003) Analyzing antibody specificity with whole proteome microarrays. Nat Biotechnol2003;21:1509-12.

G.K. Hu, S.J. Madore, B. Moldover, T. Jatkoe, D. Balaban. J. Thomas and Y. Wang (2001) Predicting splice variant from DNA chip expression data. Genome Res 11:1237-45.

G.K. Smyth and T. Speed (2003) Normalization of eDNA microarray data. Methods 31: 265-273.

H. Hakonarson, E. Halapi, R. Whelan, J. Gulcher, K. Stefansson and M.M. Grunstein (2001) Association between IL-1betajTNF-alphainduced glucocorticoid-sensitive changes in multiple gene expression and altered responsiveness in airway smooth muscle. Am J Respir Cell Mol Bioi 25:761-71.

H. Hu, J. Columbus, Y. Zhang, D. Wu, I. Lian, S. Yang, J. Goodwin, C. Luczak, M. Carter, L. Chen, M. James, R. Davis, M. Sudol, J. Rodwell and J.J. Herrero (2004) A map of WW domain family interactions. Proteomics 4: 643-655

H. Zhu, J.F. Klemic, S. Chang, P. Bertone, A. Casamayor, KG. Klemic, D. Smith, M. Gerstein, M.A. Reed and M. Snyder (2000) Analysis of yeast protein kinases using protein chips. Nat Genet 26:283-9.

H. Zhu, M. Bilgin and M. Snyder (2003) Proteomics. Annu Rev Biochem 72:783-812. H. Zhu, M. Bilgin, R. Bangham, D. Hall, A. Casamayor, P. Bertone, N. Lan, R. Jansen, S.

Bidlingmaier, T. Houfek, T. Mitchell, P. Miller, R.A. Dean, M. Gerstein and M. Snyder (2001) Global analysis of protein activities using proteome chips. Science 293:2101-2105.

H. Zhu, S. Hu, G. Jona, X. Zhu, N. Kreiswirth, B.M. Willey, T. Mazzulli, G. Liu, Q. Song, P. Chen, M. Cameron, A. Tyler, J. Wang, J. Wen, W. Chen, S. Compton and M. Snyder (2006) Severe acute respiratory syndrome diagnostics using a coronavirus protein microarray. Proc Natl Acad Sci US A 103:4011-6.

H.C. Causton, B. Ren, S.S. Koh, C.T. Harbison, E. Kanin, E.G. Jennings, T. Lee, H.L. True, E.S. Lander and R.A. Young (2001) Remodeling of yeast genome expression in response to environmental changes. Mol Biol Cell12:323-337.

I. Lonnstedt and T.P. Speed (2002) Replicated microarray data. Statistica Sinica 12: 31-46. I.K. Ko, K. Kato and H. Iwata (2005) Antibody microarray for correlating cell phenotype with

surface marker. Biomaterials 26: 687-696 J. Huang, H. Zhu, S.J. Haggarty, D.R. Spring, H. Hwang, F. Jin, M. Snyder and S.L. Schreiber

(2004) Finding new components of the target of rapamycin (TOR) signaling network



through chemical genetics and proteome chips. Proc Natl Acad Sci U S A 101:16594-16599.

J. Ptacek, G. Devgan, G. Michaud, H. Zhu, X. Zhu, J. Fasolo, H. Guo, G. Jona, A. Breitkreutz, R. Sopko, R.R. McCartney, M.C. Schmidt, N. Rachidi, S.J. Lee, A.S. Mah, L. Meng, M.J. Stark, D.F. Stern, C. De Virgilio, M. Tyers, B. Andrews, M. Gerstein, B. Schweitzer, P.F. Predki and M. Snyder (2005) Global analysis of protein phosphorylation in yeast. Nature 438:679-84.

J. Zou, S. Young, F. Zhu, F. Gheyas, S. Skeans, Y. Wan, L. Wang, W. Ding, M. Billah, T. McOanahan, R.L. Coffman, R. Egan and S. Umland (2002b) Microarray profile of differentially expressed genes in a monkey model of ailergic asthma. Genome Bioi 3(5): 0020.

J.B. Delehanty (2004) Printing functional protein microarrays using piezoelectric capillaries. Methods Mol Bioi. 264:135-143.

J.B. Delehanty and F.S. Ligler (2003) Method for printing functional protein microarrays. Biotechniques. 34:380-385.

J.D. Storey and, R. Tibshirani (2003) Statistical significance for genomewide studies. PNAS 100: 9440-9445.

J.G. Kupfner, J.J. Arcaroli, H.K. Yum, S.G. Nadler, K.Y. Yang and E. Abraham (2001) Role of NF-kappaB in endotoxemia-induced alterations of lung neutrophil apoptosis. J Immunol167:7044-51.

J.H. Lee, N. Kaminski, G. Dolganov, G. Grunig, L. Koth, C. Solomon, D.J. Erie and D. Sheppard (2001) Interleukin-13 induces dramatically different transcriptional programs in three human airway cell types. Am J Respir Cell Mol Biol25:474-85.

J.T. Streelman and T.D. Kocher (2000) From phenotype to genotype. Evol Dev. 2:166-73. K. Anderson, A. Potter, D. Bahan and K.E. Davies (2003) Protein expression changes in spinal

muscular atrophy revealed with a novel antibody array technology. Brain 126, 2052-2064

K. Sayama, M. Diehn, K. Matsuda, C. Lunderius, M. Tsai, S.Y. Tam, D. Botstein, P.O. Brown and S.J. Galli (2002) Transcriptional response of human mast ceils stimulated via the Fe (epsilon) RI and identification of mast cells as a source of IL-11. BMC Immunol3(1):5.

L.H. Boise, M. Gonzalez-Garcia, C.E. Postema, L. Ding, T. Lindsten, L.A. Turka, X. Mao, G. Nunez and C.B. Thompson (1993) bclx, a bcl-2-related gene that functions as a dominant regulator of apoptotic cell death. Cell74: 597-608.

L.J. Kricka and P. Fortina (2001) Microarray Technology and Applications: An All-Language Literature Survey Including Books and Patents. Clinical Chemistry 47:1479-1482.

M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K. Dolinski, S.S. 0\o.·ight, J.T. Eppig, M.A. Harris, D.P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J.C. Matese, J.E. Richardson, M. Ringwald, G.M. Rubin and G. Sherlock (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25-29.

M. Cargill, D. Altshuler, J. Ireland, P. Sklar, K. Ardlie, N. Patil, N. Shaw, C.R. Lane, E.P. Lim, N. KaJyanaraman, J. Nernesh, L. Ziaugra, L. Friedland, A. Rolfe, J. Warrington, R. Lipshutz, G.Q. Daley and E.S. Lander (1999) Characterization of single-nucleotide polymorphisrns in coding regions of human genes. Nat Genet 22: 231-238.

20 In Techniques of Biotechnology Ed. Ani! Gupta


M. Kato, N. Seki, S. Sugano, K. Hashimoto, Y. Masuho, M. Muramatsu, K. Kaibuchi and M. Nakafuku (2001) Identification of sonic hedgehog-responsive genes using eDNA microarray. Biochem. Biophys. Res Commun 289:472-78.

M. Raitio, K. Lindroos, M. Laukkanen, T. Pastinen, P. Sistonen, A. Sajantila and A.C. Syvanen (2001) Y-chromosomal SNPs in Finn-Ugric-speaking populations analysed by minisequencing on microarrays. Genome Res 11:471-482

M. Sabbadini, D. Barisani, E. Conforti, A. Marozzi, E. Cinelli, G. Miserocchi and R. Meneveri (2003) Gene expression analysis in interstitial lung edema induced by saline infusion. Biochim Biophys Acta 1638:149-56.

M. Schena (2003) Microarray Analysis. Hoboken, NJ: Wiley-Liss. 630p. M. Schena, D. Shalon, R.W. Davis and P.O. Brown (1995) Quantitative monitoring of gene

expression patterns with a complementary DNA microarray. Science 270:467-470 M.A. Coleman, K.A. Miller, P.T. Beernink, D.M. Yoshikawa and J.S. Albala (2003)

Identification of chromatin-related protein interactions using protein microarrays, Proteomics 3 (11): 2101-2107.

M.A. Olman, K.E. White, LB. Ware, M.T. Cross, S. Zhu and M.A. Matthay (2004) Microarray analysis indicates that pulmonary edema fluid from patients with acute lung injury mediates inflammation, mitogen gene expression, and fibroblast proliferation through bioactive interleukin-1. J Immunol172:2668-77. -

M.B. Eisen and P.O. Brown (1999) DNA arrays for analysis of Gene Expression. Methods Enzymol. 303:179-205.

M.B. Eisen,-P.T. Spellman, P.O. Brown and D. Botstein (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863-14868.

M.C. Pirrung, L. Fallon and G. McCall (1998) Proofing of photolithographic DNA syntheisis with 3' ,5'-dimethoxybenzoiny loxycarbony !-protected deoxynucleoside phosphoramidites. J Org Chern 63: 241-246.

M.F. Templin, D. Stoll, M. Schrenk, P.C. Traub, C.F. Vohringer and T.O. Joos (2002) Protein microarray technology. Trends Biotechnol20:160-166.

M.G. Kattah, G.R. Alemi, D.L. Thibault, I. Balboni and P.J. Utz (2006) A new two-color Fab labeling method for autoantigen protein microarrays. Nat Methods 2006 3: 745-51.

M.H. Brutsche, I.C. Brutsche, P. Wood, A. Brass, N. Morrison, M. Rattay, N. Mogulkoc, N. Simler, M. Craven, A. Custovic, J.J. Egan and A. Woodcock (2001) Apoptosis signals in atopy and asthma measured with eDNA arrays. Clin Exp Immunol123:181-187.

M.H. Brutsche, L. Joos, I.E. Carlen-Brutsche, R. Bissinger, M. Tamm, A. Custovic and A. Woodcock (2002) Array-based diagnostic gene-expression score for atopy and asthma. J Allergy Clin Immunol 109:271-273.

M.J. Heller (2002) DNA Microarray Technology: Devices, Systems, and Applications. Annu Rev Biomed Eng 4: 129-53.

M.K. Kerr and G.A. Churchill (2001) Experimental design for gene expression microarrays. Biostatistics 2:183-201.

N. Kaminski, J.D. Allard, J.F. Pittet, F. Zuo, M.J. Griffiths, D. Morris, X. Huang, D. Sheppard and R.A. Heller (2000) Global analysis of gene expression in pulmonary fibrosis



reveals distinct programs regulating lung inflammation and fibrosis. Proc Natl Acad Sci USA 97:1778-83.

N.A. Eckardt (2004) Cutting Edge Transcriptome Analysis: It's All about Design. The Plant Cell. 16: 2249-2251.

N.R. Hackett, A. Heguy, B.G. Harvey, T.P. O'Connor, K. Luettich, D.B. Flieder, R. Kaplan and R.G. Crystal (2003) Variability of antioxidant-related gene expression in the airway epithelium of cigarette smokers. Am J Respir Cell Mol Biol29:331-43.

P. Angenendt, J. Glokler, D. Murphy, H. Lehrach and DJ Cahill (2002) Toward optimized antibody microarrays: a comparison of current microarray support materials. Anal Biochem 309:253-60.

P. Baldi and A. Long (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17: 509-519.

P. Bertone and M. Snyder (2005) Advances in functional protein microarray technology. Febs J 272:5400-5411.

P.F. Macgregor and J.A. Squire (2002) Application of microarrays to the analysis of gene expression in cancer. Clinical chemistry 48: 1170-1177.

P.T. Charles, E.R. Goldman, J.G. Rangasammy, C.L. Schauer, M.S. Chen and C.R. Taitt (2004) Fabrication and characterization of 3D hydrogel microarrays to measure antigenicity and antibody functionality for biosensor applications. Biosens Bioelectron 20:753-764.

P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M.B. Eisen, P.O. Brown, D. Botstein and B. Futcher (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Bioi Cell 9: 3273-3297.

R. Ihaka and R. Gentleman (1996) R: a language for data analysis and graphics. Journal of Computational & Graphical Statistics 5: 299-314.

R. Speer, J.D. Wulfkuhle, L.A. Liotta, and E.F. Petricoin (2005) Reverse-phase protein rnicroarrays for tissue-based analysis. Curr Opin Mol Ther 7:240-245.

R. Temple, E. Allen, J. Fordham, S. Phipps, H.C. Schneider, K. Lindauer, L Hayes, J. Lockey, K. Pollock and R. Jupp (2001) Microarray analysis of eosinophils reveals a number of candidate survival and apoptosis genes. Am J Respir Cell Mol Bioi 25:425-33.

R. B. Jones, A. Gordus, J.A. Krall and G. MacBeath (2006) A quantitative protein interaction netvvork for the ErbB receptors using protein rnicroarrays. Nature 439:168-74.

R.B. Jones, A. Gordus, J.A. Krall and G. MacBeath (2006) A quantitative protein interaction network for the ErbB receptors using protein rnicroarrays. Nature 439:168-174.

R.C. Chambers, P. Leoni,~. Kaminski, G.J. Laurent: and R.A Heller (2003) Global expression profiling of fibroblast responses to transforming growth factor-beta1 reveals the induction of inhibitor of differentiation-1 and provides evidence of smooth muscle cell phenotypic switching. Am J Pathol162:533-46.

R.G. Crystal, P. B. Bitterman, B. Mossman, M.I. Schwarz, D. Sheppard, L. Almasy, H.A Chapman, S.L. Friedman, T.E. Jr. King, L.A. Leinwand, L. Liotta, G.R. Martin, D.A. Schwartz, G.S. Schultz, C.R. Wagner and R.A. Musson (2002) Future research



directions in idiopathic pulmonary fibrosis: summary of a National Heart, Lung, and Blood Institute working group. Am J Respir Crit Care Med 15(166):236-46.

R.J. Cho, M.J. Campbell, E.A. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T.G. Wolfsberg, A.E. Gabrielian, D. Landsman, D.J. Lockhart and R.W. Davis (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 2:65-73.

R.J. Lipshutz, S.P. Fodor, T.R. Gingeras and D.J. Lockhart (1999) High density synthetic oligonucleotide arrays. Nat Genet 21:20-24.

R.K. Saiki, P.S. Walsh, C.H. Levenson and H.A. Erlich (1989) Genetic analysis of amplified DNA with immobilized sequence-specific oligonucleotide probes. Proc Natl Acad Sci USA 86: 6230-6234.

R.W. Doerge (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nat Rev Genet 3:43-52.

S. Dudoit, Y.H. Yang and B. Bolstad (2002a) Using R for the analysis of DNA microarray data. R News 2:24-32.

S. Dudoit, Y.H. Yang, M.J. Callow and T.P. Speed (2002b) Statistical methods for identifying differentially expressed genes in replicated eDNA microarray experiments. Statistica Sinica 12: 111-139.

S. Fuke, T. Betsuyaku, Y. Nasuhara, T. Morikawa, H. Katoh and M. Nishimura (2004) Chemokines in bronchiolar epithelium in the development of chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol31:405-12.

S. Fukui, T. Feizi, C. Galustian, A.M. Lawson and W. Chai (2002) Oligosaccharide microarrays for high-throughput detection and specificity assignments of carbohydrate-protein interactions. Nat. Biotechnol. 20: 1011-1017

S. Katsuma, K. Nishi, K. Tanigawara, H. Ikawa, S. Shiojima, K. Takagaki, Y. Kaminishi, Y. Suzuki, A. Hirasawa, T. Ohgi, J. Yano, Y. Murakami and G. Tsujimoto (2001) Molecular monitoring of bleomycin-induced pulmonary fibrosis by eDNA microarraybased gene expression profiling. Biochem Biophys Res Commun 288:747-51.

S. Perkowski, J. Sun, S. Singhal, J. Santiago, G.D. Leikauf and S.M. Albelda (2003) Gene expression profiling of the early pulmonary response to hyperoxia in mice. Am J Respir Cell Mol Bioi 28:682-96.

S.A. McDowell, K. Gammon, C.J. Bachurski, J.S. Wiest, J.E. Leikauf, D.R. Prows and G.D. Leikauf (2000) Differential gene expression in the initiation and progression of nickelinduced acute lung injury. Am J Respir Cell Mol Biol23:466-74.

S.K. Banerjee, H.W. Young, J.B. Volmer and M.R. Blackburn (2002) Gene expression profiling in inflammatory airway disease associated with elevated adenosine. Am J Physiol Lung Cell Mol Physiol 282: 169-182.

S.K. Yadav, S.L.S. Pareek and A. Pareek (2006) Transcriptome analysis. Techniques in soil microbiology. Eds. Varma, A. and Oelmuller. Springer Verlag. Pp.

S.L. Harmer and S.A. Kay (2000) Microarrays: Determining the balance of cellular transcription. The Plant Cell12: 613-615.

S.P. Fodor, J.L. Read, M.C. Pirrung, L. Stryer, A. T. Lu and D. Salas (1991) Light-directed, spatially addressable parallel chemical synthesis. Science 251: 767-773.

23 In Techniques of Biotechnology Ed. Ani] Gupta


S.W. Cole, Z. Galic and J.A. Zack (2003) Controlling false negative errors in microarray differential expression analysis: a PRIM approach. Bioinformatics 19: 1808-1816.

T. Liu, S.M. Dhanasekaran, H. Jin, B. Hu, S.A. Tomlins, A.M. Chinnaiyan and S.H. Phan (2004) FIZZ1 stimulation of myofibroblast differentiation. Am J Pathol164:1315-26.

T. Nakajima, K. Matsumoto, H. Suto, K. Tanaka, M. Ebisawa, H. Tomita, K. Yuki, T. Katsunuma, A. Akasawa, R. Hashida, Y. Sugita, H. Ogawa, C. Ra and H. Saito (2001) Gene expression screening of human mast cells and eosinophils using high-density oligonucleotide probe arrays: abundant expression of major basic protein in mast cells. Blood 98:1127-34.

T. Park, S.G. Yi, S.H. Kang, S.Y. Lee, Y.S. Lee and R. Simon (2003) Evaluation of normalization methods for microarray data. BMC Bioinformatics 4: 33.

T.A. Clark, C.W. Sugnet and M. Jr. Ares (2002) Genomewide analysis of mRNA processing in yeast using splicing specific microarrays. Science 296: 907-10

T.L. Ferea, D. Botstein_ P.O. Brown and R.F. Rosenzweig (1999) Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc Natl Acad Sci USA 96:9721-26.

T.M. Straub and D.P. Chandler (2003) Towards a unified system for detecting waterborne pathogens. J. Microbial. Methods 53, 185-197

T.R. Hughes, M. Mao, A.R. Jones, J. Burchard, M.J. Marton, K.W. Shannon, S.M. Lefkowitz, M. Ziman, J.M. Schelter, M.R. Meyer, S. Kobayashi, C. Davis, H. Dai, Y.D. He, S.B. Stephaniants, G. C..avet, W.L. Walker, A. VVest, E. Coffey, D.D. Shoemaker, R. Stoughton, A.P. Blanchard, S.H. Friend and P.S. Linsley (2001) Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 19:342-47.

U. Maskos and E. M. Southern (1992) Parallel analysis of oligodeoxyribonucleotide (oligonucleotide) interactions. I. Analysis of factors influencing oligonucleotide duplex formation. Nucleic Acids Res 20: 1675-1678.

V.G. Cheung, M. Morley, F. Aguilar, A. Massimi, R. Kucherlapati and G. Childs (1999) Making and Reading Microarrays. Nat Genet 21(1 Suppl): 15-9.

V.G. Tusher, R. Tibshirani and G. Chu (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci C 5 A. 98:5116-5121.

W. Hueber, B.A. Kidd, B.H. Tornooka, B.J. Lee, B. Bruce, J.F. Fries, G. Sonderstrup, P. Monach, J.W. Drijfhout, W.J. van Venrooij, P.J. Utz, M.C. Genovese and W.H. Robinson (2005) Antigen microarray profiling of autoantibodies in rheumatoid arthritis. Arthritis Rheum 52:2645-55.

W. Kusnezow, A. Jacob, A. Walijew, F. Diehl and J.D. Hoheisel (2003) Antibody microarrays: an evaluation of production parameters. Proteomics 3:254-64.

Y. Yamanaka, M. Tamari, T. Nakahata andY. Nakamura (2001) Gene expression profiles of human small airway epithelial cells treated with low doses of 14- and 16-membered macrolides. Biochem Biophys Res Comrnun 287:198-203.

Y.H. Yang, S. Dudoit, P. Luu, D.M. Lin, V. Peng, J. Ngai and T.P. Speed (2002) Normalization for eDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30 e15.



Z. Guo, R.A. Guilfoyle, A.J. Thiel, R. Wang and L.M. Smith (1994) Direct fluorescence analysis of genetic polymorphisms by hybridization with oligonucleotide arrays on glass supports. Nucleic Acids Res 22:5456-5465.

History: 1953: Watson and Crick described DNA double helix and their denaturation 1961: Marmur and Doty described DNA renaturation or molecular hybridization 1965: Gillespie and Spiegelman measured the interaction between an RNA molecule and the

DNA from which it was transcribed 1966: Horana and coworkers demonstrated synthesis of complex nucleic acids. 1969: Jones and Robertson discovered in situ hybridization 1971: Kleppe provided the primers needed for the polymerase chain reaction (PCR)

Engvall and Perlman developed ELISA 1975: Grunstein and Hogness described colony hybridization 1977: Benton and Davis described plaque hybridization 1979: Kafatos introduced dot blot

Wallace introduced synthetic oligonucleotides as hybridization probes 1987: Wan, Fortuna and Furmanski described tissue microarray technique 1990: Ink-jet spotting methods for in situ synthesis of 60-mer oligo spots on glass slides 1991: Photolithographic printing (Affymetrix) developed by Fodor and colleagues 1992: Ward and coworkers introduced multicolor fluorescent labeling techniques in FISH 1994: First eDNA collections were developed at Stranford University

This was first step toward microarrays Hoheisel and coworkers used multiple libraries for genome analysis Hoheisel and coworkers replaced manual procedures by robotics.

1995: Quantitative eDNA microarray. 1996: Commercialization of oligonucleotide arrays (GeneChipTm by Affymetrix) 1997: Genome- wide expression analysis inS. cerevisiae (yeast) 1998: Kononen and coworkers described tissue microarray technology 1999: Bulyk and coworkers described DNA-protein interactions in a microarray format

Mendoza and coworkers developed high-throughput microarray-based ELISA. 2000: Cancer signatures detected

Ciphergen Biosystems Inc. established protein chip array technology de Wildt and coworkers first used antibody arrays to study antibody-antigen interactions Afanassiev and coworkers describe protein microarrays on glass slides coated with an agarose film

2001: Zhu and coworkers first used protein microarray to study protein-protein interactions in yeast

2003: Clinical application of microarray Rubina and coworkers describe protein microarrays on glass slides coated with polyacrylamide Angenendt and coworkers developed Multiple Spotting Technology (MIST)

2004: Whole human genome analysis by single microarray .

Iri Techniques of Biotechnology Ed. Anil Gupta 25


Kersten and coworkers describe protein microarrays on glass slides coated with nitro cellulose


Chapter 9 Abiotic stress responses: complexities in gene expression

Abiotic stress responses: complexities in gene expression Vaishali Punjabi-Sabharwal, Ratna Karan, Tanveer Khan and Ashwani Pareek*

Stress Physiology and Molecular Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi 100067, India. Fax: 91-11-26704504

* Author for correspondence Email: [email protected]

Running title: stress related gene expression in plants

In "Abiotic Sress Adaptation in Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, S.K.Sopory, H.J.Bohnert and Govindjee, Springer Dordrecht, Netherlands


Summary .............................................................................................. 3 I. Introduction ............................................................................. . II. Signal Trans,duction Pathways under Abiotic Stresses ............................. . III. Resources for Identification of Novel Genes ........................................ . IV. Functional Genomics Approaches for Understanding Abiotic Stresses ............ .

A. Identification of QTLs (Quantitative Trait Loci) for Tolerance to Abiotic Stresses ........................ .

B. Analysis of Transcript Profiles: Transcriptomics .................................. . 1. High-Throughput Techniques for Transcriptome Analysis ................. .

a. Differential Display PCR ................................... . b. cDNA-AFLP ...................................................... . c. Subtractive Hybridization ............................................... . d. Microarray .................................................. . e. SAGE ....................................................................... .

2. Transcriptional Profiling Reveals that Metabolic Adjustment is a Hallmark of Abiotic Stress Response ......................................... .

a. Kinetics of Gene Expression Pattern: Early versus Late Responses ............ .

b. Kinetics of Gene Expression Pattern: Developmental Stage-JOrgan Specific Regulation ..................................................... .

c. Cross-Talk between various Abiotic Stress Responses ................. . C. Large Scale Study of Proteins: Proteomics ................................... . D. Metabolic Engineering .................................................... ..

V. Interact orne ....................................................................... . A. Interacting Partners of Two-Component System ................................... . B. High-Throughput Yeast Two-Hybrid Analysis .................................. .. C. Predction of Protein-Protein Interactions using Bioinformatics and

Development of Protein Interactome Databases ................................... . VI. Future Projection ....................................................................... .

Acknowledgement ............................................................................ . References ........................................................................................... .


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Summary: Abiotic stresses are being considered as one of the major threat to agriculture. Studies carried out by plant breeders as well as molecular biologists have clearly documented the response of plants towards these stresses to be multigenic in nature. Plants have been documented to have evolved delicate mechanisms to cope with these abuses. The availability of whole genome sequences and tools to analyze regulation of its "member components" at transcript and protein levels have really revolutionized the way stress biology is being studied in the present time. These investigations have given insight into how, extracellular signals are perceived and transmitted through signal transduction cascades in a given plant. It has been established that upon receipt and transmission of the stress signal(s), expression of a number of genes are altered, leading to stress adaptation in plant cells. Recent studies carried out at genome level using microarrays have shown the significance and contributions of these gene regulatory networks in making a given plant "stress tolerant" or "stress sensitive". Presently, our understanding for proteinprotein interactions, post translational modifications or metabolite fluxes is less developed as compared to that of transcriptional changes. With the recent technological leaps, it is hoped that these gaps in our knowledge will be filled soon and thus will enable us to "tame" these abiotic stress in a better way.


3

Chapter9 Abiotic stress responses: complexities in gene expression

Abrre,iations ROS MAPK LEA sos CDPK ABA ABRE ESTs EMS QTL SNP

ERFs EREBPs RLK AREB ORE CRT CBF DREB PCR DDPCR SSH SAGE cDNAAFLP ORF GST APX MALDI-TOF MeCAT ICP-MS ESJ-MS Q-TOF ECM proteins GC HPLC CE NMR DBD Cvt AtPID

Reactive oxygen species, Mitogen Activated Protein kinase, Late Embryogenesis Abundant proteins, Salt Overly Sensistive, Calcium Dependent Protein kinase, Abscisic acid, Abscisisc acid Responsive Element, Expressed Sequence Tags, Ethyl Methyl Sulphonate, Quantitative Trait Loci, Single Nucleotide Polymorphism, Ethylene Response Factors Ethylene Responsive Element Binding Proteins Receptor Like Kinase ABRE Binding Protein (this needs expansion) Dehydration Responsive Element C-repeat CRT Binding Factor ORE Binding Factor (this needs expansionj Polymerase Chain Reaction Differential Display Polymerase Chain Reaction Suppression Subtractive Hybridization Serial Analysis of Gene Expression eDNA Amplified Fragment Length Polymorphism Open Reading Frame Glutathione-S-transferase Ascorbate peroxidase Matrix Assisted Laser Desorption-Ionization time of Right Metal Coded Tagging Inductively Coupled Plasma Mass spectrometry Electrospray (electrospin check??) Ionization mass spectrometry Quadrupole Time of Right Extracellular Matrix Proteins Gas Chromatography High Performance Liquid Chromatography Capillary Electrophoresis ~uclear Magnetic Resonance D~A Binding Domain Cytoplasm to vacuole targeting Arabidopsis thaliana Protein Interactome Database


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I. Introduction Plants undergo exposure to various biotic and abiotic stresses in their natural environment. World-wide, abiotic stress conditions cause a major loss to the agricultural productivity. The wide array of abiotic factors affecting crop productivity includes salinity, drought, submergence, high and low temperature and heavy metals. Almost a third of the total area farmed, is affected by salt, the associated water logging or alkalinity. The gains in agricultural output provided by Green revolution have reached their maximum whereas; the world population continues to rise. In this regard, it has been estimated that in order to meet the needs of the growing population, plant productivity needs to be increased by at least 20% in the developed countries and 60% in the developing countries. This increasing demand, coupled with shrinking resources has fuelled scientific research in elucidating the mechanisms by which plants respond to stress. It is being hoped that successful application of biotechnology and classical plant breeding may lead to the development of stress-tolerant crop plants with enhanced food production.

The present chapter is an attempt to present our current understanding related to signaling under stress in plants. We present various technologies which have been utilized in recent past to explore the gene expression changes in plants under stress conditions. These interactions appear to be quite complex as revealed by studies targeted towards understanding alterations at transcriptome, proteome and protein-protein interaction levels. For brevity sake, we have only provided the indications about these interactions among signaling members, the details about which can be found in other chapters of this book.

II. Signal Transduction Pathways under Abiotic Stresses Signaling pathways are induced in response to environmental stresses and recent molecular and genetic studies have revealed that these pathways involve a host of diverse responses (Chinuusamy et al 2004, Sreenivasulu et al 2007). A stress response is initiated when a plant recognizes stress at the cellular level. Signal recognition activates signal transduction pathways which result in altered gene expression and metabolism readjustment at the cellular level, some of which get translated to altered physiological state that results in better acclimatization of the plant to abiotic stress conditions. Sensors are molecules that perceive the initial stress signal. Sensors initiate (or suppress) a cascade to transmit the signal intracellularly and in many cases, activate nuclear transcription factors to induce the expression of specific sets of genes. Drought, salt and cold stresses have been shown to induce transient Ca2

+ influx into the cell cytoplasm which arises due to influx from the apoplastic space or release from internal stores (Zhu et al., 2002). The major signaling pathways operative under abiotic stress in plants which lead to changes in gene expression and ultimately determines the stress response include (details of these pathways are presented as Box I):

• Gene expression changes that are brought in via the MAPK pathway (to regulate oxidation in the cell).

• Gene expression changes for LEA-Type Genes (to check cellular desiccation in the cells).

• Changes in SOS pathway signaling (to regulates ion homeostasis in the cells). • Gene alterations as mediated via hormone-signaling pathways


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III. Resources for Identification of Novel Genes The comparative genomics approach has proven to be a highly valuable tool for

unveiling the key genetic contributors to the complex physiological processes involved in abiotic stress tolerance (Eddy and Storey 2008). Comparative stress genomics essentially mean~ scoring of various commonalities and differences in expression patterns of different genes among populations that differ in stress tolerance. The identification of various abiotic stress specific changes in gene expression can be achieved by comparing gene expression in non-induced and stress-induced tissues or by comparing genetica11y different genotypes such as contrasting cultivars (Grover et al., 1993; Kumari et aL, 2008). Prokaryotic extremophiles have also been used as a source of useful genes as they can survive under the conditions where plants cannot (Figure 1 ). Similarly, creation of variability is another important target of plant scientists which has also served as a useful tool for gene expression. An example for this approach is the identification of SOS pathwa) and its specific mutants in Arabidopsis (Zhu et al., 2004). Among other approaches, availability of contrasting genotypes in crop species has been well exploited and ~everal reports have come in recent past where molecular mechanisms of stress response have been looked into, using these genotypes. In one such study, it was noted that a fewer number of genes were induced under salt stress in Thellungiella halophila (salt cress -a salt tolerant relative of Arabidopsis) in contrast to Arabidopsis (lnan et al., 2004 ). This analysis revealed an important aspect of stress tolerance mechanism. where the stress tolerance of salt cress may be due to constitutive overexpression of many genes that function in salt tolerance and are only stress inducible in Arabidopsis (Taji et aL, 2004). Similarly, global gene expression analyses in rice revealed a strikingly large spectrum of gene expression changes induced by salinity stress in salt sensitive genotypes as compared to the tolerant lines (Walia et al., 2005, Walia et al., 2007). A total of 164 ESTs were upregulated in the tolerant line (FL478) under salinity stress. A nearly equal number of ESTs were found to be significantly down-regulated under salinity stress in FL4 78. In contrast. a total of 456 probe sets were induced by salinity stress in salt sensitive IR29 and the expression of I 84 ESTs was suppressed. However, only eight ESTs were common between the two during salinity stress (Walia et al., 2005). Similar observation~ were also made in tomato where a total I 1 genes were upregulated and 14 downregulated in a tolerant genotype (LA27I 1) specific manner, while 43 and 76 were upregulated and downregulated only in the sensitive genotype (ZS-5). A total of 26 and 31 genes were commonly upregulated and downregulated between the two genotypes (Ouyang et al., 2006). Our own work related to analysis of salinity stress responsive gene alterations in rice and brassica is also in corroboration with the above observations (Kumari et al., 2008, Kumar et al., 2008). Thus, increasing evidence are accumulating in a range of plant species which indicate towards the constitutive gene expression patterns to be a major mechanism responsible for observed difference in basal stress tolerance in plants (Figure 2).

The stress-tolerant genotypes of important plant species may serve as an important gene pool for identification of useful genes (Kumar et 2008). Also, the identification of these genes which may be involved in the stress responses contributing towards stress tolerance may prove useful for understanding the underlying molecular mechanisms. With this view, major EST sequencing efforts have been initiated for the halophyte Mesembryanthemum crystallinum


6


(www.compbio.dfci.harvard.edu/tgi/gi/mcgi). The EST sequencing has also been initiated in bryophyte Tortula ruralis and Physcomitrella patens for identifying desiccation tolerance related genes (Oliver et al., 2000).

Besides EST sequencing . projects, other approaches are also being used for identification of useful stress responsive genes. For example, insertional mutagenesis in plants has the advantage of its genome-wide distribution with preferential insertion in ~ genic regions. Saturation mutagenesis has been achieved for both rice and Arabidopsis with T-DNA gene insertions in covering more than 90% of the genes. They have been widely employed to characterize abiotic stress responsive genes. The retrotransposon Tosl7 has also been used for large scale mutagenesis in rice (Hirochika, 2001 www .genoscope.cns.fr/spip/Oryza-sativa-retrotransposon-Tos 17). These lines have been used to study the role of stress-responsive rice genes such as metallothionin (Wong et al., 2004), histidine kinases (Pareek et al 2006) and OsTPCI-a putative voltage gated Ca2

+

permeable channel (Kenji et al., 2004) etc. In rice, random mutants have been generated in cv IR64 using chemical mutants such as EMS and irridation by fast neutrons or gamma-rays. These mutants have been used to generate pools for TILLING - a highthroughput technology useful for identifying mutations in selected genes or a variant allele (Till et al., 2007). IV. Functional Genomics Approaches for Understanding the response of plants towards Abiotic Stress

It has now been clearly established that abiotic stress response is a complex trait governed by multiple genes. During last 15 years, basic biological research has undergone a major revolution with main endeavors of scientific research being switched from studying the expression of single genes or proteins to large number of genes or gene products simultaneously enabling genome-wide expression strategies for better understanding of this complex traits. During the first part of genomics era, researchers concentrated on accumulating DNA sequence information from a range of model plants as well as those having economical importance. The first complete genome sequence of a plant, Arabidopsis thaliana, was reported in 2000 (www.araidopsis.org). The dicot flowering plant is an important model system for identifying genes and determining their functions. Similarly, the complete genome sequence for rice, one of the world's most important food plants was reported in 2005 (International Rice genome sequencing project, 2005). Rice, the model plant for the Poaceae family has important syntenic l relationships with the other cereal species (http://www.gramene.org). A total of 37,544 nontransposable-element-related protein-coding genes have been identified, of which 71% have a putative homologue in Arabidopsis. Interestingly, twenty-nine per cent of the predicted genes in rice appear in clustered gene families. This situation indicates towards a possible 'regulon' operating in rice similar to what has been recently in Arabidopsis (Ma and Bohnert 2008). Similarly, the map-based sequence of rice has proven useful for the identification of genes underlying agronomic traits. The additional single-nucleotide polymorphisms and simple sequence repeats identified should accelerate our understanding of stress response in rice.

Since, genomics information has become available for a broad range of organisms, an era has arisen in where the data generated can be used as a resource to answers many biological problems which may thus provide rapid systematic ways to identify genes for crop improvement. Deriving meaningful knowledge from DNA

In "Abiotic Sress Adaptation in Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, SKSopory, H.J.Bohnert and Govindjee, Springer Dordrecht, Netherlands

7


sequences will define biologica1 research through the coming decades and require the expertise of biologi~ts. chemists, engineers, and computational scientists, among others. With the whole genome sequences in hand, some research challenges that need to be taken up include - studies on determination of gene number, exact locations, and functions; gene regulation: coordination of gene expression, protein synthesis, and posttranslational events: interaction of proteins; correlation of SNPs (single--base DNA variations among individuals) with agronomically important traits and their prediction based on gene sequence variation.

Thanks to the technological leaps which have been taken in recent years, high throughput technologies have been proven extremely useful in addressing the complexities in gene regulations in plants. These advancements includes technologies available for the studies targeting the QTL analysis, transcript kinetics. proteomics as well as post-translations modifications. In the next section, we present details for some of these advancements which have enabled us to comment on abiotic stresses in a systems biology approach.

A. Identification of QTLs for Tolerance to Abiotic Stresses

The availability of both Arabidopsis and rice genome sequences has lead to the identification of thousands of molecular markers making map-based cloning a viable option for global functional genomics (lander et al., 2002). The QTL mapping approach has been employed to dissect QTLs controlling the highly complex abiotic-stress tolerance trait. Severa] QTLs involved in the stress response have been recently reported (Lin et al., 2004. Ren et al., 2005, Xu et al., 2005, Salvi and Tuberosa, 2006). Most of these QTLs have been identified using the positional cloning approach. This strategy allows the use of a pr.enotype to determine the position of an allele by examining linkage of markers whose position in the genome is already known. Employing an F2 population derived from a cross between a salt-tolerant indica variety (Nona Bokra) and a susceptible japonica variety (Koshihikari) 10 QTLs responsible for variation in K+ and Na+ content were identified in rice (Lin et al., 2004). One of these QTLs. SALTOL. located on the short arm of chromosome I was found to explain 40t;C of the variation in salinity tolerance. High resolution mapping of backcross population, lead to the identification of SKC 1 gene and found to code for an HKT -type transporter (Ren et aL 2005).

B. Analysis of Transcript Profiles: Transcrip~omics

Submergence of rice (Ory::.a sativa) by flash flooding is a major constraint to rice production in coastal areas. Rice cultivars vary in their capacity to tolerate complete submergence: quantitative trait loci analysis has revealed that a large portion of this variation in submergence tolerance can be explained by one locus (Sub/) on chromosome 9. Two recently published reports (Kenong Xu et al., 2006 and Takeshi Fukao et al., 2008) suggest that a transcription factor belonging to the B-2 subgroup of the ethylene response factors {ERFs)/ethylene-responsive element binding proteins (EREBPs)/apetala 2-like proteins (AP2) within the Sub] locus determines submergence tolerance in rice.

In 'Abiotic Sress Adaptaton m Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, S.K.Sopory, H.J.Bohnert and Govindjee, Springer Dordrecht, Netherlands

8

Chapter 9 Abiotic stress responses: com'plexities in gene expression

These genes control highly conserved hormonal, physiological and developmental processes that determine the rate of elongation when submerged.

All living organisms have thousands of unique genes encoded in their genome, of ~~ich only a small fractio~, pe~, are expresse~ in a~~ in~~al c~II. Therefore, It Is the temporal and spatial regulatiOn In-ge-n,e~expresswn~thati'letermme~ hfe processes. Thus, expression profiling has become an important tool to investigate how an organism responds to environmental changes. Plants, being sessile, have the ability to alter their gene expression patterns in response to environmental changes such as temperature, water availability or the presence of deleterious levels of ions (Hazen et al., 2003). Sometimes, these transcriptional changes are successful adaptations leading to tolerance, while at other times, the plant ultimately fails to adapt to the new environment and is considered to be sensitive to that condition. Expression profiling can define both tolerant and sensitive responses (Figure 3). These profiles of plant response to environmental extremes are expected to lead to regulators that will be useful in biotechnological approaches to improve stress tolerance as well as to new tools for studying regulatory genetic circuitry.

Certain high-throughput techniques have been employed to identify the gene whose expression at transcript level is differentially regulated in response to various environmental stresses in higher plants. Such methods (for details of these methods, see Box 2) include differential display polymerase chain reaction (DDPCR), suppression subtractive hybridization (SSH), serial analysis of gene expression (SAGE), DNA-chip and microarray, and eDNA-amplified fragment length polymorphism (cDNA-AFLP). However, genes identified, isolated and cloned by such approaches would need to be functionally-characterized.

1. High-Throughput Techniques for Transcriptome Analysis a. Differential display PCR: It was reported that 17 eDNA clones were isolated

from sunflower by means of DD-PCR. Genes corresponding to 13 of these cDNAs were confirmed by quantitative RT-PCR to be expressed differentially in response to osmotic stress. Their expression patterns were further analyzed in leaves of drought-stressed plants, and in roots and shoots of drought- or salinity- stressed seedlings (Liu and Baird, 2003).

b. cDNA-AFLP: The cDNA-AFLP technique was used to analyze differentially expressed genes in

wheat RH8706-49, a salt-stress resistant line (SR) and H8706-34, a salt-stress sensitive line (SS) with or without NaCl stress. A large number of gene fragments related to salt stress were found. Among them, a eDNA encoding glycogen synthase kinase-shaggy kinase (TaGSK I) showed to be induced by NaCl stress, and expressed more strongly in SR than in SS suggesting that TaGSKl is involved in its response to salt stress as a part of the signal transduction component (Ch et al., 2003). This technique has also been used to isolate differentially expressed ESTs during cold acclimatization in Physcomitrella patens (Sun et al., 2007), drought responses in Festuca mairei (Wang and Bugrara 2007), water-stress tolerance in wild barley (Suprunova et al., 2007), salinity stress in soybean (Aoki et al., 2005) and potato (Hmida-Sayari et al., 2005).


9


c. Subtracth:e Hybridization A PCR-based subtraction method has been employed for isolation and expression

analysis of salt stress-associated ESTs from contrasting rice cultivars (Sahi et al., 2003, Kumari et al., 2008). In order to understand the gene expression patterns and isolate drought induced cDNAs from maize,. a subtractive eDNA library was constructed from PEG-treated maize seedlings (Jia et al., 2006). Suppression subtractive hybridization was carried out to identify early salt stress response genes in tomato root (Ouyang et aL 2007). This approach has also been employed for studying long-term transcript accumulation during development for dehydration adaptation in Cicer arietinum (Boominathan et al .. 2004).

d. Microarray Gene expression profiling using eDNA microarrays or gene chips is a useful

approach for analyzing the expression patterns of genes under conditions of drought, cold and high salinity. The use of microarray to study global gene expression profiling in response to abiotic stress in rice was first reported by Kawa'iaki et al., 200 l. Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle has also been performed using microarray (Zhou et al., 2007). Genome-wide transcriptional analysis of salinity stressed rice genotypes during vegetative and panicle initiation stage has also been carried out using microarrays (Walia et aL 2005, Walia et al., 2007). Despite the advantage of this precise technique, several problems have arisen and are expected to be resolved in the near future, including the high cost, identifying appropriate software for analysis of results, and standardizing methods to allow comparison between results from different labs (Richmond and Somerville, 2000: Van Hal et al., 2000).

e. Serial Analysis of Gene Expression (SAGE) Among the various techniques used to assess transcript abundance, the most

powerful is probably SAGE. Serial analysis of gene expression was used to profile transcript levels in Arabidopsis roots and assess their responses to 2.4,6-trinitrotoluene (Thl"f) exposure. SAGE libraries representing control and T~l-exposed seedling root transcripts were constructed, and each was sequenced to a depth of roughly 32,000 tags. More than 19,000 unique tags were identified overall. The second most highly induced tag (27-fold increase) represented a glutathione S-transferase. Cytochrome P450 enzymes, as well as an ABC transporter and a probable nitroreductase, were highly induced by T~ exposure. Analyses also revealed an oxidative stress response upon TNT exposure (Ekmar. et al., 2003 ). The SAGE technology has been employed to reveal changes in gene e~pression in Arabidopsis leaves and pollen undergoing cold stress (Jung et al., 2003, Lee et al., 2003).

2. Transcriptional Profiling Revels that Metabolic Re-adjustment is a Hallmark of Abiotic Stress Response The metabolic readjustment in response to abiotic stress is brought about by a

cascade of events involving perception and transduction of stress signal through a chain of signaling molecules that ultimately affect regulatory elements of stress inducible genes to initiate synthesis of different classes of proteins, i.e. effectors which include transcription factors, enzymes. molecular chaperons, ion channels, transporters, scavengers of ROS, or alter their activity. Over the last decade. FunCat has been established as a robust and stable annotation scheme that offers both meaningful and


10


manageable functional classification of the extensive transcriptome data generated using genome wide approaches. The FunCat annotation scheme consists of 28 main categories (Ruepp et al., 2004). These include metabolism, energy, storage protein, cell cycle and DNA processing, transcription, protein synthesis, protein fate, protein with binding function or cofactor requirement, regulation of metabolism and protein function, cellular transport, cellular communication/signal transduction mechanism, cell rescue, defense and virulence, interaction with the environment, systemic interaction with the environment, transposable elements, viral and plasmid proteins, cell fate, development (systemic), biogenesis of cellular components, cell type differentiation, tissue differentiation, organ differentiation, subcellular localization, cell type localization, tissue localization, organ localization, classification not yet clear-cut and unclassified proteins. A comparison of various studies on genome wide transcriptome analyses of responses to various abiotic stress conditions in plants indicate that unknown genes form the largest category (upto 30-40%), followed by genes required for metabolism (15%), cell rescue and defense (1 0% ), protein synthesis (1 0% ), signal transduction and cellular communication (8-1 0% ), while those belonging to other functional categories constituted less than 5% of the transcripts (Table I).

a. Kinetics of Gene Expression Pattern: Early vs Late Responses Plant tolerance towards abiotic stresses is a complex trait controlled by several {

genes and factors, some of which are induced within 15 min of stress imposition. Although these fast responding genes are feJ"" in number, they are critical for transcriptome reprogramming under abiotic stress. An instantaneous response (15 min) was detected for only 2% of the genes in Pokkali. These genes included GRPs and CDPK (Kawasaki et al., 2001). In another study, it was reported that the early response genes mainly belonge(ii to two functional categories i.e. cellular communication /signal transduction and transcription. The transcription factors or TF-related factors induced a J

0 p

calmodulin-binding transcription factor, a PHD-finger, a zinc finger, a NAC transcription ~ factor, and other DNA binding proteins. The cellular communication/signal transduction ~ included kinases such as OsCDPK7 and some new stress induced kinases (Chao et al., 2005).

An adaptive response of plants to salinity stress is induced after 3-24 hrs of salinity stress in rice. This response includes three aspects: ion homeostasis, damage control and growth regulation. At the gene expression level, these factors are controlled by regulation of genes belonging to four functional categories: cellular transport, cellular

'--.:::2 =-=-~and defense, energy and metabolism. Af~ one hour salt stress treatment, upregulationwas aet'ectea fm33%of the genes in rice (Kawasaki et al., 2001 ), which largely included genes involved in protein synth~~is i.e. rih.omos_Gma1...pr.oteins.,_elongation f~tors, and proteinJUo.difjGati0n-sucn-aS=PFGtoose-inhibitor-s._~ch may be involved in restructuring of protein synthesis apparatus. The genes, which were upregulated within D-. 6 hrs of stress consisted of those involved in growth such as sucrose synthase 2 and those involved in hormonal induction such as B-glucosidases. Upregulation of the genes involved in de~e~s~ainst reactive oxygen sp~efks,_(~b._~QSJ and,APX) domir,t'!!L the transcriptsdii'Cfilced ~sition. Study by Chao et al., 2005 showed rapid inductidi'l'tilcell rescue and defense genes in this category with 76.5% of the genes being upregulated. This rapid response is vital to salt tolerance because high salt can lead to stress damage that quickly becomes irreversible. These genes function in every aspect


11


of damage control or repair, playing roles in removal of ROS and other stress induced toxins, repair of protein and DNA, protection against enzyme activity and maintenance of osmotic homeostasis. These included antioxidant enzymes such as glutathione reductase, dehydroascorbate reductase, gluthathione peroxidase, and those functioning in repair including heat shock (DNAJ and Clp protease) and those maintaining osmotic homeostasis (LEA. trehalose synthesis enzymes). Induction in cellular transport genes is related to ion - homeostasis. Many genes involved in protein degradation such as proteases were also induced as an adaptive response to salinity stress.

A rapid down-regulation was obsen,ed in photosynthesis and metabolism related genes reflecting growth inhibition observed in salt stressed plants. Many of these genes encode enzymes that catalyze carbohydrate metabolism whose down-regulation can contribute to accumulation of protective osmol)'tes or inhibition of destructive foldases and proteases. A downregulation of the genes involved in biosynthesis and nitrogen fixation was also detected in tomato within 2 hrs of salinity stress (Ouyang et al., 2007). Only a few genes showed an increased expression after 7-days of stress. These included metallothionins (Kawasaki et al., 2001, Chao et al., 2005).

A comparison of the early and late response transcripts was conducted for drought induced stress maize seedlings (Zheng et al., 2004). The highly upregulated clones at early time points (0.5 to I) included ORE binding factor, protein kinase, protein phosphatase 2C, nucleic acid binding protein, WD domain contaimng protein, while the late induced transcripts (2-6 hrs) consisted of GST, ribomosomal proteins, glutaredoxin etc.

b. Kinetics of Gene Expression Patterns: Developmental Stage/Organ -Specific Regulation

Sensiti\ity of crops to abiotic stress varies with the growth stage. In general, rice plants are highly sensitive to salinity stress at young seedling stages (Rowers and Yeo, 1981 ). From an agronomic point of view, tiller number and number of spikelets per panicle have been reported to be the most salinity sensitive yield component. These components are determined at vegetative and panicle initiation stages respectively. Centroradialis (CEN), a gene which plays a role in phase transition and panicle morphology, was induced in both the sensitive genotypes, M103 and IR29 in response to salinity (Walia et al 2005, 2007). One of the most striking stress responses of IR29 during the vegetative stage experiment was the induction of genes involved in the flavonoid pathway which include those encoding for phenylalanine ammonia lyase, chalcone synthase, dihydroflavonol 4-reductase, and flavonone 3-hydroxylase. Therefore, upregulation of the flavonoid pathway as a response to salinity stress appears to be a general characteristic of IR29. Rice is more susceptible to damage caused by water deficit at particular growth stages. A given level of drought at the vegetative stage can cause a moderate reduction in yield, but the same stress can eliminate yield entirely if it coincides with pollen meiosis or fertilization (OToole, 1982). The degree of overlap in the expression of stress responsive genes in various rice organs viz. flag leaf, panicle, shoot (four-leaf vegetative stage), was studied (Zhou et al., 2007). Limited degree of overlap was detected with only a small number of gene expression patterns being shared between a pair of organs. The greatest overlap was observed between root and flag leaf under both high salinity and drought conditions. Interestingly, transcription factor genes under high salinity and drought conditions were expressed in an organ-specific manner. Among the

In "Abiotic Sress Adaptabon in Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, S.K.Sopory, H.J.Bohnert and Govindjee, Spr,inger Dordrecht, Netherlands

12


186 induced transcription factor genes, only 12 were in all the three organs following drought or salinity stress.

c. Cross Talk between various Abiotic Stress Responses Based on microarray analysis, Rabbani et al., (2003) identified 36, 62, 57 and 43

genes induced by cold, drought, high salinity and ABA response in rice respectively. Fifty-six genes were induced by both drought and high salinity, 25 genes were induced by both drought and cold stress, and 22 genes were induced by cold and high salinity stresses. Similarly, 43 genes were up-regulated by both drought and ABA application, whereas only 17 genes were identified as cold- and AHA-inducible genes. More than 98% of the high salinity- and 100% of AHA-inducible genes were also induced by drought stress, which indicates a strong relationship not only between drought and highsalinity responses but also between drought and ABA responses. These results indicate a greater cross-talk between drought and high salinity stress and between drought and ABA signaling than between cold and high salinity stress or between cold and ABA signaling. Similar observations were made in Arabidopsis where an overlap was also detected between drought and high salinity responsive gene expression (Shiozaki and YamaguchiShinozaki, 1999; Seki et al., 2002). However, contradictory observations were made by Kerps et al., (2002) in cold and high salinity stressed Arabidopsis and by Ozturk et al., (2002) in drought and high salinity stressed barley (Hordeum vulgare). A comparison between the two abiotic stresses, high salinity and drought, was also carried in rice in various organs (Zhou et al., 2007). It was observed that upto half of the genes upregulated or downregulated by drought stress also exhibited a similar expression pattern under high salinity. The remainder of drought responsive genes exhibited different expression patterns under high salinity. Among the genes specifically induced by drought stress included: DREB 1 A, LEA protein, WS 176, MAP65, NAM, HLH, G-box binding, Zinc finger, AP2 transcription factors and some kinases. C. Large Scale Study of Proteins : Proteomics

The transcriptome analyses of gene expression at the mRNA level have greatly contributed to our understanding of abiotic stress responses in model plants i.e. Arabidopsis and rice. However, the level of mRNA does not always correlate well with levels of protein, the key player in cell. The level of transcription of a gene gives only a rough estimate of its level of expression into a protein. An mRNA produced in abundance may be degraded rapidly or translated inefficiently, resulting in still lower abundance of protein.

Recent studies have indicated that out of a given pool of mRNA, only a fraction is recruited into polyribosome assembly for translation (Serres JB, 1999). It is intriguing that what factors are responsible for the differential recruitment of mRNA into translational assembly? Many proteins experience post-translational modifications that profoundly affect their activities; for example some proteins are not active until they become phosphorylated. Methods such as phosphoproteomics and glycoproteomics are used to study post-translational modifications. Further, many transcripts give rise to more than one protein, through alternative splicing or alternative post-translational modifications.

The study of tobacco leaf apoplast proteome in response to salt stress identified 20 proteins whose expression changed in response to stress. These included several wellknown stress-associated proteins, together with chitinases, germin-Jike protein and lipid


13


transfer proteins (Dani et al., 2005). Proteome analysis was performed to study the effect of cold stress on rice anthers at the young microspore stage. 70 of the total 3000 proteins showed differential expression in response to cold stress. Seven of the 18 proteins identified by MALDI-TOF-MS were observed to be partially degraded, reflecting the effect of cold stress at the young microspore stage (lmin et aJ., 2004). Similarly, in an analysis of the rice cold stress proteome, proteins from unstressed seedlings were compared with those from seedlings exposed to temperatures of 15, I 0 and 5 oc. Of a total of I 700 protein spots separated by 2DGE, 60 proteins were up-regulated with a decrease in tempernture. The identities of 4 I of these proteins were established by MALO I-TOF-MS or ESUMS/MS, and these mainly included chaperones, proteases, detoxifying enzymes. and enzymes linked to cell wall biosynthesis, energy pathways and signal transduction. These results emphasize the importance of maintaining protein quality control via chaperones and proteases, together with an increase in cell wall components, during the cold stress response (Cui et al., 2005 ). In addition, a rice proteome database is available which catalogues information from 23 reference maps of 2DGE analysis of proteins from diverse biological samples. The database contains, in total, 13,129 identified proteins and the amino acid sequences of 5092 proteins (Komatsu et al., 2005).

Protein phosphorylation plays a key role in signal transduction in cells. Since phosphoproteins are present in low abundance either radiolabeling or enrichment methods are required for their analysis (For a review on technological advancements in the area of phosphoproteomics, see Nita-Lazar et al., 2008). Proteomics approach has

~ been employed to identify the phosphoproteome in rice and Arabidopsis (Khan et aL

12005, Kwon et al., 2007). Phosphoproteome in rice was detected by in-vitro protein

I phosphorylation achieved by incubating the crude protein extract with [y2P] ATP i 1 followed by 2-DE. Forty-four phosphoproteins were identified by Q-TOF MS/MS and 1 MADLI-TOF MS. Amongst these. the largest percentage was involved in signaling

(30% ). Thirteen of these proteins were regulated differentially by various hormones and stress treatments. In another study the prosphoproteome of Arabidopsis was identified using the enrichment methods. A strategy that replaces the phosphate moieties on serine and threonine residues with a biotin-containing tag via a series of chemical reactions was employed. Ribulose 15-bis-phosphate carboxylase/oxygenase ( R UBISCO )-depleted protein extracts prepared from Arabidopsis seedlings were chemically modified for 'biotin-tagging'. The biotinylated (previously phosphorylated) proteins were then selectively isolated by avidin-biotin affinity chromatography, followed by twodimensional gel electrophoresis (2-DE) and MALDI-TOF MS. This led to the identification of 31 protein spots, representing 18 different proteins, which are implicated in a variety of cellular processes.

In order to gain a better understanding of dehydration response in the food legume, chickpea (Cicer arietinum L.) a comparative nuclear proteome analysis was carried out (Pandey et al., 2007). Approximately, 205 protein spots were found to be differentially regulated under dehydration. Mass spectrometry a.Ttalysis allowed the identification of 147 differentially expressed proteins, presumably involved in a variety of functions including gene transcription and replication, molecular chaperones, cell signaling and chromatin remodeling. In plants, cell wall or extracellular matrix (ECM) A serves as the repository for most of the components of the cell signaling process and acts J ~


14


as a frontline defense (Bhushan et al., 2007). A proteomics approach was therefore employed to identify dehydration-responsive ECM proteins in a food legume, chickpea. The comparative proteomics analysis led to the identification of 134 differentially expressed proteins that include predicted and novel dehydration-responsive proteins.

Since the signaling processes in plants that initiate cellular responses to abiotic stresses are believed to be located in the plasma membrane, a sub-cellular proteomics approach was applied to monitor changes in abundance of plasma membrane-associated protein in response to salinity (Malakshah et al., 2007). D. Metabolic Engineering

The metabolome represents the collection of all metabolites in a biological organism, which are the end products of its gene expression. Thus, while mRNA gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell, metabolic profiling can give an instantaneous snapshot of the physiology of the cell. The word was coined in analogy with transcriptomics and proteomics; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method. Recent technological advances in mass spectrometry have made possible reliable and highly sensitive measurements of metabolites. A wide range of analytical technologies are now available for the analysis of metabolites.

Metabolomics has been utilized not only to investigate plant metabolism but also to identify unknown gene functions by comparing the profiles between wild-type and genetically altered plants or during developmental changes. The popular metabolomics strategy is to focus on the pattern of metabolite concentrations under the given conditions. Such quantitative information on metabolites has been used either to predict gene functions directly involved in metabolic processes, to delineate metabolism and its regulatory networks, or to distinguish metabolic phenotypes. Metabolomics is now finding use in the analysis of plant perturbed by abiotic changes. In one such study, metabolite profiling was employed to characterize the freezing tolerance response of A. thaliana and understand the function of the cold-response regulatory pathway that is regulated by the CBF transcription family. This study showed that the physiological process of cold acclimation significantly influenced the concentration of three-quarters of the >400 metabolite peaks that were detected by GC-TOF MS, and that the levels of most of these peaks were influenced by the CBF-mediated cold response pathway (Cook et al. 2004). V. Interactome

Proteins are actual molecular entities inside the cell required for most of the biological processes. Proteins function by interacting with other biomolecules like proteins, lipids, nucleic acids and low molecular weight compounds. Biochemical activity inside the cell is mostly carried out by formation of transient or stable protein complexes. It has been found that the small number of genes and proteins make the individual less complex to more complex multicellular organism, so there must be interaction among the proteins for making the organism more complex (Bird 1995; Rubin 2001; Szathmary et al., 2001). Therefore a comprehensive knowledge of protein interactions is an important source of information to understand the cellular processes on a genome wide level. The collection of all protein interactions in an organism is typically referred to as


15


Interactome (Magdalena, 2005). One of the major goals in the post genomic era is to analyse the complete protein linkage map i.e interactome of the organism to understand the signaling pathways operating inside the cell to respond and adapt in the fluctuating environmental conditions. Availability of huge protein database and its expression and localization has led to the study of prediction of protein-protein interaction by bioinformaticians. The protein-protein interactions have been studied at three levels, A. simple yeast two hybrid system to identify the interacting partners B. use of high throughput yeast two hybrid analysis by tandem affinity purification and tandem mass spectrometry and C. exploiting the available protein database to predict the protein -protein interactions. A. Interacting Partners of Two Component System

As an example, yeast two hybrid system has been used to find out interacting partners of member of two component system of eukaryotes (Figure 4). This signaling pathway involves the transfer of phosphate group from A TP to the conserved Histidine residue of Histidine kinasae. this phosphate group is further transferred to conserved Aspartate residue of response protein in simple histidine kinase. In sensory hybrid type histidine kinase. the transfer of phosphate group from histidine kinase to response regulator is mediated by histidine- containing phosphotransfer protein. B. High Throughput Yeast Two Hybrid Analysis

Several research projects have been initiated with the goal of comprehensively mapping the networks of protein- protein interactions in yea'>t and nematode worm (Ito et al., 2000, Walhout et al., 2000) and produced a huge lnteractome data by high throughtput experiments. Mapping of protein-protein interactions using global approaches is a major thrust in the post genomic era to understand biological processes of cellular organisms. The yeast two hybrid system is the technique that requires manipulation of D~A to search for interacting partners. Basic informations of large number of genes and gene products are available along with their expression. localization and interaction with other proteins. Therefore, to obtain global maps of expression, localization and interaction, the currently available methods need to be converted into standardized functional assays that should be amenable to automation. It will also help in understanding the function of unknown proteins by knowing the interaction with known proteins by manipulating the DNA to decipher the pathways operating inside the cell. The first genomic analysis using the two-hybrid system was carried out from Escherichia coli bacteriophage nand a protein linkage map was created (Bartel et aL 1996).

Major challenge in the post genomic era is to understand the roles of all the predicted ORFs gene products and how they interact to create a eukaryotic organism. Saccharomyces cereviceae genome sequencing has predicted 6, 144 open reading frames (ORF). In this direction, a comprehensive analysis of protein-protein interactions in Saccharomyces cereviceae was done by array screening (http:/depts .. washington.edu./sfields/) and library screeing by cloning 5,345 ORFs out of 6,144 ORFs of Saccharomyces cereviceae (Uetz et al., 2000). This analysis revealed the categorization of some functionally unclassified proteins into a biological context. For example, YGRO lOW and YLR328W (77% identical) the two proteins of unknown function were observed to interact with each other, both proteins also bind to ornithine amino transferase. which indicates that they may be involved in arginim: metabolism. Data from this study also provided evidence of links between two proteins involved in


16


autophagy and cytoplasm-to-vacuole targeting (Cvt) proteins. Autophagy is a degradation pathway which operates under nutrient stress condition to nonselectively recycle the cytoplasmic proteins and organelles to their constituent components. A large scale identification of interactions between integral membrane proteins of Saccharomyces cereviceae has been done where, among 705 annotated integral membrane proteins, 1 ,985 putative interactions has been identified by 536 proteins (Miller et al., 2005). Yeast two hybrid interaction screening was started with a subset of metazoan specific proteins that are directly or indirectly related to multicellular functions was selected as baits and more than 4000 interactions were identified from high throughput experiment (Li et al., 2004) and the data obtained was further validated by coaffinity purification glutathione Stransferase pull down assays experiment by exploiting the ORFeome 1.1 of C elegans present in Gateway vector. Worm Interactome version 5 (WI5) has been developed by integrating data generated for individual biological processes such as vulval development, protein degradation, DNA damage response and germline formation. The current version of the worm Interactome map (WI5) contains around 5500 interactions. Protein protein interactions are likely to play an important role in response to abiotic stress. It will help in understanding how cells perceive and transducer stress signals to trigger the genetic system responsible for appropriate plant response. There are few studies on protein interaction mapping in plants. The interaction map of the Arabidopsis thaliana MADS_box transcription factors has been developed and revealed regulatory loops providing links between floral organ development and floral induction (DeFolter et al., 2005) to know the signal transduction cascade. An interaction network of proteins associated with abiotic stress response and development in wheat (Triticum aestivum) has been generated (Tardif et al., 2007) using specific protein protein interaction studies. The interaction is comprised of 73 proteins, generating 97 interaction pairs and 21 interactions were confirmed by bimolecular fluorescent complementation in Nicotiana benthamiana. The Interactome also revealed the presence of a "cluster of proteins involved in flowering control" which gives insight into the complex relationships among transcription factors known to play central role in vernalization, flower initiation, abscisic acid signaling as well as associations with regulatory and signaling proteins involved in abiotic stress. C. Prediction of Protein Protein Interactions using Bioinformatics and Development of Protein Interactome Databases

The sequence based annotations have led to the identification of a number of cellular proteins and its localization inside the cellular compartments. As many proteins function by physically interacting with other proteins therefore proteins of unknown function can also be characterized by identifying the interacting partners within a large network of molecular interactions to understand the complex cellular functions of an organism. Interactomics is increasingly becoming a new tool in post genomics ·era to comprehensively deduce the network of protein protein interactions based on the available published protein protein interaction literature. High throughput experiments have produced a large scale networks of protein protein interactions in yeast, fruitfly, nematode worm and human (Uetz et al., 2000; Giot et al., 2003; Li et al., 2004; Miller et al., 2005 and Gandhi et al., 2006). An Interactome of Arabidopsis thaliana has been developed by prediction from interacting orthologs in yeast, nematode worm, fruitfly and human using bioinformatics, where a total of 1,159 high confidence, 5913 medium confidence and 12907 low confidence interactions were identified for 3617 conserved


17

ChapterS Abiotic stress responses: complexities in gene expression

Arabidopsis proteins (Geisler-Lee et al., 2007). Similarly an Arabidopsis thaliana protein interaction databse -AtPID has been developed that integrates data from several bioinformatics prediction methods and manually collected information from literature (Cui et al .. 2007). In this database, information about 28062 protein-protein interactions have been included and information about its subcellular location, ortholog maps domain attributes and gene regulation is also given (http:/atpid.biosino.orgf) which provides a rich source of information for system level understanding of gene function and biological processes in Arabidopsis thaliana. The dynamics of Interactome network will be considered to address where and when interactions take place and how they are regulated. VI. Future Projection The study of abiotic stress response in plants is not new, but a<; the newer tools and techniques are being developed, we are witnessing more and more complexities in these responses. We have moved from an era of single gene analysis to genome level analysis tools. However, as it appears, we need to analyse the complexities brought in at genome level, transcriptome leveL translational and recently, the interactome levels. Further, these analysis need to be viewed in tissue, time, dose and developmental windows. A wiser approach using a combination of above tools and technologies ca.'l pave our way towards understanding the stress response in plants which, at this moment seem to be very complex in nature.

Acknowledgment Authors would like to acknowledge the receipt of financial support received from Jawaharlal ~ehru Cniversity, International Foundation of Science. Sweden, IAEA (Vienna), DST and DBT, Govt. of India. Ratna Karan ackno\\ ledges the award of Senior Research Fellowship from University Grants Commission, New Delhi, India.


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References Abe H, Urao T, Ito T, Seki M, Shinozaki K and Yamaguchi-Shinozaki Y (2003) Arabidopsis AtMYC2 (bHLH) and AtMYB2 (MYB) function as transcriptional activators in abscisic acid signaling. Plant Cell 15: 63--78

Aoki A, Kanegami' A, Mihara M, Kojima T, Shiraiwa M and Takahara H (2005) Molecular cloning and characterization of a novel soybean gene encoding a leucinezipper-like protein induced to salt stress. Gene 356: 135--145

Bae MS, Cho EJ, Choi E-Y and Park OK (2003) Analysis of the Arabidopsis nuclear proteome and its response to cold stress. Plant J 36: 652--663

Bartel PL, Roecklein JA, Sengupta D and Fields SA. (1996) A protein linkage map of Escherichia coli bacteriophage T7. Nature Genet. 12: 72--77

Bhushan D, Pandey A, Choudhary MK, Datta A, Chakraborty S and Chakraborty N (2007) Comparative proteomics analysis of differentially expressed proteins in chickpea extracellular matrix during dehydration stress. Mol Cell Proteomics 6: 1868--1884

Bird AP (1995) Gene number, noise reduction and biological complexity. Trends Genet 11: 94--100

Boominathan P, Shukla R, Kumar A, Manna D, Negi D, Verma PK and Chattopadhyay D (2004) Long term transcript accumulation during the development of dehydration adaptation in Cicer arietinum. Plant Physiol. 135: 1608-1620

Boudsocq M and Lauriere C (2005) Osmotic signaling in plants: Multiple pathways by emerging kinase families. Plant Physiol, 138:1185--1194

Caron H, van Schaik B, van der Mee M, Baas F, Riggins G, van Sluis P, Hermus MC, van Asperen R, Boon K, Voute P A, Heisterkamp S, van Kampen A and Versteeg R (2001) The human transcriptome map: clustering of highly expressed genes in chromosomal domains. Science 291 : 1289--1292

Chao DY, Lou YH, Shi M, Lou D and Lin HX (2005) Salt-responsive genes in rice revealed by eDNA microarray analysis. Cell Res 15: 796-810

Chinnusamy V, Schumaker K and Zhu JK (2004) Molecular genetic perspectives on cross-talk and specificity in abiotic stress signalling in plants. J Exp Bot 395: 225-236

Cooke, R., et al. (1996) Further progress towards a catalogue of all Arabidopsis genes: Analysis of a set of 5000 non-redundant ESTs. Plant J 9: 101--124

Cui J, Li P, Li G, Xu F, Zhao C, Li Y, Yang Z, Wang G, Yu Q, Li Y and Shi T (2008) AtPID: Arabidopsis thaliana protein interactome database--an integrative platform for plant systems biology. Nucleic Acids Res 36: D999--l 008

In "Abiotic Sress Adaptation in Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, S.K.Sopory,.H.J.Bohnert and Govindjee, Springer Dordrecht, Netherlands

19


Cui S, Huang F. Wang J, MaX, Cheng Y and Liu J (2005) A proteomic analysis of cold stress responses in rice seedlings. Proteomics 5: 3162-3172

Dani V, Simon WJ. Duranti M and Croy RRD (2005) Changes in the tobacco leaf apoplast proteome in response to salt stress. Proteomics 5: 737--745

Defolter S and lmmink RG (2005) Comprehensive interaction map of the Arabidopsis MADS Box transcriptions. Plant Cell 17: 1424-1433

Diatchenko L Chris Lau YF, Campbell AP, Chenchik A, Moqadam Fand Huang B. ( 1996) Suppression subtractive hybridization: A method for generating differentially regulated or tissue-specific eDNA probes and libraries. Proc Nat) Acad Sci USA 93: 6025-6030 Eddy SF and Storey KB (2008) Comparative molecular physiological genomics: Heterologous prob:ng of cD~A arrays. Methods Mol Bioi 410: 81-110

Ekman DR, Lorenz \\'W, Przybyla AE, Wolfe NL and Dean JFD (2003) SAGE Analysis of Transcriptome Responses in Arabidopsis Roots Exposed to 2,4,6-Trinitrotoluene1

.

Plant Physiology 133: 1397-1406.

Flowers TJ and Yeo AR (1981) Variability in the resistance of sodium chloride salinity within rice (Ory::.a latil·a L) varieties. New Phytol 88: 363--373

Gandhi TK, Zhong J, Mathivanan S et al. (2006) Analysis of human protein interactome and comparison wiLl-t yeast, wom1 and fly interaction datasets. Nat Genet 38: 285--293

Geisler-Lee J, O'Toole N. Ammar R, Provart NJ, Millar AH, Geisler M. et al. (2007) A predicted interactome for Arabidopsis. Plant Physiol. 145: 317--329

Giot L, Bader JS, Brouwer C. Chaudhuri A, Kuang B, Li Y et al. (2003) A Protein Interaction Map of Drosophila melanogaster. Science 302: 1727--1736

Gui PC, Maa WS, Huanga ZJ, Xu T, Xueb YB and Shen YZ (2003) Isolation and characterization of TaGSK 1 involved in wheat salt tolerance. Plant Sci 165: 1369-1375

Hazen Sp, Wu Y and Kreps JA (2003) Gene expression profiling of plant responses to abiotic stresses. Fuctional and Integrative Genomics. 3: I 05-111.

Hirochika H (2001) Contribution of the Tosl7 retrotransposon to rice functional genomics. Curr Opin:on in Plant Bioi. 4118--122.

Hmida-Sayari A. Costa A, Leone A, Jaoua S and Gargouri-Bouzid R (2005) Identification of salt stress-induced transcripts in potato leaves by cD~A-AA....P. Mol Biotechnol 30: 31--40

In "Abiotic Sress Adapta!ion in Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, S.K.Sopory, H.J.Bohnert and Govindjee, Springer Dordrecht, Netherlands

20


Imin N, Kerim T, Weinman JJ and Rolfe BG (2006) Low temperature treatment at the young microspore stage induces protein changes in rice anthers. Mol Cell Proteomics 5: 274-292

Ito T, Tashiro K, Muta S, Ozawa R, Chiba T, Nishizawa M, Yamamoto K, Kuhara Sand Sakaki Y (2000) Toward a Protein-protein Interaction Map of the Budding Yeast: a Comprehensive System to Examine Two-hybrid Interactions in All Possible Combinations Between the Yeast Proteins. Proc Nat) Acad Sci 97: 1143--1147

Jia J, Fu J, Zheng J, Zhou X, Huai J, Wang J, Wang M, Zhang Y, Chen X, Zhang J, Zhao J, Su Z, Lv Y and Wang G (2006) Annotation and expression profile analysis of 2073 full-length cDNAs from stress-induced maize (Zea mays L.) seedlings. Plant J 48: 710--727

Inan G, Zhang Q, Li P, Wang Z, Cao Z, Zhang H, Zhang C,Quist TM, Goodwin SM, Zhu J K. (2004): Salt cress. A halophyteand cryophyte Arabidopsis relative model system and its applicability to molecular ·genetic analyses of growth and development of extremophiles. Plant Physiol 135:1718--1737.

Karan R, Pareek SS and Pareek A (2008) Members of two component system (TCS) are differentially regulated in contrasting genotypes and co-localized with QTLs for salinity tolerance in rice. Func Int Genomics (communicated)

Kawasaki S, Borchert C, Deyholos M, Wang H, Brazille S, Kawai K, Galbraith D and Bohnert HJ (2001) Gene expression profiles during the initial phase of salt stress in rice. Plant Cell 13: 889-905

Khan M, Takasaki H and Komatsu S (2005) Comprehensive phosphoproteome analysis in rice and identification of phosphoproteins responsive to different hormones/stresses. J Proteome Res 4: 1592-1599

Kijne JW (2006) Abiotic stress and water scarcity: Identifying and resolving conflicts from plant level to global levels. Field crop Res 97(1 )3- 18.

Komatsu S (2005) Rice proteome database: a step toward functional analysis of the rice genome. Plant Mol Bioi 59: 179-190

Kreps JA, Wu Y, Chang HS, Zhu T, Wang X and Harper JF (2002) Transcriptome changes for Arabidopsis in response to salt, and cold stress. Plant Physiol 130: 2129-2141

Kumari, S, Panjabi, VS, Kushwaha, H, Sopory, SK, Pareek, SL and Pareek, A (2008) Transcriptome map for seedling stage specific salinity stress response indicates a specific


21


set of genes as candidate for saline tolerance in Oryza sativa. L. Func. Int. Genomics (In Press)

Lee S, Chen J, Zhou G and Wang SM (2001) Generation of high-quantity and quality tag/ditag cDNAs for SAGE analysis. Biotechniques 31: 348--354

Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PQ, Han JJ, et al. (2004) A map of the Interactome network of the metazoan C. elegans. Science 303: 540--543

Lin HX, Zhu MZ, Yano M, Gao JP, Liang ZW, Su WA, Hu XH, Ren ZH, and Chao DY (2004) QTLs for !"'ra ... and K+ uptake of the shoots and roots controlling rice salt tolerance. Theor Appl Genet 108: 253-260

Liu X and Baird WV (2003) Differential Expression of genes regulated in response to drought or salinity stress in sunflower. Crop Sci 43: 678--687

Ma S and Bohnert H J (2008) Gene networks for the integration and understanding of gene expression characteristics. Weed Science wees-56-02-10.3d

Magdalena S (2005) ~etwork biology: a protein network of one's own protein. Nat Rev Genet 6: 800

Miller JP, Lo RS. Ben-Haur A, Desmarais C, Stagljar I, Noble WS and Fields S (2005) Large scale identification of yeast integral membrane protein interactions. Proc Nat] Acad Sci USA 102:12123--12128

Nita-lazar A, Saito-Benz H and White FM (2008) Quantitative phosphoproteomics by mass spectrometry: Pa<;t. present and future. Proteomics 8: 4433-4443 Nohzadeh Malakshah S, Habibi Rezaei M, Heidari M and Salekdeh GH (2007) Proteomics reveals new salt responsive proteins associated with rice plasma membrane. Biosci Biotechnol Biochem. 71:2144-2154

O'Toole JC and Baldia EP ( 1982) Water deficits and mineral uptake in rice. Crop Sci 22: 1144--1150 Oliver MJ, Velten J ar.d Wood AJ (2000) Bryophytes as experimental models for the study of environmental stress'tolerance: tortula ruralis and desiccation-tolerance in mosses. Plant Ecolog)' 151 :73--84.

Ouyang B, Yang T. Li H, Zhang L, Zhang Y, Zhang J, Fei Z and Ye Z (2007) Identification of early salt stress response genes in tomato root by suppression subtractive hybridization and microarray analysis. J Exp Bot: 58: 507-520

Ozturk ZN, Talame V, Deyholos M, Michalowski CB, Galbraith OW, Gozukirmizi N, Tuberosa R and Bohnert HJ (2002) Monitoring large-scale changes in transcript abundance in drought- and salt-stressed barley. Plant Mol Bioi 48: 551--573


22


Pandey A, Chakraborty S, Datta A and Chakraborty N (2007) Proteomics approach to identify dehydration responsive nuclear proteins from chickpea (Cicer arietinum L.). Mol Cell Proteomics 7: 88-107

Rabbani, MA, Maruyama K, Abe H, Khan MA, Katsura K, Ito Y, Yoshiwara K, Seki M, Shinozaki K, and Yamaguchi-Shinozaki K (2003) Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using eDNA microarray and RNA gel-blot analyses. Plant Physiol 133: 1755--1767

Ren Z-H, Gao JP, Li L-G, 6, Cai X-L, Huang W, Chao D-Y, Zhu M-Z, Wang Z-Y, Luan S and Lin H-X (2005) A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nat Genet 37: 1141--1146

Rual J-F, Venkatesan K, HaoT, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437: 1173--1178

Rubin GM (2001) The draft sequences. Comparing species. Nature 409: 820-821

Ruepp A, Zolner A, Maier D, Alberman K, Hani J, Mokrejs M, Tetko I, Guldener U, MAnnhaupt G, Munsterkotter M and Mewes MW (2004). Nucleic Acid Res.32 5539--5545.

Sahi C, Agarwal M, Reddy M, Sopory S and Grover A (2003) Isolation and expression analysis of salt stress-associated ESTs from contrasting rice cultivars using a PCR-based subtraction method. Theor Appl Genet I 06: 620--628

Salekdeh GH, Siopongco J, Wade LJ, Ghareyazie B and Bennett J (2002) Proteomic analysis of rice leaves during drought stress and recovery. Proteomics 2: 1131--1145

Salvi S and Tuberosa R (2005) QTL cloning in plants: Achievements and perspectives. Trends Plant Sci 10: 297--304

Schena M, Shalon D, Davis RW and Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270: 467-470

Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A, Nakajima M, Enju A, Sakurai T, et al. (2002) Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length eDNA microarray. Plant J 1: 279--292

Seki M, Kamei A, Shinozaki KY and Shinozaki K (2003) Molecular responses to drought, salinity and frost: common and different paths for plant protection. Curr Opinion in Biotech.114 (2):194--199.


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Chapter 9 Abiotic stress responses: complexities In gene expression

Serres JB ( 1 999) Selective translation of cytoplasmic mRNAs in plants. Trends in Plant Sci 4(4): 142--148.

Shinozaki K and Y amaguchi-Shinozaki K (1999) Molecular responses to drought stress. In K Shinozaki. K Y amaguchi-Shinozaki, eds, Molecular Responses to Cold. Drought, Heat and Salt Stress in Higher Plants. R.G. Landes, Austin, TX, pp 1 1-28

Sreenivasulu N, Sopory SK and Kavi Kishor PB (2007) Deciphering the regulatory mechanisms of abiotic stress tolerance in plants by genomic approaches. Gene 388: 1-13

Stenhouse J and Kijne JW (2006) Prospects for productive use of saline water in West Asia and North Africa. Comprehensive assessment research report no. II, Colombo, Sri Lanka: Comprehensive assessment secretariat. 41 pp

Sun MM, Li LH, Xie H. Ma RC and He YK (2007) Differentia1Jy expressed genes under cold acclimation in Physcomitrella patens. J Biochem Mol Bioi 40: 986--1001

Suprunova T. Krugman T. Distelfeld A, Fahima T, Nevo E and Korol A (2007) Identification of a novel gene (Hsdr4) involved in water-stress tolerance in wild barley. Plant Mol Bioi 64: 17-34

Szathmary E, Jordan F and Pal C (2001) Molecular biology and evolution. Can genes explain biological complexity? Science 292:1315-1316

Tardif G, Kane NA, Adam H, Labrie L, Major G, Gulick P, Sarhan F and Jean-Fran<;ois Laliberte (2007) lr.teraction network of proteins associated with abiotic stress response and development in wheat. Plant Mol Bioi 63: 703-718

Uetz P. Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D. Narayan V, Srinivasan M, Pochart P, eta] (2000) A comprehensive analysis of protein interactions in Saccharomyces cern·iceae. Nature 403: 623--627

Uno Y. Furihata T, Abe H, Yoshida R, Shinozaki K and Yamaguchi-Shinozaki K (2000) Arabidopsis basic leucine zipper transcription factors involved in an abscisic aciddependent signal transduction pathway under drought and high-salinity conditions. Proc Nat! Acad Sci t:SA 97: 11632-11637

Velculescu VE. Zhang L. Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270: 484-487

Walhout AJM .. Sorde11a R, Lu X, Hartley JL, Temple GF. Brasch MA, Thierry-Mieg N and Vidal M (2000) Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 287: 116--122

Walia H, Wilson C, Condamine P, Liu X, Ismail AM, Zeng L, Wanamaker SI, Mandai J, Xu J, Cui X and Close TJ (2005) Close comparative transcriptional profiling of two

In ·Abiotic Sress Adaptation in Plants: Physiological, Molecular and Genomic Foundation" Eds: A. Pareek, S.K.Sopory, H.J.Bohnert and Govindjee, Springer Dordrecht, Netherlands

24


contrasting rice genotypes under salinity stress during the vegetative growth stage. Plant Physiol 139: 822-835

Walia H, Wilson C, Zeng L, Ismail AM, Condamine P and Close TJ (2007) Genomewide transcriptional analysis of salinity stressed japonica and indica rice genotypes during panicle initiation stage. Plant Mol Bioi 63: 609-623

Wang JP and Bughrara SS (2007) Monitoring of gene expression profiles and identification of candidate genes involved in drought responses in Festuca mairei: Mol Genet Genomics 277:571-587

Wang W, Vinocur B, Altman A (2003): Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance. Planta 218: 1-14

Wong CE, Li Y, Labbe A, Guevara D, Nuin P, Whitty B, Diaz C, Golding GB, Gray OR, Weretilnyk EA, et al. (2006) Transcriptional profiling implicates novel interactions between abiotic stress and hormonal responses in Thellungiella, a close relative of Arabidopsis. Plant Physiol 140: 1437--1450

Wong HL, Sakamoto T, Kawasaki T, Umemura K and Shimamoto K (2004) Downregulation of metallothionein, a reactive oxygen scavenger, by the small GTPase OsRacl in rice. Plant Physiol 135: 1447--1456

Xiong L and Zhu JK (200 I) Abiotic stress signal transduction in plants: A molecular and genetic perspective. Physiologia Plantarum 112 (2):152--166.

Xu K, Xu X, Fukao T, Canlas P, Maghirang-Rodriguez R, Heuer S, et al. (2006) SubiA is an ethylene-response-factor-like gene that confers submergence to tolerance to rice. Nat Lett 442: 705--708

Yan SP, Zhang QY, Tang ZC, Su WA and Sun WN (2006) Comparative proteomic analysis provides new insights into chilling stress responses in rice. Mol Cell Proteomics 5:484--496

Zheng BS, Yang L, Zhang WP, Mao CZ, Wu YR, Yi KK, Liu FY and Wu P (2003) Mapping QTLs and candidate genes for rice root traits under different water-supply conditions and comparative analysis across three populations. Theor Appl Genet 107: 1505--1515 Zhou J, Wang X, Jiao Y, Qin Y, Liu X, He K, Chen C, MaL, Wang J, Xiong L, Zhang Q, Fan L and Deng XW (2007) Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle. Plant Mol Bioi 63: 591--608

Figure Legends


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Figure 1. Sources of genes for raising stress tolerant plants. Stress responsive genes from mutant population, Prokaryotic extremophiles, contrasting genotypes and tolerant wild relatives can be used to raise stress tolerant plants.

Figure 2. Preparedness of the tolerant cultivar by maintenance of relatively higher level of transcription of stress related genes.

Figure 3. Stress response at transcriptome level. Transcript profile changes during developmental stages of plants and also by the duration of applied stress. Expression of some genes are specific at a particular stage, while some genes expression are common during different stages of growth and duration of stress.

Fig 4. Schematic representation of interacting partners of two component system. Histidine kinase interacts with histidine phosphotransfer protein which further interacts with response regulator. In Saccharomyces cereviceae (Posas et al., 1996 ), Histidine kinase (SLN 1) interacts with histidine phosphotransfer protein (YPD 1) and YPD 1 interacts with response regulator (SSKI). In Arabidopsis thaliana, ATHKI selectively interacts with ATHPI not with ATHP2 and ATHP3, wheareas ATHP2 and ATHP3 interacts redundantly with A TRR4 (Urao et al., 2000). A THP3 also interacts with A TRR 1. In tree plant Populus deltoides, HKl interacts with HPT2 (Chefdor et al., 2006).

BOX 1: The major signaling pathways operative under abiotic stress in plants

MAPK pathway: The MAPK pathway is activated by ROS and also by receptors/sensors such as protein tyrosine kinases, G-protein coupled receptors, or two-component histidine kinases. The oxidative burst i.e. production of reactive oxygen species is one of earliest response of plant to any kind of stress. The reaction of ROS species with plant proteins I lipids can have a detrimental effect on the plant and may eventually lead to its death. Plant cells have therefore developed mechanisms to constantly monitor and manage the levels of reactive oxygen species (ROS) accumulating within their cytosol and organelles. One of the earliest responses to ROS perturbations in plants cells is the activation of specific mitogen-activated protein (MAP) kinases eventually triggering the synthesis of enzymes involved in oxidative protection. The osmotic adjustment, a hallmark of abiotic stress tolerance in plants, is also mediated by the MAPK signal transduction pathway (Zhu et al., 2002).

LEA genes: LEA or Late Embryogenesis abundant proteins are expressed during cellular dessication and are named so because during later stages of embryo development, seeds accumulate these proteins at high concentrations. Water deficit, high osmolarity, and low temperature stress results in the accumulation of a group of LEA proteins (Wang et al., 2003). Such proteins may preserve protein structure and membrane integrity by binding water, preventing protein denaturation or renaturing unfolded proteins, and sequestering


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ions in stressed tissues. LEA proteins and chaperones have shown to be involved in protecting macromolecules like enzymes, lipids and mRNAs from dehydration. The CDPK pathway seems to be involved in increasing the expression of LEA proteins from cellular desiccation.

SOS Pathway: SOS signaling appears to be relatively specific for the ionic aspect of salt stress. The targets of this pathway are ion transporters that control ion homeostasis under salt stress (Seki et al., 2003). An early response to sodium stress is a transient increase in the calcium level. The ionic aspect of salt stress is signaled via the SOS pathway where a calcium responsive SOS3-SOS2 protein kinase complex controls the expression and activity of ion trans~orters such as SOSI. The Ca2

+ signaling is perceived by the calcineurin-B-like Ca + sensor SOS3, which in tum interacts and activates protein kinase SOS2 by phosphorylation. The activated SOS2 then modulates the activity of plasma membrane localized Na+/H+ antiporter SOSl.

ABA mediated pathway: ABA is a phytohormone that is extensively involved in responses towards abiotic stresses such as drought, low temperature, and osmotic stress. In plants, two kinds of cis-acting sequences have been reported to be involved in ABAmediated gene expression. The first one is the ABA-responsive element (ABRE). It has been shown that the bZIP transcription factors like ABRE-binding factor (ABF)/ABRE binding protein (AREB) can activate the stress-responsive RD29A promoter through binding to the ABRE motifs (Boudsocq and Lauriere, 2005). It has been shown that MYB and MYC transcription factors are involved in an ABA-dependent pathway leading to the expression of drought-responsive genes like RD22 (Abe et al., 2003). This ABAindependent expression of stress-responsive genes can occur through dehydrationresponsive element (DRE)/C-repeat (CRT) cis-acting elements. The binding factors CBF/DREBl (CRT-binding factor/ORE-binding factor I) and DREB2 mediate gene expression in response to cold and drought/salinity, respectively. These aspects of stress signaling have been dealt in details in chapter three ************************************************************************

BOX 2: Recent techniques being used for analysis of stress response in plants Transcriptomics: Transcriptomics is often considered as a step next to genomics in the study of biological systems, after genomics. The transcriptome is the set of all mRNA molecules, or "transcripts", produced in one organism or cell type under a given set of conditions. It is more complex than genomics, mostly because while an organism's genome is rather constant (with the exception of mutations), a transcriptome differs from cell to cell and constantly changes with external environmental conditions. Thus, the transcriptome reflects the genes that are being actively expressed at any given time, with the exception of mRNA degradation phenomena such as transcriptional attenuation.

Differential Display PCR: Different primer combinations (oligo-dTplus primersT12NA, Tl2NT, T12NG or T12NC) are used in a reverse transcriptase (RT)-PCR to generate cDNAs from mRNAs expressed in a given cell. By comparing the cDNAs derived from multiple cell types, or from a single cell type under different conditions, it is possible to detect differences in transcription products derived from the diverse


27


conditions. These differentiaiJy expressed products are identified on an acrylamide gel, excised. eluted, re-amplified, and eventually sequenced.

eDNA AFLP: The cD~A-AFLP is a RNA finger printing technique used to analyze genes that are differentially expressed in genotypes with contrasting stress tolerance grown under normal and stress conditions. Here, the double stranded eDNA is digested with two restriction enzymes, adapter molecules are ligated to the cDNAs and PCR amplification is performed with a primer that is complementary to the adapter, but has an additional 1-3 selective bases.

Subtractive Hybridization: Suppression subtractive hybridization (SSH) has been a powerful approach to identify and isolate cDNAs of differentially expressed genes. It involves hybridization of eDNA from one population (tester) to an excess of mRNA (eDNA) from other population (driver) and then the separation of the unhybridized fraction (target) from hybridized common sequences, and their cloning in desired vector to generate a subtractive library. These subtraction techniques often involve multiple or repeated subtraction steps, and are labor intensive (Diatchenko et al., 1996 ).

Microarray: The DNA chip technology has revolutionized the study of gene expression profiles (Schena et al.. 1995) by allowing the entire gene complement of the genome to be studied in a single experiment. This technique makes use of microscopic slides on which specific D!'IA fragments (eDNA clones, EST clones, anonymous genomic clones or D~A amplified from open reading frames (ORFs)) determined from a database have been printed at indexed positions using a computer control1ed high speed robot.

SAGE: This technique invented first to quantify gene expression in yeast (Velculescu et aL 1995: 1997 ), comprises the production of a short I 0-14 nucJeotide tag. with each tag representing a uniq':le transcript present in a cell (all possible combination of 4 bases in a 10 bp sequence, 410 gives more than I miiJion different sequences). Determination of the sequence of a tag allows identification of the corresponding gene, and the frequency of a tag represents the steady state level of the mRNA from which it was derived. The unique advantages of SAGE over alternative techniques for transcript analysis are: high sensitivity. threshold detection of one transcript in three cells (Ishii et al.. 2000), scalability, the technique can be used on any size of sample, from a few cel1s upwards: detection of all genes. including those of unknown function: avoidance of amplification bias: the data are digital and not derived (e.g. from an analogue fluorescent signal): the data are immortal and can be used at any time in a comparative study: data s.ets generated by one lab can be related directly to those produced by another. To date. however, this technique has found limited utility in the plant research. ************************************************************************

BOX 3: Tools of proteomics Research in proteomics requires resolving proteins on a massive scale. Protein separation can be performed using two-dimensional gel electrophoresis, which usuaiJy separates proteins first by isoelectric point and then by molecular weight. Protein spots in a gel can be visualized using a variety of chemical stains or fluorescent markers. Proteins can often


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be quantified by the intensity of their stain. Once proteins are separated and quantified, they are identified. Individual spots are cut out of the gel and cleaved into peptides with proteolytic enzymes. These peptides can then be identified by mass spectrometry, specifically matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry. In this procedure, a peptide is placed on a matrix, which causes the peptide to form crystals. Then the peptide on the matrix is ionized with a laser beam and an increase in voltage at the matrix is used to shoot the ions towards a detector, in which the time taken by an ion to reach the detector depends on its mass. The higher the mass, the longer, the time of flight (TOF) of the ion. High-throughput proteomic techniques are based on mass spectrometry, commonly peptide mass fingerprinting on MALDI-TOF instruments, or de novo repeat detection MS/MS on instruments capable of more than one round of mass spectrometry. MS/MS data can be analyzed by simple database searches as is the case for PMFs and additionally, they can be analyzed by de novo sequencing and homology searching in darabases such as Mascot, PEAKS OMSSA, or SEQUEST. This particular approach allows to even identify similar (homolog) proteins, e.g. across the species in case a protein was derived from an organism with unsequenced genome. Further, ICP-MS combined with MeCAT- Metal Coded Tagging- technology is used for ultrasensitive quantification of proteins and peptides (down to low attomol range).

Gas Chromatography: Gas chromatography, in combination with mass spectrometry (GC-MS), is one of the most widely used and powerful methods. It offers very high chromatographic resolution, but requires chemical derivatization for many biomolecules: only volatile chemicals can be analysed without derivatization. Some large and polar metabolites cannot be analysed by GC. Compared to GC, HPLC has lower chromatographic resolution, but it does have the advantage that a much wider range of analytes can potentially be measured. Capillary electrophoresis (CE) though less popular than other techniques, has a higher theoretical separation efficiency than HPLC, and is suitable for use with a wider range of metabolite classes than is GC. As for all electrophoretic techniques, it is most appropriate for charged analytes. GC-MS is the most popular combination of the three, and was the first to be developed. Nuclear magnetic resonance (NMR) spectroscopy is the only detection technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses. All kinds of small molecule metabolites can be measured simultaneously. Practically, however, it is relatively insensitive compared to mass spectrometry-based techniques; additionally, NMR spectra can be very difficult to interpret for complex mixtures.

Yeast two hybrid system: The principle of yeast two hybrid system is that a functional transcription factor consists of two different domains: a DNA binding domain (DBD) and a transactivation domain (AD). In the yeast two hybrid system, these two domains are separated and each one is fused to a protein of interest (X and Y, respectively). Physical interaction between DBD-X and AD-Y reconstitutes a transcription factor that can activate the transcription of reporter genes regulated by DBD binding sites ***********************************************************************************


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q>u6 fications - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/14649/13/13_publication.pdf · q>u6 fications Funct Integr Genomics DOl 10.1007/s10142-009-0119-x I SHORT COMMUNICATION