estimated using sandwich ELISA. denaturation at 95uC for 15 s; step 3-annealing at 57uC for 15 s; step 4-extension at 72uC for 20 s; step 5-melting curve analysis. Steps from 2 to 4 were repeated for 40 cycles. The specificity of respective amplicons was confirmed from the melting curve analysis. The amplification of each gene was carried out in triplicates for each group. The threshold cycle values obtained from above runs were used for calculating the expression levels of genes by REST-384 version 2 software. The expressions of genes were normalized against that of a housekeeping gene, bactin, and plotted as relative change in the expression with respect to control. Statistical analysis Data are presented as mean 6 SEM. The statistical analysis was done using ANOVA with Microcal Origin 6.0 software followed by post-hoc analysis using Schiffe’s test. refers to p,0.01, as compared to vehicle treated PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/22179956 control and # refers to p,0.01, as compared to Con A or LPS stimulated cells. Log rank test was used to compare GVHD related mortality in mice injected with control or UA treated allogenic lymphocytes. Quantitative real-time RT-PCR mRNA levels in the samples were quantified by quantitative real-time RT-PCR as described previously. Briefly, total RNA was isolated from the samples using Trizol reagent following the manufacturer’s instructions and was dissolved in deionised DEPC-treated water. From this RNA 2 mg was converted to cDNA by reverse transcription following the manufacturer’s instruction. qPCR was carried out using the Rotor Gene 3000 machine. The PCR was setup by mixing 106 SYBR green PCR mix with 5 ml cDNA, 10 picomoles each of forward and reverse primers, and PCR-grade water in 20 ml reaction system. The above reaction mixtures were amplified in the following steps: step 1-denaturation at 95uC for 5 min; step 2- Acknowledgments The authors would like to acknowledge the help of Ms. Bincy Bhaskar and the technical assistance of Ms. Jisha Menon, Mr. Prayag Amin, Mr. Kashinath Munankar and Mr. Deepak Kathole. ~~ Grain size is an important yield component, and thought to be partly controlled by sizes of glumes; the lemma and palea. Enlargement of these organs results in a bigger grain, when grainfilling ability remains unchanged. To date, two genes controlling lemma/palea length and two genes involved in grain width have been isolated and characterized. A major QTL for grain length, GS3, 910232-84-7 web encodes a protein with four different domains including the plant-specific organ size domain. This domain is necessary and sufficient to function as a negative regulator of rice grain length. Another grain length gene Small and round seed 3 encodes a kinesin13 protein. Constitutive expression of SRS3 rescued and complemented the short grain phenotype of an srs3 mutant, suggesting a positive role for SRS3 in rice grain length. A QTL for seed width on chromosome 5, qSW5, was shown to control cell numbers of the lemma and palea, and identified to be functional nucleotide polymorphisms for a putative nuclear protein. A QTL for grain width on chromosome 2, GW2, encodes a RING-type ubiquitin E3 ligase and controls cell numbers of the lemma and palea. Although grain size is controlled by a complex genetic network in 
which the above mentioned four genes are also involved, it is expected that many genes are yet to be identified. Basic helix-loop-helix proteins are the second largest class of plant transcription factors. They comprise two distinct functional reg