Distinct morpho-physiological and biochemical features of arid and hyper-arid ecotypes of Ziziphus nummularia under drought suggest its higher tolerance compared with semi-arid ecotype.

Tree species in the arid and semi-arid regions use various strategies to combat drought stress. Ziziphus nummularia (Burm. f.) Wight et Arn., native to the Thar Desert in India, is highly drought-tolerant. To identify the most drought-tolerant ecotype of Z. nummularia, one ecotype each from semi-arid (Godhra, annual rainfall >750 mm), arid (Bikaner, 250-350 mm) and hyper-arid (Jaisalmer, <150 mm) regions was selected along with two other Ziziphus species, Ziziphus mauritiana Lamk. and Ziziphus rotundifolia Lamk., and screened for parameters contributing to drought tolerance. Among these, Z. nummularia (Jaisalmer) (CIAHZN-J) was the most drought - tolerant. The tolerance nature of CIAHZN-J was associated with increased membrane stability, root length and number, length of hairs and thorns, root dry/fresh weight ratio, seed germination (at -0.5 MPa), proline content (31-fold), catalase and sugar content (two- to three-fold). Apart from these characteristics, it also exhibited the longest duration to reach highest cumulative drought stress rating, maintained higher relative water content for a longer period of time with reduced leaf size, leaf rolling and falling of older leaves, and displayed sustained shoot growth during drought stress. To determine drought tolerance in Ziziphus, we developed a morphological symptom-based screening technique in this study. Additionally, transcriptome profiling of CIAHZN-J in response to drought revealed the up-regulation of genes involved in sugar metabolism and transport, abscisic acid biosynthesis, osmoregulation, reactive oxygen species homeostasis and maintaining water potential. Expression profiles and semi-quantitative reverse transcription PCR results further correlated with the physiological and biochemical mechanisms. In conclusion, CIAHZN-J is an excellent genetic stock for the identification of drought-responsive genes and can also be deployed in crop improvement programs for drought tolerance.

CyclinPred: a SVM-based method for predicting cyclin protein sequences.

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server--CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods.