Comparative analyses of genotype dependent expressed sequence tags and stress-responsive transcriptome of chickpea wilt illustrate predicted and unexpected genes and novel regulators of plant immunity

<p>Abstract</p> <p>Background</p> <p>The ultimate phenome of any organism is modulated by regulated transcription of many genes. Characterization of genetic makeup is thus crucial for understanding the molecular basis of phenotypic diversity, evolution and response to intra- and extra-cellular stimuli. Chickpea is the world's third most important food legume grown in over 40 countries representing all the continents. Despite its importance in plant evolution, role in human nutrition and stress adaptation, very little ESTs and differential transcriptome data is available, let alone genotype-specific gene signatures. Present study focuses on <it>Fusarium </it>wilt responsive gene expression in chickpea.</p> <p>Results</p> <p>We report 6272 gene sequences of immune-response pathway that would provide genotype-dependent spatial information on the presence and relative abundance of each gene. The sequence assembly led to the identification of a <it>Ca</it>Unigene set of 2013 transcripts comprising of 973 contigs and 1040 singletons, two-third of which represent new chickpea genes hitherto undiscovered. We identified 209 gene families and 262 genotype-specific SNPs. Further, several novel transcription regulators were identified indicating their possible role in immune response. The transcriptomic analysis revealed 649 non-cannonical genes besides many unexpected candidates with known biochemical functions, which have never been associated with pathostress-responsive transcriptome.</p> <p>Conclusion</p> <p>Our study establishes a comprehensive catalogue of the immune-responsive root transcriptome with insight into their identity and function. The development, detailed analysis of <it>Ca</it>EST datasets and global gene expression by microarray provide new insight into the commonality and diversity of organ-specific immune-responsive transcript signatures and their regulated expression shaping the species specificity at genotype level. This is the first report on differential transcriptome of an unsequenced genome during vascular wilt.</p

Fermion to Boson Mapping

The enveloping algebra,$D_{n}$,of fermions is extended on the lattice to include the discrete space invariance.This extended algebra,denoted X, has the space symmetry as a factor : $X/D_{n}$ = space group

<span style="font-size:11.0pt;line-height:115%; font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family: "Times New Roman";mso-fareast-theme-font:minor-fareast;mso-hansi-theme-font: minor-latin;mso-bidi-font-family:"Times New Roman";mso-ansi-language:EN-US; mso-fareast-language:EN-US;mso-bidi-language:AR-SA">Graft copolymerisation of methyl methacrylate onto silk fibre using Ce⁺³/K₂S₂O_g as redox initiator under visible light in a limited aqueous medium</span>

285-288<span style="font-size:11.0pt;line-height:115%; font-family:" calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family:="" "times="" new="" roman";mso-fareast-theme-font:minor-fareast;mso-hansi-theme-font:="" minor-latin;mso-bidi-font-family:"times="" roman";mso-ansi-language:en-us;="" mso-fareast-language:en-us;mso-bidi-language:ar-sa"="">Photograft copolymerisation of methyl methacrylate (MMA) onto mulberry silk fibre has been done in limited aqueous medium using cerrous ammonium sulphate (Ce+3)/potassium persulphate (K2S2Og) as the redox initiator and compared with the graft copolymerization done in dark under the same conditions. Grafting, total conversion, grafting efficiency and pH dependency of the system have been studied and analysed. The mechanism of polymerisation and graft copolymer formation are discussed. FTIR spectra and SEM studies show the evidence of grafting on the fibre backbone.</span

Dehydration-responsive reversible and irreversible changes in the extracellular matrix: comparative proteomics of chickpea genotypes with contrasting tolerance

Dehydration is the most crucial environmental factor that limits plant growth, development, and productivity affecting agriculture throughout the world. Studies on genetic variations for dehydration tolerance in plants is crucial because divergent cultivars with contrasting traits aid the identification of key cellular components that confer better adaptability. The extracellular matrix (ECM) is a dynamic structure that serves as the repository for important signaling components and acts as a front-line defense. To better understand dehydration adaptation, a proteomic study was performed on the extracellular matrix of ICCV-2, a dehydration-susceptible genotype of chickpea. The proteome was generated with ECM-enriched fractions using two-dimensional gel electrophoresis. The LC−ESI−MS/MS analysis led to the identification of 81 dehydration-responsive proteins. The proteome was then compared with that of JG-62, a tolerant genotype. Comparative proteomics revealed genotype-specific expression of many proteins involved in a variety of cellular functions. Further, the reversible and irreversible changes in the proteomes revealed their differing ability to recover from dehydration-induced damage. We propose that cell wall restructuring and superior homeostasis, particularly the management of reactive oxygen species, may render better dehydration-adaptation. To our knowledge, this is the first report on the comprehensive comparison of dehydration-responsive organellar proteome of two genotypes with contrasting tolerance

Comparative proteomics reveals a role for seed storage protein AmA1 in cellular growth, development, and nutrient accumulation

Seed storage proteins are known to be utilized as carbon and nitrogen source for growing seedlings and thus are considered as potential candidates for nutritional improvement. However, their precise function remains unknown. We have earlier shown that ectopic expression of a seed storage protein, AmA1, leads to increase in protein besides high tuber yield in potato. To elucidate the AmA1-regulated molecular mechanism affecting increased protein synthesis, reserve accumulation, and enhanced growth, a comparative proteomics approach has been applied to tuber life-cycle between wild-type and AmA1 potato. The differential display of proteomes revealed 150 AmA1-responsive protein spots (ARPs) that change their intensities more than 2.5-fold. The LC–ESI-MS/MS analyses led to the identification of 80 ARPs presumably associated with cell differentiation, regulating diverse functions, viz., protein biogenesis and storage, bioenergy and metabolism, and cell signaling. Metabolome study indicated up-regulation of amino acids paralleling the proteomics analysis. To validate this, we focused our attention on anatomical study that showed differences in cell size in the cortex, premedullary zone and pith of the tuber, coinciding with AmA1 expression and localization. Further, we interrogated the proteome data using one-way analysis of variance, cluster, and partial correlation analysis that identified two significant protein modules and six small correlation groups centered around isoforms of cysteine protease inhibitor, actin, heat shock cognate protein 83 and 14-3-3, pointing toward AmA1-regulated overlapping processes of protein enhancement and cell growth perhaps through a common mechanism of function. A model network was constructed using the protein data sets, which aim to show how target proteins might work in coordinated fashion and attribute to increased protein synthesis and storage reserve accumulation in AmA1 tubers on one hand and organ development on the other

Automatic brain segmentation in preterm infants with post-hemorrhagic hydrocephalus using 3D Bayesian U-Net

Post-hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two-dimensional measurements of the ventricles. Automatic and reliable three-dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U-Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U-Net, and ensemble learning using DenseNets and U-Nets. A total of 41 T -weighted MRIs from 27 preterm infants were manually segmented into lateral ventricles, external CSF, white and cortical gray matter, brainstem, and cerebellum. These segmentations were used as ground truth for model evaluation. All methods were trained and evaluated using 4-fold cross-validation and segmentation endpoints, with additional uncertainty endpoints for the Bayesian method. In the lateral ventricles, segmentation endpoint values for the DenseNet, U-Net, ensemble learning, and Bayesian U-Net methods were mean Dice score = 0.814 ± 0.213, 0.944 ± 0.041, 0.942 ± 0.042, and 0.948 ± 0.034 respectively. Uncertainty endpoint values for the Bayesian U-Net were mean recall = 0.953 ± 0.037, mean negative predictive value = 0.998 ± 0.005, mean accuracy = 0.906 ± 0.032, and mean AUC = 0.949 ± 0.031. To conclude, the Bayesian U-Net showed the best segmentation results across all methods and provided accurate uncertainty maps. This method may be used in clinical practice for automatic brain segmentation of preterm infants with PHH, and lead to better PHH monitoring and more informed treatment decisions