Fruit deformity in papaya: field screening, nutrient composition and amelioration by boron application

870-875Boron deficiency is a major production constraint of papaya under alkali (light) soils of India. Fruit deformity is an emerging physiological anomaly in developing papaya fruits. Systematic field screening was conducted in eighteen papaya germplasm to observe the severity of this disorder under spring transplanted crop. The disorder severity, fruit yield and economic losses due to physiological disorders were also observed. Germplasm, Pune Selection-3 was most sensitive (67.02 %) for the disorder, while PL (13/96), PN (13/86), Pusa Selection Red, Pusa Nanha and FPL-6 were most tolerant (≤4.46%) under agro-climatic conditions of India. The nutrient analysis of leaf and fruits of papaya plants indicated that the fruit deformity was caused due to boron deficiency. Basal application of borax @ 5 g/plant was most effective in increasing the B level in leaf and fruits (73.6 and 49.1 ppm, respectively) without any toxicity

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Not AvailableSclerotinia stem rot caused by Sclerotinia sclerotiorum (Lib.) de Bary was observed for the first time on pigeonpea in Pusa, Bihar (India) during the years 2012–2014. Typical symptoms of the disease included blighting of twigs, stem lesions, and development of cottony white mycelium and numerous large sclerotia on diseased plant parts. The fungus was identified on the basis of cultural and morphological characteristics, and its pathogenicity was also established. The internal transcribed spacer (ITS) region of pathogen was amplified with primers ITS1 and ITS4, which revealed 100% query coverage along with 99% identity with S. sclerotiorum recovered from different hosts. The highest disease incidence was recorded on cultivar ‘ICPL151’, whereas cultivars ‘MAL13’ and ‘Kudrat’ were observed to have significantly less disease under natural conditionsNot Availabl

Novel Insights into Understanding the Molecular Dialogues between Bipolaroxin and the Gα and Gβ Subunits of the Wheat Heterotrimeric G-Protein during Host–Pathogen Interaction

Spot blotch disease of wheat, caused by the fungus Bipolaris sorokiniana (Sacc.) Shoem., produces several toxins which interact with the plants and thereby increase the blightening of the wheat leaves, and Bipolaroxin is one of them. There is an urgent need to decipher the molecular interaction between wheat and the toxin Bipolaroxin for in-depth understanding of host–pathogen interactions. In the present study, we have developed the three-dimensional structure of G-protein alpha subunit from Triticum aestivum. Molecular docking studies were performed using the active site of the modeled G-protein alpha and cryo-EM structure of beta subunit from T. aestivum and ‘Bipolaroxin’. The study of protein–ligand interactions revealed that six H-bonds are mainly formed by Glu29, Ser30, Lys32, and Ala177 of G-alpha with Bipolaroxin. In the beta subunit, the residues of the core beta strand domain participate in the ligand interaction where Lys256, Phe306, and Leu352 formed seven H-bonds with the ligand Bipolaroxin. All-atoms molecular dynamics (MD) simulation studies were conducted for G-alpha and -beta subunit and Bipolaroxin complexes to explore the stability, conformational flexibility, and dynamic behavior of the complex system. In planta studies clearly indicated that application of Bipolaroxin significantly impacted the physio-biochemical pathways in wheat and led to the blightening of leaves in susceptible cultivars as compared to resistant ones. Further, it interacted with the Gα and Gβ subunits of G-protein, phenylpropanoid, and MAPK pathways, which is clearly supported by the qPCR results. This study gives deeper insights into understanding the molecular dialogues between Bipolaroxin and the Gα and Gβ subunits of the wheat heterotrimeric G-protein during host–pathogen interaction

Mean and range of grain iron, zinc, protein and thousand kernel weight in RIL population grown in rabi 2012–13 and 2013–14 at ICAR-IARI, GBPUA&T, and Pusa Bihar.

<p>Mean and range of grain iron, zinc, protein and thousand kernel weight in RIL population grown in rabi 2012–13 and 2013–14 at ICAR-IARI, GBPUA&T, and Pusa Bihar.</p

Frequency distributions of grain iron and zinc concentration measured during 2012–13 and 2013–14 in the parents and their RIL population.

<p>Frequency distributions of grain iron and zinc concentration measured during 2012–13 and 2013–14 in the parents and their RIL population.</p

QTLs identified for grain iron, zinc, protein and thousand kernel weight in RILs of WH 542 × Synthetic derivative in rabi 2012–13 and 2013–14 at ICAR-IARI, GBPUA&T and Pusa Bihar.

<p>QTLs identified for grain iron, zinc, protein and thousand kernel weight in RILs of WH 542 × Synthetic derivative in rabi 2012–13 and 2013–14 at ICAR-IARI, GBPUA&T and Pusa Bihar.</p

Estimates of phenotypic correlation coefficients for grain iron, zinc, protein and thousand kernel weight in RILs of WH542 × Synthetic derivative in rabi 2012–2013 and 2013–14 grown at ICAR-IARI, GBPUA&T, and Pusa Bihar.

<p>Estimates of phenotypic correlation coefficients for grain iron, zinc, protein and thousand kernel weight in RILs of WH542 × Synthetic derivative in rabi 2012–2013 and 2013–14 grown at ICAR-IARI, GBPUA&T, and Pusa Bihar.</p

Molecular mapping of the grain iron and zinc concentration, protein content and thousand kernel weight in wheat (<i>Triticum aestivum</i> L.)

<div><p>Genomic regions responsible for accumulation of grain iron concentration (Fe), grain zinc concentration (Zn), grain protein content (PC) and thousand kernel weight (TKW) were investigated in 286 recombinant inbred lines (RILs) derived from a cross between an old Indian wheat variety WH542 and a synthetic derivative (<i>Triticum dicoccon</i> PI94624/<i>Aegilops squarrosa</i> [409]//BCN). RILs were grown in six environments and evaluated for Fe, Zn, PC, and TKW. The population showed the continuous distribution for all the four traits, that for pooled Fe and PC was near normal, whereas, for pooled Zn, RILs exhibited positively skewed distribution. A genetic map spanning 2155.3cM was constructed using microsatellite markers covering the 21 chromosomes and used for QTL analysis. 16 quantitative trait loci (QTL) were identified in this study. Four QTLs (<i>QGFe</i>.<i>iari-2A</i>, <i>QGFe</i>.<i>iari-5A</i>, <i>QGFe</i>.<i>iari-7A</i> and <i>QGFe</i>.<i>iari-7B</i>) for Fe, five QTLs (<i>QGZn</i>.<i>iari-2A</i>, <i>QGZn</i>.<i>iari-4A</i>, <i>QGZn</i>.<i>iari-5A</i>, <i>QGZn</i>.<i>iari-7A</i> and <i>QGZn</i>.<i>iari-7B</i>) for Zn, two QTLs (<i>QGpc</i>.<i>iari-2A</i> and <i>QGpc</i>.<i>iari-3A</i>) for PC, and five QTLs (<i>QTkw</i>.<i>iari-1A</i>, <i>QTkw</i>.<i>iari-2A</i>, <i>QTkw</i>.<i>iari-2B</i>, <i>QTkw</i>.<i>iari-5B</i> and <i>QTkw</i>.<i>iari-7A</i>) for TKW were identified. The QTLs together explained 20.0%, 32.0%, 24.1% and 32.3% phenotypic variation, respectively, for Fe, Zn, PC and TKW. <i>QGpc</i>.<i>iari-2A</i> was consistently expressed in all the six environments, whereas, <i>QGFe</i>.<i>iari-7B</i> and <i>QGZn</i>.<i>iari-2A</i> were identified in two environments each apart from pooled mean. <i>QTkw</i>.<i>iari-2A</i> and <i>QTkw</i>.<i>iari-7A</i>, respectively, were identified in four and three environments apart from pooled mean. A common region in the interval of <i>Xgwm359-Xwmc407</i> on chromosome 2A was associated with Fe, Zn, and PC. One more QTL for TKW was identified on chromosome 2A but in a different chromosomal region (<i>Xgwm382-Xgwm359</i>). Two more regions on 5A (<i>Xgwm126-Xgwm595</i>) and 7A (<i>Xbarc49-Xwmc525</i>) were found to be associated with both Fe and Zn. A QTL for TKW was identified (<i>Xwmc525-Xbarc222</i>) in a different chromosomal region on the same chromosome (7A). This reflects at least a partly common genetic basis for the four traits. It is concluded that fine mapping of the regions of the three chromosomes of A genome involved in determining the accumulation of Fe, Zn, PC, and TKW in this mapping population may be rewarding.</p></div

Frequency distributions of grain protein content measured during 2012–13 and 2013–14 in the parents and their RIL population.

<p>Frequency distributions of grain protein content measured during 2012–13 and 2013–14 in the parents and their RIL population.</p