Progress in Breeding Groundnut Varieties Resistant to Peanut Bud Necrosis Virus and its Vector

Peanut bud necrosis disease (PBND), caused by peanut bud necrosis virus (PBNV), and transmitted by Thrips palmi is an important disease of groundnut in South and Southeast Asia. Several cultivated groundnut germplasm lines showed consistently low disease incidence under field conditions (field resistance). Eight accessions of wild Arachis species did not show disease under field conditions. Field resistance could be due to vector and/or to virus resistance. The current breeding strategy includes improving the level of resistance to thrips and PBNV, and combining them into superior agronomic backgrounds. Several high-yielding varieties with high levels of resistance to PBND have been developed. These varieties possess moderate resistance to the vector. Two of these, ICGV 86031 and JCGV 86388, show resistance to PBNV when mechanically sap-inoculated with low virus concentration (10-2). Considering the level of resistance to the vector and PBNV, it appears that further improvement in the level of resistance through conventional breeding may be difficult to achiev

Hydrodynamic Processes and Inlet Dynamics of Chilka, A Tropical Lagoon on East Coast of India

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Registration of ICGV 86031 Peanut Germplasm

ICGV 86031 (Reg. no. GP-58, PI no. 561917) is a spanishtype peanut (Arachis hypogaea L. subsp. fastigiata Waldron var. vulgaris Hartz) developed at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. It was released in 1991 by the Plant Materials Identification Committee of ICRISAT because of its resistance to thrips (Thripspalmi Karny), jassid (Empoasca kerri Pruthi), spodoptera [Spodoptera litura (Fabricius)], groundnut leaf miner (Aproaerema modicella Deventer) and bud necrosis virus (BNV), which causes bud necrosis disease (END) in peanut. ICGV 86031 has also been found to be photoperiod insensitive and resistant to iron deficiency chlorosis (4)

Diagnosis and Resistance Breeding of Peanut Bud Necrosis Virus

he occurrence of peanut bud necrosis (PBN) disease in India was first reported in 1968. The high incidence of PBN disease during the 1960s coincided with large-scale imports of the peanut cultivars Asiria Mwitundae and Spanish Improved, both of which are highly susceptible to PBN. Since then, a number of reports have been published in India describing bud necrosis under at least seven different names (Reddy 1988). Crop losses due to PBN have been estimated at USD89 million per year in India during 1976–1986. The disease is also currently recognized as economically important in Nepal (Sharma 1996), in Sri Lanka, and in Thailand (Wongkaew 1995). The causal agent of PBN was originally reported as tomato spotted wilt virus (TSWV) (Ghanekar et al. 1979). Since then, methods to purify the causal virus of PBN have been developed, which facilitated the production of good quality antisera. On the basis of serological relationships, some physicochemical properties, and thrips transmission, it was shown that the causal virus of PBN in India was a distinct tospovirus that was named peanut bud necrosis virus (PBNV, Reddy et al. 1992). These results were subsequently confirmed by Adam et al. (1993). Later, monoclonal antibodies (MAbs) have been produced against the nucleocapsid (N) protein of PBNV (Poul et al. 1992). Antibodies from nine clones failed to react with a TSWV-lettuce (TSWV-L) isolate and with an impatiens necrotic spot virus (INSV) by triple-antibody sandwich enzyme-linked immunosorbent assay (TAS-ELISA) (coating of PBNV polyclonal antiserum, addition of antigen followed by addition of MAbs and antimouse IgGs conjugated to alkaline phosphatase). Of 16 MAbs produced against TSWV-L (Hsu et al. 1990), 12 H5 Al (f), 12 H5 H5 (

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)