Chemo-profiling of Purpureocillium lilacinum and Paecilomyces variotii isolates using GC-MS analysis, and evaluation of their metabolites against M. incognita.

Nematophagous fungi are the best alternatives to chemical nematicides for managing nematodes considering environmental health. In the current study, activity of metabolites from ten isolates of Purpureocillium lilacinum (Thom) Luangsa-ard (Hypocreales: Ophiocordycipitaceae) and two isolates of Paecilomyces variotii Bainier (Eurotiales: Trichocomaceae), were examined to inhibit the hatching of Meloidogyne incognita (Kofoid & White) Chitwood (Tylenchida: Heteroderidae) eggs. At 100%, 50%, and 25% concentrations, respectively, the culture filtrate of the isolate P. lilacinum 6887 prevented 97.55%, 90.52%, and 62.97% of egg hatching. Out of all the isolates, Pl 6887, Pl 6553, and Pl 2362 showed the greatest results in the hatching inhibition experiment.Gas chromatography-mass spectrometry (GC-MS) analysis revealed a variety of nematicidal compounds from different isolates. A total of seven nematicidal compounds, including four very potent nematicidal fatty acids were found in the isolate Pl 6553. Secondary metabolites of the same isolate possess the highest M. incognita juvenile mortality, i.e., 43.33% and 92% after 48 hrs of treatment at 100 and 200 ppm concentrations, respectively. Significant difference was observed in juvenile mortality percentage among the isolate having highest and lowest nematicidal compounds. Nematicidal fatty acids like myristic and lauric acid were found for the first time in P. lilacinum. Multiple vacuole-like droplets were found inside the unhatched eggs inoculated with the culture filtrate of isolate Pl 6887, and also in the juveniles that perished in the ethyl acetate extract of isolate Pl 6553.

Microbial interaction mediated programmed cell death in plants.

Food demand of growing population can only be met by finding solutions for sustaining the crop yield. The understanding of basic mechanisms employed by microorganisms for the establishment of parasitic relationship with plants is a complex phenomenon. Symbionts and biotrophs are dependent on living hosts for completing their life cycle, whereas necrotrophs utilize dead cells for their growth and establishment. Hemibiotrophs as compared to other microbes associate themselves with plants in two phase's, viz. early bio-phase and later necro-phase. Plants and microbes interact with each other using receptors present on host cell surface and elicitors (PAMPs and effectors) produced by microbes. Plant-microbe interaction either leads to compatible or incompatible reaction. In response to various biotic and abiotic stress factors, plant undergoes programmed cell death which restricts the growth of biotrophs or hemibiotrophs while necrotrophs as an opportunist starts growing on dead tissue for their own benefit. PCD regulation is an outcome of plant-microbe crosstalk which entirely depends on various biochemical events like generation of reactive oxygen species, nitric oxide, ionic efflux/influx, CLPs, biosynthesis of phytohormones, phytoalexins, polyamines and certain pathogenesis-related proteins. This phenomenon mostly occurs in resistant and non-host plants during invasion of pathogenic microbes. The compatible or incompatible host-pathogen interaction depends upon the presence or absence of host plant resistance and pathogenic race. In addition to host-pathogen interaction, the defense induction by beneficial microbes must also be explored and used to the best of its potential. This review highlights the mechanism of microbe- or symbiont-mediated PCD along with defense induction in plants towards symbionts, biotrophs, necrotrophs and hemibiotrophs. Here we have also discussed the possible use of beneficial microbes in inducing systemic resistance in plants against pathogenic microbes.

Draft genome sequencing and secretome profiling of Sclerotinia sclerotiorum revealed effector repertoire diversity and allied broad-host range necrotrophy.

White mold commonly known as Sclerotinia sclerotiorum causes stem rot disease and has emerged as one of the major fungal pathogens of oilseed Brassica across the world. In the present study, consistently virulent S. sclerotiorum isolate "ESR-01" was sequenced and an assembly size of ~ 41 Mb with 328 scaffolds having N50 of 447,128 was obtained. Additionally, 27,450 single nucleotide polymorphisms (SNPs) were identified from 155 scaffolds against S. sclerotiorum 1980 isolate, with an average SNP density of ~ 1.5 per kb genome. 667 repetitive elements were identified and approximately comprised 7% of the total annotated genes. The DDE_1 with 454 in numbers was found to be the most abundant and accounts for 68% of the total predicted repetitive elements. In total, 3844 simple sequence repeats are identified in the 328 scaffolds. A total of 9469 protein-coding genes were predicted from the whole genome assembly with an average gene length of 1587 bp and their distribution as 230.95 genes per Mb in the genome. Out of 9469 predicted protein-coding genes, 529 genes were observed encoding the CAZymes (Carbohydrate-Active enzymes) capable of degradation of the complex polysaccharides. Glycosyltransferase (GT) families were most abundant (49.71%) among the predicted CAZymes and GT2 (23%), GT4 (20%), and glycoside hydrolase (GH) 23% with GH18 (11%) were the prominent cell wall degrading enzyme families in the ESR-01 secretome. Besides this, 156 genes essential for the pathogen-host interactions were also identified. The effector analysis in the whole genome proteomics dataset revealed a total of 57 effector candidates (ECs) and 27 of them were having their analogs whereas the remaining 30 were novel ones. Eleven selected ECs were validated experimentally by analyzing the expression profile of the ESR-01 isolate of S. sclerotiorum. Together, the present investigation offers a better understanding of the S. sclerotiorum genome, secretome, and its effector repertoire which will help in refining the present knowledge on S. sclerotiorum-Brassica interactions and necrotrophic lifestyle of the phytopathogen in general.

Genetic Diversity Studies Based on Morphological Variability, Pathogenicity and Molecular Phylogeny of the Sclerotinia sclerotiorum Population From Indian Mustard (Brassica juncea).

White mold or stem rot disease are ubiquitously distributed throughout the world and the causal organism of this disease Sclerotinia sclerotiorum (Lib.) de Bary, is known to infect over 400 plant species. Sclerotinia stem rot is one of the most devastating fungal diseases and poses a serious threat to the worldwide cultivation of oilseed Brassica including India. S. sclerotiorum pathogen usually infects the stem but in severe cases leaves and pods also affected at different developmental stages that deteriorate not only the oil quality but also causing the seed and oil yield losses up to 90% depending on the severity of the disease infestation. This study investigated the morphological and molecular characterization of pathogenic S. sclerotiorum (Lib) de Bary geographical isolates from oilseed Brassica including Brassica juncea (Indian mustard). The aim of this study was to compare isolates of S. sclerotiorum originated from different agro-climatic conditions and to analyse similarity or differences between them as well as to examine the virulence of this pathogen specifically in Brassica for the first time. The collection of S. sclerotiorum isolates from symptomatic Brassica plants was done and analyzed for morphological features, and molecular characterization. The virulence evaluation test of 65 isolates on four Brassica cultivars has shown 5 of them were highly virulent, 46 were virulent and 14 were moderately virulent. Phylogenetic analysis encompassing all the morphological features, SSR polymorphism, and ITS sequencing has shown the existence of high genetic diversity among the isolates that categorized all the isolates in three evolutionary lineages in the derived dendrogram. Further, genetic variability analysis based on sequences variation in ITS region of all the isolates has shown the existence of either insertions or deletions of the nucleotides in the ITS region has led to the interspecies variability and observed the variation were in a clade-specific manner. Together this analysis observed the existence of higher heterogeneity and genetic variability in S. sclerotiorum isolates collection and indicates the presence of clonal and sexual progenies of the pathogen in the mustard growing regions of India surveyed in this study. With a higher level of genetic variability and diversity among the S. sclerotiorum population needs robust screening approaches to identify the donor parent and utilize them in resistance breeding program for effectively counter the menace of stem rot disease in Brassica.

Affinity learning with diffusion on tensor product graph.

In many applications, we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples. Often the pairwise similarities (which are also called affinities) are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples. As observed in several recent approaches, more reliable similarities can be obtained if the original similarities are diffused in the context of other data points, where the context of each point is a set of points most similar to it. Compared to the existing methods, our approach differs in two main aspects. First, instead of diffusing the similarity information on the original graph, we propose to utilize the tensor product graph (TPG) obtained by the tensor product of the original graph with itself. Since TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities. However, it comes at the price of higher order computational complexity and storage requirement. The key contribution of the proposed approach is that the information propagation on TPG can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph. We prove that a graph diffusion process on TPG is equivalent to a novel iterative algorithm on the original graph, which is guaranteed to converge. After its convergence we obtain new edge weights that can be interpreted as new, learned affinities. We stress that the affinities are learned in an unsupervised setting. We illustrate the benefits of the proposed approach for data manifolds composed of shapes, images, and image patches on two very different tasks of image retrieval and image segmentation. With learned affinities, we achieve the bull's eye retrieval score of 99.99 percent on the MPEG-7 shape dataset, which is much higher than the state-of-the-art algorithms. When the data- points are image patches, the NCut with the learned affinities not only significantly outperforms the NCut with the original affinities, but it also outperforms state-of-the-art image segmentation methods.

Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis.

A new procedure for resolving noisy overlapped peaks in DNA separations by capillary electrophoresis (CE) is developed. The procedure combines both a wavelet-based denoising method that effectively denoises the signal and a novel approximate deconvolution technique that resolves the fragment peaks and improves the ability to separate highly overlapped peaks early in the electrophoresis process. Different kinds of overlapped peaks with and without noise simulated by computer as well as some DNA experimental electropherograms were submitted to the new procedure. A second order differential operator with variable coefficients is applied to the entire electrophoresis signal at any given time and approximate deconvolutions of the individual Gaussian peaks are performed. The operator incorporates the effect of the superposition and gives exact annihilation in the neighborhood of each peak. Overlapped peaks with a resolution higher than 0.46 can be resolved directly. Also, the method can determine the peak components of signals with a signal to noise ratio higher than 1.4