In Silico functional and phylogenetic analyses of fungal immunomodulatory proteins of some edible mushrooms

Abstract Mushrooms are a well known source of many bioactive and nutritional compounds with immense applicability in both the pharmaceutical and food industries. They are widely used to cure various kinds of ailments in traditional medicines. They have a low amount of fats and cholesterol and possess a high number of proteins. Immunomodulators have the ability which can improve immunity and act as defensive agents against pathogens. One such class of immunomodulators is fungal immunomodulatory proteins (FIPs). FIPs have potential roles in the treatment of cancer, and immunostimulatory effects and show anti-tumor activities. In the current study, 19 FIPs from edible mushrooms have been used for comparison and analysis of the conserved motifs. Phylogenetic analysis was also carried out using the FIPs. The conserved motif analysis revealed that some of the motifs strongly supported their identity as FIPs while some are novel. The fungal immunomodulatory proteins are important and have many properties which can be used for treating ailments and diseases and this preliminary study can be used for the identification and functional characterization of the proposed novel motifs and in unraveling the potential roles of FIPs for developing newer drugs

An Overview of Some Biopesticides and Their Importance in Plant Protection for Commercial Acceptance

Biopesticides are natural, biologically occurring compounds that are used to control various agricultural pests infesting plants in forests, gardens, farmlands, etc. There are different types of biopesticides that have been developed from various sources. This paper underscores the utility of biocontrol agents composed of microorganisms including bacteria, cyanobacteria, and microalgae, plant-based compounds, and recently applied RNAi-based technology. These techniques are described and suggestions are made for their application in modern agricultural practices for managing crop yield losses due to pest infestation. Biopesticides have several advantages over their chemical counterparts and are expected to occupy a large share of the market in the coming period

Signaling Pathways and Downstream Effectors of Host Innate Immunity in Plants

Phytopathogens, such as biotrophs, hemibiotrophs and necrotrophs, pose serious stress on the development of their host plants, compromising their yields. Plants are in constant interaction with such phytopathogens and hence are vulnerable to their attack. In order to counter these attacks, plants need to develop immunity against them. Consequently, plants have developed strategies of recognizing and countering pathogenesis through pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). Pathogen perception and surveillance is mediated through receptor proteins that trigger signal transduction, initiated in the cytoplasm or at the plasma membrane (PM) surfaces. Plant hosts possess microbe-associated molecular patterns (P/MAMPs), which trigger a complex set of mechanisms through the pattern recognition receptors (PRRs) and resistance (R) genes. These interactions lead to the stimulation of cytoplasmic kinases by many phosphorylating proteins that may also be transcription factors. Furthermore, phytohormones, such as salicylic acid, jasmonic acid and ethylene, are also effective in triggering defense responses. Closure of stomata, limiting the transfer of nutrients through apoplast and symplastic movements, production of antimicrobial compounds, programmed cell death (PCD) are some of the primary defense-related mechanisms. The current article highlights the molecular processes involved in plant innate immunity (PII) and discusses the most recent and plausible scientific interventions that could be useful in augmenting PII

Botanicals against some important nematodal diseases: Ascariasis and hookworm infections

Ascariasis and intestinal parasitic nematodes are the leading cause of mass mortality infecting many people across the globe. In light of the various deleterious side effects of modern chemical-based allopathic drugs, our preferences have currently shifted towards the use of traditional plant-based drugs or botanicals for treating diseases. The defensive propensities in the botanicals against parasites have probably evolved during their co-habitation with parasites, humans and plants in nature and hence their combative interference in one another’s defensive mechanisms has occurred naturally ultimately being very effective in treating diseases. This article broadly outlines the utility of plant-based compounds or botanicals prepared from various medicinal herbs that have the potential to be developed as effective therapies against the important parasites causing ascariasis and intestinal hookworm infections leading to ascariasis & infections and thereby human mortality, wherein allopathic treatments are less effective and causes enormous side-effects

Image_1_Autofluorescence−spectral imaging for rapid and invasive characterization of soybean for pre-germination anaerobic stress tolerance.jpeg

The autofluorescence-spectral imaging (ASI) technique is based on the light-emitting ability of natural fluorophores. Soybean genotypes showing contrasting tolerance to pre-germination anaerobic stress can be characterized using the photon absorption and fluorescence emission of natural fluorophores occurring in seed coats. In this study, tolerant seeds were efficiently distinguished from susceptible genotypes at 405 nm and 638 nm excitation wavelengths. ASI approach can be employed as a new marker for the detection of photon-emitting compounds in the tolerant and susceptible soybean seed coats. Furthermore, the accuracy of rapid characterization of genotypes using this technique can provide novel insights into soybean breeding.</p

DataSheet_1_Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.pdf

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs</p

Table_1_Identification of quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight in soybean [Glycine max (L.) Merr.].xlsx

Seed size and shape are important traits determining yield and quality in soybean. Seed size and shape are also desirable for specialty soy foods like tofu, natto, miso, and edamame. In order to find stable quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight, the current study used vegetable type and seed soybean-derived F2 and F2:3 mapping populations. A total of 42 QTLs were mapped, which were dispersed across 13 chromosomes. Of these, seven were determined to be stable QTLs and five of them were major QTLs, namely qSL-10-1, qSW-4-1, qSV-4-1, qSLW-10-1, and qSLH-10-1. Thirteen of the 42 QTLs detected in the current study were found at known loci, while the remaining 29 were discovered for the first time. Out of these 29 novel QTLs, 17 were major QTLs. Based on Protein Analysis Through Evolutionary Relationships (PANTHER), gene annotation information, and literature search, 66 genes within seven stable QTLs were predicted to be possible candidate genes that might regulate seed shape and seed weight in soybean. The current study identified the key candidate genes and quantitative trait loci (QTLs) controlling soybean seed shape and weight, and these results will be very helpful in marker-assisted breeding for developing soybean varieties with improved seed weight and desired seed shape.</p