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

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.

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

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.

In silico analysis of angiotensin-converting enzyme inhibitory compounds obtained from soybean [Glycine max (L.) Merr.].

Cardiovascular diseases (CVDs) are one of the major reasons for deaths globally. The renin-angiotensin-aldosterone system (RAAS) regulates body hypertension and fluid balance which causes CVD. Angiotensin-converting enzyme I (ACE I) is the central Zn-metallopeptidase component of the RAAS playing a crucial role in maintaining homeostasis of the cardiovascular system. The available drugs to treat CVD have many side effects, and thus, there is a need to explore phytocompounds and peptides to be utilized as alternative therapies. Soybean is a unique legume cum oilseed crop with an enriched source of proteins. Soybean extracts serve as a primary ingredient in many drug formulations against diabetes, obesity, and spinal cord-related disorders. Soy proteins and their products act against ACE I which may provide a new scope for the identification of potential scaffolds that can help in the design of safer and natural cardiovascular therapies. In this study, the molecular basis for selective inhibition of 34 soy phytomolecules (especially of beta-sitosterol, soyasaponin I, soyasaponin II, soyasaponin II methyl ester, dehydrosoyasaponin I, and phytic acid) was evaluated using in silico molecular docking approaches and dynamic simulations. Our results indicate that amongst the compounds, beta-sitosterol exhibited a potential inhibitory action against ACE I.

Angiotensin-converting enzyme inhibitory peptides and isoflavonoids from soybean [Glycine max (L.) Merr.].

Angiotensin-converting enzyme I (ACE I) is a zinc-containing metallopeptidase involved in the renin-angiotensin system (RAAS) that helps in the regulation of hypertension and maintains fluid balance otherwise, which results in cardiovascular diseases (CVDs). One of the leading reasons of global deaths is due to CVDs. RAAS also plays a central role in maintaining homeostasis of the CV system. The commercial drugs available to treat CVDs possess several fatal side effects. Hence, phytochemicals like peptides having plant-based origin should be explored and utilized as alternative therapies. Soybean is an important leguminous crop that simultaneously possesses medicinal properties. Soybean extracts are used in many drug formulations for treating diabetes and other disorders and ailments. Soy proteins and its edible products such as tofu have shown potential inhibitory activity against ACE. Thus, this review briefly describes various soy proteins and products that can be used to inhibit ACE thereby providing new scope for the identification of potential candidates that can help in the design of safer and natural treatments for CVDs.

Harnessing heterosis and male sterility in soybean [Glycine max (L.) Merrill]: A critical revisit.

Soybean is a predominantly self-pollinated crop. It is also one of the important oilseed legumes. Soybean is an excellent crop having industrial, traditional, culinary, feeding, and cultural roles. Genetic diversity in breeding programs is of prime importance as it ensures the success of any breeding by enhancing the outcomes and results of the plants. The phenomenon wherein the progeny exhibits greater biomass (yield) and a faster rate of development and fertility than its parents is referred to as heterosis. As of now, heterosis is mainly limited to the trait of seed yield and is considered the basis for the development of better (superior) varieties. Male sterility (MS) is extensively used for the production of seeds and the improvement of crops coupled with the traditional breeding programs and molecular technology. Therefore, deployment of MS and heterosis in breeding soybean could yield better outcomes. This review aims to focus on two aspects, namely, MS and heterosis in soybean with its scope for crop improvement.

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

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.

Differential accumulation of ß-carotene and tissue specific expression of phytoene synthase (MaPsy) gene in banana (Musa sp) cultivars.

An experiment was conducted with twelve major Indian banana cultivars to investigate the molecular relationship between the differential accumulation of ß-carotene in peel and pulp of the banana fruit and carotenoid biosynthetic pathway genes. The high performance liquid chromatography showed that all banana cultivars accumulated two-three fold more ß-carotene in non-edible portion of the banana fruit. However, Nendran, a famous orange fleshed cultivar of South India, had high ß-carotene content (1362 µg/100 g) in edible pulp. The gene encoding Musa accuminata phytoene synthase (MaPsy) was successfully amplified using a pair of degenerate primers designed from Oncidium orchid. The deduced amino acid sequences shared a high level of identity to phytoene synthase gene from other plants. Gene expression analysis confirmed the presence of two isoforms (MaPsy1 and MaPsy2) of MaPsy gene in banana fruits. Presence of two isoforms of MaPsy gene in peel and one in pulp confirmed the differential accumulation of ß-carotene in banana fruits. However, Nendran accumulated more ß-carotene in edible pulp due to presence of both the isoforms of MaPsy gene. Thus, carotenoid accumulation is a tissue specific process strongly dependent on differential expression pattern of two isoforms of MaPsy gene in banana.

Analysis of non-synonymous single-nucleotide polymorphisms and population variability of PLD2 gene associated with hypertension.

We have determined the non-synonymous Single-Nucleotide Polymorphisms (nsSNPs) of PLD2 gene and its variations in different populations to understand its role in hypertension. Out of 350 SNPs, six are found to be non-synonymous, of which two showed significant damaging effect and SNP variability with large differences among the Minor Allele Frequency (MAF) observed in various populations. The amino acid change found for rs2286672 is from arginine to cysteine, i.e., from largest amino acid containing guanidino group to a small amino acid containing sulfhydryl group, and for rs3764897, it is from glycine to serine, i.e., from a hydrophobic amino acid to a hydrophilic amino acid. Hence, owing to the complete change in side chains and polarity of the amino acid residues brought about by these SNPs, the structure of the protein might be altered and hence the function might be affected, leading to dysregulation of blood pressure.