Protein and Peptide Letters - Volume 28, Issue 8, 2021

The main focus of this review is to discuss the current status of the use of GWAS for fodder quality and biofuel owing to its similarity of traits. Sorghum is a potential multipurpose crop, popularly cultivated for various uses as food, feed fodder, and biomass for ethanol. Production of a huge quantity of biomass and genetic variation for complex sugars are the main motivations not only to use sorghum as fodder for livestock nutritionists but also as a potential candidate for biofuel generation. Few studies have been reported on the knowledge transfer that can be used from the development of biofuel technologies to complement improved fodder quality and vice versa. With recent advances in genotyping technologies, GWAS became one of the primary tools used to identify the genes/genomic regions associated with the phenotype. These modern tools and technologies accelerate the genomic assisted breeding process to enhance the rate of genetic gains. Hence, this mini-review focuses on GWAS studies on genetic architecture and dissection of traits underpinning fodder quality and biofuel traits and their limited comparison with other related model crop species.

Plastids in higher plants carry out specialized roles such as photosynthesis, nitrogen assimilation, biosynthesis of amino acids, fatty acids, isoprenoids, and various metabolites. Plastids arise from undifferentiated precursors known as proplastids, which are found in the root and shoot meristems. They are highly dynamic as they change their number, morphology, and physiology according to the tissue they are present. In addition to housing various metabolic activities, plastids also serve as a global sensor for both internal and external environmental cues including different stresses, and help plants to respond/adjust accordingly. They relay information to the nucleus, which then responds by changing the expression levels of specific genes. It has been shown that plants with impaired plastid functions exhibit abnormalities. One of the sources emanating these signals to the nucleus is plastid transcription. Normal plastid functioning is therefore critical for plant survival. Despite immense significance for plant acclimation, the plastid transcriptome is largely an unstudied research area. In this review, we discuss the importance of plastid transcriptomics for the acclimation of plants under changing environmental conditions and summarize the key literature published in this field.

Abiotic stresses in plants such as salinity, drought, heavy metal toxicity, heat, and nutrients limitations significantly reduce agricultural production worldwide. The genome editing techniques such as transcriptional activator-like effector nucleases (TALENs) and zinc finger nucleases (ZFNs) have been used for genome manipulations in plants. However, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technique has recently emerged as a promising tool for genome editing in plants to acquire desirable traits. The CRISPR/Cas9 system has a great potential to develop crop varieties with improved tolerance against abiotic stresses. This review is centered on the biology and potential application of the CRISPR/Cas9 system to improve abiotic stress tolerance in plants. Furthermore, this review highlighted the recent advancements of CRISPR/Cas9-mediated genome editing for sustainable agriculture.

Wheat is a widely cultivated cereal, consumed by nearly 80% of the total population in the world. Although wheat is growing on 215 million hectares annually, its production is still inadequate to meet the future demand of feeding the 10 billion human population. Global food security is the biggest challenge as climate change is threatening crop production. There is a need to fast-- track the wheat breeding by devising modern biotechnological tools. Climate-smart wheat having greater stress resilience, better adaptability and improved agronomic traits are vital to guarantee food security. Substantial understanding and knowledge of vital biochemical pathways and regulatory networks is required for achieving stress resilience in wheat. Metabolomics has emerged as a fascinating technology to speed up the crop improvement programs by deciphering unique metabolic pathways for abiotic/biotic stress tolerance. State-of-the-art metabolomics tools such as nuclear magnetic resonance (NMR) and advanced mass spectrometry (MS) has opened new horizons for detailed analysis of wheat metabolome. The identification of unique metabolic pathways offers various types of stress tolerance and helps to screen the elite wheat cultivars. In this review, we summarize the applications of metabolomics to probe the stress-responsive metabolites and stress-inducive regulatory pathways that govern abiotic/biotic stress tolerance in wheat and highlight the significance of metabolic profiling to characterize wheat agronomics traits. Furthermore, we also describe the potential of metabolomics-assisted speed breeding for wheat improvement and propose future directions.

Sustainable crop improvement can help to feed the exploding human population in an era of shrinking cultivable lands and dwindling water resources. In this scenario, crop improvement using OMICS technologies may help to ensure food security and alleviate the rural poverty in poor countries. Additionally, the improved crops may help to cope with the problem of malnutrition in the different parts of the world, especially Africa. OMICS technologies are based on the knowledge gained through genomics, transcriptomics, proteomics, metabolomics, interactomics and phenomics. This expert review article congregates recent knowledge of the emerging OMICS technologies and evaluates how their integrated application is improving important crops and the potential of these technologies in bringing a revolution in agriculture. Moreover, we have provided an analysis of various technical challenges and difficulties arising during application of OMICS technologies to crop plants which pose major restrictions to the implementation of these strategies.

Background: Production of biofuels from lignocellulosic crop biomass is an alternative to reduce greenhouse gas emissions. The biofuel production involves collecting biomass, breaking down cell wall components followed by the conversion of sugars to ethanol. The lingo-cellulosic biomass comprises 40-50% cellulose, 20-30% hemicellulose, and 10-25% lignin. Sorghum is a widely adapted energy crop for biofuel production. Biomass with low lignin, high cellulose, and high hemicellulose contents are exploited to attain maximum biofuel production efficiency. Resistance to lodging, pest, disease, and abiotic stresses related to cell wall components is well documented, and quantitative trait loci were identified to understand these traits' genetic correlation. Selection for reduced lignin and increased cellulose content in stover can increase the ethanol yield. The Genome-Wide Association Studies (GWAS) is a complementary approach to evaluating the marker and phenotype associations among large diversity panels. Single nucleotide polymorphisms were scanned to identify loci associated with the traits of interest. In this study, the GWAS was performed on 245 sorghum minicore genotypes to analyze agronomic traits (days to 50%flowering, fresh biomass yield, dry biomass yield) and cell wall components (cellulose, hemicellulose, and lignin). Further, in-silico validation of the candidate genes was performed in a global gene expression data from large-scale RNA sequencing studies in sorghum available in the NCBI GEO database was used. Objective: The objectives of this study are to evaluate native variations in biofuel related agronomic traits and stalk cell wall components and to identify significant SNPs or loci related to the cell wall components. Methods: In this article, an association mapping panel, comprising of 245 sorghum minicore germplasm accessions, was evaluated during two post rainy seasons of 2013 and 2014, and observations were recorded on the whole plot- for days to 50% flowering, fresh biomass yield (tha^-1), and dry biomass yield (tha^-1). The biomass of sun-dried plants from both seasons was collected separately, chopped, dried, and ground to powder. The cellulose, hemicellulose, and lignin contents were determined in the powdered. The content of each of these three components in sorghum was expressed in percent of dry matter. The data on agronomic traits and composition analysis was subjected to Analysis of Variance. For the current study, we remapped the raw GBS data with the sorghum assembly version v3.1. A total of 27,589 SNPs were obtained with a minor allele frequency (MAF) >1% and missing data <50%. The GWAS was performed in a single minicore population using FarmCPU, in R software. The synteny positions of the identified significant SNPs between sorghum and other model crop species viz., maize, switchgrass, and Arabidopsis were represented using CIRCOS software for traits viz., dry biomass yield, cellulose, hemicellulose, and lignin. The transcriptome dataset from where sorghum gene atlas studies of grain, sweet, and bioenergy sorghums are available through NCBI's Gene Expression Omnibus (GEO) under accession number GSE49879, was used to cross-validate the identified SNPs for cellulose, hemicellulose, and lignin through GWAS. Results: High broad-sense heritability was exhibited for all the traits in individual seasons along with significant genotype × environment interaction across seasons except lignin. Association mapping with a P < 1×10^-4 revealed genomic regions associated with the- (i) agronomic traits (days to 50% flowering, fresh and dry biomass), and (ii) biochemical traits (cellulose, hemicellulose, and lignin) associated with biofuels production, in individual seasons. Twelve significant SNPs for flowering time, 30 fresh biomass yields, and 24 for dry biomass yield, 25 for cellulose, 7 for hemicellulose, and 21 for lignin were identified. CIRCOS plot was constructed to identify and analyze similarities and differences while comparing the sorghum genome with different crops. For cellulose high similarity of >80% was observed for all sorghum gene sequences with the maize homologs. The overall similarity of sorghum homologs with foxtail millet was >65%, for Arabidopsis from 30.6% to 48.6%, and rice from 28.2% to 92.8%. SNPs for hemicellulose displayed maximum similarity to foxtail millet followed by maize. The sequence similarity of lignin SNPs in sorghum was highest with the maize genome followed by Arabidopsis. Both rice and foxtail millet showed >55% similarity to the sorghum genome. Conclusion: This study reports large variability for agronomic and biofuel traits in the sorghum minicore collection with high heritability. The genetic architecture of cell wall components using the GWAS approach was studied and candidate genes for each component were annotated. These results give a better understanding of the genetic basis of the sorghum cell wall composition. The association analysis identified regions of the genome that could be targeted to enhance the quality of biomass and yield along with the desired composition promoting breeding efficiency for enhanced biofuel yield.

Background: Resistance Gene Analogues (RGAs) are an important source of disease resistance in crop plants and have been extensively studied for their identification, tagging and mapping of Quantitative Trait Loci (QTLs). Tracking these RGAs in sugarcane can be of great help for the selection and screening of disease resistant clones. Objective: In the present study expression of different Resistance Gene Analogues (RGAs) was assessed in indigenous elite sugarcane genotypes which include resistant, highly resistant, susceptible and highly susceptible to disease infestation. Methods: Total cellular DNA and RNA were isolated from fourteen indigenous elite sugarcane genotypes. PCR, semi-quantitative RT PCR and real time qPCR analyses were performed. The resultant amplicons were sequence characterized, chromosomal localization and phylogenetic analysis were performed. Results: All of the 15 RGA primers resulted in amplification of single or multiple fragments from genomic DNA whereas only five RGA primers resulted in amplification from cDNA. Sequence characterization of amplified fragments revealed 86-99% similarity with disease resistance proteins indicating their potential role in disease resistance response. Phylogenetic analysis also validated these findings. Further, expression of RGA-012, RGA-087, RGA-118, RGA-533 and RGA-542 appeared to be upregulated and down regulated in disease resistant and susceptible genotypes, respectively, after inoculation with Colletotrichum falcatum. Conclusion: RGAs are present in most of our indigenous genotypes. Anyhow, differential expression of five RGAs indicated that they have some critical role in disease resistance. So, the retrieved results can not only be employed to devise molecular markers for the screening of disease resistant genotypes but can also be used to develop disease resistant plants through transgenic technology.

This article reviews and discusses the relationship between surface hydrophobicity and other surface properties of proteins and the possibility of using surface hydrophobicity as a key indicator to predict and evaluate the changes in the surface properties of a protein. Hydrophobicity is the main driving force of protein folding; it affects the structure and functions. Surface hydrophobicity and other surface properties of proteins are controlled by their spatial structures. Due to the hydrophobic interactions, most proteins fold into their globular structures, and they lack sufficient hydrophobic residues on the molecular surface; thus, they do not exhibit excellent surface properties. Surface hydrophobicity is closely related to the changes in the surface property of proteins because it directly reflects the actual distribution of the hydrophobic residues on the surface of a protein. The molecular structure of a protein can be changed or modified to remove the constraints of spatial structures and expose more hydrophobic residues on the molecular surface, which may improve the surface properties of proteins. Therefore, the changes in the surface hydrophobicity caused by changes in the molecular structure can be an ideal key indicator to predict and evaluate the changes in the surface properties of a protein.

Given that conventional therapies are ineffective for COVID-19, obtained exosomes from stem cells have been proposed as a sustainable and effective treatment. Exosomes are subsets with lengths between 30 and 100 nanometers, and they can be secreted by different cells. Exosomes are containing different types of miRNAs, mRNAs, and different proteins. The role of immune system modulation of exosomes of mesenchymal stem cells has been studied and confirmed in more than one study. Exosome miRNAs detect and reduce cytokines that cause cytokine storms such as IL-7, IL-2, IL-6, etc. These miRNAs include miR-21, miR-24, miR-124, miR-145, etc. The risks associated with treatment with exosomes from different cells are relatively small compared to other treatments because transplanted cells do not stimulate the host immune system and also has reduced infection transmission. Due to the ineffectiveness of existing drugs in reducing inflammation and preventing cytokine storms, the use of immune-boosting systems may be suggested as another way to control cytokine storm.

Background: B-cell epitope prediction is a computational approach originally developed to support the design of peptide-based vaccines for inducing protective antibody-mediated immunity, as exemplified by neutralization of biological activity (e.g., pathogen infectivity). Said approach is benchmarked against experimentally obtained data on paratope-epitope binding; but such data are curated primarily on the basis of immune-complex structure, obscuring the role of antigen conformational disorder in the underlying immune recognition process. Objective: This work aimed to critically analyze the curation of epitope-paratope binding data that are relevant to B-cell epitope prediction for peptide-based vaccine design. Methods: Database records on neutralizing monoclonal antipeptide antibody immune-complex structure were retrieved from the Immune Epitope Database (IEDB) and analyzed in relation to other data from both IEDB and external sources including the Protein Data Bank (PDB) and published literature, with special attention to data on conformational disorder among paratope-bound and unbound peptidic antigens. Results: Data analysis revealed key examples of antipeptide antibodies that recognize conformationally disordered B-cell epitopes and thereby neutralize the biological activity of cognate targets (e.g., proteins and pathogens), with inconsistency noted in the mapping of some epitopes due to reliance on immune-complex structural details, which vary even among experiments utilizing the same paratope-epitope combination (e.g., with the epitope forming part of a peptide or a protein). Conclusion: The results suggest an alternative approach to curating paratope-epitope binding data based on neutralization of biological activity by polyclonal antipeptide antibodies, with reference to immunogenic peptide sequences and their conformational disorder in unbound antigen structures.