Genomic blueprints of soybean (Glycine max) pathogen resistance: revealing the key genes for sustainable agriculture

Soybean (Glycine max ) is an important oilseed, protein and biodiesel crop. It faces significant threats from bacterial, fungal and viral pathogens, which cause economic losses and jeopardises global food security. In this article, we explore the relationship between soybeans and these pathogens, focusing on the molecular responses that are crucial for soybeans defence mechanisms. Molecular responses involve small RNAs and specific genes, including resistance (R) genes that are pivotal in triggering immune responses. Functional genomics, which makes use of cutting-edge technologies, such as CRISPR Cas9 gene editing, allows us to identify genes that provide insights into the defence mechanisms of soybeans with the focus on using genomics to understand the mechanisms involved in host pathogen interactions and ultimately improve the resilience of soybeans. Genes like GmKR3 and GmVQ58 have demonstrated resistance against soybean mosaic virus and common cutworm, respectively. Genetic studies have identified quantitative trait loci (QTLs) including those linked with soybean cyst nematode, root-knot nematode and Phytophthora root and stem rot resistance. Additionally, resistance against Asian soybean rust and soybean cyst nematode involves specific genes and their variations in terms of different copy numbers. To address the challenges posed by evolving pathogens and meet the demands of a growing population, accelerated soybean breeding efforts leveraging functional genomics are imperative. Targeted breeding strategies based on a deeper understanding of soybean gene function and regulation will enhance disease resistance, ensuring sustainable agriculture and global food security. Collaborative research and continued technological advancements are crucial for securing a resilient and productive agricultural future.

Genome-wide association study of soybean (Glycine max [L.] Merr.) germplasm for dissecting the quantitative trait nucleotides and candidate genes underlying yield-related traits

Soybean (Glycine max [L.] Merr.) is one of the most significant crops in the world in terms of oil and protein. Owing to the rising demand for soybean products, there is an increasing need for improved varieties for more productive farming. However, complex correlation patterns among quantitative traits along with genetic interactions pose a challenge for soybean breeding. Association studies play an important role in the identification of accession with useful alleles by locating genomic sites associated with the phenotype in germplasm collections. In the present study, a genome-wide association study was carried out for seven agronomic and yield-related traits. A field experiment was conducted in 2015/2016 at two locations that include 155 diverse soybean germplasm. These germplasms were genotyped using SoySNP50K Illumina Infinium Bead-Chip. A total of 51 markers were identified for node number, plant height, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield using a multi-locus linear mixed model (MLMM) in FarmCPU. Among these significant SNPs, 18 were putative novel QTNs, while 33 co-localized with previously reported QTLs. A total of 2,356 genes were found in 250 kb upstream and downstream of significant SNPs, of which 17 genes were functional and the rest were hypothetical proteins. These 17 candidate genes were located in the region of 14 QTNs, of which ss715580365, ss715608427, ss715632502, and ss715620131 are novel QTNs for PH, PPP, SDPP, and TY respectively. Four candidate genes, Glyma.01g199200, Glyma.10g065700, Glyma.18g297900, and Glyma.14g009900, were identified in the vicinity of these novel QTNs, which encode lsd one like 1, Ergosterol biosynthesis ERG4/ERG24 family, HEAT repeat-containing protein, and RbcX2, respectively. Although further experimental validation of these candidate genes is required, several appear to be involved in growth and developmental processes related to the respective agronomic traits when compared with their homologs in Arabidopsis thaliana. This study supports the usefulness of association studies and provides valuable data for functional markers and investigating candidate genes within a diverse germplasm collection in future breeding programs.

Molecular and genetic insights into secondary metabolic regulation underlying insect-pest resistance in legumes

Insect pests pose a major threat to agricultural production, resulting in significant economic losses for countries. A high infestation of insects in any given area can severely reduce crop yield and quality. This review examines the existing resources for managing insect pests and highlights alternative eco-friendly techniques to enhance insect pest resistance in legumes. Recently, the application of plant secondary metabolites has gained popularity in controlling insect attacks. Plant secondary metabolites encompass a wide range of compounds such as alkaloids, flavonoids, and terpenoids, which are often synthesized through intricate biosynthetic pathways. Classical methods of metabolic engineering involve manipulating key enzymes and regulatory genes to enhance or redirect the production of secondary metabolites in plants. Additionally, the role of genetic approaches, such as quantitative trait loci mapping, genome-wide association (GWAS) mapping, and metabolome-based GWAS in insect pest management is discussed, also, the role of precision breeding, such as genome editing technologies and RNA interference for identifying pest resistance and manipulating the genome to develop insect-resistant cultivars are explored, highlighting the positive contribution of plant secondary metabolites engineering-based resistance against insect pests. It is suggested that by understanding the genes responsible for beneficial metabolite compositions, future research might hold immense potential to shed more light on the molecular regulation of secondary metabolite biosynthesis, leading to advancements in insect-resistant traits in crop plants. In the future, the utilization of metabolic engineering and biotechnological methods may serve as an alternative means of producing biologically active, economically valuable, and medically significant compounds found in plant secondary metabolites, thereby addressing the challenge of limited availability.

Genome-Wide Identification and Analysis of the Hsp40/J-Protein Family Reveals Its Role in Soybean (Glycine max) Growth and Development

The J-protein family comprises molecular chaperones involved in plant growth, development, and stress responses. Little is known about this gene family in soybean. Hence, we characterized J-protein genes in soybean, with the most highly expressed and responsive during flower and seed development. We also revealed their phylogeny, structure, motif analysis, chromosome location, and expression. Based on their evolutionary links, we divided the 111 potential soybean J-proteins into 12 main clades (I-XII). Gene-structure estimation revealed that each clade had an exon-intron structure resembling or comparable to others. Most soybean J-protein genes lacked introns in Clades I, III, and XII. Moreover, transcriptome data obtained from a publicly accessible soybean database and RT-qPCR were used to examine the differential expression of DnaJ genes in various soybean tissues and organs. The expression level of DnaJ genes indicated that, among 14 tissues, at least one tissue expressed the 91 soybean genes. The findings suggest that J-protein genes could be involved in the soybean growth period and offer a baseline for further functional research into J-proteins' role in soybean. One important application is the identification of J-proteins that are highly expressed and responsive during flower and seed development in soybean. These genes likely play crucial roles in these processes, and their identification can contribute to breeding programs to improve soybean yield and quality.

Genetic diversity and population structure analysis in cultivated soybean (Glycine max [L.] Merr.) using SSR and EST-SSR markers

Soybean (Glycine max) is an important legume that is used to fulfill the need of protein and oil of large number of population across the world. There are large numbers of soybean germplasm present in the USDA germplasm resources. Finding and understanding genetically diverse germplasm is a top priority for crop improvement programs. The current study used 20 functional EST-SSR and 80 SSR markers to characterize 96 soybean accessions from diverse geographic backgrounds. Ninety-six of the 100 markers were polymorphic, with 262 alleles (average 2.79 per locus). The molecular markers had an average polymorphic information content (PIC) value of 0.44, with 28 markers ≥ 0.50. The average major allele frequency was 0.57. The observed heterozygosity of the population ranged from 0-0.184 (average 0.02), while the expected heterozygosity ranged from 0.20-0.73 (average 0.51). The lower value for observed heterozygosity than expected heterozygosity suggests the likelihood of a population structure among the germplasm. The phylogenetic analysis and principal coordinate analysis (PCoA) divided the total population into two major groups (G1 and G2), with G1 comprising most of the USA lines and the Australian and Brazilian lines. Furthermore, the phylogenetic analysis and PCoA divided the USA lines into three major clusters without any specific differentiation, supported by the model-based STRUCTURE analysis. Analysis of molecular variance (AMOVA) showed 94% variation among individuals in the total population, with 2% among the populations. For the USA lines, 93% of the variation occurred among individuals, with only 2% among lines from different US states. Pairwise population distance indicated more similarity between the lines from continental America and Australia (189.371) than Asia (199.518). Overall, the 96 soybean lines had a high degree of genetic diversity.

Identification of QTLs and joint QTL segments of leaflet traits at different canopy layers in an interspecific RIL population of soybean

A leaflet trait on different canopy layers may have different QTLs; leaflet trait QTLs may cluster to form joint QTL segments; all canopy layer QTLs form a complete QTL system for a leaflet trait. As the main part of the plant canopy structure, leaf/leaflet size and shape affect the plant architecture and yield. To explore the leaflet trait QTL system, a population composed of 199 recombinant inbred lines derived from Changling (annual wild, narrow leaflet) and Yiqianli (landrace, broad leaflet) with their parents was tested for leaflet length (LL), width (LW) and length to width (LLW). The population was genotyped with specific-locus amplified fragment sequencing (SLAF-seq) and applied for linkage mapping of the leaflet traits. The results showed that the leaflet traits varied greatly even within a plant, which supported a stratified leaflet sampling strategy to evaluate these traits at top, middle and bottom canopy layers. Altogether, 13 LL, 10 LW and 9 LLW in a total of 32 plus 3 duplicated QTLs were identified, in which, 17 QTLs were new ones, and 48.6%, 28.6% and 22.8% of QTLs were from the top, middle and bottom layers, respectively, indicating the genetic importance of the top layer leaves. Since a leaflet trait may have layer-specific QTLs, all layer QTLs form a complete QTL system. Five QTL clusters each with their QTL supporting intervals overlapped were designated as joint QTL segments (JQSs). In JQS-16, with its linkage map further validated using PCR markers, two QTLs, qLW-16-1 and qLLW-16-1 of the top layer leaflet, were identified six QTL·times. Six candidate genes were predicted, with Glyma.16G127900 as the most potential one for LW and LLW. Three PCR markers, Gm16PAV0653, BARCSOYSSR_16_0796 and YC-16-3, were suggested for marker-assisted selection for LW and LLW in JQS-16.

Cyanobacteria derived compounds: Emerging drugs for cancer management

The wide diversity of cyanobacterial species and their role in a variety of biological activities have been reported in the previous few years. Cyanobacteria, especially from marine sources, constitutes a major source of biologically active metabolites that have gained great attention especially due to their anticancer potential. Numerous chemically diverse metabolites from various cyanobacterial species have been recognized to inhibit the growth and progression of tumor cells through the induction of apoptosis in many different types of cancers. These metabolites activate the apoptosis in the cancer cells by different molecular mechanisms, however, the dysregulation of the mitochondrial pathway, death receptors signaling pathways, and the activation of several caspases are the crucial mechanisms that got considerable interest. The array of metabolites and the range of mechanisms involved may also help to overcome the resistance acquired by the different tumor types against the ongoing therapeutic agents. Therefore, the primary or secondary metabolites from the cyanobacteria as well as their synthetic derivates could be used to develop novel anticancer drugs alone or in combination with other chemotherapeutic agents. In this study, we have discussed the role of cyanobacterial metabolites in the induction of cytotoxicity and the potential to inhibit the growth of cancer cells through the induction of apoptosis, cell signaling alteration, oxidative damage, and mitochondrial dysfunctions. Moreover, the various metabolites produced by cyanobacteria have been summarized with their anticancer mechanisms. Furthermore, the ongoing trials and future developments for the therapeutic implications of these compounds in cancer therapy have been discussed.

Engineering broad-spectrum resistance to cotton leaf curl disease by CRISPR-Cas9 based multiplex editing in plants

Advances in genome editing technologies have tremendous potential to address the limitations of classical resistance breeding. CRISPR-Cas9 based gene editing has been applied successfully in plants to tolerate virus infections. In this study, we successfully tested CRISPR-Cas9 system to counteract cotton leaf curl disease (CLCuD) caused by whitefly transmitted cotton leaf curl viruses (CLCuVs). We also analyzed the ability of CLCuV to escape the Cas9 endonuclease activity. Targeting overlapping genes of most prevalent CLCuVs with three gRNAs resulted in virus interference, as validated by low virus titer. Furthermore, multiplex CRISPR-Cas9 construct simultaneously targeting six genes of CLCuV, was found more effective to interfere with virus proliferation compared to targeting single region individually. Additionally, transgenic N. benthamiana plants expressing multiple gRNAs simultaneously showed enhanced tolerance against CLCuV infection when compared to wild-type plants. T7 Endonuclease-I (T7EI) assay, showing indels in the CLCuV genome, confirmed the occurrence of double strand breaks (DSBs) in DNA at target sequence induced by Cas9 endonuclease. We observed that targeting CLCuV genome at multiple sites simultaneously resulted in better interference, also with inefficient recovery of altered virus molecules. Next, we tested multiplex construct in cotton to interfere CLCuV infection. We found significant decrease in virus accumulation in cotton leaves co-infiltrated with multiplex cassette and virus compared to cotton leaves infiltrated with virus only. The results demonstrate future use of CRISPR-Cas9 system for engineering virus resistance in crops. Moreover, our results also advocate that resistance to mixed virus infections can be engineered using multiplex genome editing.

Identification of Additive-Epistatic QTLs Conferring Seed Traits in Soybean Using Recombinant Inbred Lines

Seed weight and shape are important agronomic traits that affect soybean quality and yield. In the present study, we used image analysis software to evaluate 100-seed weight and seed shape traits (length, width, perimeter, projection area, length/width, and weight/projection area) of 155 novel recombinant inbred soybean lines (NJRISX) generated by crossing "Su88-M21" and "XYXHD". We examined quantitative trait loci (QTLs) associated with the six traits (except seed weight per projection area), and identified 42 additive QTLs (5-8 QTLs per trait) accounting for 24.9-37.5% of the phenotypic variation (PV). Meanwhile, 2-4 epistatic QTL pairs per trait out of a total of 18 accounted for 2.5-7.2% of the PV; and unmapped minor QTLs accounted for the remaining 35.0-56.7% of the PV. A total of 28 additive and 11 epistatic QTL pairs were concentrated in nine joint QTL segments (JQSs), indicating that QTLs associated with seed weight and shape are closely related and interacted. An interaction was also detected between additive and epistatic QTL pairs and environment, which made significant contributions of 1.4-9.5% and 0.4-0.8% to the PV, respectively. We annotated 18 candidate genes in the nine JQSs, which were important for interpreting the close relationships among the six traits. These findings indicate that examining the interactions between closely related traits rather than only analyzing individual trait provides more useful insight into the genetic system of the interrelated traits for which there has been limited QTL information.