The phenomenal increase in the use of nitrogenous fertilizers coupled with poor nitrogen use efficiency is among the most important threats to the environment, economic, and social health. During the last 2 decades, a number of genomic regions associated with nitrogen use efficiency (NUE) and related traits have been reported by different research groups, but none of the stable and major effect QTL have been utilized in the marker-assisted introgression/pyramiding program. Compiling the data available in the literature could be very useful in identifying stable and major effect genomic regions associated with the root and NUE-related trait improving the rice grain yield. In the present study, we performed meta-QTL analysis on 1,330 QTL from 29 studies published in the past 2 decades. A total of 76 MQTL with a stable effect over different genetic backgrounds and environments were identified. The significant reduction in the confidence interval of the MQTL compared to the initial QTL resulted in the identification of annotated and putative candidate genes related to the traits considered in the present study. A hot spot region associated with correlated traits on chr 1, 4, and 8 and candidate genes associated with nitrate transporters, nitrogen content, and ammonium uptake on chromosomes 2, 4, 6, and 8 have been identified. The identified MQTL, putative candidate genes, and their orthologues were validated on our previous studies conducted on rice and wheat. The research-based interventions such as improving nitrogen use efficiency via identification of major genomic regions and candidate genes can be a plausible, simple, and low-cost solution to address the challenges of the crop improvement program.

GRAS genes belong to the plant-specific transcription factors (TF's) family that are known to be involved in plant growth and development. In this study, we have identified 37 genes from the bottle gourd genome that encodes for GRAS TF's. Except for the SCLA, we were able to identify at least one gene from each of the 17 subfamilies. Gene structure and chromosomal analysis showed that maximum seven genes are present on Chr7 followed by six genes on Chr1. The subcellular location analysis revealed that most of the genes were localized in the nucleus, except for a few in chloroplast and mitochondria. Additionally, we have identified one tandem gene duplication event on Chr7 and three major motifs that were present in all the GRAS genes. Furthermore, the protein-protein interaction prediction and gene expression analysis showed five candidate hub-genes interact with various other genes and thus probably control the expression of interacting partners in different plant tissues. Overall, this study provides a comprehensive analysis of GRAS transcription factors in bottle gourd genome which could be further extended to other vegetable crops. Transcription factors (TFs) are the class of proteins that control the functioning of various genes by binding with their promoters and thus involved in the gene regulation process 1. Previously, more than 320K TFs belonging to 58 transcription factor families have been reported from 165 different plant species. Out of these, GRAS represents one of the major families that are involved in plant growth, development, cell signaling, and stress tolerance 2. GRAS family was first reported in bacteria and characterized by the three TFs i.e. (i) GAI (gibberellicacid insensitive); (ii) RGA (repressor of GAI), and (iii) SCR (scarecrow) with the size range from 400 to 770 amino acids 3,4. It was observed that GAI and RGA are the part of DELLA proteins that take part in gibberellin (GA) and Jasmonate (JA) response as well as light signaling. Likewise, SCR and Short Root (SHR) played a key role in the radial organization of root by forming the SCR/SHR complex 5. Previously, it has also been reported that the C-terminal region of GRAS members is highly conserved, while the N-terminal is highly divergent that might provide the specificity to each protein 6. Furthermore, the evolutionary analysis suggested the phenomenon of horizontal gene transfer (HGT) from bacteria to plants. GRAS gene family is further categorized into different subfamilies such as eight in Arabidopsis and rice 7 , while 8 to 13 in tomato, popular, castor beans, etc 8-10. Recently, Cenci et al. classified the GRAS family into 17 subfamilies such as DELLA, Lateral suppressor (LS), Hairy meristem (HAM), and SCR 11. Previous studies showed that it is one of the widely explored transcription factor family in the various plant species including tomato, potato, buckwheat, and sweet orange 12-14. Bottle gourd (Lagenaria siceraria), a member of the Cucurbitaceae family is commonly cultivated in the tropical and subtropical regions an...

Nitrogen transport is one of the most important processes in plants mediated by specialized transmembrane proteins. Plants have two main systems for nitrogen uptake from soil and its transport within the system—a low-affinity transport system and a high-affinity transport system. Nitrate transporters are of special interest in cereal crops because large amount of money is spent on N fertilizers every year to enhance the crop productivity. Till date four gene families of nitrate transporter proteins; NPF (nitrate transporter 1/peptide transporter family), NRT2 (nitrate transporter 2 family), the CLC (chloride channel family), and the SLAC/SLAH (slow anion channel-associated homologues) have been reported in plants. In our study, in silico mining of nitrate transporter genes along with their detailed structure, phylogenetic and expression analysis was carried out. A total of 412 nitrate transporter genes were identified in hexaploid wheat genome using HMMER based homology searches in IWGSC Refseq v2.0. Out of those twenty genes were root specific, 11 leaf/shoot specific and 17 genes were grain/spike specific. The identification of nitrate transporter genes in the close proximity to the previously identified 67 marker-traits associations associated with the nitrogen use efficiency related traits in nested synthetic hexaploid wheat introgression library indicated the robustness of the reported transporter genes. The detailed crosstalk between the genome and proteome and the validation of identified putative candidate genes through expression and gene editing studies may lay down the foundation to improve nitrogen use efficiency of cereal crops.

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Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population’s hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.