Changes in microbial community structure and yield responses with the use of nano-fertilizers of nitrogen and zinc in wheat-maize system.

The growing popularity of nano-fertilization around the world for enhancing yield and nutrient use efficiency has been realized, however its influence on soil microbial structure is not fully understood. The purpose of carrying out this study was to assess the combined effect of nano and conventional fertilizers on the soil biological indicators and crop yield in a wheat-maize system. The results indicate that the at par grain yield of wheat and maize was obtained with application of 75% of recommended nitrogen (N) with full dose of phosphorus (P) and potassium (K) through conventional fertilizers along with nano-N (nano-urea) or nano-N plus nano-Zn sprays and N100PK i.e. business as usual (recommended dose of fertilizer). Important soil microbial property like microbial biomass carbon was found statistically similar with nano fertilizer-based management (N75PK + nano-N, and N75PK + nano-N + nano-Zn) and conventional management (N100PK), during both wheat and maize seasons. The experimental data indicated that the application of foliar spray of nano-fertilizers along with 75% N as basal is a sustainable nutrient management approach with respect to growth, yield and rhizosphere biological activity. Furthermore, two foliar sprays of nano-N or nano-N + nano-Zn curtailed N requirement by 25%, furthermore enhanced soil microbial diversity and the microbial community structure. The specific microbial groups, including Actinobacteria, Bacteroidia, and Proteobacteria, were present in abundance and were positively correlated with wheat and maize yield and soil microbial biomass carbon. Thus, one of the best nutrient management approaches for sustaining productivity and maintaining sound microbial diversity in wheat-maize rotation is the combined use of nano-fertilizers and conventional fertilizers.

Meta-QTL analysis in wheat: progress, challenges and opportunities.

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Identification of Novel QTLs/Defense Genes in Spring Wheat Germplasm Panel for Seedling and Adult Plant Resistance to Stem Rust and Their Validation Through KASP Marker Assays.

Stem rust is one of the major diseases threatening wheat production globally. To identify novel resistance quantitative trait loci (QTLs), we performed 35K Axiom Array SNP genotyping assays on an association mapping panel of 400 germplasm accessions, including Indian landraces, in conjunction with phenotyping for stem rust at seedling and adult plant stages. Association analyses using three genome wide association study (GWAS) models (CMLM, MLMM, and FarmCPU) revealed 20 reliable QTLs for seedling and adult plant resistance. Among these 20 QTLs, five QTLs were found consistent with three models, i.e., four QTLs on chromosome 2AL, 2BL, 2DL, and 3BL for seedling resistance and one QTL on chromosome 7DS for adult plant resistance. Further, we identified a total of 21 potential candidate genes underlying QTLs using gene ontology analysis, including a leucine rich repeat receptor (LRR) and P-loop nucleoside triphosphate hydrolase, which have a role in pathogen recognition and disease resistance. Furthermore, four QTLs (Qsr.nbpgr-3B_11, QSr.nbpgr-6AS_11, QSr.nbpgr-2AL_117-6, and QSr.nbpgr-7BS_APR) were validated through KASP located on chromosomes 3B, 6A, 2A, and 7B. Out of these QTLs, QSr.nbpgr-7BS_APR was identified as a novel QTL for stem rust resistance which has been found effective in both seedling as well as the adult plant stages. Identified novel genomic regions and validated QTLs have the potential to be deployed in wheat improvement programs to develop disease resistant varieties for stem rust and can diversify the genetic basis of resistance.

Functional response of an egg parasitoid, Trichogramma chilonis Ishii to sublethal imidacloprid exposure.

BACKGROUND: The effectiveness of a biological control agent depends on how well it can control pests and how compatible it is with pesticides. Therefore, we reported the multigenerational effect of a commonly used insecticide, imidacloprid, on the functional response of a widely acclaimed egg parasitoid, Trichogramma chilonis Ishii, to different densities of the host Corcyra cephalonica Stainton eggs. The study investigated the outcomes of the median lethal concentration (LC50 ) and sublethal concentrations (LC5 , LC30 ), along with control treatments for five continuous generations (F1 to F5 ). RESULTS: The results showed that the F5 generation of LC30 , both of the F1 and F5 generations of LC50 , and the control all had a Type II functional response. A Type I functional response was exhibited for the F1 generation of LC30 and both generations of LC5 . The attack rate on host eggs treated with LC5 and LC30 did not change (decrease) with the shift in the type of functional response as compared to the control. A significant increase in the searching efficiency (a) was observed in the later generation (F5 ) under the exposure of LC5 and LC30 imidacloprid concentrations. A lower handling time (Th ) in both generations of the LC5 followed by LC30 treated individuals was observed when compared with the control and LC50 treatments. The per capita parasitization efficiency (1/Th ) and the rate of parasitization per handling time (a/Th ) were also considerably higher in both the generations of LC5 and LC30 than in the control and LC50 , thereby implying positive effects of imidacloprid on the parasitization potential of T. chilonis. CONCLUSION: Altogether, these multigenerational outcomes on the functional response of T. chilonis could be leveraged to control the intractable lepidopteran pests under the mild exposure of imidacloprid in integrated pest management (IPM) programs as well as in the mass rearing of the parasitoid, T. chilonis. © 2023 Society of Chemical Industry.

Comparative transcriptome profiling of near isogenic lines PBW343 and FLW29 to unravel defense related genes and pathways contributing to stripe rust resistance in wheat.

Stripe rust (Sr), caused by Puccinia striiformis f. sp. tritici (Pst), is the most devastating disease that poses serious threat to the wheat-growing nations across the globe. Developing resistant cultivars is the most challenging aspect in wheat breeding. The function of resistance genes (R genes) and the mechanisms by which they influence plant-host interactions are poorly understood. In the present investigation, comparative transcriptome analysis was carried out by involving two near-isogenic lines (NILs) PBW343 and FLW29. The seedlings of both the genotypes were inoculated with Pst pathotype 46S119. In total, 1106 differentially expressed genes (DEGs) were identified at early stage of infection (12 hpi), whereas expressions of 877 and 1737 DEGs were observed at later stages (48 and 72 hpi) in FLW29. The identified DEGs were comprised of defense-related genes including putative R genes, 7 WRKY transcriptional factors, calcium, and hormonal signaling associated genes. Moreover, pathways involved in signaling of receptor kinases, G protein, and light showed higher expression in resistant cultivar and were common across different time points. Quantitative real-time PCR was used to further confirm the transcriptional expression of eight critical genes involved in plant defense mechanism against stripe rust. The information about genes are likely to improve our knowledge of the genetic mechanism that controls the stripe rust resistance in wheat, and data on resistance response-linked genes and pathways will be a significant resource for future research.

Regulatory Networks of lncRNAs, miRNAs, and mRNAs in Response to Heat Stress in Wheat (Triticum Aestivum L.): An Integrated Analysis.

Climate change has become a major source of concern, particularly in agriculture, because it has a significant impact on the production of economically important crops such as wheat, rice, and maize. In the present study, an attempt has been made to identify differentially expressed heat stress-responsive long non-coding RNAs (lncRNAs) in the wheat genome using publicly available wheat transcriptome data (24 SRAs) representing two conditions, namely, control and heat-stressed. A total of 10,965 lncRNAs have been identified and, among them, 153, 143, and 211 differentially expressed transcripts have been found under 0 DAT, 1 DAT, and 4 DAT heat-stress conditions, respectively. Target prediction analysis revealed that 4098 lncRNAs were targeted by 119 different miRNA responses to a plethora of environmental stresses, including heat stress. A total of 171 hub genes had 204 SSRs (simple sequence repeats), and a set of target sequences had SNP potential as well. Furthermore, gene ontology analysis revealed that the majority of the discovered lncRNAs are engaged in a variety of cellular and biological processes related to heat stress responses. Furthermore, the modeled three-dimensional (3D) structures of hub genes encoding proteins, which had an appropriate range of similarity with solved structures, provided information on their structural roles. The current study reveals many elements of gene expression regulation in wheat under heat stress, paving the way for the development of improved climate-resilient wheat cultivars.

Recent advances on genome-wide association studies (GWAS) and genomic selection (GS); prospects for Fusarium head blight research in Durum wheat.

PURPOSE: Wheat is an important cereal crop that is cultivated in different parts of the world. The biotic stresses are the major concerns in wheat-growing nations and are responsible for production loss globally. The change in climate dynamics makes the pathogen more virulent in foothills and tropical regions. There is growing concern about FHB in major wheat-growing nations, and until now, there has been no known potential source of resistance identified in wheat germplasm. The plant pathogen interaction activates the cascade of pathways, genes, TFs, and resistance genes. Pathogenesis-related genes' role in disease resistance is functionally validated in different plant systems. Similarly, Genomewide association Studies (GWAS) and Genomic selection (GS) are promising tools and have led to the discovery of resistance genes, genomic regions, and novel markers. Fusarium graminearum produces deoxynivalenol (DON) mycotoxins in wheat kernels, affecting wheat productivity globally. Modern technology now allows for detecting and managing DON toxin to reduce the risk to humans and animals. This review offers a comprehensive overview of the roles played by GWAS and Genomic selection (GS) in the identification of new genes, genetic variants, molecular markers and DON toxin management strategies. METHODS: The review offers a comprehensive and in-depth analysis of the function of Fusarium graminearum virulence factors in Durum wheat. The role of GWAS and GS for Fusarium Head Blight (FHB) resistance has been well described. This paper provides a comprehensive description of the various statistical models that are used in GWAS and GS. In this review, we look at how different detection methods have been used to analyze and manage DON toxin exposure. RESULTS: This review highlights the role of virulent genes in Fusarium disease establishment. The role of genome-based selection offers the identification of novel QTLs in resistant wheat germplasm. The role of GWAS and GS selection has minimized the use of population development through breeding technology. Here, we also emphasized the function of recent technological developments in minimizing the impact of DON toxins and their implications for food safety.

Validation of Novel spot blotch disease resistance alleles identified in unexplored wheat (Triticum aestivum L.) germplasm lines through KASP markers.

BACKGROUND: During the last few decades, the diverse sources of resistance, several genes and QTLs for spot blotch resistance have been identified. However, a large set of germplasm lines are still unexplored that have the potential to develop highly resistant wheat cultivars for the target environments. Therefore, the identification of new sources of resistance to spot blotch is essential for breeding programmes to develop spot blotch resistant cultivars and sustain wheat production. The association mapping panel of 294 diverse bread wheat accessions was used to explore new sources of spot blotch disease resistance and to identify genomic regions using genome wide association analysis (GWAS). The genotypes were tested in replicated trials for spot blotch disease at three major hot spots in India (Varanasi in UP, Pusa in Bihar, and Cooch Behar in West Bengal). The area under the disease progress curve (AUDPC) was calculated to assess the level of resistance in each genotype. RESULTS: A total of 19 highly and 76 moderately resistant lines were identified. Three accessions (EC664204, IC534306 and IC535188) were nearly immune to spot blotch disease. The genotyping of all accessions resulted in a total of 16,787 high-quality polymorphic SNPs. The GWAS was performed using a Compressed Mixed Linear Model (CMLM) and a Mixed Linear Model (MLM). A total of seven significant MTAs, common in both the models and consistent across the environment, were further validated to develop KASP markers. Four MTAs (AX-94710084, AX-94865722, AX-95135556, and AX-94529408) on three chromosomes (2AL, 2BL, and 3BL) have been successfully validated through the KASP marker. CONCLUSIONS: The new source of resistance was identified from unexplored germplasm lines. The genomic regions identified through GWAS were validated through KASP markers. The marker information and the highly resistant sources are valuable resources to rapidly develop immune or near immune wheat varieties.

Draft genome sequencing and secretome profiling of Sclerotinia sclerotiorum revealed effector repertoire diversity and allied broad-host range necrotrophy.

White mold commonly known as Sclerotinia sclerotiorum causes stem rot disease and has emerged as one of the major fungal pathogens of oilseed Brassica across the world. In the present study, consistently virulent S. sclerotiorum isolate "ESR-01" was sequenced and an assembly size of ~ 41 Mb with 328 scaffolds having N50 of 447,128 was obtained. Additionally, 27,450 single nucleotide polymorphisms (SNPs) were identified from 155 scaffolds against S. sclerotiorum 1980 isolate, with an average SNP density of ~ 1.5 per kb genome. 667 repetitive elements were identified and approximately comprised 7% of the total annotated genes. The DDE_1 with 454 in numbers was found to be the most abundant and accounts for 68% of the total predicted repetitive elements. In total, 3844 simple sequence repeats are identified in the 328 scaffolds. A total of 9469 protein-coding genes were predicted from the whole genome assembly with an average gene length of 1587 bp and their distribution as 230.95 genes per Mb in the genome. Out of 9469 predicted protein-coding genes, 529 genes were observed encoding the CAZymes (Carbohydrate-Active enzymes) capable of degradation of the complex polysaccharides. Glycosyltransferase (GT) families were most abundant (49.71%) among the predicted CAZymes and GT2 (23%), GT4 (20%), and glycoside hydrolase (GH) 23% with GH18 (11%) were the prominent cell wall degrading enzyme families in the ESR-01 secretome. Besides this, 156 genes essential for the pathogen-host interactions were also identified. The effector analysis in the whole genome proteomics dataset revealed a total of 57 effector candidates (ECs) and 27 of them were having their analogs whereas the remaining 30 were novel ones. Eleven selected ECs were validated experimentally by analyzing the expression profile of the ESR-01 isolate of S. sclerotiorum. Together, the present investigation offers a better understanding of the S. sclerotiorum genome, secretome, and its effector repertoire which will help in refining the present knowledge on S. sclerotiorum-Brassica interactions and necrotrophic lifestyle of the phytopathogen in general.

Integrated model for genomic prediction under additive and non-additive genetic architecture.

Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM's performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.

SNP Discovery Using BSR-Seq Approach for Spot Blotch Resistance in Wheat (Triticum aestivum L.), an Essential Crop for Food Security.

The pathogenic fungus, Bipolaris sorokiniana, that causes spot blotch (SB) disease of wheat, is a major production constraint in the Eastern Gangetic Plains of South Asia and other warm, humid regions of the world. A recombinant inbred line population was developed and phenotyped at three SB-prone locations in India. The single nucleotide polymorphism (SNP) for SB resistance was identified using a bulked segregant RNA-Seq-based approach, referred to as "BSR-Seq." Transcriptome sequencing of the resistant parent (YS#24), the susceptible parent (YS#58), and their resistant and susceptible bulks yielded a total of 429.67 million raw reads. The bulk frequency ratio (BFR) of SNPs between the resistant and susceptible bulks was estimated, and selection of SNPs linked to resistance was done using sixfold enrichments in the corresponding bulks (BFR >6). With additional filtering criteria, the number of transcripts was further reduced to 506 with 1055 putative polymorphic SNPs distributed on 21 chromosomes of wheat. Based on SNP enrichment on chromosomal loci, five transcripts were found to be associated with SB resistance. Among the five SB resistance-associated transcripts, four were distributed on the 5B chromosome with putative 52 SNPs, whereas one transcript with eight SNPs was present on chromosome 3B. The SNPs linked to the trait were exposed to a tetra-primer ARMS-PCR assay, and an SNP-based allele-specific marker was identified for SB resistance. The in silico study of these five transcripts showed homology with pathogenesis-related genes; the metabolic pathway also exhibits similar results, suggesting their role in the plant defense mechanism.

Multi-locus genome-wide association studies (ML-GWAS) reveal novel genomic regions associated with seedling and adult plant stage leaf rust resistance in bread wheat (Triticum aestivum L.).

Leaf rust is one of the important diseases limiting global wheat production and productivity. To identify quantitative trait nucleotides (QTNs) or genomic regions associated with seedling and adult plant leaf rust resistance, multilocus genome-wide association studies (ML-GWAS) were performed on a panel of 400 diverse wheat genotypes using 35 K single-nucleotide polymorphism (SNP) genotyping assays and trait data of leaf rust resistance. Association analyses using six multi-locus GWAS models revealed a set of 201 significantly associated QTNs for seedling and 65 QTNs for adult plant resistance (APR), explaining 1.98-31.72% of the phenotypic variation for leaf rust. Among these QTNs, 51 reliable QTNs for seedling and 15 QTNs for APR were consistently detected in at least two GWAS models and were considered reliable QTNs. Three genomic regions were pleiotropic, each controlling two to three pathotype-specific seedling resistances to leaf rust. We also identified candidate genes, such as leucine-rich repeat receptor-like (LRR) protein kinases, P-loop containing nucleoside triphosphate hydrolase and serine-threonine/tyrosine-protein kinases (STPK), which have a role in pathogen recognition and disease resistance linked to the significantly associated genomic regions. The QTNs identified in this study can prove useful in wheat molecular breeding programs aimed at enhancing resistance to leaf rust and developing next-generation leaf rust-resistant varieties.

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Genetic diversity and population structure analysis for morphological traits in upland cotton (Gossypium hirsutum L.).

A total of 96 different genotypes of upland cotton (Gossypium hirsutum) were selected from the breeding material and germplasm available at CCS HAU, India, to find the novel marker-trait associations for morphological traits used for registration of variety in upland cotton. Twenty-three morphological traits of the selected genotypes were recorded in field trials conducted in two replication of randomized block design during Kharif 2018 and 2019. A total of 11 traits showed sufficient variations in the screened germplasm and the same were further used for association mapping. A total of 168 SSRs were used for genotyping, of which 97 SSRs showed polymorphism amplifying 293 different alleles with an average of 3.02 alleles per SSR. Clustering, principal component analysis, and population structure analysis advocated that the current germplasm panel has enough diversity to be considered for association mapping. A total of 20 significant marker-trait associations were identified by the mixed linear model (MLM) and compressed mixed linear model (CMLM), of which 15 were common to both models, hence considered as promising associations. To the best of our knowledge, it is a first attempt to identify the linked markers in relation to morphological traits for the cotton crop. Results of the present study will be highly useful in speeding up variety registration programmes of upland cotton complementing to Distinctiveness, Uniformity, and Stability (DUS) testing.

A Deep Clustering-based Novel Approach for Binning of Metagenomics Data.

Background: One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets. Objective: To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures. Methods: In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used. Results: In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods. Conclusion: An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.

Foraging behaviour of Scymnus coccivora Ayyar against cotton mealybug Phenacoccus solenopsis Tinsley.

Predation is one of the significant biotic mortality factors reducing the insect pest population as functional response and the numerical response of the predator are the key factor regulating the population dynamics of predator prey species. This study is aimed to evaluate the functional response of all the developmental stages of Scymnus coccivora Ayyar (Coleoptera: Coccinellidae) against the different densities of cotton mealybug, Phenacoccus solenopsis (Tinsley) (Hemiptera: Pseudococcidae) and the numerical response of female predator. Experiments were carried out in controlled environment laboratory conditions at 25 ± 1 °C temperature, 60 ± 5% relative humidity and photoperiod of 16 h. Number of eggs consumed, number of eggs laid and the Efficiency of Conversion of Ingested food (ECI) were recorded daily. Results from the study revealed that all the developmental stages of S. coccivora exhibited a Type II response. Different parameters such as attack rate (a'), handling time (Th) and the maximum rate of predation were estimated using Roger's random attack equation and Holling Disc equation in which Rogers random attack equation was found best fit. Female has shown the highest attack rate (a') followed by IVth instar grub, male, IIIrd, IInd and Ist instar grub. With low handling time, IVth instar grub has shown maximum predation rate of 76.40 per day followed by female (75.86), male (58.79), IIIrd (22.84), IInd (19.65) and Ist instar grub (15.39). The numerical response increase was curvilinearly related to different prey densities with the highest number of eggs (11.8 ± 3.44) produced at highest prey density (160). The Efficiency of Conversion of Ingested food (ECI) was highest (64.49 ± 8.03) at prey density of 10. Understanding the factors that lead to variation in functional response of predator in natural population will advance our understanding of the effects of predation on individual and the effectiveness of coccinellid predators as biocontrol agent against cotton mealybug.

Identification of Novel Marker-Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs.

Improving the yield of lint is the main objective for most of the cotton crop improvement programs throughout the world as it meets the demand of fiber for textile industries. In the current study, 96 genotypes of Gossypium hirsutum were used to find novel simple sequence repeat marker-based associations for lint yield contributing traits by linkage disequilibrium. Extensive phenotyping of 96 genotypes for various agronomic traits was done for two consecutive years (2018 and 2019) in early, normal, and late sown environments. Out of 168 SSR markers screened over the 96 genotypes, a total of 97 polymorphic markers containing 293 alleles were used for analysis. Three different models, i.e., mixed linear model (MLM), compressed mixed linear model (CMLM), and multiple locus mixed linear model (MLMM), were used to detect the significant marker-trait associations for six different environments separately. A total of 38 significant marker-trait associations that were common to at least two environments were considered as promising associations and detailed annotation of the significant markers has been carried out. Twenty-two marker-trait associations were found to be novel in the current study. These results will be very useful for crop improvement programs using marker-assisted cotton breeding.

Crosses with spelt improve tolerance of South Asian spring wheat to spot blotch, terminal heat stress, and their combination.

Spot blotch and terminal heat are two of the most important stresses for wheat in South Asia. A study was initiated to explore the use of spelt (Triticum spelta) to improve tolerance to these stresses in spring wheat (T. aestivum). We assessed 185 recombinant inbred lines (RILs) from the cross T. spelta (H + 26) × T. aestivum (cv. HUW234), under the individual stresses and their combination. H + 26 showed better tolerance to the single stresses and also their combination; grain yield in RILs was reduced by 21.9%, 27.7% and 39.0% under spot blotch, terminal heat and their combined effect, respectively. However, phenological and plant architectural traits were not affected by spot blotch itself. Multivariate analysis demonstrated a strong negative correlation between spikelet sterility and grain yield under spot blotch, terminal heat and their combination. However, four recombinant lines demonstrated high performance under both stresses and also under their combined stress. The four lines were significantly superior in grain yield and showed significantly lower AUDPC than the better parent. This study demonstrates the potential of spelt wheat in enhancing tolerance to spot blotch and terminal heat stresses. It also provides comprehensive evidence about the expression of yield and phenological traits under these stresses.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach.

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.