Enhancement of grain Fe and Zinc Concentration in Cereals-Biofortification approach

Micronutrient malnutrition alone afflicts more than two billion people, mostly among resource-poor families in developing countries, with Zn, Fe, I and vitamin A deficiencies most prevalent globally. More than five million childhood deaths occur from micronutrient malnutrition every year. Currently, mineral malnutrition is considered to be among the most serious global challenges to human kind and is avoidable. Among different micronutrients, Fe, Zn, deficiency is a well-documented problem in food crops, causing decreased crop yields and nutritional quality. Generally, the regions in the world with Fe, Zn-deficient soils are also characterized by widespread Fe, Zn deficiency in humans. Recent estimates indicate that nearly half of world population suffers from Fe, Zn deficiency. Cereal crops play an important role in satisfying daily calorie intake in developing world, but they are inherently very low in Fe, Zn concentrations in grain, particularly when grown on Fe, Zn-deficient soils. The reliance on cereal-based diets may induce Zn deficiency-related health problems in humans, such as impairments in physical development, Immune system and brain function. Among the strategies being discussed as major solution to Fe, Zn deficiency, genetic biofortification appears to be a most sustainable and cost-effective approach useful in improving Fe, Zn concentrations in grain. Scientific evidence shows this is technically feasible without compromising agronomic productivity

Discovery and validation of candidate genes for grain iron and zinc metabolism in pearl millet [Pennisetum glaucum (L.) R. Br.]

Pearl millet is an important crop for alleviating micronutrient malnutrition through genomics-assisted breeding for grain Fe (GFeC) and Zn (GZnC) content. In this study, we identified candidate genes related to iron (Fe) and zinc (Zn) metabolism through gene expression analysis and correlated it with known QTL regions for GFeC/GZnC. From a total of 114 Fe and Zn metabolism-related genes that were selected from the related crop species, we studied 29 genes. Different developmental stages exhibited tissue and stage-specific expressions for Fe and Zn metabolism genes in parents contrasting for GFeC and GZnC. Results revealed that PglZIP, PglNRAMP and PglFER gene families were candidates for GFeC and GZnC. Ferritin-like gene, PglFER1 may be the potential candidate gene for GFeC. Promoter analysis revealed Fe and Zn deficiency, hormone, metal-responsive, and salt-regulated elements. Genomic regions underlying GFeC and GZnC were validated by annotating major QTL regions for grain Fe and Zn. Interestingly, PglZIP and PglNRAMP gene families were found common with a previously reported linkage group 7 major QTL region for GFeC and GZnC. The study provides insights into the foundation for functional dissection of different Fe and Zn metabolism genes homologs and their subsequent use in pearl millet molecular breeding programs globally

Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations

The escalating risk of collisions and the accumulation of space debris in Low Earth Orbit (LEO) has reached critical concern due to the ever increasing number of spacecraft. Addressing this crisis, especially in dealing with non-cooperative and unidentified space debris, is of paramount importance. This paper contributes to efforts in enabling autonomous swarms of small chaser satellites for target geometry determination and safe flight trajectory planning for proximity operations in LEO. Our research explores on-orbit use of the You Only Look Once v5 (YOLOv5) object detection model trained to detect satellite components. While this model has shown promise, its inherent lack of interpretability hinders human understanding, a critical aspect of validating algorithms for use in safety-critical missions. To analyze the decision processes, we introduce Probabilistic Explanations for Entropic Knowledge extraction (PEEK), a method that utilizes information theoretic analysis of the latent representations within the hidden layers of the model. Through both synthetic in hardware-in-the-loop experiments, PEEK illuminates the decision-making processes of the model, helping identify its strengths, limitations and biases

Genetic variability, genotype × environment interaction and correlation analysis for grain iron and zinc contents in recombinant inbred line population of pearl millet [Pennisetum glaucum (L). R.

Micronutrient malnutrition is one of the major health problems, especially iron (Fe) and zinc (Zn) deficiencies that are widespread coupled with inadequate food supply in the developing world. Pearl millet grains are a good source of Fe and Zn elements making it a potential staple crop for overcoming hidden-hunger and micronutrient deficiencies. Breeding pearl millet with high levels of grain Zn and Fe contents represents a major opportunity to enhance the intake of these minerals for poor and malnourished people. A precise understanding of the genetic variability, correlation of mineral nutrients, genotype × environment (G × E) interaction is important for developing improved lines with high Fe and Zn content. To get fair estimates, we used a bi-parental recombinant inbred lines (RIL) mapping population representing F2 phenotypic variance. A total of 317 RILs were evaluated for grain iron and zinc content in two seasons, Summer 2016 (E1) and Summer 2017 (E2). The result from the analysis of variance exhibited a large variability for grain Fe and Zn content across the two environments. The G × E for high grain Fe were significant at P < 0.01. The mean performance across the two environments data for grain Fe ranged from 22.9 to 154.5 mg kg-1 (ppm) and Zn content ranged from 19.3 to 121 mg kg-1. The correlation coefficient for grain Fe and Zn was 0.9, and 0.8 and across the two (E1 and E2) environments. The value of correlation coefficient (0.9) was found to be highly significant at P < 0.01 level, that indicated good opportunities for simultaneous genetic improvement of both iron and zinc contents in pearl millet

Impact of heat and drought stresses on grain nutrient content in chickpea: Genome-wide marker-trait associations for protein, Fe and Zn

Chickpea is a cheap source of protein and micronutrients to the poor and vegetarian population living in south- Asia and sub-Saharan Africa. Due to changes in climatic conditions and cropping systems, the crop is being exposed to severe drought and heat stress during its reproductive period, which leads to significant yield losses and fluctuations in grain nutrient accumulation. The study was conducted with 140 diverse genotypes under nonstress, drought, and heat stress conditions to estimate their effects on grain nutrient (protein, Fe and Zn) contents and identify the marker-trait associations. Analysis of variance revealed highly significant differences among genotypes for nutrient content under respective planting conditions. The seed yield was negatively associated with the grain Fe (r

Discerning combining ability loci for divergent environments using chromosome segment substitution lines (CSSLs) in pearl millet

Pearl millet is an important crop for arid and semi-arid regions of the world. Genomic regions associated with combining ability for yield-related traits under irrigated and drought conditions are useful in heterosis breeding programs. Chromosome segment substitution lines (CSSLs) are excellent genetic resources for precise QTL mapping and identifying naturally occurring favorable alleles. In the present study, testcross hybrid populations of 85 CSSLs were evaluated for 15 grain and stover yield-related traits for summer and wet seasons under irrigated control (CN) and moisture stress (MS) conditions. General combining ability (GCA) and specific combining ability (SCA) effects of all these traits were estimated and significant marker loci linked to GCA and SCA of the traits were identified. Heritability of the traits ranged from 53–94% in CN and 63–94% in MS. A total of 40 significant GCA loci and 36 significant SCA loci were identified for 14 different traits. Five QTLs (flowering time, panicle number and panicle yield linked to Xpsmp716 on LG4, flowering time and grain number per panicle with Xpsmp2076 on LG4) simultaneously controlled both GCA and SCA, demonstrating their unique genetic basis and usefulness for hybrid breeding programs. This study for the first time demonstrated the potential of a set of CSSLs for trait mapping in pearl millet. The novel combining ability loci linked with GCA and SCA values of the traits identified in this study may be useful in pearl millet hybrid and population improvement programs using marker-assisted selection (MAS)

Towards defining heterotic gene pools using SSR markers in pearl millet [Pennisetum glaucum (L.) R. Br.]

Pearl millet is a climate resilient crop and the most widely grown millet worldwide. In a maiden attempt to identify potential heterotic groups for grain yield in pearl millet, a total of 88 polymorphic SSR markers were used to genotype 343 hybrid parental lines of pearl millet. The SSR markers generated a total of 532 alleles with a mean value of 6.05 alleles per locus, mean gene diversity of 0.55, and an average PIC of 0.50. Out of 532 alleles, 443 (83.27%) alleles were contributed by B- lines with a mean of 5.03 alleles per locus. R- lines contributed 476 alleles (89.47%) with a mean of 5.41, while 441 (82.89%) alleles were shared commonly between B- and R- lines. The gene diversity and PIC were high among R- lines (0.55 and 0.50) than B- lines (0.49 and 0.44) revealed that R- lines were more diverse than B- lines. The unweighted neighbor-joining tree based on simple matching dissimilarity matrix obtained from SSR data clearly differentiated B- lines into 10 sub-clusters (B1, B2, B3, B4, B5, B6, B7, B8, B9 and B10), and Rlines into 11 sub-clusters (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10 and R11). The parents, three checks and 99 hybrids generated by crossing between representative lines of each of the B- cluster with that of each of the R- cluster were evaluated in line ? tester design over three environments. Based on pooled mean performance, the cross combinations generated between clusters B1 and R3, B2 and R4, B3 and R5, B4 and undetermined cluster, B5 and 11R, B6 and R3, B8 and R4, B9 and R7 and B10 and R5 had shown higher grain yield per plant compared to their counterparts. Based on per se performance, high sca effects and standard heterosis over superior check, F1s generated from crosses between representatives of groups B3 and B10 with representative of group R5 resulted in best heterotic combinations for grain yield. These represent putative heterotic gene pools in pearl millet.publishersversionPeer reviewe

Fine mapping of the linkage group 2 drought tolerance QTL in pearl millet [Pennisetum glaucum (L.) R. Br.]

Pearl millet is an important cereal crop capable of growing in semi-arid, arid and marginal regions of the world. Drought is a major abiotic constraint affecting pearl millet production globally. Previously identified and validated major linkage group 2 (LG2) drought tolerance (DT) QTL contributing to hybrid grain and stover yield potential to terminal drought stress is being fine mapped. The fine mapping population was generated by crossing two near-isogenic lines (NILs) differing for the LG2 DT QTL. A modified double digest restriction site associated DNA (ddRAD) technique using SphI and MluCI enzyme combination was employed. A total of 290 out of 6,067 F2 mapping population of the cross (H77/833-2-P10 × ICMR 01029-P10), and parents were genotyped on Illumina HiSeq2500. Out of 52,028 SNPs that were identified from a total of 888.85 million reads at a read depth of 10 in the mapping population, a total of 6,821 SNPs were used for mapping. The genotypic data of these SNPs were used in combination with that of seven SSRs that had known linkage relationship with LG2 DT QTL interval. Linkage map was constructed using QTL IciMapping 4.1 software at a LOD threshold of 3.0. A total of 189 SNPs anchored to seven SSRs were mapped to the LG2 DT QTL. The length of linkage group (LG) was 639.72 cM (Haldane units) with an average inter-marker distance of 6.73 cM. In order to refine the fine mapping process, ddRAD technique is being further employed for genotyping rest of the fine mapping population

Metabolic pathway responsive gene encoding enzyme anchored EST–SSR markers based genetic and population assessment among Capsicum accessions

Gene encoding enzyme based EST–SSR markers are more potent or functional marker system to evaluate astounding genetic and structural differentiation in plants. It is very useful in shaping divergences in metabolic fingerprinting, ecological interactions, conservation and adaptation among plants. Therefore, gene encoding enzyme mediated EST–SSR markers system were used presently to evaluate genetic and population structure among 48 Capsicum accessions. Total of 35 gene encoding enzyme based EST–SSR markers was used and generated 184 alleles at 35 loci with an average of 5.25 alleles per locus. The average value of polymorphic information content, marker index and discriminating power was 0.40, 0.232, and 0.216 respectively which revealed noteworthy degree of marker efficacy and their competency was further supported by primer polymorphism (93.57%) and cross transferability (44.52%). A significant genetic variability (Na = 1.249, Ne = 1.269, I = 0.247, He = 0.163, and uHe = 0.183) was identified among the Capsicum accession using EST–SSR markers. The mean value for Nei gene diversity, total species diversity (Ht), and diversity within population (Hs) were 0.277, 0.240 and 0.170 respectively. The coefficient of gene differentiation (Gst) was 0.296 indicating significant genetic differentiation within the population and Gene flow (Nm) was 1.189, which reflect a constant gene flow among populations. AMOVA revealed more genetic differentiation within the population which is similarly supported by principal coordinate analysis among the different Capsicum population. Thus, gene encoding enzyme based EST–SSR markers represent a potent system for estimation of genetic and structural relationship and is helpful for estimation of relationships or variations studies in plants