The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
David E. Salt - One of the best experts on this subject based on the ideXlab platform.
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1,135 ionomes reveals the global pattern of leaf and seed mineral nutrient and trace element diversity in Arabidopsis thaliana.
The Plant journal : for cell and molecular biology, 2021Co-Authors: A. C. A. L. Campos, W. F. A. Van Dijk, Priya Ramakrishna, Tom C. Giles, Pamela Korte, Alex Douglas, Pete Smith, David E. SaltAbstract:Soil is a heterogenous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically-based variation in the elemental composition 'ionome', of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool- IonExplorer to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory genetic mapping studies to identify genes underlying natural variation in leaf and seed ionome, and genetic adaptation of plants to soil conditions.
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Full species-wide leaf and seed ionomic diversity of Arabidopsis thaliana
2020Co-Authors: A. C. A. L. Campos, W. F. A. Van Dijk, Priya Ramakrishna, Tom C. Giles, Pamela Korte, Alex Douglas, Pete Smith, David E. SaltAbstract:O_LISoil is a heterogenous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically-based variation in the elemental composition ionome, of plants, both within and between species. C_LIO_LIWe explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. C_LIO_LIWe present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool-IonExplorer to interact with the dataset. C_LIO_LIWe describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory QTL, GWA studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions. C_LI
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Plant Ionomics: From Elemental Profiling to Environmental Adaptation
Molecular Plant, 2016Co-Authors: Xin-yuan Huang, David E. SaltAbstract:Ionomics is a high-throughput elemental profiling approach to study the molecular mechanistic basis underlying mineral nutrient and trace element composition (also known as the ionome) of living organisms. Since the concept of Ionomics was first introduced more than 10 years ago, significant progress has been made in the identification of genes and gene networks that control the ionome. In this update, we summarize the progress made in using the Ionomics approach over the last decade, including the identification of genes by forward genetics and the study of natural ionomic variation. We further discuss the potential application of Ionomics to the investigation of the ecological functions of ionomic alleles in adaptation to the environment.
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Natural Variants of AtHKT1 Enhance Naþ Accumulation in Two Wild
2015Co-Authors: Populations Of Arabidopsis, Balasubramaniam Muthukumar, Elena Yakubova, Jeff Gustin, Ivan Baxter, Brett Lahner, Ana Rus, David E. SaltAbstract:Plants are sessile and therefore have developed mechanisms to adapt to their environment, including the soil mineral nutrient composition. Ionomics is a developing functional genomic strategy designed to rapidly identify the genes and gene networks involved in regulating how plants acquire and accumulate these mineral nutrients from the soil. Here, we report on the coupling of high-throughput elemental profiling of shoot tissue from various Arabidopsis accessions with DNA microarray-based bulk segregant analysis and reverse genetics, for the rapid identification of genes from wild populations of Arabidopsis that are involved in regulating how plants acquire and accumulate Naþ from the soil. Elemental profiling of shoot tissue from 12 different Arabidopsis accessions revealed that two coastal populations of Arabidopsis collected from Tossa del Mar, Spain, and Tsu, Japan (Ts-1 and Tsu-1, respectively), accumulate higher shoot levels of Naþ than do Col-0 and other accessions. We identify AtHKT1, known to encode a Naþ transporter, as being the causal locus driving elevated shoot Naþ in both Ts-1 and Tsu-1. Furthermore, we establish that a deletion in a tandem repeat sequence approximately 5 kb upstream of AtHKT1 is responsible for the reduced root expression of AtHKT1 observed in these accessions. Reciprocal grafting experiments establish that this loss of AtHKT1 expression in roots is responsible for elevated shoot Naþ. Interestingly, and in contrast to the hkt1–1 null mutant, under NaCl stress conditions, this novel AtHKT1 allele not only does not confer NaCl sensitivity but also cosegregates with elevated NaCl tolerance. We also present all our elemental profiling data in a new open access Ionomics database, the Purdue Ionomics Information Management System (PiiMS
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Natural Variants of AtHKT1 Enhance Na þ Accumulation in Two Wild
2013Co-Authors: Populations Of Arabidopsis, Balasubramaniam Muthukumar, Elena Yakubova, Jeff Gustin, Ivan Baxter, Brett Lahner, Ana Rus, David E. SaltAbstract:Plants are sessile and therefore have developed mechanisms to adapt to their environment, including the soil mineral nutrient composition. Ionomics is a developing functional genomic strategy designed to rapidly identify the genes and gene networks involved in regulating how plants acquire and accumulate these mineral nutrients from the soil. Here, we report on the coupling of high-throughput elemental profiling of shoot tissue from various Arabidopsis accessions with DNA microarray-based bulk segregant analysis and reverse genetics, for the rapid identification of genes from wild populations of Arabidopsis that are involved in regulating how plants acquire and accumulate Na þ from the soil. Elemental profiling of shoot tissue from 12 different Arabidopsis accessions revealed that two coastal populations of Arabidopsis collected from Tossa del Mar, Spain, and Tsu, Japan (Ts-1 and Tsu-1, respectively), accumulate higher shoot levels of Na þ than do Col-0 and other accessions. We identify AtHKT1, known to encode a Na þ transporter, as being the causal locus driving elevated shoot Na þ in both Ts-1 and Tsu-1. Furthermore, we establish that a deletion in a tandem repeat sequence approximately 5 kb upstream of AtHKT1 is responsible for the reduced root expression of AtHKT1 observed in these accessions. Reciprocal grafting experiments establish that this loss of AtHKT1 expression in roots is responsible for elevated shoot Na þ. Interestingly, and in contrast to the hkt1–1 null mutant, under NaCl stress conditions, this novel AtHKT1 allele not only does not confer NaCl sensitivity but also cosegregates with elevated NaCl tolerance. We also present all our elemental profiling data in a new open access Ionomics database, the Purdue Ionomics Information Management System (PiiMS
Ivan Baxter - One of the best experts on this subject based on the ideXlab platform.
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Intraspecific variation in elemental accumulation and its association with salt tolerance in Paspalum vaginatum
2021Co-Authors: David M. Goad, Ivan Baxter, Elizabeth A. Kellogg, K. M. OlsenAbstract:Most plant species, including most crops, perform poorly in salt-affected soils because high sodium levels are cytotoxic and can disrupt uptake of water and important nutrients. Halophytes are species that have evolved adaptations to overcome these challenges and may be a useful source of knowledge for salt tolerance mechanisms and genes that may be transferable to crop species. The salt content of saline habitats can vary dramatically by location, providing ample opportunity for different populations of halophytic species to adapt to their local salt concentrations; however, the extent of this variation, and the physiology and polymorphisms that drive it, remain poorly understood. Differential accumulation of inorganic elements between genotypes or populations may play an important role in local salinity adaptation. To test this, we investigated the relationships between population structure, tissue ion concentrations (i.e., ionomic profiles) and salt tolerance in 17 "fine-textured" genotypes of the halophytic turfgrass seashore paspalum (Paspalum vaginatum Swartz). A high-throughput Ionomics pipeline was used to quantify the shoot concentration of 18 inorganic elements across three salinity treatments. We found a significant relationship between population structure and ion accumulation, with strong correlations between principal components derived from genetic and ionomic data. Additionally, genotypes with higher salt tolerance accumulated more K and Fe and less Ca than less tolerant genotypes. Together these results indicate that differences in ion accumulation between P. vaginatum populations may reflect locally adapted salt stress responses.
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High-throughput profiling and analysis of plant responses over time to abiotic stress
2017Co-Authors: Kira M. Veley, Ivan Baxter, Jeffrey C. Berry, Sarah J. Fentress, Daniel P. Schachtman, Rebecca BartAbstract:Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G x E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G x E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.
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should we treat the ionome as a combination of individual elements or should we be deriving novel combined traits
Journal of Experimental Botany, 2015Co-Authors: Ivan BaxterAbstract:It has been more than 10 years since the concept of the ionome, all of the mineral nutrients in a cell tissue or organism, was introduced. In the intervening years, Ionomics, high throughput elemental profiling, has been used to analyse over 400 000 samples from at least 10 different organisms. There are now multiple published examples where an Ionomics approach has been used to find genes of novel function, find lines or environments that produce foods with altered nutritional profiles, or define gene by environmental effects on elemental accumulation. In almost all of these studies, the ionome has been treated as a collection of independent elements, with the analysis repeated on each measured element. However, many elements share chemical properties, are known to interact with each other, or have been shown to have similar interactions with biological molecules. Accordingly, there is strong evidence from ionomic studies that the elements of the ionome do not behave independently and that combinations of elements should be treated as the phenotypes of interest. In this review, I will consider the evidence that we have for the interdependence of the ionome, some of its causes, methods for incorporating this interdependence into analyses and the benefits, drawbacks, and challenges of taking these approaches.
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Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding
Metabolomics, 2015Co-Authors: Miyako Kusano, Ivan Baxter, Atsushi Fukushima, Akira Oikawa, Yozo Okazaki, Ryo Nakabayashi, Denise J. Bouvrette, Frederic Achard, Andrew R. Jakubowski, Joan M. BallamAbstract:Information on crop genotype- and phenotype-metabolite associations can be of value to trait development as well as to food security and safety. The unique study presented here assessed seed metabolomic and ionomic diversity in a soybean lineage representing ~35 years of breeding (launch years 1972–2008) and increasing yield potential. Selected varieties included six conventional and three genetically modified (GM) glyphosate-tolerant lines. A metabolomics approach utilizing capillary electrophoresis (CE)-time-of-flight-mass spectrometry (TOF-MS), gas chromatography (GC)-TOF-MS and liquid chromatography (LC)-quadrupole (q)-TOFMS resulted in measurement of a total of 732 annotated peaks. Ionomics through inductively-coupled plasma (ICP)-MS profiled twenty mineral elements. Orthogonal partial least squares-discriminant analysis (OPLS-DA) of the seed data successfully differentiated newer higher-yielding soybean from earlier lower-yielding accessions at both field sites. This result reflected genetic fingerprinting data that demonstrated a similar distinction between the newer and older soybean. Correlation analysis also revealed associations between yield data and specific metabolites. There were no clear metabolic differences between the conventional and GM lines. Overall, observations of metabolic and genetic differences between older and newer soybean varieties provided novel and significant information on the impact of varietal development on biochemical variability. Proposed applications of omics in food and feed safety assessments will need to consider that GM is not a major source of metabolite variability and that trait development in crops will, of necessity, be associated with biochemical variation.
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Natural Variants of AtHKT1 Enhance Naþ Accumulation in Two Wild
2015Co-Authors: Populations Of Arabidopsis, Balasubramaniam Muthukumar, Elena Yakubova, Jeff Gustin, Ivan Baxter, Brett Lahner, Ana Rus, David E. SaltAbstract:Plants are sessile and therefore have developed mechanisms to adapt to their environment, including the soil mineral nutrient composition. Ionomics is a developing functional genomic strategy designed to rapidly identify the genes and gene networks involved in regulating how plants acquire and accumulate these mineral nutrients from the soil. Here, we report on the coupling of high-throughput elemental profiling of shoot tissue from various Arabidopsis accessions with DNA microarray-based bulk segregant analysis and reverse genetics, for the rapid identification of genes from wild populations of Arabidopsis that are involved in regulating how plants acquire and accumulate Naþ from the soil. Elemental profiling of shoot tissue from 12 different Arabidopsis accessions revealed that two coastal populations of Arabidopsis collected from Tossa del Mar, Spain, and Tsu, Japan (Ts-1 and Tsu-1, respectively), accumulate higher shoot levels of Naþ than do Col-0 and other accessions. We identify AtHKT1, known to encode a Naþ transporter, as being the causal locus driving elevated shoot Naþ in both Ts-1 and Tsu-1. Furthermore, we establish that a deletion in a tandem repeat sequence approximately 5 kb upstream of AtHKT1 is responsible for the reduced root expression of AtHKT1 observed in these accessions. Reciprocal grafting experiments establish that this loss of AtHKT1 expression in roots is responsible for elevated shoot Naþ. Interestingly, and in contrast to the hkt1–1 null mutant, under NaCl stress conditions, this novel AtHKT1 allele not only does not confer NaCl sensitivity but also cosegregates with elevated NaCl tolerance. We also present all our elemental profiling data in a new open access Ionomics database, the Purdue Ionomics Information Management System (PiiMS
Brett Lahner - One of the best experts on this subject based on the ideXlab platform.
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Natural Variants of AtHKT1 Enhance Naþ Accumulation in Two Wild
2015Co-Authors: Populations Of Arabidopsis, Balasubramaniam Muthukumar, Elena Yakubova, Jeff Gustin, Ivan Baxter, Brett Lahner, Ana Rus, David E. SaltAbstract:Plants are sessile and therefore have developed mechanisms to adapt to their environment, including the soil mineral nutrient composition. Ionomics is a developing functional genomic strategy designed to rapidly identify the genes and gene networks involved in regulating how plants acquire and accumulate these mineral nutrients from the soil. Here, we report on the coupling of high-throughput elemental profiling of shoot tissue from various Arabidopsis accessions with DNA microarray-based bulk segregant analysis and reverse genetics, for the rapid identification of genes from wild populations of Arabidopsis that are involved in regulating how plants acquire and accumulate Naþ from the soil. Elemental profiling of shoot tissue from 12 different Arabidopsis accessions revealed that two coastal populations of Arabidopsis collected from Tossa del Mar, Spain, and Tsu, Japan (Ts-1 and Tsu-1, respectively), accumulate higher shoot levels of Naþ than do Col-0 and other accessions. We identify AtHKT1, known to encode a Naþ transporter, as being the causal locus driving elevated shoot Naþ in both Ts-1 and Tsu-1. Furthermore, we establish that a deletion in a tandem repeat sequence approximately 5 kb upstream of AtHKT1 is responsible for the reduced root expression of AtHKT1 observed in these accessions. Reciprocal grafting experiments establish that this loss of AtHKT1 expression in roots is responsible for elevated shoot Naþ. Interestingly, and in contrast to the hkt1–1 null mutant, under NaCl stress conditions, this novel AtHKT1 allele not only does not confer NaCl sensitivity but also cosegregates with elevated NaCl tolerance. We also present all our elemental profiling data in a new open access Ionomics database, the Purdue Ionomics Information Management System (PiiMS
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Natural Variants of AtHKT1 Enhance Na þ Accumulation in Two Wild
2013Co-Authors: Populations Of Arabidopsis, Balasubramaniam Muthukumar, Elena Yakubova, Jeff Gustin, Ivan Baxter, Brett Lahner, Ana Rus, David E. SaltAbstract:Plants are sessile and therefore have developed mechanisms to adapt to their environment, including the soil mineral nutrient composition. Ionomics is a developing functional genomic strategy designed to rapidly identify the genes and gene networks involved in regulating how plants acquire and accumulate these mineral nutrients from the soil. Here, we report on the coupling of high-throughput elemental profiling of shoot tissue from various Arabidopsis accessions with DNA microarray-based bulk segregant analysis and reverse genetics, for the rapid identification of genes from wild populations of Arabidopsis that are involved in regulating how plants acquire and accumulate Na þ from the soil. Elemental profiling of shoot tissue from 12 different Arabidopsis accessions revealed that two coastal populations of Arabidopsis collected from Tossa del Mar, Spain, and Tsu, Japan (Ts-1 and Tsu-1, respectively), accumulate higher shoot levels of Na þ than do Col-0 and other accessions. We identify AtHKT1, known to encode a Na þ transporter, as being the causal locus driving elevated shoot Na þ in both Ts-1 and Tsu-1. Furthermore, we establish that a deletion in a tandem repeat sequence approximately 5 kb upstream of AtHKT1 is responsible for the reduced root expression of AtHKT1 observed in these accessions. Reciprocal grafting experiments establish that this loss of AtHKT1 expression in roots is responsible for elevated shoot Na þ. Interestingly, and in contrast to the hkt1–1 null mutant, under NaCl stress conditions, this novel AtHKT1 allele not only does not confer NaCl sensitivity but also cosegregates with elevated NaCl tolerance. We also present all our elemental profiling data in a new open access Ionomics database, the Purdue Ionomics Information Management System (PiiMS
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Large-scale plant Ionomics
Methods of Molecular Biology, 2012Co-Authors: John Danku, Elena Yakubova, Brett Lahner, David E. SaltAbstract:Large-scale phenotyping methods are at the heart of efficiently deciphering the functions of genes and gene networks in the postgenomic era. In order to obtain meaningful results when comparing natural variants, and mutants and wild-types during large-scale quantitative analyses, necessary precautions must be employed throughout the whole process. Here, we describe large-scale elemental profiling in Arabidopsis thaliana and other genetic model organisms using high-throughput analytical methodologies. We also include a description of workflow management and data storage systems.
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Mapping of Ionomic Traits in Mimulus guttatus Reveals Mo and Cd QTLs That Colocalize with MOT1 Homologues
PloS one, 2012Co-Authors: David B. Lowry, Brett Lahner, David E. Salt, Calvin C. Sheng, Zhirui Zhu, Thomas E. Juenger, John H. WillisAbstract:Natural variation in the regulation of the accumulation of mineral nutrients and trace elements in plant tissues is crucial to plant metabolism, development, and survival across different habitats. Studies of the genetic basis of natural variation in nutrient metabolism have been facilitated by the development of Ionomics. Ionomics is a functional genomic approach for the identification of the genes and gene networks that regulate the elemental composition, or ionome, of an organism. In this study, we evaluated the genetic basis of divergence in elemental composition between an inland annual and a coastal perennial accession of Mimulus guttatus using a recombinant inbred line (RIL) mapping population. Out of 20 elements evaluated, Mo and Cd were the most divergent in accumulation between the two accessions and were highly genetically correlated in the RILs across two replicated experiments. We discovered two major quantitative trait loci (QTL) for Mo accumulation, the largest of which consistently colocalized with a QTL for Cd accumulation. Interestingly, both Mo QTLs also colocalized with the two M. guttatus homologues of MOT1, the only known plant transporter to be involved in natural variation in molybdate uptake.
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the leaf ionome as a multivariable system to detect a plant s physiological status
Proceedings of the National Academy of Sciences of the United States of America, 2008Co-Authors: Ivan Baxter, Balasubramaniam Muthukumar, Brett Lahner, Olga Vitek, Monica Borghi, Joe Morrissey, Mary Lou Guerinot, David E. SaltAbstract:The contention that quantitative profiles of biomolecules contain information about the physiological state of the organism has motivated a variety of high-throughput molecular profiling experiments. However, unbiased discovery and validation of biomolecular signatures from these experiments remains a challenge. Here we show that the Arabidopsis thaliana (Arabidopsis) leaf ionome, or elemental composition, contains such signatures, and we establish statistical models that connect these multivariable signatures to defined physiological responses, such as iron (Fe) and phosphorus (P) homeostasis. Iron is essential for plant growth and development, but potentially toxic at elevated levels. Because of this, shoot Fe concentrations are tightly regulated and show little variation over a range of Fe concentrations in the environment, making them a poor probe of a plant's Fe status. By evaluating the shoot ionome in plants grown under different Fe nutritional conditions, we have established a multivariable ionomic signature for the Fe response status of Arabidopsis. This signature has been validated against known Fe-response proteins and allows the high-throughput detection of the Fe status of plants with a false negative/positive rate of 18%/16%. A “metascreen” of previously collected ionomic data from 880 Arabidopsis mutants and natural accessions for this Fe response signature successfully identified the known Fe mutants frd1 and frd3. A similar approach has also been taken to identify and use a shoot ionomic signature associated with P homeostasis. This study establishes that multivariable ionomic signatures of physiological states associated with mineral nutrient homeostasis do exist in Arabidopsis and are in principle robust enough to detect specific physiological responses to environmental or genetic perturbations.
Zhaohua Peng - One of the best experts on this subject based on the ideXlab platform.
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Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods
BMC plant biology, 2020Co-Authors: Shuai Liu, Hua Zhong, Xiaoxi Meng, Tong Sun, Shannon R. M. Pinson, Sam K.c. Chang, Zhaohua PengAbstract:Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for Ionomics flooded, Ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value
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Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods
2020Co-Authors: Shuai Liu, Hua Zhong, Xiaoxi Meng, Tong Sun, Shannon R. M. Pinson, Sam Chang, Zhaohua PengAbstract:Abstract BackgroundRice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. ResultsTo identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for Ionomics flooded, Ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value <1.53×10-8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~20%) of the 250 QTLs were coinciding with previous reported QTLs/genes, about 201 (~80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. ConclusionsWhile 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.
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Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods
2020Co-Authors: Shuai Liu, Hua Zhong, Xiaoxi Meng, Tong Sun, Shannon R. M. Pinson, Sam Chang, Zhaohua PengAbstract:Abstract Background: Rice is an important human staple food vulnerable to heavy metal contamination due to its unique physiology and growth environment. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge. To identify candidate QTLs for rice yield and heavy metal content, sixteen ionomic traits and thirteen agronomic traits of the USDA Rice mini-core collection were analyzed using both univariate and multivariate GWAS methods in this study. The USDA Rice Mini-Core Collection contains about 1% of the whole Rice Collection of the National Small Grains Collection (NSGC), USA.Results: Using the p-value <1.53×10-8, this criterium p-value was determined by the Bonferroni correction for p-value of 0.05, 106, 47, and 97 QTLs were identified for Ionomics in flooded environment, unflooded environment, and agronomic traits, respectively. A large number of QTLs coincide well with previous report results while many of the QTLs are new QTLs, suggesting the efficiency of GWAS methods and the reliability of this study. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs. When univariate methods failed to identify QTLs for a trait, the multivariate methods frequently detected QTLs. However, when many QTLs were detected by univariate methods, the number of QTLs detected by multivariate methods were reduced in many cases. These analyses suggest that using multiple GWAS methods can complement each other in QTL identification. In addition, several candidate genes involved in ionomic and agronomic traits control were identified by analyzing the sequences of the candidate QTL regions.Conclusions: Significant QTLs for heavy metal, mineral, and agronomic traits are presented in the rice genome and some of them have been fine mapped in the rice genome in this study. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing an important foundation for further studies on reducing heavy-metal contamination and improving crop yields. In addition, the comparison analysis of the GAWS methods showed that each method has unique feature and different method can complement each other.
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Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods
2020Co-Authors: Shuai Liu, Hua Zhong, Xiaoxi Meng, Tong Sun, Shannon R. M. Pinson, Sam Chang, Zhaohua PengAbstract:Abstract Rice is an important human staple food vulnerable to heavy metal contamination due to its unique physiology and growth environment. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge. In this report, a comprehensive GWAS analyses for ionomic and agronomic traits based on 3,259,478 SNPs were performed using two univariate methods and two multivariate methods. Under the criterium p-value <1.53×10-8, 106, 47, and 97 QTLs were identified for Ionomics in flooded environment, unflooded environment, and agronomic traits, respectively. Detailed analysis of the QTLs revealed that many of the identified QTLs are co-localized with the QTLs reported in prior ionomic and agronomic studies or posited near the genes with known functions in the related traits, suggesting that our GWAS analyses are reliable. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs. When univariate methods failed to identify QTLs for a trait, the multivariate methods frequently detected QTLs. However, when many QTLs were detected by univariate methods, the number of QTLs detected by multivariate methods were reduced in many cases. These analyses suggest that using multiple GWAS methods can complement each other in QTL identification and some methods may be more powerful with less false discovery rate. In addition, several candidate genes involved in ionomic and agronomic traits control were identified by sequence analysis of the QTL regions. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing an important foundation for further studies on reducing heavy-metal contamination and improving crop yields.
John Danku - One of the best experts on this subject based on the ideXlab platform.
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High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome
BMC Genomics, 2012Co-Authors: John Danku, Mourad Ouzzani, Ivan Baxter, Sungjin Kim, Olena K. Vatamaniuk, Olga Vitek, David E. SaltAbstract:Background To balance the demand for uptake of essential elements with their potential toxicity living cells have complex regulatory mechanisms. Here, we describe a genome-wide screen to identify genes that impact the elemental composition (‘ionome’) of yeast Saccharomyces cerevisiae . Using inductively coupled plasma – mass spectrometry (ICP-MS) we quantify Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, S and Zn in 11890 mutant strains, including 4940 haploid and 1127 diploid deletion strains, and 5798 over expression strains. Results We identified 1065 strains with an altered ionome, including 584 haploid and 35 diploid deletion strains, and 446 over expression strains. Disruption of protein metabolism or trafficking has the highest likelihood of causing large ionomic changes, with gene dosage also being important. Gene over expression produced more extreme ionomic changes, but over expression and loss of function phenotypes are generally not related. Ionomic clustering revealed the existence of only a small number of possible ionomic profiles suggesting fitness tradeoffs that constrain the ionome. Clustering also identified important roles for the mitochondria, vacuole and ESCRT pathway in regulation of the ionome. Network analysis identified hub genes such as PMR1 in Mn homeostasis, novel members of ionomic networks such as SMF3 in vacuolar retrieval of Mn, and cross-talk between the mitochondria and the vacuole. All yeast ionomic data can be searched and downloaded at http://www.Ionomicshub.org . Conclusions Here, we demonstrate the power of high-throughput ICP-MS analysis to functionally dissect the ionome on a genome-wide scale. The information this reveals has the potential to benefit both human health and agriculture.
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High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome.
BMC genomics, 2012Co-Authors: John Danku, Mourad Ouzzani, Ivan Baxter, Sungjin Kim, Olena K. Vatamaniuk, Olga Vitek, David E. SaltAbstract:To balance the demand for uptake of essential elements with their potential toxicity living cells have complex regulatory mechanisms. Here, we describe a genome-wide screen to identify genes that impact the elemental composition (‘ionome’) of yeast Saccharomyces cerevisiae. Using inductively coupled plasma – mass spectrometry (ICP-MS) we quantify Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, S and Zn in 11890 mutant strains, including 4940 haploid and 1127 diploid deletion strains, and 5798 over expression strains. We identified 1065 strains with an altered ionome, including 584 haploid and 35 diploid deletion strains, and 446 over expression strains. Disruption of protein metabolism or trafficking has the highest likelihood of causing large ionomic changes, with gene dosage also being important. Gene over expression produced more extreme ionomic changes, but over expression and loss of function phenotypes are generally not related. Ionomic clustering revealed the existence of only a small number of possible ionomic profiles suggesting fitness tradeoffs that constrain the ionome. Clustering also identified important roles for the mitochondria, vacuole and ESCRT pathway in regulation of the ionome. Network analysis identified hub genes such as PMR1 in Mn homeostasis, novel members of ionomic networks such as SMF3 in vacuolar retrieval of Mn, and cross-talk between the mitochondria and the vacuole. All yeast ionomic data can be searched and downloaded at http://www.Ionomicshub.org . Here, we demonstrate the power of high-throughput ICP-MS analysis to functionally dissect the ionome on a genome-wide scale. The information this reveals has the potential to benefit both human health and agriculture.
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Large-scale plant Ionomics
Methods of Molecular Biology, 2012Co-Authors: John Danku, Elena Yakubova, Brett Lahner, David E. SaltAbstract:Large-scale phenotyping methods are at the heart of efficiently deciphering the functions of genes and gene networks in the postgenomic era. In order to obtain meaningful results when comparing natural variants, and mutants and wild-types during large-scale quantitative analyses, necessary precautions must be employed throughout the whole process. Here, we describe large-scale elemental profiling in Arabidopsis thaliana and other genetic model organisms using high-throughput analytical methodologies. We also include a description of workflow management and data storage systems.
