Advancing Genetic Theory and Application by Metabolic Quantitative Trait Loci Analysis

Kliebenstein, Daniel J.

doi:10.1105/tpc.109.067611

Cited by 67 publications

(62 citation statements)

References 109 publications

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“…These seven genes belong to the aromatic amino acid decarboxylase family; two genes (GRMZM2G021388 and GRMZM2G021277; annotated as tryptophan decarboxylase) of which have been found associated with tryptophan in maize kernel in our previous study (Wen et al, 2014). These duplicated genes may diverge from each other in the pattern of neofunctionalization, where one of the duplicates obtains a novel function, or subfunctionalization, in which case the duplicate copies obtain differential expression patterns in terms of tissue specificity or stress response (Kliebenstein, 2009). Despite the high identity of the two TDC genes in predicted amino acid sequence, large divergence was found in their 59 untranslated region (UTR) and 39 UTR (Supplemental Figure 3).…”

Section: A Maize Primary Metabolic Network Involving Key Genes and Mementioning

confidence: 99%

“…Nevertheless, the present result still suggests that epistasis is widely prevalent in the determination of metabolite levels within this RIL population. Possible mechanisms underlying genetic epistasis in natural populations could refer to physical or functional interactions between gene products or gene and gene products that are connected within a biochemical or regulatory pathway (Phillips, 2008;Kliebenstein, 2009;Bassel et al, 2012). Regulatory or enzymatic interactions can generate genetic epistasis through functional epistasis, for instance, transcription factors regulating enzymes, protein-protein interactions, and epigenetic modification.…”

Section: Qtl Identification and Genetic Epistasis For Maize Metabolicmentioning

confidence: 99%

“…Understanding the functional divergence of duplicated genes will necessitate further experimentation including cloning of the causal genes and marker development for metabolic improvement. Moreover, further study of metabolic QTLs may shed light on evolutionary questions regarding the fate of duplicated genes, as implicated by Kliebenstein (2009).…”

Section: A Maize Primary Metabolic Network Involving Key Genes and Mementioning

confidence: 99%

See 2 more Smart Citations

Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population

et al. 2015

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Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R 2 = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R 2 >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement.

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Section: A Maize Primary Metabolic Network Involving Key Genes and Mementioning

confidence: 99%

Section: Qtl Identification and Genetic Epistasis For Maize Metabolicmentioning

confidence: 99%

Section: A Maize Primary Metabolic Network Involving Key Genes and Mementioning

confidence: 99%

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Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population

et al. 2015

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“…The effect of such alterations can vary from neutral to structural diversification of protein function but also to differences in transcription and response to environmental cues. Duplication events, for instance, are a major source of neofunctionalization and subfunctionalization in gene families (Bikard et al, 2009;Kliebenstein, 2009), whereas point mutations can lead to subtle quantitative differences in biological processes (Fridman et al, 2004). Mutations that have accumulated during the evolutionary history of a species might persist in different lineages or radiate in outcrossing species through genomic recombination and selection.…”

Section: Natural Diversitymentioning

confidence: 99%

Metabolic Networks: How to Identify Key Components in the Regulation of Metabolism and Growth

2009

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Plants display enormous diversity in their metabolism. Although the biosynthesis and function of the myriad plant metabolites have been studied for decades, we have little understanding of the interactions between metabolites, metabolite signaling, interactions with development, and the role of metabolism in genotype-to-phenotype relationships. Technologies for the analysis of metabolites have made tremendous progress in recent years, both in terms of the number of metabolites that are identified and of throughput. Recent developments allow the construction of metabolic networks and study of the role of these networks in plant growth and development. In this review, we discuss what types of information can be obtained from measurements of metabolites and what requirements they have with respect to the comprehensiveness of coverage and the precision of identification and quantification, and we outline procedures that can be implemented to validate the measurements. We then discuss what sorts of perturbations can be used to disturb metabolic networks, including environmental and physiological treatments, chemicals, reverse genetics, and the use of natural genetic diversity.Plants are the most consummate and sophisticated chemical system in the world. They use light energy to convert CO 2 into carbohydrates in their leaves. They absorb nutrients like nitrate, phosphate, and sulfate via their roots and convert them to amino acids and nucleotides, using light energy in the leaves in the day and energy derived from respiration in leaves in the dark and in nonphotosynthetic tissues. Carbohydrates, amino acids, and nucleotides are then transported to growing tissues, where they are converted into macromolecular cellular components like proteins, nucleic acids, cell walls, pigments, and lipids. Plants also synthesize tens of thousands of secondary metabolites, including phenylpropanoids and flavonoids, terpenoids, glucosinolates, and alkaloids. These have important roles in cellular function, in signaling, and in adaptation to abiotic and biotic stress. Their unique synthetic ability is the result of a highly complex and sophisticated metabolic apparatus. Its complexity and flexibility were already appreciated by the 1980s, as biochemical studies of cellular compartmentation revealed that many basic pathways like glycolysis, the oxidative pentose phosphate pathway, and organic acid metabolism were present in more than one compartment (Lunn, 2007), and the diversity of plant secondary metabolites was unveiled. It was further underlined as genome sequencing uncovered families of genes for enzymes in central metabolism, including starch and Suc synthesis and degradation, glycolysis, many reactions of the tricarboxylic acid cycle and nucleotide degradation, and huge families of genes that encode enzymes that introduce modifications into basic metabolite structures, like the alcohol dehydrogenases, 2-oxoglutarate dioxygenases, acyl transferases, UDP glucosyltransferases, O-methyl transferases, nitrilases, cytochrome P50s, myr...

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“…Interestingly, a number of important traits, such as stress resistance and postharvest processing, are largely dependent on metabolic content [8], implying a vast potential for manipulation of metabolic phenotypes via classical breeding [26,27,29,30,98,99]. This approach has several advantages in relation to genetic markers, as it does not rely on the genome sequence nor does it depend on understanding the complex mixture of segregating patterns among the progenies.…”

Section: Plant Metabolomicsmentioning

confidence: 99%

Metabolomics applied in bioenergy

Abdelnur

Caldana

Martins

2014

Chem. Biol. Technol. Agric.

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Metabolomics, which represents all the low molecular weight compounds present in a cell or organism in a particular physiological condition, has multiple applications, from phenotyping and diagnostic analysis to metabolic engineering and systems biology. In this review, we discuss the use of metabolomics for selecting microbial strains and engineering novel biochemical routes involved in plant biomass production and conversion. These aspects are essential for increasing the production of biofuels to meet the energy needs of the future. Additionally, we provide a broad overview of the analytic techniques and data analysis commonly used in metabolomics studies.

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Advancing Genetic Theory and Application by Metabolic Quantitative Trait Loci Analysis

Cited by 67 publications

References 109 publications

Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population

Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population

Metabolic Networks: How to Identify Key Components in the Regulation of Metabolism and Growth

Metabolomics applied in bioenergy

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