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Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways.

ABSTRACT: Phenotypic variation between individuals of a species is often under quantitative genetic control. Genomic analysis of gene expression polymorphisms between individuals is rapidly gaining popularity as a way to query the underlying mechanistic causes of variation between individuals. However, there is little direct evidence of a linkage between global gene expression polymorphisms and phenotypic consequences. In this report, we have mapped quantitative trait loci (QTLs)-controlling glucosinolate content in a population of 403 Arabidopsis Bay x Sha recombinant inbred lines, 211 of which were previously used to identify expression QTLs controlling the transcript levels of biosynthetic genes. In a comparative study, we have directly tested two plant biosynthetic pathways for association between polymorphisms controlling biosynthetic gene transcripts and the resulting metabolites within the Arabidopsis Bay x Sha recombinant inbred line population. In this analysis, all loci controlling expression variation also affected the accumulation of the resulting metabolites. In addition, epistasis was detected more frequently for metabolic traits compared to transcript traits, even when both traits showed similar distributions. An analysis of candidate genes for QTL-controlling networks of transcripts and metabolites suggested that the controlling factors are a mix of enzymes and regulatory factors. This analysis showed that regulatory connections can feedback from metabolism to transcripts. Surprisingly, the most likely major regulator of both transcript level for nearly the entire pathway and aliphatic glucosinolate accumulation is variation in the last enzyme in the biosynthetic pathway, AOP2. This suggests that natural variation in transcripts may significantly impact phenotypic variation, but that natural variation in metabolites or their enzymatic loci can feed back to affect the transcripts.

SUBMITTER: Wentzell AM

PROVIDER: S-EPMC1976331 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.

Project description:BackgroundPhytophthora nicotianae Breda de Haan (Phytophthora parasitica Dastur) causes severe damage to citrus crops worldwide. A population of citrandarins was created from the cross between the susceptible parent Citrus sunki Hort. Ex Tan. and the resistant parent Poncirus trifoliata (L.) Raf. cv. Rubidoux, both parents and two reference rootstocks (Rangpur lime and Swingle citrumelo) were grafted in a greenhouse on Rangpur lime. Inoculations were performed at 10 cm and 15 cm above the grafting region and the resulting lesions were evaluated by measuring the lesion length 60 days after inoculation. As control, non-inoculated plants of each genotype were used. In addition, we evaluated the expression of 19 candidate genes involved in citrus defense response 48 h after pathogen infection by quantitative Real-Time PCR (qPCR). We mapped genomic regions of Quantitative Trait Loci (QTLs) and Expression Quantitative Trait Loci (eQTLs) associated with resistance to P. parasitica in the linkage groups (LGs) of the previously constructed maps of C. sunki and P. trifoliata.ResultsWe found disease severity differences among the generated hybrids, with lesion lengths varying from 1.15 to 11.13 mm. The heritability of the character was 65%. These results indicate that there is a great possibility of success in the selection of resistant hybrids within this experiment. The analysis of gene expression profile demonstrated a great variation of responses regarding the activation of plant defense pathways, indicating that citrandarins have several defense strategies to control oomycete infection. The information of the phenotypic and gene expression data made possible to detect genomic regions associated with resistance. Three QTLs and 84 eQTLs were detected in the linkage map of P. trifoliata, while one QTL and 110 eQTLs were detected in C. sunki.ConclusionsThis is the first study to use eQTLs mapping in the Phytophthora-citrus interaction. Our results from the QTLs and eQTLs mapping allow us to conclude that the resistance of some citrandarins to the infection by P. parasitica is due to a favorable combination of QTLs and eQTLs transmitted by both parents.

Project description:BackgroundA wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds.ResultsThe developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates.ConclusionWith this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts.

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Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways.

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