Final Report Summary - NETWORK QTL MAPPING (Network QTL mapping of circadian clock)
Summary of the project and objectives
Understanding the molecular and genetic basis of complex quantitative traits is an important goal in genetics with wide ranging ramifications across the scientific community. Phenotypes within species are not fixed and instead have significant levels of natural genetic variation that distinguishes individuals. This includes traits ranging from development to metabolism to pathogen resistance, with selection often maintaining the underlying genetic variation. One network that is known to be naturally variable while also having global ramifications for organism across metabolism and development is the circadian clock.
Natural variation in circadian clocks affects interactions between environmental factors and the clock (Edwards et al., 2006) and could lead to complex genotype by environment interactions, perhaps causing large effects on transcript or metabolite levels. Previous works using a single model genotype of Arabidopsis thaliana have indicated that the circadian clock may alter the regulation of plant metabolism (Fukushima et al., 2009). Following this research line, the team of Dr. Kliebenstein from UC Davis, USA (host Institute) studied in deep the relationship between natural variability in circadian clocks and secondary metabolism by using quantitative genetics and microarray data in the model species A. thaliana. Quantitative trait locus (QTL) were founded for natural variation altering circadian clock outputs and those were linked to pre-existing metabolomic QTLs from this same population, thereby identifying possible links between alterations in clock function and metabolism (Kerwin et al., 2011). The Arabidopsis/Brassica system is an important example of both the challenges and opportunities associated with extrapolation of genomic information from facile models to economic important crops. In the coordinator Research Institute (Misión Biológica de Galicia, CSIC) a research line is focused in breeding of different Brassica crops and investigating the environmental effect on secondary metabolites (such as glucosinolates and phenolic compounds) content as well as identifying genes involved on the biosynthetic pathways. Currently, the identification of the molecular mechanisms underlying connections between circadian outputs and metabolomic networks is not clear and requires intensive efforts. Thus, the objectives of the project were: 1) Cloning three metabolic loci controlling circadian output variation in Arabidopsis. 2) Identification of homologous genes in B. oleracea and B. rapa.
Description of the work performed since the beginning of the project
To clone the three loci controlling the Arabidopsis Bay × Sha circadian clock variation, we utilized a combination of fine-scale genetic mapping and in silico candidate gene identification. For fine mapping, we obtained heterogeneous inbred families (HIFs) heterozygous for a region that included each confidence interval. An initial screen of the progeny from each HIF’s revealed differences in secondary metabolites content and morphological traits (bolting date, rosette diameter and flowering time) that correlated with the genotypes for two of the three QTL regions under study. To identify genes that differentially regulate the phenotype on the population, we generate a high-resolution mapping population. For that, specific HIF pairs were crossed to optimize the phenotypic consequence of variation at each of the single loci. PCR-based markers flanking each locus were developed and used to screen 1,000 F2 progeny for recombination events. All recombinant progeny was then self-fertilized to generate F3 seeds to validate the F2 measurements. This strategy allowed us to identify recombination breakpoints and locate smaller regions of the tested locus, enabling the narrowing regions of about 30 genes. Simultaneously, we also constructed networks for all loci on those regions. Since genes in the same pathways or in the same functional complexes often exhibit similar expression patterns under diverse temporal and physiological conditions, we connected each candidate gene to co-expressed genes across 1,388 microarray experiments (http://atted.jp). Besides, as we found different phenotypes among Bay-0 and Sha alleles, we expect different expression on the causative genes. For this reason, we filtered the networks to keep only co-expressed genes with an eQTL in the location of the candidate genes, indicative of a regulatory relationship. Once we reduce the number of candidates genes, we started to search for homologous genes within B.oleracea and B. rapa genomes. The data from GEO (Gene Expression Omnibus) provides transcriptome sequencing in different tissues of B. rapa (GSE43245) and B. oleracea (GSE42891), this is allowing the differential expression study of orthologous genes and paralogs among different plant tissues.
Results
It was possible fine map two of the QTLs under study and reduce the list of the causative genes for each phenotype of interest. So far, we are focused on three candidate genes for glucosinolate content, and five candidate genes for flowering time, all of them exhibit a high circadian shift rhythms. To test if these candidate genes control the variability for the mentioned phenotypes, different approaches were carried on. On one hand we obtained homozygous knockout t-DNA lines defectives for each locus, and on the other hand we constructed overesspresed transgenic lines with the CaMV 35S promoter. Those mutant and transgenic lines were tested and the three candidates for glucosinolates and two for flowering display the expected phenotype. These results confirmed that our candidate genes are involved on the QTL natural variation and will help to describe the function of those genes in the metabolomic and circadian network. This information is now easily applicable to found homologues genes within B. oleracea and B. rapa genome.
Socio-economic impact of the project
Understanding the molecular basis for the accumulation of secondary metabolites in response to the circadian clock in the model specie A. thaliana as well as in Brassica crops could be a significant step forward on natural genetic variation and also provide unique knowledge from functional, ecological, and evolutionary perspectives. Brassica genus comprises major oil, vegetable, fodder and mustard crops grown worldwide. Currently, Brassica crops together with cereals represent the basis of world supplies. B. oleracea and B. rapa, the so-called 'cole crops', comprise many of the vegetables in our daily diet that includes: cauliflower, broccoli, turnip, cabbage or Brussels sprouts. The results from the model species will have great scientific importance, both in terms of the advancement of knowledge in the organization of plant genomes and its application to facilitate the isolation and cloning of genes of importance in horticultural species.
References
Edwards et al. 2005. Genetics 170: 387-400
Fukushima et al. 2009. Proceedings of the National Academy of Sciences of the United States of America 106: 7251-7256
Kerwin et al. The Plant Cell 22(2): 471-485
Understanding the molecular and genetic basis of complex quantitative traits is an important goal in genetics with wide ranging ramifications across the scientific community. Phenotypes within species are not fixed and instead have significant levels of natural genetic variation that distinguishes individuals. This includes traits ranging from development to metabolism to pathogen resistance, with selection often maintaining the underlying genetic variation. One network that is known to be naturally variable while also having global ramifications for organism across metabolism and development is the circadian clock.
Natural variation in circadian clocks affects interactions between environmental factors and the clock (Edwards et al., 2006) and could lead to complex genotype by environment interactions, perhaps causing large effects on transcript or metabolite levels. Previous works using a single model genotype of Arabidopsis thaliana have indicated that the circadian clock may alter the regulation of plant metabolism (Fukushima et al., 2009). Following this research line, the team of Dr. Kliebenstein from UC Davis, USA (host Institute) studied in deep the relationship between natural variability in circadian clocks and secondary metabolism by using quantitative genetics and microarray data in the model species A. thaliana. Quantitative trait locus (QTL) were founded for natural variation altering circadian clock outputs and those were linked to pre-existing metabolomic QTLs from this same population, thereby identifying possible links between alterations in clock function and metabolism (Kerwin et al., 2011). The Arabidopsis/Brassica system is an important example of both the challenges and opportunities associated with extrapolation of genomic information from facile models to economic important crops. In the coordinator Research Institute (Misión Biológica de Galicia, CSIC) a research line is focused in breeding of different Brassica crops and investigating the environmental effect on secondary metabolites (such as glucosinolates and phenolic compounds) content as well as identifying genes involved on the biosynthetic pathways. Currently, the identification of the molecular mechanisms underlying connections between circadian outputs and metabolomic networks is not clear and requires intensive efforts. Thus, the objectives of the project were: 1) Cloning three metabolic loci controlling circadian output variation in Arabidopsis. 2) Identification of homologous genes in B. oleracea and B. rapa.
Description of the work performed since the beginning of the project
To clone the three loci controlling the Arabidopsis Bay × Sha circadian clock variation, we utilized a combination of fine-scale genetic mapping and in silico candidate gene identification. For fine mapping, we obtained heterogeneous inbred families (HIFs) heterozygous for a region that included each confidence interval. An initial screen of the progeny from each HIF’s revealed differences in secondary metabolites content and morphological traits (bolting date, rosette diameter and flowering time) that correlated with the genotypes for two of the three QTL regions under study. To identify genes that differentially regulate the phenotype on the population, we generate a high-resolution mapping population. For that, specific HIF pairs were crossed to optimize the phenotypic consequence of variation at each of the single loci. PCR-based markers flanking each locus were developed and used to screen 1,000 F2 progeny for recombination events. All recombinant progeny was then self-fertilized to generate F3 seeds to validate the F2 measurements. This strategy allowed us to identify recombination breakpoints and locate smaller regions of the tested locus, enabling the narrowing regions of about 30 genes. Simultaneously, we also constructed networks for all loci on those regions. Since genes in the same pathways or in the same functional complexes often exhibit similar expression patterns under diverse temporal and physiological conditions, we connected each candidate gene to co-expressed genes across 1,388 microarray experiments (http://atted.jp). Besides, as we found different phenotypes among Bay-0 and Sha alleles, we expect different expression on the causative genes. For this reason, we filtered the networks to keep only co-expressed genes with an eQTL in the location of the candidate genes, indicative of a regulatory relationship. Once we reduce the number of candidates genes, we started to search for homologous genes within B.oleracea and B. rapa genomes. The data from GEO (Gene Expression Omnibus) provides transcriptome sequencing in different tissues of B. rapa (GSE43245) and B. oleracea (GSE42891), this is allowing the differential expression study of orthologous genes and paralogs among different plant tissues.
Results
It was possible fine map two of the QTLs under study and reduce the list of the causative genes for each phenotype of interest. So far, we are focused on three candidate genes for glucosinolate content, and five candidate genes for flowering time, all of them exhibit a high circadian shift rhythms. To test if these candidate genes control the variability for the mentioned phenotypes, different approaches were carried on. On one hand we obtained homozygous knockout t-DNA lines defectives for each locus, and on the other hand we constructed overesspresed transgenic lines with the CaMV 35S promoter. Those mutant and transgenic lines were tested and the three candidates for glucosinolates and two for flowering display the expected phenotype. These results confirmed that our candidate genes are involved on the QTL natural variation and will help to describe the function of those genes in the metabolomic and circadian network. This information is now easily applicable to found homologues genes within B. oleracea and B. rapa genome.
Socio-economic impact of the project
Understanding the molecular basis for the accumulation of secondary metabolites in response to the circadian clock in the model specie A. thaliana as well as in Brassica crops could be a significant step forward on natural genetic variation and also provide unique knowledge from functional, ecological, and evolutionary perspectives. Brassica genus comprises major oil, vegetable, fodder and mustard crops grown worldwide. Currently, Brassica crops together with cereals represent the basis of world supplies. B. oleracea and B. rapa, the so-called 'cole crops', comprise many of the vegetables in our daily diet that includes: cauliflower, broccoli, turnip, cabbage or Brussels sprouts. The results from the model species will have great scientific importance, both in terms of the advancement of knowledge in the organization of plant genomes and its application to facilitate the isolation and cloning of genes of importance in horticultural species.
References
Edwards et al. 2005. Genetics 170: 387-400
Fukushima et al. 2009. Proceedings of the National Academy of Sciences of the United States of America 106: 7251-7256
Kerwin et al. The Plant Cell 22(2): 471-485