Limits...
Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations.

Lisec J, Meyer RC, Steinfath M, Redestig H, Becher M, Witucka-Wall H, Fiehn O, Törjék O, Selbig J, Altmann T, Willmitzer L - Plant J. (2007)

Bottom Line: QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P < or = 0.05, with >80% agreement on the allele effects.Some of the differences could be attributed to epistatic interactions.This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.

View Article: PubMed Central - PubMed

Affiliation: Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany. lisec@mpimp-golm.mpg.de

ABSTRACT
Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P < or = 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24-67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.

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myo-inositol QTL analysis reveals direct candidate genes for three of four determined QTL (1/18, 4/0 and 4/65). A LOD curve calculated using two independent programs (PLABQTL, red lines; QTL Cartographer, blue lines) is shown at the top. Horizontal lines indicate 0.05 (solid) and 0.25 (dotted) significance thresholds calculated based on 5000 permutations. Vertical lines indicate marker positions. At the bottom, the three relevant reaction steps according to the mQTL as connected by arrows are presented (pathways from left to right are inositol oxidation, stachyose biosynthesis and phospholipids biosynthesis). The pictograms in the center indicate the total number and location of genes known per pathway. Twelve genes (from six pathways) for enzymes catalyzing reactions in which myo-inositol is involved directly are known. The insert shows a comprehensive view of all AGI codes associated with myo-inositol (red, direct; black, pathway), indicating mQTL support intervals (blue), approximate LOD (number) and IL confirmation threshold reached (asterisk). A similar plot for all known metabolites is shown in Supplementary Figure S1.
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fig03: myo-inositol QTL analysis reveals direct candidate genes for three of four determined QTL (1/18, 4/0 and 4/65). A LOD curve calculated using two independent programs (PLABQTL, red lines; QTL Cartographer, blue lines) is shown at the top. Horizontal lines indicate 0.05 (solid) and 0.25 (dotted) significance thresholds calculated based on 5000 permutations. Vertical lines indicate marker positions. At the bottom, the three relevant reaction steps according to the mQTL as connected by arrows are presented (pathways from left to right are inositol oxidation, stachyose biosynthesis and phospholipids biosynthesis). The pictograms in the center indicate the total number and location of genes known per pathway. Twelve genes (from six pathways) for enzymes catalyzing reactions in which myo-inositol is involved directly are known. The insert shows a comprehensive view of all AGI codes associated with myo-inositol (red, direct; black, pathway), indicating mQTL support intervals (blue), approximate LOD (number) and IL confirmation threshold reached (asterisk). A similar plot for all known metabolites is shown in Supplementary Figure S1.

Mentions: Initial analyses of detected metabolic QTL with respect to underlying biochemical pathways show that it is possible to identify candidate genes even at the rather low mapping resolution that can be achieved using an RIL population. For example, inspection of the available information on pathways involving myo-inositol suggested candidate genes for three of four identified QTL (Table S1 and Figure 3). The AraCyc section of the TAIR database lists only 12 loci representing enzymes that catalyze reactions on myo-inositol. Three of these loci co-locate with determined mQTL: a myo-inositol oxygenase (AT1G14520, inositol oxidation pathway), a phosphatidyltransferase (AT4G38570, phospholipid biosynthesis pathway) and a stachyose synthase (AT4G01970, stachyose biosynthesis pathway). If all genes from pathways involving myo-inositol are considered, it is possible to find candidates for the remaining mQTL of this metabolite.


Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations.

Lisec J, Meyer RC, Steinfath M, Redestig H, Becher M, Witucka-Wall H, Fiehn O, Törjék O, Selbig J, Altmann T, Willmitzer L - Plant J. (2007)

myo-inositol QTL analysis reveals direct candidate genes for three of four determined QTL (1/18, 4/0 and 4/65). A LOD curve calculated using two independent programs (PLABQTL, red lines; QTL Cartographer, blue lines) is shown at the top. Horizontal lines indicate 0.05 (solid) and 0.25 (dotted) significance thresholds calculated based on 5000 permutations. Vertical lines indicate marker positions. At the bottom, the three relevant reaction steps according to the mQTL as connected by arrows are presented (pathways from left to right are inositol oxidation, stachyose biosynthesis and phospholipids biosynthesis). The pictograms in the center indicate the total number and location of genes known per pathway. Twelve genes (from six pathways) for enzymes catalyzing reactions in which myo-inositol is involved directly are known. The insert shows a comprehensive view of all AGI codes associated with myo-inositol (red, direct; black, pathway), indicating mQTL support intervals (blue), approximate LOD (number) and IL confirmation threshold reached (asterisk). A similar plot for all known metabolites is shown in Supplementary Figure S1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2268983&req=5

fig03: myo-inositol QTL analysis reveals direct candidate genes for three of four determined QTL (1/18, 4/0 and 4/65). A LOD curve calculated using two independent programs (PLABQTL, red lines; QTL Cartographer, blue lines) is shown at the top. Horizontal lines indicate 0.05 (solid) and 0.25 (dotted) significance thresholds calculated based on 5000 permutations. Vertical lines indicate marker positions. At the bottom, the three relevant reaction steps according to the mQTL as connected by arrows are presented (pathways from left to right are inositol oxidation, stachyose biosynthesis and phospholipids biosynthesis). The pictograms in the center indicate the total number and location of genes known per pathway. Twelve genes (from six pathways) for enzymes catalyzing reactions in which myo-inositol is involved directly are known. The insert shows a comprehensive view of all AGI codes associated with myo-inositol (red, direct; black, pathway), indicating mQTL support intervals (blue), approximate LOD (number) and IL confirmation threshold reached (asterisk). A similar plot for all known metabolites is shown in Supplementary Figure S1.
Mentions: Initial analyses of detected metabolic QTL with respect to underlying biochemical pathways show that it is possible to identify candidate genes even at the rather low mapping resolution that can be achieved using an RIL population. For example, inspection of the available information on pathways involving myo-inositol suggested candidate genes for three of four identified QTL (Table S1 and Figure 3). The AraCyc section of the TAIR database lists only 12 loci representing enzymes that catalyze reactions on myo-inositol. Three of these loci co-locate with determined mQTL: a myo-inositol oxygenase (AT1G14520, inositol oxidation pathway), a phosphatidyltransferase (AT4G38570, phospholipid biosynthesis pathway) and a stachyose synthase (AT4G01970, stachyose biosynthesis pathway). If all genes from pathways involving myo-inositol are considered, it is possible to find candidates for the remaining mQTL of this metabolite.

Bottom Line: QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P < or = 0.05, with >80% agreement on the allele effects.Some of the differences could be attributed to epistatic interactions.This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.

View Article: PubMed Central - PubMed

Affiliation: Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany. lisec@mpimp-golm.mpg.de

ABSTRACT
Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P < or = 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24-67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.

Show MeSH
Related in: MedlinePlus