| 1. |
- Bylesjö, Max, et al.
(author)
-
Data integration in plant biology the O2PLS method for combined modeling of transcript and metabolite data
- 2007
-
In: The Plant Journal. - 0960-7412 .- 1365-313X. ; 52:6, s. 1181-1191
-
Journal article (peer-reviewed)abstract
- The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.
|
|
| 2. |
- Jonsson, Pär, et al.
(author)
-
A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS
- 2004
-
In: Analytical Chemistry. - Columbus, OH : American Chemical Society. - 0003-2700 .- 1520-6882. ; 76:6, s. 1738-1745
-
Journal article (peer-reviewed)abstract
- In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.
|
|
| 3. |
- Jonsson, Pär, et al.
(author)
-
Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data : a potential tool for multi-parametric diagnosis
- 2006
-
In: Journal of Proteome Research. - : American Chemical Society. - 1535-3893 .- 1535-3907. ; 5:6, s. 1407-1414
-
Journal article (peer-reviewed)abstract
- A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.
|
|
| 4. |
- Kusano, Miyako, et al.
(author)
-
Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice
- 2007
-
In: Journal of chromatography. B. - Amsterdam : Elsevier. - 1570-0232 .- 1873-376X. ; 855:1, s. 71-79
-
Journal article (peer-reviewed)abstract
- We have developed a comprehensive method combining analytical techniques of one-dimensional (I D) and two-dimensional (GC x GC) gas chromatography-time-of-flight (TOF)-mass spectrometry. This method was applied to the metabolic phenotyping of natural variants in rice for the 68 world rice core collection (WRC) and two other varieties. Ten metabolites were selected as metabolite representatives, and the selected ion current of each metabolite peak obtained from both techniques were statistically compared. Our method of combining I D- and GC x GC-TOF/MS is useful for the metabolic phenotyping of natural variants in rice for further studies in breeding programs.
|
|
| 5. |
- Kusano, Miyako, et al.
(author)
-
Metabolite signature during short-day induced growth cessation in populus
- 2011
-
In: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 2
-
Journal article (peer-reviewed)abstract
- The photoperiod is an important environmental signal for plants, and influences a wide range of physiological processes. For woody species in northern latitudes, cessation of growth is induced by short photoperiods. In many plant species, short photoperiods stop elongational growth after a few weeks. It is known that plant daylength detection is mediated by Phytochrome A (PHYA) in the woody hybrid aspen species. However, the mechanism of dormancy involving primary metabolism remains unclear. We studied changes in metabolite profiles in hybrid aspen leaves (young, middle, and mature leaves) during short-day-induced growth cessation, using a combination of gas chromatography–time-of-flight mass spectrometry, and multivariate projection methods. Our results indicate that the metabolite profiles in mature source leaves rapidly change when the photoperiod changes. In contrast, the differences in young sink leaves grown under long and short-day conditions are less distinct. We found short daylength induced growth cessation in aspen was associated with rapid changes in the distribution and levels of diverse primary metabolites. In addition, we conducted metabolite profiling of leaves of PHYA overexpressor (PHYAOX) and those of the control to find the discriminative metabolites between PHYAOX and the control under the short-day conditions. The metabolite changes observed in PHYAOX leaves, together with those in the source leaves, identified possible candidates for the metabolite signature (e.g., 2-oxo-glutarate, spermidine, putrescine, 4-amino-butyrate, and tryptophan) during short-day-induced growth cessation in aspen leaves.
|
|
| 6. |
- Kusano, Miyako, et al.
(author)
-
Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana
- 2007
-
In: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 1:53, s. 1-17
-
Journal article (peer-reviewed)abstract
- BACKGROUND: Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (methionine-over accumulation 1 [mto1] and transparent testa4 [tt4]) plants regarding the alteration of metabolite accumulation in Arabidopsis thaliana. RESULTS: In the GC-TOF/MS analysis, we acquired quantitative information regarding over 170 metabolites, which has been analyzed by a novel score (ZMC, z-score of metabolite correlation) describing a characteristic metabolite in terms of correlation. Although the 2 mutants revealed no apparent morphological abnormalities, the overall correlation values in mto1 were much lower than those of the wild-type and tt4 plants, indicating the loss of overall network stability due to the uncontrolled accumulation of methionine. In the tt4 mutant, a new correlation between malate and sinapate was observed although the levels of malate, sinapate, and sinapoylmalate remain unchanged, suggesting an adaptive reconfiguration of the network. Gene-expression correlations presumably responsible for these metabolic networks were determined using the metabolite correlations as clues. CONCLUSION: Two Arabidopsis mutants, mto1 and tt4, exhibited the following changes in entire metabolome networks: the overall loss of metabolic stability (mto1) or the generation of a metabolic network of a backup pathway for the lost physiological functions (tt4). The expansion of metabolite correlation to gene-expression correlation provides detailed insights into the systemic understanding of the plant cellular process regarding metabolome and transcriptome.
|
|
| 7. |
- Redestig, Henning, et al.
(author)
-
Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data
- 2009
-
In: Analytical Chemistry. - Washington : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 81:19, s. 7974-7980
-
Journal article (peer-reviewed)abstract
- Most mass spectrometry based metabolomics studies are semiquantitative and depend on efficient normalization techniques to suppress systematic error. A common approach is to include isotope-labeled internal standards (ISs) and then express the estimated metabolite abundances relative to the IS. Because of problems such as insufficient chromatographic resolution, however, the analytes may directly influence estimates of the IS, a phenomenon known as cross-contribution (CC). Normalization using ISs that suffer from CC effects will cause significant loss of information if the interfering analytes are associated with the studied factors. We present a novel normalization algorithm, which compensates for systematic CC effects that can be traced back to a linear association with the experimental design. The proposed method was found to be superior at purifying the signal of interest compared to current normalization methods when applied to two biological data sets and a multicomponent dilution mixture. Our method is applicable to data from randomized and designed experiments that use ISs to monitor the systematic error.
|
|
