Metabolomic Data Processing, Analysis, and Interpretation Using MetaboAnalyst

Xia, Jianguo; Wishart, David S.

doi:10.1002/0471250953.bi1410s34

Cited by 217 publications

(172 citation statements)

References 41 publications

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“…The PCA, heat map, and t test for metabolomics data were performed using MetaboAnalyst 3.0 (42,43). One-way analysis of variance followed by the Tukey post-test was used for statistical analysis.…”

Section: Discussionmentioning

confidence: 99%

Metabolomic profiling reveals a finely tuned, starvation-induced metabolic switch in Trypanosoma cruzi epimastigotes

Barisón

Rapado

Merino

et al. 2017

Journal of Biological Chemistry

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Edited by Chris WhitfieldTrypanosoma cruzi, the etiological agent of Chagas disease, is a protozoan parasite with a complex life cycle involving a triatomine insect and mammals. Throughout its life cycle, the T. cruzi parasite faces several alternating events of cell division and cell differentiation in which exponential and stationary growth phases play key biological roles. It is well accepted that arrest of the cell division in the epimastigote stage, both in the midgut of the triatomine insect and in vitro, is required for metacyclogenesis, and it has been previously shown that the parasites change the expression profile of several proteins when entering this quiescent stage. However, little is known about the metabolic changes that epimastigotes undergo before they develop into the metacyclic trypomastigote stage. We applied targeted metabolomics to measure the metabolic intermediates in the most relevant pathways for energy metabolism and oxidative imbalance in exponentially growing and stationary growth-arrested epimastigote parasites. We show for the first time that T. cruzi epimastigotes transitioning from the exponential to the stationary phase exhibit a finely tuned adaptive metabolic mechanism that enables switching from glucose to amino acid consumption, which is more abundant in the stationary phase. This metabolic plasticity appears to be crucial for survival of the T. cruzi parasite in the myriad different environmental conditions to which it is exposed during its life cycle.Cell growth, both in natural environments or in in vitro culture, usually presents in two phases: the exponential phase where cells divide at a roughly constant rate, and the stationary phase where cells slow down or stop the cell cycle and division. During the exponential phase, cells go through the well described canonical cell cycle of G 1 3 S 3 G 2 3 M. In the stationary phase, cells leave the regular cell cycle to enter a resting state known as quiescence, which is highly relevant because it corresponds to the physiological state in which most cells from both unicellular and multicellular organisms spend most of their life (1).Trypanosoma cruzi, the etiological agent of Chagas disease, is a protozoan parasite with a complex life cycle involving a triatomine insect and mammals. Throughout its life cycle, the T. cruzi parasite faces several alternated events of cell division and cell differentiation. Briefly, insects are infected during a blood meal from a mammal having non-dividing forms of the parasite, denominated trypomastigotes, present in their blood. Once in the midgut of the triatomine insect, the trypomastigotes differentiate to replicative non-infective epimastigotes, which proliferate and colonize the digestive tube. After the blood meal, nutrients are consumed by the intestinal epithelium, the parasite, and the intestinal microbiota population; therefore, the replicative epimastigotes eventually face nutrient starvation. Under this situation of metabolic stress, cell division is arrested, and the parasites adhe...

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Section: Discussionmentioning

confidence: 99%

Metabolomic profiling reveals a finely tuned, starvation-induced metabolic switch in Trypanosoma cruzi epimastigotes

Barisón

Rapado

Merino

et al. 2017

Journal of Biological Chemistry

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“…Metabolites were identified from MS spectra using the BinBase algorithm (34). Raw abundance data for all known and unknown metabolites, consisting of unique ion peak heights, were analyzed with MetaboAnalyst (54). Principal component analysis (PCA) was applied to raw data as a quality-control measure to observe sample replicate groupings (Fig.…”

Section: Methodsmentioning

confidence: 99%

The circadian oscillator in Synechococcus elongatus controls metabolite partitioning during diurnal growth

Diamond

Jun

Rubin

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

124

158

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Synechococcus elongatus PCC 7942 is a genetically tractable model cyanobacterium that has been engineered to produce industrially relevant biomolecules and is the best-studied model for a prokaryotic circadian clock. However, the organism is commonly grown in continuous light in the laboratory, and data on metabolic processes under diurnal conditions are lacking. Moreover, the influence of the circadian clock on diurnal metabolism has been investigated only briefly. Here, we demonstrate that the circadian oscillator influences rhythms of metabolism during diurnal growth, even though lightdark cycles can drive metabolic rhythms independently. Moreover, the phenotype associated with loss of the core oscillator protein, KaiC, is distinct from that caused by absence of the circadian output transcriptional regulator, RpaA (regulator of phycobilisome-associated A). Although RpaA activity is important for carbon degradation at night, KaiC is dispensable for those processes. Untargeted metabolomics analysis and glycogen kinetics suggest that functional KaiC is important for metabolite partitioning in the morning. Additionally, output from the oscillator functions to inhibit RpaA activity in the morning, and kaiC-null strains expressing a mutant KaiC phosphomimetic, KaiC-pST, in which the oscillator is locked in the most active output state, phenocopies a ΔrpaA strain. Inhibition of RpaA by the oscillator in the morning suppresses metabolic processes that normally are active at night, and kaiC-null strains show indications of oxidative pentose phosphate pathway activation as well as increased abundance of primary metabolites. Inhibitory clock output may serve to allow secondary metabolite biosynthesis in the morning, and some metabolites resulting from these processes may feed back to reinforce clock timing.C yanobacteria comprise a promising engineering platform for the production of fuels and industrial chemicals. These organisms already have been engineered to produce ethanol, isobutyraldehyde, alkanes, and hydrogen (1-4). However, the efficient industrial-scale application of these photosynthetic organisms will require their growth and maintenance in the outdoors where they will be subjected to light-dark (LD) cycles (5). Phototrophic cyanobacteria present a completely different engineering challenge relative to heterotrophic bacteria such as Escherichia coli: their cellular activities respond strongly to the presence and absence of light because their metabolism is centered on photosynthesis (6, 7). Diverse cyanobacteria also possess a true circadian clock that synchronizes with external LD cycles and has been demonstrated to drive both gene expression and metabolic rhythms (8-10). It is important to understand how signals from the external environment and the internal circadian clock are integrated to modulate metabolic processes in environmentally relevant LD cycles to optimize the engineering of these organisms. In this work we attempt to separate the influences of environment and circadian control using the cyan...

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“…To further reduce the number of variables in the regression model, the linear stepwise regression procedure was conducted using a p value of less than 0.05 as threshold for inclusion and a p value of greater than 0.10 as exclusion criteria. To detect time, as well as group-specific plasma metabolic differences between trained and untrained animals, metabolic data were analyzed using free online software MetaboAnalyst 2.0 (http://www.metaboanalyst.ca [23]). Data were normalized by logarithmic transformation using software-integrated procedures.…”

Section: Methodsmentioning

confidence: 99%

Physical Exercise Induces Specific Adaptations Resulting in Reduced Organ Injury and Mortality During Severe Polymicrobial Sepsis

Soßdorf

Fischer

Meyer

et al. 2013

Critical Care Medicine

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Metabolomic Data Processing, Analysis, and Interpretation Using MetaboAnalyst

Cited by 217 publications

References 41 publications

Metabolomic profiling reveals a finely tuned, starvation-induced metabolic switch in Trypanosoma cruzi epimastigotes

Metabolomic profiling reveals a finely tuned, starvation-induced metabolic switch in Trypanosoma cruzi epimastigotes

The circadian oscillator in Synechococcus elongatus controls metabolite partitioning during diurnal growth

Physical Exercise Induces Specific Adaptations Resulting in Reduced Organ Injury and Mortality During Severe Polymicrobial Sepsis

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