Normalizing and Integrating Metabolomics Data

Livera, Alysha M De; Dias, Daniel A.; Souza, David P. De; Rupasinghe, Thusitha; Pyke, James; Tull, Dedreia; Roessner, Ute; McConville, Malcolm J.; Speed, Terence P.

doi:10.1021/ac302748b

Cited by 181 publications

(190 citation statements)

References 20 publications

(63 reference statements)

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“…Metabolite abundance was determined by LC-MS peak height and is normalized to the average for untreated samples from the same plate. Statistical analyses utilized Welch's t test (␣ ϭ 0.05) and Pearson's correlation (Microsoft Excel), as well as hierarchical clustering analysis (HCA) and principal-component analysis (PCA) using the Metabolomics package in R (36). All LC-MS data are included in the supplemental material (see Data Set S1; the full IDEOM file is available at https://dx.doi.org/10.4225/03/57A80CE1503CD) and were deposited in the NIH Metabolomics Workbench under accession no.…”

Section: Cell Culture and Drug Incubations For Lc-ms Metabolomics Anamentioning

confidence: 99%

“…Seventy metabolites were identified based on the unique fragmentation profiles and retention time and validated using authentic standards. All metabolite peak intensities were subjected to natural logarithmic transformation (36), and the data were normalized to data for untreated samples in each plate. Pearson's correlation and Student's t test (Microsoft Excel; ␣ ϭ 0.05), as well as HCA (Metabolomics package in R), were performed for statistical analyses.…”

Section: Cell Culture and Drug Incubations For Lc-ms Metabolomics Anamentioning

confidence: 99%

See 1 more Smart Citation

Metabolomics-Based Screening of the Malaria Box Reveals both Novel and Established Mechanisms of Action

Creek

Chua

Cobbold

et al. 2016

Antimicrob Agents Chemother

116

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e High-throughput phenotypic screening of chemical libraries has resulted in the identification of thousands of compounds with potent antimalarial activity, although in most cases, the mechanism(s) of action of these compounds remains unknown. Here we have investigated the mode of action of 90 antimalarial compounds derived from the Malaria Box collection using high-coverage, untargeted metabolomics analysis. Approximately half of the tested compounds induced significant metabolic perturbations in in vitro cultures of Plasmodium falciparum. In most cases, the metabolic profiles were highly correlated with known antimalarials, in particular artemisinin, the 4-aminoquinolines, or atovaquone. Select Malaria Box compounds also induced changes in intermediates in essential metabolic pathways, such as isoprenoid biosynthesis (i.e., 2-C-methyl-D-erythritol 2,4-cyclodiphosphate) and linolenic acid metabolism (i.e., traumatic acid). This study provides a comprehensive database of the metabolic perturbations induced by chemically diverse inhibitors and highlights the utility of metabolomics for triaging new lead compounds and defining specific modes of action, which will assist with the development and optimization of new antimalarial drugs. Malaria remains a major global health problem, and there is a pressing need to discover and develop new antimalarials. Traditional antimalarial drugs have been severely undermined by the emergence of drug-resistant strains and current treatments rely heavily on artemisinin-based combination therapies. Alarmingly, artemisinin resistance has arisen in Southeast Asia during the last decade, highlighting the urgent need for new antimalarials (1). Extensive efforts to produce a malaria vaccine have met limited success and a pipeline of new antimalarial drugs will be required to mitigate extensive morbidity and mortality from malaria for the foreseeable future (2).High-throughput phenotypic screening of chemical libraries against the malaria parasite, Plasmodium falciparum, has provided a major step forward in the discovery of novel antimalarial compounds. Almost 30,000 compounds that selectively inhibit growth of cultured P. falciparum asexual red blood cell (RBC) stages have been identified in these screens, providing excellent starting points for the discovery of new antimalarial drugs (3-5). A prioritized collection of these "hit" compounds, known as the Malaria Box, has been assembled by the Medicines for Malaria Venture (MMV) and provided free to the research community to facilitate this drug development pipeline (6).A key limitation to the further optimization of many of these compounds is the lack of information on their mode of action. While not essential for registration, information on the mode of action of inhibitors discovered in phenotypic screens will significantly accelerate drug development by allowing structure-based drug design, monitoring of activity, toxicity and resistance, and facilitation of rational clinical usage in combination with other medicines. Furthermore, in...

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Section: Cell Culture and Drug Incubations For Lc-ms Metabolomics Anamentioning

confidence: 99%

Section: Cell Culture and Drug Incubations For Lc-ms Metabolomics Anamentioning

confidence: 99%

Metabolomics-Based Screening of the Malaria Box Reveals both Novel and Established Mechanisms of Action

Creek

Chua

Cobbold

et al. 2016

Antimicrob Agents Chemother

116

View full text Add to dashboard Cite

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“…The median value is divided by each summed value to create a correction factor which is multiplied to the original intensity values to give the normalized sum scaled measurement. [12,13] The corrections factors used in the sum-scaling procedure were computed at the PSM level.…”

Section: Tmtcalibratormentioning

confidence: 99%

Combined tissue and fluid proteomics with Tandem Mass Tags to identify low-abundance protein biomarkers of disease in peripheral body fluid: An Alzheimer's Disease case study

Russell

Heslegrave

Mitra

et al. 2016

Rapid Commun. Mass Spectrom.

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RATIONALE:Ideal biomarkers are present in readily accessible samples including plasma and cerebrospinal fluid (CSF), and are directly derived from diseased tissue, therefore likely to be of relatively low abundance. Traditional unbiased proteomic approaches for biomarker discovery have struggled to detect low-abundance markers due to the high dynamic range of proteins, the predominance of hyper-abundant proteins, and the use of data-dependent acquisition mass spectrometry (MS). To overcome these limitations and improve biomarker discovery in peripheral fluids, we have developed TMTcalibrator ™ ; a novel MS workflow combining isobarically labelled diseased tissue digests in parallel with an appropriate set of labelled body fluids to increase the chance of identifying low-abundance, tissue-derived biomarkers. METHODS: A disease relevant cell line was labelled with TMT® in a range of concentrations generating a multi-point calibration curve. Peripheral biofluid samples were labelled with the remaining tags and quantitative analysis was performed using an Orbitrap Fusion Tribrid mass spectrometer with a Top10 CID-HCD MS3 synchronous precursor selection (SPS) method. SPS allowed direct analysis of non-depleted, unfractionated CSF samples with complete profiling of six individual samples requiring only 15 hours of MS time, equivalent to 1.5 h per sample. RESULTS: Using the TMTcalibrator ™ workflow allowed the identification of several markers of microglia activation that are differentially quantified in the CSF of patients with Alzheimer's disease (AD). We report peptides from 41 proteins that have not previously been detected in the CSF, that appear to be regulated by at least 60% in AD.CONCLUSIONS: This study has demonstrated the benefits of the new TMTcalibrator ™ workflow and the results suggest this is a suitable and efficient method of detecting low-abundance peptides within biological fluids. The use of TMTcalibrator ™ in further biomarker discovery studies should be considered to overcome some of the limitations commonly associated with more conventional approaches.

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“…As the sample quality is affected by the day of processing and differences in the staff and laboratories, so-called batch effects are produced (Leek et al, 2010;De Livera et al, 2012;Lazar et al, 2013). Because we included more than 650 samples in our study, we could demonstrate that removing such batch effects from our data set by appropriate experimental designs and by quality control samples allows comparison between mutants (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The COMBAT algorithm (Johnson et al, 2007) may not be the best choice for adjusting batch effects in metabolomics data sets (Chen et al, 2011). A number of batch adjustment methods are available (De Livera et al, 2012;Lazar et al, 2013), and we are in the process of comparing these methods for their enhancement of the interpretation of metabolomics data sets.…”

Section: Discussionmentioning

confidence: 99%

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

et al. 2014

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Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc. riken.jp/meko/.

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Normalizing and Integrating Metabolomics Data

Cited by 181 publications

References 20 publications

Metabolomics-Based Screening of the Malaria Box Reveals both Novel and Established Mechanisms of Action

Metabolomics-Based Screening of the Malaria Box Reveals both Novel and Established Mechanisms of Action

Combined tissue and fluid proteomics with Tandem Mass Tags to identify low-abundance protein biomarkers of disease in peripheral body fluid: An Alzheimer's Disease case study

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

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