Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

Chong, Jasmine; Wishart, David S.; Xia, Jianguo

doi:10.1002/cpbi.86

Cited by 1,864 publications

(1,547 citation statements)

References 60 publications

Supporting

Mentioning

1,533

Contrasting

Unclassified

Order By: Relevance

“…The output from GNPS deconvolution, annotations, and molecular network analysis can be exported for use in a statistical analysis environment such as Qiime 4748 , Qiita 49 , or MetaboAnalyst 50,51 , or for data visualization in tools such as Cytoscape 52 or Gephi 53 (e.g. Supplementary Figures S4-S7 , Figure 3, 4e-g), or for molecular cartography in ‘ili 54 (Figure 4 a-d).…”

Section: Resultsmentioning

confidence: 99%

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Aksenov¹,

Laponogov²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

show abstract

Section: Resultsmentioning

confidence: 99%

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Aksenov¹,

Laponogov²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Further enrichment analysis of the metabolites decreased in feces indicated the potential importance of microbiome-derived metabolites in many physiological and pathological conditions. Using MetaboAnalyst 27 we discovered that changes in those metabolites are significantly associated with many metabolic syndromes including Hartnup disease, Short-Bowel disease, among others; and more intriguingly they are indicated in central neural system disorders including seizures and Schizophrenia, re-affirming the functioning of gut-brain axis is largely via various microbial metabolites ( Figure S10 ). Enrichment results also include “Metabolites affected by diurnal variations” (10-fold enrichement, 15 th in terms of significance value), and since we established that part of those metabolites are largely dependent on the presence of gut microbiome, diurnal variations in gut microbiome can be assumed to be underlying the diurnal variations in the metabolites themselves.…”

Section: Resultsmentioning

confidence: 99%

Reconstruction and dynamics of human intestinal microbiome observedin situ

Liu

Dai

et al. 2020

Preprint

View full text Add to dashboard Cite

45Gut microbiome are studied primarily using fecal samples in humans and we gained 46 vital knowledge of compositional and functional capacities of gastro-intestinal 47 microbial communities. Yet, fecal materials limit our ability to investigate microbial 48 dynamics in different locations along GI-tract (in situ), nor in finer temporal scales as 49 they are infrequent. With a technology developed originally for fecal material 50 transplantation, colonic transendoscopic enteral tubing, we were able to sample 51 ileocecal microbiome twice daily, and carried out metagenomic as well as 52 metatranscriptomic analyses. Ileocecal and fecal microbiome are similar in 53 metagenomic profiling, yet their active genes (in metatranscriptomes) are highly 54 distinct. Both were perturbed after laxatives and then became more similar to 55 microbiome prior to treatment, demonstrating resilience as an innate property of gut 56 microbiome. Ileocecal microbiome transcriptomes sampled during day and night 57 revealed diurnal rhythmes exist in certain bacterial species and functional pathways, in 58 particular those related to short-chain fatty acid production. Lastly, metabolomic 59 analysis in fecal and urine samples mirrored the perturbance and recovery in gut 60 microbiome, indicating crucial contribution of gut microbiome to many of the key 61 metabolites involved in host health. Our study provides interesting novel insights into 62 human gut microbiome, and demonstrates the inner resilience, diurnal rhythmes and 63 potential consequences to the host. 64 65 66 (Figure 1, Figure S1). Yet those microbial species do not always occupy the majority 131 in RNA reads, rather, species Ruminococcus torques, Methanobrevibacter, 132

show abstract

“…Cut off values for SPE and leverage were computed as described in the methods section taking α=0.05. The t-test, ANOVA, ASCA analysis and Principal component analysis (PCA) was performed in MetaboAnalyst 4.0 45 . Hierarchical clustering analysis and heatmap plotting was performed with Perseus (version 1.6.10.0) 46 .…”

Section: Methodsmentioning

confidence: 99%

Growth phase-dependent responses of an in vitro gut microbiome to metformin

Zhang

Ning

et al. 2020

Preprint

View full text Add to dashboard Cite

In vitro gut microbiota models are often used to study drug-microbiome interaction. Similar to culturing individual microbial strains, the biomass accumulation of in vitro gut microbiota follows a logistic growth curve. Current studies on in vitro gut microbiome responses introduce drug stimulation during different growth stages, e.g. lag phase or stationary phase. However, in vitro gut microbiota in different growth phases may respond differently to same stimuli. Therefore, in this study, we used a 96-deep well plate-based culturing model (MiPro) to culture the human gut microbiota. Metformin, as the stimulus, was added at the lag, log and stationary phases of growth. Microbiome samples were collected at different time points for optical density and metaproteomic functional analysis. Results show that in vitro gut microbiota responded differently to metformin added during different growth phases, in terms of the growth curve, alterations of taxonomic and functional compositions. The addition of drugs at log phase leads to the greatest decline of bacterial growth. Metaproteomic analysis suggested that the strength of the metformin effect on the gut microbiome functional profile was ranked as lag phase > log phase > stationary phase. Our results showed that metformin added at lag phase resulted in a significantly reduced abundance of the Clostridiales order as well as an increased abundance of the Bacteroides genus, which was different from stimulation during the rest of the growth phase.Metformin also resulted in alterations of several pathways, including energy production and conversion, lipid transport and metabolism, translation, ribosomal structure and biogenesis. Our results indicate that the timing for drug stimulation should be considered when studying drugmicrobiome interactions in vitro.

show abstract

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

Cited by 1,864 publications

References 60 publications

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Reconstruction and dynamics of human intestinal microbiome observedin situ

Growth phase-dependent responses of an in vitro gut microbiome to metformin

Contact Info

Product

Resources

About