Mass spectrometry-based metabolomics in microbiome investigations

Bauermeister, Anelize; Mannochio-Russo, Helena; Costa-Lotufo, Letı́cia V.; Jarmusch, Alan K.; Dorrestein, Pieter C.

doi:10.1038/s41579-021-00621-9

Cited by 171 publications

(65 citation statements)

References 172 publications

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“…In summary, differently than Principal Component Analysis (PCA) which measures correlations among the samples, PCoA analysis is used to calculate distances among them, and the way these distances are calculated can result in different clustering trends in the plots. When the Euclidean distance is used in PCoA analysis, the result will be the same as if PCA was employed ( Mohammadi and Prasanna, 2003 ; Bauermeister et al, 2022 ). To evaluate the impact of different extraction protocols, PCoA plots obtained by Bray–Curtis distance metric showed that the two extraction solvents resulted in very different metabolomic profiles on both ionization modes (for positive ionization mode: PERMANOVA F = 40.39, p = 0.001; for negative ionization mode: PERMANOVA F = 38.37, p = 0.001).…”

Section: Resultsmentioning

confidence: 99%

“…It is now possible to support DNA-based phylogenetic studies at a molecular level and assist in elaborating evolutionary hypotheses based on natural products and metabolomics analyses ( Schmitt and Barker, 2009 ). The rapid development of natural product bioinformatics tools and databases ( Li and Gaquerel, 2021 ; Medema, 2021 ; Bauermeister et al, 2022 ) can be of great value to assist and accelerate more comprehensive chemosystematics studies aiming at taxa-specific metabolic pathways.…”

Section: Introductionmentioning

confidence: 99%

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Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family

et al. 2022

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Natural products produced by plants are one of the most investigated natural sources, which substantially contributed to the development of the natural products field. Even though these compounds are widely explored, the literature still lacks comprehensive investigations aiming to explore the evolution of secondary metabolites produced by plants, especially if classical methodologies are employed. The development of sensitive hyphenated techniques and computational tools for data processing has enabled the study of large datasets, being valuable assets for chemosystematic studies. Here, we describe a strategy for chemotaxonomic investigations using the Malpighiaceae botanical family as a model. Our workflow was based on MS/MS untargeted metabolomics, spectral searches, and recently described in silico classification tools, which were mapped into the latest molecular phylogeny accepted for this family. The metabolomic analysis revealed that different ionization modes and extraction protocols significantly impacted the chemical profiles, influencing the chemotaxonomic results. Spectral searches within public databases revealed several clades or genera-specific molecular families, being potential chemical markers for these taxa, while the in silico classification tools were able to expand the Malpighiaceae chemical space. The classes putatively annotated were used for ancestral character reconstructions, which recovered several classes of metabolites as homoplasies (i.e., non-exclusive) or synapomorphies (i.e., exclusive) for all sampled clades and genera. Our workflow combines several approaches to perform a comprehensive evolutionary chemical study. We expect it to be used on further chemotaxonomic investigations to expand chemical knowledge and reveal biological insights for compounds classes in different biological groups.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family

et al. 2022

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“…More generally, we end by observing that approaches that have been employed to identify polymicrobial and/or microbe-host interactions mediated by secondary metabolites in the context of the gut microbiome (Agus et al, 2021;Vernocchi et al, 2016) can and should be leveraged more strongly in the context of infectious disease. While some studies have begun to light the way (Bauermeister et al, 2021;Garg et al, 2017), there remains tremendous potential for discovery of secondary metabolite-mediated effects that shape clinical outcomes. To identify endogenously produced molecules that impact antimicrobial susceptibility, we must ask: Who is there?…”

Section: Discussionmentioning

confidence: 99%

Redox-active secondary metabolites act as interspecies modulators of antibiotic resilience

Meirelles

Newman

2021

Preprint

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Bacterial opportunistic pathogens make a wide range of secondary metabolites both in the natural environment and when causing infections, yet how these molecules mediate microbial interactions and their consequences for antibiotic treatment are still poorly understood. Here, we explore the role of two redox-active secondary metabolites, pyocyanin and toxoflavin, as interspecies modulators of antibiotic resilience. We find that these molecules dramatically change susceptibility levels of diverse bacteria to clinical antibiotics. Pyocyanin is made by Pseudomonas aeruginosa, while toxoflavin is made by Burkholderia gladioli, organisms that infect cystic fibrosis and other immunocompromised patients. Both molecules alter the susceptibility profile of pathogenic species within the “Burkholderia cepacia complex” to different antibiotics, either antagonizing or potentiating their effects, depending on the drug’s class. Defense responses regulated by the redox-sensitive transcription factor SoxR potentiate the antagonistic effects these metabolites have against fluoroquinolones, and the presence of genes encoding SoxR and the efflux systems it regulates can be used to predict how these metabolites will affect antibiotic susceptibility of different bacteria. Finally, we demonstrate that inclusion of secondary metabolites in standard protocols used to assess antibiotic resistance can dramatically alter the results, motivating the development of new tests for more accurate clinical assessment.

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“…Metabolites are the intermediate or end product of interactions between microbes and host cells [110]. Short-chain fatty acids (SCFA) and bile acids are metabolites that can have beneficial or damaging impacts on the host tissue [111] and have shown associations with a range of illnesses [102,[112][113][114][115][116]. This promotes the idea of incorporating metabolite information in disease state analyses.…”

Section: Gut Microbiome and Metabolitesmentioning

confidence: 99%

It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

Curry

Nute

Treangen

2021

Emerging Topics in Life Sciences

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Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.

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Mass spectrometry-based metabolomics in microbiome investigations

Cited by 171 publications

References 172 publications

Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family

Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family

Redox-active secondary metabolites act as interspecies modulators of antibiotic resilience

It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

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