Untargeted metabolomics links glutathione to bacterial cell cycle progression

Hartl, Johannes; Kiefer, Patrick; Kaczmarczyk, Andreas; Mittelviefhaus, Maximilian; Vonderach, Thomas; Hattendorf, Bodo; Jenal, Urs; Vorholt, Julia A.

doi:10.1038/s42255-019-0166-0

Cited by 42 publications

(35 citation statements)

References 88 publications

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“…Notably, tedious sample pretreatment (e.g., derivatization) and lengthy chromatographic separation (≈40 min) are required to reduce biosample complexity and enrich target molecules in conventional MS, including liquid chromatography‐mass spectrometry and gas chromatography‐mass spectrometry. [ 28 ] In contrast to the conventional MS, our approach avoided the sample treatment procedure, suitable for large scale clinical use with enhanced analytical speed (approximately seconds), reduced sample consumption (down to ≈nL), and low cost (≈€3 per test). [ 15b,29 ]…”

Section: Figurementioning

confidence: 99%

Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition

Lin

Gao

et al. 2020

Advanced Science

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Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer‐aided diagnosis of stroke is performed using SMF based multi‐modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi‐modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single‐modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

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Section: Figurementioning

confidence: 99%

Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition

Lin

Gao

et al. 2020

Advanced Science

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“…Global or untargeted metabolomics is increasingly used to investigate metabolic changes of various biological or environmental systems in an unbiased manner [1,2]. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) has become the main workhorse for global metabolomics [3,4].…”

Section: Introductionmentioning

confidence: 99%

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics

et al. 2020

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Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

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“…Glutathione can reduce oxidized glutaredoxins, which serve as antioxidants. Both glutathione and thioredoxin have been shown to play a role in cell cycle regulation, and their concentrations vary between different growth phases [ 62 , 63 ]. However, Cu can also form a catalytic complex with glutathione, generating additional ROS instead of depleting them [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

Environmental Conditions Modulate the Transcriptomic Response of Both Caulobacter crescentus Morphotypes to Cu Stress

et al. 2021

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Bacteria encounter elevated copper (Cu) concentrations in multiple environments, varying from mining wastes to antimicrobial applications of copper. As the role of the environment in the bacterial response to Cu ion exposure remains elusive, we used a tagRNA-seq approach to elucidate the disparate responses of two morphotypes of Caulobacter crescentus NA1000 to moderate Cu stress in a complex rich (PYE) medium and a defined poor (M2G) medium. The transcriptome was more responsive in M2G, where we observed an extensive oxidative stress response and reconfiguration of the proteome, as well as the induction of metal resistance clusters. In PYE, little evidence was found for an oxidative stress response, but several transport systems were differentially expressed, and an increased need for histidine was apparent. These results show that the Cu stress response is strongly dependent on the cellular environment. In addition, induction of the extracytoplasmic function sigma factor SigF and its regulon was shared by the Cu stress responses in both media, and its central role was confirmed by the phenotypic screening of a sigF::Tn5 mutant. In both media, stalked cells were more responsive to Cu stress than swarmer cells, and a stronger basal expression of several cell protection systems was noted, indicating that the swarmer cell is inherently more Cu resistant. Our approach also allowed for detecting several new transcription start sites, putatively indicating small regulatory RNAs, and additional levels of Cu-responsive regulation.

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Untargeted metabolomics links glutathione to bacterial cell cycle progression

Cited by 42 publications

References 88 publications

Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition

Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics

Environmental Conditions Modulate the Transcriptomic Response of Both Caulobacter crescentus Morphotypes to Cu Stress

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