Quantitative metabolic network profiling of Escherichia coli: An overview of analytical methods for measurement of intracellular metabolites

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Acute Care Utilization After Recovery Coaching Linkage During Substance-Related Inpatient Admission: Results of Two Randomized Controlled Trials

BACKGROUND

For patients with substance use disorder (SUD), a peer recovery coach (PRC) intervention increases engagement in recovery services; effective support services interventions have occasionally demonstrated cost savings through decreased acute care utilization.

OBJECTIVE

Examine effect of PRCs on acute care utilization.

DESIGN

Combined results of 2 parallel 1:1 randomized controlled trials.

PARTICIPANTS

Inpatient adults with substance use disorder INTERVENTIONS: Inpatient PRC linkage and follow-up contact for 6 months vs usual care (providing contact information for SUD resources and PRCs) MAIN MEASURES: Acute care encounters (emergency and inpatient) 6 months before and after enrollment; encounter type by primary diagnosis code category (mental/behavioral vs medical); 30-day readmissions with Lace+ readmission risk scores.

KEY RESULTS

A total of 193 patients were randomized: 95 PRC; 98 control. In the PRC intervention, 66 patients had a pre-enrollment acute care encounter and 56 had an encounter post-enrollment, compared to the control group with 59 pre- and 62 post-enrollment (odds ratio [OR] = -0.79, P = 0.11); there was no significant effect for sub-groups by encounter location (emergency vs inpatient). There was a significant decrease in mental/behavioral ED visits (PRC: pre-enrollment 17 vs post-enrollment 10; control: pre-enrollment 13 vs post-enrollment 16 (OR = -2.62, P = 0.02)) but not mental/behavioral inpatient encounters or medical emergency or inpatient encounters. There was no significant difference in 30-day readmissions corrected for Lace+ scores (15.8% PRC vs 17.3% control, OR = 0.19, P = 0.65).

CONCLUSIONS

PRCs did not decrease overall acute care utilization but may decrease emergency encounters related to substance use.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT04098601, NCT04098614).

Bacterial Metabolomics: Sample Preparation Methods

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important "omics" tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Unraveling antimicrobial resistance using metabolomics

The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies. TEASER: Novel treatment strategies are needed to address the emerging threat of antimicrobial resistance (AMR) in bacterial pathogens. Metabolomics approaches may help to unravel the biochemical underpinnings of AMR, thereby facilitating the discovery of metabolism-associated drug targets and treatment strategies.

Combined Metabolome and Lipidome Analyses for In-Depth Characterization of Lipid Accumulation in the DHA Producing Aurantiochytrium sp. T66

Thraustochytrids are marine heterotrophic microorganisms known for their potential to accumulate docosahexaenoic acid (DHA)-enriched lipids. There have been many attempts to improve thraustochytrid DHA bioprocesses, especially through traditional optimization of cultivation and media conditions. Nevertheless, thraustochytrid-based bioprocesses are still not commercially competitive for high volume-low cost production of DHA. Thus, it is realized that genetic and metabolic engineering strategies are needed for the development of commercially competitive thraustochytrid DHA cell factories. Here, we present an analytical workflow for high resolution phenotyping at metabolite and lipid levels to generate deeper insight into the thraustochytrid physiology, with particular focus on central carbon and redox metabolism. We use time-series sampling during unlimited growth and nitrogen depleted triggering of DHA synthesis and lipid accumulation (LA) to show-case our methodology. The mass spectrometric absolute quantitative metabolite profiling covered glycolytic, pentose phosphate pathway (PPP) and tricarboxylic acid cycle (TCA) metabolites, amino acids, complete (deoxy)nucleoside phosphate pools, CoA and NAD metabolites, while semiquantitative high-resolution supercritical fluid chromatography MS/MS was applied for the lipid profiling. Interestingly, trace amounts of a triacylglycerols (TG) with DHA incorporated in all three acyl positions was detected, while TGs 16:0_16:0_22:6 and 16:0_22:6_22:6 were among the dominant lipid species. The metabolite profiling data indicated that lipid accumulation is not limited by availability of the acyl chain carbon precursor acetyl-CoA nor reducing power (NADPH) but rather points to the TG head group precursor glycerol-3-phosphate as the potential cause at the metabolite level for the gradual decline in lipid production throughout the cultivation. This high-resolution phenotyping provides new knowledge of changes in the central metabolism during growth and LA in thraustochytrids and will guide target selection for metabolic engineering needed for further improvements of this DHA cell factory.

Absolute Quantification of the Central Carbon Metabolome in Eight Commonly Applied Prokaryotic and Eukaryotic Model Systems

Absolute quantification of intracellular metabolite pools is a prerequisite for modeling and in-depth biological interpretation of metabolomics data. It is the final step of an elaborate metabolomics workflow, with challenges associated with all steps—from sampling to quantifying the physicochemically diverse metabolite pool. Chromatographic separation combined with mass spectrometric (MS) detection is the superior platform for high coverage, selective, and sensitive detection of metabolites. Herein, we apply our quantitative MS-metabolomics workflow to measure and present the central carbon metabolome of a panel of commonly applied biological model systems. The workflow includes three chromatographic methods combined with isotope dilution tandem mass spectrometry to allow for absolute quantification of 68 metabolites of glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and the amino acid and (deoxy) nucleoside pools. The biological model systems; Bacillus subtilis, Saccharomyces cerevisiae, two microalgal species, and four human cell lines were all cultured in commonly applied culture media and sampled in exponential growth phase. Both literature and databases are scarce with comprehensive metabolite datasets, and existing entries range over several orders of magnitude. The workflow and metabolite panel presented herein can be employed to expand the list of reference metabolomes, as encouraged by the metabolomics community, in a continued effort to develop and refine high-quality quantitative metabolomics workflows.

Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H₂O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

Quenching for Microalgal Metabolomics: A Case Study on the Unicellular Eukaryotic Green Alga Chlamydomonas reinhardtii

Capturing a valid snapshot of the metabolome requires rapid quenching of enzyme activities. This is a crucial step in order to halt the constant flux of metabolism and high turnover rate of metabolites. Quenching with cold aqueous methanol is treated as a gold standard so far, however, reliability of metabolomics data obtained is in question due to potential problems connected to leakage of intracellular metabolites. Therefore, we investigated the influence of various parameters such as quenching solvents, methanol concentration, inclusion of buffer additives, quenching time and solvent to sample ratio on intracellular metabolite leakage from Chlamydomonas reinhardtii. We measured the recovery of twelve metabolite classes using gas chromatography mass spectrometry (GC-MS) in all possible fractions and established mass balance to trace the fate of metabolites during quenching treatments. Our data demonstrate significant loss of intracellular metabolites with the use of the conventional 60% methanol, and that an increase in methanol concentration or quenching time also resulted in higher leakage. Inclusion of various buffer additives showed 70 mM HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) to be suitable. In summary, we recommend quenching with 60% aqueous methanol supplemented with 70 mM HEPES (−40 °C) at 1:1 sample to quenching solvent ratio, as it resulted in higher recoveries for intracellular metabolites with subsequent reduction in the metabolite leakage for all metabolite classes.

Improved phosphometabolome profiling applying isotope dilution strategy and capillary ion chromatography-tandem mass spectrometry

The phosphometabolome is comprised of all phosphorylated metabolites including the major metabolite classes sugar phosphates and nucleoside phosphates. Phosphometabolites are invaluable in any cell as a part of primary- and energy- metabolism, and as building blocks in the biosynthesis of macromolecules. Here, we report quantitative profiling of the phosphometabolome by applying capillary ion chromatography-tandem mass spectrometry (capIC-MS/MS), ensuring improved chromatographic separation, robustness and quantitative precision. Baseline separation was achieved for six out of eight tested hexose phosphates. Quantitative precision and reproducibility was improved by introducing a fully uniformly (U) ¹³C-labeled biological extract and applying an isotope dilution (ID) correction strategy. A ¹³C-labeled biological extract does in principle contain internal standards (IS) for all metabolites, but low abundant metabolites pose a challenge, and solutions to this are discussed. The extreme reproducibility and reliability of this capIC-MS/MS method was demonstrated by running the instrumentation continuously for ten days.

Influence of direct and sequential extraction methodology on metabolic profiling

A systematic comparison was made of the detected metabolite profiles for two plant materials (black beans and soybeans) and a dietary supplement (black cohosh) extracted using sequential (hexane, ethyl acetate, and 50% aqueous methanol) and direct extraction with three solvent systems (80% aqueous methanol, methanol/chloroform/water (2.5:1:1, v/v/v) and water). Extracts were analyzed by LC-MS (without derivatization) and GC-FID (with BSTFA/TMCS derivatizations). For sequential extraction, HPLC-UV and BSTFA/TMCS-derivatized GC-FID detection were more responsive to the polar molecules with a rough distribution of 10%, 10%, and 80% of the total signals in hexane, ethyl acetate, and 50% aqueous methanol, respectively. With HPLC-MS detection, the distribution of signals was more balanced, roughly 40%, 30%, and 30% for the same extracts (hexane, ethyl acetate, and 50% aqueous methanol). For direct extraction, HPLC-UV and BSTFA/TMCS-derivatized 4GC-FID provided signals between 60% and 150% of the total sequential extracted signals. The overlap of signals for the 3 sequential extracts ranged from 1% to 3%. The overlap of the signals for direct extraction with the total for sequential extraction ranged from 15% to 98%. With HPLC-MS detection, signals varied from 30% to 40% of the total signals for sequential extraction. Multivariate analysis showed that the components for the sequential and direct extracts were statistically different. However, each extract, sequential or direct, allowed discrimination between the 3 plant materials.

How close are we to complete annotation of metabolomes?

The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.

Mass Defect-Based N,N-Dimethyl Leucine Labels for Quantitative Proteomics and Amine Metabolomics of Pancreatic Cancer Cells

Mass spectrometry-based stable isotope labeling has become a key technology for protein and small-molecule analyses. We developed a multiplexed quantification method for simultaneous proteomics and amine metabolomics analyses via nano reversed-phase liquid chromatography-tandem mass spectrometry (nanoRPLC-MS/MS), called mass defect-based N,N-dimethyl leucine (mdDiLeu) labeling. The duplex mdDiLeu reagents were custom-synthesized with a mass difference of 20.5 mDa, arising from the subtle variation in nuclear binding energy between the two DiLeu isotopologues. Optimal MS resolving powers were determined to be 240K for labeled peptides and 120K for labeled metabolites on the Orbitrap Fusion Lumos instrument. The mdDiLeu labeling does not suffer from precursor interference and dynamic range compression, providing excellent accuracy for MS¹-centric quantification. Quantitative information is only revealed at high MS resolution without increasing spectrum complexity and overlapping isotope distribution. Chromatographic performance of polar metabolites was dramatically improved by mdDiLeu labeling with modified hydrophobicity, enhanced ionization efficiency, and picomole levels of detection limits. Paralleled proteomics and amine metabolomics analyses using mdDiLeu were systematically evaluated and then applied to pancreatic cancer cells.

Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems

Metabolome analyses are a suite of analytical approaches that enable us to capture changes in the metabolome (small molecular weight components, typically less than 1500 Da) in biological systems. Mass spectrometry (MS) has been widely used for this purpose. The key challenge here is to be able to capture changes in a reproducible and reliant manner that is representative of the events that take place in vivo Typically, the analysis is carried out in vitro, by isolating the system and extracting the metabolome. MS-based approaches enable us to capture metabolomic changes with high sensitivity and resolution. When developing the technique for different biological systems, there are similarities in challenges and differences that are specific to the system under investigation. Here, we review some of the challenges in capturing quantitative changes in the metabolome with MS based approaches, primarily in microbial and mammalian systems.This article is part of the themed issue 'Quantitative mass spectrometry'.