Phenotyping of gut microbiota: Focus on capillary electrophoresis

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Metabolomics reveals synergy between Ag and g-C3N4 in Ag/g-C3N4 composite photocatalysts: a unique feature among Ag-doped biocidal materials

INTRODUCTION

The silver/graphitic carbon nitride (Ag/g-C₃N₄) composite system exerts biocidal activity against the pathogenic bacterium Escherichia coli 1337-H that is stronger than that of well-known silver and titanium oxide (TiO₂)-based composites. However, whether the Ag/g-C₃N₄ composite system has biocidal properties that the parent components do or do not have as separate chemical entities and whether they differ from those in Ag/TiO₂ composite photocatalysts have not been clarified.

OBJECTIVE

We investigated the chemical (cooperative charge handling and electronic properties) and biological (metabolic) effects exerted by the addition of Ag to g-C₃N₄ and to TiO₂.

METHODS

In this work, we undertook metabolome-wide analysis by liquid chromatography-electrospray ionization-quadrupole-time of flight-mass spectrometry to compare the metabolite profiles of untreated E. coli 1337-H cells or those subjected to disinfection with Ag, g-C₃N₄, 2Ag/g-C₃N₄, TiO₂ and 2Ag/TiO₂.

RESULTS

While Ag or g-C₃N₄ moderately affected microbial metabolism according to the mean of the altered metabolites, multiple cell systems contributing to rapid cell death were immediately affected by the light-triggered radical species produced when Ag and g-C₃N₄ were as xAg/g-C₃N_4. The effects include drastically reduced production of small metabolites essential for detoxifying reactive oxygen species and those that regulate DNA replication fidelity, cell morphology and energy status. These biological consequences were different from those caused by Ag/TiO₂-based biocides, demonstrating the uniqueness of the Ag/g-C₃N₄ system.

CONCLUSIONS

Our results support the idea that the unique Ag/g-C₃N₄ biocidal properties are based on synergistic action and reveal new directions for designing future photocatalysts for use in disinfection and microbial control.

Capillary Electrophoresis Mass Spectrometry as a Tool for Untargeted Metabolomics

Although capillary electrophoresis (CE) coupled to mass spectrometry (MS) is a separation technique not extensively implemented, it offers differential possibilities in the study of polar and ionic metabolites in complex matrices with minimum sample treatment. However, in order to get successful results, some efforts at early stages and following specific recommendations are necessary.In this chapter, we describe our updated and well-tested methods for untargeted metabolomics using CE-MS-TOF for common biological samples: urine, serum or plasma, feces, tissues, and cells. Sample treatment, as well as separation and detection conditions are described in detail and other steps in the workflow for untargeted metabolomics are also explained. Special attention is paid to instrumental setup and advices for daily practice.Characteristic electropherograms obtained with each type of sample are depicted as well as groups of metabolites easily measured by this technique. Their global or individual comparisons have been given undoubtedly important information to unveil altered metabolic pathways, diagnosis, and prognosis or biomarker discovery in the study of diseases or conditions over decades.

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.