A quantitative metabolomics study of bacterial metabolites in different domains

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

The Next Frontier of 3D Bioprinting: Bioactive Materials Functionalized by Bacteria

3D bioprinting has become a flexible technical means used in many fields. Currently, research on 3D bioprinting is mainly focused on the use of mammalian cells to print organ and tissue models, which has greatly promoted progress in the fields of tissue engineering, regenerative medicine, and pharmaceuticals. In recent years, bacterial bioprinting has gradually become a rapidly developing research fields, with a wide range of potential applications in basic research, biomedicine, bioremediation, and other field. Here, this works reviews new research on bacterial bioprinting, and discuss its future research direction.

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.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Interactions between Enterohemorrhagic Escherichia coli (EHEC) and Gut Commensals at the Interface of Human Colonoids

The interactions between the gut microbiota and pathogens are complex and can determine the outcome of an infection. Enterohemorrhagic Escherichia coli (EHEC) is a major human enteric pathogen that colonizes the colon through attaching and effacing (AE) lesions and uses microbiota-derived molecules as cues to control its virulence. Different gut commensals can modulate EHEC virulence. However, the lack of an animal model that recapitulates the human pathophysiology of EHEC infection makes it challenging to investigate how variations in microbiota composition could affect host susceptibility to this pathogen. Here, we addressed these interactions building from simple to more complex in vitro systems, culminating with the use of the physiological relevant human colonoids as a model to study the interactions between EHEC and different gut commensals. We demonstrated that Bacteroides thetaiotaomicron and Enterococcus faecalis enhance virulence expression and AE lesion formation in cultured epithelial cells, as well as on the colonic epithelium, while commensal E. coli did not affect these phenotypes. Importantly, in the presence of these three commensals together, virulence and AE lesion are enhanced. Moreover, we identified specific changes in the metabolic landscape promoted by different members of the gut microbiota and showed that soluble factors released by E. faecalis can increase EHEC virulence gene expression. Our study highlights the importance of interspecies bacterial interactions and chemical exchange in the modulation of EHEC virulence. IMPORTANCE Enterohemorrhagic E. coli (EHEC) is a natural human pathogen that poorly colonizes mice. Hence, the use of murine models to understand features of EHEC infection is a challenge. In this study, we use human colonoids as a physiologically relevant model to study interactions between EHEC and gut commensals. We demonstrate that the ability of EHEC to form AE lesions on the intestinal epithelium is enhanced by the presence of certain gut commensals, such as B. thetaiotaomicron and E. faecalis, while it is not affected by commensal E. coli. Furthermore, we show that commensal bacteria differently impact the metabolic landscape. These data suggest that microbiota compositions can differentially modulate EHEC-mediated disease.

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.

Metabolomic analysis of Trichophyton rubrum and Microsporum canis during keratin degradation

Keratin is important and needed for the growth of dermatophytes in the host tissue. In turn, the ability to invade keratinised tissues is defined as a pivotal virulence attribute of this group of medically important fungi. The host–dermatophyte interaction is accompanied by an adaptation of fungal metabolism that allows them to adhere to the host tissue as well as utilize the available nutrients necessary for their survival and growth. Dermatophyte infections pose a significant epidemiological and clinical problem. Trichophyton rubrum is the most common anthropophilic dermatophyte worldwide and its typical infection areas include skin of hands or feet and nail plate. In turn, Microsporum canis is a zoophilic pathogen, and mostly well known for ringworm in pets, it is also known to infect humans. The aim of the study was to compare the intracellular metabolite content in the T. rubrum and M. canis during keratin degradation using liquid chromatography system coupled with tandem mass spectrometer (LC-MS/MS). The metabolite “fingerprints” revealed compounds associated with amino acids metabolism, carbohydrate metabolism related to the glycolysis and the tricarboxylic acid cycle (TCA), as well as nucleotide and energy metabolism. The metabolites such as kynurenic acid, l-alanine and cysteine in case of T. rubrum as well as cysteine and riboflavin in case of M. canis were detected only during keratin degradation what may suggest that these compounds may play a key role in the interactions of T. rubrum and M. canis with the host tissue. The metabolomic results were completed by qPCR gene expression assay. Our findings suggest that metabolomic analysis of T. rubrum and M. canis growing in culture media that mimic the dermatophyte infection could allow the understanding of processes involved in the pathogenesis of dermatophytes.

The Azospirillum brasilense Core Chemotaxis Proteins CheA1 and CheA4 Link Chemotaxis Signaling with Nitrogen Metabolism

Bacterial chemotaxis affords motile bacteria the ability to navigate the environment to locate niches for growth and survival. At the molecular level, chemotaxis depends on chemoreceptor signaling arrays that interact with cytoplasmic proteins to control the direction of movement. In Azospirillum brasilense, chemotaxis is mediated by two distinct chemotaxis pathways: Che1 and Che4. Both Che1 and Che4 are critical in the A. brasilense free-living and plant-associated lifestyles. Here, we use whole-cell proteomics and metabolomics to characterize the role of chemotaxis in A. brasilense physiology. We found that mutants lacking CheA1 or CheA4 or both are affected in nonchemotaxis functions, including major changes in transcription, signaling transport, and cell metabolism. We identify specific effects of CheA1 and CheA4 on nitrogen metabolism, including nitrate assimilation and nitrogen fixation, that may depend, at least, on the transcriptional control of rpoN, which encodes RpoN, a global regulator of metabolism, including nitrogen. Consistent with proteomics, the abundance of several nitrogenous compounds (purines, pyrimidines, and amino acids) changed in the metabolomes of the chemotaxis mutants relative to the parental strain. Further, we uncover novel, and yet uncharacterized, layers of transcriptional and posttranscriptional control of nitrogen metabolism regulators. Together, our data reveal roles for CheA1 and CheA4 in linking chemotaxis and nitrogen metabolism, likely through control of global regulatory networks.

IMPORTANCE Bacterial chemotaxis is widespread in bacteria, increasing competitiveness in diverse environments and mediating associations with eukaryotic hosts ranging from commensal to beneficial and pathogenic. In most bacteria, chemotaxis signaling is tightly linked to energy metabolism, with this coupling occurring through the sensory input of several energy-sensing chemoreceptors. Here, we show that in A. brasilense the chemotaxis proteins have key roles in modulating nitrogen metabolism, including nitrate assimilation and nitrogen fixation, through novel and yet unknown regulations. These results are significant given that A. brasilense is a model bacterium for plant growth promotion and free-living nitrogen fixation and is used as a bio-inoculant for cereal crops. Chemotaxis signaling in A. brasilense thus links locomotor behaviors to nitrogen metabolism, allowing cells to continuously and reciprocally adjust metabolism and chemotaxis signaling as they navigate gradients.

Making the Most of Trait-Based Approaches for Microbial Ecology

There is an increasing interest in applying trait-based approaches to microbial ecology, but the question of how and why to do it is still lagging behind. By anchoring our discussion of these questions in a framework derived from epistemology, we broaden the scope of trait-based approaches to microbial ecology from one oriented mostly around explanation towards one inclusive of the predictive and integrative potential of these approaches. We use case studies from macro-organismal ecology to concretely show how these goals for knowledge development can be fulfilled and propose clear directions, adapted to the biological reality of microbes, to make the most of recent advancements in the measurement of microbial phenotypes and traits.