1H-NMR-based metabolomics to investigate the effects of Phoenix dactylifera seed extracts in LPS-IFN-γ-induced RAW 264.7 cells.

Inflammation has been revealed to play a central role in the onset and progression of many illnesses. Nuclear magnetic resonance (NMR) based metabolomics method was adopted to evaluate the effects of Phoenix dactylifera seeds, in particular the Algerian date variety of Deglet on the metabolome of the LPS-IFN-γ-induced RAW 264.7 cells. Variations in the extracellular and intracellular profiles emphasized the differences in the presence of tyrosine, phenylalanine, alanine, proline, asparagine, isocitrate, inosine and lysine. Principal component analysis (PCA) revealed noticeable clustering patterns between the treated and induced RAW cells based on the metabolic profile of the extracellular metabolites. However, the effects of treatment on the intracellular metabolites appears to be less distinct as suggested by the PCA and heatmap analyses. A clear group segregation was observed for the intracellular metabolites from the treated and induced cells based on the orthogonal partial least squares-discriminant analysis (OPLS-DA) score plot. Likewise, 11 of the metabolites in the treated cells were significantly different from those in the induced groups, including amino acids and succinate. The enrichment analysis demonstrated that treatment with Deglet seed extracts interfered with the energy and of amino acids metabolism. Overall, the obtained data reinforced the possible application of Deglet seeds as a functional food with anti-inflammatory properties.

NMR metabolomics for evaluating passage number and harvesting effects on mammalian cell metabolome.

The variation in the extracellular metabolites of RAW 264.7 cells obtained from different passage numbers (passage 9, 12 and 14) was examined. The impact of different harvesting protocols (trypsinization and scraping) on recovery of intracellular metabolites was then assessed. The similarity and variation in the cell metabolome was investigated using 1H NMR metabolic profiling modeled using multivariate data analysis. The characterization and quantification of metabolites was performed to determine the passage-related and harvesting-dependent effects on impacted metabolic networks. The trypsinized RAW cells from lower passages gave higher intensities of most identified metabolites, including asparagine, serine and tryptophan. Principal component analysis revealed variation between cells from different passages and harvesting methods, as indicated by the formation of clusters in score plot. Analysis of S-plots revealed metabolites that acted as biomarkers in discriminating cells from different passages including acetate, serine, lactate and choline. Meanwhile lactate, glutamine and pyruvate served as biomarkers for differentiating trypsinized and scraped cells. In passage-dependent effects, glycolysis and TCA cycle were influential, whereas glycerophospholipid metabolism was affected by the harvesting method. Overall, it is proposed that typsinized RAW cells from lower passage numbers are more appropriate when conducting experiments related to NMR metabolomics.

Characterization of Plant Volatiles Reveals Distinct Metabolic Profiles and Pathways among 12 Brassicaceae Vegetables.

Plants emit characteristic organic volatile compounds (VOCs) with diverse biological/ecological functions. However, the links between plant species/varieties and their phytochemical emission profiles remain elusive. Here, we developed a direct headspace solid-phase microextraction (HS-SPME) technique and combined with non-targeted gas chromatography‒high-resolution mass spectrometry (GC-HRMS) platform to investigate the VOCs profiles of 12 common Brassicaceae vegetables (watercress, rocket, Brussels sprouts, broccoli, kai lan, choy sum, pak choi, cabbage, Chinese cabbage, cauliflower, radish and cherry radish). The direct HS-SPME sampling approach enabled reproducible capture of the rapid-emitting VOCs upon plant tissue disruption. The results revealed extensive variation in VOCs profiles among the 12 Brassicaceae vegetables. Furthermore, principal component analysis (PCA) showed that the VOC profiles could clearly distinguish the 12 Brassicaceae vegetables, and that these profiles well reflected the classical morphological classification. After multivariate statistical analysis, 44 VOCs with significant differences among the Brassicaceae vegetables were identified. Pathway analysis showed that three secondary metabolism pathways, including the fatty acid pathway, methylerythritol phosphate (MEP) pathway and glucosinolate (GLS) pathway, behave distinctively in these vegetables. These three pathways are responsible for the generation and emission of green leaf volatiles (GLVs), terpenes and isothiocyanates (ITCs), respectively. Correlation analysis further showed that volatile metabolites formed via the common pathway had significantly positive correlations, whereas metabolites from different pathways had either non-significant or significantly negative correlations. Genetic influences on these metabolites across various vegetable types were also evaluated. These findings extend our phytochemical knowledge of the 12 edible Brassicaceae vegetables and provide useful information on their secondary metabolism.

Keystone Species in Pregnancy Gingivitis: A Snapshot of Oral Microbiome During Pregnancy and Postpartum Period.

It is well known that pregnancy is under the constant influence of hormonal, metabolic and immunological factors and this may impact the oral microbiota toward pregnancy gingivitis. However, it is still not clear how the oral microbial dysbiosis can modulate oral diseases as oral microbiome during pregnancy is very poorly characterized. In addition, the recent revelation that placental microbiome is akin to oral microbiome further potentiates the importance of oral dysbiosis in adverse pregnancy outcomes. Hence, leveraging on the 16S rRNA gene sequencing technology, we present a snapshot of the variations in the oral microbial composition with the progression of pregnancy and in the postpartum period and its association with pregnancy gingivitis. Despite the stability of oral microbial diversity during pregnancy and postpartum period, we observed that the microbiome makes a pathogenic shift during pregnancy and reverts back to a healthy microbiome during the postpartum period. Co-occurrence network analysis provided a mechanistic explanation of the pathogenicity of the microbiome during pregnancy and predicted taxa at hubs of interaction. Targeting the taxa which form the ecological guilds in the underlying microbiome would help to modulate the microbial pathogenicity during pregnancy, thereby alleviating risk for oral diseases and adverse pregnancy outcomes. Our study has also uncovered the possibility of novel species in subgingival plaque and saliva as the key players in the causation of pregnancy gingivitis. The keystone species hold the potential to open up avenues for designing microbiome modulation strategies to improve host health during pregnancy.

Extracellular Electron Transfer Powers Enterococcus faecalis Biofilm Metabolism.

Enterococci are important human commensals and significant opportunistic pathogens. Biofilm-related enterococcal infections, such as endocarditis, urinary tract infections, wound and surgical site infections, and medical device-associated infections, often become chronic upon the formation of biofilm. The biofilm matrix establishes properties that distinguish this state from free-living bacterial cells and increase tolerance to antimicrobial interventions. The metabolic versatility of the enterococci is reflected in the diversity and complexity of environments and communities in which they thrive. Understanding metabolic factors governing colonization and persistence in different host niches can reveal factors influencing the transition to biofilm pathogenicity. Here, we report a form of iron-dependent metabolism for Enterococcus faecalis where, in the absence of heme, extracellular electron transfer (EET) and increased ATP production augment biofilm growth. We observe alterations in biofilm matrix depth and composition during iron-augmented biofilm growth. We show that the ldh gene encoding l-lactate dehydrogenase is required for iron-augmented energy production and biofilm formation and promotes EET.IMPORTANCE Bacterial metabolic versatility can often influence the outcome of host-pathogen interactions, yet causes of metabolic shifts are difficult to resolve. The bacterial biofilm matrix provides the structural and functional support that distinguishes this state from free-living bacterial cells. Here, we show that the biofilm matrix can immobilize iron, providing access to this growth-promoting resource which is otherwise inaccessible in the planktonic state. Our data show that in the absence of heme, Enterococcus faecalis l-lactate dehydrogenase promotes EET and uses matrix-associated iron to carry out EET. Therefore, the presence of iron within the biofilm matrix leads to enhanced biofilm growth.

Enterococcal Metabolite Cues Facilitate Interspecies Niche Modulation and Polymicrobial Infection.

Enterococcus faecalis is frequently associated with polymicrobial infections of the urinary tract, indwelling catheters, and surgical wound sites. E. faecalis co-exists with Escherichia coli and other pathogens in wound infections, but mechanisms that govern polymicrobial colonization and pathogenesis are poorly defined. During infection, bacteria must overcome multiple host defenses, including nutrient iron limitation, to persist and cause disease. In this study, we investigated the contribution of E. faecalis to mixed-species infection when iron availability is restricted. We show that E. faecalis significantly augments E. coli biofilm growth and survival in vitro and in vivo by exporting L-ornithine. This metabolic cue facilitates E. coli biosynthesis of the enterobactin siderophore, allowing E. coli growth and biofilm formation in iron-limiting conditions that would otherwise restrict its growth. Thus, E. faecalis modulates its local environment by contributing growth-promoting cues that allow co-infecting organisms to overcome iron limitation and promotes polymicrobial infections.

Coordinate Regulation of Metabolite Glycosylation and Stress Hormone Biosynthesis by TT8 in Arabidopsis.

Secondary metabolites play a key role in coordinating ecology and defense strategies of plants. Diversity of these metabolites arises by conjugation of core structures with diverse chemical moieties, such as sugars in glycosylation. Active pools of phytohormones, including those involved in plant stress response, are also regulated by glycosylation. While much is known about the enzymes involved in glycosylation, we know little about their regulation or coordination with other processes. We characterized the flavonoid pathway transcription factor TRANSPARENT TESTA8 (TT8) in Arabidopsis (Arabidopsis thaliana) using an integrative omics strategy. This approach provides a systems-level understanding of the cellular machinery that is used to generate metabolite diversity by glycosylation. Metabolomics analysis of TT8 loss-of-function and inducible overexpression lines showed that TT8 coordinates glycosylation of not only flavonoids, but also nucleotides, thus implicating TT8 in regulating pools of activated nucleotide sugars. Transcriptome and promoter network analyses revealed that the TT8 regulome included sugar transporters, proteins involved in sugar binding and sequestration, and a number of carbohydrate-active enzymes. Importantly, TT8 affects stress response, along with brassinosteroid and jasmonic acid biosynthesis, by directly binding to the promoters of key genes of these processes. This combined effect on metabolite glycosylation and stress hormones by TT8 inducible overexpression led to significant increase in tolerance toward multiple abiotic and biotic stresses. Conversely, loss of TT8 leads to increased sensitivity to these stresses. Thus, the transcription factor TT8 is an integrator of secondary metabolism and stress response. These findings provide novel approaches to improve broad-spectrum stress tolerance.

Ecogenomics reveals metals and land-use pressures on microbial communities in the waterways of a megacity.

Networks of engineered waterways are critical in meeting the growing water demands in megacities. To capture and treat rainwater in an energy-efficient manner, approaches can be developed for such networks that use ecological services from microbial communities. Traditionally, engineered waterways were regarded as homogeneous systems with little responsiveness of ecological communities and ensuing processes. This study provides ecogenomics-derived key information to explain the complexity of urban aquatic ecosystems in well-managed watersheds with densely interspersed land-use patterns. Overall, sedimentary microbial communities had higher richness and evenness compared to the suspended communities in water phase. On the basis of PERMANOVA analysis, variation in structure and functions of microbial communities over space within same land-use type was not significant. In contrast, this difference was significant between different land-use types, which had similar chemical profiles. Of the 36 environmental parameters from spatial analysis, only three metals, namely potassium, copper and aluminum significantly explained between 7% and 11% of the variation in taxa and functions, based on distance-based linear models (DistLM). The ecogenomics approach adopted here allows the identification of key drivers of microbial communities and their functions at watershed-scale. These findings can be used to enhance microbial services, which are critical to develop ecologically friendly waterways in rapidly urbanizing environments.

Plant metabolomics: from experimental design to knowledge extraction.

Metabolomics is one of the most recent additions to the functional genomics approaches. It involves the use of analytical chemistry techniques to provide high-density data of metabolic profiles. Data is then analyzed using advanced statistics and databases to extract biological information, thus providing the metabolic phenotype of an organism. Large variety of metabolites produced by plants through the complex metabolic networks and their dynamic changes in response to various perturbations can be studied using metabolomics. Here, we describe the basic features of plant metabolic diversity and analytical methods to describe this diversity, which includes experimental workflows starting from experimental design, sample preparation, hardware and software choices, combined with knowledge extraction methods. Finally, we describe a scenario for using these workflows to identify differential metabolites and their pathways from complex biological samples.

Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis.

Identification of the factors critical to the tumor-initiating cell (TIC) state may open new avenues in cancer therapy. Here we show that the metabolic enzyme glycine decarboxylase (GLDC) is critical for TICs in non-small cell lung cancer (NSCLC). TICs from primary NSCLC tumors express high levels of the oncogenic stem cell factor LIN28B and GLDC, which are both required for TIC growth and tumorigenesis. Overexpression of GLDC and other glycine/serine enzymes, but not catalytically inactive GLDC, promotes cellular transformation and tumorigenesis. We found that GLDC induces dramatic changes in glycolysis and glycine/serine metabolism, leading to changes in pyrimidine metabolism to regulate cancer cell proliferation. In the clinic, aberrant activation of GLDC correlates with poorer survival in lung cancer patients, and aberrant GLDC expression is observed in multiple cancer types. This link between glycine metabolism and tumorigenesis may provide novel targets for advancing anticancer therapy.

datPAV--an online processing, analysis and visualization tool for exploratory investigation of experimental data.

SUMMARY: Data processing, analysis and visualization (datPAV) is an exploratory tool that allows experimentalist to quickly assess the general characteristics of the data. This platform-independent software is designed as a generic tool to process and visualize data matrices. This tool explores organization of the data, detect errors and support basic statistical analyses. Processed data can be reused whereby different step-by-step data processing/analysis workflows can be created to carry out detailed investigation. The visualization option provides publication-ready graphics. Applications of this tool are demonstrated at the web site for three cases of metabolomics, environmental and hydrodynamic data analysis. AVAILABILITY: datPAV is available free for academic use at http://www.sdwa.nus.edu.sg/datPAV/.

MetDAT: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation.

SUMMARY: Analysis of high throughput metabolomics experiments is a resource-intensive process that includes pre-processing, pre-treatment and post-processing at each level of experimental hierarchy. We developed an interactive user-friendly online software called Metabolite Data Analysis Tool (MetDAT) for mass spectrometry data. It offers a pipeline of tools for file handling, data pre-processing, univariate and multivariate statistical analyses, database searching and pathway mapping. Outputs are produced in the form of text and high-quality images in real-time. MetDAT allows users to combine data management and experiment-centric workflows for optimization of metabolomics methods and metabolite analysis. AVAILABILITY: MetDAT is available free for academic use from http://smbl.nus.edu.sg/METDAT2/. CONTACT: sanjay@nus.edu.sg