Systems biology: metabolite turns master regulator

Efficient lipidomic approach for the discovery of lipid ligands for immune receptors by combining LC-HRMS/MS analysis with fractionation and reporter cell assay

C-type lectin receptors (CLRs), which are pattern recognition receptors responsible for triggering innate immune responses, recognize damaged self-components and immunostimulatory lipids from pathogenic bacteria; however, several of their ligands remain unknown. Here, we propose a new analytical platform combining liquid chromatography-high-resolution tandem mass spectrometry with microfractionation capability (LC-FRC-HRMS/MS) and a reporter cell assay for sensitive activity measurements to develop an efficient methodology for searching for lipid ligands of CLR from microbial trace samples (crude cell extracts of approximately 5 mg dry cell/mL). We also developed an in-house lipidomic library containing accurate mass and fragmentation patterns of more than 10,000 lipid molecules predicted in silico for 90 lipid subclasses and 35 acyl side chain fatty acids. Using the developed LC-FRC-HRMS/MS system, the lipid extracts of Helicobacter pylori were separated and fractionated, and HRMS and HRMS/MS spectra were obtained simultaneously. The fractionated lipid extract samples in 96-well plates were thereafter subjected to reporter cell assays using nuclear factor of activated T cells (NFAT)-green fluorescent protein (GFP) reporter cells expressing mouse or human macrophage-inducible C-type lectin (Mincle). A total of 102 lipid molecules from all fractions were annotated using an in-house lipidomic library. Furthermore, a fraction that exhibited significant activity in the NFAT-GFP reporter cell assay contained α-cholesteryl glucoside, a type of glycolipid, which was successfully identified as a lipid ligand molecule for Mincle. Our analytical platform has the potential to be a useful tool for efficient discovery of lipid ligands for immunoreceptors.

Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli

The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.

Conversion of Escherichia coli into Mixotrophic CO2 Assimilation with Malate and Hydrogen Based on Recombinant Expression of 2-Oxoglutarate:Ferredoxin Oxidoreductase Using Adaptive Laboratory Evolution

We report the mixotrophic growth of Escherichia coli based on recombinant 2-oxoglutarate:ferredoxin oxidoreductase (OGOR) to assimilate CO₂ using malate as an auxiliary carbon source and hydrogen as an energy source. We employ a long-term (~184 days) two-stage adaptive evolution to convert heterotrophic E. coli into mixotrophic E. coli. In the first stage of evolution with serine, diauxic growth emerges as a prominent feature. At the end of the second stage of evolution with malate, the strain exhibits mixotrophy with CO₂ as an essential substrate for growth. We expect this work will open new possibilities in the utilization of OGOR for microbial CO₂ assimilation and future hydrogen-based electro-microbial conversion.

Metabolic alteration of Methylococcus capsulatus str. Bath during a microbial gas-phase reaction

This study demonstrates the metabolic alteration of Methylococcus capsulatus (Bath), a representative bacterium among methanotrophs, in microbial gas-phase reactions. For comparative metabolome analysis, a bioreactor was designed to be capable of supplying gaseous substrates and liquid nutrients continuously. Methane degradation by M. capsulatus (Bath) was more efficient in a gas-phase reaction operated in the bioreactor than in an aqueous phase reaction operated in a batch reactor. Metabolome analysis revealed remarkable alterations in the metabolism of cells in the gas-phase reaction; in particular, pyruvate, 2-ketoglutarate, some amino acids, xanthine, and hypoxanthine were accumulated, whereas 2,6-diaminopimelate was decreased. Based on the results of metabolome analysis, cells in the gas-phase reaction seemed to alter their metabolism to reduce the excess ATP and NADH generated upon increased availability of methane and oxygen. Our findings will facilitate the development of efficient processes for methane-based bioproduction with low energy consumption.

The remodel of the "central dogma": a metabolomics interaction perspective

BACKGROUND

In 1957, Francis Crick drew a linear diagram on a blackboard. This diagram is often called the "central dogma." Subsequently, the relationships between different steps of the "central dogma" have been shown to be considerably complex, mostly because of the emerging world of small molecules. It is noteworthy that metabolites can be generated from the diet through gut microbiome metabolism, serve as substrates for epigenetic modifications, destabilize DNA quadruplexes, and follow Lamarckian inheritance. Small molecules were once considered the missing link in the "central dogma"; however, recently they have acquired a central role, and their general perception as downstream products has become reductionist. Metabolomics is a large-scale analysis of metabolites, and this emerging field has been shown to be the closest omics associated with the phenotype and concomitantly, the basis for all omics.

AIM OF REVIEW

Herein, we propose a broad updated perspective for the flux of information diagram centered in metabolomics, including the influence of other factors, such as epigenomics, diet, nutrition, and the gut- microbiome.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Metabolites are the beginning and the end of the flux of information.

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

Carbon/Nitrogen Metabolic Balance: Lessons from Cyanobacteria

Carbon and nitrogen are the two most abundant nutrient elements for all living organisms, and their metabolism is tightly coupled. What are the signaling mechanisms that cells use to sense and control the carbon/nitrogen (C/N) metabolic balance following environmental changes? Based on studies in cyanobacteria, it was found that 2-phosphoglycolate derived from the oxygenase activity of Rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) and 2-oxoglutarate from the Krebs cycle act as the carbon- and nitrogen-starvation signals, respectively, and their concentration ratio likely reflects the status of the C/N metabolic balance. We will present and discuss the regulatory principles underlying the signaling mechanisms, which are likely to be conserved in other photosynthetic organisms. These concepts may also contribute to developments in the field of biofuel engineering or improvements in crop productivity.

The Emergence of 2-Oxoglutarate as a Master Regulator Metabolite

The metabolite 2-oxoglutarate (also known as α-ketoglutarate, 2-ketoglutaric acid, or oxoglutaric acid) lies at the intersection between the carbon and nitrogen metabolic pathways. This compound is a key intermediate of one of the most fundamental biochemical pathways in carbon metabolism, the tricarboxylic acid (TCA) cycle. In addition, 2-oxoglutarate also acts as the major carbon skeleton for nitrogen-assimilatory reactions. Experimental data support the conclusion that intracellular levels of 2-oxoglutarate fluctuate according to nitrogen and carbon availability. This review summarizes how nature has capitalized on the ability of 2-oxoglutarate to reflect cellular nutritional status through evolution of a variety of 2-oxoglutarate-sensing regulatory proteins. The number of metabolic pathways known to be regulated by 2-oxoglutarate levels has increased significantly in recent years. The signaling properties of 2-oxoglutarate are highlighted by the fact that this metabolite regulates the synthesis of the well-established master signaling molecule, cyclic AMP (cAMP), in Escherichia coli.

Protocol: a fast, comprehensive and reproducible one-step extraction method for the rapid preparation of polar and semi-polar metabolites, lipids, proteins, starch and cell wall polymers from a single sample

BACKGROUND

The elucidation of complex biological systems requires integration of multiple molecular parameters. Accordingly, high throughput methods like transcriptomics, proteomics, metabolomics and lipidomics have emerged to provide the tools for successful system-wide investigations. Unfortunately, optimized analysis of different compounds requires specific extraction procedures in combination with specific analytical instrumentation. However, the most efficient extraction protocols often only cover a restricted number of compounds due to the different physico-chemical properties of these biological compounds. Consequently, comprehensive analysis of several molecular components like polar primary metabolites next to lipids or proteins require multiple aliquots to enable the specific extraction procedures required to cover these diverse compound classes. This multi-parallel sample handling of different sample aliquots is therefore not only more sample intensive, it also requires more time and effort to obtain the required extracts.

RESULTS

To circumvent large sample amounts, distributed into several aliquots for the comprehensive extraction of most relevant biological compounds, we developed a simple, robust and reproducible two-phase liquid-liquid extraction protocol. This one-step extraction protocol allows for the analysis of polar-, semi-polar and hydrophobic metabolites, next to insoluble or precipitated compounds, including proteins, starch and plant cell wall components, from a single sample. The method is scalable regarding the used sample amounts but also the employed volumes and can be performed in microcentrifuge tubes, enabling high throughput analysis. The obtained fractions are fully compatible with common analytical methods, including spectroscopic, chromatographic and mass spectrometry-based techniques. To document the utility of the described protocol, we used 25 mg of Arabidopsis thaliana rosette leaves for the generation of multi-omics data sets, covering lipidomics, metabolomics and proteomics. The obtained data allowed us to measure and annotate more than 200 lipid compounds, 100 primary metabolites, 50 secondary metabolites and 2000 proteins.

CONCLUSIONS

The described extraction protocol provides a simple and straightforward method for the efficient extraction of lipids, metabolites and proteins from minute amounts of a single sample, enabling the targeted but also untargeted high-throughput analyses of diverse biological tissues and samples.

The Crc protein participates in down-regulation of the Lon gene to promote rhamnolipid production and rhl quorum sensing in Pseudomonas aeruginosa

Rhamnolipid acts as a virulence factor during Pseudomonas aeruginosa infection. Here, we show that deletion of the catabolite repression control (crc) gene in P. aeruginosa leads to a rhamnolipid-negative phenotype. This effect is mediated by the down-regulation of rhl quorum sensing (QS). We discover that a disruption of the gene encoding the Lon protease entirely offsets the effect of crc deletion on the production of both rhamnolipid and rhl QS signal C4-HSL. Crc is unable to bind lon mRNA in vitro in the absence of the RNA chaperon Hfq, while Crc contributes to Hfq-mediated repression of the lon gene expression at a posttranscriptional level. Deletion of crc, which results in up-regulation of lon, significantly reduces the in vivo stability and abundance of the RhlI protein that synthesizes C4-HSL, causing the attenuation of rhl QS. Lon is also capable of degrading the RhlI protein in vitro. In addition, constitutive expression of rhlI suppresses the defects of the crc deletion mutant in rhamnolipid, C4-HSL and virulence on lettuce leaves. This study therefore uncovers a novel posttranscriptional regulatory cascade, Crc-Hfq/Lon/RhlI, for the regulation of rhamnolipid production and rhl QS in P. aeruginosa.

Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions

Living organisms have sophisticated but well-organized regulation system. It is important to understand the metabolic regulation mechanisms in relation to growth environment for the efficient design of cell factories for biofuels and biochemicals production. Here, an overview is given for carbon catabolite regulation, nitrogen regulation, ion, sulfur, and phosphate regulations, stringent response under nutrient starvation as well as oxidative stress regulation, redox state regulation, acid-shock, heat- and cold-shock regulations, solvent stress regulation, osmoregulation, and biofilm formation, and quorum sensing focusing on Escherichia coli metabolism and others. The coordinated regulation mechanisms are of particular interest in getting insight into the principle which governs the cell metabolism. The metabolism is controlled by both enzyme-level regulation and transcriptional regulation via transcription factors such as cAMP-Crp, Cra, Csr, Fis, P(II)(GlnB), NtrBC, CysB, PhoR/B, SoxR/S, Fur, MarR, ArcA/B, Fnr, NarX/L, RpoS, and (p)ppGpp for stringent response, where the timescales for enzyme-level and gene-level regulations are different. Moreover, multiple regulations are coordinated by the intracellular metabolites, where fructose 1,6-bisphosphate (FBP), phosphoenolpyruvate (PEP), and acetyl-CoA (AcCoA) play important roles for enzyme-level regulation as well as transcriptional control, while α-ketoacids such as α-ketoglutaric acid (αKG), pyruvate (PYR), and oxaloacetate (OAA) play important roles for the coordinated regulation between carbon source uptake rate and other nutrient uptake rate such as nitrogen or sulfur uptake rate by modulation of cAMP via Cya.

Metabolic sensor governing bacterial virulence in Staphylococcus aureus

An effective metabolism is essential to all living organisms, including the important human pathogen Staphylococcus aureus. To establish successful infection, S. aureus must scavenge nutrients and coordinate its metabolism for proliferation. Meanwhile, it also must produce an array of virulence factors to interfere with host defenses. However, the ways in which S. aureus ties its metabolic state to its virulence regulation remain largely unknown. Here we show that citrate, the first intermediate of the tricarboxylic acid (TCA) cycle, binds to and activates the catabolite control protein E (CcpE) of S. aureus. Using structural and site-directed mutagenesis studies, we demonstrate that two arginine residues (Arg145 and Arg256) within the putative inducer-binding cavity of CcpE are important for its allosteric activation by citrate. Microarray analysis reveals that CcpE tunes the expression of 126 genes that comprise about 4.7% of the S. aureus genome. Intriguingly, although CcpE is a major positive regulator of the TCA-cycle activity, its regulon consists predominantly of genes involved in the pathogenesis of S. aureus. Moreover, inactivation of CcpE results in increased staphyloxanthin production, improved ability to acquire iron, increased resistance to whole-blood-mediated killing, and enhanced bacterial virulence in a mouse model of systemic infection. This study reveals CcpE as an important metabolic sensor that allows S. aureus to sense and adjust its metabolic state and subsequently to coordinate the expression of virulence factors and bacterial virulence.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

BACKGROUND

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.

METHOD

Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.

RESULTS

Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.

CONCLUSIONS

Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Major depressive disorder subtypes to predict long-term course

BACKGROUND

Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here.

METHODS

Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due to MDD).

RESULTS

Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor.

CONCLUSIONS

Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.

Bacterial growth: global effects on gene expression, growth feedback and proteome partition

The function of endogenous as well as synthetic genetic circuits is generically coupled to the physiological state of the cell. For exponentially growing bacteria, a key characteristic of the state of the cell is the growth rate and thus gene expression is often growth-rate dependent. Here we review recent results on growth-rate dependent gene expression. We distinguish different types of growth-rate dependencies by the mechanisms of regulation involved and the presence or absence of an effect of the gene product on growth. The latter can lead to growth feedback, feedback mediated by changes of the global state of the cell. Moreover, we discuss how growth rate dependence can be used as a guide to study the molecular implementation of physiological regulation.