Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches

Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.

Multi-omics analysis unravels a segregated metabolic flux network that tunes co-utilization of sugar and aromatic carbons in Pseudomonas putida

Pseudomonas species thrive in different nutritional environments and can catabolize divergent carbon substrates. These capabilities have important implications for the role of these species in natural and engineered carbon processing. However, the metabolic phenotypes enabling Pseudomonas to utilize mixed substrates remain poorly understood. Here, we employed a multi-omics approach involving stable isotope tracers, metabolomics, fluxomics, and proteomics in Pseudomonas putida KT2440 to investigate the constitutive metabolic network that achieves co-utilization of glucose and benzoate, respectively a monomer of carbohydrate polymers and a derivative of lignin monomers. Despite nearly equal consumption of both substrates, metabolite isotopologues revealed nonuniform assimilation throughout the metabolic network. Gluconeogenic flux of benzoate-derived carbons from the tricarboxylic acid cycle did not reach the upper Embden-Meyerhof-Parnas pathway nor the pentose-phosphate pathway. These latter two pathways were populated exclusively by glucose-derived carbons through a cyclic connection with the Entner-Doudoroff pathway. We integrated the ¹³C-metabolomics data with physiological parameters for quantitative flux analysis, demonstrating that the metabolic segregation of the substrate carbons optimally sustained biosynthetic flux demands and redox balance. Changes in protein abundance partially predicted the metabolic flux changes in cells grown on the glucose:benzoate mixture versus on glucose alone. Notably, flux magnitude and directionality were also maintained by metabolite levels and regulation of phosphorylation of key metabolic enzymes. These findings provide new insights into the metabolic architecture that affords adaptability of P. putida to divergent carbon substrates and highlight regulatory points at different metabolic nodes that may underlie the high nutritional flexibility of Pseudomonas species.

Quantitative imaging for discovery and assembly of the metabo-regulome

Little is known about regulatory networks that control metabolic flux in plant cells. Detailed understanding of regulation is crucial for synthetic biology. The difficulty of measuring metabolites with cellular and subcellular precision is a major roadblock. New tools have been developed for monitoring extracellular, cytosolic, organellar and vacuolar ion and metabolite concentrations with a time resolution of milliseconds to hours. Genetically encoded sensors allow quantitative measurement of steady-state concentrations of ions, signaling molecules and metabolites and their respective changes over time. Fluorescence resonance energy transfer (FRET) sensors exploit conformational changes in polypeptides as a proxy for analyte concentrations. Subtle effects of analyte binding on the conformation of the recognition element are translated into a FRET change between two fused green fluorescent protein (GFP) variants, enabling simple monitoring of analyte concentrations using fluorimetry or fluorescence microscopy. Fluorimetry provides information averaged over cell populations, while microscopy detects differences between cells or populations of cells. The genetically encoded sensors can be targeted to subcellular compartments or the cell surface. Confocal microscopy ultimately permits observation of gradients or local differences within a compartment. The FRET assays can be adapted to high-throughput analysis to screen mutant populations in order to systematically identify signaling networks that control individual steps in metabolic flux.

Multiplex methods provide effective integration of multi-omic data in genome-scale models

BACKGROUND

Genomic, transcriptomic, and metabolic variations shape the complex adaptation landscape of bacteria to varying environmental conditions. Elucidating the genotype-phenotype relation paves the way for the prediction of such effects, but methods for characterizing the relationship between multiple environmental factors are still lacking. Here, we tackle the problem of extracting network-level information from collections of environmental conditions, by integrating the multiple omic levels at which the bacterial response is measured.

RESULTS

To this end, we model a large compendium of growth conditions as a multiplex network consisting of transcriptomic and fluxomic layers, and we propose a multi-omic network approach to infer similarity of growth conditions by integrating layers of the multiplex network. Each node of the network represents a single condition, while edges are similarities between conditions, as measured by phenotypic and transcriptomic properties on different layers of the network. We then fuse these layers into one network, therefore capturing a global network of conditions and the associated similarities across two omic levels. We apply this multi-omic fusion to an updated genome-scale reconstruction of Escherichia coli that includes underground metabolism and new gene-protein-reaction associations.

CONCLUSIONS

Our method can be readily used to evaluate and cross-compare different collections of conditions among different species. Acquiring multi-omic information on the topology of the space of experimental conditions makes it possible to infer the position and to build condition-specific models of untested or incomplete profiles for which experimental data is not available. Our weighted network fusion method for genome-scale models is freely available at https://github.com/maxconway/SNFtool .

Metabolic remodelling of glucose, fatty acid and redox pathways in the heart of type 2 diabetic mice

KEY POINTS

Hearts from type 2 diabetic animals display perturbations in excitation-contraction coupling, impairing myocyte contractility and delaying relaxation, along with altered substrate consumption patterns. Under high glucose and β-adrenergic stimulation conditions, palmitate can, at least in part, offset left ventricle (LV) dysfunction in hearts from diabetic mice, improving contractility and relaxation while restoring coronary perfusion pressure. Fluxome calculations of central catabolism in diabetic hearts show that, in the presence of palmitate, there is a metabolic remodelling involving tricarboxylic acid cycle, polyol and pentose phosphate pathways, leading to improved redox balance in cytoplasmic and mitochondrial compartments. Under high glucose and increased energy demand, the metabolic/fluxomic redirection leading to restored redox balance imparted by palmitate helps explain maintained LV function and may contribute to designing novel therapeutic approaches to prevent cardiac dysfunction in diabetic patients.

ABSTRACT

Type-2 diabetes (T2DM) leads to reduced myocardial performance, and eventually heart failure. Excessive accumulation of lipids and glucose is central to T2DM cardiomyopathy. Previous data showed that palmitate (Palm) or glutathione preserved heart mitochondrial energy/redox balance under excess glucose, rescuing β-adrenergic-stimulated cardiac excitation-contraction coupling. However, the mechanisms underlying the accompanying improved contractile performance have been largely ignored. Herein we explore in intact heart under substrate excess the metabolic remodelling associated with cardiac function in diabetic db/db mice subjected to stress given by β-adrenergic stimulation with isoproterenol and high glucose compared to their non-diabetic controls (+/+, WT) under euglycaemic conditions. When perfused with Palm, T2DM hearts exhibited improved contractility/relaxation compared to WT, accompanied by extensive metabolic remodelling as demonstrated by metabolomics-fluxomics combined with bioinformatics and computational modelling. The T2DM heart metabolome showed significant differences in the abundance of metabolites in pathways related to glucose, lipids and redox metabolism. Using a validated computational model of heart's central catabolism, comprising glucose and fatty acid (FA) oxidation in cytoplasmic and mitochondrial compartments, we estimated that fluxes through glucose degradation pathways are ∼2-fold lower in heart from T2DM vs. WT under all conditions studied. Palm addition elicits improvement of the redox status via enhanced β-oxidation and decreased glucose uptake, leading to flux-redirection away from redox-consuming pathways (e.g. polyol) while maintaining the flux through redox-generating pathways together with glucose-FA 'shared fuelling' of oxidative phosphorylation. Thus, available FAs such as Palm may help improve function via enhanced redox balance in T2DM hearts during peaks of hyperglycaemia and increased workload.

Strategies for Extending Metabolomics Studies with Stable Isotope Labelling and Fluxomics

This is a perspective from the peer session on stable isotope labelling and fluxomics at the Australian & New Zealand Metabolomics Conference (ANZMET) held from 30 March to 1 April 2016 at La Trobe University, Melbourne, Australia. This report summarizes the key points raised in the peer session which focused on the advantages of using stable isotopes in modern metabolomics and the challenges in conducting flux analyses. The session highlighted the utility of stable isotope labelling in generating reference standards for metabolite identification, absolute quantification, and in the measurement of the dynamic activity of metabolic pathways. The advantages and disadvantages of different approaches of fluxomics analyses including flux balance analysis, metabolic flux analysis and kinetic flux profiling were also discussed along with the use of stable isotope labelling in in vivo dynamic metabolomics. A number of crucial technical considerations for designing experiments and analyzing data with stable isotope labelling were discussed which included replication, instrumentation, methods of labelling, tracer dilution and data analysis. This report reflects the current viewpoint on the use of stable isotope labelling in metabolomics experiments, identifying it as a great tool with the potential to improve biological interpretation of metabolomics data in a number of ways.

Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.

Applications of stable isotope-based metabolomics and fluxomics toward synthetic biology of cyanobacteria

Unique features of cyanobacteria (e.g., photosynthesis and nitrogen fixation) make them potential candidates for production of biofuels and other value-added biochemicals. As prokaryotes, they can be readily engineered using synthetic and systems biology tools. Metabolic engineering of cyanobacteria for the synthesis of desired compounds requires in-depth knowledge of central carbon and nitrogen metabolism, pathway fluxes, and their regulation. Metabolomics and fluxomics offer the comprehensive analysis of metabolism by directly characterizing the biochemical activities of cells. This information is acquired by measuring the abundance of key metabolites and their rates of interconversion, which can be achieved by labeling cells with stable isotopes, quantifying metabolite pool sizes and isotope incorporation by gas chromatography/liquid chromatography-mass spectrometry GC/LC-MS or nuclear magnetic resonance (NMR), and mathematical modeling to estimate in vivo metabolic fluxes. Herein, we review progress that has been made to adapt metabolomics and fluxomics tools to examine model cyanobacterial species. We summarize the application of metabolic flux analysis (MFA) strategies to identify metabolic bottlenecks that can be targeted to boost cell growth, improve stress tolerance, or enhance biochemical production in cyanobacteria. Despite the advances in metabolomics, fluxomics, and other synthetic and systems biology tools during the past years, further efforts are required to increase our understanding of cyanobacterial metabolism in order to create efficient photosynthetic hosts for the production of value-added compounds. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

13C Metabolic Flux Analysis Indicates Endothelial Cells Attenuate Metabolic Perturbations by Modulating TCA Activity

Disrupted endothelial metabolism is linked to endothelial dysfunction and cardiovascular disease. Targeted metabolic inhibitors are potential therapeutics; however, their systemic impact on endothelial metabolism remains unknown. In this study, we combined stable isotope labeling with ¹³C metabolic flux analysis (¹³C MFA) to determine how targeted inhibition of the polyol (fidarestat), pentose phosphate (DHEA), and hexosamine biosynthetic (azaserine) pathways alters endothelial metabolism. Glucose, glutamine, and a four-carbon input to the malate shuttle were important carbon sources in the baseline human umbilical vein endothelial cell (HUVEC) ¹³C MFA model. We observed two to three times higher glutamine uptake in fidarestat and azaserine-treated cells. Fidarestat and DHEA-treated HUVEC showed decreased ¹³C enrichment of glycolytic and TCA metabolites and amino acids. Azaserine-treated HUVEC primarily showed ¹³C enrichment differences in UDP-GlcNAc. ¹³C MFA estimated decreased pentose phosphate pathway flux and increased TCA activity with reversed malate shuttle direction in fidarestat and DHEA-treated HUVEC. In contrast, ¹³C MFA estimated increases in both pentose phosphate pathway and TCA activity in azaserine-treated cells. These data show the potential importance of endothelial malate shuttle activity and suggest that inhibiting glycolytic side branch pathways can change the metabolic network, highlighting the need to study systemic metabolic therapeutic effects.

Kinetic modelling of phospholipid synthesis in Plasmodium knowlesi unravels crucial steps and relative importance of multiple pathways

BACKGROUND

Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species.

RESULTS

Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step.

CONCLUSION

We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance.

Metabolic Flux Analysis of Catechin Biosynthesis Pathways Using Nanosensor

(+)-Catechin is an important antioxidant of green tea (Camelia sinensis (L.) O. Kuntze). Catechin is known for its positive role in anticancerous activity, extracellular matrix degradation, cell death regulation, diabetes, and other related disorders. As a result of enormous interest in and great demand for catechin, its biosynthesis using metabolic engineering has become the subject of concentrated research with the aim of enhancing (+)-catechin production. Metabolic flux is an essential concept in the practice of metabolic engineering as it helps in the identification of the regulatory element of a biosynthetic pathway. In the present study, an attempt was made to analyze the metabolic flux of the (+)-catechin biosynthesis pathway in order to decipher the regulatory element of this pathway. Firstly, a genetically encoded fluorescence resonance energy transfer (FRET)-based nanosensor (FLIP-Cat, fluorescence indicator protein for (+)-catechin) was developed for real-time monitoring of (+)-catechin flux. In vitro characterization of the purified protein of the nanosensor showed that the nanosensor was pH stable and (+)-catechin specific. Its calculated K_d was 139 µM. The nanosensor also performed real-time monitoring of (+)-catechin in bacterial cells. In the second step of this study, an entire (+)-catechin biosynthesis pathway was constructed and expressed in E. coli in two sets of plasmid constructs: pET26b-PT7-rbs-PAL-PT7-rbs-4CL-PT7-rbs-CHS-PT7-rbs-CHI and pET26b-T7-rbs-F3H-PT7-rbs- DFR-PT7-rbs-LCR. The E. coli harboring the FLIP-Cat was transformed with these plasmid constructs. The metabolic flux analysis of (+)-catechin was carried out using the FLIP-Cat. The FLIP-Cat successfully monitored the flux of catechin after adding tyrosine, 4-coumaric acid, 4-coumaroyl CoA, naringenin chalcone, naringenin, dihydroquercetin, and leucocyanidin, individually, with the bacterial cells expressing the nanosensor as well as the genes of the (+)-catechin biosynthesis pathway. Dihydroflavonol reductase (DFR) was identified as the main regulatory element of the (+)-catechin biosynthesis pathway. Information about this regulatory element of the (+)-catechin biosynthesis pathway can be used for manipulating the (+)-catechin biosynthesis pathway using a metabolic engineering approach to enhance production of (+)-catechin.

Effective Estimation of Dynamic Metabolic Fluxes Using (13)C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability

The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and ¹³C labeling to identify piecewise-affine (PWA) flux functions has been described recently. Obtaining flux estimates nevertheless still required frequent manual reinitalization to obtain a good reproduction of the experimental data and, moreover, did not optimize on all observables simultaneously (metabolites and isotopomer concentrations). In our contribution we focus on measures to achieve faster and robust dynamic flux estimation which leads to a high dimensional parameter estimation problem. Specifically, we address the following challenges within the PWA problem formulation: (1) Fast selection of sufficient domains for the PWA flux functions, (2) Control of over-fitting in the concentration space using shape-prescriptive modeling and (3) robust and efficient implementation of the parameter estimation using the hybrid implicit filtering algorithm. With the improvements we significantly speed up the convergence by efficiently exploiting that the optimization problem is partly linear. This allows application to larger-scale metabolic networks and demonstrates that the proposed approach is not purely theoretical, but also applicable in practice.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.

Exploring the Glucose Fluxotype of the E. coli y-ome Using High-Resolution Fluxomics

We have developed a robust workflow to measure high-resolution fluxotypes (metabolic flux phenotypes) for large strain libraries under fully controlled growth conditions. This was achieved by optimizing and automating the whole high-throughput fluxomics process and integrating all relevant software tools. This workflow allowed us to obtain highly detailed maps of carbon fluxes in the central carbon metabolism in a fully automated manner. It was applied to investigate the glucose fluxotypes of 180 Escherichia coli strains deleted for y-genes. Since the products of these y-genes potentially play a role in a variety of metabolic processes, the experiments were designed to be agnostic as to their potential metabolic impact. The obtained data highlight the robustness of E. coli’s central metabolism to y-gene deletion. For two y-genes, deletion resulted in significant changes in carbon and energy fluxes, demonstrating the involvement of the corresponding y-gene products in metabolic function or regulation. This work also introduces novel metrics to measure the actual scope and quality of high-throughput fluxomics investigations.

Designing and Development of FRET-Based Nanosensor for Real Time Analysis of N-Acetyl-5-Neuraminic Acid in Living Cells

N-acetyl-5-neuraminic acid (NeuAc) plays crucial role in improving the growth, brain development, brain health maintenance, and immunity enhancement of infants. Commercially, it is used in the production of antiviral drugs, infant milk formulas, cosmetics, dietary supplements, and pharmaceutical products. Because of the rapidly increasing demand, metabolic engineering approach has attracted increasing attention for NeuAc biosynthesis. However, knowledge of metabolite flux in biosynthetic pathways is one of the major challenges in the practice of metabolic engineering. So, an understanding of the flux of NeuAc is needed to determine its cellular level at real time. The analysis of the flux can only be performed using a tool that has the capacity to measure metabolite level in cells without affecting other metabolic processes. A Fluorescence Resonance Energy Transfer (FRET)-based genetically-encoded nanosensor has been generated in this study to monitor the level of NeuAc in prokaryotic and eukaryotic cells. Sialic acid periplasmic binding protein (SiaP) from Haemophilus influenzae was exploited as a sensory element for the generation of nanosensor. The enhanced cyan fluorescent protein (ECFP) and Venus were used as Fluroscence Resonance Energy Transfer (FRET) pair. The nanosensor, which was termed fluorescent indicator protein for sialic acid (FLIP-SA), was successfully transformed into, and expressed in Escherichia coli BL21 (DE3) cells. The expressed protein of the nanosensor was isolated and purified. The purified nanosensor protein was characterized to assess the affinity, specificity, and stability in the pH range. The developed nanosensor exhibited FRET change after addition to NeuAc. The developed nanosensor was highly specific, exhibited pH stability, and detected NeuAc levels in the nanomolar to milimolar range. FLIP-SA was successfully introduced in bacterial and yeast cells and reported the real-time intracellular levels of NeuAc non-invasively. The FLIP-SA is an excellent tool for the metabolic flux analysis of the NeuAc biosynthetic pathway and, thus, may help unravel the regulatory mechanism of the metabolic pathway of NeuAc. Furthermore, FLIP-SA can be used for the high-throughput screening of E. coli mutant libraries for varied NeuAc production levels.

Analysis of metabolic flux in felid spermatozoa using metabolomics and 13C-based fluxomics†

Spermatozoa from three feline species-the domestic cat (Felis catus), the cheetah (Acinonyx jubatus), and the clouded leopard (Neofelis nebulosa)-were analyzed using metabolomic profiling and 13C-based fluxomics to address questions raised regarding their energy metabolism. Metabolic profiles and utilization of 13C-labeled energy substrates were detected and quantified using gas chromatography-mass spectrometry (GC-MS). Spermatozoa were collected by electroejaculation and incubated in media supplemented with 1.0 mM [U13C]-glucose, [U13C]-fructose, or [U13C]-pyruvate. Evaluation of intracellular metabolites following GC-MS analysis revealed the uptake and utilization of labeled glucose and fructose in sperm, as indicated by the presence of heavy ions in glycolytic products lactate and pyruvate. Despite evidence of substrate utilization, neither glucose nor fructose had an effect on the sperm motility index of ejaculated spermatozoa from any of the three felid species, and limited entry of pyruvate derived from these hexose substrates into mitochondria and the tricarboxylic acid cycle was detected. However, pathway utilization was species-specific for the limited number of individuals (four to seven males per species) assessed in these studies. An inhibitor of fatty acid beta-oxidation (FAO), etomoxir, altered metabolic profiles of all three felid species but decreased motility only in the cheetah. While fluxomic analysis provided direct evidence that glucose and fructose undergo catabolic metabolism, other endogenous substrates such as endogenous lipids may provide energy to fuel motility.

Increasing field strength versus advanced isotope labeling for NMR-based fluxomics

Nuclear magnetic resonance (NMR)-based fluxomics seeks to measure the incorporation of isotope labels in selected metabolites to follow kinetically the synthesis of the latter. It can however equally be used to understand the biosynthetic origin of the same metabolites. We investigate here different NMR approaches to optimize such experiments in terms of resolution and time requirement. Using the isoleucine biosynthesis as an example, we explore the use of different field strengths ranging from 500 MHz to 1.1 GHz. Because of the different field dependence of chemical shift and heteronuclear J couplings, the spectra change at different field strengths. We equally explore the approach to silence the leucine/valine methyl signals through the use of a suitable deuterated precursor, thereby allowing selective observation of the Ile ¹³ C labeling pattern. Combining both approaches, we arrive at an efficient procedure for the NMR-based exploration of Ile biosynthesis.

Construction of a Nanosensor for Non-Invasive Imaging of Hydrogen Peroxide Levels in Living Cells

Simple Summary

Spatially and temporally defined H₂O₂ signatures are essential parts of various signaling pathways. Therefore, monitoring H₂O₂ dynamics with high spatio–temporal resolution is significantly important to understand how this ubiquitous signaling molecule can control diverse cellular responses. In this study, we designed and characterized a Fluorescence Resonance Energy Transfer (FRET)-based genetically encoded H₂O₂ sensor that provides a powerful tool to monitor the spatio–temporal dynamics of H₂O₂ fluxes. We have used this sensor to monitor the flux of H₂O₂ in live cells under stress conditions. Using this sensor, real-time information of the H₂O₂ level can be obtained non-invasively and would help to understand the adverse effect of H₂O₂ on cell physiology and its role in redox signaling.

Abstract

Hydrogen peroxide (H₂O₂) serves fundamental regulatory functions in metabolism beyond the role as damage signal. During stress conditions, the level of H₂O₂ increases in the cells and causes oxidative stress, which interferes with normal cell growth in plants and animals. The H₂O₂ also acts as a central signaling molecule and regulates numerous pathways in living cells. To better understand the generation of H₂O₂ in environmental responses and its role in cellular signaling, there is a need to study the flux of H₂O₂ at high spatio–temporal resolution in a real-time fashion. Herein, we developed a genetically encoded Fluorescence Resonance Energy Transfer (FRET)-based nanosensor (FLIP-H₂O₂) by sandwiching the regulatory domain (RD) of OxyR between two fluorescent moieties, namely ECFP and mVenus. This nanosensor was pH stable, highly selective to H₂O₂, and showed insensitivity to other oxidants like superoxide anions, nitric oxide, and peroxynitrite. The FLIP-H₂O₂ demonstrated a broad dynamic range and having a binding affinity (Kd) of 247 µM. Expression of sensor protein in living bacterial, yeast, and mammalian cells showed the localization of the sensor in the cytosol. The flux of H₂O₂ was measured in these live cells using the FLIP-H₂O₂ under stress conditions or by externally providing the ligand. Time-dependent FRET-ratio changes were recorded, which correspond to the presence of H₂O₂. Using this sensor, real-time information of the H₂O₂ level can be obtained non-invasively. Thus, this nanosensor would help to understand the adverse effect of H₂O₂ on cell physiology and its role in redox signaling.

A Non-Invasive Tool for Real-Time Measurement of Sulfate in Living Cells

Sulfur (S) is an essential element for all forms of life. It is involved in numerous essential processes because S is considered as the primary source of one of the essential amino acids, methionine, which plays an important role in biological events. For the control and regulation of sulfate in a metabolic network through fluxomics, a non-invasive tool is highly desirable that opens the door to monitor the level of the sulfate in real time and space in living cells without fractionation of the cells or tissue. Here, we engineered a FRET (fluorescence resonance energy transfer) based sensor for sulfate, which is genetically-encoded and named as FLIP-SP (Fluorescent indicator protein for sulfate). The FLIP-SP can measure the level of the sulfate in live cells. This sensor was constructed by the fusion of fluorescent proteins at the N- and C-terminus of sulfate binding protein (sbp). The FLIP-SP is highly specific to sulfate, and showed pH stability. Real-time monitoring of the level of sulfate in prokaryotic and eukaryotic cells showed sensor bio-compatibility with living cells. We expect that this sulfate sensor offers a valuable strategy in the understanding of the regulation of the flux of sulfate in the metabolic network.

Application of Carbon-13 Isotopomer Analysis to Assess Perinatal Myocardial Glucose Metabolism in Sheep

Ovine models of pregnancy have been used extensively to study maternal-fetal interactions and have provided considerable insight into nutrient transfer to the fetus. Ovine models have also been utilized to study congenital heart diseases. In this work, we demonstrate a comprehensive assessment of heart function and metabolism using a perinatal model of heart function with the addition of a [U-13C]glucose as tracer to study central energy metabolism. Using nuclear magnetic resonance spectroscopy, and metabolic modelling, we estimate myocardial citric acid cycle turnover (normalized for oxygen consumption), substrate selection, and anaplerotic fluxes. This methodology can be applied to studying acute and chronic effects of hormonal signaling in future studies.

Metabolic flux reprogramming in Mycobacterium tuberculosis-infected human macrophages

Metabolic fluxes are at the heart of metabolism and growth in any living system. During tuberculosis (TB) infection, the pathogenic Mycobacterium tuberculosis (Mtb) adapts its nutritional behaviour and metabolic fluxes to survive in human macrophages and cause infection. The infected host cells also undergo metabolic changes. However, our knowledge of the infected host metabolism and identification of the reprogrammed metabolic flux nodes remains limited. In this study, we applied systems-based 13C-metabolic flux analysis (MFA) to measure intracellular carbon metabolic fluxes in Mtb-infected human THP-1 macrophages. We provide a flux map for infected macrophages that quantified significantly increased fluxes through glycolytic fluxes towards pyruvate synthesis and reduced pentose phosphate pathway fluxes when compared to uninfected macrophages. The tri carboxylic acid (TCA) cycle fluxes were relatively low, and amino acid fluxes were reprogrammed upon Mtb infection. The knowledge of host metabolic flux profiles derived from our work expands on how the host cell adapts its carbon metabolism in response to Mtb infection and highlights important nodes that may provide targets for developing new therapeutics to improve TB treatment.

Selected Ion Monitoring for Orbitrap-Based Metabolomics

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

Omic Studies on In Vitro Cystinosis Model: siRNA-Mediated CTNS Gene Silencing in HK-2 Cells

Cystinosis is an autosomal recessive disease caused by mutations in the CTNS gene encoding a protein called cystinosine, which is a lysosomal cystine transporter. Disease-causing mutations lead to accumulation of cystine crystals in the lysosomes, thereby causing dysfunction of vital organs. Determination of the increased leukocyte cystine level is one of the most used methods for diagnosis. However, this method is expensive, difficult to perform, and may yield different results in different laboratories. In this study, a disease model was created with CTNS gene-silenced HK2 cells, which can mimic cystinosis in cell culture, and multiomics methods (ie, proteomics, metabolomics, and fluxomics) were implemented at this cell culture to investigate new biomarkers for the diagnosis. CTNS-silenced cell line exhibited distinct metabolic profiles compared with the control cell line. Pathway analysis highlighted significant alterations in various metabolic pathways, including alanine, aspartate, and glutamate metabolism; glutathione metabolism; aminoacyl-tRNA biosynthesis; arginine and proline metabolism; beta-alanine metabolism; ascorbate and aldarate metabolism; and histidine metabolism upon CTNS silencing. Fluxomics analysis revealed increased cycle rates of Krebs cycle intermediates such as fumarate, malate, and citrate, accompanied by enhanced activation of inorganic phosphate and ATP production. Furthermore, proteomic analysis unveiled differential expression levels of key proteins involved in crucial cellular processes. Notably, peptidyl-prolyl cis-trans isomerase A, translation elongation factor 1-beta (EF-1beta), and 60S acidic ribosomal protein decreased in CTNS-silenced cells. Additionally, levels of P0 and tubulin α-1A chain were reduced, whereas levels of 40S ribosomal protein S8 and Midasin increased. Overall, our study, through the utilization of an in vitro cystinosis model and comprehensive multiomics approach, led to the way toward the identification of potential new biomarkers while offering valuable insights into the pathogenesis of cystinosis.