Real-time metabolome profiling of the metabolic switch between starvation and growth

RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking

MOTIVATION

Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra.

RESULTS

We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community

Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in-and even dominate-the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.

(p)ppGpp-Dependent Regulation of the Nucleotide Hydrolase PpnN Confers Complement Resistance in Salmonella enterica Serovar Typhimurium

The stringent response is an essential mechanism of metabolic reprogramming during environmental stress that is mediated by the nucleotide alarmones guanosine tetraphosphate and pentaphosphate [(p)ppGpp]. In addition to physiological adaptations, (p)ppGpp also regulates virulence programs in pathogenic bacteria, including Salmonella enterica serovar Typhimurium. S Typhimurium is a common cause of acute gastroenteritis, but it may also spread to systemic tissues, resulting in severe clinical outcomes. During infection, S Typhimurium encounters a broad repertoire of immune defenses that it must evade for successful host infection. Here, we examined the role of the stringent response in S Typhimurium resistance to complement-mediated killing and found that the (p)ppGpp synthetase-hydrolase, SpoT, is required for bacterial survival in human serum. We identified the nucleotide hydrolase, PpnN, as a target of the stringent response that is required to promote bacterial fitness in serum. Using chromatography and mass spectrometry, we show that PpnN hydrolyzes purine and pyrimidine monophosphates to generate free nucleobases and ribose 5'-phosphate, and that this metabolic activity is required for conferring resistance to complement killing. In addition to PpnN, we show that (p)ppGpp is required for the biosynthesis of the very long and long O-antigen in the outer membrane, known to be important for complement resistance. Our results provide new insights into the role of the stringent response in mediating evasion of the innate immune system by pathogenic bacteria.

Propionate as the preferred carbon source to produce 3-indoleacetic acid in B. subtilis: comparative flux analysis using five carbon sources

3-Indoleacetic acid (IAA) is a phytohormone that promotes plant root growth, improving the use of nutrients and crop yield and it is been reported that bacteria of the genus Bacillus are capable of producing this phytohormone under various growth conditions. Considering this metabolic capability, in this work, Bacillus subtilis was cultivated in five different carbon sources: glucose, acetate, propionate, citrate and glycerol; and l-tryptophan (Trp) was used as an inducer for the IAA production. Based on the experimental results it was observed that the highest growth rate was achieved using glucose as a carbon source (μ = 0.12 h-1) and the lowest value was for citrate (μ = 0.08 h-1). On the other hand, the highest IAA production was obtained using propionate Yp/s = 0.975 (gIAA gTrp-1) and the lowest was when glucose was the substrate Yp/s = 0.803 (gIAA gTrp-1). In order to explore the metabolism and understand these differences, the experimental data was used to calculate the flux distribution using the genomic-scale metabolic model of Bacillus subtilis. Performing a comparative analysis it is observed that the fluxes towards precursors increase when propionate is the carbon source.

Exometabolite Dynamics over Stationary Phase Reveal Strain-Specific Responses

Microbial exponential growth is expected to occur infrequently in environments that have long periods of nutrient starvation punctuated by short periods of high nutrient flux. These conditions likely impose nongrowth states for microbes. However, nongrowth states are uncharacterized for the majority of environmental bacteria, especially in regard to exometabolite production. We compared exometabolites produced over stationary phase across three environmental bacteria: Burkholderia thailandensis E264 (ATCC 700388), Chromobacterium violaceum ATCC 31532, and Pseudomonas syringae pv. tomato DC3000 (ATCC BAA-871). We grew each strain in monoculture and investigated exometabolite dynamics from mid-exponential to stationary phases. We focused on exometabolites that were released into the medium and accumulated over 45 h, including approximately 20 h of stationary phase. We also analyzed transcripts (transcriptome sequencing [RNA-seq]) to interpret exometabolite output. We found that the majority of exometabolites released were strain specific, with a subset of identified exometabolites involved in both central and secondary metabolism. Transcript analysis supported that exometabolites were released from intact cells, as various transporters had either increased or consistent transcripts through time. Interestingly, we found that succinate was one of the most abundant identifiable exometabolites for all strains and that each strain rerouted their metabolic pathways involved in succinate production during stationary phase. These results show that nongrowth states can be metabolically dynamic and that environmental bacteria can enrich a minimal environment with diverse chemical compounds as a consequence of growth and postgrowth maintenance in stationary phase. This work provides insights into microbial community interactions via exometabolites under conditions of growth cessation or limitation.IMPORTANCE Nongrowth states are common for bacteria that live in environments that are densely populated and predominantly nutrient exhausted, and yet these states remain largely uncharacterized in cellular metabolism and metabolite output. Here, we investigated and compared stationary-phase exometabolites and RNA transcripts for each of three environmental bacterial strains. We observed that diverse exometabolites were produced and provide evidence that these exometabolites accumulate over time through release by intact cells. Additionally, each bacterial strain had a characteristic exometabolite profile and exhibited dynamics in exometabolite composition. This work affirms that stationary phase is metabolically dynamic, with each strain tested creating a unique chemical signature in the extracellular space and altering metabolism in stationary phase. These findings set the stage for understanding how bacterial populations can support surrounding neighbors in environments with prolonged nutrient exhaustion through exometabolite-mediated interspecies interactions.

Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach

Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.

How Does the Alarmone ppGpp Change Bacterial Cell Metabolism? From Genome-wide Approaches to Structure to Physiology

Wang et al. (2020) show that binding of the second messenger ppGpp to inosine-guanosine kinase (Gsk) in E. coli modulates the levels of the key metabolite phosphoribosyl pyrophosphate (pRpp), decreasing purine synthesis to favor amino acid synthesis during stress adaptation.

Cadmium-mediated lung injury is exacerbated by the persistence of classically activated macrophages

Heavy metals released into the environment have a significant effect on respiratory health. Lung macrophages are important in mounting an inflammatory response to injury, but they are also involved in repair of injury. Macrophages develop mixed phenotypes in complex pathological conditions and polarize to a predominant phenotype depending on the duration and stage of injury and/or repair. Little is known about the reprogramming required for lung macrophages to switch between these divergent functions; therefore, understanding the mechanism(s) by which macrophages promote metabolic reprogramming to regulate lung injury is essential. Here, we show that lung macrophages polarize to a pro-inflammatory, classically activated phenotype after cadmium-mediated lung injury. Because metabolic adaptation provides energy for the diverse macrophage functions, these classically activated macrophages show metabolic reprogramming to glycolysis. RNA-Seq revealed up-regulation of glycolytic enzymes and transcription factors regulating glycolytic flux in lung macrophages from cadmium-exposed mice. Moreover, cadmium exposure promoted increased macrophage glycolytic function with enhanced extracellular acidification rate, glycolytic metabolites, and lactate excretion. These observations suggest that cadmium mediates the persistence of classically activated lung macrophages to exacerbate lung injury.

Rapid antibiotic susceptibility testing of bacteria from patients' blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy

Blood stream infection is one of the major public health issues characterized with high cost and high mortality. Timely effective antibiotics usage to control infection is crucial for patients’ survival. The standard microbiological diagnosis of infection however can last days. The delay in accurate antibiotic therapy would lead to not only poor clinical outcomes, but also to a rise in antibiotic resistance due to widespread use of empirical broad-spectrum antibiotics. An important measure to tackle this problem is fast determination of bacterial antibiotic susceptibility to optimize antibiotic treatment. We show that a protocol based on surface-enhanced Raman spectroscopy can obtain consistent antibiotic susceptibility test results from clinical blood-culture samples within four hours. The characteristic spectral signatures of the obtained spectra of Staphylococcus aureus and Escherichia coli—prototypic Gram-positive and Gram-negative bacteria—became prominent after an effective pretreatment procedure removed strong interferences from blood constituents. Using them as the biomarkers of bacterial metabolic responses to antibiotics, the protocol reported the susceptibility profiles of tested drugs against these two bacteria acquired from patients’ blood with high specificity, sensitivity and speed.

Toward fine-tuned metabolic networks in industrial microorganisms

There are numerous microorganisms in nature capable of synthesizing diverse useful compounds; however, these natural microorganisms are generally inefficient in the production of target products on an industrial scale, relative to either chemical synthesis or extraction methods. To achieve industrial production of useful compounds, these natural microorganisms must undergo a certain degree of mutation or effective fine-tuning strategies. This review describes how to achieve an ideal metabolic fine-tuned process, including static control strategies and dynamic control strategies. The static control strategies mainly focus on various matabolic engineering strategies, including protein engineering, upregulation/downregulation, and combinatrorial control of these metabolic engineering strategies, to enhance the flexibility of their application in fine-tuned metabolic metworks. Then, we focus on the dynamic control strategies for fine-tuned metabolic metworks. The design principles derived would guide us to construct microbial cell factories for various useful compounds.

β-Oxidation and autophagy are critical energy providers during acute glucose depletion in Saccharomyces cerevisiae

The ability to tolerate and thrive in diverse environments is paramount to all living organisms, and many organisms spend a large part of their lifetime in starvation. Upon acute glucose starvation, yeast cells undergo drastic physiological and metabolic changes and reestablish a constant-although lower-level of energy production within minutes. The molecules that are rapidly metabolized to fuel energy production under these conditions are unknown. Here, we combine metabolomics and genetics to characterize the cells' response to acute glucose depletion and identify pathways that ensure survival during starvation. We show that the ability to respire is essential for maintaining the energy status and to ensure viability during starvation. Measuring the cells' immediate metabolic response, we find that central metabolites drastically deplete and that the intracellular AMP-to-ATP ratio strongly increases within 20 to 30 s. Furthermore, we detect changes in both amino acid and lipid metabolite levels. Consistent with this, both bulk autophagy, a process that frees amino acids, and lipid degradation via β-oxidation contribute in parallel to energy maintenance upon acute starvation. In addition, both these pathways ensure long-term survival during starvation. Thus, our results identify bulk autophagy and β-oxidation as important energy providers during acute glucose starvation.

Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation

Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real-time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd-Au synthetic alloys are reported for mass-spectrometry-based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core-shell structure is designed using magnetic core particles to support Pd-Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application-driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future.

Real-time toxicity prediction of Aconitum stewing system using extractive electrospray ionization mass spectrometry

Due to numerous obstacles such as complex matrices, real-time monitoring of complex reaction systems (e.g., medicinal herb stewing system) has always been a challenge though great values for safe and rational use of drugs. Herein, facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry, a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time. A complete analytical strategy, including data acquisition, data mining, and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry quantification. The complex Fuzi (the lateral root of Aconitum)–meat stewing systems were real-timely monitored in 150 min by qualitative and quantitative analysis of the nine key alkaloids accurately. The results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated accordingly. Stewing with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%, which was recommended to be used practically for treating rheumatic arthritis and enhancing immunity. The established strategy was versatile, simple, and accurate, which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.

Bacterial Glycogen Provides Short-Term Benefits in Changing Environments

Changing nutritional conditions challenge microbes and shape their evolutionary optimization. Here, we used real-time metabolomics to investigate the role of glycogen in the dynamic physiological adaptation of Escherichia coli to fluctuating nutrients following carbon starvation. After the depletion of environmental glucose, we found significant metabolic activity remaining, which was linked to rapid utilization of intracellular glycogen. Glycogen was depleted by 80% within minutes of glucose starvation and was similarly replenished within minutes of glucose availability. These fast time scales of glycogen utilization correspond to the short-term benefits that glycogen provided to cells undergoing various physiological transitions. Cells capable of utilizing glycogen exhibited shorter lag times than glycogen mutants when starved between periods of exposure to different carbon sources. The ability to utilize glycogen was also important for the transition between planktonic and biofilm lifestyles and enabled increased glucose uptake during pulses of limited glucose availability. While wild-type and mutant strains exhibited comparable growth rates in steady environments, mutants deficient in glycogen utilization grew more poorly in environments that fluctuated on minute scales between carbon availability and starvation. Taken together, these results highlight an underappreciated role of glycogen in rapidly providing carbon and energy in changing environments, thereby increasing survival and competition capabilities under fluctuating and nutrient-poor conditions.IMPORTANCE Nothing is constant in life, and microbes in particular have to adapt to frequent and rapid environmental changes. Here, we used real-time metabolomics and single-cell imaging to demonstrate that the internal storage polymer glycogen plays a crucial role in such dynamic adaptations. Glycogen is depleted within minutes of glucose starvation and similarly is replenished within minutes of glucose availability. Cells capable of utilizing glycogen exhibited shorter lag times than glycogen mutants when starved between periods of exposure to different carbon sources. While wild-type and mutant strains exhibited comparable growth rates in steady environments, mutants deficient in glycogen utilization grew more poorly in environments that fluctuated on minute scales between carbon availability and starvation. These results highlight an underappreciated role of glycogen in rapidly providing carbon and energy in changing environments, thereby increasing survival and competition capabilities under fluctuating and nutrient-poor conditions.

Co-Incorporated Mesoporous Carbon Material-Assisted Laser Desorption/Ionization Ion Source as an Online Interface of In Vivo Microdialysis Coupled with Mass Spectrometry

The combination of microdialysis and mass spectrometry (MS) provides the potential for rapidly monitoring diverse metabolites in vivo. Unfortunately, the high concentration of salt in biological microdialysates hindered the sensitive and online detection of these small molecular compounds. In this study, we synthesized Co-incorporated mesoporous carbon material (Co-NC) and developed a Co-NC-assisted laser desorption/ionization (LDI) ion source as an online interface of in vivo microdialysis coupled with MS for the direct analysis of diverse metabolites in microdialysates. The Co-NC could be used as a matrix for surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) analysis of small molecular compounds, even under high concentration salt conditions. The Co-NC possessed the adsorption ability for small molecular compounds, and it was believed that the adsorption ability of Co-NC might separate the analytes from the salt in microdialysates at a microscopic level, which might facilitate the desorption and ionization of the analytes and finally improved the salt-tolerance ability as a matrix. Furthermore, the Co-NC-assisted LDI ion source as a novel interface of in vivo microdialysis coupled with MS has been applied to the online monitoring of liver metabolites from the CCl₄-induced liver injury rat model for the first time.

Metabolomic change and pathway profiling reveal enhanced ansamitocin P-3 production in Actinosynnema pretiosum with low organic nitrogen availability in culture medium

Ansamitocin P-3 (AP-3), a 19-membered polyketide macrocyclic lactam, has potent antitumor activity. Our previous study showed that a relatively low organic nitrogen concentration in culture medium could significantly improve AP-3 production of Actinosynnema pretiosum. In the present study, we aimed to reveal the possible reasons for this improvement through metabolomic and gene transcriptional analytical methods. At the same time, a metabolic pathway profile based on metabolome data and pathway correlation information was performed to obtain a systematic view of the metabolic network modulations of A. pretiosum. Orthogonal partial least squares discriminant analysis showed that nine and eleven key metabolites directly associated with AP-3 production at growth phase and ansamitocin production phase, respectively. In-depth pathway analysis results highlighted that low organic nitrogen availability had significant impacts on central carbon metabolism and amino acid metabolic pathways of A. pretiosum and these metabolic responses were found to be beneficial to precursor supply and ansamitocin biosynthesis. Furthermore, real-time PCR results showed that the transcription of genes involved in precursor and ansamitocin biosynthetic pathways were remarkably upregulated under low organic nitrogen condition thus directing increased carbon flux toward ansamitocin biosynthesis. More importantly, the metabolic pathway analysis demonstrated a competitive relationship between fatty acid and AP-3 biosynthesis could significantly affect the accumulation of AP-3. Our findings provided new knowledge on the organic nitrogen metabolism and ansamitocin biosynthetic precursor in A. pretiosum and identified several important rate-limiting steps involved in ansamitocin biosynthesis thus providing a theoretical basis of further improvement in AP-3 production.

LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism

BACKGROUND

The systems-scale analysis of cellular metabolites, "metabolomics," provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. Finding a way to retain the advantages of FBA's linear structure while relaxing some of its assumptions could allow us to account for metabolite levels and metabolite-dependent regulation in strain design tools built from FBA, improving the accuracy of predictions made by these tools. We designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), to satisfy these specifications. Our strategy adds constraints describing the dynamics and regulation of metabolism that are strictly linear. We evaluated LK-DFBA against alternative modeling frameworks using simulated noisy data from a small in silico model and a larger model of central carbon metabolism in E. coli, and compared each framework's ability to recapitulate the original system.

RESULTS

In the smaller model, we found that we could use regression from a dynamic flux estimation (DFE) with an optional non-linear parameter optimization to reproduce metabolite concentration dynamic trends more effectively than an ordinary differential equation model with generalized mass action rate laws when tested under realistic data sampling frequency and noise levels. We observed detrimental effects across all tested modeling approaches when metabolite time course data were missing, but found these effects to be smaller for LK-DFBA in most cases. With the E. coli model, we produced qualitatively reasonable results with similar properties to the smaller model and explored two different parameterization structures that yield trade-offs in computation time and accuracy.

CONCLUSIONS

LK-DFBA allows for calculation of metabolite concentrations and considers metabolite-dependent regulation while still retaining many computational advantages of FBA. This provides the proof-of-principle for a new metabolic modeling framework with the potential to create genome-scale dynamic models and the potential to be applied in strain engineering tools that currently use FBA.

Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

Hyperpolarized product selective saturating-excitations for determination of changes in metabolic reaction rates in real-time

Investigation of hyperpolarized substrate metabolism has been showing utility in real-time determination of in-cell and in vivo enzymatic activities. Intracellular reaction rates may vary during the course of a measurement, even on the very short time scales of visibility on hyperpolarized MR, due to many factors such as the availability of the substrate and co-factors in the intracellular space. Despite this potential variation, the kinetic analysis of hyperpolarized signals typically assumes that the same rate constant (and in many cases, the same rate) applies throughout the course of the reaction as observed via the build-up and decay of the hyperpolarized signals. We demonstrate here an acquisition approach that can null the need for such an assumption and enable the detection of instantaneous changes in the rate of the reaction during an ex vivo hyperpolarized investigation, (i.e. in the course of the decay of one hyperpolarized substrate dose administered to a viable tissue sample ex vivo). This approach utilizes hyperpolarized product selective saturating-excitation pulses. Similar pulses have been previously utilized in vivo for spectroscopic imaging. However, we show here favorable consequences to kinetic rate determinations in the preparations used. We implement this acquisition strategy for studies on perfused tissue slices and develop a theory that explains why this particular approach enables the determination of changes in enzymatic rates that are monitored via the chemical conversions of hyperpolarized substrates. Real-time changes in intracellular reaction rates are demonstrated in perfused brain, liver, and xenograft breast cancer tissue slices and provide another potential differentiation parameter for tissue characterization.

Microbial energy management-A product of three broad tradeoffs

Wherever thermodynamics allows, microbial life has evolved to transform and harness energy. Microbial life thus abounds in the most unexpected places, enabled by profound metabolic diversity. Within this diversity, energy is transformed primarily through variations on a few core mechanisms. Energy is further managed by the physiological processes of cell growth and maintenance that use energy. Some aspects of microbial physiology are streamlined for energetic efficiency while other aspects seem suboptimal or even wasteful. We propose that the energy that a microbe harnesses and devotes to growth and maintenance is a product of three broad tradeoffs: (i) economic, trading enzyme synthesis or operational cost for functional benefit, (ii) environmental, trading optimization for a single environment for adaptability to multiple environments, and (iii) thermodynamic, trading energetic yield for forward metabolic flux. Consideration of these tradeoffs allows one to reconcile features of microbial physiology that seem to opposingly promote either energetic efficiency or waste.

Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics

In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.

Engineering the transmission efficiency of the noncyclic glyoxylate pathway for fumarate production in Escherichia coli

BACKGROUND

Fumarate is a multifunctional dicarboxylic acid in the tricarboxylic acid cycle, but microbial engineering for fumarate production is limited by the transmission efficiency of its biosynthetic pathway.

RESULTS

Here, pathway engineering was used to construct the noncyclic glyoxylate pathway for fumarate production. To improve the transmission efficiency of intermediate metabolites, pathway optimization was conducted by fluctuating gene expression levels to identify potential bottlenecks and then remove them, resulting in a large increase in fumarate production from 8.7 to 16.2 g/L. To further enhance its transmission efficiency of targeted metabolites, transporter engineering was used by screening the C₄-dicarboxylate transporters and then strengthening the capacity of fumarate export, leading to fumarate production up to 18.9 g/L. Finally, the engineered strain E. coli W3110△4-P_(H)CAI_(H)SC produced 22.4 g/L fumarate in a 5-L fed-batch bioreactor.

CONCLUSIONS

In this study, we offered rational metabolic engineering and flux optimization strategies for efficient production of fumarate. These strategies have great potential in developing efficient microbial cell factories for production of high-value added chemicals.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases

Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non-invasiveness for diagnostics of the future. Present urine detection affords unsatisfactory performance and requires advanced materials to extract molecular information, due to the limited biomarkers and high sample complexity. Herein, we report plasmonic polymer@Ag for laser desorption/ionization mass spectrometry (LDI-MS) and sparse-learning-based metabolic diagnosis of kidney diseases. Using only 1 μL of urine without enrichment or purification, polymer@Ag afforded urine metabolic fingerprints (UMFs) by LDI-MS in seconds. Analysis by sparse learning discriminated lupus nephritis from various other non-lupus nephropathies and controls. We combined UMFs with urine protein levels (UPLs) and constructed a new diagnostic model to characterize subtypes of kidney diseases. Our work guides urine-based diagnosis and leads to new personalized analytical tools for other diseases.

In situ molecular vibration insights into the antibacterial behavior of silicon nitride bioceramic versus gram-negative Escherichia coli

Gram-negative bacteria represent a substantial fraction of pathogens responsible for periprosthetic infections. Given the increasing resistance of such bacteria to antibiotics, significant efforts are nowadays paid in developing new biomaterial surfaces, which offer resistance against bacterial adhesion and/or possess inherent antibacterial effects. Non-oxide silicon nitride (Si₃N₄) bioceramic in its polycrystalline form is a biomaterial with inherent antibacterial properties. Building upon previous phenomenological findings, the present study focuses on vibrational analyses of the metabolic response of Escherichia coli at the molecular level. A time-lapse study is conducted upon exposing the bacteria in vitro to Si₃N₄ bioceramic surfaces. A comparison is carried out with the as-cultured bacterial strain and with bacteria exposed to other commercially available biomaterials, namely, Ti-alloy (Ti6Al4V-ELI) and zirconia-toughened alumina (ZTA) oxide bioceramic tested under exactly the same experimental conditions. The metabolic pathways before and after exposure to different substrates were monitored by means of Raman and FTIR spectroscopies. Results indicated the development of significant osmotic stress in the bacterial strain and constant concentration decreases of its cellular compounds markers over time upon exposure to Si₃N₄. This ultimately led to bacterial lysis (also confirmed by conventional fluorescence microscopy assays). The main antibacterial effect was of chemical origin and driven by the elution of nitrogen ions from the Si₃N₄ surface, successively converted into ammonia (NH₃) or ammonium (NH₄)⁺ in aqueous solution, depending on environmental pH. The presence of these nitrogen species created osmotic stress in the cytoplasmic space. In answer to the osmotic stress, metabolic rates changed rapidly, the bacterial membrane was damaged, and lysis occurred almost completely within 48 h exposure. The antibacterial behavior exerted by the Si₃N₄ substrate on E. coli was more effective than that observed on the biomedical Ti6Al4V alloy. Conversely, no lysis but bacterial proliferation was recorded for E. coli exposed to ZTA bioceramic oxide substrates.

Systematic identification of metabolites controlling gene expression in E. coli

Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.

Interactions between metabolites and transcription factors are known to control gene expression but analyzing these events at genome-scale is challenging. Here, the authors integrate dynamic metabolome and transcriptome data from E.coli to predict regulatory metabolite-transcription factor interactions.

Microfluidic Irreversible Electroporation-A Versatile Tool to Extract Intracellular Contents of Bacteria and Yeast

Exploring the dynamic behavior of cellular metabolism requires a standard laboratory method that guarantees rapid sampling and extraction of the cellular content. We propose a versatile sampling technique applicable to cells with different cell wall and cell membrane properties. The technique is based on irreversible electroporation with simultaneous quenching and extraction by using a microfluidic device. By application of electric pulses in the millisecond range, permanent lethal pores are formed in the cell membrane of Escherichia coli and Saccharomyces cerevisiae, facilitating the release of the cellular contents; here demonstrated by the measurement of glucose-6-phosphate and the activity of the enzyme glucose-6-phosphate dehydrogenase. The successful application of this device was demonstrated by pulsed electric field treatment in a flow-through configuration of the microfluidic chip in combination with sampling, inactivation, and extraction of the intracellular content in a few seconds. Minimum electric field strengths of 10 kV/cm for E. coli and 7.5 kV/cm for yeast S. cerevisiae were required for successful cell lysis. The results are discussed in the context of applications in industrial biotechnology, where metabolomics analyses are important.

Bacterial Metabolism and Antibiotic Efficacy

Antibiotics target energy-consuming processes. As such, perturbations to bacterial metabolic homeostasis are significant consequences of treatment. Here, we describe three postulates that collectively define antibiotic efficacy in the context of bacterial metabolism: (1) antibiotics alter the metabolic state of bacteria, which contributes to the resulting death or stasis; (2) the metabolic state of bacteria influences their susceptibility to antibiotics; and (3) antibiotic efficacy can be enhanced by altering the metabolic state of bacteria. Altogether, we aim to emphasize the close relationship between bacterial metabolism and antibiotic efficacy as well as propose areas of exploration to develop novel antibiotics that optimally exploit bacterial metabolic networks.

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli

How microbes dynamically coordinate uptake and simultaneous utilization of nutrients in complex nutritional ecosystems is still an open question. Here, we develop a constraint-based modeling approach that exploits non-targeted exo-metabolomics data to unravel adaptive decision-making processes in dynamic nutritional environments. We thereby investigate metabolic adaptation of Escherichia coli to continuously changing conditions during batch growth in complex medium. Unexpectedly, model-based analysis of time resolved exo-metabolome data revealed that fastest growth coincides with preferred catabolism of amino acids, which, in turn, reduces glucose uptake and increases acetate overflow. We show that high intracellular levels of the amino acid degradation metabolites pyruvate and oxaloacetate can directly inhibit the phosphotransferase system (PTS), and reveal their functional role in mediating regulatory decisions for uptake and catabolism of alternative carbon sources. Overall, the proposed methodology expands the spectrum of possible applications of flux balance analysis to decipher metabolic adaptation mechanisms in naturally occurring habitats and diverse organisms.

Dynamic 13C Labeling of Fast Turnover Metabolites for Analysis of Metabolic Fluxes and Metabolite Channeling

Dynamic or isotopically nonstationary ¹³C labeling experiments are a powerful tool not only for precise carbon flux quantification (e.g., metabolic flux analysis of photoautotrophic organisms) but also for the investigation of pathway bottlenecks, a cell's phenotype, and metabolite channeling. In general, isotopically nonstationary metabolic flux analysis requires three main components: (1) transient isotopic labeling experiments; (2) metabolite quenching and isotopomer analysis using LC-MS; (3) metabolic network construction and flux quantification. Labeling dynamics of key metabolites from ¹³C-pulse experiments allow flux estimation of key central pathways by solving ordinary differential equations to fit time-dependent isotopomer distribution data. Additionally, it is important to provide biomass requirements, carbon uptake rates, specific growth rates, and carbon excretion rates to properly and precisely balance the metabolic network. Labeling dynamics through cascade metabolites may also identify channeling phenomena in which metabolites are passed between enzymes without mixing with the bulk phase. In this chapter, we outline experimental protocols to probe metabolic pathways through dynamic labeling. We describe protocols for labeling experiments, metabolite quenching and extraction, LC-MS analysis, computational flux quantification, and metabolite channeling observations.

Assessing Cofactor Usage in Pseudoclostridium thermosuccinogenes via Heterologous Expression of Central Metabolic Enzymes

Pseudoclostridium thermosuccinogenes and Hungateiclostridium thermocellum are being studied for their potential to contribute to a more sustainable bio-based economy. Both species were shown previously to rely on GTP or pyrophosphate instead of ATP as cofactors in specific reactions of central energy metabolism for reasons that are not well understood yet. Since it is often impossible to predict cofactor specificity from the primary protein structure, thirteen enzymes from P. thermosuccinogenes were cloned and heterologous expressed in Escherichia coli to assess the cofactor usage in vitro and paint a more complete picture of the cofactor usage in the central metabolism of P. thermosuccinogenes. The assays were conducted with heat-treated E. coli cell-free extract devoid of background activity to allow the quick assessment of a relatively large number of (thermophilic) enzymes. Selected enzymes were also purified to allow the determination of the enzyme kinetics for competing cofactors. Following the results of the glucokinase (GK), galactokinase, xylulokinase (XK), and ribokinase assays, it seems that phosphorylation of monosaccharides by and large is mainly GTP-dependent. Some possible implications of this relating to the adenylate/guanylate energy charge are discussed here. Besides the highly expressed pyrophosphate-dependent 6-phosphofructokinase, another 6-phosphofructokinase was found to be equally dependent on ATP and GTP, while no 6-phosphofructokinase activity could be demonstrated for a third. Both type I glyceraldehyde 3-phosphate dehydrogenases were found to be NAD+-dependent, and further, acetate kinase, isocitrate dehydrogenase, and three enzymes predicted to be responsible for the interconversion of phosphoenolpyruvate and pyruvate (i.e., pyruvate kinase; pyruvate, phosphate dikinase; phosphoenolpyruvate synthase), were also assessed.

Continuous in vivo Metabolism by NMR

Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa, a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems.

(p)ppGpp Regulates a Bacterial Nucleosidase by an Allosteric Two-Domain Switch

The stringent response alarmones pppGpp and ppGpp are essential for rapid adaption of bacterial physiology to changes in the environment. In Escherichia coli, the nucleosidase PpnN (YgdH) regulates purine homeostasis by cleaving nucleoside monophosphates and specifically binds (p)ppGpp. Here, we show that (p)ppGpp stimulates the catalytic activity of PpnN both in vitro and in vivo causing accumulation of several types of nucleobases during stress. The structure of PpnN reveals a tetramer with allosteric (p)ppGpp binding sites located between subunits. pppGpp binding triggers a large conformational change that shifts the two terminal domains to expose the active site, providing a structural rationale for the stimulatory effect. We find that PpnN increases fitness and adjusts cellular tolerance to antibiotics and propose a model in which nucleotide levels can rapidly be adjusted during stress by simultaneous inhibition of biosynthesis and stimulation of degradation, thus achieving a balanced physiological response to constantly changing environments.

Metabolomics and Isotope Tracing

Great strides have been made over the past decade toward comprehensive study of metabolism. Mass spectrometry (MS) has played a central role by enabling measurement of many metabolites simultaneously. Tracking metabolite labeling from stable isotope tracers can in addition reveal pathway activities. Here, we describe the basics of metabolite measurement by MS, including sample preparation, metabolomic analysis, and data interpretation. In addition, drawing on examples of successful experiments, we highlight the ways in which metabolomics and isotope tracing can illuminate biology.

In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids

Tapping into the metabolic cross-talk between a host and its virus can reveal unique strategies employed during infection. Viral infection is a dynamic process that generates an evolving metabolic landscape. Gaining a continuous view into the infection process is highly challenging and is limited by current metabolomics approaches, which typically measure the average of the entire population at various stages of infection. Here, we took an innovative approach to study the metabolic basis of host-virus interactions between the bloom-forming alga Emiliania huxleyi and its specific virus. We combined a classical method in virology, the plaque assay, with advanced mass spectrometry imaging (MSI), an approach we termed ‘in plaque-MSI’. Taking advantage of the spatial characteristics of the plaque, we mapped the metabolic landscape induced during infection in a high spatiotemporal resolution, unfolding the infection process in a continuous manner. Further unsupervised spatially-aware clustering, combined with known lipid biomarkers, revealed a systematic metabolic shift during infection towards lipids containing the odd-chain fatty acid pentadecanoic acid (C15:0). Applying ‘in plaque-MSI’ might facilitate the discovery of bioactive compounds that mediate the chemical arms race of host-virus interactions in diverse model systems.

Mass Spectrometry-Based Microbial Metabolomics: Techniques, Analysis, and Applications

The demand for understanding the roles genes play in biological systems has steered the biosciences into the direction the metabolome, as it closely reflects the metabolic activities within a cell. The importance of the metabolome is further highlighted by its ability to influence the genome, transcriptome, and proteome. Consequently, metabolomic information is being used to understand microbial metabolic networks. At the forefront of this work is mass spectrometry, the most popular metabolomics measurement technique. Mass spectrometry-based metabolomic analyses have made significant contributions to microbiological research in the environment and human disease. In this chapter, we break down the technical aspects of mass spectrometry-based metabolomics and discuss its application to microbiological research.

Pharmacognosy in the digital era: shifting to contextualized metabolomics

Humans have co-evolved alongside numerous other organisms, some having a profound effect on health and nutrition. As the earliest pharmaceutical subject, pharmacognosy has evolved into a meta-discipline devoted to natural biomedical agents and their functional properties. While the acquisition of expanding data volumes is ongoing, contextualization is lagging. Thus, we assert that the establishment of an integrated and open databases ecosystem will nurture the discipline. After proposing an epistemological framework of knowledge acquisition in pharmacognosy, this study focuses on recent computational and analytical approaches. It then elaborates on the flux of research data, where good practices could foster the implementation of more integrated systems, which will in turn help shaping the future of pharmacognosy and determine its constitutional societal relevance.