Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

A parallel reaction monitoring-mass spectrometric method for studying lipid biosynthesis in vitro using 13C16-palmitate as an isotope tracer

BACKGROUND

Palmitate, which is the end product of fatty acid synthase, is the key fatty acid for understanding of lipid biosynthetic process in mammalian cells. Mass spectrometry (MS) methodology using 13C-palmitate can trace the lipid biosynthesis such as glycerolipids, glycerophospholipids, and sphingolipids. However, due to the interferences of natural heavy isotopes, accurate measurement of 13C-labeled lipid species has been limited. Here we describe a high-throughput isotope tracing experiment to assess lipid biosynthesis using parallel reaction monitoring-MS (PRM-MS) with 13C16-palmitate as an isotope tracer.

RESULTS

The developed method can trace 14 13C16-labeled lipid classes without disturbance from the heavy isotope patterns of natural lipids. Lipid class-based separation was achieved through hydrophilic interaction liquid chromatography (HILIC) which allows facile identification of lipid, and PRM-MS was performed for accurate detection of the 13C16-labeled lipids. A fibroblast (NIH/3T3) cell line was used as an in vitro model, and the NIH/3T3 cells were treated with bovine serum albumin (BSA)-bound 13C16-palmitate. The isotopic disturbance from natural lipid was eliminated using 13C16-palmitate, rather than 13C1-palmitate, as an isotope tracer. After 24 h of incubation with 0.1 mmol/L of BSA-bound 13C16-palmitate in the fibroblasts, NIH/3T3 cells synthesized the 127 13C16-labeled lipid species of glycerolipids, glycerophospholipids, and sphingolipids. Finally, in the NIH/3T3 cells incubated for 1, 6, and 24 h after the treatment of the isotope tracer exhibited an increased profile of 13C16-labeled lipidome, depending on duration of incubation.

SIGNIFICANCE

The HILIC/PRM-MS method using 13C16-palmitate as an isotope tracer enables identification of 13C16-labeled lipid species by annotating 13C16-labeled position, including the 13C16-fatty acyl chain and 13C16-sphingolipid headgroup, without interference of natural heavy isotope patterns. This lipidomic flux analysis using PRM approach is expected to provide insights into assessment of isotope-labeled lipids.

A relative metabolic flux analysis model of glucose anaplerosis

Glucose provides substrate for the predominant anaplerotic pathway which involves the activity of pyruvate carboxylase (PC). PC-mediated anaplerosis has been extensively studied as a metabolic regulator in glycolytic cells during tumorigenesis and metastasis. Herein, inaccuracies in established methods to measure relative intracellular flux through PC are highlighted and a compartmentalized condensed metabolic network (CCMN) is used to resolve the total malate pool into relative contributions from PC and other sources by metabolic flux analysis (MFA) with [U-13C6]glucose tracing. Performance of the CCMN method is evaluated in breast cancer cell lines that are exposed to small molecules targeting metabolism. Across conditions and cell lines, the CCMN approach yields results nearest to an accepted gold-standard methodology, using [3-13C]glucose, or even exposes the gold standard's limitations. The CCMN method does not require a separate experiment with a much more costly and generally less informative metabolic tracer, such as [3-13C]glucose, and in some cases, may outperform its application.

A process-based dynamic model for succicinic acid production by Actinobacillus succinogenes: regulatory role of ATP/ADP balance

Introduction

Succinic acid is an important chemical compound for biotechnological productions, being used as a basic platform to produce many industrial products in major business applications. It can be produced as fermentation end-product of anaerobic metabolism of different bacterial species, among which Actinobacillus succinogenes is largely used. Modeling microbial metabolic processes in controlled bioreactor systems is recognized as a useful tool to optimize growth conditions aimed at maximizing yield.

Methods

A novel model is presented based on System Dynamics approach in which the maintenance of the ATP/ADP balance is introduced as a key regulatory process of A. succinogenes metabolism.

Results and discussion

Model simulations accurately reproduce microbial growth and succinic acid production in anaerobic batch cultures at different initial glucose concentrations. Results reveal that the main limitations to maximal succinic acid production are glucose uptake restrictions and energy homeostasis costs (ATP/ADP balance) of the microbial population. The process-based modeling approach effectively describes the main metabolic processes and their regulation, providing a useful tool to define working conditions and overcome the criticalities of the SA fermentation process.

Multiscale Mathematical Modeling in Systems Biology: A Framework to Boost Plant Synthetic Biology

Global food insecurity and environmental degradation highlight the urgent need for more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology offers the potential for enhanced crop traits, it also entails risks of extensive environmental damage. This review highlights the complexities and risks associated with plant synthetic biology, while presenting the potential of multiscale mathematical modeling to assess and mitigate those risks effectively. Despite its potential, applying multiscale mathematical models in plants remains underutilized. Here, we advocate for integrating technological advancements in agricultural data analysis to develop a comprehensive understanding of crops across biological scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper establishes a foundation for creating and utilizing integrated multiscale mathematical models. Through modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, thereby enhancing the safety of genetically engineered species. To demonstrate the potential of this approach, ongoing efforts are highlighted to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga (Striga spp.) and drought.

Revealing novel protein interaction partners of glyphosate in Escherichia coli

Despite all debates about its safe use, glyphosate remains the most widely applied active ingredient in herbicide products, with renewed approval in the European Union until 2033. Non-target organisms are commonly exposed to glyphosate as a matter of its mode of application, with its broader environmental and biological impacts remaining under investigation. Glyphosate displays structural similarity to phosphoenolpyruvate (PEP), thereby competitively inhibiting the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), crucial for the synthesis of aromatic amino acids in plants, fungi, bacteria, and archaea. Most microbes, including the gut bacterium Escherichia coli (E. coli), possess a glyphosate-sensitive class I EPSPS, making them vulnerable to glyphosate's effects. Yet, little is known about glyphosate's interactions with other bacterial proteins or its broader modes of action at the proteome level. Here, we employed a quantitative proteomics and thermal proteome profiling (TPP) approach to identify novel protein binding partners of glyphosate in the E. coli proteome. Glyphosate exposure significantly altered amino acid synthesizing pathways. The abundance of shikimate pathway proteins was increased, suggesting a compensatory mechanism. Extracellular riboflavin concentrations were elevated upon glyphosate exposure, while intracellular levels remained stable. Beyond the target enzyme EPSPS, thermal proteome profiling indicated an effect of glyphosate on the thermal stability of certain proteins, including AroH and ProA, indicating interactions. Similar to the competitive binding between PEP and glyphosate at EPSPS, one reason for the interaction of AroH and ProA with the herbicide could be a high structural similarity between their substrates and glyphosate. Overall, glyphosate induced metabolic disturbances in E. coli, extending beyond its primary target, thereby providing new insights into glyphosate's broader impact on microbial systems.

Biological production of nicotinamide mononucleotide: a review

Nicotinamide mononucleotide (NMN) presents significant therapeutic potential against aging-related conditions, such as Alzheimer's disease, due to its consistent and strong pharmacological effects. Aside from its anti-aging effect, NMN is also an emerging noncanonical cofactor for orthogonal metabolic pathways in the field of biomanufacturing. This has significant advantages in the field of metabolic engineering, allowing cells to produce unnatural chemicals without disrupting the natural cellular processes. NMN is produced through both the chemical and biological methods, with the latter being more environmentally sustainable. The primary biological production pathway centers on the enzyme nicotinamide phosphoribosyltransferase, which transforms nicotinamide and phosphoribosyl pyrophosphate to NMN. Efforts to increase NMN production have been explored in microorganisms, such as: Escherichia coli, Bacillus subtilis, and yeast, serving as biocatalysts, by rewiring their metabolic processes. Although most researchers are focusing on genetically and metabolically manipulating microorganisms to act as biocatalysts, a growing number of studies on cell-free synthesis are emerging as a promising strategy for producing NMN. This review explores the different biological production techniques of NMN employing microorganisms. This article, in particular, is essential to those who are working on NMN production using microbial strain engineering and cell-free systems.

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Unraveling Diabetic Kidney Disease: The Roles of Mitochondrial Dysfunction and Immunometabolism

Mitochondria are essential for cellular energy production and are implicated in numerous diseases, including diabetic kidney disease (DKD). Current evidence indicates that mitochondrial dysfunction results in alterations in several metabolic pathways within kidney cells, thereby contributing to the progression of DKD. Furthermore, mitochondrial dysfunction can engender an inflammatory milieu, leading to the activation and recruitment of immune cells to the kidney tissue, potentially perturbing intrarenal metabolism. In addition, this inflammatory microenvironment has the potential to modify immune cell metabolism, which may further accentuate the immune-mediated kidney injury. This understanding has led to the emerging field of immunometabolism, which views DKD as not just a metabolic disorder caused by hyperglycemia but also one with significant immune contributions. Targeting mitochondrial function and immunometabolism may offer protective effects for the kidneys, complementing current therapies and potentially mitigating the risk of DKD progression. This comprehensive review examines the impact of mitochondrial dysfunction and the potential role of immunometabolism in DKD. We also discuss tools for investigating these mechanisms and propose avenues for integrating this research with existing therapies. These insights underscore the modulation of mitochondrial function and immunometabolism as a critical strategy for decelerating DKD progression.

Apparent Saturation of Branched-Chain Amino Acid Catabolism After High Dietary Milk Protein Intake in Healthy Adults

CONTEXT

Milk protein contains high concentrations of branched-chain amino acids (BCAA) that play a critical role in anabolism and are implicated in the onset of obesity and chronic disease. Characterizing BCAA catabolism in the postprandial phase could elucidate the impact of protein intake on obesity risk established in the "early protein hypothesis."

OBJECTIVE

To examine the acute effects of protein content of young child formulas as test meals on BCAA catabolism, observing postprandial plasma concentrations of BCAA in relation to their degradation products.

METHODS

The TOMI Add-On Study is a randomized, double-blind crossover study in which 27 healthy adults consumed 2 isocaloric young child formulas with alternating higher (HP) and lower (LP) protein and fat content as test meals during separate interventions, while 9 blood samples were obtained over 5 hours. BCAA, branched-chain α-keto acids (BCKA), and acylcarnitines were analyzed using a fully targeted HPLC-ESI-MS/MS approach.

RESULTS

Mean concentrations of BCAA, BCKA, and acylcarnitines were significantly higher after HP than LP over the 5 postprandial hours, except for the BCKA α-ketoisovalerate (KIVA). The latter metabolite showed higher postprandial concentrations after LP. With increasing mean concentrations of BCAA, concentrations of corresponding BCKA, acylcarnitines, and urea increased until a breakpoint was reached, after which concentrations of degradation products decreased (for all metabolites except valine and KIVA and Carn C4:0-iso).

CONCLUSION

BCAA catabolism is markedly influenced by protein content of the test meal. We present novel evidence for the apparent saturation of the BCAA degradation pathway in the acute postprandial phase up to 5 hours after consumption.

Enhancement of essential cofactors for in vivo biocatalysis

A scarcity of cofactors, necessary metabolites or substrates for in vivo enzymatic reactions, is among the major barriers for product synthesis in metabolically engineered cells. This work compares our recently developed cofactor-boosting strategy, which uses xylose reductase (XR) and lactose to increase the intracellular levels of reduced or oxidized nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), adenosine triphosphate (ATP) and acetyl coenzymeA (acetyl-CoA), with other previously reported methods. We demonstrated that the XR/lactose approach enhances levels of sugar alcohols and sugar phosphates, which leads to elevated levels of crucial cofactors required by specific metabolic pathways. The patterns of cofactor enhancement are not uniform and depend upon the specific pathway components that are overexpressed. We term this model the "user-pool" model. Here, we investigated metabolite alteration in the fatty-alcohol-producing system in the presence of XR/lactose within an early time frame (5 min after the bioconversion started). All metabolite data were analyzed using untargeted metabolomics. We found that the XR/lactose system could improve fatty-alcohol production as early as 5 min after the bioconversion started. The enhancement of key cofactors and intermediates, such as hexitol, NAD(P)H, ATP, 3-phosphoglycerate, acetyl-CoA, 6-phosphogluconate (6-PG) and glutathione, was consistent with those previously reported on a longer time scale (after 1 h). However, measurements performed at the early time reported here showed detectable differences in metabolite enhancement patterns, such as those of ATP, NADPH, acetyl-CoA and glutathione. These data could serve as a basis for future analysis of metabolic flux alteration by the XR/lactose system. Comparative analysis of the cofactor enhancement by XR and other methods suggests that XR/lactose can serve as a simple tool to increase levels of various cofactors for microbial cell factories.

Host-pathogen interactions from a metabolic perspective: methods of investigation

Metabolism shapes immune homeostasis in health and disease. This review presents the range of methods that are currently available to investigate the dialog between metabolism and immunity at the systemic, tissue and cellular levels, particularly during infection.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

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

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Segmentation and evaluation of pathway module efficiency: Quantitative approach to monitor and overcome evolving bottlenecks in xylose to ethanol pathway

Engineering microbes that can efficiently ferment xylose to ethanol is critical to the development of renewable fuels from lignocellulosic biomass. To accelerate the strain optimization process, a method termed Segmentation and Evaluation of Pathway Module Efficiency (SEPME) was developed to enable rapid and iterative identification and removal of metabolic bottlenecks. Using SEPME, the overall pathway was segmented into two modules: the upstream xylose assimilation pathway and the downstream pentose phosphate pathway, glycolysis, and fermentation. The efficiencies of both modules were then quantified to identify the rate controlling module, followed by analyses of control coefficients, reaction rates, and byproduct concentrations to narrow down targets within the module. SEPME analysis revealed that as the strain was engineered with increasing xylose-to-ethanol yields, the bottlenecks shifted within a module and across the two modules. Guided by SEPME, these bottlenecks were removed one by one, and a strain approaching the theoretical ethanol yield was obtained.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Ex Vivo 13C-Metabolic Flux Analysis of Porcine Circulating Immune Cells Reveals Cell Type-Specific Metabolic Patterns and Sex Differences in the Pentose Phosphate Pathway

In general, females present with stronger immune responses than males, but scarce data are available on sex-specific differences in immunometabolism. In this study, we characterized porcine peripheral blood mononuclear cell (PBMC) and granulocyte energy metabolism using a Bayesian 13C-metabolic flux analysis, which allowed precise determination of the glycolytic, pentose phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) fluxes, together with an assessment of the superoxide anion radical (O2•-) production and mitochondrial O2 consumption. A principal component analysis allowed for identifying the cell type-specific patterns of metabolic plasticity. PBMCs displayed higher TCA cycle activity, especially glutamine-derived aspartate biosynthesis, which was directly related to mitochondrial respiratory activity and inversely related to O2•- production. In contrast, the granulocytes mainly utilized glucose via glycolysis, which was coupled to oxidative PPP utilization and O2•- production rates. The granulocytes of the males had higher oxidative PPP fluxes compared to the females, while the PBMCs of the females displayed higher non-oxidative PPP fluxes compared to the males associated with the T helper cell (CD3+CD4+) subpopulation of PBMCs. The observed sex-specific differences were not directly attributable to sex steroid plasma levels, but we detected an inverse correlation between testosterone and aldosterone plasma levels and showed that aldosterone levels were related with non-oxidative PPP fluxes of both cell types.

13C-Metabolic flux analysis detected a hyperoxemia-induced reduction of tricarboxylic acid cycle metabolism in granulocytes during two models of porcine acute subdural hematoma and hemorrhagic shock

Introduction

Supplementation with increased inspired oxygen fractions has been suggested to alleviate the harmful effects of tissue hypoxia during hemorrhagic shock (HS) and traumatic brain injury. However, the utility of therapeutic hyperoxia in critical care is disputed to this day as controversial evidence is available regarding its efficacy. Furthermore, in contrast to its hypoxic counterpart, the effect of hyperoxia on the metabolism of circulating immune cells remains ambiguous. Both stimulating and detrimental effects are possible; the former by providing necessary oxygen supply, the latter by generation of excessive amounts of reactive oxygen species (ROS). To uncover the potential impact of increased oxygen fractions on circulating immune cells during intensive care, we have performed a 13C-metabolic flux analysis (MFA) on PBMCs and granulocytes isolated from two long-term, resuscitated models of combined acute subdural hematoma (ASDH) and HS in pigs with and without cardiovascular comorbidity.

Methods

Swine underwent resuscitation after 2 h of ASDH and HS up to a maximum of 48 h after HS. Animals received normoxemia (PaO2 = 80 - 120 mmHg) or targeted hyperoxemia (PaO2 = 200 - 250 mmHg for 24 h after treatment initiation, thereafter PaO2 as in the control group). Blood was drawn at time points T1 = after instrumentation, T2 = 24 h post ASDH and HS, and T3 = 48 h post ASDH and HS. PBMCs and granulocytes were isolated from whole blood to perform electron spin resonance spectroscopy, high resolution respirometry and 13C-MFA. For the latter, we utilized a parallel tracer approach with 1,2-13C2 glucose, U-13C glucose, and U-13C glutamine, which covered essential pathways of glucose and glutamine metabolism and supplied redundant data for robust Bayesian estimation. Gas chromatography-mass spectrometry further provided multiple fragments of metabolites which yielded additional labeling information. We obtained precise estimations of the fluxes, their joint credibility intervals, and their relations, and characterized common metabolic patterns with principal component analysis (PCA).

Results

13C-MFA indicated a hyperoxia-mediated reduction in tricarboxylic acid (TCA) cycle activity in circulating granulocytes which encompassed fluxes of glutamine uptake, TCA cycle, and oxaloacetate/aspartate supply for biosynthetic processes. We further detected elevated superoxide levels in the swine strain characterized by a hypercholesterolemic phenotype. PCA revealed cell type-specific behavioral patterns of metabolic adaptation in response to ASDH and HS that acted irrespective of swine strains or treatment group.

Conclusion

In a model of resuscitated porcine ASDH and HS, we saw that ventilation with increased inspiratory O2 concentrations (PaO2 = 200 - 250 mmHg for 24 h after treatment initiation) did not impact mitochondrial respiration of PBMCs or granulocytes. However, Bayesian 13C-MFA results indicated a reduction in TCA cycle activity in granulocytes compared to cells exposed to normoxemia in the same time period. This change in metabolism did not seem to affect granulocytes' ability to perform phagocytosis or produce superoxide radicals.

Chemical Reaction Models in Synthetic Promoter Design in Bacteria

We discuss the formalism of chemical reaction networks (CRNs) as a computer-aided design interface for using formal methods in engineering living technologies. We set out by reviewing formal methods within a broader view of synthetic biology. Based on published results, we illustrate, step by step, how mathematical and computational techniques on CRNs can be used to study the structural and dynamic properties of the designed systems. As a case study, we use an E. coli two-component system that relays the external inorganic phosphate concentration signal to genetic components. We show how CRN models can scan and explore phenotypic regimes of synthetic promoters with varying detection thresholds, thereby providing a means for fine-tuning the promoter strength to match the specification.

Plant Life with and without Oxygen: A Metabolomics Approach

Oxygen deficiency is an environmental challenge which affects plant growth, the development and distribution in land and aquatic ecosystems, as well as crop yield losses worldwide. The capacity to exist in the conditions of deficiency or the complete lack of oxygen depends on a number of anatomic, developmental and molecular adaptations. The lack of molecular oxygen leads to an inhibition of aerobic respiration, which causes energy starvation and the acceleration of glycolysis passing into fermentations. We focus on systemic metabolic alterations revealed with the different approaches of metabolomics. Oxygen deprivation stimulates the accumulation of glucose, pyruvate and lactate, indicating the acceleration of the sugar metabolism, glycolysis and lactic fermentation, respectively. Among the Krebs-cycle metabolites, only the succinate level increases. Amino acids related to glycolysis, including the phosphoglycerate family (Ser and Gly), shikimate family (Phe, Tyr and Trp) and pyruvate family (Ala, Leu and Val), are greatly elevated. Members of the Asp family (Asn, Lys, Met, Thr and Ile), as well as the Glu family (Glu, Pro, Arg and GABA), accumulate as well. These metabolites are important members of the metabolic signature of oxygen deficiency in plants, linking glycolysis with an altered Krebs cycle and allowing alternative pathways of NAD(P)H reoxidation to avoid the excessive accumulation of toxic fermentation products (lactate, acetaldehyde, ethanol). Reoxygenation induces the downregulation of the levels of major anaerobically induced metabolites, including lactate, succinate and amino acids, especially members of the pyruvate family (Ala, Leu and Val), Tyr and Glu family (GABA and Glu) and Asp family (Asn, Met, Thr and Ile). The metabolic profiles during native and environmental hypoxia are rather similar, consisting in the accumulation of fermentation products, succinate, fumarate and amino acids, particularly Ala, Gly and GABA. The most intriguing fact is that metabolic alterations during oxidative stress are very much similar, with plant response to oxygen deprivation but not to reoxygenation.

In Vivo Detection of Low Molecular Weight Platform Chemicals and Environmental Contaminants by Genetically Encoded Biosensors

Genetically encoded biosensor systems operating in living cells are versatile, cheap, and transferable tools for the detection and quantification of a broad range of small molecules. This review presents state-of-the-art biosensor designs and assemblies, featuring transcription factor-, riboswitch-, and enzyme-coupled devices, highly engineered fluorescent probes, and emerging two-component systems. Importantly, (bioinformatic-assisted) strategies to resolve contextual issues, which cause biosensors to miss performance criteria in vivo, are highlighted. The optimized biosensing circuits can be used to monitor chemicals of low molecular mass (<200 g mol-1) and physicochemical properties that challenge conventional chromatographical methods with high sensitivity. Examples herein include but are not limited to formaldehyde, formate, and pyruvate as immediate products from (synthetic) pathways for the fixation of carbon dioxide (CO2), industrially important derivatives like small- and medium-chain fatty acids and biofuels, as well as environmental toxins such as heavy metals or reactive oxygen and nitrogen species. Lastly, this review showcases biosensors capable of assessing the biosynthesis of platform chemicals from renewable resources, the enzymatic degradation of plastic waste, or the bioadsorption of highly toxic chemicals from the environment. These applications offer new biosensor-based manufacturing, recycling, and remediation strategies to tackle current and future environmental and socioeconomic challenges including the wastage of fossil fuels, the emission of greenhouse gases like CO2, and the pollution imposed on ecosystems and human health.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Role of Essential Amino Acids in Age-Induced Bone Loss

Age-induced osteoporosis is a global problem. Essential amino acids (EAAs) work as an energy source and a molecular pathway modulator in bone, but their functions have not been systematically reviewed in aging bone. This study aimed to discuss the contribution of EAAs on aging bone from in vitro, in vivo, and human investigations. In aged people with osteoporosis, serum EAAs were detected changing up and down, without a well-established conclusion. The supply of EAAs in aged people either rescued or did not affect bone mineral density (BMD) and bone volume. In most signaling studies, EAAs were proven to increase bone mass. Lysine, threonine, methionine, tryptophan, and isoleucine can increase osteoblast proliferation, activation, and differentiation, and decrease osteoclast activity. Oxidized L-tryptophan promotes bone marrow stem cells (BMSCs) differentiating into osteoblasts. However, the oxidation product of tryptophan called kynurenine increases osteoclast activity, and enhances the differentiation of adipocytes from BMSCs. Taken together, in terms of bone minerals and volume, more views consider EAAs to have a positive effect on aging bone, but the function of EAAs in bone metabolism has not been fully demonstrated and more studies are needed in this area in the future.