High-resolution 13C metabolic flux analysis

Microbial central carbon metabolism in a tidal freshwater marsh and an upland mixed conifer soil under oxic and anoxic conditions

The central carbon (C) metabolic network is responsible for most of the production of energy and biosynthesis in microorganisms and is therefore key to a mechanistic understanding of microbial life in soil communities. Many upland soil communities have shown a relatively high C flux through the pentose phosphate (PP) or the Entner-Doudoroff (ED) pathway, thought to be related to oxidative damage control. We tested the hypothesis that the metabolic organization of the central C metabolic network differed between two ecosystems, an anoxic marsh soil and oxic upland soil, and would be affected by altering oxygen concentrations. We expected there to be high PP/ED pathway activity under high oxygen concentrations and in oxic soils and low PP/ED activity in reduced oxygen concentrations and in marsh soil. Although we found high PP/ED activity in the upland soil and low activity in the marsh soil, lowering the oxygen concentration for the upland soil did not reduce the relative PP/ED pathway activity as hypothesized, nor did increasing the oxygen concentration in the marsh soil increase the PP/ED pathway activity. We speculate that the high PP/ED activity in the upland soil, even when exposed to low oxygen concentrations, was related to a high demand for NADPH for biosynthesis, thus reflecting higher microbial growth rates in C-rich soils than in C-poor sediments. Further studies are needed to explain the observed metabolic diversity among soil ecosystems and determine whether it is related to microbial growth rates.IMPORTANCEWe observed that the organization of the central carbon (C) metabolic processes differed between oxic and anoxic soil. However, we also found that the pentose phosphate pathway/Entner-Doudoroff (PP/ED) pathway activity remained high after reducing the oxygen concentration for the upland soil and did not increase in response to an increase in oxygen concentration in the marsh soil. These observations contradicted the hypothesis that oxidative stress is a main driver for high PP/ED activity in soil communities. We suggest that the high PP/ED activity and NADPH production reflect higher anabolic activities and growth rates in the upland soil compared to the anaerobic marsh soil. A greater understanding of the molecular and biochemical processes in soil communities is needed to develop a mechanistic perspective on microbial activities and their relationship to soil C and nutrient cycling. Such an increased mechanistic perspective is ecologically relevant, given that the central carbon metabolic network is intimately tied to the energy metabolism of microbes, the efficiency of new microbial biomass production, and soil organic matter formation.

Research progress on biosynthesis of erythritol and multi-dimensional optimization of production strategies

Erythritol, as a new type of natural sweetener, has been widely used in food, medical, cosmetics, pharmaceutical and other fields due to its unique physical and chemical properties and physiological functions. In recent years, with the continuous development of strategies such as synthetic biology, metabolic engineering, omics-based systems biology and high-throughput screening technology, people's understanding of the erythritol biosynthesis pathway has gradually deepened, and microbial cell factories with independent modification capabilities have been successfully constructed. In this review, the cheap feedstocks for erythritol synthesis are introduced in detail, the environmental factors affecting the synthesis of erythritol and its regulatory mechanism are described, and the tools and strategies of metabolic engineering involved in erythritol synthesis are summarized. In addition, the study of erythritol derivatives is helpful in expanding its application field. Finally, the challenges that hinder the effective production of erythritol are discussed, which lay a foundation for the green, efficient and sustainable production of erythritol in the future and breaking through the bottleneck of production.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Engineering yeasts to Co-utilize methanol or formate coupled with CO2 fixation

The development of synthetic microorganisms that could use one-carbon compounds, such as carbon dioxide, methanol, or formate, has received considerable interest. In this study, we engineered Pichia pastoris and Saccharomyces cerevisiae to both synthetic methylotrophy and formatotrophy, enabling them to co-utilize methanol or formate with CO2 fixation through a synthetic C1-compound assimilation pathway (MFORG pathway). This pathway consisted of a methanol-formate oxidation module and the reductive glycine pathway. We first assembled the MFORG pathway in P. pastoris using endogenous enzymes, followed by blocking the native methanol assimilation pathway, modularly engineering genes of MFORG pathway, and compartmentalizing the methanol oxidation module. These modifications successfully enabled the methylotrophic yeast P. pastoris to utilize both methanol and formate. We then introduced the MFORG pathway from P. pastoris into the model yeast S. cerevisiae, establishing the synthetic methylotrophy and formatotrophy in this organism. The resulting strain could also successfully utilize both methanol and formate with consumption rates of 20 mg/L/h and 36.5 mg/L/h, respectively. The ability of the engineered P. pastoris and S. cerevisiae to co-assimilate CO2 with methanol or formate through the MFORG pathway was also confirmed by 13C-tracer analysis. Finally, production of 5-aminolevulinic acid and lactic acid by co-assimilating methanol and CO2 was demonstrated in the engineered P. pastoris and S. cerevisiae. This work indicates the potential of the MFORG pathway in developing different hosts to use various one-carbon compounds for chemical production.

Tanshinone IIA alleviates pulmonary fibrosis by modulating glutamine metabolic reprogramming based on [U-13C5]-glutamine metabolic flux analysis

INTRODUCTION

Glutamine metabolic reprogramming, mediated by glutaminase (GLS), is an important signal during pulmonary fibrosis (PF) progression. Tanshinone IIA (Tan IIA) is a naturally lipophilic diterpene with antioxidant and antifibrotic properties. However, the potential mechanisms of Tan IIA for regulating glutamine metabolic reprogramming are not yet clear.

OBJECTIVES

This study aimed was to evaluate the role of Tan IIA in intervening in glutamine metabolic reprogramming to exert anti-PF and to explore the potential new mechanisms of metabolic regulation.

METHODS

Fibrotic characteristics was detected via immunofluorescence and western blotting analysis. Cell proliferation was examined with EdU Assay. Cell metabolites were labeled by using stable isotope [U-13C5]-glutamine. By utilizing 100% 13C glutamine tracers and employing network analysis to investigate the activation of metabolic pathways in fibroblasts, as well as evaluating the impact of Tan IIA on these pathways, we accurately quantified the absolute flux of glutaminolysis, proline synthesis, and the TCA cycle pathway using isotopomer network compartmental analysis (INCA), a user-friendly software tool for 13C metabolic flux analysis (13C-MFA). Molecular docking was used for identifying the binding of Tan IIA with target protein.

RESULTS

Tan IIA ameliorate TGF-β1-induced myofibroblast proliferation, reduce collagen I and III and α-SMA protein expression in MRC-5 and NIH-3T3 cells. Furthermore, Tan IIA regulate mitochondrial energy metabolism by modulating TGF-β1-stimulated glutamine metabolic reprogramming in NIH-3T3 cells and inhibiting GLS1 expression, which reduced the metabolic flux of glutamine into mitochondria in myofibroblasts, and also targeted inhibited the expression of Δ1-pyrroline-5-carboxylate synthase (P5CS), P5C reductase 1 (PYCR1), and phosphoserine aminotransferase 1 (PSAT1), and reduced proline hydroxylation and blocked the collagen synthesis pathway.

CONCLUSION

Tan IIA reverses glutamine metabolic reprogramming, reduces mitochondrial energy expenditure, and inhibits collagen matrix synthesis by modulating potential targets in glutamine metabolism. This novel perspective sheds light on the essential role of glutamine metabolic reprogramming in PF.

Elucidating uptake and metabolic fate of dipeptides in CHO cell cultures using 13C labeling experiments and kinetic modeling

The rapidly growing market of biologics including monoclonal antibodies has stimulated the need to improve biomanufacturing processes including mammalian host systems such as Chinese Hamster Ovary (CHO) cells. Cell culture media formulations continue to be enhanced to enable intensified cell culture processes and optimize cell culture performance. Amino acids, major components of cell culture media, are consumed in large amounts by CHO cells. Due to their low solubility and poor stability, certain amino acids including tyrosine, leucine, and phenylalanine can pose major challenges leading to suboptimal bioprocess performance. Dipeptides have the potential to replace amino acids in culture media. However, very little is known about the cleavage, uptake, and utilization kinetics of dipeptides in CHO cell cultures. In this study, replacing amino acids, including leucine and tyrosine by their respective dipeptides including but not limited to Ala-Leu and Gly-Tyr, supported similar cell growth, antibody production, and lactate profiles. Using 13C labeling techniques and spent media studies, dipeptides were shown to undergo both intracellular and extracellular cleavage in cultures. Extracellular cleavage increased with the culture duration, indicating cleavage by host cell proteins that are likely secreted and accumulate in cell culture over time. A kinetic model was built and for the first time, integrated with 13C labeling experiments to estimate dipeptide utilization rates, in CHO cell cultures. Dipeptides with alanine at the N-terminus had a higher utilization rate than dipeptides with alanine at the C-terminus and dipeptides with glycine instead of alanine at N-terminus. Simultaneous supplementation of more than one dipeptide in culture led to reduction in individual dipeptide utilization rates indicating that dipeptides compete for the same cleavage enzymes, transporters, or both. Dipeptide utilization rates in culture and cleavage rates in cell-free experiments appeared to follow Michaelis-Menten kinetics, reaching a maximum at higher dipeptide concentrations. Dipeptide utilization behavior was found to be similar in cell-free and cell culture environments, paving the way for future testing approaches for dipeptides in cell-free environments prior to use in large-scale bioreactors. Thus, this study provides a deeper understanding of the fate of dipeptides in CHO cell cultures through an integration of cell culture, 13C labeling, and kinetic modeling approaches providing insights in how to best use dipeptides in media formulations for robust and optimal mammalian cell culture performance.

Hippo-YAP/TAZ signalling coordinates adipose plasticity and energy balance by uncoupling leptin expression from fat mass

Adipose tissues serve as an energy reservoir and endocrine organ, yet the mechanisms that coordinate these functions remain elusive. Here, we show that the transcriptional coregulators, YAP and TAZ, uncouple fat mass from leptin levels and regulate adipocyte plasticity to maintain metabolic homeostasis. Activating YAP/TAZ signalling in adipocytes by deletion of the upstream regulators Lats1 and Lats2 results in a profound reduction in fat mass by converting mature adipocytes into delipidated progenitor-like cells, but does not cause lipodystrophy-related metabolic dysfunction, due to a paradoxical increase in circulating leptin levels. Mechanistically, we demonstrate that YAP/TAZ-TEAD signalling upregulates leptin expression by directly binding to an upstream enhancer site of the leptin gene. We further show that YAP/TAZ activity is associated with, and functionally required for, leptin regulation during fasting and refeeding. These results suggest that adipocyte Hippo-YAP/TAZ signalling constitutes a nexus for coordinating adipose tissue lipid storage capacity and systemic energy balance through the regulation of adipocyte plasticity and leptin gene transcription.

Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

Carnosine regulation of intracellular pH homeostasis promotes lysosome-dependent tumor immunoevasion

Tumor cells and surrounding immune cells undergo metabolic reprogramming, leading to an acidic tumor microenvironment. However, it is unclear how tumor cells adapt to this acidic stress during tumor progression. Here we show that carnosine, a mobile buffering metabolite that accumulates under hypoxia in tumor cells, regulates intracellular pH homeostasis and drives lysosome-dependent tumor immune evasion. A previously unrecognized isoform of carnosine synthase, CARNS2, promotes carnosine synthesis under hypoxia. Carnosine maintains intracellular pH (pHi) homeostasis by functioning as a mobile proton carrier to accelerate cytosolic H+ mobility and release, which in turn controls lysosomal subcellular distribution, acidification and activity. Furthermore, by maintaining lysosomal activity, carnosine facilitates nuclear transcription factor X-box binding 1 (NFX1) degradation, triggering galectin-9 and T-cell-mediated immune escape and tumorigenesis. These findings indicate an unconventional mechanism for pHi regulation in cancer cells and demonstrate how lysosome contributes to immune evasion, thus providing a basis for development of combined therapeutic strategies against hepatocellular carcinoma that exploit disrupted pHi homeostasis with immune checkpoint blockade.

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.

Viral AMGs-driven pentose phosphate pathway in natural wetland

Viruses exist anywhere on earth where there is life, and among them, virus-encoded auxiliary metabolic genes (AMGs) can maintain ecosystem balance and play a major role in the global ecosystem. Although the function of AMGs has been widely reported, the genetic diversity of AMGs in natural ecosystems is still poorly understood. Exploring the genetic diversity of viral community-wide AMGs is essential to gain insight into the complex interactions between viruses and hosts. In this article, we studied the phylogenetic tree, principal co-ordinates analysis (PCoA), α diversity, and metabolic pathways of viral auxiliary metabolism genes involved in the pentose phosphate pathway (PPP) through metagenomics, and the changes of metabolites and genes of host bacteria were further studied by using Pseudomonas mandelii SW-3 and its lytic phage based on metabolic flow and AMGs expression. We found that the viral AMGs in the Napahai plateau wetland were created by a combination of various external forces, which contributed to the rich genetic diversity, uniqueness, and differences of the virus, which promoted the reproduction of offspring and better adaptation to the environment. Overall, this study systematically describes the genetic diversity of AMGs associated with the PPP in plateau wetland ecosystems and further expands the understanding of phage-host unique interactions.

Tanshinone IIA ameliorates energy metabolism dysfunction of pulmonary fibrosis using 13C metabolic flux analysis

Evidence indicates that metabolic reprogramming characterized by the changes in cellular metabolic patterns contributes to the pathogenesis of pulmonary fibrosis (PF). It is considered as a promising therapeutic target anti-PF. The well-documented against PF properties of Tanshinone IIA (Tan IIA) have been primarily attributed to its antioxidant and anti-inflammatory potency. Emerging evidence suggests that Tan IIA may target energy metabolism pathways, including glycolysis and tricarboxylic acid (TCA) cycle. However, the detailed and advanced mechanisms underlying the anti-PF activities remain obscure. In this study, we applied [U-13C]-glucose metabolic flux analysis (MFA) to examine metabolism flux disruption and modulation nodes of Tan IIA in PF. We identified that Tan IIA inhibited the glycolysis and TCA flux, thereby suppressing the production of transforming growth factor-β1 (TGF-β1)-dependent extracellular matrix and the differentiation and proliferation of myofibroblasts in vitro. We further revealed that Tan IIA inhibited the expression of key metabolic enzyme hexokinase 2 (HK2) by inhibiting phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR)/hypoxia-inducible factor 1α (HIF-1α) pathway activities, which decreased the accumulation of abnormal metabolites. Notably, we demonstrated that Tan IIA inhibited ATP citrate lyase (ACLY) activity, which reduced the collagen synthesis pathway caused by cytosol citrate consumption. Further, these results were validated in a mouse model of bleomycin-induced PF. This study was novel in exploring the mechanism of the occurrence and development of Tan IIA in treating PF using 13C-MFA technology. It provided a novel understanding of the mechanism of Tan IIA against PF from the perspective of metabolic reprogramming.

Dual strategy for 13C-Metabolic flux analysis of central carbon and energy metabolism in Mammalian cells based on LC-isoMRM-MS

Central carbon and energy metabolism are the most concerned metabolic pathways in 13C-Metabolic flux analysis (13C-MFA). However, some α-keto acids, ribonucleoside triphosphate (NTPs) and deoxyribonucleoside triphosphate (dNTPs) involved in central carbon and energy metabolism pathways were unstable or reactive, leading to inaccurate metabolic flux analysis. To achieve accurate 13C-MFA of central carbon and energy metabolism, we proposed a dual strategy for the detection of 101 metabolites in glucose metabolism pathways. N-Methylphenylethylamine (MPEA) was utilized for derivatization of 4 carboxyl (α-keto acids) and 8 phosphate metabolites (NTPs and dNTPs). After derivatization, the MPEA derivatives were investigated to be stable for 4 weeks under 4 °C and detected with high intensity in ∼104 cells. On the other hand, we analyzed an additional 89 metabolites in central carbon and energy metabolic pathways were directly analyzed by liquid chromatography tandem mass spectrometry (LC-MRM-MS). The limit of detection (LODs) of our method were as low as 0.05 ng/mL and the linear range was at least two orders of magnitude with determination coefficient (R2) > 0.9701. The relative standard divisions (RSDs) of intra- and inter-day of 95% metabolites were below 20%. In addition, the isotope list of 82 detected metabolites in central carbon and energy metabolism were generated according to isotopologues and isotopomers for each metabolite resulting from 13C incorporation. Accurate assessment of mass isotopomer distributions (MIDs) of intracellular 13C-labeled metabolites was achieved in [U-13C]-glucose cultured HepG2 cells by our dual strategy. Finally, we performed MID analysis of 101 metabolites in central carbon and energy metabolism. Overall, this dual method is reproducible and robust for application on 13C-MFA and has a great potential for studying clinical isotope labeled samples.

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Metabolic flux analysis in adipose tissue reprogramming

Obesity is a growing epidemic in the United States and worldwide and is associated with insulin resistance and cardiovascular disease, among other comorbidities. Understanding of the pathology that links overnutrition to these disease processes is ongoing. Adipose tissue is a heterogeneous organ comprised of multiple different cell types and it is likely that dysregulated metabolism within these cell populations disrupts both inter- and intracellular interactions and is a key driver of human disease. In recent years, metabolic flux analysis, which offers a precise quantification of metabolic pathway fluxes in biological systems, has emerged as a candidate strategy for uncovering the metabolic changes that stoke these disease processes. In this mini review, we discuss metabolic flux analysis as an experimental tool, with a specific emphasis on mass spectrometry with isotope tracing as this is the technique most frequently used for metabolic flux analysis in adipocytes. Furthermore, we examine existing literature that uses metabolic flux analysis to further our understanding of adipose tissue biology. Our group has a specific interest in understanding the role of white adipose tissue inflammation in the progression of cardiometabolic disease, as we know that in obesity the accumulation of pro-inflammatory adipose tissue macrophages is associated with significant morbidity, so we use this as a paradigm throughout our review for framing the application of these experimental techniques. However, there are many other biological applications to which they can be applied to further understanding of not only adipose tissue biology but also systemic homeostasis.

ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology

BACKGROUND

Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights.

RESULTS

To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering.

CONCLUSION

ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .

Recent advances in systems metabolic engineering

Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.

Microbial production of L-methionine and its precursors using systems metabolic engineering

L-methionine is an essential amino acid with versatile applications in food, feed, cosmetics and pharmaceuticals. At present, the production of L-methionine mainly relies on chemical synthesis, which conflicts with the concern over serious environmental problems and sustainable development goals. In recent years, microbial production of natural products has been amply rewarded with the emergence and rapid development of system metabolic engineering. However, efficient L-methionine production by microbial fermentation remains a great challenge due to its complicated biosynthetic pathway and strict regulatory mechanism. Additionally, the engineered production of L-methionine precursors, L-homoserine, O-succinyl-L-homoserine (OSH) and O-acetyl-L-homoserine (OAH), has also received widespread attention because they can be catalyzed to L-methionine via a high-efficiently enzymatic reaction in vitro, which is also a promising alternative to chemical route. This review provides a comprehensive overview on the recent advances in the microbial production of L-methionine and its precursors, highlighting the challenges and potential solutions for developing L-methionine microbial cell factories from the perspective of systems metabolic engineering, aiming to offer guidance for future engineering.

Quantification and isotope abundance determination of 13C labeled intracellular sugar metabolites with hydrophilic interaction liquid chromatography

Metabolic flux analysis (MFA) using stable isotope labeled tracers is a powerful tool to estimate fluxes through metabolic pathways. It finds applications in studying metabolic changes in diseases, regulation of cellular energetics, and novel strategies for metabolic engineering. Accurate and precise quantification of the concentration of metabolites and their labeling states is critical for correct MFA results. Utilizing an ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) system, an analytical method for simultaneously quantifying the concentration of sugar metabolites and their mass isotopologue distribution (MID) was developed. The method performs with good linearity and coefficient of determination (R2) > 0.99, while the detection limit ranged from 0.1 to 50 mg L-1. Seven sugar metabolites were detected in a labeled Brevibacterium flavum sample using the method. The detected quantities ranged from 6.15 to 3704.21 mg L-1, and 13C abundance was between 12.77% and 66.67% in the fermentation fluid and 16.28% and 91.93% in the bacterial body. Overall, the method is efficient, accurate, and suitable for analysis of labeled sugar metabolites in 13C MFA studies.

Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis

The capability of cyanobacteria to produce sucrose from CO2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. We used targeted metabolomics, 13C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.

Metabolic flux phenotyping of secondary metabolism in cyanobacteria

Cyanobacteria generate energy from photosynthesis and produce various secondary metabolites with diverse commercial and pharmaceutical applications. Unique metabolic and regulatory pathways in cyanobacteria present new challenges for researchers to enhance their product yields, titers, and rates. Therefore, further advancements are critically needed to establish cyanobacteria as a preferred bioproduction platform. Metabolic flux analysis (MFA) quantitatively determines the intracellular flows of carbon within complex biochemical networks, which elucidate the control of metabolic pathways by transcriptional, translational, and allosteric regulatory mechanisms. The emerging field of systems metabolic engineering (SME) involves the use of MFA and other omics technologies to guide the rational development of microbial production strains. This review highlights the potential of MFA and SME to optimize the production of cyanobacterial secondary metabolites and discusses the technical challenges that lie ahead.

Two-step conversion of polyethylene into recombinant proteins using a microbial platform

BACKGROUND

The increasing prevalence of plastic waste combined with the inefficiencies of mechanical recycling has inspired interest in processes that can convert these waste streams into value-added biomaterials. To date, the microbial conversion of plastic substrates into biomaterials has been predominantly limited to polyhydroxyalkanoates production. Expanding the capabilities of these microbial conversion platforms to include a greater diversity of products generated from plastic waste streams can serve to promote the adoption of these technologies at a larger scale and encourage a more sustainable materials economy.

RESULTS

Herein, we report the development of a new strain of Pseudomonas bacteria capable of converting depolymerized polyethylene into high value bespoke recombinant protein products. Using hexadecane, a proxy for depolymerized polyethylene, as a sole carbon nutrient source, we optimized media compositions that facilitate robust biomass growth above 1 × 109 cfu/ml, with results suggesting the benefits of lower hydrocarbon concentrations and the use of NH4Cl as a nitrogen source. We genomically integrated recombinant genes for green fluorescent protein and spider dragline-inspired silk protein, and we showed their expression in Pseudomonas aeruginosa, reaching titers of approximately 10 mg/L when hexadecane was used as the sole carbon source. Lastly, we demonstrated that chemically depolymerized polyethylene, comprised of a mixture of branched and unbranched alkanes, could be converted into silk protein by Pseudomonas aeruginosa at titers of 11.3 ± 1.1 mg/L.

CONCLUSION

This work demonstrates a microbial platform for the conversion of a both alkanes and plastic-derived substrates to recombinant, protein-based materials. The findings in this work can serve as a basis for future endeavors seeking to upcycle recalcitrant plastic wastes into value-added recombinant proteins.

Understanding glycosylation: Regulation through the metabolic flux of precursor pathways

Glycosylation is how proteins and lipids are modified with complex carbohydrates known as glycans. The post-translational modification of proteins with glycans is not a template-driven process in the same way as genetic transcription or protein translation. Glycosylation is instead dynamically regulated by metabolic flux. This metabolic flux is determined by the concentrations and activities of the glycotransferase enzymes, which synthesise glycans, the metabolites that act as their precursors and transporter proteins. This review provides an overview of the metabolic pathways underlying glycan synthesis. Pathological dysregulation of glycosylation, particularly increased glycosylation occurring during inflammation, is also elucidated. The resulting inflammatory hyperglycosylation acts as a glycosignature of disease, and we report on the changes in the metabolic pathways which feed into glycan synthesis, revealing alterations to key enzymes. Finally, we examine studies in developing metabolic inhibitors targeting these critical enzymes. These results provide the tools for researchers investigating the role of glycan metabolism in inflammation and have helped to identify promising glycotherapeutic approaches to inflammation.

FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis

13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.

Circadian disruption decreases gluconeogenic flux in late-gestation, nonlactating dairy cows

Cattle exposed to shifts in light-dark phases during late pregnancy develop hypoglycemia and insulin resistance. Our objective was to investigate if differences in liver carbon flux for gluconeogenesis were driving circadian-disrupted metabolic alterations in glucose homeostasis, and relate changes in carbon flux to hepatic gene expression. We hypothesized circadian disruption would decrease hepatic carbon flux for glucose synthesis. Milking was ceased in late-gestation Holstein cows (n = 8) at 60 d before expected calving (BEC), and animals were assigned to either a control (n = 4) or a phase-shifted (PS; n = 4) group. From d 35 to 21 BEC both groups of cows were exposed to 16 h of light and 8 h of dark, but for the PS, light was shifted forward 6 h every 3 d. On d 21 BEC, liver biopsies were collected, subdivided, and incubated in 1.0 mM [U-13C] propionate for 2 h. Total RNA was isolated from a separate liver sample and used for RNA-sequencing analysis. Postincubation 13C mass isotopologue distribution was determined for aspartate, serine, alanine, and glutamate and used to calculate metabolic flux ratios. Enrichment of serine to enrichment of aspartate ratio (eSer:eAsp) was lower for PS (0.75 ± 0.02) cows compared with control (0.81 ± 0.04), indicating a reduction in carbon flux toward glucose for PS animals. eSer:eAsp ratio was negatively correlated to propionyl-CoA carboxylase (PCCB; r = -0.79) and succinate dehydrogenase subunit D (SDHD; r = -0.82). These relationships indicate that when dairy cattle are exposed to circadian disruption during late gestation, propionate carbon is preferentially used for energy rather than gluconeogenesis.

Chemical inhibitors of hexokinase-2 enzyme reduce lactate accumulation, alter glycosylation processing, and produce altered glycoforms in CHO cell cultures

Chinese hamster ovary (CHO) cells, predominant hosts for recombinant biotherapeutics production, generate lactate as a major glycolysis by-product. High lactate levels adversely impact cell growth and productivity. The goal of this study was to reduce lactate in CHO cell cultures by adding chemical inhibitors to hexokinase-2 (HK2), the enzyme catalyzing the conversion of glucose to glucose 6-phosphate, and examine their impact on lactate accumulation, cell growth, protein titers, and N-glycosylation. Five inhibitors of HK2 enzyme at different concentrations were evaluated, of which 2-deoxy- d-glucose (2DG) and 5-thio- d-glucose (5TG) successfully reduced lactate accumulation with only limited impacts on CHO cell growth. Individual 2DG and 5TG supplementation led to a 35%-45% decrease in peak lactate, while their combined supplementation resulted in a 60% decrease in peak lactate. Inhibitor supplementation led to at least 50% decrease in moles of lactate produced per mol of glucose consumed. Recombinant EPO-Fc titers peaked earlier relative to the end of culture duration in supplemented cultures leading to at least 11% and as high as 32% increase in final EPO-Fc titers. Asparagine, pyruvate, and serine consumption rates also increased in the exponential growth phase in 2DG and 5TG treated cultures, thus, rewiring central carbon metabolism due to low glycolytic fluxes. N-glycan analysis of EPO-Fc revealed an increase in high mannose glycans from 5% in control cultures to 25% and 37% in 2DG and 5TG-supplemented cultures, respectively. Inhibitor supplementation also led to a decrease in bi-, tri-, and tetra-antennary structures and up to 50% lower EPO-Fc sialylation. Interestingly, addition of 2DG led to the incorporation of 2-deoxy-hexose (2DH) on EPO-Fc N-glycans and addition of 5TG resulted in the first-ever observed N-glycan incorporation of 5-thio-hexose (5TH). Six percent to 23% of N-glycans included 5TH moieties, most likely 5-thio-mannose and/or 5-thio-galactose and/or possibly 5-thio-N-acetylglucosamine, and 14%-33% of N-glycans included 2DH moieties, most likely 2-deoxy-mannose and/or 2-deoxy-galactose, for cultures treated with different concentrations of 5TG and 2DG, respectively. Our study is the first to evaluate the impact of these glucose analogs on CHO cell growth, protein production, cell metabolism, N-glycosylation processing, and formation of alternative glycoforms.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Turn air-captured CO2 with methanol into amino acid and pyruvate in an ATP/NAD(P)H-free chemoenzymatic system

The use of gaseous and air-captured CO₂ for technical biosynthesis is highly desired, but elusive so far due to several obstacles including high energy (ATP, NADPH) demand, low thermodynamic driving force and limited biosynthesis rate. Here, we present an ATP and NAD(P)H-free chemoenzymatic system for amino acid and pyruvate biosynthesis by coupling methanol with CO₂. It relies on a re-engineered glycine cleavage system with the NAD(P)H-dependent L protein replaced by biocompatible chemical reduction of protein H with dithiothreitol. The latter provides a higher thermodynamic driving force, determines the reaction direction, and avoids protein polymerization of the rate-limiting enzyme carboxylase. Engineering of H protein to effectively release the lipoamide arm from a protected state further enhanced the system performance, achieving the synthesis of glycine, serine and pyruvate at g/L level from methanol and air-captured CO₂. This work opens up the door for biosynthesis of amino acids and derived products from air.

Reversible Recognition-Based Boronic Acid Probes for Glucose Detection in Live Cells and Zebrafish

Glucose, a critical source of energy, directly determines the homeostasis of the human body. However, due to the lack of robust imaging probes, the mechanism underlying the changes of glucose homeostasis in the human body remains unclear. Herein, diboronic acid probes with good biocompatibility and high sensitivity were synthesized based on an ortho-aminomethylphenylboronic acid probe, phenyl(di)boronic acid (PDBA). Significantly, by introducing the water-solubilizing group -CN directly opposite the boronic acid group and -COOCH₃ or -COOH groups to the β site of the anthracene in PDBA, we obtained the water-soluble probe Mc-CDBA with sensitive response (F/F₀ = 47.8, detection limit (LOD) = 1.37 μM) and Ca-CDBA with the highest affinity for glucose (K_a = 4.5 × 10³ M^-1). On this basis, Mc-CDBA was used to identify glucose heterogeneity between normal and tumor cells. Finally, Mc-CDBA and Ca-CDBA were used for imaging glucose in zebrafish. Our research provides a new strategy for designing efficient boronic acid glucose probes and powerful new tools for the evaluation of glucose-related diseases.

Functional comparison of metabolic networks across species

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

13C-Metabolic flux analysis of 3T3-L1 adipocytes illuminates its core metabolism under hypoxia

Hypoxia has been identified as a major factor in the pathogenesis of adipose tissue inflammation, which is a hallmark of obesity and obesity-linked type 2 diabetes mellitus. In this study, we have investigated the impact of hypoxia (1% oxygen) on the physiology and metabolism of 3T3-L1 adipocytes, a widely used cell culture model of adipose. Specifically, we applied parallel labeling experiments, isotopomer spectral analysis, and ¹³C-metabolic flux analysis to quantify the impact of hypoxia on adipogenesis, de novo lipogenesis and metabolic flux reprogramming in adipocytes. We found that 3T3-L1 cells can successfully differentiate into lipid-accumulating adipocytes under hypoxia, although the production of lipids was reduced by about 40%. Quantitative flux analysis demonstrated that short-term (1 day) and long-term (7 days) exposure to hypoxia resulted in similar reprogramming of cellular metabolism. Overall, we found that hypoxia: 1) reduced redox and energy generation by more than 2-fold and altered the patterns of metabolic pathway contributions to production and consumption of energy and redox cofactors; 2) redirected glucose metabolism from pentose phosphate pathway and citric acid cycle to lactate production; 3) rewired glutamine metabolism, from net glutamine production to net glutamine catabolism; 4) suppressed branched chain amino acid consumption; and 5) reduced biosynthesis of odd-chain fatty acids and mono-unsaturated fatty acids, while synthesis of saturated even-chain fatty acids was not affected. Together, these results highlight the profound impact of extracellular microenvironment on adipocyte metabolic activity and function.

Probing Single-Cell Fermentation Fluxes and Exchange Networks via pH-Sensing Hybrid Nanofibers

The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that, when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of exchange networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single-cell flux analysis. In this study, we provide direct experimental characterization of such exchange networks. We devise a method to quantify single-cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single-cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected "hubs" (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e., by pairwise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method addresses issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, allowing for real-time noninvasive single-cell metabolic flux analysis.

Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments

Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA.

ONE SENTENCE SUMMARY

An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.

Engineering and adaptive laboratory evolution of Escherichia coli for improving methanol utilization based on a hybrid methanol assimilation pathway

Engineering Escherichia coli for efficient methanol assimilation is important for developing methanol as an emerging next-generation feedstock for industrial biotechnology. While recent attempts to engineer E. coli as a synthetic methylotroph have achieved great success, most of these works are based on the engineering of the prokaryotic ribulose monophosphate (RuMP) pathway. In this study, we introduced a hybrid methanol assimilation pathway which consists of prokaryotic methanol dehydrogenase (Mdh) and eukaryotic xylulose monophosphate (XuMP) pathway enzyme dihydroxyacetone synthase (Das) into E. coli and reprogrammed E. coli metabolism to improve methanol assimilation by combining rational design and adaptive laboratory evolution. By deletion and down-regulation of key genes in the TCA cycle and glycolysis to increase the flux toward the cyclic XuMP pathway, methanol consumption and the assimilation of methanol to biomass were significantly improved. Further improvements in methanol utilization and cell growth were achieved via adaptive laboratory evolution and a final evolved strain can grow on methanol with only 0.1 g/L yeast extract as co-substrate. ¹³C-methanol labeling assay demonstrated significantly higher labeling in intracellular metabolites in glycolysis, TCA cycle, pentose phosphate pathway, and amino acids. Transcriptomics analysis showed that the expression of fba, dhak, and part of pentose phosphate pathway genes were highly up-regulated, suggesting that the rational engineering strategies and adaptive evolution are effective for activating the cyclic XuMP pathway. This study demonstrated the feasibility and provided new strategies to construct synthetic methylotrophy of E. coli based on the hybrid methanol assimilation pathway with Mdh and Das.

Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

In situ diversity of metabolism and carbon use efficiency among soil bacteria

The central carbon (C) metabolic network harvests energy to power the cell and feed biosynthesis for growth. In pure cultures, bacteria use some but not all of the network's major pathways, such as glycolysis and pentose phosphate and Entner-Doudoroff pathways. However, how these pathways are used in microorganisms in intact soil communities is unknown. Here, we analyzed the incorporation of ¹³C from glucose isotopomers into phospholipid fatty acids. We showed that groups of Gram-positive and Gram-negative bacteria in an intact agricultural soil used different pathways to metabolize glucose. They also differed in C use efficiency (CUE), the efficiency with which a substrate is used for biosynthesis. Our results provide experimental evidence for diversity among microbes in the organization of their central carbon metabolic network and CUE under in situ conditions. These results have important implications for our understanding of how community composition affects soil C cycling and organic matter formation.

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

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

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Cardio-Onco-Metabolism - Metabolic vulnerabilities in cancer and the heart

Cancer and cardiovascular diseases (CVDs) are the leading cause of death worldwide. Metabolic remodeling is a hallmark of both cancer and the failing heart. Tumors reprogram metabolism to optimize nutrient utilization and meet increased demands for energy provision, biosynthetic pathways, and proliferation. Shared risk factors for cancer and CVDs suggest intersecting mechanisms for disease pathogenesis and progression. In this review, we aim to highlight the role of metabolic remodeling in cancer and its potential to impair cardiac function. Understanding these mechanisms will help us develop biomarkers, better therapies, and identify patients at risk of developing heart disease after surviving cancer.

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.

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

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Hypoxia-Mediated ATF4 Induction Promotes Survival in Detached Conditions in Metastatic Murine Mammary Cancer Cells

Regions of hypoxia are common in solid tumors and drive changes in gene expression that increase risk of cancer metastasis. Tumor cells must respond to the stress of hypoxia by activating genes to modify cell metabolism and antioxidant response to improve survival. The goal of the current study was to determine the effect of hypoxia on cell metabolism and markers of oxidative stress in metastatic (metM-Wntlung) compared with nonmetastatic (M-Wnt) murine mammary cancer cell lines. We show that hypoxia induced a greater suppression of glutamine to glutamate conversion in metastatic cells (13% in metastatic cells compared to 7% in nonmetastatic cells). We also show that hypoxia increased expression of genes involved in antioxidant response in metastatic compared to nonmetastatic cells, including glutamate cysteine ligase catalytic and modifier subunits and malic enzyme 1. Interestingly, hypoxia increased the mRNA level of the transaminase glutamic pyruvic transaminase 2 (Gpt2, 7.7-fold) only in metM-Wntlung cells. The change in Gpt2 expression was accompanied by transcriptional (4.2-fold) and translational (6.5-fold) induction of the integrated stress response effector protein activating transcription factor 4 (ATF4). Genetic depletion ATF4 demonstrated importance of this molecule for survival of hypoxic metastatic cells in detached conditions. These findings indicate that more aggressive, metastatic cancer cells utilize hypoxia for metabolic reprogramming and induction of antioxidant defense, including activation of ATF4, for survival in detached conditions.

TCR activation directly stimulates PYGB-dependent glycogenolysis to fuel the early recall response in CD8+ memory T cells

Glycolysis facilitates the rapid recall response of CD8⁺ memory T (Tm) cells. However, it remains unclear whether Tm cells uptake exogenous glucose or mobilize endogenous sugar to fuel glycolysis. Here, we show that intracellular glycogen rather than extracellular glucose acts as the major carbon source for the early recall response. Following antigenic stimulation, Tm cells exhibit high glycogen phosphorylase (brain form, PYGB) activity, leading to glycogenolysis and release of glucose-6-phosphate (G6P). Elevated G6P mainly flows to glycolysis but is also partially channeled to the pentose phosphate pathway, which maintains the antioxidant capacity necessary for later recall stages. Mechanistically, TCR signaling directly induces phosphorylation of PYGB by LCK-ZAP70. Functionally, the glycogenolysis-fueled early recall response of CD8⁺ Tm cells accelerates the clearance of OVA-Listeria monocytogenes in an infected mouse model. Thus, we uncover a specific dependency on glycogen for the initial activation of memory T cells, which may have therapeutic implications for adaptive immunity.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.