Towards better understanding of industrial cell factories: novel approaches for 13C metabolic flux analysis in complex nutrient environments

What are the signals that control catabolite repression in Pseudomonas?

Metabolically versatile bacteria exhibit a global regulatory response known as carbon catabolite repression (CCR), which prioritizes some carbon sources over others when all are present in sufficient amounts. This optimizes growth by distributing metabolite fluxes, but can restrict yields in biotechnological applications. The molecular mechanisms and preferred substrates for CCR vary between bacterial groups. Escherichia coli prioritizes glucose whereas Pseudomonas sp. prefer certain organic acids or amino acids. A significant issue in understanding (and potentially bypassing) CCR is the lack of information about the signals that trigger this regulatory response. In E. coli, several key compounds act as flux sensors, governing the flow of metabolites through catabolic pathways and preventing imbalances. These flux sensors can also modulate the CCR response. It has been suggested that the order of substrate preference is determined by carbon uptake flux rather than substrate identity. For Pseudomonas, much less information is available, as the signals that induce CCR are poorly understood. This article briefly discusses the available evidence on the signals that trigger CCR and the questions that remain to be answered in Pseudomonas.

Design and Construction of Artificial Biological Systems for One-Carbon Utilization

The third-generation (3G) biorefinery aims to use microbial cell factories or enzymatic systems to synthesize value-added chemicals from one-carbon (C1) sources, such as CO2, formate, and methanol, fueled by renewable energies like light and electricity. This promising technology represents an important step toward sustainable development, which can help address some of the most pressing environmental challenges faced by modern society. However, to establish processes competitive with the petroleum industry, it is crucial to determine the most viable pathways for C1 utilization and productivity and yield of the target products. In this review, we discuss the progresses that have been made in constructing artificial biological systems for 3G biorefineries in the last 10 years. Specifically, we highlight the representative works on the engineering of artificial autotrophic microorganisms, tandem enzymatic systems, and chemo-bio hybrid systems for C1 utilization. We also prospect the revolutionary impact of these developments on biotechnology. By harnessing the power of 3G biorefinery, scientists are establishing a new frontier that could potentially revolutionize our approach to industrial production and pave the way for a more sustainable future.

Time-resolved, deuterium-based fluxomics uncovers the hierarchy and dynamics of sugar processing by Pseudomonas putida

Pseudomonas putida, a microbial host widely adopted for metabolic engineering, processes glucose through convergent peripheral pathways that ultimately yield 6-phosphogluconate. The periplasmic gluconate shunt (PGS), composed by glucose and gluconate dehydrogenases, sequentially transforms glucose into gluconate and 2-ketogluconate. Although the secretion of these organic acids by P. putida has been extensively recognized, the mechanism and spatiotemporal regulation of the PGS remained elusive thus far. To address this challenge, we adopted a dynamic 13C- and 2H-metabolic flux analysis strategy, termed D-fluxomics. D-fluxomics demonstrated that the PGS underscores a highly dynamic metabolic architecture in glucose-dependent batch cultures of P. putida, characterized by hierarchical carbon uptake by the PGS throughout the cultivation. Additionally, we show that gluconate and 2-ketogluconate accumulation and consumption can be solely explained as a result of the interplay between growth rate-coupled and decoupled metabolic fluxes. As a consequence, the formation of these acids in the PGS is inversely correlated to the bacterial growth rate-unlike the widely studied overflow metabolism of Escherichia coli and yeast. Our findings, which underline survival strategies of soil bacteria thriving in their natural environments, open new avenues for engineering P. putida towards efficient, sugar-based bioprocesses.

Fungal Pigments: Carotenoids, Riboflavin, and Polyketides with Diverse Applications

Natural pigments and colorants have seen a substantial increase in use over the last few decades due to their eco-friendly and safe properties. Currently, customer preferences for more natural products are driving the substitution of natural pigments for synthetic colorants. Filamentous fungi, particularly ascomycetous fungi (Monascus, Fusarium, Penicillium, and Aspergillus), have been shown to produce secondary metabolites containing a wide variety of pigments, including β-carotene, melanins, azaphilones, quinones, flavins, ankaflavin, monascin, anthraquinone, and naphthoquinone. These pigments produce a variety of colors and tints, including yellow, orange, red, green, purple, brown, and blue. Additionally, these pigments have a broad spectrum of pharmacological activities, including immunomodulatory, anticancer, antioxidant, antibacterial, and antiproliferative activities. This review provides an in-depth overview of fungi gathered from diverse sources and lists several probable fungi capable of producing a variety of color hues. The second section discusses how to classify coloring compounds according to their chemical structure, characteristics, biosynthetic processes, application, and present state. Once again, we investigate the possibility of employing fungal polyketide pigments as food coloring, as well as the toxicity and carcinogenicity of particular pigments. This review explores how advanced technologies such as metabolic engineering and nanotechnology can be employed to overcome obstacles associated with the manufacture of mycotoxin-free, food-grade fungal pigments.

Engineering Strategies for Efficient Bioconversion of Glycerol to Value-Added Products by Yarrowia lipolytica

Yarrowia lipolytica has been a valuable biotechnological workhorse for the production of commercially important biochemicals for over 70 years. The knowledge gained so far on the native biosynthetic pathways, as well as the availability of numerous systems and synthetic biology tools, enabled not only the regulation and the redesign of the existing metabolic pathways, but also the introduction of novel synthetic ones; further consolidating the position of the yeast in industrial biotechnology. However, for the development of competitive and sustainable biotechnological production processes, bioengineering should be reinforced by bioprocess optimization strategies. Although there are many published reviews on the bioconversion of various carbon sources to value-added products by Yarrowia lipolytica, fewer works have focused on reviewing up-to-date strain, medium, and process engineering strategies with an aim to emphasize the significance of integrated engineering approaches. The ultimate goal of this work is to summarize the necessary knowledge and inspire novel routes to manipulate at a systems level the yeast biosynthetic machineries by combining strain and bioprocess engineering. Due to the increasing surplus of biodiesel-derived waste glycerol and the favored glycerol-utilization metabolic pathways of Y. lipolytica over other carbon sources, the present review focuses on pure and crude glycerol-based biomanufacturing.

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use ¹³ C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with ¹³ C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of ¹³ C-based proteomics for uncovering microbial interactions.

Co-cultures of Propionibacterium freudenreichii and Bacillus amyloliquefaciens cooperatively upgrade sunflower seed milk to high levels of vitamin B12 and multiple co-benefits

BACKGROUND

Sunflower seeds (Helianthus annuus) display an attractive source for the rapidly increasing market of plant-based human nutrition. Of particular interest are press cakes of the seeds, cheap residuals from sunflower oil manufacturing that offer attractive sustainability and economic benefits. Admittedly, sunflower seed milk, derived therefrom, suffers from limited nutritional value, undesired flavor, and the presence of indigestible sugars. Of specific relevance is the absence of vitamin B12. This vitamin is required for development and function of the central nervous system, healthy red blood cell formation, and DNA synthesis, and displays the most important micronutrient for vegans to be aware of. Here we evaluated the power of microbes to enrich sunflower seed milk nutritionally as well as in flavor.

RESULTS

Propionibacterium freudenreichii NCC 1177 showed highest vitamin B12 production in sunflower seed milk out of a range of food-grade propionibacteria. Its growth and B12 production capacity, however, were limited by a lack of accessible carbon sources and stimulants of B12 biosynthesis in the plant milk. This was overcome by co-cultivation with Bacillus amyloliquefaciens NCC 156, which supplied lactate, amino acids, and vitamin B7 for growth of NCC 1177 plus vitamins B2 and B3, potentially supporting vitamin B12 production by the Propionibacterium. After several rounds of optimization, co-fermentation of ultra-high-temperature pre-treated sunflower seed milk by the two microbes, enabled the production of 17 µg (100 g)-1 vitamin B12 within four days without any further supplementation. The fermented milk further revealed significantly enriched levels of L-lysine, the most limiting essential amino acid, vitamin B3, vitamin B6, improved protein quality and flavor, and largely eliminated indigestible sugars.

CONCLUSION

The fermented sunflower seed milk, obtained by using two food-grade microbes without further supplementation, displays an attractive, clean-label product with a high level of vitamin B12 and multiple co-benefits. The secret of the successfully upgraded plant milk lies in the multifunctional cooperation of the two microbes, which were combined, based on their genetic potential and metabolic signatures found in mono-culture fermentations. This design by knowledge approach appears valuable for future development of plant-based milk products.

Isotopically nonstationary 13C metabolic flux analysis in resting and activated human platelets

Platelet metabolism is linked to platelet hyper- and hypoactivity in numerous human diseases. Developing a detailed understanding of the link between metabolic shifts and platelet activation state is integral to improving human health. Here, we show the first application of isotopically nonstationary ¹³C metabolic flux analysis to quantitatively measure carbon fluxes in both resting and thrombin activated platelets. Metabolic flux analysis results show that resting platelets primarily metabolize glucose to lactate via glycolysis, while acetate is oxidized to fuel the tricarboxylic acid cycle. Upon activation with thrombin, a potent platelet agonist, platelets increase their uptake of glucose 3-fold. This results in an absolute increase in flux throughout central metabolism, but when compared to resting platelets they redistribute carbon dramatically. Activated platelets decrease relative flux to the oxidative pentose phosphate pathway and TCA cycle from glucose and increase relative flux to lactate. These results provide the first report of reaction-level carbon fluxes in platelets and allow us to distinguish metabolic fluxes with much higher resolution than previous studies.

Kendrick Mass Defect Variation to Decipher Isotopic Labeling in Brain Metastases Studied by Mass Spectrometry Imaging

Besides many other applications, isotopic labeling is commonly used to decipher the metabolism of living biological systems. By giving a stable isotopically labeled compound as a substrate, the biological system will use this labeled nutrient as it would with a regular substrate and incorporate stable heavy atoms into new metabolites. Utilizing mass spectrometry, by comparing heavy atom enriched isotopic profiles and naturally occurring ones, it is possible to identify these metabolites and deduce valuable information about metabolism and biochemical pathways. The coupling of this approach with mass spectrometry imaging (MSI) allows one then to obtain 2D maps of metabolisms used by living specimens. As metabolic networks are convoluted, a global overview of the isotopically labeled data set to detect unexpected metabolites is crucial. Unfortunately, due to the complexity of MSI spectra, such untargeted processing approaches are difficult to decipher. In this technical note, we demonstrate the potential of a variation around the Kendrick analysis concept to detect the incorporation of stable heavy atoms into metabolites. The Kendrick analysis uses as a base unit the difference between the mass of the most abundant isotope and the mass of the corresponding stable isotopic tracer (namely, ¹²C and ¹³C). The resulting Kendrick plot offers an alternative method to process the MSI data set with a new perspective allowing for the rapid detection of the ¹³C-enriched metabolites and separating unrelated compounds. This processing method of MS data could therefore be a useful tool to decipher isotopic labeling and study metabolic networks, especially as it does not require advanced computational capabilities.

Genome-based selection and application of food-grade microbes for chickpea milk fermentation towards increased L-lysine content, elimination of indigestible sugars, and improved flavour

Background

Plant-based milk alternatives are more popular than ever, and chickpea-based milks are among the most commercially relevant products. Unfortunately, limited nutritional value because of low levels of the essential amino acid l-lysine, low digestibility and unpleasant taste are challenges that must be addressed to improve product quality and meet consumer expectations.

Results

Using in-silico screening and food safety classifications, 31 strains were selected as potential l-lysine producers from approximately 2,500 potential candidates. Beneficially, 30% of the isolates significantly accumulated amino acids (up to 1.4 mM) during chickpea milk fermentation, increasing the natural level by up to 43%. The best-performing strains, B. amyloliquefaciens NCC 156 and L. paracasei subsp. paracasei NCC 2511, were tested further. De novo lysine biosynthesis was demonstrated in both strains by ¹³C metabolic pathway analysis. Spiking small amounts of citrate into the fermentation significantly activated l-lysine biosynthesis in NCC 156 and stimulated growth. Both microbes revealed additional benefits in eliminating indigestible sugars such as stachyose and raffinose and converting off-flavour aldehydes into the corresponding alcohols and acids with fruity and sweet notes.

Conclusions

B. amyloliquefaciens NCC 156 and L. paracasei subsp. paracasei NCC 2511 emerged as multi-benefit microbes for chickpea milk fermentation with strong potential for industrial processing of the plant material. Given the high number of l-lysine-producing isolates identified in silico, this concept appears promising to support strain selection for food fermentation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01595-2.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Genomic, transcriptomic, and metabolic characterizations of Escherichia coli adapted to branched-chain higher alcohol tolerance

Microbial-produced branched-chain higher alcohols (BCHAs), such as isopropanol, isobutanol, and isopentanol in Escherichia coli, have emerged as promising alternative biofuels under development. Elucidating and improving the tolerance of E. coli to BCHAs are important issues for microbial production of BCHAs due to their physiological inhibitory effect. Previous works aimed at understanding the genetic basis of E. coli tolerance to BCHAs with a comparative genome, reverse engineering, or transcriptome approach have gained some important insights into the mechanism of tolerance. However, investigation on BCHA tolerance from the whole-genomic, transcriptomic, and metabolic levels via a systematic approach has not yet been completely elucidated. Here, in this study, genomic, transcriptomic, and ¹³C-metabolic flux analyses (¹³C-MFA) of an evolved E. coli strain adapted to BCHA tolerance were conducted. Genome mutation of negative regulation factor (rssB, acrB, and clpX) of RpoS level suggested upregulation of RpoS activity in BCHA tolerance of E. coli. From a more detailed perspective, enhanced energy metabolism was observed to be the main characteristic of E. coli strain tolerant to BCHAs. Enhanced energy metabolism has been achieved through several routes, which included redistribution of the central carbon metabolism, upregulation of the energy generation machinery, and facilitating the operation of electron transferring chain. Evidence of multiple solutions of genotype modification toward BCHA tolerance was also revealed through comparative analysis of previous works from different groups.

Quantitative analysis of glycine related metabolic pathways for one-carbon synthetic biology

Glycine is an essential one-carbon (C1) metabolite nested in a complex network of cellular metabolism. Glycine and its related metabolic pathways have important biochemical and biomedical implications and have thus been studied for a long time. However, quantitative and systems level knowledge about the interactions and regulations of the pathways are severely limited, especially for the purpose of reengineering the relevant pathways for C1-based biotechnological processes using synthetic biology and metabolic engineering approaches. In fact, quantitative analytic methods are missing for some of the key players of the glycine-related pathways, prominently the glycine cleavage system and folate cycle, particularly for intracellular processes under physiological conditions. Here, we pinpoint the existing gaps and highlight the need and challenges for future development.

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

¹³C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. ¹³C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying ¹³C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of ¹³C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for ¹³C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of ¹³C MFA.

13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol

BACKGROUND

Bioprocessing offers a sustainable and green approach to manufacture various chemicals and materials. Development of bioprocesses requires transforming common producer strains to cell factories. ¹³C metabolic flux analysis (¹³C-MFA) can be applied to identify relevant targets to accomplish the desired phenotype, which has become one of the major tools to support systems metabolic engineering. In this research, we applied ¹³C-MFA to identify bottlenecks in the bioconversion of glycerol into acetol by Escherichia coli. Valorization of glycerol, the main by-product of biodiesel, has contributed to the viability of biofuel economy.

RESULTS

We performed ¹³C-MFA and measured intracellular pyridine nucleotide pools in a first-generation acetol producer strain (HJ06) and a non-producer strain (HJ06C), and identified that engineering the NADPH regeneration is a promising target. Based on this finding, we overexpressed nadK encoding NAD kinase or pntAB encoding membrane-bound transhydrogenase either individually or in combination with HJ06, obtaining HJ06N, HJ06P and HJ06PN. The step-wise approach resulted in increasing the acetol titer from 0.91 g/L (HJ06) to 2.81 g/L (HJ06PN). To systematically characterize and the effect of mutation(s) on the metabolism, we also examined the metabolomics and transcriptional levels of key genes in four strains. The pool sizes of NADPH, NADP⁺ and the NADPH/NADP⁺ ratio were progressively increased from HJ06 to HJ06PN, demonstrating that the sufficient NADPH supply is critical for acetol production. Flux distribution was optimized towards acetol formation from HJ06 to HJ06PN: (1) The carbon partitioning at the DHAP node directed gradually more carbon from the lower glycolytic pathway through the acetol biosynthetic pathway; (2) The transhydrogenation flux was constantly increased. In addition, ¹³C-MFA showed the rigidity of upper glycolytic pathway, PP pathway and the TCA cycle to support growth. The flux patterns were supported by most metabolomics data and gene expression profiles.

CONCLUSIONS

This research demonstrated how ¹³C-MFA can be applied to drive the cycles of design, build, test and learn implementation for strain development. This succeeding engineering strategy can also be applicable for rational design of other microbial cell factories.