Combining systems and synthetic biology for in vivo enzymology.

Enzymatic parameters are classically determined in vitro, under conditions that are far from those encountered in cells, casting doubt on their physiological relevance. We developed a generic approach combining tools from synthetic and systems biology to measure enzymatic parameters in vivo. In the context of a synthetic carotenoid pathway in Saccharomyces cerevisiae, we focused on a phytoene synthase and three phytoene desaturases, which are difficult to study in vitro. We designed, built, and analyzed a collection of yeast strains mimicking substantial variations in substrate concentration by strategically manipulating the expression of geranyl-geranyl pyrophosphate (GGPP) synthase. We successfully determined in vivo Michaelis-Menten parameters (KM, Vmax, and kcat) for GGPP-converting phytoene synthase from absolute metabolomics, fluxomics and proteomics data, highlighting differences between in vivo and in vitro parameters. Leveraging the versatility of the same set of strains, we then extracted enzymatic parameters for two of the three phytoene desaturases. Our approach demonstrates the feasibility of assessing enzymatic parameters directly in vivo, providing a novel perspective on the kinetic characteristics of enzymes in real cellular conditions.

PhysioFit: a software to quantify cell growth parameters and extracellular fluxes.

SUMMARY: Quantification of growth parameters and extracellular uptake and production fluxes is central in systems and synthetic biology. Fluxes can be estimated using various mathematical models by fitting time-course measurements of the concentration of cells and extracellular substrates and products. A single tool is available to non-computational biologists to calculate extracellular fluxes, but it is hardly interoperable and is limited to a single hard-coded growth model. We present our open-source flux calculation software, PhysioFit, which can be used with any growth model and is interoperable by design. PhysioFit includes some of the most common growth models, and advanced users can implement additional models to calculate extracellular fluxes and other growth parameters for metabolic systems or experimental setups that follow alternative kinetics. PhysioFit can be used as a Python library and offers a graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. AVAILABILITY AND IMPLEMENTATION: PhysioFit v3 is implemented in Python 3 and was tested on Windows, Unix, and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/PhysioFit/ and https://physiofit.readthedocs.io/.

Chromatin-Bound MDM2 Regulates Serine Metabolism and Redox Homeostasis Independently of p53.

The mouse double minute 2 (MDM2) oncoprotein is recognized as a major negative regulator of the p53 tumor suppressor, but growing evidence indicates that its oncogenic activities extend beyond p53. Here, we show that MDM2 is recruited to chromatin independently of p53 to regulate a transcriptional program implicated in amino acid metabolism and redox homeostasis. Identification of MDM2 target genes at the whole-genome level highlights an important role for ATF3/4 transcription factors in tethering MDM2 to chromatin. MDM2 recruitment to chromatin is a tightly regulated process that occurs during oxidative stress and serine/glycine deprivation and is modulated by the pyruvate kinase M2 (PKM2) metabolic enzyme. Depletion of endogenous MDM2 in p53-deficient cells impairs serine/glycine metabolism, the NAD(+)/NADH ratio, and glutathione (GSH) recycling, impacting their redox state and tumorigenic potential. Collectively, our data illustrate a previously unsuspected function of chromatin-bound MDM2 in cancer cell metabolism.

CARD9 in neutrophils protects from colitis and controls mitochondrial metabolism and cell survival.

OBJECTIVES: Inflammatory bowel disease (IBD) results from a combination of genetic predisposition, dysbiosis of the gut microbiota and environmental factors, leading to alterations in the gastrointestinal immune response and chronic inflammation. Caspase recruitment domain 9 (Card9), one of the IBD susceptibility genes, has been shown to protect against intestinal inflammation and fungal infection. However, the cell types and mechanisms involved in the CARD9 protective role against inflammation remain unknown. DESIGN: We used dextran sulfate sodium (DSS)-induced and adoptive transfer colitis models in total and conditional CARD9 knock-out mice to uncover which cell types play a role in the CARD9 protective phenotype. The impact of Card9 deletion on neutrophil function was assessed by an in vivo model of fungal infection and various functional assays, including endpoint dilution assay, apoptosis assay by flow cytometry, proteomics and real-time bioenergetic profile analysis (Seahorse). RESULTS: Lymphocytes are not intrinsically involved in the CARD9 protective role against colitis. CARD9 expression in neutrophils, but not in epithelial or CD11c+cells, protects against DSS-induced colitis. In the absence of CARD9, mitochondrial dysfunction increases mitochondrial reactive oxygen species production leading to the premature death of neutrophilsthrough apoptosis, especially in oxidative environment. The decreased functional neutrophils in tissues might explain the impaired containment of fungi and increased susceptibility to intestinal inflammation. CONCLUSION: These results provide new insight into the role of CARD9 in neutrophil mitochondrial function and its involvement in intestinal inflammation, paving the way for new therapeutic strategies targeting neutrophils.

An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.

INTRODUCTION: Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking. OBJECTIVES: To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism. METHODS: The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of 13C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism. RESULTS: The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time. CONCLUSION: The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.

Simultaneous Measurement of Metabolite Concentration and Isotope Incorporation by Mass Spectrometry.

Studies of the topology, functioning, and regulation of metabolic systems are based on two main types of information that can be measured by mass spectrometry: the (absolute or relative) concentration of metabolites and their isotope incorporation in 13C-labeling experiments. These data are currently obtained from two independent experiments because the 13C-labeled internal standard (IS) used to determine the concentration of a given metabolite overlaps the 13C-mass fractions from which its 13C-isotopologue distribution (CID) is quantified. Here, we developed a generic method with a dedicated processing workflow to obtain these two sets of information simultaneously in a unique sample collected from a single cultivation, thereby reducing by a factor of 2 both the number of cultivations to perform and the number of samples to collect, prepare, and analyze. The proposed approach is based on an IS labeled with other isotope(s) that can be resolved from the 13C-mass fractions of interest. As proof-of-principle, we analyzed amino acids using a doubly labeled 15N13C-cell extract as IS. Extensive evaluation of the proposed approach shows a similar accuracy and precision compared to state-of-the-art approaches. We demonstrate the value of this approach by investigating the dynamic response of amino acids metabolism in mammalian cells upon activation of the protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), a key component of the unfolded protein response. Integration of metabolite concentrations and isotopic profiles reveals a reduced de novo biosynthesis of amino acids upon PERK activation. The proposed approach is generic and can be applied to other (micro)organisms, analytical platforms, isotopic tracers, or classes of metabolites.

IsoCor: isotope correction for high-resolution MS labeling experiments.

SUMMARY: Mass spectrometry (MS) is widely used for isotopic studies of metabolism and other (bio)chemical processes. Quantitative applications in systems and synthetic biology require to correct the raw MS data for the contribution of naturally occurring isotopes. Several tools are available to correct low-resolution MS data, and recent developments made substantial improvements by introducing resolution-dependent correction methods, hence opening the way to the correction of high-resolution MS (HRMS) data. Nevertheless, current HRMS correction methods partly fail to determine which isotopic species are resolved from the tracer isotopologues and should thus be corrected. We present an updated version of our isotope correction software (IsoCor) with a novel correction algorithm which ensures to accurately exploit any chemical species with any isotopic tracer, at any MS resolution. IsoCor v2 also includes a novel graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. AVAILABILITY AND IMPLEMENTATION: IsoCor v2 is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/IsoCor/ and https://isocor.readthedocs.io/.

Functional analysis of isoprenoid precursors biosynthesis by quantitative metabolomics and isotopologue profiling.

INTRODUCTION: Isoprenoids are amongst the most abundant and diverse biological molecules and are involved in a broad range of biological functions. Functional understanding of their biosynthesis is thus key in many fundamental and applicative fields, including systems biology, medicine and biotechnology. However, available methods do not yet allow accurate quantification and tracing of stable isotopes incorporation for all the isoprenoids precursors. OBJECTIVES: We developed and validated a complete methodology for quantitative metabolomics and isotopologue profiling of isoprenoid precursors in the yeast Saccharomyces cerevisiae. METHODS: This workflow covers all the experimental and computational steps from sample collection and preparation to data acquisition and processing. It also includes a novel quantification method based on liquid chromatography coupled to high-resolution mass spectrometry. Method validation followed the Metabolomics Standards Initiative guidelines. RESULTS: This workflow ensures accurate absolute quantification (RSD < 20%) of all mevalonate and prenyl pyrophosphates intermediates with a high sensitivity over a large linear range (from 0.1 to 50 pmol). In addition, we demonstrate that this workflow brings crucial information to design more efficient phytoene producers. Results indicate stable turnover rates of prenyl pyrophosphate intermediates in the constructed strains and provide quantitative information on the change of the biosynthetic flux of phytoene precursors. CONCLUSION: This methodology fills one of the last technical gaps for functional studies of isoprenoids biosynthesis and should be applicable to other eukaryotic and prokaryotic (micro)organisms after adaptation of some organism-dependent steps. This methodology also opens the way to 13C-metabolic flux analysis of isoprenoid biosynthesis.

E4F1 controls a transcriptional program essential for pyruvate dehydrogenase activity.

The mitochondrial pyruvate dehydrogenase (PDH) complex (PDC) acts as a central metabolic node that mediates pyruvate oxidation and fuels the tricarboxylic acid cycle to meet energy demand. Here, we reveal another level of regulation of the pyruvate oxidation pathway in mammals implicating the E4 transcription factor 1 (E4F1). E4F1 controls a set of four genes [dihydrolipoamide acetlytransferase (Dlat), dihydrolipoyl dehydrogenase (Dld), mitochondrial pyruvate carrier 1 (Mpc1), and solute carrier family 25 member 19 (Slc25a19)] involved in pyruvate oxidation and reported to be individually mutated in human metabolic syndromes. E4F1 dysfunction results in 80% decrease of PDH activity and alterations of pyruvate metabolism. Genetic inactivation of murine E4f1 in striated muscles results in viable animals that show low muscle PDH activity, severe endurance defects, and chronic lactic acidemia, recapitulating some clinical symptoms described in PDC-deficient patients. These phenotypes were attenuated by pharmacological stimulation of PDH or by a ketogenic diet, two treatments used for PDH deficiencies. Taken together, these data identify E4F1 as a master regulator of the PDC.

Methodology for the Validation of Isotopic Analyses by Mass Spectrometry in Stable-Isotope Labeling Experiments.

Stable-isotope labeling experiments (ILEs) are widely used to investigate the topology and operation of metabolic networks. The quality of isotopic data collected in ILEs is of utmost importance to ensure reliable biological interpretations, but current evaluation approaches are limited due to a lack of suitable reference material and relevant evaluation criteria. In this work, we present a complete methodology to evaluate mass spectrometry (MS) methods used for quantitative isotopic studies of metabolic systems. This methodology, based on a biological sample containing metabolites with controlled labeling patterns, exploits different quality metrics specific to isotopic analyses (accuracy and precision of isotopologue masses, abundances, and mass shifts and isotopic working range). We applied this methodology to evaluate a novel LC-MS method for the analysis of amino acids, which was tested on high resolution (Orbitrap operating in full scan mode) and low resolution (triple quadrupole operating in multiple reaction monitoring mode) mass spectrometers. Results show excellent accuracy and precision over a large working range and revealed matrix-specific as well as mode-specific characteristics. The proposed methodology can identify reliable (and unreliable) isotopic data in an easy and straightforward way and efficiently supports the identification of sources of systematic biases as well as of the main factors that influence the overall accuracy and precision of measurements. This approach is generic and can be used to validate isotopic analyses on different matrices, analytical platforms, labeled elements, or classes of metabolites. It is expected to strengthen the reliability of isotopic measurements and thereby the biological value of ILEs.

Sphagnum Species Modulate their Phenolic Profiles and Mycorrhizal Colonization of Surrounding Andromeda polifolia along Peatland Microhabitats.

Sphagnum mosses mediate long-term carbon accumulation in peatlands. Given their functional role as keystone species, it is important to consider their responses to ecological gradients and environmental changes through the production of phenolics. We compared the extent to which Sphagnum phenolic production was dependent on species, microhabitats and season, and how surrounding dwarf shrubs responded to Sphagnum phenolics. We evaluated the phenolic profiles of aqueous extracts of Sphagnum fallax and Sphagnum magellanicum over a 6-month period in two microhabitats (wet lawns versus dry hummocks) in a French peatland. Phenolic profiles of water-soluble extracts were measured by UHPLC-QTOF-MS. Andromeda polifolia mycorrhizal colonization was quantified by assessing the intensity of global root cortex colonization. Phenolic profiles of both Sphagnum mosses were species-, season- and microhabitat- dependant. Sphagnum-derived acids were the phenolics mostly recovered; relative quantities were 2.5-fold higher in S. fallax than in S. magellanicum. Microtopography and vascular plant cover strongly influenced phenolic profiles, especially for minor metabolites present in low abundance. Higher mycorrhizal colonization of A. polifolia was found in lawns as compared to hummocks. Mycorrhizal abundance, in contrast to environmental parameters, was correlated with production of minor phenolics in S. fallax. Our results highlight the close interaction between mycorrhizae such as those colonizing A. polifolia and the release of Sphagnum phenolic metabolites and suggest that Sphagnum-derived acids and minor phenolics play different roles in this interaction. This work provides new insight into the ecological role of Sphagnum phenolics by proposing a strong association with mycorrhizal colonization of shrubs.

Allelopathic effect of a native species on a major plant invader in Europe.

Biological invasions have become a major global issue in ecosystem conservation. As formalized in the "novel weapon hypothesis", the allelopathic abilities of species are actively involved in invasion success. Here, we assume that allelopathy can also increase the biotic resistance of native species against invasion. We tested this hypothesis by studying the impact of the native species Sambucus ebulus on the colonization of propagules of the invasive species Fallopiaxbohemica and the subsequent development of plants from these. Achenes and rhizome fragments from two natural populations were grown in a greenhouse experiment for 50 days. We used an experimental design that involved "donor" and "target" pots in order to separate resource competition from allelopathy. An allelopathic treatment effect was observed for plant growth but not for propagule establishment. Treatment affected, in particular, the growth of Fallopia plants originating from achenes, but there was less influence on plants originating from rhizomes. By day 50, shoot height had decreased by 27% for plants originating from rhizomes and by 38% for plants originating from achenes. The number of leaves for plants originating from achenes had only decreased by 20%. Leaf and above- and below-ground dry masses decreased with treatment by 40, 41 and 25% for plants originating from rhizomes and 70, 61 and 55% for plants originating from achenes, respectively. S. ebulus extracts were analysed using high-performance chromatography, and the choice of test molecules was narrowed down. Our results suggest native species use allelopathy as a biotic containment mechanism against the naturalization of invasive species.

Differential gene expression and metabolomic analyses of Brachypodium distachyon infected by deoxynivalenol producing and non-producing strains of Fusarium graminearum.

BACKGROUND: Fusarium Head Blight (FHB) caused primarily by Fusarium graminearum (Fg) is one of the major diseases of small-grain cereals including bread wheat. This disease both reduces yields and causes quality losses due to the production of deoxynivalenol (DON), the major type B trichothecene mycotoxin. DON has been described as a virulence factor enabling efficient colonization of spikes by the fungus in wheat, but its precise role during the infection process is still elusive. Brachypodium distachyon (Bd) is a model cereal species which has been shown to be susceptible to FHB. Here, a functional genomics approach was performed in order to characterize the responses of Bd to Fg infection using a global transcriptional and metabolomic profiling of B. distachyon plants infected by two strains of F. graminearum: a wild-type strain producing DON (Fgdon+) and a mutant strain impaired in the production of the mycotoxin (Fgdon-). RESULTS: Histological analysis of the interaction of the Bd21 ecotype with both Fg strains showed extensive fungal tissue colonization with the Fgdon+ strain while the florets infected with the Fgdon- strain exhibited a reduced hyphal extension and cell death on palea and lemma tissues. Fungal biomass was reduced in spikes inoculated with the Fgdon- strain as compared with the wild-type strain. The transcriptional analysis showed that jasmonate and ethylene-signalling pathways are induced upon infection, together with genes encoding putative detoxification and transport proteins, antioxidant functions as well as secondary metabolite pathways. In particular, our metabolite profiling analysis showed that tryptophan-derived metabolites, tryptamine, serotonin, coumaroyl-serotonin and feruloyl-serotonin, are more induced upon infection by the Fgdon+ strain than by the Fgdon- strain. Serotonin was shown to exhibit a slight direct antimicrobial effect against Fg. CONCLUSION: Our results show that Bd exhibits defense hallmarks similar to those already identified in cereal crops. While the fungus uses DON as a virulence factor, the host plant preferentially induces detoxification and the phenylpropanoid and phenolamide pathways as resistance mechanisms. Together with its amenability in laboratory conditions, this makes Bd a very good model to study cereal resistance mechanisms towards the major disease FHB.

Analysis of hydroxycinnamic acid degradation in Agrobacterium fabrum reveals a coenzyme A-dependent, beta-oxidative deacetylation pathway.

The soil- and rhizosphere-inhabiting bacterium Agrobacterium fabrum (genomospecies G8 of the Agrobacterium tumefaciens species complex) is known to have species-specific genes involved in ferulic acid degradation. Here, we characterized, by genetic and analytical means, intermediates of degradation as feruloyl coenzyme A (feruloyl-CoA), 4-hydroxy-3-methoxyphenyl-ß-hydroxypropionyl-CoA, 4-hydroxy-3-methoxyphenyl-ß-ketopropionyl-CoA, vanillic acid, and protocatechuic acid. The genes atu1416, atu1417, and atu1420 have been experimentally shown to be necessary for the degradation of ferulic acid. Moreover, the genes atu1415 and atu1421 have been experimentally demonstrated to be essential for this degradation and are proposed to encode a phenylhydroxypropionyl-CoA dehydrogenase and a 4-hydroxy-3-methoxyphenyl-ß-ketopropionic acid (HMPKP)-CoA ß-keto-thiolase, respectively. We thus demonstrated that the A. fabrum hydroxycinnamic degradation pathway is an original coenzyme A-dependent ß-oxidative deacetylation that could also transform p-coumaric and caffeic acids. Finally, we showed that this pathway enables the metabolism of toxic compounds from plants and their use for growth, likely providing the species an ecological advantage in hydroxycinnamic-rich environments, such as plant roots or decaying plant materials.

Evidence for biological denitrification inhibition (BDI) by plant secondary metabolites.

Previous studies on the effect of secondary metabolites on the functioning of rhizosphere microbial communities have often focused on aspects of the nitrogen (N) cycle but have overlooked biological denitrification inhibition (BDI), which can affect plant N-nutrition. Here, we investigated the BDI by the compounds of Fallopia spp., an invasive weed shown to be associated with a low potential denitrification of the soil. Fallopia spp. extracts were characterized by chromatographic analysis and were used to test the BDI effects on the metabolic and respiratory activities of denitrifying bacteria, under aerobic and anaerobic (denitrification) conditions. The BDI of Fallopia spp. extracts was tested on a complex soil community by measuring denitrification enzyme activity (DEA), substrate induced respiration (SIR), as well as abundances of denitrifiers and total bacteria. In 15 strains of denitrifying bacteria, extracts led to a greater BDI (92%) than respiration inhibition (50%). Anaerobic metabolic activity reduction was correlated with catechin concentrations and the BDI was dose dependent. In soil, extracts reduced the DEA/SIR ratio without affecting the denitrifiers: total bacteria ratio. We show that secondary metabolite(s) from Fallopia spp. inhibit denitrification. This provides new insight into plant-soil interactions and improves our understanding of a plant's ability to shape microbial soil functioning.

The secondary metabolism glycosyltransferases UGT73B3 and UGT73B5 are components of redox status in resistance of Arabidopsis to Pseudomonas syringae pv. tomato.

Secondary metabolism plant glycosyltransferases (UGTs) ensure conjugation of sugar moieties to secondary metabolites (SMs) and glycosylation contributes to the great diversity, reactivity and regulation of SMs. UGT73B3 and UGT73B5, two UGTs of Arabidopsis thaliana (Arabidopsis), are involved in the hypersensitive response (HR) to the avirulent bacteria Pseudomonas syringae pv. tomato (Pst-AvrRpm1), but their function in planta is unknown. Here, we report that ugt73b3, ugt73b5 and ugt73b3 ugt73b5 T-DNA insertion mutants exhibited an accumulation of reactive oxygen species (ROS), an enhanced cell death during the HR to Pst-AvrRpm1, whereas glutathione levels increased in the single mutants. In silico analyses indicate that UGT73B3 and UGT73B5 belong to the early salicylic acid (SA)-induced genes whose pathogen-induced expression is co-regulated with genes related to cellular redox homeostasis and general detoxification. Analyses of metabolic alterations in ugt mutants reveal modification of SA and scopoletin contents which correlate with redox perturbation, and indicate quantitative modifications in the pattern of tryptophan-derived SM accumulation after Pst-AvrRpm1 inoculation. Our data suggest that UGT73B3 and UGT73B5 participate in regulation of redox status and general detoxification of ROS-reactive SMs during the HR to Pst-AvrRpm1, and that decreased resistance to Pst-AvrRpm1 in ugt mutants is tightly linked to redox perturbation.

A metabolic crosstalk between liposarcoma and muscle sustains tumor growth.

Dedifferentiated and Well-differentiated liposarcoma are characterized by a systematic amplification of the Murine Double Minute 2 (MDM2) oncogene. We demonstrate that p53-independent metabolic functions of chromatin-bound MDM2 are exacerbated in liposarcoma and mediate an addiction to serine metabolism to sustain tumor growth. However, the origin of exogenous serine remains unclear. Here, we show that elevated serine levels in mice harboring liposarcoma-patient derived xenograft, released by distant muscle is essential for liposarcoma cell survival. Repressing interleukine-6 expression, or treating liposarcoma cells with Food and Drugs Administration (FDA) approved anti-interleukine-6 monoclonal antibody, decreases de novo serine synthesis in muscle, impairs proliferation, and increases cell death in vitro and in vivo. This work reveals a metabolic crosstalk between muscle and liposarcoma tumor and identifies anti-interleukine-6 as a plausible treatment for liposarcoma patients.

Unexpected phytostimulatory behavior for Escherichia coli and Agrobacterium tumefaciens model strains.

Plant-beneficial effects of bacteria are often underestimated, especially for well-studied strains associated with pathogenicity or originating from other environments. We assessed the impact of seed inoculation with the emblematic bacterial models Agrobacterium tumefaciens C58 (plasmid-cured) or Escherichia coli K-12 on maize seedlings in nonsterile soil. Compared with the noninoculated control, root biomass (with A. tumefaciens or E. coli) and shoot biomass (with A. tumefaciens) were enhanced at 10 days for 'PR37Y15' but not 'DK315', as found with the phytostimulator Azospirillum brasilense UAP-154 (positive control). In roots as well as in shoots, Agrobacterium tumefaciens and E. coli triggered similar (in PR37Y15) or different (in DK315) changes in the high-performance liquid chromatography profiles of secondary metabolites (especially benzoxazinoids), distinct from those of Azospirillum brasilense UAP-154. Genome sequence analysis revealed homologs of nitrite reductase genes nirK and nirBD and siderophore synthesis genes for Agrobacterium tumefaciens, as well as homologs of nitrite reductase genes nirBD and phosphatase genes phoA and appA in E. coli, whose contribution to phytostimulation will require experimental assessment. In conclusion, the two emblematic bacterial models had a systemic impact on maize secondary metabolism and resulted in unexpected phytostimulation of seedlings in the Azospirillum sp.-responsive cultivar.

Mitochondria Transfer from Mesenchymal Stem Cells Confers Chemoresistance to Glioblastoma Stem Cells through Metabolic Rewiring.

Glioblastomas (GBM) are heterogeneous tumors with high metabolic plasticity. Their poor prognosis is linked to the presence of glioblastoma stem cells (GSC), which support resistance to therapy, notably to temozolomide (TMZ). Mesenchymal stem cells (MSC) recruitment to GBM contributes to GSC chemoresistance, by mechanisms still poorly understood. Here, we provide evidence that MSCs transfer mitochondria to GSCs through tunneling nanotubes, which enhances GSCs resistance to TMZ. More precisely, our metabolomics analyses reveal that MSC mitochondria induce GSCs metabolic reprograming, with a nutrient shift from glucose to glutamine, a rewiring of the tricarboxylic acid cycle from glutaminolysis to reductive carboxylation and increase in orotate turnover as well as in pyrimidine and purine synthesis. Metabolomics analysis of GBM patient tissues at relapse after TMZ treatment documents increased concentrations of AMP, CMP, GMP, and UMP nucleotides and thus corroborate our in vitro analyses. Finally, we provide a mechanism whereby mitochondrial transfer from MSCs to GSCs contributes to GBM resistance to TMZ therapy, by demonstrating that inhibition of orotate production by Brequinar (BRQ) restores TMZ sensitivity in GSCs with acquired mitochondria. Altogether, these results identify a mechanism for GBM resistance to TMZ and reveal a metabolic dependency of chemoresistant GBM following the acquisition of exogenous mitochondria, which opens therapeutic perspectives based on synthetic lethality between TMZ and BRQ. Significance: Mitochondria acquired from MSCs enhance the chemoresistance of GBMs. The discovery that they also generate metabolic vulnerability in GSCs paves the way for novel therapeutic approaches.

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.