Isotope Labeling Patterns Research Articles

Abstract 3512: Joint probabilistic modeling of multimodal metabolic data with UnitedMet

Abstract Metabolites play a crucial role in the functioning of biological systems. Measurements of metabolite abundances by metabolomics and metabolic pathway activity by isotope-tracing are powerful approaches to study metabolic phenotypes. However, large-scale measurements of metabolite levels and pathway activity remain extremely scarce due to the technical challenges. Motivated by the strength of RNA-metabolite covariation, we present UnitedMet, a Bayesian probabilistic method for end-to-end joint modeling of RNA-sequencing and metabolic datasets that is capable of dimensionality reduction, data integration and metabolic modality prediction. UnitedMet demonstrates high accuracy predicting metabolite levels and isotopologue distributions from gene expression data in human tumor samples. To evaluate UnitedMet’s efficacy in clinical applications, we inferred metabolomic profiles and isotope labeling patterns from the RNA sequencing data of clear cell renal cell carcinoma (ccRCC) patients enrolled in TCGA and clinical trials. Correlation analysis between genetic mutations and predicted metabolic phenotypes revealed that BAP1 mutations were associated with increased contribution of glucose to TCA cycle in ccRCC. Increased TCA cycle activity was found to be associated with disease progression and poorer clinical outcome. UnitedMet, therefore, provides a solution that could be broadly applicable to enhance multimodal metabolomic datasets and facilitates comprehensive study of interplays between metabolic profiles, molecular alterations and human phenotypes. Citation Format: Amy X. Xie, Casey Bradshaw, Christopher Tosh, Wesley Tansey, Ed Reznik. Joint probabilistic modeling of multimodal metabolic data with UnitedMet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3512.

Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution

The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.

Hexamethylene amiloride binds the SARS-CoV-2 envelope protein at the protein-lipid interface.

The SARS-CoV-2 envelope (E) protein forms a five-helix bundle in lipid bilayers whose cation-conducting activity is associated with the inflammatory response and respiratory distress symptoms of COVID-19. E channel activity is inhibited by the drug 5-(N,N-hexamethylene) amiloride (HMA). However, the binding site of HMA in E has not been determined. Here we use solid-state NMR to measure distances between HMA and the E transmembrane domain (ETM) in lipid bilayers. 13 C, 15 N-labeled HMA is combined with fluorinated or 13 C-labeled ETM. Conversely, fluorinated HMA is combined with 13 C, 15 N-labeled ETM. These orthogonal isotopic labeling patterns allow us to conduct dipolar recoupling NMR experiments to determine the HMA binding stoichiometry to ETM as well as HMA-protein distances. We find that HMA binds ETM with a stoichiometry of one drug per pentamer. Unexpectedly, the bound HMA is not centrally located within the channel pore, but lies on the lipid-facing surface in the middle of the TM domain. This result suggests that HMA may inhibit the E channel activity by interfering with the gating function of an aromatic network. These distance data are obtained under much lower drug concentrations than in previous chemical shift perturbation data, which showed the largest perturbation for N-terminal residues. This difference suggests that HMA has higher affinity for the protein-lipid interface than the channel pore. These results give insight into the inhibition mechanism of HMA for SARS-CoV-2 E.

TMET-28. A METHOD FOR EX VIVO STABLE ISOTOPE TRACING IN SURGICALLY EXPLANTED GLIOMA ORGANOIDS

Abstract Stable isotope tracing is a powerful method for elucidating tumor metabolism. While in vivo stable isotope tracing is one of the most faithful approaches for studying metabolic activity under pathophysiologically relevant conditions, this method is technically challenging and costly to perform. We therefore sought to optimize an ex vivo stable isotope tracing approach under conditions that recapitulate the in vivo tumor microenvironment (TME), focusing on three key features: cellular heterogeneity, nutrient availability, and oxygenation. Surgically eXplanted Organoids (SXOs) are 3-dimensional glioma models that retain the diverse cell populations in human brain tumors. We tested whether glioma SXOs can be cultured in Human Plasma-Like Medium (HPLM) under brain-relevant oxygen levels (5%) for the purpose of stable isotope tracing. We cultured SXOs in conventional media, HPLM for 24 hours, or HPLM for 120 hours under 5% oxygen. We observed no significant differences in markers of tumor architecture, cellular proliferation rates, or stemness profiles. We then performed stable isotope tracing in SXOs cultured in HLPM at 5% oxygen. We acclimated SXOs to HPLM for 24 hours or 120 hours before replacing media with either HPLM (unlabeled) or HPLM with 15N2 glutamine (labeled). 15N isotopic labeling patterns in various metabolites were compared between the 24- and 120-hour conditions by linear regression. These patterns were strongly concordant over time (r2 = 0.9544), indicating that steady-state metabolic fluxes are stable in these cultures. We then analyzed intermediates that occupy key nodes in amino acid metabolism, redox homeostasis, and nucleotide synthesis pathways and found similarly strong concordance between labeling patterns at the individual metabolite level. Our findings outline an effective approach for conducting ex vivo stable isotope tracing in heterocellular glioma models under conditions that mimic the TME. This approach may complement and enhance analogous in vivo methods to provide new insights into glioma metabolism.

A simple protocol for the production of highly deuterated proteins for biophysical studies

Highly deuterated protein samples expand the biophysics and biological tool kit by providing, among other qualities, contrast matching in neutron diffraction experiments and reduction of dipolar spin interactions from normally protonated proteins in magnetic resonance studies, impacting both electron paramagnetic resonance and NMR spectroscopy. In NMR applications, deuteration is often combined with other isotopic labeling patterns to expand the range of conventional NMR spectroscopy research in both solution and solid-state conditions. However, preparation of deuterated proteins is challenging. We present here a simple, effective, and user-friendly protocol to produce highly deuterated proteins in Escherichia coli cells. The protocol utilizes the common shaker flask growth method and the well-known pET system (which provides expression control via the T7 promotor) for large-scale recombinant protein expression. One liter expression typically yields 5 to 50 mg of highly deuterated protein. Our data demonstrate that the optimized procedure produces a comparable quantity of protein in deuterium (2H2O) oxide M9 medium compared with that in 1H2O M9 medium. The protocol will enable a broader utilization of deuterated proteins in a number of biophysical techniques.

Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness.

Stable isotope-assisted metabolic flux analysis (MFA) is a powerful method to estimate carbon flow and partitioning in metabolic networks. At its core, MFA is a parameter estimation problem wherein the fluxes and metabolite pool sizes are model parameters that are estimated, via optimization, to account for measurements of steady-state or isotopically-nonstationary isotope labeling patterns. As MFA problems advance in scale, they require efficient computational methods for fast and robust convergence. The structure of the MFA problem enables it to be cast as an equality-constrained nonlinear program (NLP), where the equality constraints are constructed from the MFA model equations, and the objective function is defined as the sum of squared residuals (SSR) between the model predictions and a set of labeling measurements. This NLP can be solved by using an algebraic modeling language (AML) that offers state-of-the-art optimization solvers for robust parameter estimation and superior scalability to large networks. When implemented in this manner, the optimization is performed with no distinction between state variables and model parameters. During each iteration of such an optimization, the system state is updated instead of being calculated explicitly from scratch, and this occurs concurrently with improvement in the model parameter estimates. This optimization approach starkly contrasts with traditional “shooting” methods where the state variables and model parameters are kept distinct and the system state is computed afresh during each iteration of a stepwise optimization. Our NLP formulation uses the MFA modeling framework of Wiechert et al. [1], which is amenable to incorporation of the model equations into an NLP. The NLP constraints consist of balances on either elementary metabolite units (EMUs) or cumomers. In this formulation, both the steady-state and isotopically-nonstationary MFA (inst-MFA) problems may be solved as an NLP. For the inst-MFA case, the ordinary differential equation (ODE) system describing the labeling dynamics is transcribed into a system of algebraic constraints for the NLP using collocation. This large-scale NLP may be solved efficiently using an NLP solver implemented on an AML. In our implementation, we used the reduced gradient solver CONOPT, implemented in the General Algebraic Modeling System (GAMS). The NLP framework is particularly advantageous for inst-MFA, scaling well to large networks with many free parameters, and having more robust convergence properties compared to the shooting methods that compute the system state and sensitivities at each iteration. Additionally, this NLP approach supports the use of tandem-MS data for both steady-state and inst-MFA when the cumomer framework is used. We assembled a software, eiFlux, written in Python and GAMS that uses the NLP approach and supports both steady-state and inst-MFA. We demonstrate the effectiveness of the NLP formulation on several examples, including a genome-scale inst-MFA model, to highlight the scalability and robustness of this approach. In addition to typical inst-MFA applications, we expect that this framework and our associated software, eiFlux, will be particularly useful for applying inst-MFA to complex MFA models, such as those developed for eukaryotes (e.g. algae) and co-cultures with multiple cell types.

An isotopic labeling approach linking natural products with biosynthetic gene clusters.

Major advances in genome sequencing and large-scale biosynthetic gene cluster (BGC) analysis have prompted an age of natural product discovery driven by genome mining. Still, connecting molecules to their cognate BGCs is a substantial bottleneck for this approach. We have developed a mass spectrometry-based parallel stable isotope labeling platform, termed IsoAnalyst, which assists in associating metabolite stable isotope labeling patterns with BGC structure prediction in order to connect natural products to their corresponding BGCs. Here we show that IsoAnalyst can quickly associate both known metabolites and unknown analytes with BGCs to elucidate the complex chemical phenotypes of these biosynthetic systems. We validate this approach for a range of compound classes, using both the type strain Saccharopolyspora erythraea and an environmentally isolated Micromonospora sp. We further demonstrated the utility of this tool with the discovery of lobosamide D, a new and structurally unique member of the family of lobosamide macrolactams.

Stable isotope tracing to assess tumor metabolism in vivo.

Cancer cells undergo diverse metabolic adaptations to meet the energetic demands imposed by dysregulated growth and proliferation. Assessing metabolism in intact tumors allows the investigator to observe the combined metabolic effects of numerous cancer cell-intrinsic and -extrinsic factors that cannot be fully captured in culture models. We have developed methods to use stable isotope-labeled nutrients (e.g., [13C]glucose) to probe metabolic activity within intact tumors in vivo, in mice and humans. In these methods, the labeled nutrient is introduced to the circulation through an intravenous catheter prior to surgical resection of the tumor and adjacent nonmalignant tissue. Metabolism within these tissues during the infusion transfers the isotope label into metabolic intermediates from pathways supplied by the infused nutrient. Extracting metabolites from surgical specimens and analyzing their isotope labeling patterns provides information about metabolism in the tissue. We provide detailed information about this technique, from introduction of the labeled tracer through data analysis and interpretation, including streamlined approaches to quantify isotope labeling in informative metabolites extracted from tissue samples. We focus on infusions with [13C]glucose and the application of mass spectrometry to assess isotope labeling in intermediates from central metabolic pathways, including glycolysis, the tricarboxylic acid cycle and nonessential amino acid synthesis. We outline practical considerations to apply these methods to human subjects undergoing surgical resections of solid tumors. We also discuss the method's versatility and consider the relative advantages and limitations of alternative approaches to introduce the tracer, harvest the tissue and analyze the data.

Uncovering metabolic reservoir cycles in MYC-transformed lymphoma B cells using stable isotope resolved metabolomics

The use of metabolomic technologies and stable isotope labeling recently enabled us to discover an unexpected role of N-acetyl-aspartyl-glutamate (NAAG): NAAG is a glutamate reservoir for cancer cells. In the current study, we first found that glucose carbon contributes to the formation of NAAG and its precursors via glycolysis, demonstrating the existence of a glucose-NAAG-glutamate cycle in cancer cells. Second, we found that glucose carbon and, unexpectedly, glutamine carbon contribute to the formation of lactate via glutaminolysis. Importantly, lactate carbon can be incorporated into glucose via gluconeogenesis, demonstrating the existence of a glutamine-lactate-glucose cycle. While a glucose-lactate-glucose cycle was expected, the finding of a glutamine-lactate-glucose cycle was unforeseen. And third, we discovered that glutamine carbon is incorporated into γ-aminobutyric acid (GABA), revealing a glutamate-GABA-succinate cycle. Thus, NAAG, lactate, and GABA can play important roles as storage molecules for glutamate, glucose, and succinate carbon in oncogenic MYC-transformed P493 lymphoma B cells (MYC-ON cells) but not in non-oncogenic MYC-OFF cells. Altogether, examining the isotopic labeling patterns of metabolites derived from labeled 13C6-glucose or 13C515N2-glutamine helped reveal the presence of what we have named “metabolic reservoir cycles” in oncogenic cells.

The metabolic origins of non-photorespiratory CO2 release during photosynthesis: a metabolic flux analysis.

Respiration in the light (RL) releases CO2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since RL lowers net carbon fixation, understanding RL could help improve plant carbon-use efficiency and models of crop photosynthesis. Although RL was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to RL, we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13CO2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N2; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that RL releases 5.2 μmol CO2 g-1 FW h-1 of CO2, which is relatively consistent with a value of 9.3 μmol CO2 g-1 FW h-1 measured by CO2 gas exchange. The results indicated that ≤10% of RL results from TCA cycle reactions, which are widely considered to dominate RL. Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >93% of CO2 released by RL.

In Vitro Production of Ergothioneine Isotopologues.

Ergothioneine is an emerging component of the redox homeostasis system in human cells and in microbial pathogens, such as Mycobacterium tuberculosis and Burkholderia pseudomallei. The synthesis of stable isotope-labeled ergothioneine derivatives may provide important tools for deciphering the distribution, function, and metabolism of this compound in vivo. We describe a general protocol for the production of ergothioneine isotopologues with programmable 2 H, 15 N, 13 C, 34 S, and 33 S isotope labeling patterns. This enzyme-based approach makes efficient use of commercial isotope reagents and is also directly applicable to the synthesis of radio-isotopologues.

In Vitro Production of Ergothioneine Isotopologues

AbstractErgothioneine is an emerging component of the redox homeostasis system in human cells and in microbial pathogens, such as Mycobacterium tuberculosis and Burkholderia pseudomallei. The synthesis of stable isotope‐labeled ergothioneine derivatives may provide important tools for deciphering the distribution, function, and metabolism of this compound in vivo. We describe a general protocol for the production of ergothioneine isotopologues with programmable 2H, 15N, 13C, 34S, and 33S isotope labeling patterns. This enzyme‐based approach makes efficient use of commercial isotope reagents and is also directly applicable to the synthesis of radio‐isotopologues.

Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

Collision energy-breakdown curves – An additional tool to characterize MS/MS methods

BackgroundIn tandem mass spectrometry, analyte detection is based on collision-induced fragmentation, which is modulated by the collision energy (CE) setting. Variation in CE leads to differential ion yield, and optimization is usually performed empirically as “tuning” during method development. Our aim was to build a method to objectify the impact of collision energy settings on ion yield for individual compounds. MethodsCollision energy (CE)-breakdown curves were generated based on acquisition files in which a large number of quasi-identical mass transitions were recorded simultaneously, with variation of CE over a defined range within a single injection. Ion yield (normalized to an internal standard recorded with a locked collision energy) was plotted as a curve versus CE settings. Piperacillin and testosterone were studied as exemplary analytes in matrix-free and serum matrix-based liquid chromatography tandem mass spectrometry (LC-MS/MS) measurements. More detailed testosterone CE-breakdown curves were investigated with regard to sample preparation techniques and the isotope labeling pattern of corresponding internal standards. ResultsCE-breakdown curves were found characteristically for the piperacillin quantifier transition with respect to CE-related maximum ion yield, as well as curve width and shape. A diverging curve profile was observed for the piperacillin qualifier transition. For testosterone analyses, no impact from different sample preparation techniques or the isotope labeling patterns on the selected CE was shown. ConclusionCE-breakdown curves are a convenient and valuable tool to verify LC-MS/MS methods regarding consistent fragmentation characteristics between sample sources or native analytes and isotope-labeled counterparts.

Abstract P3-02-06: Tracing glucose catabolism in human triple negative breast cancer tumors

Abstract Triple negative breast cancer (TNBC) is an aggressive tumor type for which few targeted treatments exist. Altered tumor metabolism may provide novel diagnostic and therapeutic opportunities. Recent genetic analyses in TNBC demonstrate augmented expression of pathways related to glucose metabolism, including glycolysis, the pentose phosphate (PPP), and serine biosynthetic pathways. No studies, however, have directly probed how glucose is utilized by TNBCs in vivo. Here, we trace these metabolic fates by infusing TNBC patients with isotopically labeled 1,2-13C-glucose, a tracer that can determine which of these pathways predominate. Upon achieving stable tracer enrichment in serum, patient primary TNBC biopsies were collected and flash frozen. Subsequent analysis by liquid chromatography mass spectroscopy (LC-MS) revealed most TNBC tumors locally ferment glucose to lactate, with limited exchange of pyruvate/lactate with the systemic circulation. This contrasts with lung cancer, where pyruvate/lactate exchange is extensive. In addition, isotopic labeling patterns indicate that glycolysis dominates the PPP in terms of total flux. Nevertheless, ribose-phosphate for nucleotide synthesis consistently arises from the oxidative branch of the PPP. Furthermore, a subset of patients demonstrated high levels of de novo serine production. To better understand these observations, next generation sequencing (NGS) and reverse phase protein array (RPPA) analyses on adjacent biopsies from these patients are underway. Together, these findings provide the first comprehensive in vivo characterization of glucose metabolism in TNBC and raise the potential to use tracing to guide development of metabolic therapeutics. Citation Format: Jonathan Michael Ghergurovich, Joyce O’Shaughnessy, Maren K Levin, Aaron Killian, William Hendricks, Virginia Espina, Joshua D Rabinowitz. Tracing glucose catabolism in human triple negative breast cancer tumors [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-02-06.

Automatic structure-based NMR methyl resonance assignment in large proteins

Isotopically labeled methyl groups provide NMR probes in large, otherwise deuterated proteins. However, the resonance assignment constitutes a bottleneck for broader applicability of methyl-based NMR. Here, we present the automated MethylFLYA method for the assignment of methyl groups that is based on methyl-methyl nuclear Overhauser effect spectroscopy (NOESY) peak lists. MethylFLYA is applied to five proteins (28–358 kDa) comprising a total of 708 isotope-labeled methyl groups, of which 612 contribute NOESY cross peaks. MethylFLYA confidently assigns 488 methyl groups, i.e. 80% of those with NOESY data. Of these, 459 agree with the reference, 6 were different, and 23 were without reference assignment. MethylFLYA assigns significantly more methyl groups than alternative algorithms, has an average error rate of 1%, modest runtimes of 0.4–1.2 h, and can handle arbitrary isotope labeling patterns and data from other types of NMR spectra.

Small Molecule Isotope Resolved Formula Enumeration: A Methodology for Assigning Isotopologues and Metabolite Formulas in Fourier Transform Mass Spectra.

Improvements in Fourier transform mass spectrometry (FT-MS) enable increasingly more complex experiments in the field of metabolomics. What is directly detected in FT-MS spectra are spectral features (peaks) that correspond to sets of adducted and charged forms of specific molecules in the sample. The robust assignment of these features is an essential step for MS-based metabolomics experiments, but the sheer complexity of what is detected and a variety of analytically introduced variance, errors, and artifacts has hindered the systematic analysis of complex patterns of observed peaks with respect to isotope content. We have developed a method called SMIRFE that detects small biomolecules and determines their elemental molecular formula (EMF) using detected sets of isotopologue peaks sharing the same EMF. SMIRFE does not use a database of known metabolite formulas; instead a nearly comprehensive search space of all isotopologues within a mass range is constructed and used for assignment. This search space can be tailored for different isotope labeling patterns expected in different stable isotope tracing experiments. Using consumer-level computing equipment, a large search space of 2000 Da was constructed, and assignment performance was evaluated and validated using verified assignments on a pair of peak lists derived from spectra containing unlabeled and 15N-labeled versions of amino acids derivatized using ethylchloroformate. SMIRFE identified 18 of 18 predicted derivatized EMFs, and each assignment was evaluated statistically and assigned an e-value representing the probability to occur by chance.

Selective isotope labeling strategy and computational interpretation of spectra for protein NMR analyses

Signal assignment is a mandatory step for the site-specific analyses of proteins by NMR. In challenging cases, such as low solubility target proteins or in-cell NMR, amino-acid selective stable isotope labeling facilitates the main-chain assignment, since it can determine the amino-acid type of each signal, thus providing complementary information to that obtained by standard three-dimensional triple resonance experiments. Inspired by information science methodologies, we developed a combinatorial selective labeling strategy, named stable isotope encoding (SiCode), to enable amino-acid typing even from noisy NMR spectra, with small numbers of labeled samples. The high noise-resistance of SiCode is achieved by both optimization of the isotope labeling pattern according to the information distance between amino acids and model-based computational retrieval of the amino-acid information from spectra, using replica exchange Monte Carlo computation. We demonstrate amino-acid typing with simulated low signal-to-noise-ratio spectra, with sample concentrations as low as micromolar order. Since the main-chain signal assignment is often a bottleneck process for various amide-signal-based NMR analyses of challenging target proteins, SiCode will facilitate and accelerate such analyses.

NanoSIMS and tissue autoradiography reveal symbiont carbon fixation and organic carbon transfer to giant ciliate host

The giant colonial ciliate Zoothamnium niveum harbors a monolayer of the gammaproteobacteria Cand. Thiobios zoothamnicoli on its outer surface. Cultivation experiments revealed maximal growth and survival under steady flow of high oxygen and low sulfide concentrations. We aimed at directly demonstrating the sulfur-oxidizing, chemoautotrophic nature of the symbionts and at investigating putative carbon transfer from the symbiont to the ciliate host. We performed pulse-chase incubations with 14C- and 13C-labeled bicarbonate under varying environmental conditions. A combination of tissue autoradiography and nanoscale secondary ion mass spectrometry coupled with transmission electron microscopy was used to follow the fate of the radioactive and stable isotopes of carbon, respectively. We show that symbiont cells fix substantial amounts of inorganic carbon in the presence of sulfide, but also (to a lesser degree) in the absence of sulfide by utilizing internally stored sulfur. Isotope labeling patterns point to translocation of organic carbon to the host through both release of these compounds and digestion of symbiont cells. The latter mechanism is also supported by ultracytochemical detection of acid phosphatase in lysosomes and in food vacuoles of ciliate cells. Fluorescence in situ hybridization of freshly collected ciliates revealed that the vast majority of ingested microbial cells were ectosymbionts.

Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii.

Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.