Utilization of 13C-13C Coupling in Structural and Biosynthetic Studies Double Labeling Method

A new method which utilizes ¹³C-¹³C coupling for structural and biosynthetic studies on acetate-derived metabolites is described. The ¹³C-NMR spectra of dihydrolatumcidins separately labeled with ¹³CH₃¹³C0₂Na and with a 1: 1 mixture of ¹³CH₃CO₂-Na and CH313C02-Na gave enough information to establish its structure.     

Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes

The composition of a cell in terms of macromolecular building blocks and other organic molecules underlies the metabolic needs and capabilities of a species. Although some core biomass components such as nucleic acids and proteins are evident for most species, the essentiality of the pool of other organic molecules, especially cofactors and prosthetic groups, is yet unclear. Here we integrate biomass compositions from 71 manually curated genome-scale models, 33 large-scale gene essentiality datasets, enzyme-cofactor association data and a vast array of publications, revealing universally essential cofactors for prokaryotic metabolism and also others that are specific for phylogenetic branches or metabolic modes. Our results revise predictions of essential genes in Klebsiella pneumoniae and identify missing biosynthetic pathways in models of Mycobacterium tuberculosis. This work provides fundamental insights into the essentiality of organic cofactors and has implications for minimal cell studies as well as for modeling genotype-phenotype relations in prokaryotic metabolic networks.     

Using Data-Driven Model-Brain Mappings to Constrain Formal Models of Cognition

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings.     

Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach

Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.     

Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems.     

Nic1 Inactivation Enables Stable Isotope Labeling with 13C615N4-Arginine in Schizosaccharomyces pombe

Stable Isotope Labeling by Amino Acids (SILAC) is a commonly used method in quantitative proteomics. Because of compatibility with trypsin digestion, arginine and lysine are the most widely used amino acids for SILAC labeling. We observed that Schizosaccharomyces pombe (fission yeast) cannot be labeled with a specific form of arginine, ¹³C₆¹⁵N₄-arginine (Arg-10), which limits the exploitation of SILAC technology in this model organism. We hypothesized that in the fission yeast the guanidinium group of ¹³C₆¹⁵N₄-arginine is catabolized by arginase and urease activity to ¹⁵N₁-labeled ammonia that is used as a precursor for general amino acid biosynthesis. We show that disruption of Ni²⁺-dependent urease activity, through deletion of the sole Ni²⁺ transporter Nic1, blocks this recycling in ammonium-supplemented EMMG medium to enable ¹³C₆¹⁵N₄-arginine labeling for SILAC strategies in S. pombe. Finally, we employed Arg-10 in a triple-SILAC experiment to perform quantitative comparison of G1 + S, M, and G2 cell cycle phases in S. pombe.Stable Isotope Labeling by Amino acids in Cell culture (SILAC)¹ is one of the most widely used methods in quantitative proteomics (1). It involves in vivo metabolic labeling of cell cultures (or small organisms) with different versions of stable isotope-labeled amino acids (2). To maximize the number of peptides that can be quantified after proteome digestion with trypsin, proteins are usually differentially labeled with different forms of lysine and arginine (3): l-lysine (Lys-0) and l-arginine (Arg-0); ²H₄-lysine(Lys-4) and ¹³C₆-arginine (Arg-6); or ¹³C₆-¹⁵N₂-lysine (Lys-8) and ¹³C₆-¹⁵N₄-arginine (Arg-10). The availability of multiple forms of labeled lysine and arginine support the application of SILAC in duplex (comparison of two states) or triplex (comparison of three states) formats. Efficient anabolic pathways mean that lysine and arginine are not essential for growth of wild type yeast cells. Auxotrophic mutants that are defective in these pathways can be used to switch yeast to an absolute dependence upon the provision of these amino acids in the external medium. Consequently, mutations in arginine and lysine biosynthesis pathways can be used to drive the complete labeling of all tryptic peptides with specific forms of these amino acids (4, 5). SILAC has been used in quantitative proteomics in several yeast species, but most widely in Saccharomyces cerevisiae (6) (budding yeast) and Schizosaccharomyces pombe (fission yeast). S. pombe is extensively exploited to study cell cycle control (7), heterochromatin (8), and differentiation (9) and is increasingly the subject of large-scale quantitative proteomic studies (10, 11).A major challenge that is faced when using SILAC in fission yeast, is metabolic conversion of arginine to other amino acids such as proline, glutamine, and lysine (5). This partial labeling of additional amino acids after the conversion event produces spectra with complex isotope clusters that makes the downstream analysis challenging and error-prone. Inactivation of the “arginine conversion pathway” by removal of the orthinine transferase, Car2, effectively overcomes this problem to support the use of arginine labeling in SILAC-based experiments (5). Although this exploitation of the car2.Δ mutation now enables SILAC technology in fission yeast, the choice of amino acids that can be employed remains limited. Only one form of heavy arginine (R6) is currently used alongside three forms of heavy lysine (Lys-4, Lys-6, and Lys-8) (5, 12). Surprisingly, we could not find any studies that use arginine (Arg-10) in fission yeast, even though this is a widely exploited reagent for labeling other cell types (1).Here, we show that labeling of fission yeast with Arg-10 leads to a general misincorporation of the stable isotope label that prevents the identification of labeled peptides. We hypothesize that successive arginase and urease activities catabolize the guanidinium group of Arg-10 to ¹⁵N₁-labeled ammonia. This labeled ammonium is then used as a general precursor for amino acid biosynthesis. Disruption of Ni²⁺-dependent urease activity through deletion of Ni²⁺ transporter Nic1 in ammonium-supplemented medium, blocked this recycling to support ¹³C₆¹⁵N₄-arginine labeling SILAC strategies. As a proof of principle we employ Arg-10 in a triple-SILAC experiment to perform quantitative comparison of G1 + S, M, and G2 cell cycle phases in S. pombe.     

An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.     

5-溴脱氧尿嘧啶核苷标记酵母基因组DNA的方法

胸腺嘧啶类似物5-溴脱氧尿嘧啶核苷(BrdU)标记技术是一种研究DNA复制、修复等生命过程的有效手段。由于酿酒酵母(Saccharomyces cerevisiae)中缺少胸腺嘧啶核苷酸补救途径,胞外BrdU不能有效的渗入到基因组中,使该技术在酿酒酵母中的应用受到极大制约。通过在基因组中引入单纯疱疹病毒胞苷激酶(HSV-TK)和人类平衡核苷转运蛋白(hENT1)基因,工作建立了BrdU标记酵母基因组DNA的方法。在生长对数中期加入0.2mg/ml BrdU,离体检测法检测发现,标记3h的荧光信号较1h、5h时强;胞内检测法结果显示,标记3h时55.3%的基因组DNA中能够渗入BrdU。该工作为酿酒酵母DNA复制、修复等方面提供了直接有效的研究方法。     

Site-Specific 13C Labeling of DNA to Deduce DNA Repair Mechanisms of Uracil-DNA Glycosyiase and UV Endonuclease V

Abstract

The oligodeoxynucleotide d(GCGUGCG) was synthesized with [1′,3′ -¹³C₂)U labeling. The uracil unit was removed with uracil-DNA glycosylase to generate an abasic site and the resulting oligonucleotide was paired with the possible d(CGCNCGC) structures. One of these heteroduplexes was a substrate for W endonuclease V. The ¹³C NMR spectra of these heteroduplexes describe the structure of the abasic site and the mechanism of the endonuclease reaction.     

Forward Modeling of Fluctuating Dietary 13C Signals to Validate 13C Turnover Models of Milk and Milk Components from a Diet-Switch Experiment

Isotopic variation of food stuffs propagates through trophic systems. But, this variation is dampened in each trophic step, due to buffering effects of metabolic and storage pools. Thus, understanding of isotopic variation in trophic systems requires knowledge of isotopic turnover. In animals, turnover is usually quantified in diet-switch experiments in controlled conditions. Such experiments usually involve changes in diet chemical composition, which may affect turnover. Furthermore, it is uncertain if diet-switch based turnover models are applicable under conditions with randomly fluctuating dietary input signals. Here, we investigate if turnover information derived from diet-switch experiments with dairy cows can predict the isotopic composition of metabolic products (milk, milk components and feces) under natural fluctuations of dietary isotope and chemical composition. First, a diet-switch from a C₃-grass/maize diet to a pure C₃-grass diet was used to quantify carbon turnover in whole milk, lactose, casein, milk fat and feces. Data were analyzed with a compartmental mixed effects model, which allowed for multiple pools and intra-population variability, and included a delay between feed ingestion and first tracer appearance in outputs. The delay for milk components and whole milk was ∼12 h, and that of feces ∼20 h. The half-life (t_½) for carbon in the feces was 9 h, while lactose, casein and milk fat had a t_½ of 10, 18 and 19 h. The ¹³C kinetics of whole milk revealed two pools, a fast pool with a t_½ of 10 h (likely representing lactose), and a slower pool with a t_½ of 21 h (likely including casein and milk fat). The diet-switch based turnover information provided a precise prediction (RMSE ∼0.2 ‰) of the natural ¹³C fluctuations in outputs during a 30 days-long period when cows ingested a pure C3 grass with naturally fluctuating isotope composition.     

A Method for Labeling Vasculature in Embryonic Mice

The establishment of a functional blood vessel network is an essential part of organogenesis, and is required for optimal organ function. For example, in the thymus proper vasculature formation and patterning is essential for thymocyte entry into the organ and mature T-cell exit to the periphery. The spatial arrangement of blood vessels in the thymus is dependent upon signals from the local microenvironment, namely thymic epithelial cells (TEC). Several recent reports suggest that disruption of these signals results in thymus blood vessel defects ^1,2. Previous studies have described techniques used to label the neonatal and adult thymus vasculature ^1,2. We demonstrate here a technique for labeling blood vessels in the embryonic thymus. This method combines the use of FITC-dextran or Griffonia (Bandeiraea) Simplicifolia Lectin I (GSL 1 - isolectin B₄) facial vein injections and CD31 antibody staining to identify thymus vascular structures and PDGFR-β to label thymic perivascular mesenchyme ^3-5. The option of using cryosections or vibratome sections is also provided. This protocol can be used to identify thymus vascular defects, which is critical for defining the roles of TEC-derived molecules in thymus blood vessel formation. As the method labels the entire vasculature, it can also be used to analyze the vascular networks in multiple organs and tissues throughout the embryo including skin and heart ^6-10. Download video file.^{(59M, mov)}     

Complex Glutamate Labeling from [U-13C]glucose or [U-13C]lactate in Co-cultures of Cerebellar Neurons and Astrocytes

Glutamate metabolism was studied in co-cultures of mouse cerebellar neurons (predominantly glutamatergic) and astrocytes. One set of cultures was superfused (90 min) in the presence of either [U-¹³C]glucose (2.5 mM) and lactate (1 mM) or [U-¹³C]lactate (1 mM) and glucose (2.5 mM). Other sets of cultures were incubated in medium containing [U-¹³C]lactate (1 mM) and glucose (2.5 mM) for 4 h. Regardless of the experimental conditions cell extracts were analyzed using mass spectrometry and nuclear magnetic resonance spectroscopy. ¹³C labeling of glutamate was much higher than that of glutamine under all experimental conditions indicating that acetyl-CoA from both lactate and glucose was preferentially metabolized in the neurons. Aspartate labeling was similar to that of glutamate, especially when [U-¹³C]glucose was the substrate. Labeling of glutamate, aspartate and glutamine was lower in the cells incubated with [U-¹³C]lactate. The first part of the pyruvate recycling pathway, pyruvate formation, was detected in singlet and doublet labeling of alanine under all experimental conditions. However, full recycling, detectable in singlet labeling of glutamate in the C-4 position was only quantifiable in the superfused cells both from [U-¹³C]glucose and [U-¹³C]lactate. Lactate and alanine were mostly uniformly labeled and labeling of alanine was the same regardless of the labeled substrate present and higher than that of lactate when superfused in the presence of [U-¹³C]glucose. These results show that metabolism of pyruvate, the precursor for lactate, alanine and acetyl-CoA is highly compartmentalized. Special issue dedicated to John P. Blass.     

A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, ¹³C Metabolic Flux Analysis (¹³C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during ¹³C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of ¹³C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based ¹³C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species.     

Fitting Regression Models to Ordinal Data

This paper deals with the analysis of ordinal data by means of a threshold model. Maximum likelihood estimation is discussed and two examples are used to illustrate the methods.     

A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks

Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.     

SIMPLE: A Sequential Immunoperoxidase Labeling and Erasing Method

The ability to simultaneously visualize expression of multiple antigens in cells and tissues can provide powerful insights into cellular and organismal biology. However, standard methods are limited to the use of just two or three simultaneous probes and have not been widely adopted for routine use in paraffin-embedded tissue. We have developed a novel approach called sequential immunoperoxidase labeling and erasing (SIMPLE) that enables the simultaneous visualization of at least five markers within a single tissue section. Utilizing the alcohol-soluble peroxidase substrate 3-amino-9-ethylcarbazole, combined with a rapid non-destructive method for antibody–antigen dissociation, we demonstrate the ability to erase the results of a single immunohistochemical stain while preserving tissue antigenicity for repeated rounds of labeling. SIMPLE is greatly facilitated by the use of a whole-slide scanner, which can capture the results of each sequential stain without any information loss. (J Histochem Cytochem 57:899–905, 2009)     

Rapid Method to Determine Labeling Specificity of Radioactive Enteroviruses

The specificity of labeling enteroviruses with (32)P-labeled NaH(2)PO(4) or (14)C-leucine can be determined by comparing the percent adsorption of infective particles and radioactivity to membrane (Millipore) filters.