Pentose Phosphate Pathway Reactions in Photosynthesizing Cells

The pentose phosphate pathway (PPP) is divided into an oxidative branch that makes pentose phosphates and a non-oxidative branch that consumes pentose phosphates, though the non-oxidative branch is considered reversible. A modified version of the non-oxidative branch is a critical component of the Calvin–Benson cycle that converts CO2 into sugar. The reaction sequence in the Calvin–Benson cycle is from triose phosphates to pentose phosphates, the opposite of the typical direction of the non-oxidative PPP. The photosynthetic direction is favored by replacing the transaldolase step of the normal non-oxidative PPP with a second aldolase reaction plus sedoheptulose-1,7-bisphosphatase. This can be considered an anabolic version of the non-oxidative PPP and is found in a few situations other than photosynthesis. In addition to the strong association of the non-oxidative PPP with photosynthesis metabolism, there is recent evidence that the oxidative PPP reactions are also important in photosynthesizing cells. These reactions can form a shunt around the non-oxidative PPP section of the Calvin–Benson cycle, consuming three ATP per glucose 6-phosphate consumed. A constitutive operation of this shunt occurs in the cytosol and gives rise to an unusual labeling pattern of photosynthetic metabolites while an inducible shunt in the stroma may occur in response to stress.

Graceful Labeling and Skolem Graceful Labeling on the U-star Graph and It’s Application in Cryptography

Graceful Labeling on graph G=(V, E) is an injective function f from the set of the vertex V(G) to the set of numbers {0,1,2,...,|E(G)|} which induces bijective function f from the set of edges E(G) to the set of numbers {1,2,...,|E(G)|} such that for each edge uv e E(G) with u,v e V(G) in effect f(uv)=|f(u)-f(v)|. Meanwhile, the Skolem graceful labeling is a modification of the Graceful labeling. The graph has graceful labeling or Skolem graceful labeling is called graceful graph or Skolem graceful labeling graph. The graph used in this study is the U-star graph, which is denoted by U(Sn). The purpose of this research is to determine the pattern of the graceful labeling and Skolem graceful labeling on graph U(Sn) apply it to cryptography polyalphabetic cipher. The research begins by forming a graph U(Sn) and they are labeling it with graceful labeling and Skolem graceful labeling. Then, the labeling results are applied to the cryptographic polyalphabetic cipher. In this study, it is found that the U(Sn) graph is a graceful graph and a Skolem graceful graph, and the labeling pattern is obtained. Besides, the labeling results on a graph it U(Sn) can be used to form a table U(Sn) polyalphabetic cipher. The table is used as a key to encrypt messages.

AccuCor2 Isotope Natural Abundance Correction for Dual-Isotope Tracer Experiments

Stable isotope labeling techniques have been widely applied in the field of metabolomics and proteomics. Before the measured mass spectrum data can be used for quantitative analysis, it must be accurately corrected for isotope natural abundance and tracer isotopic impurity. Despite the increasing popularity of dual-isotope tracing strategy such as ¹³C-¹⁵N or ¹³C-²H, there is no accurate tool for correcting isotope natural abundance for such experiments. Here, we present AccuCor2 as an R-based tool to perform the correction for ¹³C-¹⁵N or ¹³C-²H labeling experiments. Our results show that the dual-isotope experiments often require a mass resolution that is high enough to resolve ¹³C and ¹⁵N or ¹³C and ²H.Otherwise the labeling pattern is not solvable. However, this mass resolution may not be sufficiently high to resolve other non-tracer elements such as oxygen or sulfur from the tracer elements. Therefore, we design AccuCor2 to perform the correction based on the actual mass resolution of the measurements. Using both simulated and experimental data, we show that AccuCor2 performs accurate and resolution dependent correction for dual-isotope tracer data.

AccuCor2 Isotope Natural Abundance Correction for Dual-Isotope Tracer Experiments

Stable isotope labeling techniques have been widely applied in the field of metabolomics and proteomics. Before the measured mass spectrum data can be used for quantitative analysis, it must be accurately corrected for isotope natural abundance and tracer isotopic impurity. Despite the increasing popularity of dual-isotope tracing strategy such as ¹³C-¹⁵N or ¹³C-²H, there is no accurate tool for correcting isotope natural abundance for such experiments. Here, we present AccuCor2 as an R-based tool to perform the correction for ¹³C-¹⁵N or ¹³C-²H labeling experiments. Our results show that the dual-isotope experiments often require a mass resolution that is high enough to resolve ¹³C and ¹⁵N or ¹³C and ²H.Otherwise the labeling pattern is not solvable. However, this mass resolution may not be sufficiently high to resolve other non-tracer elements such as oxygen or sulfur from the tracer elements. Therefore, we design AccuCor2 to perform the correction based on the actual mass resolution of the measurements. Using both simulated and experimental data, we show that AccuCor2 performs accurate and resolution dependent correction for dual-isotope tracer data.

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.

Biosynthetic origin of butyrolactol A, an antifungal polyketide produced by a marine-derived Streptomyces

Butyrolactol A is an antifungal polyketide of Streptomyces bearing an uncommon tert-butyl starter unit and a polyol system in which eight hydroxy/acyloxy carbons are contiguously connected. Except for its congener butyrolactol B, there exist no structurally related natural products to date. In this study, inspired by our previous genomic analysis, incorporation of 13C- and 2H-labeled precursors into butyrolactol A was investigated. Based on the labeling pattern and sequencing analytical data, we confirmed that the tert-butyl group is derived from valine and its C-methylation with methionine and the polyol carbons are derived from a glycolysis intermediate, possibly hydroxymalonyl-ACP.

High-resolution mapping reveals topologically distinct cellular pools of phosphatidylserine

Phosphatidylserine (PS) plays a central role in cell signaling and in the biosynthesis of other lipids. To date, however, the subcellular distribution and transmembrane topology of this crucial phospholipid remain ill-defined. We transfected cells with a GFP-tagged C2 domain of lactadherin to detect by light and electron microscopy PS exposed on the cytosolic leaflet of the plasmalemma and organellar membranes. Cytoplasmically exposed PS was found to be clustered on the plasma membrane, and to be associated with caveolae, the trans-Golgi network, and endocytic organelles including intraluminal vesicles of multivesicular endosomes. This labeling pattern was compared with the total cellular distribution of PS as visualized using a novel on-section technique. These complementary methods revealed PS in the interior of the ER, Golgi complex, and mitochondria. These results indicate that PS in the lumenal monolayer of the ER and Golgi complex becomes exposed cytosolically at the trans-Golgi network. Transmembrane flipping of PS may contribute to the exit of cargo from the Golgi complex.