Induction of brain natriuretic peptide and monocyte chemotactic protein-1 gene expression by oxidized low-density lipoprotein: relevance to ischemic heart failure

Brain natriuretic peptide (BNP) and monocyte chemotactic protein-1 (MCP-1) are biomarkers of heart failure (HF). The aim of the present study was to determine the role of oxidized low-density lipoprotein (Ox-LDL) in the induction of these biomarkers and the signaling pathways involved in vitro. Incubation of HL-1 cardiomyocytes and human myocytes with Ox-LDL induced the expression of BNP and MCP-1 genes, while native LDL had no effect. When peroxides associated with Ox-LDL were reduced to hydroxides, the ability to induce BNP and MCP-1 gene expression was abolished. Furthermore, exposure of HL-1 cells to ischemic conditions alone had no effect on BNP gene expression, while ischemia followed by reperfusion resulted in increased expression of BNP gene. Inhibitors of ERK and JNK inhibited the induction of BNP. Signaling array results suggested that the induction of both MAPK and NF-κB pathways is involved in the induction of BNP by Ox-LDL. These results suggest that Ox-LDL or peroxidized lipids formed in oxidatively stressed myocytes during ischemia-reperfusion injury may play a role in the induction of BNP and MCP-1.     

S-glutathionylation activates STIM1 and alters mitochondrial homeostasis

Oxidant stress influences many cellular processes, including cell growth, differentiation, and cell death. A well-recognized link between these processes and oxidant stress is via alterations in Ca²⁺ signaling. However, precisely how oxidants influence Ca²⁺ signaling remains unclear. Oxidant stress led to a phenotypic shift in Ca²⁺ mobilization from an oscillatory to a sustained elevated pattern via calcium release–activated calcium (CRAC)–mediated capacitive Ca²⁺ entry, and stromal interaction molecule 1 (STIM1)– and Orai1-deficient cells are resistant to oxidant stress. Functionally, oxidant-induced Ca²⁺ entry alters mitochondrial Ca²⁺ handling and bioenergetics and triggers cell death. STIM1 is S-glutathionylated at cysteine 56 in response to oxidant stress and evokes constitutive Ca²⁺ entry independent of intracellular Ca²⁺ stores. These experiments reveal that cysteine 56 is a sensor for oxidant-dependent activation of STIM1 and demonstrate a molecular link between oxidant stress and Ca²⁺ signaling via the CRAC channel.     

Genetically and Phenotypically Distinct Pseudomonas aeruginosa Cystic Fibrosis Isolates Share a Core Proteomic Signature

The opportunistic pathogen Pseudomonas aeruginosa is among the main colonizers of the lungs of cystic fibrosis (CF) patients. We have isolated and sequenced several P. aeruginosa isolates from the sputum of CF patients and compared them with each other and with the model strain PAO1. Phenotypic analysis of CF isolates showed significant variability in colonization and virulence-related traits suggesting different strategies for adaptation to the CF lung. Genomic analysis indicated these strains shared a large set of core genes with the standard laboratory strain PAO1, and identified the genetic basis for some of the observed phenotypic differences. Proteomics revealed that in a conventional laboratory medium PAO1 expressed 827 proteins that were absent in the CF isolates while the CF isolates shared a distinctive signature set of 703 proteins not detected in PAO1. PAO1 expressed many transporters for the uptake of organic nutrients and relatively few biosynthetic pathways. Conversely, the CF isolates expressed a narrower range of transporters and a broader set of metabolic pathways for the biosynthesis of amino acids, carbohydrates, nucleotides and polyamines. The proteomic data suggests that in a common laboratory medium PAO1 may transport a diverse set of “ready-made” nutrients from the rich medium, whereas the CF isolates may only utilize a limited number of nutrients from the medium relying mainly on their own metabolism for synthesis of essential nutrients. These variations indicate significant differences between the metabolism and physiology of P. aeruginosa CF isolates and PAO1 that cannot be detected at the genome level alone. The widening gap between the increasing genomic data and the lack of phenotypic data means that researchers are increasingly reliant on extrapolating from genomic comparisons using experimentally characterized model organisms such as PAO1. While comparative genomics can provide valuable information, our data suggests that such extrapolations may be fraught with peril.     

New stochastic simulation capability applied to the GREET model

Background, Aims and Scope

In 1995, the Center for Transportation Research (CTR) of Argonne National Laboratory (ANL) began to develop a model, called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation), for estimating the full fuel-cycle energy and emissions impacts of alternative transportation fuels and advanced vehicle technologies. The parametric assumptions used in the GREET model involve uncertainties. A new stochastic simulation tool, developed by Vishwamitra Research Institute (VRI), is built into the GREET model to address uncertainties. This paper presents the methodology and features of this new stochastic simulation tool and evaluates the performance of the sampling techniques in the tool.

Methods

The new tool is interfaced through the graphical user interface (GUI) to perform the stochastic simulation. In general, five steps need to be followed to run a complete simulation: 1) Specify probability distribution functions; 2) Indicate the number of samples and the sampling technique; 3) Define the forecast variables; 4) Delete distribution functions (if necessary); and 5) Propagate the uncertainties and statistically analyze the outputs. The GREET model contains more than 700 default distribution functions for a wide variety of key parameters and as many as 3000 forecast variables. The stochastic simulation tool has been developed to incorporate 11 probability distribution function types for representing uncertain parameters and four sampling techniques (Monte Carlo sampling [MCS], Hammersley Sequence sampling [HSS], Latin Hypercube sampling [LHS] and Latin Hypercube Hammersley sampling [LHHS]) for stochastic simulation. To evaluate the performance of the four sampling techniques, 16 independent stochastic simulation runs were conducted in GREET and the output results were analyzed and compared.

Results and Discussion

With the same number of samples, the output distribution curve simulated by HSS is the smoothest corresponding to the highest level of uniformity. To achieve the same level of smoothness as HSS with 1,000 samples, LHHS needs to be simulated with ～1500 samples and LHS and MCS with ～3,000 samples. As a result, HSS can achieve more than 200% reduction in running time compared to LHS or MCS without compromising the accuracy and quality of the prediction curves. The simulated mean values are close enough to the actual mean value (within ±1%) despite the selection of sampling technique and the number of samples (between 1,000 and 4,000). The standard deviation values from each other are close enough as well (within ±5%). It shows the trend that the increasing number of samples makes the simulated mean value marginally closer to the actual mean value; however, the improvement effect is negligible. The simulation time is strictly positive-correlated with the number of samples; therefore, the trade-off between extending simulation time and improving the smoothness of the output distribution curve needs to be carefully assessed.

Conclusion

A new stochastic simulation tool has been developed to be built into Argonne’s GREET model to enhance its capability for addressing uncertainty. This new tool guides the user in each step of the process through the user-friendly GUI windows. According to the performance comparison among the four sampling techniques, HSS was found to be the most efficient technique. Therefore, HSS was set as the default technique in GREET.     

Strain engineering to prevent norleucine incorporation during recombinant protein production in Escherichia coli

Incorporation of norleucine in place of methionine residues during recombinant protein production in Escherichia coli is well known. Continuous feeding of methionine is commonly used in E. coli recombinant protein production processes to prevent norleucine incorporation. Although this strategy is effective in preventing norleucine incorporation, there are several disadvantages associated with continuous feeding. Continuous feeding increases the operational complexity and the overall cost of the fermentation process. In addition, the continuous feed leads to undesirable dilution of the fermentation medium possibly resulting in lower cell densities and recombinant protein yields. In this work, the genomes of three E. coli hosts were engineered by introducing chromosomal mutations that result in methionine overproduction in the cell. The recombinant protein purified from the fermentations using the methionine overproducing hosts had no norleucine incorporation. Furthermore, these studies demonstrated that the fermentations using one of the methionine overproducing hosts exhibited comparable fermentation performance as the control host in three different recombinant protein production processes. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:204–211, 2015