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Death Phase (death + phase)

Distribution by Scientific Domains
Life Sciences83%

Selected Abstracts

Staphylococcus aureus ClpC ATPase is a late growth phase effector of metabolism and persistence

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 5 2009
Indranil Chatterjee Dr.
Abstract Staphylococcus aureus Clp ATPases (molecular chaperones) alter normal physiological functions including an aconitase-mediated effect on post-stationary growth, acetate catabolism, and entry into death phase (Chatterjee et al., J. Bacteriol. 2005, 187, 4488,4496). In the present study, the global function of ClpC in physiology, metabolism, and late-stationary phase survival was examined using DNA microarrays and 2-D PAGE followed by MALDI-TOF MS. The results suggest that ClpC is involved in regulating the expression of genes and/or proteins of gluconeogenesis, the pentose-phosphate pathway, pyruvate metabolism, the electron transport chain, nucleotide metabolism, oxidative stress, metal ion homeostasis, stringent response, and programmed cell death. Thus, one major function of ClpC is balancing late growth phase carbon metabolism. Furthermore, these changes in carbon metabolism result in alterations of the intracellular concentration of free NADH, the amount of cell-associated iron, and fatty acid metabolism. This study provides strong evidence for ClpC as a critical factor in staphylococcal energy metabolism, stress regulation, and late-stationary phase survival; therefore, these data provide important insight into the adaptation of S. aureus toward a persister state in chronic infections. [source]

Profiling of N -glycosylation gene expression in CHO cell fed-batch cultures

BIOTECHNOLOGY & BIOENGINEERING, Issue 3 2010
Danny Chee Furng Wong
Abstract One of the goals of recombinant glycoprotein production is to achieve consistent glycosylation. Although many studies have examined the changes in the glycosylation quality of recombinant protein with culture, very little has been done to examine the underlying changes in glycosylation gene expression as a culture progresses. In this study, the expression of 24 genes involved in N -glycosylation were examined using quantitative RT PCR to gain a better understanding of recombinant glycoprotein glycosylation during production processes. Profiling of the N -glycosylation genes as well as concurrent analysis of glycoprotein quality was performed across the exponential, stationary and death phases of a fed-batch culture of a CHO cell line producing recombinant human interferon-, (IFN-,). Of the 24 N -glycosylation genes examined, 21 showed significant up- or down-regulation of gene expression as the fed-batch culture progressed from exponential, stationary and death phase. As the fed-batch culture progressed, there was also an increase in less sialylated IFN-, glycoforms, leading to a 30% decrease in the molar ratio of sialic acid to recombinant IFN-,. This correlated with decreased expression of genes involved with CMP sialic acid synthesis coupled with increased expression of sialidases. Compared to batch culture, a low glutamine fed-batch strategy appears to need a 0.5,mM glutamine threshold to maintain similar N -glycosylation genes expression levels and to achieve comparable glycoprotein quality. This study demonstrates the use of quantitative real time PCR method to identify possible "bottlenecks" or "compromised" pathways in N -glycosylation and subsequently allow for the development of strategies to improve glycosylation quality. Biotechnol. Bioeng. 2010;107: 516,528. © 2010 Wiley Periodicals, Inc. [source]

Enhancing glycoprotein sialylation by targeted gene silencing in mammalian cells

BIOTECHNOLOGY & BIOENGINEERING, Issue 6 2010
Min Zhang
Abstract Recombinant glycoproteins produced by mammalian cells represent an important category of therapeutic pharmaceuticals used in human health care. Of the numerous sugars moieties found in glycoproteins, the terminal sialic acid is considered particularly important. Sialic acid has been found to influence the solubility, thermal stability, resistance to protease attack, antigenicity, and specific activity of various glycoproteins. In mammalian cells, it is often desirable to maximize the final sialic acid content of a glycoprotein to ensure its quality and consistency as an effective pharmaceutical. In this study, CHO cells overexpressing recombinant human interferon gamma (hIFN,) were treated using short interfering RNA (siRNA) and short-hairpin RNA (shRNA) to reduce expression of two newly identified sialidase genes, Neu1 and Neu3. By knocking down expression of Neu3 we achieved a 98% reduction in sialidase function in CHO cells. The recombinant hIFN, was examined for sialic acid content that was found to be increased 33% and 26% respectively with samples from cell stationary phase and death phase as compared to control. Here, we demonstrate an effective targeted gene silencing strategy to enhance protein sialylation using RNA interference (RNAi) technology. Biotechnol. Bioeng. 2010;105: 1094,1105. © 2009 Wiley Periodicals, Inc. [source]

Logistic Equations Effectively Model Mammalian Cell Batch and Fed-Batch Kinetics by Logically Constraining the Fit

BIOTECHNOLOGY PROGRESS, Issue 4 2005
Chetan T. Goudar
A four-parameter logistic equation was used to fit batch and fed-batch time profiles of viable cell density in order to estimate net growth rates from the inoculation through the cell death phase. Reduced three-parameter forms were used for nutrient uptake and metabolite/product formation rate calculations. These logistic equations constrained the fits to expected general concentration trends, either increasing followed by decreasing (four-parameter) or monotonic (three-parameter). The applicability of this approach was first verified for Chinese hamster ovary (CHO) cells cultivated in 15-L batch bioreactors. Cell density, metabolite, and nutrient concentrations were monitored over time and used to estimate the logistic parameters by nonlinear least squares. The logistic models fit the experimental data well, supporting the validity of this approach. Further evidence to this effect was obtained by applying the technique to three previously published batch studies for baby hamster kidney (BHK) and hybridoma cells in bioreactors ranging from 100 mL to 300 L. In 27 of the 30 batch data sets examined, the logistic models provided a statistically superior description of the experimental data than polynomial fitting. Two fed-batch experiments with hybridoma and CHO cells in benchtop bioreactors were also examined, and the logistic fits provided good representations of the experimental data in all 25 data sets. From a computational standpoint, this approach was simpler than classical approaches involving Monod-type kinetics. Since the logistic equations were analytically differentiable, specific rates could be readily estimated. Overall, the advantages of the logistic modeling approach should make it an attractive option for effectively estimating specific rates from batch and fed-batch cultures. [source]

Profiling of N -glycosylation gene expression in CHO cell fed-batch cultures

BIOTECHNOLOGY & BIOENGINEERING, Issue 3 2010
Danny Chee Furng Wong
Abstract One of the goals of recombinant glycoprotein production is to achieve consistent glycosylation. Although many studies have examined the changes in the glycosylation quality of recombinant protein with culture, very little has been done to examine the underlying changes in glycosylation gene expression as a culture progresses. In this study, the expression of 24 genes involved in N -glycosylation were examined using quantitative RT PCR to gain a better understanding of recombinant glycoprotein glycosylation during production processes. Profiling of the N -glycosylation genes as well as concurrent analysis of glycoprotein quality was performed across the exponential, stationary and death phases of a fed-batch culture of a CHO cell line producing recombinant human interferon-, (IFN-,). Of the 24 N -glycosylation genes examined, 21 showed significant up- or down-regulation of gene expression as the fed-batch culture progressed from exponential, stationary and death phase. As the fed-batch culture progressed, there was also an increase in less sialylated IFN-, glycoforms, leading to a 30% decrease in the molar ratio of sialic acid to recombinant IFN-,. This correlated with decreased expression of genes involved with CMP sialic acid synthesis coupled with increased expression of sialidases. Compared to batch culture, a low glutamine fed-batch strategy appears to need a 0.5,mM glutamine threshold to maintain similar N -glycosylation genes expression levels and to achieve comparable glycoprotein quality. This study demonstrates the use of quantitative real time PCR method to identify possible "bottlenecks" or "compromised" pathways in N -glycosylation and subsequently allow for the development of strategies to improve glycosylation quality. Biotechnol. Bioeng. 2010;107: 516,528. © 2010 Wiley Periodicals, Inc. [source]

Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis

BIOTECHNOLOGY PROGRESS, Issue 4 2009
Gürkan Sin
Abstract The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base-consumption were found low compared to the large uncertainty observed in the antibiotic and off-gas CO2 predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass-transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source]