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Cellular Regulation (cellular + regulation)
Selected AbstractsA steady-state modeling approach to validate an in vivo mechanism of the GAL regulatory network in Saccharomyces cerevisiaeFEBS JOURNAL, Issue 20 2004Malkhey Verma Cellular regulation is a result of complex interactions arising from DNA,protein and protein,protein binding, autoregulation, and compartmentalization and shuttling of regulatory proteins. Experiments in molecular biology have identified these mechanisms recruited by a regulatory network. Mathematical models may be used to complement the knowledge-base provided by in vitro experimental methods. Interactions identified by in vitro experiments can lead to the hypothesis of multiple candidate models explaining the in vivo mechanism. The equilibrium dissociation constants for the various interactions and the total component concentration constitute constraints on the candidate models. In this work, we identify the most plausible in vivo network by comparing the output response to the experimental data. We demonstrate the methodology using the GAL system of Saccharomyces cerevisiae for which the steady-state analysis reveals that Gal3p neither dimerizes nor shuttles between the cytoplasm and the nucleus. [source] Modulation of p53 function in cellular regulationFEBS JOURNAL, Issue 10 2001Ettore Appella No abstract is available for this article. [source] MBSJ MCC Young Scientist Award 2009 REVIEW: Selective autophagy regulates various cellular functionsGENES TO CELLS, Issue 9 2010Masaaki Komatsu Autophagy is a self-eating system conserved among eukaryotes, in which cellular components including organelles are entrapped into a double membrane structure called the autophagosome and then degraded by lysosomal hydrolases. In addition to its role in supplying amino acids in response to nutrient starvation, autophagy is involved in quality control to maintain cell health. Thus, inactivation of autophagy causes the formation of cytoplasmic protein inclusions, which comprise misfolded proteins and the accumulation of many degenerated organelles, resulting in liver injury, diabetes, myopathy and neurodegeneration. Furthermore, although autophagy has been considered nonselective, increasing evidence points to the selectivity of autophagy in sorting vacuolar enzymes and removal of aggregate-prone proteins and unwanted organelles. Such selectivity allows diverse cellular regulation, similar to the ubiquitin proteasome pathway. In this review, we discuss the physiological roles of selective autophagy and their molecular mechanisms. [source] Signalling events involved in interferon-,-inducible macrophage nitric oxide generationIMMUNOLOGY, Issue 4 2003Julie Blanchette Summary Nitric oxide (NO) produced by macrophages (M,) in response to interferon-, (IFN-,) plays a pivotal role in the control of intracellular pathogens. Current knowledge of the specific biochemical cascades involved in this IFN-,-inducible M, function is still limited. In the present study, we evaluated the participation of various second messengers , Janus kinase 2 (JAK2), signal transducer and activator of transcription (STAT) 1,, MAP kinase kinase (MEK1/2), extracellular signal-regulated kinases 1 and 2 (Erk1/Erk2) and nuclear factor kappa B (NF-,B) , in the regulation of NO production by IFN-,-stimulated J774 murine M,. The use of specific signalling inhibitors permitted us to establish that JAK2/STAT1,- and Erk1/Erk2-dependent pathways are the main players in IFN-,-inducible M, NO generation. To determine whether the inhibitory effect was taking place at the pre- and/or post-transcriptional level, we evaluated the effect of each antagonist on inducible nitric oxide synthase (iNOS) gene and protein expression, and on the capacity of IFN-, to induce JAK2, Erk1/Erk2 and STAT1, phosphorylation. All downregulatory effects occurred at the pretranscriptional level, except for NF-,B, which seems to exert its role in NO production through an iNOS-independent event. In addition, electrophoretic mobility shift assay (EMSA) analysis revealed that STAT1, is essential for IFN-,-inducible iNOS expression and NO production, whereas the contribution of NF-,B to this cellular regulation seems to be minimal. Moreover, our data suggest that Erk1/Erk2 are responsible for STAT1, Ser727 residue phosphorylation in IFN-,-stimulated M,, thus contributing to the full activation of STAT1,. Taken together, our results indicate that JAK2, MEK1/2, Erk1/Erk2 and STAT1, are key players in the IFN-,-inducible generation of NO by M,. [source] Serum and glucocorticoid-regulated protein kinases: Variations on a themeJOURNAL OF CELLULAR BIOCHEMISTRY, Issue 6 2006Maude Tessier Abstract The phosphatidylinositol 3, kinase (PI3K)-signaling pathway plays a critical role in a variety of cellular responses such as modulation of cell survival, glucose homeostasis, cell division, and cell growth. PI3K generates important lipid second messengers,phosphatidylinositides that are phosphorylated at the 3, position of their inositol ring head-group. These membrane restricted lipids act by binding with high affinity to specific protein domains such as the pleckstrin homology (PH) domain. Effectors of PI3K include molecules that harbor such domains such as phosphoinositide-dependent kinase (PDK1) and protein kinase B (PKB), also termed Akt. The mammalian genome encodes three different PKB genes (,, ,, and ,; Akt1, 2, and 3, respectively) and each is an attractive target for therapeutic intervention in diseases such as glioblastoma and breast cancer. A second family of three protein kinases, termed serum and glucocorticoid-regulated protein kinases (SGKs), is structurally related to the PKB family including regulation by PI3K but lack a PH domain. However, in addition to PH domains, a second class of 3, phosphorylated inositol phospholipid-binding domains exists that is termed Phox homology (PX) domain: this domain is found in one of the SGKs (SGK3). Here, we summarize knowledge of the three SGK isoforms and compare and contrast them to PKB with respect to their possible importance in cellular regulation and potential as therapeutic targets. J. Cell. Biochem. © 2006 Wiley-Liss, Inc. [source] NF-,B DNA-binding activity after high peak power pulsed microwave (8.2 GHz) exposure of normal human monocytesBIOELECTROMAGNETICS, Issue 4 2002Mohan Natarajan Abstract The hypothesis investigated is that exposure of a mammalian cell to high peak power pulsed RF, at the frequency of 8.2 GHz, can result in the activation of an important eukaryotic transcriptional regulator, nuclear factor kappa B (NF-,B). This DNA-binding protein controls genes involved in long term cellular regulation. The selection of 8.2 GHz was based on the availability of a high peak power pulsed RF transmitter. In these studies, triplicate cultures of human monocytes (Mono Mac-6) were exposed to the pulsed wave radiation. The peak to average power ratio was 455:1 (2.2 ,s pulse width and pulse repetition rate of 1000 pulses/s). The average power density at the position of exposure was 50 W/m2, and the mean SAR at the bottom of the culture flask was 10.8,±,7.1 W/kg. The FDTD analysis indicated that 10% of the cells had an SAR of 22,29 W/kg. The cells were exposed continuously for 90 min at 37 °C, reincubated at this temperature, and harvested 4 h postexposure. The nuclear extracts were analyzed by electrophoretic mobility shift assay. The results showed a profound increase (3.6-fold) in the DNA binding activity of NF-,B in monocytes at 4 h after the pulsed RF exposure compared to sham irradiated controls. Competition experiments with cold NF-,B- specific oligonucleotides confirmed the specificity of the DNA binding activity. These results provide evidence that high peak power pulsed radiofrequency radiation can perturb the cell and initiate cell signaling pathways. However, at this point, we are not prepared to advocate that the cause is a nonthermal mechanism. Because of the broad distribution of SAR's in the flask, experiments need to be performed to determine if the changes observed are associated with cells exposed to high or low SARs. Bioelectromagnetics 23:271,277, 2002. © 2002 Wiley-Liss, Inc. [source] Prediction of metabolic function from limited data: Lumped hybrid cybernetic modeling (L-HCM)BIOTECHNOLOGY & BIOENGINEERING, Issue 2 2010Hyun-Seob Song Abstract Motivated by the need for a quick quantitative assessment of metabolic function without extensive data, we present an adaptation of the cybernetic framework, denoted as the lumped hybrid cybernetic model (L-HCM), which combines the attributes of the classical lumped cybernetic model (LCM) and the recently developed HCM. The basic tenet of L-HCM and HCM is the same, that is, they both view the uptake flux as being split among diverse pathways in an optimal way as a result of cellular regulation such that some chosen metabolic objective is realized. The L-HCM, however, portrays this flux distribution to occur in a hierarchical way, that is, first among lumped pathways, and next among individual elementary modes (EM) in each lumped pathway. Both splits are described by the cybernetic control laws using operational and structural return-on-investments, respectively. That is, the distribution of uptake flux at the first split is dynamically regulated according to environmental conditions, while the subsequent split is based purely on the stoichiometry of EMs. The resulting model is conveniently represented in terms of lumped pathways which are fully identified with respect to yield coefficients of all products unlike classical LCMs based on instinctive lumping. These characteristics enable the model to account for the complete set of EMs for arbitrarily large metabolic networks despite containing only a small number of parameters which can be identified using minimal data. However, the inherent conflict of questing for quantification of larger networks with smaller number of parameters cannot be resolved without a mechanism for parameter tuning of an empirical nature. In this work, this is accomplished by manipulating the relative importance of EMs by tuning the cybernetic control of mode-averaged enzyme activity with an empirical parameter. In a case study involving aerobic batch growth of Saccharomyces cerevisiae, L-HCM is compared with LCM. The former provides a much more satisfactory prediction than the latter when parameters are identified from a few primary metabolites. On the other hand, the classical model is more accurate than L-HCM when sufficient datasets are involved in parameter identification. In applying the two models to a chemostat scenario, L-HCM shows a reasonable prediction on metabolic shift from respiration to fermentation due to the Crabtree effect, which LCM predicts unsatisfactorily. While L-HCM appears amenable to expeditious estimates of metabolic function with minimal data, the more detailed dynamic models [such as HCM or those of Young et al. (Young et al., Biotechnol Bioeng, 2008; 100: 542,559)] are best suited for accurate treatment of metabolism when the potential of modern omic technology is fully realized. However, in view of the monumental effort surrounding the development of detailed models from extensive omic measurements, the preliminary insight into the behavior of a genotype and metabolic engineering directives that can come from L-HCM is indeed valuable. Biotechnol. Bioeng. 2010;106: 271,284. © 2010 Wiley Periodicals, Inc. [source] | ||||||||||