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      Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

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          Abstract

          Gene switching dynamics is a major source of randomness in genetic networks, also in the case of large concentrations of the transcription factors. In this work, we consider a common network motif - the positive feedback of a transcription factor on its own synthesis - and assess its response to extrinsic noises perturbing gene deactivation in a variety of settings where the network might operate. These settings are representative of distinct cellular types, abundance of transcription factors and ratio between gene switching and protein synthesis rates. By investigating noise-induced transitions among the different network operative states, our results suggest that gene switching rates are key parameters to shape network response to external perturbations, and that such response depends on the particular biological setting, i.e. the characteristic time scales and protein abundance. These results might have implications on our understanding of irreversible transitions for noise-related phenomena such as cellular differentiation. In addition these evidences suggest to adopt the appropriate mathematical model of the network in order to analyze the system consistently to the reference biological setting.

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          Stochastic protein expression in individual cells at the single molecule level.

          In a living cell, gene expression--the transcription of DNA to messenger RNA followed by translation to protein--occurs stochastically, as a consequence of the low copy number of DNA and mRNA molecules involved. These stochastic events of protein production are difficult to observe directly with measurements on large ensembles of cells owing to lack of synchronization among cells. Measurements so far on single cells lack the sensitivity to resolve individual events of protein production. Here we demonstrate a microfluidic-based assay that allows real-time observation of the expression of beta-galactosidase in living Escherichia coli cells with single molecule sensitivity. We observe that protein production occurs in bursts, with the number of molecules per burst following an exponential distribution. We show that the two key parameters of protein expression--the burst size and frequency--can be either determined directly from real-time monitoring of protein production or extracted from a measurement of the steady-state copy number distribution in a population of cells. Application of this assay to probe gene expression in individual budding yeast and mouse embryonic stem cells demonstrates its generality. Many important proteins are expressed at low levels, and are thus inaccessible by current genomic and proteomic techniques. This microfluidic single cell assay opens up possibilities for system-wide characterization of the expression of these low copy number proteins.
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            Control, exploitation and tolerance of intracellular noise.

            Noise has many roles in biological function, including generation of errors in DNA replication leading to mutation and evolution, noise-driven divergence of cell fates, noise-induced amplification of signals, and maintenance of the quantitative individuality of cells. Yet there is order to the behaviour and development of cells. They operate within strict parameters and in many cases this behaviour seems robust, implying that noise is largely filtered by the system. How can we explain the use, rejection and sensitivity to noise that is found in biological systems? An exploration of the sources and consequences of noise calls for the use of stochastic models.
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              Epidermal growth factor receptor gene and protein and gefitinib sensitivity in non-small-cell lung cancer.

              Gefitinib is a selective inhibitor of the epidermal growth factor (EGFR) tyrosine kinase, which is overexpressed in many cancers, including non-small-cell lung cancer (NSCLC). We carried out a clinical study to compare the relationship between EGFR gene copy number, EGFR protein expression, EGFR mutations, and Akt activation status as predictive markers for gefitinib therapy in advanced NSCLC. Tumors from 102 NSCLC patients treated daily with 250 mg of gefitinib were evaluated for EGFR status by fluorescence in situ hybridization (FISH), DNA sequencing, and immunohistochemistry and for Akt activation status (phospho-Akt [P-Akt]) by immunohistochemistry. Time to progression, overall survival, and 95% confidence intervals (CIs) were calculated and evaluated by the Kaplan-Meier method; groups were compared using the log-rank test. Risk factors associated with survival were evaluated using Cox proportional hazards regression modeling and multivariable analysis. All statistical tests were two-sided. Amplification or high polysomy of the EGFR gene (seen in 33 of 102 patients) and high protein expression (seen in 58 of 98 patients) were statistically significantly associated with better response (36% versus 3%, mean difference = 34%, 95% CI = 16.6 to 50.3; P<.001), disease control rate (67% versus 26%, mean difference = 40.6%, 95% CI = 21.5 to 59.7; P<.001), time to progression (9.0 versus 2.5 months, mean difference = 6.5 months, 95% CI = 2.8 to 10.3; P<.001), and survival (18.7 versus 7.0 months, mean difference = 11.7 months, 95% CI = 2.1 to 21.4; P = .03). EGFR mutations (seen in 15 of 89 patients) were also statistically significantly related to response and time to progression, but the association with survival was not statistically significant, and 40% of the patients with mutation had progressive disease. In multivariable analysis, only high EGFR gene copy number remained statistically significantly associated with better survival (hazard ratio = 0.44, 95% CI = 0.23 to 0.82). Independent of EGFR assessment method, EGFR+/P-Akt+ patients had a statistically significantly better outcome than EGFR-, P-Akt-, or EGFR+/P-Akt- patients. High EGFR gene copy number identified by FISH may be an effective molecular predictor for gefitinib efficacy in advanced NSCLC.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                03 June 2016
                2016
                : 6
                : 26980
                Affiliations
                [1 ]European Institute of Oncology, Department of Experimental Oncology , Milano, Italy
                [2 ]Università degli Studi di Milano-Bicocca, Dipartimento di Informatica, Sistemistica e Comunicazione , Milano, Italy
                [3 ]School of Informatics, University of Edinburgh , Edinburgh, UK
                [4 ]International Prevention Research Institute , Lyon, France
                Author notes
                Article
                srep26980
                10.1038/srep26980
                4891709
                27256916
                443b549d-f9c8-454f-9976-1f286eaa88cc
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 17 August 2015
                : 11 May 2016
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