scholarly journals Controlling Cell-Free Gene Expression Behavior by Tuning Membrane Transport Properties

2019 ◽  
Author(s):  
Patrick M. Caveney ◽  
Rosemary M. Dabbs ◽  
William T. McClintic ◽  
S. Elizabeth Norred ◽  
C. Patrick Collier ◽  
...  
Keyword(s):  
Gene Expression ◽  
Membrane Permeability ◽  
Treatment Method ◽  
Lipid Vesicle ◽  
Molecular Signaling ◽  
Expression Variability ◽  
Reaction Chambers ◽  
Free Gene ◽  
Optical Treatment ◽  
Membrane Engineering

SummaryControlled transport of molecules across boundaries for energy exchange, sensing, and communication is an essential step toward cell-like synthetic systems. This communication between the gene expression compartment and the external environment requires reaction chambers that are permeable to molecular species that influence expression. In lipid vesicle reaction chambers, species that support expression – from small ions to amino acids – may diffuse across membranes and amplify protein production. However, vesicle-to-vesicle variation in membrane permeability may lead to low total expression and high variability in this expression. We demonstrate a simple optical treatment method that greatly reduces the variability in membrane permeability. When transport across the membrane was essential for expression, this optical treatment increased mean expression level by ~6-fold and reduced expression variability by nearly two orders of magnitude. These results demonstrate membrane engineering may enable essential steps toward cell-like synthetic systems. The experimental platform described here provides a means of understanding controlled transport motifs in individual cells and groups of cells working cooperatively through cell-to-cell molecular signaling.

scholarly journals Self-organization controls expression more than abundance of molecular components of transcription and translation in confined cell-free gene expression

2018 ◽  
Author(s):  
P.M. Caveney ◽  
R. Dabbs ◽  
G. Chauhan ◽  
S.E. Norred ◽  
C.P. Collier ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Production ◽  
Self Organization ◽  
Large Variability ◽  
Expression Noise ◽  
Expression Efficiency ◽  
Cell Extracts ◽  
Reaction Chambers ◽  
Free Gene ◽  
Molecular Components

AbstractCell-free gene expression using purified components or cell extracts has become an important platform for synthetic biology that is finding a growing numBer of practical applications. Unfortunately, at cell-relevant reactor volumes, cell-free expression suffers from excessive variability (noise) such that protein concentrations may vary by more than an order of magnitude across a population of identically constructed reaction chambers. Consensus opinion holds that variability in expression is due to the stochastic distribution of expression resources (DNA, RNAP, ribosomes, etc.) across the population of reaction chambers. In contrast, here we find that chamber-to-chamber variation in the expression efficiency generates the large variability in protein production. Through analysis and modeling, we show that chambers self-organize into expression centers that control expression efficiency. Chambers that organize into many centers, each having relatively few expression resources, exhibit high expression efficiency. Conversely, chambers that organize into just a few centers where each center has an abundance of resources, exhibit low expression efficiency. A particularly surprising finding is that diluting expression resources reduces the chamber-to-chamber variation in protein production. Chambers with dilute pools of expression resources exhibit higher expression efficiency and lower expression noise than those with more concentrated expression resources. In addition to demonstrating the means to tune expression noise, these results demonstrate that in cell-free systems, self-organization may exert even more influence over expression than the abundance of the molecular components of transcription and translation. These observations in cell-free platform may elucidate how self-organized, membrane-less structures emerge and function in cells.

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

scholarly journals Improving cell-free glycoprotein synthesis by characterizing and enriching native membrane vesicles

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jasmine M. Hershewe ◽  
Katherine F. Warfel ◽  
Shaelyn M. Iyer ◽  
Justin A. Peruzzi ◽  
Claretta J. Sullivan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Synthetic Biology ◽  
Membrane Protein ◽  
Membrane Vesicles ◽  
Processing Methods ◽  
Glycoprotein Synthesis ◽  
On Demand ◽  
Free Gene ◽  
Membrane Bound

AbstractCell-free gene expression (CFE) systems from crude cellular extracts have attracted much attention for biomanufacturing and synthetic biology. However, activating membrane-dependent functionality of cell-derived vesicles in bacterial CFE systems has been limited. Here, we address this limitation by characterizing native membrane vesicles in Escherichia coli-based CFE extracts and describing methods to enrich vesicles with heterologous, membrane-bound machinery. As a model, we focus on bacterial glycoengineering. We first use multiple, orthogonal techniques to characterize vesicles and show how extract processing methods can be used to increase concentrations of membrane vesicles in CFE systems. Then, we show that extracts enriched in vesicle number also display enhanced concentrations of heterologous membrane protein cargo. Finally, we apply our methods to enrich membrane-bound oligosaccharyltransferases and lipid-linked oligosaccharides for improving cell-free N-linked and O-linked glycoprotein synthesis. We anticipate that these methods will facilitate on-demand glycoprotein production and enable new CFE systems with membrane-associated activities.

Early response of gene clusters is associated with mouse lung resistance or sensitivity to cigarette smoke

2009 ◽  
Vol 296 (3) ◽  
pp. L418-L429 ◽  
Author(s):  
Eleonora Cavarra ◽  
Paolo Fardin ◽  
Silvia Fineschi ◽  
Annamaria Ricciardi ◽  
Giovanna De Cunto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cigarette Smoke ◽  
Gene Clusters ◽  
Early Response ◽  
Cigarette Smoke Exposure ◽  
Mouse Lung ◽  
Individual Response ◽  
Molecular Signaling ◽  
Gene Expression Response ◽  
Real Time Quantitative Pcr

We have investigated the effects of cigarette smoke exposure in three different strains of mice. DBA/2 and C57BL/6J are susceptible to smoke and develop different lung changes in response to chronic exposure, whereas ICR mice are resistant to smoke and do not develop emphysema. The present study was carried out to determine early changes in the gene expression profile of mice exposed to cigarette smoke with either a susceptible or resistant phenotype. The three strains of mice were exposed to smoke from three cigarettes per day, 5 days/wk, for 4 wk. Microarray analysis was carried out on total RNA extracted from the lung using the Affymetrix platform. Cigarette smoke modulates several clusters of genes (i.e., proemphysematous, acute phase response, and cell adhesion) in smoke-sensitive DBA/2 or C57BL/6J strains, but the same genes are not altered by smoke in ICR resistant mice. Only a few genes were commonly modulated by smoke in the three strains of mice. This pattern of gene expression suggests that the response to smoke is strain-dependent and may involve different molecular signaling pathways. Real-time quantitative PCR was used to verify the pattern of modulation of selected genes and their potential biological relevance. We conclude that gene expression response to smoke is highly dependent on the mouse genetic background. We speculate that the definition of gene clusters associated, to various degrees, with mouse susceptibility or resistance to smoke may be instrumental in defining the molecular basis of the individual response to smoke-induced lung injury in humans.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals Inferring gene regulation dynamics from static snapshots of gene expression variability

2021 ◽  
Vol 104 (4) ◽  
Author(s):  
Euan Joly-Smith ◽  
Zitong Jerry Wang ◽  
Andreas Hilfinger
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Expression Variability
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