scholarly journals A suppressor tRNA-mediated feedforward loop eliminates leaky gene expression in bacteria

2020 ◽  
Author(s):  
Joanne M L Ho ◽  
Corwin A Miller ◽  
Sydney E Parks ◽  
Jacob R Mattia ◽  
Matthew R Bennett
Keyword(s):  
Gene Expression ◽  
Cancer Detection ◽  
Background Subtraction ◽  
Fold Change ◽  
Genetic Circuits ◽  
Persistent Problem ◽  
Suppressor Trna ◽  
Feedforward Loop ◽  
Genetic Systems ◽  
Fold Induction

Abstract Ligand-inducible genetic systems are the mainstay of synthetic biology, allowing gene expression to be controlled by the presence of a small molecule. However, ‘leaky’ gene expression in the absence of inducer remains a persistent problem. We developed a leak dampener tool that drastically reduces the leak of inducible genetic systems while retaining signal in Escherichia coli. Our system relies on a coherent feedforward loop featuring a suppressor tRNA that enables conditional readthrough of silent non-sense mutations in a regulated gene, and this approach can be applied to any ligand-inducible transcription factor. We demonstrate proof-of-principle of our system with the lactate biosensor LldR and the arabinose biosensor AraC, which displayed a 70-fold and 630-fold change in output after induction of a fluorescence reporter, respectively, without any background subtraction. Application of the tool to an arabinose-inducible mutagenesis plasmid led to a 540-fold change in its output after induction, with leak decreasing to the level of background mutagenesis. This study provides a modular tool for reducing leak and improving the fold-induction within genetic circuits, demonstrated here using two types of biosensors relevant to cancer detection and genetic engineering.

scholarly journals An automated model test system for systematic development and improvement of gene expression models

2017 ◽  
Author(s):  
Alexander C. Reis ◽  
Howard M. Salis
Keyword(s):  
Gene Expression ◽  
Model Test ◽  
Translation Initiation ◽  
Test System ◽  
Biophysical Model ◽  
Genetic Circuits ◽  
Initiation Rate ◽  
Information Theoretic ◽  
New Information ◽  
Genetic Systems

ABSTRACTGene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

scholarly journals Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 271
Author(s):  
Chentao Yong ◽  
Andras Gyorgy
Keyword(s):  
Cell Fate ◽  
Rational Design ◽  
Resource Competition ◽  
Dynamic Properties ◽  
Mechanistic Model ◽  
Robustness Analysis ◽  
System Level ◽  
Genetic Circuits ◽  
Scarce Resources ◽  
Genetic Systems

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.

scholarly journals Species diversity and spatial distribution of CL/VL vectors: assessing bioclimatic effect on expression plasticity of genes possessing vaccine properties isolated from wild-collected sand flies in endemic areas of Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ali Bordbar ◽  
Parviz Parvizi
Keyword(s):  
Gene Expression ◽  
Spatial Distribution ◽  
Interpolation Method ◽  
Diversity Index ◽  
Fold Change ◽  
Control Measures ◽  
Pcr Analysis ◽  
Physiological Status ◽  
Sand Flies ◽  
Risk Maps

Abstract Background Leishmaniasis is one of the ten most important neglected tropical diseases worldwide. Understanding the distribution of vectors of visceral and cutaneous leishmaniasis (VL/CL) is one of the significant strategic frameworks to control leishmaniasis. In this study, the extent of the bioclimatic variability was investigated to recognize a rigorous cartographic of the spatial distribution of VL/CL vectors as risk-maps using ArcGIS modeling system. Moreover, the effect of bioclimatic diversity on the fold change expression of genes possessing vaccine traits (SP15 and LeIF) was evaluated in each bioclimatic region using real-time PCR analysis. Methods The Inverse Distance Weighting interpolation method was used to obtain accurate geography map in closely-related distances. Bioclimatic indices were computed and vectors spatial distribution was analyzed in ArcGIS10.3.1 system. Species biodiversity was calculated based on Shannon diversity index using Rv.3.5.3. Expression fold change of SP15 and LeIF genes was evaluated using cDNA synthesis and RT-qPCR analysis. Results Frequency of Phlebotomus papatasi was predominant in plains areas of Mountainous bioclimate covering the CL hot spots. Mediterranean region was recognized as an important bioclimate harboring prevalent patterns of VL vectors. Semi-arid bioclimate was identified as a major contributing factor to up-regulate salivary-SP15 gene expression (P = 0.0050, P < 0.05). Also, Mediterranean bioclimate had considerable effect on up-regulation of Leishmania-LeIF gene in gravid and semi-gravid P. papatasi population (P = 0.0109, P < 0.05). Conclusions The diversity and spatial distribution of CL/VL vectors associated with bioclimatic regionalization obtained in our research provide epidemiological risk maps and establish more effectively control measures against leishmaniasis. Oscillations in gene expression indicate that each gene has its own features, which are profoundly affected by bioclimatic characteristics and physiological status of sand flies. Given the efficacy of species-specific antigens for vaccine production, it is essential to consider bioclimatic factors that have a fundamental role in affecting the regulatory regions of environmentally responsive loci for genes used in vaccine design.

scholarly journals High-performance chemical- and light-inducible recombinases in mammalian cells and mice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Benjamin H. Weinberg ◽  
Jang Hwan Cho ◽  
Yash Agarwal ◽  
N. T. Hang Pham ◽  
Leidy D. Caraballo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
High Performance ◽  
Genome Engineering ◽  
Genetic Circuits ◽  
Control Of Gene Expression ◽  
Expression Control ◽  
Gene Expression Control ◽  
High Performance Systems ◽  
Spatiotemporal Control

Abstract Site-specific DNA recombinases are important genome engineering tools. Chemical- and light-inducible recombinases, in particular, enable spatiotemporal control of gene expression. However, inducible recombinases are scarce due to the challenge of engineering high performance systems, thus constraining the sophistication of genetic circuits and animal models that can be created. Here we present a library of >20 orthogonal inducible split recombinases that can be activated by small molecules, light and temperature in mammalian cells and mice. Furthermore, we engineer inducible split Cre systems with better performance than existing systems. Using our orthogonal inducible recombinases, we create a genetic switchboard that can independently regulate the expression of 3 different cytokines in the same cell, a tripartite inducible Flp, and a 4-input AND gate. We quantitatively characterize the inducible recombinases for benchmarking their performances, including computation of distinguishability of outputs. This library expands capabilities for multiplexed mammalian gene expression control.

Abstract 212: Prophylactic Anti-inflammatory Treatment Attenuates The Neuroinflammatory And Behavioral Effects Of Silent Cerebral Infarction

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
Christina L Nemeth ◽  
Gretchen N Neigh
Keyword(s):  
Gene Expression ◽  
Cardiac Surgery ◽  
Open Field ◽  
Treatment Strategies ◽  
Fold Change ◽  
Inflammatory Activity ◽  
Central Tendency ◽  
Negative Consequences ◽  
Anti Inflammatory

Silent brain infarction is a frequent complication of cardiac surgery and is associated with mood changes and cognitive disruption. Microsphere embolism (ME) rodent models recapitulate both the diffuse ischemic infarcts and the delayed subtle behavioral disturbances characteristic to silent infarction (SI). Previously, we have shown that ME leads to increased hippocampal inflammation, weakening of the blood brain barrier, and the infiltration of peripherally circulating inflammatory cells in rats. Given long-term increases in inflammatory activity following SI, the current study tests the efficacy of anti-inflammatory versus anti-depressant treatment strategies to reduce the inflammatory and behavioral sequelae of injury. Adult rats were administered either chronic meloxicam (preferential COX-2 inhibitor) or fluoxetine (SSRI) beginning five days prior to ME surgeries. After a two week recovery, animals were tested for anxiety-like behaviors in the open field paradigm and the hippocampus was examined for gene expression of inflammatory cytokines. Meloxicam treated animals showed a decrease in hippocampal gene expression of inflammatory markers (SPP1; p = 0.0272) and greater than a 3-fold change improvement in open field central tendency (p = 0.0003). No differences in inflammatory gene expression were observed in fluoxetine treated animals (SPP1; p = 0.3288); however, fluoxetine treatment resulted in a 2-fold change improvement in open field central tendency (p = 0.0138) suggesting that while both treatment strategies attenuate SI induced behavioral disruption, only meloxicam acts via inflammatory mechanisms. Given the long term negative consequences of increased central and peripheral inflammatory activity, the data suggest that anti-inflammatory therapeutic strategies may benefit patients at risk for SI as well as cardiac surgery candidates.

scholarly journals Characterization, modelling and mitigation of gene expression burden in mammalian cells

2019 ◽  
Author(s):  
T Frei ◽  
F Cella ◽  
F Tedeschi ◽  
J Gutierrez ◽  
GB Stan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Genome Engineering ◽  
Host Cells ◽  
Genetic Circuits ◽  
Circuit Performance ◽  
Resource Requirements ◽  
Synthetic Circuit ◽  
Resource Aware

AbstractDespite recent advances in genome engineering, the design of genetic circuits in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here we demonstrate that competition for limited transcriptional and translational resources dynamically couples otherwise independent co-expressed exogenous genes, leading to diminished performance and contributing to the divergence between intended and actual function. We also show that the expression of endogenous genes is likewise impacted when genetic payloads are expressed in the host cells. Guided by a resource-aware mathematical model and our experimental finding that post-transcriptional regulators have a large capacity for resource redistribution, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the novel use of endogenous miRNAs as integral components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.

scholarly journals A modular toolset for electrogenetics

2021 ◽  
Author(s):  
Joshua M Lawrence ◽  
Yutong Yin ◽  
Paolo Bombelli ◽  
Alberto Scarampi ◽  
Marko Storch ◽  
...  
Keyword(s):  
Gene Expression ◽  
Industrial Applications ◽  
Electrochemical Activation ◽  
Genetic Circuits ◽  
Control Of Gene Expression ◽  
Promoter Library ◽  
Redox Responsive ◽  
Electrochemical Control ◽  
Redox Molecules ◽  
New Applications

Synthetic biology research and its industrial applications rely on the deterministic spatiotemporal control of gene expression. Recently, electrochemical control of gene expression has been demonstrated in electrogenetic systems (redox-responsive promoters used alongside redox inducers and an electrode), allowing for the direct integration of electronics with complex biological processes for a variety of new applications. However, the use of electrogenetic systems is limited by poor activity, tunability and standardisation. Here, we have developed a variety of genetic and electrochemical tools that facilitate the design and vastly improve the performance of electrogenetic systems. We developed a strong, unidirectional, redox-responsive promoter before deriving a mutant promoter library with a spectrum of strengths. We then constructed genetic circuits with these parts and demonstrated their activation by multiple classes of redox molecules. Finally, we demonstrated electrochemical activation of gene expression in aerobic conditions utilising a novel, modular bioelectrochemical device. This toolset provides researchers with all the elements needed to design and build optimised electrogenetic systems for specific applications.

scholarly journals The impact of androgen receptor (AR) and histone deacetylase 1 (HDAC1) expression on the prognosis of ductal carcinoma in situ (DCIS)

2020 ◽  
Author(s):  
Choong Man Lee ◽  
Jisun Kim ◽  
Yang Soon Park ◽  
Gyung Won Yoon ◽  
Hwi Gyeong Jo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Androgen Receptor ◽  
Histone Deacetylase ◽  
Gene Expression Analysis ◽  
Ductal Carcinoma ◽  
External Validation ◽  
Fold Change ◽  
Histone Deacetylase 1

Abstract Background Ductal carcinoma in situ (DCIS) display favorable outcome but little is known about the factors associated with invasive recurrence. To identify better prognostic biomarkers, we performed gene expression analysis followed by immunohistochemistry (IHC) staining validation. Methods Differential gene expression (DGE) analysis of 24 pure DCIS patients was performed using a nanostring platform. RNA was extracted from paraffin blocks from age/estrogen receptor matched recurrence-free (n=16) and invasive-recurrence (n=8) cases (disease-free interval >5 years). External validation was done among independent 61 cases, invasive-recurrence (n=16) and recurrence-free (n=45) pure DCIS cases by IHC staining. Results Eight differentially expressed genes were found statistically significant (log 2-fold change <–1 or >1 and p<0.001). Less than ½ fold expression of CUL1, AR, RPS27A, CTNNB1, MAP3K1, PRKACA, GNG12, MGMT genes were observed in REC cases compared to NED cases. Androgen receptor (AR) and histone deacetylase 1 (HDAC1) were selected for external validation (AR: log 2-fold change –1.35, p<0.001, and HDAC1; log 2-fold change –0.774, p<0.001). AR and HDAC1 protein expression was externally validated by IHC staining of 61 pure DCIS cases (16 invasive-recurrence versus 45 recurrence-free). Absence of AR and high HDAC1 expression was an independent risk factor for invasive recurrence (hazard ratio 5.04, 95% CI: 1.24, 20.4; p=0.023, hazard ratio 3.07, 95% CI: 1.04, 9.04; p=0.042). High nuclear grade (NG 3) was also associated with long term invasive recurrence. Conclusion Comparative gene expression analysis of pure DCIS revealed 8 genes differentially expressed among recurred cases. Immunohistochemistry validation within an independent cohort suggests that, absence of AR and overexpression of HDAC1 was associated with greater risk of long term invasive recurrence among pure DCIS.

scholarly journals Hands-free control of heterologous gene expression in batch cultures

2017 ◽  
Author(s):  
Olivier Borkowski ◽  
Drew Endy ◽  
Pakpoom Subsoontorn
Keyword(s):  
Gene Expression ◽  
Batch Culture ◽  
Communication Systems ◽  
Heterologous Gene Expression ◽  
Cell Communication ◽  
Culture Conditions ◽  
Heterologous Gene ◽  
Synthetic Cell ◽  
Genetic Systems ◽  
Application Specific

AbstractBackgroundAutonomous cell-based control of heterologous gene expression can simplify batch-culture bioprocessing by eliminating external monitoring and extrinsic control of culture conditions. Existing approaches use auto-induction media, synthetic cell-cell communication systems, or application-specific biosensors. A simpler, resource-efficient, and general-purpose expression control system responsive to common changes during batch culture would be useful.ResultsWe used nativeE.colipromoters and recombinase-based switches to repurpose endogenous transcription signals for control of heterologous gene expression. Specifically, natural changes in transcription from endogenous promoters result in recombinase expression at different phases of batch culture. So-expressed recombinases invert a constitutive promoter regulating expression of arbitrary heterologous genes. We realized reversible and single-use switching, reduced static and dynamic cell-to-cell variation, and overall expression amplification. We used “off-the-shelf” genetic parts and abstraction-based composition frameworks to realize reliable forward engineering of our synthetic genetic systems.ConclusionWe engineered autonomous control systems for regulating heterologous gene expression. Our system uses generic endogenous promoters to sense and control heterologous expression during growth-phase transitions. Our system does not require specialized auto-induction media, production or activation of quorum sensing, or the development of application-specific biosensors. Cells programmed to control themselves could simplify existing bioprocess operations and enable the development of more powerful synthetic genetic systems.

scholarly journals Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping-I Lin ◽  
Mohammad Ali Moni ◽  
Susan Shur-Fen Gau ◽  
Valsamma Eapen
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Microarray Data ◽  
Social Communication ◽  
Language Impairment ◽  
Cholesterol Biosynthesis ◽  
Learning Algorithms ◽  
Fold Change ◽  
Machine Learning Algorithms ◽  
Communication Deficits

Objectives: The identification of subgroups of autism spectrum disorder (ASD) may partially remedy the problems of clinical heterogeneity to facilitate the improvement of clinical management. The current study aims to use machine learning algorithms to analyze microarray data to identify clusters with relatively homogeneous clinical features.Methods: The whole-genome gene expression microarray data were used to predict communication quotient (SCQ) scores against all probes to select differential expression regions (DERs). Gene set enrichment analysis was performed for DERs with a fold-change &gt;2 to identify hub pathways that play a role in the severity of social communication deficits inherent to ASD. We then used two machine learning methods, random forest classification (RF) and support vector machine (SVM), to identify two clusters using DERs. Finally, we evaluated how accurately the clusters predicted language impairment.Results: A total of 191 DERs were initially identified, and 54 of them with a fold-change &gt;2 were selected for the pathway analysis. Cholesterol biosynthesis and metabolisms pathways appear to act as hubs that connect other trait-associated pathways to influence the severity of social communication deficits inherent to ASD. Both RF and SVM algorithms can yield a classification accuracy level &gt;90% when all 191 DERs were analyzed. The ASD subtypes defined by the presence of language impairment, a strong indicator for prognosis, can be predicted by transcriptomic profiles associated with social communication deficits and cholesterol biosynthesis and metabolism.Conclusion: The results suggest that both RF and SVM are acceptable options for machine learning algorithms to identify AD subgroups characterized by clinical homogeneity related to prognosis.

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