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bioRxiv2017

Stringency of synthetic promoter sequences in Clostridium revealed and circumvented by tuning promoter library mutation rates

Collections of characterized promoters of different strengths are key resources for synthetic biology, but are not well established for many important organisms, including industrially-relevant Clostridium spp. When generating promoters, reporter constructs are used to measure expression, but classical fluorescent reporter proteins are oxygen-dependent and hence inactive in anaerobic bacteria like Clostridium. We directly compared oxygen-independent reporters of different types in Clostridium acetobutylicum and found that glucuronidase (GusA) from E. coli performed best. Using GusA, a library of synthetic promoters was first generated by a typical approach entailing complete randomization of a constitutive thiolase gene promoter (Pthl) except for the consensus -35 and -10 elements. In each synthetic promoter, the chance of each degenerate position matching Pthl was 25%. Surprisingly, none of the synthetic promoters from this library were functional in C. acetobutylicum, even though they functioned as expected in E. coli. Next, instead of complete randomization, we specified lower promoter mutation rates using oligonucleotide primers synthesized using custom mixtures of nucleotides. Using these primers, two promoter libraries were constructed in which the chance of each degenerate position matching Pthl was 79% or 58%, instead of 25% as before. Synthetic promoters from these stringent libraries functioned well in C. acetobutylicum, covering a wide range of strengths. The promoters functioned similarly in the distantly-related species Clostridium sporogenes, and allowed predictable metabolic engineering of C. acetobutylicum for acetoin production. Besides generating the desired promoters and demonstrating their useful properties, this work indicates an unexpected stringency of promoter sequences in Clostridium, not reported previously.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC=\"FIGDIR/small/216853_ufig1.gif\" ALT=\"Figure 1000\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@52f2borg.highwire.dtl.DTLVardef@5be460org.highwire.dtl.DTLVardef@ee8b05org.highwire.dtl.DTLVardef@e92801_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2022

Dynamic cybergenetic control of bacterial co-culturecomposition via optogenetic feedback

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. Additionally, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities.

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bioRxiv2025

SpyRing-Mediated Cyclization of TEV Protease, Guided by AlphaFold, Improves Thermostability

Cyclization is a promising strategy to enhance protein stability, but its applicability is often limited by structural constraints such as the distance between terminal regions. Here, we report the rational design and characterization of a cyclized Tobacco Etch Virus protease (cTEVp) using the SpyRing system, which enables covalent cyclization through SpyTag/SpyCatcher-mediated isopeptide bond formation. We applied this approach to a widely used engineered TEVp variant (L56V, S135G, S219V, {Delta}238-242) and employed AlphaFold structure prediction to optimize linker length and domain positioning. Despite the ~40 [A] separation between the N- and C-termini of native TEVp, AlphaFold modeling suggested that the fused SpyTag and SpyCatcher domains can adopt a favorable configuration for intramolecular cyclization. The resulting cTEVp exhibited proteolytic activity comparable to the non-cyclized TEVp, indicating that structural constraint via SpyRing-mediated cyclization did not impair enzymatic function. Importantly, cTEVp displayed significantly improved thermostability relative to its non-cyclized counterpart, as demonstrated by higher retention of soluble enzyme and residual activity following heat treatment at 50{degrees}C. Our findings validate the effectiveness of SpyRing-mediated cyclization in improving TEVp stability and highlight the utility of computationally guided cyclization as a generalizable strategy for engineering thermally resilient proteins. This study establishes a framework that integrates structure prediction and rational protein engineering, contributing to the development of robust biocatalysts for synthetic biology and industrial biotechnology applications. Table of Contents (TOC) / Abstract Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/646106v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@4efc7eorg.highwire.dtl.DTLVardef@1a2eab8org.highwire.dtl.DTLVardef@e00b2forg.highwire.dtl.DTLVardef@fe78a8_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2023

Engineering Programmable Material-To-Cell Pathways Via Synthetic Notch Receptors To Spatially Control Cellular Phenotypes In Multi-Cellular Constructs

Synthetic Notch (synNotch) receptors are modular synthetic components that are genetically engineered into mammalian cells to detect signals presented by neighboring cells and respond by activating prescribed transcriptional programs. To date, synNotch has been used to program therapeutic cells and pattern morphogenesis in multicellular systems. However, cell-presented ligands have limited versatility for applications that require spatial precision, such as tissue engineering. To address this, we developed a suite of materials to activate synNotch receptors and serve as generalizable platforms for generating user-defined material-to-cell signaling pathways. First, we demonstrate that synNotch ligands, such as GFP, can be conjugated to cell- generated ECM proteins via genetic engineering of fibronectin produced by fibroblasts. We then used enzymatic or click chemistry to covalently link synNotch ligands to gelatin polymers to activate synNotch receptors in cells grown on or within a hydrogel. To achieve microscale control over synNotch activation in cell monolayers, we microcontact printed synNotch ligands onto a surface. We also patterned tissues comprising cells with up to three distinct phenotypes by engineering cells with two distinct synthetic pathways and culturing them on surfaces microfluidically patterned with two synNotch ligands. We showcase this technology by co-transdifferentiating fibroblasts into skeletal muscle or endothelial cell precursors in user-defined spatial patterns towards the engineering of muscle tissue with prescribed vascular networks. Collectively, this suite of approaches extends the synNotch toolkit and provides novel avenues for spatially controlling cellular phenotypes in mammalian multicellular systems, with many broad applications in developmental biology, synthetic morphogenesis, human tissue modeling, and regenerative medicine.

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bioRxiv2026

Genetic code expansion enables programmable covalent protein design

Covalent chemistry has transformed small-molecule drug discovery, yet analogous strategies for proteins remain largely inaccessible because covalent warheads cannot be readily integrated into biologics. Conventional genetic code expansion requires engineering a dedicated aminoacyl-tRNA synthetase for each new amino acid, rendering broad warhead screening impractical. Here we introduce AminoX, a platform that bypasses this limitation through direct tRNA acylation, enabling site-specific incorporation of chemically diverse non-standard amino acids (nsAAs), including covalent warhead nsAAs compatible with scalable biologic manufacturing and multifunctional nsAAs. Using a pooled mRNA display workflow, we screened more than 2,000 warhead-position combinations in machine learning-designed de novo miniproteins targeting CTLA-4, enabling parallel interrogation of covalent chemistry, linker geometry, and incorporation site. We confirmed covalent engagement on cells together with enhanced functional blockade. Finally, we demonstrate multifunctional nsAAs that combine covalent warheads with fluorogenic reporters for real-time detection of target engagement, as well as dual nsAA incorporation for macrocyclization and fluorescent imaging of covalent binding on cell surfaces. By uniting synthetic biology, chemical biology, generative protein design, and high-throughput functional selection, AminoX compresses covalent protein engineering timelines by orders of magnitude, accelerating the development of next-generation therapeutics, biosensors, and chemical probes.

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bioRxiv2020

Development of a clostridia-based cell-free system for prototyping genetic parts and metabolic pathways

Gas fermentation by autotrophic bacteria, such as clostridia, offers a sustainable path to numerous bioproducts from a range of local, highly abundant, waste and low-cost feedstocks, such as industrial flue gases or syngas generated from biomass or municipal waste. Unfortunately, designing and engineering clostridia remains laborious and slow. The ability to prototype individual genetic parts, gene expression, and biosynthetic pathway performance in vitro before implementing them in cells could help address these bottlenecks by speeding up design. Unfortunately, a high-yielding cell-free gene expression (CFE) system from clostridia has yet to be developed. Here, we report the development and optimization of a high-yielding (236 {+/-} 24 {micro}g/mL) batch CFE platform from the industrially relevant anaerobe, Clostridium autoethanogenum. A key feature of the platform is that both circular and linear DNA templates can be applied directly to the CFE reaction to program protein synthesis. We demonstrate the ability to prototype gene expression, and quantitatively map cell-free metabolism in lysates from this system. We anticipate that the C. autoethanogenum CFE platform will not only expand the protein synthesis toolkit for synthetic biology, but also serve as a platform in expediting the screening and prototyping of gene regulatory elements in non-model, industrially relevant microbes.

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bioRxiv2024

Enhancing lipid production in plant cells through high-throughput genome editing and phenotyping via a scalable automated pipeline

Plant bioengineering is a time-consuming and labor-intensive process, with no guarantee of achieving the desired trait. Here we report a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB). FAST-PB achieves gene cloning, genome editing, and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrate that FAST-PB can streamline the Golden Gate cloning process, with the capacity to construct 96 vectors in parallel. To prove the concept, using FAST-PB, we first found that PEG2050 significantly increases transfection efficiency by over 45%. To validate the pipeline, we established a reporter-gene-free method for CRISPR editing via mutation of HCF136, affecting cellular chlorophyll fluorescence. Next, we applied this pipeline for lipid production and found that diverse lipids were significantly enhanced up to sixfold through introducing multi-gene cassettes via CRISPR activation, and regenerated plant using this platform. Lastly, we harnessed FAST-PB to achieve high-throughput single-cell lipid profiling through the integration of MALDI-MS with the biofoundry, and differentiated engineered and unengineered cells using the single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering, and change what is possibly using single-cell metabolomics in plants.

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bioRxiv2015

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression

Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA-RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.

Synthetic Biology↗

bioRxiv2020

Two-stage Dynamic Deregulation of Metabolism Improves Process Robustness & Scalability in Engineered E. coli.

We report improved strain and bioprocess robustness as a result of the dynamic deregulation of central metabolism using two-stage dynamic control. Dynamic control is implemented using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes in the context of a standardized two-stage bioprocesses. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. The deregulated network is more robust to environmental conditions improving process robustness, which in turn leads to predictable scalability from high throughput small scale screens to fully instrumented bioreactors as well as to pilot scale production. Additionally, as these two-stage bioprocesses are standardized, a need for traditional process optimization is minimized. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology. In this work we demonstrate that the improved robustness of E. coli strains engineered for the improved scalability of the important industrial chemicals alanine, citramalate and xylitol, from microtiter plates to pilot reactors.

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bioRxiv2018

De-Novo-Designed Translational Repressors for Multi-Input Cellular Logic

Synthetic biology aims to apply engineering principles toward the development of novel biological systems for biotechnology and medicine. Despite efforts to expand the set of high-performing parts for genetic circuits, achieving more complex circuit functions has often been limited by the idiosyncratic nature and crosstalk of commonly utilized parts. Here, we present a molecular programming strategy that implements RNA-based repression of translation using de-novo-designed RNAs to realize high-performance orthogonal parts with mRNA detection and multi-input logic capabilities. These synthetic post-transcriptional regulators, termed toehold repressors and three-way junction (3WJ) repressors, efficiently suppress translation in response to cognate trigger RNAs with nearly arbitrary sequences using thermodynamically and kinetically favorable linear-linear RNA interactions. Automated in silico optimization of thermodynamic parameters yields improved toehold repressors with up to 300-fold repression, while in-cell SHAPE-Seq measurements of 3WJ repressors confirm their designed switching mechanism in living cells. Leveraging the absence of sequence constraints, we identify eight- and 15-component sets of toehold and 3WJ repressors, respectively, that provide high orthogonality. The modularity, wide dynamic range, and low crosstalk of the repressors enable their direct integration into ribocomputing devices that provide universal NAND and NOR logic capabilities and can perform multi-input RNA-based logic. We demonstrate these capabilities by implementing a four-input NAND gate and the expression NOT((A1 AND A2) OR (B1 AND B2)) in Escherichia coli. These features make toehold and 3WJ repressors important new classes of translational regulators for biotechnological applications.

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bioRxiv2025

Bistability in Gene Regulation: Simulating Positive Feedback and Toggle Circuits Using Python and Hill Functions

Cellular decision-making relies heavily on bistable gene regulatory networks, which allow systems to respond to internal or external stimuli by switching between several stable expression states. Processes like cell differentiation, epigenetic memory, and the creation of artificial biological switches all depend on these dynamics. In this work, we introduce a simple and reproducible Python framework for modeling bistability in genetic feedback systems by means of ordinary differential equations (ODEs) driven by Hill functions. We employ two fundamental motifs, both of which are recognized for their ability to generate bistable behavior: a two-gene mutual inhibition toggle switch and a single-gene positive feedback loop. We investigate the effects of different Hill coefficients, production rates, and initial expression levels on system dynamics by numerical integration using SciPy. Our simulations show phase-plane convergence to several attractors, map expression outcomes over a grid of beginning circumstances, and illustrate the onset of bistability above a key Hill threshold. The delicate reliance of final states on cooperativity and beginning values is further demonstrated by heatmaps and bifurcation-like graphs. For accessibility, all code is hosted at Google Colab and is written in open-source Python. This study promotes research and teaching in synthetic biology, systems biology, and computational modeling while providing a simple yet effective computational framework for investigating the fundamentals of gene circuit bistability.

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bioRxiv2021

LOICA: Logical Operators for Integrated Cell Algorithms

1Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.

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bioRxiv2024

Optimisation of surfactin yield in Bacillus using active learning and high-throughput mass spectrometry

Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160% yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/576661v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@14c41ceorg.highwire.dtl.DTLVardef@1745ad2org.highwire.dtl.DTLVardef@1f6bedforg.highwire.dtl.DTLVardef@6d56c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2024

A new mRNA structure prediction based approach to identifying improved signal peptides for bone morphogenetic protein 2

BackgroundSignal peptide (SP) engineering has proven able to improve production of many proteins yet is a laborious process that still relies on trial and error. mRNA structure around the translational start site is important in translation initiation and has rarely been considered in this context, with recent improvements in in silico mRNA structure potentially rendering it a useful predictive tool for SP selection. Here we attempt to create a method to systematically screen candidate signal peptide sequences in silico based on both their nucleotide and amino acid sequences. Several recently released computational tools were used to predict signal peptide activity (SignalP), localization target (DeepLoc) and predicted mRNA structure (MXFold2). The method was tested with Bone Morphogenetic Protein 2 (BMP2), an osteogenic growth factor used clinically for bone regeneration. It was hoped more effective BMP2 SPs could improve BMP2-based gene therapies and reduce the cost of recombinant BMP2 production. ResultsAmino acid sequence analysis indicated 2,611 SPs from the TGF-{beta} superfamily were predicted to function when attached to BMP2. mRNA structure prediction indicated structures at the translational start site were likely highly variable. The five sequences with the most accessible translational start sites, a codon optimized BMP2 SP variant and the well-established hIL2 SP sequence were taken forward to in vitro testing. The top five candidates showed non-significant improvements in BMP2 secretion in HEK293T cells. All showed reductions in secretion versus the native sequence in C2C12 cells, with several showing large and significant decreases. None of the tested sequences were able to increase alkaline phosphatase activity above background in C2C12s. The codon optimized control sequence and hIL2 SP showed reasonable activity in HEK293T but very poor activity in C2C12. ConclusionsThese results support the use of peptide sequence based in silico tools for basic predictions around signal peptide activity in a synthetic biology context. However, mRNA structure prediction requires improvement before it can produce reliable predictions for this application. The poor activity of the codon optimized BMP2 SP variant in C2C12 emphasizes the importance of codon choice, mRNA structure, and cellular context for SP activity.

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bioRxiv2024

Expanding the toolkit for ploidy manipulation in Chlamydomonas reinhardtii

SummaryO_LIWhole genome duplications, widely observed in plant lineages, have significant evolutionary and ecological impacts. Yet, our current understanding of the direct implications of ploidy shifts on short- and long-term plant evolution remains fragmentary, necessitating further investigations across multiple ploidy levels. C_LIO_LIChlamydomonas reinhardtii, a haploid green alga, is a valuable model organism with profound potential to study the impact of ploidy increase on the longer-term in a laboratory environment. This is partly due to the ability to successfully increase the ploidy level. C_LIO_LIWe developed a strategy to engineer ploidy in Chlamydomonas reinhardtii using a collection of non-interfering antibiotic selectable markers. This approach allows to induce higher ploidy levels in Chlamydomonas reinhardtii and is applicable to field isolates, which expands beyond specific auxotroph laboratory strains and broadens the genetic diversity of parental haploid strains that can be crossed. We implement flow cytometry for precise measurement of the genome size of strains of different ploidy. C_LIO_LIWe demonstrate the creation of diploids, triploids and tetraploids by engineering North American field isolates, broadening the application of synthetic biology principles in Chlamydomonas reinhardtii. C_LIO_LIOur study greatly facilitates the application of Chlamydomonas reinhardtii to study polyploidy, both in fundamental and applied settings. C_LI

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bioRxiv2023

Towards AI-designed genomes using a variational autoencoder

Genomes encode elaborate networks of genes whose products must seamlessly interact to support living organisms. Humans capacity to understand these biological systems is limited by their sheer size and complexity. In this work, we develop a proof of concept framework for training a machine learning algorithm to model bacterial genome composition. To achieve this, we create simplified representations of genomes in the form of binary vectors that indicate the encoded genes, henceforth referred to as genome vectors. A denoising variational autoencoder was trained to accept corrupted genome vectors, in which most genes had been masked, and reconstruct the original. The resulting model, DeepGenomeVector, effectively captures complex dependencies in genomic networks, as evaluated by both qualitative and quantitative metrics. An in-depth functional analysis of a generated genome vector shows that its encoded pathways are interconnected, near complete, and ecologically cohesive. On the test set, where the models ability to reconstruct uncorrupted genome vectors was evaluated, AUC and F1 scores of 0.98 and 0.83, respectively, support the models strong performance. This work showcases the power of machine learning approaches for synthetic biology and highlights the possibility that AI agents may one day be able to design genomes that animate carbon-based cells.

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bioRxiv2022

SLICER: Seamless Loss of Integrated Cassettes Using Endonuclease Cleavage and Recombination in Deinococcus radiodurans

Methods for creating seamless genome modifications are an essential part of the microbial genetic toolkit that allows for strain engineering through the recycling of selectable markers. Here, we report the development of a method, termed SLICER, which can be used to create seamless genome modifications in D. radiodurans. We used SLICER to sequentially target four putative restriction-modification (R-M) system genes, recycling the same selective and screening markers for each subsequent deletion. A fifth R-M gene was replaced by a selectable marker to create a final D. radiodurans strain with 5 of the 6 putative R-M systems deleted. While we observed no significant increase in transformation efficiency, SLICER is a promising method to obtain a fully restriction-minus strain and expand the synthetic biology applications of D. radiodurans including as an in vivo DNA assembly platform. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/512367v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@13a4f39org.highwire.dtl.DTLVardef@127655corg.highwire.dtl.DTLVardef@16faddforg.highwire.dtl.DTLVardef@10023cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2020

Recombination of 2Fe-2S ferredoxins reveals differences in the inheritance of thermostability and midpoint potential

ABSTRACTHomologous recombination can be used to create enzymes that exhibit distinct activities and stabilities from proteins in nature, allowing researchers to overcome component limitations in synthetic biology. To investigate how recombination affects the physical properties of an oxidoreductase that transfers electrons, we created ferredoxin (Fd) chimeras by recombining distantly-related cyanobacterial and cyanomyophage Fds that present similar midpoint potentials but distinct thermostabilities. Fd chimeras having a wide range of amino acid substitutions retained the ability to coordinate an iron-sulfur cluster, although their thermostabilities varied with the fraction of residues inherited from each parent. The midpoint potentials of chimeric Fds also varied. However, all of the synthetic Fds exhibited midpoint potentials outside of the parental protein range. Each of the chimeric Fds could also be used to build synthetic pathways that support electron transfer between Fd-NADP reductase and sulfite reductase in Escherichia coli, although the chimeric Fds varied in the expression required to support similar levels of cellular electron transfer. These results show how recombination can be used to rapidly diversify the physical properties of protein electron carriers and reveal differences in the inheritance of thermostability and electrochemical properties. Furthermore, they illustrate how electron transfer efficiencies of chimeric Fds can be rapidly evaluated using a synthetic electron transfer pathway.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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