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A mechanical metamaterial made from a DNA hydrogel : Nature Nanotechnology : Nature Publishing Group

A mechanical metamaterial made from a DNA hydrogel : Nature Nanotechnology : Nature Publishing Group | SynBioFromLeukipposInstitute | Scoop.it

DNA Nanotechnology: A metamaterial with memory
by
Ju Li & Liyuan Bai
http://bit.ly/UgZN4U

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A mechanical metamaterial made from a DNA hydrogel

by
Jong Bum Lee, Songming Peng, Dayong Yang, Young Hoon Roh, Hisakage Funabashi, Nokyoung Park, Edward J. Rice, Liwei Chen, Rong Long, Mingming Wu & Dan Luo

"Metamaterials are artificial substances that are structurally engineered to have properties not typically found in nature. To date, almost all metamaterials have been made from inorganic materials such as silicon and copper1, 2, which have unusual electromagnetic or acoustic properties1, 2, 3, 4, 5 that allow them to be used, for example, as invisible cloaks6, 7, 8, 9, superlenses10, 11, 12 or super absorbers for sound13. Here, we show that metamaterials with unusual mechanical properties can be prepared using DNA as a building block. We used a polymerase enzyme to elongate DNA chains and weave them non-covalently into a hydrogel. The resulting material, which we term a meta-hydrogel, has liquid-like properties when taken out of water and solid-like properties when in water. Moreover, upon the addition of water, and after complete deformation, the hydrogel can be made to return to its original shape. The meta-hydrogel has a hierarchical internal structure and, as an example of its potential applications, we use it to create an electric circuit that uses water as a switch."

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Rational Design of Evolutionarily Stable Microbial Kill Switches

The evolutionary stability of synthetic genetic circuits is key to both the understanding and application of genetic control elements. One useful but challenging situation is a switch between life and death depending on environment. Here are presented "essentializer" and "cryodeath" circuits, which act as kill switches in Escherichia coli. The essentializer element induces cell death upon the loss of a bi-stable cI/Cro memory switch. Cryodeath makes use of a cold-inducible promoter to express a toxin. We employ rational design and a toxin/antitoxin titering approach to produce and screen a small library of potential constructs, in order to select for constructs that are evolutionarily stable. Both kill switches were shown to maintain functionality in vitro for at least 140 generations. Additionally, cryodeath was shown to control the growth environment of a population, with an escape frequency of less than 1 in 105 after 10 days of growth in the mammalian gut.
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Multiscale memory and bioelectric error correction in the cytoplasm-cytoskeleton-membrane system

A fundamental aspect of life is the modification of anatomy, physiology, and behavior in the face of changing conditions. This is especially illustrated by the adaptive regulation of growth and form that underlies the ability of most organisms-from single cells to complex large metazoa-to develop, remodel, and regenerate to specific anatomical patterns. What is the relationship of the genome and other cellular components to the robust computations that underlie this remarkable pattern homeostasis? Here we examine the role of constraints defined at the cellular level, especially endogenous bioelectricity, in generating and propagating biological information. We review evidence that the genome is only one of several multi-generational biological memories. Focusing on the cell membrane and cytoplasm, which is physically continuous across all of life in evolutionary timeframes, we characterize the environment as an interstitial space through which messages are passed via bioelectric and biochemical codes. We argue that biological memory is a fundamental phenomenon that cannot be understood at any one scale, and suggest that functional studies of information propagated in non-genomic cellular structures will not only strongly impact evolutionary developmental biology, but will also have implications for regenerative medicine and synthetic bioengineering. For further resources related to this article, please visit the WIREs website.
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CRISPR-Cas9D10A Nickase-Assisted Genome Editing in Lactobacillus casei

Lactobacillus casei has drawn increasing attention as a health-promoting probiotic, while effective genetic manipulation tools are often not available, e.g., the single-gene knockout in L. casei still depends on the classic homologous recombination-dependent double-crossover strategy, which is quite labor-intensive and time-consuming. In the present study, a rapid and precise genome editing plasmid, pLCNICK, was established for L. casei genome engineering based on CRISPR-Cas9D10A. In addition to the P23-Cas9D10A and Pldh-sgRNA (single guide RNA) expression cassettes, pLCNICK includes the homologous arms of the target gene as repair templates. The ability and efficiency of chromosomal engineering using pLCNICK were evaluated by in-frame deletions of four independent genes and chromosomal insertion of an enhanced green fluorescent protein (eGFP) expression cassette at the LC2W_1628 locus. The efficiencies associated with in-frame deletions and chromosomal insertion is 25 to 62%. pLCNICK has been proved to be an effective, rapid, and precise tool for genome editing in L. casei, and its potential application in other lactic acid bacteria (LAB) is also discussed in this study.
IMPORTANCE The lack of efficient genetic tools has limited the investigation and biotechnological application of many LAB. The CRISPR-Cas9D10A nickase-based genome editing in Lactobacillus casei, an important food industrial microorganism, was demonstrated in this study. This genetic tool allows efficient single-gene deletion and insertion to be accomplished by one-step transformation, and the cycle time is reduced to 9 days. It facilitates a rapid and precise chromosomal manipulation in L. casei and overcomes some limitations of previous methods. This editing system can serve as a basic technological platform and offers the possibility to start a comprehensive investigation on L. casei. As a broad-host-range plasmid, pLCNICK has the potential to be adapted to other Lactobacillus species for genome editing.
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Growing DNA Strands

Growing DNA Strands | SynBioFromLeukipposInstitute | Scoop.it
Synthetic biologists and nanobiologists are repurposing DNA as a smart and stable self-assembling material to build nanofactories, drug-delivering nanostructures and molecular devices that can sense their environment and respond in different ways by, for example, detecting inflammation in the body...
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Autonomous control of metabolic state by a quorum sensing (QS)-mediated regulator for bisabolene production in engineered E. coli

Inducible gene expression systems are widely used in microbial host strains for protein and commodity chemical production because of their extensive characterization and ease of use. However, some of these systems have disadvantages such as leaky expression, lack of dynamic control, and the prohibitively high costs of inducers associated with large-scale production. Quorum sensing (QS) systems in bacteria control gene expression in response to population density, and the LuxI/R system from Vibrio fischeri is a well-studied example. A QS system could be ideal for biofuel production strains as it is self-regulated and does not require the addition of inducer compounds, which reduce operational costs for inducer. In this study, a QS system was developed for inducer-free production of the biofuel compound bisabolene from engineered E. coli. Seven variants of the Sensor plasmid, which carry the luxI-luxR genes, and four variants of the Response plasmid, which carry bisabolene producing pathway genes under the control of the PluxI promoter, were designed for optimization of bisabolene production. Furthermore, a chromosome-integrated QS strain was engineered with the best combination of Sensor and Response plasmid and produced bisabolene at a titer of 1.1g/L without addition of external inducers. This is a 44% improvement from our previous inducible system. The QS strain also displayed higher homogeneity in gene expression and isoprenoid production compared to an inducible-system strain.
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Structure-guided chemical modification of guide RNA enables potent non-viral in vivo genome editing

Structure-guided chemical modification of guide RNA enables potent non-viral in vivo genome editing | SynBioFromLeukipposInstitute | Scoop.it
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Synthetic circuits can harvest light energy

Synthetic circuits can harvest light energy | SynBioFromLeukipposInstitute | Scoop.it
By organizing pigments on a DNA scaffold, an MIT-led team of researchers has designed a light-harvesting material that closely mimics the structure of naturally occurring photosynthetic structures. This type of structure could be incorporated into materials such as glass or textiles, enabling them to harvest or store energy from sunlight.
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Hypersymbiont Dress

Hypersymbiont Dress | SynBioFromLeukipposInstitute | Scoop.it
Photo: Photo: Installation view (detail) with video mapping at “Technology and Emotions”, Oslo
The Hypersymbiont Dress is a dress stained and video mapped with bacteria that may have the potentia
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Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing

Front Microbiol. 2017 Oct 24;8:2060. doi: 10.3389/fmicb.2017.02060. eCollection 2017. Review
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Policy Challenges and Ethical Issues with the Breakthrough Technology: The Case of Synthetic Biology

Policy Challenges and Ethical Issues with the Breakthrough Technology: The Case of Synthetic Biology | SynBioFromLeukipposInstitute | Scoop.it
Synthetic biology is an exciting field which has enormous potentials for solving various problems human beings are facing up such as genetic disease, food shortage and global warming. Many countries such as the United States, the United Kingdom and China have invested heavily in this field but the negative aspect of such scientific breakthrough draws little attention. Since the harms synthetic biology can cause is not certain, it is neither safe nor proper to leave it to the hands of only experts. Currently researches on synthetic biology are being conducted without proper public discourse and consideration. One of the reasons for the lack of public discussion on synthetic biology is the speed of the development in the field. The field is innovating so fast that people have little chance to digest the consequence of such advances. Also the confusion on the definition of synthetic biology contributes to the lack of proper public discussion on the issue.

This article provides a new typology for definition of synthetic biology conceptualised by the authors and analyses the current state of synthetic biology in major countries. In addition, ethical issues associated with synthetic biology are discussed. Scientific transparency and participatory process are suggested as policy options to deal with them.
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Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber

Learning the underlying details of a gene network is a major challenge in cellular and synthetic biology. We address this challenge by building a chemical kinetic model that utilizes information encoded in the stochastic protein expression trajectories typically measured in experiments. The applicability of the proposed method is demonstrated in an auto-activating genetic circuit, a common motif in natural and synthetic gene networks. Our approach is based on the principle of maximum caliber (MaxCal)-a dynamical analog of the principle of maximum entropy-and builds a minimal model using only three constraints: 1) protein synthesis, 2) protein degradation, and 3) positive feedback. The MaxCal-generated model (described with four parameters) was benchmarked against synthetic data generated using a Gillespie algorithm on a known reaction network (with seven parameters). MaxCal accurately predicts underlying rate parameters of protein synthesis and degradation as well as experimental observables such as protein number and dwell-time distributions. Furthermore, MaxCal yields an effective feedback parameter that can be useful for circuit design. We also extend our methodology and demonstrate how to analyze trajectories that are not in protein numbers but in arbitrary fluorescence units, a more typical condition in experiments. This "top-down" methodology based on minimal information-in contrast to traditional "bottom-up" approaches that require ad hoc knowledge of circuit details-provides a powerful tool to accurately infer underlying details of feedback circuits that are not otherwise visible in experiments and to help guide circuit design.
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Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.
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Design and Application of Genetically-Encoded Malonyl-CoA Biosensors for Metabolic Engineering of Microbial Cell Factories

Malonyl-CoA is the basic building block for synthesizing a range of important compounds including fatty acids, phenylpropanoids, flavonoids and non-ribosomal polyketides. Centering around malonyl-CoA, we summarized here the various metabolic engineering strategies employed recently to regulate and control malonyl-CoA metabolism and improve cellular productivity. Effective metabolic engineering of microorganisms requires the introduction of heterologous pathways and dynamically rerouting metabolic flux towards products of interest. Transcriptional factor-based biosensors translate an internal cellular signal to a transcriptional output and drive the expression of the designed genetic/biomolecular circuits to compensate the activity loss of the engineered biosystem. Recent development of genetically-encoded malonyl-CoA sensor has stood out as a classical example to dynamically reprogram cell metabolism for various biotechnological applications. Here, we reviewed the design principles of constructing a transcriptional factor-based malonyl-CoA sensor with superior detection limit, high sensitivity and broad dynamic range. We discussed various synthetic biology strategies to remove pathway bottleneck and how genetically-encoded metabolite sensor could be deployed to improve pathway efficiency. Particularly, we emphasized that integration of malonyl-CoA sensing capability with biocatalytic function would be critical to engineer efficient microbial cell factory. Biosensors have also advanced beyond its classical function of a sensor actuator for in situ monitoring of intracellular metabolite concentration. Applications of malonyl-CoA biosensors as a sensor-invertor for negative feedback regulation of metabolic flux, a metabolic switch for oscillatory balancing of malonyl-CoA sink pathway and source pathway and a screening tool for engineering more efficient biocatalyst are also presented in this review. We envision the genetically-encoded malonyl-CoA sensor will be an indispensable tool to optimize cell metabolism and cost-competitively manufacture malonyl-CoA-derived compounds.
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CRIPSR-Cas expands dynamic range of gene expression from T7RNAP promoters

Abstract
BACKGROUND:
Reducing leaky gene expression is critical for improving protein yield of recombinant bacteria and stability of engineered cellular circuits in synthetic biology. Leaky gene expression occurs when a genetic promoter is not fully repressed, leading to unintended protein synthesis in the absence of stimuli. Existing work has devised specific molecular strategies for reducing leaky gene expression of each promoter. Main Method and Results: In contrast, we describe a repurposed, modular CRISPRi system that attenuates leaky gene expression using a series of single-guide RNAs targeting the PT7/LacO1 . Furthermore, we demonstrate the efficacy of CRISPRi to significantly increase the dynamic range of T7 RNA Polymerase (T7RNAP) promoters. In addition, we demonstrate that the CRISPRi system can be applied to enhance growth of bacteria that suffer from leaky expression of a toxic protein.
CONCLUSIONS AND IMPLICATIONS:
Our work establishes a new application of CRISPRi in genomic engineering to improve the control of recombinant gene expression. The approach is potentially generalizable to other gene expression system by changing the single-guide RNAs.
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Discovery and design of self-assembling peptides

eptides are ubiquitous in nature and useful in many fields, from agriculture as pesticides, in medicine as antibacterial and antifungal drugs founded in the innate immune systems, to medicinal chemistry as hormones. However, the concept of peptides as materials was not recognized until 1990 when a self-assembling peptide as a repeating segment in a yeast protein was serendipitously discovered. Peptide materials are so called because they have bona fide materials property and are made from simple amino acids with well-ordered nanostructures under physiological conditions. These structures include well-ordered nanofibres, nanotubes and nanovesicles. These peptide materials have been used for: (i) three-dimensional tissue cell cultures of primary cells and stem cells, (ii) three-dimensional tissue printing, (iii) sustained releases of small molecules, growth factors, monoclonal antibody and siRNA, (iv) accelerated wound healing in reparative and regenerative medicine as well as tissue engineering, (v) used to stabilize membrane proteins including difficult G-protein coupled receptors and photosystem I for designing nanobiodevices, (vi) a few self-assembling peptides have been used in human clinical trials for accelerated wound healings in surgical uses and (vii) in human clinical trials for siRNA delivery for treatment of cancers. It is likely that these self-assembling peptides will open doors for more and more diverse uses. The field of self-assembling peptides is growing in a number of directions in areas of materials, synthetic biology, and clinical medicine and beyond.
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Scientists invent new way of folding and protecting recombinant proteins

Scientists invent new way of folding and protecting recombinant proteins | SynBioFromLeukipposInstitute | Scoop.it
November 13, 2017 A team from the NUS Yong Loo Lin School of Medicine (NUS Medicine) has invented a fundamentally new way of folding and protecting recombinant proteins. Sourced from the rapidly expanding field of synthetic biology, this protein-in-a-protein technology can improve functional...
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RNA-based dynamic genetic controllers: development strategies and applications

Dynamic regulation of gene expression in response to various molecules is crucial for both basic science and practical applications. RNA is considered an attractive material for creating dynamic genetic controllers because of its specific binding to ligands, structural flexibility, programmability, and small size. Here, we review recent advances in strategies for developing RNA-based dynamic controllers and applications. First, we describe studies that re-engineered natural riboswitches to generate new dynamic controllers. Next, we summarize RNA-based regulatory mechanisms that have been exploited to build novel artificial dynamic controllers. We also discuss computational methods and high-throughput selection approaches for de novo design of dynamic RNA controllers. Finally, we explain applications of dynamic RNA controllers for metabolic engineering and synthetic biology.
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CRISPR Can Now Edit Genes Using Nanoparticles Instead of Viruses

CRISPR Can Now Edit Genes Using Nanoparticles Instead of Viruses | SynBioFromLeukipposInstitute | Scoop.it
The new delivery mechanism completely turned off a gene responsible for high cholesterol in mice.
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Zipping DNA

Zipping DNA | SynBioFromLeukipposInstitute | Scoop.it
ETH researchers have developed a method that allows large amounts of genetic information to be compressed and then decompressed again in cells. This could aid in the development of new therapies.
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Researchers fold a protein within a protein

Researchers fold a protein within a protein | SynBioFromLeukipposInstitute | Scoop.it
A team from the NUS Yong Loo Lin School of Medicine (NUS Medicine) has invented a fundamentally new way of folding and protecting recombinant proteins. Sourced from the rapidly expanding field of synthetic biology, this protein-in-a-protei
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A New Method for the 3-D Printing of Living Tissues

The approach could revolutionize regenerative medicine
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Genetic circuit characterization and debugging using RNA-seq

Genetic circuits implement computational operations within a cell. Debugging them is difficult because their function is defined by multiple states (e.g., combinations of inputs) that vary in time. Here, we develop RNA-seq methods that enable the simultaneous measurement of: (i) the states of internal gates, (ii) part performance (promoters, insulators, terminators), and (iii) impact on host gene expression. This is applied to a three-input one-output circuit consisting of three sensors, five NOR/NOT gates, and 46 genetic parts. Transcription profiles are obtained for all eight combinations of inputs, from which biophysical models can extract part activities and the response functions of sensors and gates. Various unexpected failure modes are identified, including cryptic antisense promoters, terminator failure, and a sensor malfunction due to media-induced changes in host gene expression. This can guide the selection of new parts to fix these problems, which we demonstrate by using a bidirectional terminator to disrupt observed antisense transcription. This work introduces RNA-seq as a powerful method for circuit characterization and debugging that overcomes the limitations of fluorescent reporters and scales to large systems composed of many parts.
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NPL, Imperial College London launch new virtual lab

NPL, Imperial College London launch new virtual lab | SynBioFromLeukipposInstitute | Scoop.it
NPL and Imperial College London have unveiled a new £7 million virtual lab to help the UK’s synthetic biology industry enhance the manufacturing and adoption of new drugs, therapies and other products. - News - PharmaTimes
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Bottom-up synthetic biology: modular design for making artificial platelets

Engineering artificial cells to mimic one or multiple fundamental cell biological functions is an emerging area of synthetic biology. Reconstituting functional modules from biological components in vitro is a challenging yet an important essence of bottom-up synthetic biology. Here we describe the concept of building artificial platelets using bottom-up synthetic biology and the four functional modules that together could enable such an ambitious effort.
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Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5' UTRs as well as native S. cerevisiae 5' UTRs. The model additionally was used to computationally evolve highly active 5' UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.
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