SynBioFromLeukipposInstitute

The CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated protein) system is known to protect bacteria against foreign invading DNA, usually from phages (viruses that infect bacteria) or plasmids (circular DNA found in the cytoplasm of bacteria). Since the first demonstration of CRISPR-Cas functionality a decade ago (1), mechanistic understanding of CRISPR-Cas has not only enabled genome editing but also revolutionized our appreciation of bacterial defense against their viruses. CRISPR-Cas systems show a high degree of sophistication in providing immunity against phages, including elaborate mechanisms to accurately identify the invading DNA, safety checks to prevent self-targeting (2), and high diversity of target destruction mechanisms among different types of CRISPR-Cas systems (3). Kazlauskiene et al. (4), on page 605 of this issue, and a study by Niewoehner et al. (5) report the discovery of an unexpected aspect of CRISPR-Cas immunity: intracellular signaling.

The precise spatial and temporal control of gene expression, cell differentiation, and tissue morphogenesis has widespread application in regenerative medicine and the study of tissue development. In this work, we applied optogenetics to control cell differentiation and new tissue formation. Specifically, we engineered an optogenetic "on" switch that provides permanent transgene expression following a transient dose of blue light illumination. To demonstrate its utility in controlling cell differentiation and reprogramming, we incorporated an engineered form of the master myogenic factor MyoD into this system in multipotent cells. Illumination of cells with blue light activated myogenic differentiation, including upregulation of myogenic markers and fusion into multinucleated myotubes. Cell differentiation was spatially patterned by illumination of cell cultures through a photomask. To demonstrate the application of the system to controlling in vivo tissue development, the light inducible switch was used to control the expression of VEGF and angiopoietin-1, which induced angiogenic sprouting in a mouse dorsal window chamber model. Live intravital microscopy showed illumination-dependent increases in blood-perfused microvasculature. This optogenetic switch is broadly useful for applications in which sustained and patterned gene expression is desired following transient induction, including tissue engineering, gene therapy, synthetic biology, and fundamental studies of morphogenesis.

The availability of simple, specific, and inexpensive on-site detection methods is of key importance for deployment of pathogen surveillance networks. We developed a nontechnical and highly specific colorimetric assay for detection of pathogen-derived peptides based on Saccharomyces cerevisiae-a genetically tractable model organism and household product. Integrating G protein-coupled receptors with a visible, reagent-free lycopene readout, we demonstrate differential detection of major human, plant, and food fungal pathogens with nanomolar sensitivity. We further optimized a one-step rapid dipstick prototype that can be used in complex samples, including blood, urine, and soil. This modular biosensor can be economically produced at large scale, is not reliant on cold-chain storage, can be detected without additional equipment, and is thus a compelling platform scalable to global surveillance of pathogens.

Mathematical modelling of signalling pathways aids experimental investigation in system and synthetic biology. Ever increasing data availability prompts the development of large dynamic models with numerous parameters. In this paper, we investigate how the number of unknown parameters affects the convergence of three frequently used optimisation algorithms and four objective functions. We compare objective functions that use data-driven normalisation of the simulations with those that use scaling factors. The data-driven normalisation of the simulation approach implies that simulations are normalised in the same way as the data, making both directly comparable. The scaling factor approach, which is commonly used for parameter estimation in dynamic systems, introduces scaling factors that multiply the simulations to convert them to the scale of the data. Here we show that the scaling factor approach increases, compared to data-driven normalisation of the simulations, the degree of practical non-identifiability, defined as the number of directions in the parameter space, along which parameters are not identifiable. Further, the results indicate that data-driven normalisation of the simulations greatly improve the speed of convergence of all tested algorithms when the overall number of unknown parameters is relatively large (74 parameters in our test problems). Data-driven normalisation of the simulations also markedly improve the performance of the non-gradient-based algorithm tested even when the number of unknown parameters is relatively small (10 parameters in our test problems). As the models and the unknown parameters increase in size, the data-driven normalisation of the simulation approach can be the preferred option, because it does not aggravate non-identifiability and allows for obtaining parameter estimates in a reasonable amount of time.

Xenotransplantation is a promising strategy to alleviate the shortage of organs for human transplantation. In addition to the concern on pig-to-human immunological compatibility, the risk of cross-species transmission of porcine endogenous retroviruses (PERVs) has impeded the clinical application of this approach. Earlier, we demonstrated the feasibility of inactivating PERV activity in an immortalized pig cell line. Here, we confirmed that PERVs infect human cells, and observed the horizontal transfer of PERVs among human cells. Using CRISPR-Cas9, we inactivated all the PERVs in a porcine primary cell line and generated PERV-inactivated pigs via somatic cell nuclear transfer. Our study highlighted the value of PERV inactivation to prevent cross-species viral transmission and demonstrated the successful production of PERV-inactivated animals to address the safety concern in clinical xenotransplantation.

The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications.
METHODS:
We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN.
RESULTS:
Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network sizes and levels of stochastic noise. We found our HRNN method to be superior in terms of accuracy for nonlinear data sets with higher amounts of noise.
CONCLUSIONS:
The proposed method identifies time-delayed gene-gene interactions of GRNs. The topology-based advancement of our HRNN worked as expected by more effectively modeling nonlinear data sets. As a non-fully connected network, an added benefit to HRNN was how it helped to find the few genes which regulated the target gene over different time delays.

Synthetic control of peptide-based supramolecular assemblies can provide molecular cues to understand protein aggregation while also inspiring the development of novel chemical biology tools to deliver cargoes inside cells. Here we show that the trans-to-cis photoisomerization of a pendant azo-group covalently attached to a Phe-Phe dipeptide can comprehensively 'turn-off' its native fibrillation propensity as well as provide an optical handle to reversibly switch the aggregate morphology from fibril to vesicle.

Engineering an efficient biomolecular circuit often requires time-consuming iterations of optimization. Cell-free protein expression systems allow rapid testing of biocircuits in vitro, speeding the design-build-test cycle of synthetic biology. In this paper, we combine this with droplet microfluidics to densely scan a transcription-translation biocircuit space. Our system assays millions of parameter combinations per hour, providing a detailed map of function. The ability to comprehensively map biocircuit parameter spaces allows accurate modeling to predict circuit function and identify optimal circuits and conditions.