A partially self-regenerating synthetic cell

Self-regeneration is a fundamental function of all living systems. Here we demonstrate partial molecular self-regeneration in a synthetic cell. By implementing a minimal transcription-translation system within microfluidic reactors, the system is able to regenerate essential protein components from DNA templates and sustain synthesis activity for over a day. By quantitating genotype-phenotype relationships combined with computational modeling we find that minimizing resource competition and optimizing resource allocation are both critically important for achieving robust system function. With this understanding, we achieve simultaneous regeneration of multiple proteins by determining the required DNA ratios necessary for sustained self-regeneration. This work introduces a conceptual and experimental framework for the development of a self-replicating synthetic cell. A fundamental function of living systems is regenerating essential components. Here the authors design an artificial cell using a minimal transcription-translation system in microfluidic reactors for sustained regeneration of multiple essential proteins

iSLIM: a comprehensive approach to mapping and characterizing gene regulatory networks

Mapping gene regulatory networks is a significant challenge in systems biology, yet only a few methods are currently capable of systems-level identification of transcription factors (TFs) that bind a specific regulatory element. We developed a microfluidic method for integrated systems-level interaction mapping of TF-DNA interactions, generating and interrogating an array of 423 full-length Drosophila TFs. With integrated systems-level interaction mapping, it is now possible to rapidly and quantitatively map gene regulatory networks of higher eukaryote

A 1024-sample serum analyzer chip for cancer diagnostics

We present a platform that combines microarrays and microfluidic techniques to measure four protein biomarkers in 1024 serum samples for a total of 4096 assays per device. Detection is based on a surface fluorescence sandwich immunoassay with a limit of detection of similar to 1 pM for most of the proteins measured: PSA, TNF-alpha, IL-1 beta, and IL-6. To validate the utility of our platform, we measured these four biomarkers in 20 clinical human serum samples, 10 from prostate cancer patients and 10 female and male controls. We compared the results of our platform to a conventional ELISA and found a good correlation between them. However, compared to a classical ELISA, our device reduces the total cost of reagents by 4 orders of magnitude while increasing throughput by 2 orders of magnitude. Overall, we demonstrate an integrated approach to perform low-cost and rapid quantification of protein biomarkers from over one thousand serum samples. This new high-throughput technology will have a significant impact on disease diagnosis and management

Implementation of cell-free biological networks at steady state

Living cells maintain a steady state of biochemical reaction rates by exchanging energy and matter with the environment. These exchanges usually do not occur in in vitro systems, which consequently go to chemical equilibrium. This in turn has severely constrained the complexity of biological networks that can be implemented in vitro. We developed nanoliter-scale microfluidic reactors that exchange reagents at dilution rates matching those of dividing bacteria. In these reactors we achieved transcription and translation at steady state for 30 h and implemented diverse regulatory mechanisms on the transcriptional, translational, and posttranslational levels, including RNA polymerases, transcriptional repression, translational activation, and proteolysis. We constructed and implemented an in vitro genetic oscillator and mapped its phase diagram showing that steady-state conditions were necessary to produce oscillations. This reactor-based approach will allow testing of whether fundamental limits exist to in vitro network complexity

Massively parallel measurements of molecular interaction kinetics on a microfluidic platform

Quantitative biology requires quantitative data. No high-throughput technologies exist capable of obtaining several hundred independent kinetic binding measurements in a single experiment. We present an integrated microfluidic device (k-MITOMI) for the simultaneous kinetic characterization of 768 biomolecular interactions. We applied k-MITOMI to the kinetic analysis of transcription factor (TF)—DNA interactions, measuring the detailed kinetic landscapes of the mouse TF Zif268, and the yeast TFs Tye7p, Yox1p, and Tbf1p. We demonstrated the integrated nature of k-MITOMI by expressing, purifying, and characterizing 27 additional yeast transcription factors in parallel on a single device. Overall, we obtained 2,388 association and dissociation curves of 223 unique molecular interactions with equilibrium dissociation constants ranging from 2×10^(-6)M to 2×10^(-9)M, and dissociation rate constants of approximately 6s^(-1) to 8.5×10^(-3)s^(-1). Association rate constants were uniform across 3 TF families, ranging from 3.7x10^6 M^(-1)s^(-1) to 9.6x10^7 M^(-1)s^(-1), and are well below the diffusion limit. We expect that k-MITOMI will contribute to our quantitative understanding of biological systems and accelerate the development and characterization of engineered systems

Rapid cell-free forward engineering of novel genetic ring oscillators

While complex dynamic biological networks control gene expression in all living organisms, the forward engineering of comparable synthetic networks remains challenging. The current paradigm of characterizing synthetic networks in cells results in lengthy design-build-test cycles, minimal data collection, and poor quantitative characterization. Cell-free systems are appealing alternative environments, but it remains questionable whether biological networks behave similarly in cell-free systems and in cells. We characterized in a cell-free system the 'repressilator,' a three-node synthetic oscillator. We then engineered novel three, four, and five-gene ring architectures, from characterization of circuit components to rapid analysis of complete networks. When implemented in cells, our novel 3-node networks produced population-wide oscillations and 95% of 5-node oscillator cells oscillated for up to 72 hours. Oscillation periods in cells matched the cell-free system results for all networks tested. An alternate forward engineering paradigm using cell-free systems can thus accurately capture cellular behavior

Single Molecule Localization and Discrimination of DNA–Protein Complexes by Controlled Translocation Through Nanocapillaries

Through the use of optical tweezers we performed controlled translocations of DNA-protein complexes through nanocapillaries. We used RNA polymerase (RNAP) with two binding sites on a 7.2 kbp DNA fragment and a dCas9 protein tailored to have five binding sites on lambda-DNA (48.5 kbp). Measured localization of binding sites showed a shift from the expected positions on the DNA that we explained using both analytical fitting and a stochastic model. From the measured force versus stage curves we extracted the non equilibrium work done during the translocation of a DNA-protein complex and used it to obtain an estimate of the effective charge of the complex. In combination with conductivity measurements, we provided a proof of concept for discrimination between different DNA protein complexes simultaneous to the localization of their binding sites

Does Positive Selection Drive Transcription Factor Binding Site Turnover? A Test with Drosophila Cis-Regulatory Modules

Transcription factor binding site(s) (TFBS) gain and loss (i.e., turnover) is a well-documented feature of cis-regulatory module (CRM) evolution, yet little attention has been paid to the evolutionary force(s) driving this turnover process. The predominant view, motivated by its widespread occurrence, emphasizes the importance of compensatory mutation and genetic drift. Positive selection, in contrast, although it has been invoked in specific instances of adaptive gene expression evolution, has not been considered as a general alternative to neutral compensatory evolution. In this study we evaluate the two hypotheses by analyzing patterns of single nucleotide polymorphism in the TFBS of well-characterized CRM in two closely related Drosophila species, Drosophila melanogaster and Drosophila simulans. An important feature of the analysis is classification of TFBS mutations according to the direction of their predicted effect on binding affinity, which allows gains and losses to be evaluated independently along the two phylogenetic lineages. The observed patterns of polymorphism and divergence are not compatible with neutral evolution for either class of mutations. Instead, multiple lines of evidence are consistent with contributions of positive selection to TFBS gain and loss as well as purifying selection in its maintenance. In discussion, we propose a model to reconcile the finding of selection driving TFBS turnover with constrained CRM function over long evolutionary time