designing-automated-high-throughput-continuous-cell-growth

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring ...

Boston University

Harvard University

Clonal populations of cells exhibit cell-to-cell variation in the transcription of individual genes. In addition to this noise in gene expression, heterogeneity in the proteome and the proteostasis network expands the phenotypic diversity of a population. Heat shock factor 1 (Hsf1) regulates chaperone gene expression, thereby coupling transcriptional noise to proteostasis. Here we show that cell-to-cell variation in Hsf1 activity is an important determinant of phenotypic plasticity. Budding yeast cells with high Hsf1 activity were enriched for the ability to acquire resistance to an antifungal drug, and this enrichment depended on Hsp90, a known phenotypic capacitor and canonical Hsf1 target. We show that Hsf1 phosphorylation promotes cell-to-cell variation, and this variation, rather than absolute Hsf1 activity, promotes antifungal resistance. We propose that Hsf1 phosphorylation enables differential tuning of the proteostasis network in individual cells, allowing populations to access a range of phenotypic states.

Hsf1 Phosphorylation Generates Cell-to-Cell Variation in Hsp90 Levels and Promotes Phenotypic Plasticity.

Chemical modifications to DNA and histone proteins are involved in epigenetic programs underlying cellular differentiation and development. Regulatory networks involving molecular writers and readers of chromatin marks are thought to control these programs. Guided by this common principle, we established an orthogonal epigenetic regulatory system in mammalian cells using N6-methyladenine (m6A), a DNA modification not commonly found in metazoan epigenomes. Our system utilizes synthetic factors that write and read m6A and consequently recruit transcriptional regulators to control reporter loci. Inspired by models of chromatin spreading and epigenetic inheritance, we used our system and mathematical models to construct regulatory circuits that induce m6A-dependent transcriptional states, promote their spatial propagation, and maintain epigenetic memory of the states. These minimal circuits were able to program epigenetic functions de novo, conceptually validating "read-write" architectures. This work provides a toolkit for investigating models of epigenetic regulation and encoding additional layers of epigenetic information in cells.

Engineering Epigenetic Regulation Using Synthetic Read-Write Modules.

Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic circuits.

Article	Year
Hsf1 Phosphorylation Generates Cell-to-Cell Variation in Hsp90 Levels and Promotes Phenotypic Plasticity. Cell reports\| PubMed ID: 29562166	2018
Engineering Epigenetic Regulation Using Synthetic Read-Write Modules. Cell\| PubMed ID: 30528434	2019
Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies. Science (New York, N.Y.)\| PubMed ID: 31000590	2019

Ahmad S. Khalil

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