Impact of variability in cell cycle periodicity on cell population dynamics.

The cell cycle consists of a series of orchestrated events controlled by molecular sensing and feedback networks that ultimately drive the duplication of total DNA and the subsequent division of a single parent cell into two daughter cells. The ability to block the cell cycle and synchronize cells within the same phase has helped understand factors that control cell cycle progression and the properties of each individual phase. Intriguingly, when cells are released from a synchronized state, they do not maintain synchronized cell division and rapidly become asynchronous. The rate and factors that control cellular desynchronization remain largely unknown. In this study, using a combination of experiments and simulations, we investigate the desynchronization properties in cervical cancer cells (HeLa) starting from the G1/S boundary following double-thymidine block. Propidium iodide (PI) DNA staining was used to perform flow cytometry cell cycle analysis at regular 8 hour intervals, and a custom auto-similarity function to assess the desynchronization and quantify the convergence to an asynchronous state. In parallel, we developed a single-cell phenomenological model the returns the DNA amount across the cell cycle stages and fitted the parameters using experimental data. Simulations of population of cells reveal that the cell cycle desynchronization rate is primarily sensitive to the variability of cell cycle duration within a population. To validate the model prediction, we introduced lipopolysaccharide (LPS) to increase cell cycle noise. Indeed, we observed an increase in cell cycle variability under LPS stimulation in HeLa cells, accompanied with an enhanced rate of cell cycle desynchronization. Our results show that the desynchronization rate of artificially synchronized in-phase cell populations can be used a proxy of the degree of variance in cell cycle periodicity, an underexplored axis in cell cycle research.

Uncoupling gene expression noise along the central dogma using genome engineered human cell lines.

Eukaryotic protein synthesis is an inherently stochastic process. This stochasticity stems not only from variations in cell content between cells but also from thermodynamic fluctuations in a single cell. Ultimately, these inherently stochastic processes manifest as noise in gene expression, where even genetically identical cells in the same environment exhibit variation in their protein abundances. In order to elucidate the underlying sources that contribute to gene expression noise, we quantify the contribution of each step within the process of protein synthesis along the central dogma. We uncouple gene expression at the transcriptional, translational, and post-translational level using custom engineered circuits stably integrated in human cells using CRISPR. We provide a generalized framework to approximate intrinsic and extrinsic noise in a population of cells expressing an unbalanced two-reporter system. Our decomposition shows that the majority of intrinsic fluctuations stem from transcription and that coupling the two genes along the central dogma forces the fluctuations to propagate and accumulate along the same path, resulting in increased observed global correlation between the products.

Robust Filtering and Noise Suppression in Intragenic miRNA-Mediated Host Regulation.

MicroRNAs (miRNAs) are short non-coding RNA molecules that regulate gene expression post-transcriptionally by binding to target messenger RNAs (mRNAs). Many human miRNAs are intragenic, located within introns of protein-coding sequence (host). Intriguingly, a percentage of intragenic miRNAs downregulate the host transcript forming an incoherent feedforward motif topology. Here, we study intragenic miRNA-mediated host gene regulation using a synthetic gene circuit stably integrated within a safe-harbor locus of human cells. When the intragenic miRNA is directed to inhibit the host transcript, we observe a reduction in reporter expression accompanied by output filtering and noise reduction. Specifically, the system operates as a filter with respect to promoter strength, with the threshold being robust to promoter strength and measurement time. Additionally, the intragenic miRNA regulation reduces expression noise compared to splicing-alone architecture. Our results provide a new insight into miRNA-mediated gene expression, with direct implications to gene therapy and synthetic biology applications.

Mapping the operational landscape of microRNAs in synthetic gene circuits.

MicroRNAs are a class of short, noncoding RNAs that are ubiquitous modulators of gene expression, with roles in development, homeostasis, and disease. Engineered microRNAs are now frequently used as regulatory modules in synthetic biology. Moreover, synthetic gene circuits equipped with engineered microRNA targets with perfect complementarity to endogenous microRNAs establish an interface with the endogenous milieu at the single-cell level. The function of engineered microRNAs and sensor systems is typically optimized through extensive trial-and-error. Here, using a combination of synthetic biology experimentation in human embryonic kidney cells and quantitative analysis, we investigate the relationship between input genetic template abundance, microRNA concentration, and output under microRNA control. We provide a framework that employs the complete operational landscape of a synthetic gene circuit and enables the stepwise development of mathematical models. We derive a phenomenological model that recapitulates experimentally observed nonlinearities and contains features that provide insight into the microRNA function at various abundances. Our work facilitates the characterization and engineering of multi-component genetic circuits and specifically points to new insights on the operation of microRNAs as mediators of endogenous information and regulators of gene expression in synthetic biology.

Reconfigurable hybrid interface for molecular marker diagnostics and in-situ reporting.

Combinations of molecular signals such as transcription factors and microRNAs in cells are a reliable indicator of multi-gene disorders. A system capable of detecting these conditions in-situ may be used as a tool for diagnosis and monitoring of disease. Here, we engineer genetic circuits that sense endogenous levels of the androgen receptor (AR), the glucocorticoid receptor (GR), and the microRNA hsa-miR-21 (miR-21) in cervical cancer cells (HeLa). Furthermore, using the mediator molecule human chorionic gonadotropin (hCG), we interface the intracellular information to enzyme-linked immunosorbent assay (ELISA) test strips. We demonstrate that this hybrid genetic circuit and test-strip interface can accommodate combinatorial, low-cost, and in-situ reporting, a versatile profiling tool.