Liquid-liquid phase transitions in complex mixtures of proteins and other molecules produce crowded compartments supporting in vitro transcription and translation. We developed a method based on picoliter water-in-oil droplets to induce coacervation in Escherichia coli cell lysate and follow gene expression under crowded and noncrowded conditions. Coacervation creates an artificial cell-like environment in which the rate of mRNA production is increased significantly. Fits to the measured transcription rates show a two orders of magnitude larger binding constant between DNA and T7 RNA polymerase, and five to six times larger rate constant for transcription in crowded environments, strikingly similar to in vivo rates. The effect of crowding on interactions and kinetics of the fundamental machinery of gene expression has a direct impact on our understanding of biochemical networks in vivo. Moreover, our results show the intrinsic potential of cellular components to facilitate macromolecular organization into membranefree compartments by phase separation.microdroplets | macromolecular crowding P rotocells are minimal compartmentalized systems exhibiting key characteristics of cellular function, including metabolism and replication (1, 2). Lipid vesicles are considered the prototypical protocell as they can form functional microscopic spherical assemblies suited for in vitro gene expression (3, 4). Compartmentalization via lipid bilayers is considered essential for the emergence of cells (4), but there are alternative models based on liquid-liquid phase transitions that lead to the emergence of compartments (5, 6). Compartmentalization is but one characteristic, as protocells ideally also mimic the highly crowded interior of living cells, which have total macromolecule concentrations in excess of 300 g/L (7). Examples in which compartmentalization and high local concentrations are obtained concurrently, include DNA brushes (8), aqueous two-phase systems (9), and liquid coacervates (10). Phase separation or coacervation occurs in a wide range of polymer and protein solutions, often triggered by changes in temperature or salt concentration, or by the addition of coacervating agents (11). The (complex) coacervate droplets that are formed in such systems present macromolecularly crowded, aqueous, physical compartments, 1-100 μm in diameter (12). Recent work has identified similar liquid phase transitions in vivo in the formation of intracellular non-membrane-bound compartments exhibiting liquid-like properties, slowed down diffusion, and strongly interacting macromolecular components (13,14). Well-studied examples are the intracellular localization of DNA or RNA and proteins in Cajal bodies, P granules, and nucleoli (15-17), which can contain over 100 components. Such complexity has not been achieved in two-phase systems in vitro (18,19). Although the physics of coacervates is well understood, progress in their development as protocell models has stalled, because of the lack of demonstrations of complex biochemical proce...

Understanding the dynamics of complex enzymatic reactions in highly crowded small volumes is crucial for the development of synthetic minimal cells. Compartmentalised biochemical reactions in cell-sized containers exhibit a degree of randomness due to the small number of molecules involved. However, it is unknown how the physical environment contributes to the stochastic nature of multistep enzymatic processes. Here, we present a robust method to quantify gene expression noise in vitro using droplet microfluidics. We study the changes in stochasticity in cellfree gene expression of two genes compartmentalised within droplets as a function of DNA copy number and macromolecular crowding. We find that decreased diffusion caused by a crowded environment leads to the spontaneous formation of heterogeneous micro-environments of mRNA as local production rates exceed diffusion rates of macromolecules. This heterogeneity leads to a higher probability of the molecular machinery to stay in the same microenvironment, directly increasing the system's stochasticity.Noise is present in all living cells. It has been studied in prokaryotes and eukaryotes 1 , as well as stem 2,3 and cancer cells 4 , and cells expressing viruses 5 . Gene expression is a key example of a complex stochastic enzymatic process. Careful analysis of variations in mRNA and protein levels has revealed the importance of both amplitude and typical decay time of noise and the ability of cells to exploit or suppress noise in gene expression [6][7][8][9] . Unlike deterministic models of gene expression, which are used to predict dynamics over large populations, stochastic models can correctly predict the dynamics of gene expression at the Reprints and permission information is available online at http://npg.nature.com/reprintsandpermissions/.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use

Transcription is an episodic process characterized by probabilistic bursts, but how the transcriptional noise from these bursts is modulated by cellular physiology remains unclear. Using simulations and single-molecule RNA counting, we examined how cellular processes influence cell-to-cell variability (noise). The results show that RNA noise is higher in the cytoplasm than the nucleus in ∼85% of genes across diverse promoters, genomic loci, and cell types (human and mouse). Measurements show further amplification of RNA noise in the cytoplasm, fitting a model of biphasic mRNA conversion between translation- and degradation-competent states. This multi-state translation-degradation of mRNA also causes substantial noise amplification in protein levels, ultimately accounting for ∼74% of intrinsic protein variability in cell populations. Overall, the results demonstrate how noise from transcriptional bursts is intrinsically amplified by mRNA processing, leading to a large super-Poissonian variability in protein levels.

Stochastic fluctuations in gene expression (‘noise’) are often considered detrimental, but fluctuations can also be exploited for benefit (e.g., dither). We show here that DNA base-excision repair amplifies transcriptional noise to facilitate cellular reprogramming. Specifically, the DNA-repair protein Apex1, which recognizes both naturally occurring and unnatural base modifications, amplifies expression noise while homeostatically maintaining mean-expression levels. This amplified expression noise originates from shorter duration, higher intensity, transcriptional bursts generated by Apex1-mediated DNA supercoiling. The remodeling of DNA topology first impedes and then accelerates transcription to maintain mean levels. This mechanism, which we term Discordant Transcription through Repair (DiThR; pronounced /’dither’/), potentiates cellular reprogramming and differentiation. Our study reveals a potential functional role for transcriptional fluctuations mediated by DNA base modifications in embryonic development and disease.

Diverse biological systems utilize fluctuations ("noise") in gene expression to drive lineage-commitment decisions. However, once a commitment is made, noise becomes detrimental to reliable function, and the mechanisms enabling post-commitment noise suppression are unclear. Here, we find that architectural constraints on noise suppression are overcome to stabilize fate commitment. Using single-molecule and time-lapse imaging, we find that-after a noise-driven event-human immunodeficiency virus (HIV) strongly attenuates expression noise through a non-transcriptional negative-feedback circuit. Feedback is established through a serial cascade of post-transcriptional splicing, whereby proteins generated from spliced mRNAs auto-deplete their own precursor unspliced mRNAs. Strikingly, this auto-depletion circuitry minimizes noise to stabilize HIV's commitment decision, and a noise-suppression molecule promotes stabilization. This feedback mechanism for noise suppression suggests a functional role for delayed splicing in other systems and may represent a generalizable architecture of diverse homeostatic signaling circuits.