Promoter architecture dictates cell-to-cell variability in gene expression

Variability in gene expression among genetically identical cells has emerged as a central preoccupation in the study of gene regulation; however, a divide exists between the predictions of molecular models of prokaryotic transcriptional regulation and genome-wide experimental studies suggesting that this variability is indifferent to the underlying regulatory architecture. We constructed a set of promoters in Escherichia coli in which promoter strength, transcription factor binding strength, and transcription factor copy numbers are systematically varied, and used messenger RNA (mRNA) fluorescence in situ hybridization to observe how these changes affected variability in gene expression. Our parameter-free models predicted the observed variability; hence, the molecular details of transcription dictate variability in mRNA expression, and transcriptional noise is specifically tunable and thus represents an evolutionarily accessible phenotypic parameter

Tuning Promoter Strength through RNA Polymerase Binding Site Design in Escherichia coli

One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will. As a step in that direction, we have constructed a set of 18 unique binding sites for E. coli RNA Polymerase (RNAP) σ^(70) holoenzyme, designed using a model of sequence-dependent binding energy combined with a thermodynamic model of transcription to produce a targeted level of gene expression. This promoter set allows us to determine the correspondence between the absolute numbers of mRNA molecules or protein products and the predicted promoter binding energies measured in K_(B)T energy units. These binding sites adhere on average to the predicted level of gene expression over orders of magnitude in constitutive gene expression, to within a factor of in both protein and mRNA copy number. With these promoters in hand, we then place them under the regulatory control of a bacterial repressor and show that again there is a strict correspondence between the measured and predicted levels of expression, demonstrating the transferability of the promoters to an alternate regulatory context. In particular, our thermodynamic model predicts the expression from our promoters under a range of repressor concentrations between several per cell up to over 100 per cell. After correcting the predicted polymerase binding strength using the data from the unregulated promoter, the thermodynamic model accurately predicts the expression for the simple repression strains to within 30%. Demonstration of modular promoter design, where parts of the circuit (such as RNAP/TF binding strength and transcription factor copy number) can be independently chosen from a stock list and combined to give a predictable result, has important implications as an engineering tool for use in synthetic biology

Using synthetic biology to make cells tomorrow's test tubes

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting

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One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will. As a step in that direction, we have constructed a set of 18 unique binding sites for E. coli RNA Polymerase (RNAP) s70 holoenzyme, designed using a model of sequence-dependent binding energy combined with a thermodynamic model of transcription to produce a targeted level of gene expression. This promoter set allows us to determine the correspondence between the absolute numbers of mRNA molecules or protein products and the predicted promoter binding energies measured in kBT energy units. These binding sites adhere on average to the predicted level of gene expression over 3 orders of magnitude in constitutive gene expression, to within a factor of 3 in both protein and mRNA copy number. With these promoters in hand, we then place them under the regulatory control of a bacterial repressor and show that again there is a strict correspondence between the measured and predicted levels of expression, demonstrating the transferability of the promoters to an alternate regulatory context. In particular, our thermodynamic model predicts the expression from our promoters under a range of repressor concentrations between several per cell up to over 100 per cell. After correcting the predicted polymerase binding strength using the data from the unregulated promoter, the thermodynamic model accurately predicts the expression for the simple repression strains to within 30%. Demonstration of modular promoter design, where parts of the circuit (such as RNAP/TF binding strength and transcription factor cop

Effect of transcription factor resource sharing on gene expression noise

Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it\u27s TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression

Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression

Quantifying the regulatory role of individual transcription factors in Escherichia coli [preprint]

Transcription factors (TFs) modulate gene expression by binding to regulatory DNA sequences surrounding target genes. To isolate the fundamental regulatory interactions of E. coli TFs, we measure regulation of TFs acting on synthetic target genes that are designed to isolate the individual TF regulatory effect. This data is interpreted through a thermodynamic model that decouples the role of TF copy number and TF binding affinity from the interactions of the TF on RNA polymerase through two distinct mechanisms: (de)stabilization of the polymerase and (de)acceleration of transcription initiation. We find the contribution of each mechanism towards the observed regulation depends on TF identity and binding location; for the set of TFs studied here, regulation immediately downstream of the promoter is not sensitive to TF identity, however these same TFs regulate through distinct mechanisms at an upstream binding site. Furthermore, depending on binding location, these two mechanisms of regulation can act coherently, to reinforce the observed regulatory role (activation or repression), or incoherently, where the TF regulates two distinct steps with opposing effect

Scaling of Gene Expression with Transcription-Factor Fugacity

The proteins associated with gene regulation are often shared between multiple pathways simultaneously. By way of contrast, models in regulatory biology often assume these pathways act independently. We demonstrate a framework for calculating the change in gene expression for the interacting case by decoupling repressor occupancy across the cell from the gene of interest by way of a chemical potential. The details of the interacting regulatory architecture are encompassed in an effective concentration, and thus, a single scaling function describes a collection of gene expression data from diverse regulatory situations and collapses it onto a single master curve

Single-molecule analysis of RAG-mediated V(D)J DNA cleavage

The recombination-activating gene products, RAG1 and RAG2, initiate V(D)J recombination during lymphocyte development by cleaving DNA adjacent to conserved recombination signal sequences (RSSs). The reaction involves DNA binding, synapsis, and cleavage at two RSSs located on the same DNA molecule and results in the assembly of antigen receptor genes. We have developed single-molecule assays to examine RSS binding by RAG1/2 and their cofactor high-mobility group-box protein 1 (HMGB1) as they proceed through the steps of this reaction. These assays allowed us to observe in real time the individual molecular events of RAG-mediated cleavage. As a result, we are able to measure the binding statistics (dwell times) and binding energies of the initial RAG binding events and characterize synapse formation at the single-molecule level, yielding insights into the distribution of dwell times in the paired complex and the propensity for cleavage on forming the synapse. Interestingly, we find that the synaptic complex has a mean lifetime of roughly 400 s and that its formation is readily reversible, with only ∼40% of observed synapses resulting in cleavage at consensus RSS binding sites

Flexibility and constraint in preimplantation gene regulation in mouse [preprint]

Although many features of embryonic development exhibit remarkable stability in the face of environmental perturbations, it is also clear that some aspects of early embryogenesis can be modulated by non-genetic influences during and after fertilization. Among potential perturbations experienced during reproduction, understanding the consequences of differing ex vivo fertilization methods at a molecular level is imperative for comprehending both the basic biology of early development and the potential consequences of assisted reproduction. Here, we set out to explore stable and flexible aspects of preimplantation gene expression using single-embryo RNA-sequencing of mouse embryos fertilized by natural mating, in vitro fertilization, or intracytoplasmic sperm injection, as well as oocytes parthenogenetically activated to develop (parthenotes). This dataset comprises a resource of over eight hundred individual embryos, which we use for three primary analyses. First, we characterize the effects of each fertilization method on early embryonic gene regulation, most notably finding decreased expression of trophectoderm markers at later stages of preimplantation development in ICSI embryos. Second, we find massive gene misregulation in parthenotes beyond the expected defects in imprinted gene expression, and show that many of these changes can be suppressed by sperm total RNA. Finally, we make use of the single-embryo resolution of our dataset to identify both stably-expressed genes and highly-variable genes in the early mouse embryo. Together, our data provide a detailed survey of the molecular consequences of different fertilization methods, establish parthenotes as a “tabula rasa” for understanding the role for sperm RNAs in preimplantation gene regulation, and identify subtypes of preimplantation embryos based on their expression of epivariable gene modules