Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Double-edged role of resource competition in gene expression noise and control

Despite extensive investigation demonstrating that resource competition can significantly alter the circuits' deterministic behaviors, a fundamental issue is how resource competition contributes to the gene expression noise and how the noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: the competition decreases noise through a resource constraint but generates its own type of noise which we name as ''resource competitive noise.'' Utilization of orthogonal resources enables retaining the noise reduction conferred by resource constraint while removing the added resource competitive noise. The noise reduction effects are studied using three negative feedback controller types: negatively competitive regulation (NCR), local, and global controllers, each having four placement architectures in the protein biosynthesis pathway (mRNA or protein inhibition on transcription or translation). Our results show that both local and NCR controllers with mRNA-mediated inhibition are efficacious at reducing noise, with NCR controllers demonstrating a superior noise-reduction capability. We also find that combining negative feedback controllers with orthogonal resources can improve the local controllers. This work provides deep insights into the origin of stochasticity in gene circuits with resource competition and guidance for developing effective noise control strategies.

Reduction in gene expression noise by targeted increase in accessibility at gene loci

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this “noisy” transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable.

Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria

Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as “noise”. Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.

An emerging and variant viral promoter of HIV-1 subtype C exhibits low-level gene expression noise

Background We observe the emergence of several promoter-variant viral strains in India during recent years. The variant viral promoters contain additional copies of transcription factor binding sites present in the viral modulatory region or enhancer, including RBEIII, LEF-1, Ap-1 and/or NF-κB. These sites are crucial for governing viral gene expression and latency. Here, we infer that one variant viral promoter R2N3-LTR containing two copies of RBF-2 binding sites (an RBEIII site duplication) and three copies of NF-κB motifs may demonstrate low levels of gene expression noise as compared to the canonical RN3-LTR or a different variant R2N4-LTR (a duplication of an RBEIII site and an NF-κB motif). To demonstrate this, we constructed a panel of sub-genomic viral vectors of promoter-variant LTRs co-expressing two reporter proteins (mScarlet and Gaussia luciferase) under the dual-control of Tat and Rev. We established stable pools of CEM.NKR-CCR5 cells (CEM-CCR5RL reporter cells) and evaluated reporter gene expression under different conditions of cell activation. Results The R2N3-LTR established stringent latency that was highly resistant to reversal by potent cell activators such as TNF-α or PMA, or even to a cocktail of activators, compared to the canonical RN3- or the variant R2N4-LTR. The R2N3-LTR exhibited low-level basal gene expression in the absence of cell activation that enhanced marginally but significantly when activated. In the presence of Tat and Rev, trans-complemented in the form of an infectious virus, the R2N3-LTR demonstrated gene expression at levels comparable to the wild-type viral promoter. The R2N3-LTR is responsive to Tat and Rev factors derived from viral strains representing diverse genetic subtypes. Conclusion With extremely low-level transcriptional noise, the R2N3-LTR can serve as an excellent model to examine the establishment, maintenance, and reversal of HIV-1 latency. The R2N3-LTR would also be an ideal viral promoter to develop high-throughput screening assays to identify potent latency-reversing agents since the LTR is not affected by the usual background noise of the cell.

Maximized redundant and synergistic information transfers predict the rise in the output gene expression noise in a generic class of coherent type-1 feed-forward loop networks

We apply the partial information decomposition principle to a generic coherent type-1 feed-forward loop (C1-FFL) motif with tunable direct and indirect transcriptional regulations of the output gene product and quantify the redundant, synergistic, and unique information transfers from the regulators to their target output species. Our results which are obtained within the small-noise regime of a Gaussian framework reveal that the redundant and synergistic information transfers are antagonistically related to the output noise. Most importantly, these two information flavors are maximized prior to the minimization and subsequent growth of the output noise. Therefore, we hypothesize that the dynamic information redundancy and synergy maxima may possibly be utilized as efficient statistical predictors to forecast the increasing trend of the fluctuations associated with the output gene expression dynamics in the C1-FFL class of network motifs. Our core analytical finding is supported by exact stochastic simulation data and furthermore validated for a diversified repertoire of biologically plausible parameters. Since, the output gene product serves essential physiological purposes in the cell, a predictive estimate of its noise level is supposed to be of considerable biophysical utility.

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis , a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

Synthetic cell-based materials extract positional information from morphogen gradients

Dynamic biomaterials composed of synthetic cellular structures have the potential to adapt and functionally differentiate guided by physical and chemical cues from their environment. Inspired by developing biological systems, which efficiently extract positional information from chemical morphogen gradients in the presence of environmental uncertainties, we here investigate the analogous question: how well can a synthetic cell determine its position within a synthetic multicellular structure? In order to calculate positional information in such systems, we created and analyzed a large number of replicas of synthetic cellular assemblies, which were composed of emulsion droplets connected via lipid bilayer membranes. The droplets contained cell-free two-node feedback gene circuits that responded to gradients of a genetic inducer acting as a morphogen. We found that in our system, simple anterior-posterior differentiation is possible, but positional information is limited by gene expression noise, and is also critically affected by the temporal evolution of the morphogen gradient and the life-time of the cell-free expression system contained in the synthetic cells. Using a 3D printing approach, we demonstrate morphogen-based differentiation also in larger tissue-like assemblies.