A quantitative comparison of sRNA-based and protein-based gene regulation

Programming anti-ribozymes to sense trigger RNAs for modulating gene expression in mammalian cells

Synthetic RNA-based switches provide distinctive merits in modulating gene expression. Simple and flexible RNA-based switches are crucial for advancing the field of gene regulation, paving the way for innovative tools that can sense and manipulate cellular processes. In this research, we have developed programmable ribozymes that are capable of suppressing gene expression in response to specific, endogenously expressed trigger RNAs. We engineer ribozymes by introducing upstream antisense sequences (anti-ribozymes) to inhibit the self-cleaving activity of the hammerhead ribozyme and open the expression of the target gene. The trigger RNA is designed to recognize and bind to complementary sequences within the anti-ribozymes, thereby inhibiting their ability to direct protein synthesis. The anti-ribozyme performance is optimized by regulating the essential sequence modules that play a crucial role in determining the specificity and efficiency of the anti-ribozyme's interaction with its trigger RNA. By applying this switch mechanism to various ribozyme designs, we have shown that it is possible to achieve control over gene expression across a wide range of trigger RNAs. By exploiting these programmable anti-ribozymes, we aim to create a powerful tool for controlling gene expression in mammalian cells, which could have important implications for basic research, disease diagnosis, and therapeutic interventions.

Suppression of HIV-1 transcription and latency reversal via ectopic expression of the viral antisense transcript AST

The mechanisms that regulate HIV-1 latency are not fully elucidated. Our previous studies showed that an HIV-1 antisense transcript (AST) promotes the deposition of histone modifications at the HIV-1 5' long terminal repeat, causing a closed chromatin state that suppresses viral transcription. Here, we report that ectopic expression of AST in CD4+ T cells from people living with HIV-1 undergoing antiretroviral therapy hinders the reactivation of viral transcription in response to ex vivo stimulation with pharmacologic and T cell receptor agonists, thus preventing the reversal of latency. We defined the structural domains and sequence motifs of AST that contribute to its latency-promoting functions. Last, we carried out an unbiased proteomic screen of AST interactors that revealed an array of host factors both previously known and not known to suppress HIV-1 expression. Our studies identify AST as a first-in-class biological molecule that is capable of enforcing HIV-1 latency and with actionable curative potential.

Exact distributions of threshold crossing times of proteins under post-transcriptional regulation by small RNAs

The timings of several cellular events like cell lysis, cell division, or pore formation in endosomes are regulated by the time taken for the relevant proteins to cross a threshold in number or concentration. Since protein synthesis is stochastic, the threshold crossing time is a first passage problem. The exact distributions of these first passage processes have been obtained recently for unregulated and autoregulated genes. Many proteins are however regulated by post-transcriptional regulation, controlled by small noncoding RNAs (sRNAs). Certain mathematical models of gene expression with post-transcriptional sRNA regulation have been recently exactly mapped to models without sRNA regulation. Utilizing this mapping and the exact distributions, we calculate exact results on fluctuations (full distribution, all cumulants, and characteristic times) of protein threshold crossing times in the presence of sRNA regulation. We derive two interesting predictions from these exact results. We show that the size of the fluctuation of the threshold crossing times have a nonmonotonic U-shaped behavior as a function of the rates of binding and unbinding of the sRNA-mRNA complex. Thus there are optimal parameters that minimize noise. Furthermore, the fluctuations in models with sRNA regulation may be higher or lower compared to the model without regulation, depending on the mean protein burst size.

miRNA circuit modules for precise, tunable control of gene expression

The ability to express transgenes at specified levels is critical for understanding cellular behaviors, and for applications in gene and cell therapy. Transfection, viral vectors, and other gene delivery methods produce varying protein expression levels, with limited quantitative control, while targeted knock-in and stable selection are inefficient and slow. Active compensation mechanisms can improve precision, but the need for additional proteins or lack of tunability have prevented their widespread use. Here, we introduce a toolkit of compact, synthetic miRNA-based circuit modules that provide precise, tunable control of transgenes across diverse cell types. These circuits, termed DIMMERs (Dosage-Invariant miRNA-Mediated Expression Regulators) use multivalent miRNA regulatory interactions within an incoherent feed-forward loop architecture to achieve nearly uniform protein expression over more than two orders of magnitude variation in underlying gene dosages or transcription rates. They also allow coarse and fine control of expression, and are portable, functioning across diverse cell types. In addition, a heuristic miRNA design algorithm enables the creation of orthogonal circuit variants that independently control multiple genes in the same cell. These circuits allowed dramatically improved CRISPR imaging, and super-resolution imaging of EGFR receptors with transient transfections. The toolbox provided here should allow precise, tunable, dosage-invariant expression for research, gene therapy, and other biotechnology applications.

RNA cis-regulators are important for Streptococcus pneumoniae in vivo success

Bacteria have evolved complex transcriptional regulatory networks, as well as many diverse regulatory strategies at the RNA level, to enable more efficient use of metabolic resources and a rapid response to changing conditions. However, most RNA-based regulatory mechanisms are not well conserved across different bacterial species despite controlling genes important for virulence or essential biosynthetic processes. Here, we characterize the activity of, and assess the fitness benefit conferred by, twelve cis-acting regulatory RNAs (including several riboswitches and a T-box), in the opportunistic pathogen Streptococcus pneumoniae TIGR4. By evaluating native locus mutants of each regulator that result in constitutively active or repressed expression, we establish that growth defects in planktonic culture are associated with constitutive repression of gene expression, while constitutive activation of gene expression is rarely deleterious. In contrast, in mouse nasal carriage and pneumonia models, strains with either constitutively active and repressed gene expression are significantly less fit than matched control strains. Furthermore, two RNA-regulated pathways, FMN synthesis/transport and pyrimidine synthesis/transport display exceptional sensitivity to mis-regulation or constitutive gene repression in both planktonic culture and in vivo environments. Thus, despite lack of obvious phenotypes associated with constitutive gene expression in vitro, the fitness benefit conferred on bacteria via fine-tuned metabolic regulation through cis-acting regulatory RNAs is substantial in vivo, and therefore easily sufficient to drive the evolution and maintenance of diverse RNA regulatory mechanisms.

Stochastic models of regulation of transcription in biological cells

In this paper we study an important global regulation mechanism of transcription of biological cells using specific macro-molecules, 6S RNAs. The functional property of 6S RNAs is of blocking the transcription of RNAs when the environment of the cell is not favorable. We investigate the efficiency of this mechanism with a scaling analysis of a stochastic model. The evolution equations of our model are driven by the law of mass action and the total number of polymerases is used as a scaling parameter. Two regimes are analyzed: exponential phase when the environment of the cell is favorable to its growth, and the stationary phase when resources are scarce. In both regimes, by defining properly occupation measures of the model, we prove an averaging principle for the associated multi-dimensional Markov process on a convenient timescale, as well as convergence results for "fast" variables of the system. An analytical expression of the asymptotic fraction of sequestrated polymerases in stationary phase is in particular obtained. The consequences of these results are discussed.

Small regulatory RNAs are mediators of the Streptococcus mutans SloR regulon

Dental caries is among the most prevalent chronic diseases worldwide. Streptococcus mutans, the chief causative agent of caries, uses a 25-kDa manganese-dependent SloR protein to coordinate the uptake of essential manganese with the transcription of its virulence attributes. Small non-coding RNAs (sRNAs) can either enhance or repress gene expression, and reports in the literature ascribe an emerging role for sRNAs in the environmental stress response. Herein, we focused our attention on 18-50 nt sRNAs as mediators of the S. mutans SloR and manganese regulons. Specifically, the results of RNA sequencing revealed 19 sRNAs in S. mutans, which were differentially transcribed in the SloR-proficient UA159 and SloR-deficient GMS584 strains, and 10 sRNAs that were differentially expressed in UA159 cells grown in the presence of low vs high manganese. We describe SmsR1532 and SmsR1785 as SloR- and manganese-responsive sRNAs that are processed from large transcripts and that bind SloR directly in their promoter regions. The predicted targets of these sRNAs include regulators of metal ion transport, growth management via a toxin-antitoxin operon, and oxidative stress tolerance. These findings support a role for sRNAs in coordinating intracellular metal ion homeostasis with virulence gene control in an important oral cariogen. IMPORTANCE Small regulatory RNAs (sRNAs) are critical mediators of environmental signaling, particularly in bacterial cells under stress, but their role in Streptococcus mutans is poorly understood. S. mutans, the principal causative agent of dental caries, uses a 25-kDa manganese-dependent protein, called SloR, to coordinate the regulated uptake of essential metal ions with the transcription of its virulence genes. In the present study, we identified and characterized sRNAs that are both SloR and manganese responsive. Taken together, this research can elucidate the details of regulatory networks that engage sRNAs in an important oral pathogen and that can enable the development of an effective anti-caries therapeutic.

Small regulatory RNAs are mediators of the Streptococcus mutans SloR regulon

Dental caries is among the most prevalent chronic infectious diseases worldwide. Streptococcus mutans , the chief causative agent of caries, uses a 25 kDa manganese dependent SloR protein to coordinate the uptake of essential manganese with the transcription of its virulence attributes. Small non-coding RNAs (sRNAs) can either enhance or repress gene expression and reports in the literature ascribe an emerging role for sRNAs in the environmental stress response. Herein, we identify 18-50 nt sRNAs as mediators of the S. mutans SloR and manganese regulons. Specifically, the results of sRNA-seq revealed 56 sRNAs in S. mutans that were differentially transcribed in the SloR-proficient UA159 and SloR-deficient GMS584 strains, and 109 sRNAs that were differentially expressed in UA159 cells grown in the presence of low versus high manganese. We describe SmsR1532 and SmsR1785 as SloR- and/or manganese-responsive sRNAs that are processed from large transcripts, and that bind SloR directly in their promoter regions. The predicted targets of these sRNAs include regulators of metal ion transport, growth management via a toxin-antitoxin operon, and oxidative stress tolerance. These findings support a role for sRNAs in coordinating intracellular metal ion homeostasis with virulence gene control in an important oral cariogen.

IMPORTANCE

Small regulatory RNAs (sRNAs) are critical mediators of environmental signaling, particularly in bacterial cells under stress, but their role in Streptococcus mutans is poorly understood. S. mutans, the principal causative agent of dental caries, uses a 25 kDa manganese-dependent protein, called SloR, to coordinate the regulated uptake of essential metal ions with the transcription of its virulence genes. In the present study, we identified and characterize sRNAs that are both SloR- and manganese-responsive. Taken together, this research can elucidate the details of regulatory networks that engage sRNAs in an important oral pathogen, and that can enable the development of an effective anti-caries therapeutic.

Origin and functional role of antisense transcription in endogenous and exogenous retroviruses

Most proteins expressed by endogenous and exogenous retroviruses are encoded in the sense (positive) strand of the genome and are under the control of regulatory elements within the 5' long terminal repeat (LTR). A number of retroviral genomes also encode genes in the antisense (negative) strand and their expression is under the control of negative sense promoters within the 3' LTR. In the case of the Human T-cell Lymphotropic Virus 1 (HTLV-1), the antisense protein HBZ has been shown to play a critical role in the virus lifecycle and in the pathogenic process, while the function of the Human Immunodeficiency Virus 1 (HIV-1) antisense protein ASP remains unknown. However, the expression of 3' LTR-driven antisense transcripts is not always demonstrably associated with the presence of an antisense open reading frame encoding a viral protein. Moreover, even in the case of retroviruses that do express an antisense protein, such as HTLV-1 and the pandemic strains of HIV-1, the 3' LTR-driven antisense transcript shows both protein-coding and noncoding activities. Indeed, the ability to express antisense transcripts appears to be phylogenetically more widespread among endogenous and exogenous retroviruses than the presence of a functional antisense open reading frame within these transcripts. This suggests that retroviral antisense transcripts may have originated as noncoding molecules with regulatory activity that in some cases later acquired protein-coding function. Here, we will review examples of endogenous and exogenous retroviral antisense transcripts, and the ways through which they benefit viral persistence in the host.

The effect of microRNA on protein variability and gene expression fidelity

Small regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such posttranscriptional regulation has been recently hypothesized to reduce the stochastic variability of gene expression around average levels. Here, we quantify noise in stochastic gene expression models with and without such regulation. Our results suggest that silencing mRNA posttranscriptionally will always increase, rather than decrease, gene expression noise when the silencing of mRNA also increases its degradation, as is expected for microRNA interactions with mRNA. In that regime, we also find that silencing mRNA generally reduces the fidelity of signal transmission from deterministically varying upstream factors to protein levels. These findings suggest that microRNA binding to mRNA does not generically confer precision to protein expression.

Regulation of neutrophil myeloperoxidase inhibitor SPIN by the small RNA Teg49 in Staphylococcus aureus

Teg49 is a Staphylococcus aureus trans-acting regulatory sRNA derived from cleavage of the sarA P3 transcript. We showed by RNA-Seq here that the 5' trident-like structure in Teg49 regulates transcriptionally (direct and indirect) 22 genes distinct from sarA. Among these, Teg49 was noted to repress spn, encoding a 102 residue preprotein which yields the mature 73 residue peptide which inhibits the catalytic activity of myeloperoxidase in human neutrophils. Teg49 was found to regulate spn mRNA post-transcriptionally in strain SH1000 through 9-nt base-pairing between hairpin loop 2 of Teg49 and an exposed bulge of the spn mRNA. Mutations of the Teg49 binding site disrupted the repression of spn, leading to reduced degradation, and increased half-life of spn mRNA in the Teg49 mutant. The spn-Teg49 interaction was also confirmed with a synonymous spn mutation to yield enhanced spn expression in the mutant vs. the parent. The Teg49 mutant with increased spn expression exhibited enhanced resistance to MPO activity in vitro. Killing assays with human neutrophils showed that the Teg49 mutant was more resistant to killing after phagocytosis. Altogether, this study shows that Teg49 in S. aureus has a distinct and important regulatory profile whereby this sRNA modulates resistance to myeloperoxidase-mediated killing by human neutrophils.

Gene regulation by a protein translation factor at the single-cell level

Gene expression is inherently stochastic and pervasively regulated. While substantial work combining theory and experiments has been carried out to study how noise propagates through transcriptional regulations, the stochastic behavior of genes regulated at the level of translation is poorly understood. Here, we engineered a synthetic genetic system in which a target gene is down-regulated by a protein translation factor, which in turn is regulated transcriptionally. By monitoring both the expression of the regulator and the regulated gene at the single-cell level, we quantified the stochasticity of the system. We found that with a protein translation factor a tight repression can be achieved in single cells, noise propagation from gene to gene is buffered, and the regulated gene is sensitive in a nonlinear way to global perturbations in translation. A suitable mathematical model was instrumental to predict the transfer functions of the system. We also showed that a Gamma distribution parameterized with mesoscopic parameters, such as the mean expression and coefficient of variation, provides a deep analytical explanation about the system, displaying enough versatility to capture the cell-to-cell variability in genes regulated both transcriptionally and translationally. Overall, these results contribute to enlarge our understanding on stochastic gene expression, at the same time they provide design principles for synthetic biology.

In the cell, proteins can bind to DNA to regulate transcription as well as to RNA to regulate translation. However, cells have mainly evolved to exploit transcription factors as specific gene regulators, while translation factors have remained as global modulators of expression. Consequently, transcription regulation has attracted much attention over the last years to unveil design principles of genetic organization and to engineer synthetic circuits for cell reprogramming. In this work, the phage MS2 coat protein was exploited to regulate the expression of a green fluorescent protein at the level of translation. This synthetic system was instrumental to gain fundamental knowledge on stochasticity and regulation at an overlooked level within the genetic information flow.

Stability and Bifurcation Analysis of a Diffusive miR-9/Hes1 Network With Time Delay

In this paper, a model of miR-9/Hes1 interaction network involving one time delay and diffusion effect under the Neumann boundary conditions is studied. First of all, the stability of the positive equilibrium and the existence of local Hopf bifurcation and Turing-Hopf bifurcation are investigated by analyzing the associated characteristic equation. Second, a algorithm for determining the direction, stability and period of the corresponding bifurcating periodic solutions is presented. The obtained results suggest that the quiescent progenitors (high steady-state Hes1) can be easily excited into oscillation by time delay whereas the differentiated state (low steady-state Hes1) is basically unaffected, and the integrated effect of delay and diffusion can induce the occurrence of spatially inhomogeneous patterns. More importantly, spatially homogeneous/inhomogeneous periodic solutions can exist simultaneously when the diffusion coefficients of Hes1 mRNA and Hes1 protein are appropriately small, conversely, there is only spatially homogeneous periodic solutions. Intriguingly, both temporal patterns and spatial-temporal patterns show that time delay can prompt Hes1 protein to shift from the high concentration state to the low concentration one ("ON" → "OFF"), where Hes1 protein shows low level whereas miR-9 shows high level. Finally, some numerical examples are presented to verify and visualize theoretical results.

Information transmission in a two-step cascade: interplay of activation and repression

We present an information-theoretic formalism to study signal transduction in four architectural variants of a model two-step cascade with increasing input population. Our results categorize these four types into two classes depending upon the effect of activation and repression on mutual information, net synergy, and signal-to-noise ratio. Using the Gaussian framework and linear noise approximation, we derive the analytic expressions for these metrics to establish their underlying relationships in terms of the biochemical parameters. We also verify our approximations through stochastic simulations.

Kinetic modeling reveals additional regulation at co-transcriptional level by post-transcriptional sRNA regulators

Small RNAs (sRNAs) are important gene regulators in bacteria. Many sRNAs act post-transcriptionally by affecting translation and degradation of the target mRNAs upon base-pairing interactions. Here we present a general approach combining imaging and mathematical modeling to determine kinetic parameters at different levels of sRNA-mediated gene regulation that contribute to overall regulation efficacy. Our data reveal that certain sRNAs previously characterized as post-transcriptional regulators can regulate some targets co-transcriptionally, leading to a revised model that sRNA-mediated regulation can occur early in an mRNA’s lifetime, as soon as the sRNA binding site is transcribed. This co-transcriptional regulation is likely mediated by Rho-dependent termination when transcription-coupled translation is reduced upon sRNA binding. Our data also reveal several important kinetic steps that contribute to the differential regulation of mRNA targets by an sRNA. Particularly, binding of sRNA to the target mRNA may dictate the regulation hierarchy observed within an sRNA regulon.

Reyer et al. use fluorescent microscopy and kinetic modeling to find that two sRNAs canonically described as post-transcriptional regulators can regulate their targets co-transcriptionally and determine the in vivo kinetic parameters that dictate differential regulation efficiency.

Hierarchy in Hfq Chaperon Occupancy of Small RNA Targets Plays a Major Role in Their Regulation

Bacterial small RNAs (sRNAs) are posttranscriptional regulators of gene expression that base pair with complementary sequences on target mRNAs, often in association with the chaperone Hfq. Here, using experimentally identified sRNA-target pairs, along with gene expression measurements, we assess basic principles of regulation by sRNAs. We show that the sRNA sequence dictates the target repertoire, as point mutations in the sRNA shift the target set correspondingly. We distinguish two subsets of targets: targets showing changes in expression levels under overexpression of their sRNA regulator and unaffected targets that interact more sporadically with the sRNA. These differences among targets are associated with their Hfq occupancy, rather than with the sRNA-target base-pairing potential. Our results suggest that competition among targets over Hfq binding plays a major role in the regulatory outcome, possibly awarding targets with higher Hfq binding efficiency an advantage in the competition over binding to the sRNA.

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Basic concepts of regulation by small RNAs are revealed from large-scale data

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Small changes in the small RNA sequence shift the target repertoire accordingly

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A regulatory sRNA affects the expression levels of only a subset of its targets

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Competition among targets over Hfq binding plays a major role in their regulation

Diverse Mechanisms and Circuitry for Global Regulation by the RNA-Binding Protein CsrA

The carbon storage regulator (Csr) or repressor of stationary phase metabolites (Rsm) system of Gammaproteobacteria is among the most complex and best-studied posttranscriptional regulatory systems. Based on a small RNA-binding protein, CsrA and homologs, it controls metabolism, physiology, and bacterial lifestyle decisions by regulating gene expression on a vast scale. Binding of CsrA to sequences containing conserved GGA motifs in mRNAs can regulate translation, RNA stability, riboswitch function, and transcript elongation. CsrA governs the expression of dozens of transcription factors and other regulators, further expanding its influence on cellular physiology, and these factors can participate in feedback to the Csr system. Expression of csrA itself is subject to autoregulation via translational inhibition and indirect transcriptional activation. CsrA activity is controlled by small noncoding RNAs (sRNAs), CsrB and CsrC in Escherichia coli, which contain multiple high affinity CsrA binding sites that compete with those of mRNA targets. Transcription of CsrB/C is induced by certain nutrient limitations, cellular stresses, and metabolites, while these RNAs are targeted for degradation by the presence of a preferred carbon source. Consistent with these findings, CsrA tends to activate pathways and processes that are associated with robust growth and repress stationary phase metabolism and stress responses. Regulatory loops between Csr components affect the signaling dynamics of the Csr system. Recently, systems-based approaches have greatly expanded our understanding of the roles played by CsrA, while reinforcing the notion that much remains to be learned about the Csr system.

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.

IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Timing effciency in small-RNA-regulated post-transcriptional processes

Gene regulation in a cellular environment is a stochastic phenomenon leading to a large variability in mRNAs and protein numbers that are often produced in bursts. The regulation leading to varied protein dynamics can be ascribed to transcriptional or post-transcriptional mechanisms. In transcriptional regulation, the gene dynamically switches between an active and an inactive state, while in the post-transcriptional regulation small RNAs tune the activity of mRNAs. In either scenario, it is possible to calculate the time-dependent probability distribution of proteins and address the interesting question pertaining to their first passage time statistics. The coefficient of variation of first passage time can be considered to be an indicator of efficiency in controlling regulatory pathways and we show that post-transcriptional regulation performs better than simple transcriptional regulation for comparable protein yields.

APERO: a genome-wide approach for identifying bacterial small RNAs from RNA-Seq data

Small non-coding RNAs (sRNAs) regulate numerous cellular processes in all domains of life. Several approaches have been developed to identify them from RNA-seq data, which are efficient for eukaryotic sRNAs but remain inaccurate for the longer and highly structured bacterial sRNAs. We present APERO, a new algorithm to detect small transcripts from paired-end bacterial RNA-seq data. In contrast to previous approaches that start from the read coverage distribution, APERO analyzes boundaries of individual sequenced fragments to infer the 5′ and 3′ ends of all transcripts. Since sRNAs are about the same size as individual fragments (50–350 nucleotides), this algorithm provides a significantly higher accuracy and robustness, e.g., with respect to spontaneous internal breaking sites. To demonstrate this improvement, we develop a comparative assessment on datasets from Escherichia coli and Salmonella enterica, based on experimentally validated sRNAs. We also identify the small transcript repertoire of Dickeya dadantii including putative intergenic RNAs, 5′ UTR or 3′ UTR-derived RNA products and antisense RNAs. Comparisons to annotations as well as RACE-PCR experimental data confirm the precision of the detected transcripts. Altogether, APERO outperforms all existing methods in terms of sRNA detection and boundary precision, which is crucial for comprehensive genome annotations. It is freely available as an open source R package on https://github.com/Simon-Leonard/APERO

Self-replenishment cycles generate a threshold response

Many metabolic cycles, including the tricarboxylic acid cycle, glyoxylate cycle, Calvin cycle, urea cycle, coenzyme recycling, and substrate cycles, are well known to catabolize and anabolize different metabolites for efficient energy and mass conversion. In terms of stoichiometric structure, this study explicitly identifies two types of metabolic cycles. One is the well-known, elementary cycle that converts multiple substrates into different products and recycles one of the products as a substrate, where the recycled substrate is supplied from the outside to run the cycle. The other is the self-replenishment cycle that merges multiple substrates into two or multiple identical products and reuses one of the products as a substrate. The substrates are autonomously supplied within the cycle. This study first defines the self-replenishment cycles that many scientists have overlooked despite its functional importance. Theoretical analysis has revealed the design principle of the self-replenishment cycle that presents a threshold response without any bistability nor cooperativity. To verify the principle, three detailed kinetic models of self-replenishment cycles embedded in an E. coli metabolic system were simulated. They presented the threshold response or digital switch-like function that steeply shift metabolic status.

Competing endogenous RNA crosstalk at system level

microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and away from stationarity. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features in silico in large-scale miRNA-RNA network reconstructions. We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network's topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.

Synthetic negative feedback circuits using engineered small RNAs

Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal, steep input–output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.

Post-Transcriptional Noise Control

Recent evidence indicates that transcriptional bursts are intrinsically amplified by messenger RNA cytoplasmic processing to generate large stochastic fluctuations in protein levels. These fluctuations can be exploited by cells to enable probabilistic bet-hedging decisions. But large fluctuations in gene expression can also destabilize cell-fate commitment. Thus, it is unclear if cells temporally switch from high to low noise, and what mechanisms enable this switch. Here, the discovery of a post-transcriptional mechanism that attenuates noise in HIV is reviewed. Early in its life cycle, HIV amplifies transcriptional fluctuations to probabilistically select alternate fates, whereas at late times, HIV utilizes a post-transcriptional feedback mechanism to commit to a specific fate. Reanalyzing various reported post-transcriptional negative feedback architectures reveals that they attenuate noise more efficiently than classic transcriptional autorepression, leading to the derivation of an assay to detect post-transcriptional motifs. It is hypothesized that coupling transcriptional and post-transcriptional autoregulation enables efficient temporal noise control to benefit developmental bet-hedging decisions.

Realizing 'integral control' in living cells: how to overcome leaky integration due to dilution?

A major problem in the design of synthetic genetic circuits is robustness to perturbations and uncertainty. Because of this, there have been significant efforts in recent years in finding approaches to implement integral control in genetic circuits. Integral controllers have the unique ability to make the output of a process adapt perfectly to disturbances. However, implementing an integral controller is challenging in living cells. This is because a key aspect of any integral controller is a 'memory' element that stores the accumulation (integral) of the error between the output and its desired set-point. The ability to realize such a memory element in living cells is fundamentally challenged by the fact that all biomolecules dilute as cells grow, resulting in a 'leaky' memory that gradually fades away. As a consequence, the adaptation property is lost. Here, we propose a general principle for designing integral controllers such that the performance is practically unaffected by dilution. In particular, we mathematically prove that if the reactions implementing the integral controller are all much faster than dilution, then the adaptation error due to integration leakiness becomes negligible. We exemplify this design principle with two synthetic genetic circuits aimed at reaching adaptation of gene expression to fluctuations in cellular resources. Our results provide concrete guidance on the biomolecular processes that are most appropriate for implementing integral controllers in living cells.

Modeling slow-processing of toxin messenger RNAs in type-I toxin-antitoxin systems: post-segregational killing and noise filtering

In type-I toxin-antitoxin (TA) systems, the action of growth-inhibiting toxin proteins is counteracted by the antitoxin small RNAs (sRNAs) that prevent the translation of toxin messenger RNAs (mRNAs). When a TA module is encoded on a plasmid, the short lifetime of antitoxin sRNA compared to toxin mRNAs mediates post-segregational killing (PSK) that contribute the plasmid maintenance, while some of the chromosomal encoded TA loci have been reported to contribute to persister formation in response to a specific upstream signal. Some of the well studied type-I TA systems such as hok/sok are known to have a rather complex regulatory mechanism. Transcribed full-length toxin mRNAs fold such that the ribosome binding site is not accessible and hence cannot be translated. The mRNAs are slowly processed by RNases, and the truncated mRNAs can be either translated or bound by antitoxin sRNA to be quickly degraded. We analyze the role of this extra processing by a mathematical model. We first consider the PSK scenario, and demonstrate that the extra processing compatibly ensures the high toxin expression upon complete plasmid loss, without inducing toxin expression upon acquisition of a plasmid or decrease of plasmid number to a non-zero number. We further show that the extra processing help filtering the transcription noise, avoiding random activation of toxins in transcriptionally regulated TA systems as seen in chromosomal ones. The present model highlights impacts of the slow processing reaction, offering insights on why the slow processing reactions are commonly identified in multiple type-I TA systems.

Kinetic Modelling of Competition and Depletion of Shared miRNAs by Competing Endogenous RNAs

Non-coding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting, e.g., the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOI’s translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.

Competition for shared cellular resources often renders genetic circuits poorly predictable. Here the authors design a biomolecular quasi-integral controller that allows gene expression to adapt to variable demand in translation resources.

Transcriptomic analysis of MicroRNA expression in enamel-producing cells

There is little evidence for the involvement of microRNAs (miRNAs) in the regulation of circadian rhythms during enamel development. Few studies have used ameloblast-like cell line LS8 to study the circadian rhythm of gene activities related to enamel formation. However, the transcriptomic analysis of miRNA expression in LS8 cells has not been established yet. In this study, we analyze the oscillations of miRNAs in LS8 cells during one-day cycle of 24 h by next generation deep sequencing. After removal of low quality reads, contaminants, and ligation products, we obtained a high number of clean reads in all 12 samples from four different time points. The length distribution analysis indicated that 77.5% of clean reads were between 21 and 24 nucleotides (nt), of which 35.81% reads exhibited a length of 22 nt. In total, we identified 1471 miRNAs in LS8 cells throughout all four time-points. 1330 (90.41%) miRNAs were identified as known miRNA sequences, whereas 139 (9.59%) were unannotated and classified as novel miRNA sequences. The differential expression analysis showed that 191 known miRNAs exhibited significantly (P-value < 0.01) different levels of expression across three time-points investigated (T6, T12, and T18) compared to T0. Verification of sequencing data using qRT-PCR on six selected miRNAs suggested good correlation between the two methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment of predicted target genes of differentially expressed miRNAs. The present study shows that miRNAs are highly expressed in LS8 cells and that a significant number of them oscillate during one-day cycle of 24 h. This is the first transcriptomic analysis of miRNAs in ameloblast-like cell line LS8 that can be potentially used to further characterize the epigenetic regulation of miRNAs during enamel formation.

Regulatory sRNAs in Cyanobacteria

As the transcriptional and post-transcriptional regulators of gene expression, small RNAs (sRNAs) play important roles in every domain of life in organisms. It has been discovered gradually that bacteria possess multiple means of gene regulation using RNAs. They have been continuously used as model organisms for photosynthesis, metabolism, biotechnology, evolution, and nitrogen fixation for many decades. Cyanobacteria, one of the most ancient life forms, constitute all kinds of photoautotrophic bacteria and exist in almost any environment on this planet. It is believed that a complex RNA-based regulatory mechanism functions in cyanobacteria to help them adapt to changes and stresses in diverse environments. Although lagging far behind other model microorganisms, such as yeast and Escherichia coli, more and more non-coding regulatory sRNAs have been recognized in cyanobacteria during the past decades. In this article, by focusing on cyanobacterial sRNAs, the approaches for detection and targeting of sRNAs will be summarized, four major mechanisms and regulatory functions will be generalized, eight types of cis-encoded sRNA and four types of trans-encoded sRNAs will be reviewed in detail, and their possible physiological functions will be further discussed.

Interplay of synergy and redundancy in diamond motif

The formalism of partial information decomposition provides a number of independent components which altogether constitute the total information provided by the source variable(s) about the target variable(s). These non-overlapping terms are recognized as unique information, synergistic information, and redundant information. The metric of net synergy conceived as the difference between synergistic and redundant information is capable of detecting effective synergy, effective redundancy, and information independence among stochastic variables. The net synergy can be quantified using appropriate combinations of different Shannon mutual information terms. The utilization of the net synergy in network motifs with the nodes representing different biochemical species, involved in information sharing, uncovers rich store for exciting results. In the current study, we use this formalism to obtain a comprehensive understanding of the relative information processing mechanism in a diamond motif and two of its sub-motifs, namely, bifurcation and integration motif embedded within the diamond motif. The emerging patterns of effective synergy and effective redundancy and their contribution toward ensuring high fidelity information transmission are duly compared in the sub-motifs. Investigation on the metric of net synergy in independent bifurcation and integration motifs are also executed. In all of these computations, the crucial roles played by various systemic time scales, activation coefficients, and signal integration mechanisms at the output of the network topologies are especially emphasized. Following this plan of action, we become confident that the origin of effective synergy and effective redundancy can be architecturally justified by decomposing a diamond motif into bifurcation and integration motif. According to our conjecture, the presence of a common source of fluctuations creates effective redundancy. Our calculations reveal that effective redundancy empowers signal fidelity. Moreover, to achieve this, input signaling species avoids strong interaction with downstream intermediates. This strategy is capable of making the diamond motif noise-tolerant. Apart from the topological features, our study also puts forward the active contribution of additive and multiplicative signal integration mechanisms to nurture effective redundancy and effective synergy.

Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Mathematical Modeling of RNA-Based Architectures for Closed Loop Control of Gene Expression

Feedback allows biological systems to control gene expression precisely and reliably, even in the presence of uncertainty, by sensing and processing environmental changes. Taking inspiration from natural architectures, synthetic biologists have engineered feedback loops to tune the dynamics and improve the robustness and predictability of gene expression. However, experimental implementations of biomolecular control systems are still far from satisfying performance specifications typically achieved by electrical or mechanical control systems. To address this gap, we present mathematical models of biomolecular controllers that enable reference tracking, disturbance rejection, and tuning of the temporal response of gene expression. These controllers employ RNA transcriptional regulators to achieve closed loop control where feedback is introduced via molecular sequestration. Sensitivity analysis of the models allows us to identify which parameters influence the transient and steady state response of a target gene expression process, as well as which biologically plausible parameter values enable perfect reference tracking. We quantify performance using typical control theory metrics to characterize response properties and provide clear selection guidelines for practical applications. Our results indicate that RNA regulators are well-suited for building robust and precise feedback controllers for gene expression. Additionally, our approach illustrates several quantitative methods useful for assessing the performance of biomolecular feedback control systems.

Gene network analysis identifies a central post-transcriptional regulator of cellular stress survival

Cells adapt to shifts in their environment by remodeling transcription. Measuring changes in transcription at the genome scale is now routine, but defining the functional significance of individual genes within large gene expression datasets remains a major challenge. We applied a network-based algorithm to interrogate publicly available gene expression data to predict genes that serve major functional roles in Caulobacter crescentus stress survival. This approach identified GsrN, a conserved small RNA that is directly activated by the general stress sigma factor, σ^T, and functions as a potent post-transcriptional regulator of survival across distinct conditions including osmotic and oxidative stress. Under hydrogen peroxide stress, GsrN protects cells by base pairing with the leader of katG mRNA and activating expression of KatG catalase/peroxidase protein. We conclude that GsrN convenes a post-transcriptional layer of gene expression that serves a central functional role in Caulobacter stress physiology.

Realizing 'integral control' in living cells: how to overcome leaky integration due to dilution?

A major problem in the design of synthetic genetic circuits is robustness to perturbations and uncertainty. Because of this, there have been significant efforts in recent years in finding approaches to implement integral control in genetic circuits. Integral controllers have the unique ability to make the output of a process adapt perfectly to disturbances. However, implementing an integral controller is challenging in living cells. This is because a key aspect of any integral controller is a 'memory' element that stores the accumulation (integral) of the error between the output and its desired set-point. The ability to realize such a memory element in living cells is fundamentally challenged by the fact that all biomolecules dilute as cells grow, resulting in a 'leaky' memory that gradually fades away. As a consequence, the adaptation property is lost. Here, we propose a general principle for designing integral controllers such that the performance is practically unaffected by dilution. In particular, we mathematically prove that if the reactions implementing the integral controller are all much faster than dilution, then the adaptation error due to integration leakiness becomes negligible. We exemplify this design principle with two synthetic genetic circuits aimed at reaching adaptation of gene expression to fluctuations in cellular resources. Our results provide concrete guidance on the biomolecular processes that are most appropriate for implementing integral controllers in living cells.

Photoresponse Mechanism in Cyanobacteria: Key Factor in Photoautotrophic Chassis

As the oldest oxygenic photoautotrophic prokaryotes, cyanobacteria have outstanding advantages as the chassis cell in the research field of synthetic biology. Cognition of photosynthetic mechanism, including the photoresponse mechanism under high-light (HL) conditions, is important for optimization of the cyanobacteria photoautotrophic chassis for synthesizing biomaterials as "microbial cell factories." Cyanobacteria are well-established model organisms for the study of oxygenic photosynthesis and have evolved various acclimatory responses to HL conditions to protect the photosynthetic apparatus from photodamage. Here, we reviewed the latest progress in the mechanism of HL acclimation in cyanobacteria. The subsequent acclimatory responses and the corresponding molecular mechanisms are included: (1) acclimatory responses of PSII and PSI; (2) the degradation of phycobilisome; (3) induction of the photoprotective mechanisms such as state transitions, OCP-dependent non-photochemical quenching, and the induction of HLIP family; and (4) the regulation mechanisms of the gene expression under HL.

Integration of Bacterial Small RNAs in Regulatory Networks

Small RNAs (sRNAs) are central regulators of gene expression in bacteria, controlling target genes posttranscriptionally by base pairing with their mRNAs. sRNAs are involved in many cellular processes and have unique regulatory characteristics. In this review, we discuss the properties of regulation by sRNAs and how it differs from and combines with transcriptional regulation. We describe the global characteristics of the sRNA-target networks in bacteria using graph-theoretic approaches and review the local integration of sRNAs in mixed regulatory circuits, including feed-forward loops and their combinations, feedback loops, and circuits made of an sRNA and another regulator, both derived from the same transcript. Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network.

Sub-cellular mRNA localization modulates the regulation of gene expression by small RNAs in bacteria

Small non-coding RNAs can exert significant regulatory activity on gene expression in bacteria. In recent years, substantial progress has been made in understanding bacterial gene expression by sRNAs. However, recent findings that demonstrate that families of mRNAs show non-trivial sub-cellular distributions raise the question of how localization may affect the regulatory activity of sRNAs. Here we address this question within a simple mathematical model. We show that the non-uniform spatial distributions of mRNA can alter the threshold-linear response that characterizes sRNAs that act stoichiometrically, and modulate the hierarchy among targets co-regulated by the same sRNA. We also identify conditions where the sub-cellular organization of cofactors in the sRNA pathway can induce spatial heterogeneity on sRNA targets. Our results suggest that under certain conditions, interpretation and modeling of natural and synthetic gene regulatory circuits need to take into account the spatial organization of the transcripts of participating genes.

Dissecting miRNA gene repression on single cell level with an advanced fluorescent reporter system

Despite major advances on miRNA profiling and target predictions, functional readouts for endogenous miRNAs are limited and frequently lead to contradicting conclusions. Numerous approaches including functional high-throughput and miRISC complex evaluations suggest that the functional miRNAome differs from the predictions based on quantitative sRNA profiling. To resolve the apparent contradiction of expression versus function, we generated and applied a fluorescence reporter gene assay enabling single cell analysis. This approach integrates and adapts a mathematical model for miRNA-driven gene repression. This model predicts three distinct miRNA-groups with unique repression activities (low, mid and high) governed not just by expression levels but also by miRNA/target-binding capability. Here, we demonstrate the feasibility of the system by applying controlled concentrations of synthetic siRNAs and in parallel, altering target-binding capability on corresponding reporter-constructs. Furthermore, we compared miRNA-profiles with the modeled predictions of 29 individual candidates. We demonstrate that expression levels only partially reflect the miRNA function, fitting to the model-projected groups of different activities. Furthermore, we demonstrate that subcellular localization of miRNAs impacts functionality. Our results imply that miRNA profiling alone cannot define their repression activity. The gene regulatory function is a dynamic and complex process beyond a minimalistic conception of "highly expressed equals high repression".

Small Antisense RNA RblR Positively Regulates RuBisCo in Synechocystis sp. PCC 6803

Small regulatory RNAs (sRNAs) function as transcriptional and post-transcriptional regulators of gene expression in organisms from all domains of life. Cyanobacteria are thought to have developed a complex RNA-based regulatory mechanism. In the current study, by genome-wide analysis of differentially expressed small RNAs in Synechocystis sp. PCC 6803 under high light conditions, we discovered an asRNA (RblR) that is 113nt in length and completely complementary to its target gene rbcL, which encodes the large chain of RuBisCO, the enzyme that catalyzes carbon fixation. Further analysis of the RblR(+)/(−) mutants revealed that RblR acts as a positive regulator of rbcL under various stress conditions; Suppressing RblR adversely affects carbon assimilation and thus the yield, and those phenotypes of both the wild type and the overexpressor could be downgraded to the suppressor level by carbonate depletion, indicated a regulatory role of RblR in CO₂ assimilation. In addition, a real-time expression platform in Escherichia coli was setup and which confirmed that RblR promoted the translation of the rbcL mRNA into the RbcL protein. The present study is the first report of a regulatory RNA that targets RbcL in Synechocystis sp. PCC 6803, and provides strong evidence that RblR regulates photosynthesis by positively modulating rbcL expression in Synechocystis.

Hierarchical Post-transcriptional Regulation of Colicin E2 Expression in Escherichia coli

Post-transcriptional regulation of gene expression plays a crucial role in many bacterial pathways. In particular, the translation of mRNA can be regulated by trans-acting, small, non-coding RNAs (sRNAs) or mRNA-binding proteins, each of which has been successfully treated theoretically using two-component models. An important system that includes a combination of these modes of post-transcriptional regulation is the Colicin E2 system. DNA damage, by triggering the SOS response, leads to the heterogeneous expression of the Colicin E2 operon including the cea gene encoding the toxin colicin E2, and the cel gene that codes for the induction of cell lysis and release of colicin. Although previous studies have uncovered the system's basic regulatory interactions, its dynamical behavior is still unknown. Here, we develop a simple, yet comprehensive, mathematical model of the colicin E2 regulatory network, and study its dynamics. Its post-transcriptional regulation can be reduced to three hierarchically ordered components: the mRNA including the cel gene, the mRNA-binding protein CsrA, and an effective sRNA that regulates CsrA. We demonstrate that the stationary state of this system exhibits a pronounced threshold in the abundance of free mRNA. As post-transcriptional regulation is known to be noisy, we performed a detailed stochastic analysis, and found fluctuations to be largest at production rates close to the threshold. The magnitude of fluctuations can be tuned by the rate of production of the sRNA. To study the dynamics in response to an SOS signal, we incorporated the LexA-RecA SOS response network into our model. We found that CsrA regulation filtered out short-lived activation peaks and caused a delay in lysis gene expression for prolonged SOS signals, which is also seen in experiments. Moreover, we showed that a stochastic SOS signal creates a broad lysis time distribution. Our model thus theoretically describes Colicin E2 expression dynamics in detail and reveals the importance of the specific regulatory components for the timing of toxin release.

The Tip of the Iceberg: On the Roles of Regulatory Small RNAs in the Virulence of Enterohemorrhagic and Enteropathogenic Escherichia coli

Enterohemorrhagic and enteropathogenic Escherichia coli are gastrointestinal pathogens that disrupt the intestinal microvilli to form attaching and effacing (A/E) lesions on infected cells and cause diarrhea. This pathomorphological trait is encoded within the pathogenicity island locus of enterocyte effacement (LEE). The LEE houses a type 3 secretion system (T3SS), which upon assembly bridges the bacterial cytosol to that of the host and enables the bacterium to traffic dozens of effectors into the host where they hijack regulatory and signal transduction pathways and contribute to bacterial colonization and disease. Owing to the importance of the LEE to EHEC and EPEC pathogenesis, much of the research on these pathogens has centered on its regulation. To date, over 40 proteinaceous factors have been identified that control the LEE at various hierarchical levels of gene expression. In contrast, RNA-based regulatory mechanisms that converge on the LEE have only just begun to be unraveled. In this minireview, we highlight major breakthroughs in small RNAs (sRNAs)-dependent regulation of the LEE, with an emphasis on their mechanisms of action and/or LEE-encoded targets.

A self-regulating biomolecular comparator for processing oscillatory signals

While many cellular processes are driven by biomolecular oscillators, precise control of a downstream on/off process by a biochemical oscillator signal can be difficult: over an oscillator's period, its output signal varies continuously between its amplitude limits and spends a significant fraction of the time at intermediate values between these limits. Further, the oscillator's output is often noisy, with particularly large variations in the amplitude. In electronic systems, an oscillating signal is generally processed by a downstream device such as a comparator that converts a potentially noisy oscillatory input into a square wave output that is predominantly in one of two well-defined on and off states. The comparator's output then controls downstream processes. We describe a method for constructing a synthetic biochemical device that likewise produces a square-wave-type biomolecular output for a variety of oscillatory inputs. The method relies on a separation of time scales between the slow rate of production of an oscillatory signal molecule and the fast rates of intermolecular binding and conformational changes. We show how to control the characteristics of the output by varying the concentrations of the species and the reaction rates. We then use this control to show how our approach could be applied to process different in vitro and in vivo biomolecular oscillators, including the p53-Mdm2 transcriptional oscillator and two types of in vitro transcriptional oscillators. These results demonstrate how modular biomolecular circuits could, in principle, be combined to build complex dynamical systems. The simplicity of our approach also suggests that natural molecular circuits may process some biomolecular oscillator outputs before they are applied downstream.

Redundancy in information transmission in a two-step cascade

We present a stochastic framework to study signal transmission in a generic two-step cascade S→X→Y. Starting from a set of Langevin equations obeying Gaussian noise processes we calculate the variance and covariance while considering both linear and nonlinear production terms for different biochemical species of the cascade. These quantities are then used to calculate the net synergy within the purview of partial information decomposition. We show that redundancy in information transmission is essentially an important consequence of Markovian property of the two-step cascade motif. We also show that redundancy increases fidelity of the signaling pathway.

Transcript degradation and noise of small RNA-controlled genes in a switch activated network in Escherichia coli

Post-transcriptional regulatory processes may change transcript levels and affect cell-to-cell variability or noise. We study small-RNA downregulation to elucidate its effects on noise in the iron homeostasis network of Escherichia coli. In this network, the small-RNA RyhB undergoes stoichiometric degradation with the transcripts of target genes in response to iron stress. Using single-molecule fluorescence in situ hybridization, we measured transcript numbers of the RyhB-regulated genes sodB and fumA in individual cells as a function of iron deprivation. We observed a monotonic increase of noise with iron stress but no evidence of theoretically predicted, enhanced stoichiometric fluctuations in transcript numbers, nor of bistable behavior in transcript distributions. Direct detection of RyhB in individual cells shows that its noise is much smaller than that of these two targets, when RyhB production is significant. A generalized two-state model of bursty transcription that neglects RyhB fluctuations describes quantitatively the dependence of noise and transcript distributions on iron deprivation, enabling extraction of in vivo RyhB-mediated transcript degradation rates. The transcripts’ threshold-linear behavior indicates that the effective in vivo interaction strength between RyhB and its two target transcripts is comparable. Strikingly, the bacterial cell response exhibits Fur-dependent, switch-like activation instead of a graded response to iron deprivation.

Noise processing by microRNA-mediated circuits: The Incoherent Feed-Forward Loop, revisited

The intrinsic stochasticity of gene expression is usually mitigated in higher eukaryotes by post-transcriptional regulation channels that stabilise the output layer, most notably protein levels. The discovery of small non-coding RNAs (miRNAs) in specific motifs of the genetic regulatory network has led to identifying noise buffering as the possible key function they exert in regulation. Recent in vitro and in silico studies have corroborated this hypothesis. It is however also known that miRNA-mediated noise reduction is hampered by transcriptional bursting in simple topologies. Here, using stochastic simulations validated by analytical calculations based on van Kampen's expansion, we revisit the noise-buffering capacity of the miRNA-mediated Incoherent Feed Forward Loop (IFFL), a small module that is widespread in the gene regulatory networks of higher eukaryotes, in order to account for the effects of intermittency in the transcriptional activity of the modulator gene. We show that bursting considerably alters the circuit's ability to control static protein noise. By comparing with other regulatory architectures, we find that direct transcriptional regulation significantly outperforms the IFFL in a broad range of kinetic parameters. This suggests that, under pulsatile inputs, static noise reduction may be less important than dynamical aspects of noise and information processing in characterising the performance of regulatory elements.