Efficient degradation and expression prioritization with small RNAs

Function of Non-coding RNA in Helicobacter pylori-Infected Gastric Cancer

Gastric cancer is a common malignant tumor of the digestive system. Its occurrence and development are the result of a combination of genetic, environmental, and microbial factors. Helicobacter pylori infection is a chronic infection that is closely related to the occurrence of gastric tumorigenesis. Non-coding RNA has been demonstrated to play a very important role in the organism, exerting a prominent role in the carcinogenesis, proliferation, apoptosis, invasion, metastasis, and chemoresistance of tumor progression. H. pylori infection affects the expression of non-coding RNA at multiple levels such as genetic polymorphisms and signaling pathways, thereby promoting or inhibiting tumor progression or chemoresistance. This paper mainly introduces the relationship between H. pylori-infected gastric cancer and non-coding RNA, providing a new perspective for gastric cancer treatment.

Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit

Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model of regulation of stochastic gene expression by sRNAs, wherein interaction between constitutively expressed sRNAs and mRNAs leads to stoichiometric mutual degradation. The exact solution of this model is analytically intractable given the nonlinear interaction term between sRNAs and mRNAs, and theoretical approaches typically invoke the mean-field approximation. However, mean-field results are inaccurate in the limit of strong interactions and low abundances; thus, alternative theoretical approaches are needed. In this work, we obtain analytical results for the canonical model of regulation of stochastic gene expression by sRNAs in the strong interaction limit. We derive analytical results for the steady-state generating function of the joint distribution of mRNAs and sRNAs in the limit of strong interactions and use the results derived to obtain analytical expressions characterizing the corresponding protein steady-state distribution. The results obtained can serve as building blocks for the analysis of genetic circuits involving sRNAs and provide new insights into the role of sRNAs in regulating stochastic gene expression in the limit of strong interactions.

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.

Quantitative Characteristic of ncRNA Regulation in Gene Regulatory Networks

RNA is mostly known for its role in protein synthesis, where it encodes information for protein sequence in its messenger RNA (mRNA) form (translation). Yet, RNA molecules regulate several cellular processes other than translation. Here, we present an overview of several mathematical models that help understanding and characterizing the role of noncoding RNA molecules (ncRNAs) in regulating gene expression and protein synthesis. First, we discuss relatively simple models where ncRNAs can modulate protein synthesis via targeting a mRNA. Then, we consider the case of feedback interactions between ncRNAs and their target proteins, and discuss several biological applications where these feedback architectures modulate a cellular phenotype and control the levels of intrinsic and extrinsic noise. Building from these simple circuit motifs, we examine feed-forward circuit motifs involving ncRNAs that generate precise spatial and temporal patterns of protein expression. Further, we investigate the competition between ncRNAs and other endogenous RNA molecules and show that the cross talk between coding and noncoding RNAs can form large genetic circuits that involve up to hundreds of chemical species. Finally, we discuss the role of ncRNAs in modulating cell-cell signaling pathways and therefore the dynamics of spatiotemporal pattern formation in a tissue.

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.

On the role of extrinsic noise in microRNA-mediated bimodal gene expression

Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels provide experimental evidence of phenotypic differentiation, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate almost no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations without requiring fine tuning. Furthermore, we characterise the protein distribution's dependence on protein half-life.

Redox cofactor engineering in industrial microorganisms: strategies, recent applications and future directions

NAD and NADP, a pivotal class of cofactors, which function as essential electron donors or acceptors in all biological organisms, drive considerable catabolic and anabolic reactions. Furthermore, they play critical roles in maintaining intracellular redox homeostasis. However, many metabolic engineering efforts in industrial microorganisms towards modification or introduction of metabolic pathways, especially those involving consumption, generation or transformation of NAD/NADP, often induce fluctuations in redox state, which dramatically impede cellular metabolism, resulting in decreased growth performance and biosynthetic capacity. Here, we comprehensively review the cofactor engineering strategies for solving the problematic redox imbalance in metabolism modification, as well as their features, suitabilities and recent applications. Some representative examples of in vitro biocatalysis are also described. In addition, we briefly discuss how tools and methods from the field of synthetic biology can be applied for cofactor engineering. Finally, future directions and challenges for development of cofactor redox engineering are presented.

Analysis of network motifs in cellular regulation: Structural similarities, input-output relations and signal integration

Much of the complexity of regulatory networks derives from the necessity to integrate multiple signals and to avoid malfunction due to cross-talk or harmful perturbations. Hence, one may expect that the input-output behavior of larger networks is not necessarily more complex than that of smaller network motifs which suggests that both can, under certain conditions, be described by similar equations. In this review, we illustrate this approach by discussing the similarities that exist in the steady state descriptions of a simple bimolecular reaction, covalent modification cycles and bacterial two-component systems. Interestingly, in all three systems fundamental input-output characteristics such as thresholds, ultrasensitivity or concentration robustness are described by structurally similar equations. Depending on the system the meaning of the parameters can differ ranging from protein concentrations and affinity constants to complex parameter combinations which allows for a quantitative understanding of signal integration in these systems. We argue that this approach may also be extended to larger regulatory networks.

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.

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.

Frequency modulation of stochastic gene expression bursts by strongly interacting small RNAs

The sporadic nature of gene expression at the single-cell level-long periods of inactivity punctuated by bursts of mRNA or protein production-plays a critical role in diverse cellular processes. To elucidate the cellular role of bursting in gene expression, synthetic biology approaches have been used to design simple genetic circuits with bursty mRNA or protein production. Understanding how such genetic circuits can be designed with the ability to control burst-related parameters requires the development of quantitative stochastic models of gene expression. In this work, we analyze stochastic models for the regulation of gene expression bursts by strongly interacting small RNAs. For the parameter range considered, results based on mean-field approaches are significantly inaccurate and alternative analytical approaches are needed. Using simplifying approximations, we obtain analytical results for the corresponding steady-state distributions that are in agreement with results from stochastic simulations. These results indicate that regulation by small RNAs, in the strong interaction limit, can be used to effectively modulate the frequency of bursting. We explore the consequences of such regulation for simple genetic circuits involving feedback effects and switching between promoter states.

Quantitative effect of target translation on small RNA efficacy reveals a novel mode of interaction

Small regulatory RNAs (sRNAs) in bacteria regulate many important cellular activities under normal conditions and in response to stress. Many sRNAs bind to the mRNA targets at or near the 5′ untranslated region (UTR) resulting in translation inhibition and accelerated degradation. Often the sRNA-binding site is adjacent to or overlapping with the ribosomal binding site (RBS), suggesting a possible interplay between sRNA and ribosome binding. Here we combine quantitative experiments with mathematical modeling to reveal novel features of the interaction between small RNAs and the translation machinery at the 5′UTR of a target mRNA. By measuring the response of a library of reporter targets with varied RBSs, we find that increasing translation rate can lead to increased repression. Quantitative analysis of these data suggests a recruitment model, where bound ribosomes facilitate binding of the sRNA. We experimentally verified predictions of this model for the cell-to-cell variability of target expression. Our findings offer a framework for understanding sRNA silencing in the context of bacterial physiology.

A mixed incoherent feed-forward loop allows conditional regulation of response dynamics

Expression of the SodA superoxide dismutase (MnSOD) in Escherichia coli is regulated by superoxide concentration through the SoxRS system and also by Fur (Ferric uptake regulator) through a mixed incoherent feed forward loop (FFL) containing the RyhB small regulatory RNA. In this work I theoretically analyze the function of this feed forward loop as part of the network controlling expression of the two cytoplasmic superoxide dismutases, SodA and SodB. I find that feed forward regulation allows faster response to superoxide stress at low intracellular iron levels compared to iron rich conditions. That is, it can conditionally modulate the response time of a superimposed transcriptional control mechanism.

A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes

It is well known that, under suitable conditions, microRNAs are able to fine tune the relative concentration of their targets to any desired value. We show that this function is particularly effective when one of the targets is a Transcription Factor (TF) which regulates the other targets. This combination defines a new class of feed-forward loops (FFLs) in which the microRNA plays the role of master regulator. Using both deterministic and stochastic equations, we show that these FFLs are indeed able not only to fine-tune the TF/target ratio to any desired value as a function of the miRNA concentration but also, thanks to the peculiar topology of the circuit, to ensure the stability of this ratio against stochastic fluctuations. These two effects are due to the interplay between the direct transcriptional regulation and the indirect TF/Target interaction due to competition of TF and target for miRNA binding (the so called "sponge effect"). We then perform a genome wide search of these FFLs in the human regulatory network and show that they are characterized by a very peculiar enrichment pattern. In particular, they are strongly enriched in all the situations in which the TF and its target have to be precisely kept at the same concentration notwithstanding the environmental noise. As an example we discuss the FFL involving E2F1 as Transcription Factor, RB1 as target and miR-17 family as master regulator. These FFLs ensure a tight control of the E2F/RB ratio which in turns ensures the stability of the transition from the G0/G1 to the S phase in quiescent cells.

Small RNA-mediated switch-like regulation in bacterial quorum sensing

Quorum sensing (QS) is a signalling mechanism by which bacteria produce, release and then detect and respond to changes in their density and biosignals called autoinducers (AIs). There are multiple feedback loops in the QS system of Vibrio harveyi. However, how these feedback loops function to control signal processing remains unclear. In this study, the authors present a computational model for the switch-like regulation of signal transduction by small regulatory RNA-mediated QS based on intertwined network involving AIs, LuxO, LuxU, Qrr sRNAs and LuxR. In agreement with experimental observations, the model suggests that different feedbacks play critical roles in the switch-like regulation. The authors results reveal that V. harveyi uses multiple feedbacks to precisely control signal transduction.

Regulating the many to benefit the few: role of weak small RNA targets

Small regulatory RNAs are central players in the regulation of many cellular processes across all kingdoms of life. Experiments in mouse and human have shown that a typical small RNA may regulate the expression of many different genes, suggesting that small RNAs act as global regulators. It is noted though that most targets respond only weakly to the presence of the small RNA. At the same time, evidence in bacteria and animals suggest that the phenotypes associated with small RNA mutants are only due to a few of their targets. Here we assume that targets regulated by a small RNA to control function is in fact small, and propose that the role of the many other weak targets is to confer robustness to the regulation of these few principal targets. Through mathematical modeling we show that auxiliary targets may significantly buffer both number and kinetic fluctuations of the principal targets, with only minor slowdown in the kinetics of response. Analysis of genomic data suggests that auxiliary targets experience a nonspecific evolutionary pressure, playing a role at the system level. Our work is of importance for studies on small RNA functions, and impacts on the understanding of small RNA evolution.

Conditional cooperativity of toxin - antitoxin regulation can mediate bistability between growth and dormancy

Many toxin-antitoxin operons are regulated by the toxin/antitoxin ratio by mechanisms collectively coined "conditional cooperativity". Toxin and antitoxin form heteromers with different stoichiometric ratios, and the complex with the intermediate ratio works best as a transcription repressor. This allows transcription at low toxin level, strong repression at intermediate toxin level, and then again transcription at high toxin level. Such regulation has two interesting features; firstly, it provides a non-monotonous response to the concentration of one of the proteins, and secondly, it opens for ultra-sensitivity mediated by the sequestration of the functioning heteromers. We explore possible functions of conditional regulation in simple feedback motifs, and show that it can provide bistability for a wide range of parameters. We then demonstrate that the conditional cooperativity in toxin-antitoxin systems combined with the growth-inhibition activity of free toxin can mediate bistability between a growing state and a dormant state.

A computational method to preclude multistationarity in networks of interacting species

MOTIVATION

Modeling and analysis of complex systems are important aspects of understanding systemic behavior. In the lack of detailed knowledge about a system, we often choose modeling equations out of convenience and search the (high-dimensional) parameter space randomly to learn about model properties. Qualitative modeling sidesteps the issue of choosing specific modeling equations and frees the inference from specific properties of the equations. We consider classes of ordinary differential equation (ODE) models arising from interactions of species/entities, such as (bio)chemical reaction networks or ecosystems. A class is defined by imposing mild assumptions on the interaction rates. In this framework, we investigate whether there can be multiple positive steady states in some ODE models in a given class.

RESULTS

We have developed and implemented a method to decide whether any ODE model in a given class cannot have multiple steady states. The method runs efficiently on models of moderate size. We tested the method on a large set of models for gene silencing by sRNA interference and on two publicly available databases of biological models, KEGG and Biomodels. We recommend that this method is used as (i) a pre-screening step for selecting an appropriate model and (ii) for investigating the robustness of non-existence of multiple steady state for a given ODE model with respect to variation in interaction rates.

AVAILABILITY AND IMPLEMENTATION

Scripts and examples in Maple are available in the Supplementary Information.

CONTACT

wiuf@math.ku.dk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modelling Competing Endogenous RNA Networks

MicroRNAs (miRNAs) are small RNA molecules, about 22 nucleotide long, which post-transcriptionally regulate their target messenger RNAs (mRNAs). They accomplish key roles in gene regulatory networks, ranging from signaling pathways to tissue morphogenesis, and their aberrant behavior is often associated with the development of various diseases. Recently it has been experimentally shown that the way miRNAs interact with their targets can be described in terms of a titration mechanism. From a theoretical point of view titration mechanisms are characterized by threshold effect at near-equimolarity of the different chemical species, hypersensitivity of the system around the threshold, and cross-talk among targets. The latter characteristic has been lately identified as competing endogenous RNA (ceRNA) effect to mark those indirect interactions among targets of a common pool of miRNAs they are in competition for. Here we propose a stochastic model to analyze the equilibrium and out-of-equilibrium properties of a network of [Formula: see text] miRNAs interacting with [Formula: see text] mRNA targets. In particular we are able to describe in detail the peculiar equilibrium and non-equilibrium phenomena that the system displays in proximity to the threshold: (i) maximal cross-talk and correlation between targets, (ii) robustness of ceRNA effect with respect to the model's parameters and in particular to the catalyticity of the miRNA-mRNA interaction, and (iii) anomalous response-time to external perturbations.

Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments

Competitive endogenous (ce)RNAs cross-regulate each other through sequestration of shared microRNAs and form complex regulatory networks based on their microRNA signature. However, the molecular requirements for ceRNA cross-regulation and the extent of ceRNA networks remain unknown. Here, we present a mathematical mass-action model to determine the optimal conditions for ceRNA activity in silico. This model was validated using phosphatase and tensin homolog (PTEN) and its ceRNA VAMP (vesicle-associated membrane protein)-associated protein A (VAPA) as paradigmatic examples. A computational assessment of the complexity of ceRNA networks revealed that transcription factor and ceRNA networks are intimately intertwined. Notably, we found that ceRNA networks are responsive to transcription factor up-regulation or their aberrant expression in cancer. Thus, given optimal molecular conditions, alterations of one ceRNA can have striking effects on integrated ceRNA and transcriptional networks.

Origins of binary gene expression in post-transcriptional regulation by microRNAs

MicroRNA-mediated regulation of gene expression is characterised by some distinctive features that set it apart from unregulated and transcription-factor-regulated gene expression. Recently, a mathematical model has been proposed to describe the dynamics of post-transcriptional regulation by microRNAs. The model explains the observations made in single-cell experiments quite well. In this paper, we introduce some additional features into the model and consider two specific cases. In the first case, a noncooperative positive feedback loop is included in the transcriptional regulation of the target gene expression. In the second case, a stochastic version of the original model is considered in which there are random transitions between the inactive and active expression states of the gene. In the first case we show that bistability is possible in a parameter regime, due to the presence of a nonlinear protein decay term in the gene expression dynamics. In the second case, we derive the conditions for obtaining stochastic binary gene expression. We find that this type of gene expression is more favourable in the case of regulation by microRNAs as compared to the case of unregulated gene expression. The theoretical predictions relating to binary gene expression are experimentally testable.

Multi-target regulation by small RNAs synchronizes gene expression thresholds and may enhance ultrasensitive behavior

Cells respond to external cues by precisely coordinating multiple molecular events. Co-regulation may be established by the so-called single-input module (SIM), where a common regulator controls multiple targets. Using mathematical modeling, we compared the ability of SIM architectures to precisely coordinate protein levels despite environmental fluctuations and uncertainties in parameter values. We find that post-transcriptional co-regulation as exemplified by bacterial small RNAs (sRNAs) is particularly robust: sRNA-mediated regulation establishes highly synchronous gene expression thresholds for all mRNA targets without a need for fine-tuning of kinetic parameters. Our analyses reveal that the non-catalytic nature of sRNA action is essential for robust gene expression synchronization, and that sRNA sequestration effects underlie coupling of multiple mRNA pools. This principle also operates in the temporal regime, implying that sRNAs could robustly coordinate the kinetics of mRNA induction as well. Moreover, we observe that multi-target regulation by a small RNA can strongly enhance ultrasensitivity in mRNA expression when compared to the single-target case. Our findings may explain why bacterial small RNAs frequently coordinate all-or-none responses to cellular stress.

Genetic and phenotypic characterization of the RyhB regulon in Salmonella Typhimurium

Salmonella encodes two homologs of RyhB, a small RNA (sRNA) involved in iron homeostasis. In Salmonella Typhimurium, the expression of both RyhB-1 and RyhB-2 is negatively regulated by the Fur repressor, while stationary phase is the primary signal inducing RyhB-2 expression. To identify the target mRNAs of RyhB-1 and RyhB-2, 9 predicted target genes were analyzed by quantitative RT-PCR to monitor differential transcript levels between wild type and each of three mutants (ΔryhB-1, ΔryhB-2 and ΔryhB-1ΔryhB-2) under conditions that maximize the expression of both sRNAs. Our results, along with bioinformatic predictions, suggest that the genes acnA, sodB, ftn, STM1273.1n, and acnB are the primary targets of at least one of these sRNAs. To understand the biological roles of the RyhB regulon, the aforementioned deletions were created in either wild type or Δfur backgrounds and were subjected to various phenotypic assays. The results showed that these sRNAs are singularly or additively involved in the expression of multiple phenotypes, including acid resistance, resistance to hydrogen peroxide, and sensitivity to bactericidal antibiotics. The results support a model whereby RyhB-1 and RyhB-2 have a global regulatory effect on diverse cellular pathways in response to multiple environmental cues via post-transcriptional regulation of distinct sets of overlapping targets.

A simple method for incorporating dynamic effects of intronic miRNA mediated regulation

The importance of microRNA (miRNA) in modulating gene expression at the post-transcriptional level is well known. Such regulation has been shown to influence the dynamics of several regulatory networks including the cell cycle. In this study we incorporated regulatory effects of intronic miRNA into an existing mathematical model of the cell cycle through the use of an existing 'proxy' protein--the host protein. It was observed that the incorporation of intronic miRNA mediated regulation improved the performance of the model resulting in a closer match to experimental results. To test the universality of this approach we compared the effects of intronic miRNA mediated regulation and host protein mediated regulation. Further, we compared miRNA mediated and protein mediated positive and negative feedback regulations of the target protein. We found that the target protein profiles were predominantly similar. These observations show the applicability of our method for incorporating intronic miRNA mediated dynamic effects in models for regulation of gene expression.

Stochastic modeling of regulation of gene expression by multiple small RNAs

A wealth of new research has highlighted the critical roles of small noncoding RNAs (sRNAs) in diverse processes, such as quorum sensing and cellular responses to stress. The pathways controlling these processes often have a central motif composed of a master regulator protein whose expression is controlled by multiple sRNAs. However, the stochastic gene expression of a single target gene regulated by multiple sRNAs is currently not well understood. To address this issue, we analyze a stochastic model of regulation of gene expression by multiple sRNAs. For this model, we derive exact analytic results for the regulated protein distribution, including compact expressions for its mean and variance. The derived results provide insights into the roles of multiple sRNAs in fine-tuning the noise in gene expression. In particular, we show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a mechanism for independently controlling the mean and variance of the regulated protein distribution.

Mechanisms generating bistability and oscillations in microRNA-mediated motifs

The importance of post-transcriptional regulation by microRNAs (miRNAs) has recently been recognized in almost all cellular processes. When participating in cellular processes, miRNAs mainly mediate mRNA degradation or translational repression. Recently computational and experimental studies have identified an abundance of motifs involving miRNAs and transcriptional factors (TFs). The simplest motif is a two-node miRNA-mediated feedback loop (MFL) in which a TF regulates an miRNA and the TF itself is negatively regulated by the miRNA. In this paper we present a general computational model for the MFL based on biochemical regulations and explore its dynamics by using bifurcation analysis. Our results show that the MFL can behave either as switches or as oscillators, depending on the TF as a repressor or an activator. These functional features are consistent with the widespread appearance of miRNAs in fate decisions such as proliferation, differentiation, and apoptosis during development. We found that under the interplay of a TF and an miRNA, the MFL model can behave as switches for wide ranges of parameters even without cooperative binding of the TF. In addition, oscillations induced by the miRNA in the MFL model require neither an additional positive feedback loop, nor self-activation of the gene, nor cooperative binding of the TF, nor saturated degradation. Therefore, the MFL may provide a general network structure to induce bistability or oscillations. It is hoped that the results presented here will provide a new view on how gene expression is regulated by miRNAs and further guidance for experiments. Moreover, the insight gained from this study is also expected to provide a basis for the investigation of more complex networks assembled by simple building blocks.

Multiple factors dictate target selection by Hfq-binding small RNAs

Hfq-binding small RNAs (sRNAs) in bacteria modulate the stability and translational efficiency of target mRNAs through limited base-pairing interactions. While these sRNAs are known to regulate numerous mRNAs as part of stress responses, what distinguishes targets and non-targets among the mRNAs predicted to base pair with Hfq-binding sRNAs is poorly understood. Using the Hfq-binding sRNA Spot 42 of Escherichia coli as a model, we found that predictions using only the three unstructured regions of Spot 42 substantially improved the identification of previously known and novel Spot 42 targets. Furthermore, increasing the extent of base-pairing in single or multiple base-pairing regions improved the strength of regulation, but only for the unstructured regions of Spot 42. We also found that non-targets predicted to base pair with Spot 42 lacked an Hfq-binding site, folded into a secondary structure that occluded the Spot 42 targeting site, or had overlapping Hfq-binding and targeting sites. By modifying these features, we could impart Spot 42 regulation on non-target mRNAs. Our results thus provide valuable insights into the requirements for target selection by sRNAs.

Transcriptome complexity and riboregulation in the human pathogen Helicobacter pylori

The Gram-negative Epsilonproteobacterium Helicobacter pylori is considered as one of the major human pathogens and many studies have focused on its virulence mechanisms as well as genomic diversity. In contrast, only very little is known about post-transcriptional regulation and small regulatory RNAs (sRNAs) in this spiral-shaped microaerophilic bacterium. Considering the absence of the common RNA chaperone Hfq, which is a key-player in post-transcriptional regulation in enterobacteria, H. pylori was even regarded as an organism without riboregulation. However, analysis of the H. pylori primary transcriptome using RNA-seq revealed a very complex transcriptional output from its small genome. Furthermore, the identification of a wealth of sRNAs as well as massive antisense transcription indicates that H. pylori uses riboregulation for its gene expression control. The ongoing functional characterization of sRNAs along with the identification of associated RNA binding proteins will help to understand their potential roles in Helicobacter virulence and stress response. Moreover, research on riboregulation in H. pylori will provide new insights into its virulence mechanisms and will also help to shed light on post-transcriptional regulation in other Epsilonproteobacteria, including widespread and emerging pathogens such as Campylobacter.

EFFECT OF NON-CODING RNA ON BISTABILITY AND OSCILLATIONS IN THE mRNA-PROTEIN INTERPLAY

The feedbacks between the mRNA and protein synthesis may result in kinetic bistability and oscillations. Two generic models predicting bistability include, respectively, a gene with positive regulation of the mRNA production by protein and two genes with mutual suppression of the mRNA production due to negative regulation of the gene transcription by protein. The simplest model predicting oscillations describes a gene with negative regulation of the mRNA production by protein formed via mRNA translation and a few steps of conversion. We complement these models by the steps of non-coding RNA (ncRNA) formation and ncRNA-mRNA association and degradation. With this extension, the bistability can often be observed as well. Without and with ncRNA, the biochemistry behind the steady states may be different. In the latter case, for example, ncRNA may control the mRNA population in the situations when this population is relatively small, and one can observe a switch in the mRNA, protein and ncRNA populations. Our analysis of oscillatory kinetics of the mRNA-protein interplay shows that with ncRNA the oscillations may be observed in a wider range of parameters and the amplitude of oscillations may be larger.

Bacterial transcriptomics: what is beyond the RNA horiz-ome?

Over the past 3 years, bacterial transcriptomics has undergone a massive revolution. Increased sequencing capacity and novel tools have made it possible to explore the bacterial transcriptome to an unprecedented depth, which has revealed that the transcriptome is more complex and dynamic than expected. Alternative transcripts within operons challenge the classic operon definition, and many small RNAs involved in the regulation of transcription, translation and pathogenesis have been discovered. Furthermore, mRNAs may localize to specific areas in the cell, and the spatial organization and dynamics of the chromosome have been shown to be important for transcription. Epigenetic modifications of DNA also affect transcription, and RNA processing affects translation. Therefore, transcription in bacteria resembles that in eukaryotes in terms of complexity more closely than was previously thought. Here we will discuss the contribution of 'omics' approaches to these discoveries as well as the possible impact that they are expected to have in the future.

Quantifying negative feedback regulation by micro-RNAs

Micro-RNAs (miRNAs) play a crucial role in post-transcriptional gene regulation by pairing with target mRNAs to repress protein production. It has been shown that over one-third of human genes are targeted by miRNA. Although hundreds of miRNAs have been identified in mammalian genomes, the function of miRNA-based repression in the context of gene regulation networks still remains unclear. In this study, we explore the functional roles of feedback regulation by miRNAs. In a model where repression of translation occurs by sequestration of mRNA by miRNA, we find that miRNA and mRNA levels are anti-correlated, resulting in larger fluctuation in protein levels than theoretically expected assuming no correlation between miRNA and mRNA levels. If miRNA repression is due to a catalytic suppression of translation rates, we analytically show that the protein fluctuations can be strongly repressed with miRNA regulation. We also discuss how either of these modes may be relevant for cell function.

Regulation by small RNAs via coupled degradation: mean-field and variational approaches

Regulatory genes called small RNAs (sRNAs) are known to play critical roles in cellular responses to changing environments. For several sRNAs, regulation is effected by coupled stoichiometric degradation with messenger RNAs (mRNAs). The nonlinearity inherent in this regulatory scheme indicates that exact analytical solutions for the corresponding stochastic models are intractable. Here, we present a variational approach to analyze a well-studied stochastic model for regulation by sRNAs via coupled degradation. The proposed approach is efficient and provides accurate estimates of mean mRNA levels as well as higher-order terms. Results from the variational ansatz are in excellent agreement with data from stochastic simulations for a wide range of parameters, including regions of parameter space where mean-field approaches break down. The proposed approach can be applied for quantitative modeling of stochastic gene expression in complex regulatory networks.

Microbial linguistics: perspectives and applications of microbial cell-to-cell communication

Inter-cellular communication via diffusible small molecules is a defining character not only of multicellular forms of life but also of single-celled organisms. A large number of bacterial genes are regulated by the change of chemical milieu mediated by the local population density of its own species or others. The cell density-dependent "autoinducer" molecules regulate the expression of those genes involved in genetic competence, biofilm formation and persistence, virulence, sporulation, bioluminescence, antibiotic production, and many others. Recent innovations in recombinant DNA technology and micro-/nano-fluidics systems render the genetic circuitry responsible for cell-to-cell communication feasible to and malleable via synthetic biological approaches. Here we review the current understanding of the molecular biology of bacterial intercellular communication and the novel experimental protocols and platforms used to investigate this phenomenon. A particular emphasis is given to the genetic regulatory circuits that provide the standard building blocks which constitute the syntax of the biochemical communication network. Thus, this review gives focus to the engineering principles necessary for rewiring bacterial chemo-communication for various applications, ranging from population-level gene expression control to the study of host-pathogen interactions.

Small RNA biology is systems biology

During the last decade small regulatory RNA (srRNA) emerged as central players in the regulation of gene expression in all kingdoms of life. Multiple pathways for srRNA biogenesis and diverse mechanisms of gene regulation may indicate that srRNA regulation evolved independently multiple times. However, small RNA pathways share numerous properties, including the ability of a single srRNA to regulate multiple targets. Some of the mechanisms of gene regulation by srRNAs have significant effect on the abundance of free srRNAs that are ready to interact with new targets. This results in indirect interactions among seemingly unrelated genes, as well as in a crosstalk between different srRNA pathways. Here we briefly review and compare the major srRNA pathways, and argue that the impact of srRNA is always at the system level. We demonstrate how a simple mathematical model can ease the discussion of governing principles. To demonstrate these points we review a few examples from bacteria and animals.

Connecting protein and mRNA burst distributions for stochastic models of gene expression

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression.

Bistability and oscillations in gene regulation mediated by small noncoding RNAs

The interplay of small noncoding RNAs (sRNAs), mRNAs, and proteins has been shown to play crucial roles in almost all cellular processes. As key post-transcriptional regulators of gene expression, the mechanisms and roles of sRNAs in various cellular processes still need to be fully understood. When participating in cellular processes, sRNAs mainly mediate mRNA degradation or translational repression. Here, we show how the dynamics of two minimal architectures is drastically affected by these two mechanisms. A comparison is also given to reveal the implication of the fundamental differences. This study may help us to analyze complex networks assembled by simple modules more easily. A better knowledge of the sRNA-mediated motifs is also of interest for bio-engineering and artificial control.

Modeling RNA interference in mammalian cells

Background

RNA interference (RNAi) is a regulatory cellular process that controls post-transcriptional gene silencing. During RNAi double-stranded RNA (dsRNA) induces sequence-specific degradation of homologous mRNA via the generation of smaller dsRNA oligomers of length between 21-23nt (siRNAs). siRNAs are then loaded onto the RNA-Induced Silencing multiprotein Complex (RISC), which uses the siRNA antisense strand to specifically recognize mRNA species which exhibit a complementary sequence. Once the siRNA loaded-RISC binds the target mRNA, the mRNA is cleaved and degraded, and the siRNA loaded-RISC can degrade additional mRNA molecules. Despite the widespread use of siRNAs for gene silencing, and the importance of dosage for its efficiency and to avoid off target effects, none of the numerous mathematical models proposed in literature was validated to quantitatively capture the effects of RNAi on the target mRNA degradation for different concentrations of siRNAs. Here, we address this pressing open problem performing in vitro experiments of RNAi in mammalian cells and testing and comparing different mathematical models fitting experimental data to in-silico generated data. We performed in vitro experiments in human and hamster cell lines constitutively expressing respectively EGFP protein or tTA protein, measuring both mRNA levels, by quantitative Real-Time PCR, and protein levels, by FACS analysis, for a large range of concentrations of siRNA oligomers.

Results

We tested and validated four different mathematical models of RNA interference by quantitatively fitting models' parameters to best capture the in vitro experimental data. We show that a simple Hill kinetic model is the most efficient way to model RNA interference. Our experimental and modeling findings clearly show that the RNAi-mediated degradation of mRNA is subject to saturation effects.

Conclusions

Our model has a simple mathematical form, amenable to analytical investigations and a small set of parameters with an intuitive physical meaning, that makes it a unique and reliable mathematical tool. The findings here presented will be a useful instrument for better understanding RNAi biology and as modelling tool in Systems and Synthetic Biology.

Theoretical analysis of catalytic-sRNA-mediated gene silencing

Small regulatory RNA (sRNA) that acts by an antisense mechanism is critical for gene regulation at the posttranscriptional level. Recently, an Hfq-dependent sRNA named MicM, which is related to the regulation of outer membrane protein, was verified as a novel antisense sRNA due to its catalytic mode of regulation. Here we propose a simple kinetic model for the enzyme-like regulation mode of sRNA and study in detail the noise properties of the target gene under various recycling rates of the regulator. We predict that the recycling rate of sRNA and other relative parameters have significant influence on the noise strength of target expression. In comparison with the stoichiometric regulatory mode, a lesser fluctuation of target expression was observed near the threshold at which the transcription rates of both sRNA and target mRNA equal each other. We also found that the new mode is better in terms of rapid response to external signals. However, it needs more time to achieve target recovery if the stimulating signal disappears. Additionally, the obtained time evolution results of the MicM-ybfM interaction system based on our model are consistent with previous experimental results, serving as experimental evidence to back up our theoretical analysis.

Hierarchical genetic networks and noncoding RNAs

In eukaryotic cells, many genes are transcribed into noncoding RNAs. Such RNAs may associate with mRNAs and inhibit their translation and facilitate degradation. To clarify what may happen in this case, we propose a kinetic model describing the effect of noncoding RNAs on a mRNA-protein network with the hierarchical three-layer architecture. For positive regulation of the layers, our model predicts either bistability with a fairly narrow hysteresis loop or a unique steady state. For negative or mixed regulation, the steady state is found to be unique.

Transcriptional and post-transcriptional regulation of the Escherichia coli luxS mRNA; involvement of the sRNA MicA

Background

The small RNA (sRNA) MicA has been shown to post-transcriptionally regulate translation of the outer membrane protein A (OmpA) in Escherichia coli. It uses an antisense mechanism to down-regulate OmpA protein synthesis and induce mRNA degradation. MicA is genomically localized between the coding regions of the gshA and luxS genes and is divergently transcribed from its neighbours. Transcription of the luxS gene which originates within or upstream of the MicA sequence would thus be complementary to the sRNA. LuxS regulation is as yet unclear.

Methodology/Principal Findings

In this report, I show that the luxS mRNA exists as three long (major) transcripts of sizes that suggest just such interaction. The sRNA MicA's expression affects the abundance of each of these luxS transcripts. The involvement of the ribonuclease, RNase III in the accumulation of the shortest transcript is demonstrated. When MicA accumulates during growth, or is induced to be over-expressed, the cleaved mRNA species is observed to increase in intensity. Using primer extension and 5′-RACE experiments in combination with sRNA overexpression plasmids, I identify the exact origin of two of the three luxS transcripts, one of which is seen to result from a previously unidentified σ^S dependent promoter.

Conclusions/Significance

The presented data provides strong evidence that MicA functions in cis and in trans, targeting both luxS mRNA as well as the previously established ompA and phoP regulation. The proposed luxS regulation by MicA would be in tandem with another sRNA CyaR, shown recently to be involved in inhibiting translation of the luxS mRNA. Regulation of luxS expression is additionally shown to occur on a transcriptional level via σ^S with variable transcript levels in different growth phases unlike what was previously assumed. This is the first known case of an sRNA in E. coli which targets both in cis (luxS mRNA) and in trans (ompA and phoP mRNAs).

Applications of Little's Law to stochastic models of gene expression

The intrinsic stochasticity of gene expression can lead to large variations in protein levels across a population of cells. To explain this variability, different sources of messenger RNA (mRNA) fluctuations ("Poisson" and "telegraph" processes) have been proposed in stochastic models of gene expression. Both Poisson and telegraph scenario models explain experimental observations of noise in protein levels in terms of "bursts" of protein expression. Correspondingly, there is considerable interest in establishing relations between burst and steady-state protein distributions for general stochastic models of gene expression. In this work, we address this issue by considering a mapping between stochastic models of gene expression and problems of interest in queueing theory. By applying a general theorem from queueing theory, Little's Law, we derive exact relations which connect burst and steady-state distribution means for models with arbitrary waiting-time distributions for arrival and degradation of mRNAs and proteins. The derived relations have implications for approaches to quantify the degree of transcriptional bursting and hence to discriminate between different sources of intrinsic noise in gene expression. To illustrate this, we consider a model for regulation of protein expression bursts by small RNAs. For a broad range of parameters, we derive analytical expressions (validated by stochastic simulations) for the mean protein levels as the levels of regulatory small RNAs are varied. The results obtained show that the degree of transcriptional bursting can, in principle, be determined from changes in mean steady-state protein levels for general stochastic models of gene expression.

Simplified models of biological networks

The function of living cells is controlled by complex regulatory networks that are built of a wide diversity of interacting molecular components. The sheer size and intricacy of molecular networks of even the simplest organisms are obstacles toward understanding network functionality. This review discusses the achievements and promise of a bottom-up approach that uses well-characterized subnetworks as model systems for understanding larger networks. It highlights the interplay between the structure, logic, and function of various types of small regulatory circuits. The bottom-up approach advocates understanding regulatory networks as a collection of entangled motifs. We therefore emphasize the potential of negative and positive feedback, as well as their combinations, to generate robust homeostasis, epigenetics, and oscillations.

Post-transcriptional regulation of noise in protein distributions during gene expression

The intrinsic stochasticity of gene expression can lead to a large variability of protein levels across a population of cells. Variability (or noise) in protein distributions can be modulated by cellular mechanisms of gene regulation; in particular, there is considerable interest in understanding the role of post-transcriptional regulation. To address this issue, we propose and analyze a stochastic model for post-transcriptional regulation of gene expression. The analytical solution of the model provides insight into the effects of different mechanisms of post-transcriptional regulation on the noise in protein distributions. The results obtained also demonstrate how different sources of intrinsic noise in gene expression can be discriminated based on observations of regulated protein distributions.

Base pairing small RNAs and their roles in global regulatory networks

Bacteria use a range of RNA regulators collectively termed small RNAs (sRNAs) to help respond to changes in the environment. Many sRNAs regulate their target mRNAs through limited base-pairing interactions. Ongoing characterization of base-pairing sRNAs in bacteria has started to reveal how these sRNAs participate in global regulatory networks. These networks can be broken down into smaller regulatory circuits that have characteristic behaviors and functions. In this review, we describe the specific regulatory circuits that incorporate base-pairing sRNAs and the importance of each circuit in global regulation. Because most of these circuits were originally identified as network motifs in transcriptional networks, we also discuss why sRNAs may be used over protein transcription factors to help transduce environmental signals.

Quantifying mRNA synthesis and decay rates using small RNAs

Regulation of mRNA decay is a critical component of global cellular adaptation to changing environments. The corresponding changes in mRNA lifetimes can be coordinated with changes in mRNA transcription rates to fine-tune gene expression. Current approaches for measuring mRNA lifetimes can give rise to secondary effects due to transcription inhibition and require separate experiments to estimate changes in mRNA transcription rates. Here, we propose an approach for simultaneous determination of changes in mRNA transcription rate and lifetime using regulatory small RNAs (sRNAs) to control mRNA decay. We analyze a stochastic model for coupled degradation of mRNAs and sRNAs and derive exact results connecting RNA lifetimes and transcription rates to mean abundances. The results obtained are then generalized to include nonstoichiometric coupled degradation of sRNAs. Our analysis suggests experimental protocols for determining parameters controlling the efficiency of stoichiometric regulation by small RNAs and for analyzing factors and processes regulating changes in mRNA transcription and decay.

Dynamic features of gene expression control by small regulatory RNAs

Small regulatory RNAs (sRNAs) in eukaryotes and bacteria play an important role in the regulation of gene expression either by binding to regulatory proteins or directly to target mRNAs. Two of the best-characterized bacterial sRNAs, Spot42 and RyhB, form a complementary pair with the ribosome binding region of their target mRNAs, thereby inhibiting translation or promoting mRNA degradation. To investigate the steady-state and dynamic potential of such sRNAs, we examine the 2 key parameters characterizing sRNA regulation: the capacity to overexpress the sRNA relative to its target mRNA and the speed at which the target mRNA is irreversibly inactivated. We demonstrate different methods to determine these 2 key parameters, for Spot42 and RyhB, which combine biochemical and genetic experiments with computational analysis. We have developed a mathematical model that describes the functional properties of sRNAs with various characteristic parameters. We observed that Spot42 and RyhB function in distinctive parameter regimes, which result in divergent mechanisms.