Gene dosage compensation calibrates four regulatory RNAs to control Vibrio cholerae quorum sensing

Quorum sensing is a mechanism of cell‐to‐cell communication that allows bacteria to coordinately regulate gene expression in response to changes in cell‐population density. At the core of the Vibrio cholerae quorum‐sensing signal transduction pathway reside four homologous small RNAs (sRNAs), named the quorum regulatory RNAs 1–4 (Qrr1–4). The four Qrr sRNAs are functionally redundant. That is, expression of any one of them is sufficient for wild‐type quorum‐sensing behaviour. Here, we show that the combined action of two feedback loops, one involving the sRNA‐activator LuxO and one involving the sRNA‐target HapR, promotes gene dosage compensation between the four qrr genes. Gene dosage compensation adjusts the total Qrr1–4 sRNA pool and provides the molecular mechanism underlying sRNA redundancy. The dosage compensation mechanism is exquisitely sensitive to small perturbations in Qrr levels. Precisely maintained Qrr levels are required to direct the proper timing and correct patterns of expression of quorum‐sensing‐regulated target genes.     

Quorum sensing and alternative sigma factor RpoN regulate type VI secretion system I (T6SSVA1) in fish pathogen <Emphasis Type="Italic">Vibrio alginolyticus</Emphasis>

Type VI secretion system (T6SS) is a highly conserved bacterial protein secretion system and is precisely regulated in Gram-negative pathogens. In Vibrio alginolyticus, an important fish pathogen, two complete T6SS gene clusters (T6SSVA1 and T6SSVA2) were identified. In this study, expression of a hemolysin coregulated protein (Hcp1), which is one of the hallmarks of T6SS, was found to be strictly regulated in this bacterium. We showed that the expression of Hcp1 was growth phase-dependent and the production of Hcp1 reached a maximum in the exponential phase. The expression of Hcp1 was positively and negatively regulated by quorum sensing regulators LuxO and LuxR, respectively. In addition, we observed that Hcp1 expression required the alternative sigma factor RpoN and the enhancer-binding protein VasH, which is encoded in T6SSVA1 gene cluster. Moreover, LuxR, RpoN, and VasH could positively regulate the expression of other T6SS genes. Taken together, we demonstrated that the expression of T6SS in V. alginolyticus was under the regulation of quorum sensing and alternative sigma factor.     

Quorum regulatory small RNAs repress type VI secretion in Vibrio cholerae

Type VI secretion is critical for Vibrio cholerae to successfully combat phagocytic eukaryotes and to survive in the presence of competing bacterial species. V. cholerae type VI secretion system genes are encoded in one large and two small clusters. In V. cholerae, type VI secretion is controlled by quorum sensing, the cell–cell communication process that enables bacteria to orchestrate group behaviours. The quorum‐sensing response regulator LuxO represses type VI secretion genes at low cell density and the quorum‐sensing regulator HapR activates type VI secretion genes at high cell density. We demonstrate that the quorum regulatory small RNAs (Qrr sRNAs) that function between LuxO and HapR in the quorum‐sensing cascade are required for these regulatory effects. The Qrr sRNAs control type VI secretion via two mechanisms: they repress expression of the large type VI secretion system cluster through base pairing and they repress HapR, the activator of the two small type VI secretion clusters. This regulatory arrangement ensures that the large cluster encoding many components of the secretory machine is expressed prior to the two small clusters that encode the secreted effectors. Qrr sRNA‐dependent regulation of the type VI secretion system is conserved in pandemic and non‐pandemic V. cholerae strains.     

Multivariate analysis of noise in genetic regulatory networks

Stochasticity is an intrinsic property of genetic regulatory networks due to the low copy numbers of the major molecular species, such as, DNA, mRNA, and regulatory proteins. Therefore, investigation of the mechanisms that reduce the stochastic noise is essential in understanding the reproducible behaviors of real organisms and is also a key to design synthetic genetic regulatory networks that can reliably work. We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix. In the analysis of fluctuations of multiple molecular species, the covariance is an important measure of noise. However, usually the representation of a covariance matrix in the natural coordinate system, i.e. the copy numbers of the molecular species, is intractably complicated because reactions change copy numbers of more than one molecular species simultaneously. Decoupling of a stoichiometric matrix, which is a transformation of variables, significantly simplifies the representation of a covariance matrix and elucidates the mechanisms behind the observed fluctuations in the copy numbers. We apply our method to three types of fundamental genetic regulatory networks, that is, a single-gene autoregulatory network, a two-gene autoregulatory network, and a mutually repressive network. We have found that there are multiple noise components differently originating. Each noise component produces fluctuation in the characteristic direction. The resulting fluctuations in the copy numbers of the molecular species are the sum of these fluctuations. In the examples, the limitation of the negative feedback in noise reduction and the trade-off of fluctuations in multiple molecular species are clearly explained. The analytical representations show the full parameter dependence. Additionally, the validity of our method is tested by stochastic simulations.     

Formulas for intrinsic noise evaluation in oscillatory genetic networks

The linear noise approximation is a useful method for stochastic noise evaluations in genetic regulatory networks, where the covariance equation described as a Lyapunov equation plays a central role. We discuss the linear noise approximation method for evaluations of an intrinsic noise in autonomously oscillatory genetic networks; in such oscillatory networks, the covariance equation becomes a periodic differential equation that provides generally an unbounded covariance matrix, so that the standard method of noise evaluation based on the covariance matrix cannot be adopted directly. In this paper, we develop a new method of noise evaluation in oscillatory genetic networks; first, we investigate structural properties, e.g., orbital stability and periodicity, of the solutions to the covariance equation given as a periodic Lyapunov differential equation by using the Floquet-Lyapunov theory, and propose a global measure for evaluating stochastic amplitude fluctuations on the periodic trajectory; we also derive an evaluation formula for the period fluctuation. Finally, we apply our method to a model of circadian oscillations based on negative auto-regulation of gene expression, and show validity of our method by comparing the evaluation results with stochastic simulations.     

Enriched partial correlations in genome-wide gene expression profiles of hybrids (A. thaliana): a systems biological approach towards the molecular basis of heterosis

Heterosis is a well-known phenomenon but the underlying molecular mechanisms are not yet established. To contribute to the understanding of heterosis at the molecular level, we analyzed genome-wide gene expression profile data of Arabidopsis thaliana in a systems biological approach. We used partial correlations to estimate the global interaction structure of regulatory networks. Our hypothesis states that heterosis comes with an increased number of partial correlations which we interpret as increased numbers of regulatory interactions leading to enlarged adaptability of the hybrids. This hypothesis is true for mid-parent heterosis for our dataset of gene expression in two homozygous parental lines and their reciprocal crosses. For the case of best-parent heterosis just one hybrid is significant regarding our hypothesis based on a resampling analysis. Summarizing, both metabolome and gene expression level of our illustrative dataset support our proposal of a systems biological approach towards a molecular basis of heterosis.     

Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae

Stochasticity is a hallmark of cellular processes, and different classes of genes show large differences in their cell-to-cell variability (noise). To decipher the sources and consequences of this noise, we systematically measured pairwise correlations between large numbers of genes, including those with high variability. We find that there is substantial pathway variability shared across similarly regulated genes. This induces quantitative correlations in the expression of functionally related genes such as those involved in the Msn2/4 stress response pathway, amino-acid?biosynthesis, and mitochondrial maintenance. Bioinformatic analyses and genetic perturbations suggest that fluctuations in PKA and Tor signaling contribute to pathway-specific variability. Our results argue that a limited number of well-delineated "noise regulons" operate across a yeast cell and that such coordinated fluctuations enable a stochastic but coherent induction of functionally related genes. Finally, we show that pathway noise is a quantitative tool for exploring pathway features and regulatory relationships in un-stimulated systems.     

Stochastic Actin Polymerization and Steady Retrograde Flow Determine Growth Cone Advancement

Neuronal growth is an extremely complex yet reliable process that is directed by a dynamic lamellipodial structure at the tip of every growing neurite, called the growth cone. Lamellipodial edge fluctuations are controlled by the interplay between actin polymerization pushing the edge forward and molecular motor driven retrograde actin flow retracting the actin network. The leading edge switches randomly between extension and retraction processes. We identify switching of “on/off” states in actin polymerization as the main determinant of lamellipodial advancement. Our analysis of motility statistics allows for a prediction of growth direction. This was used in simulations explaining the amazing signal detection capabilities of neuronal growth by the experimentally found biased stochastic processes. Our measurements show that the intensity of stochastic fluctuations depend on changes in the underlying active intracellular processes and we find a power law η = a^∗x^α with exponent α = 2.63 ± 0.12 between noise intensity η and growth cone activity x, defined as the sum of protrusion and retraction velocity. Differences in the lamellipodial dynamics between primary neurons and a neuronal cell line further suggests that active processes tune the observed stochastic fluctuations. This hints at a possible role of noise intensity in determining signal detection sensitivity.     

Robust and sensitive control of a quorum‐sensing circuit by two interlocked feedback loops

The quorum‐sensing (QS) response of Vibrio fischeri involves a rapid switch between low and high induction states of the lux operon over a narrow concentration range of the autoinducer (AI) 3‐oxo‐hexanoyl‐L ‐homoserine lactone. In this system, LuxR is an AI‐dependent positive regulator of the lux operon, which encodes the AI synthase. This creates a positive feedback loop common in many bacterial species that exhibit QS‐controlled gene expression. Applying a combination of modeling and experimental analyses, we provide evidence for a LuxR autoregulatory feedback loop that allows LuxR to increase its concentration in the cell during the switch to full lux activation. Using synthetic lux gene fragments, with or without the AI synthase gene, we show that the buildup of LuxR provides more sensitivity to increasing AI, and promotes the induction process. Elevated LuxR levels buffer against spurious variations in AI levels ensuring a robust response that endows the system with enhanced hysteresis. LuxR autoregulation also allows for two distinct responses within the same cell population.