Dominant negative autoregulation limits steady-state repression levels in gene networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Semsey S
PROVIDER: S-EPMC2704710 | biostudies-literature | 2009 Jul
REPOSITORIES: biostudies-literature
Publications
Dominant negative autoregulation limits steady-state repression levels in gene networks.
Semsey Szabolcs S Krishna Sandeep S Erdossy János J Horváth Péter P Orosz László L Sneppen Kim K Adhya Sankar S
Journal of bacteriology 20090508 14
Many transcription factors repress transcription of their own genes. Negative autoregulation has been shown to reduce cell-cell variation in regulatory protein levels and speed up the response time in gene networks. In this work we examined transcription regulation of the galS gene and the function of its product, the GalS protein. We observed a unique operator preference of the GalS protein characterized by dominant negative autoregulation. We show that this pattern of regulation limits the rep ...[more]
Similar Datasets
Project description:Autonomic nerves control organ function through the sympathetic and parasympathetic branches, which have opposite effects. In the bone marrow, sympathetic (adrenergic) nerves promote hematopoiesis; however, how parasympathetic (cholinergic) signals modulate hematopoiesis is unclear. Here, we show that B lymphocytes are an important source of acetylcholine, a neurotransmitter of the parasympathetic nervous system, which reduced hematopoiesis. Single-cell RNA sequencing identified nine clusters of cells that expressed the cholinergic α7 nicotinic receptor (Chrna7) in the bone marrow stem cell niche, including endothelial and mesenchymal stromal cells (MSCs). Deletion of B cell-derived acetylcholine resulted in the differential expression of various genes, including Cxcl12 in leptin receptor+ (LepR+) stromal cells. Pharmacologic inhibition of acetylcholine signaling increased the systemic supply of inflammatory myeloid cells in mice and humans with cardiovascular disease.
| S-EPMC8989652 | biostudies-literature
Project description:Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.
| S-EPMC3938831 | biostudies-literature
Project description:Contractile actomyosin network flows are crucial for many cellular processes including cell division and motility, morphogenesis and transport. How local remodeling of actin architecture tunes stress production and dissipation and regulates large-scale network flows remains poorly understood. Here, we generate contracting actomyosin networks with rapid turnover in vitro, by encapsulating cytoplasmic Xenopus egg extracts into cell-sized 'water-in-oil' droplets. Within minutes, the networks reach a dynamic steady-state with continuous inward flow. The networks exhibit homogeneous, density-independent contraction for a wide range of physiological conditions, implying that the myosin-generated stress driving contraction and the effective network viscosity have similar density dependence. We further find that the contraction rate is roughly proportional to the network turnover rate, but this relation breaks down in the presence of excessive crosslinking or branching. Our findings suggest that cells use diverse biochemical mechanisms to generate robust, yet tunable, actin flows by regulating two parameters: turnover rate and network geometry.
| S-EPMC6871652 | biostudies-literature
Project description:We investigated the effect of codon optimization on the expression levels of heterologous proteins in Aspergillus oryzae, using the mite allergen Der f 7 as a model protein. A codon-optimized Der f 7 gene was synthesized according to the frequency of codon usage in A. oryzae by recursive PCR. Both native and optimized Der f 7 genes were expressed under the control of a high-level-expression promoter with their own signal peptides or in a fusion construct with A. oryzae glucoamylase (GlaA). Codon optimization markedly increased protein and mRNA production levels in both nonfused and GlaA-fused Der f 7 constructs. For constructs with native codons, analysis by 3' rapid amplification of cDNA ends revealed that poly(A) tracts tended to be added within the coding region, producing aberrant mRNAs that lack a termination codon. Insertion of a termination codon between the carrier GlaA and native Der f 7 proteins in the GlaA fusion construct resulted in increases in mRNA and secreted-carrier-GlaA levels. These results suggested that mRNAs without a termination codon as a result of premature polyadenylation are degraded, possibly through the nonstop mRNA decay pathway. We suggest that codon optimization in A. oryzae results in elimination of cryptic polyadenylation signals in native Der f 7, thereby circumventing the production of truncated transcripts and resulting in an increase in steady-state mRNA levels.
| S-EPMC2576710 | biostudies-literature
Project description:We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.
| S-EPMC3743784 | biostudies-literature
Project description:For correct application and interpretation of cerebral autoregulation (CA) measurements in research and in clinical care, it is essential to understand differences and similarities between dynamic and steady-state CA. The present study found no correlation between dynamic and steady-state CA indices in healthy older adults. There was variability between individuals in all (steady-state and dynamic) autoregulatory indices, ranging from low (almost absent) to highly efficient CA in this healthy population. These findings challenge the assumption that assessment of a single CA parameter or a single set of parameters can be generalized to overall CA functioning. Therefore, depending on specific research purposes, the choice for either steady-state or dynamic measures or both should be weighed carefully.The present study aimed to investigate the relationship between dynamic (dCA) and steady-state cerebral autoregulation (sCA). In 28 healthy older adults, sCA was quantified by a linear regression slope of proportionate (%) changes in cerebrovascular resistance (CVR) in response to proportionate (%) changes in mean blood pressure (BP) induced by stepwise sodium nitroprusside (SNP) and phenylephrine (PhE) infusion. Cerebral blood flow (CBF) was measured at the internal carotid artery (ICA) and vertebral artery (VA) and CBF velocity at the middle cerebral artery (MCA). With CVR = BP/CBF, Slope-CVRICA , Slope-CVRVA and Slope-CVRiMCA were derived. dCA was assessed (i) in supine rest, analysed with transfer function analysis (gain and phase) and autoregulatory index (ARI) fit from spontaneous oscillations (ARIBaseline ), and (ii) with transient changes in BP using a bolus injection of SNP (ARISNP ) and PhE (ARIPhE ). Comparison of sCA and dCA parameters (using Pearson's r for continuous and Spearman's ρ for ordinal parameters) demonstrated a lack of linear correlations between sCA and dCA measures. However, comparisons of parameters within dCA and within sCA were correlated. For sCA slope-CVRVA with Slope-CVRiMCA (r = 0.45, P < 0.03); for dCA ARISNP with ARIPhE (ρ = 0.50, P = 0.03), ARIBaseline (ρ = 0.57, P = 0.03) and PhaseLF (ρ = 0.48, P = 0.03); and for GainVLF with GainLF (r = 0.51, P = 0.01). By contrast to the commonly held assumption based on an earlier study, there were no linear correlations between sCA and dCA. As an additional observation, there was strong inter-individual variability, both in dCA and sCA, in this healthy group of elderly, in a range from low to high CA efficiency.
| S-EPMC5556173 | biostudies-other
Project description:MotivationIdentifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.ResultsIn this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.
| S-EPMC4514654 | biostudies-literature
Project description:The validity of using transcranial Doppler measurement of cerebral blood flow velocity (CBFV) to assess cerebral autoregulation (CA) still is a concern. This study measured CBFV in the middle cerebral artery using transcranial Doppler and volumetric cerebral blood flow (CBF) in the internal carotid artery (ICA) using color-coded duplex ultrasonography to assess CA during steady-state changes in mean arterial pressure (MAP). Twenty-one healthy adults participated. MAP was changed stepwise by intravenous infusion of sodium nitroprusside and phenylephrine. Changes in CBFV, CBF, cerebrovascular resistance (CVR=MAP/CBF), or cerebrovascular resistance index (CVRi=MAP/CBFV) were measured to assess CA by linear regression analysis. The relationship between changes in ICA diameter and MAP was assessed. All values were normalized as percentage changes from baseline. Drug-induced changes in MAP were from -26% to 31%. Changes in CBFV and CVRi in response to MAP were linear, and the regression slopes were similar between middle cerebral artery and ICA. However, CBF in ICA remained unchanged despite large changes in MAP. Consistently, a steeper slope of changes in CVR relative to CVRi was observed (0.991 versus 0.804; P<0.05). The ICA diameter changed inversely in response to MAP (r=-0.418; P<0.05). These findings indicate that CA can be assessed with transcranial Doppler measurements of CBFV and CVRi in middle cerebral artery. However, it is likely to be underestimated when compared with the measurements of CBF and CVR in ICA. The inverse relationship between changes in ICA diameter and MAP suggests that large cerebral arteries are involved in CA.
| S-EPMC3893684 | biostudies-literature
Project description:Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.
| S-EPMC6065372 | biostudies-literature
Project description:Living cells maintain a steady state of biochemical reaction rates by exchanging energy and matter with the environment. These exchanges usually do not occur in in vitro systems, which consequently go to chemical equilibrium. This in turn has severely constrained the complexity of biological networks that can be implemented in vitro. We developed nanoliter-scale microfluidic reactors that exchange reagents at dilution rates matching those of dividing bacteria. In these reactors we achieved transcription and translation at steady state for 30 h and implemented diverse regulatory mechanisms on the transcriptional, translational, and posttranslational levels, including RNA polymerases, transcriptional repression, translational activation, and proteolysis. We constructed and implemented an in vitro genetic oscillator and mapped its phase diagram showing that steady-state conditions were necessary to produce oscillations. This reactor-based approach will allow testing of whether fundamental limits exist to in vitro network complexity.
| S-EPMC3791785 | biostudies-literature