A gene regulatory network for root hair development

Journal of Plant Research - Root hairs play important roles for the acquisition of nutrients, microbe interaction and plant anchorage. In addition, root hairs provide an excellent model system to...     

A Markov-blanket-based model for gene regulatory network inference

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.     

Interaction of anions and ATP with the coated vesicle proton pump

ATP-driven proton transport in intact clathrin-coated vesicles requires the presence of a permeant anion, such as Cl-, to provide charge compensation during the electrogenic movement of protons. Using the purified (H+)-ATPase from clathrin-coated vesicles in both the detergent-solubilized and reconstituted states, we have studied the direct effects of anions on the activity of this enzyme. Both proton transport and ATP hydrolysis by the purified enzyme are independent of the presence of Cl-. In addition, proton transport does not occur even at high Cl- concentrations unless K+ and valinomycin are present to dissipate the membrane potential generated. These results indicate that the anion channel which provides for Cl- flux in intact coated vesicles is not a component of the purified (H+)-ATPase. Inhibition of ATPase activity is observed in the presence of I-, NO3-, or SO4(2-), with 50% inhibition occurring at 350 mM I-, 50 mM NO3-, or 40 mM SO4(2-). The presence of ATP lowers the concentration of I- required for 50% inhibition from 350 mM to 100 mM and increases the maximal inhibition observed in the presence of NO3- from 65% to 100%. Two separate mechanisms appear to be responsible for anion inhibition of the (H+)-ATPase. Thus, I- and high concentrations of NO3- (in the presence of ATP) cause inhibition by dissociation of the (H+)-ATPase complex, while SO4(2-) and NO3- (in the absence of ATP) cause inhibition without dissociation of the complex, suggesting the existence of an inhibitory anion binding site on the enzyme.(ABSTRACT TRUNCATED AT 250 WORDS)     

H+/ATP stoichiometry of proton pump of turtle urinary bladder

Urinary acidification in the turtle urinary bladder is due to a reversible proton-translocating ATPase. To estimate the H+/ATP stoichiometry of this pump, we measured the delta G'ATP in the epithelial cells and the maximum e.m.f. generated by the pump. The latter is the maximal transepithelial electrochemical gradient for protons placed across the epithelium that is needed to nullify the rate of transport and averaged 179 +/- 7 mV. The delta G'ATP averaged 50.1 kJ/mol. The H+/ATP stoichiometry of these bladders was 2.92 +/- 0.1. In other experiments, the bladders were poisoned by iodoacetate and cyanide and a variable transepithelial electrochemical gradient for protons was placed across them. It was noted that ATP synthesis occurred at a transepithelial electrochemical gradient for protons greater than 120 mV. The delta G'ATP in other bladders treated identically averaged 40.0 kJ/mol, giving a H+/ATP stoichiometry of 3.4 +/- 0.1. We conclude that the H+/ATP stoichiometry of the proton pump of turtle urinary bladder is approximately 3.     

A gene regulatory network orchestrates neural crest formation

The neural crest is a multipotent, migratory cell population that is unique to vertebrate embryos and gives rise to many derivatives, ranging from the peripheral nervous system to the craniofacial skeleton and pigment cells. A multimodule gene regulatory network mediates the complex process of neural crest formation, which involves the early induction and maintenance of the precursor pool, emigration of the neural crest progenitors from the neural tube via an epithelial to mesenchymal transition, migration of progenitor cells along distinct pathways and overt differentiation into diverse cell types. Here, we review our current understanding of these processes and discuss the molecular players that are involved in the neural crest gene regulatory network.     

Integrating heterogeneous gene expression data for gene regulatory network modelling

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.     

Characterization of an electrogenic ATP and chloride-dependent proton translocating pump from rat renal medulla

To study acidification mechanisms in the distal nephron, microsomes were prepared from rat renal medulla by differential centrifugation. Microsomes were enriched in the enzyme marker gamma-glutamyl transferase and contained an ATP-dependent proton pump, as evidenced by ATP-dependent, 3,3',4',5-tetrachlorosalicylanilide-reversible quenching of acridine orange fluorescence. Acidification was vanadate-insensitive, but was completely inhibited by micromolar N-ethylmaleimide. Maximal acidification was achieved in the presence of halide (Cl-, Br-) only and was not attainable with potassium-valinomycin diffusion potentials without halide ion. Microsomal ATPase activity was neither chloride- nor N-ethylmaleimide-sensitive. A chloride conductance was observed only with vesicles which had undergone ATP-dependent acidification. An ATP-dependent, N-ethylmaleimide-inhibitable, 3,3',4',5-tetrachlorosalicylanilide-reversible, and chloride-attenuated quench of bis(1,3-dibutylbarbituric acid-(5] pentamethinoxonol fluorescence was seen, consistent with net transfer of positive charge into the vesicles. Nonetheless, positive intravesicular potentials increased the ATP-dependent initial acidification rate, perhaps by increasing availability of chloride ion to the transport site. Our results are consistent with an electrogenic, ATP-dependent proton pump regulated by a voltage-sensitive chloride site.     

A comprehensive gene regulatory network for the diauxic shift in Saccharomyces cerevisiae

Existing machine-readable resources for large-scale gene regulatory networks usually do not provide context information characterizing the activating conditions for a regulation and how targeted genes are affected. Although this information is essentially required for data interpretation, available networks are often restricted to not condition-dependent, non-quantitative, plain binary interactions as derived from high-throughput screens. In this article, we present a comprehensive Petri net based regulatory network that controls the diauxic shift in Saccharomyces cerevisiae. For 100 specific enzymatic genes, we collected regulations from public databases as well as identified and manually curated >400 relevant scientific articles. The resulting network consists of >300 multi-input regulatory interactions providing (i) activating conditions for the regulators; (ii) semi-quantitative effects on their targets; and (iii) classification of the experimental evidence. The diauxic shift network compiles widespread distributed regulatory information and is available in an easy-to-use machine-readable form. Additionally, we developed a browsable system organizing the network into pathway maps, which allows to inspect and trace the evidence for each annotated regulation in the model.     

Two-component regulatory systems in Pseudomonas aeruginosa: an intricate network mediating fimbrial and efflux pump gene expression

Pseudomonas aeruginosa is responsible for chronic and acute infections in humans. Chronic infections are associated with production of fimbriae and the formation of a biofilm. The two-component system Roc1 is named after its role in the regulation of cup genes, which encode components of a machinery allowing assembly of fimbriae. A non-characterized gene cluster, roc2, encodes components homologous to the Roc1 system. We show that cross-regulation occurs between the Roc1 and Roc2 signalling pathways. We demonstrate that the sensors RocS2 and RocS1 converge on the response regulator RocA1 to control cupC gene expression. This control is independent of the response regulator RocA2. Instead, we show that these sensors act via the RocA2 response regulator to repress the mexAB-oprM genes. These genes encode a multidrug efflux pump and are upregulated in the rocA2 mutant, which is less susceptible to antibiotics. It has been reported that in cystic fibrosis lungs, in which P. aeruginosa adopts the biofilm lifestyle, most isolates have an inactive MexAB-OprM pump. The concomitant RocS2-dependent upregulation of cupC genes (biofilm formation) and downregulation of mexAB-oprM genes (antibiotic resistance) is in agreement with this observation. It suggests that the Roc systems may sense the environment in the cystic fibrosis lung.     

14-3-3 protein regulation of proton pumps and ion channels

In addition to their regulation of cytoplasmic enzymes, the 14-3-3 proteins are important regulators of membrane localised proteins. In particular, many of the cells' ion pumps and channels are either directly or indirectly modulated by 14-3-3 proteins. Binding of 14-3-3 can lead to the activation of pump activity as in the case of the plasma membrane H⁺-ATPase or inhibition as in the case of the F-type ATP synthase complexes. 14-3-3 binding can also lead to surprising results such as the recruitment of `sleepy' outward rectifiying K⁺ channels in tomato cells. Our present knowledge extends to an initial understanding of isoform-specific binding of 14-3-3 to certain membrane proteins and a perception of the protein kinases and phosphatases that maintain the regulatory process in a state of flux.     

Study of lung cancer regulatory network that involves erbB4 and tumor marker gene

Our purpose is to screen out serum tumor markers closely correlated to the nature of solitary pulmonary nodule (SPN) and to draw a regulatory network containing genes correlated to lung cancer. Two hundred and sixty cases of SPN patients confirmed through pathological diagnosis were collected as subjects, factors closely correlated to the nature of SPN were screened out from eight tumor markers through Fisher discriminant method, and functional annotation and pathway analysis were conducted on erbB4 as well as its tumor marker genes by GO and KEGG databases. Four key tumor markers: CYFRA21-1, CA125, SCC-Ag and CA153 were successfully screened out and the first three proteins’ corresponding gene were KRT19, MUC16 and SERPINB3 while that of CA153 was not found. GO analysis on erbB4, KRT19, MUC16 and SERPINB3 showed that they covered three domains, cell components, molecular function and biological process; meanwhile, combined with KEGG database and based on signal pathway of erbB4, a regulatory network of lung cancer cells escaping from apoptosis was successfully made. This study indicates that serum tumor marker genes play an important role in the occurrence and development of lung cancer, besides, this study primarily discussed the molecular mechanism of these tumor markers in predicting tumor, which provides a basis for in-depth information about lung cancer.     

Temporal graphical models for cross-species gene regulatory network discovery

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.     

A computational framework for gene regulatory network inference that combines multiple methods and datasets

Background  

Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective.     

Evolution of the bone gene regulatory network

Current fossil, embryological and genetic data shed light on the evolution of the gene regulatory network (GRN) governing bone formation. The key proteins and genes involved in skeletogenesis are well accepted. We discuss when these essential components of the GRN evolved and propose that the Runx genes, master regulators of skeletogenesis, functioned in early cartilages well before they were co-opted to function in the making of bone. Two rounds of whole genome duplication, together with additional tandem gene duplications, created a genetic substrate for segregation of one GRN into several networks regulating the related tissues of cartilage, bone, enamel, and dentin. During this segregation, Runx2 assumed its position at the top of the bone GRN, and Sox9 was excluded from bone, retaining its ancient role in cartilage.     

Stochastic S-system modeling of gene regulatory network

Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomycescerevisiae yeast, and (2) the SOS DNA repair network in Escherichiacoli.     

结核分枝杆菌感染对人巨噬细胞离子通道及其调控元件转录表达的影响

为研究人巨噬细胞的离子通道及其调控元件是否参与了抗结核分枝杆菌感染免疫,利用表达谱芯片技术研究细菌感染后主巨噬细胞基因的表达情况,在全局表达谱分析的基础上,重点分析了离子通道及其调控元件的表达,并比较无毒株和临床分离有毒株在诱导离子通道及其调控元件表达方面的差异。结果表明,细菌感染影响离子通道及其调控元件基因的表达,涉及的离子通道包括钾通道、钠通道、氯通道、钙通道,差异表达的调控元件包括G蛋白、G蛋白偶联受体、蛋白质激酶和磷酸化酶;临床株感染影响的离子通道及其调控元件较无毒株广泛和丰富。这些观察结果提示,离子通道及其调控元件参与了宿主细胞对感染细菌的免疫应答,有关离子通道及其调控元件在抗结核免疫中的作用有待进一步研究。芯片研究的结果为将来的研究提供了线索。