Biomedical subjects

James C Liao

Publications and source records attributed to James C Liao.

At least 19 recordsLinked to original sources

PubMed2006

Rhodopseudomonas palustris CGA009 has two functional ppsR genes, each of which encodes a repressor of photosynthesis gene expression.

The PpsR protein is a regulator of redox-dependent photosystem development in purple phototrophic bacteria. In contrast to most species, Rhodopseudomonas palustris contains two ppsR genes. We show that the inactivation of each of the R. palustris strain CGA009 ppsR genes results in an elevated level of formation of the photosystem under dark aerobic conditions. Absorption spectra of the two PpsR mutants revealed qualitative and quantitative differences in light-harvesting peak amplitude increases. A sequence difference in the helix-turn-helix DNA binding motif of PpsR2 (Arg 439 to Cys) between R. palustris strains CEA001 and CGA009 is shown to be a natural polymorphism that does not inactivate the repressor activity of the protein. To evaluate which photosynthesis genes are regulated by the two PpsR proteins, transcriptome profiles of the CGA009 and PpsR mutant strains were analyzed in microarray experiments. Transcription of most but not all photosystem genes was derepressed in the mutant strains to levels consistent with the in vivo absorption spectra, mathematical analyses of peak shapes and amplitudes, reaction center protein levels, and real-time PCR of selected mRNAs. Closely spaced PpsR binding motif repeats were identified 5' of genes that were derepressed in the transcriptome analysis of PpsR mutants. This work shows that both the PpsR1 and PpsR2 proteins from R. palustris strain CGA009 function as oxygen-responsive transcriptional repressors.

Aerobiosis↗

PubMed2006

A Gibbs sampler for the identification of gene expression and network connectivity consistency.

MOTIVATION: Data from DNA microarrays and ChIP-chip binding assays often form the basis of transcriptional regulatory analyses. However, experimental noise in both data types combined with environmental dependence and uncorrelation between binding and regulation in ChIP-chip binding data complicate analyses that utilize these complimentary data sources. Therefore, to minimize the impact of these inaccuracies on transcription analyses it is desirable to identify instances of gene expression-ChIP-chip agreement, under the premise that inaccuracies are less likely to be present when separate data sources corroborate each other. Current methods for such identification either make key assumptions that limit their applicability and/or yield high false positive and false negative rates. The goal of this work was to develop a method with a minimal amount of assumptions, and thus widely applicable, that can identify agreement between gene expression and ChIP-chip data at a higher confidence level than current methods. RESULTS: We demonstrate in Saccharomyces cerevisiae that currently available ChIP-chip binding data explain microarray data from a variety of environments only as well as randomized networks with the same connectivity density. This suggests a high degree of inconsistency between the two data types and illustrates the need for a method that can identify consistency between the two data sources. Here we have developed a Gibbs sampling technique to identify genes whose expression and ChIP-chip binding data are mutually consistent. Compared to current methods that could perform the same task, the Gibbs sampling method developed here exceeds their ability at high levels (>50%) of transcription network and gene expression error, while performing similarly at lower levels. Using this technique, we show that on average 73% more gene expression features can be captured per gene as compared to the unfiltered use of gene expression and ChIP-chip-derived network connectivity data. It is important to note that the method described here can be generalized to other transcription connectivity data (e.g. sequence analysis, etc.). AVAILABILITY: Our algorithm is available on request from the authors and soon to be posted on the web. See author's homepage for details, http://www.seas.ucla.edu/~liaoj/

Algorithms↗

PubMed2006

Versatility and connectivity efficiency of bipartite transcription networks.

The modulation of promoter activity by DNA-binding transcription regulators forms a bipartite network between the regulators and genes, in which a smaller number of regulators control a much lager number of genes. To facilitate representation of gene expression data with the simplest possible network structure, we have characterized the ability of bipartite networks to describe data. This has led to the classification of two types of bipartite networks, versatile and nonversatile. Versatile networks can describe any data of the same rank, and are indistinguishable from one another. Nonversatile networks require constraints to be present in data they describe, which may be used to distinguish between different network topologies. By quantifying the ability of bipartite networks to represent data we were able to define connectivity efficiency, which is a measure of how economic the use of connections is within a network with respect to data representation and generation. We postulated that it may be desirable for an organism to maximize its gene expression range per network edge, since development of a regulatory connection may have some evolutionary cost. We found that the transcriptional regulatory networks of both Saccharomyces cerevisiae and Escherichia coli lie close to their respective connectivity efficiency maxima, suggesting that connectivity efficiency may have some evolutionary influence.

Escherichia coli Proteins↗

PubMed2006

Transcriptome network component analysis with limited microarray data.

UNLABELLED: Network component analysis (NCA) is a method to deduce transcription factor (TF) activities and TF-gene regulation control strengths from gene expression data and a TF-gene binding connectivity network. Previously, this method could analyze a maximum number of regulators equal to the total sample size because of the identifiability limit in data decomposition. As such, the total number of source signal components was limited to the total number of experiments rather than the total number of biological regulators. However, networks that have less transcriptome data points than the number of regulators are of interest. Thus it is imperative to develop a theoretical basis that allows realistic source signal extraction based on relatively few data points. On the other hand, such methods would inherently increase numerical challenges leading to multiple solutions. Therefore, solutions to both the problems are needed. RESULTS: We have improved NCA for transcription factor activity (TFA) estimation, based on the observation that most genes are regulated by only a few TFs. This observation leads to the derivation of a new identifiability criterion which is tested during numerical iteration that allows us to decompose data when the number of TFs is greater than the number of experiments. To show that our method works with real microarray data and has biological utility, we analyze Saccharomyces cerevisiae cell cycle microarray data (73 experiments) using a TF-gene connectivity network (96 TFs) derived from ChIP-chip binding data. We compare the results of NCA analysis with the results obtained from ChIP-chip regression methods, and we show that NCA and regression produce TFAs that are qualitatively similar, but the NCA TFAs outperform regression in statistical tests. We also show that NCA can extract subtle TFA signals that correlate with known cell cycle TF function and cell cycle phase. Overall we determined that 31 TFs have statistically periodic TFAs in one or more experiments, 75% of which are known cell cycle regulators. In addition, we find that the 12 TFAs that are periodic in two or more experiments correspond to well-known cell cycle regulators. We also investigated TFA sensitivity to the choice of connectivity network we constructed two networks using different ChIP-chip p-value cut-offs. AVAILABILITY: The NCA Toolbox for MATLAB is available at http://www.seas.ucla.edu/~liaoj/download.htm.

Algorithms↗

PubMed2006

The role of the lateral line and vision on body kinematics and hydrodynamic preference of rainbow trout in turbulent flow.

The ability to detect water flow using the hair cells of the lateral line system is a unique feature found in anamniotic aquatic vertebrates. Fishes use their lateral line to locate prey, escape from predators and form cohesive schooling patterns. Despite the prevalence of complex flows in nature, almost nothing is known about the function of the lateral line and its relationship to other sensory modalities for freely swimming fishes in turbulent flows. Past studies indicate that under certain conditions the lateral line is not needed to swim steadily in uniform flow. This paper examines how the lateral line and vision affect body kinematics and hydrodynamic habitat selection of rainbow trout (Oncorhynchus mykiss) exposed to vortices generated behind a cylinder. Trout Kármán gaiting (i.e. exploiting vortices to hold station in a vortex street) with a pharmacologically blocked lateral line display altered kinematics; body wavelength and wave speed increase compared to control animals. When visual cues are withheld by performing experiments in the dark, almost all Kármán gait kinematics measured for fish with and without a functional lateral line are the same. The lateral line, rather than vision, plays a larger role in affecting body kinematics when trout hold station in a vortex street. Trout show a preference to Kármán gait in the light but not in the dark, which may be attributed to physiological state rather than hydrodynamic or sensorimotor reasons. In the dark, trout both with and without a functional lateral line hold station near the downstream suction region of the cylinder wake (i.e. entraining) and avoid the vortex street. Vision therefore plays a larger role in the preference to associate with a turbulent vortex street. Trout in the light with a blocked lateral line show individual variation in their preference to Kármán gait or entrain. In the dark, entraining trout with an intact lateral line will alternate between right and left sides of the cylinder throughout the experiment, showing an ability to explore their environment. By contrast, when the lateral line is blocked these fish display a strong fidelity to one side of the cylinder and are not inclined to explore other regions of the flow tank. Both entraining and Kármán gaiting probably represent energetically favorable strategies for holding station relative to the earth frame of reference in fast flows. The ability to decipher how organisms collect and process sensory input from their environment has great potential in revealing the mechanistic basis of how locomotor behaviors are produced as well as how habitat selection is modulated.

Animals↗

PubMed2005

Targeted disruption of glycerol kinase gene in mice: expression analysis in liver shows alterations in network partners related to glycerol kinase activity.

Glycerol kinase deficiency (GKD) is an X-linked inborn error of metabolism with metabolic and neurological crises. Liver shows the highest level of glycerol kinase (GK) activity in humans and mice. Absence of genotype-phenotype correlations in patients with GKD indicates the involvement of modifier genes, including other network partners. To understand the molecular pathogenesis of GKD, we performed microarray analysis on liver mRNA from neonatal glycerol kinase (Gyk) knockout (KO) and wild-type (WT) mice. Unsupervised learning revealed that the overall gene expression profile of the KO mice was different from that of WT. Real-time PCR confirmed the differences for selected genes. Functional gene enrichment analysis was used to find 56 increased and 37 decreased gene functional categories. PathwayAssist analysis identified changes in gene expression levels of genes involved in organic acid metabolism indicating that GK was part of the same metabolic network which correlates well with the patients with GKD having metabolic acidemia during their episodic crises. Network component analysis (NCA) showed that transcription factors sterol regulatory element-binding protein (SREBP)-1c, carbohydrate response element-binding protein (ChREBP), hepatocyte nuclear factor-4 alpha (HNF-4alpha) and peroxisome proliferative-activated receptor-alpha (PPARalpha) had increased activity in the Gyk KO mice compared with WT mice, whereas SREBP-2 was less active in the Gyk KO mice. These studies show that Gyk deletion causes alterations in expression of genes in several regulatory networks and is the first time NCA has been used to expand on microarray data from a mouse KO model of a human disease.

Animals↗

PubMed2005

A global regulatory role of gluconeogenic genes in Escherichia coli revealed by transcriptome network analysis.

In bacterial adaptation to the dynamic environment, metabolic genes are typically thought to be the executors, whereas global transcription regulators are regarded as the decision makers. Although the feedback from metabolic consequence is believed to be important, much less is understood. This work demonstrates that the gluconeogenic genes in Escherichia coli, ppsA, sfcA, and maeB, provide a feedback loop to the global regulator, cAMP receptor protein (CRP), in carbon source transition. Disruption of one of the gluconeogenic pathways has no phenotype in balanced growth, but causes a significant delay in the diauxic transition from glucose to acetate. To investigate the underlying mechanism, we measured the transcriptome profiles during the transition using DNA microarray, and network component analysis was employed to obtain the transcription factor activities. Results showed that one of the global regulators, CRP, was insufficiently activated during the transition in the ppsA deletion mutant. Indeed, addition of cAMP partially rescued the delay in transition. These results suggest that the gluconeogenic flux to phosphoenolpyruvate is important for full activation of adenylate cyclase through the phosphorylated enzyme IIA(glu) of the phosphotransferase system. Reduction of this flux causes insufficient activation of CRP and a global metabolic deficiency, which exemplifies a significant feedback interaction from metabolism to the a global regulatory system.

Acetates↗

PubMed2005

Inferring yeast cell cycle regulators and interactions using transcription factor activities.

BACKGROUND: Since transcription factors are often regulated at the post-transcriptional level, their activities, rather than expression levels may provide valuable information for investigating functions and their interactions. The recently developed Network Component Analysis (NCA) and its generalized form (gNCA) provide a robust framework for deducing the transcription factor activities (TFAs) from various types of DNA microarray data and transcription factor-gene connectivity. The goal of this work is to demonstrate the utility of TFAs in inferring transcription factor functions and interactions in Saccharomyces cerevisiae cell cycle regulation. RESULTS: Using gNCA, we determined 74 TFAs from both wild type and fkh1 fkh2 deletion mutant microarray data encompassing 1529 ORFs. We hypothesized that transcription factors participating in the cell cycle regulation exhibit cyclic activity profiles. This hypothesis was supported by the TFA profiles of known cell cycle factors and was used as a basis to uncover other potential cell cycle factors. By combining the results from both cluster analysis and periodicity analysis, we recovered nearly 90% of the known cell cycle regulators, and identified 5 putative cell cycle-related transcription factors (Dal81, Hap2, Hir2, Mss11, and Rlm1). In addition, by analyzing expression data from transcription factor knockout strains, we determined 3 verified (Ace2, Ndd1, and Swi5) and 4 putative interaction partners (Cha4, Hap2, Fhl1, and Rts2) of the forkhead transcription factors. Sensitivity of TFAs to connectivity errors was determined to provide confidence level of these predictions. CONCLUSION: By subjecting TFA profiles to analyses based upon physiological signatures we were able to identify cell cycle related transcription factors consistent with current literature, transcription factors with potential cell cycle dependent roles, and interactions between transcription factors.

Cell Cycle↗

PubMed2005

A synthetic gene-metabolic oscillator.

Autonomous oscillations found in gene expression and metabolic, cardiac and neuronal systems have attracted significant attention both because of their obvious biological roles and their intriguing dynamics. In addition, de novo designed oscillators have been demonstrated, using components that are not part of the natural oscillators. Such oscillators are useful in testing the design principles and in exploring potential applications not limited by natural cellular behaviour. To achieve transcriptional and metabolic integration characteristic of natural oscillators, here we designed and constructed a synthetic circuit in Escherichia coli K12, using glycolytic flux to generate oscillation through the signalling metabolite acetyl phosphate. If two metabolite pools are interconverted by two enzymes that are placed under the transcriptional control of acetyl phosphate, the system oscillates when the glycolytic rate exceeds a critical value. We used bifurcation analysis to identify the boundaries of oscillation, and verified these experimentally. This work demonstrates the possibility of using metabolic flux as a control factor in system-wide oscillation, as well as the predictability of a de novo gene-metabolic circuit designed using nonlinear dynamic analysis.

Biological Clocks↗

PubMed2005

Single-gene disorders: what role could moonlighting enzymes play?

Single-gene disorders with "simple" Mendelian inheritance do not always imply that there will be an easy prediction of the phenotype from the genotype, which has been shown for a number of metabolic disorders. We propose that moonlighting enzymes (i.e., metabolic enzymes with additional functional activities) could contribute to the complexity of such disorders. The lack of knowledge about the additional functional activities of proteins could result in a lack of correlation between genotype and phenotype. In this review, we highlight some notable and recent examples of moonlighting enzymes and their possible contributions to human disease. Because knowledge and cataloging of the moonlighting activities of proteins are essential for the study of cellular function and human physiology, we also review recently reported and recommended methods for the discovery of moonlighting activities.

Animals↗

PubMed2005

Erythrocyte nitric oxide transport reduced by a submembrane cytoskeletal barrier.

Encapsulation of hemoglobin (Hb) within red blood cells (RBCs) preserves nitric oxide (NO) bioactivity. With encapsulation, millimolar concentrations of Hb quench only a fraction of NO bioactivity, whereas mere micromolar concentrations of cell-free Hb completely quench NO bioactivity. A submembrane cytoskeletal barrier has been hypothesized to account for the lowered quenching of NO bioactivity. In order to substantiate this hypothesis, here, the underlying submembrane cytoskeletal barrier was physically reduced and the rate of NO entry into the modified RBC measured. The submembrane cytoskeletal barrier of normal and depleted RBCs was characterized using atomic force microscopy and the lipid to protein ratio measured. The reduction in the submembrane cytoskeletal barrier resulted in an increase in the rate of NO entry. We suggest that the underlying submembrane cytoskeleton may be a key component of RBC mediated regulation of NO bioavailability.

Biological Transport↗

PubMed2005

Markov Chain modeling of pyelonephritis-associated pili expression in uropathogenic Escherichia coli.

Pyelonephritis-associated pili (Pap) expression in uropathogenic Escherichia coli is regulated by a complex phase variation mechanism involving the competition between leucine-responsive regulatory protein (Lrp) and DNA adenine methylase (Dam). Population dynamics of pap gene expression has been studied extensively and the detailed molecular mechanism has been largely elucidated, providing sufficient information for mathematical modeling. Although the Gillespie algorithm is suited for modeling of stochastic systems such as the pap operon, it becomes computationally expensive when detailed molecular steps are explicitly modeled in a population. Here we developed a Markov Chain model to simplify the computation. Our model is analytically derived from the molecular mechanism. The model presented here is able to reproduce results presented using the Gillespie method, but since the regulatory information is incorporated before simulation, our model runs more efficiently and allows investigation of additional regulatory features. The model predictions are consistent with experimental data obtained in this work and in the literature. The results show that pap expression in uropathogenic E. coli is initial-state-dependent, as previously reported. However, without environment stimuli, the pap-expressing fraction in a population will reach an equilibrium level after approximately 50-100 generations. The transient time before reaching equilibrium is determined by PapI stability and Lrp and Dam copy numbers per cell. This work demonstrates that the Markov Chain model captures the essence of the complex molecular mechanism and greatly simplifies the computation.

Algorithms↗

PubMed2005

Analysis of nitric oxide donor effectiveness in resistance vessels.

Decreased nitric oxide (NO) bioavailability is associated with a number of pathological conditions. Administration of a supplemental source of NO can counter the pathological effects arising from decreased NO bioavailability. A class of NO-nucleophile adducts that spontaneously release NO (NONOates) has been developed, and its members show promise as therapeutic sources of NO. Because the NONOates release NO spontaneously, a significant portion of the NO may be consumed by the myriad of NO reactive species present in the body. Here we develop a model to analyze the efficacy of NO delivery, by membrane-impermeable NONOates, in the resistance arterioles. Our model identifies three features of blood vessels that will enhance NONOate efficacy: 1) the amount of NO delivered to the abluminal region increases with lumen radius; 2) the presence of a flow-induced red blood cell-free zone will augment NO delivery; and 3) extravasation of the NONOate into the interstitial space will increase abluminal NO delivery. These results suggest that NONOates may be more effective in larger vessels and that NONOate efficacy can be altered by modifying permeability to the interstitial space.

Arterioles↗

PubMed2005

gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation.

Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to classical approaches such as principal component analysis or independent component analysis, NCA makes use of the connectivity structure from transcriptional regulatory networks to restrict the decomposition to a unique solution. However, the existing version of NCA cannot incorporate information beyond the network topology such as information obtained from regulatory gene knockouts that constrain the dynamics of regulatory signals. The ability of incorporating such information enables a more accurate and self-consistent analysis over different experiments and extends NCA to systems that may not satisfy the identifiability criteria of NCA. In this paper, we derive a generalized form of NCA, gNCA, which significantly expands the capability of transcription network analysis by incorporating regulatory signal constraints arising from genetic knockouts. The theoretical bases including criteria for uniqueness of solution and distinguishability between networks are derived. In addition, numerical techniques for robust decomposition are discussed. gNCA is then demonstrated using an Escherichia coli wild-type strain and an isogenic arcA deletion mutant during a carbon source transition.

Bacterial Outer Membrane Proteins↗

PubMed2005

Determination of functional interactions among signalling pathways in Escherichia coli K-12.

Interaction among different signalling pathways has been noted repeatedly. However, no systematic method has been developed to identify and quantify such interactions. Here we reported that network component analysis (NCA) was able to determine interactions among various signalling pathways in Escherichia coli K-12 based on known transcription factor (TF)-promoter connectivity information and microarray data from genetic knockout strains. The TF activities determined from NCA allow the quantitation of functional interactions, barring gross errors in the connectivity and microarray data. By using a robust statistical test, 37 pairs of functional interactions were identified. Eighteen interaction pairs confirmed previous implications, while 19 others represent new predictions. These results demonstrate that the functional interactions among various signalling pathways may be rather significant. With reasonable TF-promoter connectivity, NCA coupled with genetic knockouts and microarray experiments provides a systematic way to elucidate interaction networks. As this approach cannot distinguish between cross-talks and unidentified direct regulation, the results should provide incentives for further experimental testing.

Escherichia coli K12↗

PubMed2005

Heat shock response of Archaeoglobus fulgidus.

The heat shock response of the hyperthermophilic archaeon Archaeoglobus fulgidus strain VC-16 was studied using whole-genome microarrays. On the basis of the resulting expression profiles, approximately 350 of the 2,410 open reading frames (ORFs) (ca. 14%) exhibited increased or decreased transcript abundance. These span a range of cell functions, including energy production, amino acid metabolism, and signal transduction, where the majority are uncharacterized. One ORF called AF1298 was identified that contains a putative helix-turn-helix DNA binding motif. The gene product, HSR1, was expressed and purified from Escherichia coli and was used to characterize specific DNA recognition regions upstream of two A. fulgidus genes, AF1298 and AF1971. The results indicate that AF1298 is autoregulated and is part of an operon with two downstream genes that encode a small heat shock protein, Hsp20, and cdc48, an AAA+ ATPase. The DNase I footprints using HSR1 suggest the presence of a cis-binding motif upstream of AF1298 consisting of CTAAC-N5-GTTAG. Since AF1298 is negatively regulated in response to heat shock and encodes a protein only distantly related to the N-terminal DNA binding domain of Phr of Pyrococcus furiosus, these results suggest that HSR1 and Phr may belong to an evolutionarily diverse protein family involved in heat shock regulation in hyperthermophilic and mesophilic Archaea organisms.

Amino Acid Sequence↗

PubMed2004

Vocabulon: a dictionary model approach for reconstruction and localization of transcription factor binding sites.

MOTIVATION: Gene expression arrays enable measurements of transcription values for a large number or all genes in the genome. In order to better interpret these results and to use them to reconstruct transcription networks, information on location of binding sites for regulatory proteins in the entire genome is needed. In particular, this represents an open problem in Escherichia coli. RESULTS: We describe the first implementation of dictionary-style models to the study of transcription factors binding sites in an entire genome. Vocabulon's unique feature is that it can both reconstruct binding sites characterized by unknown motifs and impute locations of known binding sites in long sequences by simultaneous search. On one hand, the dictionary model specifies a probability for the entire sequence taking simultaneously into account all the possible binding sites. This greatly reduces the number of false positives. On the other hand, the possibility of refining motif description, as an increasing number of binding sites are identified, augments the sensitivity of the method. We illustrate these properties with examples in E.coli. The results of gene expression arrays are used both to guide the search and corroborate it.

Algorithms↗

PubMed2004

Stochastic modeling of the phase-variable pap operon regulation in uropathogenic Escherichia coli.

Regulation of the pap operon in uropathogenic Escherichia coli is phase variable. This phase variation arises from competition between regulatory proteins at two sites within the regulatory region, GATC(dist) and GATC(prox). We have used the available literature data to design a stochastic model of the molecular interactions of pap regulation and expression during growth in a non-glucose environment at 37 degrees C. The resulting wild-type model is consistent with reported data. The wild-type model served as a basis for two "in silico" mutant models for investigating the role of key regulatory components, the GATC(dist) binding site and the PapI interaction with Lrp at the GATC(prox) site. Our results show that competition at GATC(dist) is required for phase variation, as previously reported. However, our results suggest that removal of competition at GATC(dist) does not affect initial state dependence. Additionally, the PapI involvement in Lrp translocation from GATC(prox) to GATC(dist) is required for the initial state dependence but not for phase variation. Our results also predict that pap expression is maximized at low growth rates and minimized at high growth rates. These predictions provide a basis for further experimental investigation.

DNA-Binding Proteins↗