Peter J. Woolf

A Gene Regulatory Network for Root Epidermis Cell Differentiation in Arabidopsis

The root epidermis of Arabidopsis provides an exceptional model for studying the molecular basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell differentiation, we used a genome-wide transcriptome approach to define and organize a large set of genes into a transcriptional regulatory network. Using cell fate mutants that produce only one of the two epidermal cell types, together with fluorescence-activated cell-sorting to preferentially analyze the root epidermis transcriptome, we identified 1,582 genes differentially expressed in the root-hair or non-hair cell types, including a set of 208 “core” root epidermal genes. The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each genes transcript accumulation. In addition, temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network. Further, a detailed functional analysis of likely bHLH regulatory genes within the network, including MYC1, bHLH54, bHLH66, and bHLH82, showed that three distinct subfamilies of bHLH proteins participate in root epidermis development in a stage-specific manner. The integration of genetic, genomic, and computational analyses provides a new view of the composition, architecture, and logic of the root epidermal transcriptional network, and it demonstrates the utility of a comprehensive systems approach for dissecting a complex regulatory network.

Temporal Dynamics of Host Molecular Responses Differentiate Symptomatic and Asymptomatic Influenza A Infection

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.

Bayesian analysis of signaling networks governing embryonic stem cell fate decisions

MOTIVATION Signaling events that direct mouse embryonic stem (ES) cell self-renewal and differentiation are complex and accordingly difficult to understand in an integrated manner. We address this problem by adapting a Bayesian network learning algorithm to model proteomic signaling data for ES cell fate responses to external cues. Using this model we were able to characterize the signaling pathway influences as quantitative, logic-circuit type interactions. Our experimental dataset includes measurements for 28 signaling protein phosphorylation states across 16 different factorial combinations of cytokine and matrix stimuli as reported previously. RESULTS The Bayesian network modeling approach allows us to uncover previously reported signaling activities related to mouse ES cell self-renewal, such as the roles of LIF and STAT3 in maintaining undifferentiated ES cell populations. Furthermore, the network predicts novel influences such as between ERK phosphorylation and differentiation, or RAF phosphorylation and differentiated cell proliferation. Visualization of the influences detected by the Bayesian network provides intuition about the underlying physiology of the signaling pathways. We demonstrate that the Bayesian networks can capture the linear, nonlinear and multistate logic interactions that connect extracellular cues, intracellular signals and consequent cell functional responses.

A Spatial Focusing Model for G Protein Signals REGULATOR OF G PROTEIN SIGNALING (RGS) PROTEIN-MEDIATED KINETIC SCAFFOLDING

Regulators of G protein signaling (RGS) are GTPase-accelerating proteins (GAPs), which can inhibit heterotrimeric G protein pathways. In this study, we provide experimental and theoretical evidence that high concentrations of receptors (as at a synapse) can lead to saturation of GDP-GTP exchange making GTP hydrolysis rate-limiting. This results in local depletion of inactive heterotrimeric G-GDP, which is reversed by RGS GAP activity. Thus, RGS enhances receptor-mediated G protein activation even as it deactivates the G protein. Evidence supporting this model includes a GTP-dependent enhancement of guanosine 5′-3-O-(thio)triphosphate (GTPγS) binding to Gi by RGS. The RGS domain of RGS4 is sufficient for this, not requiring the NH2- or COOH-terminal extensions. Furthermore, a kinetic model including only the GAP activity of RGS replicates the GTP-dependent enhancement of GTPγS binding observed experimentally. Finally in a Monte Carlo model, this mechanism results in a dramatic “spatial focusing” of active G protein. Near the receptor, G protein activity is maintained even with RGS due to the ability of RGS to reduce depletion of local Gα-GDP levels permitting rapid recoupling to receptor and maintained G protein activation near the receptor. In contrast, distant signals are suppressed by the RGS, since Gα-GDP is not depleted there. Thus, a novel RGS-mediated “kinetic scaffolding” mechanism is proposed which narrows the spatial range of active G protein around a cluster of receptors limiting the spill-over of G protein signals to more distant effector molecules, thus enhancing the specificity of Gi protein signals.

A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data

Background Quantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle () standard curve quantification, which requires the time- and labor-intensive construction of a standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as standard curve quantification. Principal Findings We have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as standard curve quantification for a variety of DNA targets and a wide range of concentrations. Significance We anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sample throughput, where construction of a standard curve is impractical.

The transcription factor osterix (SP7) regulates BMP6-induced human osteoblast differentiation.

The transcription factor Osterix (Sp7) is essential for osteoblastogenesis and bone formation in mice. Genome wide association studies have demonstrated that Osterix is associated with bone mineral density in humans; however, the molecular significance of Osterix in human osteoblast differentiation is poorly described. In this study we have characterized the role of Osterix in human mesenchymal progenitor cell (hMSC) differentiation. We first analyzed temporal microarray data of primary hMSC treated with bone morphogenetic protein‐6 (BMP6) using clustering to identify genes that are associated with Osterix expression. Osterix clusters with a set of osteoblast‐associated extracellular matrix (ECM) genes, including bone sialoprotein (BSP) and a novel set of proteoglycans, osteomodulin (OMD), osteoglycin, and asporin. Maximum expression of these genes is dependent upon both the concentration and duration of BMP6 exposure. Next we overexpressed and repressed Osterix in primary hMSC using retrovirus. The enforced expression of Osterix had relatively minor effects on osteoblastic gene expression independent of exogenous BMP6. However, in the presence of BMP6, Osterix overexpression enhanced expression of the aforementioned ECM genes. Additionally, Osterix overexpression enhanced BMP6 induced osteoblast mineralization, while inhibiting hMSC proliferation. Conversely, Osterix knockdown maintained hMSC in an immature state by decreasing expression of these ECM genes and decreasing mineralization and hMSC proliferation. Overexpression of the Osterix regulated gene OMD with retrovirus promoted mineralization of hMSC. These results suggest that Osterix is necessary, but not sufficient for hMSC osteoblast differentiation. Osterix regulates the expression of a set of ECM proteins which are involved in terminal osteoblast differentiation. J. Cell. Physiol. 227: 2677–2685, 2012.

Untangling Ligand Induced Activation and Desensitization of G-Protein-Coupled Receptors

Long-term treatment with a drug to a G-protein-coupled receptor (GPCR) often leads to receptor-mediated desensitization, limiting the therapeutic lifetime of the drug. To better understand how this therapeutic window might be controlled, we created a mechanistic Monte Carlo model of the early steps in GPCR signaling and desensitization. Using this model we found that the rates of G-protein activation and receptor phosphorylation can be partially decoupled by varying the drug-receptor dissociation rate constant, k(off), and the drugs efficacy, alpha. The maximum ratio of G-protein activation to receptor phosphorylation (GARP) was found for drugs with an intermediate k(off) value and small alpha-value. Changes to the cellular environment, such as changes in the diffusivity of membrane molecules and the G-protein inactivation rate constant, affected the GARP value of a drug but did not change the characteristic shape of the GARP curve. These model results are examined in light of experimental data for a number of GPCRs and are found to be in good agreement, lending support to the idea that the desensitization properties of a drug might be tailored to suit a specific application.

Monte Carlo simulations of receptor dynamics: Insights into cell signaling

Many receptor-level processes involve the diffusion and reaction of receptors with other membrane-localized molecules. Monte Carlo simulation is a powerful technique that allows us to track the motions and discrete reactions of individual receptors, thus simulating receptor dynamics and the early events of signal transduction. In this paper, we discuss simulations of two receptor processes, receptor dimerization and G-protein activation. Our first set of simulations demonstrates how receptor dimerization can create clusters of receptors via partner switching and the relevance of this clustering for receptor cross-talk and integrin signaling. Our second set of simulations investigates the activation and desensitization of G-protein coupled receptors when either a single agonist or both an agonist and an antagonist are present. For G-protein coupled receptor systems in the presence of an agonist alone, the dissociation rate constant of agonist is predicted to affect the ratio of G-protein activation to receptor phosphorylation. Similarly, this ratio is affected by the antagonist dissociation rate constant when both agonist and antagonist are present. The relationship of simulation predictions to experimental findings and potential applications of our findings are also discussed.

poolMC: Smart pooling of mRNA samples in microarray experiments

BackgroundTypically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements.ResultsA theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment.ConclusionsThe theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size.

miniTUBA: medical inference by network integration of temporal data using Bayesian analysis

MOTIVATION Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference, such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data. RESULTS miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. AVAILABILITY miniTUBA is available at http://www.minituba.org.