Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes.

The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

Microbial inactivation of Pseudomonas putida and Pichia pastoris using gene silencing.

Antisense deoxyoligonucleotide (ASO) gene silencing was investigated as a potential disinfection tool for industrial and drinking water treatment application. ASOs bind with their reverse complementary mRNA transcripts thereby blocking protein translation. While ASO silencing has mainly been studied in medicine, it may be useful for modulating gene expression and inactivating microorganisms in environmental applications. In this proof of concept work, gene targets were sh ble (zeocin resistance) and todE (catechol-2,3-dioxygenase) in Pichia pastoris and npt (kanamycin resistance) in Pseudomonas putida. A maximum 0.5-fold decrease in P. pastoris cell numbers was obtained following a 120 min incubation with single-stranded DNA (ssDNA) concentrations ranging from 0.2 to 200 nM as compared to the no ssDNA control. In P. putida, a maximum 5.2-fold decrease was obtained after 90 min with 400 nM ssDNA. While the silencing efficiencies varied for the 25 targets tested, these results suggest that protein activity as well as microbial growth can be altered using ASO gene silencing-based tools. If successful, this technology has the potential to eliminate some of the environmental and health issues associated with the use of strong chemical biocides. However, prior to its dissemination, more research is needed to increase silencing efficiency and develop effective delivery methods.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Reciprocal in vivo regulation of myocardial G protein-coupled receptor kinase expression by beta-adrenergic receptor stimulation and blockade.

BACKGROUND: Impaired myocardial beta-adrenergic receptor (betaAR) signaling, including desensitization and functional uncoupling, is a characteristic of congestive heart failure. A contributing mechanism for this impairment may involve enhanced myocardial beta-adrenergic receptor kinase (betaARK1) activity because levels of this betaAR-desensitizing G protein-coupled receptor kinase (GRK) are increased in heart failure. An hypothesis has emerged that increased sympathetic nervous system activity associated with heart failure might be the initial stimulus for betaAR signaling alterations, including desensitization. We have chronically treated mice with drugs that either activate or antagonize betaARs to study the dynamic relationship between betaAR activation and myocardial levels of betaARK1. METHODS AND RESULTS: Long-term in vivo stimulation of betaARs results in the impairment of cardiac +betaAR signaling and increases the level of expression (mRNA and protein) and activity of +betaARK1 but not that of GRK5, a second GRK abundantly expressed in the myocardium. Long-term beta-blocker treatment, including the use of carvedilol, improves myocardial betaAR signaling and reduces betaARK1 levels in a specific and dose-dependent manner. Identical results were obtained in vitro in cultured cells, demonstrating that the regulation of GRK expression is directly linked to betaAR signaling. CONCLUSIONS: This report demonstrates, for the first time, that betaAR stimulation can significantly increase the expression of betaARK1 , whereas beta-blockade decreases expression. This reciprocal regulation of betaARK1 documents a novel mechanism of ligand-induced betaAR regulation and provides important insights into the potential mechanisms responsible for the effectiveness of beta-blockers, such as carvedilol, in the treatment of heart failure.

Enhanced myocardial relaxation in vivo in transgenic mice overexpressing the beta2-adrenergic receptor is associated with reduced phospholamban protein.

To assess the effect of targeted myocardial beta-adrenergic receptor (AR) stimulation on relaxation and phospholamban regulation, we studied the physiological and biochemical alterations associated with overexpression of the human beta2-AR gene in transgenic mice. These mice have an approximately 200-fold increase in beta-AR density and a 2-fold increase in basal adenylyl cyclase activity relative to negative littermate controls. Mice were catheterized with a high fidelity micromanometer and hemodynamic recordings were obtained in vivo. Overexpression of the beta2-AR altered parameters of relaxation. At baseline, LV dP/dt(min) and the time constant of LV pressure isovolumic decay (Tau) in the transgenic mice were significantly shorter compared with controls, indicating markedly enhanced myocardial relaxation. Isoproterenol stimulation resulted in shortening of relaxation velocity in control mice but not in the transgenic mice, indicating maximal relaxation in these animals. Immunoblotting analysis revealed a selective decrease in the amount of phospholamban protein, without a significant change in the content for either sarcoplasmic reticulum Ca2+ ATPase or calsequestrin, in the transgenic hearts compared with controls. This study indicates that myocardial relaxation is both markedly enhanced and maximal in these mice and that conditions associated with chronic beta-AR stimulation can result in a selective reduction of phospholamban protein.

Beta-adrenergic receptor kinase: identification of a novel protein kinase that phosphorylates the agonist-occupied form of the receptor.

Agonist-promoted desensitization of adenylate cyclase is intimately associated with phosphorylation of the beta-adrenergic receptor in mammalian, avian, and amphibian cells. However, the nature of the protein kinase(s) involved in receptor phosphorylation remains largely unknown. We report here the identification and partial purification of a protein kinase capable of phosphorylating the agonist-occupied form of the purified beta-adrenergic receptor. The enzyme is prepared from a supernatant fraction from high-speed centrifugation of lysed kin- cells, a mutant of S49 lymphoma cells that lacks a functional cAMP-dependent protein kinase. The beta-agonist isoproterenol induces a 5- to 10-fold increase in receptor phosphorylation by this kinase, which is blocked by the antagonist alprenolol. Fractionation of the kin- supernatant on molecular-sieve HPLC and DEAE-Sephacel results in a 50- to 100-fold purified beta-adrenergic receptor kinase preparation that is largely devoid of other protein kinase activities. The kinase activity is insensitive to cAMP, cGMP, cAMP-dependent kinase inhibitor, Ca2+-calmodulin, Ca2+-phospholipid, and phorbol esters and does not phosphorylate general kinase substrates such as casein and histones. Phosphate appears to be incorporated solely into serine residues. The existence of this novel cAMP-independent kinase, which preferentially phosphorylates the agonist-occupied form of the beta-adrenergic receptor, suggests a mechanism that may explain the homologous or agonist-specific form of adenylate cyclase desensitization. It also suggests a general mechanism for regulation of receptor function in which only the agonist-occupied or "active" form of the receptor is a substrate for enzymes inducing covalent modification.

Phorbol esters promote alpha 1-adrenergic receptor phosphorylation and receptor uncoupling from inositol phospholipid metabolism.

DDT1 MF-2 cells, which are derived from hamster vas deferens smooth muscle, contain alpha 1-adrenergic receptors (54,800 +/- 2700 sites per cell) that are coupled to stimulation of inositol phospholipid metabolism. Incubation of these cells with tumor-promoting phorbol esters, which stimulate calcium- and phospholipid-dependent protein kinase, leads to a marked attenuation of the ability of alpha 1-receptor agonists such as norepinephrine to stimulate the turnover of inositol phospholipids. This turnover was measured by determining the 32P content of phosphatidylinositol and phosphatidic acid after prelabeling of the cellular ATP pool with 32Pi. These phorbol ester-treated cells also displayed a decrease in binding affinity of cellular alpha 1 receptors for agonists with no change in antagonist affinity. By using affinity chromatography on the affinity resin Affi-Gel-A55414, the alpha 1 receptors were purified approximately equal to 300-fold from control and phorbol ester-treated 32Pi-prelabeled cells. As assessed by NaDodSO4/polyacrylamide gel electrophoresis, the Mr 80,000 alpha 1-receptor ligand-binding subunit is a phosphopeptide containing 1.2 mol of phosphate per mol of alpha 1 receptor. After phorbol ester treatment this increased to 3.6 mol of phosphate per mol of alpha 1 receptor. The effect of phorbol esters on norepinephrine-stimulated inositol phospholipid turnover and alpha 1-receptor phosphorylation showed the same rapid time course with a t1/2 less than 2 min. These results indicate that calcium- and phospholipid-dependent protein kinase may play an important role in regulating the function of receptors that are coupled to the inositol phospholipid cycle by phosphorylating and deactivating them.

Features of programmed cell death in intact Xenopus oocytes and early embryos revealed by near-infrared fluorescence and real-time monitoring.

Factors influencing apoptosis of vertebrate eggs and early embryos have been studied in cell-free systems and in intact embryos by analyzing individual apoptotic regulators or caspase activation in static samples. A novel method for monitoring caspase activity in living Xenopus oocytes and early embryos is described here. The approach, using microinjection of a near-infrared caspase substrate that emits fluorescence only after its proteolytic cleavage by active effector caspases, has enabled the elucidation of otherwise cryptic aspects of apoptotic regulation. In particular, we show that brief caspase activity (10 min) is sufficient to cause apoptotic death in this system. We illustrate a cytochrome c dose threshold in the oocyte, which is lowered by Smac, a protein that binds thereby neutralizing the inhibitor of apoptosis proteins. We show that meiotic oocytes develop resistance to cytochrome c, and that the eventual death of oocytes arrested in meiosis is caspase-independent. Finally, data acquired through imaging caspase activity in the Xenopus embryo suggest that apoptosis in very early development is not cell-autonomous. These studies both validate this assay as a useful tool for apoptosis research and reveal subtleties in the cell death program during early development. Moreover, this method offers a potentially valuable screening modality for identifying novel apoptotic regulators.

Night-time neuronal activation of Cluster N in a day- and night-migrating songbird.

Magnetic compass orientation in a night-migratory songbird requires that Cluster N, a cluster of forebrain regions, is functional. Cluster N, which receives input from the eyes via the thalamofugal pathway, shows high neuronal activity in night-migrants performing magnetic compass-guided behaviour at night, whereas no activation is observed during the day, and covering up the birds' eyes strongly reduces neuronal activation. These findings suggest that Cluster N processes light-dependent magnetic compass information in night-migrating songbirds. The aim of this study was to test if Cluster N is active during daytime migration. We used behavioural molecular mapping based on ZENK activation to investigate if Cluster N is active in the meadow pipit (Anthus pratensis), a day- and night-migratory species. We found that Cluster N of meadow pipits shows high neuronal activity under dim-light at night, but not under full room-light conditions during the day. These data suggest that, in day- and night-migratory meadow pipits, the light-dependent magnetic compass, which requires an active Cluster N, may only be used during night-time, whereas another magnetosensory mechanism and/or other reference system(s), like the sun or polarized light, may be used as primary orientation cues during the day.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.