Glutamate 301 of the mouse gonadotropin-releasing hormone receptor confers specificity for arginine 8 of mammalian gonadotropin-releasing hormone

The Arg residue at position 8 of mammalian GnRH is necessary for high affinity binding to mammalian GnRH receptors. This requirement has been postulated to derive from an electrostatic interaction of Arg8 with a negatively charged receptor residue. In order to identify such a residue, 8 conserved acidic residues of the mouse GnRH receptor were mutated to isosteric Asn or Gln. Mutant receptors were tested for decreased preference for Arg8-containing ligands by ligand binding and inositol phosphate production. One of the mutants, in which the Glu301 residue was mutated to Gln, exhibited a 56-fold decrease in apparent affinity for mammalian GnRH. The mutant receptor also exhibited decreased affinity for [Lys8]GnRH, but its affinity for [Gln8]GnRH was unchanged compared with the wild type receptor. The apparent affinity of the mutant receptor for the acidic analogue, [Glu8]GnRH, was increased more than 10-fold. The mutant receptor did not, therefore, distinguish mammalian GnRH from analogues with amino acid substitutions at position 8 as effectively as the wild type receptor. This loss of discrimination was specific for the residue at position 8, because the mutant receptor did distinguish mammalian GnRH from analogues with favorable substitutions at positions 5, 6, and 7. These findings show that Glu301 of the GnRH receptor plays a role in receptor recognition of Arg8 in the ligand and are consistent with an electrostatic interaction between these 2 residues

ProbCD: enrichment analysis accounting for categorization uncertainty

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for
the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation

Proteome Profiling of Breast Tumors by Gel Electrophoresis and Nanoscale Electrospray Ionization Mass Spectrometry

We have conducted proteome-wide analysis of fresh surgery specimens derived from breast cancer patients, using an approach that integrates size-based intact protein fractionation, nanoscale liquid separation of peptides, electrospray ion trap mass spectrometry, and bioinformatics. Through this approach, we have acquired a large amount of peptide fragmentation spectra from size-resolved fractions of the proteomes of several breast tumors, tissue peripheral to the tumor, and samples from patients undergoing noncancer surgery. Label-free quantitation was used to generate protein abundance maps for each proteome and perform comparative analyses. The mass spectrometry data revealed distinct qualitative and quantitative patterns distinguishing the tumors from healthy tissue as well as differences between metastatic and non-metastatic human breast cancers including many established and potential novel candidate protein biomarkers. Selected proteins were evaluated by Western blotting using tumors grouped according to histological grade, size, and receptor expression but differing in nodal status. Immunohistochemical analysis of a wide panel of breast tumors was conducted to assess expression in different types of breast cancers and the cellular distribution of the candidate proteins. These experiments provided further insights and an independent validation of the data obtained by mass spectrometry and revealed the potential of this approach for establishing multimodal markers for early metastasis, therapy outcomes, prognosis, and diagnosis in the future. © 2008 American Chemical Society

Functional microdomains in G-protein-coupled receptors: the conserved arginine-cage motif in the gonadotropin-releasing hormone receptor

An Arg present in the third transmembrane domain of all rhodopsin-like G-protein-coupled receptors is required for efficient signal transduction. Mutation of this Arg in the gonadotropin-releasing hormone receptor to Gln, His, or Lys abolished or severely impaired agonist-stimulated inositol phosphate generation, consistent with Arg having a role in receptor activation. To investigate the contribution of the surrounding structural domain in the actions of the conserved Arg, an integrated microdomain modeling and mutagenesis approach has been utilized. Two conserved residues that constrain the Arg side chain to a limited number of conformations have been identified. In the inactive wild-type receptor, the Arg side chain is proposed to form an ionic interaction with Asp3.49(138). Experimental results for the Asp3. 49(138) --> Asn mutant receptor show a modestly enhanced receptor efficiency, consistent with the hypothesis that weakening the Asp3. 49(138)-Arg3.50(139) interaction by protonation of the Asp or by the mutation to Asn favors activation. With activation, the Asp3. 49(138)-Arg3.50(139) ionic bond would break, and the unrestrained Arg would be prevented from orienting itself toward the water phase by a steric clash with Ile3.54(143). The mutation Ile3.54(143) --> Ala, which eliminates this clash in simulations, causes a marked reduction in measured receptor signaling efficiency, implying that solvation of Arg3.50(139) prevents it from functioning in the activation of the receptor. These data are consistent with residues Asp3.49(138) and Ile3.54(143) forming a structural motif, which helps position Arg in its appropriate inactive and active receptor conformations

The acceleration of the universe and the physics behind it

Using a general classification of dark enegy models in four classes, we discuss the complementarity of cosmological observations to tackle down the physics beyond the acceleration of our universe. We discuss the tests distinguishing the four classes and then focus on the dynamics of the perturbations in the Newtonian regime. We also exhibit explicitely models that have identical predictions for a subset of observations.Comment: 18 pages, 18 figure

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

<p>Abstract</p> <p>Background</p> <p>Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.</p> <p>Results</p> <p><it>GOrilla </it>is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression). <it>GOrilla </it>employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the <it>top </it>of a ranked gene list. Building on a complete theoretical characterization of the underlying distribution, called mHG, <it>GOrilla </it>computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations. This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds. The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms.</p> <p>Conclusion</p> <p><it>GOrilla </it>is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools. <it>GOrilla</it>'s unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation. <it>GOrilla </it>is publicly available at: <url>http://cbl-gorilla.cs.technion.ac.il</url></p

Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak

In its largest outbreak, Ebola virus disease is spreading through Guinea, Liberia, Sierra Leone, and Nigeria. We sequenced 99 Ebola virus genomes from 78 patients in Sierra Leone to ~2000× coverage. We observed a rapid accumulation of interhost and intrahost genetic variation, allowing us to characterize patterns of viral transmission over the initial weeks of the epidemic. This West African variant likely diverged from central African lineages around 2004, crossed from Guinea to Sierra Leone in May 2014, and has exhibited sustained human-to-human transmission subsequently, with no evidence of additional zoonotic sources. Because many of the mutations alter protein sequences and other biologically meaningful targets, they should be monitored for impact on diagnostics, vaccines, and therapies critical to outbreak response.Organismic and Evolutionary Biolog

Planck 2013 results. XXII. Constraints on inflation

We analyse the implications of the Planck data for cosmic inflation. The Planck nominal mission temperature anisotropy measurements, combined with the WMAP large-angle polarization, constrain the scalar spectral index to be ns = 0:9603 _ 0:0073, ruling out exact scale invariance at over 5_: Planck establishes an upper bound on the tensor-to-scalar ratio of r < 0:11 (95% CL). The Planck data thus shrink the space of allowed standard inflationary models, preferring potentials with V00 < 0. Exponential potential models, the simplest hybrid inflationary models, and monomial potential models of degree n _ 2 do not provide a good fit to the data. Planck does not find statistically significant running of the scalar spectral index, obtaining dns=dln k = 0:0134 _ 0:0090. We verify these conclusions through a numerical analysis, which makes no slowroll approximation, and carry out a Bayesian parameter estimation and model-selection analysis for a number of inflationary models including monomial, natural, and hilltop potentials. For each model, we present the Planck constraints on the parameters of the potential and explore several possibilities for the post-inflationary entropy generation epoch, thus obtaining nontrivial data-driven constraints. We also present a direct reconstruction of the observable range of the inflaton potential. Unless a quartic term is allowed in the potential, we find results consistent with second-order slow-roll predictions. We also investigate whether the primordial power spectrum contains any features. We find that models with a parameterized oscillatory feature improve the fit by __2 e_ _ 10; however, Bayesian evidence does not prefer these models. We constrain several single-field inflation models with generalized Lagrangians by combining power spectrum data with Planck bounds on fNL. Planck constrains with unprecedented accuracy the amplitude and possible correlation (with the adiabatic mode) of non-decaying isocurvature fluctuations. The fractional primordial contributions of cold dark matter (CDM) isocurvature modes of the types expected in the curvaton and axion scenarios have upper bounds of 0.25% and 3.9% (95% CL), respectively. In models with arbitrarily correlated CDM or neutrino isocurvature modes, an anticorrelated isocurvature component can improve the _2 e_ by approximately 4 as a result of slightly lowering the theoretical prediction for the ` <_ 40 multipoles relative to the higher multipoles. Nonetheless, the data are consistent with adiabatic initial conditions

Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems