Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.     

Species composition, seasonal dynamics and numerical responses of arthropod predators in organic strawberry fields

The species composition and relative abundance of predatory arthropod fauna were studied in organic strawberry fields in northwestern Turkey using sweep net sampling and pitfall trapping (activity density). Arachnida constituted 13.1 and 11.5% of the sweep net and pitfall trap catches, respectively. Among the predatory insects, the most abundant groups were Heteroptera (26.7%), Diptera (25.9%), Coleoptera (16.9%) and Orthoptera (10.8%) in sweep net samples. Coleoptera (84.2%) dominated the pitfall trap catches. Many aphid specific and polyphagous predators reached peak abundance during June and July. Pearson's two-tailed correlations showed a significant and positive relationship between syrphid, coccinellid or chrysopid predator numbers and strawberry aphid density. Aphid density was not significantly correlated with carabid or with nabid abundance. Examination of spatial distribution patterns of all predatory arthropod groups using Taylor's Power Law indicated that most arthropod predators, except carabids, exhibited aggregated dispersion patterns. Coccinellids changed their spatial patterns from a uniform to an aggregated distribution through the season. There was also a significant linear correlation between Anterastes sp. abundance (larvae+adults) and Isophya rectipennis+Poecilimon ricteri (larvae+adults) density at both locations. We observed, for the first time, Anterastes preying on Isophya and Poecilimon species. The seasonal abundance of the major predatory groups were described, and their potential importance in controlling strawberry aphid and other pests is discussed.     

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx.     

Selenium effects on arsenic cytotoxicity and protein phosphorylation in human kidney cells using chip-based nanoLC-MS/MS

Although arsenic toxicity is well known, little is known of how it exerts its effects at the proteome level. Protein phosphorylation is an important post-translational modification in the regulation of cell signaling. Despite the importance of protein phosphorylation, the identification and characterization of phosphorylated proteins, as influenced by interaction between arsenic and selenium species have not been fully studied. The aim of this study is to identify phosphorylation in arsenic toxified cells, with and without selenium present. Here, we identify the phosphorylated proteins related to post translational modifications (PTMs) after inorganic arsenic (iAs) and selenomethionine (SeMet) were inoculated together with HEK293 human kidney cells. In this study, using TiO(2)-based nanoLC-phosphochip? coupled to ESI-MS we observed phosphorylated peptide enrichment and significant reduction in sample complexity. The identification of phosphorylated proteins in highly complex digests of cell lysate were markedly different with As toxification only, or when in the presence of SeMet. Several phosphorylation sites and proteins are identified using Spectrum Mill and Mascot protein data base search engines. Cytotoxicity studies showed that SeMet significantly reduces the cytotoxic effect of iAs in HEK293 cells, while inorganic selenium did not.     

Phosphorelays Provide Tunable Signal Processing Capabilities for the Cell

Achieving a complete understanding of cellular signal transduction requires deciphering the relation between structural and biochemical features of a signaling system and the shape of the signal-response relationship it embeds. Using explicit analytical expressions and numerical simulations, we present here this relation for four-layered phosphorelays, which are signaling systems that are ubiquitous in prokaryotes and also found in lower eukaryotes and plants. We derive an analytical expression that relates the shape of the signal-response relationship in a relay to the kinetic rates of forward, reverse phosphorylation and hydrolysis reactions. This reveals a set of mathematical conditions which, when satisfied, dictate the shape of the signal-response relationship. We find that a specific topology also observed in nature can satisfy these conditions in such a way to allow plasticity among hyperbolic and sigmoidal signal-response relationships. Particularly, the shape of the signal-response relationship of this relay topology can be tuned by altering kinetic rates and total protein levels at different parts of the relay. These findings provide an important step towards predicting response dynamics of phosphorelays, and the nature of subsequent physiological responses that they mediate, solely from topological features and few composite measurements; measuring the ratio of reverse and forward phosphorylation rate constants could be sufficient to determine the shape of the signal-response relationship the relay exhibits. Furthermore, they highlight the potential ways in which selective pressures on signal processing could have played a role in the evolution of the observed structural and biochemical characteristic in phosphorelays.     

Simulating the evolution of signal transduction pathways

We use a generic model of a network of proteins that can activate or deactivate each other to explore the emergence and evolution of signal transduction networks and to gain a basic understanding of their general properties. Starting with a set of non-interacting proteins, we evolve a signal transduction network by random mutation and selection to fulfill a complex biological task. In order to validate this approach we base selection on a fitness function that captures the essential features of chemotactic behavior as seen in bacteria. We find that a system of as few as three proteins can evolve into a network mediating chemotaxis-like behavior by acting as a "derivative sensor". Furthermore, we find that the dynamics and topology of such networks show many similarities to the natural chemotaxis pathway, that the response magnitude can increase with increasing network size and that network behavior shows robustness towards variations in some of the internal parameters. We conclude that simulating the evolution of signal transduction networks to mediate a certain behavior may be a promising approach for understanding the general properties of the natural pathway for that behavior.     

The roles of integration in molecular systems biology

A common way to think about scientific practice involves classifying it as hypothesis- or data-driven. We argue that although such distinctions might illuminate scientific practice very generally, they are not sufficient to understand the day-to-day dynamics of scientific activity and the development of programmes of research. One aspect of everyday scientific practice that is beginning to gain more attention is integration. This paper outlines what is meant by this term and how it has been discussed from scientific and philosophical points of view. We focus on methodological, data and explanatory integration, and show how they are connected. Then, using some examples from molecular systems biology, we will show how integration works in a range of inquiries to generate surprising insights and even new fields of research. From these examples we try to gain a broader perspective on integration in relation to the contexts of inquiry in which it is implemented. In today's environment of data-intensive large-scale science, integration has become both a practical and normative requirement with corresponding implications for meta-methodological accounts of scientific practice. We conclude with a discussion of why an understanding of integration and its dynamics is useful for philosophy of science and scientific practice in general.     

Changes in composition and sulfation patterns of glycoaminoglycans in renal cell carcinoma

Glycosaminoglycans (GAGs) are heterogeneous, linear, highly charged, anionic polysaccharides consisting of repeating disaccharides units. GAGs have some biological significance in cancer progression (invasion and metastasis) and cell signaling. In different cancer types, GAGs undergo specific structural changes. In the present study, in depth investigation of changes in sulfation pattern and composition of GAGs, heparan sulfate (HS)/heparin (HP), chondroitin sulfate (CS)/dermatan sulfate and hyaluronan (HA) in normal renal tissue (NRT) and renal cell carcinoma tissue (RCCT) were evaluated. The statistical evaluation showed that alteration of the HS (HS_NRT = 415.1 ± 115.3; HS_RCCT = 277.5 ± 134.3), and CS (CS_NRT = 35.3 ± 12.3; CS_RCCT = 166.7 ± 108.8) amounts (in ng/mg dry tissue) were statistically significant (p < 0.05). Sulfation pattern in NRT and RCCT was evaluated to reveal disaccharide profiles. Statistical analyses showed that RCCT samples contain significantly increased amounts (in units of ng/mg dry tissue) of 4S_CS (NRT = 25.7 ± 9.4; RCCT = 117.1 ± 73.9), SE_CS (NRT = 0.7 ± 0.3; RCCT = 4.7 ± 4.5), 6S_CS (NRT = 6.1 ± 2.7; RCCT = 39.4 ± 34.7) and significantly decreased amounts (in units of ng/mg dry tissue) of NS6S_HS (RCCT = 28.6 ± 6.5, RCCT = 10.2 ± 8.0), NS2S_HS (RCCT = 44.2 ± 13.8; RCCT = 27.2 ± 15.0), NS_HS (NRT = 68.4 ± 15.8; RCCT = 50.4 ± 21.2), 2S6S_HS (NRT = 1.0 ± 0.4; RCCT = 0.4 ± 0.3), and 6S_HS (NRT = 60.6 ± 17.5; RCCT = 24.9 ± 12.3). If these changes in GAGs are proven to be specific and sensitive, they may serve as potential biomarkers in RCC. Our findings are likely to help us to show the direction for further investigations to be able to bring different diagnostic and prognostic approaches in renal tumors.     

Evolution under Fluctuating Environments Explains Observed Robustness in Metabolic Networks

A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments.     

NMR structure of the second intracellular loop of the alpha 2A adrenergic receptor: evidence for a novel cytoplasmic helix

A major, unresolved question in signal transduction by G protein coupled receptors (GPCRs) is to understand how, at atomic resolution, a GPCR activates a G protein. A step toward answering this question was made with the determination of the high-resolution structure of rhodopsin; we now know the intramolecular interactions that characterize the resting conformation of a GPCR. To what degree does this structure represent a structural paradigm for other GPCRs, especially at the cytoplasmic surface where GPCR-G protein interaction occurs and where the sequence homology is low among GPCRs? To address this question, we performed NMR studies on approximately 35-residue-long peptides including the critical second intracellular loop (i2) of the alpha 2A adrenergic receptor (AR) and of rhodopsin. To stabilize the secondary structure of the peptide termini, 4-12 residues from the adjacent transmembrane helices were included and structures determined in dodecylphosphocholine micelles. We also characterized the effects on an alpha 2A AR peptide of a D130I mutation in the conserved DRY motif. Our results show that in contrast to the L-shaped loop in the i2 of rhodopsin, the i2 of the alpha 2A AR is predominantly helical, supporting the hypothesis that there is structural diversity within GPCR intracellular loops. The D130I mutation subtly modulates the helical structure. The spacing of nonpolar residues in i2 with helical periodicity is a predictor of helical versus loop structure. These data should lead to more accurate models of the intracellular surface of GPCRs and of receptor-mediated G protein activation.