Relatedness does not explain geographic variation in queen cooperation in the seed-harvester ant Messor pergandei

The desert seed-harvester ant Messor pergandei shows sharp regional differences in social structure: in central Arizona, queens initially form group nests but become aggressive following worker emergence and reduce to a single queen (secondary monogyny). In much of the rest of the species range, however, co-founding queens do not display aggression and retain multiple queens throughout the colony lifecycle (primary polygyny). One hypothesis to explain why queen behavior differs between regions is that relatedness among co-foundresses and, therefore, the potential for kin-selected cooperation, varies geographically. To test whether primary polygyny is associated with greater kin association, we used highly polymorphic microsatellites to estimate within-group relatedness for co-foundress associations in the field at two secondarily monogynous sites and five primarily polygynous sites. To determine whether queens can potentially use nestmate identity as a proxy for genetic relatedness, we compared these values to similarly sized samples of worker nestmates from adult colonies at the same sites. We found that foundresses do not preferentially form groups with relatives regardless of the ultimate fate of foundress groups. Mean relatedness values for co-foundresses did not differ significantly from zero irrespective of social structure. In contrast, adult colony worker nestmates were significantly positively related at all sites. These results indicate that kin-selected benefits are not likely to be responsible for the absence of fatal competition in the polygynous region; instead, the cause of geographic variation in queen cooperation must lie in ecological factors that alter the costs and benefits of retaining additional queens into colony maturity.     

BiP-mediated closing of the Sec61 channel limits Ca(2+) leakage from the ER

In mammalian cells, signal peptide-dependent protein transport into the endoplasmic reticulum (ER) is mediated by a dynamic protein-conducting channel, the Sec61 complex. Previous work has characterized the Sec61 channel as a potential ER Ca(2+) leak channel and identified calmodulin as limiting Ca(2+) leakage in a Ca(2+)-dependent manner by binding to an IQ motif in the cytosolic aminoterminus of Sec61α. Here, we manipulated the concentration of the ER lumenal chaperone BiP in cells in different ways and used live cell Ca(2+) imaging to monitor the effects of reduced levels of BiP on ER Ca(2+) leakage. Regardless of how the BiP concentration was lowered, the absence of available BiP led to increased Ca(2+) leakage via the Sec61 complex. When we replaced wild-type Sec61α with mutant Sec61αY344H in the same model cell, however, Ca(2+) leakage from the ER increased and was no longer affected by manipulation of the BiP concentration. Thus, BiP limits ER Ca(2+) leakage through the Sec61 complex by binding to the ER lumenal loop 7 of Sec61α in the vicinity of tyrosine 344.     

How strongly do sequence conservation patterns and empirical scales correlate with exposure patterns of transmembrane helices of membrane proteins?

Given the difficulty in determining high-resolution structures of helical membrane proteins, sequence-based prediction methods can be useful in elucidating diverse physiological processes mediated by this important class of proteins. Predicting the angular orientations of transmembrane (TM) helices about the helix axes, based on the helix parameters from electron microscopy data, is a classical problem in this regard. This problem has triggered the development of a number of different empirical scales. Recently, sequence conservation patterns were also made use of for improved predictions. Empirical scales and sequence conservation patterns (collectively termed as "prediction scales") have also found frequent applications in other research areas of membrane proteins: for example, in structure modeling and in prediction of buried TM helices. This trend is expected to grow in the near future unless there are revolutionary developments in the experimental characterization of membrane proteins. Thus, it is timely and imperative to carry out a comprehensive benchmark test over the prediction scales proposed so far to determine their pros and cons. In the current analysis, we use exposure patterns of TM helices as a golden standard, because if one develops a prediction scale that correlates perfectly with exposure patterns of TM helices, it will enable one to predict buried residues (or buried faces) of TM helices with an accuracy of 100%. Our analysis reveals several important points. (1) It demonstrates that sequence conservation patterns are much more strongly correlated with exposure patterns of TM helices than empirical scales. (2) Scales that were specifically parameterized using structure data (structure-based scales) display stronger correlation than hydrophobicity-based scales, as expected. (3) A nonnegligible difference is observed among the structure-based scales in their correlational property, suggesting that not every learning algorithm is equally effective. (4) A straightforward framework of optimally combining sequence conservation patterns and empirical scales is proposed, which reveals that improvements gained from combining the two sources of information are not dramatic in almost all cases. In turn, this calls for the development of fundamentally different scales that capture the essentials of membrane protein folding for substantial improvements.     

A spatial model of the chromatophore vesicles of Rhodobacter sphaeroides and the position of the Cytochrome bc1 complex

The photosynthetic apparatus of purple bacteria is generally considered a well-studied and understood system. However, recent atomic force microscopy images of flattened chromatophore vesicles from Rhodobacter sphaeroides restarted a debate about the stoichiometry and positions of the membrane proteins, with the interpretations of the observed images only partly being in agreement with earlier models. The most puzzling observation from the recent images is that the Cytochrome bc(1) complex, which is a central part of the photosynthetic apparatus, seems to be missing on the chromatophore vesicles, even when these were extracted from photosynthetically grown bacteria. From the available information on the geometry of the vesicle and of the proteins we reconstructed here a three-dimensional model vesicle at molecular resolution. Its central feature, also determining its diameter of approximately 45 nm, is an equatorial array of LH1 dimers, lined by a region of LH2 rings. This naturally puts the Cytochrome bc(1) complexes and the ATPase at the vesicle's poles. This spatial model may explain why the vesicle's endcaps with the bc(1) complexes are lost during the preparatory steps of the imaging process together with the ATPase and are therefore absent from the available images.     

The Arabidopsis cinnamoyl CoA reductase irx4 mutant has a delayed but coherent (normal) program of lignification

Previous studies have indicated that the Arabidopsis thalianairregular xylem 4 (irx4) mutant is severely lignin-deficient, forming abnormal lignin from aberrant monomers. Studies of lignin structure in dwarfed cinnamoyl CoA reductase (CCR)-downregulated tobacco were also previously reported to incorporate feruloyl tyramine derivatives. The lignin in the Arabidopsis irx4 mutant was re-investigated at 6 weeks and at maturation (9 weeks). Application of (1)H, (13)C, 2D Heteronuclear Multiple Quantum Coherence and 2D Heteronuclear Multiple Bond Coherence spectroscopic analyses to the lignin-enriched isolates from both Arabidopsis wild-type (Ler) and the CCR-irx4 mutant at both developmental stages revealed that only typical guaiacyl/syringyl lignins were formed. For the irx4 mutant, the syringyl content at 6 weeks growth was lower, in accordance with a delayed but coherent program of lignification. At maturation, however, the syringyl/guaiacyl ratio of the irx4 mutant approached that of wild-type. There was no evidence for feruloyl tyramines, or homologues thereof, accumulating as a chemical signature in lignins resulting from CCR mutation. Nor were there any noticeable increases in other phenolic components, such as hydroxycinnamic acids. These findings were further confirmed by application of thioacidolysis, alkaline nitrobenzene oxidation and acetyl bromide analyses. Moreover, in the case of CCR downregulation in tobacco, there were no NMR spectroscopic correlations that demonstrated feruloyl tyramines being incorporated into the lignin biopolymers. This study thus found no evidence that abnormal lignin formation occurs when CCR activity is modulated.     

CMIP and ATP2C2 Modulate Phonological Short-Term Memory in Language Impairment

Specific language impairment (SLI) is a common developmental disorder characterized by difficulties in language acquisition despite otherwise normal development and in the absence of any obvious explanatory factors. We performed a high-density screen of SLI1, a region of chromosome 16q that shows highly significant and consistent linkage to nonword repetition, a measure of phonological short-term memory that is commonly impaired in SLI. Using two independent language-impaired samples, one family-based (211 families) and another selected from a population cohort on the basis of extreme language measures (490 cases), we detected association to two genes in the SLI1 region: that encoding c-maf-inducing protein (CMIP, minP = 5.5 × 10⁻⁷ at rs6564903) and that encoding calcium-transporting ATPase, type2C, member2 (ATP2C2, minP = 2.0 × 10⁻⁵ at rs11860694). Regression modeling indicated that each of these loci exerts an independent effect upon nonword repetition ability. Despite the consistent findings in language-impaired samples, investigation in a large unselected cohort (n = 3612) did not detect association. We therefore propose that variants in CMIP and ATP2C2 act to modulate phonological short-term memory primarily in the context of language impairment. As such, this investigation supports the hypothesis that some causes of language impairment are distinct from factors that influence normal language variation. This work therefore implicates CMIP and ATP2C2 in the etiology of SLI and provides molecular evidence for the importance of phonological short-term memory in language acquisition.     

Contribution of charged and polar residues for the formation of the E1–E2 heterodimer from Hepatitis C Virus

The transmembrane domains of the envelope glycoprotein E1 and E2 have crucial multifunctional roles in the biogenesis of hepatitis C virus. We have performed molecular dynamics simulations to investigate a structural model of the transmembrane segments of the E1–E2 heterodimer. The simulations support the key role of the Lys370–Asp728 ion pair for mediating the E1–E2 heterodimerization. In comparison to these two residues, the simulation results also reveal the differential effect of the conserved Arg730 residue that has been observed in experimental studies. Furthermore, we discovered the formation of inter-helical hydrogen bonds via Asn367 that stabilize dimer formation. Simulations of single and double mutants further demonstrate the importance of the ion-pair and polar interactions between the interacting helix monomers. The conformation of the E1 fragment in the simulation of the E1–E2 heterodimer is in close agreement with an NMR structure of the E1 transmembrane segment. The proposed model of the E1–E2 heterodimer supports the postulated cooperative insertion of both helices by the translocon complex into the bilayer.     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

     

Bridging the gap: linking molecular simulations and systemic descriptions of cellular compartments

Metabolic processes in biological cells are commonly either characterized at the level of individual enzymes and metabolites or at the network level. Often these two paradigms are considered as mutually exclusive because concepts from neither side are suited to describe the complete range of scales. Additionally, when modeling metabolic or regulatory cellular systems, often a large fraction of the required kinetic parameters are unknown. This even applies to such simple and extensively studied systems like the photosynthetic apparatus of purple bacteria. Using the chromatophore vesicles of Rhodobacter sphaeroides as a model system, we show that a consistent kinetic model emerges when fitting the dynamics of a molecular stochastic simulation to a set of time dependent experiments even though about two thirds of the kinetic parameters in this system are not known from experiment. Those kinetic parameters that were previously known all came out in the expected range. The simulation model was built from independent protein units composed of elementary reactions processing single metabolites. This pools-and-proteins approach naturally compiles the wealth of available molecular biological data into a systemic model and can easily be extended to describe other systems by adding new protein or nucleic acid types. The automated parameter optimization, performed with an evolutionary algorithm, reveals the sensitivity of the model to the value of each parameter and the relative importances of the experiments used. Such an analysis identifies the crucial system parameters and guides the setup of new experiments that would add most knowledge for a systemic understanding of cellular compartments. The successful combination of the molecular model and the systemic parametrization presented here on the example of the simple machinery for bacterial photosynthesis shows that it is actually possible to combine molecular and systemic modeling. This framework can now straightforwardly be applied to other currently less well characterized but biologically more relevant systems.     

Dopamine regulation of amiloride-sensitive sodium channels in lung cells

Dopamine increases lung fluid clearance. This is partly due to activation of basolateral Na-K-ATPase. However, activation of Na-K-ATPase by itself is unlikely to produce large changes in transepithelial transport. Therefore, we examined apical and basolateral dopamine's effect on apical, highly selective sodium channels [epithelial sodium channels (ENaC)] in monolayers of an alveolar type 2 cell line (L2). Dopamine increased channel open probability (P(o)) without changing the unitary current. The D(1) receptor blocker SCH-23390 blocked the dopamine effect, but the D(2) receptor blocker sulpiride did not. The dopamine-mediated increase in ENaC activity was not a secondary effect of dopamine stimulation of Na-K-ATPase, since ouabain applied to the basolateral surface to block the activity of Na-K-ATPase did not alter dopamine-mediated ENaC activity. Protein kinase A (PKA) was not responsible for dopamine's effect since a PKA inhibitor, H89, did not reduce dopamine's effect. However, cpt-2-O-Me-cAMP, which selectively binds and activates EPAC (exchange protein activated by cAMP) but not PKA, increased ENaC P(o). An Src inhibitor, PP2, and the phosphatidylinositol-3-kinase inhibitor, LY-294002, blocked dopamine's effect on ENaC. In addition, an MEK blocker, U0126, an inhibitor of phospholipase A(2), and a protein phosphatase inhibitor also blocked the effect of dopamine on ENaC P(o). Finally, since the cAMP-EPAC-Rap1 pathway also activates DARPP32 (32-kDa dopamine response protein phosphatase), we confirmed that dopamine phosphorylates DARPP32, and okadaic acid, which blocks phosphatases (DARPP32), also blocks dopamine's effect. In summary, dopamine increases ENaC activity by a cAMP-mediated alternative signaling pathway involving EPAC and Rap1, signaling molecules usually associated with growth-factor-activated receptors.