Communication breakdown : dissecting the COM interfaces between the subunits of nonribosomal peptide synthetases

Nonribosomal peptides are a structurally diverse and bioactive class of natural products constructed by multidomain enzymatic assembly lines known as nonribosomal peptide synthetases (NRPSs). While the core catalytic domains and even entire protein subunits of NRPSs have been structurally elucidated, little biophysical work has been reported on the docking domains that promote interactions—and thus transfer of biosynthetic intermediates—between subunits. In the present study, we closely examine the COM domains that mediate COMmunication between donor epimerization (E) and acceptor condensation (C) domains found at the termini of NRPS subunits. Through a combination of X-ray crystallography, circular dichroism spectroscopy, solution- and solid-state NMR spectroscopy, and molecular dynamics (MD) simulations, we provide direct evidence for an intrinsically disordered donor COM region that folds into a dynamic helical motif upon binding to a suitable acceptor. Furthermore, our NMR titration and carbene footprinting experiments illuminate the residues involved at the COM interaction interface, and our MD simulations demonstrate folding consistent with experimental data. Although our results lend credence to the previously proposed helix-hand mode of interaction, they also underscore the importance of viewing COM interfaces as dynamic ensembles rather than single rigid structures and suggest that engineering experiments should account for the interactions which transiently guide folding in addition to those which stabilize the final complex. Through activity assays and affinity measurements, we further substantiate the role of the donor COM region in binding the acceptor C domain and implicate this short motif as readily transposable for noncognate domain crosstalk. Finally, our bioinformatics analyses show that COM domains are widespread in natural product pathways and function at interfaces beyond the canonical type described above, setting a high priority for thorough characterization of these docking domains. Our findings lay the groundwork for future attempts to rationally engineer NRPS domain–domain interactions with the ultimate goal of generating bioactive molecules

Mining phenotypes for gene function prediction

<p>Abstract</p> <p>Background</p> <p>Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships.</p> <p>Results</p> <p>We present results on a study where we use a large set of phenotype data – in textual form – to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations.</p> <p>Conclusion</p> <p>The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.</p

Methylation at Global LINE-1 Repeats in Human Blood Are Affected by Gender but Not by Age or Natural Hormone Cycles

Previously, we reported on inter-individual and gender specific variations of LINE-1 methylation in healthy individuals. In this study, we investigated whether this variability could be influenced by age or sex hormones in humans. To this end, we studied LINE-1 methylation in vivo in blood-derived DNA from individuals aged 18 to 64 years and from young healthy females at various hormone levels during the menstrual cycle. Our results show that no significant association with age was observed. However, the previously reported increase of LINE-1 methylation in males was reconfirmed. In females, although no correlation between LINE-1 or Alu methylation and hormone levels was observed, a significant stable individual specific level of methylation was noted. In vitro results largely confirmed these findings, as neither estrogen nor dihydrotestosterone affected LINE-1 or Alu methylation in Hek293T, HUVEC, or MDA-kb2 cell lines. In contrast, a decrease in methylation was observed in estrogen-treated T47-Kbluc cell lines strongly expressing estrogen receptor. The very low expression of estrogen receptor in blood cells could explain the observed insensitivity of methylation at LINE-1 to natural hormonal variations in females. In conclusion, neither natural cycle of hormones nor age has a detectable effect on the LINE-1 methylation in peripheral blood cells, while gender remains an important factor

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans.

To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (P < 5 × 10(-8)), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.Please refer to the manuscript or visit the publisher's website for funding infomation

Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis

R.: Neural Networks – A Model of Boolean Functions

This paper deals with the representation of Boolean functions using artificial neural networks and points out three important results. First, using a polynomial as transfer function, a single neuron is able to represent a non-monotonous Boolean function. Second, the number of inputs in the neural network can be decreased if the binary values of the Boolean variables are encoded. This approach simplifies significantly the necessary number of neurons in the artificial neural network. Finally, an algorithm to compute the minimal number of neurons was developed. The lower bound, calculated by this algorithm, corresponds to a suggested structure of artificial neural networks. An example shows, how such a simple artificial neural network may represent a Boolean function.

Fast Diffusion of the Unassembled PetC1-GFP Protein in the Cyanobacterial Thylakoid Membrane

Biological membranes were originally described as a fluid mosaic with uniform distribution of proteins and lipids. Later, heterogeneous membrane areas were found in many membrane systems including cyanobacterial thylakoids. In fact, cyanobacterial pigment&ndash;protein complexes (photosystems, phycobilisomes) form a heterogeneous mosaic of thylakoid membrane microdomains (MDs) restricting protein mobility. The trafficking of membrane proteins is one of the key factors for long-term survival under stress conditions, for instance during exposure to photoinhibitory light conditions. However, the mobility of unbound &lsquo;free&rsquo; proteins in thylakoid membrane is poorly characterized. In this work, we assessed the maximal diffusional ability of a small, unbound thylakoid membrane protein by semi-single molecule FCS (fluorescence correlation spectroscopy) method in the cyanobacterium Synechocystis sp. PCC6803. We utilized a GFP-tagged variant of the cytochrome b6f subunit PetC1 (PetC1-GFP), which was not assembled in the b6f complex due to the presence of the tag. Subsequent FCS measurements have identified a very fast diffusion of the PetC1-GFP protein in the thylakoid membrane (D = 0.14 &minus; 2.95 &micro;m2s&minus;1). This means that the mobility of PetC1-GFP was comparable with that of free lipids and was 50&ndash;500 times higher in comparison to the mobility of proteins (e.g., IsiA, LHCII&mdash;light-harvesting complexes of PSII) naturally associated with larger thylakoid membrane complexes like photosystems. Our results thus demonstrate the ability of free thylakoid-membrane proteins to move very fast, revealing the crucial role of protein&ndash;protein interactions in the mobility restrictions for large thylakoid protein complexes

Presence of UV filters in surface water and the effects of phenylbenzimidazole sulfonic acid on rainbow trout (Oncorhynchus mykiss) following a chronic toxicity test

UV filters belong to a group of compounds that are used by humans and are present in municipal waste-waters, effluents from sewage treatment plants and surface waters. Current information regarding UV filters and their effects on fish is limited. In this study, the occurrence of three commonly used UV filters - 2-phenylbenzimidazole-5-sulfonic acid (PBSA), 2-hydroxy-4-methoxybenzophenone (benzophenone-3, BP-3) and 5-benzoyl-4-hydroxy-2-methoxy-benzenesulfonic acid (benzophenone-4, BP-4) - in South Bohemia (Czech Republic) surface waters is presented. PBSA concentrations (up to 13μgL(-1)) were significantly greater than BP-3 or BP-4 concentrations (up to 620 and 390ngL(-1), respectively). On the basis of these results, PBSA was selected for use in a toxicity test utilizing the common model organism rainbow trout (Oncorhynchus mykiss). Fish were exposed to three concentrations of PBSA (1, 10 and 1000µgL(-1)) for 21 and 42 days. The PBSA concentrations in the fish plasma, liver and kidneys were elevated after 21 and 42 days of exposure. PBSA increased activity of certain P450 cytochromes. Exposure to PBSA also changed various biochemical parameters and enzyme activities in the fish plasma. However, no pathological changes were obvious in the liver or gonads