SMART 5: domains in the context of genomes and networks

The Simple Modular Architecture Research Tool (SMART) is an online resource () used for protein domain identification and the analysis of protein domain architectures. Many new features were implemented to make SMART more accessible to scientists from different fields. The new ‘Genomic’ mode in SMART makes it easy to analyze domain architectures in completely sequenced genomes. Domain annotation has been updated with a detailed taxonomic breakdown and a prediction of the catalytic activity for 50 SMART domains is now available, based on the presence of essential amino acids. Furthermore, intrinsically disordered protein regions can be identified and displayed. The network context is now displayed in the results page for more than 350 000 proteins, enabling easy analyses of domain interactions

Antibiotic Conjugates with an Artificial MECAM-Based Siderophore Are Potent Agents against Gram-Positive and Gram-Negative Bacterial Pathogens

The development of novel drugs against Gram-negative bacteria represents an urgent medical need. To overcome their outer cell membrane, we synthesized conjugates of antibiotics and artificial siderophores based on the MECAM core, which are imported by bacterial iron uptake systems. Structures, spin states, and iron binding properties were predicted in silico using density functional theory. The capability of MECAM to function as an effective artificial siderophore in Escherichia coli was proven in microbiological growth recovery and bioanalytical assays. Following a linker optimization focused on transport efficiency, five β-lactam and one daptomycin conjugates were prepared. The most potent conjugate 27 showed growth inhibition of Gram-positive and Gram-negative multidrug-resistant pathogens at nanomolar concentrations. The uptake pathway of MECAMs was deciphered by knockout mutants and highlighted the relevance of FepA, CirA, and Fiu. Resistance against 27 was mediated by a mutation in the gene encoding ExbB, which is involved in siderophore transport

Gas hydrate technology: state of the art and future possibilities for Europe

Interest in natural gas hydrates has been steadily increasing over the last few decades, with the understanding that exploitation of this abundant unconventional source may help meet the ever-increasing energy demand and assist in reduction of CO2 emission (by replacing coal). Unfortunately, conventional technologies for oil and gas exploitation are not fully appropriate for the specific exploitation of gas hydrate. Consequently, the technology chain, from exploration through production to monitoring, needs to be further developed and adapted to the specific properties and conditions associated with gas hydrates, in order to allow for a commercially and environmentally sound extraction of gas from gas hydrate deposits. Various academic groups and companies within the European region have been heavily involved in theoretical and applied research of gas hydrate for more than a decade. To demonstrate this, Fig. 1.1 shows a selection of leading European institutes that are actively involved in gas hydrate research. A significant number of these institutes have been strongly involved in recent worldwide exploitation of gas hydrate, which are shown in Fig. 1.2 and summarized in Table 1.1. Despite the state of knowledge, no field trials have been carried out so far in European waters. MIGRATE (COST action ES1405) aims to pool together expertise of a large number of European research groups and industrial players to advance gas-hydrate related activity with the ultimate goal of preparing the setting for a field production test in European waters. This MIGRATE report presents an overview of current technologies related to gas hydrate exploration (Chapter 2), production (Chapter 3) and monitoring (Chapter 4), with an emphasis on European activity. This requires covering various activities within different disciplines, all of which contribute to the technology development needed for future cost-effective gas production. The report points out future research and work areas (Chapter 5) that would bridge existing knowledge gaps, through multinational collaboration and interdisciplinary approaches

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

<p>Abstract</p> <p>Background</p> <p>Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis.</p> <p>Methods</p> <p>We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation.</p> <p>Results</p> <p>On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL.</p> <p>Conclusion</p> <p>The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.</p

Number of classes <i>not</i> enriched in GO-terms with high significance.

<p>A Number of classes <i>not</i> significantly enriched below the level after Bonferroni-correction in any of the GO categories biological process, molecular function or cellular compartment. B Number of classes <i>not</i> significantly enriched below the level after Bonferroni correction in at least one the GO categories biological process (BP), molecular function (MF) or cellular compartment (CC). Note the different scales. Clearly, the non-diagonal models consistently produce a lower number of classes which are not enriched in functional annotation. This can be seen as an indication that the non-diagonal models not only represent the network better, but the inferred groups also correspond better to known biology.</p

Experiment type to link weight transformation.

<p>We valued the different experiments compiled in the HPRD database differently, giving lowest weight to interactions found in yeast-2-hybrid experiments only and highest to those interactions found in vivo, in vitro and <i>Y2H</i> experiments. These weights are only to represent a ranking of a practitioners belief in their validity.</p

An example network and possible image graphs.

<p>A A simple example network of nodes of 4 different types identified by their structural position. Nodes of types and are densely connected among themselves. The nodes of type have connections to both nodes of types and , but not among themselves, i.e. they mediate between types and . The nodes of type only have connections to nodes of type , but not among each other, i.e. they form a periphery to type nodes. B and C Two possible image graphs for the functional understanding of this network show the connections among groups of nodes. A typical network clustering will aggregate nodes into clusters densely connected internally but only sparsely connected to the rest, as depicted in the left image graph. This will result in grouping nodes of types and together and nodes of type and together. Because of aggregating nodes into cohesive groups, any such algorithm will never recognize nodes of type and as different and hence miss essential part of the network's structure. On the opposite, the right image graph correctly captures the network structure of the 4 different types as the 4 different nodes in the image graph. D and E The adjacency matrices of our example network with rows and columns ordered according to the two decompositions shown above. A black square in position indicates the existence of a link connecting node with node . Rows and columns are ordered such that nodes in the same group are adjacent. The internal order of the nodes in the groups is random. Each block in the matrix corresponds to a possible edge in the image graph. The left matrix shows the adjacency matrix for the output of a typical clustering algorithm which groups nodes of type and , as well as and together. Clearly, we see dense blocks along the diagonal and sparse blocks on the off-diagonal of the matrix as expected. The right matrix depicts the adjacency matrix with rows and columns according to the actual types of the nodes. All empty blocks in this matrix correspond to a missing edge in the image graph and all populated blocks are represented by an edge in the image graph. We see that for this network, the image graph perfectly captures the structure of the network.</p

Fit scores and generalization error.

<p>A Comparison of highest fit scores (4) and (5) for the full HPRD dataset with 32,331 interactions. Aggregating nodes into cohesive groups (diagonal image graphs) cannot improve the score beyond a certain limit, while non-diagonal image graphs are able to capture more and more structure as the image graph gets larger and larger. For comparison, the analysis was repeated on a randomized (RND) version of the original network. Standard deviation is smaller than symbol size. The fit scores we obtain on the real data show that the structure we find is far from random. B After removing a test-set of links from the network, we optimized the assignment of nodes into classes according to (2) using only the remaining links and keeping the image graphs fixed to those found in the runs that lead to figure A. With the assignment of nodes into classes for this training set of links, we computed the score on the test set of links. The figure shows average and standard deviation over 10 repetitions of this experiment. C p-values of Student's t-test for a statistically significant difference in the means of the test scores of panel B. For higher numbers of classes and thus larger differences in the fit scores of diagonal and non-diagonal image graphs, all differences become significant at the level.</p