Animal or Plant: Which Is the Better Fog Water Collector?

Occasional fog is a critical water source utilised by plants and animals in the Namib Desert. Fog basking beetles (Onymacris unguicularis, Tenebrionidae) and Namib dune bushman grass (Stipagrostris sabulicola, Poaceae) collect water directly from the fog. While the beetles position themselves optimally for fog water collection on dune ridges, the grass occurs predominantly at the dune base where less fog water is available. Differences in the fog-water collecting abilities in animals and plants have never been addressed. Here we place beetles and grass side-by-side in a fog chamber and measure the amount of water they collect over time. Based on the accumulated amount of water over a two hour period, grass is the better fog collector. However, in contrast to the episodic cascading water run-off from the grass, the beetles obtain water in a steady flow from their elytra. This steady trickle from the beetles' elytra to their mouth could ensure that even short periods of fog basking – while exposed to predators – will yield water. Up to now there is no indication of specialised surface properties on the grass leafs, but the steady run-off from the beetles could point to specific property adaptations of their elytra surface

Wind-Powered Wheel Locomotion, Initiated by Leaping Somersaults, in Larvae of the Southeastern Beach Tiger Beetle (Cicindela dorsalis media)

Rapid movement is challenging for elongate, soft-bodied animals with short or no legs. Leaping is known for only a few animals with this “worm-like” morphology. Wheel locomotion, in which the animal's entire body rolls forward along a central axis, has been reported for only a handful of animals worldwide. Here we present the first documented case of wind-powered wheel locomotion, in larvae of the coastal tiger beetle Cicindela dorsalis media. When removed from their shallow burrows, larvae easily can be induced to enter a behavioral sequence that starts with leaping; while airborne, larvae loop their body into a rotating wheel and usually either “hit the ground rolling” or leap again. The direction larvae wheel is closely related to the direction in which winds are blowing; thus, all our larvae wheeled up-slope, as winds at our study site consistently blew from sea to land. Stronger winds increased both the proportion of larvae wheeling, and the distance traveled, exceeding 60 m in some cases. In addition, the proportion of larvae that wheel and the distance traveled by wheeling larvae are significantly greater on smooth sandy beaches than on beach surfaces made rough and irregular by pedestrian, equestrian, and vehicular traffic. Like other coastal species of tiger beetles, C. dorsalis media has suffered major declines in recent years that are clearly correlated with increased human impacts. The present study suggests that the negative effects of beach traffic may be indirect, preventing larvae from escaping from predators using wheel locomotion by disrupting the flat, hard surface necessary for efficient wheeling

Using structural motif descriptors for sequence-based binding site prediction

All authors are with the Biotechnological Center, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany and -- Wan Kyu Kim is with the Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USABackground: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites. -- Results: We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Swiss-Prot annotation "ATP-binding", we achieve a recall of 25% with a precision of 89%, whereas Prosite's P-loop motif recognizes an equal amount of hits at the expense of a much higher number of false positives (precision: 57%). Our method yields 771 hits with a precision of 96% that were not previously picked up by any Prosite-pattern. -- Conclusion: The automatically generated descriptors are a useful complement to known Prosite/InterPro motifs. They serve to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available.Institute for Cellular and Molecular [email protected]

Hypolithic and soil microbial community assembly along an aridity gradient in the Namib Desert

The Namib Dessert is considered the oldest desert in the world and hyperarid for the last 5 million years. However, the environmental buffering provided by quartz and other translucent rocks supports extensive hypolithic microbial communities. In this study, open soil and hypolithic microbial communities have been investigated along an East–West transect characterized by an inverse fog-rainfall gradient. Multivariate analysis showed that structurally different microbial communities occur in soil and in hypolithic zones. Using variation partitioning, we found that hypolithic communities exhibited a fog-related distribution as indicated by the significant East– West clustering. Sodium content was also an important environmental factor affecting the composition of both soil and hypolithic microbial communities. Finally, although null models for patterns in microbial communities were not supported by experimental data, the amount of unexplained variation (68–97 %) suggests that stochastic processes also play a role in the assembly of such communities in the Namib Desert.Web of Scienc

Spatio-Temporal Differentiation and Sociality in Spiders

Species that differ in their social system, and thus in traits such as group size and dispersal timing, may differ in their use of resources along spatial, temporal, or dietary dimensions. The role of sociality in creating differences in habitat use is best explored by studying closely related species or socially polymorphic species that differ in their social system, but share a common environment. Here we investigate whether five sympatric Anelosimus spider species that range from nearly solitary to highly social differ in their use of space and in their phenology as a function of their social system. By studying these species in Serra do Japi, Brazil, we find that the more social species, which form larger, longer–lived colonies, tend to live inside the forest, where sturdier, longer lasting vegetation is likely to offer better support for their nests. The less social species, which form single-family groups, in contrast, tend to occur on the forest edge where the vegetation is less robust. Within these two microhabitats, species with longer-lived colonies tend to occupy the potentially more stable positions closer to the core of the plants, while those with smaller and shorter-lived colonies build their nests towards the branch tips. The species further separate in their use of common habitat due to differences in the timing of their reproductive season. These patterns of habitat use suggest that the degree of sociality can enable otherwise similar species to differ from one another in ways that may facilitate their co-occurrence in a shared environment, a possibility that deserves further consideration

From RNAi Screens to Molecular Function in Embryonic Stem Cells

The ability of embryonic stem (ES) cells to generate any of the around 220 cell types of the adult body has fascinated scientists ever since their discovery. The capacity to re-program fully differentiated cells into induced pluripotent stem (iPS) cells has further stimulated the interest in ES cell research. Fueled by this interest, intense research has provided new insights into the biology of ES cells in the recent past. The development of large-scale and high throughput RNAi technologies has made it possible to sample the role of every gene in maintaining ES cell identity. Here, we review the RNAi screens performed in ES cells to date and discuss the challenges associated with these large-scale experiments. Furthermore, we provide a perspective on how to streamline the molecular characterization following the initial phenotypic description utilizing bacterial artificial chromosome (BAC) transgenesis

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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