Conservation of regulatory elements between two species of Drosophila

Background  

One of the important goals in the post-genomic era is to determine the regulatory elements within the non-coding DNA of a given organism's genome. The identification of functional cis-regulatory modules has proven difficult since the component factor binding sites are small and the rules governing their arrangement are poorly understood. However, the genomes of suitably diverged species help to predict regulatory elements based on the generally accepted assumption that conserved blocks of genomic sequence are likely to be functional. To judge the efficacy of strategies that prefilter by sequence conservation it is important to know to what extent the converse assumption holds, namely that functional elements common to both species will fall within these conserved blocks. The recently completed sequence of a second Drosophila species provides an opportunity to test this assumption for one of the experimentally best studied regulatory networks in multicellular organisms, the body patterning of the fly embryo.     

FITBAR: a web tool for the robust prediction of prokaryotic regulons

Background  

The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.     

Structural and Functional Roles of Coevolved Sites in Proteins

Background

Understanding the residue covariations between multiple positions in protein families is very crucial and can be helpful for designing protein engineering experiments. These simultaneous changes or residue coevolution allow protein to maintain its overall structural-functional integrity while enabling it to acquire specific functional modifications. Despite the significant efforts in the field there is still controversy in terms of the preferable locations of coevolved residues on different regions of protein molecules, the strength of coevolutionary signal and role of coevolution in functional diversification.

Methodology

In this paper we study the scale and nature of residue coevolution in maintaining the overall functionality and structural integrity of proteins. We employed a large scale study to investigate the structural and functional aspects of coevolved residues. We found that the networks representing the coevolutionary residue connections within our dataset are in general of ‘small-world’ type as they have clustering coefficient values higher than random networks and also show smaller mean shortest path lengths similar and/or lower than random and regular networks. We also found that altogether 11% of functionally important sites are coevolved with any other sites. Active sites are found more frequently to coevolve with any other sites (15%) compared to protein (11%) and ligand (9%) binding sites. Metal binding and active sites are also found to be more frequently coevolved with other metal binding and active sites, respectively. Analysis of the coupling between coevolutionary processes and the spatial distribution of coevolved sites reveals that a high fraction of coevolved sites are located close to each other. Moreover, ∼80% of charge compensatory substitutions within coevolved sites are found at very close spatial proximity (< = 5Å), pointing to the possible preservation of salt bridges in evolution.

Conclusion

Our findings show that a noticeable fraction of functionally important sites undergo coevolution and also point towards compensatory substitutions as a probable coevolutionary mechanism within spatially proximal coevolved functional sites.     

Comparative phosphoproteomics reveals evolutionary and functional conservation of phosphorylation across eukaryotes

Background  

Reversible phosphorylation of proteins is involved in a wide range of processes, ranging from signaling cascades to regulation of protein complex assembly. Little is known about the structure and evolution of phosphorylation networks. Recent high-throughput phosphoproteomics studies have resulted in the rapid accumulation of phosphopeptide datasets for many model organisms. Here, we exploit these novel data for the comparative analysis of phosphorylation events between different species of eukaryotes.     

Discovering functional interaction patterns in protein-protein interaction networks

Background  

In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI) network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks.     

Seed dispersal by rivers in tropical dry forests: An overlooked process in tropical central Mexico

Aims

Rivers are important corridors for the movement, migration and dispersal of aquatic organisms, including seeds from riparian plants. Although tropical dry forests (TDF) are among the most extensive and floristically rich ecosystems of tropical habitats, and the most globally endangered ecosystem, less attention has been given to riparian corridors within this ecosystem. Although most TDFs manifest peak seed dispersal during dry seasons, we hypothesized that riparian corridors may show a dispersal peak during the rainy season, due to an anticipated ‘sweep or drag effect’, resulting from river overflow and bank erosion. Our main aims were to investigate whether there were any differences in the seed communities transported by the river to sites in rainy as opposed to dry seasons, and to evaluate any possible relationship between the riparian seed community and river flow.

Location

Amacuzac River, drainage of the Balsas basin, State of Morelos, Mexico.

Methods

To evaluate the above assumption, we associated Amacuzac River flow with the number of species and seeds dispersed by water. We also characterized and evaluated differences between seed communities transported by the river during the rainy and dry seasons, and between four different sites located along the river. We used univariate and ordination NMDS techniques to evaluate patterns between seasons at the community level.

Results

Forty‐five plant species were identified from 909 seeds collected from the river. The composition of riparian seed communities was markedly different between seasons but not between sites. Seed abundances were significantly higher in the rainy than in the dry season and varied between sites. Seed species diversity in the river (H’ = 1.6–1.9) showed no significant differences between seasons or sites, but species assemblages and dominance varied according to season. Ordination techniques and subsequent fitting analyses showed that seed species composition was positively associated with river flow.

Conclusions

Seed dispersal patterns generated by rivers are significant mechanisms for structuring the composition and distribution of the riparian plant community in Mexican TDF. Varying species assemblages and seed abundance dispersed by the river throughout the year is a relevant and until now unknown consequence that may affect the dynamics and composition of riparian plant communities in this region. This study initiative will promote new avenues of research regarding plant establishment and succession.     

Alien plants have greater impact than habitat fragmentation on native insect flower visitation networks

Aim

Habitat fragmentation and alien species are among the leading causes of biodiversity loss. In an attempt to reduce the impact of forestry on natural systems, networks of natural corridors and patches of natural habitat are often maintained within the afforested matrix, yet these can be subject to degradation by invasion of non‐native species. Both habitat fragmentation and alien invasive species disrupt the complex interaction networks typical of native communities. This study examines whether an invasive plant and/or the fragmented nature of the forestry landscape influences natural flower visitation networks (FVNs), flower–visitor abundance and richness or flower/visitor species composition.

Location

The species rich and diverse grasslands in the KwaZulu‐Natal Midlands, South Africa is under threat from transformation, particularly by commercial forestry plantations, restricting much of the remaining untransformed grasslands into remnant grassland patches (RGPs). Remaining patches are under additional threat from the invasive Rubus cuneifolius Pursh (bramble). Sites were established in RGPs and in a nearby protected area (PA), with and without brambles present for both areas.

Results

Flower abundance and flower area of native plant species were greater within RGP than in PA, but only in the absence of R. cuneifolius. Flower–visitor assemblages differed between invaded and uninvaded sites and also differed between PA and RGP sites. Both areas lost specialist flower–visitor species in the presence of brambles. Network modularity was greatly reduced by the presence of bramble, indicating a reduction in complexity and organization. The structure of FVNs was otherwise unaffected by presence of bramble or being located in RGPs or the PA.

Main conclusions

The RPGs contribute to regional biodiversity conservation through additional compositional diversity and intact FVNs. Rubus cuneifolius reduces ecological complexity of both RGPs and PAs, however, and its removal must be prioritized to conserve FVNs.     

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Background  

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees.     

Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction

Background  

Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels.     

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

Background  

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.