Comparative metagenomic analyses reveal viral-induced shifts of host metabolism towards nucleotide biosynthesis

BACKGROUND: Viral genomes often contain metabolic genes that were acquired from host genomes (auxiliary genes). It is assumed that these genes are fixed in viral genomes as a result of a selective force, favoring viruses that acquire specific metabolic functions. While many individual auxiliary genes were observed in viral genomes and metagenomes, there is great importance in investigating the abundance of auxiliary genes and metabolic functions in the marine environment towards a better understanding of their role in promoting viral reproduction. RESULTS: In this study, we searched for enriched viral auxiliary genes and mapped them to metabolic pathways. To initially identify enriched auxiliary genes, we analyzed metagenomic microbial reads from the Global Ocean Survey (GOS) dataset that were characterized as viral, as well as marine virome and microbiome datasets from the Line Islands. Viral-enriched genes were mapped to a “global metabolism network” that comprises all KEGG metabolic pathways. Our analysis of the viral-enriched pathways revealed that purine and pyrimidine metabolism pathways are among the most enriched pathways. Moreover, many other viral-enriched metabolic pathways were found to be closely associated with the purine and pyrimidine metabolism pathways. Furthermore, we observed that sequential reactions are promoted in pathways having a high proportion of enriched genes. In addition, these enriched genes were found to be of modular nature, participating in several pathways. CONCLUSIONS: Our naïve metagenomic analyses strongly support the well-established notion that viral auxiliary genes promote viral replication via both degradation of host DNA and RNA as well as a shift of the host metabolism towards nucleotide biosynthesis, clearly indicating that comparative metagenomics can be used to understand different environments and systems without prior knowledge of pathways involved

A diverse epigenetic landscape at human exons with implication for expression

DNA methylation is an important epigenetic marker associated with gene expression regulation in eukaryotes. While promoter methylation is relatively well characterized, the role of intragenic DNA methylation remains unclear. Here, we investigated the relationship of DNA methylation at exons and flanking introns with gene expression and histone modifications generated from a human fibroblast cell-line and primary B cells. Consistent with previous work we found that intragenic methylation is positively correlated with gene expression and that exons are more highly methylated than their neighboring intronic environment. Intriguingly, in this study we identified a unique subset of hypomethylated exons that demonstrate significantly lower methylation levels than their surrounding introns. Furthermore, we observed a negative correlation between exon methylation and the density of the majority of histone modifications. Specifically, we demonstrate that hypo-methylated exons at highly expressed genes are associated with open chromatin and have a characteristic histone code comprised of significantly high levels of histone markings. Overall, our comprehensive analysis of the human exome supports the presence of regulatory hypomethylated exons in protein coding genes. In particular our results reveal a previously unrecognized diverse and complex role of the epigenetic landscape within the gene body

Comparative analysis of fungal protein kinases and associated domains

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation is responsible for a large portion of the regulatory functions of eukaryotic cells. Although the list of sequenced genomes of filamentous fungi has grown rapidly, the kinomes of recently sequenced species have not yet been studied in detail. The objective of this study is to apply a comparative analysis of the kinase distribution in different fungal phyla, and to explore its relevance to understanding the evolution of fungi and their taxonomic classification. We have analyzed in detail 12 subgroups of kinases and their distribution over 30 species, as well as their potential use as a classifier for members of the fungal kingdom.</p> <p>Results</p> <p>Our findings show that despite the similarity of the kinase distribution in all fungi, their domain distributions and kinome density can potentially be used to classify them and give insight into their evolutionary origin. In general, we found that the overall representation of kinase groups is similar across fungal genomes, the only exception being a large number of tyrosine kinase-like (TKL) kinases predicted in <it>Laccaria bicolor</it>. This unexpected finding underscores the need to continue to sequence fungal genomes, since many species or lineage-specific properties may remain to be discovered. Furthermore, we found that the domain organization significantly varies between the fungal species. Our results suggest that protein kinases and their functional domains strongly reflect fungal taxonomy.</p> <p>Conclusions</p> <p>Comparison of the predicted kinomes of sequenced fungi suggests essential signaling functions common to all species, but also specific adaptations of the signal transduction networks to particular species.</p

Patch Finder Plus (PFplus): A web server for extracting and displaying positive electrostatic patches on protein surfaces

Positively charged electrostatic patches on protein surfaces are usually indicative of nucleic acid binding interfaces. Interestingly, many proteins which are not involved in nucleic acid binding possess large positive patches on their surface as well. In some cases, the positive patches on the protein are related to other functional properties of the protein family. PatchFinderPlus (PFplus) http://pfp.technion.ac.il is a web-based tool for extracting and displaying continuous electrostatic positive patches on protein surfaces. The input required for PFplus is either a four letter PDB code or a protein coordinate file in PDB format, provided by the user. PFplus computes the continuum electrostatics potential and extracts the largest positive patch for each protein chain in the PDB file. The server provides an output file in PDB format including a list of the patch residues. In addition, the largest positive patch is displayed on the server by a graphical viewer (Jmol), using a simple color coding

A computational approach for genome-wide mapping of splicing factor binding sites

A computational method is presented for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and evolutionary conservation

SFmap: a web server for motif analysis and prediction of splicing factor binding sites

Alternative splicing (AS) is a post-transcriptional process considered to be responsible for the huge diversity of proteins in higher eukaryotes. AS events are regulated by different splicing factors (SFs) that bind to sequence elements on the RNA. SFmap is a web server for predicting putative SF binding sites in genomic data (http://sfmap.technion.ac.il). SFmap implements the COS(WR) algorithm, which computes similarity scores for a given regulatory motif based on information derived from its sequence environment and its evolutionary conservation. Input for SFmap is a human genomic sequence or a list of sequences in FASTA format that can either be uploaded from a file or pasted into a window. SFmap searches within a given sequence for significant hits of binding motifs that are either stored in our database or defined by the user. SFmap results are provided both as a text file and as a graphical web interface

Experimentally based contact energies decode interactions responsible for protein–DNA affinity and the role of molecular waters at the binding interface

A major obstacle towards understanding the molecular basis of transcriptional regulation is the lack of a recognition code for protein–DNA interactions. Using high-quality crystal structures and binding data on the promiscuous family of C2H2 zinc fingers (ZF), we decode 10 fundamental specific interactions responsible for protein–DNA recognition. The interactions include five hydrogen bond types, three atomic desolvation penalties, a favorable non-polar energy, and a novel water accessibility factor. We apply this code to three large datasets containing a total of 89 C2H2 transcription factor (TF) mutants on the three ZFs of EGR. Guided by molecular dynamics simulations of individual ZFs, we map the interactions into homology models that embody all feasible intra- and intermolecular bonds, selecting for each sequence the structure with the lowest free energy. These interactions reproduce the change in affinity of 35 mutants of finger I (R2 = 0.998), 23 mutants of finger II (R2 = 0.96) and 31 finger III human domains (R2 = 0.94). Our findings reveal recognition rules that depend on DNA sequence/structure, molecular water at the interface and induced fit of the C2H2 TFs. Collectively, our method provides the first robust framework to decode the molecular basis of TFs binding to DNA