Domain-oriented edge-based alignment of protein interaction networks

Motivation: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein–protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment

DIMA 3.0: Domain Interaction Map

Domain Interaction MAp (DIMA, available at http://webclu.bio.wzw.tum.de/dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46 900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software

Zygomycetes, Microsporidia, and the Evolutionary Ancestry of Sex Determination

Zygomycetes and their alleged sister taxon, the microsporidia, exclusively share the presence of a cluster of three genes encoding a sugar transporter, a high mobility group (HMG)-type transcription factor, and an RNA helicase. In zygomycetes, the HMG-type transcription factor acts as the sole sex determinant. This intimately ties the evolutionary history of this gene cluster to the evolution of sex determination. Here, we have unraveled the relationships of the two gene clusters by vicariously analyzing the sugar transporters and the RNA helicases. We show that if the two gene clusters share a common ancestry, it dates back to the early days of eukaryotic evolution. As a consequence, the zygomycete MAT locus would be old enough to represent the archetype of fungal and animal sex determination. However, the evolutionary scenario that has to be invoked is complex. An independent assembly of the two clusters deserves therefore consideration. In either case, shared ancestry or convergent evolution, the presence of the gene cluster in microsporidia and in zygomycetes represents at best a plesiomorphy. Hence, it is not phylogenetically informative. A further genome-wide reanalysis of gene order conservation reveals that gene order is not significantly more similar between microsporidia and zygomycetes than between microsporidia and any other fungal taxon or even humans. Consequently, the phylogenetic placement of microsporidia as sister to the zygomycetes needs to be reconsidered

DOMINE: a comprehensive collection of known and predicted domain-domain interactions

DOMINE is a comprehensive collection of known and predicted domain–domain interactions (DDIs) compiled from 15 different sources. The updated DOMINE includes 2285 new domain–domain interactions (DDIs) inferred from experimentally characterized high-resolution three-dimensional structures, and about 3500 novel predictions by five computational approaches published over the last 3 years. These additions bring the total number of unique DDIs in the updated version to 26 219 among 5140 unique Pfam domains, a 23% increase compared to 20 513 unique DDIs among 4346 unique domains in the previous version. The updated version now contains 6634 known DDIs, and features a new classification scheme to assign confidence levels to predicted DDIs. DOMINE will serve as a valuable resource to those studying protein and domain interactions. Most importantly, DOMINE will not only serve as an excellent reference to bench scientists testing for new interactions but also to bioinformaticans seeking to predict novel protein–protein interactions based on the DDIs. The contents of the DOMINE are available at http://domine.utdallas.edu

Are sites with multiple single nucleotide variants in cancer genomes a consequence of drivers, hypermutable sites or sequencing errors?

Across independent cancer genomes it has been observed that some sites have been recurrently hit by single nucleotide variants (SNVs). Such recurrently hit sites might be either (i) drivers of cancer that are postively selected during oncogenesis, (ii) due to mutation rate variation, or (iii) due to sequencing and assembly errors. We have investigated the cause of recurrently hit sites in a dataset of >3 million SNVs from 507 complete cancer genome sequences. We find evidence that many sites have been hit significantly more often than one would expect by chance, even taking into account the effect of the adjacent nucleotides on the rate of mutation. We find that the density of these recurrently hit sites is higher in non-coding than coding DNA and hence conclude that most of them are unlikely to be drivers. We also find that most of them are found in parts of the genome that are not uniquely mappable and hence are likely to be due to mapping errors. In support of the error hypothesis, we find that recurently hit sites are not randomly distributed across sequences from different laboratories. We fit a model to the data in which the rate of mutation is constant across sites but the rate of error varies. This model suggests that ∼4% of all SNVs are errors in this dataset, but that the rate of error varies by thousands-of-fold between sites

DOMINE: a database of protein domain interactions

DOMINE is a database of known and predicted protein domain interactions compiled from a variety of sources. The database contains domain–domain interactions observed in PDB entries, and those that were predicted by eight different computational approaches. DOMINE contains a total of 20 513 unique domain–domain interactions among 4036 Pfam domains, out of which 4349 are inferred from PDB entries and 17 781 were predicted by at least one computational approach. This database will serve as a valuable resource to those working in the field of protein and domain interactions. DOMINE may not only serve as a reference to experimentalists who test for new protein and domain interactions, but also offers a consolidated dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that control the topological structure of interaction networks. DOMINE is freely available at http://domine.utdallas.edu

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes

Single cell RNA-seq reveals profound transcriptional similarity between Barrett's oesophagus and oesophageal submucosal glands

Barrett’s oesophagus is a precursor of oesophageal adenocarcinoma. In this common condition, squamous epithelium in the oesophagus is replaced by columnar epithelium in response to acid reflux. Barrett’s oesophagus is highly heterogeneous and its relationships to normal tissues are unclear. Here we investigate the cellular complexity of Barrett’s oesophagus and the upper gastrointestinal tract using RNA-sequencing of single cells from multiple biopsies from six patients with Barrett’s oesophagus and two patients without oesophageal pathology. We find that cell populations in Barrett’s oesophagus, marked by LEFTY1 and OLFM4, exhibit a profound transcriptional overlap with oesophageal submucosal gland cells, but not with gastric or duodenal cells. Additionally, SPINK4 and ITLN1 mark cells that precede morphologically identifiable goblet cells in colon and Barrett’s oesophagus, potentially aiding the identification of metaplasia. Our findings reveal striking transcriptional relationships between normal tissue populations and cells in a premalignant condition, with implications for clinical practice

Domain-Domain Interactions Underlying Herpesvirus-Human Protein-Protein Interaction Networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host

Distinct spatiotemporal patterns and PARP dependence of XRCC1 recruitment to single-strand break and base excision repair

Single-strand break repair (SSBR) and base excision repair (BER) of modified bases and abasic sites share several players. Among them is XRCC1, an essential scaffold protein with no enzymatic activity, required for the coordination of both pathways. XRCC1 is recruited to SSBR by PARP-1, responsible for the initial recognition of the break. The recruitment of XRCC1 to BER is still poorly understood. Here we show by using both local and global induction of oxidative DNA base damage that XRCC1 participation in BER complexes can be distinguished from that in SSBR by several criteria. We show first that XRCC1 recruitment to BER is independent of PARP. Second, unlike SSBR complexes that are assembled within minutes after global damage induction, XRCC1 is detected later in BER patches, with kinetics consistent with the repair of oxidized bases. Third, while XRCC1-containing foci associated with SSBR are formed both in eu- and heterochromatin domains, BER complexes are assembled in patches that are essentially excluded from heterochromatin and where the oxidized bases are detected