PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes

How Far Are We from the Completion of the Human Protein Interactome Reconstruction?

After more than fifteen years from the first high-throughput experiments for human protein–protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses

Protein–protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

Abstract Background It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein–protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. Methods The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. Results The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. Conclusions The implemented workflow could be used for other multifactorial diseases

Nerve tissue-specific human glutamate dehydrogenase that is thermolabile and highly regulated by adp / P. Shashidharan, Donald D. Clarke, Naveed Ahmed, Nicholas Moschonas, and Andreas Plaitakis Department of Neurology, Mount Sinai School of Medicine, New York; Department of Chemistry, Fordham University, Bronx New York, USA; and Department of Biology and School of Health Sciences, University of Crete, Crete, Greece

Glutamate dehydrogenase (GDH), an enzyme that is central to the metabolism of glutamate, is present at high levels in the mammalian brain. Studies on human leukocytes and rat brain suggested the presence of two GDH activities differing in thermal stability and allosteric regulation, but molecular biological investigations led to the cloning of two human GDH-specific genes encoding highly homologous polypeptides. The first gene, designated GLUD1, is expressed in all tissues (housekeeping GDH), whereas the second gene, designated GLUD2, is expressed specifically in neural and testicular tissues. In this study, we obtained both GDH isoenzymes in pure form by expressing a GLUD1 eDNA and a GLUD2 eDNA in Sf9 cells and studied their properties. The enzymes generated showed comparable catalytic properties when fully activated by 1 mM ADP. However, in the absence of ADP, the nerve tissue-specific GDH showed only 5% of its maximal activity, compared with ,....,40% showed by the housekeeping enzyme. Low physiological levels of ADP (0.05-0.25 mM) induced a concentration-dependent enhancement of enzyme activity that was proportionally greater for the nerve tissue GDH (by 550- 1 ,300%) than of the housekeeping enzyme {by 120- 150%). Magnesium chloride (1-2 mM) inhibited the nonactivated housekeeping GDH (by 45-64%); this inhibition was reversed almost completely by ADP. In contrast, Mg 2+ did not affect the nonstimulated nerve tissuespecific GDH, although the cation prevented much of the allosteric activation of the enzyme at low ADP levels (0.05-0.25 mM). Heat-inactivation experiments revealed that the half-life of the housekeeping and nerve tissuespecific GDH was 3.5 and 0.5 h, respectively. Hence, the nerve tissue-specific GDH is relatively thermolabile and has evolved into a highly regulated enzyme. These allosteric properties may be of importance for regulating brain glutamate fluxes in vivo under changing energy demand

The underlying PICKLE 2.0 structure.

<p>The genetic information ontology network (left) comprises three classes of biological entities, UniProt Entry (U), Gene (G) and nucleotide sequence (mRNA) (m), linked through the genetic information flow. Primary PPIs are stored as pairings of biological entities forming a heterogeneous integrated network (right). Each stored PPI is associated with sets of evidence attributes. It should be noted that each set of evidence attributes is associated with a single or multiple publications.</p

Data sources and entity identifier types for the reconstruction of the genetic information ontology network.

<p>The biological entity classes (gray boxes) are respectively populated with the RHCP UniProt entries and the RHCP-associated Entrez Gene IDs, Ensembl gene IDs, RefSeq and EMBL nucleotide IDs, according to UniProt. Other types of entity identifiers (all shown in white boxes) are also collected by a multitude of sources (shown in parentheses). The sources for the links between the various biological entities are shown, indicating the path of identifier normalization to the UniProt or gene level.</p

Snapshots of the PICKLE 2.0 web interface.

<p>(A) The entity identification and the interaction querying section followed by a part of the result section; (B) An example of the page that provides the original forms in which a PPI is reported by the source databases and the associated experimental evidences.</p