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

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.

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.

Comprehensive subcellular topologies of polypeptides in Streptomyces.

BACKGROUND: Members of the genus Streptomyces are Gram-positive bacteria that are used as important cell factories to produce secondary metabolites and secrete heterologous proteins. They possess some of the largest bacterial genomes and thus proteomes. Understanding their complex proteomes and metabolic regulation will improve any genetic engineering approach. RESULTS: Here, we performed a comprehensive annotation of the subcellular localization of the proteome of Streptomyces lividans TK24 and developed the Subcellular Topology of Polypeptides in Streptomyces database (SToPSdb) to make this information widely accessible. We first introduced a uniform, improved nomenclature that re-annotated the names of ~ 4000 proteins based on functional and structural information. Then protein localization was assigned de novo using prediction tools and edited by manual curation for 7494 proteins, including information for 183 proteins that resulted from a recent genome re-annotation and are not available in current databases. The S. lividans proteome was also linked with those of other model bacterial strains including Streptomyces coelicolor A3(2) and Escherichia coli K-12, based on protein homology, and can be accessed through an open web interface. Finally, experimental data derived from proteomics experiments have been incorporated and provide validation for protein existence or topology for 579 proteins. Proteomics also reveals proteins released from vesicles that bleb off the membrane. All export systems known in S. lividans are also presented and exported proteins assigned export routes, where known. CONCLUSIONS: SToPSdb provides an updated and comprehensive protein localization annotation resource for S. lividans and other streptomycetes. It forms the basis for future linking to databases containing experimental data of proteomics, genomics and metabolomics studies for this organism.

Data correction strategy for metabolomics analysis using gas chromatography-mass spectrometry.

Gas chromatography-mass spectrometry metabolomics requires the original sample's derivatization. Therefore, systematic biases that might distort the one-to-one proportional relationship between the original metabolite concentration and derivative peak area profiles have to be considered. The first type of such biases change only the value of the proportionality constant between the two profiles among samples and are corrected by the use of an internal standard. The second type, however, might distort the one-to-one relationship and also change the proportionality constant between the two profiles among samples to a different fold-extent for each metabolite. Metabolomic profiles should be corrected from these biases, because changes due only to chemical kinetics could be assigned biological significance. This paper presents the first streamlined data correction and validation strategy that does not jeopardize the high-throughput nature of metabolomic analysis. This context allowed also for the chemical annotation of 15 currently unknown derivative peaks of (NH(2))-group containing compounds.

Alternative pathways of galactose assimilation: could inverse metabolic engineering provide an alternative to galactosemic patients?

The galactose assimilation pathway has been extensively studied as an example of a genetic regulatory switch. Besides the importance of this pathway as a tool in basic biological research, unraveling its structure and regulation is also of major medical importance. Impairment of galactose assimilation is the cause of the genetic metabolic disease known as "galactosemia", while the in vivo activity of the pathway affects the production of glycans. The latter have been connected to tumor metastasis, anti-cancer drug resistance and various cardiovascular diseases. Despite the vast amount of studies, however, galactose assimilation and its interaction with other parts of the metabolic network have not been fully elucidated yet. In yeast and higher eukaryotes, it is still being studied as comprising only the linear Leloir pathway. Recent observations, however, indicate that alternative pathways of galactose assimilation identified in prokaryotes and fungi might also be present in yeast. Such a result is valuable per se, because it could lead to the discovery of these pathways in humans. Even more importantly, these pathways provide alternative phenotypes with known genetic fingerprints that can be used in the context of classical and inverse metabolic engineering to examine and treat the mechanisms of defects of galactose assimilation.

Ion-trap mass spectrometry used in combination with gas chromatography for high-resolution metabolic flux determination.

Metabolic fluxes provide a detailed metric of the cellular metabolic state. Fluxes are estimated indirectly from available measurements, and various methods have been developed for this purpose. Of particular interest are methods that make use of stable isotopic tracers because they enable flux estimation at a fine resolution. In this report, we present a protocol for the use of ion-trap mass spectrometry (MS) in combination with gas chromatography to measure the mass isotopomer distribution of biomass hydrolysates. At physiological steady-state, these measurements directly reflect the isotopic tracer distribution in the amino acid central carbon metabolism precursors. Because the extent to which a metabolic flux network can be accurately resolved strongly depends on the reliability and precision of the MS measurements and, in light of the current need for quantitative high-throughput biological analysis at the microscale, we discuss every step of the measurement process, indicate possible sources of error, and suggest solutions to avoid them. Potential advantages to using ion-trap versus quadrupole MS are also addressed. The final protocol requires 0.5 mg of dry biomass to detect the mass isotopomer distribution of 2-4 fragments of 13 amino acids, with a relative variance less than 1% for the most abundant peaks.