Biomedical subjects

Gabriel F Berriz

Publications and source records attributed to Gabriel F Berriz.

8 recordsLinked to original sources

PubMed2005

Metabolomic identification of novel biomarkers of myocardial ischemia.

BACKGROUND: Recognition of myocardial ischemia is critical both for the diagnosis of coronary artery disease and the selection and evaluation of therapy. Recent advances in proteomic and metabolic profiling technologies may offer the possibility of identifying novel biomarkers and pathways activated in myocardial ischemia. METHODS AND RESULTS: Blood samples were obtained before and after exercise stress testing from 36 patients, 18 of whom demonstrated inducible ischemia (cases) and 18 of whom did not (controls). Plasma was fractionated by liquid chromatography, and profiling of analytes was performed with a high-sensitivity electrospray triple-quadrupole mass spectrometer under selected reaction monitoring conditions. Lactic acid and metabolites involved in skeletal muscle AMP catabolism increased after exercise in both cases and controls. In contrast, there was significant discordant regulation of multiple metabolites that either increased or decreased in cases but remained unchanged in controls. Functional pathway trend analysis with the use of novel software revealed that 6 members of the citric acid pathway were among the 23 most changed metabolites in cases (adjusted P=0.04). Furthermore, changes in 6 metabolites, including citric acid, differentiated cases from controls with a high degree of accuracy (P<0.0001; cross-validated c-statistic=0.83). CONCLUSIONS: We report the novel application of metabolomics to acute myocardial ischemia, in which we identified novel biomarkers of ischemia, and from pathway trend analysis, coordinate changes in groups of functionally related metabolites.

Adenosine Monophosphate↗

PubMed2005

Towards a proteome-scale map of the human protein-protein interaction network.

Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

Cloning, Molecular↗

PubMed2005

Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis.

Although numerous fundamental aspects of development have been uncovered through the study of individual genes and proteins, system-level models are still missing for most developmental processes. The first two cell divisions of Caenorhabditis elegans embryogenesis constitute an ideal test bed for a system-level approach. Early embryogenesis, including processes such as cell division and establishment of cellular polarity, is readily amenable to large-scale functional analysis. A first step toward a system-level understanding is to provide 'first-draft' models both of the molecular assemblies involved and of the functional connections between them. Here we show that such models can be derived from an integrated gene/protein network generated from three different types of functional relationship: protein interaction, expression profiling similarity and phenotypic profiling similarity, as estimated from detailed early embryonic RNA interference phenotypes systematically recorded for hundreds of early embryogenesis genes. The topology of the integrated network suggests that C. elegans early embryogenesis is achieved through coordination of a limited set of molecular machines. We assessed the overall predictive value of such molecular machine models by dynamic localization of ten previously uncharacterized proteins within the living embryo.

Algorithms↗

PubMed2004

Evidence for dynamically organized modularity in the yeast protein-protein interaction network.

In apparently scale-free protein-protein interaction networks, or 'interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the 'hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: 'party' hubs, which interact with most of their partners simultaneously, and 'date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes--or modules--to each other, whereas party hubs function inside modules.

Computer Simulation↗

PubMed2004

Global mapping of the yeast genetic interaction network.

A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Amino Acid Sequence↗

PubMed2004

A map of the interactome network of the metazoan C. elegans.

To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains approximately 5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.

Animals↗

PubMed2003

Characterizing gene sets with FuncAssociate.

SUMMARY: FuncAssociate is a web-based tool to help researchers use Gene Ontology attributes to characterize large sets of genes derived from experiment. Distinguishing features of FuncAssociate include the ability to handle ranked input lists, and a Monte Carlo simulation approach that is more appropriate to determine significance than other methods, such as Bonferroni or idák p-value correction. FuncAssociate currently supports 10 organisms (Vibrio cholerae, Shewanella oneidensis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Caenorhaebditis elegans, Drosophila melanogaster, Mus musculus, Rattus norvegicus and Homo sapiens). AVAILABILITY: FuncAssociate is freely accessible at http://llama.med.harvard.edu/Software.html. Source code (in Perl and C) is freely available to academic users 'as is'.

Algorithms↗

PubMed2003

GoFish finds genes with combinations of Gene Ontology attributes.

SUMMARY: GoFish is a Java application that allows users to search for gene products with particular gene ontology (GO) attributes, or combinations of attributes. GoFish ranks gene products by the degree to which they satisfy a Boolean query. Four organisms are currently supported: Saccaromyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and M.musculus.

Amino Acid Sequence↗