A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

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Network biology: understanding the cell's functional organization

Over 225,000 independent Agrobacterium transferred DNA (T-DNA) insertion events in the genome of the reference plant Arabidopsis thaliana have been created that represent near saturation of the gene space. The precise locations were determined for more than 88,000 T-DNA insertions, which resulted in the identification of mutations in more than 21,700 of the approximately 29,454 predicted Arabidopsis genes. Genome-wide analysis of the distribution of integration events revealed the existence of a large integration site bias at both the chromosome and gene levels. Insertion mutations were identified in genes that are regulated in response to the plant hormone ethylene.

https://science.sciencemag.org/content/sci/301/5633/653.full.pdf

Genome-Wide Insertional Mutagenesis of Arabidopsis thaliana

The most highly connected proteins in the cell are the most important for its survival. Proteins are traditionally identified on the basis of their individual actions as catalysts, signalling molecules, or building blocks in cells and microorganisms. But our post-genomic view is expanding the protein's role into an element in a network of protein–protein interactions as well, in which it has a contextual or cellular function within functional modules1,2. Here we provide quantitative support for this idea by demonstrating that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions.

Lethality and centrality in protein networks

We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.

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Genomic expression programs in the response of yeast cells to environmental changes.

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE
 .

/pdf/an-automated-method-for-finding-molecular-complexes-in-large-1xr24lgr4a.pdf

An automated method for finding molecular complexes in large protein interaction networks.

The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.

/pdf/functional-characterization-of-the-s-cerevisiae-genome-by-5264a47hhf.pdf

Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis

As part of EUROFAN (European Functional Analysis Network), we investigated 21 novel yeast open reading frames (ORFs) by growth and sporulation tests of deletion mutants. Two genes (YNL026w and YNL075w) are essential for mitotic growth and three deletion strains (ynl080c, ynl081c and ynl225c) grew with reduced rates. Two genes (YNL223w and YNL225c) were identified to be required for sporulation. In addition we also performed green fluorescent protein (GFP) tagging for localization studies. GFP labelling indicated the spindle pole body (Ynl225c-GFP) and the nucleus (Ynl075w-GFP) as the sites of action of two proteins. Ynl080c-GFP and Ynl081c-GFP fluorescence was visible in dot-shaped and elongated structures, whereas the Ynl022c-GFP signal was always found as one spot per cell, usually in the vicinity of nuclear DNA. The remaining C-terminal GFP fusions did not produce a clearly identifiable fluorescence signal. For 10 ORFs we constructed 5'-GFP fusions that were expressed from the regulatable GAL1 promoter. In all cases we observed GFP fluorescence upon induction but the localization of the fusion proteins remained difficult to determine. GFP-Ynl020c and GFP-Ynl034w strains grew only poorly on galactose, indicating a toxic effect of the overexpressed fusion proteins. In summary, we obtained a discernible GFP localization pattern in five of 20 strains investigated (25%). A deletion phenotype was observed in seven of 21 (33%) and an overexpression phenotype in two of 10 (20%) cases.

Nicole Liebundguth

Papers

Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis

Analysis of deletion phenotypes and GFP fusions of 21 novel Saccharomyces cerevisiae open reading frames.