Inventories to insights

“In the long course of cell life on this earth it remained, for our age, for our generation, to receive the full ownership of our inheritance. We have entered the cell, the Mansion of our birth and started the inventory of our acquired wealth.” (Albert Claude, Nobel lecture, 1974

Network motif analysis of a multi-mode genetic-interaction network

Statistical and computational methods for the extraction of biological information from dense multi-mode genetic-interaction networks were developed and implemented in open-source software

Antisense Transcription Controls Cell Fate in Saccharomyces cerevisiae

SummaryEntry into meiosis is a key developmental decision. We show here that meiotic entry in Saccharomyces cerevisiae is controlled by antisense-mediated regulation of IME4, a gene required for initiating meiosis. In MAT a/α diploids the antisense IME4 transcript is repressed by binding of the a1/α2 heterodimer at a conserved site located downstream of the IME4 coding sequence. MAT a/α diploids that produce IME4 antisense transcript have diminished sense transcription and fail to initiate meiosis. Haploids that produce the sense transcript have diminished antisense transcription and manifest several diploid phenotypes. Our data are consistent with transcription interference as a regulatory mechanism at the IME4 locus that determines cell fate

Control of Signaling in a MAP-kinase Pathway by an RNA-Binding Protein

Signaling-protein mRNAs tend to have long untranslated regions (UTRs) containing binding sites for RNA-binding proteins regulating gene expression. Here we show that a PUF-family RNA-binding protein, Mpt5, represses the yeast MAP-kinase pathway controlling differentiation to the filamentous form. Mpt5 represses the protein levels of two pathway components, the Ste7 MAP-kinase kinase and the Tec1 transcriptional activator, and negatively regulates the kinase activity of the Kss1 MAP kinase. Moreover, Mpt5 specifically inhibits the output of the pathway in the absence of stimuli, and thereby prevents inappropriate cell differentiation. The results provide an example of what may be a genome-scale level of regulation at the interface of signaling networks and protein-RNA binding networks

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

BACKGROUND: The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types. RESULTS: SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform. CONCLUSION: SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research

Prediction of phenotype and gene expression for combinations of mutations

Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified

Derivation of genetic interaction networks from quantitative phenotype data

We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways

System-based proteomic analysis of the interferon response in human liver cells

BACKGROUND: Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS). RESULTS: In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform. CONCLUSIONS: Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection

Maximal Extraction of Biological Information from Genetic Interaction Data

Targeted genetic perturbation is a powerful tool for inferring gene function in model organisms. Functional relationships between genes can be inferred by observing the effects of multiple genetic perturbations in a single strain. The study of these relationships, generally referred to as genetic interactions, is a classic technique for ordering genes in pathways, thereby revealing genetic organization and gene-to-gene information flow. Genetic interaction screens are now being carried out in high-throughput experiments involving tens or hundreds of genes. These data sets have the potential to reveal genetic organization on a large scale, and require computational techniques that best reveal this organization. In this paper, we use a complexity metric based in information theory to determine the maximally informative network given a set of genetic interaction data. We find that networks with high complexity scores yield the most biological information in terms of (i) specific associations between genes and biological functions, and (ii) mapping modules of co-functional genes. This information-based approach is an automated, unsupervised classification of the biological rules underlying observed genetic interactions. It might have particular potential in genetic studies in which interactions are complex and prior gene annotation data are sparse