Timothy Galitski

Modular organization of cellular networks

We investigated the organization of interacting proteins and protein complexes into networks of modules. A network-clustering method was developed to identify modules. This method of network-structure determination was validated by clustering known signaling-protein modules and by identifying module rudiments in exclusively high-throughput protein-interaction data with high error frequencies and low coverage. The signaling network controlling the yeast developmental transition to a filamentous form was clustered. Abstraction of a modular network-structure model identified module-organizer proteins and module-connector proteins. The functions of these proteins suggest that they are important for module function and intermodule communication.

Antisense Transcription Controls Cell Fate in Saccharomyces cerevisiae

Entry 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/alpha diploids the antisense IME4 transcript is repressed by binding of the a1/alpha2 heterodimer at a conserved site located downstream of the IME4 coding sequence. MAT a/alpha 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.

Transcriptome profiling to identify genes involved in peroxisome assembly and function.

Yeast cells were induced to proliferate peroxisomes, and microarray transcriptional profiling was used to identify PEX genes encoding peroxins involved in peroxisome assembly and genes involved in peroxisome function. Clustering algorithms identified 224 genes with expression profiles similar to those of genes encoding peroxisomal proteins and genes involved in peroxisome biogenesis. Several previously uncharacterized genes were identified, two of which, YPL112c and YOR084w, encode proteins of the peroxisomal membrane and matrix, respectively. Ypl112p, renamed Pex25p, is a novel peroxin required for the regulation of peroxisome size and maintenance. These studies demonstrate the utility of comparative gene profiling as an alternative to functional assays to identify genes with roles in peroxisome biogenesis.

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

BackgroundInterferons (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).ResultsIn 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.ConclusionsIntegration 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.

Identification of androgen-coregulated protein networks from the microsomes of human prostate cancer cells

BackgroundAndrogens play a critical role in the development of prostate cancer-dysregulation of androgen-regulated growth pathways can led to hormone-refractory prostate cancer. A comprehensive understanding of androgen-regulated cellular processes has not been achieved to date. To this end, we have applied a large-scale proteomic approach to define cellular processes that are responsive to androgen treatment in LNCaP prostate cancer cells.ResultsUsing isotope-coded affinity tags and mass spectrometry we identified and quantified the relative abundance levels of 1,064 proteins and found that distinct cellular processes were coregulated by androgen while others were essentially unaffected. Subsequent pharmacological perturbation of the cellular process for energy generation confirmed that androgen starvation had a profound effect on this pathway.ConclusionsOur results provide evidence for the role of androgenic hormones in coordinating the expression of critical components involved in distinct cellular processes and further establish a foundation for the comprehensive reconstruction of androgen-regulated protein networks and pathways in prostate cancer cells.

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.

Maximal extraction of biological information from genetic interaction data.

Extraction of all the biological information inherent in large-scale genetic interaction datasets remains a significant challenge for systems biology. The core problem is essentially that of classification of the relationships among phenotypes of mutant strains into biologically informative “rules” of gene interaction. Geneticists have determined such classifications based on insights from biological examples, but it is not clear that there is a systematic, unsupervised way to extract this information. In this paper we describe such a method that depends on maximizing a previously described context-dependent information measure to obtain maximally informative biological networks. We have successfully validated this method on two examples from yeast by demonstrating that more biological information is obtained when analysis is guided by this information measure. The context-dependent information measure is a function only of phenotype data and a set of interaction rules, involving no prior biological knowledge. Analysis of the resulting networks reveals that the most biologically informative networks are those with the greatest context-dependent information scores. We propose that these high-complexity networks reveal genetic architecture at a modular level, in contrast to classical genetic interaction rules that order genes in pathways. We suggest that our analysis represents a powerful, data-driven, and general approach to genetic interaction analysis, with particular potential in the study of mammalian systems in which interactions are complex and gene annotation data are sparse.

Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins.

Regulation of gene expression involves the orchestrated interaction of a large number of proteins with transcriptional regulatory elements in the context of chromatin. Our understanding of gene regulation is limited by the lack of a protein measurement technology that can systematically detect and quantify the ensemble of proteins associated with the transcriptional regulatory elements of specific genes. Here, we introduce a set of selected reaction monitoring (SRM) assays for the systematic measurement of 464 proteins with known or suspected roles in transcriptional regulation at RNA polymerase II transcribed promoters in Saccharomyces cerevisiae. Measurement of these proteins in nuclear extracts by SRM permitted the reproducible quantification of 42% of the proteins over a wide range of abundances. By deploying the assay to systematically identify DNA binding transcriptional regulators that interact with the environmentally regulated FLO11 promoter in cell extracts, we identified 15 regulators that bound specifically to distinct regions along ∼600 bp of the regulatory sequence. Importantly, the dataset includes a number of regulators that have been shown to either control FLO11 expression or localize to these regulatory regions in vivo. We further validated the utility of the approach by demonstrating that two of the SRM-identified factors, Mot3 and Azf1, are required for proper FLO11 expression. These results demonstrate the utility of SRM-based targeted proteomics to guide the identification of gene-specific transcriptional regulators.

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)