Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae

Collins, Sean R.; Kemmeren, Patrick; Zhao, Xüna; Greenblatt, Jack; Spencer, Forrest; Holstege, Frank C.P.; Weissman, Jonathan S.; Krogan, Nevan J.

doi:10.1074/mcp.m600381-mcp200

Cited by 733 publications

(835 citation statements)

References 48 publications

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“…Heat shock proteins are methylated in response to stress (Chiang et al, 1996). Yeast Aco1 interacts with calmodulin Cmd1 (Krogan et al, 2006), which itself interacts with actin Act1 (Collins et al, 2007) and the nucleoside diphosphate kinase Ynk1 (Ho et al, 2002). All these data support that yeast Sam1 and A. nidulans SasA seem to (directly or indirectly) interact with multiple protein partners.…”

Section: Discussionsupporting

confidence: 59%

Fungal S-adenosylmethionine synthetase and the control of development and secondary metabolism in Aspergillus nidulans

Gerke

Bayram

Braus

2012

Fungal Genetics and Biology

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a b s t r a c tThe filamentous fungus Aspergillus nidulans carries a single gene for the S-adenosylmethionine (SAM) synthetase SasA, whereas many other organisms possess multiple SAM synthetases. The conserved enzyme catalyzes the reaction of methionine and ATP to the ubiquitous methyl group donor SAM. SAM is the main methyl group donor for methyltransferases to modify DNA, RNA, protein, metabolites, or phospholipid target substrates. We show here that the single A. nidulans SAM synthetase encoding gene sasA is essential. Overexpression of sasA, encoding a predominantly cytoplasmic protein, led to impaired development including only small sterile fruiting bodies which are surrounded by unusually pigmented auxiliary Hülle cells. Hülle cells are the only fungal cell type which does not contain significant amounts of SasA. Sterigmatocystin production is altered when sasA is overexpressed, suggesting defects in coordination of development and secondary metabolism. SasA interacts with various metabolic proteins including methionine or mitochondrial metabolic enzymes as well as proteins involved in fungal morphogenesis. SasA interaction to histone-2B might reflect a putative epigenetic link to gene expression. Our data suggest a distinct role of SasA in coordinating fungal secondary metabolism and development.

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Section: Discussionsupporting

confidence: 59%

Fungal S-adenosylmethionine synthetase and the control of development and secondary metabolism in Aspergillus nidulans

Gerke

Bayram

Braus

2012

Fungal Genetics and Biology

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“…Indeed, although Adh1p is frequently described as a cytoplasmic protein, it is also associated with the plasma membrane (92) like many other fermentation enzymes (Pgi1p, Tpi1p, Eno1p, Eno2p, Tdh1p, Tdh2p, Tdh3p, Pgk1p, Pyk1p). In addition, these enzymes display protein-protein interactions in yeast (93) and also in Human (94), suggesting they may form a large metabolon complex whose plasma membrane localization (92) may be useful for the rapid processing of the glucose entering the cell. This is particularly true for larger cells in which independent cytosolic proteins have less chance to be close together and form an enzyme-to-enzyme channeling of glycolytic intermediates.…”

Section: Discussionmentioning

confidence: 99%

Linking Post-Translational Modifications and Variation of Phenotypic Traits

Albertin

Marullo

Bely

et al. 2013

Molecular & Cellular Proteomics

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Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of posttranslational modifications in the diversity of metabolic and life-history traits. Molecular & Cellular Proteomics 12: 10.1074/mcp.M112.024349, 720 -735, 2013.The key problem in understanding genotype-phenotype relationships is the complexity arising from multiple levels of cellular functioning. Among them, metabolic networks involve series of interconnected chemical reactions catalyzed by enzymes, allowing the transformation of input substrates into intermediate or final metabolites. These networks play an essential role in an organism's growth, reproduction, and ability to maintain cell integrity and to respond to environmental changes (1, 2). The metabolic fluxes, as well as the metabolite concentrations, are governed by the activity of the enzymes, which depends on three types of factors: kinetic parameters, enzyme abundance, and activation state of the enzyme. The kinetic parameters are determined by the sequence and the three-dimensional structure of the protein (3). The abundance of the enzymes is the result of numerous molecular processes taking place from the transcriptional to the translational level, including epigenetic modifications of the DNA and chromatin (4), transcriptional regulation by transcription factors (5), mRNA capping and splicing and small RNA regulation (6), protein turnover (7), etc. The enzyme activation state is primarily because of post-translational modifications of the native protein, themselves highly regulated (8, 9). Other mechanisms involved in enzyme activity are proteinprotein interactions and allosteric regulation, such mechanisms being sometimes mediated through post-translational modifications (10, 11). The resulting isoforms can display differences in activity, affinity for partners (protein or effectors), and stability (12). The most studied modification is the reversibl...

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“…The prediction of GFA is compared with the results reported by CODEC using the w 1 scoring scheme, which gives CODEC the best performance. To compare with CEZANNE, the same PPI network from [48] and microarray data [49] considered in [26] are used to construct the input for GFA. All edges with confidence scores below 0.2 (one of the cutoffs considered in [48]) are removed and the edge weights (i.e., specific functional modules, it predicted only 14 complexes/modules, which led to very low sensitivities (e.g., 14.1% on GO:macromolecular complex) but high specificities (e.g., 84.6% on GO:macromolecular complex).…”

Section: B Comparison With Existing Methodsmentioning

confidence: 99%

A Max-Flow-Based Approach to the Identification of Protein Complexes Using Protein Interaction and Microarray Data

Feng

Jiang

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network and then breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our tests on three widely used protein-protein interaction datasets and comparisons with several latest methods for protein complex identification demonstrate the strong performance of our method in predicting novel protein complexes in terms of its specificity and efficiency. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes. Index TermsProtein complex, protein-protein interaction network, microarray, dense subgraph, maximum network flow, efficient algorithm Jianxing Feng is with

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Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae

Cited by 733 publications

References 48 publications

Fungal S-adenosylmethionine synthetase and the control of development and secondary metabolism in Aspergillus nidulans

Fungal S-adenosylmethionine synthetase and the control of development and secondary metabolism in Aspergillus nidulans

Linking Post-Translational Modifications and Variation of Phenotypic Traits

A Max-Flow-Based Approach to the Identification of Protein Complexes Using Protein Interaction and Microarray Data

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