FEBS Letters 583 (2009) 3895–3899

journal homepage: www.FEBSLetters .org

Review

Systems biology from a yeast omics perspective

Michael Snyder *, Jennifer E.G. GallagherDepartment of Genetics, 300 Pasteur Dr, Stanford University, Stanford, CA 94305, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 23 October 2009Revised 5 November 2009Accepted 5 November 2009Available online 11 November 2009

Edited by Stefan Hohmann

Keywords:Systems biologyGenomicsProteomicsTranscriptomePhosphorylomeSaccharomyces cerevisiae

0014-5793/$36.00 � 2009 Federation of European Biodoi:10.1016/j.febslet.2009.11.011

* Corresponding author.E-mail address: mpsnyder@stanford.edu (M. Snyd

Systems biology represents a paradigm shift from the study of individual genes, proteins or othercomponents to that of the analysis of entire pathways, cellular, developmental, or organismal pro-cesses. Large scale studies, primarily initiated in Saccharomyces cerevisiae, have allowed the identi-fication and characterization of components on an unprecedented level. Large scale interaction,transcription factor binding and phosphorylation data have enabled the elucidation of global regu-latory networks. These studies have helped provide an understanding of cellular pathways and pro-cesses at a global and systems level.� 2009 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

1. Introduction

Systems Biology represents the study of entire pathways, pro-cesses or even organismal interactions usually at a molecular level.This larger analysis has allowed elucidation of basic principles thatmay not be apparent at the level of individual components. Suchinformation is expected to be valuable to understanding how anentire system operates.

2. A paradigm shift

Until the early 1990s nearly all biological research was focusedon the detailed study of individual components in which proteinsor genes were studied one at a time, a very laborious and ineffi-cient process. This approach shifted in the early and mid-1990swith several types of large-scale studies that allow the analysisof large numbers of components systematically and/or simulta-neously for the first time (Fig. 1 and Table 1). As the field matured,analysis moved from qualitative to quantitative experiments as aprerequisite to compare and draw meaningful conclusions fromlarge datasets. The first study was our transposon tagging projectwhich tagged a large number of yeast genes allowing for large-scale gene expression, protein localization, and disruption pheno-type analyzes as well as identification of unannotated sequences[1]. The second was DNA microarrays, which allowed the monitor-

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ing of gene expression of large numbers of, and ultimately all,known and potential genes within an organism [2]. Subsequently,studies to systematically disrupt gene function and biochemicallycharacterize proteins on large-scale emerged (see Table 1). Eachof these projects usually required development of a new method(RNAi for multicellular organisms; protein expression collectionsfor biochemical assays).

The vast majority of these projects were first pioneered in yeastbecause of (a) its relatively small genome and numbers of genes(approximately 6000), (b) the genome was one of the first se-quenced (completed in 1996; [3]) and (c) its facile genetics. Table1 summarizes the different large-scale projects performed foryeast. Projects for characterizing gene expression, protein localiza-tion, gene disruption, transcription factor binding, biochemicalstudies and protein profiling have been carried out providing awealth of information about each gene, and its correspondingRNA and protein in the cell. Perhaps most importantly, these stud-ies have generated valuable collections of tagged strains and mu-tants that have proven very valuable to the scientific community.The culture of sharing reagents and information within the yeastcommunity has greatly enhanced the entire field and community.Subsequent to the launch of the different yeast studies, parallelprojects have been performed for Caenorhabditis elegans, Drosoph-ila, Arabidopsis and vertebrates and enabled extensive gene charac-terization in those organisms [4–8]. Many of these have beenperformed as systematic efforts by consortia e.g. modENCODE tosystematically analyze the genomic association of all transcriptionfactors [9,10].

lsevier B.V. All rights reserved.

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Fig. 1. Timeline of yeast projects.

Table 1Large scale yeast projects and methods.

Project/method Goal Reference

Transposon tagging Gene expression; protein localization; disruption phenotypes [1,28,55]DNA microarrays Gene expression [22]Systematic knockouts Phenotypes [56,57]Protein localization using directed tagging Subcellular protein localization [1,28,55,58]Biochemical protein characterization Biochemical activities [59,60]Protein microarrays Biochemical activities; protein modifications; interactions [21,45,60]Mass spectrometry Protein profiling; Quantitative protein levels [61–63]Protein–protein Interactions two hybrid Protein interaction maps; protein function prediction [14–16]Protein–protein interactions complex purification Protein interaction maps; protein function predication [19,20,64]Genetic interactions Global synthetic screens [37,39,65,66]Phosphorylation Kinase substrate map; large-scale phosophylation mapping [41,42,46,67]Other post-translational modifications Mapping of glycosylation; SUMO, acetylation, ubiquitination [68–72]

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These studies enabled assignment of functions to genes at a ra-pid pace. For example, when the yeast genome sequenced wascompleted in 1996 the function of two-thirds of the encoded geneswas not known [3]. Now 13 years later this figure has been reducedto 10% [11]. Through the integration of different types of large-scale data [12,13], some level of function can be inferred aboutmost yeast genes, although this information is by no means com-prehensive, and new function of proteins continue to emerge. Asa consequence of these various global studies, we also have a muchdeeper understanding of entire biological processes and pathways.

3. Analysis of networks and regulatory circuits

In conjunction with the advent of large-scale characterization ofgenes and proteins, came the large-scale analysis of their interac-tions and regulation. To date a number of interaction networkshave been generated including protein–protein interaction andtranscription factor binding to DNA. These global interaction stud-ies have help elucidate entire pathways as well as regulatory net-works controlling biological processes.

3.1. Protein–protein interactions

The first of the interaction studies were protein–protein inter-action projects. Initial research focused on high throughput yeasttwo-hybrid studies; these studies were initially incompletealthough a more recent study and related protein complementa-tion method generated much larger datasets [14–18]. Thesestudies generally identify direct interactions among protein. Sub-sequent to the initial two-hybrid studies large-scale studies usingaffinity purification of tagged proteins and identification of associ-

ated proteins by mass spectrometry were performed [19,20]. Thisapproach tends to identify members of a complex, usually at phys-iological levels in vivo and was a dramatic shift away from assign-ing protein function to pathways from protein–proteinassociations rather than classical mutant characterization. Smallerscale in vitro interaction studies using protein microarrays havealso been performed [21] and allowed computational detectionof non-canonical small molecule binding motifs. In general, theoverlap between the approaches is rather modest. This is partly be-cause these approaches themselves are incomplete and are rarelyperformed to saturation. Moreover, each method has its biasesand limitations and this likely also contributes to the observedincomplete overlap.

3.2. Transcription factors

A second major area for the analysis of regulatory networks isthe monitoring of gene expression using DNA microarrays. Expres-sion profiling has now been performed for a large number oforganisms and now thousands of microarray experiments havebeen performed for yeast, C. elegans, Drosophila, Arabidopsis, miceand humans [2,22–24]. More recent studies avoid DNA microarraysand involve direct sequencing of RNA (RNA-Seq; [25,26]), which ismuch more sensitive and accurate due to lack of cross hybridiza-tion [27]. These different studies have allowed the profiling of allannotated genes under diverse conditions, different tissues and/or different developmental stages. By correlating gene patterns, ithas been possible to determine which genes work together and of-ten identify common DNA sequence motifs in promoter regions.

Other large-scale studies have been performed to characterizetranscription factor binding sites. We have found that 27% of

M. Snyder, J.E.G. Gallagher / FEBS Letters 583 (2009) 3895–3899 3897

epitope-tagged proteins localize to the nucleus and the majority ofthese exhibit punctuate patterns of staining of chromosomes usingmeiotic chromosome spreads [28]. Together with Dr. PatrickBrown’s laboratory, we invented the ChIP-chip method for large-scale identification of binding sites throughout the yeast genome[29,30]. This method involves immunoprecipitation of a transcrip-tion factor along with its associated DNA, followed by probing ofDNA microarrays containing genomic sequences. This method ledto several large-scale studies to map DNA binding sites of manyfactors during either vegetative growth or under different condi-tions [30–32]. A modification of the ChIP method is ChIP-Seq,which uses high-throughput DNA sequencing as its readout[33,34]. A version for yeast uses bar coding of samples so that largenumbers of samples can be analyzed at once [35]. ChIP-chip andChIP-Seq, when combined with gene expression studies, are a par-ticularly powerful method for dissecting regulatory circuits. Todate most of the global DNA binding studies have been performedat a single time point; however recent studies have demonstratedthat transcription factor binding sites can have vary temporallypresumably reflected the combinatorial effects of multiple bindingpartners [32]. After an initial challenge to yeast, changes in thetranscription factor binding can be graphed over time (Fig. 2). Pos-sible outcomes are no change, a rapid increase/decrease, a lag inchange transcription factor binding or a transient binding dissoci-ation of the transcription factor. Adding temporal component tomapping transcription factor binding allows a dynamic picture ofcellular response to environmental changes.

3.3. Genetic interactions

Genetic screens uncover interactions that are not necessarilyphysical, but rather indicate functional relationships between pro-teins at the pathway, cellular, or even organismal level. The mostcommon of these are synthetic lethal screen in which strains con-taining combinations of gene mutations in have a much more se-vere phenotype (typically death) than that of mutations inindividual genes [36]. The first screens systematically examinedthe effects of combining different null mutations for the ‘‘knockoutcollections” [37]. More recent studies using a collection of DAMPalleles, which contain hypomorphs of essential proteins [38], haverevealed synthetic interactions of essential genes. These types ofstudies have been extended to not only examine negative interac-tions (synthetic lethal interactions), but also positive interactions(suppression) between mutated genes. The analysis of multipleglobal screens can also reveal parallel pathways by clusteringinteractions by the similarities in of genetic interactions (E-MAPs)

Fig. 2. Types of temporal patterns of transcription factors binding to DNA. (A) Constitutivsignal). (C) Delayed binding (lag in binding and then no change in binding). (D) Trandissociation (immediate response and no reassociation). (F) Delayed dissociation (lag breassociation). (Reproduced from Ref. [32].)

that would otherwise be missed by analysis of only physical inter-actions [39,40].

3.4. Phosphorylome

A fourth area of global analysis is protein phosphorylation. Ini-tial estimates have suggested that 30% of proteins in yeast and hu-mans are phosphoproteins; although, this estimate is likely to be asignificant underestimate. However, from the limited number ofrecent studies, over 12 000 high confidence phosphorylation siteson �50% of yeast proteins have been identified in yeast using massspectrometry [41–44] and the number of phosphorylated sites isexpected to grow even higher as additional sites continue to bemapped. A significant challenge has been linking kinases to theirsubstrates: yeast have 122 putative kinases whereas humans have518 [45]. Thus far several methods have been employed for this. Tobegin to systematically identify 95 kinase substrates, we used pro-tein microarray containing 4400 yeast proteins to find in vitro tar-gets for 95 yeast kinases [46,47]. From the first 87 kinases, 4200substrates were identified, an average of 47 targets per kinases.Among the many interesting findings from this study was theobservation that closely related kinases such as the three proteinkinase A homologs, Tpk1, Tpk2 and Tpk3, each have different tar-gets. Likewise, the cyclin-dependent kinase, Pho85, phosphory-lated different targets when associated with different cyclins. Arelated approach has been to incubate purified kinases with GSTfusion proteins or to use modified kinases that only use a modifiedATP [48–50]. Another strategy has been to identify differences inphosphorylation patterns in the presence and absence of a proteinkinase [51]. Phosphoproteins are purified from cells lacking a pro-tein kinase and identified using mass spectrometry. Phosphoryla-tion is a dynamic post-translational modification crucial for rapidresponse in cell signaling pathways and mapping these sites canprovide connections between pathways.

3.5. Integration of datasets

Individually, these studies have provided a wealth of informa-tion for specific types of regulation. However, considerableemphasis is now being placed on integration of different datatypes. Initial studies integrated gene expression and transcriptionfactor binding data to determine the roles of transcription factors(e.g. positive or negative regulation). Moreover, studies have beenperformed to integrate additional data types such as protein–pro-tein interaction data, phosphorylation data [46], and even metab-olite data [52] into large ‘‘meta network” or ‘‘ridiculograms,”

e binding (no change). (B) Rapid binding (immediate response and no attenuation ofsient binding (immediate response then return to steady-state levels). (E) Rapidefore complete dissociation). (G) Transient dissociation (initial dissociation before

Fig. 3. Simple motifs/modules found in complex regulatory networks. Seven different motifs of interactions described between kinates: kinases, their substrates andtranscription factors. (Reproduced from Ref. [53].)

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which are visually stunning networks containing extensive inter-actions. The combined data in these meta networks can besearched for motifs or modules that are overrepresented in thenetworks. Examples of overrepresented modules are shown inFig. 3, including the scaffold module, in which protein kinaseand their substrates each interacting with a third proteins andthe ‘‘ménage à trois” module in which kinases interact with twointeraction partners [46,53].

4. Interdisciplinary research

System Biology by its very nature operates at the interface ofBiology and Computational Biology. Biologists are typically respon-sible for collecting large data sets often involving many data pointsand computational biologist are typically responsible for both ini-tial scoring of results and more general analyses. The production ofthese dataset also provides materials to propose mathematicalmodels for pathways that can identify and/or predict key regula-tors. Furthermore, engineers are having a profound impact onreducing sample size therefore saving reagents and increasingthroughput. In some areas of systems biology, chemists and biolo-gist are designing small molecular probes or inhibitors to specificproteins or a class of proteins to probe function.

Regardless of the project, Systems Biology by necessity isbecoming more quantitative, both in data collecting and modelingpathways and biological processes. These models make predictionswhich in turn can be tested through additional experimentationwith respect to data collection as well as with regard to the mod-eling of biological pathways and processes.

5. Conclusion

Science today is different from that of 20 years ago. Whole gen-omes have been sequenced and the opportunity for comprehensiveanalysis of biological pathways has arrived. Not only is it possibleto uncover the biological components and events that occur, butthe ability to obtain a quantitative description is now possible. Thisinformation will be extremely valuable in medicine for under-standing how pathways go awry in human disease. Often commondiseases arise from complex interactions between genetics (poly-morphisms in coding and non-coding regions) influencing tran-scription and translation the proteome and the environment.Global analysis of molecular components and states during humandiseases is expected to be valuable for diagnostics and monitoringtreatments [54].

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