AbstractRecent computational and experimental work suggests that functional modules underlie much of cellular physiology and are auseful unit of cellular organization from the perspective of systems biology. Because interactions among modules can giverise to higher-level properties that are essential to cellular function, a complete knowledge of these interactions is necessaryfor future work in systems biology, including in silico modeling and metabolic engineering. Here we present a computationalmethod for the systematic identification and analysis of functional modules whose activity is coordinated at the level oftranscription. We applied this method, Search for Pairwise Interactions (SPIN), to obtain a global view of functional moduleconnectivity in Saccharomyces cerevisiae and to provide insight into the biological mechanisms underlying this coordination. We also examined this global networkat higher resolution to obtain detailed information about the interactions of particular module pairs. For instance, our resultsreveal possible transcriptional coordination of glycolysis and lipid metabolism by the transcription factor Gcr1p, and furthersuggest that glycolysis and phosphoinositide signaling may regulate each other reciprocally.

• Biological Science 2nd Edition Freeman 2005 Gmc Yukon
• Biological Science Freeman 5th Edition

The phenotype of a unicellular organism is determined by an integrated network of genes, proteins, and metabolites that participatein reciprocal regulatory relationships. Creating a quantitative description of this network—a goal that has recently engenderedthe dedicated discipline of systems biology—is essential to understanding, predicting, and manipulating cellular behavior.A first step toward this goal is deciphering the connectivity of the network, that is, the pattern of interactions among itscomponents. Given the complexity of this undertaking, the integrated network is often treated as a group of superimposed subnetworks,including the gene regulatory, protein, and metabolic networks. A corollary task is to determine whether the network's topologyreflects its organizational principles.

Using biological networks with relatively well-characterized connectivity, quantitativeanalyses of network topology have revealed that these networks are modular—they can be clustered into nodes that are moredensely connected to each other than to nodes in other clusters (;; ).In both natural and man-made systems, modularity is a fundamental design principle whereby components are partitioned accordingto common physical, regulatory, or functional properties. In biology, the exact meaning of modularity depends on the networkunder consideration. For example, modules in the protein network often have a straightforward physical interpretation as astatic molecular complex (such as the ribosome) or a dynamic signaling pathway (such as a MAP kinase cascade) (; ). Gene regulatory networks, in contrast, tend to display regulatory modules, in which every gene is controlled by the sametranscription factors (TFs) under the same environmental conditions (; ). Both networks exhibit functional modularity. Defined as groups of genes, proteins, and other molecules involved in a commonsubcellular process, functional modules transcend the heuristic subdivision of the integrated network into gene regulatory,protein, and metabolic networks. It has been proposed that the functional module is the most relevant organizational unitof a cell from the perspective of systems biology , and a growing body of work supports the idea that such modules underlie much of cellular physiology.For natural and man-made systems, the relative independence of modules has significant implications for system engineering(by evolution or by man): A module can be selectively altered without perturbing the behavior of the rest of the system.

However,higher-level properties that emerge from intermodule coordination are often critical to the behavior of the system as a whole. This need for module integration is balanced with the benefits of module independence by ensuring that only a few componentsin each module interface with other modules. With respect to (biological) functional modularity, modules are partially isolatedfrom each other by the biochemical specificity of protein-protein and protein-DNA interactions. They contain a large numberof internal components that do not interact with other modules, and a small number of input and/or output components thatdo.Most investigations of functional modularity have sought to define the modules' components (;;;; ), and several databases use published literature to categorize every gene in Saccharomyces cerevisiae according to its cellular function (; ). However, several studies have found examples of physical and regulatory interactions between modules (Ihmels et al.,;;;;;; ). For instance, Gal4p, a well-characterized transcription factor traditionally associated with galactose metabolism, wasalso found to regulate genes associated with uracil metabolism, such as the uracil transporter gene FUR4, thereby facilitating the addition of uridine 5′-diphosphate to galactose.A more global understanding of functional module interactions is essential to several endeavors of systems biology, such aspredicting cellular-level phenotypes arising from, for example, genetic manipulation of microorganisms, and creating accuratemathematical models of cellular function. This understanding will also help answer broader questions about how intermodule regulatory connections are distributedacross the cell.

One possibility is that most cellular functions are coordinated by a central organizing module; another,that the connections are uniformly distributed among modules.These considerations motivated us to automate the identification of potentially coregulated functional module pairs in S. Such coregulation can be achieved through a variety of mechanisms, including enzymatic reactions, shared metabolites, andcoordinated transcription. We focus on transcription, which, by promoting the coexistence of biochemical components necessaryfor coordination by other mechanisms, may represent the most fundamental level of coregulation. Drawing on experimental evidencethat a transcription factor can coordinate the activity of two pathways by regulating genes in both pathways (;; ), we developed an algorithm, SPIN (Search for Pairwise INteractions), that examines genes from a pair of functional modules,and uses expression data to assess the possibility that a given TF(s) transcriptionally coregulates genes in both modules.To obtain a comprehensive view of module coordination at a desired level of functional resolution, we applied SPIN to allpairwise combinations of 72 functional modules (Supplemental Fig. S1), which we defined according to the second-tier functionalcategories of the MIPS database. For each module pair, the extent of transcriptional coregulation was determined with respect to four microarray expressiondata sets and each of 175 TFs. The results suggest extensive coregulation among functional modules, and afford a high-levelview of interprocess coordination consistent with biological knowledge.

SPIN also identifies previously unknown functionalinteractions and the TFs that mediate them, thereby providing novel insight into the overall functional and regulatory organizationof S. Results Single-TF analysisSPIN accepts as input four data sets, including one set of transcription factor binding site (TFBS) data (for several TFs),one set of gene expression data, and two gene sets, each representing a predefined functional module. For each TF, SPIN determineswhether the TF binds to genes in both modules. If so, it uses gene expression data to determine whether the TF confers uponthese target genes an expression profile that is coherent and distinct from the expression profiles that characterize thefunctional categories. The output of SPIN is a list of statistically significant “triplets,” that is, sets of two functional modules and one TFthat appears to coordinate the modules' activity by coregulating genes in each.

We consider only triplets in which each modulecontains at least four target genes.To generate the results reported below, we used several combinations of TFBS and gene expression data. TFBS information fora total of 175 TFs was taken from two pre-existing data sets: One set, referred to as MSA (Multiple Sequence Alignment), wasobtained using the sequence-alignment algorithm AlignACE and the pattern-matching algorithm ScanACE to identify TF-binding motifs and their genomic locations, respectively, as describedin Pilpel et al. and Hughes et al. ; the second data set, referred to as Chip2, was obtained using ChIP-chip technology, and therefore constitutes direct physicalevidence of in vivo TF binding. Because each DNA-binding motif in the MSA set is assumed to represent a binding site for a cognate TF, “TF” or “TFBS” willhenceforth also refer to DNA motifs. Four microarray data sets, which measure mRNA expression profiles during the cell cycle,diauxic shift, response to environmental stress, and perturbations of the MAPK signaling pathways, were used. Functional moduleswere defined according to the second-tier functional categories in the MIPS database (in which published, largely experimental,evidence is manually curated in order to assign gene function).

(Supplemental Fig. However, SPIN can be used with anyscheme of functional categorization. This is essential, because module definitions will inevitably coevolve with the annotationof the yeast genome. Figure 1.( A) General approach.

( B) Global module interaction network (MSA TFs). Each node represents a functional module in the second-highest level of theMIPS hierarchy, and its color corresponds to the highest-level MIPS category of which the node is a member (see ). Each edge represents one or more MSA TFs that coordinate the transcription of genes in both modules in a statisticallysignificant manner.

Edge color represents the expression data set from which the interaction was inferred: (purple) cell cycle,(gray) diauxic shift, (turquoise) environmental stress, (green) MAPK. Supplemental Figure S4 is a more fully annotated versionof this figure. This graph and others like it were drawn using Pajek. To obtain a global view of module coordination at this level of functional resolution, we applied SPIN to all pairs of MIPSfunctional modules, using each TFBS data set and each expression data set. Summarizes these data for each data set combination (complete results in Supplemental Figs. Results from the MSATFs reveal extensive coordination among modules: 34 TFs were found to mediate 316 pairwise interactions involving 55 functionalmodules. Many module pairs are coordinated by more than one TF and/or in more than one set of expression data.

Those coregulatedby at least one TF under at least one set of expression data are shown in (annotated version in Supplemental Fig. Results obtained using the Chip2 data set include 102 pairwise interactionsamong 36 categories, mediated by 25 TFs. A substantial portion (48%) of these interactions was also found using the MSA results(Supplemental Fig. Overall, these results comprise a rich data set with which intermodule coregulation can be studiedat different levels of detail, as we discuss below. At low resolution, the results afford a global view of module connectivity that reveals the extent to which control of cellularactivity is centralized, and the conditions under which each module is active. For both TFBS sets, the distribution of moduleconnectivity, defined as the number of other modules with which a given module is coregulated, is not uniform. Those related to storage and transmission of genetic information, such as the mRNA, Cell cycle, tRNA, Nucleotides, Differentiation, and DNA modules, are among the most highly connected (see for module abbreviations).

Although there is no evidence that a single module acts as a cellular “central processing unit,”these modules appear to have a particularly strong influence on the organization of cellular behavior. This is consistentwith data on physical interaction modules in the protein-protein interaction network (;;; ). The functional module connectivity distributions show general (but imperfect) correlation between module size and numberof partner modules (Pearson correlation coefficients = 0.87 Chip2 and 0.64 MSA). A general correlation is expected, sincecentral cellular processes may (1) involve many molecules, and (2) interface with numerous other processes.

Connectivity distributionsbroken down by expression data set (Supplemental Fig. S6A-C) reveal that fewer interactions were deemed significant usingthe environmental stress expression data.

The conditions in that data set elicit a broad transcriptional response known asthe environmental stress response (ESR) , but may affect few functional modules in a manner that is distinct from the ESR. From a TF-centric perspective, the global view reveals a nonuniform distribution of TF connectivity (the number of pairwiseinteractions that a given TF mediates) analogous to the distribution of module connectivity. This supports the notion that some TFs (such as mRRPE, PAC, and SFF) are relatively general and are therefore involvedin many processes, whereas others are more specific. The module pairs coregulated by each TF, and the conditions under which the coregulation was inferred, are largely consistentwith known TF activities. For example, MCB, whose target genes are active during the G 1 phase of the cell cycle, was found to coregulate many modules related to cell cycle progression and transcription, all ofwhich were identified using cell cycle expression data. Moreover, TFs that are known to act synergistically are often foundto mediate the same pairwise interaction.

For instance, many interactions mediated by mRRPE3 and PAC, which are thought tocooperatively regulate the expression of rRNA transcription and processing genes , involve one or more of the modules rRNA, tRNA, and Amino-acyl tRNA synthetases, and were inferred using diauxic shift expression data (Supplemental Fig. This may reflect the decrease in ribosomebiogenesis that accompanies the diauxic shift.Although the complete network is too large and highly connected to be informative about the details of transcriptional coordination,subnetworks of interest can be extracted and examined in increasing levels of detail. For instance, all modules that are coregulatedwith Cell cycle by MSA TFs are shown in Supplemental Figure S7; specific module pairs are discussed below.Intersection of MSA and Chip2 resultsTo identify a small subset of the most biologically significant results, we selected statistically significant pairwise interactionsthat were identified using both binding site data sets (regardless of the identity of the motifs mediating the interaction).The resulting filtered data set contains 29 pairwise interactions among 22 modules, mediated by 39 motifs (; Supplemental Fig.

Many of these interactions and the TFs that mediate them are biologically well-grounded, but severalless predictable relationships were also discovered. With respect to the former, there is a clique consisting of the Cell cycle, DNA, mRNA, and Differentiation modules.

Every interaction within the clique is mediated by MBP1 (Chip2) and MCB (MSA), both of which correspond to the TFMbfp. (MBP1 and SWI6 interact to form the Mbfp complex, which binds to the MCB DNA motif and regulates passage through STARTduring the cell cycle.)Other foreseeable relationships include the interactions of Assembly of protein complexes with Ionic homeostasis and Mitochondrial transport. The TFs HAP4 (Chip2) and HAP2/3/4 (MSA) (which represents a complex composed of HAP2, HAP3, and HAP4) mediate all of theseinteractions. Figure 2.( A) Functional module connectivity histogram. For each module, the number of partner modules with which it is coordinated byat least one TF in at least one expression data set is shown.

(Purple columns) MSA TFs, (green columns) Chip2 TFs, (pink line)module size. ( B) TF utilization histogram. The number of pairwise module interactions mediated by each TF is shown on the left-hand axis.

The right-hand axis shows the number of genome-wide promoter targets for each TF. (Purple column) MSA TFs, (pink column) Chip2 TFs,(pink line) promoter targets per TF. Analysis of module coregulation with respect to individual transcription factorsIn addition to the global study, we investigated the regulatory relationships between several pairs of functional modulesin detail. It is this level of analysis that yields the greatest number of practical biological insights: SPIN efficientlyreproduces results from numerous previous studies, and provides new information that allows us to synthesize known and novelresults into revised, more complete, biological hypotheses.

Here, we discuss the coordination of “Carbon compound and carbohydratemetabolism” ( Carbon) with “Lipid, fatty-acid, and isoprenoid metabolism” ( Lipids).The metabolic relationship between glucose and lipid metabolism—that each process generates intermediates of the other—iswell-established (; ). However, recently identified physical and signaling interactions between glycolysis and lipid metabolism imply that theseprocesses are further coordinated by more elaborate, multilevel regulatory mechanisms that are not fully understood. In orderto study this coordination at the level of transcription, our algorithm was applied to the functional module pairs Carbon and Lipids, and Glycolysis and Lipids.Using stringent and lenient filtering criteria (see Methods), we found that the activity of Carbon and Lipids is coordinated by a variety of TFs using a variety of expression data sets. It appears that different TFs coordinate differentsubsets of genes in each process in a condition-specific manner. Using MAPK expression data, for instance, SWI4 (Chip2) wasfound to coordinate genes in each process that are associated with cell wall biosynthesis.

Using cell cycle data, however,GCR1 (MSA) was found to coordinate the Carbon-Lipids interaction. We focus on this result because, compared to the other TFs that mediate this interaction, GCR1 is more specificto the glycolytic genes.GCR1 is the primary transcriptional activator of the glycolytic enzymes , but has not been previously implicated in the metabolism of lipids, fatty acids, or isoprenoids. Here, however, GCR1 wasassociated with the expression of six genes associated with Lipids. These genes are involved in biosynthesis of inositol and phosphoinositides, such as the signaling molecule PI(4,5)P 2 ( MSS4 and INM1), fatty acid biosynthesis ( ELO1), ergosterol biosynthesis ( FPS1), glycerol import ( ERG20), and inositol-dependent transcriptional regulation of other phospholipid biosynthetic genes ( UME6) (; ).These results draw together several recent studies, each of which reports on a different aspect of the relationship betweenglycolysis and lipid metabolism. Using a yeast proteome chip, some glycolytic enzymes and glucose transporters were observedto bind phosphoinositides , consistent with independent reports that most glycolytic enzymes are localized to the cell wall (in addition to the cytoplasm)(;;;;; ).

Biological Science 2nd Edition Freeman 2005 Gmc Yukon

Other work has shown that glucose stimulates cleavage and subsequent signal transduction by the phosphoinositide PI(4,5)P 2, as well as transcription of INM1 which encodes a PI(4,5)P 2 biosynthetic enzyme.Taken together, these data suggest that phosphoinositides may couple glycolysis with PI(4,5)P 2 synthesis and glucose-induced PI(4,5)P 2 signaling. This raises the possibility that phosphoinositides or phosphoinositide-mediated signal transduction regulate theactivity of glycolytic enzymes in a feedback loop. Our evidence for transcriptional coordination of glucose and lipid metabolismsuggests that joint transcription by GCR1 enables components of both processes to coexist. DiscussionRecent computational and experimental work has yielded increasing evidence that the cell is organized into functional modules—groupsof genes, proteins, and other molecules that serve a particular cellular function. Although each module acts in relative isolationfrom the rest of the cell, higher-level properties that emerge from the coordination of and interactions among functionalmodules may be essential to cellular survival. Future work in systems biology may necessitate a more complete knowledge ofthese interactions than we currently possess.

SPIN facilitates the identification of functional modules whose activity istranscriptionally coordinated, and yields qualitative information about the relationships between those modules. We analyzedseveral module interactions in detail, and discuss that between Carbon and Lipids. Published experimental results point to various interactions between these functional modules, but their relationship hasnot been studied systematically. Our results indicate the TFs likely responsible for the coordination, the environmental conditionsunder which the TFs effect the coordination, and the genes that act at the interface of the modules, all of which suggesta mechanism and/or rationale for the interaction. For example, the analysis of the interaction between carbon and lipid metabolismcorroborates and integrates previous evidence that glycolysis can occur at the cell wall, and that glycolysis may regulate(or be regulated by) phosphoinositide signaling.

Further experimental directions may be to determine whether phospholipidsactivate or inhibit glycolytic enzymes; if PI(4,5)P 2 signaling influences glycolysis; and whether glycolysis (or glucose alone) is necessary for the observed effect of glucoseon PI(4,5)P 2 signaling. Figure 4.Interaction between Lipid, fatty-acid, and isoprenoid metabolism and Carbon compound and carbohydrate metabolism.

The two modules (colored ovals) are coregulated by the MSA TF GCR1 (purple edge) using the cell cycle expression data set.GCR1 targets in each module are shown below the modules in purple or green type; those associated with glycolysis are organized according to the superimposed chart ofglycolysis. Gene names preceded by a blue diamond were shown to bind phosphoinositides using proteome chips in Zhu et al. In addition to this analysis, we performed a global analysis of module coordination in order to understand, at a particularfunctional resolution, how regulatory control is distributed across the cell.

SPIN was applied to all pairwise combinationsof 72 functional modules, and revealed extensive coordination among 55 of these modules, many of which are coordinated withmore than one partner module, by more than one TF, and/or in more than one experimental condition. The distribution of moduleconnectivity is not uniform. Although a single master regulator module was not identified, processes related to storage andtransmission of genetic information, such as mRNA, Cell cycle, tRNA, Nucleotides, Differentiation, and DNA are most highly connected.

(However, the observed distribution of connectivity may be different if different sets of expressiondata are used.) This suggests a control hierarchy in which these basic processes are central to the orchestration of cellularbehavior. In related work, however, it has been shown that many functional modules that are coexpressed in S. Cerevisiae are not coexpressed in other organisms. Extending the genome-wide analysis of module coordination to additional organisms might shed light on the evolution ofinteractions within complex cellular networks.

Methods OverviewSPIN was motivated by experimental evidence that functional modules can be coordinately regulated via a TF that induces thecoexpression of a subset of genes associated with each module. Given two gene sets (each corresponding to a functional module),one TFBS data set, and one microarray expression data set, the algorithm loops through each TF to identify genes in each modulethat contain a corresponding TFBS. These “target genes” are scored using expression data to assess the evidence that the TFinfluences their expression. Two modules are said to be coordinately regulated by a particular TF if its target genes in eachmodule (at least four) are coexpressed more coherently than, and in a pattern that is distinct from, non-target genes in thosemodules. The input data sets, scoring metrics, and methods of assessing statistical significance are discussed in detail below.Functional module definitionsEach functional module is defined as the set of genes assigned to a particular functional category in the MIPS (Munich InformationCenter for Protein Sequences) S. Cerevisiae database (; Supplemental Fig.

Using published information, MIPS assigns genes to functional categories in a hierarchical fashion,and each gene can be assigned to multiple functional categories. SPIN is designed to analyze pairs of functional categoriesthat are from the same level of the hierarchy. Although any level can be used, we present results obtained using categoriesin the second-highest level of the hierarchy that correspond to defined physiological processes (e.g., cell cycle, ribosomebiogenesis, the TCA cycle, and intracellular signal transduction). Category names are italicized, and lists abbreviations for some commonly used ones.Genome sequence dataUpstream region sequence data were downloaded from the AlignACE Web site (; ) and the Saccharomyces Genome Database (SGD). These data contain 5018 upstream sequences, representing 6186 non-mitochondrial genes.Transcription factor binding dataWe used two different transcription factor (TF) binding data sets, each being a matrix in which the value of element ( i, j) is 1 if the promoter of gene i contains a binding site for transcription factor j and 0 if not.

The “MSA” set (Multiple Sequence Alignment) (Supplemental Figs. S14, S15) contains data for 62 TF DNA-bindingmotifs, represented as position-specific weight matrices (PSWMs), that were generated using the literature and the multiplesequence alignment program AlignACE as described in Hughes et al. , Pilpel et al. , and Roth et al.

The location of each motif in each promoter was determined using the pattern-matching program ScanACE , which identifies and scores close matches to each consensus motif. A motif was considered present in a promoter if itsmatch in the promoter scored equal to or better than one standard deviation below the mean score of the sequences constitutingthe PSWM (results obtained using a different score threshold are similar data not shown).

For comparison, TF-binding datagenerated by an independent method were also used; the “Chip2” data set (Supplemental Figs. S16, S17) contains location datafor 113 TFs, whose binding sites in 6270 promoters were determined using combined chromatin immunoprecipitation microarrayhybridization (ChIP-chip) technology. Every pair of modules was analyzed twice—once with each TFBS data set. Figure 5.Coherence of target gene expression profiles. ( A) Distinctness of target gene expression profiles.

The cube represents a three-dimensional expression space. Each dot representsa gene from functional module 1 (blue) or functional module 2 (red), and the location of the gene on an axis represents theexpression level of the gene at the time point or condition represented by the axis. The distance that separates two genesis inversely proportional to the “coherence” of their expression profiles. Target genes are shown as regulated by the TF (arrow).This distribution of genes represents a hypothetical scenario in which the intermodule coherence between target genes is higherthan that between non-target genes.

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( B) Notation as in A. This distribution of genes represents a hypothetical scenario in which target genes and non-target genes have very differentexpression profiles, even though the intermodule coherence between target genes is similar to that of non-target genes. ( C) Triplet scoring notation. M 1 = genes in module 1; T = genes in genome containing binding site for TF T; m 1 = genes in M containing binding site for T (“target genes”); M 1′ = genes in M 1 that do not contain a binding site for T (“non-target genes”); r x ( M 1′) = x randomly selected genes from M 1′; M 2, m 2, M 2′ r x ( M 2′) defined analogously. Aggregate target genes = ( m 1 ∪ m 2) or ( m 1∪ m 2∪ I T); aggregate non-target genes =( M 1′ ∪ M 2′) or ( M 1′ ∪ I ∪ M 2′); I and I T represent non-target and target genes, respectively, that are present in both modules. ( D) Quartet scoring notation.

Variables are analogous to those in C, but here there are two TFs, X and Y. T x = genes in genome containing binding site for X; T y = genes in genome containing binding site for Y. B, X, Y, and N represent module components that are targets of both X and Y, X only, Y only, or neither TF, respectively:. The Expression Profile Convergence score, C, determines whether the coherence between the disjoint target genes (gene sets m 1 and m 2) is greater than that between the disjoint non-target genes (gene sets M 1 and M 2) (; Supplemental Fig.

S18):The significance of the C score is ensured by controlling the False Discovery Rate (FDR); q-values were computed from p-values using the Q-value software.