On cycles in the transcription network of Saccharomyces cerevisiae
 Jieun Jeong^{1}Email author and
 Piotr Berman^{1}
DOI: 10.1186/17520509212
© Jeong and Berman; licensee BioMed Central Ltd. 2008
Received: 10 April 2007
Accepted: 31 January 2008
Published: 31 January 2008
Abstract
Background
We investigate the cycles in the transcription network of Saccharomyces cerevisiae. Unlike a similar network of Escherichia coli, it contains many cycles. We characterize properties of these cycles and their place in the regulatory mechanism of the cell.
Results
Almost all cycles in the transcription network of Saccharomyces cerevisiae are contained in a single strongly connected component, which we call LSCC (L for "largest"), except for a single cycle of two transcription factors. The fact that LSCC includes almost all cycles is well explained by the properties of a random graph with the same in and outdegrees of the nodes.
Among different physiological conditions, cell cycle has the most significant relationship with LSCC, as the set of 64 transcription interactions that are active in all phases of the cell cycle has overlap of 27 with the interactions of LSCC (of which there are 49).
Conversely, if we remove the interactions that are active in all phases of the cell cycle (25% of interactions to transcription factors), the LSCC would have only three nodes and 5 edges, many fewer than expected. This subgraph of the transcription network consists mostly of interactions that are active only in the stress response subnetwork.
We also characterize the role of LSCC in the topology of the network. We show that LSCC can be used to define a natural hierarchy in the network and that in every physiological subnetwork LSCC plays a pivotal role.
Conclusion
Apart from those welldefined conditions, the transcription network of Saccharomyces cerevisiae is devoid of cycles. It was observed that two conditions that were studied and that have no cycles of their own are exogenous: diauxic shift and DNA repair, while cell cycle and sporulation are endogenous. We claim that in a certain sense (slow recovery) stress response is endogenous as well.
Background
Cycles have a central role in control of continuing processes (for an example, see Hartwell [1]). Therefore we expect the regulatory mechanism of a cell to have many cycles of interactions. Only some of these interactions have the form of a transcription factor (TF for short) regulating expression of a target gene. Our question is: given that there are cycles of transcription interactions, are they important in the regulation of life processes?
Graph properties of the regulatory networks have been reported in a number of papers. ShenOrr et al. [2] analyzed the regulatory networks statistically and observed certain characteristic motifs that are more frequent than in the random model and which have functional significance (while other small subgraphs are significantly less frequent). Cycles, or feedback loops also may have some typical regulatory role, e.g. they may be related to multiple steady states [3–5].
Luscombe et al. [6] studied the dynamics of the regulatory network of Saccharomyces cerevisiae as it changes for multiple conditions and proposed a method for the statistical analysis of network dynamics. They have found large changes in the topology of the network and compared it with random graphs. We have found that the transcription network of Saccharomyces cerevisiae contains a single large strongly connected component (a union of overlapping cycles), which we call LSCC, and that the topology changes discussed by Luscombe et al. [6] are well reflected within LSCC, in spite of its small size.
Comparisons of biological networks with random graphs were subject of methodological investigations of Barabasi and Albert [8] who proposed a scalefree model. This model is difficult to apply here. While the networks we investigated have the key property of scalefree networks, i.e. they have many nodes with degree much higher than the average, the distribution of the degrees is too irregular to match with a particular power law. In a scale free network the ratio #{nodes with degrees k to 2k  1} to #{nodes with degrees 2k to 4k  1} is convergent, but in our networks it varies widely for different k's (for recent study of scalefree nature on biological networks, see also [9, 10]). Therefore Milo et al. [11] (see also Newman et al. [12]) proposed several methods of generating graphs that have the same in and outdegrees as the reference network. We used their "matching algorithm" whenever possible, as well as faster and somewhat biased variants.
Results and Discussion
In the data set of Luscombe et al. [6] we can see the LSCC with 25 TFs and one small strongly connected component with two TFs.
To see if the cycles of the LSCC are significant, we checked how the topological changes of the transcription network during various physiological conditions are reflected inside the LSCC, we checked several graph characteristics of the TFs in the LSCC, and we compared the characteristics of the LSCC to the cycles in random networks.
General characterization of the cycles
Size of LSCC is relatively small
The cycles form two connected components, one "degenerate", consisting of 2 TFs, and one "large", consisting of 25 TFs.
The degenerate component consists of two TFs with indistinguishable interactions that have selfloops, thus they are TFs of themselves, and of each other. This may be a result of a relatively recent gene duplication. Thus we will ignore this cycle in our discussions.
The size of the largest cyclic component, 25, is rather small compared with random models (averages 42–43), with pvalue ca. 0.025. The number of nodes in the remaining cycles, 2, is not very different from the average (0.8 to 1.3).
By the way of contrast, the transcription network of Escherichia coli is either devoid of cycles or it contains very few of them (depending on the data set, see Cosentino Lagomarsino et al. [13]).
LSCC connected very strongly to the cell cycle
The transcription network reported by Luscombe et al. [6] has 142 TFs and 7074 interactions, of which we disregard 21 "selfloop" interactions of the remainder 254 are TF to TF; we use ITF to denote the latter set (interactions to transcription factors). 25 TFs and 49 interactions form the LSCC. The subnetworks associated with the 5 stages of the cell cycle have 64 interactions in common (we name this set CCC, "common to cell cycle"), all of them directed to TFs (hence in ITF) and 27 of them are present in the LSCC. If even one of these two sets, LSCC or CCC, is random, the expected number of common elements would be smaller than 13 (49 × 64/254) and the probability of LSCC ∩ CCC ≥ 27 would be below 10^{6} (estimated by binomial formula). This shows that LSCC is very strongly related to the cell cycle.
Cycles of subnetworks other than cell cycle
Stress response is special in the sense that it has cycles of its own, all of which involve YAP6 that is not active in any other subnetwork. It seems that the cyclic interaction of this TF with two other TFs is a differentiating part of stress response condition from other exogenous conditions, diauxic shift and DNA damage. The latter have similar sets of active interactions in LSCC, but they lack 5 interactions involving YAP6.
One cycle consists of 3 interactions that are common to all conditions, REB1 → SIN3 → HSF1 → REB1. Note that HSF1 is a Heat Stress Factor, very important in the stress response, but also in "basal level sustained transcription" (see Mager and Ferreira [14]). One possible role of cycles in stress response is slowing down the recovery transition from the stress condition, so it can last several hours [14]. During the recovery, sporulation and cell cycle activities are suppressed. In this sense, stress response is partially endogenous to use the classification of Luscombe et al. [6] (they group Cell Cycle and Sporulation as endogenous and the other conditions as exogenous).
LSCC has an orderly layout
In the diagram, al (apricot color) marks the nodes present in the cycles of all subnetworks. The cycles in the diauxic shift and DNA damage subnetworks contain only these nodes. (Note that an interaction of LSCC can be active in a subnetwork without belonging to a cycle in that subnetwork.)
The cycles in the sporulation subnetwork sp contain apricot and strawberry nodes.
The cycles in the cell cycle subnetwork cc contain apricot, strawberry and cerulean nodes.
The cycles in the stress response subnetwork sr contain apricot and sienna nodes.
Nodes that are not included in the cycles of any subnetwork are black.
We managed to find an orderly layout for LSCC, in which few edges are long while nodes with the same color are grouped together.
LSCC has small feedback vertex set
Another property of LSCC is that it has a small and unique minimum feedback vertex set, a set of nodes whose removal destroys all cycles.
We can use F to distinguish three natural cyclic units within LSCC, S_{ b }for each b ∈ F. We can think that b is the "boss" of S_{ b }. We define S_{ b }as the union of all simple cycles that go through b but not through F  {b}. Only one node can have two bosses: {4} = S_{1} ∩ S_{25}. Because there is only one path from 1 to 4 and three disjoint paths from 25 to 4, we remove 4 from S_{1} to make our units disjoint. The three sets coincide well with functional categories: S_{3} = {3, 21, 24} are the nodes on cycles of LSCC_{ sr }, S_{1} are the nodes on cycles of LSCC_{ sp }, and S_{25} are the nodes on cycles of LSCC_{ cc }minus S_{1} (observe that S 1 is contained in LSCC_{ cc }). (Actually, S_{25} has 11 nodes and it has one node that is not in LSCC_{ cc }, 18, and one node of the cell cycle network is missed, 8.)
Differences and similarities of subnetworks are reflected in LSCC
For subnetwork A we define LSCC_{ A }as the set of interactions of A that are also in LSCC; to measure the difference between two sets we use A ⊕ B, the number of elements that are in one of the sets A and B but not in both.
Statistic profile of the TFs from the LSCC for three different original networks
We tested properties of LSCC in randomly generated networks. We also tabulated results of random tests based on two larger data sets. In our tables, we refer to the networks using names of the first authors of the paper in which they were published [6, 7, 15], hence we call them Luscombe, Yu and Balaji.
In our random networks we kept all original connections from TFs to Terminal Targets (i.e. regulated genes which are not TFs themselves. Later we refer to them with abbreviation TTs). The remaining connections were "rewired" at random, using three criteria, R, F and B. Criterion R was a uniformly random permutation of the edge ends, conditional on obtaining a "correct network" – no selfloops or duplicated edges. Criterion F was creating a bias in the selection of the permutation so the resulting number of feedforward loops was close to the actual value in the original network. Criterion B was similar, but with bifans rather than feedforward loops.
When we refer to our computed average value we used form x (y, z) to denote "average obtained using criterion R (F, B)".
Average size and outdegree
The size of LSCC is quite a bit smaller than the average, 25 versus 42 (41, 43), with pvalue of 0.025 (0.04, 0.02), and the situation is similar for Yu and Balaji. (The sizes of LSCC, as well as the classes defined in the next section in terms of LSCC, are in Tables 1, 2, 3.)
Average sizes of classes compared with random model R
INLSCC  LSCC  OUTLSCC  SIMPLE  SSCC  INT  EXCP  

Luscombe  
actual  9  25  68  38  2  0  2 
average  17.1  42.3  43.2  33.8  1.00  2.8  2.8 
pvalue  0.02  0.025  0.001  0.062  0.6  0.097  0.58 
Yu  
actual  20  63  114  77  5  6  5 
average  32.5  69.5  102.8  69.6  0.44  6.3  4.2 
pvalue  0.001  0.002  0.020  0.22  0.01  0.32  0.34 
Balaji  
actual  21  60  58  14  0  3  1 
average  20.9  74.4  45.6  14.3  0.2  1.2  0.5 
pvalue  0.53  0.002  0.002  0.57  0.92  0.14  0.35 
Average sizes of classes compared with random model F
INLSCC  LSCC  OUTLSCC  SIMPLE  SSCC  INT  EXCP  

Luscombe  
actual  9  25  68  38  2  0  2 
average  16.2  40.7  45.0  34.1  1.25  2.9  3.15 
pvalue  0.043  0.041  0.004  0.081  0.30  0.086  0.555 
Yu  
actual  20  63  114  77  5  6  5 
average  29.0  66.0  107.0  71.0  0.7  6.9  4.9 
pvalue  0.032  0.35  0.19  0.081  0.022  0.48  0.48 
Balaji  
actual  21  60  58  14  0  3  1 
average  19.8  72.1  47.7  14.8  0.2  1.6  0.8 
pvalue  0.39  0.006  0.011  0.45  0.99  0.23  0.46 
Average sizes of classes compared with random model B
INLSCC  LSCC  OUTLSCC  SIMPLE  SSCC  INT  EXCP  

Luscombe  
actual  9  25  68  38  2  0  2 
average  17.5  43.0  42.5  33.4  0.85  2.7  2.7 
pvalue  0.018  0.017  0.001  0.05  0.26  0.105  0.57 
Yu  
actual  20  63  114  77  5  6  5 
average  33.7  67.5  99.7  71.8  0.43  7.4  4.9 
pvalue  0.002  0.26  0.025  0.12  0.013  0.4  0.47 
Balaji  
actual  21  60  58  14  0  3  1 
average  22.6  74.2  43.8  14.4  0.2  1.4  0.7 
pvalue  0.39  0.003  0.000  0.56  0.91  0.18  0.46 
Average outdegrees compared with three random models
in LSCC  among all TFs  

OUT  OUTF  OUT  OUTF  
model  R  F  B  R  F  B  
Luscombe  
actual  128.08  4.92  49.67  1.79  
average  97.68  100.67  96.55  3.88  3.97  3.85     
pvalue  0.003  0.013  0.001  0.003  0.009  0.001     
Yu  
actual  85.54  6.35  29.27  2.01  
average  75.08  79.26  75.61  5.68  5.96  5.71     
pvalue  0.030  0.148  0.032  0.037  0.169  0.038     
Balaji  
actual  146.78  4.87  81.99  3.12  
average  131.61  134.80  130.69  5.31  5.43  5.26     
pvalue  0.002  0.015  0.002  0.005  0.031  0.009     
Position of LSCC in the hierarchy
Only 9 TFs belong to the incomponent of the LSCC (denoted InLSCC) in the sense that there are paths from these TFs to the LSCC; of these 9 paths 8 are single edges and one consists of two edges. If we consider that path to be exception, collectively the LSCC has unambiguous hierarchical position 2nd from the top. In a random network, on the average we have 17 (16, 17.5) TFs in InLSCC. In this sense, the LSCC is higher in the hierarchy than the average in the random models.
Almost all paths with more than 2 edges are related to the LSCC in the following sense: either they include a TF from the LSCC, or form the final part of a path that starts in the LSCC. Two TFs form an exception to that rule, namely they can start a path with more than 2 edges that is not such a final part.
After collapsing scc's to single nodes we measured for each TF the maximum path length (for paths to which it belongs), and we call it MPL. For 38 TFs the value of MPL is at most 2, and they form a rather separate part of the transcription network which we call SIMPLE. 104 TFs have MPL of at least 3. Maximum of MPL is 13, more than the average in random networks that is 8.3 (8.4, 8.5). (The maximum length of a simple path is perhaps a better measure, but it requires a much more complex program to compute it. It is closely related to the feedback vertex set problem.)
Yu and Gerstein [7] propose a partition of networks according to the length of shortest paths to those TFs that have only TTs as their targets. This definition would not work with the length of the shortest paths to TTs: this length is 1 for all TFs but ten, and for that ten, it is 2, so the hierarchy would be trivial. Because LSCC has such a special and statistically significant position in the network, we propose to partition TFs by their relation to LSCC, as it is indicated in Fig. 1. In particular, TFs with a path to or from LSCC are partitioned into hierarchy INLSCC, LSCC and the outcomponent of LSCC (denoted OUTLSCC), while the remaining TFs are classified according to MPL; if MPL is at most 2, they are in SIMPLE, if it is more than 3, they are in EXCP, and if it is equal 3, we place them in the intermediate class INT (which is empty in Luscombe data set).
We performed our study using the data of Luscombe et al. [6] because we wanted to compare the cycles with physiological subnetworks described in their paper. Nevertheless, we compared our definition of a hierarchy with that of Yu and Gerstein [7], who performed their investigation in a larger transcription network.
When we apply our program to the latter network, the proportions between the class sizes remain similar (here we included INT in SIMPLE): INLSCC (20), LSCC (63), OUTLSCC (114), SIMPLE (83) and EXCP (5). Tables 1, 2, 3 show detailed comparison of class sizes.
We performed two tests applied by Yu and Gerstein to their classes (see Fig. 2 for the partition of Yu network into classes).
When we checked the percentage of essential genes in our classes, we got 15% in INLSCC and LSCC, 13% in OUTLSCC and 12% in SIMPLE, a more uniform distribution than among classes of Yu and Gerstein. A more striking difference exists when we check the percentage of cancer related genes: 10% in INLSCC, 9.5% in LSCC, 3.5% in SIMPLE and 2.6% in OUTLSCC.
The division we propose is closely related to the notion proposed by Yu and Gerstein: a division of transcription control mechanisms into reflex processes and cogitation processes. SIMPLE clearly corresponds to reflex processes. In a cogitation process, one that involves a long path of interactions, we can partition the process into beginning, middle and the ending part. As the various paths have very different lengths, identifying LSCC as the middle is both "objective" and independent from the path length, and in the same time quite arbitrary. However, we show in the next subsection that LSCC has a "switchboard" property even in the physiological conditions in which paths do not form cycles, and we just have seen that the percentage of cancer related genes sharply drops as we move from the middle to the final part of the long paths.
Topological changes inside LSCC
Luscombe et al. [6] measured the following topological characteristic in the subnetworks: the average length of shortest paths from TFs to TTs. By its very nature, LSCC is disproportionally involved in that characteristic. In the full network there are 113,000 such paths, and the average length is 4.81; among those paths, 100,910 go through LSCC, and their average length is 5.11, while only 12,090 paths does not go through LSCC and their average length is 2.63.
This domination of LSCC directly follows from the fact that every TF in LSCC and InLSCC has a path to every TT that can be reached from LSCC, as a result, on the average one can reach 2968 TTs from these TFs through LSCC (in LSCC this number is contant, but in InLSCC it can be smaller because some TTs reachable through LSCC may have shorter paths directly from InLSCC). The average number of TTs reachable not through LSCC is 103 (for 117 TFs outside LSCC).
In other words, only 12% of shortest connections between TFs and TTs does not go through LSCC, and these paths contribute only 5.8% to the sum of lengths.
Importance of LSCC in the paths of different subnetworks
subnetwork  cc  sp  sr  ds  dd 

average path length  4.64  3.55  2.31  2.10  1.94 
PERCENTPATH  87.1  69.4  72.1  57.8  54.6 
PERCENTLENGTH  94.2  78.0  81.6  64.4  59.0 
Proteins that form nodes in Fig. 3
node  code  TF  node  code  TF 

1  YBR049C  REB1  14  YLR183C  TOS4 
2  YDR207C  UME6  15  YLR256W  HAP1 
3  YDR259C  YAP6  16  YML007W  YAP1 
4  YDR501W  PLM2  17  YML027W  YOX1 
5  YER111C  SWI4  18  YNL068C  FKH2 
6  YGL073W  HSF1  19  YNL216W  RAP1 
7  YIL122W  POG1  20  YOL004W  SIN3 
8  YJR060W  CBF1  21  YOR028C  CIN5 
9  YKL043W  PHD1  22  YOR372C  NDD1 
10  YKL062W  MSN4  23  YPL177C  CUP9 
11  YKL112W  ABF1  24  YPR065W  ROX1 
12  YLR131C  ACE2  25  YPR104C  FHL1 
13  YLR182W  SWI6 
Conclusion
We inspected graphtheoretic properties of the cycles in the transcription network of Saccharomyces cerevisiae. While in general cycles are "avoided" by the network, interactions common to all phases of the cell cycle form a big exception, and interactions specific to the stress response form a smaller exception. In spite of their modest number (they involve 25 of 142 transcription factors that were included in the data set), the transcription factors that are included in cycles have a large topological impact: most of the shortest paths between transcription factors and terminal targets go through them.
One should compile many kinds of data to establish the exact role of the cycles of transcription interactions in controlling life processes. In particular, cell cycle, which is closely related to cancer, possesses a long cycle that can be easily interrupted at many different points, and the process itself can be interrupted by a number of different conditions (like DNA damage).
We have shown that LSCC is a key part of the regulatory network and that it can be divided into functional subunits. Further work will yield fuller and clearer picture of these subunits and their interactions under various conditions.
Methods
Data
We used supplementary materials for [6] ; we also used supplementary materials of [7, 15] and the list of yeast homologs of human cancer genes personally communicated by Haiyuan Yu.
Graphtheoretic definitions
A graph of a network consists of nodes (which correspond to TFs, transcription factors and TTs, terminal targets) and directed edges/interactions.
A path in a graph is a sequence of nodes (u_{0}, ..., u_{k1}) such that each consecutive pair (u_{i1}, u_{ i }) is an edge. If additionally there exists an edge (u_{k1}, u_{0}) we say that this is a cycle.
A single node (u) forms a degenerate cycle.
Nodes in a graph are partitioned into strongly connected components, or SCC's. A node u is contained in SCC(u) which is the union of the node sets of all cycles that contain u.
SCC's with one node are called trivial.
For graph G we define strong component graph G_{SCC}, the graph of SCC's of G. Nodes of G_{SCC} are scc's of G, and edges are pairs of the form (SCC(u), SCC(v)) such that (u, v) is an edge of G.
G_{SCC} cannot have cycles of its own, and therefore it is easy to compute longest paths in that graphs (the algorithm is considered folklore). The paths lengths in that graph are used in Fig. 1.
We use LSCC to denote the largest strongly connected component in a graph. We apply this definition when the majority of elements of nontrivial scc's belongs to one of them, so there is no ambiguity as to which one is "the largest".
Algorithms
To compute nontrivial scc's we first obtained a "dictionary" protein code ↔ number followed by pairs of numbers representing the edges. We computed scc's and the graph of scc's using the method described in section 22.5 of Cormen et al. [16].
Shortest paths used in subsection on Position of LSCC in the hierarchy were computed using breadth first search.
Defining motifs, generating random graphs
We define a feedforward loop (3 for short) as a triple of nodes {u_{0}, u_{1}, u_{2}} such that there exists three edges: two form a path (u_{0}, u_{1}, u_{2}) while the third forms a shortcut, (u_{0}, u_{2}). A bifan is a quadruple of nodes (u_{0}, u_{1}, v_{0}, v_{1}) such that all of the 4 possible edges of the form (u_{ i }, v_{ j }) exist.
When we count ffl's and bifans we remove the selfloops (edges of the form (u, u)) from the graph.
Moreover, every triple/quadruple is counted separately, even when they share nodes.
To count ffl's and bifans we made a table Overlap that for a pair of TFs stored the number of common targets. For every positive entry k = Overlap(a, b) we add k(k  1)/ 2 to the count of bifans, and if there is an edge from a to b, we add Overlap(a, b) to the count of ffl's.
We generated networks to make statistic comparisons. First, we generated random networks, or R. For Luscombe network, we permutated TF entries of adjacency lists at random. After permutation, lists could contain errors; a TF that "owns" the respective list, or a TF that has another copy earlier on the list. We repeated random permutations until errorfree list were obtained, a process that took 1–2 seconds.
For Yu and Balaji, this provably unbiased approach [11] had no results within 30 minutes, so we used a variation of metropolis random walk. Starting from the original network, we repeatedly selected pairs of edges at random to swap their endpoints; a swap introducing new errors was performed with probability β and rejected otherwise. We set β so the process would result in an errorfree network in a reasonable time (several seconds or several millions attempts on the average)
Random networks were modified to boost the number of motifs, either feedforward loops (version F) or bifans (version B). Boosting was performed via a metropolis process in which a randomly selected swap was rejected if it decreased the number of desired motifs by k (more precisely, such a swap was rejected with probability 1  α^{ k }for some α), or if it increased the number of errors by l (a swap was rejected with probability 1  β^{ l }). Parameter α was adapted by the algorithm; decreased if the number of motifs was too small and not growing, and increased when it was too large.
Abbreviations
 TF:

transcription factor. TT, terminal target. LSCC, large(st) strongly connected component. SCC, strongly connected component. SSCC, small cyclic SCC's. Various networks in Luscombe et al. [6] data al, all interactions, cc, interactions of the cell cycle, dd, interactions of the DNA damage, ds, interactions of the diauxic shift, sp, interactions of sporulation, sr, interactions of the stress response. Ccc, interactions in common in 5 stages of the cell cycle. ITF, interactions from TF to TF. For a class of interactions X :(like ITF and LSCC) and a subnetwork yy (like cc and dd), X_{ yy }denotes the intersection. F, feedback node set. MPL, the maximal path length for a path that includes a given TF. INLSCC, the incomponent of LSCC. OUTLSCC, the outcomponent of LSCC. SIMPLE, TFs whose longest paths to which they belong is at most 1 or 2. INT, TFs whose longest paths to which they belong is at most 3. EXCP, TFs that are not in any of INLSCC, LSCC, OUTLSCC, or SIMPLE. ffl, feedforward loop. In tables, we used Luscombe, Yu and Balaji to refer to networks from the data sets published in [6, 7, 15] respectively, and we used R, F and B to refer to random models generated with simple metropolis method (R), a variation of that method that increased the number of ffls (F) to the actually observed value, and a similar variation for the bifan motifs (B). The terms PERCENTPATH and PERCENTLENGTH are explained in detail in the caption of Table 5.
Declarations
Acknowledgements
JJ is grateful to Arthur Lesk for posing the problem and reviewing the manuscript. The authors thank Arthur Lesk and L. Aravind for providing inspiring discussions and many helpful suggestions. Haiyuan Yu supplied one of the data sets.
Authors’ Affiliations
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