Responses to reviewer 1 comments
-- 1.1 – The descriptive nature of our results -Reviewer
Comment
… the main question is whether the presented results are
sufficiently profound and at the same time well-corroborated
to be "natureworthy". In my admittedly subjective view, the
study does not meet this standard. The findings I cite above
are quite interesting, but I feel that they add up to a
rather descriptive analysis, which the authors augment with
informed speculation about the biological significance of
their findings.
… I could thus envision a one-page communication that
focuses on one of the findings listed above, such as
combinatorial regulation, and presents an in depth analysis
of its extent, an analysis that is based on the authors'
approach.
Author
Response
Out interpretation of the referee’s comments is that: (i) he finds our results
interesting, (ii) some of the findings may be worth publishing in Nature, (iii) but
there are doubts about the “Nature-worthiness” of the paper as it provides a
descriptive analysis.
We would like to address this point (iii) by demonstrating that:
1. Nature and Science have recently published many papers providing
descriptive analyses of molecular biological networks.
2. studying the dynamics of networks is an issue of general interest.
1. Recent studies in Nature and Science that have analyses biological
networks
Many prominent journals, including Nature and Science, have recently published
many papers describing the architectural features of molecular biological
networks. Most studies have focused on the protein-protein, transcriptional
regulatory or metabolic networks, and have made biological inferences from their
observations. Below we list a selection of these papers, including some important
reviews:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Agrawal, H. (2002). "Extreme self-organization in networks constructed from gene expression data." Phys
Rev Lett 89(26): 268702.
Alon, U. (2003). "Biological networks: the tinkerer as an engineer." Science 301(5641): 1866-7.
Bader, G. D. and C. W. Hogue (2002). "Analyzing yeast protein-protein interaction data obtained from
different sources." Nat Biotechnol 20(10): 991-7.
Barabasi, A. L. and Z. N. Oltvai (2004). "Network biology: understanding the cell's functional
organization." Nat Rev Genet 5(2): 101-13.
Bar-Joseph, Z., G. K. Gerber, et al. (2003). "Computational discovery of gene modules and regulatory
networks." Nat Biotechnol 21(11): 1337-42.
Barolo, S. and J. W. Posakony (2002). "Three habits of highly effective signaling pathways: principles of
transcriptional control by developmental cell signaling." Genes Dev 16(10): 1167-81.
Bray, D. (2003). "Molecular networks: the top-down view." Science 301(5641): 1864-5.
de Menezes, M. A. and A. L. Barabasi (2004). "Fluctuations in network dynamics." Phys Rev Lett 92(2):
028701.
Guelzim, N., S. Bottani, et al. (2002). "Topological and causal structure of the yeast transcriptional
regulatory network." Nat Genet 31(1): 60-3.
Holtzendorff, J., D. Hung, et al. (2004). "Oscillating Global Regulators Control the Genetic Circuit Driving
a Bacterial Cell Cycle." Science.
Ihmels, J., G. Friedlander, et al. (2002). "Revealing modular organization in the yeast transcriptional
network." Nat Genet 31(4): 370-7.
Jansen, R., H. Yu, et al. (2003). "A Bayesian networks approach for predicting protein-protein interactions
from genomic data." Science 302(5644): 449-53.
Jenssen, T. K., A. Laegreid, et al. (2001). "A literature network of human genes for high-throughput
analysis of gene expression." Nat Genet 28(1): 21-8.
Jeong, H., S. P. Mason, et al. (2001). "Lethality and centrality in protein networks." Nature 411(6833): 412.
Jeong, H., B. Tombor, et al. (2000). "The large-scale organization of metabolic networks." Nature
407(6804): 651-4.
16. Laub, M. T., H. H. McAdams, et al. (2000). "Global analysis of the genetic network controlling a bacterial
cell cycle." Science 290(5499): 2144-8.
17. Lee, T. I., N. J. Rinaldi, et al. (2002). "Transcriptional regulatory networks in Saccharomyces cerevisiae."
Science 298(5594): 799-804.
18. Mangan, S. and U. Alon (2003). "Structure and function of the feed-forward loop network motif." Proc
Natl Acad Sci U S A 100(21): 11980-5.
19. Maslov, S. and K. Sneppen (2002). "Specificity and stability in topology of protein networks." Science
296(5569): 910-3.
20. McAdams, H. H. and L. Shapiro (2003). "A bacterial cell-cycle regulatory network operating in time and
space." Science 301(5641): 1874-7.
21. Milo, R., S. Itzkovitz, et al. (2004). "Superfamilies of evolved and designed networks." Science 303(5663):
1538-42.
22. Milo, R., S. Shen-Orr, et al. (2002). "Network motifs: simple building blocks of complex networks."
Science 298(5594): 824-7.
23. Odom, D. T., N. Zizlsperger, et al. (2004). "Control of pancreas and liver gene expression by HNF
transcription factors." Science 303(5662): 1378-81.
24. Oltvai, Z. N. and A. L. Barabasi (2002). "Systems biology. Life's complexity pyramid." Science 298(5594):
763-4.
25. Podani, J., Z. N. Oltvai, et al. (2001). "Comparable system-level organization of Archaea and Eukaryotes."
Nat Genet 29(1): 54-6.
26. Ravasz, E., A. L. Somera, et al. (2002). "Hierarchical organization of modularity in metabolic networks."
Science 297(5586): 1551-5.
27. Segal, E., M. Shapira, et al. (2003). "Module networks: identifying regulatory modules and their conditionspecific regulators from gene expression data." Nat Genet 34(2): 166-76.
28. Shen-Orr, S. S., R. Milo, et al. (2002). "Network motifs in the transcriptional regulation network of
Escherichia coli." Nat Genet 31(1): 64-8.
29. Strogatz, S. H. (2001). "Exploring complex networks." Nature 410(6825): 268-76.
30. Tavazoie, S., J. D. Hughes, et al. (1999). "Systematic determination of genetic network architecture." Nat
Genet 22(3): 281-5.
31. von Mering, C., R. Krause, et al. (2002). "Comparative assessment of large-scale data sets of proteinprotein interactions." Nature 417(6887): 399-403.
32. Watts, D. J. and S. H. Strogatz (1998). "Collective dynamics of 'small-world' networks." Nature 393(6684):
440-2.
33. Wuchty, S., Z. N. Oltvai, et al. (2003). "Evolutionary conservation of motif constituents in the yeast protein
interaction network." Nat Genet 35(2): 176-9.
34. Zeitlinger, J., I. Simon, et al. (2003). "Program-specific distribution of a transcription factor dependent on
partner transcription factor and MAPK signaling." Cell 113(3): 395-404.
Our paper does indeed provide a description of the changes experienced by a
transcriptional regulatory network under different cellular conditions. As shown
above, Nature has recently published a large number of papers providing
descriptive analyses of the structure and function of molecular biological
networks. Therefore, our paper is clearly within the scope of what the journal
considers to be worth publishing.
2. Importance of studying network dynamics
Assessing the dynamic nature of the transcriptional regulatory network is an
important problem of wide interest. In a review in Nature, Strogatz (Strogatz 2001)
explicitly states that studying network dynamics is a topic of great interest that is
not restricted to biological systems. Within the field of transcription regulation
Barolo and Posakony (Barolo and Posakony 2002) have written that
understanding how the regulatory system determines gene expression remains a
challenge. Furthermore in their prominent paper in Science (Lee et al. 2002),
Young and co-workers clearly cite that examining the dynamics of the
transcriptional regulatory network as an important future goal. Over the past 10
months, Young and co-workers have made moves to further our understanding in
this area by publishing papers in Cell (Zeitlinger et al. 2003) and Science (Odom
et al. 2004). However, studies of regulatory network dynamics have so far been
limited to a handful of transcription factors.
Our paper is the first that considers this issue on a genomic scale; we use a
dataset comprising a substantial proportion of the regulatory system in yeast. As
described in the paper, and in section 3.1 of the response to referees, many of
our findings are completely unexpected given current perception of biological
networks. Clearly, given the interest in network analysis, and in particular
regulatory network dynamics, the publication of our paper is timely and will be of
great interest to readers.
-- 1.2 – Length of the paper -Reviewer
Comment
I also note that the paper is rather long but that some of
the key findings could be briefly summarized in one
sentence.
Author
Response
We agree with the referee that the current version of the paper is longer than
anticipated for a publication in Nature. The reason for this is that we have
addressed all of the scientific issues raised by the referees in the current revision.
In order to emphasize that we have dealt with all of these issues, we have
incorporated most of the changes into the main manuscript.
We stress that the paper can be readily shortened without compromising its
scientific quality, and we plan to make those changes in a later version. Many
sections of the paper can be moved to the supplementary website; for example
the Methods section (section 7), many of the detailed observations on network
topology and motif usage (section 4), and detailed descriptions of individual
genes (sections 3 and 5).
Responses to reviewer 2 comments
-- 2.1 – Abstract -Reviewer
Comment
1. The abstract is well written, but I am wondering whether
it could be made a little more informative. I believe that
the
most
exciting,
really
biologically
meaningful
observation in the paper is the difference of network
topologies between endogenous and exogenous conditions. The
authors explain it well on p. 6. I was thinking that some of
it perhaps could be adapted for the abstract, the current
version reading somewhat overly general.
Author
Response
We thank the referee for his positive comments and for the many helpful
recommendations that clarify the paper.
Regarding the first comment, we have now modified the abstract (section 1 of the
paper) to describe the topological changes that are observed in the network.
-- 2.2 – Clarification of text in section 3.3 -Reviewer
Comment
2. On p. 5 (2nd paragraph), the meaning of the following
phrase somehow escapes me:
'...there is a dramatic reversal of the trend observed for
individual TFs."
I fail to see the 'reversal', I think the wording should be
changed and/or clarified.
Author
Response
We have now changed the text in section 3.3 of the paper to clarify this point.
-- 2.3 – Incoming degree distribution -Reviewer
Comment
3. In the last paragraph on p. 5, it is first indicated that
'the associated exponent for the distribution of number of
TFs pert target gene' remains constant, than that beta=0.8
for the static network, and, finally, that different active
subnetworks have 'constant beta=0.6'. It is not quite clear
what is going on: is one of the above values a typo? Or are
the active subnetworks truly distinct from the static
network in this respect? How significant is the difference,
then?
Author
Response
We have now made substantial changes to the text in section 4.1 of the paper to
clarify this matter.
As detailed in the paper, and in section 3.1 of the response to referees, the
incoming degree does indeed change between the different networks. This is
accompanied by corresponding changes in the exponential exponents for the
incoming degree distributions (Figure 2).
As detailed in the paper and section 3.6 of the response to referees, these
changes are statistically significant when assessed using a U-test.
-- 2.4 – Power-law exponents -Reviewer
Comment
4. For the power law distributions discussed on p. 6, gamma
values <1 (especially gamma=0.6 for the static network) is
very low, considerably lower than in other scalefree
networks. It would be useful if the authors briefly
discussed this.
Author
Response
-- 2.5 – Network motif occurrence -Reviewer
Comment
5. In section 4.3 on the utilizatino of network motifs, I
think it would be useful to include some statistical
results, i.e., how does the abundance of these motif differ
from random expectation.
Author
Response
As detailed in section 4.3 of the paper, and section 3.6 of the response to
referees, we have generated random sub-networks corresponding to each cellular
condition. The patterns of motif usage in the condition-specific sub-networks are
significantly different to what is expected at random. For example, feed-forward
loops are overrepresented in the endogenous sub-networks, whereas single input
motifs are overrepresented in the exogenous conditions.
Responses to reviewer 3 comments
-- 3.1 – The obviousness of our results -Reviewer
Comment
Studying the dynamic nature of the regulatory network in
itself is an important problem of wide interest. However, as
detailed below, several of the results presented here seem
either obvious or unsufficiently rigorous.
Author
Response
Our interpretation of the above comment is that the referee is critical about the
novelty and obviousness of the results that we present in our paper.
We will address this by:
i. Demonstrating we address issues that are both timely and of great
interest because of the attention they have gained in prominent journals
(especially Nature and Science) recently.
ii. Presenting the current views about the issues and discussing the current
expectations given these views.
iii. Summarizing our main findings regarding each issue and showing that
they are not obvious because:
a. the issue has not been studied previously, and therefore the
results are unknown, and therefore are not obvious.
or b. there is a debate surrounding the issue, and we contribute to the
discussion by presenting results supporting one side of the
argument.
or c. there is a common perception of the issue, and our results
question this view by adding an entirely new and unexpected
perspective.
Our main findings are:
1. Dynamic usage of regulatory interaction.
2. Global changes in the topology of the regulatory network.
3. Local changes in usage of network motifs.
4. Dynamic usage of transcriptional regulatory hubs.
5. Tandem use of serial and parallel inter-regulation.
6. Dynamics of transcription factor combination usage.
Main finding 1: Dynamic usage of regulatory interactions
i. Interest in the issue
As stated by the referee, studying the dynamics of the regulatory interactions in
the transcriptional network is of great interest. In a recent review in Nature Review
Genetics (Barabasi and Oltvai 2004), assessing the temporal nature of links in
biological networks was raised as a future direction in analyzing these systems.
Recent papers in Cell and Science by Young and co-workers have highlighted the
importance of this issue by examining the dynamics of regulatory interaction
usage for selected transcription factors in human and yeast (Zeitlinger et al. 2003;
Odom et al. 2004).
ii. Current view of issue
Studies of regulatory interaction dynamics have so far been conducted on a
single gene basis. Even in using a functional genomic approach, Young and coworkers restricted their analysis to a total of four transcription factors (Zeitlinger et
al. 2003; Odom et al. 2004). Therefore the full extent of the dynamics of the
regulatory system and use of new regulatory interactions under different cellular
conditions has not been assessed yet.
iii. Summary of findings relevant to issues in (i), and how they were previously
unknown, and thus novel and not obvious given (ii)
Our study is the first genomic scale analysis of this issue, and we conduct our
study using a substantial proportion of the known transcription factors in yeast.
We discuss our findings in detail in section 3.2 of the paper, and sections 3.4 of
the response to referees. Briefly, there are significant changes in the use of
regulatory interactions; Figures 1 and 2 highlight the substantial rewiring that
takes place in transferring between the five cellular conditions. We also examine
the extent to which individual transcription factors produce new regulatory
interactions in different conditions (Figure 3). Different transcription factors
exchange or maintain their regulatory interactions in very different proportions.
Main finding 2: Global changes in regulatory network topology
i. Interest in the issue
Many recent papers in Nature, Science and Physical Review Letters have
discussed the large-scale topologies of molecular biological and related systems
(Watts and Strogatz 1998; Barabasi and Albert 1999; Jeong et al. 2000; Jenssen
et al. 2001; Jeong et al. 2001; Agrawal 2002; Albert and Barabasi 2002; Guelzim
et al. 2002; Oltvai and Barabasi 2002; von Mering et al. 2002; Alon 2003;
Barabasi and Oltvai 2004; Tong et al. 2004).. These include work on proteinprotein interactions, metabolic pathways, literature co-citations, and
transcriptional regulatory system. The complex topology of these networks is
captured by assessing graph-theoretic measures that describe their architectural
features.
ii. Current view of issue
As detailed in section 4.1 of the paper, the prevailing view is that molecular
biological networks, including the transcriptional regulatory system, are
remarkably consistent in their topologies (Wagner 2001; Featherstone and
Broadie 2002 and references above). For example, cross-species comparisons of
metabolic pathways show that the very divergent networks of parasitic bacteria
and large multi-cellular organisms preserve the same scale-free topology with the
same degree exponents (Jeong et al. 2000; Wagner and Fell 2001). Small-world
characteristics and high level of clustering is retained as shown by invariant
average path length, and clustering coefficient values, regardless of network size
(Jeong et al. 2000; Ravasz et al. 2002). The same is true for comparisons of the
E.coli and yeast regulatory networks (Luscombe, unpublished results).
In fact, the random controls that we conduct match the current expectations. As
detailed in section 4.1 of the paper and section 3.6 of the response to referees,
we follow the referee’s recommendation to generate random sub-networks
corresponding to each cellular condition. The topologies of the random subnetworks remain constant despite large differences in network sizes and sampling
of divergent sections of the network.
Therefore, the expectation is for the condition-specific sub-networks to have very
similar topological measures.
iii. Summary of findings relevant to issues in (i), and how they are not obvious
given the prevailing view in (ii)
Contrary to the prevailing view, there are surprisingly large changes between the
sub-networks used under the different cellular conditions (paper section 4.1 and
Figure 2). The outgoing degree doubles from the endogenous to exogenous
conditions, and incoming degree decreases by a fifth. Exogenous networks
display much more small-world character, and a higher level of clustering is seen
in the endogenous conditions. As detailed in section 4.1 of the paper and section
3.6 of the response to referees, the changes are statistically significant, and they
are also significantly different compared with the randomly generated subnetworks.
The global topological changes that we observe can be rationalised in the context
of the cellular conditions in which they occur. They demonstrate that the
regulatory system as a whole has clearly evolved to respond effectively to diverse
biological demands (paper section 4.1). For example the shorter path lengths and
diameters for the exogenous sub-networks mean that perturbations in the
surrounding environment would reach the required targets very quickly. The
longer path lengths in the endogenous conditions are most likely due to the
formation of regulatory chains, in which intermediate transcription factors regulate
sequential phases of the cell cycle and sporulation.
We stress that these large scale topological changes are completely unexpected
given current views, and random expectation in the control networks.
Main finding 3: Local changes in network motif usage
i. Interest in the issue
The large-scale use of network motifs in the regulatory system was introduced by
Alon and co-workers in Nature Genetics (Shen-Orr et al. 2002). As highlighted by
numerous recent publications in Nature and Science (Milo et al. 2002; Milo et al.
2004), the analysis of motif usage has become an integral part of network
analysis at a local scale, particularly for the transcriptional regulatory system (Lee
et al. 2002).
ii. Current view of the issue
As reported by Alon and co-workers in Science, the prevailing view is that the
usage pattern of motifs is highly conserved across different networks; for example
the regulatory systems of diverse organisms such as B. subtilis, E. coli, and yeast
highly favour the use of feed-forward loops (Milo et al. 2004). In another paper in
Science, they reported that the relative occurrence of motifs does not change
when smaller sub-networks of various sizes are sampled (Milo et al. 2002).
In fact, our random controls match the current expectation. As detailed in section
4.3 of the paper and section 3.6 of the response to referees, motif usage patterns
in the random sub-networks do not differ from the static network. In other words,
the motif occurrence does not change between the random sub-networks.
Therefore, the expectation is for the condition-specific sub-networks to show very
similar usage patterns of network motifs.
Iii. Summary of findings relevant to issues in (i), and how they are not obvious
given the prevailing view in (ii)
We examine the occurrence of the three most prevalent motifs in the regulatory
network (paper section 4.3, Figure 3). The change in usage pattern is dramatic.
The direct-acting motifs (single input and multiple input) are highly favoured in the
exogenous conditions (80% of regulatory interactions). Indirect-acting motifs are
overrepresented in the endogenous conditions (50% of regulatory interaction). As
detailed in the paper and in section 3.6 of the response to referees, the difference
in usage between conditions is statistically significant, and also substantially
different to that expected at random.
Different network motifs have been ascribed specific information processing
tasks. We rationalize the observations in the context of the different kinetic and
properties of the motifs, and the regulatory requirements of the biological contexts
in which they are found.
Again, we stress that the large differences we observe in motif usage are
completely unexpected given current views, and random expectation in the
control networks.
Main finding 4: Dynamic usage of regulatory hubs
i. Interest in the issue
As highlighted by numerous publications in Nature and Science, there has been
great interest in the scale-free topology of biological networks (see references in
main finding 2). The behaviour is indicative of a hub-containing topology; as they
are influential components of networks, and dictate the overall structure of the
graph, these hubs have been identified as being central to the system under
consideration (Barabasi and Albert 1999; Jeong et al. 2001; Barabasi and Oltvai
2004; Tong et al. 2004).
ii. Current view of the issue
There is currently a lot of discussion about the identity of hubs in transcriptional
networks as their regulatory role is disputed. As detailed in section 3.7 of the
response to referees, there are two competing views on the regulatory role of
hubs. (a) On the one hand, they are portrayed as general regulators that target
genes across a wide spectrum of functions and conditions (Guelzim et al. 2002;
Lee et al. 2002; Martinez-Antonio and Collado-Vides 2003; Barabasi and Oltvai
2004). (b) On the other hand, because of the modular nature of the regulatory
network, hubs are described as being condition-specific, with a much narrower
functional remit networks (Hartwell et al. 1999; Guelzim et al. 2002; Oltvai and
Barabasi 2002; Alon 2003; Barabasi and Oltvai 2004; Wall et al. 2004).
As detailed in section 4.2 of the paper and section 3.6 of the response to
referees, random expectation supports the first view, that hubs are multifunctional. In spite of large differences in size and starting points, the random
networks converge on the same set of transcription factor hubs (overlap in TF
membership is 77-96%).
iii. Summary of findings relevant to issues in (i), and how they are not obvious
given the discussion surrounding it in (ii)
Contrary to random expectation, the overlap in transcription factors that are
considered to be hubs is low (36-74% depending on the conditions).
As detailed in Figure 5, section 4.2 of the paper, and section 3.7 of the response
to referees, there are two general types of hubs: condition-independent, and
condition-dependent. The first are hubs regardless of cellular condition, and tend
to comprise general and house-keeping regulators.
The second act as hubs in one particular condition, and are consequently much
less active in the other conditions. These comprise most of the regulatory hubs.
Within this group are five sub-clusters corresponding to each condition. Belonging
to these sub-clusters are transcription factors known to be important for the
particular conditions, as well as some functionally uncharacterised ones for which
we can now provide additional annotation.
Though there are a limited number of general hubs that are active in all
conditions. Our findings support the view that they tend to be condition-specific.
Given the present discussion surrounding the functions of hubs, and the random
expectation that they would be multi-functional, our results are clearly not
obvious.
Main finding 5: Tandem use of serial and parallel inter-regulation
i. Interest in the issue
Examining the cross-talk between different biological macromolecules is a topic of
great interest in understanding cell signaling eg. (Yuan et al. 2003). As highlighted
by papers in Cell, Science, and other prominent journals, this interest is extended
to understanding how transcription factors regulate each other (Guelzim et al.
2002; Horak et al. 2002; Shen-Orr et al. 2002; Segal et al. 2003). In particular, the
publications by Young and co-workers have highlighted the time-dependent interregulation between transcription factors during the cell cycle (Simon et al. 2001;
Lee et al. 2002).
ii. Current view of the issue
Young and co-workers examined the temporal regulation of the cell cycle for 11
transcription factors that display periodic expression profiles (see references
above). They coined the term “serial inter-regulation” to describe the circuitry of
regulators in which factors from one phase of the cell cycle target those in
subsequent phases to drive the process forward. This effectively produces a
chain of regulatory events.
The time-dependent regulation of transcription factors during multi-phase
processes is clearly important in understanding the dynamics of the regulatory
system. However the full extent of inter-regulation between all transcription
factors in these processes is currently unknown.
iii. Summary of findings relevant to issues in (i), how they extend our current
knowledge in (ii), and are therefore not obvious
As detailed in section 4.1 of the paper, the endogenous conditions have more
clustered sub-networks, which indicates that there is substantial inter-connections
between transcription factors. We investigate the roots of this topological feature
by examining the time-dependent inter-regulation between transcription factors.
As described in section 5 of the paper, we focus on the inter-regulatory network of
all 70 transcription factors that are actively used in the cell cycle (Figure 6).
We confirm Young and co-workers’ previous results describing serial interregulation, adding weight to the validity to the methods that we employ. We also
greatly expand the scope of the serial circuitry by describing the full extent of
inter-regulation between the phase-specific factors.
In addition, we describe a new type of inter-regulatory network that has not been
observed before. In analogy to the “serial” circuitry coined by Young and coworkers, we term this “parallel” inter-regulation. In this, the ubiquitously active
transcription factors inter-regulate with the phase-specific ones. As many of the
factors involved are also condition-independent hubs, we propose that parallel
inter-regulation allows for cross-talk between the cell cycle and house-keeping
apparatus.
At present, it is not obvious that such a regulatory relationship exists, and this is
the first report for serial and parallel inter-regulation to operate in tandem.
Main finding 6: Dynamics of transcription factor combinations
i. Interest in the issue.
The importance of transcription factor combinations is highlighted by the
publication by Church and co-workers in Nature Genetics (Pilpel et al. 2001). As
detailed in section 3.5 of the response to referees, the topic is of current interest
(Berman et al. 2002).
ii. Current view of Issue
As detailed in section 3.5 of the response to referees, the previous studies have
been (a) restricted to a small number of transcription factors, and (b) based on
predictions of binding site co-occurrence.
iii. Summary of findings relevant to issues in (i), and how we contribute to the
present state of knowledge in (ii)
As detailed in section 3.3 of the paper and section 3.5 of the response to
referees, we examine the use of transcription factor combinations as a major
method of conferring regulatory specificity in different cellular conditions. In
contrast to the large overlap in the repertoire of active transcription factors
between conditions, there is only slight overlap in the use of pair-wise
transcription factor combinations (Figure 3).
In addition, we expand on previous findings because: (a) we use a much larger
dataset comprising 142 transcription factors, (b) base our observations on actual
binding data rather than predictions, and (c) consider transcription factor
combinations in the context of regulatory network dynamics.
We stress that this is not the only finding in our paper, but given the importance of
the issue in conferring regulatory specificity in different cellular conditions we
cannot ignore it. In addition, the general agreement of this result with previous
publications actually strengthens our paper as it validates the methods that we
use.
-- 3.2 Network description of the regulatory system –-Reviewer
Comment
The authors motivate their work by observing, in Section 2,
that "[no studies] have explored the dynamic nature of
[biological] networks". But this merely rephrases a wellknown fact - namely, that many transcription factors and the
genes controlled by them are only active in specific
conditions - in the "networks" language.
Author
Response
We agree with the referee: it is well known that transcription factors and genes
controlled by them are only active in specific conditions. This is exactly why we
were motivated to investigate the dynamic usage of the transcriptional regulatory
system.
Until to now, the condition-specific usage of transcription factors has been
assessed only on a gene-by-gene basis. As detailed in sections 1.1 of the
response to referees, the only exceptions are two recent studies by Young and
co-workers (Zeitlinger et al. 2003; Odom et al. 2004). Here, condition/tissuespecific usage of four transcription factors was examined using the ChIp-chip
technique. That these papers were published in Cell and Science underlines the
continued importance of studying this well-known fact.
What sets apart our paper from earlier work is that it is the first to address this
question on a genomic scale. Our regulatory dataset comprises 142 transcription
factors, 3840 genes, and 7074 regulatory interactions. This is a substantial
proportion of the regulatory system in yeast. Given the size of this dataset, a
network description of the system is clearly appropriate.
An analogy is the popularity and the importance (given the publication in Nature
and Science) of analysing protein-protein interaction data (eg Bader and Hogue
2002; von Mering et al. 2002). One could argue that it is well known that proteins
interact with each other. However, the fact that this is well-known has only served
to increase the importance of studying these interactions on a genomic scale.
Furthermore, many of these prominent studies have taken a network perspective.
-- 3.3 Simplicity of the backtracking algorithm –-Reviewer
Comment
This also suggests that the simple backtracking procedure
used by the authors may greatly overestimate the number of
truly "active" transcription factors in a given condition.
Author
Response
Though the referee criticises the simplicity of the backtracking algorithm, we do
feel that the method that we used is reasonable. The underlying assumption –
that transcription factors connected to the differentially expressed genes are
potentially involved in regulating them – is biological realistic. As outlined in
response to referees section 3.1, we have shown that our results are consistent
with previous findings; for example in the use of transcription factor combinations
and correct classification of the phase-specific transcription factors during the cell
cycle. The novel findings are backed by biological plausible explanations.
In response to the referee’s view, we tested several back-tracking models that
account for the presence or absence, and therefore the regulatory activity of
transcription factors, in different ways. As shown in the supplementary website all
the methods, including the one used in the original manuscript, yields very similar
results.
The method we focus on in the current manuscript elaborates on the original
algorithm by accounting for the expression levels of the transcription factors.
Details of the method are provided in section 7.2 of the paper. Briefly, we classify
a transcription factor as being present or absent in each condition by considering
their expression levels during the condition. We then backtrack from the
differentially expressed genes through sections of the static network that contain
only present transcription factors.
-- 3.4 Interchange of regulatory interactions –-Reviewer
Comment
The far more interesting (and far less studied) issue of the
potentially dynamic nature of the regulatory connections
themselves is mentioned by the authors at the beginning of
the Conclusion (Section 6) but not addressed by the present
study.
Author
Response
The dynamic nature of the regulatory interactions is indeed of great interest. As
discussed in earlier sections of the response to referees, this is stressed by the
publications of Young and co-workers in Cell and Science (Zeitlinger et al. 2003;
Odom et al. 2004). In the first, Zeitlinger et al examined the condition-specific
interactions made by Ste12 in two cellular states. In the second, Odom et al.
examined the use of three transcription factors in human liver and pancreatic
cells.
Despite the interest shown in this subject, no study has so far assessed the
extent to which regulatory interactions are exchanged across many transcription
factors.
This is an issue that we could have readily addressed in the original manuscript
but didn’t and we thank the referee for highlighting this as a topic that would
interest readers. We follow the referee’s suggestion and we now elaborate on this
topic by discussing it in a new section in the paper (section 3.2, Figure 3).
Most regulatory interactions are unique to a particular condition, and as shown in
Figure 2, there is substantial rewiring in the regulatory network in different cellular
conditions. Therefore, creation and exchange of regulatory interactions clearly
plays a critical part of regulating alternative cellular conditions. In the recent
review in Nature Review Genetics (Barabasi and Oltvai 2004), Barabasi and coworkers discussed the interest in identifying “hot links” in biological networks,
which are most actively used. For the first time, we now identify a small number of
such links that are retained across many different conditions. Many of the
transcription factors involved are condition-independent hubs.
We also measure the extent to which transcription factors exchange or conserve
regulatory interactions by introducing an exchange index. Low values indicate
retention of most interactions, and high values indicate exchange of most
interactions between conditions. As shown in Figure 3, there are three types of
transcription factors: (i) those that are not dynamic, and maintain regulatory
interactions across multiple conditions, (ii) those that are very dynamic and whose
regulatory interactions change over drastically between conditions and (iii) those
that lie between the two extremes. Interestingly, the highly dynamic transcription
factors shift regulatory functions as they create alternative regulatory interactions.
-- 3.5 Combinatorial usage of transcription factors –-Reviewer
Comment
Another major conclusion of the manuscript, the importance
of combinatorial regulation of the same gene by two or more
transcription factors as a mechanism for increasing the
specificity of the transcriptional response, has also
recently become established in the literature: see e.g.
Pilpel et al (cited Ref. 34 in the ms) and a number of
papers used clustering of transcription factor binding sites
to find functional cis-regulatory modules in Drosophila
(e.g. BP Berman et al, Proc Natl Acad Sci U S A. 2002 Jan
22;99(2):757-62; M Markstein et al, Proc Natl Acad Sci U S
A. 2002 Jan 22;99(2):763-8).
Author
Response
The referee states that our conclusions regarding the combinatorial use of
transcription factors have already been established. Indeed this issue has been of
recent interest, and several papers have been published in prominent journals
including Nature Genetics (Pilpel et al. 2001; Berman et al. 2002). Without
criticizing these pioneering studies – as they were conducted when there were
less data available – we would like to point out two disadvantages of the papers
cited by the referee: (i) first, both papers consider a small set of transcription
factors (5 and 27 respectively), and (ii) second, the transcription factors
combinations are predicted based on the co-occurrence of binding site motifs.
Our work greatly expands on the previous publications in three ways (section 3.3
in the paper): (i) first we use a much larger dataset comprising 142 transcription
factors, making our work a truly genomic assessment of the phenomenon, (ii)
second the regulatory combinations we report are not predictions but based on
actual DNA-binding data, and (iii) third we consider the combinatorial usage of
transcription factors in the context of dynamic usage of the regulatory network.
Furthermore, although combinatorial transcription factor usage is indeed one of
the results of our paper, we emphasize that it is only one of the many conclusions
we make (see section 3.1 of response to referees). Despite previous publications
relating to this issue, we cannot ignore it because of the important role that
combinatorial usage plays in conferring regulatory specificity. In fact, the general
agreement of our results with previous studies adds weight and validity to the
other findings in out paper.
To highlight that the main messages conveyed by our paper is obviously different
to the results in the papers cited by the referee, we copy the abstracts for the
respective papers below:
(Note: we feel that the referee is slightly misleading in citing the work by
Markstein and co-workers (Markstein et al. 2002). This paper does not study
combinatorial transcription factor usage but searches for clusters of binding sites
for a single factor, dorsal, in fly.)
Our paper:
“Transcriptional regulatory networks play a central role in directing gene expression
changes in response to internal and external stimuli. Here for the first time, we integrate
gene expression data for five cellular conditions with known transcriptional regulatory
relationships to study the dynamic nature of this network in yeast. Distinct and specific
sections of the regulatory network are used under each condition. Contrary to
expectations about the invariant nature of graph architecture, the conditionally active
networks reveal large-scale structural changes. Networks used during the cell cycle and
sporulation display a denser clustering of transcription factors whereas environmentally
responsive conditions have substantially more small-world character. Changes also occur
locally in which network motifs of different kinetic properties are favoured depending on
the condition. These observations clearly demonstrate that the regulatory system has
evolved to respond effectively to diverse biological demands. We also examine the
temporal usage of the regulatory system during multiple stages of the cell cycle and
sporulation, and we introduce the concept of serial and parallel inter-regulation
operating in tandem to advance the cell through these conditions. Moreover we show that
different sets of transcription factors become key regulatory hubs at different times,
portraying a network that shifts its weight between different foci to bring about distinct
cellular states.”
Pilpel et al (Pilpel et al. 2001):
“Several computational methods based on microarray data are currently used to study
genome-wide transcriptional regulation. Few studies, however, address the combinatorial
nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a
new approach using microarray data to uncover novel functional motif combinations in
the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif
combinations that affect expression patterns during the cell cycle, sporulation and various
stress responses, we observed regulatory cross-talk among several of these processes. We
have also generated motif-association maps that provide a global view of transcription
networks. The maps are highly connected, suggesting that a small number of transcription
factors are responsible for a complex set of expression patterns in diverse conditions. This
approach may be useful for modeling transcriptional regulatory networks in more
complex eukaryotes.”
Berman et al (Berman et al. 2002):
“A major challenge in interpreting genome sequences is understanding how the genome
encodes the information that specifies when and where a gene will be expressed. The first
step in this process is the identification of regions of the genome that contain regulatory
information. In higher eukaryotes, this cis-regulatory information is organized into
modular units [cis-regulatory modules (CRMs)] of a few hundred base pairs. A common
feature of these cis-regulatory modules is the presence of multiple binding sites for
multiple transcription factors. Here, we evaluate the extent to which the tendency for
transcription factor binding sites to be clustered can be used as the basis for the
computational identification of cis-regulatory modules. By using published DNA binding
specificity data for five transcription factors active in the early Drosophila embryo, we
identified genomic regions containing unusually high concentrations of predicted binding
sites for these factors. A significant fraction of these binding site clusters overlap known
CRMs that are regulated by these factors. In addition, many of the remaining clusters are
adjacent to genes expressed in a pattern characteristic of genes regulated by these
factors. We tested one of the newly identified clusters, mapping upstream of the gap gene
giant (gt), and show that it acts as an enhancer that recapitulates the posterior expression
pattern of gt.”
Markstein et al (Markstein et al. 2002):
“Metazoan genomes contain vast tracts of cis-regulatory DNA that have been identified
typically through tedious functional assays. As a result, it has not been possible to
uncover a cis-regulatory code that links primary DNA sequences to gene expression
patterns. In an initial effort to determine whether coordinately regulated genes share a
common "grammar," we have examined the distribution of Dorsal recognition sequences
in the Drosophila genome. Dorsal is one of the best-characterized sequence-specific
transcription factors in Drosophila. The homeobox gene zerknullt (zen) is repressed
directly by Dorsal, and this repression is mediated by a 600-bp silencer, the ventral
repression element (VRE), which contains four optimal Dorsal binding sites. The
arrangement and sequence of the Dorsal recognition sequences in the VRE were used to
develop a computational algorithm to search the Drosophila genome for clusters of
optimal Dorsal binding sites. There are 15 regions in the genome that contain three or
more optimal sites within a span of 400 bp or less. Three of these regions are associated
with known Dorsal target genes: sog, zen, and Brinker. The Dorsal binding cluster in sog
is shown to mediate lateral stripes of gene expression in response to low levels of the
Dorsal gradient. Two of the remaining 12 clusters are shown to be associated with genes
that exhibit asymmetric patterns of expression across the dorsoventral axis. These results
suggest that bioinformatics can be used to identify novel target genes and associated
regulatory DNAs in a gene network.”
-- 3.6 Random network statistics –-Reviewer
Comment
A technical concern about the conclusions drawn by the
authors regarding the difference in global and local network
statistics they find between the "endogenous" and the
"exogenous" conditions is the following. As the authors
note, far more genes are found to be active in the "exo"
conditions than in the "endo" conditions. It is quite
possible that the difference in network statistics reported
in Figure 2 could partly or solely due to this difference in
the number of active genes. An appropriate control would be
to randomly sample a given number of "active"genes from the
set of all targets, redo the backtracking step, and evaluate
the various network statistics. This necessary control
however is lacking in the current manuscript.
Author
Response
We thank the referee for recommending a suitable control. In response we have
extended our approach which strengthens the validity of our findings.
We generated 1,000 random networks for each cellular condition. (i) We randomly
sampled the same number of differentially expressed genes from all yeast genes
for a given condition. (ii) We then randomly sampled the same number of present
transcription factors from all regulators in the dataset. (iii) Finally we repeated the
backtracking procedure from the randomly picked genes, using just the randomly
present transcription factors.
Below we outline the results from the random networks. (1) First we compare
between random networks, (2) next we compare between the conditionally active
sub-networks, and (3) finally we compare the conditionally active sub-networks
with the random controls. (4) In addition, we describe a sensitivity test we
conducted to check the robustness of our findings.
(1) Comparison between random networks
(i) Topological measures. We compare topological measures of the random
networks. As described in section 4.1 of the paper, despite large differences in
network size, there is very little change in topological measures. The differences
are not statistically significant. The only measure that changes is the average
outgoing degree, but this is expected as outgoing edges must be shared from a
limited pool of transcription factors.
(ii) Network motifs. We compare the occurrence of network motifs in the random
networks. As described in section 4.3 of the paper, the usage pattern is the same
as observed in the static network, and the differences are not statistically
significant.
(iii) Regulatory hubs. We test whether the random networks produce different sets
of regulatory hubs. As described in section 4.2 of the paper and section 3.7 of the
response to referees, there is high overlap (77-86%) in the transcription factors
that are identified as hubs. Pair-wise correlation coefficients of the numbers of
target genes for each transcription factor are also high (r > 0.9). Therefore the
random networks converge on the same sets of regulatory hubs.
(2) Comparisons between condition-specific networks
(i) Topological measures. As described in section 4.1 of the paper, the topological
measures of the condition-specific networks differ substantially. Briefly, the
exogenous conditions have much greater outgoing degree and smaller incoming
degree. The path lengths, diameters and clustering coefficients are larger for the
endogenous conditions. The differences in values are statistically significant.
(ii) Network motifs. As described in section 4.3 of the paper, the occurrence of
network motifs changes dramatically between conditions. The direct-acting motifs
(single input and multiple input) are highly favoured in the exogenous conditions.
The indirect-acting motif (feed-forward loop) is preferred in the endogenous
conditions. The changes are statistically significant.
(iii) Regulatory hubs. As detailed in section 4.2 of the paper and section 3.7 of the
response to referees, the condition-specific sub-networks have very different sets
of regulatory hubs. The overlap in transcription factors identified as hubs is low
(30-74%). The pair-wise correlation coefficients of the numbers of target genes for
each transcription factor are also smaller (0.2 < r< 0.7).
(3) Comparison between the condition-specific sub-networks and random
sub-networks
(i) Topological measures. As described in section 4.1 of the paper, many
topological measures differ significantly between the condition-specific subnetworks and the random controls. Incoming degrees are larger than expected in
endogenous conditions, and smaller in the exogenous conditions. Outgoing
degrees are larger than expected in exogenous conditions. Path lengths and
diameters are longer than expected in endogenous conditions, and shorter in
exogenous conditions. Clustering coefficients are larger than expected in
endogenous conditions, and smaller in some exogenous conditions.
(ii) Network motifs. As described in section 4.3 of the paper, motif occurrences
differ significantly between the condition-specific and random networks.
Compared with random expectation, single input motifs are overrepresented in
exogenous conditions, and underrepresented in endogenous conditions. Feedforward loops are overrepresented in endogenous conditions, and
underrepresented in exogenous conditions.
(4) Sensitivity test
As described in sections 4.1 and 7.8 of the paper, the observations we make are
insensitive to data errors. We generated 1000 “static” networks with error-rates of
30% by adding, removing or rearranging regulatory interactions at random. For
each condition, we then backtracked through these networks using the original
sets of differentially expressed genes, and present transcription factors.
The topological measures, motif usage and hub definitions remain unchanged.
The only exception is the clustering coefficient which is lowered for all conditions,
though the same trend remains. This is unsurprising because in introducing the
random errors, we disperse the regulatory interactions and so break down
clusters.
-- 3.7 – Convergence of hubs in random networks -Reviewer
Comment
Since expression data for very different conditions (from
cell cycle to stress response) is used as input to this
procedure, it should come as no surprise that very different
multi-target transcription factors emerge as the important
"hubs" in different cellular states.
Author
Response
We feel that contrary to the referee’s view, our results are not obvious and make
a valid contribution to the current discussion on the role of hubs in biological
networks.
1. Expectation from the literature
There are currently two competing views as to the identity and regulatory function
of hubs in the transcriptional network; one side argues that hubs are multifunctional and control many processes, whereas the other side reasons that hubs
are centred on specific regulatory processes.
(i) On the one hand, hubs are described as general regulators that target genes
across a wide spectrum of functions and conditions (Barabasi and Oltvai 2004).
Collado-Vides and colleagues, the curators of RegulonDB, explicitly include this
criterion in their definition of hubs (Martinez-Antonio and Collado-Vides 2003). In
their paper in Science, Young and co-workers note that their top-ranking factor is
a general transcriptional activator (Lee et al. 2002). In their Nature Genetics
paper, Guelzim and co-workers report that hubs are found upstream in the
regulatory network (Guelzim et al. 2002); therefore they amplify their range of
control via other transcription factors, and diversify their regulatory functions.
These studies support a trans-conditional view of hubs.
(ii) On the other hand, hubs are also depicted as being condition-specific, with a
much narrower functional remit. Many papers in Nature and Science have
reported the modularity of biological networks (Hartwell et al. 1999; Guelzim et al.
2002; Oltvai and Barabasi 2002; Alon 2003; Barabasi and Oltvai 2004; Wall et al.
2004). Several studies in Nature Genetics and Nature Biotechnology have
identified functionally distinct modules within the regulatory network (Tavazoie et
al. 1999; Ihmels et al. 2002), and have shown that these modules centre about
their own particular hubs (Bar-Joseph et al. 2003; Segal et al. 2003). In a paper in
Nature, Maslov and Sneppen argue that hubs are topologically isolated from each
other to descrease the cross-talk between modules (Maslov and Sneppen 2002).
Therefore, these studies point to a condition-specific role for hubs.
2. Expectation from random controls
The random controls support view (i). The referee’s expectation is that different
transcription factors should emerge as hubs because of backtracking from
different starting points. If this were the case, then the random controls should
output very different, potentially random sets of transcription factor hubs.
The opposite happens (section 4.2 in the paper). The random networks are very
different in size, and starting points. However they converge on the same sets of
transcription factors (77-86% overlap in transcription factors), signifying that hubs
are expected to be multi-conditional.
3. Our findings
In fact, our findings are unexpected considering the current perception in the
literature, and compared with random expectation. Unlike the randomly generated
controls, the overlap is much lower between the conditionally active networks (3674% depending on the conditions).
Furthermore, we make a useful contribution to the debate of the regulatory role of
hubs (section 4.2 in the paper). There are two types of hubs (Figure 5): conditionindependent, and condition-specific. Condition-dependent hubs are active across
all cellular conditions, and thus are multi-functional. They tend to comprise
general transcriptional regulators such or house-keeping regulators. Therefore,
this group of transcription factors support view (i).
However, most of the regulatory hubs are condition-dependent; they are most
active in a particular condition, and much less active in the others. There are five
sub-clusters corresponding to each cellular condition and within these clusters are
transcription factors known to be important regulators in the condition, as well as
some functionally uncharacterised ones for which we can now provide additional
annotation. These condition-dependent hubs support view (ii).
As there are many more condition-dependent than independent hubs, we would
tend to side with view (ii).
-- 3.7 Notation of statistics –-Reviewer
Comment
The
notation
in
section
7.3
is
sometimes
unclear:
"N(actual)", "N(fitted)", and "Ntargets" are never defined;
readers may be confused by the definition of the "average
path length" as the "median of the shortest distance”.
Author
Response
We thank the referee for pointing this out. We have clarified the notation in
section 7.4 of the paper.
-- 3.8 K-means clustering parameters –-Reviewer
Comment
… and the choice of k=6 in the k-means clustering procedure
is not movitated.
Author
Response
We thank the referee for raising this.
The choice of k = 6 is motivated by the fact that there are five cellular conditions
(condition-specific hubs). We added an extra cluster for the condition-independent
hubs. We also tested k = 5 (one for each cellular condition), which actually
resulted in the combination of the diauxic shift and stress response clusters, and
the maintenance of the condition-independent cluster. k = 7 resulted in similar
clusters to k = 6, except that the condition-independent cluster was partitioned
into two.
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