BACKGROUND
basic cellular systems including the cell cycle, innate immunity, and mrna translation operate in a similar manner across a large number of species.
the proteins that participate in these systems are highly conserved, enabling many successful applications to infer gene function based on sequence similarity across species  <cit> .
while genes with very similar sequence often perform the same function, dynamic properties of conserved proteins, including expression and interactions, seem to differ substantially between species.
in studies profiling similar tissues in mouse and human, researchers found a large divergence in expression profiles  <cit>  .
the correlation of cell cycle expression between two yeasts was determined to be around  <dig>   <cit> .
similarly, in protein-dna binding studies, researchers found that only 11% of binding interactions for highly conserved transcription factors overlapped between human and mouse  <cit> .
studies of three yeast species with high sequence similarity identified only 20% overlap in binding targets  <cit>  and similar results were obtained for bacteria  <cit> .
protein interactions were also found to overlap at very low rates  <cit>  .
only an estimated 18% to 29% of negative genetic interactions between s. cerevisiae and s. pombe were found to be conserved  <cit> .
early studies have mainly focused on pairwise comparisons based on a single genomic data type.
while the results in these early papers indicated low overlap between species, no attempt was made to generalize observations to address reasons for the lower conservation of interaction data when compared to sequence data conservation.
recent high throughput experiments with better coverage  <cit>  made it possible to reassess the conservation of interaction data.
a number of possible reasons have been proposed to explain the lack of conservation for specific types of interaction data.
for example, fox et al.
<cit>  observed that interactions connecting hub proteins are more conserved when compared to interactions involving proteins with a lower degree of connectivity.
as they show using ppi data from multiple species, there is a positive correlation between the average degree of a protein and the conservation of its interacting partners.
byrne et al.
<cit>  studied the genetic interaction networks of s.cerevisiae and c.elegans and reported that while only little overlap is seen for individual interactions, the properties of their genetic interaction networks are conserved.
they proposed that changes in individual genetic interactions might be a form of evolution.
another direction suggested by roguev et al.
<cit>  demonstrated that conservation of interactions within protein complexes is higher than that of other interactions.
they compared genetic interactions between chromatin-related genes in two yeasts and determined that protein complexes and the evolution of a new biological mechanism  can help explain the minimal overlap observed, hypothesizing that protein-protein interactions pose a constraint on functional divergence in evolution.
similarly, jensen et al.
<cit>  compared cell cycle expression of a number of species and discovered that while in-time expression was not conserved at the individual gene level, it was much more conserved at the protein complex level.
van dam and snel  <cit>  showed that conservation rates for ppi within complexes in human and yeast are much higher than overall interaction conservation.
on the other hand, wang and zhang  <cit>  studied conservation of yeast, fly, and nematode ppi networks and determined that interactions in protein complexes are not conserved at levels that are higher than other interactions.
beltrao et al.
<cit>  claimed that protein complexes are correlated with higher conservation only for stable interactions, while transient interactions, including phosphoregulation, are less conserved.
the experimental methods used to obtain expression data are large scale and produce measurements for the entire genome leading to a significantly better coverage of the interactome compared to the other data types.
in addition, as there is no equivalent to protein complexes in expression data, early analysis of the conservation of dynamic properties in expression data focused on the identification of conserved expression modules across species  <cit> .
while some important expression modules were conserved, many others were not.
the above discussion illustrates several  trends observed for the conservation of interactions across species.
one of the reasons for the disagreement between the results of these observations is the fact that each was only tested on a small dataset, often for only one type of interaction data , in one specific condition and between a single pair of species.
to determine which of these trends hold more generally we performed a comprehensive analysis using four model organisms, and several genomic data types measured under a variety of conditions.
as we show below, while all the proposed directions so far indeed explain part of the differences between species, none is enough to provide a comprehensive explanation.
we have thus attempted to generalize these suggestions.
our findings suggest that while sequence and function are conserved at the individual protein level, interactions are conserved at a higher organizational level for which we use the term 'functional modules'.
these results indicate that while gene-gene interactions are not well conserved, the overall network, through the intermediate level of modules, is conserved to a much higher degree.
RESULTS
data collection and processing
we focused on four species for which large interaction datasets are available: the two yeasts s. cerevisiae and s. pombe, the nematode c. elegans, and the fruit fly d. melanogaster.
we retrieved available sequence, expression, protein-protein interaction , and genetic interaction  data as well as gene ontology  annotations for all species.
see methods for details.
to facilitate the comparison of genomic datasets across species, we converted all datasets into network representation using a probabilistic approach that assigns a score to each edge  between two genes based on their likelihood of participating in the same biological process  <cit>  .
this method was used in the past  <cit>  to determine appropriate cutoffs for correlation networks in each species .
from this point on, we refer to each data type as a network .
the co-expression, ppi, positive gi, and sequence networks were combined to create an integrated weighted network separately for each species .
additional integrated network that includes only the co-expression, ppi, and positive gi was tested as well .
only positive genetic interactions were used for the integrated network as negative gis are often found between genes in parallel pathways rather than within the same pathway  <cit> .
for each edge in the integrated networks, its score was calculated by summing up the log likelihood scores for that edge across the four individual network types.
the integrated network represents the most comprehensive functional association aggregation that we are able to achieve for each of the species in our study from the currently available experimental data.
we determined orthology relationships using genedb  <cit>  and reciprocal best blastp hits .
.
for a specific network in species a we extracted all pairs of genes ga, <dig> and ga, <dig> that are connected in that network.
if both genes have orthologs in species b we define the interaction ga,1- ga, <dig> to be directly conserved if their orthologs  have the same interaction in species b.
we first computed conservation statistics directly from the networks for each species.
most interaction datasets are not well conserved across species, including networks that are fairly complete.
the 'baseline' column in table  <dig> presents the overall conservation of interaction data  between s. cerevisiae and s. pombe, the two closest species in our study .
the overall conservation of the integrated gene network is  <dig> % for s. cerevisiae with respect to s. pombe, and  <dig> % for s. pombe with respect to s. cerevisiae .
of all the types of datasets in our analysis, expression data is the most abundant.
however, the coexpression interactions between these two yeasts are only conserved at a rate of  <dig> %/ <dig> % which is still low, although it is indeed higher than the other experimental data types.
in contrast, we find a better agreement between go edges of the two species  despite the relatively low coverage of go annotation for s. pombe.
conservation rates for s. pombe with respect to s. cerevisiae are based on the integrated networks for the following categories: baseline: the entire networks; hubs: highest rate reported for any bin based on node degree; complexes: complexes as defined by the gavin and krogran studies  <cit> ; molecular function: highest rate reported for interactions with any go molecular function; wmi: within-module interactions; wmi - no hubs: wmi excluding interactions with hubs; extended wmi: extended module interactions.
see text for further details.
conservation of hub interactions
several studies have previously analyzed specific interaction datasets in multiple species and identified trends in these datasets that differentiated conserved and non conserved interactions.
to test how these generalize to the large datasets we collected we have reformulated some of these observed trends as possible explanations to the low conservation rates and analyzed them using our integrated networks.
we first checked whether interactions involving hub proteins are more likely to be conserved.
in order to examine this, we binned the nodes according to their degrees in the integrated network, and for each bin, we calculated the conservation rates for interactions involving at least one node whose degree falls into that bin.
we found a positive correlation between the degree of the nodes and the conservation rates of the interactions that connect them with their partners .
fewer than 15% of the interactions involving nodes with low degrees , which include the vast majority of the interactions, are conserved in both s. cerevisiae and s. pombe, while for those interactions involving nodes with high degrees , 24-26% are conserved.
therefore, we conclude that hub interactions are conserved at rates that are better than average, and the effect of hubs should be considered in subsequent analyses.
nonetheless, the conservation rates of hub interactions are still much lower than the conservation of sequence data and they provide only a limited explanation for the even lower conservation rates of all interactions.
conservation of interactions within protein complexes
protein complexes were previously shown  <cit>  to have higher conservation rates.
this analysis was limited to protein-protein interactions but interactions of other genomic data types that coincide with ppi were also shown to have higher conservation rates  <cit> .
in our analysis, we checked conservation rates for protein complexes that were defined in two recent studies in s. cerevisiae  <cit> .
interactions in the integrated network that were part of the complexes defined by krogan et al.
were conserved at a rate of  <dig> % , while the  <dig> interactions that were part of the complexes identified by gavin et al.
had a conservation rate of  <dig> %.
note that this is only a one-way comparison, since the complexes are defined only for s. cerevisiae.
these results show that while conservation rates for interactions within protein complexes are indeed higher than the 'baseline' reported above, they still do not provide a complete and robust explanation to the question of conservation.
conservation of interactions by molecular activity
beltrao et al.
<cit>  observed that stable interactions are more conserved than transient interactions for specific types of interactions .
while we cannot obtain enough data to test this specific observation using our integrated networks, we did examine the role played by the various functions of proteins in distinguishing conserved and non conserved interactions.
we looked at interactions for proteins with certain molecular functions  with the rest of the genome for all molecular functions annotations in go that contains more than  <dig> genes in s. cerevisiae.
the average conservation rate for the molecular function term  is similar to the baseline for the go network .
interestingly, there are big differences for conservation rates for the different mf terms .
interactions that link transporters  exhibit significantly lower rates of conservation probably due to their dynamic nature .
a recent study on three yeast species  <cit>  showed how differential expression of abc transporters resulted in inherently different mechanisms for coping with an anti-fungal medicine.
interactions linking rna polymerase ii transcription factor activity  also have lower conservation rates , possibly due to the specific regulation in each of the species and the transient nature of the interaction  <cit> .
interactions connecting proteins annotated with kinase activity , a category that consists of  <dig> proteins, are conserved at rates of 14%/23%, but the sub category of protein kinase activity  that contains  <dig> proteins are conserved at rates of 19%/29% which is higher than the average.
interactions linking structural ribosome activity  showed a significant higher-than-average conservation rate  which is in accordance with previous findings  <cit> .
it is important to note that the size of the molecular function terms did not have any effect on the conservation rates.
to conclude, while the molecular function of a protein has an effect on the conservation rates of the interactions, we cannot establish a clear trend showing that stable interactions are always more conserved than transient interactions.
moreover, even the most conserved category, rna binding activity , shows only moderate conservation levels .
extracting modules from diverse interaction datasets
our analysis above indicates that the low conservation rates proposed so far  do not always generalize when applied to comprehensive data .
we thus hypothesized that a more general mechanism that combines elements from these proposed directions may be responsible for the low overlap between species.
specifically, we combined different types of interaction data to find gene modules, sets of highly interacting genes that often share similar function.
using these modules we studied the conservation of genomic interaction data at the network level rather than at the individual protein level.
we used the markov clustering algorithm   <cit>  to search for modules in the integrated networks for each species .
mcl partitions a graph via a simulation of random walks effectively placing each node into exactly one module.
therefore, each module is a set of highly connected proteins and often contains different types of interactions.
since mcl can incorporate edge-weight information, edges that have higher linkage scores or are observed in more than one data type are more likely to be in the same module.
mcl was also shown to be robust to random edge addition or removal  <cit> , a key issue for noisy genomic data.
modules that did not include at least  <dig> nodes were discarded from further analyses .
module sizes follow exponential distribution with very few modules containing more than  <dig> nodes .
as expected, many of the modules are significantly enriched with various functional go categories .
in addition, some of the modules in s. cerevisiae significantly overlap protein complexes derived from high throughput experiments  <cit> , though many modules are not related to protein complexes .
to evaluate the significance of our results, we created random networks for each of the real networks we studied for comparison.
we tried two randomization methods; edge switching randomization and node label randomization .
the first randomization method retains the degree distribution of the original networks and the second randomization method retains both the degree distributions and diameters.
we used these random networks to identify random modules and to compare them across species in the same way real modules were identified and analyzed.
<dig> random networks were generated for each data type and the results were averaged.
conservation of functional genomics data on the module level
we divided all interactions into two sets.
the first set is 'within-module interactions' .
these interactions connect two nodes that reside in the same module in species a. the second set is 'between-modules interactions' .
these interactions connect two nodes that reside in different modules in species a. finally, we defined an interaction as 'extended module conservation' when the interaction itself is not directly conserved, but the orthologs of the two genes connected by the interaction reside in the same module in b .
an 'extended module conservation' can indicate either a specific interaction that exists in the other species but so far has not been experimentally tested, or an interaction that is not conserved in the other species, but its functional effect is retained via the module structure .
recall that the overall interaction conservation rates between s. cerevisiae and s. pombe are  <dig> %/ <dig> %.
however, using our modules we show that this is the result of two very different sets of interactions.
the wmi conservation rates are much higher.
<dig> %/ <dig> % of wmis are conserved between the two yeasts .
in contrast, bmi conservation rates are lower than the overall conservation rates at  <dig> %/ <dig> %.
to rule out the possibility that our results merely reflect the effect of hubs that might be more abundant in modules, we excluded hubs  from our analysis.
the wmi/bmi conservation statistic became even more distinct; while wmi conservation remained almost the same or better , bmi conservation rates dropped .
these trends hold for almost all other types of genomic data as well .
the numbers of wmi and bmi interactions for all species and data types including the percentages of the wmi interactions out of the total number of interactions are listed in additional file  <dig>
integrated
conservation rates are listed for the following categories: baseline: the entire networks; bmi: between-module interactions; wmi: within-module interactions; extended wmi: extended module interactions.
: statistics based on integrated network that does not include the sequence network.
: in addition to 'no-seqs', all edges connecting paralogs  were removed.
random data does not display similar trends under the edge randomization method  and under the node label switching randomization method .
in fact, in clear contrast to the observations on the real modules, statistics for the modules based on the random networks showed that the averages of the bmi conservation ratios are higher than wmi conservation for all genomics data types and species comparison, indicating that results for real data are a function of strong non-random selection bias .
none of the  <dig> random networks we generated led to conservation rates seen in the real networks .
in fact, the rates obtained for all random networks were significantly lower than those observed for the real networks indicating that there is evolutionary pressure to maintain module conservation.
the conservation rates of extended-wmi are even higher , while extended-bmi rates have only moderately increased , indicating that even if the specific interaction type is not observed in the other species, it may be that either it is actually present but was not measured, or that its effect is mediated indirectly through other members of the module.
we extended this analysis to all  <dig> pairwise species comparisons .
figure 2b presents the results for all comparisons across the different data types .
it can be seen that while the overall conservation rates change according to the distance between the species and the coverage of the specific data types, the overall trend is similar in all comparisons.
overall wmis are more conserved than average, yet they are much less conserved in the random networks.
extended module conservation further increases the conservation rates.
the only interaction type for which most comparisons do not show an improvement is negative gi.
indeed, negative gis are often found between genes in parallel pathways rather than within the same pathway  <cit> , so they are not expected to be conserved via modules.
robustness analysis
in addition to using random networks as a control we carried out several other experiments to test the robustness of our findings and show that they are independent of the way the modules are defined, the amounts of data that are being used, or the orthology matching definitions.
to rule out the possibility that the wmi:bmi statistics are a result of the way the modules definition and parameter selection, we used an alternative graph clustering method, spici  <cit> , to partition the networks into modules and ran the same analyses.
spici uses a heuristic approach to greedily build clusters from selected seeds.
this scheme is a bottom-up approach for partitioning the network whereas the other method we used, mcl, is a top-down approach.
wmis are shown to be conserved at higher rates than bmis under this graph clustering scheme as well, for almost all species comparison and data types .
we tried using a novel method for evaluating module preservation  <cit>  to check whether modules are preserved in terms of density and connectivity between the species regardless of the parameters used to obtain the modules.
even though the method was not intended for cross species analysis few modules were found to be significantly preserved .
in addition, we tested conservation rates for modules that are based on previous knowledge rather than clustering the interaction data.
we created modules based on gene ontology terms that are defined based on direct experimental evidence only .
while the resulting networks and modules are smaller and less comprehensive compared to our interactions data, the conservation trends for the go-based modules are similar to the modules based on interaction data .
all together, these results show that our conclusions hold and are independent of the way the modules are defined, as long as there is a strong functional relationship within the module.
we also studied the effect of insufficient data coverage on our results.
missing data is the most common reason for differences between the true biological networks and our integrated networks.
this is more likely to be the case for species other than s. cerevisiae, as fewer experiments for all data types were conducted.
to this end, we randomly removed edges from the s. cerevisiae network and generated modules that are based on the trimmed networks.
calculating the conservation rates against s. pombe showed that in all cases our results regarding the large increase in wmi and extended-wmi conservation still hold .
also, many of the modules from the full s. cerevisiae network were significantly retained in the trimmed networks .
to rule out the possibility that our results are affected by the orthology definition we repeated the analysis using inparanoid  <cit>  mapping.
very similar results to the ones presented above were achieved for the one-to-one mappings generated from inparanoid .
furthermore, we checked whether using many-to-many  inparanoid mapping would change our results.
conservation definitions are slightly changed under m:n mapping definitions.
we marked an edge as conserved in the query species if any edge between possible orthologous nodes in the reference species was conserved.
while conservation statistics for both wmi and bmi in almost all species and data types naturally increased using the new definitions, the trend for wmi to have higher conservation rates is retained for most comparisons .
we further evaluated the effect of stricter orthology mappings on the conservation patterns.
we tried various orthology mappings between s. cerevisiae and s. pombe by keeping only high confidence orthology matching between the two species .
stricter orthology mapping corresponded to fewer interactions whose functions are known to be more conserved , and showed similar or higher wmi/bmi conservation rate patterns for most comparisons .
lastly, we evaluated our results using an integrated network that included only the co-expression, ppi, and gi positive and did not include the sequence networks to rule out the possibility that our results are driven by paralog conservation.
the trends we observed for our original analysis remained the same for this smaller network indicating that our module based conservation result is robust to the type of data used .
moreover, we created an additional network in which we further excluded all interactions  connecting two nodes  with blastp e-value cutoff of 1e- <dig> or less in all species.
we observed the same trends for this network as for the other networks we analyzed  indicating that module-based conservation is a general trend that is independent of sequence conservation.
conservation of modules across species
having established the within-modules conservation trend, we asked whether the modules themselves are conserved  across the species.
for this we extracted all modules with at least three members resulting in  <dig> modules for s. cerevisiae,  <dig> for s. pombe,  <dig> for c. elegans and  <dig> for d. melanogaster.
for each such module we computed the significance of its overlap with all modules in the other three species .
for s. cerevisiae,  <dig> modules were found to match s. pombe modules, with a reciprocal p-value <  <dig>  .
this number, which is 25% of all s. pombe modules, is high considering coverage limits.
a total of  <dig> matches were found for all species comparisons .
figure 4a shows a graph with significant reciprocal matches between the modules.
we next examined modules that are conserved among all species in our analysis, and  <dig> such groups were found, spanning various functional categories like signal transduction, protein folding, metabolic processes and many others.
figures 4b,c,d present some examples of such modules.
the module matches are based on the nodes, nevertheless these examples show that relatively little rewiring  had occurred between orthologous proteins that participate in these modules.
modules may also contain other proteins that do not have an ortholog.
figure 4b shows orthologous proteins from modules that are significantly enriched for proteolysis and are part of the proteasome complex.
s. cerevisiae, the most extensively studied organism in our study, shows many interactions from the various networks like co-expression, ppi, and sequence, and even other types of interactions like genes that are co-regulated by the same transcription factor  <cit> , which were not used in the module construction process.
many of the ppi interactions in the s. cerevisiae module are retained in the matched c. elegans module, and we can suspect that similar interactions should be experimentally found in s. pombe.
the many similar co-expression edges observed for s. pombe indicate that these proteins are probably present at the same time in the cell, which increases their likelihood of forming ppis.
similarly, figure 4c shows orthologous proteins from modules that are all enriched for dna replication in the s phase of the mitotic cell cycle.
s. cerevisiae and s. pombe exhibit very similar patterns of ppi and gi, which were not measured for c. elegans.
nonetheless, the co-expression and sequence edges indicate that it is likely that the ppi and gi edges should be present in c. elegans as well.
figure 4d shows an example for modules enriched for protein folding.
s. pombe exhibits many co-expression edges, especially with tcp1/cct <dig> that are absent in s. cerevisiae.
nonetheless, many of these edges are present in s. cerevisiae as ppi edges, a fact that might indicate that these modules operate in a similar manner in both species, as ppi are more likely to be co-expressed.
discussion
our results indicate that while, in general, interactions at the node  level are conserved at low rates, interactions within modules are conserved to a much greater degree.
this raises the intriguing possibility that interactions are conserved on a level different from that of the individual genes.
in other words, while there is a strong selective pressure to maintain interactions within a module, there is less pressure to maintain between-module interactions.
the within-module conservation statistics that are presented in this study are probably an underestimate for the real conservation rates due to the incompleteness of interaction data  <cit> .
our results are robust with respect to varying the amount of available data , when compared to random interaction networks, across all four species we studied.
many of the modules we discover independently in each species are significantly conserved across more than one species, and we expect this number to grow once additional data becomes available.
this refined understating of conservation may lead to better cross species search tools that can utilize the network context in addition to sequence similarity.
our results also shed new light on some recent discoveries about the relationships between genes associated with very different phenotypic outcomes in close species  <cit> .
the results suggest that while modules are conserved, interactions between modules may change at a higher pace, allowing modules involved in a specific function in one species to become involved in a different function in another species through interactions with other modules.
a possible analogy to our proposed view for module conservation is sequence conservation .
when looking at the sequence similarity between close species, we see that the overall similarity is lower than the similarity of the coding regions, as there is less evolutionary pressure to preserve intergenic regions.
similarly, the overall network similarity is lower than the similarity of the modules, as there is less evolutionary pressure to preserve between-modules interactions.
there are also cases where some nucleotide substitutions in coding regions result in functionally similar proteins .
likewise, changes in within-module interactions can result in functionally similar modules, and can be explained by redundancy or indirect interactions via a third protein, as long as the two proteins remain in the same module.
this network organization structure allows both robustness  and flexibility  which may confer advantages in evolving species.
CONCLUSIONS
our results indicate that although individual interactions in one species are generally conserved at lower levels when compared directly with a closely related species, interactions within functional modules are much more likely to be conserved.
in contrast, interactions between functional modules are usually conserved at a lower rate than the general case.
this may introduce flexibility in the evolution of networks since such between-module interactions can change more rapidly, allowing modules involved in a specific function in one species to become involved in a different function in another species through interactions with other modules.

 basic cellular systems including the cell cycle, innate immunity, and mrna translation operate in a similar manner across a large number of species. the proteins that participate in these systems are highly conserved, enabling many successful applications to infer gene function based on sequence similarity across species. while genes with very similar sequence often perform the same function, dynamic properties of conserved proteins, including expression and interactions, seem to differ substantially between species. in studies profiling similar tissues in mouse and human, researchers found a large divergence in expression profiles. the correlation of cell cycle expression between two yeasts was determined to be around. similarly, in protein-dna binding studies, researchers found that only 11% of binding interactions for highly conserved transcription factors overlapped between human and mouse. studies of three yeast species with high sequence similarity identified only 20% overlap in binding targets and similar results were obtained for bacteria. protein interactions were also found to overlap at very low rates. only an estimated 18% to 29% of negative genetic interactions between s. cerevisiae and s. pombe were found to be conserved. early studies have mainly focused on pairwise comparisons based on a single genomic data type. while the results in these early papers indicated low overlap between species, no attempt was made to generalize observations to address reasons for the lower conservation of interaction data when compared to sequence data conservation. recent high throughput experiments with better coverage made it possible to reassess the conservation of interaction data. a number of possible reasons have been proposed to explain the lack of conservation for specific types of interaction data. for example, fox et al. observed that interactions connecting hub proteins are more conserved when compared to interactions involving proteins with a lower degree of connectivity. as they show using ppi data from multiple species, there is a positive correlation between the average degree of a protein and the conservation of its interacting partners. byrne et al. studied the genetic interaction networks of s.cerevisiae and c.elegans and reported that while only little overlap is seen for individual interactions, the properties of their genetic interaction networks are conserved. they proposed that changes in individual genetic interactions might be a form of evolution. another direction suggested by roguev et al. demonstrated that conservation of interactions within protein complexes is higher than that of other interactions. they compared genetic interactions between chromatin-related genes in two yeasts and determined that protein complexes and the evolution of a new biological mechanism can help explain the minimal overlap observed, hypothesizing that protein-protein interactions pose a constraint on functional divergence in evolution. similarly, jensen et al. compared cell cycle expression of a number of species and discovered that while in-time expression was not conserved at the individual gene level, it was much more conserved at the protein complex level. van dam and snel showed that conservation rates for ppi within complexes in human and yeast are much higher than overall interaction conservation. on the other hand, wang and zhang studied conservation of yeast, fly, and nematode ppi networks and determined that interactions in protein complexes are not conserved at levels that are higher than other interactions. beltrao et al. claimed that protein complexes are correlated with higher conservation only for stable interactions, while transient interactions, including phosphoregulation, are less conserved. the experimental methods used to obtain expression data are large scale and produce measurements for the entire genome leading to a significantly better coverage of the interactome compared to the other data types. in addition, as there is no equivalent to protein complexes in expression data, early analysis of the conservation of dynamic properties in expression data focused on the identification of conserved expression modules across species. while some important expression modules were conserved, many others were not. the above discussion illustrates several trends observed for the conservation of interactions across species. one of the reasons for the disagreement between the results of these observations is the fact that each was only tested on a small dataset, often for only one type of interaction data, in one specific condition and between a single pair of species. to determine which of these trends hold more generally we performed a comprehensive analysis using four model organisms, and several genomic data types measured under a variety of conditions. as we show below, while all the proposed directions so far indeed explain part of the differences between species, none is enough to provide a comprehensive explanation. we have thus attempted to generalize these suggestions. our findings suggest that while sequence and function are conserved at the individual protein level, interactions are conserved at a higher organizational level for which we use the term'functional modules'. these results indicate that while gene-gene interactions are not well conserved, the overall network, through the intermediate level of modules, is conserved to a much higher degree.  data collection and processing we focused on four species for which large interaction datasets are available: the two yeasts s. cerevisiae and s. pombe, the nematode c. elegans, and the fruit fly d. melanogaster. we retrieved available sequence, expression, protein-protein interaction, and genetic interaction data as well as gene ontology annotations for all species. see methods for details. to facilitate the comparison of genomic datasets across species, we converted all datasets into network representation using a probabilistic approach that assigns a score to each edge between two genes based on their likelihood of participating in the same biological process. this method was used in the past to determine appropriate cutoffs for correlation networks in each species. from this point on, we refer to each data type as a network. the co-expression, ppi, positive gi, and sequence networks were combined to create an integrated weighted network separately for each species. additional integrated network that includes only the co-expression, ppi, and positive gi was tested as well. only positive genetic interactions were used for the integrated network as negative gis are often found between genes in parallel pathways rather than within the same pathway. for each edge in the integrated networks, its score was calculated by summing up the log likelihood scores for that edge across the four individual network types. the integrated network represents the most comprehensive functional association aggregation that we are able to achieve for each of the species in our study from the currently available experimental data. we determined orthology relationships using genedb and reciprocal best blastp hits.. for a specific network in species a we extracted all pairs of genes ga, and ga, that are connected in that network. if both genes have orthologs in species b we define the interaction ga,1- ga, to be directly conserved if their orthologs have the same interaction in species b. we first computed conservation statistics directly from the networks for each species. most interaction datasets are not well conserved across species, including networks that are fairly complete. the'baseline' column in table presents the overall conservation of interaction data between s. cerevisiae and s. pombe, the two closest species in our study. the overall conservation of the integrated gene network is % for s. cerevisiae with respect to s. pombe, and % for s. pombe with respect to s. cerevisiae. of all the types of datasets in our analysis, expression data is the most abundant. however, the coexpression interactions between these two yeasts are only conserved at a rate of %/ % which is still low, although it is indeed higher than the other experimental data types. in contrast, we find a better agreement between go edges of the two species despite the relatively low coverage of go annotation for s. pombe. conservation rates for s. pombe with respect to s. cerevisiae are based on the integrated networks for the following categories: baseline: the entire networks; hubs: highest rate reported for any bin based on node degree; complexes: complexes as defined by the gavin and krogran studies; molecular function: highest rate reported for interactions with any go molecular function; wmi: within-module interactions; wmi - no hubs: wmi excluding interactions with hubs; extended wmi: extended module interactions. see text for further details. conservation of hub interactions several studies have previously analyzed specific interaction datasets in multiple species and identified trends in these datasets that differentiated conserved and non conserved interactions. to test how these generalize to the large datasets we collected we have reformulated some of these observed trends as possible explanations to the low conservation rates and analyzed them using our integrated networks. we first checked whether interactions involving hub proteins are more likely to be conserved. in order to examine this, we binned the nodes according to their degrees in the integrated network, and for each bin, we calculated the conservation rates for interactions involving at least one node whose degree falls into that bin. we found a positive correlation between the degree of the nodes and the conservation rates of the interactions that connect them with their partners. fewer than 15% of the interactions involving nodes with low degrees, which include the vast majority of the interactions, are conserved in both s. cerevisiae and s. pombe, while for those interactions involving nodes with high degrees, 24-26% are conserved. therefore, we conclude that hub interactions are conserved at rates that are better than average, and the effect of hubs should be considered in subsequent analyses. nonetheless, the conservation rates of hub interactions are still much lower than the conservation of sequence data and they provide only a limited explanation for the even lower conservation rates of all interactions. conservation of interactions within protein complexes protein complexes were previously shown to have higher conservation rates. this analysis was limited to protein-protein interactions but interactions of other genomic data types that coincide with ppi were also shown to have higher conservation rates. in our analysis, we checked conservation rates for protein complexes that were defined in two recent studies in s. cerevisiae. interactions in the integrated network that were part of the complexes defined by krogan et al. were conserved at a rate of %, while the interactions that were part of the complexes identified by gavin et al. had a conservation rate of %. note that this is only a one-way comparison, since the complexes are defined only for s. cerevisiae. these results show that while conservation rates for interactions within protein complexes are indeed higher than the'baseline' reported above, they still do not provide a complete and robust explanation to the question of conservation. conservation of interactions by molecular activity beltrao et al. observed that stable interactions are more conserved than transient interactions for specific types of interactions. while we cannot obtain enough data to test this specific observation using our integrated networks, we did examine the role played by the various functions of proteins in distinguishing conserved and non conserved interactions. we looked at interactions for proteins with certain molecular functions with the rest of the genome for all molecular functions annotations in go that contains more than genes in s. cerevisiae. the average conservation rate for the molecular function term is similar to the baseline for the go network. interestingly, there are big differences for conservation rates for the different mf terms. interactions that link transporters exhibit significantly lower rates of conservation probably due to their dynamic nature. a recent study on three yeast species showed how differential expression of abc transporters resulted in inherently different mechanisms for coping with an anti-fungal medicine. interactions linking rna polymerase ii transcription factor activity also have lower conservation rates, possibly due to the specific regulation in each of the species and the transient nature of the interaction. interactions connecting proteins annotated with kinase activity, a category that consists of proteins, are conserved at rates of 14%/23%, but the sub category of protein kinase activity that contains proteins are conserved at rates of 19%/29% which is higher than the average. interactions linking structural ribosome activity showed a significant higher-than-average conservation rate which is in accordance with previous findings. it is important to note that the size of the molecular function terms did not have any effect on the conservation rates. to conclude, while the molecular function of a protein has an effect on the conservation rates of the interactions, we cannot establish a clear trend showing that stable interactions are always more conserved than transient interactions. moreover, even the most conserved category, rna binding activity, shows only moderate conservation levels. extracting modules from diverse interaction datasets our analysis above indicates that the low conservation rates proposed so far do not always generalize when applied to comprehensive data. we thus hypothesized that a more general mechanism that combines elements from these proposed directions may be responsible for the low overlap between species. specifically, we combined different types of interaction data to find gene modules, sets of highly interacting genes that often share similar function. using these modules we studied the conservation of genomic interaction data at the network level rather than at the individual protein level. we used the markov clustering algorithm to search for modules in the integrated networks for each species. mcl partitions a graph via a simulation of random walks effectively placing each node into exactly one module. therefore, each module is a set of highly connected proteins and often contains different types of interactions. since mcl can incorporate edge-weight information, edges that have higher linkage scores or are observed in more than one data type are more likely to be in the same module. mcl was also shown to be robust to random edge addition or removal, a key issue for noisy genomic data. modules that did not include at least nodes were discarded from further analyses. module sizes follow exponential distribution with very few modules containing more than nodes. as expected, many of the modules are significantly enriched with various functional go categories. in addition, some of the modules in s. cerevisiae significantly overlap protein complexes derived from high throughput experiments, though many modules are not related to protein complexes. to evaluate the significance of our results, we created random networks for each of the real networks we studied for comparison. we tried two randomization methods; edge switching randomization and node label randomization. the first randomization method retains the degree distribution of the original networks and the second randomization method retains both the degree distributions and diameters. we used these random networks to identify random modules and to compare them across species in the same way real modules were identified and analyzed. random networks were generated for each data type and the results were averaged. conservation of functional genomics data on the module level we divided all interactions into two sets. the first set is'within-module interactions'. these interactions connect two nodes that reside in the same module in species a. the second set is'between-modules interactions'. these interactions connect two nodes that reside in different modules in species a. finally, we defined an interaction as'extended module conservation' when the interaction itself is not directly conserved, but the orthologs of the two genes connected by the interaction reside in the same module in b. an'extended module conservation' can indicate either a specific interaction that exists in the other species but so far has not been experimentally tested, or an interaction that is not conserved in the other species, but its functional effect is retained via the module structure. recall that the overall interaction conservation rates between s. cerevisiae and s. pombe are %/ %. however, using our modules we show that this is the result of two very different sets of interactions. the wmi conservation rates are much higher. %/ % of wmis are conserved between the two yeasts. in contrast, bmi conservation rates are lower than the overall conservation rates at %/ %. to rule out the possibility that our results merely reflect the effect of hubs that might be more abundant in modules, we excluded hubs from our analysis. the wmi/bmi conservation statistic became even more distinct; while wmi conservation remained almost the same or better, bmi conservation rates dropped. these trends hold for almost all other types of genomic data as well. the numbers of wmi and bmi interactions for all species and data types including the percentages of the wmi interactions out of the total number of interactions are listed in additional file integrated conservation rates are listed for the following categories: baseline: the entire networks; bmi: between-module interactions; wmi: within-module interactions; extended wmi: extended module interactions.: statistics based on integrated network that does not include the sequence network.: in addition to'no-seqs', all edges connecting paralogs were removed. random data does not display similar trends under the edge randomization method and under the node label switching randomization method. in fact, in clear contrast to the observations on the real modules, statistics for the modules based on the random networks showed that the averages of the bmi conservation ratios are higher than wmi conservation for all genomics data types and species comparison, indicating that results for real data are a function of strong non-random selection bias. none of the random networks we generated led to conservation rates seen in the real networks. in fact, the rates obtained for all random networks were significantly lower than those observed for the real networks indicating that there is evolutionary pressure to maintain module conservation. the conservation rates of extended-wmi are even higher, while extended-bmi rates have only moderately increased, indicating that even if the specific interaction type is not observed in the other species, it may be that either it is actually present but was not measured, or that its effect is mediated indirectly through other members of the module. we extended this analysis to all pairwise species comparisons. figure 2b presents the results for all comparisons across the different data types. it can be seen that while the overall conservation rates change according to the distance between the species and the coverage of the specific data types, the overall trend is similar in all comparisons. overall wmis are more conserved than average, yet they are much less conserved in the random networks. extended module conservation further increases the conservation rates. the only interaction type for which most comparisons do not show an improvement is negative gi. indeed, negative gis are often found between genes in parallel pathways rather than within the same pathway, so they are not expected to be conserved via modules. robustness analysis in addition to using random networks as a control we carried out several other experiments to test the robustness of our findings and show that they are independent of the way the modules are defined, the amounts of data that are being used, or the orthology matching definitions. to rule out the possibility that the wmi:bmi statistics are a result of the way the modules definition and parameter selection, we used an alternative graph clustering method, spici, to partition the networks into modules and ran the same analyses. spici uses a heuristic approach to greedily build clusters from selected seeds. this scheme is a bottom-up approach for partitioning the network whereas the other method we used, mcl, is a top-down approach. wmis are shown to be conserved at higher rates than bmis under this graph clustering scheme as well, for almost all species comparison and data types. we tried using a novel method for evaluating module preservation to check whether modules are preserved in terms of density and connectivity between the species regardless of the parameters used to obtain the modules. even though the method was not intended for cross species analysis few modules were found to be significantly preserved. in addition, we tested conservation rates for modules that are based on previous knowledge rather than clustering the interaction data. we created modules based on gene ontology terms that are defined based on direct experimental evidence only. while the resulting networks and modules are smaller and less comprehensive compared to our interactions data, the conservation trends for the go-based modules are similar to the modules based on interaction data. all together, these results show that our conclusions hold and are independent of the way the modules are defined, as long as there is a strong functional relationship within the module. we also studied the effect of insufficient data coverage on our results. missing data is the most common reason for differences between the true biological networks and our integrated networks. this is more likely to be the case for species other than s. cerevisiae, as fewer experiments for all data types were conducted. to this end, we randomly removed edges from the s. cerevisiae network and generated modules that are based on the trimmed networks. calculating the conservation rates against s. pombe showed that in all cases our results regarding the large increase in wmi and extended-wmi conservation still hold. also, many of the modules from the full s. cerevisiae network were significantly retained in the trimmed networks. to rule out the possibility that our results are affected by the orthology definition we repeated the analysis using inparanoid mapping. very similar results to the ones presented above were achieved for the one-to-one mappings generated from inparanoid. furthermore, we checked whether using many-to-many inparanoid mapping would change our results. conservation definitions are slightly changed under m:n mapping definitions. we marked an edge as conserved in the query species if any edge between possible orthologous nodes in the reference species was conserved. while conservation statistics for both wmi and bmi in almost all species and data types naturally increased using the new definitions, the trend for wmi to have higher conservation rates is retained for most comparisons. we further evaluated the effect of stricter orthology mappings on the conservation patterns. we tried various orthology mappings between s. cerevisiae and s. pombe by keeping only high confidence orthology matching between the two species. stricter orthology mapping corresponded to fewer interactions whose functions are known to be more conserved, and showed similar or higher wmi/bmi conservation rate patterns for most comparisons. lastly, we evaluated our results using an integrated network that included only the co-expression, ppi, and gi positive and did not include the sequence networks to rule out the possibility that our results are driven by paralog conservation. the trends we observed for our original analysis remained the same for this smaller network indicating that our module based conservation result is robust to the type of data used. moreover, we created an additional network in which we further excluded all interactions connecting two nodes with blastp e-value cutoff of 1e- or less in all species. we observed the same trends for this network as for the other networks we analyzed indicating that module-based conservation is a general trend that is independent of sequence conservation. conservation of modules across species having established the within-modules conservation trend, we asked whether the modules themselves are conserved across the species. for this we extracted all modules with at least three members resulting in modules for s. cerevisiae, for s. pombe, for c. elegans and for d. melanogaster. for each such module we computed the significance of its overlap with all modules in the other three species. for s. cerevisiae, modules were found to match s. pombe modules, with a reciprocal p-value <. this number, which is 25% of all s. pombe modules, is high considering coverage limits. a total of matches were found for all species comparisons. figure 4a shows a graph with significant reciprocal matches between the modules. we next examined modules that are conserved among all species in our analysis, and such groups were found, spanning various functional categories like signal transduction, protein folding, metabolic processes and many others. figures 4b,c,d present some examples of such modules. the module matches are based on the nodes, nevertheless these examples show that relatively little rewiring had occurred between orthologous proteins that participate in these modules. modules may also contain other proteins that do not have an ortholog. figure 4b shows orthologous proteins from modules that are significantly enriched for proteolysis and are part of the proteasome complex. s. cerevisiae, the most extensively studied organism in our study, shows many interactions from the various networks like co-expression, ppi, and sequence, and even other types of interactions like genes that are co-regulated by the same transcription factor, which were not used in the module construction process. many of the ppi interactions in the s. cerevisiae module are retained in the matched c. elegans module, and we can suspect that similar interactions should be experimentally found in s. pombe. the many similar co-expression edges observed for s. pombe indicate that these proteins are probably present at the same time in the cell, which increases their likelihood of forming ppis. similarly, figure 4c shows orthologous proteins from modules that are all enriched for dna replication in the s phase of the mitotic cell cycle. s. cerevisiae and s. pombe exhibit very similar patterns of ppi and gi, which were not measured for c. elegans. nonetheless, the co-expression and sequence edges indicate that it is likely that the ppi and gi edges should be present in c. elegans as well. figure 4d shows an example for modules enriched for protein folding. s. pombe exhibits many co-expression edges, especially with tcp1/cct that are absent in s. cerevisiae. nonetheless, many of these edges are present in s. cerevisiae as ppi edges, a fact that might indicate that these modules operate in a similar manner in both species, as ppi are more likely to be co-expressed. discussion our results indicate that while, in general, interactions at the node level are conserved at low rates, interactions within modules are conserved to a much greater degree. this raises the intriguing possibility that interactions are conserved on a level different from that of the individual genes. in other words, while there is a strong selective pressure to maintain interactions within a module, there is less pressure to maintain between-module interactions. the within-module conservation statistics that are presented in this study are probably an underestimate for the real conservation rates due to the incompleteness of interaction data. our results are robust with respect to varying the amount of available data, when compared to random interaction networks, across all four species we studied. many of the modules we discover independently in each species are significantly conserved across more than one species, and we expect this number to grow once additional data becomes available. this refined understating of conservation may lead to better cross species search tools that can utilize the network context in addition to sequence similarity. our results also shed new light on some recent discoveries about the relationships between genes associated with very different phenotypic outcomes in close species. the results suggest that while modules are conserved, interactions between modules may change at a higher pace, allowing modules involved in a specific function in one species to become involved in a different function in another species through interactions with other modules. a possible analogy to our proposed view for module conservation is sequence conservation. when looking at the sequence similarity between close species, we see that the overall similarity is lower than the similarity of the coding regions, as there is less evolutionary pressure to preserve intergenic regions. similarly, the overall network similarity is lower than the similarity of the modules, as there is less evolutionary pressure to preserve between-modules interactions. there are also cases where some nucleotide substitutions in coding regions result in functionally similar proteins. likewise, changes in within-module interactions can result in functionally similar modules, and can be explained by redundancy or indirect interactions via a third protein, as long as the two proteins remain in the same module. this network organization structure allows both robustness and flexibility which may confer advantages in evolving species.  our results indicate that although individual interactions in one species are generally conserved at lower levels when compared directly with a closely related species, interactions within functional modules are much more likely to be conserved. in contrast, interactions between functional modules are usually conserved at a lower rate than the general case. this may introduce flexibility in the evolution of networks since such between-module interactions can change more rapidly, allowing modules involved in a specific function in one species to become involved in a different function in another species through interactions with other modules. 
several recent studies comparing high throughput data including expression, protein-protein, protein-dna, and genetic interactions between close species show conservation at a much lower rate than expected. however, while sequence similarity often implies functional similarity, interaction data is not well conserved even for proteins with high sequence similarity. in this work we collected comprehensive high-throughput interaction datasets for four model organisms and carried out systematic analyses in order to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data. we show that conservation is maintained between species, but mainly at the module level. our results indicate that interactions within modules are much more likely to be conserved than interactions between proteins in different modules. we combined all interaction evidences into an integrated network for each species and identified functional modules from these integrated networks. we then demonstrate that interactions that are part of functional modules are conserved at much higher rates than previous reports in the literature, while interactions that connect between distinct functional modules are conserved at lower rates. we first showed that several previously proposed hypotheses only provide a limited explanation for such lower conservation rates. this provides a network based explanation to the observed conservation rates that can also help explain why so many biological processes are well conserved despite the lower levels of conservation for the interactions of proteins participating in these processes.
 orthologous genes are highly conserved between closely related species and biological systems often utilize the same genes across different organisms. however, while sequence similarity often implies functional similarity, interaction data is not well conserved even for proteins with high sequence similarity. several recent studies comparing high throughput data including expression, protein-protein, protein-dna, and genetic interactions between close species show conservation at a much lower rate than expected.  in this work we collected comprehensive high-throughput interaction datasets for four model organisms and carried out systematic analyses in order to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data. we first showed that several previously proposed hypotheses only provide a limited explanation for such lower conservation rates. we combined all interaction evidences into an integrated network for each species and identified functional modules from these integrated networks. we then demonstrate that interactions that are part of functional modules are conserved at much higher rates than previous reports in the literature, while interactions that connect between distinct functional modules are conserved at lower rates.  we show that conservation is maintained between species, but mainly at the module level. our results indicate that interactions within modules are much more likely to be conserved than interactions between proteins in different modules. this provides a network based explanation to the observed conservation rates that can also help explain why so many biological processes are well conserved despite the lower levels of conservation for the interactions of proteins participating in these processes. accompanying website: http://www.sb.cs.cmu.edu/crosssp 