Levy et al. BMC Bioinformatics (2015) 16:164 DOI 10.1186/s12859-015-0588-y

SOFTWARE Open Access

NetCooperate: a network-based tool for inferringhost-microbe and microbe-microbe cooperationRoie Levy1, Rogan Carr1, Anat Kreimer2,3, Shiri Freilich4 and Elhanan Borenstein1,5,6*

Abstract

Background: Host-microbe and microbe-microbe interactions are often governed by the complex exchange ofmetabolites. Such interactions play a key role in determining the way pathogenic and commensal species impacttheir host and in the assembly of complex microbial communities. Recently, several studies have demonstratedhow such interactions are reflected in the organization of the metabolic networks of the interacting species, andintroduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directlyfrom network topology. Using these methods, such studies have revealed evolutionary and ecological processesthat shape species interactions and community assembly, highlighting the potential of this reverse-ecology researchparadigm.

Results: NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbecooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: theBiosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasiticor a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair ofmicrobial organisms’ niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metricsas well as a list of potential syntrophic metabolic compounds.

Conclusions: The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbeand microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolicnetwork topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-basedtool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.

Keywords: Species interactions, Microbial ecology, Community assembly, Systems biology, Metabolic networks,Reverse ecology

BackgroundIn the post-genomic era, genome scale reconstructionsof various biological networks have become a powerfultool for studying the behavior of organisms [1]. For ex-ample, genome-scale metabolic models can be used topredict growth rates of microbial species following per-turbation [2,3], signaling network models can be used topredict cell phenotypes [4], and regulatory networks canbe used to map cell-specific developmental programs [5].

* Correspondence: elbo@uw.edu1Department of Genome Sciences, University of Washington, Seattle, WA98195, USA5Department of Computer Science and Engineering, University ofWashington, Seattle, WA 98195, USAFull list of author information is available at the end of the article

© 2015 Levy et al.; licensee BioMed Central. ThCommons Attribution License (http://creativecreproduction in any medium, provided the orDedication waiver (http://creativecommons.orunless otherwise stated.

Methods for analyzing the topology of such genome-scalenetworks were shown to be especially useful for revealingsystems-level properties and for identifying design princi-ples of biological networks [6-8].More recently, several such studies focused on using

network-based analyses to predict ecological attributesof microorganisms, laying the foundation for a compre-hensive reverse-ecology framework [9]. This frameworkfocuses on the identification of topological properties inan organism’s metabolic network that may reflect adap-tation to specific environments or ecological interac-tions. Borenstein et al., for example, exploited thisprinciple to predict the effective chemical environmentsof numerous microbial species [10]. In this study, analgorithm was introduced to analyze the topology of a

is is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andiginal work is properly credited. The Creative Commons Public Domaing/publicdomain/zero/1.0/) applies to the data made available in this article,

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metabolic network to determine the set of exogenouslyacquired nutrients (termed the ‘seed set’) from which allother compounds in the network could be synthesized.Applying this algorithm to detect the seed sets of a largearray of microbial species, several fundamental proper-ties of the interface between organisms and their envi-ronments, as well as large-scale evolutionary trends,were identified. This reverse-ecology method was furtherhighlighted for its biotechnological and environmentalapplications by numerous studies (e.g., [11-13]). Theseed detection algorithm was later presented also as aweb-based tool and a software package, making thismethod easily accessible to researchers with any level ofcomputational expertise [14].Importantly though, following the introduction of this

reverse-ecology paradigm, several other techniques weredeveloped, going beyond a single-species analysis andaiming to investigate multi-species microbial systems.Exploring such systems and characterizing the complexweb of interactions between member species are crucialfor gaining a principled understanding of these complexecosystems, their assembly rules, and the fundamentalprinciples that govern microbial ecology. These methodsfocused specifically on quantifying maximal cohabitation[15], effective competition [16,17], and niche overlap[18], providing a diverse set of tools for predictingspecies competitive potential.Clearly, however, ecological interaction between spe-

cies is not limited to competition and, in nature, cooper-ation between species represents an additional crucialaspect of species interaction, with potentially markedimpact on both the interacting species and their envir-onment [19]. Following the reverse-ecology paradigmand assuming that cooperative relationships are similarlyreflected in the topology of metabolic networks, severalmetrics have recently been presented to predict the levelof potential cooperation between interacting species. Forexample, Christian et. al. described a method to quantifythe extent to which the set of metabolites a microbialspecies can synthetize in a given environment [20-22] isexpanded by (or conversely, redundant with) the set ofmetabolic reactions carried out by an interacting partner[23]. This measure of metabolic synergy was used todemonstrate that metabolic networks that are neithertoo similar nor too dissimilar stand to gain the mostfrom interaction [23] (and see also [19]). Here, we focuson two other topology-derived metrics that are specific-ally based on the reverse-ecology seed-finding algorithmdescribed above to quantify the potential strength ofcross-species ecological interactions by assessing theextent to which the biosynthetic capacity of one speciescan support or complement the nutritional requirements(i.e., the seed set) of another species. The first metric,the Biosynthetic Support Score (BSS), quantifies the

capacity of a host organism to meet the nutritional re-quirements of a parasitic endosymbiont [24]. This scorehas been used, for example, to predict the strength of in-teractions between eukaryotic hosts and potential patho-gens, and revealed gradual adaptation of parasites totheir specific hosts on an evolutionary scale. The second,the Metabolic Complementarity Index (MCI), quantifiesthe extent to which two microbial species may supportone another through biosynthetic complementarity, andprovides a measure of the potential for syntrophy thatexists between them [18]. The Metabolic Complemen-tarity Index has been used to determine community-level assembly rules in the human gut microbiome andto demonstrate that the microbiome is dominated byhabitat filtering. Notably, these two metrics do not ne-cessarily measure active parasitism or cross-feeding, butrather the metabolic potential for such interactions asreflected by the metabolic networks of the two organ-isms. The realization of this potential depends on theenvironment in which the two species are placed andthe availability of nutrients in this environment ([25]; seealso below).To make these methods easily accessible to researchers

of microbe-microbe and host-microbe interactions, herewe present NetCooperate, a web-based tool and a soft-ware package for calculating both the BSS and MCI met-rics. This tool provides a network-based framework forpredicting cooperative species interactions, and comple-ments previously introduced reverse-ecology tools in of-fering a comprehensive suite of network-based methodsfor predicting the ecological attributes and ecological in-teractions of microbial species.

ImplementationNetCooperate receives as input the metabolic networksof two species, each encoded as a directed graph withnodes representing compounds and edges connectingsubstrates to products. It then calculates and plots thepairwise Biosynthetic Support Score and MetabolicComplementarity Index of each network to its partner.These metrics are based on pairwise comparison of spe-cies’ nutritional profiles as predicted by the seed detec-tion algorithm [10]. Briefly, the seed detection algorithmutilizes a graph-theory-based method to analyze the top-ology of a metabolic network and to determine its seedset – the minimal set of compounds that should be ac-quired exogenously in order to allow the synthesis of allother compounds in the network. This set was shown tosuccessfully serve as a proxy for the effective biochem-ical environment and the natural habitat of a species[10]. Importantly, due to the existence of multiple seedset solutions, this algorithm combines seed compoundsinto seed groups, such that any compound in the groupcan serve as an alternative seed in the seed set solution.

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Using the predicted seed sets of two species, the BSS iscalculated based on the fraction of seed groups in thenetwork of the first species (the parasite) of which atleast one compound can be found in the network of thesecond species (the host; Figure 1A). Similarly, the MCIis calculated based on the fraction of seed groups in thenetwork of the first species of which at least one com-pound can be found in the network, but not in the seedset, of the second species (Figure 1B). Notably, in calcu-lating the BSS and MCI metrics, NetCooperate takesinto account all possible seed set solutions. Moreover,the software further keeps track of the set of metabolitesthat were supported (or complemented) in each species.Both BSS and MCI range from 0 to 1, with 0 denotingno potential for cooperation and 1 denoting full cooper-ation. Importantly, in interoperating these scores there isno clear threshold for determining ‘cooperation’ vs. ‘nocooperation’, and instead, as demonstrated below, thesemetrics should be used in a comparative manner toassign physiological significance and to determine whichspecies pairs exhibit strong potential for cooperationcompared to other pairs in the same settings. Futurestudies could similarly use a comparative approach thatis appropriate for the specific system under study.The NetCooperate package was implemented in Python

and allows users to quickly and easily integrate BSS andMCI calculation into existing software pipelines. TheNetCooperate web-based tool is a CGI built on top of theNetCooperate Python module (Figure 2). The user suppliestwo networks, determines various threshold parameters forboth, and can select several output options (Figure 2A).Input files can either be tab-, comma-, or space-delimitedtext files with each row representing a single directed edge,or tab-delimited text files representing network adjacency

Figure 1 A schematic illustration of the Biosynthetic Support score and Mebacterium represents a parasite that is supported by the red eukaryotic hocolored red, whereas potential products are colored orange. In this examplbe supported by the host metabolic network (grey arrows). Accordingly, thinteracting microbes, the complementation of the blue species by the rednetworks as in panel A are used). The seed A of the blue species is a produarrow). However, since F is a seed in both species, the red species cannot cblue species is therefore 0.5.

matrices. An explanation of each parameter may be foundby hovering over the adjacent tooltip button (Figure 2A), aswell as in the instruction manual available from theNetCooperate download page. The tool then calculates anddisplays the pairwise BSS and MCI metrics in both direc-tions (Figure 2B). If the user selects Show detailed seedinformation, a list of the seed compounds that are presentin the supporting network is linked to from the results. Anoption to display an interactive network visualization screenis additionally provided, allowing the user to view bothnetworks, the status of each node, and metabolic informa-tion (Figure 2C). Finally, if nodes in the network are identi-fied by KEGG compound IDs [26], the user may select theNodes are KEGG IDs option, in which case the nodes inthe network visualization serve as hyperlinks to KEGGdatabase entries with detailed chemical descriptions of eachcompound.

Results and DiscussionWe have previously successfully utilized the cooperationmetrics calculated by NetCooperate for studying a num-ber of microbial systems and have shown that they pro-vide tools for addressing fundamental questions inmicrobial ecology and evolution [18,24,27]. Such studiesdemonstrate the benefits of using systems-level toolsand the impact such tools can have on elucidating globalprinciples that govern multi-species systems. Specifically,below we discuss two such studies we have conductedthat highlight the potential of NetCooperate and itsapplicability to several systems of interest [18,24]. Thesestudies have promoted much interest and the applicationof this approach to address various challenges in bio-technological and medical settings has been highlighted[11-13]. It is our hope that providing the NetCooperate

tabolic Complementarity Index. (A) In this toy example, the bluest. The metabolic networks of both species are shown. Seeds aree, the bacterium has 2 seed groups: A and F/G/H, both of which cane BSS of the host on the parasite is 1.0. (B) In this toy example of twospecies is illustrated (for illustration purposes, the same metabolicct of the red species and can therefore be complemented (greyomplement F for the blue species. The MCI of the red species on the

Figure 2 The NetCooperate web tool interface. (A) The data input panel. The user uploads two network files and selects analysis andvisualization parameters. (B) The results panel. The BSS and MCI values are shown for all potential interactions. If the user selects ‘Show detailedseed information’ the results include links to lists of the supported (or complemented) metabolites. (C) Network visualization. If the user selects‘Visualize the scores on the networks’ both metabolic networks are plotted with seeds colored blue. Clicking on any ‘show score’ button will visuallydemonstrate compounds contributing the score: seeds which are supported (complemented) are colored red in the parasite network, andcolored green in the host network.

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tool will enable the research community at large toapply this framework to a wide array of microbialecosystems.

Predicting host-parasite interaction and characterizingpatterns of parasite adaptationParasitic species are clearly well adapted to their hosts. Inintroducing the Biosynthetic Support Score, Borensteinand Feldman aimed to examine whether such adaptationis reflected in the species’ metabolic networks andwhether it can be used to predict parasitic species andspecific host-parasite interactions [24]. To this end, theyused the Biosynthetic Support Score to quantify the

interaction between approximately 600 bacterial speciesand each of three model eukaryotic hosts (human, fruitfly, and Arabidopsis, representing a mammalian, insect,and plant host respectively). The distribution of BSSvalues of all bacteria against all hosts ranged fromapproximately 0.45 to 0.95. Importantly, a comparisonof pathogenic bacteria to free living bacteria showedthat parasitic bacteria have significantly higher BSSwith all three hosts compared to free-living bacteriaand that BSS was better in predicting parasitic speciesthan classical metrics (e.g., genome size). Moreover,the BSS of a given parasite was higher when the modelhost was phylogenetically related to the parasite’s

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natural host (e.g., mammalian parasites had significantlygreater BSS in human than in fruit fly), suggesting thatthe parasite’s metabolic network was sufficient to infernot only its parasitic life-style but also its preferredhost.To demonstrate the applicability of the Biosynthetic

Support Score to evolutionary analysis, this study furtherintegrated this cooperation score with phylogenetic ana-lysis, calculating the BSS of both extant and ancestralspecies (obtained through phylogenetic reconstruction)within the phylum Firmicutes. It was then shown thatthe biosynthetic support provided by human to anygiven bacterium increased with the phylogenetic dis-tance of the species from the common ancestor ofFirmicutes, clearly demonstrating the gradual adaptationof parasites to their host environment on a global scale.Given the success of the BSS metric in predicting host-parasite interactions, it was later also proposed as a toolfor designing culture media and for studying host-microbiome interactions [12,28].

Assessing interaction between co-occurring microbes andelucidating assembly rules in the human microbiomeThe human microbiome is a diverse and complex micro-bial ecosystem, with different individuals harboringmarkedly different sets of species. Previous surveys ofthe microbiome have revealed clear non-neutral patternsin the distribution of species and have demonstrated thatcertain species pairs tend to co-occur across microbiomesamples whereas others tend to exclude one another[29]. Yet, the underlying forces that give rise to thesepatterns were not clear. The Metabolic ComplementarityIndex was first developed to address this challenge andto study emergent organizational properties of commu-nity assembly in the human microbiome [18]. Thismetric was first validated by predicting metabolic com-plementarity among several species of the human oralmicrobiota with well-characterized and assayed interac-tions [30], to confirm that it correctly identified pre-ferred interacting partners. Indeed, in a series ofcontrolled in vitro experiments, where microbes wereplaced in a nutrient-limited saliva medium, microbeswere found to grow best in the presence of species withgreater metabolic complementarity. In such settings, theability of species to complement the nutritional require-ments of their partners translates into active cooperationand improved growth. Moving on to in vivo communi-ties of the human intestine, the MCI between all possiblepairs among >150 gut dwelling microbial species wasthen calculated. By comparing the MCI among species’co-occurring partners to their excluders, it was foundthat in fact in this nutrient-rich environment specieswith low MCI tended to co-occur, whereas species pairswith greater MCI excluded one another from a given

host-habitat. This finding suggested that in the assemblyof these communities habitat filtering outweighed theimpact of species interaction and that species relied onthe availability of nutrients in the environment ratherthan realizing the potential for cross-feeding [18,27]. Putdifferently, in this nutrient-rich environment, the poten-tial for cooperation did not necessarily materialize andspecies assortment was based on the availability of nutri-ents in the environment rather than on the presence orabsence of other species. An in depth analysis furtherrevealed that not only is MCI not an artifact of phylo-genetic relatedness, but that it was more successful atpredicting species interactions. A similar analysis wasused to investigate community assembly across multiplephylogenetic and biogeographic scales, demonstratingthat metabolic complementarity had a greater influenceon species co-occurrence patterns between members ofthe same phylum than across all species. Finally, apply-ing the MCI to species co-occurrence across and withinmultiple body sites revealed that habitat filtering is ageneral assembly rule applicable to communities inha-biting heterogeneous anatomical sites within the humanbody.

ConclusionsNetwork analysis has become an essential component inthe study of microbiology. Metabolic, regulatory, andprotein-interaction networks provide insight into thebehavior and dynamics of individual cells [31-34], whereasecological networks reveal processes defining the behaviorof entire microbial communities [29,35,36]. Yet, molecularnetwork properties are rarely used to explain patternsobserved in ecological networks, although clearly, thesetwo scales of organization are tightly linked. The reverse-ecology framework provides a powerful platform toaddress this challenge and to couple genomic informationwith environmental context. Specifically, the BiosyntheticSupport Score and the Metabolic Complementarity Indexrepresent two successful examples in which molecularnetwork analysis can be applied to ecological studies ofmicrobe-microbe and host-microbe interactions. Unfortu-nately, the implementation of such graph theory-basedmethods is not trivial, and may be beyond of the technicalcapabilities of microbiology researchers with no advancedcomputational skills. Above, we have presented Net-Cooperate, a web-based tool and Python package foreasily performing the necessary computation. NetCoo-perate can be applied on a small-scale by those studyinga microbe of interest, or it can be integrated into a lar-ger workflow for large-scale analysis of entire communi-ties. NetCooperate, along with previously introducedmethods [14,17], completes the suite of reverse-ecologyanalysis tools accessible to researchers with any level oftechnical expertise.

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Availability and requirementsProject name: NetCooperateProject home page: http://elbo.gs.washington.edu/software_netcooperate.htmlOperating system(s): Platform independentProgramming language: PythonOther requirements: Python 2.7License: GPLAny restrictions to use by non-academics: For commercialuse please contact the corresponding author

AbbreviationsBSS: Biosynthetic Support Score; MCI: Metabolic Complementarity Index.

Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsRL, RC, and EB developed and designed the Software; RL and EB performedvarious analyses using the MCI and BSS scores respectively; AK and SFprovided advice and commented on the manuscript; RL and EB wrote thepaper. All authors read and approved the final manuscript.

AcknowledgmentsRL is a National Science Foundation Graduate Research Fellow. We thank thereviewers of this paper for their thoughtful comments.

FundingThis work was supported in part by New Innovator Award DP2 AT007802-01(to EB).

Author details1Department of Genome Sciences, University of Washington, Seattle, WA98195, USA. 2Department of Electrical Engineering & Computer Science,Center for Computational Biology, UC Berkeley, Berkeley, CA 94720, USA.3Department of Bioengineering and Therapeutic Sciences, UCSF, SanFrancisco, CA 94158, USA. 4Newe Ya’ar Research Center, Agricultural ResearchOrganization, Ramat Yishay, 30095, Israel. 5Department of Computer Scienceand Engineering, University of Washington, Seattle, WA 98195, USA. 6SantaFe Institute, Santa Fe, NM 87501, USA.

Received: 12 January 2015 Accepted: 22 April 2015

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