MICROBIOLOGY OF AQUATIC SYSTEMS

Contrasting Network Features between Free-Livingand Particle-Attached Bacterial Communities in Taihu Lake

Huimin Xu1,2& Dayong Zhao1

& Rui Huang1,2& Xinyi Cao1,2

& Jin Zeng2& Zhongbo Yu1

& Katherine V. Hooker3 &

K. David Hambright3 & Qinglong L. Wu2

Received: 25 July 2017 /Accepted: 18 December 2017# Springer Science+Business Media, LLC, part of Springer Nature 2018

AbstractFree-living (FL) and particle-attached (PA) bacterial communities play critical roles in nutrient cycles, metabolite production, andas a food source in aquatic systems, and while their community composition, diversity, and functions have been well studied, weknow little about their community interactions, co-occurrence patterns, and niche occupancy. In the present study, 13 sites inTaihu Lake were selected to study the differences of co-occurrence patterns and niches occupied between the FL and PA bacterialcommunities using correlation-based network analysis. The results show that both FL and PA bacterial community networkswere non-random and significant differences of the network indexes (average path length, clustering coefficient, modularity)were found between the two groups. Furthermore, the PA bacterial community network consisted of more correlations betweenfewer OTUs, as well as higher average degree, making it more complex. The results of observed (O) to random (R) ratios of intra-or inter-phyla connections indicate more relationships such as cross-feeding, syntrophic, mutualistic, or competitive relationshipsin the PA bacterial community network. We also found that four OTUs (OTU00074, OTU00755, OTU00079, and OTU00454),which all had important influences on the nutrients cyclings, played different roles in the two networks as connectors or modulehubs. Analysis of the relationships between the module eigengenes and environmental variables demonstrated that bacterialgroups of the two networks favored quite different environmental conditions. These findings further confirmed the differentecological functions and niches occupied by the FL and PA bacterial communities in the aquatic ecosystem.

Keywords Free-living bacteria . Particle-attached bacteria . Network . Interactions

Introduction

Aquatic bacteria communities are thought to be composed oftwo distinctly separate groups, those which are free-living and

the partical-attached consortia. Particle-attached (PA) bacteriaare able to colonize nearly all types of particulate organicmatter [1] and play a crucial role in mediating nutrient cyclingin aquatic ecosystems [2–4], representing nutrient-richBhotspots^ of microbial activity even in oligotrophic environ-ments [2, 5, 6]. For decades, differences in biomass, produc-tion, activity, community composition, and function betweenfree-living (FL) and PA bacteria have been investigated inmarine ecosystems [7, 8], deep-lake environments [9–11], tur-bid estuarine ecosystems [12, 13], shallow lagoons [14], andfreshwater mesocosms [15]. Generally, PA bacteria are con-sidered to be larger in size, centralized in biomass, and higherin metabolic activity than FL bacteria [2, 16–18]. Ortega-Retuerta et al. (2013) reported that composition and structureof the two bacterial groups were different in the open sea butsimilar in coastal seas and rivers [19]. However, studies on thediversity of the two bacterial assemblages in aquatic systemsdiverge with different trophic status. Compared to FL bacteria,the diversity of PA bacteria is higher in mesotrophic and

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s00248-017-1131-7) contains supplementarymaterial, which is available to authorized users.

* Jin Zengjzeng@niglas.ac.cn

1 State Key Laboratory of Hydrology-Water Resources and HydraulicEngineering, College of Hydrology and Water Resources, HohaiUniversity, Nanjing 210098, China

2 State Key Laboratory of Lake Science and Environment, NanjingInstitute of Geography and Limnology, Chinese Academy ofSciences, Nanjing 210008, China

3 Program in Ecology and Evolutionary Biology, Department ofBiology, University of Oklahoma, Norman, OK 73019, USA

Microbial Ecologyhttps://doi.org/10.1007/s00248-017-1131-7

eutrophic lakes [10, 11, 20], whereas PA bacterial diversityhas been shown to be lower in oligotrophic marine systems[21–23]. In our previous study of Taihu Lake, a higher αdiversity of PA bacterial community was detected in compar-ison to FL bacterial community. The primary factor drivingthe community assembly of FL bacteria appeared to be deter-ministic, whereas regulation of the PA bacterial communityassembly (composition) was unknown [24]. Such differencesbetween the two bacterial fractions indicate that PA and FLbacteria could be regarded as independent components of thewhole pelagic bacterial assemblage with different species-specific interactions. To date, the possible interactions amongphylogenetic groups, as well as the ecological niches occupiedby specific groups in the FL and PA bacterial communitynetworks, are still poorly understood. However, co-occurrence relationships among phylogenetic groups in thetwo bacterial communities may provide valuable insight intothe ecological interactions of the FL and PA bacterial commu-nities [25].

High-throughput DNA sequencing technologies have con-tributed to rapid developments in the research of complexbacterial communities [26]. Many studies have focused onthe composition, structure, and diversity of bacterial commu-nities, but there has been much less attention on the direct orindirect interactions between microbial taxa coexisting in theenvironmental samples [27]. Analysis of potential interactionsbetween microbial taxa (i.e., co-occurrence patterns) in com-plicated and different microbial communities can aid in theexploration of the potential functions or environmental nichesoccupied by microbes [28, 29]. Ecological interactions be-tween species (e.g., predation, competition, and mutualism)can be viewed as complex networks [30], which can be ex-plored using network analysis tools and network thinking [31]in applications similar to those used in other fields of biology[32, 33], sociology [34], and computer science [35]. In fact,network analysis has been applied recently in the explorationof co-occurrence patterns of microbial communities in soil[27, 36], freshwater [37], and activated sludge [38]. Zhouet al. [36] reported that the structures of the microbial net-works under ambient and elevated CO2 levels were distinct.Ju et al. [38] found that bacterial communities in activatedsludge were formed non-randomly by taxonomic relatedness,and co-occurrence patterns were significantly higher thanthose expected by chance. More recently, Zhao et al. [37]documented that Cyanobacteria influence on the bacterialcommunity networks is seasonally dependent.

In the present study, we hypothesized that the networks ofPA and FL bacterial communities differ, and that this differ-ence can be ascribed to differences in habitats and communityresponses to environmental factors [24]. We used 454 pyrose-quencing of the 16S rRNA gene to explore the FL and PAbacterial communities in Taihu Lake. Thirteen sampling sitesin the lake and associated rivers were chosen to explore the co-

occurrence patterns in FL and PA bacterial communities vianetwork analysis. The following three questions were ad-dressed: (1) Are the co-occurrence patterns in PA bacterialcommunity network distinct from that in the FL bacterial com-munity network? (2) Do individual nodes in the FL and PAbacterial community network play different topological roles?(3) Do the environmental factors have different but importantimpacts on modules in FL and PA bacterial communitynetworks?

Materials and Methods

Sampling Sites and DNA Sequencing

Thirteen sites in Taihu Lake and associated rivers were select-ed. More details about the sampling sites, collection of thebacterial biomass, measurement of the environmental param-eters, DNA extraction, PCR amplification, pyrosequencing,sequence processing, and nucleotide sequence accession num-bers can be found in our previous study [24]. Briefly, wecollected water and filter 200 mL sampled water through a5-μm pore-size polycarbonate filter and a 0.22-μm pore-sizepolycarbonate membrane filter successively to collect FL andPA bacterial biomass. DNAwas extracted for PCR amplifica-tion and pyrosequencing. The sequence data were submittedto the Sequence Read Archive database (http://www.ncbi.nlm.nih.gov/sra) of the National Center for BiotechnologyInformation under accession number SRP091707. The watercharacteristics of each sample were also presented in ourprevious study [24, Table S1].

Network Construction and Characterization

From each site, an FL and a PA bacterial group were collected.The operational taxonomic units (OTUs) that occurred in atleast 8 samples of FL or PA group were selected to constructthe network to enhance the networks’ reliability. The relativeproportions of sequence numbers helped with correlationanalysis due to the different sequence numbers of individualOTUs acquired in each sample [36]. The correlation matrixwas constructed with the relative abundances of the OTUs ofeach sample in each group. The BHmisc^ package in R [39]was used to calculate the correlation matrix (R value) and thesignificance matrix (P value) by counting all the possiblepairwise Spearman’s rank correlations among all selectedOTUs of each group. Here, we only selected strong correla-tions (i.e., those with statistically significant, P ≤ 0.01,Spearman’s correlation coefficient > 0.85 (or < − 0.85)) tocomplete the network analysis [28, 40]. Various testing issueswere considered, and the possibility of obtaining false resultswas excluded using the Bqvalue^ package in R by calculatingthe Q-value, which represents the fraction of false positives or

Xu H. et al.

false negatives if a given pair is identified to be significant (Q-values < 0.05) [28]. The correlation matrix obtained abovewas then imported into Cytoscape datasets in R, and topolog-ical networks were generated and visualized in Cytoscapev2.8.2 [41]. Other important information including nodes(OTUs), edges (interactions), modules, weights, and positiveor negative correlations were also imported into Cytoscape.

The network we obtained fell into modules by the fastgreedy modularity optimization [42]. The architectures ofthe bacterial community networks were described by modu-larity, clustering coefficient, average path length, network di-ameter, average degree, and graph density using the Bigraph^package of R [43], which was also used for constructing ran-dom networks for comparison. Indices of each generated ran-dom network represent the averages and standard deviationsof 1000 random networks. Z-tests were used to assess thesignificant differences between the observed and random net-works for each group; Student’s t test was used to assess thedifferences in modularity, clustering coefficient, and averagepath length between FL and PA bacterial communitynetworks.

The Calculation of O/R Ratios between the Intra-or Inter-Phyla Interactions

The observed (O) and random (R) incidences of the co-occurrence and co-exclusion interactions among phylogeneticgroups were verified statistically using an R script. The as-sembly patterns of complicated microbial communities can betested to be non-random by results of the O/R ratio [44]. In ourstudy, the methods of calculating the observed (O) and themean value of random (R) co-occurring/co-exclusion inci-dence and their significance were carried out according toZhao et al. [37].

Distribution of Connectivity of Individual OTUs

The topological roles of individual OTUs can be decided bytwo indices called Z value and P value. Zi is the connectivityof node i within-module, while Pi is the connectivity of node iamongmodules [45]. The nodes in a network could be dividedinto the following four subcategories according to their Zvalues and P values: (i) peripheral nodes (Zi ≤ 2.5, Pi ≤ 0.62),(ii) connectors (Zi ≤ 2.5, Pi > 0.62), (iii) module hubs (Zi > 2.5,Pi ≤ 0.62), and (iv) network hubs (Zi > 2.5, Pi > 0.62) [36].

Relationships between Modules and EnvironmentalFactors

Environmental factors were included into the networks to pro-vide insight into their potential influence on the distribution ofnodes. To enhance the reliability of the network, only signif-icant correlations (P < 0.05, Q-value < 0.05) were considered.

Additionally, eigengene network analysis [46] was employedto study the relationships between modules and environmentalfactors and to simplify the visualization of the relationships.To complete eigengene network analysis, two steps were con-ducted: (1) all nodes in module i were selected, and theBWGCNA^ packages in R were used to calculate theireigengene values [47]; (2) the relationships between eacheigengene and environmental factors were calculated usingSpearman’s rank correlations.

Results and Discussion

Network Structure of FL and PA BacterialCommunities

Networks of PA and FL bacterial communities were construct-ed to reveal the ecological interactions among species in bac-terial communities. An ecological network can reflect variousbiological interactions including predation, competition, andmutualisms [36], with species (nodes) connected by pairwiseinteractions (edges). In this study, identical thresholds wereemployed to construct the two networks. Our results showedthat there were 352 nodes and 723 edges (average degree of4.11) and 281 nodes and 988 edges (average degree of 7.03) inthe networks of FL and PA bacterial communities, respective-ly (Table 1). Various indices, including modularity, clusteringcoefficient, average path length, network diameter, averagedegree, and graph density, were all significantly higher(P < 0.001) than those of their random networks assessed byZ-tests (Table 1). Furthermore, the network indices (averagepath length, average clustering coefficient, and modularity)significantly differed between the FL and PA groups as deter-mined by Student’s t test (P < 0.001). The degrees of distribu-tion in the two resulting networks were compared with thefollowing three models: (1) power law (scale free), (2) trun-cated power law (broad scale), and (3) exponential distribu-tions (i.e., faster-decaying functions). The truncated powerlaw model was the best one to describe our networks, withcoefficients of 0.987 and 0.980 for FL and PA groups, respec-tively (Fig. S1). In an analysis of seed dispersal-animal inter-actions (N = 16), pollination-animal interactions (N = 29),seed dispersal-plant interactions (N = 16), pollination-plant in-teractions (N = 29), Jordano et al. [48] found truncated powerlaw was the best fit for 12 seed dispersal-animal interactions,13 pollination-animal interactions, 13 seed dispersal-animalinteractions, and 21 pollination-plant interactions. Our resultsare consistent with these mutualistic ecological networks, sug-gesting that most species in the two bacterial groups had onlyfew connections while only a few species had manyconnections.

Table 1 shows that both of the two obtained networks showBsmall-world^ properties [49], and nodes were highly

Contrasting Network Features between Free-Living and Particle-Attached Bacterial Communities in Taihu Lake

interconnected more than would be expected by chance.Furthermore, high-modularity values (0.761 for FL and0.460 for PA) of the two bacterial community networks sug-gested that a modular structure existed in both bacterial groups[44]. The PA network had more edges, but fewer nodes thanthat of the FL bacterial group, and the average degree of PAwas nearly twice as high as that of FL (4.11 for FL and 7.03for PA, Table 1), indicating that there are more connectionsamong the species in the PA network.

The majority of the nodes in these two networks belongedto six dominant phyla/subphyla (percentage of nodes ≥ 5%).Among them,Cyanobacteriawere the most dominant phylumin the FL network, whereas Betaproteobacteria was the mostdominant phylum in the network of PA (Fig. S2). The relativeproportion of Cyanobacteria in the PA network was signifi-cantly less than that of the FL bacterial community network(P < 0.01), whereas that of Alphaproteobacteria was higher(Fig. S2). These results revealed the differences in composi-tion and structure between the FL and PA bacterial communitynetworks. Most of the OTUs affiliated with Cyanobacteria inthe two networks could be assigned to the cladeGpIIa (84.0%forCyanobacteria of the FL bacterial community network and82.7% for Cyanobacteria of the PA bacterial community net-work).GpIIa was found to be abundant in the surface layer ofthe ocean [50], and they were also found in a eutrophic fresh-water lake with much lower abundance [51]. We found 63nodes in the FL bacterial community network assigned toGpIIa, whereas the PA bacterial community network onlyhad 43 nodes affiliated with GpIIa. GpIIa probably main-tained important influences on the differences between thetwo networks. Further studies are needed to investigate theroles of GpIIa in both of the networks.

Co-Occurrence Patterns of FL and PA BacterialCommunities

The high modularity of the two bacterial community networks(Table 1) indicates a modular structure. To further explore theco-occurrence patterns of FL and PA bacterial communitynetworks, the species-species (OTU-OTU) association net-works were divided by modules. Most OTUs (nodes) withinthe same modules usually had relatively stronger positive re-lationships with each other. As shown in Fig. S3, the species-species association networks divided by modules were obvi-ously different between FL (Fig. S3A) and PA (Fig. S3B)bacterial communities. The PA bacterial community networkhad more correlations (edges) and higher average degree(Table 1) than those of the FL network, meaning that the PAnetwork was more complex than the FL network. As such, wewould hypothesize that there were more relationships likesymbiosis or competition among species in the PA bacterialcommunity than those of the FL bacterial community.Furthermore, modularity is also expected to be an essentialTa

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Xu H. et al.

ingredient of network complexity [52]. Modularity can reflectdiverse selections of habitats and clustering of those closelyconnected species [53]. Here, high-modularity values wereobserved in both FL and PA bacterial community networks,but the modularity of PA bacterial community network wassignificantly lower than that of the FL bacterial communitynetwork (Table 1, P < 0.001). The result suggested that the FLbacterial community network has more complex correlationswithin modules, whereas the PA bacterial community networkhas more complex correlations among modules. Mougi andKondoh [54] considered that increasing complexity of a com-munity with mixed interaction types results in increased sta-bility. This would indicate that the PA bacterial communitymight exhibit higher stability than the FL bacteria in responseto the changing environment. The results might be attributedto the great amounts of nutrients on particle surfaces and thefact that nutrients and/or the substratum create more suitablemicrohabitats for the PA bacterial community and their asso-ciated biogeochemical processes [14, 16]. On the contrary,nutrients were relatively scant in the lake water compared tothe particles and the FL bacterial species might be predicted tooccupy similar ecological niche and exhibit competitive orantagonistic interactions [37].

Actinobacteria, Betaproteobacteria, and Cyanobacteriawere predominant in most of the modules of both networks(Fig. S3). The results indicate that these bacterial groups couldadapt to both ecological and nutrient conditions, and this ad-aptation correlates with the high proportion of OTUs belong-ing to these phyla in the two different networks (Fig. S2).Zhou et al. [36] reported that Actinobacteria play importantroles in decomposing organic materials and producing sec-ondary metabolites with very diverse physiological roles.Actinobacteria are also considered to be the most persistentlineage of freshwater lake bacteria [55]. Betaproteobacteriaare prevalent in freshwater ecosystems and may form highabundances in lakes with different trophic status [56, 57].Additionally, Cyanobacteria are commonly abundant in eu-trophic freshwater lakes like Taihu Lake [58, 59].

Table 2 shows the intra- and inter-phylum co-occurrence/co-exclusion pattern in FL and PA bacterial community net-works. An O/R ratio > 1 means that the observed co-occur-rence/co-exclusion of two phyla/subphyla was higher to occurthan what would be expected by chance. In Table 2, more ofthe intra- or inter-phyla/subphyla O/R values were significant-ly > 1 in the PA bacterial community network than those of theFL bacterial community network, indicating more co-occurrence or co-exclusion patterns in the PA bacterial com-munity network. Co-occurrence patterns can reflect generallyfavored circumstances or cooperative behaviors, such ascross-feeding, syntrophic relationships, and mutualistic inter-actions [60], whereas co-exclusion patterns indicate some re-lationships like predation or competition. For intra-phyluminteractions, significant co-occurrence patterns (O/R = 2.83,

P < 0.05) were observed among Alphaproteobacteria in thePA network, while significant co-exclusion patterns (O/R >1, P < 0.05) were observed among Bacteroidetes in both net-works. Alphaproteobacteria are considered as the ancestralgroup for mitochondria [61] and therefore has a special rolein evolution [62]. In the present study, we interpret the co-occurrence patterns among Alphaproteobacteria in the PAnetwork, as indicating cooperative interactions amongAlphaproteobacteria associated with the particles. By con-trast, the co-exclusion patterns are observed in both net-works among Bacteroidetes. Bacteroidetes favored in theenvironmental conditions characterized by high concentra-tions of dissolved organic carbon (DOC) or alga-derivedDOC inputs [63, 64], suggesting that Bacteroidetes needmore nutrient to survive in aquatic ecosystems, and theymight compete with each other for more resources.Moreover, Epsilonproteobacteria showed significant andstrong co-occurrence relationships (O/R > 3, P < 0.05) withmany phyla/subphyla (Bacteroidetes, Betaproteobacteria,Cyanobacteria, and Firmicutes) in the PA bacterial com-munity network. However, the Epsilonproteobacteria inthe FL bacterial community network only showed robustco-occurrence patterns (O/R = 55.56, P < 0.05) with mem-bers of TM. Epsilonproteobacteria have been found inmany natural habitats, some of which were considered asextreme environments like caves [65], sulfurous springs[66], groundwater associated with oil [67], marine waterand muds [68, 69], and deep-sea hydrothermal vent [70,71]. These results confirmed that this bacterial group hasvery diverse physiology and can cooperate with other bacte-rial groups to occupy different ecological niches. This couldexplain the significant and strong co-occurrence relation-ships (O/R > 3, P < 0.05) between Epsilonproteobacteriaandother phyla.Members of theEpsilonproteobacteriahavealso been suggested to be strongly involved in the cycling ofcarbon, nitrogen, and sulfur compounds in sulfidic habitats atoxic-anoxic interfaces [72]. Additionally, Lopez-Garciaet al. [73] suggested that Epsilonproteobacteria in deepsea could maximize overall ecosystem function and thenadapt to variational geochemical conditions and metabolicversatility. This capacity helps Epsilonproteobacteria col-onize new substrates and habitats for high biomass andgrowth rates [74, 75]. Therefore, Epsilonproteobacteria,as primary colonizers and producers, have vital roles insymbiotic associations [72].

Topology of Individual Nodes in FL and PA BacterialCommunity Network Interactions

Different species (OTUs) were considered to play differenttopological roles in the network [45]. The topological rolesof the OTUs identified in the two groups are shown in Fig. 1,and more information about connectors and module hubs was

Contrasting Network Features between Free-Living and Particle-Attached Bacterial Communities in Taihu Lake

shown in Table 3. Most (98.9% for FL and 96.4% for PA) ofthe OTUs were peripherals with most of their links inside theirmodules. Of the peripheral OTUs in FL and in PA networks,76.4 and 65.7%, respectively, had no links at all with othermodules (i.e., Pi = 0). Two nodes (OTUs) were module hubs,and two nodes (OTUs) were connectors in the FL bacterialcommunity network (Fig. 1). There were ten connectors butno module hubs in the PA bacterial community network(Fig. 1). However, there were no network hubs observed inboth networks. Two module hubs of the FL network belongedto Cyanobacteria and Betaproteobacteria, and the two con-nectors of FL network were assigned to Bacteroidetes andPlanctomycetes (Table 3). Moreover, the two module hubsand two connectors in the FL bacterial community came fromfour different modules (F3, F4, F5, F7). For the PA bacterialcommunity network, the connectors were classified as theBac tero ide t es , Cyanobac te r ia , Ac t inobac ter ia ,Alphaproteobacteria, Betaproteobacteria, and Firmicutesfrom four different modules (P1, P2, P4, P5).

Table 2 Ratios of observed co-occurrence (positive)/co-exclusion(negative) incidence (O) and the random mean co-occurrence/co-exclusion incidence (R) representing the incidence of significant and

strong (P < 0.01, Q-value < 0.05; r > 0.85) intra-taxon and inter-taxainteractions between bacterial species

Node1 Nodes2 FL bacterial community network PA bacterial community network

Co-occurrence Co-exclusion Co-occurrence Co-exclusion

Intra-taxon Interaction Actinobacteria Actinobacteria 0.87 0.67 0.56** 0.38*

Alphaproteobacteria Alphaproteobacteria 0.41* 0.00 2.83* 0.44

Bacteroidetes Bacteroidetes 0.86 1.91* 0.64* 1.86*

Betaproteobacteria Betaproteobacteria 1.27 1.02 1.13 0.44*

Inter-taxon Interaction Actinobacteria Bacteroidetes 0.73* 0.62* 0.85 0.87

Alphaproteobacteria Bacteroidetes 0.85 0.24* 1.03 1.04

Actinobacteria Betaproteobacteria 0.70* 0.90 0.96 0.90

Bacteroidetes Cyanobacteria 1.23 0.92 1.09 1.68**

Bacteroidetes Epsilonproteobacteria 0.00 0.00 4.30* 1.80

Betaproteobacteria Epsilonproteobacteria 1.48 2.40 5.03** 0.00

Cyanobacteria Epsilonproteobacteria 2.42 0.00 5.45*** 0.00

Epsilonproteobacteria Firmicutes 0.00 – 20.00* 0.00

Betaproteobacteria Gammaproteobacteria 0.71 2.42 1.47 3.28**

Bacteroidetes Planctomycetes 0.84 0.81 1.33 0.30*

Actinobacteria Proteobacteria_unclassified 0.96 2.42 0.25* 0.00

Betaproteobacteria Proteobacteria_unclassified 0.00 0.00 0.21** 0.00

Epsilonproteobacteria TM 55.56* – 0.00 –

Fusobacteria TM – – 20.41* –

Alphaproteobacteria Unclassified 1.12 3.59* 0.97 0.26*

Planctomycetes Unclassified 0.00 0.00 0.34* 0.78

Cyanobacteria Verrucomicrobia 1.29 1.16 2.04* 0.00

The observed co-occurrence/co-exclusion incidence (O) of two taxa was calculated as the relative percentage of the number of observed positive/negative edges between them in the total positive/negative edges of the network, whereas the random mean co-occurrence/co-exclusion incidence (R)was calculated by constructing 1000 random networks. The italicized numbers indicate significant differences between the observed co-occurringincidence and the random mean co-occurring incidence

*P < 0.05; **P < 0.01; ***P < 0.001

Fig. 1 Z-P plot showing the distribution of OTUs based on theirtopological roles. Each symbol represents an OTU in FL (red) or PA(blue) bacterial communities. The topological role of each OTU wasdetermined according to the scatter plot of within-module connectivity(Zi) and among-module connectivity (Pi). The module hubs andconnectors are numbered and more information is shown in Table 3

Xu H. et al.

The effects of species extinctions on network depend onspecies roles. Both module hubs and connectors play differenttopological roles in the ecological network. Generally, an ex-tinction of a module hub may lead its module to be dividedinto fragments with no or few indirect influences on othermodules, whereas the extinction of connectors may lead thedisassembly of the entire network into isolated modules with-out major influences on the inner structure of individual mod-ules [52]. Our results indicated different topological roles ofindividual nodes in FL and PA bacterial community networkinteractions. As shown in Table 3, OTU00074 and OTU00755were both assigned to the genus GpIIa belonging to theCyanobacteria but playing different topological roles as mod-ule hub and connector in FL and PA bacterial communitynetworks, respectively. Cyanobacteria in freshwater lakesare generally the main constituents of blooms during summer.Some genera of Cyanobacteria including GpIIa can fix atmo-spheric nitrogen [76, 77]. Moreover, OTU00079 belonged tothe familyComamonadaceae and OTU00454 was assigned tothe genus Limnohabitans (family Comamonadaceae, phylumBetaproteobacteria). Although OTU00079 and OTU00454were both classif ied as belonging to the familyComamonadaceae, OTU00079 played an important role asa module hub in the FL bacterial community network, where-as OTU00454 was a connector in the PA bacterial communitynetwork. Members of the Comamonadaceae have beenshown to be involved in the mineralization of carbon-boundsulfur and the cycling of soil sulfate between organic andinorganic forms in the plant rhizospheres [78], whereas theyare considered able to influence a nutrient removal processunder oxygen-limited conditions in sludge [79]. These fourOTUs mentioned above (OTU00074, OTU00755,

OTU00079, and OTU00454) all have important influenceon the cycling of nutrients, whereas they play different rolesin the two bacterial community networks, indicating differentecological functions between the FL and PA bacterial commu-nities. Additionally, our results also show high abundance ofCyanobacteria and Betaproteobacteria in both networks(Fig. S2), which could be explained by their important topo-logical roles and functions in ecological networks. Modulehubs in FL bacterial community network ensured that specieswithin a module were linked more tightly, which is the reasonwhy the modularity of FL network was much higher than thatof PA. Similarly, connectors in PA network mademuch tighterinteractions among modules than that of the FL network,which also proved the differences of network structures be-tween the two bacterial groups.

Relationships between Modules and EnvironmentalFactors in the FL and PA Bacterial CommunityNetworks

Environmental factors were imported into the two networks(red and black edges represent positive and negative correla-tions, respectively) to explore the relationships between mod-ules and environmental factors in the two networks (Fig. 2).Some of the environmental factors were removed from thenetwork graphs due to few edges with nodes (OTUs) to im-prove clarity. The number of positive and negative links be-tween environmental variables and species are presented inTable S1. In Fig. 2, many significant (P < 0.05) interactionsbetween species and environmental factors could be found inboth networks and more significant negative correlations werefound in the FL network than that of PA network.

Table 3 Module hubs and connectors in the species-species association networks

No. of node OTU ID No. of module Mean abundance (%) Phylum Lowest taxonomic rank

Module hubs 1 OTU00074 F5 1.39 Cyanobacteria g_GpIIa

2 OTU00079 F3 1.37 Betaproteobacteria f_Comamonadaceae

Connectors 3 OTU00436 P4 0.53 Bacteroidetes p_Bacteroidetes

4 OTU00755 P4 0.48 Cyanobacteria g_GpIIa

5 OTU00100 P2 1.27 Actinobacteria o_Actinomycetales

6 OTU00693 P4 0.46 Alphaproteobacteria f_Rhodobacteraceae

7 OTU00012 P2 2.70 Alphaproteobacteria o_Rhizobiales

8 OTU00058 P4 1.59 Cyanobacteria g_GpVI

9 OTU00667 F4 0.41 Planctomycetes f_Planctomycetaceae

10 OTU00239 P5 0.66 Alphaproteobacteria f_Sphingomonadaceae

11 OTU01109 F7 0.36 Bacteroidetes g_Flavobacterium

12 OTU00594 P1 0.48 Firmicutes g_Exiguobacterium

13 OTU00454 P7 0.68 Betaproteobacteria g_Limnohabitans

14 OTU00060 P5 1.64 Cyanobacteria o_Cyanobacteria_order_incertae_sedis

p_, c_, o_, f_ and g_ represented phylum, class, order, family and genus, respectively

Contrasting Network Features between Free-Living and Particle-Attached Bacterial Communities in Taihu Lake

Eigengene network analysis was conducted to furtherstudy the relationships between modules and environmen-tal factors. Figure 3 shows the relationships betweeneigengenes (representing the modules in FL and PA bac-terial community networks) and environmental factors.Most of the modules in FL network appeared to be sig-nificantly negatively correlated with environmental fac-tors (P < 0.05), whereas the results were different in thePA network. There were only two of the measured envi-ronmental factors (NO3, nitrate nitrogen; TSS, totalsuspended solids) exhibiting significant positive correla-tions with the modules of F1, F3, and F7 in the networkof FL bacterial community (Fig. 3a). For the PA network,three modules (P1, P4, and P13) only had significant(P < 0.05) positive correlations with environmental fac-tors; however, four modules (P3, P9, P11 and P12) onlyhad significant (P < 0.05) negative correlations with envi-ronmental factors (Fig. 3b).

Many studies have discussed the influence of environmen-tal factors on the FL and PA bacterial communities [8, 14, 19,20]. Our previous study revealed that the particulate environ-mental factors, like particulate phosphorus, were only signif-icantly correlated with the composition of PA bacterial com-munity in Taihu Lake [17]. Modules can be considered as

groups of highly interconnected species that may constitutea biological pathway bridging the gap between individualbacteria and emergent global properties [45]. Environmentalfactors, except nitrate nitrogen (NO3) and total suspendedsolids (TSS), all had significant (P < 0.05) negative correla-tions with modules in the FL bacterial community network,indicating that species of the FL network were favored in lowconcentration of nutrients (e.g., nitrogen and phosphorus).However, modules P1, P4, and P13 in the PA networkdisplayed significantly (P < 0.05) positive correlations withall environmental factors, suggesting that species of the PAnetwork were favored in high concentrations of nutrients,which is consistent with our previous results [24]. In our pre-vious study, the results showed that environmental factorsdisplayed stronger role in the FL bacterial community thanin the PA assemblage [24]. Similar results could be found inFig. 2 that the FL bacterial community network has more linkswith environmental factors. These results suggest that the FLbacterial community is more sensitive to the changing envi-ronmental conditions, whereas PA is more stable with regardto this. This might be due to the differences in the structurecomplexity of the two bacterial community networks. In gen-eral, nutrients (i.e., nitrogen and phosphorus) have profoundimpacts on the co-occurrence networks of FL and PA bacterial

Fig. 2 Species-speciesassociation network divided bymodule and species-environmentassociation network in FL (a) andPA (b) bacterial communities.Only correlations between speciesthat were statistically significant(P < 0.01, Q-value < 0.05) andstrong (r > 0.85 or r < − 0.85)were shown by red (positivecorrelations) and black (negativecorrelations) dash lines. Onlycorrelations betweenenvironmental factors and speciesthat were significant (P < 0.05)were shown. Red solid line meanspositive correlation and blacksolid line means negativecorrelation. Different bacterialphyla were represented withdifferent colors, and the numberon each node means the IDnumber of each OTUs clustered at97% similarity. The circles consistof some nodes mean modules.Environmental variables: TN,total nitrogen; TP, totalphosphorus; TDN, total dissolvednitrogen; NO3, nitrate nitrogen;NH4, ammonia nitrogen; NO2,nitrite nitrogen; DIN, dissolvedinorganic nitrogen

Xu H. et al.

communities; however, the impacts differed considerably be-tween the two groups.

Conclusions

PA and FL bacteria are living in the same ecosystem but col-onize different niches with distinct nutrient conditions. In thisstudy, the results demonstrated that network structure and co-occurrence patterns were different between FL and PA bacte-rial communities in Taihu Lake. More prominent relationshipsin PA bacterial community network than that of the FL net-work were observed, indicating more relationships like sym-biosis or competition among species in the PA bacterial com-munity. Epsilonproteobacteria in the PA network showedquite significant and robust co-occurrence relationships(O/R > 3, P < 0.05) with the other four phyla, which mightbe attributed to their diverse physiology and high adaptabilityto the changing geochemical conditions. Moreover, the PAbacterial community network included more connectors andfewer module hubs than the network of FL bacterial group,with these connectors and hubs playing different roles in the

two bacterial groups. With the analysis of the relationshipsbetween the module eigengenes and environmental factors,we were able to visualize the complicated influences of envi-ronmental factors on the FL and PA bacterial communities,suggesting that the two bacterial groups favored quite differ-ent environmental conditions.

Acknowledgements This work was supported by the National NaturalScience Foundation of China (41571108 and 41671078), the NaturalScience Foundation of Jiangsu Province, China (BK20151614), theNational Key Technology R&D Program (2015BAD13B01), theSpecial Fund of State Key Laboratory of Hydrology-Water Resourcesand Hydraulic Engineering (20145027312, 20155019012), and QingLan Project of Jiangsu Province, and KVH was supported through a USNational Science Foundation Graduate Research Fellowship under GrantNo. 1650686.

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Contrasting Network Features between Free-Living and Particle-Attached Bacterial Communities in Taihu Lake