Jousset, Alexandre

Kowalchuk, George a

Li, Rong

Ou, Yannan

Shen, Qirong

Weidner, Simone

Xiong, Wu

Utrecht University Repository

 Open Access https://doi.org/10.48130/SSE-2023-0001Soil Science and Environment 2023, 2:1Co-occurrence networks depict common selection patterns, notinteractionsRong Li1,2, Simone Weidner1, Yannan Ou2, Wu Xiong1,2, Alexandre Jousset1, Qirong Shen2 andGeorge A Kowalchuk1*1 Ecology and Biodiversity Group, Department of Biology, Institute of Environmental Biology, Utrecht University, 3584 CH Utrecht, Netherlands2 Jiangsu  Provincial  Key  Lab  for  Solid  Organic  Waste  Utilization,  National  Engineering  Research  Center  for  Organic-based  Fertilizers,  Jiangsu  CollaborativeInnovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, P.R. China* Corresponding author, E-mail: g.a.kowalchuk@uu.nl  High-throughput interrogation of microbial communities hasprovided  a  quantum  leap  in  our  ability  to  characterize  thephylogenetic composition of our microbial  world.  However,  asecologists, we aim to go beyond pure stamp collecting of whois present in a community. We seek to understand the drivers ofcommunity  dynamics  and  the  interactions  that  dictatecommunity  functioning.  A  range  of  tools  has  been  developedto  visualize  co-occurrence  patterns,  generated  for  instance  byHigh-throughput (HTP) tag sequencing of ribosomal RNA genefragments, as networks of taxa that are positively or negativelycorrelated  in  their  distributions  (Barberán et  al.,  2012).  Whilesuch  studies  often  reveal  interesting  changes  in  network  top-ology as related to specific  environmental  gradients or experi-mental  manipulations  (Hilton et  al.,  2021; Li et  al.,  2021),interpretation  of  how  such  networks  relate  to  actual  interac-tions  and  community  drivers  has  remained  problematic  andcan  even  be  misleading  (Cardona et  al.,  2016).  Here,  we  use  aset  of  manipulated  soil-borne  microbial  communities  todemonstrate  that  extra  complex  and  tightly  knit  microbial  co-occurrence networks  can be generated by  selection processesthat  have  no  link  to  actual  ecological  interactions.  Thus,  whileco-occurrence  network  topology  and  complexity  may  indeedhave  ecological  meaning,  we  purport  that  they  are  morerelated  to  common  outcomes  of  population  selection,  asopposed to interactive activities.As  we  continue  to  develop  more  advanced  and  thoroughmeans  to  describe  complex  microbial  communities,  we  obvi-ously  wish  to  move  beyond  cataloguing  relative  populationdensities  toward  detecting  patterns  of  ecological  significance.To drive such research forward,  a  number of  approaches havebeen  employed  to  examine  evolutionary  and  ecological  sig-natures  within  large  microbial  datasets  (Horner-Devine  andBohannan, 2006; Stegen et al., 2012; Dini-Andreote et al., 2015),in  order  to  examine  how  interspecific  interactions,  be  theypositive,  negative  or  neutral,  shape  microbial  communitystructure  and assembly  (Faust  and Raes,  2012; Zelezniak et  al.,2015; Blasche et  al.,  2021).  The  use  of  co-occurrence  andcorrelation networks has increased significantly in recent years,as  a  means  of  organizing  population  distributions  across  arange  of  complex  microbial  communities  from  oceans  to  thehuman gut and for describing not only bacteria, but also fungi,protists and other organisms (Faust and Raes, 2012; Morriën etal.,  2017; Xiong et  al.,  2018; Gao et  al.,  2022).  Indeed,  a  simplesearch  of  the  Web  of  Science  (Fig.  1)  demonstrates  thewidespread and growing use of such approaches.Many  different  approaches  (correlation  networks  (CoNet),local  similarity  analysis  (LSA),  maximal  information coefficients(MIC),  random  matrix  theory  (RMT),  sparse  correlations  forcompositional  data  (SparCC),  Pearson  correlations,  Spearmancorrelations,  Bray–Curtis,  and  so  on)  and  models  (copula,  nullmodel,  ecological,  and  lotka–volterra)  have  been  explored  forconstructing  co-occurrence  networks,  and  were  reported  toexhibit  different  correlation  technique  usage  (Weiss et  al.,2016). Resulting changes in network topology are then typicallyrelated  to  specific  environmental  gradients  or  experimentalmanipulations.  While  most  researchers  recognize  that  suchnetworks  do  not  demonstrate  actual  ecological  interactionsbetween  species,  the  very  term  'network'  implies  that  suchcommunity  portrayals  yield  interactive  information  (Morales-Castilla et al., 2015; Cazelles et al., 2016; Sander et al., 2017). Realworld  interactions  are  far  more  complex  than  a  mathematicallinkage can convey. They involve specific physiologies,  higher-order  and  indirect  interactions,  changing  environmentalconditions and spatially structured environments (Alexander etal.,  2015; Thakur  and  Geisen,  2019; Dal et  al.,  2020; Vos et  al.,2013).  Network  analysis  is  clearly  booming,  but  what  insightdoes it  lend to illuminating microbial  interactions or  drivers ofcommunity structuring?Positive  or  negative  links  within  co-occurrence  networkshave  been  shown  to  be  poor  predictors  of  actual  interactionsupon  examination  of  one-to-one  effects  (Freilich et  al.,  2018).However,  such  pairwise  interactions  should  also  be  viewedwithin the context of other interactions within the community,as  interactions  may  be  indirect  and  other  species  in  thenetwork  may  impact  one  or  both  of  the  pairwise  players.Species  can  also  coexist  and  exhibit  a  correlation  in  theirabundances  through  population  selection  by  a  third  species(Holt  and Bonsall,  2017)  or  unreported abiotic  factors  (Röttjersand Faust, 2018; Lv et al., 2019). It has also been suggested thatco-occurrence  may  be  a  result  of  dispersal  limitation  (Ulrich,2004),  or  common  selection  due  to  specific  environmentalfactors, without any actual direct or indirect interaction (Peres-Neto et al., 2001; Freilich et al., 2018).We  believe  that  this  latter  explanation  is  in  many  casesdriving  the  topology  of  microbial  co-occurrence  networks.  Inorder to examine this premise, we used a set of engineered soil-borne  microbial  communities  that  differed  in  the  degree  toLETTER © The Author(s)www.maxapress.com/ssewww.maxapress.comwhich populations were segregated across replicates. We thentracked  rhizosphere  community  assembly  and  network  top-ology, via a  range  of  methods,  over  time.  Briefly,  we  used  adilution  series  of  a  soil  suspension  re-inoculated  back  into  itssterilized  origin  soil  to  create  soil  communities  that  had  beensubjected  to  different  levels  of  population  segregation(Supplemental  Fig.  S1).  With  such  a  dilution-to-extinctionexperiment  setup,  all  starting  communities  derived  from  lowdilutions  have  very  similar  species  pools,  as  nearly  all  speciesremain  present  in  the  starting  inoculum.  On  the  other  hand,replicates  at  higher  dilutions  receive  more  disparate  speciespools  due  to  the  dilution  to  extinction  of  different  subsets  ofthe initial  community.  As  such,  this  imposed community  'drift'acts  as  a  strong  experimentally  imposed  segregation  at  highdilutions,  but  not  at  low dilutions.  Soils  were  incubated for  61weeks  and  then  used  to  examine  microbial  communityassembly on plant roots via 16S rRNA gene tag sequencing byprimers  341F  (5’-CCTACGGGNBGCASCAG-3’)  and  806R  (5’-GGACTACNVGGGTWTCTAAT-3’)  based  on  the  Illumina  MiSeqplatform  and  network  analyses  in  a  replicated  design(Supplemental  Fig.  S1).  There  are  12  sequencing  samples  ineach  dilution  with  three  replicates  per  treatment  which  wasderived from one origin soil  (in a total  of  four origin soils)  andwith 446-814 OTUs in each sample,  depending on the dilutionand the sampling time, used to compute one network.Co-occurrence network analyses of rhizosphere communitiesshowed  distinct  patterns  with  respect  to  the  dilution  of  thestarting  communities,  as  well  as  the  age  of  the  plant  (Fig.  2),with denser and stronger networks with increased dilution andplant  age.  In  addition,  the  number  of  links  per  network  alsoshowed increased trends with dilution, independent of the co-occurrence  model  used  (see Supplemental  Fig.  S2;  e.g.  CoNet,Pearson  or  Spearman).  The  Spearman  correlations  betweennode  (or  link)  number  with  dilutions  in  each  network  werecalculated,  and  significant  correlations  can  be  observed  insome  networks  (see Supplemental  Table  S1).  In  our  manipu-a b Fig. 1    (a) Number of publications and (b) citations referencing microbial network analysis (Web of Science from 1985 to Dec 20, 2022; search= network and microbiology or microbes or microbiome or microbiota or microflora as two separate subjects). Fig.  2    Co-occurrence  networks  of  rhizosphere  bacteria  microbiota  from  samples  collected  from  week  1  and  week  3  across  the  given  soilsuspension dilution treatments. Connections represent significant (P < 0.01) correlation as calculated by the Pearson method. Nodes representdifferent OTUs. Nodes and edges are colored by modularity class. Co-occurrence networks depict common selection patternsPage 2 of 4   Li et al. Soil Science and Environment 2023, 2:1lated  communities,  we  have  no  reason  to  assume  that  com-munities  at  higher  dilutions  are  more  'connected'  or  containmore  microbial  interactions,  which  can  be  supported  by  thedecreased  number  of  OTUs  involved  in  the  network  (seeSupplemental  Fig.  S2).  Rather,  the  imposed  segregation  ofpopulations,  which  is  greater  at  higher  dilutions,  resulted  in  agreater preponderance of positive and negative co-occurrencepatterns  and  an  increased  number  of  nodes  derived  frommultiple  replicates  (see Supplemental  Fig.  S3).  Similarly,  net-work  strength  is  increased  with  the  continued  growth  of  theplant (Fig. 2). We believe that this result is due to the increasedselective  action  of  the  plant,  which  increases  with  plant  size.Indeed,  it  has  previously  been  observed  that  co-occurrencenetwork  complexity  increases  with  plant  growth  stage  (Shi etal., 2016; Ceja-Navarro et al., 2021).It  has  been  suggested  that  co-occurrence  networks  can  bemisleading  if  other  factors,  such  as  habitat  filtering,  result  innon-random patterns in the abundance of multiple taxa (Berryand Widder,  2014).  In  other  words,  if  one seeks  to  zoom in onone  particular  parameter  driving  co-occurrence  networkstructure,  it  is  important  to  keep  all  other  factors  that  mayinfluence  this  structure  constant.  Here,  the  numbers  of  intra-and inter-treatment links  both showed increased trends in co-occurrence  networks  of  rhizosphere  microbiota  with  the  levelof dilution (see Supplemental Fig. S4). We demonstrate that ourimposed  segregation  of  populations,  which  can  be  seen  as  arandom  selection,  resulted  in  more  tightly  knit  network  top-ologies  –  a  result  that  most  likely  has  nothing  to  do  withincreases  in  actual  interactions.  The  distribution  of  microbialspecies in the environment is clearly not independent, and wesuggest  that  estimations  of  microbial  responses  to  environ-mental  filtering  need  to  be  considered,  for  instance viageneralized  joint  attribute  modeling  (Clark et  al.,  2017).  Ourexample  provides  an  empirical  warning  regarding  the  ecolo-gical  interpretation  of  co-occurrence  networks.  We  show  thatco-occurrence  network  structure  and  complexity  can  beprincipally  driven  by  common  patterns  of  imposed  selection,thereby  providing  a  strong  cautionary  message  to  the  inter-pretation of functional interactions from such approaches.ACKNOWLEDGMENTSThis  work  was  supported  by  the  Key  project  at  centralgovernment level:  The ability establishment of sustainable usefor  valuable  Chinese  medicine  resources  (2060302);  NaturalScience  Foundation  of  Jiangsu  Province,  China  (BK20200544);the  Priority  Academic  Program  Development  of  the  JiangsuHigher  Education  Institutions  (PAPD),  the  National  NaturalScience  Foundation  of  China  (42107141),  the  Nederlandse  Or-ganisatie  voor  Wetenschappelijk  Onderzoek  (ALW.870.15.050)and  the  Koninklijke  Nederlandse  Akademie  van  Weten-schappen (530-5CDP18).Conflict of interestLi  R,  Shen  Q  and  Kowalchuk  GA  are  the  Editorial  Boardmembers  of  Journal Soil  Science  and  Environment.  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Co-occurrence networks depict common selection patterns, not interactions

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Co-occurrence networks depict common selection patterns, not interactions

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