Deterministic Assembly and Diversity Gradient Altered the Biofilm

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Environmental Processes

Deterministic assembly and diversity gradient altered the biofilm community performances of bioreactors Zhaojing Zhang, Ye Deng, Kai Feng, Weiwei Cai, Shuzhen Li, Huaqun Yin, Meiying Xu, Daliang Ning, and Yuanyuan Qu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06044 • Publication Date (Web): 07 Jan 2019 Downloaded from http://pubs.acs.org on January 10, 2019

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Environmental Science & Technology

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Deterministic assembly and diversity gradient altered the biofilm

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community performances of bioreactors

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Zhaojing Zhang,†,‡,§ Ye Deng,*,†,§,|| Kai Feng,§ Weiwei Cai,§,┴ Shuzhen Li,‡,§ Huaqun

4

Yin,# Meiying Xu,∇ Daliang Ning,○ and Yuanyuan Qu,*,‡

5 6

†Institute

for Marine Science and Technology, Shandong University, Qingdao 266237, China

7

‡State

8

Education, China), School of Environmental Science and Technology, Dalian University of

9

Technology, Dalian 116024, China

Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of

10

§Key

Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental

11

Sciences, Chinese Academy of Sciences, Beijing 100085, China

12

||College

13

100190, China

14

┴School

of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China

15

#School

of Minerals Processing and Bioengineering, Central South University, Changsha 410083,

16

China

17

∇State

18

Microbiology, Guangzhou 510070, China

19

○Institute

20

School of Civil Engineering and Environmental Science, University of Oklahoma, Norman,

21

Oklahoma 73019, USA

of Resources and Environment, University of Chinese Academy of Sciences, Beijing

Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of

for Environmental Genomics, Department of Microbiology and Plant Biology, and

1

ACS Paragon Plus Environment


22

ABSTRACT ART

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ABSTRACT

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Community assembly process (determinism vs. stochasticity) determines the

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composition and diversity of microbial community, and then shapes its functions.

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Understanding this complex process and its relationship to the community functions

29

becomes a very important task for the applications of microbial biotechnology. In this

30

study, we applied microbial electrolysis cells (MECs) with moderate species numbers and

31

easily tractable functions as a model ecosystem, and constructed a series of biofilm

32

communities with gradient biodiversity to examine the roles of community assembly in

33

determining microbial community structure and functions. After stable biofilms formed,

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the best MEC reactor performances (e.g., gas productivity, total energy efficiency) were

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achieved in the group which biofilms had the second highest -diversity, and biofilms

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with even lower diversity showed declining performance. Null model analyses indicated

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that both deterministic and stochastic assembly played roles in the formation of biofilm

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communities. When deterministic assembly dominates this formation, the higher diversity

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of biofilm community would generally show better reactor performance. However, when

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the stochasticity dominates the assembly process, the bioreactor performance would

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decline. This study provides novel evidence that the assembly mechanism could be one of

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the key processes to shift the functions, and proposes an important guidance for selecting

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the most efficient microorganisms for environmental biotechnologies.

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

INTRODUCTION

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Microorganisms, regulating all major biogeochemical cycles, serve essential roles in

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environmental biotechnology.1,2 With the attempts linking microbial communities with

48

environmental processes, researchers believe that understanding the ecological

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mechanisms of community assembly controlling community diversity and its

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relationships to community functions becomes a very important task for the application of

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microbial biotechnology.3,4 Knowledge about the process and factors controlling

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community assembly is critical to our understanding of the patterns of species

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composition and diversity. Traditional niche-based theory hypothesizes that deterministic

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factors such as species traits, interspecies interactions (e.g., competition, mutualisms, and

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predation), and environmental conditions (e.g., pH, temperature, moisture) govern

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community structure.5,6 Consequently, the deterministic process can directly determine

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the microbial communities under specific environmental conditions.7 In contrast, neutral

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theory assumes that all species are ecologically equivalent and community structures are

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governed by stochastic processes, which typically include random birth-death events,

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colonization, extinction and speciation.8 Microbial communities assembled via stochastic

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processes may exhibit less of a direct link between the environment and processes.9

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Currently it is recognized that community assembly is simultaneously influenced by both

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deterministic and stochastic processes.10-12 Although the relative importance of each

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process in controlling community structure, succession, and biogeography, has been

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intensively studied recently,13-15 its association with the community functions is still

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elusive. 4


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The relationship between biodiversity and community functions is also important.16

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Biodiversity is the most important point in community ecology, and considered to be

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closely related to the ecosystem functions.17-19 For macro-ecology, studies have shown

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that loss of biodiversity can have significant consequences for ecosystem process, for

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example, the productivity and stability of ecosystems.20,21 However, this relationship is

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poorly understood in microbial ecosystem due to its intrinsic traits, such as large number

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of species, the difficulty in controlling them, and high complexity of their interactions.22

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Since even the simplest microbial communities from natural environment could contain

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tens to more than thousands of species, it is almost impossible to experimentally verify

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which species in a habitat are actively part of the community, or are performing key

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functions.23 The diversity and assembly of natural microbial communities are also very

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difficult to control or characterize.24 Therefore, simplified model ecosystems that retain

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the key features of natural communities are desperately needed to assess the role of key

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ecological, structural and functional features of communities in a feasible way.22,25

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However, those model ecosystems are quite rare.

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Microbial electrolysis cell (MEC), as a type of bioelectrochemical system, is a

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promising technology for efficient and sustainable hydrogen production from

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biodegradable organic matter with little energy input.26,27 In the initial start-up stage, the

85

microorganisms from an original inoculation source (i.e., activated sludge, wastewater)

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are strictly selected to attach the anode of a MEC. After a stable biofilm has been

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successfully formed, the exoelectrogenic microorganisms, as major components, would

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generate electrons through the oxidation of organic matter.13 The released protons and 5



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electrons flow to the cathode to produce hydrogen on the cathodic side through the

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addition of a small voltage to the circuit.28,29 The gas productivity and relevant

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bioelectrochemical efficiency could be regarded as measurable functions for biofilm

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microbial community in macroscopic performance.30,31 Meanwhile, the whole MEC is a

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closed microbial reactor with moderate richness of bacterial species, and their taxonomy

94

and functional genes can be easily detected with novel metagenomic tools.32 This closed

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ecosystem with relatively simple microbial community and easily measurable

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performance is quite appropriate for microbial ecological studies.33 Our previous study

97

found that microbial communities in MEC bioreactors were mainly shaped by stochastic

98

processes.13 However, it is still not clear whether, and how, their relative importance

99

varies within a diversity gradient.

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In this study, we used a microbial electrolysis cell (MEC) as a model system to

101

acquire a better understanding of the role of the diversity in microbial community

102

assembly at both taxonomic and functional aspects. Experimentally, we constructed a

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series of biofilm communities with a diversity gradient by diluting the input initial

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community at 1, 102, 104, 106 times (6 replicate bioreactors in each concentration), and

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applied 16S rRNA gene sequencing to assess their diversity. The performances of those

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reactors have been simultaneously measured. We hypothesized that (i) the higher

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biodiversity may have the best performances following the general ecological rule; (ii)

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stochastic assembly is critical to form the MEC microbial communities with higher

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inoculated concentration, but the roles of deterministic assembly (i.e., selection) could

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increase along with the decrease in the inoculation concentration. 6


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

MATERIALS AND METHODS

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Reactor construction and operation. The activated sludge (AS) was collected from

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Beixiaohe Reclaimed Water Plant (Beijing, China) as the initial inoculation solution.

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Microbial electrolysis cells (MECs) used in this study were single chamber with

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membrane-less reactor (Supplementary Figure S1).34 Four groups of single chamber

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reactors, with six replicates per group, were set up with gradient diluted inoculations (A,

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1:1; B, 1:102; C, 1:104; D, 1:106, V/V) to obtain different biofilms with a diversity

118

gradient. All reactors were run under identical conditions and followed previous research

119

under neutral condition in an effort to minimize technical variations. The continuously

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recorded current could reflect reactor status timely and directly relate to anodic biofilm

121

community performance, MEC reactors can be considered to be successfully started once

122

current is higher than 1mA,35 recording as the start-up time. Five parameters of MEC

123

performance with different meanings, including hydrogen productivity rate, hydrogen

124

recovery, electrical energy recovery, substrate energy recovery, and total energy

125

efficiency, were further evaluated. Details for reactor operation and calculative processes

126

of performance parameters were provided in Supporting Information.

127

DNA extraction and microbial community analysis. At the end of MEC operation,

128

all anodic biofilm samples were collected. Microbial genomic DNA of the anodic

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biofilms was extracted following the protocol of the FastDNA® SPIN Kit for Soil

130

(MP-Biomedicals). Thereafter the V4 region of 16S rRNA was amplified using the

131

primers

132

(5’-GGACTACHVGGGTWTCTAAT-3’) for high-throughput sequencing. Sequencing

515F

(5’-GTGCCAGCMGCCGCGGTAA-3’)

7


and

806R


133

run was conducted on MiSeq (Illumina) for 2 × 250 bp paired-end sequencing in Central

134

South University (Changsha, China).

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Raw data was processed using an in-house pipeline (http://mem.rcees.ac.cn:8080)

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integrated with bioinformatics tools. UParse36 was used to remove chimeras and classify

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the sequences into operational taxonomy units (OTUs) at 97% similarity level. Random

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resampling was performed with the lowest number of sequences (> 20,000 sequences)

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among samples. This resampled OTUs summary table was used for further statistical

140

analyses. Representative sequences of each OTU were then aligned using PyNAST37

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against the GreenGenes database, and the maximum-likelihood tree was constructed with

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FastTree38 for further phylogenetic analysis. Raw sequences were submitted to the NCBI

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Sequence Read Archive (SRA) under the accession number SRP127703.

144

Ecological and Statistical analysis. In this study, we calculated α-diversity with five

145

indexes, including Shannon and Inverse Simpson indexes, Richness, Chao1, and

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Phylogenetic diversity (PD) to measure the biodiversity of microbial community in MEC.

147

Phylogenetic diversity (PD) was measured based on Faith’s approach,39 which is the sum

148

of total phylogenetic length of OTUs in each sample, and calculated using Picante

149

package in R (v.3.2.5). To determine the site-to-site variability in microbial community

150

composition of these reactors, two different metrics for measuring β-diversity were

151

evaluated, including Bray-Curties and Jaccard indexes, using vegan package (v.2.3-5) in

152

R (v.3.2.5). Non-metric multidimensional scaling (NMDS) based on Bray-Curtis distance

153

was used for representing the microbial community structure changes. Spearman

154

correlation test was applied to correlate the MEC performance with α-diversity and the 8


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relative abundance of genera Geobacter and Methanobrevibacter. The significance

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between two groups was determined by two-tailed Student’s t test, and significance

157

comparing the four groups was obtained using one-way analysis of variance (ANOVA).

158

Mantel test were used to test the correlation between the MEC performance and

159

β-diversity indexes. Other statistical methods were provided in Supporting Information.

160

Null Model Analysis. To investigate the relative importance of deterministic and

161

stochastic processes underlying community assembly, we performed the null model

162

analysis using abundance-based similarity metrics as previously reported.40-42 Here, we

163

used the Bray-Curtis distance as the dissimilarity metric (𝐷𝑜𝑏𝑠) across all communities,

164

ranging from 0 to 1. The observed similarity (𝑆𝑜𝑏𝑠) across the actual communities was

165

complementary to the dissimilarity, that was, 𝑆𝑜𝑏𝑠 = 1 ― 𝐷𝑜𝑏𝑠. Then the randomly

166

expected similarity (𝐸𝑒𝑥𝑝) of null expected communities could be generated using null

167

model algorithm keeping richness the same as observed and species occurrence

168

frequencies proportional to observed ones.40,41 This procedure was repeated 1,000 times.

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An average null expected similarity (𝐸𝑒𝑥𝑝) and its SD could be estimated based on 1,000

170

drawings. The nonparametric permutation test, permutational multivariate analysis of

171

variance (PERMANOVA), was used to test the significance of the difference of the

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biofilm communities between the observed similarity matrices with the null model

173

expectation. If community assembly is primarily shaped by deterministic processes, the

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similarity observed across the actual communities will be significantly higher than the

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random null expectation.14,43 On the contrary, if ecological drift (e.g., stochastic

176

colonization and extinction) and possibly priority effects leading to multiple stable 9



177

equilibria play predominant roles in determining community composition, the actual

178

similarity observed will be statistically indistinguishable from the random null

179

expectation.14

180

Theoretically, deterministic processes can drive ecological communities more similar

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or more dissimilarity than null expectation.44 The difference between the observed and

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average null expectation can be used to quantitatively estimate the strength of

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determinism acting against otherwise stochastic forces in shaping community

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composition and structure,7 which is also termed as deterministic ratio (DR).14 The DR

185

was measured as a proportion of the differences between the observed total similarity and

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the null expected similarity divided by the total similarity (DR = (𝑆𝑜𝑏𝑠 ― 𝐸𝑒𝑥𝑝)/𝑆𝑜𝑏𝑠), and

187

their average across all pairwise comparisons was used as a quantitative index for

188

measuring the importance of determinism vs. stochasticity in shaping biodiversity.14 The

189

relevant R code was available on http://mem.rcees.ac.cn/download/.

190

Ecological Processes Govern the Community Assembly. To quantify the

191

contributions of various ecological processes (e.g., selection and dispersal) to microbial

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community structure, we used a null-model-based statistical framework developed by

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Stegen et al.41 Firstly, we quantified βNTI (β nearest-taxa index) for all pairwise

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community comparisons using an in-house pipeline (http://mem.rcees.ac.cn:8080). The

195

βNTI is based on a null model test of the phylogenetic βMNTD (β mean nearest-taxon

196

distance), which was used to characterize the turnover in phylogenetic community

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composition. A value of |βNTI| > 2 indicates that observed turnover between a pair of

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communities is governed primarily by selection, which could be divided into 10


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homogeneous selection (βNTI < -2) and heterogeneous selection (βNTI > +2). Details for

200

calculative processes were provided in Supporting Information.

201

As part of the second major step in this procedure, pairwise comparisons with

202

nonsignificant βNTI values were further evaluated by comparing observed Bray-Curtis

203

(BCobs) to Bray-Curtis expected under the randomization (BCnull). The value of

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Bray-Curtis-based Raup-Crick (RCbary) characterizes the magnitude of deviation between

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BCobs and BCnull; the value of |RCbary| > 0.95 was considered significant by dispersal,

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containing homogenizing dispersal (RCbary < -0.95) and dispersal limitation (RCbary >

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+0.95).41 However, if |βNTI| < 2 and |RCbary| < 0.95, then drift (referred to as

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'undominated' processes) drives compositional turnover processes.45 Drift was used to

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estimate the fraction that neither selection now dispersal is the primary cause of

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between-community compositional differences.46

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

RESULTS

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Performance of MEC reactors with gradient diluted inoculation. Four different

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concentrations of activated sludge were inoculated into 24 MEC reactors. Once the

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electric current in the circuit was higher than 1 mA, the MEC reactor was considered to

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be successfully started and recorded as startup time (Table1, Supplementary Figure S2).

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Group A with highest inoculated concentration took nearly 5 days for initial startup,

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while Group D with 106 times diluted inoculation took more than one month for start-up

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(Table 1), indicating that higher gradient diluted inoculation needed more time to

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generate stable performance. In the end of MEC operation with diluted inoculation, the

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total biomass attached on the anodic biofilms were quantified according to two different 11



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analytic methods, quantitative PCR (qPCR) and volatile suspended solids (VSS)

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(Supplementary Table S1). The copy numbers of 16S rRNA genes in the biofilms from

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those four groups, ranged from 1.31×1012 ± 7.78×1011 to 2.01×1012 ± 2.21×1011 per g

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graphite brush. Meanwhile, the total cell numbers of biofilm communities derived from

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the amount of VSS showed a range from 1.65×1012 ± 2.81×1011 to 1.84×1012 ± 1.81×1011

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per g graphite brush. Both the ANOVA analysis for the four groups and Student’s t test

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for each two groups showed no obvious differences in the microbial biomass of biofilms

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operated with diluted inoculations (Supplementary Table S2).

229 230

Table 1.

MEC performance response over a gradient of diversity. H2 production

Electrical

Substrate

Hydrogen rate (mL/mL

Total energy Start-up time energy

energy

recovery (%) reactor cycle)

efficiency (%)

(day) a

recovery (%) recovery (%)

Group A 1.156 ± 0.127

72.8 ± 7.4

145.7 ± 15.3

83.7 ± 9.1

53.1 ± 5.7

5.00 ± 0.00

Group B 1.242 ± 0.094

80.7 ± 5.5

162.5 ± 11.1

89.7 ± 6.8

57.8 ± 4.2

7.33 ± 0.82

Group C 0.670 ± 0.117

42.0 ± 6.7

83.6 ± 13.9

46.1 ± 7.4

29.7 ± 4.8

25.60 ± 2.41

Group D 0.609 ± 0.050

39.6 ± 4.1

79.5 ± 8.6

45.1 ± 5.4

28.7 ± 3.2

35.4 ± 4.33

Once the electric current in the circuit is higher than 1 mA, the MEC reactor is considered to be

231

a

232

successfully started.

233 234

The other five performance parameters (hydrogen production rate, hydrogen

12


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recovery, electrical energy recovery, substrate energy recovery and total energy

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efficiency) were measured after all bioreactors were running stably (Table 1). The

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hydrogen production rates of MEC groups with diluted inoculation were further

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visualized in figure 1. Surprisingly, Group B had performed the highest hydrogen yields

239

compared with the other three groups (Student’s t test, P < 0.05), while Group C and D

240

obtained similar hydrogen yield with less difference (P > 0.05). Other performance

241

indexes (hydrogen recovery, electrical energy recovery, substrate energy recovery and

242

total energy efficiency) exhibited similar pattern with hydrogen production rate

243

(Supplementary Figure S3). Thus, it seems that the gradient diluted inoculation resulted

244

in a decrease of reactor performance in terms of the effectiveness production, but distinct

245

from, the initial inoculation with the highest concentration (Group A).

246

247

13



248

Figure 1.

Hydrogen production rates of MEC groups responding to inoculation concentrations,

249

subsidiary with one-way ANOVA analysis and Student’s t test results, t value (P-value). Bold

250

font means the significance at P < 0.05 level.

251 252

Microbial compositions of anodic biofilms in MECs. To determine the biodiversity

253

of MEC biofilm communities, 16S ribosomal RNA (rRNA) gene was amplified and

254

sequenced using high-throughput sequencing. After quality control of original reads, total

255

2,570,638 sequences were classified into 24 biofilm samples under 4 inoculated

256

concentrations. An average of 85,688 ± 59,045 sequences per sample was obtained and

257

the rarefaction curves indicated these numbers of sequences were enough to reach the

258

saturation (Supplementary Figure S4). All samples were randomly resampled at 29,220

259

reads per each.

260

To further elucidate the major players in these bioreactors, relative abundance of

261

microorganisms was investigated at phylum/class and genus levels (Figure 2,

262

Supplementary Figure S5). Generally, the most abundant phyla/classes of anodic biofilms

263

were Deltaproteobacteria, Betaproteobacteria, Bacteroidetes, Euryarchaeota, Firmicutes,

264

Epsilonproteobacteria, and Gammaproteobacteria, accounting for over 90% total

265

abundance across all four groups of biofilms, which were significantly distinct with the

266

composition from the initial AS (Supplementary Figure S5). This was especially evident

267

with the relative abundance of class Deltaproteobacteria, which was lower than 4% in the

268

initial AS, but significantly increased to more than 70% in the biofilm communities from

269

group B. 14


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271 272

Figure 2.

Genus distribution for anodic biofilms in MEC groups.

273 274

More specifically, the genus Geobacter was dominant in all MEC biofilms (Figure

275

2). Almost concordant with Deltaproteobacteria, Geobacter (accounted for more than half

276

percentage in relative abundance) predominated in those four groups, but was among the

277

rare species in the initial AS. For another dominant genus, Methanobrevibacter,

278

belonging to phylum Euryarchaeota, which had higher percentage in Groups C and D

279

(11.40% and 9.65%), was rare in the other two groups (0.17% in group A; 0.31% in

280

group B), exhibiting completely opposite trend with Geobacter.

281

Effects of diluted inoculation on biodiversity of anodic biofilms in MECs. To

282

investigate the microbial community in different groups, the shared and unique OTUs 15



283

were distinguished through a Venn diagram (Supplementary Figure S6). There were a

284

total of 5,908 OTUs observed in the initial AS and anodic biofilms with diluted

285

inoculation. The number of OTUs in the initial AS and different diluted groups, varied

286

from 5,085 to 515, with 121 of these OTUs observed in all groups (Supplementary Figure

287

S6A). In general, most initial OTUs were not observed in the anodic biofilms (65.25%,

288

3,318 of 5,085 total OTUs in initial AS). Besides, 4591 OTUs (77.71%) were identified

289

as rare taxa (only containing 2.24% of total sequences), while the abundant taxa included

290

36 OTUs (0.61%) containing 70.93% of the total sequences (Supplementary Figure S6B

291

and S6C). The distribution of rare and abundant OTUs across the initial AS and four

292

groups of anodic biofilms showed that the abundant OTUs shared by multiple groups

293

containing much higher relative abundance than those in initial AS, while the number of

294

rare OTUs decreased more sharply in the anodic biofilms operated with diluted

295

inoculations, which might contribute to the biodiversity gradient in the final biofilms

296

(Supplementary Figure S6B and S6C, Supplemental Table S4).

297

The Faith’s phylogenetic diversity (PD) of microbial communities showed that the

298

-diversity of all 24 anode biofilms (from 56.90 ± 6.10 to 14.17 ± 2.35) were

299

significantly lower than those of initial AS (138.34 ± 2.78, Figure 3, Supplementary

300

Table S3), indicating a strong selection on microbial species in MEC applying voltage

301

and substrate. The highest PD values were obtained from Group A, significantly higher

302

than the other three groups (Student’s t test, P < 0.05, Figure 3). Meanwhile, the PD

303

values of Group B were also significantly higher than those of Group C and D (Student’s

304

t test, P < 0.05), but that of Group C, D showed less difference (Figure 3). The other four 16


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diversity characteristics (Shannon index, Inverse Simpson index, Chao1 estimated

306

richness and observed richness) performed similar pattern as the PD values

307

(Supplementary Table S3, Supplementary Figure S7). These results indicated that

308

-diversities of biofilm communities were significantly decreased with the gradient

309

decreasing concentrations of inoculations for Group A, B, C and D.

310

311 312

Figure 3.

Phylogenetic diversity (PD) of the microbial communities in MEC anodic biofilms.

313

One-way ANOVA test was calculated among Group A, B, C, and D.

314 315

In terms of β-diversity, the non-metric multidimensional scaling analysis (NMDS)

316

based on Bray-Curtis distance matrix exhibited that the microbial communities of the four

317

groups were distinctly separated from other groups (Supplementary Figure S8). Further

17



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three dissimilarity tests, multiresponse permutation procedure (MRPP), analysis of

319

similarity

320

(PERMANOVA), also revealed that substantial variations in biofilm community structure

321

were observed among these biofilm communities (Supplementary Table S5).

322

Additionally, PERMANOVA also showed significant differences (P < 0.05) between

323

each pair of groups (Supplementary Table S6). Both the -diversity and β-diversity

324

analyses indicated that lower sludge inoculation concentrations could lead to lower

325

species richness, and also changed the community structures in anode biofilms.

(ANOSIM),

and

Permutational

multivariate

326

18


analysis

of

variances

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Table 2. Spearman correlation (r value, n=24) between microbial α-diversities with reactor

328

performance. (*: P < 0.05; **: P < 0.01) Hydrogen

Hydrogen

Energy

Substrate

Total energy

Production rate

Recovery

Recovery

Recovery

efficiency

Shannon

0.337

0.330

0.330

0.388

0.370

Inverse Simpson

0.312

0.334

0.334

0.369

0.360

Richness

0.674**

0.666**

0.666**

0.703**

0.691**

Chao1

0.649**

0.668**

0.668**

0.680**

0.670**

PD

0.688**

0.670**

0.670**

0.712**

0.697**

Geobacter (%)

0.396

0.388

0.388

0.338

0.350

Methanobrevibacter(%)

-0.774**

-0.761**

-0.761**

-0.759**

-0.764**

Spearman

329 330

To investigate the associations between biofilm biodiversity and reactor

331

performances, MEC performance parameters were correlated to five α-diversity indexes

332

(Table 2) and β-diversities (Supplementary Table S7) by using Spearman correlation test

333

and Mantel test, respectively. Three of the five α-diversity indexes (exceptions being

334

Shannon and Inverse Simpson indexes), showed significantly positive correlations with

335

hydrogen yield, hydrogen recovery, energy recovery, substrate recovery, and total energy

336

efficiency (P < 0.05, Table 2). The polynomial fit method was the best fitting model

337

between the hydrogen production rates and PD, indicating that diversity and function of

338

MEC matched the binomial distribution (Figure 4). These results revealed that the biofilm 19



339

communities with the highest α-diversity from MEC reactors, may not achieve the best

340

performance. Mantel test demonstrated that all MEC performances showed significant

341

correlations with β-diversity in anode biofilm community (Supplementary Table S7),

342

indicating the dissimilarity rates of microbial communities are highly associated with the

343

differences among all bioreactors.

344

345 346

Figure 4.

Fitted curve between the hydrogen production rates with the phylogenetic diversity

347

(PD) of the biofilm communities with gradient diluted inoculation. The 95% confidence interval

348

was shown by dotted lines.

349 350

Ecological processes in the community assembly of anodic biofilms in MEC. 20


Page 20 of 39

Page 21 of 39


351

The null model analysis was adapted to disentangle the importance of deterministic

352

mechanisms from stochastic mechanisms underlying community assembly. Here, the

353

regional species pool is defined as the total number of all OTUs found in all 24

354

bioreactors with diluted inoculation and 6 samples of initial AS. The permutational

355

multivariate analysis of variances (PERMANOVA) test revealed that the observed

356

similarity of actual communities was not significantly distinguishable (P = 0.152) from

357

that of the null expectation for microbial communities in Group A, indicating that the

358

stochasticity of community assembly was more important than the determinism in Group

359

A (Table3). Whereas the significant difference (P < 0.05) between the observed

360

similarities with the null expectation indicated the dominant position of deterministic

361

community assembly in Group B, C and D (Table3).

362

To further quantify the relative importance of deterministic and stochastic processes

363

in shaping the biofilm community structures, the relative ratio of determinism (DR) was

364

calculated. Meanwhile, the stochastic ratio (SR), serving as the complement of the

365

deterministic ratio (DR), was also calculated. Significant differences in DR was observed

366

for the communities across different dilution groups (PERMANOVA: F5,30 = 10.156, P
0.05) showed that there

491

were no obvious differences in the microbial biomass of biofilms operated with diluted

492

inoculations (Supplementary Table S2), suggesting that the relative abundance of the

493

major microorganisms could be compared as the actual abundance.58 As a well-known

494

functional bacterial genus in MEC related to electron delivery, Geobacter could

495

contribute to the functional performance in biofilm community.59 Kiely et al. inferred that

496

the decreased presence of Geobacter in the bacterial community could result in the poor 28


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Page 29 of 39


497

performance of MECs.60 In the present study, the genus Geobacter, which was very rare

498

(< 0.01%) in the initial community, became the most dominant species (> 50%) among

499

all of the biofilm communities after the MEC operation (Figure 2), but there is

500

non-significant correlation (P > 0.05) between the relative abundance of genus Geobacter

501

with the MEC performance (Table 2). Meanwhile, Methanobrevibacter as unexpected

502

and detrimental microorganisms for hydrogen production in MECs,61 was the

503

predominant genus in Group C and D following Geobacter, and showed significantly

504

negatively correlated (Table 2, P < 0.05) with the MEC performance. Another

505

exoelectrogen, Arcobacter, which could oxidize various organic acids and use electrode

506

as an electron acceptor directly,32,62 also occupied a higher proportion in Group D. All

507

these suggest the dilution inoculation with decreased biodiversity and increased

508

deterministic strength could alter the dominant species in biofilms, including the major

509

functional groups and detrimental groups (Figure 5).

510

Diversity is a most fundamental concept in community ecology and often implicated

511

as a cause of success or failure of a microbial community.18 There has often been an

512

assumption that high diversity is implicitly a good or desirable outcome for communities,

513

and that higher diversity is also somehow more meritorious ecologically. However, as the

514

outcome of the ecological processes, higher diversity is not necessarily better.18 The

515

relationships between diversity and emergent properties of a community, such as

516

stability, productivity, or invisibility, are much more nuanced. Turnbaugh et al.

517

demonstrated that the α-diversity indexes in both of high-fat and low-fat diets were

518

comparable in humanized mouse models.63 A comparison of two sets of replicated

519

methanogenic bioreactors responded differently to a pulse glucose shock,64,65 suggested

520

that communities with higher diversity were not necessarily more functionally stable in 29



521

the face of disturbance. On the contrary, our previous study showed that the biofilm

522

community with higher biodiversity exhibited better resilience ability (short recovery

523

time) in response to environmental disturbance.33

524

Recent experimental research has proposed the hypothesis that it is the community

525

assembly mechanisms that drives the relationships between microbial community

526

structure and function.66 Both positive and negative associations between stochasticity

527

and community function have been hypothesized.24 From one side of the debate, high

528

levels of stochasticity could enhance community functioning, proposing that high

529

diversity communities are more likely to contain more beneficial species properties on

530

average than lower diversity communities. On the contrary, high rates of stochasticity can

531

add organisms to a microbial community that are not expected to local environmental

532

conditions, which may decrease community function. Grahan et al. demonstrated that the

533

microbial assembly processes exert greater influence over the community function when

534

there is variation in the relative contributions of stochasticity and determinism among

535

communities.15 Here, in this study, our results suggested that in general the higher

536

diversity of biofilm community would gain better performance in MEC reactor, but the

537

best performance could not be achieved when the biofilm community was formed under

538

stochastic process (Figure 4; Figure 5). High level of stochasticity decreased the reactor

539

performance by increasing the proportion of non-functional taxa in the anodic biofilms,

540

which contributed more for the higher diversity. Meanwhile, the increasing of

541

deterministic strength caused by the diluted inoculation, could also decrease the reactor

542

performance, due to the enrichment of those unexpected and detrimental microorganisms,

543

such as Methanobrevibacter. This result proved high diversity might not be a desirable

544

outcome for community, especially in microbiology. The assembly mechanism could be 30


Page 30 of 39

Page 31 of 39


545

one of the key processes to shift the functions of a community when we desire a better

546

performance. Our study also has an important implication to microbial engineering (i.e.,

547

MEC) that highly complex community in initial inoculation will not always achieve high

548

performance outcomes. Increasing the selection stress or appropriately diluting the

549

inoculation concentration that brings the community assembly to determinism stage will

550

better select most efficient microorganisms from natural resources.

551

In conclusion, we investigated the relationship between the microbial diversity and

552

functions by using MEC reactors with gradient dilution ratio of inoculations under

553

identical environmental and operational conditions. A gradient diversity in biofilm

554

communities was generated. Null model analysis indicated that both deterministic and

555

stochastic processes played important roles in controlling the assembly of the anodic

556

biofilm communities in MECs, but the most important ecological process in shaping the

557

microbial diversity changed from stochasticity to determinism with the increasing

558

dilution ratio of inoculations. As a consequence, the highest MEC performance was

559

obtained by the biofilm community with the next-highest biodiversity, but not the highest

560

biodiversity biofilm resulting from the stochastic factors. The results presented in this

561

study imply that improving the biodiversity may enhance the functions of microbial

562

biofilm communities, only when the deterministic assembly becomes dominant. This

563

study provides new insights into our understanding of the relationships between

564

community assembly and biodiversity with ecosystem functions.

565 566



567

Supporting Information

568

ASSOCIATED CONTENT

Description of supplementary tests; additional figures showing schematic diagram of 31



569

MEC operation, current production schematic and performance parameters of MEC

570

reactors, rarefaction curve, community composition and distribution, biodiversity indexes

571

and deterministic ratio of microbial communities in MEC anodic biofilms; and tables

572

listing quantitative results of the microbial biomass in anodic biofilms, and the ANOVA

573

analysis and T test results of the quantitative results in the anodic biofilms with diluted

574

inoculation, α-diversity indexes of microbial communities in MEC anodic biofilms,

575

difference of rare and abundant species across all groups of biofilm communities,

576

dissimilarity of microbial communities among reactor groups, and correlation between

577

microbial β-diversities and reactor performances.

578 579



580

Corresponding Author

581

*Ye Deng, Phone: +86 (010) 6284 0082; E-mail: [email protected].

582

or Yuanyuan Qu, Phone: +86 (411) 8470 6250; E-mail: [email protected].

583

Notes

584

The authors declare no competing financial interest.

AUTHOR INFORMATION

585 586



587

This study was supported by Key Research Program of Frontier Sciences, CAS

588

(QYZDB-SSW-DQC026), the Key Research Program of the Chinese Academy of

589

Sciences (KFZD-SW-219-3), CAS 100 talent program and the Open Project Program of

590

State Key Laboratory of Applied Microbiology Southern China (SKLAM001-2015). The

591

authors are very grateful to Dr. James Walter Voordeckers for careful edition on the final

ACKNOWLEDGEMENTS

32


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Page 33 of 39

592


version.

593 594

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