Unravelling and Reconstructing the Nexus of Salinity, Electricity, and

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Unravelling and Reconstructing the Nexus of Salinity, Electricity and Microbial Ecology for Bioelectrochemical Desalination Heyang Yuan, Shan Sun, Ibrahim M. Abu-Reesh, Brian Badgley, and Zhen He Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03763 • Publication Date (Web): 28 Sep 2017 Downloaded from http://pubs.acs.org on September 29, 2017

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Environmental Science & Technology

Unravelling and Reconstructing the Nexus of Salinity, Electricity and Microbial Ecology for Bioelectrochemical Desalination

Heyang Yuan a,†, Shan Sun b,†, Ibrahim M. Abu-Reesh c, Brian D. Badgley b,* and Zhen He a,*

a

Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State

University, Blacksburg, VA 24061, USA b

Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State

University, Blacksburg, VA 24061, USA c

Department of Chemical Engineering, College of Engineering, Qatar University, P.O. Box

2713, Doha, Qatar

†

These authors have contributed equally.

*

Corresponding authors.

Phone: 540-231-9629; e-mail: [email protected] Phone: 540-231-1346; e-mail: [email protected]

1 ACS Paragon Plus Environment


1

Abstract

2

Microbial desalination cells (MDCs) are an emerging concept for simultaneous water/wastewater

3

treatment and energy recovery. The key to developing MDCs is to understand fundamental

4

problems, such as the effects of salinity on system performance and the role of microbial

5

community and functional dynamics. Herein, a tubular MDC was operated under a wide range of

6

salt concentrations (0.05 – 4 M) and the salinity effects were comprehensively examined. The

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MDC generated higher current with higher salt concentrations in the desalination chamber.

8

When fed with 4 M NaCl, the MDC achieve a current density of 300 A m-3 (anode volume),

9

which was one of the highest among BES studies. Community analysis and electrochemical

10

measurements suggested that electrochemically active bacteria Pseudomonas and Acinetobacter

11

transferred electrons extracellularly via electron shuttles and the consequent ion migration led to

12

high anode salinities and conductivity that favored their dominance. Predictive functional

13

dynamics and Bayesian networks implied that the taxa putatively not capable of EET (e.g.,

14

Bacteroidales and Clostridiales) might indirectly contribute to bioelectrochemical desalination.

15

By integrating the Bayesian network with logistic regression, current production was

16

successfully predicted from taxonomic data. This study has demonstrated uncompromised

17

system performance under high salinity and thus has highlighted the potential of MDCs as an

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energy-efficient technology to address water-energy challenges. The statistical modeling

19

approach developed in this study represents a significant step toward understating microbial

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communities and predicting system performance in engineered biological systems.

21


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TOC Art

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27

INTRODUCTION

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Microbial desalination cells (MDCs) are an emerging concept that can simultaneously treat

29

wastewater, desalination salt water and convert organic wastes into electricity.1 As a

30

bioelectrochemical system (BES), MDC produce little waste sludge and can have a lower carbon

31

foot print.2 A recent large-scale MDC system with a total liquid volume of 105 L has

32

demonstrated the potential of this technology.3 However, due to the slow microbial metabolism

33

and the consequent low desalination rate,4 MDCs are suggested to be the pre-treatment process

34

for conventional desalination technologies.5 Understanding the microbial community in MDC

35

anodes can help enhance system performance (e.g., energy production and desalination rate),6

36

and the microbial community can be better manipulated when the metabolic and

37

bioelectrochemical functions of dominant species in the community are understood.7 Anolyte

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salinity is one of the important factors that can affect the bioelectrochemical functions and

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microbial community structure, but the detailed effects remain unclear.8-11 For instance, the BES

40

performance decreased when the Cl- concentration increased from 25 to 100 mM and

41

Desulfobulbus became the major genus in the community.12

42 43

An MDC anode is constantly exposed to high salinity and provides a unique environment in

44

which the microbial community may be self-regulating by interacting with the salt from the

45

desalination chamber (Supporting Information (SI) Fig. S1). As the ions start to migrate, the

46

ohmic resistance of an MDC is reduced and the growth of electrochemically active bacteria is

47

encouraged.13 Meanwhile, the salinity in the anode increases and the metabolism becomes

48

energetically expensive (i.e., more energy expenditure for osmotic adaptation).14 The positive

49

effects of conductivity and adverse impacts of salinity on microbial activity eventually reach


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equilibrium with development of a stable microbial community. The microbial communities in

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MDC anodes have been found to be distinct from those in other BES,15 and to become less

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diverse after long-term operation.16 In contrast to the inhibitory effects of high salinity on the

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performance of other types of BES,17 the current and Coulombic efficiency of MDCs increased

54

with increased salt concentration in the desalination chamber.18, 19 These limited findings have

55

highlighted the need of a systematic study of the salinity effects on the bioelectrochemical

56

activity and microbial community in MDC anodes.20, 21

57 58

A deeper understanding of the microbial ecology in MDC anodes could also improve modeling

59

approaches for microbial communities, and thus guide system design and optimization. With the

60

recent advancements in high-throughput sequencing technologies and bioinformatic tools, the

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microbial community can be predicted using statistical modeling based on phylogenetic and

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taxonomic profiles.22-25 There have been pioneering studies integrating sequencing data with

63

novel statistical approaches, such as artificial neural networks (ANN), to reconstruct the

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microbial communities in environmental ecosystems.26, 27 These novel approaches can provide

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powerful tools to understand the microbial community and functional dynamics in the MDC

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anode, and the mathematical equations inferred by the networks can potentially predict system

67

performance. Furthermore, the knowledge obtained from microbial community modeling in

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MDCs may help identify microbial dark matter in other engineered biological systems and

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deliver insights into biogeochemical cycles in saline ecosystems.28, 29

70 71

In this study, a bench-scale tubular MDC was built (SI Fig. S2) and operated at six different salt

72

concentrations to: 1) examine the effects of salinity on system performance, 2) understand the



73

dynamics of anode microbial communities and functions associated with the salt concentrations

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and 3) develop a statistical model to reconstruct anode microbial community structure and

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predict the system performance. To achieve these objectives, the MDC performance was

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monitored and the microbial community was characterized using 16S rRNA amplicon

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sequencing. The data quantifying dominant taxa and environmental parameters were trained to

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construct Bayesian networks. To our best knowledge, this is the first study using taxonomic data

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to predict the performance of an engineered biological system.

80 81

METHODS

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Reactor setup and operation. A tubular MDC with a 350-mL anode and a150-mL desalination

83

chamber was constructed (SI Fig. S2).30 The MDC was inoculated with anaerobic sludge

84

collected from a local wastewater treatment plant. The anode was fed with synthetic wastewater

85

containing 8 g L-1 acetate and operated continuously. After star-up, the experiment started on day

86

28 and the NaCl concentration was examined in the salt chamber following the order: 2 M, 1 M,

87

0.6 M, 0.2 M, 0.05 M and 4 M (Fig. 1A). These concentrations were selected to mimic industrial

88

saline wastewater (4 M, 2M and 1 M), seawater (0.6 M), brackish water (0.2 M) and domestic

89

wastewater (0.05 M). For each concentration, the MDC was operated for 28 d to ensure

90

sufficient adaptation time.

91 92

Electrochemical measurement. Polarization curve and turnover cyclic voltammetry (CV) was

93

measured after the reactor was operated at each NaCl concentration for 24 d. The MDC was

94

drained and the refilled with fresh anolyte (8 g L-1 acetate), catholyte (25 mM PBS) and salt

95

solution (1 M NaCl) to ensure that the difference in electrochemical performance was caused by


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the anode microorganisms. The drained solutions were stored at 4 °C. Electrochemical

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measurements were completed within 6 h and the drained solutions were pumped back into the

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MDC to minimize the adverse effects on the microbial community.

99 100

DNA extraction and bioinformatics. Biomass was harvested after the reactor was operated at

101

each NaCl concentration for 28 d. The DNA extraction and sequencing analysis can be found in

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the SI Method, SI Fig. S3 and SI Fig. S4. Sequencing data are available in GenBank under

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BioProject accession number PRJNA384805. PICRUSt was used to predict the metabolic

104

dynamics of the observed microbial communities.31 Twenty-seven environmental functions from

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6 level-2 categories (Cell Motility, Carbohydrate Metabolism, Energy Metabolism, Lipid

106

Metabolism, Membrane Transport and Cellular Processes and Signaling, SI Table S1 and Table

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S2) showed significant change (ANOVA, p 1%, and thus 7 phyla, 8 orders and 29 OTUs were used to train the

113

respective networks. Networks were built with R package “bnlearn” using hill-climbing

114

algorithm and the parameters of the networks were calculated with Maximum Likelihood

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parameter estimation method. The networks were validated with Bray-Curtis similarity and

116

relative root-mean square error (RMSE) based on leave-one-out cross-validation.33 A null model

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based on average taxa abundance was performed to further validate the network modeling

118

approach.27 After validation, three final Bayesian networks at the phylum, order and OUT level



119

were constructed from the total 16 datasets. The relative abundance of the major taxa associated

120

with NaCl concentration were fitted with logistic regressions as a simplification of pure-culture

121

growth under the influence of NaCl,34 and subsequently input into the Bayesian networks for

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current simulation.

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RESULTS AND DISCUSSION

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MDC performance. The current decreased significantly from 100 mA with 2 M NaCl to 10 mA

126

with 0.05 M NaCl, and rose back to 105 mA with 4 M NaCl (Fig. 1A). The slight current

127

fluctuation under each concentration was mainly due to the dose of NaOH for pH control.

128

Interestingly, the current changed less noticeably at higher NaCl concentrations (1 - 4 M, 15-mA

129

difference) than at lower concentrations (0.05 - 0.6 M, 65-mA difference). COD removal and

130

Coulombic efficiency were analyzed to understand the possible reason of the relatively

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unchanged current (Fig. 1B). At high salt concentrations, COD removal remained stable at ~80%

132

(Analysis of Variance (ANOVA), p = 0.53) and the effluent containing up to 1200 mg L-1 COD.

133

The presence of high acetate content in the effluent was further confirmed using high-

134

performance liquid chromatography (SI Fig. S5). Such a COD concentration was in theory

135

equivalent to ~25 mA of current according to the equation of Coulombic efficiency. The results

136

thus suggested that the current production at high salt concentrations was not limited by the

137

organic matter and implied that further enhancement of the bioelectrochemical activity could be

138

inhibited by the high salinity.

139 140

Chloride ions migrated into the anode as a result of current production (Fig. 1C). The Cl-

141

concentration in the anode positively correlated with the salt concentration in the desalination


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chamber and reached the highest of 0.84 M at 4 M (Fig. 1C). In addition to migration, diffusion

143

also contributed to ion movement,35 which explained the >100% current efficiency (i.e., the ratio

144

between the transported Cl- ions and the transferred electrons) at 2 and 4 M (SI Fig. S6). The

145

sodium ions, which were mainly from NaOH dosing for pH control, had a higher concentration

146

than Cl- likely due to the presence of negatively charged acetate and the resulting bicarbonate.

147

The highly saline anode environment at high NaCl concentrations was beneficial for charge

148

transfer, but might have also suppressed microbial metabolism.36

149 150

The bioelectrochemical activity was directly evaluated using polarization curves (Fig. 1D). The

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maximum power and current densities followed the order: 4 M > 2 M ≈ 1 M > 0.6 M > 0.2 M >

152

0.05 M, which was consistent with the current production and COD removal and reflected a

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lower bioelectrochemical activity with decreased NaCl concentration. While the polarization

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curves of 0.2 – 4 M were similar in shape, a sharp decrease at a high current density was

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observed for 0.05 M, meaning that current production was limited by mass transport.37 Given the

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high substrate content of fresh anolyte, the mass transport overpotential was possibly caused by

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electron mediators.

158 159

Microbial community dynamics. Principal coordinate analysis (PCoA) based on Bray-Curtis

160

distances revealed significant differences among the microbial communities of the inoculum, the

161

anode electrode and the liquid phase (Fig. 2A, Permutational Multivariate Analysis of Variance

162

(PERMANOVA), p < 0.001). After cultivation at 2 M NaCl, the biofilm and suspended

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communities were separated from each other (PERMANOVA, p < 0.001), but the two

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communities started to converge as the salt concentration decreased and clustered closely at 0.05



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M. When the NaCl concentration was raised back to 4 M, the two communities diverged again

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and shifted towards their 2 M counterparts (Fig. 2A). The distinct biofilm and suspended

167

communities at high salinities could be attributed to multiple factors. For instance, the biofilm

168

might protect bacteria from salt stress but could also limit proton transport,38, 39 whereas the

169

planktonic bacteria were directly exposed to high osmotic pressure and substrates. In addition,

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the EET pathways of the dominant bacteria in the biofilm and liquid might differ, leading to

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divergent community structure.40 The triplicate biofilm communities showed poor repeatability

172

(Fig. 2B), possibly indicating a non-homogeneous distribution of the bacteria on the anode

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electrode. By comparison, the triplicate suspended communities at the same salinity shared high

174

similarity (Fig. 2C). It seemed that 0.6 M was the turning point for the suspended communities,

175

above which the salinities started to become the determinant of the community structure. With

176

0.6 M NaCl in the salt chamber, the anolyte conductivity reached 40 mS cm-1 (0.4 M Cl- and 0.5

177

M Na+, Fig. 1C). Such a salinity was close to that of seawater (~50 mS cm-1) and the anode

178

became a true hypersaline environment that would favor the growth of specialized halophilic

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organisms.41, 42 This likely explained the drastic community shifts above 0.6 M.

180 181

Canonical correspondence analysis (CCA) was performed to understand the relationships

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between the environmental parameters and microbial communities. The biofilm communities

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cultivated at 2 M and 4 M NaCl showed similar projection onto Cl-, indicating their adaption to

184

high salinity (SI Fig. S7A). However, the 4 M biofilm communities had many more negative

185

projections on current production, COD removal and maximum power density than other high-

186

concentration biofilm communities (p < 0.001), which did not agree with the system

187

performance (Fig. 1). In contrast, the projections of the suspended communities on performance-


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related environmental parameters roughly followed the order: 4 M ≈ 2 M > 1 M > 0.6 M > 0.2 M

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≈ 0.05 M (SI Fig. S7B), consistent with the trend of the actual MDC performance (Fig. 1). The

190

results thus implied a close connection between the suspended communities and the MDC

191

performance.

192 193

Microbial community composition. Of the 8 major orders, 36 OTUs had a relative

194

abundance >0.5% and accounted for 53-69% and 71-83% of the total relative abundance in the

195

biofilm and suspended communities, respectively (Fig. 3).

196 197

The order Pseudomonadales was dominated by 4 and 5 OTUs from the genus Pseudomonas and

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Acinetobacter, respectively (Fig. 3). Pseudomonas xanthomarina-related OTU 57 was initially

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the major Pseudomonas in the suspended communities (6.7% with 2 M), and was later replaced

200

by the more omnipresent Pseudomonas caeni-related OTU 38 (20.5% with 4 M). Pseudomonas

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is among the earliest genera isolated from BES, and is well known for the mediator-based EET

202

using phenazine derivatives.43 The electron shuttles released by Pseudomonas, which are

203

regulated by environment-induced quorum sensing (e.g., NaCl concentration),44 can also be used

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by other bacteria in mixed cultures as electron acceptors.45 In addition, phenazine derivatives can

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function as antibiotics to inhibit competitors, affect transcriptional regulation and modulate the

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physical characteristics of Pseudomonas communities.46 Based on their “social” behavior and

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ubiquitous high abundance in anodes, it is speculated that Pseudomonas spp. play a key role in

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shaping the anode community and determining the bioelectrochemical function.

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Similar to Pseudomonas, the 5 Acinetobacter-related OTUs were classified with high confidence

211

(>98% identity) (Fig. 3). Dominance shifted from OTUs 12 (Acinetobacter venetianus) and 514

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(Acinetobacter rudis) at 2 M to OTUs 1362 (Acinetobacter brisouii) and 30 (Acinetobacter

213

gandensis) at 4 M. Overall, Acinetobacter seems to prefer the biofilm habitat and high salinity.

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The high phylogenetic similarity to Pseudomonas and the fact Acinetobacter calcoaceticus

215

dominated an ethanol-oxidizing BES implied that the members from this genus might be capable

216

of EET.47 An Acinetobacter sp. strain Tol 5 was highly adhesive to the anode electrode and

217

performed EET solely via electron shuttles, e.g., phenazine derivatives from Pseudomonas.48 It

218

has been reported that Pseudomonas can provide Brevibacillus sp. PTH1 and Enterobacter

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aerogenes with phenazine for respiration, leading to their dominance in the respective

220

communities.49, 50 The high total abundance of Pseudomonas and Acinetobacter in the MDC

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under high current conditions (4 M NaCl, 17.9% in biofilm and 28.3% in liquid) suggests that

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Acinetobacter spp. may form symbiotic associations with Pseudomonas spp. and actively

223

participate in current production.

224 225

In addition to Pseudomonadales, Campylobacterales and Oceanospirillales might also contribute

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to electricity generation. Campylobacter upsaliensis-related OTU 148 was ubiquitous in the

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biofilm, while Arcobacter skirrowii-related OTU 1 and Halomonas salicampi-related OTU 20

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were abundant under high current conditions (i.e., high NaCl concentrations). Arcobacter and

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Oceanospirillaceae (the family including Halomonas) from marine sediments have been reported

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to utilize acetate to reduce manganese oxide.51 Moreover, a Halomonas sp. isolated from Soap

231

Lake could reduce Fe and Cr, implying their potential ability for EET.52 Based on the high

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phylogenetic similarity between A. skirrowii and Arcobacter butzleri ED-1 (an electrochemical


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active strain isolated from BES),53 and that between H. salicampi and the Pseudomonas-related

234

OTUs in the present study (Fig. 3), it is reasonable to speculate that the members from

235

Campylobacterales and Oceanospirillales contribute to current production in the MDC anode.

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Four of the 6 major Clostridiales-related OTUs (OTU 72, 32, 47 and 37) were frequently

238

observed under medium to high salinities (0.6 – 4 M) and might be associated with current

239

production (Fig. 3). However, the EET abilities of these taxa have not been previously reported

240

in the literature. It was speculated that their function in the MDC anode could be related to

241

scavenging byproducts of current production, such as inhibitory metabolites, biomass and HCO3-.

242

For example, Proteiniclasticum ruminis (OTU 72) does not utilize carbohydrates but can digest

243

proteins,54 and thus may feed on dead cells. On the other hand, Acetoanaerobium noterae (OTU

244

47) is known to utilize CO2 and H2 to produce acetate.55 Given the close phylogenetic

245

relationship of this OTU with OTUs 32, 77 and 100, most of the Clostridiales in the MDC anode

246

seem to perform CO2 fixation and do not directly participate in current production.

247 248

The order Bacteroidales was composed of highly diverse but poorly identified members (Fig. 3).

249

Bacteroidales are consistently found dominant in anaerobic bioreactors and can contribute to

250

degrading soluble microbial products.56 Their relatively unchanged abundance at different

251

current conditions suggests that they may not be important drivers of EET. In the remaining taxa,

252

Erysipelotrichales, Acholeplasmatales and Synergistales exhibited clear preference for low

253

salinity and the liquid environment. The total relative abundance of the OTUs from these three

254

orders in the suspended community grew with decreased NaCl concentration, and reached the

255

peak of 45.9% at 0.05 M, implying that they were not involved in electricity generation.



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Functional dynamics. Electrochemically active microorganisms can transfer electrons

258

extracellularly via membrane-bound cytochromes, shuttle molecules or conductive pili.57 To

259

understand the major EET mechanisms in the MDC anode, CV was performed with fresh

260

electrolyte and 1 M NaCl solution, and the scan rate ranged from 0.05 to 50 mV s-1. The

261

voltammograms showed the typical characteristics of a Nernstian response, including anodic

262

peak current/cathodic peak current = 1 and peak potential being independent of scan rate (SI Fig.

263

S8). Furthermore, the peak current correlated linearly with the square root of the scan rate (Fig.

264

4A), indicating that the redox reactions in the anode were reversible and diffusion-controlled.58

265

These were clear evidences that the EET in the MDC anode proceeded mainly via shuttle

266

molecules.59 However, the possibility of other EET pathways should not be ruled out, as

267

evidenced by the occurrence of Desulfuromonadales (1 – 5% at high NaCl concentrations, SI Fig.

268

S9), which includes various members that perform EET via direct contact and/or conductive

269

pili.60 The formal potentials of the shuttle molecules were identified at approximately -180 and -

270

260 mV vs. SHE (standard hydrogen electrode) (Fig. 4B), which could be assigned to the

271

phenazine derivatives, phenazine-1-carboxylic acid and phenazine-1-carboxamide.61, 62 Initially,

272

the EET was dominated by the more positive redox reaction (2 M, -180 mV vs. SHE). As the

273

NaCl concentration decreased, the EET showed a composite characteristic of both peaks and

274

then shifted to the more negative redox reaction (0.2 M, -260 mV vs. SHE). Interestingly, the

275

major EET pathway changed back to the more positive redox reaction when the NaCl

276

concentration further dropped to 0.05 M, and remained stable at 4 M. Although the variation of

277

the dominant electron-transfer sites under osmotic pressure remain unclear and warrant further

278

studies, the electrochemical results agreed well with the community composition containing high


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abundance of Pseudomonas (Fig. 3), and suggested that phenazine-mediated species are the key

280

players of current production in the highly saline anode environment.

281 282

PICRUSt was applied to understand the possible impacts of salinity on the overall microbial

283

activity in the MDC anode.31 Of the 329 predicted results, 27 environmental functions from 6

284

categories (Cell Motility, Carbohydrate Metabolism, Energy Metabolism, Lipid Metabolism,

285

Membrane Transport and Cellular Processes and Signaling) were significantly affected (ANOVA,

286

p < 0.05, Table S1 and Table S2). The suspended communities revealed clear patterns under high

287

salinity (Fig. 4C). For instance, the significantly high cell mobility (i.e., chemotaxis, motility and

288

flagella assembly) suggested that bacteria might be actively seeking shuttle molecules for

289

respiration.63 Carbon fixation also became more active at high salinity, which agreed with the

290

high Clostridiales abundance (Fig. 2 and Fig. 3) and was possibly related to the high HCO3-

291

content resulted from the rapid acetate oxidation (Fig. 1B).64 Under low NaCl concentrations,

292

important carbohydrate metabolisms such as glycolysis/gluconeogenesis, pentose phosphate

293

pathway (PPP) and the metabolism of Amino sugar/nucleotide sugar, fructose/mannose and

294

starch/sucrose are predicted to be enhanced (Fig. 4C), possibly because EET-related acetate

295

oxidation is suppressed and more substrates are available to non-EET heterotrophs, syntrophs

296

and methanogens. In agreement, methane metabolism and phosphotransferase system for sugar

297

uptake are upregulated. Overall, PICRUSt provided reasonable predictions of the suspended

298

communities. The predictions of the biofilm communities did not exhibit distinct patterns (SI Fig.

299

S10) because the functions were derived from the taxonomic data and the no noticeable trend

300

was observed from the biofilm communities (Fig. 2B, SI Fig. S7A and SI Fig. S9A). Detailed



301

functions of the biofilm communities warranted further elucidation using other functional

302

analyses.

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Bayesian network analysis. A Bayesian network modeling approach was applied to simulate

305

the dynamics of the microbial community and achieved significantly more accurate prediction

306

than the null model (SI Fig. S11, t-test, p0.8 M NaCl was one of the highest among BES studies. The bioelectrochemical

374

performance of the MDC was not inhibited by the high salinity, likely because ions accumulated

375

in the MDC anode over a period of time, which provided microorganisms sufficient time to adapt

376

to the saline environment. These findings have collectively highlighted the potential of MDCs as

377

an energy-efficient water and wastewater treatment technology. For instance, when treating low-

378

strength wastewater (e.g., domestic wastewater), a high concentration of salt solution in the salt

379

chamber may facilitate organic removal. On the other hand, in order to completely desalinate

380

industrial saline wastewater (e.g., produced water from hydraulic fracturing), an appropriate

381

carbon source needs to be supplied in the anode to achieve electron transfer and ion migration.

382 383

Based on analyses of microbial community structure and phylogeny, Pseudomonas,

384

Acinetobacter and Arcobacter are proposed to be the major electrochemically active

385

microorganisms (Fig. 3). It is for the first time demonstrated how the community in an MDC

386

anode is shaped by salinity and dominated by these shuttle-mediated electrochemically active

387

microorganisms. Interestingly, halophilic microorganisms were not dominant in the communities

388

even at the highest NaCl concentration (4 M in the salt chamber, >0.8 M in the anode), possibly

389

because that the electrochemically active bacteria developed mechanisms to cope with osmotic

390

pressure during the star-up period and the high conductivity favored their EET. The potential

391

evolution of these microorganisms during adaption is of strong interest to future studies.

392

Members of Bacteroidales and Clostridiales may not be involved in EET directly but can play



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393

critical roles in sustaining other important functions in the anode community, allowing mixed-

394

culture BES to consistently generate higher current and power than pure-culture BES.67 For

395

example, members of Bacteroidales may be scavenging dead cells on the electrode,68 thereby

396

creating space for electrochemically active bacteria and facilitating EET kinetics. Hydrogen gas

397

may be produced from biomass fermentation, and subsequently used together with HCO3- by

398

Clostridiales (e.g., A. noterae, Fig. 4C) to form acetate. Some Clostridiales, such as Clostridium

399

ljungdahlii and Clostridium aceticum, have been reported to reduce CO2 to acetate using an

400

electrode poised at -400 mV vs. SHE as electron donor.69 Similar process may occur in the MDC

401

anode, but with phenazine derivatives donating electrons. At pH 7, the redox potentials of

402

phenazine-1-carboxylic acid and phenazine-1-carboxamide are calculated to be -360 and -400

403

mV vs. SHE (onset oxidative potential in CV, SI Fig. S8), which are thermodynamically

404

favorable when coupled with CO2 reduction to acetate (-296 mV vs. SHE). The hypothesis

405

implies the importance of Clostridiales in carbon cycling and electron flow in the MDC anode,

406

which is consistent with the prediction by PICRUSt and Bayesian network (Fig. 4C and Fig. 5B),

407

but needs further studies that directly measure functions.

408 409

Bayesian networks can help identify interactions between environmental factors and dominant

410

taxa (Fig. 5),70 and have been used here for the first time to predict functional output from an

411

engineered system (SI Fig. S16 and Fig. 6). This approach is based on taxonomic data, and

412

avoids the assumption of a simplified microbial community and the estimation of empirical

413

coefficients.71 For instance, a previous MDC engineering model developed based on a

414

configuration similar to the present study (a tubular reactor with 300-mL anode and 150 mL-

415

desalination chamber) could not yield satisfactory simulation at high NaCl concentrations


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416

without optimizing some key coefficients.72 In contrast, the taxa-based statistical model in this

417

study can successfully predict reactor performance over a wide range of salt concentrations and

418

suggest optimal salinity for different application purposes. With advancements in DNA

419

sequencing technologies and biostatistics, such an approach can potentially be expanded to

420

predict the effects of other engineering factors including COD loading, hydraulic retention time,

421

external resistance and electrode size, etc., and produce a statistical model that is universally

422

applicable to various types of bioreactors. To achieve that, a bioreactor can be operated with one

423

factor varying within a certain range while other factors remaining constant. The dynamics of the

424

community structures and functions associated with this factor are captured with “omics”

425

methods. The same procedure can be applied to other factors to build a database with each factor

426

as the independent variable and the sequencing data as the dependent variables. Finally, their

427

relationships are inferred by machine learning and the equations are used to extrapolate the

428

microbial community and function given a set of parameters.

429 430

ASSOCIATED CONTENT

431

Supporting Information

432

Additional methods. Addition results: equations, coefficients and standard deviations inferred by

433

the Bayesian network at different taxonomic levels. Additional tables: PICRUSt results and

434

RMSE between real and predicted values. Additional figures: proposed hypothesis of the self-

435

regulating MDC microbial community, the MDC schematic, the concentration of acetate in the

436

anode effluent, current efficiency, rarefaction curve, α-diversity indices, CCA results, CV results,

437

PICRUSt results, Bayesian network predictions, logistic regression of the relative abundance of

438

dominant taxa.



439 440

AUTHOR INFORMATION

441

Corresponding authors

442

Phone: 540-231-9629; e-mail: [email protected]

443

Phone: 540-231-1346; e-mail: [email protected]

444 445

Author Contributions

446

†

These authors have contributed equally.

447 448

Notes

449

The authors declare no competing financial interests.

450 451

ACKNOWLEDGMENT

452

The authors would like to thank Keaton Lesnik at the Oregon State University for the discussion

453

of statistical modeling. This work was partially supported by NPRP grant # 6-289-2-125 from

454

the Qatar National Research Fund (a member of Qatar Foundation).


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Figure 1. (A) Current production; experiment started on day 28 after start-up; red arrows indicate

666

performance measurements, green arrows indicate electrochemical measurements and blue

667

arrows indicate sample collection for DNA extraction. (B) COD removal and Coulombic

668

efficiency. (C) The concentrations of Cl- and Na+ in the anode. (D) Polarization curve. Error bars

669

were calculated from triplicate data.

670



671 672

Figure 2. (A) PCoA of the biofilm and liquid samples based on the relative abundances of OTUs.

673

(B) Detailed PCoA of the biofilm samples. (C) Detailed PCoA of the liquid samples

674


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Figure 3. Phylogeny and abundance of the 36 major OTUs from 8 orders in the biofilm and

677

liquid samples. The relative abundance at each NaCl concentration was the mean of triplicate

678

results, and the OTUs were selected with a relative abundance >0.5%.

679



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Figure 4. (A) Linear correlation between the anodic peak current and the square root of the scan

682

rate (0.5 – 50 mV s-1). (B) First derivative of the CV at the scan rate of 1mV s-1 under turnover

683

condition. (C) Major functions of the anode suspended community predicted using PICRUSt and

684

visualized using Circos. The thickness of the band represents the permillage of the function in

685

the predicted data. For each function, there are six bands associated with the six NaCl

686

concentrations, and the two highest permillages were highlighted. Amino, Amino sugar and

687

nucleotide sugar metabolism; PPP, pentose phosphate pathway; ABC, ATP-binding cassette

688

transporters; PTS, phosphotransferase system.

689


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Figure 5. (A) The Bayesian network at the phylum level with 7 dominant phyla in the biofilm

692

and suspended communities and 6 environmental parameters. (B) The Bayesian network at the

693

order level with 8 dominant orders in the biofilm and suspended communities and 6

694

environmental parameters.

695 33 ACS Paragon Plus Environment


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Figure 6 The actual and simulated current at different levels. Error bars were calculated from

698

triplicate data.

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