Linking Microbial Community, Environmental Variables, and

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Linking microbial community, environmental variables and methanogenesis in anaerobic biogas digesters of chemically enhanced primary treatment sludge Feng Ju, Frankie Lau, and Tong Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b06344 • Publication Date (Web): 27 Feb 2017 Downloaded from http://pubs.acs.org on February 28, 2017

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Environmental Science & Technology

Linking microbial community, environmental variables and methanogenesis in anaerobic biogas digesters of chemically enhanced primary treatment sludge Authors: Feng Ju 1, Frankie Lau 2, Tong Zhang 1* Author affiliation:

1

Environmental Biotechnology Lab, The University of Hong

Kong SAR，China; 2Drainage Services Department, The Government of the Hong Kong Special Administrative Region, Hong Kong, China. *Corresponding author: Dr. Tong Zhang (Professor) Address: Environmental Biotechnology Lab, The University of Hong Kong, Pokfulam Road, Hong Kong Tel: 852-28591968 (lab), 28578551 (office) Fax: 852-25595337 E-mail: [email protected]

1

ACS Paragon Plus Environment


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1

Abstract

2

Understanding the influences of biotic and abiotic factors on microbial community structure

3

and methanogenesis are important for its engineering and ecological significance. In this study,

4

four biogas digesters were supplied with the same inoculum and feeding sludge, but operated

5

at different sludge retention time (7 to 16 days) and organic loading rates for 90 days to

6

determine the relative influence of biotic and environmental factors on the microbial

7

community assembly and methanogenic performance. Despite different operational

8

parameters, all digester communities were dominated by Bacteroidales, Clostridiales and

9

Thermotogales, and followed the same trend of population dynamics over time. Network and

10

multivariate analyses suggest that deterministic factors, including microbial competition

11

(involving Bacteroidales spp.), niche differentiation (e.g., within Clostridiales spp.), and

12

periodic microbial immigration (from feed sludge), are the key drivers of microbial

13

community assembly and dynamics. A yet-to-be-cultured phylotype of Bacteroidales

14

(GenBank ID: GU389558.1) is implicated as a strong competitor for carbohydrates. Moreover,

15

biogas-producing rate and methane content were significantly related with the abundances of

16

functional populations rather than any operational or physicochemical parameter, revealing

17

microbiological mediation of methanogenesis. Combined, this study enriches our

18

understandings of biological and environmental drivers of microbial community assembly and

19

performance in anaerobic digesters.

20

Keywords: Anaerobic digesters; Methanogenesis; Microbial community; Population 2


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dynamics; Network analysis

22

Introduction

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Anaerobic digestion (AD) is a microbially mediated biotechnology widely used for renewable

24

energy production and waste management 1, 2. Environmental variables, including operational

25

parameters (e.g., sludge retention time) and physicochemical conditions (e.g., volatile fatty

26

acids (VFAs), ammonium nitrogen (NH4-N)), are known to affect AD process, including

27

formation of intermediates, process stability, biogas yield, biodegradation efficiency, and 3, 4, 5.

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However, because of the lack of dense monitoring of coupled environmental and

29

microbiological data from the same anaerobic digesters, biotic and abiotic factors that govern

30

microbial community structure and their linkage with methane production are poorly

31

understood3, 6, 7.

32

Recent application of high-throughput sequencing technologies provides a comprehensive,

33

qualitative and quantitative measurement of diverse environmental microbiomes, facilitating

34

the exploration of the driving forces that structure microbial communities in marine water 8,

35

lake 9, soil

36

interpret patterns and processes of microbial community assembly

37

highlights an importance of deterministic processes, such as microbial interactions (e.g.,

38

syntrophy and competition), niche differentiation, substrate availability and operational

39

conditions 6, 11. On the contrary, neutral theory, as a null hypothesis, disregards the differences

40

among species in response to ecological conditions and only considers stochastic processes,

10-12

, and bioreactors

6, 13-15

. Both niche-based and neutral theories are used to 16

. Niche-based theory

3



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15, 17

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such as birth, death, dispersal, and colonization and immigration

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the relative role of niche and neutral forces in structuring microbiome are controversial. Some

43

researchers argue that microbiome assembly in lake water, soil, and activated sludge is driven

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by deterministic niche-based processes based on co-occurrence analysis

45

reactor experiments 6; others that neutral models support stochastic processes for interpreting

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microbiome assembly in marine water 8, activated sludge

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that niche-based and stochastic-neutral processes are both important for structuring

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microbiomes of soil and bioreactor 10.

49

Bioreactors are ideal systems to study patterns of microbial community assembly and link

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them with system performance, because microorganisms are cultivated in elaborately

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controlled and regularly monitored systems

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previously reported in quintuplicate denitrifying bioreactors

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membrane reactors

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suggests that these highly predictable microbial systems are not guided by stochastic

55

processes. However, replicate operational conditions, such as sludge retention time (SRT) and

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organic loading rate (OLR), and highly similar profiles of chemicals (e.g., VFAs and NH4-N)

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in these replicated bioreactors make it impossible to resolve the influence of different

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operational parameters on the microbial community assembly and performance. In fact,

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operational parameters including SRT and NH4-N have long been implicated to affect

60

microbial community structure and methane production

15

. Recent viewpoints on

9, 13

or replicated

and bioreactors 18; and yet more

6, 13, 14

. The reproducible community dynamics

20

19

, quadruplicate nitrifying

, and triplicate anaerobic cellulose-degrading reactors

6

congruously

3, 4

. Therefore, the examination of

4


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anaerobic digesters under different operational conditions is essential for checking the

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robustness of deterministic community assembly and dynamics observed in previous replicate

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bioreactors, examining whether different operational conditions lead to stochastic or random

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community assemblages, and determining the influences of biotic and abiotic (environmental)

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factors in shaping community structure and performance.

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Here, four anaerobic digesters were supplied with the same seeding and feeding sludge and

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operated at different SRTs and OLRs. Temporal dynamics in the microbial communities and

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environmental conditions was closely followed over 90 days and linked to methane

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production. This work was conducted to show the relationships among microbial community,

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environmental variables and methane production, to check whether microbial community

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assembly and dynamics were deterministic or stochastic, and to compare the relative

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influence of environmental variables (operational and physicochemical) and biotic factors

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(e.g., microbial interactions) in shaping microbial community assembly and performance.

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Materials and Methods

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Digester operation and sample collection

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Four 1-liter continuously stirring anaerobic digesters of chemically enhanced primary

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treatment (CEPT) sludge, designated as R1, R2, R3 and R4, were operated at 35°C (heated in

78

water bath) but under different SRTs of 7, 9, 12 and 16 days, corresponding to levels of OLR

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between 2.40 and 3.78, 1.86 and 2.93, 1.40 and 2.21, and 1.05 and 1.66 kg-VS/m3/d, 5



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respectively (Table S1). The setup of retention time below and above 10 days, which is

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deemed as the minimum retention time needed to prevent washout of slow-growing

82

methanogens by some researchers

83

recommended minimum SRT on the methanogenic performance and microbial structure.

84

More details on digester operation are available in the Supporting Method S1. For

85

microbiological analysis, the seed sludge (in triplicates), feed sludge (Day 1), and 48 digester

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samples (collected at 12 time points; Figure 1a) were fixed in 50% ethanol and stored at

87

-20°C until further processing.

88

Digester performance monitoring

89

Biogas, liquid, and sludge samples were collected for chemical analysis every 2 or 4 days

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from the four anaerobic digesters, as described in Supporting Method S2. The digesters

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showed reproducible performance in biogas production and volatile solid reduction over 90

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days (Figure S1).

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DNA extraction, sequencing and bioinformatics analysis

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16S rRNA genes were amplified with the universal primer set F515/R806 11, 21 from genomic

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DNAs extracted using FastDNA SPIN Kit for Soil. The pooled PCR products were purified

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and prepared for paired-end sequencing (2×250 bp) on Illumina Miseq. The generated data

97

were pretreated following mothur Miseq SOP

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QIIME (v 1.8.0) for open-reference operational taxonomic units (OTUs) picking and core

99

diversity analyses 23. The molecular and bioinformatics procedures are described in details in

4, 6

, allows us to test the effects of a span around the

22

, and the clean data were imported into

6


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Supporting Method S3. The sequence data are deposited into the NCBI’s Sequence Read

101

Archive Database with accession numbers of SRR3104406, SRR3104407, SRR3104408,

102

SRR3104410 and SRR3104411.

103

Statistical and network analyses

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The statistical analyses including principal coordinates analysis (PCoA), procrustes analysis,

105

canonical correspondence analysis (CCA), correlation analysis, student’s t-test, Wilcoxon

106

signed-rank test, Kruskal-Wallis tests and BIOENV analysis were performed using R

107

packages: stats

108

and co-exclusion species-species associations (SSA) were explored by calculating all pairwise

109

Spearman’s rank coefficients (ρ) among 0.97-OTUs that occurred in at least 50% of samples

110

and represented by at least five sequences on average across all samples. Details on the

111

statistical and network analyses were described in the Supporting Method S4.

112

Results

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Digester performance and operational parameter

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Four anaerobic digesters were operated to investigate the effects of different SRTs (7-16 days)

115

and OLRs (1.05-3.78 kg-VS/m3/d) on the biogas production and microbial community

116

structure over 90 days. To facilitate comparison of temporal succession of microbial

117

community, three operational stages with similar time lengths were defined: Stage I—day

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10-38, Stage II—day 39-66, and Stage III—day 67-90 (Figure 1).

24

, vegan

25

, MASS

26

and nortest 27, unless stated otherwise. Co-occurrence

7



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The four anaerobic digesters achieved good methane-producing rate (RM) (0.49-0.66 L/L/d;

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Figure 1b) and methane content (CH4%; 64-67%, Table S1) at Day 12, indicating rapid

121

adaptation of the inoculated microorganisms to the feedings of sewage sludge. From day 10 to

122

18, R1 and R2 (shorter SRTs of 7 and 9 days) had higher RM than R3 and R4 (higher SRTs of

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12 and 16 days), probably because of the higher OLRs to the former over the latter digesters

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(Figure 1a, inset). However, accumulation of VFAs (mainly acetate and propionate) were

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observed in R1 during stage I (Figure 1c and Table S2) and RM dramatically decreased on Day

126

34 (Figure 1b), closely followed by the peak of VFAs at Day 38 (Table S2). Stage II was

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characterized by OLR decrease from day 42 to 50 and OLR increase from day 54 to 66. This

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accompanied with the decrease of VFA concentrations in R1 (33-128 mg/L), as well as

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fluctuations of RM and pH in all digesters (Figure 1b and 1d). During Stage III, the digester

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OLRs were markedly increased on Day 66 (by 38%), followed by dramatic increases of RM in

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all digesters expect for R1. Two-sample statistical tests show that RM from day 10 to 90 were

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significantly (P-values < 2.3×10-4) different between R2 (0.95±0.14 L/L/d) and R3

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(0.73±0.20), between R3 and R4 (0.59±0.10), and between R2 and R1 (0.54±0.14). In

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contrast, there was no significant (P-values > 0.07) difference in CH4% between any two

135

digesters.

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Associations among environmental parameters, microbial alpha-diversity and biogas

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parameters

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Pearson’s linear correlations and/or Spearman’s rank correlations (Table S4) show that RM 8


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was negatively correlated with the concentration of organic parameters, including acetic acid

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(HAc), propionic acid (HPr) and VFAs (-(0.39-0.48), Table S4-1), as well as microbial

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alpha-diversity metrics, such as Shannon’s and Simpson’s diversity indexes (-(0.38-0.40),

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Table S4-2). Moreover, all alpha-diversity metrics were negatively correlated with operating

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time (i.e., Day; -(0.61-0.84)), inorganic carbon (IC, -(0.37-0.65)), and NH4-N (-(0.41-0.64)

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(Table S4-1). This is in agreement with the results of two-sample statistical tests that the

145

richness, evenness and diversity metrics of 0.97-OTUs significantly decreased (P-values ≤

146

0.05) with operating time from Stage I to III (Table 1).

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Microbial population dynamics in SRT-differentiated digesters

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The seed sludge was mainly composed of Bacteroidia (20.9%), Gammaproteobacteria

149

(12.7%) and Betaproteobacteria (8.5%). All the digester microbial communities rapidly

150

shifted away from the seed community (Figure 2, 0.97-OTU level; Figure S2, class level) and

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markedly decreased in richness (i.e., observed species) and Shannon’s H diversity of OTUs

152

after startup (Figure 2, diversity heatmap). PCoA biplots based on UniFrac distances (Figure 3)

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and Bray Curtis dissimilarities between 0.97-OTUs (Figure S3a) show that both microbiome

154

membership (i.e., presence or absence of rare phylotypes) and abundance were much more

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different between operating stages (Figure 3c & 3d) than between digesters (Figure 3a & 3b).

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Procrustes analysis on the principal coordinates of unweighted UniFrac and weighted UniFrac

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distances between samples over 90 days shows a significant and high correlation (0.85,

158

P-value = 0.001) between microbial community structure with and without considering 9



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membership, and highly similar community dynamics among the four digesters (Figure S3b).

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BIOENV analysis supports that operating time best explained the variations of microbial

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community abundance and membership (rho = 0.518-0.520, weighted UniFrac distance,

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Table 2). However, compared with only considering operating time, the incorporation of SRT

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slightly improved interpretation of the unweighted (but not weighted) UniFrac distances

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between the samples (rho from 0.369-0.413 to 0.442-0.481, Table 2), revealing that SRT

165

slightly determines microbial community membership. PCoA bi-plots highlighted the

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influence of SRT on the community membership at Stage I. For example, the community

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membership (Figure 3d) changed more apparently than abundance (Figure 3c) for all

168

digesters.

169

Despite the differences in SRT and operating time, the microbial communities in all digester

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samples (n = 48) were composed of Bacteroidia (42.8±13.1%), Clostridia (23.0±6.2%),

171

Thermotogae (8.7±7.0%), Bacilli (3.0±1.3%), Anaerolineae (2.7±1.2%), Actinobacteria

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(2.1±1.0%) and other populations (17.8±5.2%) (Figure S2, clusters a and c1). Moreover, the

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overall profiles of microbial abundance and diversity metrics in the four digesters were

174

similar over time (Figure S2) and the microbiome succession followed deterministic trends

175

over 90 days (Figure 3 and Figure S3b). The 50 most abundant 0.97-OTUs in the four

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digesters over 90 days (i.e., labeled from s1 to s50; Table S5) accounted for total relative

177

abundance of 71.1(±6.8)%. Among them, 38 OTUs had significant different (P-values ≤ 0.05)

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relative abundances between the seed sludge and different operating stages (29 OTUs, taxon

10


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names in red) or SRTs (10 OTUs, taxa names in green), as shown by Kruskal-Wallis tests

180

(Figure 2, P-value heatmap).

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Environment-species and species-performance associations

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The relationships among environmental (i.e., operational and physicochemical) parameters,

183

abundance of 0.97-OTUs, and methanogenic performance were explored by CCA based on

184

data from all 48 digester samples. Overall, the associations between environmental and

185

performance parameters, as shown by CCA plot, were in agreement with the results of

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correlation analysis (Table S4). CCA additionally shows that Bacteroidales OTUs including

187

s1, s13 and s49 (red nodes, Figure 4) were positively correlated with both methane-producing

188

rate (RM) and biogas-producing rate (RG) whereas negatively with CH4%. The most abundant

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OTU, s1 (GenBank ID: GU389558.1, Greengenes ID: 837605, Figure S6), was significantly

190

enriched (P-value ≤ 0.01) in all digesters, from 1.1% in the seed sludge to average relative

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abundances of 15.1% 28.0% and 45.6% at Stages I (n=16), II (n=16) and III (n=16),

192

respectively (Figure 2, cluster a). In contrast, the organic parameters, including OLR, DOC,

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VFAs, HPr and HAc, were positively correlated a group of OTUs (blue nodes, Figure 4),

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including four Clostridium OTUs (s3, s4, s19 and s36), four Syntrophomonas OTUs (s5, s12,

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s18 and s29), three Bacteroidales OTUs (s10, s15 and s46), three Verruco-5 OTUs (of

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Verrucomicrobia; s28, s38 and s48), two T78 clade OTUs (of Chloroflexi, s9 and s23) and one

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Fibrobacter succinogenes OTU (s17). The most abundant methanogen, s30, (Greengenes

198

taxonomy ID: 103247) was correlated positively with HAc, whereas negatively with SRT 11



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(Figure 4). This Methanosaeta species had significantly different relative abundances at

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different SRTs (Kruskal-Wallis test P-value ≤ 0.001; Figure 2, P-value heatmap). Likewise,

201

methanogenic populations of class Methanomicrobia and genus Methanosaeta were the most

202

enriched at the SRT of 7 days, followed by SRTs of 9, 12 and 16 days, respectively (Figure

203

S4).

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Species-species associations

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The co-occurrence (i.e., positive) and co-exclusion (i.e., negative) associations between

206

species (i.e., 0.97-OTUs) over time were explored using network analysis of 48 digester

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samples collected from Day 10 to 90. The resulting positive SSA network consisted of 101

208

nodes (i.e., 0.97-OTUs) and 317 edges (i.e., correlation; Figure 5a), while the negative SSA

209

network included 60 nodes and 131 edges (Figure 5b). Overall, 52% and 48% of OTUs in the

210

positive and negative SSA networks belonged to Bacteroidetes and Firmicutes, respectively.

211

Furthermore, 20% and 40% of all the 131 negative SSA involved the most abundant,

212

Bacteroidales s1 and other 15 Bacteroidales OTUs, respectively. Notably, the average

213

correlation coefficients of negative SSA between s1 and other OTUs (-0.71; see Figure 5d for

214

examples) were significant higher (P-values < 0.0003, Figure 5c) than those between other 15

215

Bacteroidales OTUs and between 44 non-Bacteroidales OTUs.

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Topological analysis shows that the positive SSA network had a clustering coefficient of 0.47

217

and a modularity index of 0.49. Network partitioning divided the network into seven modules

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(Figure S5a). Module I was aggregated by 25 densely connected OTUs with peak abundances

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at Stage I (see green nodes in Figure S5a against Figure S5b). However, these OTUs had

220

sharply decreasing abundance with operating time. This was accompanied with peak

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abundance of many other OTUs (mainly in Modules II and V, Figure S5a) at Stage II (39

222

OTUs; red nodes) or Stage III (14 OTUs, purple nodes; Figure S5b). Different from Module I

223

where OTUs of Actinobacteria, Tenericutes and WWE1 were prevalent (see Figure S5a

224

against 5a), Module II mainly harbored OTUs of Bacteroidetes (10 OTUs), Proteobacteria (7

225

OTUs), Verrucomicrobia (3 OTUs), and Firmicutes (3 OTUs).

226

Further statistical analysis reveals non-random (deterministic) co-exclusion patterns between

227

OTUs from different orders (i.e., inter-orders) or from the same order (i.e., intra-order). Firstly,

228

OTUs from order Bacteroidales tended to co-exclude with OTUs of other bacterial orders (i.e.,

229

Actinomycetales, Cloacamonales, Mollicutes, Anaerolineales and Synergistales) much more

230

(23%) than expected when considering observed order frequencies and random association

231

(6%, Table S6). Moreover, the observed incidence of intra-order co-exclusion for

232

Bacteroidales (10.7%) was slightly higher than expected by chance (6.8%), whereas the

233

observed incidence of intra-order co-exclusion for Clostridiales (0.8%) was much lower than

234

expected by random associations (4.4%).

13



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Discussion

236

Environmental parameters linked to methanogenic performance

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In this study, four anaerobic digesters were operated for 90 days under different SRTs and

238

OLRs with the same inoculum and primary sewage sludge as biogas feedstock. Shortening

239

SRT to 9 days, which is below the putative minimum SRT, i.e., 10 days, to prevent washout of

240

slow-growing methanogens

241

abundance of HAc-utilizing methanogens, nor in the deterioration of biogas/methane

242

production. For example, relative abundances of Methanomicrobia and Methanosaeta (Figure

243

S4) and methane-producing rate (RM, Figure 1b) markedly increased with the decrease of SRT

244

from 16 or 12 days to 9 days. However, a further decrease of SRT from 9 to 7 days led to a

245

dramatic decrease of RM from 0.95 (±0.14) to 0.54 (±0.14) L/L/d (Table S1), despite a

246

further increase of the relative abundance of methanogens (Figure S4). This non-monotonic

247

relationship between SRT/OLR and methanogen abundance suggest that they are not the sole

248

influential factors of methanogens and RM. For example, the positive/negative correlations

249

between abundance of Bacteroidales populations (i.e., s1, s3 and s49, Figure 4) and RM/CH4%

250

suggest their important roles in mediating both methane-producing rate (as further discussed

251

below) and methane content in biogas. In line with this relationship, the increasing order of

252

the relative abundance of Bacteroidales populations by SRT during stage III (i.e., 7 < 16 < 12

253

< 9 days, Figure 6) well agrees with that of the biogas-producing rate between the digesters

254

(RM, Table S3). However, results obtained in this study are inadequate to suggest that all

6, 30

, may not necessarily result in the reduction of relative

14


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digesters could reduce their SRT to below 10 days, considering the narrow range of possible

256

conditions and specific source of the seed and feed sludge. Therefore, the effects of SRT

257

should be assessed on a case-by-case basis.

258

The positive correlations between OLR and VFAs/HPr/HAc, as shown both by correlation

259

analysis (0.37-0.47, Table S4-1) and CCA (Figure 4), reflect increased hydrolysis and

260

conversion of organic macromolecules including carbohydrates, lipids and proteins to soluble

261

organic molecules with increasing OLR. In contrast, the negative correlations between RM and

262

VFAs/HPr/HAc (-0.39 to -0.48, Table S4-1) indicate that elevated levels of these soluble

263

intermediates may signify incomplete methanogenesis. The buildup of VFAs in R1 during

264

Stage I was probably an indicator of unbalanced growth between acidogenic bacteria and

265

methanogens. In contrast, no clear accumulation of VFAs in R2, R3 and R4 suggests

266

relatively stable conversion of VFAs to methane.

267

Community composition and dynamics linked to methanogenic performance

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Microbial communities in the four digesters rapidly shifted away from the inoculated

269

community, followed the same trends in succession (along PC1, Figure 3), and converged into

270

highly similar community compositions (mainly Bacteroidia, Clostridia and Thermotogae)

271

under the selective pressures imposed by SRT, OLR and feed sludge (Figure S2 and Figure 2).

272

Both the nature of substrate (primary sludge) and SRT (7-16 days) were different from those

273

of the source digester of seed sludge that treats combined primary and secondary sludge at a

15



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SRT of 6 days. Primary sludge has much higher biodegradability than secondary sludge since

275

it consists of more easily biodegradable organics, while SRT selects microorganisms by

276

growth rate and tends to wash out slowly growing organisms. Both our and other studies show

277

a positive relation between digester SRT and microbial richness 3, 4. Therefore, these selective

278

pressures may create specific niches that differentiate the community composition and

279

diversity from those of the inoculum.

280

First, both the microbial richness and evenness of 0.97-OTUs decreased over 90 days in all

281

digesters (Table 1). The decrease in diversity was mainly driven by the differences in the

282

nature of feeding sludge and SRT between this study and the source digester of the seed

283

sludge, reflecting the adaption of inoculated microorganisms to the regular feedings of CEPT

284

sludge. Moreover, many OTUs undetected or detected at low relative abundance in the seed

285

sludge (Figure 2) became the major trophic groups (Figure 6). Multiple populations capable

286

of the same metabolic functions, including hydrolyzing and fermenting organic molecules

287

(e.g., s1, s2, s3, s4, s7, s8, s13, s14, s15, s17, s22), and generating acetate (e.g., s5, s6, s12,

288

s16, s18 and s21) and methane (e.g., s30) were shared by the four digesters (Figure 2 and

289

Table S5), regardless of their different SRTs and OLRs, indicating high levels of functional

290

redundancy to maintain the process stability.

291

Hydrolysis and fermentation

292

Bacterial populations of orders Bacteroidales, Clostridiales and Thermotogales dominated

293

microbial communities in all digesters (Figure 6). Members of Clostridiales and 16


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Thermotogales are well implicated in the hydrolysis and fermentation of carbohydrates in

295

anaerobic digesters

296

mammalian gut and anaerobic digesters as hydrolyzing/fermentative bacteria of carbohydrates

297

6, 32, 33

298

bacteria in anaerobic digesters (e.g., Clostridiales).

299

The rapid enrichment and continuous dominance of Bacteroidales populations (Figure 6) and

300

their significant (P-values < 0.01) negative Spearman’s correlations with cellulolytic

301

Clostridiales (-0.44) and Thermotogales (-0.64) populations reveals Bacteroidales spp. are

302

much stronger competitors of organic molecules in the anaerobic digesters than other bacteria.

303

Likewise, Bacteroidales populations are probably substrate competitors of Cloacamonales

304

(WWE1) and Saprospirales populations, considering the strong and significant negative

305

correlations (-0.88 and -0.80; P-values < 1×10-11) between them. The functions of WWE1 and

306

Saprospirales in methanogenic environments are poorly understood 34, 35. Our results suggest

307

that in anaerobic digesters they may share overlapped niches with Bacteroidales populations.

308

As operational conditions and resource availability in the anaerobic digesters favor the

309

prosperity and dominance of Bacteroidales, WWE1, Saprospirales and Thermotogales

310

populations are outcompeted and gradually washed out from the digesters (Figure 6).

311

On the contrary, Clostridiales spp. were dramatically enriched, secondary dominant

312

populations (Figure 6). The opposite intra-order species-spices co-exclusion (i.e., negative

313

associations between species of the same order) for Bacteroidales and Clostridiales

6, 31

. In contrast, Bacteroidales populations commonly occur in the

. However, we know little about their biological interactions with other cosmopolitan

17



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314

implicates different ecological traits and rules guiding their co-dominance. Specifically,

315

Clostridiales populations have diverse metabolic capabilities of diverse organic molecules,

316

including cellulose and protein hydrolysis

317

and homoacetogenesis

318

niche differentiation may drive functionally versatile Clostridiales spp. into diverse patterns

319

of resource use to avoid exclusive competition. The metabolic versatility and phenotypic

320

plasticity with regard to the use of a broad range of organic micro- and macro-molecules in

321

Clostridiales populations should allow them to fulfill multiple functions and occupy broad

322

niches in anaerobic digesters. Based on these arguments, it is suggested that Clostridiales spp.

323

should be more ecologically divergent (or dissimilar) than Bacteroidales spp., thus

324

intra-species competition is stronger between members of Bacteroidales than Clostridiales.

325

Therefore, non-overlapped niches between Clostridiales and Bacteroidales populations could

326

make their co-dominance in the anaerobic digesters possible.

327

Acetogenesis and methanogenesis

328

Syntrophomonas, Syntrophobacterales and Clostridium can convert fatty acids to

329

methanogenic substrates (i.e., HAc and H2) 40, 41. These populations were the main acetogens

330

during anaerobic digestion (Figure 6), as also implicated by the positive correlations between

331

relative abundance of Syntrophomonas (s5, s12, s18 and s29) and Clostridium (e.g., s3, s4,

332

s19) OTUs and HAc concentration (Figure 4 and Table S5). The archaeal methanogens

333

mainly included acetoclastic Methanosarcinales and hydrogenotrophic Methanomicrobiales

36, 37

, polysaccharide fermentation 6, acetogenesis

38

, and syntrophic oxidation of fatty acids and acetate

39

. Therefore,

18


Page 19 of 40


334

and Methanobacteriale, which are widely found together or individually as the core

335

methanogens in anaerobic digesters

336

between H2-producing Syntrophomonas populations and H2-scavenging Methanomicrobiales

337

and Methanobacteriales (0.45 and 0.60, P-values

Linking Microbial Community, Environmental Variables, and

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