SUPPLEMENTARY MATERIAL for
Taxonomic Relatedness Shapes Bacterial Assembly in Activated Sludge of
Globally Distributed Wastewater Treatment Plants
Feng Ju1, Yu Xia1, Feng Guo1, Zhiping Wang1,2, Tong Zhang1*
1Environmental Biotechnology Lab, The University of Hong Kong SAR，China; 2School of
Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Submitted to Environmental Microbiology
*Corresponding
author
phone:
+852-28578551;
fax:
+852-25595337;
e-mail:
zhangt@hkucc.hku.hk
Supporting Information S1: Molecular methods
Supporting Information S2: Topological properties of the activated sludge bacterial network
Supporting Information S3: R orders used to analyse the species co-occurrence patterns
Figures
Figure S1 Flowchart of the network analysis of activated sludge bacterial community
Figure S2 Degree distribution of the nodes for the activated sludge association network
(closed circles) and random network (open squares) of identical size, respectively.
Figure S3 The relative abundance of phylum Acidobacteria (upper panel) and
Verrucomicrobia (lower panel) in the 50 activated sludge samples.
Figure S4 The relative abundance of functional genera in the 50 activated sludge samples.
Figure S5 Examples of strong and significant correlations within different groups of bacterial
genera in the activated sludge.
Tables
Table S1 The 50 activated sludge samples from globally-collected wastewater treatment
plants (WWTPs) and the 454 datasets used in the network analysis
Table S2 Strong (Spearman’s ρ <-0.6) and significant (P-value < 0.01) negative correlations
identified between bacterial genera of activated sludge.
Table S3 Clustering coefficient and average path length of the microbial co-occurring
network of activated sludge (AS) and comparisons to other real ecological networks and the
corresponding Erdös-Réyni random networks
Table S4 The observed co-occurring incidence for intra- and inter-phylum/class
co-occurrence versus that is expected by random association.
Table S5 The inter-correlations between activated sludge generalists (nodes in Figure 4) and
the number of co-occurring samples.
Supplementary Information S1: Molecular methods
For the first group of the 17 industrial AS samples, the V3-V4 regions of the 16S rRNA genes
(~465 nucleotides) were amplified with 338F (5’-ACTCCTACGGRAGGCAGCAG-3’) and
802R (5’-TACNVGGGTATCTAATCC-3’) (Claesson et al 2010). Barcodes for sample
multiplexing during sequencing were modified in the 5’ terminus of the forward primer. Each
polymerase chain reaction (PCR) was conducted in a 100 ml reaction system using
MightyAmp polymerase (TaKaRa, Otsu, Japan) and an i-Cycler (BioRad, Hercules, CA, USA)
under the following PCR conditions: initial denaturation at 94 °C for 5 min, 35 cycles at
94 °C for 50 s, 40 °C for 30 s and 72 °C for 90 s, and a final extension at 72 °C for 5 min. The
resulting barcoded PCR products for each sample were first purified, then mixed in equal
concentrations by mass, and finally sequenced on a Roche 454 FLX Titanium platform
(Roche) at the Genome Research Center of the University of Hong Kong.
Supplementary Information S2: Topological properties of the
activated sludge bacterial network
For the AS network, the observed APL (3.55), CC (0.55) and modularity (MD, 0.57; values >
0.4 suggest that the network has a modular structure (Newman 2006)) were all greater than
the APLr (2.74), CCr (0.057) and MDr (0.35) of their respective Erdös-Réyni random
networks (Table S1). The high CC/CCr ratio of 10.2 strongly suggested that the correlation
network had ‘small world’ properties, that is, nodes are more connected than in an
identical-size random network (Watts and Strogatz 1998) (see Supplementary Information S1
for more details about the network topologies).
Although the effect size (measured by log response ratio of CC/CCr) was smaller than for the
soil microbial network (3.31) (Barberán et al 2011), it is much higher than for other
ecological networks, such as food-web networks (-1.20 to 1.34) (Dunne et al 2002, Montoya
et al 2006) and functional gene networks of grassland soil (0.79 to 1.28) (Zhou et al 2010),
slightly higher than the marine three-domain (Bacteria, Archaea and Protozoa) network (1.81)
(Steele et al 2011), and comparable to the upper bound of pollinator-plant networks (2.39)
(Montoya et al 2006, Olesen et al 2006). Interestingly, a CC of 0.43 for the one-domain
(Bacteria) activated sludge microbial network in this study was higher than the 0.33 for the
two-domain (Bacteria and Archaea) soil microbial network (Barberán et al 2011), and both of
these are higher than the 0.27 for three-domain marine network (Steele et al 2011). This may
indicate a higher frequency of strong co-occurrence between taxa of the bacteria domain than
between a bacterial taxon and a taxon of archaeal or eukaryotic domain. While the higher CC
implies that the AS bacterial network included more highly correlated edges (relationships)
than other ecological networks, comparisons of topological properties between these networks
offer insights into the effects of the general characteristics of different habitat types on the
assembly structure of various microbial communities.
Based on the modularity class, the entire network could be parsed into 7 major modules (i.e.,
clusters of species that interact more among themselves than with other species, compared to
a random association), with 82 out of total 107 vertices occupied by the three largest modules:
Module I, II and III (Figure 1c). Two types of hubs (highly connected nodes) appeared in the
AS co-occurring network: (1) genera highly connected within one module (i.e.,
within-module hubs, e.g., Hyphomicrobium, Prosthecobacter, and Thiobacter) and (2) genera
acting as ‘‘connectors’’ between multiple modules (i.e., within-module hubs, such as
Clostridium XI, Variovorax, and Gp16). The occurrence of minor highly connected hubs in
the “small-world” AS bacterial network rendered it more robust to change, whereas the
elimination of hubs from the network would change its structure dramatically (Albert et al
2000). Thus, these hubs could be regarded as microbial ‘keystone species’ in AS. In particular,
the between-module hubs were important for associating different modules within the AS
network (e.g., Gp16 connects Module I and III). In Modules II and III, the number and vertex
degree of the between-module hubs far exceeded that of within-module hubs (Figure 1c),
possibly revealing a very close linkage between these two modules in the AS network.
Supporting Information S3: R orders used to analyse the species
co-occurrence patterns
a) C-score calculation
oecosimu (data.matrix, nestedchecker, method="swap",nsimul=30000)
b) Spearman’s correlation calculation
rcorr (t(data.matrix), type="spearman")
c) Multiple testing correction for P value adjustment
p.adjust (p.matrix, method="BH")
d) Generation of graph using igraph
graph.adjacency(qualified.data.matrix., weight=T, mode="undirected")
e) Generation of 10000 Erdös-Réyni random graphs
for (i in 1:10000) {
g <- erdos.renyi.game(107, 382,'gnm',weight=T,mode="undirected")
g<- simplify(g1)
V(g)$label <- taxonomy (name)
V(g)$degree <- degree(g)
write.graph(g, paste(i,".random.gml", sep = ""),format='gml'}
Supplementary Figures
454 pyrosequencing
of 16S rRNA amplicons of
50 activated sludge
Bacterial abundance
matrix
Correlation analysis
(e.g. Spearman )
Inter-phylum/class
correlation
Intra-phylum/class
correlation
Visualization of activated
sludge network
Module detection and
degree distribution
Co-occurrence patterns
within AS generalists
and functional groups
Who are the core species?
And how they interact with
each other
Bacterial incidence
matrix
Species occupancy &
checkerboard score testing
Activated sludge
generalists
Non-random cooccurrence patterns
Generation of 10000 ErdösRéyni random networks
Incidence of inter- and
intra-phylum/class cooccurrence
Network topological
characterization
Whether non-random cooccurrence or NOT?
Figure S1 Flowchart of the network analysis of activated sludge bacterial community
P(k)
0.1
2
Power (R =0.92)
0.01
2
Gaussian (R =0.91)
0
2
4
6
8
10
12
14
16
18
20
22
24
26
Vertex degree (k)
Figure S2 Degree distribution of the nodes for the activated sludge association network
(closed circles) and random network (open squares) of identical size, respectively. The node
degree (i.e., the number of edges connected to the node) is plotted against the probability P(k)
that a node would have that degree in the network. The solid line shows the power law fitting
(R2=0.92) of degree distribution in the activated sludge association network, and the dashed
line shows the Gaussian fitting to the degree distribution from the random network (R2=0.91).
For the entire activated sludge (AS) bacterial network, the node degree distribution best
follows a scale-free power law distribution (P(k)= 0.3446*(1+k)-0.9665; R2=0.92, Figure S1),
resulting from the preferential attachment of new vertices to the more highly connected
vertices. This is quite different from the Poisson shape of the random network of an identical
size (best fitted by a Gaussian curve, R2=0.91) (Figure S1). Although the exponent of power
law distribution for the AS bacteria network was much smaller than the typical coefficients of
2-4 observed for many large networks such as social or protein interaction networks (Amaral
et al 2000, Newman 2003, Palla et al 2005), this value was very close to certain other
ecological networks (e.g., a marine microbial network (Steele et al 2011) and some food webs
(Dunne et al 2002)). This structural similarity among these ecological networks, in contrast
with the Gaussian connectivity distribution predicted by the expectation of randomness, also
indicated the existence of meaningful, nonrandom associations in the AS bacterial network.
S1-S21: Municipal activated sludge
S22-S50: Industrial activated sludge
Acidobacteria
Relative abundance (%)
50
S27 Polymer
45
40
S34-37: Morpholine
12
S41-42: Acrylonitrile
S26: Texile
S29: Polymer
8
4
0
0
4
8
12
16
20
24
28
32
36
40
44
48
12
Verrucomicrobia
Relative abundance (%)
10
8
6
4
2
0
0
4
8
12
16
20
24
Sample ID
28
32
36
40
44
48
Figure S3 The relative abundance of phylum Acidobacteria (upper panel) and
Verrucomicrobia (lower panel) in the 50 activated sludge samples. The relative abundance is
calculated as the number of sequences that are assigned to the taxa divided by the number of total 16S
rRNA gene sequences in the sample.
a
0.01%
Diversity: Shannon index
Eveness: Simpson index
Richness: number of genera
0.01%
0.1%
400
1.0
300
0.9
200
0.8
5.5
0.1%
0.01%
0.1%
5.0
4.5
4.0
3.5
3.0
100
2.5
0.7
2.0
0
1.5
Municipal
Industrial
0.6
b
Percentage (%) in total 16S
pyrotags
1.0
Municipal
Industrial
100
*
*
*
*
*
*
*
*
10
*
*
*
1
0.1
Zo
o
gl
oe
a
us
cc
co
ue
ra
Tr
ic
ho
N
itr
os
ic
m
Th
a
ro
os
bi
pi
ra
um
a
ni
do
ho
ic
vi
ef
lu
D
yp
H
cu
oc
on
om
or
hl
ec
D
G
or
as
s
0
Figure S4 Comparison between the municipal (S1-S21) and industrial (S22-S50) activated
sludge (AS) samples in terms of: (I) the bacterial richness, evenness and diversity, and (II)
functional groups with significantly-different (t-test, P-value < 0.05) abundances in the two
groups of AS. In inset a, the 0.01 and 0.1% cutoffs represent the minimum supportive (relative)
abundance for a genera to be calculated as existed. In inset b, asterisk above boxes of each
genus stands for the significance of the difference: *P-value < 0.05, **P-value <0.005 and
***P-value < 0.0005. Boxplots represent observation-normalized results, where boxes
represent interquartile range, whiskers indicate 0th and 100th percentiles and plus symbols
indicate outliers. The underlined genera are AS functional generalists (occurred in at least 60%
of the AS samples) widely-distributed in all activated sludge samples. Figure S4a shows
bacterial diversity and richness of the municipal AS were higher than the industrial AS. Figure
S4b shows 8 out of total 24 functional groups (Figure 4) had significantly different abundance
(t-test, P-value < 0.05) between the municipal and industrial AS. For example,
Dechloromonas and Nitrosospria had higher abundance in the municipal AS, whereas
Hyphomicrobacterium, Defluviicoccus and Thauera were more abundant in the industrial AS.
a
Nitrosospira
0.7
Thauera
Mesorhizobium
25
Spearman's ρ = 0.64
Relative abundance (%)
Relative abundance (%)
0.6
0.5
0.4
0.3
0.2
0.1
Hyphomicrobium
Spearman's ρ = 0.63
20
15
10
5
0
0
1
5
9
b
individual WWTP
5
9
1.2
Spearman's ρ = 0.85
Relative abundance (%)
Relative abundance (%)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
13 17 21 25 29 33 37 41 45 49
WWTP id
Bifidobacterium
Blautia
13 17 21 25 29 33 37 41 45 49
WWTP id
Bifidobacterium
Lactococcus
Spearman's ρ = 0.71
1
0.8
0.6
0.4
0.2
0
1
5
9
c
1.8
13 17 21 25 29 33 37 41 45 49
WWTP id
Gp16
Gp6
Gp3
1
Relative abundance (%)
Relative abundance (%)
1.4
1.2
1
0.8
0.6
0.4
0.2
9
1.8
Spearman's ρ = 0.67-0.78
1.6
5
13 17 21 25 29 33 37 41 45 49
WWTP id
Bdellovibrio
Acidovorax
Spearman's ρ = 0.61
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
1
5
9
13 17 21 25 29 33 37 41 45 49
WWTP id
1
5
9
13 17 21 25 29 33 37 41 45 49
WWTP id
Figure S5 Examples of strong and significant correlations within different groups of bacterial
genera in the activated sludge. A correlation is considered as strong and significant when the
Spearman’s correlation coefficient (ρ) > 0.6, and P-value < 0.01. The x-axis of each subfigure
corresponds to the id (Table S1) of individual WWTP and the y-axis is relative representation
of each taxon in the corresponding total 16S rRNA pyrotag library.
Supplementary Tables
Table S1 The 50 activated sludge samples from globally-collected wastewater treatment plants (WWTPs) and the
454 datasets used in the network analysis. Samples with ID from S01 to S21 are collected from full-scale WWTPs treating
municipal wastewater; samples with ID from S22-S50 are collected from full-scale WWTPs treating different industry wastewater.
MWW: municipal wastewater; IWW: industrial wastewater.
16S datasets from
454 pyrosequencing
Activated sludge sample
Sam
ple
ID
S01
S02
S03
S04
S05
S06
S07
S08
S09
S10
S11
WWTP
Latitude
location
and
Wastewater
(city,
Longitud
constitutes
country)
e
Guelph,
43.54,
Canada
-80.24
removal
foaming
or not
or not
A/O
Yes
NA
22
01-2010
21308
224
A/A/O
Yes
NA
17
05-2010
24557
223
A/O
Yes
NA
10
04-2010
24455
224
A/O
Yes
NA
16
05-2010
26077
224
Yes
NA
16
12-2010
21714
223
time
16S
(0C)
(mm-year)
pyrotag
s
ge
length
(bp)
MWW
Columbia,
34.00,
predominent
USA
-81.03
MWW
33.24,
predominent
-84.26
MWW
Haerbin,
45.80,
predominent
A/A/O+M
China
126.53
MWW
BR
Beijing,
39.90,
China
116.40
95% MWW
A/A/O
Yes
NA
17
12-2009
23106
223
Qingdao,
36.06,
China
120.38
70% MWW
A/A/O
Yes
NA
26
12-2009
24160
223
Nanjing,
32.06,
China
118.79
NO
NA
27
11-2009
20927
223
Shanghai,
31.23,
China
121.47
CAS
NO
NA
17
11-2009
20659
224
Wuhan,
30.59,
predominent
China
114.30
MWW
OD
NO
NA
18
12-2009
22227
223
Guangzhou,
23.12,
China
113.26
60% MWW
CAS
NO
NA
13
04-2010
21525
223
90% MWW
A/O
Yes
NA
27
08-2010
19151
223
100% MWW
A/O
Yes
NA
27
08-2010
24442
223
30
11-2009
27648
223
31
11-2009
27366
223
Griffin, USA
i,Hong Kong,
22.50,
147.12
Stanley,Hong
22.50,
Kong, China
147.12
g Kong,
g Kong,
China
S17
setup
ature
Avera
predominent
Sha-Tin,Hon
S16
&
of clear
1.32,
China
S15
nitrogen
Sampling
103.77
Sha-Tin,Hon
S14
Process
Temper
Singapore
China
S13
Bulking
Ulu Pandan,
Shek-Wu-Hu
S12
85% MWW
Number
Design for
22.38,
114.19
22.38,
114.19
Tai-Po,Hong
22.44,
Kong, China
114.16
Yuen-Long,
22.44,
85% MWW
70% MWW
CAS+MB
R
Yes
95% MWW
A/O
Yes
（Jan. to
Mar.）
Yes
95% MWW
A/O
Yes
（Jan. to
Mar.）
95% MWW
A/O
Yes
NA
31
11-2011
33307
208
95% MWW
A/O
Yes
NA
30
11-2011
28600
208
Data
sourc
e
this
study
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study
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study
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study
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study
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study
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study
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study
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study
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study
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study
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study
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study
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study
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study
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study
this
Hong Kong,
114.02
study
China
SRR
S18
Buenos-Aires
-34.60,
predominent
, Argentina
-58.38
MWW
CAS
NO
NA
21
11-2008
8141
408
6277
55,
SRA
SRR
S19
Buenos-Aires
-34.60,
predominent
, Argentina
-58.38
MWW
CAS
NO
NA
19
03-2009
9596
398
6277
56,
SRA
S20
S21
Seoul, Korea
Seoul, Korea
37.56,
predominent
126.97
MWW
37.56,
predominent
126.97
MWW
Attac
A/A/O
Yes
NA
23
06-2009
43991
453
hed
AS
Susp
A/A/O
Yes
NA
23
06-2009
23695
435
ende
d AS
SRR
S22
Cordoba,
-31.39,
Whey filtering
Argentina
-64.18
IWW
CAS
NO
NA
22
05-2010
19032
420
6277
52,
SRA
S23
Buenos-Aires
-34.60,
, Argentina
-58.38
Petroleum
refinery
SRR
Yes, but
A/O
IWW
low
NA
34
05-2009
13004
421
efficiency
6277
53,
SRA
SRR
S24
Buenos-Aires
-34.60,
Pharmaceutical
, Argentina
-58.38
IWW
CAS
NO
NA
21
05-2011
22405
408
6277
54,
SRA
SRR
S25
Buenos-Aires
-34.60,
Textile dyeing
, Argentina
-58.38
IWW
CAS
NO
NA
33
09-2008
19187
418
6277
57,
SRA
SRR
S26
Buenos-Aires
-34.60,
Textile dyeing
, Argentina
-58.38
IWW
CAS
NO
NA
28
05-2011
7252
417
6277
58,
SRA
SRR
S27
Buenos-Aires
-34.60,
Acrylic
, Argentina
-58.38
polymer IWW
CAS
NO
NA
23
05-2009
15276
438
6277
59,
SRA
SRR
S28
Buenos-Aires
-34.60,
Pharmaceutical
, Argentina
-58.38
IWW
CAS
NO
NA
22
07-2011
11220
411
6277
60,
SRA
SRR
S29
Buenos-Aires
-34.60,
Acrylic
, Argentina
-58.38
polymer IWW
CAS
NO
NA
23
09-2008
13491
415
6277
61,
SRA
S30
Cordoba,
-31.39,
Whey filtering
Argentina
-64.18
IWW
SRR
CAS
NO
NA
20
06-2011
9972
423
6277
73,
SRA
S31
Buenos-Aires
-34.60,
Petroleum
, Argentina
-58.38
refinery IWW
SRR
Yes, but
A/O
low
NA
35
05-2011
5859
388
efficiency
6277
74,
SRA
SRR
S32
Buenos-Aires
-34.60,
, Argentina
-58.38
Pet food IWW
CAS
NO
NA
22
01-2011
9307
400
6277
75,
SRA
SRR
S33
Buenos-Aires
-34.60,
, Argentina
-58.38
Pet food IWW
CAS
NO
NA
22
09-2008
23234
402
6277
76,
SRA
S34
S35
S36
S37
S38
S39
S40
S41
S42
Shanghai,
31.23,
Morpholine
China
121.47
IWW
Shanghai,
31.23,
Morpholine
China
121.47
IWW
Shanghai,
31.23,
Morpholine
China
121.47
IWW
Shanghai,
31.23,
Morpholine
China
121.47
IWW
Shanghai,
31.23,
China
121.47
Shanghai,
31.23,
China
121.47
Shanghai,
31.23,
China
121.47
A/O
Yes
A/O
Yes
A/O
Yes
A/O
Yes
Coking IWW
A/A/O
Yes
Coking IWW
A/A/O
Coking IWW
Shanghai,
31.23,
Acrylonitrile
China
121.47
IWW
Shanghai,
31.23,
Acrylonitrile
China
121.47
IWW
Yes(som
25
08-2012
6886
417
25
08-2012
8699
416
25
08-2012
8675
416
25
08-2012
8066
417
NA
25
08-2012
6815
425
Yes
NA
25
08-2012
6158
425
A/A/O
Yes
NA
25
08-2012
5960
425
A/O
NA
NA
25
08-2012
7799
419
A/O
NA
NA
25
08-2012
7978
419
BCO
Yes
NA
20
10-2012
3993
402
BCO
Yes
NA
20
10-2012
3104
402
BCO
Yes
NA
20
10-2012
3496
402
BCO
Yes
NA
20
10-2012
3461
402
BCO
Yes
NA
20
10-2012
3965
403
etimes)
Yes(som
etimes)
Yes(som
etimes)
Yes(som
etimes)
this
study
this
study
this
study
this
study
this
study
this
study
this
study
this
study
this
study
80% printing
S43
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
this
study
MWW
80% printing
S44
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
this
study
MWW
80% printing
S45
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
this
study
MWW
80% printing
S46
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
this
study
MWW
80% printing
S47
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
MWW
this
study
80% printing
S48
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
BCO
Yes
NA
20
10-2012
4674
401
BCO
Yes
NA
20
10-2012
4889
402
BCO
Yes
NA
20
10-2012
3790
401
this
study
MWW
85% printing
S49
Wuxi, China
31.49,
and dyeing
120.31
IWW, 15%
this
study
chemical IWW
80% printing
S50
Wuxi, China
31.49,
and dyeing
120.31
IWW, 20%
MWW
Abbreviations: A/O, anoxic/aerobic; A/A/O, anaerobic/anoxic/aerobic; CAS, conventional activated sludge; MBR, membrane
bioreactor; NA, not available; OD: oxidation ditch; STPs, sewage treatment plants. BCO：biological contact oxidation process.
this
study
Table S2 Strong (Spearman’s ρ <-0.6) and significant (P-value < 0.01) negative correlations identified between
bacterial genera of activated sludge. In total, 20 out of total 402 pairs of significant and robust correlations were found as
negative correlations related to 23 (out of total 110) co-occurring bacterial genera. The module referred to the sub-clusters of the
positive co-occurrence network of activated sludge bacteria (Figure 1c). NA: genera not found in any module of the positive
network. Half of the 20 pairs of strong negative correlations were related to Alicycliphilus and Sphingopyxis. These two bacterial
genera were mainly detected in the industrial AS samples and were mostly negatively correlated with genera (e.g. Flavobacterium,
Blautia and Variovorax) appearing more in municipal AS (module I). Their roles in the industrial AS were most likely to be
associated with biodegradation of alicycli and hydrocarbon compounds, respectively.
Node1
Node1-affiliated
phylum/class
module
Alicycliphilus
Betaproteobacteria
III
Alicycliphilus
Betaproteobacteria
Alicycliphilus
Betaproteobacteria
Alicycliphilus
Node2
Node2-affiliated
Correlations
phylum/class
module
Flavobacterium
Bacteroidetes
I
-0.70626
III
Luteimonas
Gammaproteobacteria
I
-0.68482
III
Blautia
Firmicutes
I
-0.60155
Betaproteobacteria
III
Prosthecobacter
Verrucomicrobia
I
-0.62994
Alicycliphilus
Betaproteobacteria
III
Opitutus
Verrucomicrobia
I
-0.62136
Sphingopyxis
Alphaproteobacteria
III
Variovorax
Betaproteobacteria
I
-0.6181
Sphingopyxis
Alphaproteobacteria
III
Blautia
Firmicutes
I
-0.62385
Sphingopyxis
Alphaproteobacteria
III
Parachlamydia
Chlamydiae
II
-0.64476
Sphingopyxis
Alphaproteobacteria
III
Azoarcus
Betaproteobacteria
NA
-0.62192
Sphingopyxis
Alphaproteobacteria
III
Lewinella
Bacteroidetes
NA
-0.62018
Rhodoplanes
Alphaproteobacteria
III
Opitutus
Verrucomicrobia
I
-0.64346
Rhodoplanes
Alphaproteobacteria
III
Zoogloea
Betaproteobacteria
I
-0.64166
Sphaerobacter
Chloroflexi
III
Curvibacter
Betaproteobacteria
I
-0.66003
Sphaerobacter
Chloroflexi
III
Rubrivivax
Betaproteobacteria
NA
-0.65837
Flavobacterium
Bacteroidetes
I
Hyphomicrobium
Alphaproteobacteria
III
-0.63994
Flavobacterium
Bacteroidetes
I
Elioraea
Alphaproteobacteria
III
-0.6208
Parvibaculum
Alphaproteobacteria
I
Byssovorax
Deltaproteobacteria
II
-0.62944
Actinobacteria
Bifidobacterium
I
Caenimonas
Betaproteobacteria
V
-0.60046
Lewinella
Bacteroidetes
NA
Caenimonas
Betaproteobacteria
V
-0.64731
Lewinella
Bacteroidetes
NA
Ideonella
Betaproteobacteria
VII
-0.65017
Table S3 Clustering coefficient and average path length of the bacterial co-occurring network of activated sludge
(AS) and comparisons to other real ecological networks and the corresponding Erdös-Réyni random networks
CC a
CCr
CC/CCr
ln(CC/CCr)
APL
APLr
ln(APL/APLr)
0.56
0.067
8.36
2.12
3.42
2.57
0.28
Soil microbial network
0.33
0.012
27.5
3.31
5.53
3.88
0.35
Marine microbial network
0.27
0.044
6.14
1.81
2.99
2.62
0.13
Food-web network
0.02 to 0.43
0.03 to 0.33
0.30 to 3.80
-1.20 to 1.34
1.33 to 3.74
1.41 to 3.73
0.34 to 1.32
Pollinator-plant networks
0.72 to 1.00
0.08 to 1.0
1.0 to 10.9
0.0 to 2.39
1.0 to 2.31
ND
ND
Functional gene networks
0.10 to 0.22
0.028 to 0.099
2.22 to 3.57
0.79 to 1.28
3.09 to 4.21
3.00 to 3.84
0.030 to 0.091
AS network (this study)
Other ecological networks
a
b
CC is the average clustering coefficient for the real network; CCr is the clustering coefficient identified from a random network of
identical size; CC/Clr is the ratio of CC to CCr; APL is the average path length for the real network; APLr is the average path length for the
random network; ln(CC/CCr) and ln(APL/APLr) are the log response ratio for the average clustering coefficient and average path length
between the observed and random networks; ND indicates missing data.
b
Topological properties of the soil microbial networks were calculated using data of (Barberán et al 2011); The topological properties for
the marine microbial network, food-web network, pollinator-plant network, and functional gene networks were quoted from (Steele et al
2011).
Table S4 The observed co-occurring incidence for intra- and inter-phylum/class co-occurrence versus that is
expected by random association. The observed co-occurring incidence (O) of two taxa as the relative percentage of the number
of observed edges between them in the total 382 edges of the AS positive network, while the random co-occurring incidence (R) was
estimated using two different approaches: (I) RER was the mean value of the observed co-occurring incidences for 10,000
identical-sized Erdös-Réyni random networks; (II) RTheo was the theoretical incidence of co-occurrence calculated by considering the
phylum/class frequencies and random association. Here we only consider those strong co-occurrence patterns (between two
phyla/classes) that were consistently supported by both the above two approaches and at least 5 edges. Overall, 91 out of total 382
edges in the AS network were observed as intra-phylum co-occurrence correlations (edges), while an average of 48.4 (±6.3)
intra-phylum co-occurrence correlations were obtained from 10000 Erdös-Réyni random networks.
Affiliated phyla/class for
negatively-related nodes
Frequencies of
phylum/class
Node1
Node2
Node1
Node2
Number
of
observed
edges
α-proteobacteria
Firmicutes
β-proteobacteria
Actinobacteria
Acidobacteria
γ-proteobacteria
δ-proteobacteria
Verrucomicrobia
Bacteroidetes
Chloroflexi
α-proteobacteria
Firmicutes
β-proteobacteria
Actinobacteria
Acidobacteria
γ-proteobacteria
δ-proteobacteria
Verrucomicrobia
Bacteroidetes
Chloroflexi
23
8
24
10
8
10
8
3
4
4
23
8
24
10
8
10
8
3
4
4
Firmicutes
β-proteobacteria
α-proteobacteria
γ-proteobacteria
α-proteobacteria
α-proteobacteria
β-proteobacteria
β-proteobacteria
δ-proteobacteria
Verrucomicrobia
β-proteobacteria
β-proteobacteria
δ-proteobacteria
Firmicutes
Actinobacteria
α-proteobacteria
α-proteobacteria
δ-proteobacteria
Verrucomicrobia
β-proteobacteria
γ-proteobacteria
WS3
α-proteobacteria
Chlamydiae
Chloroflexi
ε-proteobacteria
Firmicutes
γ-proteobacteria
Verrucomicrobia
α-proteobacteria
Chloroflexi
Actinobacteria
γ-proteobacteria
β-proteobacteria
Firmicutes
Actinobacteria
Acidobacteria
Acidobacteria
Firmicutes
Acidobacteria
β-proteobacteria
Actinobacteria
δ-proteobacteria
Firmicutes
Acidobacteria
Acidobacteria
Chloroflexi
Firmicutes
Actinobacteria
γ-proteobacteria
Bacteroidetes
Bacteroidetes
Acidobacteria
γ-proteobacteria
Actinobacteria
Acidobacteria
α-proteobacteria
Chlamydiae
Actinobacteria
Actinobacteria
δ-proteobacteria
Actinobacteria
8
24
23
10
23
23
24
24
8
3
24
24
8
8
10
23
23
8
3
24
10
1
23
1
4
1
8
10
3
23
4
10
10
24
8
10
8
8
8
8
24
10
8
8
8
8
4
8
10
10
4
4
8
10
10
8
23
1
10
10
8
10
The co-occurring incidence (%)
29
14
14
12
9
5
4
2
1
1
Observed
(O)
7.59
3.66
3.66
3.14
2.36
1.31
1.05
0.52
0.26
0.26
RandomER
(RER)
4.45
0.58
4.88
0.83
0.57
0.83
0.58
0.28
0.31
0.31
RandomTheo
(RTheo)
4.46
0.49
4.87
0.79
0.49
0.79
0.49
0.05
0.11
0.11
22
18
17
13
12
11
11
10
10
10
9
9
9
7
6
6
6
6
6
5
5
5
4
4
4
4
4
4
4
3
3
5.76
4.71
4.45
3.40
3.14
2.88
2.88
2.62
2.62
2.62
2.36
2.36
2.36
1.83
1.57
1.57
1.57
1.57
1.57
1.31
1.31
1.31
1.05
1.05
1.05
1.05
1.05
1.05
1.05
0.79
0.79
1.42
4.24
9.71
1.42
4.05
3.23
3.39
3.39
1.14
1.29
4.23
3.36
1.14
1.15
1.42
1.62
3.24
1.42
0.60
1.70
0.76
0.33
4.05
0.35
0.63
0.51
0.33
1.76
0.60
3.24
0.74
1.41
4.23
9.73
1.41
4.06
3.24
3.39
3.39
1.13
1.27
4.23
3.39
1.13
1.13
1.41
1.62
3.24
1.41
0.53
1.69
0.71
0.14
4.06
0.18
0.56
0.41
0.14
1.76
0.53
3.24
0.71
O/RER
O/RTheo
1.7
6.4
0.8
3.8
4.1
1.6
1.8
1.9
0.9
0.8
1.7
7.4
0.8
4.0
4.8
1.6
2.1
9.9
2.5
2.5
4.1
1.1
0.5
2.4
0.8
0.9
0.8
0.8
2.3
2.0
0.6
0.7
2.1
1.6
1.1
1.0
0.5
1.1
2.6
0.8
1.7
4.0
0.3
3.0
1.7
2.1
3.2
0.6
1.7
0.2
1.1
4.1
1.1
0.5
2.4
0.8
0.9
0.9
0.8
2.3
2.1
0.6
0.7
2.1
1.6
1.1
1.0
0.5
1.1
3.0
0.8
1.9
9.3
0.3
5.9
1.9
2.6
7.4
0.6
2.0
0.2
1.1
δ-proteobacteria
Verrucomicrobia
Verrucomicrobia
Verrucomicrobia
WS3
α-proteobacteria
β-proteobacteria
Chlamydiae
δ-proteobacteria
γ-proteobacteria
γ-proteobacteria
Gemmatimonadet
es
WS3
α-proteobacteria
Bacteroidetes
β-proteobacteria
ε-proteobacteria
ε-proteobacteria
Firmicutes
γ-proteobacteria
Spirochaetes
Verrucomicrobia
WS3
WS3
WS3
WS3
γ-proteobacteria
Bacteroidetes
Firmicutes
Spirochaetes
δ-proteobacteria
Chlamydiae
Chloroflexi
Acidobacteria
Chlamydiae
Acidobacteria
Chlamydiae
Acidobacteria
Firmicutes
Bacteroidetes
Acidobacteria
Chlamydiae
Chloroflexi
δ-proteobacteria
Chloroflexi
Chloroflexi
β-proteobacteria
Acidobacteria
Actinobacteria
α-proteobacteria
β-proteobacteria
Chlamydiae
8
3
3
3
1
23
24
1
8
10
10
10
4
8
1
8
1
4
8
1
8
1
3
3
3
3
3
2
2
2
2
2
2
0.79
0.79
0.79
0.79
0.79
0.52
0.52
0.52
0.52
0.52
0.52
1.42
0.37
0.52
0.28
0.33
0.50
1.70
0.33
0.33
1.42
0.35
1.41
0.21
0.42
0.05
0.14
0.41
1.69
0.14
0.14
1.41
0.18
0.6
2.1
1.5
2.8
2.4
1.0
0.3
1.6
1.6
0.4
1.5
0.6
3.7
1.9
14.8
5.6
1.3
0.3
3.7
3.7
0.4
3.0
1
1
23
4
24
1
1
8
10
1
3
1
1
1
1
8
8
4
8
1
4
8
4
4
24
8
10
23
24
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
0.52
0.52
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.33
0.33
1.63
0.63
0.52
0.29
0.33
0.63
0.75
0.52
0.52
0.35
0.51
0.52
0.26
0.14
0.14
1.62
0.56
0.42
0.07
0.14
0.56
0.71
0.42
0.42
0.18
0.41
0.42
0.02
1.6
1.6
0.2
0.4
0.5
0.9
0.8
0.4
0.3
0.5
0.5
0.7
0.5
0.5
1.0
3.7
3.7
0.2
0.5
0.6
3.7
1.9
0.5
0.4
0.6
0.6
1.5
0.6
0.6
14.8
Table S5 The inter-correlations between activated sludge (AS) generalists (nodes in Figure 4) and the number of
co-occurring samples. The AS generalists are defined as genera that were widely distributed in both at least 60% municipal (12
samples) and 60% indudtrial AS (18 samples)
Cosmopolitan
affiliated phylum
Acidobacteria
Actinobacteria
Alphaproteobacteria
Betaproteobacteria
Chloroflexi
Deltaproteobacteria
Firmicutes
Gammaproteobacteri
a
Gemmatimonadetes
Cosmopolitan
Co-occurrent
Co-occurrent
affiliated phylum
spearman’s
ρ
Number of
co-occurring AS
Gp3
Gp3
Gp16
Ilumatobacter
Hyphomicrobium
Methylocystis
Paracoccus
Hyphomicrobium
Mesorhizobium
Mesorhizobium
Mesorhizobium
Bauldia
Paracoccus
Mesorhizobium
Pseudolabrys
Pseudolabrys
Pseudolabrys
Pseudolabrys
Pseudolabrys
Bauldia
Pseudolabrys
Xanthobacter
Diaphorobacter
Acidovorax
Thauera
Acidovorax
Nitrosospira
Diaphorobacter
Caldilinea
Longilinea
Sphaerobacter
Sphaerobacter
Bdellovibrio
Clostridium_XI
Clostridium_XI
Clostridium_sensu_st
ricto
Clostridium_sensu_st
ricto
Clostridium_sensu_st
ricto
Clostridium_sensu_st
ricto
Steroidobacter
Steroidobacter
Steroidobacter
Gemmatimonas
Gp4
Gp6
Gp6
Mycobacterium
Thauera
Bradyrhizobium
Clostridium_sensu_stricto
Sphaerobacter
Sphaerobacter
Hyphomicrobium
Nitrosospira
Hyphomicrobium
Gp16
Gp16
Sphaerobacter
Thauera
Nitrosospira
Hyphomicrobium
Mesorhizobium
Pseudolabrys
Gp16
Hyphomicrobium
Comamonas
Bdellovibrio
Sphaerobacter
Ferruginibacter
Hyphomicrobium
Thiobacillus
Mycobacterium
Gp3
Leucobacter
Gp16
Comamonas
Mycobacterium
Ilumatobacter
Clostridium_XI
Mycobacterium
Gp6
Gp16
Hyphomicrobium
Sphaerobacter
Thiobacillus
Gp6
Acidobacteria
Acidobacteria
Acidobacteria
Actinobacteria
Betaproteobacteria
Alphaproteobacteria
Firmicutes
Chloroflexi
Chloroflexi
Alphaproteobacteria
Betaproteobacteria
Alphaproteobacteria
Acidobacteria
Acidobacteria
Chloroflexi
Betaproteobacteria
Betaproteobacteria
Alphaproteobacteria
Alphaproteobacteria
Alphaproteobacteria
Acidobacteria
Alphaproteobacteria
Betaproteobacteria
Deltaproteobacteria
Chloroflexi
Bacteroidetes
Alphaproteobacteria
Betaproteobacteria
Actinobacteria
Acidobacteria
Actinobacteria
Acidobacteria
Betaproteobacteria
Actinobacteria
Actinobacteria
Firmicutes
Actinobacteria
Acidobacteria
Acidobacteria
Alphaproteobacteria
Chloroflexi
Betaproteobacteria
Acidobacteria
0.61
0.78
0.67
0.61
0.63
0.70
0.61
0.67
0.68
0.66
0.64
0.62
0.68
0.61
0.63
0.63
0.69
0.78
0.66
0.61
0.71
0.63
0.61
0.61
0.69
0.65
0.69
0.64
0.62
0.61
0.64
0.70
0.62
0.69
0.64
0.90
0.67
0.64
0.78
0.62
0.68
0.63
0.62
39
39
32
40
48
40
37
37
37
37
34
33
32
32
30
30
30
30
30
30
30
28
41
37
37
37
34
31
40
39
36
32
41
39
39
37
37
37
32
41
37
31
41
Reference
Albert R, Jeong H, Barabási A-L (2000). Error and attack tolerance of complex networks. Nature 406: 378-382.
Amaral LAN, Scala A, Barthélémy M, Stanley HE (2000). Classes of small-world networks. Proceedings of the
National Academy of Sciences 97: 11149-11152.
Barberán A, Bates ST, Casamayor EO, Fierer N (2011). Using network analysis to explore co-occurrence patterns in
soil microbial communities. ISME J 6: 343-351.
Claesson MJ, Wang Q, O'Sullivan O, Greene-Diniz R, Cole JR, Ross RP et al (2010). Comparison of two
next-generation sequencing technologies for resolving highly complex microbiota composition using tandem
variable 16S rRNA gene regions. Nucleic Acids Res 38: e200-e200.
Dunne JA, Williams RJ, Martinez ND (2002). Food-web structure and network theory: the role of connectance and
size. Proceedings of the National Academy of Sciences 99: 12917-12922.
Montoya JM, Pimm SL, Solé RV (2006). Ecological networks and their fragility. Nature 442: 259-264.
Newman MEJ (2003). The structure and function of complex networks. SIAM review 45: 167-256.
Newman MEJ (2006). Modularity and community structure in networks. Proceedings of the National Academy of
Sciences 103: 8577-8582.
Olesen JM, Bascompte J, Dupont YL, Jordano P (2006). The smallest of all worlds: pollination networks. J Theor
Biol 240: 270-276.
Palla G, Derényi I, Farkas I, Vicsek T (2005). Uncovering the overlapping community structure of complex
networks in nature and society. Nature 435: 814-818.
Steele JA, Countway PD, Xia L, Vigil PD, Beman JM, Kim DY et al (2011). Marine bacterial, archaeal and
protistan association networks reveal ecological linkages. ISME J 5: 1414-1425.
Watts DJ, Strogatz SH (1998). Collective dynamics of ‘small-world’networks. Nature 393: 440-442.
Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X (2010). Functional molecular ecological networks. MBio 1.