Supplementary data for

Divergent assemblage patterns of abundant and rare microbial sub-communities

in response to inorganic carbon stresses in a simultaneous anammox and

denitrification (SAD) system

Submitted to Bioresource Technology

Authors:

Duntao Shua, Hong Yuea, Yanling Hec, Gehong Weia*

Affiliation of Authors:

a State Key Laboratory of Crop Stress Biology in Arid Areas, College of Life Sciences,

Northwest A&F University, Yangling, Shaanxi 712100, China

b Key Laboratory of Beijing for Water Quality Science and Water Environmental Recovery

Engineering,Engineering Research Center of Beijing, Beijing University of Technology,

Beijing 100124, China

c School of Human Settlements & Civil Engineering, Xi'an Jiaotong University, Xi’an,

Shaanxi 710049, China

Contents

1. Methods

Method S1 DNA extraction and high-throughput amplicon sequencing

* Corresponding author. Email: weigehong@nwsuaf.edu.cn.

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Method S2 Quantitative real-time PCR (qPCR).

Method S3 Bioinformatic analysis

2. Tables

Table S1 Long-term experimental procedures.

3. Figures

Figure S1 Beta diversity between different phases. (a), (b), (c) Differences in abundant, rare and whole communities shown by principal coordinate analysis ordinations (PCOs, based on Bray-Curtis distance matrices); (d), (e), (f) Nonmetric multidimensional scaling (NMDS) plot of the different communities (based on the weighted UniFrac distance matrices).

Figure S2 Redundancy analysis triplot were applied to investigate the ecological correlation between sub-community or gene fragments. (a) Abundant sub-community; (b) Rare sub-community; (c) Transient sub-community; (d) Nitrogen functional gene fragments.

Figure S3 Network of co-occurring OTUs in abundant, rare, and transient sub-community in SAD systems, based on correlation analysis. Each edge stands for a strong (ρ> 0.6) and significant correlation (q< 0.05). For each panel, the size of each node is proportional to the relative of OTUs. The thickness of the edges is proportional to each OTUs of Spearman’s correlation coefficient.

Figure S4 Path diagrams assessing the effects of functional gene group on (a) NH4+-N, (b)

NO2--N, and (d) total nitrogen transformation rate under different inorganic carbon/NH4

+-N ratios. Solid and dash line represent indirect and direct effects, respectively.

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Method S1 DNA extraction and high-throughput amplicon sequencing

Extracted genomic DNA concentrations were determined with DS-11 Spectrophotometers

& Fluorometers (DeNovix Inc., USA) and their quality were checked in 1.0% agarose gel

electrophoresis.

Then, amplifications for genomic DNA was conducted triplicate using the primer set 338F

(5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5’-GGACTACHVGGGT- WTCTAAT-3’).

The 20 μl PCR mixture contained 4 μl of 5FastPfu buffer, 2 μl of dNTP (2.5mM), 10 ng of

DNA, 0.8 μl of forward and reverse primers (5 μM), and 0.4 μl FastPfu polymerase

(TransStart® FastPfu DNA Polymerase, Transgen, China). The PCR protocol were as follows:

95 °C for 3 min, followed by 27 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 45 s and a

final extension at 72 °C for 10 min. The PCR products were checked in agarose gels (2%).

Then, amplicons were pooled and purified with AxyPrep DNA Gel Extraction Kit (Axgen,

USA), following the manufacture’s protocol. The clone library were constructed with

TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, Inc., USA), following the

manufacture’s instruction. Finally, the amplicons were pooled in equimolar ratios and

sequence using the MiSeq Illumina platform (300 bp paired-end reads) (Shanghai Personal

Biotechnology Co., Ltd, Shanghai, China).

Following sequencing, all the raw sequences were processed in QIIME v1.9.1-dev under

the default settings (Method S1)., the raw paired-end reads were assembled with FLASH

(Ver1.2.11, http://ccb.jhu.edu/software/FLASH/), and then barcodes, primers, and low quality

reads were trimmed off by Trimmoatic (Ver 0.33, http://www.usadellab.org/cms/?

page=trimmomatic). After filtration, the remaining effective sequences were clustered into

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operational taxonomic units (OTUs) using UPARSE (Ver 10.0.240,

http://www.drive5.com/usearch/) by setting a similarity 97%. Then, the taxonomic

classification were conducted using QIIME (Ver 1.9.0, http://QIIME.org) via Silva SSU

database (Release 128, http://www.arb-silva.de) at 80% bootstrap confidence. To avoid

unequal sampling depth biases during comparison of microbial diversity, sub-sampling was

performed to equal read depths of 11,168 per sample.

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Method S2 Quantitative real-time PCR (qPCR).

The primer pairs and protocols for qPCR were summarized in our previous study. The

standard plasmids these genes were also prepared according to our previous study. The

standard plasmids of these genes, inclduing bacteria 16S rRNA, anammox 16S rRNA, AOB

amoA, AOA amoA, nxrA, nirK, nirS, napA, narG, nrfA, and nosZ genes, were in the range of

5.98×102 -5.98×109 copies, 9.05×102- 9.05×109 copies, 1.91×102-1.91×109 copies, 9.75×102-

9.75×108 copies, 8.25×102-8.25×108 copies, 1.11×103-1.11×109 copies, 7.6×103-7.6×109

copies, 4.49×103 - 4.49×109 copies, 1.13×103 - 1.13×109 copies, 6.43×103 - 6.43×109 copies,

5.18×103 -5.18×109 copies, and 4.83×103-4.83×109 copies, respectively.

Triplicate assays were conducted for all standard reactions and samples. The qPCR

amplication was performed in a 10 µl reaction mixture consisting of 5μl SYBR® Premix Ex

Taq™ II (Takara, Japan), 0.25 μl of each primer, 1 μl of genomic DNA and 3.5 μl dd H2O.

The amplification efficiencies of qPCR assays ranged from 95% to 110%, and R2 value for

each calibration curves exceeded 0.98. The Ct (threshold cycle) was used to calculated the

copy numbers of all above mentioned genes.

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Method S3 Bioinformatic analysisAfter sub-sampling and cumulative sum scaling (CSS) normalization for OTU matrix, ɑ-

diversities, including Chao 1 estimator, ACE estimator, Shannon index, Simpson index,

Good’s coverage, and rarefaction curves at a 97% similarity, were measured using the qiime

platform (https://qiime.org).

Bray-Curtis and weighted UniFrac distance matrices were calculated to assess the

discrepancies in bacterial communities (β-diversity). Nonmetric multidimensional scaling

(NMDS) was performed to evaluate the similarity between samples by using the ggplot2 and

metaMDS function of the vegan packages in R. To further assess differences in microbial

communities, Principal coordinate analysis (PCoA) was performed by using the capscale

function of the vegan and ggplot2 packages in R.

To elucidate the influences of environmental parameters on OTUs affiliated with rare and

abundant taxa, redundancy ordination analysis (RDA) was conducted using the capscale

function of vegan package in R. Spearman’s rank correlation values between environmental

parameters and relative abundances of OTUS were calculated using the corr.test function of

psych R package. Then, the significantly Spearman’s rank association values (both positive

and negative) between OTUs and environmental parameters were displayed in correlation

network generated in Cytoscape (Ver 3.5.0, http://www.cytoscape.org).

To uncover the co-occurrence patterns of the rare, abundant and transient OTUs throughout

the all phases, all possible pairwise Spearman’s rank correlations between those OTUs were

calculated using the rcorr function of Hmisc R package. A correlation between two OTUs was

considered statistically robust if the Spearman’s correlation coefficient (ρ) was >0.6 and q

value was < 0.05. Then, the correlation networks were visualized using the interactive

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platform Gephi (Ver 0.9.1, https://gephi.org). To compare the topology of the rare taxa sub-

network with the abundant taxa sub-network, network-level topological properties included

modularity (MD), clustering coefficient (CC), average path length (APL), network diameter

(ND), average degree (AD), and graph density (GD) were calculated using the igraph R

package. In addition, node-level topological features, involving degree centralization,

closeness centralization, betweenness centralization, and eigenvector centralization were also

calculated by using igraph R package.

To further investigate the potential functional genera in the above reactor, the selected

sequences from the OTU dataset were blasted against the Microbial Database for Activated

Sludge (MiDAS) database (www.midasplatform.org). In addition, the functional profiles and

key enzymes were annotated using Kyoto Encyclopedia of Genes and Genomes (KEGG) in

the Tax4Fun (https:// tax4fun.gobics.de).

Finally, the quantitative response relationships between nitrogen transformation rates and

functional gene fragments were determined via step regression models. Subsequently, direct

and indirect effects of different nitrogen gene fragments on nitrogen transformation rate were

calculated via path analysis with SEM package in R (R Core Team 2014).

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Table S1 long-term experimental procedures.

PhasesNH4

+

(mg-N L-1)NO2

-

(mg-N L-1)KHCO3 (mg L-1)

CODa

(mg L-1)

IC: NH4+-

N(mg L-1)

COD: TN(mg L-1)

Phase I (1-20 days)

190 220 1250 -- 1.3 --

Phase II (21-40 days)

190 220 0 126 0 0.31

Phase III (41-60 days)

190 220 95 126 0.1 0.31

Phase IV (61-80 days)

190 220 380 126 0.4 0.31

Phase V (81-100 days)

190 220 665 126 0.7 0.31

Phase VI (101-120 days)

190 220 950 126 1.0 0.31

Phase VII (121-140 days)

190 220 1250 126 1.3 0.31

a Mixed solution of sodium acetate and propionate was added into the reactor automatically at the end of

each feeding period to maintain an influent COD: TN of 0.31.

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Figure S1

Figure S1 Beta diversity between different phases. (a), (b), (c) Differences in abundant, rare

and whole communities shown by principal coordinate analysis ordinations (PCOs, based on

unweighted UniFrac distance matrices); (d), (e), (f) Nonmetric multidimensional scaling

(NMDS) plot of the different communities (based on the weighted UniFrac distance matrices).

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Figure S2

Figure S2 Redundancy analysis triplot were applied to investigate the ecological correlation

between sub-community or gene fragments. (a) Abundant sub-community; (b) Rare sub-

community; (c) Transient sub-community; (d) Nitrogen functional gene fragments.

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Figure S3

Figure S3 Network of co-occurring OTUs in abundant, rare, and transient sub-community in

SAD systems, based on correlation analysis. Each edge stands for a strong (ρ> 0.6) and

significant correlation (q< 0.05). For each panel, the size of each node is proportional to the

relative of OTUs. The thickness of the edges is proportional to each OTUs of Spearman’s

correlation coefficient.

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Figure S4

Figure S4 Path diagrams assessing the effects of functional gene group on (a) NH4+-N, (b)

NO2--N, and (d) total nitrogen transformation rate under different inorganic carbon/NH4

+-N

ratios. Solid and dash line represent indirect and direct effects, respectively.

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