Supplementary Material

for

Effect of TiO2 and CeO2 nanoparticles on the metabolic activity of surficial

sediment microbial communities based on oxygen microelectrodes and high-

throughput sequencing

Lingzhan Miao1, Peifang Wang*1, Chao Wang1, Jun Hou*1, Yu Yao1, Jun Liu2, Bowen

Lv1, Yangyang Yang1, Guoxiang You1, Yi Xu1, Zhilin Liu1, Songqi Liu1

1. Key Laboratory of Integrated Regulation and Resources Development on Shallow

Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing,

People’s Republic of China, 210098

2. State Key Laboratory of Biocontrol, Key Laboratory of Biodiversity Dynamics and

Conservation of Guangdong Higher Education Institutes, College of Ecology and

Evolution, Sun Yat-sen University, Guangzhou, People’s Republic of China, 510275

*Corresponding author. Address: College of Environment, Hohai University, 1

Xikang Road. Nanjing, 210098, China. Tel.: +86-25-83787332; fax: +86-25-

83787332.

E-mail: pfwang2005@hhu.edu.cn (P.F Wang)

hhuhjyhj@126.com (Jun Hou)

This Supplementary Information includes a total of 13 pages (including this page)

with 4 sections for text, references, 5 tables and 7 figures.

Test. S1. Microprofiles data analysis

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Oxygen flux (J) was estimating from the steady-state oxygen profiles according to the

first Fick’s law of diffusion:

J=−D0∂ C( z)

∂ z (1)

Where Do represents the oxygen diffusion coefficient in water (Berg et al., 1998) and

∂C (z )∂ z

the oxygen gradient in the diffusive boundary layer.

Toxicity IJ is quantified as the reduction in the flux of J before and after exposure to

an inhibitor (TiO2 and CeO2 NPs).

I J=1−(JT

J C) (2)

where JT and JC are the oxygen flux across the SWI in the MNP-treated and the

control sediments.

The numerical model PROFIL (Berg et al., 1998) was used to analyze measured O2

concentration profiles and apparent diffusivity. PROFIL calculates the rate of net

consumption as a function of depth, assuming that the concentration-depth profiles

represent steady states. This procedure is based on a series of least square fir to the

measured steady-state concentration profiles, assuming an increasing number of

consumption zones. Statistical F-testing compares the fits, so that the simplest

consumption profiles results, which reproduces the measured concentration within the

chosen statistical accuracy (Berg et al., 1998).

The Ds was calculated from the free solution molecular diffusion coefficient (D0) for

O2 and the sediment porosity (∅ ) according toNakamura et al. (2004).

Ds=∅ 2× D0 (3)

The D0 for O2 (2.09×10-5 cm2/s) at 20°C was found in the literature (Nakamura et al.,

2004). Porosity of the sediment samples used in this study is calculated with a value

of 0.81±0.06.

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Test. S2. Enzyme activities

After the MNPs exposure, subsamples were collected and four enzymes activities

(fluorescein diacetate hydrolase, dehydrogenase, alkaline phosphatase and urease)

were measured. Fluorescein diacetate (FDA) hydrolase is widely used to evaluate the

total microbial activity among various kinds of environmental samples (Huang et al.,

2015). Dehydrogenase (DHA) can be uses as a good indicator of the total oxidative

activity of microbes, which play a significant role in the biological oxidation of

organic matter (Bao et al., 2016). Phosphatase catalyzes the hydrolysis of organic

phosphorus (P) to inorganic phosphorus, and urease catalyzes hydrolysis of organic

nitrogen (N) into ammonia. Thus, alkaline phosphatase (APA) and urease (UE) are

responsible for P and N cycles in sediments. These four enzyme activities were

measured following the methods described by Bao et al (2016).

Test.S3. DNA extraction, PCR amplification, sequencing, and data analysis

Sediment samples were collected carefully and were stored at -80 °C until DNA

extraction. Genomic DNA was extracted using the E.Z.N.A.® Tissue DNA kit

(Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer’s instructions.

The extracted DNA samples were stored at 20 °C until used. The concentrations and

purities (A260/A280 ratio > 1.5) of the extracted DNA samples were analyzed by a

NanoDrop ND-2000 (Thermo Fisher Scientific, USA). The V4-V5 region of the

bacteria 16S ribosomal RNA gene of the extracted DNA was amplified by PCR（95

°C for 2 min, followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for

30 s and a final extension at 72 °C for 5 min） using primers 515F 5’-barcode-

GTGCCAGCMGCCGCGG)-3’ and 907R 5’-CCGTCAATTCMTTTRAGTTT-3’. The

triplicate PCR amplicons were multiplexed into a single pool, and concomitantly

electrophoresed by a 2% (w/v) agarose gels and purified using the AxyPrep DNA Gel

3

Extraction Kit (Axygen Biosciences, Union City, CA, U.S.) according to the

manufacturer’s instructions and quantified using QuantiFluor™ -ST (Promega, U.S.).

Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 250) on

an Illumina MiSeq platform according to the standard protocols, which was conducted

by Majorbio Bio-Pharm Technology Co., Ltd (Shanghai, China) (Lai et al., 2014;

Zhang et al., 2015).

The raw data obtained from the Illumina MiSeq sequencing were saved as paired-

end fastq. Raw fastq files were demultiplexed, quality-filtered using QIIME (version

1.17， http://qiime.org/) with the following criteria: (i) The 250 bp reads were

truncated at any site receiving an average quality score <20 over a 10 bp sliding

window, discarding the truncated reads that were shorter than 50bp. (ii) exact barcode

matching, 2 nucleotide mismatch in primer matching, reads containing ambiguous

characters were removed. (iii) only sequences that overlap longer than 10 bp were

assembled according to their overlap sequence. Reads which could not be assembled

were discarded.

After removing the barcodes and primers, the data were subsampled to 13876

sequences per sample to avoid biases related to unequal numbers of sequences (Zhang

et al., 2012). Then, the normalized samples were individually classified and analyzed

by RDP Classifier (http://rdp.cme.msu.edu/) against the silva (SSU115) 16S rRNA

database using confidence threshold of 70% (Amato et al., 2013). Operational Units

(OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7.1

http://drive5.com/uparse/) and chimeric sequences were identified and removed using

UCHIME. The rarefaction curve was constructed by random sampling for all the

sequences, which can be used to compare the species diversity between different

samples. Shannon diversity index (H) was used to study the microbial species

diversity of biofilms, it was calculated by the following equation (Shannon, 1948):

H= - ∑Pi lnPi (i=1, 2, 3…, S). Pi represents the relative abundance of the species and

S represents the number of microbial species. Heat map was conducted using R

4

program to analyze microbial community structure in genus level. Genera with

relative abundance top 100 in each sample were selected and compared with relative

abundances in other samples.

Test. S4. Aggregation of MNPs in the overlying water

Prior to the experiment, the aggregation kinetics of TiO2 and CeO2 NPs in the

filtered lake-water (through 0.22 μm) were investigated to study the stability of added

NPs in natural water environment. According to our preliminary test, concentration of

10 mg/L NPs was used to provide a good detection. The particle-size distributions

(PSD) of the NPs were measured by dynamic light scattering (DLS) using Malvern

Zetasizer Nano ZSP. The homo-aggregation experiments of NPs suspensions were

performed following the detailed procedure reported in our previous study (Miao et

al., 2016), and the particle size distributions and zeta potentials of NPs in the solution

were determined. Due to the low level of suspended solids (<0.58 mg/L) in the static

sediment incubation systems, the hetero-aggregation processes (aggregation between

nanoparticles and suspended particles in the water column) in the microcosms were

not taken consideration in this study.

As shown in Figure S4, both the two types of MNPs aggregated significantly in

the first 30 mins, with hydrodynamic diameter (HDD) increased from 342 nm (TiO2

NPs) and 223 nm (CeO2 NPs) to approximate 800 nm and 1100 nm in the water

column, respectively. The nano-aggregates were captured by SEM analysis and shown

in Figure S4. The zeta potentials of these two NPs were determined to be -6.3±0.6 mV

(TiO2 NPs) and -3.4±1.7 mV (CeO2 NPs), respectively. Low values of zeta potential

indicated weak electrostatic repulsion between the nanoparticles (Miao et al., 2016),

resulting in obvious aggregation of NPs in the solutions. Besides, the aggregation

processes were also accelerated by the high ionic strength, dissolved salts and

dissolved organic matter present in the overlying water (Table S1) (Binh et al., 2014;

Jomini et al., 2015). Realistically, when NPs were released to sediment systems,

homo-aggregation might not be the only mechanisms of NPs colloidal destabilization

5

and hetero-aggregation might occur with the presence of natural colloids in the

microcosms (Marie et al., 2014; Zhang et al., 2016).

References

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Bao, S., Wang, H., Zhang, W., Xie, Z., Fang, T., 2016. An investigation into the effects of silver nanoparticles on natural microbial communities in two freshwater sediments. Environ. Pollut. 219, 696-704.

Berg, P., Risgaard-Petersen, N., Rysgaard, S., 1998. Interpretation of measured concentration profiles in sediment pore water. Limnol. Oceanogr. 43(7), 1500-1510.

Binh, C.T.T., Tong, T., Gaillard, J.F., Gray, K.A., Kelly, J.J., 2014. Acute effects of TiO2

nanomaterials on the viability and taxonomic composition of aquatic bacterial communities assessed via high-throughput screening and next generation sequencing. PloS one. 9(8), e106280.

Huang, X., Wu, C., Hu, H., Yu, Y., Liu, J., 2015. Sorption and degradation of triclosan in sediments and its effect on microbes. Ecotoxicology and environmental safety, 116, 76-83.

Jomini, S., Clivot, H., Bauda, P., Pagnout, C., 2015. Impact of manufactured TiO2 nanoparticles on planktonic and sessile bacterial communities. Environ. Pollut. 202, 196-204.

Lai, C.Y., Yang, X., Tang, Y., Rittmann, B. E., Zhao, H.P., 2014. Nitrate shaped the selenate-reducing microbial community in a hydrogen-based biofilm reactor. Environ Sci Technol. 48(6), 3395-3402.

Miao, L., Wang, C., Hou, J., Wang, P., Ao, Y., Li, Y., Xu, Y., 2016. Effect of alginate on the aggregation kinetics of copper oxide nanoparticles (CuO NPs): bridging interaction and hetero-aggregation induced by Ca2+. Environ. Sci. Pollut. R. 23(12), 11611-11619.

Nakamura, Y., Satoh, H., Okabe, S., Watanabe, Y., 2004. Photosynthesis in sediments determined at high spatial resolution by the use of microelectrodes. Water. Res. 38(9), 2440-2448.

Shannon, C.E.A., 1948. Mathematical theory of communication. Bell Syst. Tech. J. 27, 623-656.

Zhang, X., Shen, D., Feng, H., Wang, Y., Li, N., Han, J., Long, Y., 2015. Cooperative role of electrical stimulation on microbial metabolism and selection of thermophilic

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communities for p-fluoronitrobenzene treatment. Bioresour. Technol. 189, 23-29.Zhang, T., Shao, M.F., Ye, L., 2012. 454 Pyrosequencing reveals bacterial diversity of

activated sludge from 14 sewage treatment plants. ISME J. 6(6), 1137-1147.

Table S1. Characterization of the filtered overlying water (through 0.22 μm) sampled from the Zhushan Bay of Lake Taihu, China.

Parameters Average values (n=5)

TOC (mg·L-1) 11.28±2.4

pH 7.77±0.2

K+ (mg·L-1) 4.88±0.4

Na+ (mg·L-1) 35.3±1.5

Ca2+ (mg·L-1) 35.6±2.3

Mg2+ (mg·L-1) 8.05±1.6

PO43- (mg·L-1) <0.038

SO42- (mg·L-1) 58.3±2.7

Cl- (mg·L-1) 43.1±3.1

HCO3- (mg·L-1) 97.7±2.9

CO32- (mg·L-1) <1.59

NO3- (mg·L-1) 1.06±0.1

NH4+ (mg·L-1) 0.353±0.1

Table S2. Physico-chemical parameters monitored in the water column and on the surficial sediment. Values of the day of MNPs addition (T0) and after 1, 2, 3, 4, and 5 days. The data are an average values ± standard deviation obtained from the microcosms.

pH DO (mg/L) EC (μS/cm)ORP（mv）

Water column

Surficial sediment

T0 7.69±0.4 8.49±0.3 288±17 244±19 233±26Day 1 7.83±0.2 8.52±0.4 288±14 265±26 255±43

7

Day 2 7.64±0.1 8.49±0.4 308±13 278±17 245±39Day 3 8.09±0.4 8.69±0.4 296±19 259±27 231±46Day 4 8.19±0.3 8.46±0.3 278±18 258±16 252±37Day 5 7.88±0.3 8.45±0.4 284±12 276±27 249±31

Table S3. Effects of MNPs addition on oxygen fluxes at the interface between water column and sediments.

Treatments Initial O2 flux(nmol cm-2 h-1)a

O2 flux at end(nmol cm-2 h-1) a

Inhibition(%)a

Controls 0.5472±0.052 0.5789±0.031 n.s5 mg/L TiO2

NPs0.5749±0.032 0.4445±0.049 22.7*

5 mg/L CeO2

NPs0.6875±0.031 0.4659±0.054 32.2*

a Average ± standard deviation (n=3–4), the inhibition rate was calculated from the initial O2 flux and O2 flux at end using formula (2). Significant differences (p<0.05) are marked with*. n.s.: not significant.

Table S4 Similarity-based OTUs and species richness and diversity estimates of bacterial communities in the controls (C-1, C-2, and C-3), nTiO2 treatments (TiO2-1, TiO2-2, and TiO2-3) and nCeO2 treatments (n CeO2-1, CeO2-2, CeO2-3).Sampl

e

0.97

OTU *

Ace Chao Coverage Shannon *

Simpson *

C-11870

2644(2533,2774

)

2632(2497,2796)

0.9484726.02

(5.99,6.06)

0.0123(0.0116,0.0131

)C-2

19772865

(2743,3008)

2811(2668,2984)

0.9445816.27

(6.24,6.3)

0.0069(0.0065,0.0073

)C-3

19032727

(2610,2862)

2644(2515,2801)

0.9475356.14

(6.11,6.17)

0.0095(0.0089,0.0101

)TiO2-1

20822865

(2757,29912795

(2673,2943)0.945950

6.43(6.4,6.46)

0.005(0.0048,0.0053

8

) )TiO2-2

21042895

(2786,3022)

2804(2684,2948)

0.9454456.48

(6.45,6.51)

0.0046(0.0044,0.0048

)TiO2-3

21383037

(2916,3177)

3001(2856,3174)

0.9417706.54

(6.52,6.57)

0.0041(0.0039,0.0042

)CeO2-

12191

2961(2856,3083

)

2850(2739,2984)

0.9445816.58

(6.55,6.6)

0.0039(0.0037,0.0041

)CeO2-

22145

2960(2849,3089

)

2890(2764,3041)

0.9436446.52

(6.49,6.55)

0.0042(0.004,0.0044)

CeO2-

32206

3046(2933,3176

)

2994(2863,3150)

0.9416986.62

(6.6,6.65)

0.0035(0.0034,0.0037

)*denotes a significant difference between the MNP treatments and the controls for p<0.05.

Table S5 Estimating significant differences in relative abundances of dominant phylum between the MNPs treatment sediments and the control sediments using the independent samples t- test.

C vs TiO2 C vs CeO2 TiO2 vs CeO2Proteobacteria 0.004 0.047 0.589Bacteroidetes 0.472 0.796 0.205Chloroflexi 0.062 0.019 0.253

Cyanobacteria 0.223 0.510 0.010Acidobacteria 0.759 0.415 0.303

Nitrospirae 0.026 0.052 0.849Planctomycetes 0.465 0.760 0.744

Firmicutes 0.476 0.948 0.316Chlorobi 0.056 0.396 0.796

Bacteria_unclassified 0.634 0.521 0.198Verrucomicrobia 0.108 0.434 0.226

Gemmatimonadetes 0.953 0.896 0.725Spirochaetae 0.245 0.714 0.240

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Figure S1. SEM images of TiO2 NPs and CeO2 NPs used in this study.

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TaihuLake

ZhushanBay

GonghuBay

MeiliangBay

South Taihu

Figure S2. The diagram of the sampling point in this study, and the asterisk (red) in

the Zhushan Bay (31.3794 N, 120.0249 E) represents the sampling site of the

sediment and overlying water.

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Figure S3. Schematic Illustration of the microcosms and the experimental setup for

microelectrode-based measurements.

TiO2 NPs

CeO2 NPs

Figure S4. Aggregation of TiO2 and CeO2 NPs in the filtered overlying water (through

0.22 μm). The aggregation kinetics of MNPs were measured in the first 30 mins. The

figure on the right represents the SEM images of nano-aggregates.

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Figure S5. Representative 2-D contour plots of DO concentrations near the sediment-

water interface with relative distance between two points of 5 mm. The sediment/bulk

water phase interface is at depth 0 μm.

Figure S6. 16S rRNA gene copies in the control sediments, the nTiO2 treated

sediments and the CeO2 treated sediment. Bacterial 16S rRNA gene copies were

normalized by the average mass of sediment used in the duplicate DNA extractions

per microcosm sampling point.

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Figure S7. Enzyme activities in the control sediments, the nTiO2 treated sediments

and the CeO2 treated sediment after 5-day exposure. Data are means ± SD (n=3). *

denotes a significant difference between the treatment and control for p<0.05.

14