Supplemental Material

Transcription

1 1 2 3 Internal porosity of mineral coating supports microbial activity in rapid sand filters for drinking water treatment Arda Gülay 1, Karolina Tatari 1, Sanin Musovic 1, Ramona V. Mateiu 2, Hans-Jørgen Albrechtsen 1, Barth F. Smets 1 1 Department of Environmental Engineering, Technical University of Denmark, Building 113, Miljøvej, 2800 Kgs Lyngby, Denmark. 2 DTU Cen, Center for Electron Nanoscopy, Technical University of Denmark, Building 307, 2800 Kongens Lyngby, Denmark Considered for publication in Applied and environmental microbiology Supplemental Material 20

2 Preliminary investigations & details about DWTPs Preliminary investigations aimed to observe the presence of mineral coating on the filter material and to identify any potential correlation with the amount of DNA present. These experiments used material from the top layer ( m) of the full-scale filters at Islevbro (DWTP 1) and Marbjerg (DWTP 2-FS) waterworks, as well as at the pilot-scale filter of Marbjerg waterworks (DWTP 2-PS). Samples were collected between June and August DWTP 1 receives anoxic groundwater abstracted from 163 aquifers nearby. The schematic drawing of the plant is illustrated in Fig. S1. Oxygen is introduced to the water by air injection in an aeration chamber up to the saturation level. Aeration is followed by a retention tank that provides about 20 min reaction time after aeration and before filtration. Filtration takes place in 2 steps, where pre- filters allow deposition of oxidized Fe 3+ on the filter bed. Filtration continues in the after-filters that are designed to remove residual Fe 2+, NH + 4 and Mn +2. Filter material samples were collected from the after-filter with a core sampler from three random horizontal location. Each core was divided into depth sections at10 cm intervals and stored at -20 C for further analysis. DWTP 2 was started-up between 1932 and The plant receives anoxic groundwater of a nearly constant temperature of 5 8 C all year round. The treatment train consists of a stair aeration, and of pre and after- filtration steps. Pre-filters aim to remove most of the Fe 2+ content by allowing time for oxidation to Fe 3+ and precipitation in the filter bed. After-filters aim to remove biologically NH + 4, residual Fe 2+ and Mn 2+, and therefore our sampling took place here. After-filters have a bed of quartz sand of nominal average diameter range between mm. Filter material was collected from the after-filter using a sterilized metal bucket attached to an extendable rod. Filter material samples were stored at -20 C before DNA extraction. Two pilot filters were operated at DWTP 2 to determine the effects of different filter media and optimize full scale filter operation conditions. Filter material samples in these preliminary investigations were collected from the pilot scale filter, which consist of a 0.75 m deep column. Nominal sand quartz size was mm quartz at the bottom overlaid by mm quartz and mm quartz on the top. Filter material was sampled only from the top 10 cm of the pilot column and was stored at -20 C for further analysis. DWTP 3, 4 and 5 similarly consist of an aeration step and a double (pre- and after-) filtration steps.

3 Main differences between these DWTP are the groundwater quality, the filter material type and age, the filter size and depth, the backwash strategy, the operating flow rates, and hydraulic retention time in the filters. Core samples from the after-filters of these DWTP were obtained and sliced into the different depth sections with 10 cm intervals. DNA and mineral coating extraction from each section followed the described methodology. Microbial colonization capacity experiment Pilot scale rapid sand filters properties and operation details at the full scale conditions were described in the study of Lee et al. (1). In brief two pilot columns were started-up with filter material inoculated from the full-scale filter of DWTP 1 and were operated at different conditions. The influent NH + 4 loading rate in pilot column 2 was increased from 0.56 ± 0.13 to 5.1 ± 0.3 g NH 4 -N/m 3 / h for 33 d. The average influent P concentration during this time was ± mg P/L. After day 33 of operation at the increased NH + 4 loading, P was dosed at the influent of the pilot filter to a final concentration of 0.43 ± 0.07 mg P/L. The pilot column 2 was operated with increased influent NH + 4 and P concentrations until day 45. During this high nutrient feeding period, NH effluent concentration dropped while the NH 4 removal rate increased Lee et al. (1). Filter material samples were taken at the day of 45 from the m (top) and m (middle) of the pilot column and were segregated into the same particle sizes as for the full-scale investigations, i.e and mm for the top layer and and mm for the middle layer. These fractions were directly fixed by 4% paraformaldehyde, embedded in OCT compound (Sakura Tissuetek, The Netherlands) and frozen at -20 C for spatial microbial distribution analysis and stored in -20 C for DNA extraction. The other pilot column 1 was initially also operated also at an influent NH + 4 loading rate of 0.56 ± 0.13 g NH 4 -N/m 3 /h. Loading rate was then increased to 5.0 ± 0.2 g NH 4 -N/m 3 /h together with an increase of the influent P concentration to 0.43 ± 0.09 mg P/ L for 7 days. Filter material samples were collected after 7 days of increased NH + 4 and P operation from the m (top) and m (middle) and were further segregated in four size fractions as above. Samples were frozen and prepared as above for DNA extraction Pyrosequencing Raw sequenced data generated from pyrosequencing were quality-checked (denoised) with Ampliconnoise (2) and chimeras were removed with UCHIME (3) using default settings.

4 After quality checks, all analyses were performed using QIIME software (4). High quality sequences were clustered into OTUs at 97% pairwise identity (OTU 0.03 ) using the UCLUST algorithm (3) with default settings, and representative sequences from each OTU 0.03 were aligned against the Greengenes reference alignment (5) using PyNAST (6). Aligned sequences were then used to build phylogenetic trees using the Fast Tree method (7). Taxonomy assignment of each representative sequence was implemented using BLAST algorithm (8) against Silva108 curated database (9). Sequences without a reference sequence hit below 90% similarity were considered as unclassified. Alpha diversity of OTU libraries was computed using Chao1, Shannon index, ACE algorithms implemented in QIIME. Evenness was assessed with the Gini coefficient (G corr.), calculated geometrically by calculating the area under the Lorenz curves per OTU 0.03 pair (10) with correction to minimize bias (11). Distance matrices were constructed using Unweighted and Weighted UniFrac algorithms in QIIME from the whole phylogenetic tree. For statistical testing of sample groupings in a distance matrix, we applied Unweighted and Weighted UniFrac phylogenetic significance tests and considered a probability (P-value) less than 0.05 to determine significance. For phylogenetic analysis at the strain level, sequences belonging to the OTU 0.03 s of the nitrifiers were extracted using QIIME. Ammonia oxidizing guilds at the genus taxonomic level were selected using the Table S2. OTU 0.03 s belonging to these guilds were extracted using QIIME and taxonomically classified after re-alignment with web-based SINA v (12), and then imported into the ARB software environment (13). References 1. Lee CO, Boe-Hansen R, Musovic S, Smets B, Albrechtsen H-J, Binning P Effects of dynamic operating conditions on nitrification in biological rapid sand filters for drinking water treatment. Water Res. 64C: Quince C, Lanzen A, Davenport RJ, Turnbaugh PJ Removing noise from pyrosequenced amplicons. BMC Bioinformatics 12: Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27: Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, Mcdonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R QIIME allows analysis of high- throughput community sequencing data. Nat. Methods 7:

5 DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL Greengenes, a chimera-checked 16S rrna gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72: Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26: Price MN, Dehal PS, Arkin AP FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26: Altschul SF, Madden TL, Schäffer a a, Zhang J, Zhang Z, Miller W, Lipman DJ Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25: Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35: Wittebolle L, Marzorati M, Clement L, Balloi A, Daffonchio D, Heylen K, De Vos P, Verstraete W, Boon N Initial community evenness favours functionality under selective stress. Nature 458: Edwards A, Anesio AM, Rassner SM, Sattler B, Hubbard B, Perkins WT, Young M, Griffith GW Possible interactions between bacterial diversity, microbial activity and supraglacial hydrology of cryoconite holes in Svalbard. ISME J. 5: Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, Buchner A, Lai T, Steppi S, Jobb G, Förster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, König A, Liss T, Lüssmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke M, Ludwig T, Bode A, Schleifer K-H ARB: a software environment for sequence data. Nucleic Acids Res. 32:

6 Fig.S1. Process flow scheme of at drinking water treatment plant 1 (DWTP 1) 1

7 NOB AOB Table S1. Index of nitrifiers extracted from pyrosequencing library Domain Phylum Class Order Genus Bacteria Proteobacteria Beta-proteobacteria Nitrosomonadales Nitrosomonas Bacteria Proteobacteria Beta-proteobacteria Nitrosomonadales Nitrosospira Bacteria Proteobacteria Gamma-proteobacteria Chromatiales Nitrosococcus Bacteria Proteobacteria Alpha-proteobacteria Rhizobiales Nitrobacter Bacteria Nitrospirae Nitrospira Nitrospirales Nitrospira Bacteria Proteobacteria Gamma-proteobacteria Chromatiales Nitrococcus Bacteria Proteobacteria Delta-proteobacteria Desulfobacterales Nitrospina 2

8 Fig.S2. DNA and mineral coating mass in the rapid sand filters (RSFs) at 2 drinking water treatment plants (DWTP). Filter material samples were collected from the full-scale RSFs at DWTP 1 and DWTP 2 (DWTP 2-FS) and at a pilot-scale filter operating in parallel at DWTP 2 (DWTP 2-PS). A. Stereo-microscope images show the presence and the different degrees of mineral coating on the filter materials of. B. Correlation between the extracted mass of mineral coating by acid digestion and the extracted mass of DNA in the different DWTPs and C. among the 24 samples collected from and individual filter at DWTP 1. 3

9 Fig.S3. Depth profiles of extracted mineral coating (left panel) and DNA mass (right panel) from filter materials at four different waterworks. 4

10 Fig.S4. Pearson correlations between different abiotic and biotic components of the examined filter material samples. Each box reports the correlation coefficient (top value) and its significance value (bottom value). Bold numbers indicate significant statistical correlations (α<0.05). 5

11 Table S2 Relative elemental composition (% element mass/ total mineral coating mass) of mineral coating in the investigated filter material samples. Top layer (0-0.1 m) Middle layer ( m) T T M M Ag N.D. a N.D. N.D. N.D. Al As Ba Ca Cd N.D. N.D. N.D. N.D. Co Cr N.D. N.D. N.D. N.D. Cu Fe K La N.D. N.D. N.D. N.D. Mg Mn Na Ni P Pb N.D. N.D S Se Sr Tl V N.D. N.D. N.D. N.D. Zn Other a N.D.: Not Detected, LOD Ag= 0.7 µg/l, LOD Cd=0.1 µg/l, LOD Cr=0.7 µg/l, LOD La=0.7 µg/l, LOD Pb=1.4 µg/l, LOD V=0.7 µg/l 6

12 Fig.S5 SEM micrographs coupled to energy dispersive X-ray microanalysis of mineral coating from the T filter material sample. Peak heights in the EDS spectra are proportional to relative elemental concentration 1

13 Fig.S6 SEM micrographs of a cryo-section across the mineral coating of the T filter material at the 5 µm scale of. Encrusted organic-like structures are marked with the white arrows 1

14 Normalized pore areas (µm 2 ) µm 200 µm a b c Number of pores Fig.S7 Internal pore area distribution in the mineral coating of the T filter material sample. Pore counts and sizes were assessed from SEM micrographs at the scale of 400 and 200 μm (panel a), 50 μm (panel b) and 20 μm (panel c). 5 SEM images were analyzed and plotted on panel c. The number of pores extends to 7074 in panel c. 2

15 Fig.S8 Confocal laser scanning micrographs of cryosectioned mineral coating from the T filter material sample showing the spatial distribution of microorganisms. (a) DIC observation SYTO 9 and propidium iodide staining and fluorescence observation ((b)syto 9 green, (c) PI red). 3

16 Table S3 Testing equalities of total microbial communities and AOB guilds of the examined filter material samples using the Weighted and Unweighted UniFrac algorithms. Each significant value (P-value) was calculated by averaging P-values of triplicate comparisons. Comparisons P-value Total community composition (Weighted UniFrac) Top vs. Top Top vs. Mid Top vs. Mid Top vs. Mid Top vs. Mid Mid vs. Mid AOB guild composition (Weighted UniFrac) Top vs. Top Top vs. Mid Top vs. Mid AOB guild composition (Unweighted UniFrac) Top vs. Top Top vs. Mid Top vs. Mid AOA guild composition (Unweighted UniFrac) Top vs. Mid Top vs. Mid Mid vs. Mid

17 Table S4 Microbial richness and evenness of microbial communities from the examined filter material samples Grain Fraction Chao1 Richness Observed species Phylogenetic Diversity Evenness Gini corr. T ± ± ± ±0.001 T ± ± ± ± M ± ± ± ± M ± ± ± ±

18 Fig.S9 Visualization of strong (r> 0.98) and statistically significant (P < 0.05) Pearson s correlations between phylum level taxa and abiotic measurements inferred from network analysis. Line thickness increases with strength of the interactions. Dashed lines denote negative correlations. Node sizes are proportional to the mean relative abundance of that taxon in each grain fraction. 6

19 Fig.S10 Visualization of strong (r> 0.98) and statistically significant (P < 0.05) Pearson s correlations between OTUs (at 97% similarity) and abiotic measurements inferred from network analysis. Line thickness increases with strength of the interactions. Dashed lines denote negative correlations. Node sizes are proportional to the mean relative abundance of that taxon in each grain fraction. 7

20 Table S5 Relative abundances of domain-level taxa in the examined filter material samples Taxon T T M M No blast hit 0.02% 0.00% 0.00% 0.00% Archaea 0.35% 0.23% 1.63% 1.20% Bacteria 99.63% 99.77% 98.37% 98.80% 8

21 Fig.S11 Relative abundances of major class-level taxa in the different filter grain fractions. Marine group I belongs to archaeal domain and detected only in M and M filter material samples. 9