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Supplementary Information 1

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Contents: 3

Fig. S1 Images of the sewer pipes and MICC layers............................................................. p. 2 4

Fig. S2 Rarefaction curves of MICC layer-associated microbial communities.....................p. 3 5

Table S1 Alpha diversity statistics........................................................................................p. 4 6

Materials and methods.....................................................................................................p. 5-7 7

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Figure S1 Sydney sewer sampling site (A) Internal upstream view of the pipe, dimensions 11

are 2 m high by 3 m wide with a flow depth of 0.8-1 m. (B) Close up of the corroded pipe-12

ceiling surface. (C) Scrapings of the corrosion layer taken from the field site with the large 13

pieces of aggregate removed. (D) Scanning electron microscopy image of the corrosion 14

scrapings. 15

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Figure S2 Rarefaction analysis showing the number of OTUs detected in each sample against 17

the depth of sequencing performed, and the theoretical maximum diversity. 18

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Table 1 The richness and evenness of sewer MICC layer-associated microbial communities. 21

Values are rarefied means based on 50 sub-samplings of 1,400 individual sequences per 22

sample. 23

Pipe Position Observed richness

Simpson’s (Evenness)

1 Wall 288.14 0.9711 1 Ceiling 57.84 0.4522 1 Ceiling 220.60 0.8934 1 Ceiling 260.98 0.8992 1 Ceiling 133.16 0.7032 1 Ceiling 480.88 0.9435 1 Ceiling 129.58 0.6755 2 Wall 311.92 0.9734 2 Wall 302.20 0.9729 2 Ceiling 99.42 0.6902

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Materials and Methods 25

Sample collection 26

In June 2010 samples were taken from an actively corroding gravity sewer system in Sydney, 27

Australia. This system services a large urban area and transports approximately 280 28

megalitres (ML) of domestic wastewater daily. The sampling site was located several 29

kilometres upstream of the discharge point to a wastewater treatment plant. The sewer system 30

is composed of two box culvert reinforced concrete pipes separated by a concrete wall. These 31

pipes have been in place for over 50 years and were resurfaced approximately eight years 32

ago. Sampling was conducted by scraping the corrosion layer on the surface of the sewer pipe 33

directly into a sterile 50 mL polypropylene container. Samples were collected from sewer 34

pipe walls, well above the maximum flow level, and ceilings. All sampling was performed 5-35

10 m upstream of the same sewer access point, where, from visual inspection, the corrosion 36

layer appeared to be homogenous. Once collected, samples were immediately placed onto dry 37

ice for transport to the laboratory and then stored at -80°C until DNA was extracted. Field 38

site pH measurements were also made using pH indicator strips (Merck) and a minimal 39

amount of Milli-Q water. The pH values were accurate to +/- 0.5 units. 40

Environmental monitoring 41

The sewer H2S gas levels and gas phase temperatures were determined by App-Tek OdaLog 42

(Brendale, Australia) gas detectors during data collection periods. These monitoring units 43

were installed at the field site and allowed to run undisturbed for several weeks in order to 44

acquire data unaffected by collection works. RDA analysis (using R, version 2.12.0) showed 45

no statistical difference between the two sewer pipes. The data was averaged over the 46

monitoring period to give average values for the temperature and H2S levels in each pipe. 47

Scanning electron microscopy 48

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Frozen corrosion layer samples were thawed in 4% osmium tetroxide overnight, then 49

dehydrated in a graduated series of ethanol, infiltrated with the drying agent 50

hexamethyldisilazane and allowed to dry completely in a fume hood. The fixed, dried sample 51

was coated with platinum to reduce charging and imaged using a Philips XL30 scanning 52

electron microscope under high vacuum at 5 kV. 53

DNA extraction and sequencing 54

Total DNA was extracted using the FastDNA SPIN Kit for Soil (MP Biomedicals), as per the 55

manufacturer’s instructions. The 16S rRNA genes were amplified and sequenced as 56

previously described [4]. Briefly, DNA was amplified with universal fusion primers, 926F [4] 57

and 1392wR,(5’-ACGGGCGGTGWGTRC-3’) that included Roche 454 LibL adaptor 58

sequences and unique 5 base pair (bp) multiplex identifiers (MID). The amplicon library was 59

purified and sequenced on a Genome Sequencer FLX Titanium pyrosequencer (Roche, 60

USA). 61

DNA sequence analysis 62

Sequences were quality filtered and dereplicated using the QIIME script split_libraries.py 63

with the homopolymer filter deactivated [2] and then checked for chimeras against the 64

greengenes database using UCHIME ver. 3.0.617 [3]. Homopolymer errors were corrected 65

using Acacia [1]. Sequences were then subjected to the following procedures using QIIME 66

scripts with the default settings: 1) sequences were clustered at 97% similarity into 67

operational taxonomic units (OTUs), 2) cluster representatives were selected, 3) greengenes 68

taxonomy was assigned to the cluster representatives using BLAST, 4) tables with the 69

abundance of different OTUs and their taxonomic assignments in each sample were 70

generated. The number of reads was then sub-sampled to a level of 1,400 sequences per 71

sample by re-sampling the OTU table. 72

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The mean number of OTUs (observed richness) and Simpson’s Diversity Index (evenness) 73

values [5], were calculated for the normalised datasets using QIIME. The values of Sobs and 74

differences in richness and evenness were investigated using generalized linear modelling 75

(GLM). Differences in the composition of microbial communities were visualised using 76

Principal Component Analysis (PCA) and differences in composition between pipes or 77

locations within pipes was investigated using redundancy analysis (RDA) with subsequent 78

Monte-Carlo permutation tests (999 permutations) for significance testing. PCA and RDA 79

were based on Hellinger transformed OTU abundances. All analyses were implemented using 80

R (version 2.12.0). 81

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References 83

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2. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, 86 E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., 87 Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., 88 Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., 89 Yatsunenko, T., Zaneveld, J., and Knight, R., 2010. QIIME allows analysis of high-90 throughput community sequencing data. Nature Methods, 7(5): 335-6. 91

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4. Engelbrektson, A., Kunin, V., Wrighton, K.C., Zvenigorodsky, N., Chen, F., Ochman, H., 94 and Hugenholtz, P., 2010. Experimental factors affecting PCR-based estimates of microbial 95 species richness and evenness. ISME Journal, 4(5): 642-647. 96

5. Simpson, E.H., 1949. Measurement of Diversity. Nature, 163(4148): 688-688. 97

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