Supplementary Methods

Anaerobic digester setup and biomass Sampling

Two sets of mesophilic continuous anaerobic digesters in triplicates were established in this

study. All of the six digesters had a working volume of 3.6 liters and were operated at a constant

temperature of 35 oC. The digesters were fed every 4 hours and the hydraulic retention time was

20 days. These digesters were initiated with inoculum from an operating laboratory dairy manure

anaerobic digester and established with dilute diary manure as the only feed source. All digesters

exhibited stable, similar performance prior to the addition of poultry waste, which was

comprised of chicken feces, kiln dried wood shavings, spilled feed, and feathers. The organic

loading rate (OLR) was maintained at 1.0 g volatile solids (VS)/L/day in the three control

digesters with dairy manure throughout the time period of this study. In contrast, the OLR was

raised to 1.3 g VS/L/day in the other three digesters with addition of poultry waste to the feed,

i.e., co-digesters. The OLR in the triplicated co-digesters were further raised to 1.5 g VS/L/day

by adding more poultry waste (Fig. S1). The feeding rate of dairy manure remained unchanged

in all digesters. The characteristics of dairy manure and poultry waste are shown in Table S1

(Chen et al 2012). During each OLR level, sludge samples, i.e., the digestate (material generated

after the anaerobic digestion process) exiting the anaerobic digesters, were taken from digesters

at five or six time points. To profile microbial communities in the feeding substrates, we took

dairy manure and poultry waste samples at three time points, with a 5-day interval.

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Chemical analyses

The biogas production of anaerobic digesters were measured using the water-displacement

method (Cesaro et al 2012). Methane content in biogas was determined by a Hewlett Packard

5890 Series II gas chromatograph (Agilent Technologies, Santa Clara, California, USA)

equipped with a thermal conductivity detector (TCD) and a Supelco packing column (60/80

Carbonxen®-1000; Sigma-Aldrich, St Louis, MO, USA). Argon was used as the carrier gas at a

flow rate of 5 ml min-1 under the following temperature scheme: 125°C in the oven, 150°C at the

injection port and 170°C in the detector. Acetate were quantified with a Agilent 1200 series

High-Performance Liquid Chromatography (Agilent Technologies, Santa Clara, California,

USA) equipped with a Bio-Rad Aminex HPX-87H ion exclusion column (Bio-Rad, Hercules,

California, USA) heated to 60°C with 0.005 N sulfuric acid as the eluent. Total ammonia-

nitrogen and VS were determined by the standard method (Federation and Association 2005).

Specifically, total ammonia-nitrogen was quantified using the ‘4500NH3 D’ method with an

Orion 9512 ammonia ion selective electrode (Orion Research Inc., Beverly, MA, USA). VS was

quantified using the ‘2540 E’ method (Federation and Association 2005), calculated as the

weight lost on ignition at 550°C in a muffle furnace, which offers an approximation of the

amount of organic matter. When the VS level of dairy and poultry waste were quantified, the

appropriate amounts of waste were determined to maintain the OLR of digesters. The VS

removal was calculated based on the difference between the input VS amount and the VS

amount measured in the digesters.

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DNA extraction, purification and quantitation

DNA was extracted from sludge samples using previously described protocols (Purkhold et al

2000). Briefly, the sludge sample was resuspended in 630 µL DNA-extraction buffer, followed

by treatment with 60 µL of a lysozyme mixture (37 °C, 60 min), 60 µL of a protease mixture (37

°C, 30 min), and 80 µL 20% sodium dodecyl sulfate (37 °C, 90 min). Treated sample suspension

was subsequently extracted with the phenol–chloroform–isoamyl alcohol (25:24:1) at 65 °C for

20 min, and supernatant was extracted using the chloroform–isoamyl alcohol (24:1). DNA

extract was then mixed with 0.6 volume of isopropanol and stored at 4 °C overnight. In addition,

DNA was obtained by centrifuging the pellet, washing with 70% cold ethanol, drying and

resuspension in nuclease-free water. DNA quality was evaluated with a NanoDrop

spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). Final DNA

concentrations were quantified by PicoGreen, using a FLUO star Optima instrument (BMG

Labtech, Jena, Germany). Purified DNA was stored at -80 °C.

Illumina sequencing and data processing

The V4 region of microbial 16S rRNA gene was amplified by primer pairs of 515F (50-GTG

CCA GCM GCC GCG GTA A-30) and 806R (50-GGA CTA CHV GGG TWT CTA AT-30)

(Wu et al 2015). PCR was performed at 94 °C for 1 min, 30 cycles of 94 °C for 20 s, 53 °C for

25 s, and 68 °C for 45 s, and a final extension at 68 °C for 10 min using the AccuPrime High

Fidelity Taq Polymerase (Invitrogen, Grand Island, NY, USA). PCR products were pooled and

purified using the QIAquick Gel Extraction Kit (Qiagen, Valencia, CA, USA). PCR amplicons

were subjected to sequencing using the MiSeq Illumina platform at the Institute for

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Environmental Genomics (IEG), University of Oklahoma. These sequence data are accessible in

the GenBank database under the accession number SRP070491.

To process raw sequencing data, primer sequences were trimmed from the paired-end

sequences, which were then merged using FLASH. Merged sequences were processed to

generate operational taxonomic units (OTUs) by UPARSE at the 97% sequence similarity

threshold. Taxonomy was assigned with a confidence cutoff of 50% using the RDP classifier.

The OTU matrices were rarefied to 11,558 sequences per sample. The rRNA operon copy

number for each OTU was estimated through the rrnDB database, based on its closest relatives

with known rRNA operon copy number (Stoddard et al 2014). In brief, for each OTU, the mean

operon copy number (if available) of the immediate child taxa was used as the mean copy

number. For any OTU without copy number data, the mean copy number of its parent was used.

The abundance-weighted average rRNA operon copy number was then calculated for each

sample as follows. OTU data were normalized by dividing by copy number to represent the cell

abundance. Then the abundance-weighted mean rRNA operon copy number were calculated by

taking the product of the estimated operon copy number and the relative abundance for each

OTU, and summing this value across all OTUs in a sample.

TaqMan qPCR experiments

TagMan qPCR analyses were performed with triplicate sludge samples at three time points (Day

45, 73 and 90). Genus-specific TaqMan qPCR assays were used to quantify the populations of

Methanosarcina and Methanosaeta. To determine the relative abundance of the both

methanogens in the archaeal community, a domain-specific TaqMan qPCR assay was performed

to quantify total archaeal populations. The characteristics of TaqMan primer/probe sets used in

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this study were summarized in Table S4, and the qPCR procedure was performed with a CFX96

Real-Time PCR Detection System (Bio-Rad, Hercules, California, USA) as previously described

(Chen and He 2015). In brief, the qPCR assays were performed in 25 μL reaction volumes with

15 pmol primers, 5 pmol probe, and Brilliant II QPCR Master Mix (Agilent, Santa Clara,

California, USA). The thermal cycling was started by an incubation at 50 °C for 2 min and an

initial denaturation at 95 °C for 10 min, followed by up to 45 cycles at 95 °C for 30 sec and 60

°C (for all primer/ probe sets) for 45 sec.

Experiments with GeoChip

GeoChip is a microarray-based tool which has been widely used for functional profiling of

microbial communities in a variety of environments (Ding et al 2015, Liu et al 2015, Yue et al

2015). The latest version of functional gene array, GeoChip 5.0, was used to analyze the

functional potential of microbial communities in sludge samples. GeoChip 5.0 (60 K arrays)

contains more than 57,000 oligonucleotide probes, covering over 144,000 gene sequences from

393 gene families involved in nitrogen (N), carbon (C), sulfur (S), and phosphorus (P) cycling,

metal resistance, organic remediation, and other processes (Zhang et al 2015). Among them, C

cycling genes include those for hydrolysis of C substrates, acidogenesis, acetogenesis and

methanogenesis, which allow for investigating the response of different functional groups of

anaerobic digestion to organic amendment.

In brief, 500 ng of purified DNA was labelled with Cy 3, and was then re-suspended in

hybridization solution. GeoChip hybridization was carried out at 67 °C in an Agilent

hybridization oven for 24 hrs. After hybridization, slides were washed with Agilent Wash Buffer

I for 5 min, and then Buffer II for 1 min. Then arrays were scanned with a NimbleGen MS200

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Microarray Scanner (Roche NimbleGen, Inc., Madison, WI, USA). Images were extracted and

signals were quantified by the Agilent Feature Extraction program. Raw microarray data were

processed by the GeoChip Microarray Data Manager pipeline (http://ieg.ou.edu/microarray/).

Poor quality spots or those with a signal-to-noise ratio of less than 2.0 were removed; positive

signals were normalized within each sample and across all samples; and then spots only detected

in one sample were removed. Natural logarithmic transformation was used before proceeding to

statistical analyses.

Statistical analyses

Principal coordinate analysis (PCoA) (Gower 1966) was performed to evaluate the difference in

microbial community compositions for samples with varying organic loadings, using the

function pcoa from the R ape package (Paradis et al 2004). Permutational multivariate analysis

of variance using distance matrices (Anderson 2001) was applied to indicate the significance

level in community composition difference, using the function adonis from the R vegan package

(Oksanen et al 2013). To examine the dynamics of OTUs or functional genes, heatmaps were

generated using function aheatmap from the R NMF package (Gaujoux and Seoighe 2010). In

the heatmaps, OTUs detected at least in 9 samples are clustered by Spearman correlation in

relative abundance, resulting in clusters of OTUs with similar patterns of dynamics across

samples. Three groups of OTUs were then selected based on the patterns of dynamics in relative

abundance: Group 1 includes branches of OTUs that are abundant cross all samples; Group 2 is

comprised of those enriched under co-digestion while Group 3 is comprised of those decreased

under co-digestion. The ratio of average relative abundances between co-digestion and controls

of each OTU from the three groups were further checked. For Group 1, the ratios were between

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0.9 and 1.1. For Group 2, the ratios were larger than 1.2 and the mean of co-digestion samples

were significantly larger than that of controls by t-test (one-tail, p <0.05). For Group 3, the ratios

were smaller than 0.8 and the mean of co-digestion samples were significantly smaller than that

of controls by t-test (one-tail, p <0.05). OTUs that failed to meet the criteria were then excluded

from the groups. Linear regression analysis was performed to examine correlation between the

average rRNA operon copy number of microbial community and the VS level in each co-

digester. To determine whether there was autocorrelation of residuals from the linear regression

models, the autocorrelation function (ACF) analysis (Venables and Ripley 2013) and the Wald–

Wolfowitz runs test (Gibbons and Chakraborti 2011) were performed, using function acf from

the R stats package (R Core Team 2014) and function runs.test from the R lawstat package (Hui

et al 2008), respectively. All the above statistical analyses were performed using R (version

3.1.3) (R Core Team 2014).

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Table S1. Characteristics of substrates used for anaerobic co-digestion (Chen et al 2012).

Parameters Dairy manure Poultry wasteTotal solids (TS), % wet mass 2.3 55.7Volatile solids (VS), % TS 57.6 60.6Total chemical oxygen demand (mg COD per g VS)

1460 389

Total ammonia (mg N per g VS) 18.4 14.1Total Kjeldahl nitrogen (mg N per g VS) 56.1 70.7Total alkalinity (mg CaCO3 per g VS) 270 83

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Table S2. Multiple regression of VS and ammonia on the average rRNA operon copy number.

Reactor Standardized coefficient of VS

P value Standardized coefficient of ammonia

P value R2 Model. P value

D1 0.481 0.36 0.340 0.51 0.64 0.016

D2 0.415 0.69 0.339 0.75 0.56 0.037

D3 1.44 0.01 -0.64 0.19 0.77 0.002

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Table S3. Microbial diversities in control and co-digesters at stages of the first and second OLR increase.

Time (days)

Taxonomic diversitya

Phylogenetic diversityb

Functional gene diversityc of archaea

Functional gene diversity of bacteria

Functional gene diversity of fungi

Control Co-digesters

Control Co-digesters

Control Co-digesters

Control Co-digesters

Control Co-digesters

45 1445.19 2080.94d 56.10 68.53 NA NA NA NA NA NA

62 1540.63 1703.90 57.67 57.36 5.72 5.75 9.39 9.37 6.95 6.95

66 1767.46 1667.83 62.87 57.51 5.26 5.32 9.14 9.14 6.61 6.56

69 1952.39 1432.82 70.01 52.86 5.85 5.90 9.48 9.55 7.14 7.17

73 1664.09 1503.91 56.95 50.85 NA NA NA NA NA NA

76 1828.83 1607.22 64.67 54.63 NA NA NA NA NA NA

80 1768.64 1766.42 63.17 62.60 NA NA NA NA NA NA

83 1787.89 1464.59 64.27 49.49 5.85 6.04 9.48 9.58 7.13 7.26

87 1574.05 1612.23 58.11 53.28 4.95 6.01 8.78 9.54 6.12 7.22

90 1902.04 1805.18 66.79 61.87 5.39 5.98 9.13 9.51 6.68 7.14

97 1741.48 1682.11 63.65 57.06 5.67 5.98 9.44 9.52 7.00 7.13

aTaxa diversity was calculated by Chao 1 index based on 16S sequence data;bPhylogenetic diversity was calculated by Faith’s index based on 16S sequence data;cFunctional gene diversity was calculated by Shannon index based on GeoChip data;dThe diversity indices of co-digesters are shown in bold if they are significantly different (p<0.05, t-test) from their controls.

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Table S4. Characteristics of TaqMan qPCR primer/probe sets used in this study.

Primer/probenamea

Target Sequence (5′-3′) Position E. coli no

Tm b (oC) GC (%) Amplicon size (bp)

References

Arc-787F(F)Arc-915P(P)Arc-1059R(R)

Archaea ATTAGATACCCSBGTAGTCCAGGAATTGGCGGGGGAGCACGCCATGCACCWCCTCT

787–806915–9341044–1059

61.0 70.162.3

45 6563

273 (Yu et al 2005)

Mst-702F(F)Mst-753P(P)Mst-862R(R)

Methanosaeta TAATCCTTGAAGGACCACCAACGGCAAGGGACGAAAGCTAGGCCTACGGCACCGACAAC

702-721753-774846-862

61.0 70.062.0

45 5965

161 (Yu et al 2005)

Msc-586F(F)Msc-743P(P)Msc-842R(R)

Methanosarcina CGGTTTGGTCAGTCCTCCGAACGGGTTCGACGGTGAGGGACGAACCAGACACGGTCGCGC

586-604743-766826-842

61.6 70.659.8

63 6371

257 (Chen and He 2015)

aDesignations in the parentheses: F - forward primer, R - reverse primer, and P - probebMelting temperature estimated using the Oligo Calculator (http://www.basic.northwestern.edu/biotools/oligocalc.html)

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Figure S1. The operation parameters in control and co-digesters.

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Figure S2. The heatmap showing the dynamics of OTU abundances. Each column in the heatmap means a sample from an individual bioreactor at a time point, which is ordered by sampling time. The abundance of OTUs were subjected to natural logarithmic transformation followed by normalization by the abundance of the most abundant OTU in each sample, resulting in the range of 0 to 1. The abundance is represented by the shade of blue color in the heatmap. Tree groups of OTUs are noted: abundant OTUs across all samples (Group 1); OTUs enriched under co-digestion (Group 2) and OTUs declined under co-digestion (Group 3).

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Figure S3. The autocorrelation function (ACF) plot of residuals from the linear regression models on the correlation between the community mean operon copy number and VS level in each co-digester. Blue lines in the ACF plot give the values beyond which the autocorrelations are statistically significantly different from zero.

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Figure S4. Microbial community compositions of feeding substrates and sludge in anaerobic digesters. a, principal coordinate analysis (PCoA) revealing differences in microbial community compositions between feeding substrates and sludge. b, microbial community compositions at the phylum level.

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Figure S5. Dynamics of microbial functional gene compositions across different OLR treatments revealed by principal coordinate analysis (PCoA). Control and co-digestion samples are indicated by red circles and blue squares, respectively, with the gradient of color representing sampling time.

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Figure S6. Heatmap showing the dynamics of cellobiase gene sequences. Each column in the heatmap means a sample from an individual bioreactor at a time point, which is ordered by sampling time. The normalized signal intensity of functional genes is represented by shade of blue color in the heatmap.

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Figure S7. Heatmap showing the dynamics of codh gene sequences. Each column in the heatmap means a sample from an individual bioreactor at a time point, which is ordered by sampling time. The normalized signal intensity of functional genes is represented by shade of blue color in the heatmap.

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Figure S8. Heatmap showing the dynamics of fthfs gene sequences. Each column in the heatmap means a sample from an individual bioreactor at a time point, which is ordered by sampling time. The normalized signal intensity of functional genes is represented by shade of blue color in the heatmap.

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Figure S9. The dynamics of Mathonasaeta and Methanosarcina during different OLRs as shown by qPCR and GeoChip data. Significant changes between control and co-digestion are indicated by asterisk. * P<0.1 and **P<0.05.

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