Real-time quantitative PCR (qPCR)

qPCR was used to quantify the gene copy numbers of the phylogenetic and functional marker genes. Following primer combinations were used, addressing the different target genes:

Ba519f/Ba907r (Lane, 1991) for bacterial 16S rRNA genes, Ar364f/Ar934br (Burggraf et al., 1997;

Großkopf et al., 1998a) for archaeal 16S rRNA genes, mlas-mod/mcrA-rev (Steinberg and Regan, 2008; Angel et al., 2011) for mcrA genes, and A189f/mb661 (Holmes et al., 1995; Costello and Lidstrom, 1999) for pmoA genes. All qPCR reactions were prepared on ice with minimal exposure to light. Before analysis by qPCR, all DNA samples were diluted (1:200). The measurement was performed in triplicates. Standards, containing the known numbers of gene copies for the target gene, were diluted serially and used for calibration in each reaction. The standards were amplified from Eschericha coli strain K12 for 16S rRNA genes, from Methanosarcina barkeri for archaeal 16S rRNA and mcrA genes, and from Methylomonas sp. for pmoA genes, in dilution series from 10¹ up to 10⁷ gene copies.

qPCR reactions were performed using an CFX Connect^TM Real-Time PCR Detection System (Bio-Rad, München, Germany), and contained the following components: 12.5 mL Sybr^® Green Ready Mix (Sigma-Aldrich, Karlsruhe, Germany), 0.125 mL of each primer (100 mM), 4 mL MgCl2 (25 mM, Sigma-Aldrich), 3.25 mL PCR-water (Sigma-Aldrich), and 5 mL of diluted template DNA, with a total volume 25 mL for bacterial 16S rRNA genes; 12.5 mL Sybr^® Green Ready Mix, 0.5 mL of each primer (33 mM), 3 mL MgCl2 (25 mM), 3.5 mL PCR-water, 0.1 mL BSA (20 mg mL^-1, Roche), and 5 mL of diluted template DNA, with a total volume 25 mL for archaeal 16S rRNA genes; 12.5 mL Sybr^® Green Ready Mix, 0.25 mL of each primer (25 mM), 3.5 mL MgCl2 (25 mM), 3.5 mL PCR-water, 0.1 mL BSA (20 mg mL^-1), and 5 mL of diluted template DNA, with a total volume 25 mL for mcrA genes; 7.5 mL KAPA SYBR^® FAST Master Mix (Kapa Biosystems, Boston, United States), 0.3 mL of each primer (20 mM), 1.9 mL PCR-water, and 5 mL of diluted template DNA, with a total volume 15 mL for pmoA genes. The following qPCR programs were used: 94°C for 8 min, followed by 50 cycles of 94°C for 20 s, 50°C for 20 s and 72°C for 50 s for bacterial 16S rRNA genes; 94°C for 6 min, followed by 40 cycles of 94°C for 35 s, 66°C for 30 s, 72°C for 45 s and 86.5°C 10 s for archaeal 16S rRNA genes; 94°C for 5 min, followed by 40 cycles of 95°C for 30 s, 57°C for 45 s, 72°C for 30 s, and 84°C for 10 s, for mcrA genes; 95°C for 2 min, followed by 39 cycles of 95°C for 10 s, 60°C for 20 s, and 84°C for 7 s, for pmoA genes. Melting curves were measured from 75-95°C for 16S rRNA and mcrA gene samples, and 65°C-75-95°C for pmoA samples. Resulting data was analyzed using CFX Manager Software (Version 3.0).

2.6.3 PCR amplification for illumina sequencing

Identification of the microbial rhizospheric community was done via Illumina© MiSeq amplicon Sequencing, with pair-end reading of 300 nt. To combine different target genes in one sequencing run, a universal barcode method was developed, based on (Boutin-Ganache et al., 2001), to tag amplicons of the same samples with unique barcodes. Therefore, the forward and reverse primers were tailed with a M13-sequence (5’-CAC GAC GTT GTA AAA CGA C-3’) on the 5’ end.

Combinations of the used M13-tagged primers are shown in Table 2.1. Target genes were amplified using the following reactions, with a total volume of 50 mL: 1 mL AccuPrime^TM DNA polymerase (Life Technologies, Darmstadt, Germany), 5 mL AccuPrime^TM PCR Buffer II, 1 mL of each primer (10 mM), 40 mL PCR-water, and 2 L of template DNA for bacterial 16S rRNA genes;

1 mL AccuPrime^TM DNA polymerase, 5 mL AccuPrime^TM PCR Buffer II, 1 mL of each primer (10 mM), 0.5 mL BSA (20 mg mL^-1, Roche), 39.5 mL PCR-water, and 2 L of template DNA for mcrA genes.

The PCR conditions were as following: initial denaturation (3 min at 95°C), 5 cycles (30 s at 95°C, 45 s at 48°C with a ramp of 0.1°C/s to 68°C for 35 s), followed by another 30 cycles (30 s at 95°C, 45 s at 55°C, 35 s at 68°C), and terminal extension (10 min at 68°C) for mcrA genes; initial denaturation (3 min at 95°C), 30 cycles (30 s at 95°C, 20 s at 48°C, 35 s at 68°C), and terminal extension (10 min at 68°C) for 16S rRNA genes. Two separate 50 L reactions were used per sample, pooled afterwards, and purified using the GenElute^TM Gel Extraction Kit (Sigma-Aldrich), with instructions recommended by the manufacturer. Unique barcodes of 8 nt length (Table S2.1) were also tagged with a M13-sequence (on the 3’ end) and attached to the gene amplicons, using a second PCR. The barcode-PCR reactions contained the following substances for all genes: 1 mL AccuPrime^TM DNA polymerase, 5 mL AccuPrime^TM PCR Buffer II, 2 mL of M13-barcode primer (10 mM), 0.5 mL BSA (20 mg mL^-1, Roche), 40.5 mL PCR water, and 1 L of amplicon DNA. The PCR conditions were as followed: initial denaturation (3 min at 95°C), 8 cycles (30 s at 95°C, 30 s at 48°C, 35 s at 68°C), and terminal extension (10 min at 68°C). The barcoded amplicons were purified using the GenElute^TM PCR cleanup Kit (Sigma-Aldrich) with instructions recommended by the manufacturer.

To test the sufficiency of the universal barcode system, M13-mcrA amplicons were created, combined with an M13-barcode, ligated into an pGEM®-T-easy vector system (Promega, Mannheim, Germany) and afterwards transformed into competent Escherichia coli cells (JM109, Promega). 10 randomly selected clones were sequenced on a ABI 373 DNA sequencer (Applera, Darmstadt, Germany) using the BigDye® Terminator v3.1 Cycle sequencing Kit (Life Technologies), with instructions recommended by the manufacturer. All tested clones contained

primer binding sites, M13- and barcode sequences on both sides while the M13- and barcode sequence was in a reverse and complimentary orientation on one end. Since the barcoding was sufficient, the DNA amount of all prepared and barcoded amplicons was determined by the use of a Qubit Flourometry (Thermo Fisher Scientific, Bremen, Germany). Afterwards, equimolar amounts of the amplicons were mixed and sequenced at the Beijing Genomic Institute Tech Solutions Center (Hong Kong) using the MiSeq 300 nt paired end Illumina sequencing technology.

Table 2.1 │Barcoded oligonucleotide primers used for illumina amplicon sequencing.

* nucleotide coding. A = Adenine, C = Cytosine, G = Guanine, T = Thymine; wobble nucleotides: M = A/C, R = A/G, K = G/T, V = A/C/G, D = A/G/T, B = C/G/T, N = A/C/G/T.

2.7 Data manipulation and analysis

Paired end illumina sequencing was performed in repeated runs, producing various forward and reverse data sets containing sequencing- and quality data in FASTQ-format files. The raw illumina sequencing data was tested for read quality by use of the FastQC software (Version 0.11.4, Babraham Bioinformatics). All separated forward sequences were combined using the software mothur (Version 1.36.0, (Schloss et al., 2009)), as well as all reverse sequences. Since use of different calculation methods would create different quality-values, all data preparation up to OTU clustering was performed only using the Usearch- (Edgar, 2010) and Uparse algorithms (Edgar, 2013), implemented in the Usearch software package (Version 8.1.1861, drive5 – Bioinformatics software and services). The sequence analysis was performed based on the OTU analysis pipeline (including the provided python scripts) provided by the software producer (http://drive5.com/usearch/manual/uparse_pipeline.htm , as of May, 2016). Therefore, forward- and

target gene prim er nam e prim er sequence (5' to 3') reference for untagged prim er

mcrA

M13-mlas

Steinberg and Regan, 2008 M13-mcrA-rev

M13-343Fmod

Köhler et al., 2012 M13-784Rmod

CAC GAC GTT GTA AAA CGA CGG TGG TGT MGG DTT CAC MCA RTA

CAC GAC GTT GTA AAA CGA CCG TTC ATB GCG TAG TTV GGR TAG T

bacterial 16S rRNA gene

CAC GAC GTT GTA AAA CGA CTA CGG GWG GCW GCA

CAC GAC GTT GTA AAA CGA CGG GTM TCT AAT CCB KTT

reverse sequences were merged using the overlapping paired ends. Sequences of the different gene amplicons in the respective samples were separated by use of the forward primer-and barcode sequences. The primer- and barcode sequences were trimmed from the amplicon sequences in the same process step. This step was performed in origin orientation of the sequence, as well as in the complementary-reverse orientation, since illumina reads are not directed. Afterwards, sequences were filtered for quality and globally trimmed. Therefore, sequences with an expected error per base E larger than 1 were removed, as well as sequences which did not fulfill the length criteria of the amplicons.

After the step of dereplication, analysis of the sequences was performed in different ways with respect to their gene type. Furthermore, two data sets were created for every sample, while for one of them the singletons were removed. Unique 16S bacteria rRNA gene sequences were clustered to OTUs with a similarity of 97 %. Chimeras were removed and remaining reads were mapped to OTUs with 97 % of similarity cutoff. Their taxonomy was assigned by use of the software QIIME (Version 1.9.0, (Caporaso et al., 2010)) and the Greengenes 16S rRNA gene database (http://greengenes.secondgenome.com, from May, 2013), as reference.

mcrA gene sequences were also clustered to OTUs by similarity of 97 %, but the created OTUs were pooled afterwards by a similarity of 84 %, according to (Yang et al., 2014). Chimera filtering occurred against a mcrA database created by (Angel et al., 2011). The reads were mapped to OTUs by similarity of 84 %. The taxonomy was assigned using the ARB software package (Version 6.0.2, (Westram et al., 2011)). Therefore, a mcrA gene database was created on the basis of a protein based tree, calculated from pure culture mcrA sequences generated by (Angel et al., 2011).

Further, 382 new mcrA gene sequences, originated form National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov/nuccore), were translated to protein sequences using the Framebot online tool (Wang et al., 2013). These new sequences were aligned using the ClustalW method (Thompson et al., 1994) and added to the basis tree by parsimony (Ludwig et al., 2004). Alignment of the imported sequences was performed manually against their closest relatives of the base tree. Afterwards, all sequences were used to calculate a new tree with RaxML 7.04 using rapid hill climbing algorithm and PROTMIX-JTT evolutionary model (Stamatakis, 2006).

This new tree was used as reference for taxonomic assignment of mcrA amplicon illumina reads which were assigned in the same way as described above.

2.8 Statistical analysis

Statistical analysis was done using the software environment R (Version 3.2.4, R Development Core Team, 2011). All levels of significance were defined at p ≤ 0.05. Data sets of chemical analysis and quantitative PCR were tested for normality using the Shapiro-Wilk test (Shapiro and Wilk, 1965; Royston, 1982). Further, a one-way analysis of variance (ANOVA) was calculated and means of the different samples for the levels of different measured parameters were compared by post hoc Tukey test (Miller Jr., 1981), using the R-package stats. Unless otherwise noted, plots were created by the mean and standard deviation values of the triplicates.

The processed OTU data sets for bacterial 16S rRNA and mcrA genes from illumina sequencing were used for calculation of diversity, and statistical analysis. The species richness (Chao, 1984) in samples was estimated with the R-package phyloseq (Version 1.4-5, (McMurdie and Holmes, 2013)), sample coverage (Good, 1953) with the R-package entropart (Version 1.4-6), diversity index (Chao and Shen, 2003) with the R-package iNEXT (Version 2.0.12), and species evenness (Legendre and Legendre, 1998) with asbio (Version 3.40), by use of the data set also containing the singletons. The relative abundance was calculated from all sequences within a tested gene on different taxonomic levels without singletons, using the R-package phyloseq. Ordination in form of canonical correspondence analysis (CCA, (McGarigal et al., 2013)) was performed on OTU-level by use of the vegan package (Version 2.2-1, (Oksanen et al., 2013)). Terms of environmental parameters were tested by ANOVA, and further by ordistep function (Oksanen et al., 2013).

Identification of indicator species (Dufrene and Legendre, 1997) was performed by use of the package indiscpecies (Version 1.7.4). The relative abundance of indicator species according to important environmental parameters were plotted in a heatmap by use of the R-package gplots (Version 3.0.1) and also used for a cluster analysis (Murtagh, 1985) based on the Bray-Curtis distance (Bray and Curtis, 1957). The ternary plots were created within the R-package vcd (Version 1.3-2).

III. Results

The aim of this study was to investigate the link between microbial colonization of the rice rhizosphere and its influence on the emission of methane by degradation of root derived carbon. To understand the process of root colonization it was necessary to provide different initial microbial communities of different environmental samples as seed bank for rice root colonization. Therefore it was essential to establish a system which (i) minimized the abiotic factors of the different environmental samples, but still provided different initial microbial communities; (ii) contained a healthy rice plant, placing their roots in direct proximity to the microbial seed bank; and (iii) allowed to follow the translocation of carbon from photosynthetic CO2-fixation by the plant through the release of rhizodeposits to microbial degradation to methane. Such rice planted soil-microcosms were previously developed by (Pump and Conrad, 2014; Pump et al., 2015). Based on this approach we also used inoculated synthetic soil-systems to provide different initial microbial communities for colonization of the rice rhizosphere, while coincidentally minimizing the abiotic factors of the different inocula.

However, it was necessary to modify these sand-vermiculite amended microcosms to establish reliable plant-soil model-systems, which could be used with a variety of inocula beside rice paddy soil. For this purpose we created modified synthetic soil model-systems and tested them in different ways for stimulation of the methanogenic activity.