Experiment design and sample process.
in depth) from the Pearl River Delta (22°14’50” N, 113°11’50” E) were
sampled in December 2017. Plant residues, macrofauna and large particles
were removed by hand grubbing. The resulting sediment was vigorously
homogenized and distributed to 40 g (wet weight) per share. Sterilized
pure water with or without ~7.74 mM calcium nitrate was
added to the sediments, making each tube to the volume of 45 ml. In sum,
we prepared 60 tubes with nitrate (as treatment) and 60 without nitrate
(as control). All tubes were anaerobically cultured at room temperature.
12 treatment and 12 control tubes were collected on Day 0, 4, 8, 16, 32
respectively for physiochemical characterizations and DNA extraction. We
quantified the levels of AVS, ferrous iron, total iron, pH, nitrate,
nitrate, sulfate, and TOC (total organic carbon). See supplementary
information for measurements. For convenience, we assigned all 120
samples into two groups: nitrate and control, 60 samples each. Each
group had five subgroups based on sampling time, i.e. 0-N, 4-N, 8-N,
16-N, and 32-N for nitrate group, while 0-C, 4-C, 8-C, 16-C and 32-C for
control. Each subgroup consisted of 12 replicates.
To analyze the composition of nitrate-reducing sulfur-oxidizing bacteria
(NR-SOB) in the sediment, a groups of serial dilution enrichments
(Lagier et al. 2018) with 12 series repetitions were prepared in
medium NS (nitrate as the sole electron acceptor and sulfide as the sole
electron donor). See supplementary information for medium chemical
compositions. 1g wet sediment (~80% w/w water content)
was added to 9 ml medium to get the 101 dilution
factor. The serial dilution was conducted every ten-fold until
10-8. Enrichments at each dilution factor had 12
parallels. Note that parallels here were not equivalent to replicates
since every dilution was random so that the resulting community
compositions might differ. See supplementary information for sample
cultivation. All samples were quantified of remaining nitrate and
sulfate to estimate the metabolic intensity, which was roughly
represented by the amount of nitrate consumption and sulfide/thiosulfate
consumption. The ultimate NR-SOB strains were isolated by
plate-streaking on medium NTS (thiosulfate as the sole electron donor)
plates supplemented with 1.5% agar respectively. After one-month
anaerobic cultivation, different colonies were isolated, purified and
identified by colony PCR of full length 16S rRNA gene.
Illumina Miseq sequencing and predictive functional profiling.
Sequencing of 16S ribosome RNA gene V4 region was used to track
community successions. Qualified DNA libraries were sequenced and
clustered into de novo OTUs by the Usearch (v9.2.64) pipeline
(Edgar 2010). See supplementary information for detail. Briefly, each
sample was rarefied to 10,000 clean reads. Sequencing of 16S rRNA gene
gives no direct metabolic information so we used FAPROTAX (Loucaet al. 2016) and PICRUSt (Langille et al. 2013) just to
roughly estimate the functional changes under nitrate stimulation. Clean
16S rRNA gene tags were re-clustered against the GreenGenes database
(v13.8, 97_otus.fasta) to be compatible with FAPROTAX (v1.1) and
PICRUSt (v1.1.1). FAPROTAX was used to identify putative functional
microbial members, and PICRUSt was to compare the gene abundance before
and after nitrate stimulation, especially those involved in N or S
metabolisms. Both predictions were based on the same normalized
closed-reference OTU table.
Interactive network construction and classification.
Each
network was constructed based on Spearman’s correlation of the 12
replicates by the MENA pipeline
(
http://ieg4.rccc.ou.edu/MENA/)
(Zhou
et al. 2010; Zhou
et al. 2011; Deng
et al.2012) yielding 10 networks. In network construction, the threshold was
automatically determined in the RMT-based modeling. Only OTUs present in
more than 8 out of 12 replicates were involved. Output networks were
visualized using Gephi (v0.9.2). A visualized interactive network
consisted of nodes and links (or edges). A node represented an OTU that
had significant Spearman’s correlations (above the network threshold)
with other nodes. A link between two nodes represented their negative or
positive covariation (Montoya
et al. 2006; Bascompte 2007) depend
on the calculated correlation coefficient (-1 to +1). A positive
correlation meant the two nodes were sharing a same changing pattern. A
module referred to a group of nodes that were highly interconnected
within the group and had fewer or no connections outside the group. MENA
has four built-in methods for module separation, and we selected the
greedy modularity optimization because it generated the highest
Modularity index (
M ). Generally, the number of nodes defines the
network size, while the numbers of modules and links per node define the
network complexity. See supplementary information for visualization
settings.
Statistical analysis.
Shannon’s index (H) and Simpson’s
evenness (J) were calculated to represent alpha-diversity of each
community. Community dissimilarity between control and nitrate group was
compared using PCoA and one-way ANOSIM, both were based on Brey-Curtis
distance. Genera that significantly changed in relative abundance over
time or over treatment were selected. We confirmed the compliance of
normal distribution of genus frequencies and then quantified the
dissimilarities by one-way T-test. Alpha-diversity, PCoA and ANOSIM were
performed with R (Vegan package, v2.4-6) (Oksanen et al. 2010).