Glasshouse experimental setup and soil sampling
A glasshouse experiment was conducted at Northwest Agriculture & Forestry University,
China, using soils collected from two different turfgrasses: dwarf lilyturf (O. japonicus) and perennial ryegrass (L. perennecvs.ph.D.). The seeds were obtained from were obtained in June 2017, from Bcyseed Co., Ltd.,
located in Liwan District (23.072127N, 113.207089E), Guangzhou, China. The specimen
was purchased by Bcyseed Co., Ltd., a professional seed production and sales company, who undertook the formal identification of the seeds used in this
study. Based on the unlikeliness of an erroneous identification, we have not deposited a voucher specimen. In this study, dwarf lilyturf (OJ) was
planted around buildings and trees and perennial ryegrass (LP) served as greening square in campus with good light conditions. The site has a warm temperate
continental monsoon climate, with a mean annual air temperature of 12oC and 500 mm of mean annual precipitation. Before the experiment, the sites were managed
as turfgrass for over 10 years.
To investigate impacts of shade stress on the rhizosphere bacterial communities, OJ was compared to LP as a control.The twoplants were cultured in a plastic pot (13.5×17.5×11.0 cm) filled with soil collected from their respective areas of growth, as
described above. Seeds were superficially disinfected with 0.1% sodium hypochlorite
and washed three times with purified water. Three seeds were sown per pot directly
into the soil. The plants were maintained in a greenhouse with an average temperature
of 23/18 oC (day/night), 700 μmol m-2 s-1photosynthetic active radiation from natural sunlight, and 65 % relative humidity
until the plants grew above 15 cm. Shade treatments
(approximately 230 μmol m<sup>-2 </sup>s<sup>-1</sup>) were performed under the canopy using two layers of black nylon net using the same
conditions described above. Pot treatments were randomized within a glasshouse compartment. We carried out five treatments
including: (1) Control (0 d); (2) shade for
7 d; (3) shade for 14 d; (4)
light (sunny) for 7 d;
(5) light (sunny) for 14 d. There were six replicates per treatment, giving a total of 60 pots. </p>
After shade treatment, total leaf area, root morphology, chlorophyll content and the
maximum quantum yield of PSII (Fv/Fm) were measured. The roots of each plant were separated from the soil and shaken manually
to remove the loosely attached soil. Rhizosphere soil was collected, as the soil adhering
to the roots [28](Wang et al., 2018). A rhizosphere soil sample was obtained by pooling
soil obtained from three plants growing in the same pot. As a result, each treatment
had six replicate rhizosphere samples due to the six replicate pots. Bulk soil samples
were also collected from the same pot at a depth of 0-10 cm. Soil samples were mixed
thoroughly, divided into two parts, stored in sterile 50 mL Falcon tubes, and transported
to the laboratory. One part was kept at 4oC for analysis of soil NH4+-N and NO3−-N, and to extract soil DNA within 3 days. The other part was air-dried for measurements
of soil pH, total N (TN), total P (TP), total K (TK), soil organic C (SOC), available P (AP), available K (AK).
Determination of plant growth characters
The total leaf area for each seedling was measured in the laboratory using a
LI-3000A leaf area scanner (LI-COR Inc., USA). Root morphology including total root length, root surface area, and root volume
was analyzed using a
WinRhizo-V700 root scanner (Regent Instruments Inc., Quebec, Canada). The chlorophyll content was determined spectrophotometrically using 80% acetone as a solvent
[38](Lichtenthaler, 1987). On the same leaf, a portable pulse-modulated fluorometer (PAM2100, Walz, Effeltrich, Germany) with the
PamWin software was used to measure chlorophyll fluorescence (Fv/Fm).
Soil physicochemical analyses
Soil pH was measured using a pH meter
(Mettler Toledo FE20, Switzerland) in a soil solution with a 1:2.5 soil: water ratio. The NH4+-N and NO3−-N were extracted with 2.0 M KCl and measured by a continuous flow analyzer
(Flowsys, Systea
Inc., Italy). Soil was processed for C content by first removing inorganic C through treatment with
1 M HCl. Following removal of inorganice C, soil organic C was analyzed using an auto-analy
zer (Shimadzu, Kyoto, Japan). The total N in the soils were measured on an
elemental analyzer (ECS 4024, Costech
Inc., Italy). Total P was determined by digesting samples first with HClO4-H2SO4,followed by the molybdenum blue method using an ultraviolet-visible spectrophotometer
(UV-1000, AOE Instruments, Shanghai, China). Available soil P (AP) was extracted with 0.03 M ammonium fluoride-hydrochloric acid
and measured colorimetrically as described above. Total K was determined using NaOH
fusion method, and the available K (AK) was extracted with 1.0 M ammonium acetate
and measured by flame photometry (Model 410, Sherwood, England).
Soil bacterial community analyses
DNA extraction, PCR amplification, and high-throughput sequencing
Total soil DNA was extracted 0.30 g soil collected from each soil sample using the
Power Soil DNA extraction kit (MoBio Laboratories, Carlsbad, CA) as directed by the
manufacturer’s instructions. The following PCR primers were used for amplification targeting the V4 region of the
bacterial 16S rRNA gene: 338F (5'-XXXXXXXXGTACTCCTACGGGAGGCAGCAG-3') and 533R (5'-TTACCGCGGCTGCTGGCAC-3').
Paired-end sequencing was performed at Beijing Genomics Institute (BGI)-Shenzhen,
Shenzhen, China, using a paired 250-bp Illumina HiSeq 2500 sequencing platform according
to the manufacturer’s instructions.
Sequence processing
Illumina sequencing data were pair assembled using FLASH software (v1.2.11) [39] using
a minimal overlapping length of 15 bp and mismatching ratio of overlapped region ≤
0.1. Sequences were then clustered into operational taxonomic units (OTUs) at a 97% identity
threshold using USEARCH (v7.0.1090) [40]. UCHIME (v4.2.40) against the
SILVA database was used to filter out chimeric sequences. USEARCH GLOBAL was used to align
representative sequences from individual OTUs [41]. These were taxonomically classified
using the Ribosomal Database Project (RDP) Classifier v.2.2 based on the
SILVA database, using 0.6 confidence values as cutoff.
Statistical analyses
Analysis of variance (ANOVA) according to the general linear model procedure of SPSS17.0
(SPSS Inc., Chicago, IL USA) was used to determine the effects of shade treatment,
turfgrass species, and the interactions between these factors on
plant physiological indicators and the influence of shade treatment on soil properties. Differences between treatment
means were separated by Fisher’s protected least significance difference (LSD) test
at P = 0.05. For these analyses, OTUs defined at 97% sequence similarity were used. Boxplots
and heatmaps were obtained with the R package ggplot2 (v2.2.1). Rarefaction curves
of observed OTUs were generated by software R (v3.1.1). The differences in OTU composition between different samples were displayed using
principal component analysis (PCA). Alpha diversity [Richness, Shannon diversity index
(H’) and Simpson's Evenness (E)] were analyzed based on randomly rarefied OTU abundance
matrices using mothur (v1.31.2). Bray-Curtis distances of bacterial communities using
QIIME (v1.80) was used to analyze beta diversity
. Principal coordinates analyses (PCoA),
based on Bray-Curtis dissimilarity, were used to display differences in the composition of bacterial communities between OJ and LP rhizosphere soil treatments.
Permutational multivariate analysis of variance (PERMANOVA) was conducted to test
the significance of the Bray-Curtis dissimilarity. Kruskal-Wallis tests were performed
using the R software (kruskal. test function) to assess the impact of
shade stress on soil
bacterial community structure in both species. A value of <em>P</em> < 0.05 was considered to be statistically significant. </p>
To analyze the correlations between soil physicochemical parameters and bacterial
community compositions, a Mantel test (9,999 permutations) with Spearman correlations
of the R vegan package was used.
Canonical correspondence analyses (CCA) were performed with the R package vegan (v2.4.2) to visualize the relationship between
soil physicochemical properties and bacterial communities. For the CCA analyses, the correlation of the canonical
axes with the explanatory matrix was determined with the general permutation test
and the “envfit” function was used to analyze the significance of soil physicochemical
factors on the composition of bacterial
communities.
To analyze the correlations of above-and below ground phenotypes and the composition of
bacterial communities, pairwise fitting analysis
was carried out using the “lm” function in the R vegan package. </p>
Indicator species analysis was performed using the multipatt function implemented in the indicspecies package in R with 1000 permutations. The
bioindicators of LP and OJ soil were designated as the OTUs that are part of the core
microbiome of only LP or OJ soil under different treatments while also having abundances higher in OJ according to the permutation test (P < 0.05).