Center Update: Soil Microbiology

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Understanding the Root-Associated Microbiota: Turfgrass Species Impacts

PI: Wei Shi; Student: Qing Xia


Turfgrass establishment and maintenance has long been subjecting to abiotic stresses, such as drought and heatwave. Using microbes to confer turfgrass stress tolerance can be a promising practice because plants and the associated microbiota contribute a holobiont and function as one. There are several strategies by which microbes may contribute to the ability of plant stress tolerance, including phytohormone regulation, osmolytes regulation, antioxidation regulation, and nutrient acquisition. Empirical data continues to surge in supporting microbial roles in regulating the homeostasis of plants in phytohormone, osmolytes, reactive oxygen species, and nutrients; and a number of bacteria and fungi have been found to be capable of plant growth promoting under stress. Despite that plant species is an important driver in shaping the root-associated microbiota, scant information is available on the species-dependent assembly of the turfgrass root microbiota. The objective of this work was to determine differentiations and composition of microbiotas associated with warm- and cool-season turfgrasses.


Sampling: A total of 72 intact soil cores (5 cm diameter x 10 cm depth) were collected from four turfgrass species (or genotypes) systems at three locations, with six cores at each location. The four species (or genotypes) were tall fescue (Festuca arundinacea), creeping bentgrass (Agrostis stolonifera), bermudagrass (Cynodon dactylon), and ultra-dwarf bermudagrass.

Sample preparation: A bleach-washing protocol [1] was used to separate the microbiota associated with roots from the microbiota in the rhizosphere and bulk soils. Here, the bulk soil referred to soil unattached or loosely attached to roots, and the rhizosphere was the soil tightly associated with roots. We collected bulk soil samples by gently breaking soil cores and shaking turfgrass roots. Rhizosphere samples were collected by roots shaken for two minutes in a phosphate buffer containing a 200 ppm surfactant, Silwet L-77, followed by slurry filtered through 100 μm mesh and then centrifuged at 3,000 x g for 5 minutes. Thereafter, roots were blotted clean and further washed sequentially in 50% bleach containing 0.01% Tween 20, 70% ethanol, and sterilized water via shaking for 60 seconds at each step. At least two more steps of water washing were added at the end or between each washing step to remove any solvent residuals. All the solutions used in root separation and washing steps were 0.22 μm filter-sterilized before use. Thus-obtained roots, rhizosphere samples, and aliquots of bulk soil samples were preserved at -20 ℃ prior to DNA extraction. Bulk soil samples were analyzed for physical and chemical properties.

DNA extraction and library preparation: Genomic DNA was extracted from ~ 500 mg of bulk soil samples, ~100 – 400 mg of rhizosphere samples, and ~100 – 200 mg of roots, respectively, with FastDNA Spin Kit (MP Biomedicals). DNA concentrations were determined using Nanodrop Spectrophotometer (Thermo Scientific). PCR amplification of bacterial 16S rRNA gene and fungal ITS region was performed with Illumina-overhang-added primer pairs targeting the bacterial V5-V7 and fungal ITS1-ITS2 [2, 3], respectively, in a 25 μL PCR reaction with 12.5 μL KAPA HiFi HotStart ReadyMix (KAPA Biosystems), 12.5 ng template of genomic DNA, and 5 mM of each primer. Thermal conditions for PCR amplification were initial denaturation at 95 ℃ for 3 min, 30 cycles of 98 ℃ for 30 s, 51 ℃ for 15 s, and 72 ℃ for 30 s, and then a final elongation at 72 ℃ for 5 min. The amplified PCR products were cleaned with AMPure XP beads (Beckman Coulter Genomics) and had Illumina adapters (Nextera XT index Kit, Illumina) added by a second 25 μL-PCR reaction. After a second clean-up on the PCR products, all the amplicons were diluted to 20 nM, mixed equimolarly, and paired-end sequenced (300 bp × 2) on Illumina Miseq platform (Illumina).

Bioinformatics analyses: Diversity and composition of the microbiota in the endosphere, rhizosphere, and bulk soil will be analyzed using bioinformatic tools as described in our previous work [4, 5]. Briefly, primers and adapters will be removed from demultiplexed sequencing reads by cutadapt (v 1.18). Then, reads will be processed using DADA2 (v 1.10) pipeline in the R package (3.5.1) to get a table of amplicon sequence variants (ASVs, higher-resolution analogues of operational taxonomic units, OTUs) and their copy numbers. This ASVs table will be imported into QIIME2 for downstream analysis. Singletons (ASVs with a total frequency of one) will be removed from the analysis of bacterial and fungal taxonomic classification using Greengenes database (13_8) and UNITE database (7.2), respectively.


Soil properties varied largely among 72 samples, with pH in the range of 5.6-7.5, moisture in the range of 4-34% and bulk density in the range of 0.85-1.45 g cm-3. Large variations in the environment allow a better understanding of how turfgrass species and their interactions with edaphic factors shape the root-associated microbiota. Our previous work [4] showed that turfgrass species was able to modify the diversity and composition of the microbiota in bulk soils. Given that species effects on the microbiota via exudates and phytochemicals are likely stronger within and surrounding of roots than in bulk soils, we anticipate substantial differentiations in the diversity and composition of the root-associated microbiota between turfgrass species. A bunch type and cool-season turfgrass species, such as tall fescue, is expected to form more intensive associations with arbuscular mycorrhizal fungi, belonging to the subphylum Glomeromycotina, because Glomeromycotina was more abundant in bulk soils of cool-season grasses [4]. Accordingly, we consider that cool-season grasses may be more prone to recruit microbes for helping plant nutrient acquisition. Compared to cool-season grasses, warm-season grasses are often more tolerant to summer high temperature and water scarcity. As such, we expect microbes that contribute to the ability of plant drought and heatwave tolerance will dominate the root-associated microbiota of warm-season grasses. We consider that plant responses to environment can transform into signals, substrates, and even stressors to control the assembly of the root-associated microbiota. We anticipate outcomes of this work will assist in screening elite microbes for helping plants to acquire nutrients and to tolerate environmental stresses. This work will also shed light into turfgrass practices for promoting successful interactions between grasses and the microbiota.


McPherson MR, Wang P, Marsh EL, Mitchell RB, Schachtman DP, 2018. Isolation and analysis of microbial communities in soil, rhizosphere, and roots in perennial grass experiments. Journal of Visualized Experiments 137, 57932.

Beckers B, de Beeck M, Thijs S, Truyens S, Weyens N, Boerjan W, Vangronsveld J, 2016. Performance of 16S rDNA primer pairs in the study of rhizosphere and endosphere bacterial microbiomes in metabarcoding studies. Frontiers in Microbiology 7, 650.

Toju H, Tanabe AS, Yamamoto S, Sato H, 2012. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS One 7, e40863.

Xia Q, Chen H, Yang T, Miller G, Shi W, 2019. Defoliation management and grass growth habits modulated the soil microbial community of turfgrass systems. PLoS One 14, e0218967.

Xia Q, Thomas Rufty, Wei Shi, 2020. Soil microbial diversity and composition: links to soil texture-associated pore size and resource distribution. Soil Biology and Biochemistry, in review.