Open access
8 July 2021

16S rRNA Gene Amplicon Sequencing Data from Flooded Rice Paddy Mesocosms Treated with Different Silicon-Rich Soil Amendments


How silicon-rich soil amendments impact the microbial community is unresolved. We report 16S rRNA gene sequencing data from flooded rice paddy mesocosms treated with different silicon amendments sampled over the growing season. We generated 11,678 operational taxonomic units (OTUs) and found that microbial communities were significantly different across treatments, time points, and biospheres.


Silicon (Si)-rich soil amendments can decrease inorganic As in rice (17), but how they impact the microbial community is unresolved. These amendments can release organic carbon, nutrients, and/or electron acceptors, with potential impacts on microbially driven biogeochemical cycles.
We sequenced 16S rRNA genes from samples collected from three biospheres (root plaque, rhizosphere, and bulk soil) over the rice life cycle from unamended paddy mesocosms and those amended with rice husk, husk char, or calcium silicate (n = 3 per treatment) at the University of Delaware RICE facility in Newark, DE (1). Paddy soils were amended in year 1 and then cultivated with rice for two growing seasons; the soil samples described here were collected in year 2 (8). Of the 49 rice (Oryza sativa L. cv. Jefferson) seedlings planted in each mesocosm (8), 5 random plants were contained in soil-filled 100-μm nylon mesh bags to define the rhizosphere (Table 1). At each time point, one bag was pulled per paddy, bulk soil was collected from surrounding soil, and the bagged plants were placed in ethanol-sterilized containers for immediate transport to the lab. Roots were separated from the root mass with sterilized instruments and cleaned of rhizosphere soil by vortexing twice in 25 ml of sterile water (18 MΩ · cm). Bulk soil, rhizosphere soil, and cleaned roots were frozen at −20°C prior to DNA extraction. For plaque sampling, cleaned roots were thawed, sonicated twice for 30 s in 10 ml of phosphate buffer solution, and centrifuged at 18 × g for 5 min (modified from reference 9), and DNA was immediately extracted from the pellet.
TABLE 1 Sampling time points and corresponding bag sizes
Time point (days after transplant)Rice growth stageBag size (diam by length [cm])
20Vegetative10.16 by 20.3
42Early reproduction12.7 by 20.3
71Heading15.2 by 31.8
88Grain ripening15.2 by 31.8
98Grain maturity15.2 by 31.8
DNA was extracted with the DNeasy PowerSoil DNA extraction kit (Qiagen) with modifications. Initially, 200 μl of PowerBead solution was replaced with 200 μl of phenol-chloroform-isoamyl alcohol (25:24:1) at neutral pH. The protocol was followed according to manufacturer’s instructions until column binding, when equal parts lysate, solution C4, and 100% ethanol were homogenized and then loaded onto the DNA binding column. Next, the column was washed with 100% ethanol (650 μl) and then solution C5 (500 μl). Finally, DNA was eluted with molecular biology-grade water.
The V4-V5 16S rRNA gene region was amplified using primers 515F-Y and 926R (10) at the Joint Genome Institute (JGI), according to the JGI “iTag Sample Preparation for Illumina Sequencing” protocol, available at Samples were sequenced with paired-end 2 × 300-bp Illumina MiSeq reads. Sequences were demultiplexed, quality filtered, clustered at 97% similarity, and checked for chimeras with the JGI iTagger pipeline (11). To filter out the rarest operational taxonomic units (OTUs), we used QIIME 2 (12) to remove OTUs that only occurred in one sample and OTUs that had fewer than 10 assigned reads across the entire data set. We assigned taxonomy using the sklearn naive Bayes feature classifier in QIIME 2, trained with the SILVA database version 132. R version 3.6.2 (13) was used for statistical analyses, and default parameters were used unless otherwise noted.
We obtained 69,402,988 raw reads from 130 samples with high-quality DNA extraction, 37,072,594 (53%) of which remained after quality filtering. By clustering at 97% identity, we obtained 11,678 OTUs, only 3,914 of which were assigned taxonomy at the genus level. Bacteroidetes and Proteobacteria were the dominant phyla in these samples. The community composition was significantly different between Si treatments, time points, and biospheres (Fig. 1).
FIG 1 Constrained correspondence analysis of soil samples constrained by treatment (A) (ADONIS; R2 = 0.09 and P = 0.001), time point (B) (ADONIS; R2 = 0.11 and P = 0.001), or biosphere (C) (ADONIS; R2 = 0.05 and P = 0.001). DNA was obtained from fewer plaque samples than rhizosphere or bulk soil. Ordination plots were generated in R (13) with the package phyloseq (15). DPT, days since transplantation.

Data availability.

The raw 16S rRNA gene sequencing data from this project have been deposited in the NCBI BioProject SRA database (14) under accession number PRJNA690162, with samples described here from the 2016 growing season.


We thank Matt Limmer, Patrick Wise, Kristy Northrup, Weida Wu, Ayofela Dare, Kendall McCoach, Fred Teasley, Douglas Amaral, Ruifang Hu, Alesia Hunter, Julia O’Brien, and Heather Eby for field assistance.
Sequencing for this project was completed by the Joint Genome Institute for project 503349. This work was supported by the National Science Foundation grant number 1350850, USDA NIFA grant number 2016-67013-24846, the DENIN Environmental Fellows program, the University of Delaware Doctoral Fellowship, the University of Delaware Dissertation Fellowship, and the Preston C. Townsend Biotechnology Fellowship.


Limmer MA, Mann J, Amaral DC, Vargas R, Seyfferth AL. 2018. Silicon-rich amendments in rice paddies: effects on arsenic uptake and biogeochemistry. Sci the Total Environ 624:1360–1368.
Teasley WA, Limmer MA, Seyfferth AL. 2017. How rice (Oryza sativa L.) responds to elevated As under different Si-rich soil amendments. Environ Sci Technol 51:10335–10343.
Seyfferth AL, Fendorf S. 2012. Silicate mineral impacts on the uptake and storage of arsenic and plant nutrients in rice (Oryza sativa L.). Environ Sci Technol 46:13176–13183.
Liu W-J, McGrath SP, Zhao F-J. 2014. Silicon has opposite effects on the accumulation of inorganic and methylated arsenic species in rice. Plant Soil 376:423–431.
Ma R, Shen J, Wu J, Tang Z, Shen Q, Zhao F-J. 2014. Impact of agronomic practices on arsenic accumulation and speciation in rice grain. Environ Pollut 194:217–223.
Yang Y-P, Zhang H-M, Yuan H-Y, Duan G-L, Jin D-C, Zhao F-J, Zhu Y-G. 2018. Microbe mediated arsenic release from iron minerals and arsenic methylation in rhizosphere controls arsenic fate in soil-rice system after straw incorporation. Environ Pollut 236:598–608.
Seyfferth AL, Limmer MA, Dykes GE. 2018. On the use of silicon as an agronomic mitigation strategy to decrease arsenic uptake by rice, p 49–91. In Sparks DL (ed), Advances in agronomy. Academic Press, San Diego, CA.
Limmer MA, Seyfferth AL. 2020. Carryover effects of silicon‐rich amendments in rice paddies. Soil Sci Soc Am J 85:314–327.
Edwards J, Johnson C, Santos-Medellín C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA, Sundaresan V. 2015. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci U S A 112:E911–E920.
Parada AE, Needham DM, Fuhrman JA. 2016. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18:1403–1414.
Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, Lee J, Chen F, Dangl JL, Tringe SG. 2015. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol 6:771.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, et al. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857.
R Core Team. 2019. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Leinonen R, Sugawara H, Shumway M, on behalf of the International Nucleotide Sequence Database Collaboration. 2011. The Sequence Read Archive. Nucleic Acids Res 39:D19–D21.
McMurdie PJ, Holmes S. 2013. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217.

Information & Contributors


Published In

cover image Microbiology Resource Announcements
Microbiology Resource Announcements
Volume 10Number 278 July 2021
eLocator: 10.1128/mra.00178-21
Editor: Catherine Putonti, Loyola University Chicago


Received: 17 February 2021
Accepted: 3 June 2021
Published online: 8 July 2021



Gretchen E. Dykes
Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware, USA
Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
Department of Earth Sciences, University of Delaware, Newark, Delaware, USA
Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware, USA


Catherine Putonti
Loyola University Chicago

Metrics & Citations


Note: There is a 3- to 4-day delay in article usage, so article usage will not appear immediately after publication.

Citation counts come from the Crossref Cited by service.


If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Figures and Media






Share the article link

Share with email

Email a colleague

Share on social media

American Society for Microbiology ("ASM") is committed to maintaining your confidence and trust with respect to the information we collect from you on websites owned and operated by ASM ("ASM Web Sites") and other sources. This Privacy Policy sets forth the information we collect about you, how we use this information and the choices you have about how we use such information.
FIND OUT MORE about the privacy policy