The need for sustainable agriculture is a global issue that requires urgent attention, particularly in the areas of soil fertility enhancement and management. In this study, the effects of organic and inorganic fertilizers on the rhizosphere microbial communities of maize plants were evaluated.
ABSTRACT
The need for sustainable agriculture is a global issue that requires urgent attention, particularly in the areas of soil fertility enhancement and management. In this study, the effects of organic and inorganic fertilizers on the rhizosphere microbial communities of maize plants were evaluated.
ANNOUNCEMENT
Increases in crop yields through soil fertilization ensure the sustainability of human food and livestock feed supplies. Over the years, chemical fertilizers have been used to increase soil fertility for maximum crop yield, but they have drawbacks, ranging from soil salinization, acidification, and reduction of soil quality to eutrophication (1, 2). This raises interest regarding the adoption of alternative and more sustainable means of soil nutrient enrichment through the use of organic fertilizer or composted manure (3, 4).
Rhizosphere soil samples from maize plants used in this study were sourced from an experimental field of North-West University located at the Molelwane Farm in North-West Province, South Africa (25°47′24.17604″S, 25°37′9.08328″E; 25°47′29.97048″S, 25°37′8.62428″E; 25°47′23.9604″S, 25°37′8.43348″E; 25°47′23.82252′S, 25°37′8.30064″E; and 25°47′24.11844″S, 25°37′8.18148″E; altitude, 1,012 m), with a temperature range of 22°C to 35°C and an average annual rainfall of 450 mm (5). The stabilization period for the compost prior to its use was 16 weeks. The soil was fertilized in early spring (September), followed by planting of the maize seeds (midaltitude variety of maize, Zea mays everta). Rhizosphere soil samples were collected at depths of 0 to 15 cm using an auger, 2 cm from the growing maize plants, at 7 weeks after germination. Sampling distances of 15 cm (minimum) to 50 cm (maximum) in 3 sampling points/plot were used. The sampling area was split into 3 plots for 60 kg N/ha and 3 plots for 120 kg N/ha. The control and compost treatments were split into 3 plots each. Soil samples were taken from 9 plants (3 from each replicate/treatment). Therefore, a total of 15 samples were collected (3 replicates × 5 treatments). From each subreplicated plot for the treatments, 9 subsamples that were distributed to cover the entire plot were gathered together as composite samples. Each of the 5 treatments was made up of 5 composite samples, each containing 9 subsamples. The collected samples were put in sterile plastic bags inside a box containing ice and were transported to the laboratory. Plant and root debris was sieved out using a sieve with a 2-mm pore size, and the samples were stored at −20°C for metagenomic shotgun analysis.
The microbial community DNA was extracted from soil using the PowerSoil DNA isolation kit from Mo Bio Laboratories, Inc., according to the manufacturer’s protocol. The extracted DNA was processed through a shotgun metagenomics sequencing procedure at MR DNA (Shallowater, TX, USA). The quantity of DNA for the analysis was evaluated using the Qubit double-stranded DNA (dsDNA) high-sensitivity (HS) assay kit (Life Technologies, Carlsbad, CA, USA). Then, following the manufacturer’s user guide, DNA libraries were prepared using the Nextera DNA Flex library preparation kit (Illumina). A total of 50 ng DNA from each sample was used to prepare the libraries. The samples were passed through the fragmentation processes in the presence of an added adapter sequence. Adapters were used during the 6 cycles of a limited PCR procedure in which unique indices were introduced into the sample. To measure the final library concentrations obtained, the Qubit dsDNA HS assay kit was used; this was followed by determination of the average library size with the use of a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). DNA libraries were then combined in an equimolar ratio at 0.7 nM, followed by 300 cycles of paired-end sequencing using the NovaSeq 6000 system (Illumina).
The raw metagenomic reads were uploaded to MG-RAST, where quality control procedures were performed on the reads (6). Preprocessing of the reads involved removal of artificial reads and other ambiguous base pairs; this was followed by gene annotation using the BLAT algorithm (7) and the M5NR database (8). Protein-coding gene annotation was executed by searching the M5NR database using BLAST, as well as using the SEED subsystem-level function. The microbial taxa were determined by comparing the sequences against the RefSeq database using BLAST. The BLASTX tool was used to search protein databases using a translated nucleotide query with an E value cutoff of 10E−5, a minimum alignment length of 15 bp, and a percent identity of 60%. The MG-RAST normalization tool was applied in order to make more meaningful comparisons and to enable visual exploration of data such as beta diversity, as well as for clustering analyses. The sequence data generated before and after quality control are presented in Table 1. These data were analyzed statistically using Canoco version 5 and PAST version 3 (9). Default parameters were used except where otherwise noted. The reads contain 98.2% bacteria, 1.2% eukaryotes, 0.2% viruses, and 0.4% archaea. The predominant bacterial phyla were Proteobacteria and Actinobacteria. Eukaryotes and viruses were most abundant in composted-manure-treated soil. Functional analysis of the metagenomes showed that they contained genes involved in carbon, nitrogen, and phosphorus cycling, as well as antimicrobial substance-producing genes that participate in disease-suppressing soil development.
TABLE 1.
Sequence data for the rhizosphere soil samples analyzed after treatments
| Samplea | Data before QCb
|
Data after QC |
Data after processing |
Data after alignment |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Size (bp) | No. of sequences | Sequence length (mean ± SD) (bp) | GC content (mean ± SD) (%) | No. of artificial duplicate reads | Size (bp) | No. of sequences | Sequence length (mean ± SD) (bp) | GC content(mean ± SD) (%) | No. of protein features predicted | No. of rRNA features predicted | No. of protein features identified | No. of rRNA features identified | |
| Cp4 | 1,939,891,250 | 12,070,719 | 161 ± 73 | 49 ± 11 | 8,391,961 | 526,025,382 | 2,945,816 | 179 ± 71 | 44 ± 10 | 1,342,578 | 18,398 | 387,527 | 2,577 |
| Cp8 | 2,618,758,280 | 15,575,330 | 168 ± 71 | 63 ± 12 | 1,832,282 | 2,270,368,498 | 13,083,355 | 174 ± 67 | 63 ± 10 | 11,042,146 | 36,758 | 4,547,525 | 10,755 |
| N2 | 978,960,789 | 5,558,478 | 176 ± 74 | 43 ± 11 | 2,399,030 | 553,971,710 | 2,892,203 | 192 ± 71 | 41 ± 9 | 1,164,182 | 13,662 | 418,097 | 3,170 |
| N1 | 1,694,792,733 | 9,687,815 | 175 ± 72 | 64 ± 12 | 1,078,716 | 1,474,813,072 | 8,198,530 | 180 ± 68 | 65 ± 10 | 7,188,585 | 18,817 | 3,057,707 | 6,303 |
| Cn0 | 1,430,407,056 | 7,834,687 | 183 ± 70 | 64 ± 11 | 788,921 | 1,257,716,569 | 6,780,803 | 185 ± 67 | 64 ± 10 | 6,123,837 | 17,813 | 2,612,059 | 5,936 |
Cp4, 4 tons/ha compost; Cp8, 8 tons/ha compost; N1, 60 kg/ha inorganic fertilizer; N2, 120 kg/ha inorganic fertilizer; Cn0, control.
QC, quality control; SD, standard deviation.
Data availability.
The sequence data for the rhizospheric soil metagenomes were deposited in the NCBI Sequence Read Archive (SRA) under the accession numbers SRX7764933 (control), SRX7764932 (8 tons/ha composted-manure treatment), SRX7764931 (4 tons/ha composted-manure treatment), SRX7764930 (60 kg/ha inorganic fertilizer treatment), and SRX7764929 (120 kg/ha inorganic fertilizer treatment). The BioProject number is PRJNA607213.
ACKNOWLEDGMENT
We are grateful to the National Research Foundation of South Africa for funding the research through O.O.B. (grant UID123634) and offering an NRF-TWAS/African Renaissance Doctoral Scholarship to M.C.E.
REFERENCES
- 1. Hartmann M, Frey B, Mayer J, Mäder P, Widmer F. 2015. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J 9:1177–1194. doi: 10.1038/ismej.2014.210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Zhu Y-G, Su J-Q, Cao Z, Xue K, Quensen J, Guo G-X, Yang Y-F, Zhou J, Chu H-Y, Tiedje JM. 2016. A buried Neolithic paddy soil reveals loss of microbial functional diversity after modern rice cultivation. Sci Bull 61:1052–1060. doi: 10.1007/s11434-016-1112-0. [DOI] [Google Scholar]
- 3. Bhattacharyya R, Kundu S, Prakash V, Gupta H. 2008. Sustainability under combined application of mineral and organic fertilizers in a rainfed soybean-wheat system of the Indian Himalayas. Eur J Agron 28:33–46. doi: 10.1016/j.eja.2007.04.006. [DOI] [Google Scholar]
- 4. Enebe MC, Babalola OO. 2020. Effects of inorganic and organic treatments on the microbial community of maize rhizosphere by a shotgun metagenomics approach. Ann Microbiol 70:49. doi: 10.1186/s13213-020-01591-8. [DOI] [Google Scholar]
- 5. Mokoboki K, Sebola N. 2017. Chemical composition and feed intake of Opuntia cladodes varieties offered to goats. J Anim Plant Sci 32:5096–5103. [Google Scholar]
- 6. Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA. 2008. The metagenomics RAST server: a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9:386. doi: 10.1186/1471-2105-9-386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Kent WJ. 2002. BLAT: the BLAST-like alignment tool. Genome Res 12:656–664. doi: 10.1101/gr.229202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Wilke A, Harrison T, Wilkening J, Field D, Glass EM, Kyrpides N, Mavrommatis K, Meyer F. 2012. The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools. BMC Bioinformatics 13:141. doi: 10.1186/1471-2105-13-141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Hammer Ø, Harper DA, Ryan PD. 2001. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4:4. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The sequence data for the rhizospheric soil metagenomes were deposited in the NCBI Sequence Read Archive (SRA) under the accession numbers SRX7764933 (control), SRX7764932 (8 tons/ha composted-manure treatment), SRX7764931 (4 tons/ha composted-manure treatment), SRX7764930 (60 kg/ha inorganic fertilizer treatment), and SRX7764929 (120 kg/ha inorganic fertilizer treatment). The BioProject number is PRJNA607213.