Survey of Maize Rhizosphere Microbiome Using Shotgun Metagenomics

ANNOUNCEMENT

Interactions between a plant and the rhizosphere microbiome are essential for enhancing crop productivity (1, 2). The study of the plant-microbe relationship in this region can improve sustainable agriculture and also aid the development of biofertilizers and biopesticides (1). Nevertheless, few studies exist that examine the maize rhizosphere microbiome and its functional capabilities; therefore, using shotgun metagenomics, this study aimed to unveil the microbial community structure and functional potentials lurking in the maize rhizosphere.

Soil samples were collected in triplicates from maize rhizosphere from two maize farms located at Lichtenburg (Fs) (25°59′40.8″S, 26°31′46.6″E) and Randfontein (Rs) (26°11′51.3″S, 27°33′18.6″′E), in North West and Gauteng provinces of South Africa, respectively, during the summer season. In order to collect the samples, maize plants were carefully excavated, and the soil loosely attached to the root was removed, while soils that were tightly attached to the roots were collected. Samples were transported on ice to the laboratory and stored at −80°C for downstream applications. From 5 g of each soil sample, DNA was extracted using the Qiagen DNeasy PowerSoil isolation kit following the manufacturer’s protocol. The extracted DNA was put through shotgun metagenomic sequencing at the Molecular Research Laboratory in Shallowater, TX. The Qubit double-stranded DNA (dsDNA) high-sensitivity (HS) assay kit (Life Technologies) was used to evaluate the concentration of DNA used for analysis prior to library preparation. The Nextera DNA Flex library preparation kit was employed for preparing the DNA libraries with the aid of the manufacturer’s user guide. A total concentration of 50 ng was used for preparing the libraries. The samples were subjected to simultaneous fragmentation and addition of adapter sequences, which were used during a limited-cycle PCR when unique indices were added. Measurement of the final library concentration was done using the Qubit dsDNA HS assay kit. The average library size was determined using the 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The libraries were pooled, diluted (to 0.6 nM), and sequenced paired end for 300 cycles using the NovaSeq system (Illumina).

Downstream analysis of the reads (totals of 18,942,494 for Fs and 15,666,716 for Rs) was carried out using the default settings of the Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) online server version 4.0.3 (https://www.mg-rast.org) (3). Quality control, which entails trimming of low-quality reads and dereplication, was performed on MG-RAST. Following this step, taxonomic classification and gene annotation were performed using BLAST-like alignment tool (BLAT) (4, 5) against the M5NR database as well as the SEED subsystem (6), which provides nonredundant integration of many databases. BLAT was used for searching protein databases using a translated nucleotide with an E value of 10⁻⁵, a percentage identity of 60%, and a maximum alignment length of 15 bp (default parameters were used for all software). For sample Fs, the mean read values showed 98.84% bacteria, 0.43% archaea, 0.65% eukaryotes, 0.02% viruses, and about 0.06% unclassified. For sample Rs, the mean read values showed 98.72% bacteria, 0.49% archaea, 0.70% eukaryotes, 0.02% viruses, and about 0.07% unclassified. The predominant bacterial phyla were Actinobacteria (23.99% to 34.38%), Proteobacteria (47.19% to 52.93%), Bacteroidetes (4.83% to 5.06%), Chloroflexi (2.19% to 2.93%), Firmicutes (2.70% to 2.76%), Planctomycetes (2.09% to 3.48%), and Cyanobacteria (1.02% to 1.50%). Euryarchaeota, Thaumarchaeota, Chlorophyta, and Apicomplexa were the most prevalent archaea and eukaryotes, and they constitute <1% of the total microbial composition. Functional classifications revealed genes involved in carbon fixation, nitrogen cycling, and phosphorus utilization in the metagenomes.