Metagenome assembly and annotation of data from the rhizosphere soil of drought-stressed CRN-3505 maize cultivar

Abstract

This data article reports shotgun metagenomic data obtained from drought-stressed maize rhizosphere through the Illumina Novaseq platform, utilizing the KBase online platform. 428,339,852 high-quality post-sequences were obtained, showcasing an average GC content of 65.45 %. The investigation, conducted at Molelwane farm in Mafikeng, South Africa, identified 13 metagenome-assembled genomes (MAGs). Functional annotation of these MAGs revealed their involvement in essential plant growth and development functions, such as sulfur and nitrogen metabolism. The dataset was deposited into the NCBI database, and MAGs accessions are available at DDBJ/ENA/GenBank under the accession number PRJNA101755.

Specifications Table

Subject	Microbial Ecology, Biological Sciences.
Specific subject area	Microbial Biotechnology
Type of data	Tables, Figures. Raw, Analyzed.
Data collection	The environmental samples from maize rhizosphere soil were collected. The DNA isolation from maize rhizosphere samples was performed using the DNeasy PowerSoil Pro kit (The Scientific Group (Pty) Ltd, Gauteng, South Africa), following the manufacturer's instructions. Subsequently, shotgun metagenomic sequencing was conducted using the Illumina NovaSeq platform.
Data source location	North-West University farm at (Molelwane) Mafikeng, North-West Province, South Africa. GPS location (25.85 S 25.63 E)
Data accessibility	Repository name: National Center for Biotechnology Information (NCBI) Data identification number: SRR26065293 (Y60R1); SRR26065293 (Y60R2); SRR26065293 (Y60R3); SRR26074284 (Y80R1); SRR26074283 (Y80R2); SRR26074282 (Y80R3); SRR26074281 (Y100R1); SRR26074280 (Y100R2); SRR26074279 (Y100R3) Direct URL to data: Raw sequencing data are available at the NCBI under BioProject PRJNA1017550 with Sequence Read Archive (SRA) accession number SRR26065293 (Y60R1) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26065293&display=metadata, SRR26065293 (Y60R2) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26065293&display=metadata, SRR26065293 (Y60R3) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26065293&display=metadata, SRR26074284 (Y80R1) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26074284&display=metadata, SRR26074283 (Y80R2) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26074283&display=metadata, SRR26074282 (Y80R3) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26074282&display=metadata, SRR26074281 (Y100R1) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26074281&display=metadata, SRR26074280 (Y100R2) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26074280&display=metadata, SRR26074279 (Y100R3) https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&acc=SRR26074279&display=metadata The Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession PRJNA101755. https://www.ncbi.nlm.nih.gov/datasets/genome/?bioproject=PRJNA1017550
Related research article	None

1. Value of the Data

• •The dataset provides comprehensive details on the impact of microbial communities in improving maize crops under drought conditions.
• •By examining the data, we gain an understanding of the microbial composition and their functions within the rhizosphere of drought-stressed maize.
• •This dataset provides valuable insights for farmers and scientists, enabling the development of novel methods and biotechnological approaches to enhance drought tolerance in maize cultivars.

2. Background

The significance of drought tolerance in maize farming cannot be overstated, as it directly impacts food security [1]. With climate change leading to severe drought, it becomes crucial to prioritize maize plants' ability to withstand water scarcity. This is essential for maintaining crop yields and minimizing the risk of food shortages [2,3]. The rhizosphere facilitates food safety and security, representing the soil zone adjacent to plant roots. According to Pathan et al. [4], the rhizosphere functions as a point of exchange of materials and interactions between plant roots and different microorganisms. In agriculture, understanding the ecosystem that forms in the rhizosphere influences essential aspects such as plant resistance, soil health, and food safety regulations.

Metagenomic analysis, a powerful tool for studying the genetic material of entire microbial communities, holds immense potential to explore microbial genomes associated with the rhizosphere and identify beneficial microbes that can enhance crop productivity [5]. By harnessing this knowledge, we can develop innovative agricultural practices and biotechnological solutions to improve food security in the face of environmental challenges.

3. Data Description

The dataset consists of raw sequencing data collected through shotgun metagenomic sequencing of the rhizosphere microbiome from drought-stressed maize plants. This data was generated to investigate the dynamics of microbial communities under varying drought stress conditions. The data files, in FASTQ format, have been submitted to the NCBI. Table 1 provides statistical details of the metagenomic data within the drought-stressed maize rhizosphere, including the total number of raw and cleaned paired reads for each sample and the GC content percentage of the reads.

Table 1.

Raw sequencing data metrics of maize rhizosphere metagenomes.

Samples	SRA Accession numbers	No. of raw reads (Paired ends)	Total number of bases	GC Percentage (%)	Sequences retained. (Post QC)
Y60_R1	SRR26065293	47,101,174	6856,748,270	65.76	46,422,870
Y60_R2	SRR26065293	50,797,694	7371,839,876	63.66	49,952,504
Y60_R3	SRR26065293	46,795,626	6795,416,810	65.54	46,052,458
Y80_R1	SRR26074284	43,966,888	6398,765,178	66.17	43,325,578
Y80_R2	SRR26074283	41,868,690	6115,504,992	56.98	41,273,526
Y80_R3	SRR26074282	46,124,210	6709,777,865	65.56	45,438,932
Y100_R1	SRR26074281	55,866,854	8151,987,912	63.94	55,127,916
Y100_R2	SRR26074280	43,746,608	6351,001,072	66.13	43,035,382
Y100_R3	SRR26074279	58,579,456	8520,267,788	66.32	57,710,686

After quality control, clean reads, such Y60R1, Y60R2, and Y60R3, were merged to create three combined assemblies, namely Y60R, Y80R, and Y100R [6].The Metagenome-Assembled Genomes (MAGs) were recovered using MEGAHIT v1.2.9 and Maxbin 2 v2.2.4 [7]. These MAGs had completeness of greater than 90 % and contamination of less than 5 %, achieved using dereplication, aggregation, and scoring approach [8]. The MAGs were deposited to GenBank under the accession PRJNA101755. https://www.ncbi.nlm.nih.gov/datasets/genome/?bioproject=PRJNA1017550. Table 2 shows eleven bacterial MAGs and two archaeal MAGs. For instance, in Y60R, four MAGs were classified as members of Actinobacteriota and Proteobacteria. Y80R comprised seven MAGs classified into Thermoproteota, Gemmatimonadota, Actinobacteriota, Proteobacteria, and Bacteroidota. Lastly, Y100R included two MAGs classified as members of Actinobacteriota and Thermoproteota. To provide a more detailed understanding of the functional potential of these MAGs, Fig. 1a summarizes the distribution of genes involved in metabolic processes across the MAGs identified in this study, while Fig. 1b illustrates the presence or absence of key components of the electron transport.

Table 2.

Metagenome-assembled genomes in the drought-stressed maize rhizosphere.

Samples	NO. of contigs	Average length (bp)	BioSample	Accession	Bin ID	GTBD lineage
Y60R	33,961	3,725.08	SAMN38082851 SAMN38082852 SAMN38082853 SAMN38082854	JAZDSL000000000 JAZDSM000000000 JAZDSN000000000 JAZDSO000000000	bin. 001.fastaY60R bin. 002.fastaY60R bin. 003.fastaY60R bin. 004.fastaY60R	d—Bacteria; p—Actinobacteriota; c_UBA4738; o—UBA4738; f—HRBIN12. d—Bacteria;p—Proteobacteria; c—Gammaproteobacteria; o—Pseudomonadales; f—Pseudomonadaceae; g—Pseudomonas_M. d——Bacteria;p—Proteobacteria; c—Gammaproteobacteria;o—Pseudomonadales; f—Moraxellaceae;g—Acinetobacter;s—Acinetobacter johnsonii d—Bacteria; p—Actinobacteriota; c—Acidimicrobiia; o—UBA5794; f—ZC4RG35; g—JACCTH01
Y80R	75	5,515.48	SAMN38082853 SAMN38082976 SAMN38082977 SAMN38082978 SAMN38082979 SAMN38082976 SAMN38082974	JAZDSN000000000 JAZDSR000000000 JAZDSS000000000 AZDST000000000 JAZDSU000000000 JAZDSR000000000 JAZDSP000000000	bin. 002.fastaY80R bin. 003.fastaY80R bin. 004.fastaY80R bin. 005.fastaY80 bin. 006.fastaY80R bin. 007.fastaY80R bin. 001.fastaY80R	d—Bacteria; p—Proteobacteria; c—Gammaproteobacteria; o—Pseudomonadales; f—Moraxellaceae;g—Acinetobacter;s—Acinetobacter johnsonii d—Bacteria; p—Gemmatimonadota; c—Gemmatimonadetes; o—Gemmatimonadales; f—GWC-71-9; d—Bacteria; p—Actinobacteriota; c—Acidimicrobiia; o—UBA5794; f—ZC4RG35; g—JACCTH01; d—Bacteria; p—Bacteroidota; c—Bacteroidia; o—Flavobacteriales; f—Weeksellaceae;g—Chryseobacterium;s—Chryseobacterium aquaticum d—Bacteria; p—Actinobacteriota; c—Rubrobacteria; o—Rubrobacterales; f—Rubrobacteracee; g—SCSIO-52909; d—Bacteria; p—Gemmatimonadota;c—Gemmatimonadets; o—Gemmatimonadales;f—GWC2-71-9; g—JACDDX01; d—Archaea;p—Thermoproteota;c—Nitrososphaeria; o—Nitrososphaerales;f—Nitrososphaeraceae;g—Nitrososphaera;
Y100R	45,645	3691.14	SAMN38082977 SAMN38082974	JAZDSS000000000 JAZDSS000000000	bin. 001.fastaY100R bin. 002.fastaY100R	d—Bacteria;p—Actinobacteriota; c—Acidimicrobiia; o—UBA5794; f—ZC4RG35; g—JACCTH01; d—Archaea; p—Thermoproteota; c—Nitrososphaeria; o—Nitrososphaerales; f—Nitrososphaeraceae; g—Nitrososphaera;

Fig. 1.

The interactive heatmap was constructed to show the presence of metabolic functions (a), modules' coverage, and electron transport chain components (b).

4. Experimental Design, Materials and Methods

4.1. Experimental design

This study used a drought-sensitive maize cultivar (CRN-3505) in a greenhouse at the North-West University farm in Molelwane, South Africa. The seeds were sterilized with 5 % (v/v) hypochlorite solution and planted in plastic pots. The maize plants were cultivated under a controlled environment with a 14-h light/10-h dark photoperiod. This means the plants received 14 h of light and 10 h of darkness each day, simulating a natural day-night cycle. The temperature was maintained between 24 °C during the day and 18 °C at night. Replicate rhizosphere soil samples were collected from maize plants under a well-watered control for comparison, severe drought (60 % of field capacity) and moderate drought (80 % of field capacity). These conditions were chosen to simulate different degrees of water availability. They were collected on the 4th of March 2022 at 8 cm diameter and 15 cm depth of the maize plants, transported to the laboratory and stored until further use.

4.2. DNA extraction and illumina sequencing

DNA was extracted from maize rhizosphere samples using a DNeasy PowerSoil® Pro kit following the manufacturer's instructions. The Nextera DNA Flex Library Preparation Kit was employed to prepare libraries using 50 ng of purified DNA derived from various sources. The library size was assessed using Agilent 2100 Bioanalyzer and Novaseq 6000 sequenced 300 cycles paired-end after pooling and diluting the libraries [9]. Shotgun metagenomic libraries were generated for all rhizosphere samples from well-watered and drought-induced maize plants.

4.3. Metagenomic data analysis and genome annotation

DOE's Systems Biology Knowledgebase (KBase) was used to decipher the metagenomic sequences [6]. All software utilized default settings. Using the KBase metagenomics approach analysis approach, read library qualities were checked with FastQC version 0.11.5 [10], and the barcode sequences and sections with poor quality were eliminated using the Trimmomatic v0.36 [11]. Clean reads were then assembled into contigs using MEGAHIT v1.2.9 [12]. Maxbin 2 v2.2.4 was employed to bin the contigs, recovering the Metagenome-Assembled Genomes (MAGs) [7]. The MAGs were selected based on their high quality using DASTool v1.1.2 [8]. Taxonomic classification of the MAGs was accomplished using the GTDB-Tk toolkit (Table 2) [13]. Subsequently, DRAM was used to predict and annotate the MAGs, providing genome metabolic summaries and an interactive heatmap to compare the metabolic profiles of each genome[14].

Limitations

The data presented is based on metagenomics, and MAG predictions are not necessarily indicators of actual function, so metatranscriptomic data would be required.

Ethics Statement

The authors have read and followed the ethical requirements for publication in Data in Brief and confirmed that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.

CRediT Author Statement

O.O Babalola: Conception, Supervision, Writing-review & editing. R.R. Molefe: Data curation, Writing – original draft, Visualization, Investigation. A.E Amoo Software, Validation, Writing – review & editing.

Data Availability

• Whole Genome Shotgun project (Original data) ()

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)

• Yellow maize (Original data) (NCBI)