Dataset of 16S rRNA and ITS gene amplicon sequencing of celery and parsley rhizosphere soils

Abstract

Objectives

This amplicon metagenomic study examines the relative abundance, taxonomic profiles and community structure of bacterial and fungal communities associated with the roots of parsley (Petroselinum crispum) and celery (Apium graveolens) under monocropping and intercropping systems. The study aims to provide a baseline understanding of how intercropping influences rhizosphere microbial dynamics.

Data description

The dataset provides insight into the effects of parsley-celery intercropping system on soil microbial richness, diversity and community structure. Amplicon metagenomic sequencing was performed on the DNA samples, targeting the 16S rRNA gene (V3-V4 region) and the ITS region for bacterial and fungal communities, respectively. The quantified libraries were pooled and sequenced using Illumina platforms, and the raw sequences were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME 2 version 2019.1.) pipeline. The resulting Amplicon Sequence Variant (ASV) profiles revealed Actinobacteria and Protobacteria as the most predominant bacteria phyla, followed by Bacteroidota, Gemmatimonadota and Acidobacteriaota. The most predominant taxonomic distribution of fungi at the phylum level includes Ascomycota and Mortierellomycota. The dataset includes raw sequence reads in FASTQ format (.fastq.gz), which have been deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) under the Bioproject Accession numbers; SRP540554 (16S rRNA) and SRP540675 (ITS).

Objective

Parsley (Petroselinum crispum (Mill.) Fuss) and Celery (Apium graveolens L.) are herbs widely used across the globe for their culinary and medicinal values [3, 10]. The two plants are often intercropped by farmers for their perceived complementary growth patterns and pest management approaches [1, 14]. Many farmers intercrop these vegetables for optimal gains, but there is limited understanding of the effects of their interaction with the rhizosphere microbiome. The amplicon metagenomic evaluation of the bacterial and fungal communities associated with the roots of celery and parsley in both monocropping and intercropping fields, alongside bulk soil was evaluated. This dataset explores the significance of intercropping systems on both bacterial and fungal diversity and community structure. This has significance in identifying putative plant growth promoting microbes towards sustainable celery and parsley cultivation, while providing the baseline for other researchers exploring microbiome intercropping in related agricultural contexts.

Data description

In this investigation, the rhizosphere soil tightly bound to the root surface of parsley and celery plants was aseptically collected from the cultivated monocropping and intercropping plots at a depth of 10 cm, alongside the bulk samples which were collected in an uncultivated adjacent field. The sampling was carried out during the plants’ vegetative stage at 8th week after planting (WAP) in Gauteng Province, South Africa, with latitude and longitude (26°05′41.3″ S 27°38′03.3″ E). The collections were conducted in three replicates, packed into respectively labelled plastic bags, and transported to the laboratory in an ice box, then stored at −20 °C until DNA extraction was conducted.

The whole community DNA was extracted using the NucleoSpin Soil kit (Macherey–Nagel, Germany) according to the manufacturers’ guidelines, while the metagenomic amplicon sequencing was carried out [8] using Illumina NovaSeq 6000 system (Novogene, Singapore). Metagenomic amplicon sequencing targeting the 16S rRNA (V3–V4 hypervariable regions) and ITS region was performed. The 16S rRNA gene was amplified using universal primers 515F/806R, while the fungal ITS region was amplified using primers ITS1F and ITS2. Sequencing libraries were synthesized from the amplified products. The DADA2 pipeline was employed for the structural analysis of the sequenced data [8]. Quality filtering, trimming, error correction, dereplication, and chimera removal were performed using cutadapt [7], and the Divisive Amplicon Denoising Algorithm 2 (DADA2) plugin within the QIIME version 2.1 (2019) [5] to generate high-resolution Amplicon Sequence Variants (ASVs), ensuring accurate and reliable data for downstream analysis. The raw sequence dataset in FASTQ format were demultiplexed and denoised using DADA2. For taxonomic assignment, the 16S rRNA gene sequences were classified using the Silva version 138.1 reference database [6], while ITS sequences were were annotated using a pre-trained Naïve Bayes classifier trained on the Unite_INSDC (version 8.0, 2018) as the reference database [4]. The functional prediction analyses were conducted with the use of PICRUSt2 [9, 15] and FUNGuild [13] for the bacterial and fungal ASVs respectively. Taxonomic annotation of the ASVs was processed in QIIME 2 using the classify-sklearn plugin. Downstream analyses involved the calculation of diversity metrics including alpha and beta diversity, visualization of microbial community composition, and the construction of a phylogenetic tree using the SEPP plugin, which ensured precise placement of ASVs within the phylogenetic framework. The pipeline facilitated robust and reproducible insights into microbial community structure and function. The dataset consists of raw sequences in fastq.gz format obtained through 16S and ITS Amplicon sequencing of the rhizosphere soils obtained from parsley and celery under monocropped and intercropped plots, as well as bulk soil metagenome. All raw sequences datasets have been deposited at the NCBI. SRA database), bacterial sequences under BioProject accession number SRP540554 (Dataset 1), and fungal sequences under SRP540675 (Dataset 2). The sequence information determined using (QIIME 2 (version 2019.1), is presented in Table 1, while microbial community structure is illustrated in Fig. 1A (bacteria) and Fig. 1B (fungi).

Table 1.

Overview of data files/data sets

Label	Name of data file/data set	File types file extension	Data repository and identifier (DOI or accession number)
Data file	Sequence information and microbial community structure	PDF file (pdf)	Figshare https://doi.org/10.6084/m9.figshare.28943930 [2]
Data set 1	16S rRNA amplicon sequence profiling of microbial diversity in celery and parsley cultivation	fastq files	NCBI-SRA (http://identifiers.org/insdc.sra:SRP540554) [11]
Data set 2	ITS amplicon sequence profiling of fungal diversity in celery and parsley intercrop	fastq files	NCBI-SRA (http://identifiers.org/insdc.sra:SRP540675) [12]

Fig. 1.

Phylum taxonomic classification of (A) bacteria and (B) fungal diversity under different cropping systems. Sample 1–3 (celery monocropping), Sample 4–6 (parsley monocropping), Sample 7–9 (celery and parsley intercropping), Sample 10–12 (bulk samples)

Limitation

Not applicable.

Abbreviations

DNA

Deoxyribonucleic Acid

NCBI

National Center for Biotechnology Information

SRA

Sequence Read Archive

DADA2

Divisive Amplicon Denoising Algorithm 2

QIIME

Quantitative Insights into Microbial Ecology.

ITS

Internal Transcribe Spacer

16S rRNA

16S ribosomal RNA gene

ASVs

Amplicon sequencing variants

Authors’ contributions

OOB: Conceptualization, Supervision, Software, Validation, Resources, Writing – review & editing, Project administration, Funding acquisition. FOO: Methodology, Visualization, Investigation, Software, Formal analysis, Writing – original draft, Writing – review & editing. AOA: Methodology, Investigation, Software, Formal analysis, Data curation, Visualization, Writing – review & editing.

Funding

The funding for this research was awarded to OOB by the National Research Foundation (South Africa) [grant numbers UID123634 and UID132595].

Data availability

The data described in this Data note can be freely and openly accessed on NCBI SRA under http://identifiers.org/insdc.sra:SRP540554 and http://identifiers.org/insdc.sra:SRP540675.

Declarations

Ethics approval and consent to participate

The current work follows the ethical requirements for publication in BMC Genomic Data. It does not involve human subjects, animal experiments, or any data collected from social media platforms.

Consent for publication

All authors approved the manuscript for publication.

Competing interests

The authors declare no competing interests.