Metagenomic data of Microbiota in Mangrove Soil from Lukut River, Malaysia

Description

Metagenomics sequencing The assembled genome contained 6736 shared genes between soil 1 and soil 3, 203,682 shared genes between soil 1 and soil 2, and 66,383 shared genes between soil 2 and soil 3 (Figure 1). Comparative analysis of metabolic pathways of bacterial diversity Function annotation based on the KEGG, as shown in Figure 2, reveals that most of the core genes found in mangrove soils are involved in metabolic pathways. The highest genes are involved in carbohydrate metabolism and followed by amino acid metabolism. In addition, the functional annotation based on eggNOG shows the genome isolated from the soils of mangrove trees’ rhizosphere mostly contains genes that are associated with energy production and conversion, amino acid transport, metabolism, replication, recombination and repair, as well as carbohydrate transport and metabolism pathways (Figure 3). In this study, the functional annotation based on the CAZy reveals six CAZy main functions as follows: Glycoside Hydrolase (GH), Glycosyl Transferase (GT), Polysaccharide Lyase (PL), Carbohydrate Esterases (CE), Auxiliary Activities (AA) and Carbohydrate-Binding Modules (CBM) were found in collected mangrove soils (Figure 4). Metabolic pathways show the signature of bacteria and their function Carbon fixation was found to be the most prevalent pathway, followed by methane, nitrogen, sulfur, atrazine, and dioxin degradation pathways (Figure 5). Further analysis identified Chloroflexi, Gaiellales bacterium and Acidobacteria as the major microbial taxa affiliated with the carbon fixation pathway. Methanotrophic bacteria, including Methyloceanibacter caenitepidi, M. superfactus, and M. marginalis were also detected (Figure 6). Additionally, several nitrogen-fixing bacteria, such as Chloroflexi bacterium, Acidobacteria bacterium, Actinobacteria bacterium, Frankia sp., proteobacteria bacterium, Betaproteobacteria bacterium, Anaerolineae bacterium, Bradyrhizobium liaoningense, Bradyrhizobium sp., Methyloceanibacter caenitepidi, Methyloceanibacter marginalis, Methyloceanibacter superfactus, Pseudolabrys taiwanensis, Bradyrhizobium manausense, Solirubrobacter sp., Solirubrobacter soli, and Phycicoccus jejuensis were identified (Figure 6). Furthermore, we also found the presence of sulfate-reducing bacteria (SRB), such as Thiohalobacter thiocyanaticus, Acidobacteria bacterium, Woeseia oceani, Desulfobacteraceae bacterium, Desulfobacterales bacterium, Mycolicibacterium rhodesiae, Gaiellales bacterium, Deltaproteobacteria bacterium and Myxococcales bacterium in the collected soil samples (Figure 6). The heatmap in Figure 6 also shows the presence of various bacterial species classified in the Actinobacteria phylum, including Phycicoccus jejuensis, Solirubrobacterales bacterium, Mycolicibacterium rhodesiae, M. mariokaense, Solirubrobacter sp. and S. soli in the collected soils.

Steps to reproduce

Sample collection Soil sediments were collected at a depth of 5 cm from three sampling points in the freshwater riverine mangrove of Lukut River, Negeri Sembilan, Malaysia (2o 35’ 25.2342” N; 101o 48’ 9.831”E) during low tide on 20th February 2020. Samples of soil 1 and soil 2 were collected in the dimension of 5 m X 5 m area populated with R. mucronata and A. officinalis trees, while soil 3 was collected from a riverbank near N. fruticans tree, approximately 50 m distance from soil 1 and soil 2. Isolation of DNA from soil samples for Metagenomic whole genome shotgun (mWGS). DNA from the three soil samples were extracted with the QIAGEN Power Soil Pro-Kit (Cat#QIAG-47014). DNA obtained was pooled from three soil samples from a same sampling location. The DNA purity and integrity were determined using agarose gel electrophoresis, while the accurate quantity of DNA was measured using a Qubit 2.0 flurometer. The DNA obtained was used for library preparation and DNA sequencing for mWGS. Genome sequencing and assembly The Covaris Sonicator was used for mechanical shearing to achieve a tight and highly reproducible DNA fragment distribution. Metagenome was assembled based on clean data from Illumina sequencing. The metagenomic libraries were constructed and quantified by adding adapters and using Agilent 2100/qPCR. Bioinformatics analyses were performed on samples passing quality control, with an optimized MEGAHIT protocol used for initial assembly. The clean data from all samples were mapped to assemble Scaftigs using Soap 2.21, to minimalize eukaryotic DNA, with unutilized PE reads collected using mapping parameters are –u, -2, -m 200. Mixed assembly was conducted on the utilized reads with the same assemble parameter. Scaftigs less than 500 bp, were trimmed and effective Scaftigs were used for gene prediction and abundance analysis. Gene prediction and abundance analysis In the gene prediction and abundance analysis, scaftigs (>=500bp) were used for Open Reading Frame (ORF) prediction by MetaGeneMark with single and mixed samples being considered. ORFs with a size less than 100 nt were trimmed. The CD-HIT tool was used for dereplication of ORFs with parameters –c 0.95, -G 0, -aS 09, -g 1 and –d 0 to generate gene catalogues. Dereplication was based on an identity with a default value of 95% and a coverage of 90%, with the longest gene being chosen as the representative gene (unigene). Clean data were then mapped to the gene catalogue using SoapAligner with parameters –m 200, -x 400, identity ≥ 95%, in order to calculate the quality of the data. Finally, the gene abundance was calculated by taking into account the total number of mapped reads and gene length. The function of the coding sequence was inferred based on its similarity to sequences in the databases from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Non-supervised Orthologous Groups (eggNOG) and Carbohydrate-Active enzymes Database (CAZy).

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