Data from: Metagenomics of tidal zones of pristine mangrove sediments
Spealman, Pieter et al. (2020), Data from: Metagenomics of tidal zones of pristine mangrove sediments, v3, Dryad, Dataset, https://doi.org/10.5061/dryad.gf1vhhmkz
Mangrove forests are intertidal ecosystems that constitute a large portion of the world’s coastline, as such, they are composed of, and reliant upon, microhabitats defined by the tides. However, we are only beginning to understand tidal microhabitat biodiversity and their role in nutrient cycling. The majority of metagenomic studies have so far been conducted on anthropogenically impacted areas. As even mild disruption can severely alter ecosystems and lead to decreased biodiversity and local extinctions, this is a critical issue . Here, we characterize prokaryotic populations and their involvement in nutrient cycling across the tidal zones of a pristine mangrove forest within a Brazilian Environmental Protection Area of the Atlantic Forest.
We hypothesize that tidal zones in pristine mangroves constitute distinct microhabitats, are composed of different prokaryotic communities and, consequently, distinct functional profiles. Samples were collected in triplicate from zones below, between, and above the tidal waterline. Using 16S amplicon sequencing, we find significantly different prokaryotic communities with diverse nutrient cycling related functions, as well specific taxa with varying contribution to functional abundances between zones. Our findings contrast those observed in anthropogenically impacted mangroves and suggest that some aspects of mangrove zonation may be compromised by human activity.
Mangrove tidal zone sediment selection and collection
The Serinhaém Estuary is located in the Low South Region of Bahia State, Brazil, between the coordinates 13°35'S and 14°10'S and 39°40’W and 38°50’W. The estuary is within the Pratigi Environmental Protection Area (APA), one of the few remaining Atlantic forest regions with a total area of 85,686 ha, enclosing a 32 km long portion of the lower Juliana River and emptying directly into Camamu Bay along with several smaller rivers.
For clarity, we refer to the location of a sample as a ‘site’ and the collection of sites within a tidal zone as a ‘zone’. Samples were collected from 3 tidal zones (centered around 13°42'59.0"S, 39°01'35.9"W) in the Serinhaém estuary in July 2018 during the morning low tide period. No sites exhibited signs of anthropogenic disturbance or pollution. The 3 collection zones were chosen based on tidal influence; sublittoral, intertidal, and supralittoral regions. From each tidal zone, 3 samples of superficial sediments (top 10 cm of the surface layer) were collected with a cylindrical sediment core sampler (eg. each zone was sampled in triplicate). To ensure that our replicates sampled a broad representation of each zone, sample sites were located a minimum of 15m from each other in a triangle. Plant and other organic material was manually removed from core samples, with precautions taken to avoid the disruption of rhizospheres associated with vegetation.
After collection, samples were transferred to the laboratory. For each sediment core an aliquot was separated and kept in the -20°C freezer for subsequent DNA extraction while the remainder of the sample was used for measuring organic matter content. The total genomic DNA was extracted from 0.25 g of sediment using the PowerSoil DNA Isolation Kit (Qiagen, Carlsbad, CA, USA). All DNA samples were stored at -20º C before library preparation and sequencing.
Physical-chemical parameters such as temperature, salinity and dissolved oxygen in the water column were measured using a multiparameter monitoring system (YSI model 85, Columbus). Each zone had different vegetation densities, with the sublittoral zone having the greatest plant density, and the supralittoral the least, with almost no vegetation. Metal concentrations were not collected as previous analysis performed by our lab (Jesus, T.B.) found no significant difference in metal concentrations relative to background within the Serinhaém estuary. (see 'env_variables.csv')
16S Amplicon Library Preparation and Sequencing
After DNA extraction, wde used PCR to specifically target the V4 region of the bacterial 16S rRNA using the primer pair 515F-Y (Parada, Needham and Fuhrman 2016) and 806R-XT (Caporaso et al. 2011). DNA sequencing was performed using Illumina MiSeq platform, V2 kit (300 cycles). The original sequences have been deposited as PRJNA608697 in the NCBI BioProject database: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA608697
Trimmomatic (Bolger, Lohse and Usadel 2014) was used to filter and trim demultiplexed sequences (ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:100). A minimum average read quality score of 15 was required for inclusion while the sliding window cuts any read at the point where the median quality score over a 4 nucleotide window is less than 15.
Sequence denoising and OTU clustering using QIIME2
QIIME (Caporaso et al. 2010) was used to join forward and reverse reads into single reads (join_paired_ends.py, -j 4 -p 1), (see 'join_reads.sh'). Reads were denoised using DADA2 (Callahan et al. 2016) (denoise-single, --p-trim-left 3, --p-trunc-len 0, --p-max-ee 2.0, --p-trunc-q 2) in QIIME2 (Bolyen et al. 2019), (q2cli, version 2019.4.0),(see 'run_qiime2_analysis.sh'). Denoised sequences are clustered into Operational Taxonomic Units (OTUs). Alpha-rarefaction was calculated using QIIME2 (alpha-rarefaction, max depth=17000). We performed a variety of alpha-diversity and beta-diversity tests using QIIME2 (core-metrics-phylogenetic, p-sampling depth 9340), (see 'run_qiime2_analysis.sh').
Taxonomic assignment used Vsearch (Rognes et al. 2016) in QIIME2 using Open Reference with 97% similarity (--p-perc-identity 0.97) against the reference 16S rRNA sequences in SILVA database (Silva SSU 132), (McDonald et al. 2012). QIIME2 visualizations for 1. OTU abundance, 2. proportional representation between sites and 3. taxonomy, (see 'run_qiime2_analysis.sh').
Phylogenetic reconstruction was carried out in QIIME2 using the representative sequences for each OTU and a QIIME2 feature classifier trained using the 97% similarity representative set containing only 16S sequences (e.g. silva_132_97_16S.fna), (see, 'train_qiime2_silva_132db.sh'). All groups were required to be present within at least 2 samples with a minimum of 3 reads each.
QIIME2 tree files were accessed in R using QIIME2R (version 0.99.12). Tree visualization was performed with R (version 3.4.4) using Metacoder (Foster, Sharpton and Grünwald) (version 0.3.2). Posterior analysis was performed using Phyloseq (McMurdie and Holmes 2013), (version 1.22.3). Analyses in R were plotted using ggplot2 (McMurdie and Holmes 2013; Villanueva and Chen 2019). (see 'visualize_phylogenetic_tree.r')
Environmental variable analysis
Vegan (Dixon 2003), (version 2.5-6) was used to test correlations between community structure and environmental variables. Distances were calculated using metaMDS, (engine=monoMDS, try=1000, k=3), and then fit the environmental variables using envfit (default settings, permutations=333). (see 'singificant_environmental_variables_vegan.r')
Functional analysis using PICRUSt2
Functional analysis was performed using PICRUSt2 (version 2.3.0-b) (Douglas et al. 2019; Barbera et al. 2019; Czech, Barbera and Stamatakis 2020; Louca and Doebeli 2018; Ye and Doak 2009) with default settings (see 'run_picrust2.sh'). Both the Kegg Orthologs (KOs) and MetaCyc pathways were analyzed for significant (p-value <= 0.05) differential abundances after centered log-ratio transformation (aldex.clr) using the general-linear model method (aldex.kw) of the ALDEx2 package (ver 1.18.0), (see 'run_aldex2_on_picrust2.r'). A heatmap of KOs with differential abundance between sample sites was then generated (see 'run_novembro_sigilo.sh', and 'sigilo.py').
Site-specific taxonomic and functional enrichment
In order to identify which species were significantly different in abundance in each zone we performed taxa enrichment analysis (see 'run_novembro_sigilo.sh', and 'novembro.py'). Briefly, OTU abundances were normalized by downsampling to match the least abundant zone (Intertidal). Taxa abundances are the sum of all assigned OTU abundances. For each taxa, we required that a significant difference be found between sites using a Chi-squared, 2x3 test, with correction (scipy.stats.chi2_contingency) using the mean normalized abundance. To correct for false positives due to variance between replicates we required the distributions of unnormalized OTU abundances between sites to also be significantly different (Mann-Whitney U test, scipy.stats.mannwhitneyu, p-val <= 0.05). Finally, to ensure biological relevance, we required the effect size to represent at least 5% difference in log-fold abundance between sites (see 'novembro.py').
To determine which taxa were associated with differences in functional abundance, we also calculated KO enrichment specific to each taxa at a given taxonomic level using the functional abundance results of PICRUSt2 (see 'run_novembro_sigilo.sh', and 'sigilo.py'). We required that a given taxa must have at least 10% of all KO functional abundance at the given level; that the functional abundance be significantly enriched using a Binomial exact test (Bonferroni corrected p-value <= 0.05), and the taxa must have at least three distinct KOs within a single pathway that meet these criteria. All KOs and their metabolic pathways are available in a supplemental file.
The data has been deposited as PRJNA608697 in the NCBI BioProject database
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES), Award: Finance Code 001
Fundação de Amparo à Pesquisa do Estado da Bahia – Fapesb, Award: PNE0021/2014
National Science Foundation, Award: MCB1818234
- This dataset cites https://doi.org/10.1101/833814