Metagenomes from Eastern Brazilian Amazonian Floodplains in the Wet and Dry Seasons

Floodplains and wetlands constitute 14% of the total area of the Amazon basin (1) and are considered the largest natural geographic source of methane (CH₄) in the tropics (2). Therefore, several studies have investigated the CH₄-producing and -consuming microbial communities in these sediments and their responses to a range of environmental factors using 16S rRNA amplicon sequencing (3–5). However, their overall microbial taxonomic and functional diversity remains little explored. Here, we report 12 metagenomes from two Amazonian floodplains in the wet and dry seasons.

The samplings were carried out in two floodplains in the State of Pará, Brazil, namely, one located at the Amazon River (FP2, “Maicá”, 2°28′11.2″S 54°38′49.9″W) and the other at the intersection between the Amazon and the Tapajós rivers (FP3, “Açu”, 2°22′44.8″S 54°44′21.1″W). The Amazon and Tapajós are considered whitewater and clearwater rivers, respectively, according to Junk et al. (6). Sediment samples from a depth of 0 to 10 cm were collected using a corer (5-cm diameter by 10-cm depth) at both sites in the wet and dry seasons (May and October 2016, respectively) in triplicate, totaling 12 samples, and homogenized thoroughly. Total DNA was extracted in duplicate from 0.25 g of sediment using the PowerLyzer PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany), following an optimized protocol for Amazonian sediments (7). Metagenomic libraries were constructed using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England BioLabs, Inc., Ipswich, MA) and paired-end sequenced (2 × 150 bp) on an Illumina HiSeq 2500 instrument (Illumina, Inc., San Diego, CA) at Novogene Co., Ltd. (Beijing, China). Detailed information about the study sites, sampling, sediment physicochemical properties, and DNA extraction and quantification have been described previously (5).

Metagenomic reads were imported into the KBase platform (8), and default parameters were used for all software unless otherwise specified. Reads were evaluated using FastQC v0.11.9 (9), trimmed and filtered using Trimmomatic v0.36 (adapters, TruSeq3-PE-2; seed mismatches, 5; sliding window size, 5; sliding window minimum quality, 20; head crop length, 10; leading minimum quality, 20; trailing minimum quality, 20; minimum read length, 70) (10), and again evaluated using FastQC v0.11.9 (9). Overlapping paired-end reads were joined with FASTQ-JOIN v2.0.2 (8, 11) and taxonomically classified using Kaiju v1.7.3 (taxonomic level, phylum/class; reference database, NCBI BLAST nr+euk; low abundance filter, 0.01%; subsample percent, 100%) (12). The results were plotted using ggplot2 3.3.5 (13) in R 4.1.2 (14).

The metagenomic samples had between 22 and 31 million 150-bp long paired-end reads (Table 1). After quality control, between 19 and 29 million paired-end reads remained, ranging from 70 to 140 bp. The joining of the overlapping paired-end reads resulted in samples with between 9 and 15 million reads, ranging from 76 to 274 bp. A considerable part of the reads (mean of 42% across samples) was not classified. Most of the classified reads were assigned to Bacteria, but also Archaea, Fungi, and viruses (Fig. 1). The most dominant phyla (mean relative abundance of > 10% across samples), among the 90 microbial phyla found, were Proteobacteria, Actinobacteria, and Acidobacteria.

This work was supported by the São Paulo Research Foundation (FAPESP; grant numbers 2014/50320-4, 2015/19979-2, 2018/14974-0, 2019/25924-7, and 2019/25931-3), the National Council for Scientific and Technological Development (CNPq; grant numbers 133769/2015-1, 311008/2016-0, and 314806/2021-0), the Coordination for the Improvement of Higher Education Personnel - Brasil (CAPES) - Finance Code 001, and the National Science Foundation - Dimensions of Biodiversity (DEB 1442214). A.M.V.’s research is currently funded by the Fung Global Fellows Program of the Princeton Institute for International and Regional Studies (PIIRS; Princeton University). This publication was supported by the Princeton University Library Open Access Fund.

We thank Wagner Piccinini for the assistance in the field and the Large-Scale Biosphere-Atmosphere Program (LBA), coordinated by the National Institute for Amazon Researchers (INPA), for the logistical support and infrastructure during field activities.

We declare no conflict of interest.

J.B.G. and S.M.T. designed the research with contributions from A.M.V., J.M.S.M., K.N., B.J.M.B., and J.L.M.R. J.B.G. collected the samples with A.M.V., J.M.S.M., and K.N. and conducted the molecular analyses with the help of A.M.V. and A.G.F. A.M.V. and J.B.G. analyzed the microbial data. S.M.T. contributed with field sampling logistics, reagents, materials, and analytic tools. A.M.V. wrote the article with the help of J.B.G. All authors critically revised the manuscript.