Earth is expected to continue warming and the Red Sea is a model environment for understanding the effects of global warming on ocean microbiomes due to its unusually high temperature, salinity and solar irradiance. However, most microbial diversity analyses of the Red Sea have been limited to cultured representatives and single marker gene analyses, hence neglecting the substantial uncultured majority. Here, we report 136 microbial genomes (completion minus contamination is ≥50%) assembled from 45 metagenomes from eight stations spanning the Red Sea and taken from multiple depths between 10 to 500 m. Phylogenomic analysis showed that most of the retrieved genomes belong to seven different phyla of known marine microbes, but more than half representing currently uncultured species. The open-access data presented here is the largest number of Red Sea representative microbial genomes reported in a single study and will help facilitate future studies in understanding the physiology of these microorganisms and how they have adapted to the relatively harsh conditions of the Red Sea.
Background & Summary
The Red Sea is an ideal marine environment to study microbial adaptation to physical conditions atypical of global oceans: high temperature, high salinity, and high irradiance. In late summer 2011, we undertook the King Abdullah University of Science and Technology (KAUST) Red Sea Expedition (KRSE2011) in the eastern Red Sea in order to map its diversity along environmental gradients that occur with changes in latitude, longitude, and depth1. This time of year is not only when temperatures and evaporation (and hence salinity) are highest, but also when a foreign water mass called the Gulf of Aden Intermediate Water (GAIW) intrudes into the Red Sea1,2 (Fig. 1). The GAIW brings nutrient-rich water to the Red Sea, providing nitrogen, phosphorus, and other elements to this otherwise oligotrophic sea, and is likely to introduce important microbial diversity.
Insights into the taxonomic, evolutionary, and functional diversity of the Red Sea have largely been based on studies of pure cultures3,
During the KRSE2011, eight stations were sampled along a cruise track from south to north, capturing gradients in temperature, salinity, oxygen, and nutrients, including the unique GAIW water mass (Fig. 1 and Table 1 (available online only)). At each station, samples were collected from the surface to mesopelagic depths (10, 25, 50, 100, 200, and 500 m), except for stations 12 and 34, which had depths shallower than 500 m (Fig. 1 and Table 1 (available online only)), in order to capture a greater variation in environmental parameters and microbial diversity. Here, we successfully reconstructed 136 genomes from 45 individually assembled metagenomes (Figs 1 and 2, Tables 1 and 2 (available online only), Data Citation 1: National Center for Biotechnology Information (NCBI) BioProject database PRJNA289734) by differential read coverage and tetranucleotide frequency methods. Of these, 43 were ‘near-complete’ with an estimated completion minus contamination of ≥90%, while the other 93 draft genomes had completion minus contamination of ≥50% (Table 2 (available online only)). To our knowledge, this is the largest number of microbial genomes from the Red Sea to be reported in a single study.
Phylogenomic analysis based on sets of single-copy marker genes universal to either the bacterial or archaeal domain showed that the 136 genomes encompassed seven phyla across these domains: Thaumarchaeota, Euryarchaeota, Actinobacteria, Cyanobacteria, Bdellovibrionaeota, Proteobacteria, and Marinimicrobia (Fig. 2 and Table 2 (available online only)). As expected, most of the recovered genomes were affiliated with known marine microorganisms such as phototrophic Prochlorococcus20,21 and Synechococcus22,23; representative of clades first discovered in the Sargasso Sea (SAR86, SAR116, SAR324 and SAR406)24,
To allow easy access to the genomes, all 136 genomes were functionally annotated and deposited into the National Centre for Biotechnology Information (NCBI) and Integrated Microbial Genomes (IMG) databases32. The wealth of metagenomic and genomic data described here greatly expands the repertoire of microbial genomic information from the Red Sea which might help to better understand the effects of global warming to ocean microbiomes. These datasets will also strengthen studies to better understand the drivers of marine nutrient cycling, help approaches for bioprospecting for novel thermo- and halo-philic enzymes, and allow for a better understanding of microbial adaptation strategies against high temperature, salinity and solar irradiance.
Metagenomic sequencing and assembly
Seawater samples were collected from eight stations and from different depths (10, 25, 50, 100, 200, and 500 m; locations are shown in Fig. 1) during summer as part of KRSE2011 (ref. 1). Genomic DNA was extracted from the 0.1–1.2 μm size fraction using an established phenol-chloroform extraction protocol1,33. Paired-end libraries (2×100 bp) were prepared using Nextera DNA Library Prep Kit (Illumina) and sequenced on a HiSeq 2000 (Illumina). Reads were quality checked and trimmed using PRINSEQ v0.20.4 (ref. 34) generating read lengths of ~93 bp and a total of ~10 million reads per sample with median insert sizes ranging from 183–366 bp1 (Data Citation 1: National Center for Biotechnology Information (NCBI) BioProject database PRJNA289734). Trimmed metagenome reads were individually assembled (Table 1 (available online only)) using IDBA-UD v1.1.1 (ref. 35) using the ‘--pre-correction’ option. To obtain coverage profile of contigs from each metagenomic assembly, the trimmed reads were mapped back to contigs using BWA v0.7.12 (ref. 36) with the bwa-mem algorithm.
Genome binning, refinement, and annotation
For each metagenome, genome bins were recovered based on tetranucleotide frequencies and read coverage using MetaBAT v0.26.1 (ref. 37) with default parameters. The completeness and contamination of the bins were assessed using CheckM v1.0.3 (ref. 38) using the lineage-specific workflow (Table 2 (available online only)). Bins were further refined using the CheckM ‘merge’ and ‘outliers’ commands which merge bins with complementary sets of marker genes to improve completeness and remove contigs from bins which appear to be outliers relative to reference GC and tetranucleotide distributions in order to reduce contamination38. The FinishM v0.0.7 (https://github.com/wwood/finishm) ‘roundup’ workflow which comprise of ‘wander’ and ‘gapfill’ modes was used to scaffold contigs together and fill gaps within individual bins. The ‘wander’ mode uses a de Bruijn graph (kmer length of 51 bp and coverage cutoff of 5) to determine contig ends which are connected while the ‘gapfill’ mode align the reads to regions of ambiguous nucleotides and replaces them with the appropriate nucleotides. Genome bins that passed the quality filter of completion minus contamination of ≥50% were submitted to IMG/ER32 for gene calling and functional annotation.
Genome tree construction
The archaeal and bacterial genome trees (Fig. 2) were inferred from the concatenation of 122 and 120 proteins, respectively, identified as being present in ≥90% of the genomes in their respective domains and, when present, single-copy in ≥95% of genomes (Supplementary Tables 1 and 2). These marker genes were aligned using HMMER v3.1b1 (ref. 39) and the tree inference from the concatenated alignment with FastTree v2.1.7 (ref. 40) under the WAG+GAMMA models (Data Citation 2: Figshare https://dx.doi.org/10.6084/m9.figshare.3362899.v1). Support values were determined using 100 non-parametric bootstrap replicates41. The archaeal tree was rooted with the DPANN (Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanohaloarchaeota, and Nanoarchaeota) superphylum in concordance with a recent large-scale phylogenomic study9 while the bacterial tree was ‘arbitrarily’ rooted with the phylum Chloroflexi42 but should be treated as unrooted. The trees were visualized in ARB43, annotated by iTOL44 and edited in Illustrator CC 2014 (Adobe).
All versions of third-party software and scripts used in this study are described and referenced accordingly in the Methods sub-sections for ease of access and reproducibility.
The raw Illumina sequencing paired-end reads (Table 1 (available online only)), 45 assembled metagenome sequences (Table 1 (available online only)) and 136 assembled genome sequences (Table 2 (available online only)), generated from the KAUST Red Sea Expedition 2011, are available from NCBI databases (Data Citation 1: National Center for Biotechnology Information (NCBI) BioProject database PRJNA289734). The genome trees and associated fasta amino acid alignment files are available from Figshare (Data Citation 2).
To validate the completeness and contamination of the genomes, we accessed the number of marker genes present in all bacterial and archaeal genomes using CheckM38. The genomes were also manually cleaned from vector contamination by comparing against the UniVec core database (ftp://ftp.ncbi.nlm.nih.gov/pub/UniVec/).
How to cite this article: Haroon, M. F. et al. A catalogue of 136 microbial draft genomes from Red Sea metagenomes. Sci. Data 3:160050 doi: 10.1038/sdata.2016.50 (2016).
Haroon, M. F. Figshare https://dx.doi.org/10.6084/m9.figshare.3362899.v1 (2016)
We acknowledge the people who were involved in the KAUST Red Sea Expedition 2011 and those that helped to generate the data, include, but are not limited to, those named here: Matt Cahill, Mamoon Rashid, Vinu Manikandan, David Ngugi and Ahmed Shibl. This work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Basic Industries Corporation (SABIC) fellowship to L.R.T., and SABIC presidential chair to U.S.
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