Genome-resolved metagenomics reveals novel archaeal and bacterial genomes from Amazonian forest and pasture soils¶

Andressa M. Venturini

Introduction¶

Genome-resolved metagenomics was applied to recover archaeal and bacterial metagenome-assembled genomes (MAGs) from forest and pasture soils of the Brazilian Eastern Amazon, which were subjected to different soil moisture levels (Venturini et al., 2022).

Brief sample description¶

The sampling was carried out in July 2015 in a pristine forest of the Tapajós National Forest (3°17'44.4"S 54°57'46.7"W) and an active cattle pasture (3°18'46.7"S 54°54'34.8"W) in the Brazilian Eastern Amazon. Three soil samples from 0 to 10 cm depth were collected at each site. Samples from each land use were combined, sieved, and subjected to a microcosm experiment under increasing soil moisture levels for 30 days at 25 °C. Moisture treatments were established in triplicates for each land use using 1.5 L glass jars filled with 350 g of soil each. These treatments included the original soil moisture of each site (22% for forest and 24% for pasture) and 100% of moisture at field capacity (FC, 50% for forest and 63% for pasture). Forest and pasture soils from these both moisture treatments were DNA-extracted in duplicate using the PowerLyzer PowerSoil DNA Isolation Kit, totaling 12 DNA samples. DNA samples were checked using 1% agarose gel electrophoresis and a Nanodrop 2000c spectrophotometer. Paired-end shotgun metagenomic sequencing (2 x 150 bp) was performed on an Illumina HiSeq platform at Novogene Co. Ltd. using the NEBNext Ultra II DNA Library Prep Kit for Illumina for library construction. Detailed information about the sites, their soil physicochemical properties, the design of the microcosm experiment, DNA extraction and quantification, and the metagenomic sequencing was previously reported (Lemos et al., 2020; Venturini et al., 2022).

Sample table:

ID	Land use	Treatment
A1	Forest	Original
A2	Forest	Original
A3	Forest	Original
A10	Forest	100% FC
A11	Forest	100% FC
A12	Forest	100% FC
A13	Pasture	Original
A14	Pasture	Original
A15	Pasture	Original
A22	Pasture	100% FC
A23	Pasture	100% FC
A24	Pasture	100% FC

Table of Contents¶

1. Data trimming and filtering

2. Merge libraries

3. Assemble reads

4. Binning and optimization

5. Bin quality assessment and filtering

6. Bin taxonomy

7. Bin extraction.

8. Bin abundance.

9. Genome annotation.

10. Patescibacteria comparison.

11. References.

1. Data trimming and filtering.¶

1.1. Evaluate paired-end reads using FastQC.

1.2. Trim and filter paired-end reads using Trimmomatic.

Changes in Trimmomatic: adapters (TruSeq3-PE-2)/sliding window minimum quality (20)/head crop length (10)/minimum read length (50).

1.3. Re-evaluate paired-end reads using FastQC.

2. Merge libraries.¶

2.1. Merge libraries of each land use to serve as inputs for the assemblers using Merge Reads Libraries.

3. Assemble reads.¶

3.1. Assemble paired-end reads using MEGAHIT.

Changes in MEGAHIT: parameter preset (meta-large).

4. Binning and optimization.¶

4.1. Cluster the assembled contigs in bins that correspond to putative population genomes using MetaBAT2.

4.2. Cluster the assembled contigs in bins that correspond to putative population genomes using MaxBin2.

Changes in MaxBin2: marker set (107 and 40)/minimum contig length (2500)/plot markers per contig (Y).

4.3. Cluster the assembled contigs in bins that correspond to putative population genomes using CONCOCT.

4.4. Bin optimization approach using DAS Tool.

5. Bin quality assessment and filtering.¶

5.1. Assign bin completeness and contamination scores using CheckM.

Changes in CheckM: reference tree (full tree).

5.2. Filter bins using CheckM.

Changes in CheckM for ≥ 50% of completeness and ≤ 10% of contamination: reference tree (full tree)/completeness (50)/contamination (10).

6. Bin taxonomy.¶

6.1. Provide taxonomic assignments for medium-quality bacterial and archaeal bins based on the Genome Taxonomy Database using GTDB-Tk.

7. Bin extraction.¶

7.1. Extract selected bacterial and archaeal bins (high-quality bins plus bins of environmental relevance) as individual assemblies using Extract Bins as Assemblies from BinnedContigs.

Forest: Bin.003/Bin.004/Bin.013.

Pasture: Bin.001/Bin.002/Bin.005/Bin.006/Bin.020/Bin.027/Bin.031/Bin.034/Bin.035.

8. Bin abundance.¶

8.1. Calculate the abundance of each selected bin in the merged metagenomes using Align Reads using Bowtie2.

Changes in Bowtie2: preset (very-sensitive).

9. Genome annotation.¶

9.1. Create AssemblySet with the selected bins using Build AssemblySet.

Forest: Bin.003/Bin.004/Bin.013.

Pasture: Bin.001/Bin.002/Bin.005/Bin.006/Bin.020/Bin.027/Bin.031/Bin.034/Bin.035.

9.2. Annotate the selected bins using Annotate and Distill Assemblies with DRAM.

10. Patescibacteria comparison.¶

10.1. Download genomes from NCBI public links using Upload File to Staging from Web.

The Patescibacterial genomes were previously recovered by Lemos et al. (2020) using the metagenomic data from the high-moisture (100% FC) pasture soils.

WARW00000000: https://sra-download.ncbi.nlm.nih.gov/traces/wgs01/wgs_aux/WA/RW/WARW01/WARW01.1.gbff.gz.

WARV00000000: https://sra-download.ncbi.nlm.nih.gov/traces/wgs01/wgs_aux/WA/RV/WARV01/WARV01.1.gbff.gz.

10.2. Import GenBank files as genomes using Import GenBank File as Genome from Staging Area.

Changes in Import GenBank File as Genome from Staging Area: genome type (metagenome-assembled genome (MAG))/source of the GenBank file (other).

10.3. Extract bins of Patescibacteria as individual assemblies using Extract Bins as Assemblies from BinnedContigs.

Pasture: Bin.013/Bin.036/Bin.038.

10.4. Compare genomes using Compute ANI with FastANI.

11. References.¶

Venturini, A.M., Gontijo, J.B., Mandro, J.A., Paula, F.S., Yoshiura, C.A., da França, A.G., Tsai, S.M., 2022. Genome-resolved metagenomics reveals novel archaeal and bacterial genomes from Amazonian forest and pasture soils. Microbial Genomics 8, 7. https://doi.org/10.1099/mgen.0.000853.

Venturini, A.M., Dias, N.M.S., Gontijo, J.B., Yoshiura, C.A., Paula, F.S., Meyer, K.M., Nakamura, F.M., da França, A.G., Borges, C.D., Barlow, J., Berenguer, E., Nüsslein, K., Rodrigues, J.L.M., Bohannan, B.J.M., Tsai, S.M., 2022. Increased soil moisture intensifies the impacts of forest-to-pasture conversion on methane emissions and methane-cycling communities in the Eastern Amazon. Environmental Research 212, 113139. https://doi.org/10.1016/j.envres.2022.113139

Lemos, L.N., Manoharan, L., Mendes, L.W., Venturini, A.M., Pylro, V.S., Tsai, S.M., 2020. Metagenome assembled‐genomes reveal similar functional profiles of CPR /Patescibacteria phyla in soils. Environmental Microbiology Reports 12, 651–655. https://doi.org/10.1111/1758-2229.12880

Created Object Name	Type	Description
A1_trimmed_paired	PairedEndLibrary	Trimmed Reads
A1_trimmed_unpaired_fwd	SingleEndLibrary	Trimmed Unpaired Forward Reads
A1_trimmed_unpaired_rev	SingleEndLibrary	Trimmed Unpaired Reverse Reads

Created Object Name	Type	Description
A2_trimmed_paired	PairedEndLibrary	Trimmed Reads
A2_trimmed_unpaired_fwd	SingleEndLibrary	Trimmed Unpaired Forward Reads
A2_trimmed_unpaired_rev	SingleEndLibrary	Trimmed Unpaired Reverse Reads

Created Object Name	Type	Description
A3_trimmed_paired	PairedEndLibrary	Trimmed Reads
A3_trimmed_unpaired_fwd	SingleEndLibrary	Trimmed Unpaired Forward Reads
A3_trimmed_unpaired_rev	SingleEndLibrary	Trimmed Unpaired Reverse Reads

Created Object Name	Type	Description
A10_trimmed_paired	PairedEndLibrary	Trimmed Reads
A10_trimmed_unpaired_fwd	SingleEndLibrary	Trimmed Unpaired Forward Reads
A10_trimmed_unpaired_rev	SingleEndLibrary	Trimmed Unpaired Reverse Reads

Created Object Name	Type	Description
A11_trimmed_paired	PairedEndLibrary	Trimmed Reads
A11_trimmed_unpaired_fwd	SingleEndLibrary	Trimmed Unpaired Forward Reads
A11_trimmed_unpaired_rev	SingleEndLibrary	Trimmed Unpaired Reverse Reads