ABSTRACT
This dataset describes metagenome-assembled genomes from three lab-scale (4L) expanded granular sludge bed bioreactors treating synthetic dairy wastewater. The resulting MAGs encompass 60 phyla with average genome completeness of 78.68% and average contamination of 2.29%. These genomes represent a valuable resource for studying anaerobic bioreactors used in wastewater treatment.
KEYWORDS: anaerobic digestion, metagenome-assembled genomes, methanogens
ANNOUNCEMENT
During anaerobic digestion, organic matter is degraded by microbial communities in the absence of oxygen, leading to the formation of biogas. These communities are susceptible to environmental disturbances that can impair syntrophic interactions and lead to process failure (1, 2).
Three lab-scale (4 L) expanded granular sludge bed (EGSB) anaerobic bioreactors were inoculated with granular sludge from a mesophilic (37°C) internal circulation bioreactor (650 m³) treating dairy and ethanol-production wastewater in Ballineen, Ireland (OLR: 9.5 kg COD/m³/d; HRT: 21 h). The EGSBs were fed skimmed milk powder with minerals and trace metals and operated at HRT 18 h, OLR 4.6 g COD/L/day, each under a different disturbance regime followed by an organic shock. Triplicate granular sludge samples were collected (Lat: 53.2793, Long: −9.0586), aliquoted into 1.5 mL tubes, snap frozen in liquid nitrogen, and stored at –80°C. Full experimental details are published elsewhere (3, 4).
DNA was extracted using a phenol-chloroform method (5). Stored samples were crushed in microcentrifuge tubes for a homogeneous mix; 0.2 g was added to bead tubes (Macherey-Nagel, Type A, Fisher Scientific, Dublin, Ireland). Cells were lysed by bead beating in 1% CTAB lysis buffer followed by phenol-chloroform extraction. DNA concentration was quantified using a Qubit High-Sensitivity kit (Invitrogen, Carlsbad, CA, USA) and quality checked via agarose gel electrophoresis and NanoDrop. Library preparation and sequencing were performed by Novogene (Cambridge, UK), including fragmentation, end-repair, phosphorylation, A-tailing, and adaptor ligation. Paired-end 150 bp sequencing was done on an Illumina NovaSeq 6000 with SP Reagent Kit v1.5.
Quality trimming was done using Sickle v1.200 (6) resulting in average Phred scores of <20. Any reads <50 bp were discarded, resulting in 641,256,041 reads. Co-assembly was performed using Megahit v1.1.3 (7) with the parameters --k-list 27,47,67,87 --kmin-1pass -m 0.95 --min-contig-len 1000 28, resulting in 653,274 contigs. Contigs were binned with MetaWrap v1.3.2 (8), using three different binning algorithms: metabat2 v2.12.1 (610 bins), maxbin2 v2.2.4 (520 bins), and CONCOCT v1.1.0 (381 bins). Default parameters were used, except where otherwise noted.
CheckM v1.0.12 (9) was used to determine the completion and contamination in each bin using default parameters. All bins with ≥50% completion and ≤10% contamination were consolidated, resulting in 380 metagenomic assembled genomes (MAGs). Recovered MAGs had a mean genome completion of 78.68%, mean contamination of 2.29%, mean length of 2,856,760 bps, mean number of contigs of 470, mean N50 score of 23,610, and mean GC content of 55% (Fig. 1). MAGs were taxonomically classified by GTDB-Tk release 95 (10), resulting in genomes from 60 phyla. A total of 356 MAGs were classified as bacteria, and 24 MAGs were classified as archaea. The METABOLIC v4.0 (11) pipeline was used to assign functions to each bin (12).
Fig 1.

Density plots showing distributions of MAG completeness, contamination, N50, and GC content across 380 bins.
These MAGs are a numerous and diverse source of microorganisms when compared with other resource announcements from similar environments (13, 14). The most abundant bacterial phyla included Chloroflexota (10%), Bacteroidota (10%), and Patescibacteria (9.7%). The most abundant archaeal phylum was Halobacteriota (3.68%).
ACKNOWLEDGMENTS
This work was funded by Science Foundation Ireland (SFI) through the IETSBIO3 SFI Research Professorship Program (grant number 15/RP/2763, to P.N.L.L. and supporting S.M.), and the DARE2CYCLE project carried out under the Green ERA-Hub, a Coordination and Support Action (CSA), funded through the European Union’s Horizon Europe research and innovation (R&I) programme and the Government of Ireland Department of Agriculture, Food and the Marine under Grant Agreement No. 101056828 (to G.C. and P.N.L.L). U.Z.I. was supported by EPSRC grants EP/P029329/1 and EP/V030515/1.
Contributor Information
Gavin Collins, Email: gavin.collins@universityofgalway.ie.
Frank J. Stewart, Montana State University, Bozeman, Montana, USA
DATA AVAILABILITY
Raw sequencing data can be found on the NCBIs Sequence Read Archive (SRA) under the accession number PRJEB89300 with the BioSample numbers SAMEA118255448https://www.ncbi.nlm.nih.gov/biosample/SAMEA118255448/ - SAMEA118255465. Assembled MAGs are deposited on Figshare (https://doi.org/10.6084/m9.figshare.25795075.v1).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Raw sequencing data can be found on the NCBIs Sequence Read Archive (SRA) under the accession number PRJEB89300 with the BioSample numbers SAMEA118255448https://www.ncbi.nlm.nih.gov/biosample/SAMEA118255448/ - SAMEA118255465. Assembled MAGs are deposited on Figshare (https://doi.org/10.6084/m9.figshare.25795075.v1).
