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Dataset of 111 metagenome-assembled genomes from cattle manure, soil and manured soil samples

. 2025 Aug ; 61 () : 111748. [epub] 20250603

Status PubMed-not-MEDLINE Language English Country Netherlands Media electronic-ecollection

Document type Journal Article

Links

PubMed 40599424
PubMed Central PMC12210313
DOI 10.1016/j.dib.2025.111748
PII: S2352-3409(25)00475-5
Knihovny.cz E-resources

This data report presents 111 metagenome-assembled genomes (MAGs) reconstructed from manure, soil and manured soil samples from microcosms after enriching for non-fermenting Gram-negative bacteria (NFGNB). Two independent microcosm experiments were conducted to investigate the spread of NFGNB from the fresh manure of dairy cows under antibiotic prophylaxis to the pasture soil of two organic farms. After sampling the microcosms on days 2, 14 and 28, the manure and soil samples were plated in duplicate on CHROMagar Acinetobacter medium for NFGNB enrichment and incubated at 28°C for 24 h. DNA was extracted from the cultures and sequenced using the Illumina NovaSeq 6000 platform with 150-bp paired-end reads. Reads were assembled with metaSPAdes both individually and by co-assembly. MAGs were reconstructed using MetaBAT, MaxBin, SemiBin2, COMEbin, and AVAMB, and then de-replicated at >95 % ANI (pairwise comparisons) using dRep. A total of 111 MAGs of at least medium quality (MIMAG standard) were obtained. These included 10 high-quality MAGs (>90 % completeness, <5 % contamination, rRNA genes and tRNA for at least 18 amino acids), 47 putative high-quality MAGs (>90 % completeness, <5 % contamination) and 54 medium-quality MAGs (>50 % completeness, <10 % contamination). The FASTA files of the MAGs as well as their taxonomic identifications, completeness and contamination, origin, genomic statistics and rRNA sequences are publicly available in a Zenodo dataset and the genomes in the NCBI database. The majority of MAGs (99) were assigned to Pseudomonadota, mainly Pseudomonas (28 MAGs), Stenotrophomonas (20 MAGs) and Acinetobacter (18 MAGs), while the remaining 12 MAGs belonged to Bacteroidota. Most MAGs (44) were of manure origin, followed by manured soil (38 MAGs) and soil (29 MAGs). High-quality MAGs were predominantly obtained from manure (6 high-quality, 21 putative high-quality), compared to manured soil (3 high-quality, 12 putative high-quality) and soil (1 high-quality, 14 putative high-quality). By providing their MAGs, this dataset offers a valuable resource for researchers investigating the genomic characteristics associated with the survival, environmental dispersal and ecological role of potentially hazardous NFGNB species in soil, particularly following the application of antibiotic-treated animal manure, and for comparative genomics studies in related environments.

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Sardar P., Elhottová D., Pérez-Valera E. Soil-specific responses in the antibiotic resistome of culturable Acinetobacter spp. and other non-fermentative Gram-negative bacteria following experimental manure application. FEMS Microbiol. Ecol. 2023;99:fiad148. doi: 10.1093/femsec/fiad148. PubMed DOI

Enoch D.A., Birkett C.I., Ludlam H.A. Non-fermentative Gram-negative bacteria. Int. J. Antimicrob. Agents. 2007;29:S33–S41. doi: 10.1016/S0924-8579(07)72176-3. PubMed DOI

Gales A.C., Jones R.N., Forward K.R., Liñares J., Sader H.S., Verhoef J. Emerging importance of multidrug-resistant acinetobacter species and stenotrophomonas maltophilia as pathogens in seriously ill patients: geographic patterns, epidemiological features, and trends in the SENTRY antimicrobial surveillance program (1997–1999) Clin. Infect. Dis. 2001;32:S104–S113. doi: 10.1086/320183. PubMed DOI

Bonomo R.A., Szabo D. Mechanisms of multidrug resistance in acinetobacter species and pseudomonas aeruginosa. Clin. Infect. Dis. 2006;43:S49–S56. doi: 10.1086/504477. PubMed DOI

Mulani M.S., Kamble E.E., Kumkar S.N., Tawre M.S., Pardesi K.R. Emerging strategies to combat ESKAPE pathogens in the era of antimicrobial resistance: a review. Front. Microbiol. 2019;10:539. doi: 10.3389/fmicb.2019.00539. PubMed DOI PMC

Pérez-Valera E., Kyselková M., Ahmed E., Sladecek F.X.J., Goberna M., Elhottová D. Native soil microorganisms hinder the soil enrichment with antibiotic resistance genes following manure applications. Sci. Rep. 2019;9:6760. doi: 10.1038/s41598-019-42734-5. PubMed DOI PMC

Leclercq S.O., Wang C., Sui Z., Wu H., Zhu B., Deng Y., Feng J. A multiplayer game: species of Clostridium, Acinetobacter, and Pseudomonas are responsible for the persistence of antibiotic resistance genes in manure-treated soils. Environ. Microbiol. 2016;18:3494–3508. doi: 10.1111/1462-2920.13337. PubMed DOI

Bowers R.M., Kyrpides N.C., Stepanauskas R., et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 2017;35:725–731. doi: 10.1038/nbt.3893. PubMed DOI PMC

B. Bushnell, BBMap: a fast, accurate, splice-aware aligner, (2014). https://escholarship.org/uc/item/1h3515gn (accessed November 2, 2022).

Líndez P.P., Johansen J., Kutuzova S., Sigurdsson A.I., Nissen J.N., Rasmussen S. Adversarial and variational autoencoders improve metagenomic binning. Commun. Biol. 2023;6:1–10. doi: 10.1038/s42003-023-05452-3. PubMed DOI PMC

Pan S., Zhao X.-M., Coelho L.P. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing. Bioinformatics. 2023;39:i21–i29. doi: 10.1093/bioinformatics/btad209. PubMed DOI PMC

Kang D.D., Li F., Kirton E., Thomas A., Egan R., An H., Wang Z. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359. doi: 10.7717/peerj.7359. PubMed DOI PMC

Wang Z., You R., Han H., Liu W., Sun F., Zhu S. Effective binning of metagenomic contigs using contrastive multi-view representation learning. Nat. Commun. 2024;15:585. doi: 10.1038/s41467-023-44290-z. PubMed DOI PMC

Wu Y.-W., Simmons B.A., Singer S.W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–607. doi: 10.1093/bioinformatics/btv638. PubMed DOI

Chklovski A., Parks D.H., Woodcroft B.J., Tyson G.W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods. 2023;20:1203–1212. doi: 10.1038/s41592-023-01940-w. PubMed DOI

Olm M.R., Brown C.T., Brooks B., Banfield J.F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–2868. doi: 10.1038/ismej.2017.126. PubMed DOI PMC

Chaumeil P.-A., Mussig A.J., Hugenholtz P., Parks D.H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics. 2022;38:5315–5316. doi: 10.1093/bioinformatics/btac672. PubMed DOI PMC

Letunic I., Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucl. Acids Res. 2021;49:W293–W296. doi: 10.1093/nar/gkab301. PubMed DOI PMC

Chakoory O., Comtet-Marre S., Peyret P. RiboTaxa: combined approaches for rRNA genes taxonomic resolution down to the species level from metagenomics data revealing novelties. NAR Genom. Bioinform. 2022;4:lqac070. doi: 10.1093/nargab/lqac070. PubMed DOI PMC

Song W., Zhang S., Thomas T. MarkerMAG: linking metagenome-assembled genomes (MAGs) with 16S rRNA marker genes using paired-end short reads. Bioinformatics. 2022;38:3684–3688. doi: 10.1093/bioinformatics/btac398. PubMed DOI

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