Abstract
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.
Keywords: Acinetobacter, Pseudomonas, CHROMagar acinetobacter, Non-fermenting Gram-negative bacteria, Opportunistic pathogens
Specifications Table
| Subject | Biology |
| Specific subject area | Metagenome-assembled genomes of non-fermenting Gram-negative bacteria from manure, soil and manured soil samples |
| Type of data | Table, Figure and FASTA files of MAGs |
| Data collection | Genomic DNA was extracted from bacteria from microcosms combining soil and dairy cow manure, following enrichment for NFGNB on CHROMagar Acinetobacter. Genomic DNA was isolated using the Fast DNA Spin Kit and sequenced on an Illumina Novaseq 6000 platform. Sequence reads were quality-checked and assembled using metaSPAdes. MAGs representing 111 non-redundant bacterial species were reconstructed using MetaBAT, MaxBin, SemiBin2, COMEbin, and AVAMB, and de-replicated at >95 % ANI (pairwise comparisons) using dREP |
| Data source location | Location: České Budějovice, Czech Republic. Soil and manure samples for the microcosm experiment were located at 48 °North, 14 °East |
| Data accessibility | Repository name: Dataset of 111 metagenome-assembled genomes from cattle manure, soil and manured soil samples Data identification number: NCBI BioProject PRJNA1231077, ZENODO 10.5281/zenodo.15309541 Direct URL to data: NCBI: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1231077 ZENODO: 10.5281/zenodo.15309541 |
| Related research article | [1] P. Sardar, D. Elhottová, E. Pérez-Valera, 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. 99 (2023) fiad148. https://doi.org/10.1093/femsec/fiad148. The metagenomic data and a description of the microcosm set up can be found in [1]. |
1. Value of the Data
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The dataset provides a comprehensive collection of 111 non-redundant MAGs from non-fermenting Gram-negative bacteria (NFGNB) isolated from soil and manure samples.
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This collection comprises 10 high-quality (MIMAG standard), 47 putative high-quality, and 54 medium-quality MAGs, representing 17 different genera that include Pseudomonas (28 MAGs), Stenotrophomonas (20 MAGs) and Acinetobacter (18 MAGs).
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A total of 44 MAGs originated from manure, 38 from manured soil and 29 from soil. 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).
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The genomic resources provided in this dataset can serve as a basis for future research on the survival, dissemination in the environment, and ecological role of bacterial species of potential clinical relevance.
2. Background
Non-fermenting Gram-negative bacteria (NFGNB) are becoming a growing concern due to their role in antimicrobial resistance and as healthcare-associated pathogens [2]. Acinetobacter spp. and other NFGNB such as Pseudomonas are widely distributed in nature, particularly in soil, water and the gastrointestinal tract of animals. These bacteria exhibit inherent resistance to many antibiotics [3] and readily acquire additional resistance mechanisms [4]. This adaptability has made them a key focus in clinical settings [5]. Previous research suggests that fresh manure from antibiotic-treated cattle enriches the soil with antibiotic resistance genes [6]. Acinetobacter spp. is known to thrive in soil following manure application, being a main actor that potentially contributes to the spread of antibiotic resistance in the environment [7]. In our previous study [1], we performed shotgun metagenomic sequencing to analyse the abundance, taxonomic identification and composition of the antibiotic resistome of NFGNB in manure, soil and manured soil samples. Here, we reconstructed 111 non-redundant MAGs from the metagenomes that account for approximately 91 % of the sequencing reads on average. The MAGs we provide can help to unravel the ecological and genomic mechanisms responsible for their spread and the spread of antibiotic resistance in the environment.
3. Data Description
The dataset contains 111 non-redundant (ANI >95 %) metagenome-assembled genomes (MAGs), all of which meet at least the MIMAG standard for medium quality (>50 % completeness and <10 % contamination) defined by Bowers et al. [8]. From these, we report 10 MAGs of high quality (>90 % completeness, <5 % contamination, encoding all 5S, 16S and 23S rRNAs genes and tRNAs for at least 18 of the 20 amino acids), 47 MAGs of putative high quality (>90 % completeness and <5 % contamination) and 54 MAGs of medium quality. High-quality MAGs were almost complete (98.4 ± 0.6 %, average ± SD) and showed low contamination (0.5 ± 0.6 %). Putative high-quality MAGs had a completeness of 97.5 ± 3 % and contamination of 0.6 ± 0.6 %. The remaining medium-quality MAGs had an average completeness of 70 ± 13 % and contamination of 2.6 ± 2.7 %. The dataset comprises MAGs assembled DNA reads as compressed FASTA files (.fasta.gz) and associated metadata in an Excel spreadsheet (MAGs_data.xlsx). The MAGs have been deposited in NCBI under the BioProject PRJNA1231077 and in Zenodo under https://doi.org/10.5281/zenodo.15309541. The Excel file “MAGs_data.xlsx”, included in the Zenodo dataset, details the following information: MAGs name, origin (manure, soil or manured soil), experiment (whether soil S or B), sample name, binning method, detection of 5S, 16S and 23S rRNA genes, number of nucleotides in the tRNAs, quality metrics (completeness, contamination, GC content, N50, genome size, scaffold and contig count, N90, L50 and L90), taxonomic affiliations predicted with GTDB-Tk, including best matching taxonomy and % ANI for the closest placement in GTDB (for MAGs with ANI >95 %), mapping reads in % average and maximum in a sample, NCBI information (i.e., SRA and BioSample accessions, coverage), and 16S rRNA-based identification and sequence. The tools used to extract each feature from the MAGs are also included in the Excel file. A summary of main MAG characteristics is given in Table 1. A phylogenomic tree (Fig. 1) illustrates the relationships among MAGs, their genome completeness, the percentage of contamination, and whether each MAG is a high-, putative high- or medium-quality MAG, as described above.
Table 1.
General characteristics of the reconstructed NFGNB MAGs from manure, soil or manured soil (M. soil) samples. Taxonomic classifications at the phylum and genus levels were performed using GTDB-Tk [17], and ANI values to the closest reference genomes are provided. ANI values from GTDB-Tk are only reported for MAGs identified at the species level (i.e., all ANIs provided are > 95 %). MAG quality was assessed following the MIMAG standard [8], modified to include putative high-quality MAGs as those with completeness >90 % and contamination <5 %. MAGs names were assigned based on the binning method. MAGs meeting the high-quality MIMAG standard are indicated in bold. A more detailed table, including NCBI accessions and full genome information, is available in the Zenodo dataset (https://doi.org/10.5281/zenodo.15309541).
| MAG | Origin | Original sample | Phylum | Genus (GTDB) | Species (GTDB) | Closest genome ANI (%) | MAG Quality | Completeness (%) | Contamination (%) | Binning Method | Genome size (bp) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S21 | Manure | GT28SEX | Pseudomonadota | Achromobacter | Unknown | Not assigned | pHigh | 100 | 1.21 | SemiBin2 | 6,542,997 | |
| S2 | M. soil | GT14BC2 | Pseudomonadota | Achromobacter | Unknown | Not assigned | pHigh | 99.9 | 0.38 | SemiBin2 | 5,722,719 | |
| S5 | Manure | GT14BEX | Pseudomonadota | Achromobacter | Unknown | Not assigned | pHigh | 94.5 | 1.26 | SemiBin2 | 6,165,035 | |
| V20 | Manure | GT28SEXb | Pseudomonadota | Achromobacter | Unknown | Not assigned | pHigh | 93.2 | 0.76 | AVAMB | 5,921,085 | |
| C7 | M. soil | GT28BC2 | Pseudomonadota | Achromobacter | Unknown | Not assigned | Medium | 72.2 | 3.29 | COMEbin | 5,283,433 | |
| V10 | Manure | GT28BEXa | Pseudomonadota | Achromobacter | A. denitrificans | 99.18 | pHigh | 100 | 0.45 | AVAMB | 6,582,530 | |
| V14 | Soil | GT28SAa | Pseudomonadota | Achromobacter | A. kerstersii | 98.84 | pHigh | 93.2 | 0.71 | AVAMB | 5,861,663 | |
| V18 | Soil | GT28Sac | Pseudomonadota | Achromobacter | A. marplatensis | 97.67 | pHigh | 94.5 | 0.84 | AVAMB | 6,267,628 | |
| M5 | Manure | GT28BEXc | Pseudomonadota | Achromobacter | A. mucicolens | 98.9 | pHigh | 99.7 | 0.3 | MaxBin | 5,857,610 | |
| C5 | Manure | GT14SEX | Pseudomonadota | Achromobacter | A. piechaudii | 98.26 | Medium | 68.5 | 1.58 | COMEbin | 4,666,491 | |
| C20 | M. soil | GT14BC2 | Pseudomonadota | Achromobacter | A. spanius | 95.09 | Medium | 67.6 | 2.39 | COMEbin | 4,643,289 | |
| V41 | Manure | GT14BEXa | Pseudomonadota | Achromobacter | A. veterisilvae | 97.99 | Medium | 81.7 | 1.03 | AVAMB | 5,818,176 | |
| V37 | Manure | GT2BEXb | Pseudomonadota | Acinetobacter | Unknown | Not assigned | Medium | 88.5 | 0.25 | AVAMB | 2,854,542 | |
| V23 | M. soil | GT2BC2b | Pseudomonadota | Acinetobacter | Unknown | Not assigned | Medium | 82.5 | 0.77 | AVAMB | 2,829,191 | |
| C6 | Manure | GT14SEX | Pseudomonadota | Acinetobacter | Unknown | Not assigned | Medium | 50.7 | 1.24 | COMEbin | 1,794,093 | |
| V21 | M. soil | GT2BC2b | Pseudomonadota | Acinetobacter | A. amyesii | 97.47 | pHigh | 94.6 | 0.39 | AVAMB | 3,279,076 | |
| C16 | Manure | GT2SEX | Pseudomonadota | Acinetobacter | A. baumannii | 97.68 | Medium | 75.8 | 3.3 | COMEbin | 3,202,536 | |
| C15 | Manure | GT2BEX | Pseudomonadota | Acinetobacter | A. bohemicus | 96.02 | Medium | 81.2 | 0.49 | COMEbin | 2,634,877 | |
| M13 | Soil | GT2Sab | Pseudomonadota | Acinetobacter | A. calcoaceticus | 96.32 | pHigh | 99.9 | 0.13 | MetaBAT | 3,801,409 | |
| C10 | M. soil | GT28BC2 | Pseudomonadota | Acinetobacter | A. calcoaceticus | 97.12 | Medium | 56.7 | 4.79 | COMEbin | 2,263,300 | |
| V36 | Manure | GT2BEXa | Pseudomonadota | Acinetobacter | A. faecalis | 98.95 | pHigh | 95.2 | 1.75 | AVAMB | 2,344,785 | |
| S3 | M. soil | GT14BC2 | Pseudomonadota | Acinetobacter | A. gandensis | 98.68 | High | 100 | 0.36 | SemiBin2 | 3,194,030 | |
| S24 | M. soil | GT2BC2 | Pseudomonadota | Acinetobacter | A. guillouiae | 97.73 | Medium | 86.7 | 2.58 | SemiBin2 | 3,781,671 | |
| S32 | Manure | GT2SEX | Pseudomonadota | Acinetobacter | A. johnsonii | 95.78 | pHigh | 99.9 | 0.41 | SemiBin2 | 3,375,171 | |
| V35 | Manure | GT2SEXc | Pseudomonadota | Acinetobacter | A. pseudolwoffii | 97.93 | Medium | 84.0 | 0.88 | AVAMB | 2,458,038 | |
| V8 | Soil | GT28Baa | Pseudomonadota | Acinetobacter | A. schindleri | 97.71 | pHigh | 100 | 0.16 | AVAMB | 3,060,448 | |
| S29 | M. soil | GT2SC2 | Pseudomonadota | Acinetobacter | Acinetobacter sp002135435 | 98.93 | Medium | 81.2 | 2.28 | SemiBin2 | 3,040,298 | |
| V38 | Manure | GT2BEXc | Pseudomonadota | Acinetobacter | Acinetobacter sp002365595 | 98.16 | Medium | 85.6 | 0.37 | AVAMB | 2,686,145 | |
| S30 | Manure | GT2SEX | Pseudomonadota | Acinetobacter | Acinetobacter sp013417555 | 95.76 | Medium | 58.4 | 0.37 | SemiBin2 | 1,796,683 | |
| M8 | Manure | GT2SEXc | Pseudomonadota | Acinetobacter | A. vivianii | 97.38 | High | 100 | 0.12 | MaxBin | 3,884,090 | |
| C18 | Soil | GT14BA | Pseudomonadota | Agrobacterium | A. fabacearum | 98.72 | High | 99.9 | 1.01 | COMEbin | 5,070,611 | |
| M10 | Manure | GT2SEXc | Pseudomonadota | Alcaligenes | Unknown | Not assigned | Medium | 61.1 | 7.74 | MaxBin | 3,792,446 | |
| M14 | Manure | GT2SEXa | Pseudomonadota | Alcaligenes | Alcaligenes faecalis | 98.45 | pHigh | 100 | 0.57 | MetaBAT | 4,114,606 | |
| V12 | Manure | GT28BEXc | Pseudomonadota | Alcaligenes | Alcaligenes nematophilus | 97.5 | pHigh | 90.2 | 0.6 | AVAMB | 3,979,026 | |
| V5 | Manure | GT14SEXb | Pseudomonadota | Alcaligenes | Alcaligenes sp023425645 | 97.38 | pHigh | 99.3 | 0.96 | AVAMB | 3,774,399 | |
| C14 | Manure | GT28SEX | Pseudomonadota | Bordetella | Bordetella trematum | 99.59 | pHigh | 93.7 | 0.7 | COMEbin | 4,161,353 | |
| V17 | Soil | GT28Sac | Pseudomonadota | Burkholderia | Burkholderia contaminans | 98.43 | pHigh | 97.8 | 0.49 | AVAMB | 8,062,764 | |
| V16 | Soil | GT28Sac | Bacteroidota | Chryseobacterium | C. culicis | 95.33 | Medium | 70.3 | 0.4 | AVAMB | 4,177,414 | |
| V24 | M. soil | GT2BC2c | Bacteroidota | Chryseobacterium | C. jejuense | 95.24 | pHigh | 100 | 0.61 | AVAMB | 5,212,491 | |
| S22 | M. soil | GT2BC2 | Bacteroidota | Chryseobacterium | C. joostei | 96.37 | pHigh | 94.8 | 0.13 | SemiBin2 | 4,459,574 | |
| V31 | Soil | GT2Sac | Bacteroidota | Chryseobacterium | C. rhizosphaerae | 98.08 | pHigh | 95.9 | 0.1 | AVAMB | 5,097,737 | |
| V15 | Soil | GT28SAa | Bacteroidota | Chryseobacterium | Chryseobacterium sp900156935 | 99.37 | pHigh | 99.9 | 0.14 | AVAMB | 5,184,267 | |
| S15 | M. soil | GT28BC2 | Pseudomonadota | Comamonas | Unknown | Not assigned | pHigh | 90.7 | 1.07 | SemiBin2 | 4,586,957 | |
| C12 | Manure | GT28BEX | Pseudomonadota | Comamonas | Unknown | Not assigned | Medium | 65.9 | 1.09 | COMEbin | 1,814,831 | |
| V2 | Soil | GT14Sac | Pseudomonadota | Comamonas | C. koreensis | 98.91 | pHigh | 100 | 0.08 | AVAMB | 4,875,248 | |
| M15 | Manure | GT14BEXb | Pseudomonadota | Comamonas | C. sp002472915 | 98.21 | pHigh | 100 | 0.27 | MetaBAT | 4,874,396 | |
| S28 | M. soil | GT2SC2 | Pseudomonadota | Comamonas | C. testosteroni | 98.83 | pHigh | 100 | 0.2 | SemiBin2 | 5,095,908 | |
| M11 | Manure | GT14BEXc | Pseudomonadota | Comamonas | C. tsuruhatensis | 98.17 | pHigh | 100 | 0 | MaxBin | 6,154,020 | |
| S14 | M. soil | GT28BC2 | Pseudomonadota | Cupriavidus | Cupriavidus sp000955785 | 96.49 | pHigh | 99.8 | 1.22 | SemiBin2 | 7,429,278 | |
| C8 | M. soil | GT28BC2 | Pseudomonadota | Diaphorobacter | D. nitroreducens | 98.31 | Medium | 54.3 | 1.95 | COMEbin | 2,384,275 | |
| V26 | Soil | GT2Sab | Bacteroidota | Flavobacterium | Flavobacterium sp002303885 | 98.08 | pHigh | 99.7 | 0.08 | AVAMB | 5,375,231 | |
| C9 | M. soil | GT28BC2 | Pseudomonadota | Microvirgula | Unknown | Not assigned | Medium | 77.4 | 4.05 | COMEbin | 2,880,378 | |
| C19 | Soil | GT14BA | Pseudomonadota | Paraburkholderia | P. hospita | 98.85 | Medium | 84.9 | 4.33 | COMEbin | 6,736,010 | |
| C17 | Soil | GT14BA | Pseudomonadota | Paraburkholderia | P. nemoris | 97.71 | Medium | 52.2 | 6.96 | COMEbin | 2,366,553 | |
| C2 | Soil | GT14BA | Pseudomonadota | Paraburkholderia | P. nemoris | 98.59 | Medium | 50.4 | 6.75 | COMEbin | 4,222,632 | |
| S1 | Soil | GT14BA | Pseudomonadota | Paraburkholderia | P. terricola | 99.26 | Medium | 85.3 | 0.25 | SemiBin2 | 5,799,680 | |
| V33 | Manure | GT2SEXa | Pseudomonadota | Pseudomonas | Unknown | Not assigned | pHigh | 97.1 | 0.07 | AVAMB | 5,154,236 | |
| S27 | Soil | GT2SA | Pseudomonadota | Pseudomonas | Unknown | Not assigned | Medium | 83.7 | 0.97 | SemiBin2 | 4,956,356 | |
| M2 | M. soil | GT28BC2c | Pseudomonadota | Pseudomonas | Unknown | Not assigned | Medium | 55.6 | 9.21 | MaxBin | 5,220,937 | |
| M7 | M. soil | GT2SC2b | Pseudomonadota | Pseudomonas | Unknown | Not assigned | Medium | 52.1 | 8.53 | MaxBin | 2,442,360 | |
| C4 | M. soil | GT14SC2 | Pseudomonadota | Pseudomonas | P. alloputida | 96.52 | Medium | 52.5 | 2.46 | COMEbin | 3,936,810 | |
| S31 | Manure | GT2SEX | Pseudomonadota | Pseudomonas | P. capeferrum | 99.59 | High | 99.5 | 0.51 | SemiBin2 | 5,724,253 | |
| S26 | Manure | GT2SEX | Pseudomonadota | Pseudomonas | P. helleri | 97.48 | pHigh | 99.7 | 0.14 | SemiBin2 | 5,310,282 | |
| S18 | Manure | GT28BEX | Pseudomonadota | Pseudomonas | P. kermanshahensis | 96.83 | High | 92.3 | 1.06 | SemiBin2 | 5,678,766 | |
| V28 | Soil | GT2Sac | Pseudomonadota | Pseudomonas | P. laurylsulfatiphila | 99.66 | pHigh | 98.4 | 0.07 | AVAMB | 6,282,357 | |
| M9 | Manure | GT2SEXc | Pseudomonadota | Pseudomonas | P. oleovorans | 96.86 | pHigh | 100 | 0.2 | MaxBin | 5,542,180 | |
| S9 | Manure | GT14SEX | Pseudomonadota | Pseudomonas | P. palmensis | 98.79 | High | 100 | 0.16 | SemiBin2 | 5,571,667 | |
| V32 | M. soil | GT2SC2c | Pseudomonadota | Pseudomonas | P. protegens | 98.93 | Medium | 82.9 | 3.03 | AVAMB | 5,989,150 | |
| V29 | Soil | GT2Sac | Pseudomonadota | Pseudomonas | P. protegens | 96.47 | pHigh | 93.0 | 1.19 | AVAMB | 6,564,374 | |
| V25 | Soil | GT2SAa | Pseudomonadota | Pseudomonas | P. putida | 97.21 | Medium | 55.8 | 0.18 | AVAMB | 3,376,573 | |
| C1 | Soil | GT14SA | Pseudomonadota | Pseudomonas | P. putida | 97.76 | Medium | 55.5 | 9.77 | COMEbin | 1,188,256 | |
| V22 | M. soil | GT2BC2b | Pseudomonadota | Pseudomonas | P. putida | 99.25 | pHigh | 95.4 | 0.41 | AVAMB | 5,609,907 | |
| V27 | Soil | GT2Sac | Pseudomonadota | Pseudomonas | P. putida | 98 | Medium | 64.2 | 0.09 | AVAMB | 4,083,299 | |
| C3 | M. soil | GT14SC2 | Pseudomonadota | Pseudomonas | P. putida | 97.94 | Medium | 50.3 | 2.47 | COMEbin | 3,664,077 | |
| S4 | M. soil | GT14BC2 | Pseudomonadota | Pseudomonas | P. shirazensis | 97.29 | Medium | 85.3 | 0.38 | SemiBin2 | 4,717,260 | |
| S19 | M. soil | GT28SC2 | Pseudomonadota | Pseudomonas | Pseudomonas sp000955815 | 99.69 | pHigh | 95.8 | 1.59 | SemiBin2 | 5,181,704 | |
| V19 | M. soil | GT28SC2b | Pseudomonadota | Pseudomonas | Pseudomonas sp001422615 | 96.85 | Medium | 51.8 | 0.63 | AVAMB | 2,968,572 | |
| V9 | Soil | GT28Bab | Pseudomonadota | Pseudomonas | Pseudomonas sp001655615 | 97.24 | pHigh | 99.2 | 0.93 | AVAMB | 6,293,145 | |
| S10 | Soil | GT28BA | Pseudomonadota | Pseudomonas | Pseudomonas sp020520285 | 99.26 | Medium | 89.7 | 0.22 | SemiBin2 | 6,347,120 | |
| M1 | Manure | GT14SEXb | Pseudomonadota | Pseudomonas | Pseudomonas sp025837155 | 97.95 | High | 100 | 0 | MaxBin | 4,307,444 | |
| V39 | M. soil | GT14BC2b | Pseudomonadota | Pseudomonas | Pseudomonas sp029839275 | 96.98 | pHigh | 99.8 | 0.1 | AVAMB | 5,158,824 | |
| S20 | M. soil | GT28SC2 | Pseudomonadota | Pseudomonas | Pseudomonas sp900101695 | 98.83 | pHigh | 99.1 | 2.82 | SemiBin2 | 5,501,807 | |
| V11 | Manure | GT28BEXb | Pseudomonadota | Pseudomonas | Pseudomonas sp943914515 | 98.82 | pHigh | 94.1 | 1.56 | AVAMB | 6,141,918 | |
| V42 | Manure | GT14BEXb | Pseudomonadota | Pseudomonas | P. urmiensis | 98.08 | High | 100 | 0.06 | AVAMB | 5,583,077 | |
| S16 | M. soil | GT28BC2 | Bacteroidota | Sphingobacterium | Unknown | Not assigned | pHigh | 93.3 | 0.87 | SemiBin2 | 5,619,382 | |
| S13 | M. soil | GT28BC2 | Bacteroidota | Sphingobacterium | Unknown | Not assigned | Medium | 70.0 | 0.41 | SemiBin2 | 4,277,425 | |
| S23 | M. soil | GT2BC2 | Bacteroidota | Sphingobacterium | S. paramultivorum | 99.61 | High | 92.6 | 1.59 | SemiBin2 | 5,603,380 | |
| C22 | M. soil | GT14BC2 | Bacteroidota | Sphingobacterium | S. siyangense | 97.46 | Medium | 65.4 | 3.9 | COMEbin | 4,334,548 | |
| V3 | Soil | GT14Sac | Bacteroidota | Sphingobacterium | S. sp019969845 | 98.83 | Medium | 85.2 | 2.8 | AVAMB | 3,328,530 | |
| C13 | Soil | GT28SA | Bacteroidota | Sphingobacterium | S. sp029542085 | 97.35 | Medium | 73.1 | 6.11 | COMEbin | 4,923,870 | |
| S17 | Manure | GT28BEX | Pseudomonadota | Stenotrophomonas | Unknown | Not assigned | Medium | 80.3 | 0.66 | SemiBin2 | 3,719,936 | |
| S6 | Manure | GT28BEX | Pseudomonadota | Stenotrophomonas | Unknown | Not assigned | Medium | 73.1 | 0.24 | SemiBin2 | 3,028,981 | |
| M12 | Soil | GT28Bac | Pseudomonadota | Stenotrophomonas | Unknown | Not assigned | Medium | 64.9 | 5.1 | MetaBAT | 3,278,791 | |
| V6 | Manure | GT14SEXb | Pseudomonadota | Stenotrophomonas | S. acidaminiphila | 98.57 | pHigh | 94.5 | 0.08 | AVAMB | 3,473,400 | |
| C21 | M. soil | GT14BC2 | Pseudomonadota | Stenotrophomonas | S. bentonitica | 97.85 | Medium | 59.1 | 4.09 | COMEbin | 2,872,105 | |
| V34 | Manure | GT2SEXb | Pseudomonadota | Stenotrophomonas | S. geniculata | 98.2 | pHigh | 100 | 0 | AVAMB | 4,536,706 | |
| M4 | Manure | GT28BEXb | Pseudomonadota | Stenotrophomonas | S. hibiscicola | 98.03 | pHigh | 100 | 0.83 | MaxBin | 4,279,084 | |
| V1 | Soil | GT14Sab | Pseudomonadota | Stenotrophomonas | S. indicatrix | 97.05 | pHigh | 100 | 0.08 | AVAMB | 4,490,560 | |
| M6 | M. soil | GT28SC2a | Pseudomonadota | Stenotrophomonas | S. indicatrix | 95.08 | Medium | 59.6 | 8.16 | MaxBin | 2,601,569 | |
| M3 | Manure | GT28BEXa | Pseudomonadota | Stenotrophomonas | S. lactitubi | 95.19 | pHigh | 100 | 1.34 | MaxBin | 4,408,438 | |
| V4 | Manure | GT14SEXa | Pseudomonadota | Stenotrophomonas | S. maltophilia | 97.26 | Medium | 86.2 | 0.26 | AVAMB | 3,538,935 | |
| S12 | M. soil | GT28BC2 | Pseudomonadota | Stenotrophomonas | S. maltophilia | 99.29 | Medium | 79.2 | 0.99 | SemiBin2 | 3,729,623 | |
| S25 | M. soil | GT2BC2 | Pseudomonadota | Stenotrophomonas | S. maltophilia | 98.25 | High | 100 | 0 | SemiBin2 | 4,563,766 | |
| V30 | Soil | GT2Sac | Pseudomonadota | Stenotrophomonas | S. rhizophila | 96.26 | pHigh | 96.0 | 0.4 | AVAMB | 4,608,349 | |
| V40 | M. soil | GT14BC2c | Pseudomonadota | Stenotrophomonas | S. sepilia | 95.62 | Medium | 79.1 | 0.39 | AVAMB | 3,707,479 | |
| V13 | Manure | GT28BEXc | Pseudomonadota | Stenotrophomonas | Stenotrophomonas sp002471015 | 97.88 | Medium | 63.7 | 3.24 | AVAMB | 2,761,634 | |
| S8 | M. soil | GT14SC2 | Pseudomonadota | Stenotrophomonas | Stenotrophomonas sp003086775 | 97.15 | Medium | 55.0 | 0.63 | SemiBin2 | 3,218,735 | |
| V7 | Manure | GT14SEXb | Pseudomonadota | Stenotrophomonas | Stenotrophomonas sp003484865 | 98.32 | Medium | 71.4 | 1.49 | AVAMB | 3,313,655 | |
| C11 | Manure | GT28BEX | Pseudomonadota | Stenotrophomonas | Stenotrophomonas sp004348115 | 97.75 | Medium | 88.5 | 3.32 | COMEbin | 3,986,901 | |
| S7 | M. soil | GT14SC2 | Pseudomonadota | Stenotrophomonas | Stenotrophomonas sp030549615 | 96.88 | Medium | 80.7 | 0.54 | SemiBin2 | 4,060,927 | |
| S11 | Soil | GT28BA | Pseudomonadota | Variovorax | Variovorax sp000282635 | 99.08 | Medium | 52.6 | 0.56 | SemiBin2 | 3,412,441 |
Fig. 1.
Phylogenomic tree and quality assessment of 111 metagenome-assembled genomes (MAGs) reconstructed from manure, soil and manured soil samples after enrichment with CHROMagar Acinetobacter. The tree was constructed using fastree on the MSA alignment by GTDB-tk using 120 concatenated single-copy bacterial genes. Outer rings show MAG quality classification: filled green stars indicate high-quality MAGs (MIMAG standard), filled blue stars indicate putative high-quality MAGs (>90 % completeness and <5 % contamination), while unfilled stars represent medium-quality MAGs (>50 % completeness and <10 % contamination). Red bars beneath the stars indicate contamination levels (0–10 %), whereas black bars represent completeness (50–100 %) as indicated by CheckM2.
4. Experimental Design, Materials and Methods
Metagenome-assembled genomes (MAGs) were obtained from cattle manure, soil and manured soil samples after enrichment via cultivation in CHROMagar Acinetobacter (CHROMagar, Paris, France) as described in [1]. Briefly, microcosms combining fresh manure from a private dairy farm (under antibiotic prophylaxis) and soil from two organic farms were sampled after 2, 14 and 28 days of incubation. Five grams of each of the soil, manure or manured soil samples were used to inoculate plates in duplicate containing CHROMagar Acinetobacter. After incubating the plates at 28°C for 24h, microbial biomass was harvested by resuspending and centrifuging at 12,170 RCF for 5 min. Bacterial DNA was isolated using the Fast DNA Spin Kit (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer's protocol. Shotgun metagenomic sequencing for a total of 52 samples was performed by Novogene (Hong Kong) on a NovaSeq 6000 instrument using 2×150 bp reads.
Raw reads were processed for quality-check, assembly and taxonomic analysis as described in [1]. Briefly, adapters, low-quality and contaminant reads were removed using BBMap and BBduk 38.96 [9]. For the construction of MAGs in this dataset, DNA reads were assembled individually (i.e., 52 assemblies), and co-assembled per soil, treatment and time (18 assemblies) using metaSPAdes 3.14.1. MAGs were obtained using multiple approaches that included AVAMB 4.1 [10], Semibin 2.1.0 [11], MetaBAT 2.17 [12], COMEbin 1.0.4 [13] and MaxBin 2.2.5 [14]. In the case of AVAMB, we used the individual assemblies following the recommendations. For the other binners, we used the co-assembled contigs. We assessed bin completeness and contamination using CheckM2 [15]. All MAGs were then clustered using dRep 3.5.0 [16] at > 95 % ANI (pairwise comparisons). One representative MAG from each cluster was chosen using the default score-based system in dRep. MAGs with >50 % completeness and <10 % contamination that met the MIMAG standard for at least medium quality were kept. MAGs were named sequentially according to the binning software used. Taxonomic assignment of MAGs was performed using GTDB-Tk 2.0 with the database GTDB R220 [17]. Species-level identification is only provided for MAGs with >95 % ANI to genomes in the GTDB reference database. A phylogenomic tree was constructed using fastree on the MSA alignments provided by GTDB-tk. The tree was visualized using iTol [18]. Complete (∼ 1,500 nucleotides) and near-complete (at least 1,200 nucleotides) 16S rRNA sequences were reconstructed from the raw fastq files using RiboTaxa 1.5 [19] using default parameters and linked to MAGs using MarkerMAG 1.1.28 [20].
Limitations
The dataset includes 54 medium-quality MAGs (>50 % completeness, <10 % contamination), which may exhibit a higher degree of fragmentation compared to the 10 high-quality and 47 putative high-quality MAGs (>90 % completeness, <5 % contamination). These MAGs might limit certain types of genomic investigations, such as those requiring complete genomes or genes. Additionally, the use of a cultivation-based enrichment method targeting non-fermenting Gram-negative bacteria might have introduced a bias in the representation of the broader microbial community present in the original manure and soil samples. While this method was specifically chosen to focus on potentially risky NFGNB, other microbial groups might be underrepresented or absent from the resulting dataset.
Ethics Statement
This research did not involve human subjects, animals, or any species requiring ethical approval.
CRediT authorship contribution statement
Eduardo Pérez-Valera: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Supervision, Project administration, Funding acquisition. Dana Elhottová: Conceptualization, Investigation, Resources, Writing – review & editing, Project administration, Funding acquisition.
Acknowledgments
We thank Karolína Farková and Linda Jíšová for technical assistance, Puspendu Sardar and Alper Dede for help with sequence assembly and processing, and MVDr. S. Kollar for fresh manure sampling. This work was supported by the Czech Science Foundation (17-25660S and 20-28265Y).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data Availability
References
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