Cattle feces are a reservoir of diverse Acinetobacter species with potential to spread antibiotic resistance genes

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Antibiotic resistance poses a major threat to human health, with antibiotic use in livestock contributing to the selection and spread of resistance genes. The genus Acinetobacter includes human- and animal-associated species capable of acquiring resistance, yet their diversity and resistance potential in livestock remain far less explored than in humans. In this study, we investigated Acinetobacter in cattle feces from 28 Czech farms with contrasting antibiotic use, aiming to assess species composition, resistance profiles, and the potential for resistance dissemination. We applied an integrative approach combining strain isolation and characterization, enrichment cultures, metabarcoding, and shotgun metagenomics. Results. Cattle feces harbored diverse Acinetobacter species with A. indicus and A. pseudolwoffii being the core species based on both isolated strains and metabarcoding, while A. baumannii was less common. Acinetobacter species occurrence determined by metabarcoding was driven by multiple factors, including production type, herd size, and per-head antibiotic use, while their abundance was mostly influenced by sample type (higher in feces from the farm floor than in rectal samples) and production type (higher in dairy than in beef cattle). Remarkably, 37% of the 284 isolated strains could not be assigned to validly named species and represent at least 19 putative novel species. Decreased susceptibility due to acquired resistance was observed in 57 strains; notably, A. indicus and A. pseudolwoffii from antibiotic-using farms were less susceptible to streptomycin than those from antibiotic-free farms. Shotgun metagenomics revealed a greater richness of acquired resistance genes in antibiotic-using farms, including the clinically relevant carbapenemase gene bla OXA−58 . This gene was located on putative plasmid contigs alongside streptomycin resistance determinants strA - strB , suggesting horizontal dissemination under streptomycin selection pressure. Strain analysis confirmed the co-localization of bla OXA−58 and strA - strB on a large plasmid in A. pseudolwoffii . Conclusions. Despite relatively strict regulations, Czech cattle farms constitute a reservoir of antibiotic-resistant Acinetobacter carrying mobile resistance genes of clinical concern. Commonly applied antibiotics likely co-select for such genes, posing an ongoing public health risk. Our findings reveal an unexpectedly high diversity of Acinetobacter spp. in cattle, highlighting the research bias toward human-associated species and underscoring the need for integrated One Health monitoring approaches. Antibiotic susceptibility Carbapenemase Cattle farm Diversity Identification MALDI-TOF MS Metabarcoding Metagenomics rpoB. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background The long-term use of antibiotics in livestock production has been a major driver of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs), which can be disseminated into the environment through practices such as the application of manure to agricultural soils [ 1 – 3 ]. Among livestock sectors, cattle farming is particularly significant, accounting for more than 50% of global antimicrobial use between 2019 and 2021 [ 4 ]. As part of the European Union’s broader One Health strategy, the use of antibiotics for growth promotion in livestock was banned in 2006, with further restrictions on prophylactic and metaphylactic applications introduced in 2022. However, antibiotics are still widely used for disease treatment on cattle farms, and their impact on the cattle gut resistome – and the potential risks this poses to public health – remains poorly understood. Among antibiotic-resistant bacteria associated with cattle, Acinetobacter baumannii is of particular concern. This opportunistic pathogen is a leading cause of nosocomial infections, and certain strains exhibit resistance to virtually all available antibiotics, including last-resort drugs such as carbapenems and colistin [ 5 ]. Its remarkable capacity to acquire ARGs via mobile genetic elements (MGEs) – including insertion sequences, transposons, integrons, and plasmids – plays a key role in its multidrug resistance [ 6 ]. Several cultivation-based studies have documented the occurrence of A. baumannii in cattle, with isolates obtained from nasal, oral, rectal, and fecal samples (e.g. [ 7 – 10 ]). Two large-scale studies from Germany and South Korea reported A. baumannii in 15.6% and 2.6% of cattle, respectively, suggesting an overall low prevalence in cattle populations [ 7 , 10 ]. Most cattle-associated strains belonged to novel multilocus sequence types [ 8 , 9 ], but some were genetically linked to sequence types known from human clinical isolates [ 7 , 10 ]. In addition, although the majority of cattle-derived strains were wild-type susceptible to antibiotics [ 7 , 10 ], carbapenem-resistant A. baumannii has also been isolated from cattle, harboring clinically relevant ARGs such as bla OXA−23 , bla OXA−24 , and bla OXA−58 [ 11 , 12 ] . Carbapenem-resistant Acinetobacter species other than A. baumannii have also been reported on cattle farms, including Acinetobacter indicus [ 13 ] or Acinetobacter variabilis [ 14 ], as well as potential novel species [ 15 ]. Moreover, several recent studies from China have documented multidrug-resistant (MDR) Acinetobacter spp. from cattle farms, carrying clinically relevant ARGs on plasmids, chromosomes, or both (e.g. [ 13 , 15 , 16 ]). For instance, co-localization of tet (X3) (tigecycline resistance) with bla NDM−1 or bla OXA−58 (carbapenem resistance) along with three additional ARGs on plasmids was shown in MDR A. indicus from a dairy farm [ 13 ]. In contrast, such MDR Acinetobacter spp. have not been reported in European cattle, but it remains unclear whether this difference reflects more restricted antibiotic use or simply a lack of comparable studies in Europe. Taken together, these studies suggest that non- baumannii Acinetobacter species may play an important role in the dissemination of ARGs in farm environments. Moreover, some of these species, such as A. variabilis , are also recognized opportunistic human pathogens [ 17 ]. Nevertheless, current knowledge of Acinetobacter spp. in cattle and their potential for ARG dissemination remains fragmented and largely disconnected. For instance, Acinetobacter -specific LowGC-type plasmids were previously shown to be important for the spread of ARGs, including the tigecycline resistance gene tet (X), from livestock manure to soils [ 18 – 20 ], but it remains unclear whether such plasmids are also present in the tet (X3)-positive isolates from Chinese farms and which host species they are associated with. Moreover, the co-selection of ARGs with heavy metal resistance genes (HMRGs) in the farm environment should be taken into account [ 21 ]. HMRGs are often found alongside ARGs on Acinetobacter plasmids and genomic islands, both in clinical A. baumannii and in farm-associated non- baumannii Acinetobacter spp. [ 13 , 22 ]. In addition, heavy metals such as Cu, Zn, Pb, As, and Cr are commonly present in cattle feces and manure, originating either from dietary supplements or feed contamination [ 2 , 23 ]. Consequently, the presence of these metals may exert additional selective pressure [ 21 ], promoting the development of antibiotic resistance in Acinetobacter spp. within the cattle gastrointestinal tract. Overall, comprehensive data on the abundance, species composition, and antibiotic resistance of Acinetobacter in cattle feces and manure are still lacking. Limited insights are available from studies of Pulami et al. [ 24 , 25 ] on German biogas plants processing cattle manure. For instance, the abundance of Acinetobacter spp. in input manure material was estimated at 10⁶–10⁸ 16S rRNA gene copies per gram of fresh weight [ 24 ], yet data on their abundance in fresh cattle feces remain unavailable. Factors influencing Acinetobacter species occurrence in cattle have so far been examined only for A. baumannii [ 7 ]. In the studied German cattle cohort, A. baumannii was more common in dairy than in beef cattle and calves, and its prevalence correlated with the use of third-generation cephalosporins in the preceding six months. A seasonal peak in isolation rates during summer was also observed [ 7 ], suggesting an effect of temperature and humidity on cattle colonization with A. baumannii . Yet, comparable data for other Acinetobacter species are still lacking. Therefore, we aimed to comprehensively investigate how antibiotic use and other farm- and cow-specific factors affect the abundance and diversity of Acinetobacter spp. in cattle feces, and to assess the contribution of antibiotic use to the acquisition of antibiotic resistance. We hypothesized that (i) the composition of Acinetobacter species is influenced by cattle type and prior antibiotic administration, (ii) on-farm antibiotic use selects for resistant and MDR Acinetobacter strains carrying horizontally acquired ARGs, and (iii) heavy metals present in cattle feces further co-select for ARGs. To test these hypotheses, we applied an integrative approach combining selective enrichment culture and characterization of Acinetobacter strains with culture-independent methods and metagenomics. We used fecal samples collected from farm floors and individual cows across 28 Czech cattle farms with varying levels of antibiotic use. Isolated strains were taxonomically characterized and screened for antibiotic susceptibility as well as horizontally acquired ARGs. Enrichment cultures were analyzed for Acinetobacter diversity using rpoB metabarcoding and for resistome composition using shotgun metagenomics. The abundance of Acinetobacter spp. was quantified by qPCR from total fecal DNA. All analyses were complemented with farm metadata and sample chemical characteristics, including antibiotic residues and heavy metal content. Together, this study provides a comprehensive characterization of Acinetobacter diversity and antibiotic resistance in cattle feces. Methods Farms, sampling, and sample processing Cattle feces were sampled at 28 anonymous Czech cattle farms representing varying levels of antibiotic use between April and October 2022 (Fig. 1 and Table S1 ). At each farm, a composite sample consisting of 5–10 dung subsamples was collected from the farm floor or pasture (‘floor’ samples, n = 28) using a sterile garden trowel while avoiding direct contact with the ground. Additionally, at 14 of these farms, fresh fecal samples were collected from 5–11 individual cows per farm (‘cow’ samples, n = 93), either directly from the rectum or immediately after defecation, using sterile examination gloves (see Table S2 and Table S3 for sample details). Samples were kept refrigerated at ≈ 4°C during transport and were processed the same day. Each sample was thoroughly mixed within its collection bag before being divided for subsequent analyses (Fig. 2 ). Subsamples intended for Acinetobacter culturing were prepared first, using sterile spatulas and falcon tubes. These subsamples were either stored directly at 4°C for use within 1–2 days; mixed with 2 mL of 0.9% NaCl and frozen at − 20°C for use within 6 months; or combined with 2 mL of 0.9% NaCl and 4 mL of glycerol and frozen at − 20°C for long-term storage (> 6 months). Subsamples for DNA isolation were stored in sterile Eppendorf tubes at − 20°C. Subsamples for chemical analyses were stored in plastic bags at − 80°C, except for aliquots for dry matter and pH measurements, which were measured upon arrival. Chemical composition analysis Sample dry matter content was determined according to standard protocols [ 26 ], and sample pH was measured by directly immersing an electrode and thermometer (Jenway 3510 Standard Digital pH Meter, Cole-Parmer) into a sample aliquot [ 27 ]. To assess heavy metal content, approximately 0.5 g of the homogenized, lyophilized sample was first mineralized by microwave-assisted acid digestion in a MARS 5 system (CEM Corp.) using concentrated HNO 3 . The content of metallic elements (Ag, As, Cd, Co, Cr, Cu, Ni, Pb, Sb, Se, Sr, Zn) was determined using inductively coupled plasma optical emission spectrometry (ICP-OES, Agilent Technologies). The quality of the measurements was monitored by including blank samples, control standards, and replicate measurements. Total C and N content was determined in solid state using a FLASH 2000 CHNS/O elemental analyzer (Thermo Fisher Scientific). The content of short-chain fatty acids (SCFA) was determined by Gas Chromatography-Mass Spectrometry (GC-MS) following the optimized protocol described by [ 28 ]. The content of antibiotic residues was determined by Liquid Chromatography-Mass Spectrometry (LC-MS) using a custom protocol as follows. Sample extraction was performed using an Accelerated Solvent Extractor (ASE 200; Dionex). The extraction cell was filled with approximately 1.5 g (dry weight) of the sample and extracted with methanol. The extract was then concentrated to approximately 10 mL, centrifuged, and 1 mL of the supernatant was transferred to an LC-MS vial for analysis. Targeted analyses were conducted using an Agilent Infinity 1260 liquid chromatograph coupled with an Agilent 6470 triple quadrupole mass spectrometer (LC/TQ). Chromatographic separation was achieved on a Kinetex Polar C18 (2.6 µm, 3 mm × 100 mm) column equipped with a SecurityGuard Polar C18 (2.6 µm, 3 mm × 2 mm) precolumn (Phenomenex), both heated to 40°C. The mobile phase for gradient elution consisted of phase A: 0.1% formic acid (LC‒MS grade; Honeywell) in Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™) and phase B: 0.1% formic acid in methanol (LC‒MS grade; Honeywell). The gradient elution program was as follows (time [min]/% phase B): 0/0; 1/0; 4/50; 6/50; 9/95; 10/95; 11/0; 12/0. The flow rate was set to 0.4 mL/min, with a total run time of 15 minutes and an injection volume of 2 µL. The ion source parameters were set as follows: source temperature 180°C, gas flow 10 L/min, nebulizer 20 psi, sheath gas temperature 300°C, sheath gas flow 10 L/min, capillary and nozzle voltages 2,500 V and 600 V, respectively. Standard addition was applied to mitigate matrix effects. The mass spectrometer parameters were optimized using MassHunter Workstation Optimizer and Source Optimizer (both Version 10.0, SR1; Agilent Technologies). A complete list of target analytes and settings is provided in Table S4 . Strain isolation Acinetobacter strains were isolated from ‘floor’ samples using the selective culture method described by [ 29 ]. Samples stored at 4°C for 1–2 days or mixed with saline and subsequently stored at − 20°C for up to 6 months were used for this purpose. Aliquots containing 2 g of feces were cultured aerobically with vigorous shaking in 25 mL of mineral medium supplemented with 0.5% (w/v) sodium acetate (ACE medium [ 29 ]) at 30°C and 44°C for 3 h in parallel. The two temperatures were used to reflect the growth preferences of both environmental and mammal-adapted Acinetobacter spp. After 1 h of passive sedimentation, 5 mL of supernatant was transferred to 25 mL of ACE medium and cultured at 30°C and 44°C for up to 2 days. The resulting liquid cultures were then plated onto both ACE agar and CHROMagar™ Acinetobacter (CHROMagar, France). After 24 h of incubation at 30°C and 44°C, selected colonies were subcultured on sheep blood agar plates (Oxoid). Strain taxonomic analysis Identification and classification at the species level, as well as dereplication (i.e., the exclusion of multiple isolates of the same strain obtained from a single sample) were performed using combinations of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), sequence analysis of the RNA polymerase β-subunit ( rpoB ) gene, DNA macrorestriction analysis, and additional methods applied to clarify the species status of certain strains (see Table 1 in Supplementary files ). MALDI-TOF MS was performed on a Microflex LT instrument (Bruker Daltonics) with Bruker Biotyper RTC and Compass v4.1.80 software following the standard Bruker protocol [ 30 ]. Overnight bacterial cultures were analyzed with α-cyano-4-hydroxycinnamic acid as the matrix. Mass spectra were acquired from at least 40 laser shots at 10 positions in automated mode. Identification was performed using the Bruker reference database (version 2021), supplemented with in-house entries of the type/reference strains of validly named species absent from the Bruker library [ 31 ] and the reference strains of novel taxa delineated in the present study. Assignments at the species level were classified according to the Biotyper identification parameters—score values and consistency categories (A or B). "Reliable" was assigned to scores of ≥ 2.3 (category A) or ≥ 2.3 (category B) when the second-ranked species differed by ≥ 0.3, "probable" to scores of 2.0–2.3 (category A) or ≥ 2.3 (category B) when the second-ranked species differed by ≥ 0.2, and "possible" to scores of ≥ 2.0 (category B) when the second-ranked species differed by < 0.2. Cluster analysis was performed using UPGMA (unweighted pair group method with arithmetic mean) with correlation-based distance metrics in Compass v4.1.80. Sequence analysis was performed on a 355-bp fragment of the rpoB gene (nucleotide positions 2915–3269 of Acinetobacter baumannii CIP 70.34ᵀ (GenBank DQ207471.1) [ 32 ]. Analyses were carried out in BioNumerics v7.6 (Applied Maths) as described previously [ 33 ]. The workflow included: (i) compiling a database of rpoB sequences from type strains of all validly named Acinetobacter species and sequences from this study, (ii) constructing a Neighbor-Joining phylogram from a multiple sequence alignment, and (iii) evaluating percentage identity values. Species-level assignments were defined as ≥ 97% identity to the closest type strain or reference strain of a tentative novel taxon and categorized as reliable (second-closest species differs by ≥ 2%), probable (≥ 1.5%), or possible (< 1.5%). Final identification combined MALDI-TOF MS and rpoB results to offset method-specific limitations: MALDI-TOF MS lacks resolution for closely related species, whereas partial rpoB sequences may be affected by interspecies recombination. Consensus identification levels (IL) were defined as: IL1, both rpoB and MALDI-TOF MS reliable; IL2, rpoB reliable and MALDI-TOF MS probable; IL3, rpoB probable and MALDI-TOF MS reliable; IL4, both rpoB and MALDI-TOF MS probable. All other combinations were interpreted as identification at the genus level only, unless additional taxonomic methods were applied (Table S5 ). Additional characterization of A. baumannii involved detection of the species-specific bla OXA−51−like gene to confirm species identity [ 34 ], and multiplex PCR to identify international MDR epidemic clones 1 and 2 [ 35 ] prevailing in Czech hospitals. In addition, in-house phenotypic assays were applied to distinguish phylogenetically related species within the Acinetobacter hemolytic clade [ 36 ]. Dereplication of isolates from each sample was performed in two steps. First, two spectra were obtained per isolate, and all spectra from a sample were clustered with the reference spectra of validly named species and tentative taxa. Clustering at a distance ≤ 50—below which replicate isolates from the same strain consistently clustered—was considered to indicate potential replicate isolates, from which one or two representatives were selected. Second, representatives of the same species were then subjected to macrorestriction analysis [ 37 ], and only isolates with distinct macrorestriction patterns were retained in the final set. Antibiotic susceptibility testing and screening for antibiotic-resistance genes (ARGs) Strain susceptibility to antimicrobial agents was determined by the disk diffusion test on Mueller-Hinton agar (Oxoid) at 30°C according to standard protocols [ 38 ], except for the Acinetobacter lwoffii phylogroup (including A. pseudolwoffii and Taxon 7443), Taxon 7209, Taxon 7509, Taxon 7947, and Taxon 7579 strains, which displayed poor growth on Mueller-Hinton agar and were tested on Levinthal’s agar medium (HiMedia). The antimicrobial agents (Oxoid; µg/disk) tested were amoxycillin + clavulanate (20 + 10), ampicillin (10), ampicillin + sulbactam (10 + 10), cefalotin (30), ceftazidime (30), ciprofloxacin (5), chloramphenicol (30), gentamicin (10), kanamycin (30), meropenem (10), nalidixic acid (30), neomycin (10), penicillin G (10 U), piperacillin (100), streptomycin (10), sulfamethoxazole (25), tetracycline (30), and trimethoprim (5). Minimal inhibitory concentrations (MIC) for colistin were determined using the broth microdilution method [ 38 ]. The evaluation of antibiograms aimed to distinguish between wild-type phenotypes and decreased susceptibility due to acquired resistance mechanisms. To achieve this, we visually examined the distribution of inhibition zone diameters for each antibiotic and species/taxons with at least 10 strains to identify deviations from a normal distribution, which could suggest the presence of acquired resistance mechanisms [ 39 ]. Ten species/taxa were examined in this way, whereas a group of 11 strains from the A. lwoffii phylogroup, which could not be identified at the species level, was excluded due to their potential species-level heterogeneity. In addition, A. baumannii strains were tested by the disk diffusion test against clinically relevant antibiotics primarily effective against this species: amikacin (30), doxycycline (30), imipenem (10), netilmicin (30), ofloxacin (5), piperacillin + tazobactam (100 + 10), trimethoprim + sulfamethoxazole, (1.25 + 23.75), and tobramycin (10). A. baumannii strains were classified as susceptible, intermediate, or resistant according to the recommendations of the Clinical and Laboratory Standards Institute [ 38 ]. A. pseudolwoffii strains ANC 7479, ANC 7490, and ANC 7493 were additionally analyzed for imipenem MIC using E-test (bioMérieux). Acinetobacter strains displaying non-wild-type decreased susceptibility to streptomycin, tetracycline, and sulfamethoxazole were further screened for the presence of the corresponding acquired ARGs. PCR screening of crude cell lysates targeted the following ARGs: strA , strB , and aadA27 (streptomycin resistance), tet (Y) (tetracycline resistance), as well as sul1 and sul2 (sulfamethoxazole resistance). All strains were additionally screened for the presence of the carbapenemase gene bla OXA−58 and the replication initiation gene V216rep associated with LowGC-type plasmids [ 18 ]. PCR primers and conditions are described in Table S6 . The specificity of PCR products was verified by Sanger sequencing, except for strA and strB , when they were positive in both separate amplification and co-amplification ( strA–strB fragment). DNA isolation from cattle feces Total fecal DNA was extracted from 150 mg of each fecal sample (n = 121) using the DNeasy PowerSoil Pro Kit (Qiagen), with the following modification in the bead-beating step to enhance DNA yield. The bead-beating tubes containing the sample suspension were subjected to two cycles of shaking in a FastPrep-24™ 5G instrument (MP Biomedicals) located in a cold room (8°C), with each cycle lasting 30 s at a speed of 6 m/s and separated by a 30-second pause. This was followed by an incubation step at 65°C for 5 minutes, after which the tubes were shaken and incubated again under the same conditions. Acinetobacter quantification To assess Acinetobacter abundance in cattle feces, qPCR was performed on the total fecal DNA using Acinetobacter genus-specific primers Ac436f/Ac676r (targeting the 16S rRNA gene; [ 40 ]. The qPCR reactions were prepared in duplicates, in a total volume of 20 µL, containing 10 µL of SsoFast EvaGreen Supermix (Bio-Rad), 2 µL of template DNA (diluted to 2–5 ng/µL), and 0.2 µM of each primer [ 24 ]. The thermocycling conditions were as follows: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 20 s (using the qTOWER³ touch, Analytik Jena). Finally, a melting curve was generated by analyzing the qPCR products in a temperature gradient from 60 to 95°C. In parallel, total bacteria were quantified using universal 16S rRNA primers 1108f/1132r [ 41 ]. The reaction conditions were the same as above except for a final primer concentration of 0.3 µM. The thermocycling conditions were set as follows: 95°C for 10 min, 40 cycles of 95°C for 15 s, 52.5°C for 35 s, and 72°C for 10 s. A standard for quantification of both Acinetobacter and total bacteria was prepared by amplification of the 16S rRNA gene from A. baumannii NIPH 501 T using the primers 27F/1492R [ 42 ]. The standard curve was generated by plotting Ct values against a 10-fold standard dilution series ranging from 10 9 to 10 2 gene copies in four replicates. The specificity of qPCR products was confirmed through melting curve analysis and product size verification after DNA electrophoresis. To assess the limits of detection (LOD) and quantification (LOQ) for Acinetobacter qPCR, two-fold dilutions of standards with gene copies ranging from ≈ 10 3 to < 1 were prepared in six replicates. The LOD and LOQ were calculated using a curve-fitting model implemented in an R script provided by Klymus et al. [ 43 ]. The 95% LOD was determined to be 7.7 gene copies per reaction, while the LOQ, defined by a 35% coefficient of variation threshold, was 68 gene copies per reaction. All values < LOD were replaced by the LOD, while values LOD) were replaced by the LOQ. Finally, the absolute Acinetobacter abundance was expressed as the number of Acinetobacter 16S rRNA gene copies per g of dry weight feces, while the relative abundance was calculated as the ratio of Acinetobacter /total bacteria 16S rRNA gene copies. DNA isolation from enrichment cultures Due to the insufficient abundance of acinetobacters in most fecal samples for downstream DNA-based analyses, we established enrichment cultures from all 121 fecal samples. The enrichments were done in liquid ACE medium ([ 29 ], see above), using 2 g of each sample (stored in 2 mL of 0.9% saline plus 4 mL of glycerol at − 20°C). Briefly, samples were washed twice with 0.9% saline and incubated in 25 mL of ACE medium at 30°C with shaking at 160 rpm for 3 h, followed by passive sedimentation for 30 min. Subsequently, 5 mL of the supernatant was transferred to a 100-mL Erlenmeyer flask containing 25 mL of fresh ACE medium and incubated at 30°C with shaking at 160 rpm for 2 days. DNA was extracted from the grown cultures using the DNeasy UltraClean Microbial Kit (Qiagen). Diversity and species composition assessment using rpoB metabarcoding To study the diversity of Acinetobacter spp. in cattle feces, metabarcoding of a 355-bp variable region of the rpoB gene was performed using either total fecal or enrichment DNA as templates (see above). The variable region was amplified with Acinetobacter -specific primers Ac696F and Ac1093R [ 32 ], containing custom-designed barcodes (Table S7). PCR was performed using a T100 PCR thermocycler (Bio-Rad) in a total reaction volume of 25 µL, containing 1× Q5 Reaction Buffer, 200 µM deoxynucleoside triphosphates, 0.2 µM of each primer, 0.02 U/µL Q5 High-Fidelity DNA Polymerase (New England Biolabs), 0.6 µg/µL bovine serum albumin, 1× Q5 High GC Enhancer, and 10–20 ng of template DNA. The PCR thermocycling conditions were as follows: an initial denaturation at 98°C for 2 min, followed by 35 cycles of 98°C for 30 s, 52°C for 30 s, and 72°C for 30 s, with a final extension at 72°C for 2 min. The amplified PCR products were purified using the MinElute PCR Purification kit (Qiagen) and their concentration was measured using a Qubit 2.0 fluorometer (Thermo Scientific). Subsequently, sequencing libraries were constructed using the TruSeq DNA PCR-free kit (Illumina) and sequencing was performed in-house using Illumina MiSeq (2 × 250 bp paired-end reads). Using total fecal DNA as a template, successful amplification and sequencing of the rpoB gene were achieved for only 21 out of 121 samples, while 118 samples were successfully amplified and sequenced based on enrichment DNA. The sequencing data from Illumina MiSeq were processed with SEED v2.1.2 [ 44 ]. The paired-end reads were first joined using fastq-join [ 45 ]. Sequences with an average quality score below 30 or a length exceeding 394 bp were discarded. Following primer removal, chimeric sequences were identified and eliminated using VSEARCH v2.15.0 [ 46 ]. The remaining high-quality, non-chimeric sequences were clustered at 98% similarity with VSEARCH, and the most abundant sequence within each cluster was selected as a cluster-representative sequence. Assignment of rpoB clusters to Acinetobacter species involved two steps. First, each representative sequence was taxonomically assigned at the genus level using BLASTn v2.5.0 ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ; [ 47 ] against a comprehensive rpoB database (FROGS rpoB_122017.fasta containing 44,673 bacterial entries; [ 48 ]. Only sequences (clusters) whose best hit corresponded to the genus Acinetobacter with a minimum of 95% coverage were retained. As a result, 4% of the clusters were removed from the dataset due to non- Acinetobacter affiliations (mainly Psychrobacter ). In the second step, species-level identification of the Acinetobacter -specific rpoB clusters was performed using BLASTn against our custom reference database containing 178 Acinetobacter rpoB sequences (Table S8 and Additional file 1), using ≥ 95% coverage and ≥ 97% identity thresholds. Resistome and mobilome analysis via shotgun metagenome sequencing of enrichment cultures To study Acinetobacter antibiotic and heavy metal resistance genes and their genetic context, shotgun metagenome sequencing of Acinetobacter enrichment DNA from 28 ‘floor’ samples was performed using the combination of Illumina and Oxford Nanopore platforms. Illumina library preparation (Illumina ® DNA Prep) and sequencing (NovaSeq 6000 System − 2 × 150 bp) were done at SEQme Ltd. (Czech Republic). Illumina reads were quality-controlled using fastp [ 49 ] and initially assembled with Megahit v1.2.9 [ 50 ]. The share of Acinetobacter reads in each sample was then estimated as the proportion of reads mapped to Acinetobacter -specific versus total rpoB gene sequences. This was determined by BLASTn of predicted genes (FragGeneScan v1.31; [ 51 ]) against the FROGS rpoB database (Table S9). Oxford Nanopore DNA libraries were prepared with the Native Barcoding kit 24 V14 and sequenced on two R10.4.1 flow cells using the Oxford Nanopore PromethION 2 Solo platform. The pooling of Oxford Nanopore sequencing libraries was done based on the anticipated share of Acinetobacter sequences in each sample (Table S9), with the aim of obtaining comparable numbers of Acinetobacter long-read sequences across samples. Basecalling using the SUPv4.3 (super accurate) algorithm and demultiplexing of the Nanopore reads was done by Dorado v0.5.3 (Oxford Nanopore). Extra adapter removal, quality control (reads filtered at average read quality Q > 15 and length > 1000 b) and removal of lambda DNA was done with duplex-tools ( https://github.com/nanoporetech/duplex-tools ) and chopper [ 52 ]. The final sequence assembly was performed on a per-sample basis using Flye v2.9.3 [ 53 ] for chromosome assembly and Plassembler v1.6.2 [ 54 ] for plasmid assembly. The Flye and Plassembler modes yielding optimal results are indicated in Table S10. Binning was intentionally not performed because high numbers of closely related Acinetobacter strains/species in the enrichment cultures would likely result in chimeric bins. Taxonomic classification of contigs was done with GTDBTk v2.1.0 [ 55 ] based on the taxonomy R207_v2 from the Genome Taxonomy Database (GTDB). Contigs that remained unidentified with GTDBTk (contigs with no or low numbers of marker genes) were further classified to the lowest common ancestor with CAT [ 56 ], using the NCBI nr database (accessed 2024-Apr) and parameters r = 1 and f = 0.6. Finally, we aimed to achieve accurate species-level classification for contigs exceeding 250,000 bases that were assigned to the Acinetobacter genus based on GTDB-Tk or CAT results. To accomplish this, we calculated their average nucleotide identity to Acinetobacter reference genomes using the ANIb method implemented in PYANI v0.2.10 ( https://pypi.org/project/pyani ; [ 57 ]). Two complementary approaches were employed to identify plasmid contigs. First, Plassembler searches each putative plasmid contig against the PLSDB plasmid database ([ 58 ]; 34,513 entries) and retains matches with a Mash distance < 0.1. Second, contigs carrying replication initiation ( rep ) genes characteristic of Acinetobacter plasmids were identified by querying predicted coding sequences against the Acinetobacter Plasmid Typing database ([ 59 ]; 1,846 entries) using BLASTn v2.5.0 [ 47 ]. Hits were retained if the alignment length exceeded 800 bp (given that the smallest rep genes are ≈ 850 bp) and coverage was > 95%, and were assigned to known Acinetobacter plasmid Rep types based on a 95% identity threshold [ 59 ]. The presence of ARGs in assembled metagenomic data was predicted using Abricate v1.0.1 ( https://github.com/tseemann/abricate ) with the NCBI AMRFinderPlus database ([ 60 ]; accessed 2023-Nov-04; 5,386 entries). Contigs carrying ARGs that could not be taxonomically classified as described above were further examined using NCBI BLASTn [ 47 ] against the core_nt database ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ; accessed 2025-Jun-06). Contigs showing ≥ 99% identity and ≥ 99% coverage with Acinetobacter sequences were retained for downstream analyses along with those assigned to Acinetobacter in the earlier step. Genetic context of the identified ARGs was determined using nucleotide/protein BLAST [ 47 ], RAST [ 61 ], and ISfinder [ 62 ], and visualized in SnapGene v8.1.1 [ 63 ]. To identify HMRG, coding sequences were predicted by FragGeneScan v1.31 [ 51 ] and queried against the MetalResistance database ( https://orbit.dtu.dk/en/datasets/metalresistance-collection-of-metal-resistance-genes ; 578 entries; [ 64 ]) using tBLASTx [ 47 ]. Hits with the alignment length > 50 amino acids (approximate size of the smallest HMRG), coverage > 70%, and amino acid identity > 60% were retained. A. pseudolwoffii ANC 7493 genome sequencing and analysis To confirm the presence of bla OXA−58 in A. pseudolwoffii , strain ANC 7493 was chosen for genome sequencing. The genomic DNA was extracted from an overnight culture grown on Nutrient Agar (ThermoFisher), using the DNeasy UltraClean Microbial Kit (Qiagen). A DNA library was then prepared with the Oxford Nanopore Native Barcoding Kit 24 V14 and sequenced on an R10.4.1 flow cell using the Oxford Nanopore PromethION 2 Solo platform. Basecalling and raw sequence processing were done as described above, but reads were quality-filtered at Q > 20. The genomic sequence was assembled with the Hybracter pipeline [ 65 ] and annotated with RAST [ 61 ]. The presence of ARGs was confirmed with Abricate as described above. The plasmid pANC7493.1 was further annotated using Prokka [ 66 ], Isfinder [ 62 ], and nucleotide and protein NCBI BLAST [ 47 ], and the annotated sequence was visualized with Proksee [ 67 ]. Alignments between pANC7493.1 and contig F17_297 were done in MEGA [ 68 ] using ClustalW [ 69 ] and visualized with gggenomes ( https://github.com/thackl/gggenomes ; [ 70 ]). Statistical analysis All statistical analyses were conducted using R version 4.5.1 [ 71 ] in RStudio version 2025.5.1.513 [ 72 ] at p < 0.05. Plotting was done using the ggplot2 package [ 73 ]. Abundance analysis The differences in Acinetobacter abundance between ‘floor’ samples and the per-farm mean of ‘cow’ samples were tested using the Wilcoxon rank-sum test for paired samples. Further, Acinetobacter abundance was compared between dairy and beef farms using the Wilcoxon rank-sum test and among farms with different stabling types (outdoor, indoor, indoor/outdoor) using the Kruskal-Wallis rank-sum test. Correlations between Acinetobacter abundance and factors such as per-farm antibiotic use, herd size, age of individual cows, sample dry-matter content (%), sample pH, sampling temperature, heavy-metal content, total C and N levels, presence of antibiotic residues, and total SCFA were tested using Spearman’s rank correlation, and the p -values were adjusted using the Benjamini–Hochberg (BH) method. Species diversity analysis Acinetobacter diversity based on rpoB metabarcoding data (Additional file 2) was analyzed using the Phyloseq [ 74 ], metagMisc [ 75 ] and Vegan [ 76 ] packages. To reduce potential biases in the relative abundance of Acinetobacter rpoB clusters arising from differences in strain growth rates in the enrichment cultures (see above), all relative abundance datasets were converted to presence–absence data before performing alpha- and beta-diversity analyses. Observed richness was selected as a measure of alpha diversity because it is independent of relative abundance. To calculate Observed richness, we used the phyloseq_mult_raref_div function to randomly rarefy the Acinetobacter sequence counts per sample 100 times to a sequencing depth of 6,702 while excluding two ‘cow’ samples that were below this depth. The average observed richness was then statistically compared between ‘floor’ and the per-farm mean of ‘cow’ samples, between dairy and beef farms, among farms with different stabling types and across farms with varied antibiotic use. In the case of ‘floor’ samples, the statistical tests performed were the same as those applied to compare Acinetobacter abundance across groups (see above). In the case of ‘cow’ samples, linear mixed-effects modeling was used with the compared groups as fixed effects and the farm as a random effect, and the significance of the effects was tested using F -tests with ANOVA. Prior to beta-diversity analyses, multiple rarefactions were performed using the phyloseq_mult_raref_avg function under the same conditions as above. Acinetobacter communities (based on rpoB clusters) were compared across samples using non-metric multidimensional scaling (NMDS) with a Sørensen dissimilarity matrix. Initially, we aimed to evaluate the dissimilarity in Acinetobacter communities between individual ‘cow’ samples and corresponding ‘floor’ samples from the same farm. To do this, we conducted NMDS using data from the 14 farms where both sample types were available. Based on the NMDS ordination, z-scores were calculated for the ‘floor’ samples to quantify their dissimilarity relative to the individual ‘cow’ samples from the same farm. The z-scores (z-score = (d F - ̅d c ) / σ c ) were computed using the mean ( ̅d c ) and standard deviation (σ c ) of the Euclidean distances of individual ‘cow’ samples to their farm-specific centroid, and the Euclidean distance of the ‘floor’ sample (d F ) to the corresponding farm-specific centroid. Further, separate NMDS ordinations were performed for ‘floor’ and ‘cow’ samples, onto which environmental variables (farm and cow-specific variables) were fitted using the envfit function. To partition the variance on Acinetobacter species distributions among the farm and cow-specific variables as well as to test the effect of those variables in Acinetobacter species, hierarchical modeling of species communities (HMSC; [ 77 ] was used. HMSC is a multivariate hierarchical generalized linear model that uses Bayesian inference. For HMSC analysis, Acinetobacter rpoB clusters were first grouped at the species level (Additional file 3–4) and only those species present in at least five ‘floor’ or ‘cow’ samples were included in the models. The response matrix Y consisted of presence-absence data for Acinetobacter species and a binomial model with a probit link was fitted to each species. The HMSC models were built separately for ‘floor’ and ‘cow’ samples and the selection or transformation of explanatory variables for each subset was done to optimize the species versus explanatory variable numbers, eliminate inter-correlated variables and minimize categories with a low number of observations. Notably, ‘indoor/outdoor’ category for stabling was grouped with ‘outdoor’, since cows from ‘indoor/outdoor’ stabling stay outdoors most of the year. In addition, the highly intercorrelated heavy metal content data were reduced into two principal components (PCs, Fig. S1 , Table S11), and antibiotic content data, containing many zero values, were considered as simple presence or absence of antibiotic residues in the samples. The X matrix of explanatory variables of the HMSC model for ‘floor’ samples thus included the following variables that varied between farms: production (dairy/beef), stabling (outdoor, indoor), herd size, log-transformed per-herd antibiotic use, sampling temperature, and total SCFA. The X matrix of the HMSC model for ‘cow’ samples included the above-mentioned variables (except total SCFA) as well as the following variables that varied between cows within farms: age of individual cows, sample pH, C/N ratio, total SCFA, presence of antibiotic residues and PC1 and PC2 for heavy metal content. To account for differences in sequencing depth, the log-transformed number of reads was also included as an explanatory variable for both models. Finally, the ‘floor’ model used a farm-level random effect whereas the ‘cow’ model used farm-level and cow-level random effects. The models were fitted assuming default priors and sampled the posterior distribution by running four Markov Chain Monte Carlo (MCMC) chains, each of which was run for 3,750 iterations with 1,250 discarded as burn-in. We thinned by 10 to obtain a total of 250 posterior samples per chain and 1,000 total posterior samples. To test for MCMC convergence we measured the potential scale reduction factor for the beta (capturing species responses to explanatory variables) parameters and assumed satisfactory convergence when they were close to one. The R scripts used for HMSC analysis are available at the Zenodo repository ( https://zenodo.org/ ), doi: 10.5281/zenodo.17426203 . Antibiotic susceptibility, and antibiotic and heavy metal resistance gene analysis Statistical analysis of antibiotic susceptibility data was performed for species with a sufficient number of strains – specifically, A. pseudolwoffii (n = 32) and A. indicus (n = 45; one strain with rpoB sequence identity < 97% to the type strain was excluded). Analyses were restricted to antibiotics for which these species showed non-wild-type decreased susceptibility (i.e., sulfamethoxazole, cefalotin, and streptomycin in both species, and penicillin in A. pseudolwoffii ). Inhibition zone diameters were compared between strains isolated from antibiotic-using and antibiotic-free farms using the Wilcoxon rank-sum test, or across on-farm antibiotic use categories using the Kruskal–Wallis test, and the p -values were adjusted for multiple testing using the Benjamini-Hochberg (BH) method. The richness of acquired ARGs or HMRG in the Acinetobacter enrichment metagenomes was compared between antibiotic-using and antibiotic-free farms with the Wilcoxon rank-sum test. Comparisons were performed for both the raw counts of unique ARG or HMRG types and for counts normalized by assembly length (log-transformed). Results Farm and sample description This study included 28 Czech cattle farms sampled from spring to autumn 2022, aiming to encompass diverse farm settings typical of Czechia. The farms thus varied in their production type (17 dairy and 11 beef farms), herd size (ranging from 5 to 1,250 cows), and stabling system (indoor, outdoor, or a combination with indoor overwintering) (Fig. 1 and Table S1 ). According to farmers' reports, 11 farms had not administered any antibiotics to their cattle in the six months prior to sampling. Of the remaining 17 farms, four used less than 100 g of antibiotics in total, eight used several hundred grams, and five used quantities in the thousands of grams (Fig. 1 ). The types of antibiotics used varied considerably between farms (Table S1 ). The most commonly reported antibiotics were procaine benzylpenicillin, amoxicillin (both β-lactams), and dihydrostreptomycin (aminoglycoside) (Fig. 1 and Fig. S2 ). Antibiotic use was primarily associated with dairy cattle, largely due to frequent treatment of mastitis. Dairy herds are also more likely to be housed indoors than beef cattle. This imbalance in the dataset reflects the practical realities of cattle farming in Czechia. Sampling included composite fecal samples collected from farm floors or pastures (one per farm, referred to as ‘floor’ samples, Table S2 ), as well as samples from individual cows with known age, breed, and recent antibiotic history, collected on 14 farms (referred to as ‘cow’ samples, n = 93, Table S3 ). Thus, 121 samples were obtained in total. Antibiotic residues and heavy metals are present in cattle feces Antibiotic residues were sporadically detected in cattle feces (Table S2 and Table S3 ), even on farms reporting high antibiotic usage. The detected antibiotics included β-lactams (procaine benzylpenicillin and penicillin G), fluoroquinolones (marbofloxacin and enrofloxacin), aminoglycosides (dihydrostreptomycin and novobiocin), tetracyclines (oxytetracycline), lincosamides (lincomycin), and rifamycins (rifaximin). These antibiotics generally reflected those reportedly used on the farms. However, the β-lactams amoxicillin and ceftiofur were not detected in any sample, despite their frequent application. Penicillin G was occasionally detected in low quantities on farms or in cows with no recent record of antibiotic treatment. Heavy metals were present in all samples (Table S2 and Table S3 ). The detected heavy metals were arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), antimony (Sb), selenium (Se), strontium (Sr), and zinc (Zn). Among them, Cu, Zn, and Sr were detected at the highest levels (dozens to hundreds of µg/g), and Cu and Zn levels were positively correlated with on-farm antibiotic use (Rho = 0.6) (Fig. S3 ). Acinetobacter strains from cattle feces are taxonomically diverse and include putative novel species Acinetobacter isolates were recovered from all 28 ‘floor’ fecal samples included in the study. Approximately 800 isolates were analyzed in total, and dereplication resulted in 284 distinct strains. Table 1 summarizes their consensus identification/classification and distribution across samples. Detailed results are provided in Table S5 , and a genus-wide rpoB phylogram is shown in Fig. S4 . A total of 179 strains (63%) were assigned to 16 validly named species. The most frequently recovered species were A. indicus (46 strains from 20 ‘floor’ samples), Acinetobacter pseudolwoffii (32 from 16), Acinetobacter thermotolerans (24 from 11), Acinetobacter gandensis (18 from 13), A . baumannii (13 from 6; none of the strains belonged to international clone 1 or 2), Acinetobacter faecalis (13 from 8), and A. variabilis (12 from 8). Acinetobacter amyesii , Acinetobacter pecorum , and Acinetobacter vivianii were each represented by five strains, while six additional species were recovered only once (Table 1 ). Eighty-one strains (28.5%) were classified into 13 novel taxa, i.e., taxonomically unique groups each comprising at least two strains from different samples with distinct macro-restriction profiles. These taxa most likely represent novel species, as supported by their unique, nearly homogeneous MALDI-TOF MS and rpoB profiles, which reliably separate them from known species and from each other (Fig. S4 , Table S5 ). Each taxon was designated by the strain number of the reference isolate used for MALDI-TOF MS and rpoB analysis (Table S5 ). For example, Taxon 7509 was designated by the reference strain ANC 7509. The most frequent taxa were Taxon 7506 (12 strains from 5 samples), Taxon 7655 (12 from 7), Taxon 7209 (10 from 6), Taxon 7509 (9 from 6), Taxon 7384 (7 from 5), and Taxon 7947 (7 from 3) (Table 1 ). The remaining 24 strains (8.5%) could not be assigned or classified at the species level. Based on their unique profiles, seven strains most likely represent six additional novel species (including two highly similar strains from a single sample). Eleven strains belonged to A. lwoffii phylogroup, i.e., a phylogenetic lineage encompassing A. lwoffii , A. pseudolwoffii , A. pecorum , and Taxon 7443; however, the available data did not permit conclusive identification or classification. Similar situations were observed for one strain within the phylogroup typified by Acinetobacter terrae [ 78 ] and for two strains related to Acinetobacter schindleri . Finally, conflicting MALDI-TOF MS and rpoB sequencing results for the last three strains precluded any taxonomic conclusion (Table 1 ). Selective isolation at 30°C yielded 241 strains representing all identified species, taxa, and other taxonomic types, except for Taxon 7947 and two taxonomically unique strains. In contrast, selective isolation at 44°C recovered only 43 strains, belonging to A. baumannii , A. thermotolerans , two novel taxa, and two taxonomically unique strains. Acinetobacter strains display decreased susceptibility to multiple antibiotics In the disk diffusion test using a panel of 18 antibiotics, some Acinetobacter strains displayed no inhibition zones when tested with penicillin (n = 27), ampicillin (n = 15), cefalotin (n = 31), streptomycin (n = 28), tetracycline (n = 2), sulfamethoxazole (n = 19), trimethoprim (n = 45), or chloramphenicol (n = 9), indicating non-susceptibility to these antibiotics (Table S5 ). However, interpreting these results requires consideration of the species context to differentiate between intrinsic and acquired resistance. For instance, A. baumannii is intrinsically resistant to penicillin, ampicillin, cefalotin, and chloramphenicol. Therefore, we further examined the inhibition zone size distribution within species with sufficient strain numbers, where small zones deviating from normal distribution indicate the presence of acquired resistance mechanisms (Fig. S5 –14). This analysis indicated the presence of acquired resistance to cefalotin (n = 4 strains), nalidixic acid (n = 1), penicillin G (n = 2), streptomycin (n = 36), sulfamethoxazole (n = 13), tetracycline (n = 2), and trimethoprim (n = 12) across nine Acinetobacter species (Fig. 3 A). As several strains showed acquired resistance to more than one antibiotic, the total number of strains with acquired resistance to at least one antibiotic was 57. Notably, three MDR Acinetobacter strains (i.e. displaying acquired non-susceptibility to at least one agent in three or more antimicrobial categories [ 79 ]) were identified within A. faecalis (strain ANC 7486), A. thermotolerans (ANC 7955), and Taxon 7209 (ANC 7562). All three strains were isolated from farms that had used several hundred grams of antibiotics within the previous six months. In contrast, all A. baumannii strains were wild-type susceptible to all tested antibiotics except one strain, which showed reduced susceptibility to streptomycin. Furthermore, A. baumannii strains were susceptible to an extended panel of clinically relevant antibiotics (Fig. S14). The colistin MIC values of all 284 strains remained ≤ 2 mg L − 1 , suggesting an overall good susceptibility. Detailed analysis of streptomycin inhibition zone diameters in A. indicus and A. pseudolwoffii (the species with the highest number of strains) revealed a significant reduction in streptomycin susceptibility among strains from antibiotic-using farms compared with those from antibiotic-free farms (Fig. 3BC). Moreover, susceptibility differed across the categories of on-farm antibiotic use, with the lowest median inhibition zones observed in isolates from high-antibiotic-use farms (Fig. S15). Acquired antibiotic resistance genes and LowGC-type plasmids are present in Acinetobacter strains PCR screening for acquired ARGs initially focused on those frequently found in European farm environments, as well as genes previously identified in a subset of strains with available whole genome sequences obtained in this project [ 31 , 80 ]. These included strA , strB , and aadA27 (streptomycin resistance), tet (Y) (tetracycline resistance), and sul1 and sul2 (sulfamethoxazole resistance) (Table S5 ). Of the 36 strains with reduced susceptibility to streptomycin, 28 carried both strA and strB , while five carried aadA27 . Both strains with reduced susceptibility to tetracycline harbored tet (Y). Eight strains with reduced susceptibility to sulfamethoxazole (out of 13) carried sul2 , while none was sul1 -positive. Screening was further extended to include strains with small inhibition zones (≤ 12 mm), where reduced susceptibility could not be clearly attributed to intrinsic or acquired mechanisms due to low numbers of strains per taxon. This notably raised the number of strA-strB -positive strains to 53 (out of 65 examined), spanning 10 species or novel taxa, suggesting their wide distribution (Table S5 ). In addition, these genes occurred in combination with sul2 and/or tet (Y) in 11 strains. Notably, two strains, ANC 7562 and ANC 7968, belonging to the novel Taxon 7209 and Taxon 7947, respectively, carried strA-strB , tet (Y), and sul2. All strains were additionally screened for the presence of the carbapenemase gene bla OXA−58 , which was detected in the Acinetobacter enrichment cultures (see below). This screening revealed the presence of bla OXA−58 in three strains of A. pseudolwoffii , i.e. ANC 7479, ANC 7490, and ANC 7493. These strains were susceptible to meropenem (Table S5 ) and imipenem (MIC values 0.125–0.5 mg/L) but had decreased susceptibility to streptomycin and harbored strA and strB . Finally, PCR screening targeted Acinetobacter -specific LowGC-type plasmids, known to mediate ARG transfer in livestock settings [ 18 ]. Three strains belonging to A. pecorum (ANC 7879), A. variabilis (ANC 7728), and the A. lwoffii phylogroup (ANC 7878) were positive and all showed large inhibition zones for all tested antibiotics except trimethoprim. Acinetobacter abundance in cattle feces is low, but increases in feces deposited on farm floor Based on qPCR analysis, Acinetobacter spp. in ‘floor’ samples accounted for an average of ≈ 1% of the total bacteria (average absolute abundance 4.33 × 10 9 16S rRNA gene copies per g dry weight), whereas nearly 50% of the ‘cow’ samples fell below the detection limit ≈ 10 6 16S rRNA gene copies per g dry weight). Reflecting this, average Acinetobacter abundance in ‘floor’ samples was at least an order of magnitude higher than the average per-farm value from ‘cow’ samples ( p = 0.01) (Fig. 4 A). Due to the high proportion of ‘cow’ samples below the limit of detection, subsequent analyses focused solely on the ‘floor’ samples. Here, Acinetobacter abundance in feces from dairy farms was at least 10 times greater than beef farms ( p = 0.03; Fig. 4 B), while the differences among the different stabling types remained insignificant. Among the factors tested for correlation with Acinetobacter abundance in ‘floor’ samples, on-farm antibiotic use showed no correlation while total SCFA showed a positive correlation (Rho = 0.51, p = 0.03) (Fig. S16A) and the C/N ratio showed a negative correlation (Rho = -0.53, p = 0.03) (Fig. S16B). rpoB metabarcoding confirms the high diversity of Acinetobacter species and indicates the main environmental drivers of species occurrence Metabarcoding of the taxonomic marker gene rpoB was used to study Acinetobacter taxonomic diversity and species composition. Due to the low abundance of acinetobacters in most samples, metabarcoding was performed on enrichment cultures, and presence–absence data were used to minimize potential bias arising from differential strain growth during enrichment. In total, 12,959 rpoB clusters were identified, with an average observed richness of 412 clusters per sample. Given that the 98% sequence identity threshold used for rpoB sequence clustering corresponds to subspecies-level differentiation for most Acinetobacter species [ 81 ], these results indicate a remarkably high Acinetobacter strain-level diversity in cattle feces. No significant differences in observed richness were detected across sample types, antibiotic usage, production systems, or stabling conditions. Applying a 97% identity threshold between representative rpoB cluster sequences and their closest reference rpoB sequences (Additional file 1), we classified 6,637 rpoB clusters into 55 Acinetobacter taxa, comprising 33 validly named species and 22 putative novel taxa or unclassified singletons. Almost the same number of clusters (6,322) remained unclassified at the species level. Clusters assigned to A. pseudolwoffii , A. pecorum , A. lwoffii (all members of the A. lwoffii phylogroup), and A. indicus were detected in more than 80% of ‘cow’ samples, indicating that these species represent the core Acinetobacter species in the cattle intestine. In contrast, the next most prevalent species, A. variabilis , was found in only 52% of ‘cow’ samples. Clusters assigned to the four core species also occurred in more than 90% of ‘floor’ samples. NMDS ordination was used to assess differences in Acinetobacter community composition (based on all rpoB clusters) between ‘cow’ and ‘floor’ samples from the same farm (14 farms included, Fig. S17). Acinetobacter communities were not clearly clustered according to sample type or farm, showing considerable between-cow variability within certain farms. However, in all but three farms, the ‘floor’ samples fell within two standard deviations of the individual ‘cow’ samples’ distance to the farm-specific centroid. This suggests that, in most cases, the ‘floor’ samples provided a representative approximation of the average Acinetobacter community composition among the cows on the same farm. Separate NMDS ordinations were subsequently performed for ‘floor’ and ‘cow’ samples to explore Acinetobacter community composition in relation to selected farm and cow-specific factors (Fig. 5 ). The ‘floor’ samples tended to separate along NMDS1 based on stabling type (Fig. S18), with indoor floor samples being exclusively in the right part of the plot. Farms with high per-head antibiotic usage (e.g. F01, F13, F27, F28) also tended to cluster together based on their ‘floor’ sample profiles, but certain farms with low or no antibiotic usage (but with indoor stabling, e.g. F07 and F24) clustered with them. This indicates that Acinetobacter community composition in ‘floor’ samples is likely shaped by a combination of multiple factors, whose individual effects are difficult to disentangle by NMDS. Similarly, NMDS analysis based on ‘cow’ samples showed trends for clustering according to farm (e.g. F03, F09 and F25), production, stabling, and breed (e.g. Hereford and Holstein), with none of these factors showing a clear independent effect on Acinetobacter community composition (Fig. 5 and Fig. S18–S20).The ‘cow’ NMDS plots were further correlated with herd size, per-herd antibiotic use, pH, SCFA and heavy metal content, indicating possible effects of these factors on Acinetobacter community composition. The high between-cow variability could not be clearly explained by recent (within 6 months prior to sampling) antibiotic administration. Though some of the treated cows were distant from their untreated counterparts on the NMDS plots, untreated cows from certain farms displayed high dispersion as well (Fig. S21). To disentangle the effects of various farm- and cow-specific factors on Acinetobacter community composition, HMSC analysis was conducted separately on ‘floor’ and ‘cow’ samples. This analysis was done at the Acinetobacter species level (i.e., rpoB clusters classified to the same species were grouped) to facilitate interpretation. Variance partitioning (Fig. 6 A) revealed that Acinetobacter species composition in the ‘floor’ samples was mainly determined by per-head antibiotic use (explaining on average 19.2% variance), followed by stabling (18.3%), herd size (17.9%) and production type (15.3%). These relatively similar contributions are in line with NMDS results, showing no overriding effect of any single factor. Similarly, production type, per-head antibiotic use, and herd size were the primary factors shaping Acinetobacter species composition in ‘cow’ samples, explaining 14.2%, 10.1%, and 8.8% of variance, respectively (Fig. 6 B). In contrast, cow-specific factors such as cow age, SCFA or C/N content contributed relatively little to the explained variance. Notably, the random cow effect accounted for 23.9% variance, indicating that Acinetobacter community composition might be highly structured at the individual-cow level, potentially due to unmeasured host-specific factors or microenvironmental variation. Heatmaps of species niches based on HMSC β-parameters revealed both common and contrasting responses of the individual Acinetobacter species to various farm- and cow-level factors (Fig. S22). Most of the Acinetobacter spp. (as identified with rpoB metabarcoding) exhibited a higher likelihood of occurrence in dairy farms as compared to beef farms, both in ‘floor’ and individual ‘cow’ fecal samples. Most species were also positively associated with high levels of SCFA in feces; notably A. gandensis , A. indicus , A. pecorum , and A. pseudolwoffii showed a significant positive relationship (with > 90% posterior support) in both ‘floor’ and ‘cow’ samples. In addition, the majority of species were positively associated with higher sampling temperatures (related to the summer sampling season). In contrast, antibiotic use at the farm level was negatively associated with the occurrence of most Acinetobacter species in ‘cow’ and ‘floor’ fecal samples, with the only exception being Taxon 7384 in ‘floor’ samples, which showed a significant positive association. Herd size had an overall negative effect on the occurrence of Acinetobacter spp. in ‘cow’ samples, whereas in ‘floor’ samples, this effect was species specific, with A. thermotolerans and A. towneri showing significant preference for larger herds. Considering stabling, contrasting results were obtained for ‘floor’ and ‘cow’ samples. All species detected in the ‘floor’ samples showed a positive trend towards indoor conditions, while it was generally the opposite for ‘cow’ samples, with the main exception being A. indicus . Interestingly, A. pecorum , A. terrae , and A. variabilis present in ‘cow’ fecal samples were significantly positively associated with metal PC2, representing higher Pb and Cd content. Shotgun sequencing of enrichment cultures provides Acinetobacter MAG and plasmid sequences Shotgun sequencing of Acinetobacter enrichment cultures from 28 ‘floor’ samples yielded 11,250 contigs (287 Mb in total) affiliated with the genus Acinetobacter (Table S10). Contigs longer than 250,000 bp, representing Acinetobacter chromosomes or large chromosome fragments, were further classified at the species level using the ANIb approach (Table S12). Of the 207 contigs examined, 116 were successfully classified to known species based on the 96% ANIb threshold [ 82 ]. They represented A. faecalis , A. gandensis , A. indicus , A. pecorum , A. pseudolwoffii , and A. schindleri . Six contigs exhibited > 99% completeness and > 5% contamination and represented single-contig, circular, high-quality metagenome-assembled genomes (MAGs) [ 83 ]. Two of these MAGs were classified as A. indicus and A. pecorum based on the 96% ANIb threshold, while one showed an ANIb value of 95.7% to A. amyesii . The three remaining MAGs (F01_chromosome_61, F18_chromosome_3, and F22_chromosome_7) likely represent novel Acinetobacter taxa, sharing 99.4–100% identity across a 355-bp fragment of the rpoB with reference strains of Taxon 7683, Taxon 7579, and Taxon 7655, respectively. In total, 599 putative plasmid contigs were identified across the Acinetobacter assemblies, of which 514 showed significant sequence similarity to PLSDB entries (Table S12) and 213 contained homologues of plasmid replication initiation ( rep ) genes listed in the Acinetobacter Plasmid Typing database (128 of these contigs met both criteria; Table S13). Among the 213 rep homologues, 120 could be confidently assigned to known Rep types (Table S13), belonging to the three major families, i.e. R1, R3, and RP, with R3 predominating. The remaining 93 rep genes shared < 95% nucleotide sequence identity with described Rep types and likely represent novel variants. The rep genes corresponding to LowGC-type plasmids (i.e., R3-T20 type), were not detected in the Acinetobacter assemblies. Shotgun sequencing of enrichment cultures revealed a rich Acinetobacter resistome with potential to spread clinically important antibiotic resistance genes Abricate analysis revealed the presence of 19 distinct ARGs across the Acinetobacter assemblies, with a total of 116 Abricate hits (Fig. 7 , Table S14). These ARGs were predicted to confer resistance to aminoglycosides, amphenicols, β-lactams (including carbapenems), tetracyclines, and sulfonamides. Of these, 78 ARGs were located on plasmid-derived contigs. Our further analyses focused on horizontally acquired ARGs, excluding chromosomally encoded intrinsic carbapenemase genes, which do not confer significant carbapenem resistance unless placed under a strong promoter provided by an upstream insertion sequence [ 84 ]. Therefore, we examined the genetic context of all identified carbapenemase genes and excluded those lacking adjacent insertion sequences. These carbapenemase genes (identified by Abricate as bla OXA−235 , bla OXA−282 , bla OXA−258 , bla OXA−537 , bla OXA−646 , and bla OXA−648 ) were all located between the metalloprotease and molecular chaperone DnaK genes, suggesting a chromosomal origin. Thus, the only carbapenemase genes retained for further analyses were the ones bracketed by IS Aba3 and present on contigs classified as Acinetobacter plasmids; their sequences were 100% identical to the bla OXA−58 sequence of A. baumannii MAD (GenBank accession number AY665723). All other identified ARGs are either known to be horizontally transferable or were associated with mobile genetic elements. The complete set of horizontally transferable ARGs was then compared between antibiotic-using and antibiotic-free farms (Fig. 7 , Fig. S23). We detected significantly more horizontally transferable ARG types in antibiotic-using farms, as compared to antibiotic-free farms ( p = 0.014), though the effect of antibiotic usage was small (median = 0 and 3 ARGs, respectively). Notably, the bla OXA−58 gene was found only in farms where antibiotics were used. Carbapenems are not administered to cattle, suggesting that these genes may be co-selected with other antibiotic or heavy metal resistance genes in the farms. Supporting this, genetic analyses showed co-localization of the bla OXA−58 gene with the strA-strB genes on two contigs (Fig. 8 A), indicating that carbapenem resistance may be co-selected with aminoglycosides, which are frequently applied in cattle. Another important case of co-localization is the presence of tet (X3) and floR on a single contig (Fig. 8 B). While tet (X3) confers resistance to the last-resort antibiotic tigecycline, floR confers resistance to florfenicol, which is occasionally used on cattle farms. The co-localization of bla OXA−58 and tet (X3) with mobile genetic elements (Fig. 8AB) on contigs sharing highly similar regions with Acinetobacter plasmids suggests their potential for dissemination through horizontal gene transfer. Of note, another contig carrying tet (X3) (F20_1806) showed 100% identity to the LowGC-type plasmid pHH1107 [ 18 ] over 5,117 bp, but it lacked the rep gene required for reliable classification. A total of 466 HMRGs hits were obtained across the Acinetobacter assemblies, representing 19 distinct HMRG types (Table S15). The most frequently (> 10 occurrences) detected HMRGs were golT (gold/copper resistance), arsB, arsC , and arsH (arsenic resistance), dpsA (iron resistance), czcA and czcD (cadmium/zinc/cobalt resistance), and nreB (cobalt/nickel). Only 37 HMRGs were localized on plasmid contigs. Co-localization of HMRGs with ARGs was rare, observed on only eight contigs, and primarily involved intrinsic carbapenemase genes on contigs likely representing Acinetobacter chromosomes. In a single instance, the dpsA gene co-occurred with strA and strB , although not in close proximity. Overall, these findings indicate that co-selection of heavy metal and antibiotic resistance genes is uncommon in the studied farms. This is further supported by the finding that HMRG counts did not differ significantly between antibiotic-using and antibiotic-free farms (Fig. S24). Genome sequencing confirms the presence of bla OXA-58 on A. pseudolwoffii ANC 7493 plasmid The genome assembly of A. pseudolwoffii ANC 7493 consisted of a single circular chromosome (2,764,499 bp) and one circular plasmid (167,549 bp; designated pANC7493.1). Abricate analysis confirmed the presence of the bla OXA−58 , strA , and strB genes in the genome, all located in close proximity on the plasmid pANC7493.1 (Fig. 9 ). These genes were part of a region spanning positions 93,403–100,027 bp, which shared > 99.9% sequence identity with the metagenomic contig F17_297 (positions 1–6,322), differing only by the absence of a LysE family translocator gene between strB and an AraC family transcriptional regulator gene from the contig (Fig S25). The bla OXA−58 was flanked by IS Aba3 insertion sequences, with the upstream copy being truncated (i.e., IS Aba3- like element). Plasmid pANC7493.1 contained a single rep gene, enabling its classification as the R3-T103 type according to the Acinetobacter Plasmid Typing scheme [ 59 ]. No relaxase or mating-pair formation genes were detected, suggesting that the plasmid is neither mobilizable nor self-transmissible. In addition, the plasmid encoded several stability and maintenance systems, including restriction–modification and toxin–antitoxin modules, as well as metabolic and putative host-adaptive genes. These included the outer membrane protein A gene ompA , a known A. baumannii virulence factor [ 85 ], a large gene encoding Ig-like domain-containing protein typical of biofilm-associated proteins [ 86 ], and two siderophore receptor genes, fhuE and fatA . The plasmid also carried several HMRGs, including an arsenic resistance operon, the cadmium/cobalt/zinc resistance gene czcD , and a mercuric resistance operon regulator gene merR1 . According to NCBI BLASTn results, the highest sequence coverage between pANC7493.1 and entries in the NCBI nucleotide collection was 52%, indicating that the plasmid represents a novel genetic element. The best BLASTn matches were three A. pseudolwoffii plasmids: CP084301.1 (52% coverage, 98.35% identity) and CP183900.1 and CP183904.1 (both 46% coverage, 98.79% identity), originating from chicken and bovine samples in China. Mapping of these plasmid sequences onto pANC7493.1 (Fig. 9 ) revealed that while all four plasmids shared HMRG regions, the ARG region was unique to pANC7493.1. Discussion The cattle fecal microbiome is important for both animal and human health, particularly regarding zoonotic transmission and the agricultural application of cattle manure. Multiple studies have characterized its composition and consistently reported a predominance of members of the bacterial phyla Bacteroidetes and Firmicutes , whereas Proteobacteria generally account for less than 5% of the total bacterial community [ 87 , 88 ]. While most studies on Proteobacteria have focused on Escherichia coli [ 89 , 90 ], Acinetobacter species remain underexplored despite their importance in pathogenesis and dissemination of ARGs [ 13 , 15 , 16 ]. To address the low abundance of Acinetobacter in cattle feces, we combined culturing strains with metabarcoding and metagenomic analysis of enrichment cultures. This approach uncovered a rich diversity of Acinetobacter species, including putative novel species, and provided insights into their response to antibiotic selection pressure. Both pure strain isolation and rpoB metabarcoding revealed a high diversity of Acinetobacter spp. in cattle feces. Of the 284 strains recovered, 63% were assigned to 16 validly named species, whereas the remaining 37% comprised either putative novel species awaiting formal description or unclassified singletons (Table 1 ). Strains obtained in this study have already contributed to the delineation of A. amyesii [ 91 ] and A. thermotolerans [ 31 ] as well as to the emended description of A. faecalis [ 80 ]. Thus, the share of strains belonging to described species prior to the start of this project was only ≈ 48%, highlighting that cattle represent a reservoir of largely unexploited Acinetobacter diversity. It should be noted that some of the novel Acinetobacter taxa, including A. thermotolerans , were recovered at the cultivation temperature of 44°C. As growth at elevated temperatures is considered a prerequisite for mammalian pathogenicity (this temperature was used to improve A. baumannii recovery), these taxa warrant further investigation regarding their potential virulence. The rpoB sequences obtained from metabarcoding of enrichment cultures were classified into 33 validly named species and 22 putative novel species or taxonomically unique singletons, comprising the aforementioned taxa and singletons included in our custom rpoB database. The higher species count relative to isolate-based data was anticipated, as strain isolation requires an additional culture step on agar plates, where certain taxa may fail to form colonies on ACE agar, thus limiting the number of isolates that can be examined. In addition, this analysis was conducted across ‘cow’ and ‘floor’ samples, whereas only ‘floor’ samples were used for strain isolation because of the low throughput of the isolate-based approach. Nonetheless, the taxonomic assignments based on rpoB metabarcoding need to be interpreted with caution, as they were not complemented by additional methods such as MALDI-TOF MS profiling or phenotypic characterization, which could be applied only to the strains. Although the number of species detected with either method may appear high given that only 87 Acinetobacter species are currently validly described ( https://szu.gov.cz/wp-content/anemec/Classification.pdf ), previous studies have indicated that a substantial portion of the genus’s phylogenetic diversity remains undescribed [ 17 , 92 ]. The diversity revealed in this study may thus represent only a small fraction of the genus’s taxonomic breadth, highlighting the need for continued exploration of Acinetobacter in animal-associated environments. The core Acinetobacter species detected in most cattle fecal samples based on rpoB metabarcoding were A. indicus and members of the A. lwoffii phylogroup ( A. lwoffii , A. pecorum , and A. pseudolwoffii ). Consistently, A. pseudolwoffii and A. indicus were also represented by the largest number of isolated strains, whereas strains belonging to other members of the A. lwoffii phylogroup were recovered at lower frequencies (Table 1 ). The occurrence of A. indicus , A. lwoffii , and A. pseudolwoffii in cattle manure has been documented previously [ 13 , 24 , 93 ]. In contrast, A. pecorum was only recently described based on isolates from sheep and chickens [ 94 ]. Our study thus demonstrates the presence of A. pecorum in cattle feces and provides the first complete MAG of this species from cattle. Compared with the core taxa, A. baumannii was detected less frequently, occurring in fewer than 5% of ‘cow’ and ‘floor’ samples by metabarcoding of enrichment cultures and in 21% of ‘floor’ samples by strain isolation (using two different growth temperatures). These findings are consistent with previous studies, which suggested that A. baumannii likely originates from environmental sources rather than being a primary colonizer of cattle [ 7 , 10 ]. This raises the question of whether the Acinetobacter species and taxa detected in this study are capable of stable colonization of the cattle intestine, or whether they represent transient passengers of the intestinal tract. Acinetobacter species are strict aerobes [ 17 ] and thus appear unlikely to thrive under the largely anaerobic conditions of the cattle gut. However, A. baumannii has been shown to proliferate in the colonic crypts of mice [ 95 ], suggesting the existence of intestinal micro-niches with oxygen levels sufficient to support Acinetobacter growth. Based on our data, we propose that the Acinetobacter communities observed in cattle feces consist of both stable colonizers—represented in particular by the core species identified in this study—and transient species acquired from environmental sources, such as A. baumannii and other less frequently detected taxa. This interpretation is further supported by our metabarcoding analysis of species composition in cattle feces, which revealed that farm- and cow-associated variables explained only a limited portion of the variation, while a substantial proportion (on average 24%) remained attributable to a random cow effect (Fig. 6 ). When examined by species, the random cow effect was low for A. indicus , A. pecorum , and A. pseudolwoffii (≈ 2–7%), supporting their role as stable colonizers, whereas it was substantially higher for A. lwoffii (47%). At the genus level, Acinetobacter abundance was substantially lower in ‘cow’ samples (feces collected per rectum or immediately upon defecation) than in ‘floor’ samples (feces deposited on the farm floor). This observation further supports the notion that growth of Acinetobacter in the cattle intestine is limited, whereas these bacteria can rapidly proliferate once exposed to external conditions. The increased abundance in deposited feces may result from rapid growth under oxygen-rich conditions, supported by the availability of short-chain fatty acids in cattle feces as a suitable carbon source, and by the competitive advantage over major intestinal taxa that favor anaerobic conditions (e.g., Bacteroidetes and Firmicutes [ 87 ]). Additional contributions could arise from colonization by airborne Acinetobacter – an aspect that merits further investigation. Observed abundances in deposited feces (typically 10⁷–10⁹ 16S rRNA gene copies per gram of dry weight, Fig. 4 ) were comparable to values reported in manure inputs for biogas plants in Germany [ 24 ], where 10⁶–10⁸ copies per gram of fresh material were detected, equivalent to roughly tenfold higher levels on a dry weight basis. Notably, Acinetobacter abundance was higher in dairy than beef cattle samples (Fig. 4 ), with most species showing a general preference for dairy farms (Fig. S22). Previous studies have documented differences in the intestinal microbiome between beef and dairy cattle [ 87 ] and variation in A. baumannii isolation rates [ 7 ]. This result is difficult to explain based on the current data, but it may be related to particular cow breeds, types of nutrition or higher levels of human contact in dairy farms. Analysis of the antibiotic susceptibility phenotypes of strains supported the hypothesis that ongoing antibiotic use in Czech cattle farming provides selective pressure for resistance acquisition. Based on data from two core species, A. pseudolwoffii and A. indicus , strains from antibiotic-using farms displayed lower streptomycin susceptibility than those from antibiotic-free farms (Fig. 3 ). Moreover, three MDR strains identified in this study originated exclusively from antibiotic-using farms. These strains belonged to the recently described species A. faecalis [ 80 ], A. thermotolerans [ 31 ], and a novel taxon (Taxon 7209), underscoring the potential role of newly recognized taxa in the dissemination of antimicrobial resistance. Nevertheless, reduced antibiotic susceptibility in these strains was limited to antibiotics not classified as critically important for human medicine (i.e., tetracycline, streptomycin, sulfamethoxazole, and trimethoprim). This contrasts with findings from China, where MDR Acinetobacter isolates displayed resistance to clinically critical agents such as carbapenems and tigecycline [ 13 , 15 ]. Consistent with this, all A. baumannii isolates recovered in this study were susceptible to all clinically relevant antibiotics. Even though strain-level analyses suggested that the overall health risk associated with Czech cattle farms is low, resistome profiling by shotgun metagenomic sequencing of enrichment cultures revealed the presence of clinically relevant ARGs, which were further corroborated by strain-level data. Notably, the carbapenemase gene bla OXA−58 was detected on several sequence contigs from antibiotic-using farms and was confirmed in three strains of A. pseudolwoffii . All three strains were susceptible to carbapenems, but the inconsistency between genotype and phenotype is not uncommon [ 96 ], likely owing to insufficient gene expression. The co-localization of bla OXA−58 with strA-strB (either on the same contig in metagenomic data or within the same strains) suggests that carbapenem resistance may be co-selected by aminoglycoside use in farm settings. In addition, metagenomic data indicated a possible co-selection of the tigecycline resistance gene tet (X) with florfenicol. Both gene clusters are associated with transposable elements and plasmids, indicating their potential for high mobility within and between bacterial hosts (Fig. 8 ). Together, these findings highlight the capacity of cattle-associated Acinetobacter spp. to serve as reservoirs of clinically relevant resistance determinants that could, under suitable conditions, be mobilized into pathogenic bacteria. The co-localization of bla OXA−58 with strA and strB on a plasmid was confirmed through whole-genome sequencing of one of the bla OXA−58 -positive strains, A. pseudolwoffii ANC 7493 (Fig. 9 and Fig. S25). Since this strain was susceptible to carbapenems, we assume that the IS Aba3 -like element located upstream of bla OXA−58 does not provide a strong promoter sufficient for bla OXA−58 expression within this host. The expression of bla OXA−58 in A. baumannii and other Acinetobacter spp. may be enhanced by the insertion of IS Aba2 , IS 18 , and other insertion sequence types within the IS Aba3 -like element through the provision of hybrid promoter sequences [ 97 , 98 ], but these structures were not observed here. The plasmid pANC7493.1 appears to be non-mobilizable and non–self-transmissible, but since natural competence is widespread among Acinetobacter spp. [ 92 ], horizontal gene transfer via transformation cannot be ruled out. Although the plasmid appears to be novel, similar scaffolds have been recovered from A. pseudolwoffii isolates obtained from chicken and bovine samples in China (Fig. 9 ), suggesting that related plasmids may circulate globally. PCR screening of Acinetobacter strains further identified the hosts of the sul2 , strA-strB , and tet (Y) genes, which have been reported from farm environments in Europe and elsewhere [ 2 , 18 , 99 ]. The strA-strB genes were particularly widespread, occurring in 10 species or novel taxa, likely reflecting the frequent administration of aminoglycosides (e.g., dihydrostreptomycin) in cattle. Regarding aminoglycoside resistance, we also demonstrated that aadA27 , encoding the ANT(3’’)-II aminoglycoside nucleotidyltransferase and originally described in A. lwoffii from permafrost [ 100 ], is present in at least four species or novel taxa (including A. faecalis , in which we recently reported the gene [ 80 ]). These findings highlight the widespread dissemination of aminoglycoside resistance determinants across diverse Acinetobacter lineages, suggesting that they may constitute a long-standing component of the environmental resistome. LowGC-type plasmids, previously thought to mediate the transfer of ARGs from livestock manure to soil and to contribute to the environmental spread of antibiotic resistance [ 2 , 18 , 20 ], were scarce in our dataset. They were detected only in three Acinetobacter strains (i.e., 1%), which were mostly susceptible to antibiotics. Within the scope of this study, therefore, LowGC-type plasmids do not appear to represent a dominant vehicle for ARG dissemination. Nonetheless, our results newly identify their hosts as A. variabilis and members of the A. lwoffii phylogroup, including A. pecorum —information that was overlooked in earlier studies, which had recovered such plasmids mainly from manured soil via exogenous plasmid isolation [ 2 , 18 ]. By contrast, we detected several other Rep-type plasmids (mostly of the Rep3 family) in Acinetobacter enrichment cultures from cattle feces, some of which carried ARGs such as strA-strB. The above-mentioned plasmid pANC7493.1 carrying strA-strB and bla OXA−58 also belonged to the Rep3 family. Reference plasmids of these Rep-types have been reported from diverse geographical regions and sources, including animal feces, clinical isolates, and hospital sewage (Table S12; [ 59 ], indicating their broad host range and global circulation. Together, these findings suggest that while LowGC-type plasmids may not be central to ARG spread in cattle, cattle-associated acinetobacters nonetheless harbor plasmid backbones capable of facilitating the dissemination of resistance genes across environments and host species. Livestock manure represents an environment conducive to horizontal gene transfer of ARGs due to its high bacterial density and diversity, abundant nutrients, and the presence of antibiotic residues exerting selective pressure [ 101 ]. We assume similar conditions apply to Acinetobacter spp. in cattle feces, given their high diversity, access to suitable carbon sources such as short-chain fatty acids, and increased abundance in feces deposited on the farm floor, which indicates active growth. Antibiotic residues were also present in both ‘cow’ and ‘floor’ samples in our study, although certain commonly used antibiotics, such as some β-lactams, were not detected—likely reflecting their low stability [ 18 , 102 ]. In addition, heavy metals were consistently present in all fecal samples, representing another selective factor that could potentially drive co-selection of antibiotic resistance [ 21 ]. The concentrations of Cu, Zn, Cd, Pb, Cr, and As were within the ranges reported in cattle farms [ 23 , 103 ], indicating that such metal contamination is likely widespread. Consistently, our shotgun metagenome analysis identified 19 distinct types of heavy metal resistance genes. However, they were infrequently localized on plasmid contigs and, in a single instance only, co-occurred on the same contig with horizontally acquired antibiotic resistance genes. Although the co-selection of heavy metal and antibiotic resistance cannot be entirely excluded – particularly given the presence of multiple heavy metal resistance genes on plasmid pANC7493.1 (Fig. 9 ) – the overall data indicate that such events are infrequent in the studied farm environments. This study demonstrates the value of an integrative approach for characterizing low-abundance members of the animal microbiome. While each of the methods used carries inherent strengths and limitations, together they provided a complementary and comprehensive picture of Acinetobacter populations in cattle feces. The analysis of total community DNA (i.e., without culturing) enabled accurate assessment of Acinetobacter abundance. Although this strategy is now widely employed for diversity profiling through metabarcoding [ 104 ], it was not feasible in our study because most samples did not yield Acinetobacter -specific amplicons. Consequently, our diversity assessment relied on enrichment cultures. This approach is, however, inherently biased, as species or strains differ in their growth performance in the liquid ACE medium used for enrichment. To mitigate this bias, we based our analysis strictly on presence–absence data rather than relative abundances. Still, it must be acknowledged that some slow-growing strains may have remained below the detection threshold, whereas fast-growing strains were more readily detected. Such biases are not unique to our enrichment strategy but also occur in widely used PCR-based community profiling approaches employing universal bacterial primers [ 104 ]. Coupling enrichment cultures with shotgun metagenomics provided valuable insights into the Acinetobacter resistome without the need to sequence individual strains. Unlike rpoB metabarcoding, this approach did not rely on Acinetobacter -specific primers, making it necessary to carefully filter the data for non- Acinetobacter sequences. This precaution was important because non- Acinetobacter bacteria may also proliferate in liquid ACE medium, particularly when traces of alternative carbon sources from cattle feces are present. As a result, ARGs carried on broad-host-range plasmids or represented by contigs too short for confident taxonomic assignment may have remained undetected in this study. Nevertheless, this approach is very useful for revealing the resistome of low-abundance taxa from complex samples, as previously shown by Marano et al. [ 105 ]. The culture of pure strains offered the advantage of direct phenotypic testing, which could then be linked to genotypic traits through PCR or whole-genome sequencing. Our use of ACE medium for isolation has previously been demonstrated to be effective for recovering a broad range of Acinetobacter spp. from diverse environments [ 17 ]. Combined with MALDI-TOF MS (amended with a custom Acinetobacter database) and sequencing of a variable region of the rpoB gene [ 32 ], this approach allowed for reliable species identification and delineation of novel taxonomic clusters. Conclusions The present study provides considerable insights into the taxonomic diversity of Acinetobacter and its antimicrobial resistance in cattle feces. Our findings revealed a surprisingly high diversity of Acinetobacter species, including several putative novel species. We identified A. indicus , A. pseudolwoffii , and other members of the A. lwoffii phylogroup as core Acinetobacter species associated with cattle. Consistent with previous reports, A. baumannii was rare in cattle feces, and the strains recovered here did not appear to pose an immediate health risk. Nevertheless, we detected clinically relevant resistance genes, including bla OXA−58 and tet (X), in cattle-associated Acinetobacter , despite relatively strict antibiotic use regulations on Czech farms. The association of these genes with mobile genetic elements highlights their potential for dissemination under favorable conditions and emphasizes the need for continued improvements in cattle health management to further reduce reliance on antibiotics. Declarations Ethics approval and consent to participate All methods were carried out in accordance with relevant guidelines and regulations. The study involved only the collection of bovine fecal samples, either from the farm floor or by non-invasive rectal sampling performed by a licensed veterinarian using a sterile examination glove. These procedures did not cause pain, suffering, distress, or lasting harm to the animals and fall below the threshold defined in Article 1(5)(f) of Directive 2010/63/EU, which excludes such non-harmful practices from the scope of animal experimentation legislation. Therefore, in accordance with Czech and EU regulations, no formal animal experiment approval was required for this study. Sampling was conducted with the consent of the farm owners. Consent for publication Not applicable. Availability of data and material The nucleotide sequence datasets generated during the current study are available as follows. Partial rpoB sequences from 284 Acinetobacter strains are available at the NCBI GenBank repository (https://www.ncbi.nlm.nih.gov/nuccore/) under accession numbers PX405702–PX405985. Partial sequences of aadA27 , bla OXA-58 , tet (Y), sul2 , and rep amplified from Acinetobacter strains are available at NCBI GenBank under accession numbers PX380145–PX380148, PX359217–PX359219, PX359220–PX359221, PX380135–PX380144, and PX396045–PX396047, respectively. Raw reads corresponding to rpoB metabarcoding data (Illumina MiSeq) from 118 cattle feces samples are available at NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra/), under accession numbers SRR34872364–SRR34872481. Raw Illumina NovaSeq and Oxford Nanopore reads from 28 shotgun metagenomes are available at NCBI SRA under accession numbers SRR34902429–SRR34902456 and SRR34878076–SRR34878103, respectively, and the corresponding assemblies are available at the Zenodo repository (https://zenodo.org/) under the identifier 17176853. The complete genome A. pseudolwoffii ANC 7493 is available at the NCBI GenBank repository under the accession number JBSSNL000000000. The R scripts used for HMSC analysis are available at the Zenodo repository under the identifier 17426203. Other data generated or analyzed during this study are included in this published article as supplementary tables or additional files. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Czech Science Foundation (project 22-05373S; contribution: design of the study and collection, analysis, and interpretation of data, and writing the manuscript) and the Ministry of Education, Youth and Sports of the Czech Republic (project CZ.02.01.01/00/22_008/0004635—AdAgriF; contribution: interpretation of data and writing the manuscript). Anitha Ravi was also supported by the Charles University Grant Agency (project 160125; contribution: interpretation of data and writing the manuscript). Authors' contributions AR performed sample processing, prepared enrichment cultures, isolated DNA, conducted qPCR and metabarcoded PCR, analyzed metabarcoding and shotgun metagenomic data, and was a major contributor to writing the manuscript. VS analyzed the isolates by MALDI-TOF MS. PTD conceptualized and performed bioinformatic analyses, including the processing of raw reads and sequence assemblies. HS contributed substantially to sample processing, enrichment cultures, and DNA isolation. MM isolated and dereplicated the Acinetobacter strains. JS conceptualized the assessment of sample chemical composition and contributed to the analysis of C, N, and antibiotic content. ANeh analyzed the antibiotic content. MV performed farm sampling and data collection and conducted rpoB sequencing of strains. IO conceptualized and performed the HMSC analysis. HSS carried out PCR screening of strains for antibiotic resistance genes. TV contributed to the conceptualization and execution of shotgun metagenomic data annotation. SM analyzed the antibiotic susceptibility of strains. TC conceptualized the assessment of sample chemical composition and analyzed SCFA content. EP coordinated and performed farm sampling and coordinated isolate rpoB sequencing. ANem coordinated strain isolation, performed taxonomic analysis of strains, analyzed susceptibility data, drafted the corresponding parts of the manuscript, and revised the manuscript. MK designed and coordinated the study, acquired funding, contributed to sample processing and shotgun metagenomic data analysis and interpretation, and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements We are grateful to the 28 anonymous farmers for providing access to their farms and farm metadata. We thank Eva Vlková, Kateřina Jochová, and Miroslav Joch (Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague) for organization of cattle-farm sampling, and Lenka Michalčíková, Mirka Petrovová, Tereza Michalová (Institute of Microbiology of the Czech Academy of Sciences, Prague), Lucie Malíková, Ladislav Čermák, Štěpánka Dvořáková, and Anna Mašlejová (Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague) for technical assistance. We gratefully acknowledge Jaroslav Kukla and Filip Křivohlavý (Laboratory of Environmental Chemistry and Soil Analysis, Institute of Environmental Sciences, Faculty of Science, Charles University) for their invaluable laboratory support in chemical analyses. 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Health","correspondingAuthor":false,"prefix":"","firstName":"Violetta","middleName":"","lastName":"Shestivska","suffix":""},{"id":584206362,"identity":"85e2653e-ea59-47a6-bd3c-a4a625d29fb5","order_by":2,"name":"Priscila Thiago Dobbler","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Priscila","middleName":"Thiago","lastName":"Dobbler","suffix":""},{"id":584206363,"identity":"1777bed4-1e2b-4def-80a3-c406233d4d1e","order_by":3,"name":"Hana Sechovcova","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hana","middleName":"","lastName":"Sechovcova","suffix":""},{"id":584206364,"identity":"fbd905b0-f6e5-4df5-999d-ffcd3fe6aacd","order_by":4,"name":"Martina Maixnerova","email":"","orcid":"","institution":"National Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Maixnerova","suffix":""},{"id":584206368,"identity":"dc3b8c18-f205-4326-b1b2-04a1e954eb36","order_by":5,"name":"Jaroslav Semerad","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jaroslav","middleName":"","lastName":"Semerad","suffix":""},{"id":584206369,"identity":"82d5dffa-7f94-499e-a7bc-992f60849393","order_by":6,"name":"Alena Nehasilova","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alena","middleName":"","lastName":"Nehasilova","suffix":""},{"id":584206370,"identity":"07ad6d20-794d-4cc0-a3eb-ffd8f6c292b9","order_by":7,"name":"Mariana Vadronova","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Vadronova","suffix":""},{"id":584206371,"identity":"308912ad-536d-4556-a6c9-928a674ff1a1","order_by":8,"name":"Inaki Odriozola","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Inaki","middleName":"","lastName":"Odriozola","suffix":""},{"id":584206372,"identity":"ec48cd1f-5434-40e3-93ad-4834687a6ce1","order_by":9,"name":"Hana Subrtova Salmonova","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Hana","middleName":"Subrtova","lastName":"Salmonova","suffix":""},{"id":584206373,"identity":"01ffe216-be1f-41a1-9c3c-25d758c2930c","order_by":10,"name":"Tomas Vetrovsky","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tomas","middleName":"","lastName":"Vetrovsky","suffix":""},{"id":584206374,"identity":"f07e5b94-c4de-4370-8e05-669bc780abb7","order_by":11,"name":"Sarka Musilova","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Sarka","middleName":"","lastName":"Musilova","suffix":""},{"id":584206375,"identity":"9e0309b4-271f-43c0-a81c-c7c3e81f3726","order_by":12,"name":"Tomas Cajthaml","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tomas","middleName":"","lastName":"Cajthaml","suffix":""},{"id":584206376,"identity":"85e17b9d-c306-4c1c-931b-593c6051e17d","order_by":13,"name":"Eva Pechouckova","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Pechouckova","suffix":""},{"id":584206377,"identity":"060d31c0-d433-45af-85fe-70a49e0e6bac","order_by":14,"name":"Alexandr Nemec","email":"","orcid":"","institution":"National Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Alexandr","middleName":"","lastName":"Nemec","suffix":""},{"id":584206378,"identity":"49c0ced0-a8b6-4945-867d-dff218cd6847","order_by":15,"name":"Martina Kyselkova","email":"data:image/png;base64,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","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Martina","middleName":"","lastName":"Kyselkova","suffix":""}],"badges":[],"createdAt":"2025-12-12 09:09:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8343889/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8343889/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42523-026-00568-3","type":"published","date":"2026-04-22T15:59:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101801158,"identity":"12c5cc82-3d3d-4f9f-88c6-4744f74b16c8","added_by":"auto","created_at":"2026-02-03 18:11:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of cattle farms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwenty-eight anonymous cattle farms (referred to as F01─F28) were sampled in Czechia, mostly in the region of Central Bohemia (the approximate sampling area is shown as a light green oval on the map). Total on-farm antibiotic use during six months preceding the sampling is displayed in the bar plot, with main antibiotic types represented by colors. Based on the total antibiotic use, the farms were grouped into No (0 g), Low (\u0026lt; 100 g), Moderate (100–1000 g), and High (1,000 g) antibiotic use categories (first row on the heatmap). The next rows of the heatmap show the categorization of farms according to the herd size, main production type and stabling type. More details can be found in Tables S1–S3.\u003c/p\u003e","description":"","filename":"Fig1overviewofSampleanalyses.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/ce2d5ae5ade4bf3ea3fe30cd.png"},{"id":101801143,"identity":"070c6731-a12a-40c7-8191-dc37e3719cbb","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":445608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of sample analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisual summary of analyses performed on fecal samples from individual cows (‘cow’ samples) and composite samples from the farm floor (‘floor’ samples) from 28 cattle farms. See Methods for further details. Created with BioRender https://BioRender.com/8att3th.\u003c/p\u003e","description":"","filename":"Fig2overviewofSampleanalyses.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/b94e206054918150317d2786.png"},{"id":101880712,"identity":"4bcba3a4-cb64-44ea-ac2e-33f2c497f633","added_by":"auto","created_at":"2026-02-04 15:05:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":288790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAntibiotic susceptibility in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAcinetobacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e strains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Numbers of strains with reduced susceptibility to antibiotics per \u003cem\u003eAcinetobacter\u003c/em\u003e species. Only species comprising ≥ 10 strains were analyzed and the total number of strains per species is indicated in parentheses after the species name (note that individual strains may show reduced susceptibility to multiple antibiotics). \u003cstrong\u003eB\u003c/strong\u003e) and \u003cstrong\u003eC\u003c/strong\u003e) – Analysis of streptomycin susceptibility in strains of \u003cem\u003eA. pseudolwoffii \u003c/em\u003e(\u003cstrong\u003eB\u003c/strong\u003e) and \u003cem\u003eA. indicus \u003c/em\u003e(\u003cstrong\u003eC\u003c/strong\u003e). Streptomycin inhibition zone size was compared between isolates from antibiotic-using (“ATB-used”) and antibiotic-free (“ATB not used”) farms using Wilcoxon rank sum test (p.adj is BH-corrected \u003cem\u003ep\u003c/em\u003e-value). Boxes represent the interquartile range (Q1– Q3), with median drawn as a horizontal line. Whiskers indicate the smallest and largest values within 1.5 times the interquartile range. Outliers are drawn as points.\u003c/p\u003e","description":"","filename":"Fig3strainsusceptibilitytoATB.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/379fb9e743ef08b6f828ce71.png"},{"id":101801145,"identity":"a50ee3e2-5628-4977-afb5-2fa5a5c0c102","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAcinetobacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e abundance in cattle feces\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of \u003cem\u003eAcinetobacter\u003c/em\u003eabundance in cattle feces as determined by qPCR. \u003cstrong\u003eA\u003c/strong\u003e) Comparison of \u003cem\u003eAcinetobacter\u003c/em\u003eabundance between ‘floor’ fecal samples and the corresponding per-farm mean values from ‘cow’ samples (paired samples Wilcoxon test). \u003cstrong\u003eB\u003c/strong\u003e) Comparison of \u003cem\u003eAcinetobacter\u003c/em\u003e abundance between beef and dairy farms based on ‘floor’ fecal samples (Wilcoxon rank-sum test).Boxes represent the interquartile range (Q1– Q3), with median drawn as a horizontal line. Whiskers indicate the smallest and largest values within 1.5 times the interquartile range. The gray shading denotes the method limit of detection (LOD), adjusted for sample dry-matter content.\u003c/p\u003e","description":"","filename":"Fig4Acinetobacterabundance.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/a4b81c6d482864ea6e4853c9.png"},{"id":101801146,"identity":"156c0993-c052-4eae-92af-434edb4f07d7","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":425228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-metric multidimensional scaling (NMDS) analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAcinetobacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNMDS plots based on the presence or absence of \u003cem\u003eAcinetobacter\u003c/em\u003e \u003cem\u003erpoB\u003c/em\u003e clusters from ‘floor’ (\u003cstrong\u003eA\u003c/strong\u003e) and ‘cow’ (\u003cstrong\u003eB\u003c/strong\u003e) samples. The ‘floor’ samples originated from all 28 farms and are labeled with farm numbers. The ‘cow’ samples originated from a subset of 14 farms and the farm of origin is represented by color. Data point size represents log(per-head antibiotic use). Farm- and cow-related explanatory variables significantly correlated with the ordinations (envfit, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are shown as vectors (for numerical variables) or centroids (for categorical variables).\u003c/p\u003e","description":"","filename":"Fig5NMDSAcinetobactercommunities.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/abf40ddb03f98d278ce2c876.png"},{"id":101801157,"identity":"7d1355fa-189b-4b3d-980a-094b63ce6cf5","added_by":"auto","created_at":"2026-02-03 18:11:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":429286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariance partitioning of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAcinetobacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003especies composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariance partitioning of \u003cem\u003eAcinetobacter\u003c/em\u003especies composition (species occurrence based on \u003cem\u003erpoB\u003c/em\u003e metabarcoding) in ‘floor’ (\u003cstrong\u003eA\u003c/strong\u003e) and ‘cow’ (\u003cstrong\u003eB\u003c/strong\u003e) samples as determined by HMSC analysis. The explanatory variables in A) included farm-level factors (production type, stabling, herd size, log-transformed per-herd antibiotic use, air temperature, and total SCFA) and random farm effects. The explanatory variables in B) included farm-level factors (as above, except total SCFA), cow-level factors (age, pH, C/N ratio, metal PC1 and PC2, presence of antibiotic residues, and total SCFA), and random farm- and cow-effects. Log-transformed sequencing depth was included in both HMSC models but is not displayed. The values next to the legend represent the average variance (%) explained by individual variables across all analyzed species.\u003c/p\u003e","description":"","filename":"Fig6VariancepartitioningAcinetobacterspecies.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/8d0b539299ee5911c3de260f.png"},{"id":101801149,"identity":"9f146aa6-ead2-41b4-aa08-b50f6eea57b6","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":88773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAcquired antibiotic resistance genes in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAcinetobacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eshotgun metagenome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Number of unique ARGs (ARG richness) detected per farm, compared between antibiotic-using (“ATB-used”) and antibiotic-free (“ATB not used”) farms using Wilcoxon rank sum test. \u003cstrong\u003eB)\u003c/strong\u003e Same data as in plot A, but normalized by log(assembly length). Boxes represent the interquartile range (Q1–Q3), with the median drawn as a horizontal line. Whiskers represent the smallest and largest values within 1.5 times the interquartile range from the quartiles. \u003cstrong\u003eC\u003c/strong\u003e) Overview of acquired ARGs detected in antibiotic-free and antibiotic-using farms.\u003c/p\u003e","description":"","filename":"Fig7VariancepartitioningAcinetobacterspecies.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/42cac80c6f1d81e25c6d56bc.png"},{"id":101801147,"identity":"4daa66f6-2d49-4897-a4ff-09d32186c878","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":50301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetagenomic contigs carrying \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ebla\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cstrong\u003eOXA-58\u003c/strong\u003e\u003c/sub\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003etet\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(X3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eContig F17_297 carrying the carbapenemase resistance gene \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA-58\u003c/sub\u003e, flanked by insertion sequence \u003cem\u003eIS\u003c/em\u003eAba3, as well as the streptomycin resistance genes \u003cem\u003estrA\u003c/em\u003e and \u003cem\u003estrB. \u003c/em\u003eThe total length of the contig is 35,741 bp, with only the first 10,000 bp shown.\u003cem\u003e\u0026nbsp; \u003c/em\u003e\u003cstrong\u003eB)\u003c/strong\u003e Contig F12_440 carrying the tigecycline resistance gene \u003cem\u003etet\u003c/em\u003e(X3), flanked by recombinase and integrase genes, as well as the florfenicol resistance gene \u003cem\u003efloR.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig8VariancepartitioningAcinetobacterspecies.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/6220df13cf4fb5ee40dbc08e.png"},{"id":101801150,"identity":"98050a74-02e6-41df-8f5f-607e366b6c12","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2482311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlasmid map of pANC7493.1 and its comparison to similar plasmids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic content of the plasmid pANC7493.1 obtained from the whole-genome sequencing of \u003cem\u003eA. pseudolwoffii\u003c/em\u003e ANC 7493. The total length of the plasmid is 167,549 bp. The outermost ring depicts the predicted coding regions, color-coded according to their putative functions (see color legend). The second ring represents the plasmid G+C content, while the third, fourth, and fifth rings (from outside) show BLASTn alignments (Prokka; E-value \u0026lt; 1e-10) with the most similar plasmids from the NCBI nr/nt database (accessed 2025-Oct-22): CP183900.1 (blue), CP183904.1 (gold), and CP183900.1 (green).\u003c/p\u003e","description":"","filename":"Fig9plasmidmap.png","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/926ad67b35886d6e1cd403a9.png"},{"id":107928092,"identity":"d8f4da61-3816-4de6-b0c4-5b5ac2830c5e","added_by":"auto","created_at":"2026-04-27 16:07:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4100402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/1a42c369-396d-499a-80ae-de0c8843656c.pdf"},{"id":101969840,"identity":"72d355d7-4ba0-4d1e-8de6-875e2513f44f","added_by":"auto","created_at":"2026-02-05 14:25:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278134,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/1be70806e7efa5648c8bde1f.pdf"},{"id":101801155,"identity":"b56cc08f-a4fd-4576-b149-608cd354a47b","added_by":"auto","created_at":"2026-02-03 18:11:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1526209,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables20251030.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/34412e070fa55a435455ba83.xlsx"},{"id":101801151,"identity":"6d5ad8a6-2b7a-41f5-903e-620ec7348e2d","added_by":"auto","created_at":"2026-02-03 18:11:25","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7730510,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures20251030.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/a4faa88cba7577ae8cb2f63f.pdf"},{"id":101801153,"identity":"da2be0f2-ea99-4af5-a9a2-f58897b211ae","added_by":"auto","created_at":"2026-02-03 18:11:26","extension":"fasta","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":619543,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.fasta","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/ef53ae0b3b26d9349dd7b308.fasta"},{"id":101880918,"identity":"6fcd393f-5c7d-4a63-85ee-39dea79e8d4c","added_by":"auto","created_at":"2026-02-04 15:07:49","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":5016444,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2OTUtaxatablerpoBclusteresfilteredenrich20251211.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/1b5324ab10abe9e8d386c22e.xlsx"},{"id":101881172,"identity":"937df886-0c5c-4166-ab59-25f3b642b1b2","added_by":"auto","created_at":"2026-02-04 15:10:26","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13818,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3OTUtablespeciesFLOORnonchimers97sim20251210.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/4d9d1c9355f6fff148f2dddc.xlsx"},{"id":101801156,"identity":"c042182a-9f27-456c-afe1-88ae53a7b020","added_by":"auto","created_at":"2026-02-03 18:11:28","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":22204,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4OTUtablespeciesCOWnonchimers97sim20251210.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8343889/v1/f16daab77f89fdf4c8234f44.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cattle feces are a reservoir of diverse Acinetobacter species with potential to spread antibiotic resistance genes","fulltext":[{"header":"Background","content":"\u003cp\u003eThe long-term use of antibiotics in livestock production has been a major driver of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs), which can be disseminated into the environment through practices such as the application of manure to agricultural soils [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among livestock sectors, cattle farming is particularly significant, accounting for more than 50% of global antimicrobial use between 2019 and 2021 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As part of the European Union’s broader One Health strategy, the use of antibiotics for growth promotion in livestock was banned in 2006, with further restrictions on prophylactic and metaphylactic applications introduced in 2022. However, antibiotics are still widely used for disease treatment on cattle farms, and their impact on the cattle gut resistome – and the potential risks this poses to public health – remains poorly understood.\u003c/p\u003e \u003cp\u003eAmong antibiotic-resistant bacteria associated with cattle, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e is of particular concern. This opportunistic pathogen is a leading cause of nosocomial infections, and certain strains exhibit resistance to virtually all available antibiotics, including last-resort drugs such as carbapenems and colistin [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its remarkable capacity to acquire ARGs via mobile genetic elements (MGEs) – including insertion sequences, transposons, integrons, and plasmids – plays a key role in its multidrug resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several cultivation-based studies have documented the occurrence of \u003cem\u003eA. baumannii\u003c/em\u003e in cattle, with isolates obtained from nasal, oral, rectal, and fecal samples (e.g. [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]). Two large-scale studies from Germany and South Korea reported \u003cem\u003eA. baumannii\u003c/em\u003e in 15.6% and 2.6% of cattle, respectively, suggesting an overall low prevalence in cattle populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Most cattle-associated strains belonged to novel multilocus sequence types [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but some were genetically linked to sequence types known from human clinical isolates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, although the majority of cattle-derived strains were wild-type susceptible to antibiotics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], carbapenem-resistant \u003cem\u003eA. baumannii\u003c/em\u003e has also been isolated from cattle, harboring clinically relevant ARGs such as \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−23\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−24\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−58\u003c/sub\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eCarbapenem-resistant \u003cem\u003eAcinetobacter\u003c/em\u003e species other than \u003cem\u003eA. baumannii\u003c/em\u003e have also been reported on cattle farms, including \u003cem\u003eAcinetobacter indicus\u003c/em\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] or \u003cem\u003eAcinetobacter variabilis\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], as well as potential novel species [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, several recent studies from China have documented multidrug-resistant (MDR) \u003cem\u003eAcinetobacter\u003c/em\u003e spp. from cattle farms, carrying clinically relevant ARGs on plasmids, chromosomes, or both (e.g. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]). For instance, co-localization of \u003cem\u003etet\u003c/em\u003e(X3) (tigecycline resistance) with \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM−1\u003c/sub\u003e or \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−58\u003c/sub\u003e (carbapenem resistance) along with three additional ARGs on plasmids was shown in MDR \u003cem\u003eA. indicus\u003c/em\u003e from a dairy farm [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, such MDR \u003cem\u003eAcinetobacter\u003c/em\u003e spp. have not been reported in European cattle, but it remains unclear whether this difference reflects more restricted antibiotic use or simply a lack of comparable studies in Europe.\u003c/p\u003e \u003cp\u003eTaken together, these studies suggest that non-\u003cem\u003ebaumannii Acinetobacter\u003c/em\u003e species may play an important role in the dissemination of ARGs in farm environments. Moreover, some of these species, such as \u003cem\u003eA. variabilis\u003c/em\u003e, are also recognized opportunistic human pathogens [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, current knowledge of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in cattle and their potential for ARG dissemination remains fragmented and largely disconnected. For instance, \u003cem\u003eAcinetobacter\u003c/em\u003e-specific LowGC-type plasmids were previously shown to be important for the spread of ARGs, including the tigecycline resistance gene \u003cem\u003etet\u003c/em\u003e(X), from livestock manure to soils [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], but it remains unclear whether such plasmids are also present in the \u003cem\u003etet\u003c/em\u003e(X3)-positive isolates from Chinese farms and which host species they are associated with.\u003c/p\u003e \u003cp\u003eMoreover, the co-selection of ARGs with heavy metal resistance genes (HMRGs) in the farm environment should be taken into account [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. HMRGs are often found alongside ARGs on \u003cem\u003eAcinetobacter\u003c/em\u003e plasmids and genomic islands, both in clinical \u003cem\u003eA. baumannii\u003c/em\u003e and in farm-associated non-\u003cem\u003ebaumannii Acinetobacter\u003c/em\u003e spp. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, heavy metals such as Cu, Zn, Pb, As, and Cr are commonly present in cattle feces and manure, originating either from dietary supplements or feed contamination [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consequently, the presence of these metals may exert additional selective pressure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], promoting the development of antibiotic resistance in \u003cem\u003eAcinetobacter\u003c/em\u003e spp. within the cattle gastrointestinal tract.\u003c/p\u003e \u003cp\u003eOverall, comprehensive data on the abundance, species composition, and antibiotic resistance of \u003cem\u003eAcinetobacter\u003c/em\u003e in cattle feces and manure are still lacking. Limited insights are available from studies of Pulami et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] on German biogas plants processing cattle manure. For instance, the abundance of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in input manure material was estimated at 10⁶–10⁸ 16S rRNA gene copies per gram of fresh weight [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], yet data on their abundance in fresh cattle feces remain unavailable. Factors influencing \u003cem\u003eAcinetobacter\u003c/em\u003e species occurrence in cattle have so far been examined only for \u003cem\u003eA. baumannii\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the studied German cattle cohort, \u003cem\u003eA. baumannii\u003c/em\u003e was more common in dairy than in beef cattle and calves, and its prevalence correlated with the use of third-generation cephalosporins in the preceding six months. A seasonal peak in isolation rates during summer was also observed [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], suggesting an effect of temperature and humidity on cattle colonization with \u003cem\u003eA. baumannii\u003c/em\u003e. Yet, comparable data for other \u003cem\u003eAcinetobacter\u003c/em\u003e species are still lacking. Therefore, we aimed to comprehensively investigate how antibiotic use and other farm- and cow-specific factors affect the abundance and diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in cattle feces, and to assess the contribution of antibiotic use to the acquisition of antibiotic resistance.\u003c/p\u003e \u003cp\u003eWe hypothesized that (i) the composition of \u003cem\u003eAcinetobacter\u003c/em\u003e species is influenced by cattle type and prior antibiotic administration, (ii) on-farm antibiotic use selects for resistant and MDR \u003cem\u003eAcinetobacter\u003c/em\u003e strains carrying horizontally acquired ARGs, and (iii) heavy metals present in cattle feces further co-select for ARGs. To test these hypotheses, we applied an integrative approach combining selective enrichment culture and characterization of \u003cem\u003eAcinetobacter\u003c/em\u003e strains with culture-independent methods and metagenomics. We used fecal samples collected from farm floors and individual cows across 28 Czech cattle farms with varying levels of antibiotic use. Isolated strains were taxonomically characterized and screened for antibiotic susceptibility as well as horizontally acquired ARGs. Enrichment cultures were analyzed for \u003cem\u003eAcinetobacter\u003c/em\u003e diversity using \u003cem\u003erpoB\u003c/em\u003e metabarcoding and for resistome composition using shotgun metagenomics. The abundance of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. was quantified by qPCR from total fecal DNA. All analyses were complemented with farm metadata and sample chemical characteristics, including antibiotic residues and heavy metal content. Together, this study provides a comprehensive characterization of \u003cem\u003eAcinetobacter\u003c/em\u003e diversity and antibiotic resistance in cattle feces.\u003c/p\u003e \n\n \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \n\n\n\n \n\n \n\n \n\n "},{"header":"Methods","content":"\u003ch3\u003eFarms, sampling, and sample processing\u003c/h3\u003e\u003cp\u003eCattle feces were sampled at 28 anonymous Czech cattle farms representing varying levels of antibiotic use between April and October 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). At each farm, a composite sample consisting of 5–10 dung subsamples was collected from the farm floor or pasture (‘floor’ samples, n = 28) using a sterile garden trowel while avoiding direct contact with the ground. Additionally, at 14 of these farms, fresh fecal samples were collected from 5–11 individual cows per farm (‘cow’ samples, n = 93), either directly from the rectum or immediately after defecation, using sterile examination gloves (see Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e for sample details). Samples were kept refrigerated at ≈ 4°C during transport and were processed the same day. Each sample was thoroughly mixed within its collection bag before being divided for subsequent analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsamples intended for \u003cem\u003eAcinetobacter\u003c/em\u003e culturing were prepared first, using sterile spatulas and falcon tubes. These subsamples were either stored directly at 4°C for use within 1–2 days; mixed with 2 mL of 0.9% NaCl and frozen at − 20°C for use within 6 months; or combined with 2 mL of 0.9% NaCl and 4 mL of glycerol and frozen at − 20°C for long-term storage (\u0026gt; 6 months). Subsamples for DNA isolation were stored in sterile Eppendorf tubes at − 20°C. Subsamples for chemical analyses were stored in plastic bags at − 80°C, except for aliquots for dry matter and pH measurements, which were measured upon arrival.\u003c/p\u003e\u003ch2\u003eChemical composition analysis\u003c/h2\u003e\u003cp\u003eSample dry matter content was determined according to standard protocols [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and sample pH was measured by directly immersing an electrode and thermometer (Jenway 3510 Standard Digital pH Meter, Cole-Parmer) into a sample aliquot [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To assess heavy metal content, approximately 0.5 g of the homogenized, lyophilized sample was first mineralized by microwave-assisted acid digestion in a MARS 5 system (CEM Corp.) using concentrated HNO\u003csub\u003e3\u003c/sub\u003e. The content of metallic elements (Ag, As, Cd, Co, Cr, Cu, Ni, Pb, Sb, Se, Sr, Zn) was determined using inductively coupled plasma optical emission spectrometry (ICP-OES, Agilent Technologies). The quality of the measurements was monitored by including blank samples, control standards, and replicate measurements. Total C and N content was determined in solid state using a FLASH 2000 CHNS/O elemental analyzer (Thermo Fisher Scientific). The content of short-chain fatty acids (SCFA) was determined by Gas Chromatography-Mass Spectrometry (GC-MS) following the optimized protocol described by [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe content of antibiotic residues was determined by Liquid Chromatography-Mass Spectrometry (LC-MS) using a custom protocol as follows. Sample extraction was performed using an Accelerated Solvent Extractor (ASE 200; Dionex). The extraction cell was filled with approximately 1.5 g (dry weight) of the sample and extracted with methanol. The extract was then concentrated to approximately 10 mL, centrifuged, and 1 mL of the supernatant was transferred to an LC-MS vial for analysis. Targeted analyses were conducted using an Agilent Infinity 1260 liquid chromatograph coupled with an Agilent 6470 triple quadrupole mass spectrometer (LC/TQ). Chromatographic separation was achieved on a Kinetex Polar C18 (2.6 µm, 3 mm × 100 mm) column equipped with a SecurityGuard Polar C18 (2.6 µm, 3 mm × 2 mm) precolumn (Phenomenex), both heated to 40°C. The mobile phase for gradient elution consisted of phase A: 0.1% formic acid (LC‒MS grade; Honeywell) in Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™) and phase B: 0.1% formic acid in methanol (LC‒MS grade; Honeywell). The gradient elution program was as follows (time [min]/% phase B): 0/0; 1/0; 4/50; 6/50; 9/95; 10/95; 11/0; 12/0. The flow rate was set to 0.4 mL/min, with a total run time of 15 minutes and an injection volume of 2 µL. The ion source parameters were set as follows: source temperature 180°C, gas flow 10 L/min, nebulizer 20 psi, sheath gas temperature 300°C, sheath gas flow 10 L/min, capillary and nozzle voltages 2,500 V and 600 V, respectively. Standard addition was applied to mitigate matrix effects. The mass spectrometer parameters were optimized using MassHunter Workstation Optimizer and Source Optimizer (both Version 10.0, SR1; Agilent Technologies). A complete list of target analytes and settings is provided in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e\u003ch3\u003eStrain isolation\u003c/h3\u003e\u003cp\u003e \u003cem\u003eAcinetobacter\u003c/em\u003e strains were isolated from ‘floor’ samples using the selective culture method described by [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Samples stored at 4°C for 1–2 days or mixed with saline and subsequently stored at − 20°C for up to 6 months were used for this purpose. Aliquots containing 2 g of feces were cultured aerobically with vigorous shaking in 25 mL of mineral medium supplemented with 0.5% (w/v) sodium acetate (ACE medium [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]) at 30°C and 44°C for 3 h in parallel. The two temperatures were used to reflect the growth preferences of both environmental and mammal-adapted \u003cem\u003eAcinetobacter\u003c/em\u003e spp. After 1 h of passive sedimentation, 5 mL of supernatant was transferred to 25 mL of ACE medium and cultured at 30°C and 44°C for up to 2 days. The resulting liquid cultures were then plated onto both ACE agar and CHROMagar™ Acinetobacter (CHROMagar, France). After 24 h of incubation at 30°C and 44°C, selected colonies were subcultured on sheep blood agar plates (Oxoid).\u003c/p\u003e\u003ch3\u003eStrain taxonomic analysis\u003c/h3\u003e\u003cp\u003eIdentification and classification at the species level, as well as dereplication (i.e., the exclusion of multiple isolates of the same strain obtained from a single sample) were performed using combinations of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), sequence analysis of the RNA polymerase β-subunit (\u003cem\u003erpoB\u003c/em\u003e) gene, DNA macrorestriction analysis, and additional methods applied to clarify the species status of certain strains (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1 in Supplementary files\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMALDI-TOF MS was performed on a Microflex LT instrument (Bruker Daltonics) with Bruker Biotyper RTC and Compass v4.1.80 software following the standard Bruker protocol [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Overnight bacterial cultures were analyzed with α-cyano-4-hydroxycinnamic acid as the matrix. Mass spectra were acquired from at least 40 laser shots at 10 positions in automated mode. Identification was performed using the Bruker reference database (version 2021), supplemented with in-house entries of the type/reference strains of validly named species absent from the Bruker library [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the reference strains of novel taxa delineated in the present study. Assignments at the species level were classified according to the Biotyper identification parameters—score values and consistency categories (A or B). \"Reliable\" was assigned to scores of ≥ 2.3 (category A) or ≥ 2.3 (category B) when the second-ranked species differed by ≥ 0.3, \"probable\" to scores of 2.0–2.3 (category A) or ≥ 2.3 (category B) when the second-ranked species differed by ≥ 0.2, and \"possible\" to scores of ≥ 2.0 (category B) when the second-ranked species differed by \u0026lt; 0.2. Cluster analysis was performed using UPGMA (unweighted pair group method with arithmetic mean) with correlation-based distance metrics in Compass v4.1.80.\u003c/p\u003e\u003cp\u003eSequence analysis was performed on a 355-bp fragment of the \u003cem\u003erpoB\u003c/em\u003e gene (nucleotide positions 2915–3269 of \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e CIP 70.34ᵀ (GenBank DQ207471.1) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Analyses were carried out in BioNumerics v7.6 (Applied Maths) as described previously [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The workflow included: (i) compiling a database of \u003cem\u003erpoB\u003c/em\u003e sequences from type strains of all validly named \u003cem\u003eAcinetobacter\u003c/em\u003e species and sequences from this study, (ii) constructing a Neighbor-Joining phylogram from a multiple sequence alignment, and (iii) evaluating percentage identity values. Species-level assignments were defined as ≥ 97% identity to the closest type strain or reference strain of a tentative novel taxon and categorized as reliable (second-closest species differs by ≥ 2%), probable (≥ 1.5%), or possible (\u0026lt; 1.5%).\u003c/p\u003e\u003cp\u003eFinal identification combined MALDI-TOF MS and \u003cem\u003erpoB\u003c/em\u003e results to offset method-specific limitations: MALDI-TOF MS lacks resolution for closely related species, whereas partial \u003cem\u003erpoB\u003c/em\u003e sequences may be affected by interspecies recombination. Consensus identification levels (IL) were defined as: IL1, both \u003cem\u003erpoB\u003c/em\u003e and MALDI-TOF MS reliable; IL2, \u003cem\u003erpoB\u003c/em\u003e reliable and MALDI-TOF MS probable; IL3, \u003cem\u003erpoB\u003c/em\u003e probable and MALDI-TOF MS reliable; IL4, both \u003cem\u003erpoB\u003c/em\u003e and MALDI-TOF MS probable. All other combinations were interpreted as identification at the genus level only, unless additional taxonomic methods were applied (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditional characterization of \u003cem\u003eA. baumannii\u003c/em\u003e involved detection of the species-specific \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−51−like\u003c/sub\u003e gene to confirm species identity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and multiplex PCR to identify international MDR epidemic clones 1 and 2 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] prevailing in Czech hospitals. In addition, in-house phenotypic assays were applied to distinguish phylogenetically related species within the \u003cem\u003eAcinetobacter\u003c/em\u003e hemolytic clade [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDereplication of isolates from each sample was performed in two steps. First, two spectra were obtained per isolate, and all spectra from a sample were clustered with the reference spectra of validly named species and tentative taxa. Clustering at a distance ≤ 50—below which replicate isolates from the same strain consistently clustered—was considered to indicate potential replicate isolates, from which one or two representatives were selected. Second, representatives of the same species were then subjected to macrorestriction analysis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and only isolates with distinct macrorestriction patterns were retained in the final set.\u003c/p\u003e\u003ch3\u003eAntibiotic susceptibility testing and screening for antibiotic-resistance genes (ARGs)\u003c/h3\u003e\u003cp\u003eStrain susceptibility to antimicrobial agents was determined by the disk diffusion test on Mueller-Hinton agar (Oxoid) at 30°C according to standard protocols [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], except for the \u003cem\u003eAcinetobacter lwoffii\u003c/em\u003e phylogroup (including \u003cem\u003eA. pseudolwoffii\u003c/em\u003e and Taxon 7443), Taxon 7209, Taxon 7509, Taxon 7947, and Taxon 7579 strains, which displayed poor growth on Mueller-Hinton agar and were tested on Levinthal’s agar medium (HiMedia). The antimicrobial agents (Oxoid; µg/disk) tested were amoxycillin + clavulanate (20 + 10), ampicillin (10), ampicillin + sulbactam (10 + 10), cefalotin (30), ceftazidime (30), ciprofloxacin (5), chloramphenicol (30), gentamicin (10), kanamycin (30), meropenem (10), nalidixic acid (30), neomycin (10), penicillin G (10 U), piperacillin (100), streptomycin (10), sulfamethoxazole (25), tetracycline (30), and trimethoprim (5). Minimal inhibitory concentrations (MIC) for colistin were determined using the broth microdilution method [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The evaluation of antibiograms aimed to distinguish between wild-type phenotypes and decreased susceptibility due to acquired resistance mechanisms. To achieve this, we visually examined the distribution of inhibition zone diameters for each antibiotic and species/taxons with at least 10 strains to identify deviations from a normal distribution, which could suggest the presence of acquired resistance mechanisms [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Ten species/taxa were examined in this way, whereas a group of 11 strains from the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup, which could not be identified at the species level, was excluded due to their potential species-level heterogeneity. In addition, \u003cem\u003eA. baumannii\u003c/em\u003e strains were tested by the disk diffusion test against clinically relevant antibiotics primarily effective against this species: amikacin (30), doxycycline (30), imipenem (10), netilmicin (30), ofloxacin (5), piperacillin + tazobactam (100 + 10), trimethoprim + sulfamethoxazole, (1.25 + 23.75), and tobramycin (10). \u003cem\u003eA. baumannii\u003c/em\u003e strains were classified as susceptible, intermediate, or resistant according to the recommendations of the Clinical and Laboratory Standards Institute [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. \u003cem\u003eA. pseudolwoffii\u003c/em\u003e strains ANC 7479, ANC 7490, and ANC 7493 were additionally analyzed for imipenem MIC using E-test (bioMérieux).\u003c/p\u003e\u003cp\u003e \u003cem\u003eAcinetobacter\u003c/em\u003e strains displaying non-wild-type decreased susceptibility to streptomycin, tetracycline, and sulfamethoxazole were further screened for the presence of the corresponding acquired ARGs. PCR screening of crude cell lysates targeted the following ARGs: \u003cem\u003estrA\u003c/em\u003e, \u003cem\u003estrB\u003c/em\u003e, and \u003cem\u003eaadA27\u003c/em\u003e (streptomycin resistance), \u003cem\u003etet\u003c/em\u003e(Y) (tetracycline resistance), as well as \u003cem\u003esul1\u003c/em\u003e and \u003cem\u003esul2\u003c/em\u003e (sulfamethoxazole resistance). All strains were additionally screened for the presence of the carbapenemase gene \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−58\u003c/sub\u003e and the replication initiation gene \u003cem\u003eV216rep\u003c/em\u003e associated with LowGC-type plasmids [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. PCR primers and conditions are described in Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e. The specificity of PCR products was verified by Sanger sequencing, except for \u003cem\u003estrA\u003c/em\u003e and \u003cem\u003estrB\u003c/em\u003e, when they were positive in both separate amplification and co-amplification (\u003cem\u003estrA–strB\u003c/em\u003e fragment).\u003c/p\u003e\u003ch3\u003eDNA isolation from cattle feces\u003c/h3\u003e\u003cp\u003eTotal fecal DNA was extracted from 150 mg of each fecal sample (n = 121) using the DNeasy PowerSoil Pro Kit (Qiagen), with the following modification in the bead-beating step to enhance DNA yield. The bead-beating tubes containing the sample suspension were subjected to two cycles of shaking in a FastPrep-24™ 5G instrument (MP Biomedicals) located in a cold room (8°C), with each cycle lasting 30 s at a speed of 6 m/s and separated by a 30-second pause. This was followed by an incubation step at 65°C for 5 minutes, after which the tubes were shaken and incubated again under the same conditions.\u003c/p\u003e\u003cp\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003equantification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess \u003cem\u003eAcinetobacter\u003c/em\u003e abundance in cattle feces, qPCR was performed on the total fecal DNA using \u003cem\u003eAcinetobacter\u003c/em\u003e genus-specific primers Ac436f/Ac676r (targeting the 16S rRNA gene; [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The qPCR reactions were prepared in duplicates, in a total volume of 20 µL, containing 10 µL of SsoFast EvaGreen Supermix (Bio-Rad), 2 µL of template DNA (diluted to 2–5 ng/µL), and 0.2 µM of each primer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The thermocycling conditions were as follows: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 20 s (using the qTOWER³ touch, Analytik Jena). Finally, a melting curve was generated by analyzing the qPCR products in a temperature gradient from 60 to 95°C. In parallel, total bacteria were quantified using universal 16S rRNA primers 1108f/1132r [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The reaction conditions were the same as above except for a final primer concentration of 0.3 µM. The thermocycling conditions were set as follows: 95°C for 10 min, 40 cycles of 95°C for 15 s, 52.5°C for 35 s, and 72°C for 10 s. A standard for quantification of both \u003cem\u003eAcinetobacter\u003c/em\u003e and total bacteria was prepared by amplification of the 16S rRNA gene from \u003cem\u003eA. baumannii\u003c/em\u003e NIPH 501\u003csup\u003eT\u003c/sup\u003e using the primers 27F/1492R [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The standard curve was generated by plotting Ct values against a 10-fold standard dilution series ranging from 10\u003csup\u003e9\u003c/sup\u003e to 10\u003csup\u003e2\u003c/sup\u003e gene copies in four replicates. The specificity of qPCR products was confirmed through melting curve analysis and product size verification after DNA electrophoresis.\u003c/p\u003e\u003cp\u003eTo assess the limits of detection (LOD) and quantification (LOQ) for \u003cem\u003eAcinetobacter\u003c/em\u003e qPCR, two-fold dilutions of standards with gene copies ranging from ≈ 10\u003csup\u003e3\u003c/sup\u003e to \u0026lt; 1 were prepared in six replicates. The LOD and LOQ were calculated using a curve-fitting model implemented in an R script provided by Klymus et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The 95% LOD was determined to be 7.7 gene copies per reaction, while the LOQ, defined by a 35% coefficient of variation threshold, was 68 gene copies per reaction. All values \u0026lt; LOD were replaced by the LOD, while values \u0026lt; LOQ (but \u0026gt; LOD) were replaced by the LOQ. Finally, the absolute \u003cem\u003eAcinetobacter\u003c/em\u003e abundance was expressed as the number of \u003cem\u003eAcinetobacter\u003c/em\u003e 16S rRNA gene copies per g of dry weight feces, while the relative abundance was calculated as the ratio of \u003cem\u003eAcinetobacter\u003c/em\u003e/total bacteria 16S rRNA gene copies.\u003c/p\u003e\u003ch2\u003eDNA isolation from enrichment cultures\u003c/h2\u003e\u003cp\u003eDue to the insufficient abundance of acinetobacters in most fecal samples for downstream DNA-based analyses, we established enrichment cultures from all 121 fecal samples. The enrichments were done in liquid ACE medium ([\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], see above), using 2 g of each sample (stored in 2 mL of 0.9% saline plus 4 mL of glycerol at − 20°C). Briefly, samples were washed twice with 0.9% saline and incubated in 25 mL of ACE medium at 30°C with shaking at 160 rpm for 3 h, followed by passive sedimentation for 30 min. Subsequently, 5 mL of the supernatant was transferred to a 100-mL Erlenmeyer flask containing 25 mL of fresh ACE medium and incubated at 30°C with shaking at 160 rpm for 2 days. DNA was extracted from the grown cultures using the DNeasy UltraClean Microbial Kit (Qiagen).\u003c/p\u003e\u003cp\u003e \u003cb\u003eDiversity and species composition assessment using\u003c/b\u003e \u003cb\u003erpoB\u003c/b\u003e \u003cb\u003emetabarcoding\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo study the diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in cattle feces, metabarcoding of a 355-bp variable region of the \u003cem\u003erpoB\u003c/em\u003e gene was performed using either total fecal or enrichment DNA as templates (see above). The variable region was amplified with \u003cem\u003eAcinetobacter\u003c/em\u003e-specific primers Ac696F and Ac1093R [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], containing custom-designed barcodes (Table S7). PCR was performed using a T100 PCR thermocycler (Bio-Rad) in a total reaction volume of 25 µL, containing 1× Q5 Reaction Buffer, 200 µM deoxynucleoside triphosphates, 0.2 µM of each primer, 0.02 U/µL Q5 High-Fidelity DNA Polymerase (New England Biolabs), 0.6 µg/µL bovine serum albumin, 1× Q5 High GC Enhancer, and 10–20 ng of template DNA. The PCR thermocycling conditions were as follows: an initial denaturation at 98°C for 2 min, followed by 35 cycles of 98°C for 30 s, 52°C for 30 s, and 72°C for 30 s, with a final extension at 72°C for 2 min. The amplified PCR products were purified using the MinElute PCR Purification kit (Qiagen) and their concentration was measured using a Qubit 2.0 fluorometer (Thermo Scientific). Subsequently, sequencing libraries were constructed using the TruSeq DNA PCR-free kit (Illumina) and sequencing was performed in-house using Illumina MiSeq (2 × 250 bp paired-end reads). Using total fecal DNA as a template, successful amplification and sequencing of the \u003cem\u003erpoB\u003c/em\u003e gene were achieved for only 21 out of 121 samples, while 118 samples were successfully amplified and sequenced based on enrichment DNA.\u003c/p\u003e\u003cp\u003eThe sequencing data from Illumina MiSeq were processed with SEED v2.1.2 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The paired-end reads were first joined using fastq-join [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Sequences with an average quality score below 30 or a length exceeding 394 bp were discarded. Following primer removal, chimeric sequences were identified and eliminated using VSEARCH v2.15.0 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The remaining high-quality, non-chimeric sequences were clustered at 98% similarity with VSEARCH, and the most abundant sequence within each cluster was selected as a cluster-representative sequence.\u003c/p\u003e\u003cp\u003eAssignment of \u003cem\u003erpoB\u003c/em\u003e clusters to \u003cem\u003eAcinetobacter\u003c/em\u003e species involved two steps. First, each representative sequence was taxonomically assigned at the genus level using BLASTn v2.5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] against a comprehensive \u003cem\u003erpoB\u003c/em\u003e database (FROGS rpoB_122017.fasta containing 44,673 bacterial entries; [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Only sequences (clusters) whose best hit corresponded to the genus \u003cem\u003eAcinetobacter\u003c/em\u003e with a minimum of 95% coverage were retained. As a result, 4% of the clusters were removed from the dataset due to non-\u003cem\u003eAcinetobacter\u003c/em\u003e affiliations (mainly \u003cem\u003ePsychrobacter\u003c/em\u003e). In the second step, species-level identification of the \u003cem\u003eAcinetobacter\u003c/em\u003e-specific \u003cem\u003erpoB\u003c/em\u003e clusters was performed using BLASTn against our custom reference database containing 178 \u003cem\u003eAcinetobacter rpoB\u003c/em\u003e sequences (Table S8 and Additional file 1), using ≥ 95% coverage and ≥ 97% identity thresholds.\u003c/p\u003e\u003ch3\u003eResistome and mobilome analysis via shotgun metagenome sequencing of enrichment cultures\u003c/h3\u003e\u003cp\u003eTo study \u003cem\u003eAcinetobacter\u003c/em\u003e antibiotic and heavy metal resistance genes and their genetic context, shotgun metagenome sequencing of \u003cem\u003eAcinetobacter\u003c/em\u003e enrichment DNA from 28 ‘floor’ samples was performed using the combination of Illumina and Oxford Nanopore platforms. Illumina library preparation (Illumina ® DNA Prep) and sequencing (NovaSeq 6000 System − 2 × 150 bp) were done at SEQme Ltd. (Czech Republic). Illumina reads were quality-controlled using fastp [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and initially assembled with Megahit v1.2.9 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The share of \u003cem\u003eAcinetobacter\u003c/em\u003e reads in each sample was then estimated as the proportion of reads mapped to \u003cem\u003eAcinetobacter\u003c/em\u003e-specific versus total \u003cem\u003erpoB\u003c/em\u003e gene sequences. This was determined by BLASTn of predicted genes (FragGeneScan v1.31; [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]) against the FROGS \u003cem\u003erpoB\u003c/em\u003e database (Table S9). Oxford Nanopore DNA libraries were prepared with the Native Barcoding kit 24 V14 and sequenced on two R10.4.1 flow cells using the Oxford Nanopore PromethION 2 Solo platform. The pooling of Oxford Nanopore sequencing libraries was done based on the anticipated share of \u003cem\u003eAcinetobacter\u003c/em\u003e sequences in each sample (Table S9), with the aim of obtaining comparable numbers of \u003cem\u003eAcinetobacter\u003c/em\u003e long-read sequences across samples. Basecalling using the SUPv4.3 (super accurate) algorithm and demultiplexing of the Nanopore reads was done by Dorado v0.5.3 (Oxford Nanopore). Extra adapter removal, quality control (reads filtered at average read quality Q \u0026gt; 15 and length \u0026gt; 1000 b) and removal of lambda DNA was done with duplex-tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nanoporetech/duplex-tools\u003c/span\u003e\u003cspan address=\"https://github.com/nanoporetech/duplex-tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and chopper [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe final sequence assembly was performed on a per-sample basis using Flye v2.9.3 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] for chromosome assembly and Plassembler v1.6.2 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] for plasmid assembly. The Flye and Plassembler modes yielding optimal results are indicated in Table S10. Binning was intentionally not performed because high numbers of closely related \u003cem\u003eAcinetobacter\u003c/em\u003e strains/species in the enrichment cultures would likely result in chimeric bins.\u003c/p\u003e\u003cp\u003eTaxonomic classification of contigs was done with GTDBTk v2.1.0 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] based on the taxonomy R207_v2 from the Genome Taxonomy Database (GTDB). Contigs that remained unidentified with GTDBTk (contigs with no or low numbers of marker genes) were further classified to the lowest common ancestor with CAT [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], using the NCBI nr database (accessed 2024-Apr) and parameters \u003cem\u003er\u003c/em\u003e = 1 and \u003cem\u003ef\u003c/em\u003e = 0.6. Finally, we aimed to achieve accurate species-level classification for contigs exceeding 250,000 bases that were assigned to the \u003cem\u003eAcinetobacter\u003c/em\u003e genus based on GTDB-Tk or CAT results. To accomplish this, we calculated their average nucleotide identity to \u003cem\u003eAcinetobacter\u003c/em\u003e reference genomes using the ANIb method implemented in PYANI v0.2.10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pypi.org/project/pyani\u003c/span\u003e\u003cspan address=\"https://pypi.org/project/pyani\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]).\u003c/p\u003e\u003cp\u003eTwo complementary approaches were employed to identify plasmid contigs. First, Plassembler searches each putative plasmid contig against the PLSDB plasmid database ([\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; 34,513 entries) and retains matches with a Mash distance \u0026lt; 0.1. Second, contigs carrying replication initiation (\u003cem\u003erep\u003c/em\u003e) genes characteristic of \u003cem\u003eAcinetobacter\u003c/em\u003e plasmids were identified by querying predicted coding sequences against the Acinetobacter Plasmid Typing database ([\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]; 1,846 entries) using BLASTn v2.5.0 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Hits were retained if the alignment length exceeded 800 bp (given that the smallest \u003cem\u003erep\u003c/em\u003e genes are ≈ 850 bp) and coverage was \u0026gt; 95%, and were assigned to known \u003cem\u003eAcinetobacter\u003c/em\u003e plasmid Rep types based on a 95% identity threshold [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe presence of ARGs in assembled metagenomic data was predicted using Abricate v1.0.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/abricate\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/abricate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e with the NCBI AMRFinderPlus database ([\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]; accessed 2023-Nov-04; 5,386 entries). Contigs carrying ARGs that could not be taxonomically classified as described above were further examined using NCBI BLASTn [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] against the core_nt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed 2025-Jun-06). Contigs showing ≥ 99% identity and ≥ 99% coverage with \u003cem\u003eAcinetobacter\u003c/em\u003e sequences were retained for downstream analyses along with those assigned to \u003cem\u003eAcinetobacter\u003c/em\u003e in the earlier step. Genetic context of the identified ARGs was determined using nucleotide/protein BLAST [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], RAST [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and ISfinder [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and visualized in SnapGene v8.1.1 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo identify HMRG, coding sequences were predicted by FragGeneScan v1.31 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and queried against the MetalResistance database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://orbit.dtu.dk/en/datasets/metalresistance-collection-of-metal-resistance-genes\u003c/span\u003e\u003cspan address=\"https://orbit.dtu.dk/en/datasets/metalresistance-collection-of-metal-resistance-genes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; 578 entries; [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]) using tBLASTx [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Hits with the alignment length \u0026gt; 50 amino acids (approximate size of the smallest HMRG), coverage \u0026gt; 70%, and amino acid identity \u0026gt; 60% were retained.\u003c/p\u003e\u003cp\u003e \u003cb\u003eA. pseudolwoffii\u003c/b\u003e \u003cb\u003eANC 7493 genome sequencing and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo confirm the presence of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA−58\u003c/sub\u003e in \u003cem\u003eA. pseudolwoffii\u003c/em\u003e, strain ANC 7493 was chosen for genome sequencing. The genomic DNA was extracted from an overnight culture grown on Nutrient Agar (ThermoFisher), using the DNeasy UltraClean Microbial Kit (Qiagen). A DNA library was then prepared with the Oxford Nanopore Native Barcoding Kit 24 V14 and sequenced on an R10.4.1 flow cell using the Oxford Nanopore PromethION 2 Solo platform. Basecalling and raw sequence processing were done as described above, but reads were quality-filtered at Q \u0026gt; 20. The genomic sequence was assembled with the Hybracter pipeline [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] and annotated with RAST [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The presence of ARGs was confirmed with Abricate as described above. The plasmid pANC7493.1 was further annotated using Prokka [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], Isfinder [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and nucleotide and protein NCBI BLAST [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and the annotated sequence was visualized with Proksee [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Alignments between pANC7493.1 and contig F17_297 were done in MEGA [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] using ClustalW [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] and visualized with gggenomes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/thackl/gggenomes\u003c/span\u003e\u003cspan address=\"https://github.com/thackl/gggenomes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using R version 4.5.1 [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] in RStudio version 2025.5.1.513 [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. Plotting was done using the ggplot2 package [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eAbundance analysis\u003c/h2\u003e\u003cp\u003eThe differences in \u003cem\u003eAcinetobacter\u003c/em\u003e abundance between ‘floor’ samples and the per-farm mean of ‘cow’ samples were tested using the Wilcoxon rank-sum test for paired samples. Further, \u003cem\u003eAcinetobacter\u003c/em\u003e abundance was compared between dairy and beef farms using the Wilcoxon rank-sum test and among farms with different stabling types (outdoor, indoor, indoor/outdoor) using the Kruskal-Wallis rank-sum test. Correlations between \u003cem\u003eAcinetobacter\u003c/em\u003e abundance and factors such as per-farm antibiotic use, herd size, age of individual cows, sample dry-matter content (%), sample pH, sampling temperature, heavy-metal content, total C and N levels, presence of antibiotic residues, and total SCFA were tested using Spearman’s rank correlation, and the \u003cem\u003ep\u003c/em\u003e-values were adjusted using the Benjamini–Hochberg (BH) method.\u003c/p\u003e\u003ch2\u003eSpecies diversity analysis\u003c/h2\u003e\u003cp\u003e \u003cem\u003eAcinetobacter\u003c/em\u003e diversity based on \u003cem\u003erpoB\u003c/em\u003e metabarcoding data (Additional file 2) was analyzed using the Phyloseq [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], metagMisc [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and Vegan [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] packages. To reduce potential biases in the relative abundance of \u003cem\u003eAcinetobacter rpoB\u003c/em\u003e clusters arising from differences in strain growth rates in the enrichment cultures (see above), all relative abundance datasets were converted to presence–absence data before performing alpha- and beta-diversity analyses. Observed richness was selected as a measure of alpha diversity because it is independent of relative abundance. To calculate Observed richness, we used the phyloseq_mult_raref_div function to randomly rarefy the \u003cem\u003eAcinetobacter\u003c/em\u003e sequence counts per sample 100 times to a sequencing depth of 6,702 while excluding two ‘cow’ samples that were below this depth. The average observed richness was then statistically compared between ‘floor’ and the per-farm mean of ‘cow’ samples, between dairy and beef farms, among farms with different stabling types and across farms with varied antibiotic use. In the case of ‘floor’ samples, the statistical tests performed were the same as those applied to compare \u003cem\u003eAcinetobacter\u003c/em\u003e abundance across groups (see above). In the case of ‘cow’ samples, linear mixed-effects modeling was used with the compared groups as fixed effects and the farm as a random effect, and the significance of the effects was tested using \u003cem\u003eF\u003c/em\u003e-tests with ANOVA.\u003c/p\u003e\u003cp\u003ePrior to beta-diversity analyses, multiple rarefactions were performed using the phyloseq_mult_raref_avg function under the same conditions as above. \u003cem\u003eAcinetobacter\u003c/em\u003e communities (based on \u003cem\u003erpoB\u003c/em\u003e clusters) were compared across samples using non-metric multidimensional scaling (NMDS) with a Sørensen dissimilarity matrix. Initially, we aimed to evaluate the dissimilarity in \u003cem\u003eAcinetobacter\u003c/em\u003e communities between individual ‘cow’ samples and corresponding ‘floor’ samples from the same farm. To do this, we conducted NMDS using data from the 14 farms where both sample types were available. Based on the NMDS ordination, z-scores were calculated for the ‘floor’ samples to quantify their dissimilarity relative to the individual ‘cow’ samples from the same farm. The z-scores (z-score = (d\u003csub\u003eF\u003c/sub\u003e - ̅d\u003csub\u003ec\u003c/sub\u003e) / σ\u003csub\u003ec\u003c/sub\u003e) were computed using the mean ( ̅d\u003csub\u003ec\u003c/sub\u003e) and standard deviation (σ\u003csub\u003ec\u003c/sub\u003e) of the Euclidean distances of individual ‘cow’ samples to their farm-specific centroid, and the Euclidean distance of the ‘floor’ sample (d\u003csub\u003eF\u003c/sub\u003e) to the corresponding farm-specific centroid. Further, separate NMDS ordinations were performed for ‘floor’ and ‘cow’ samples, onto which environmental variables (farm and cow-specific variables) were fitted using the envfit function.\u003c/p\u003e\u003cp\u003eTo partition the variance on \u003cem\u003eAcinetobacter\u003c/em\u003e species distributions among the farm and cow-specific variables as well as to test the effect of those variables in \u003cem\u003eAcinetobacter\u003c/em\u003e species, hierarchical modeling of species communities (HMSC; [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] was used. HMSC is a multivariate hierarchical generalized linear model that uses Bayesian inference. For HMSC analysis, \u003cem\u003eAcinetobacter rpoB\u003c/em\u003e clusters were first grouped at the species level (Additional file 3–4) and only those species present in at least five ‘floor’ or ‘cow’ samples were included in the models. The response matrix Y consisted of presence-absence data for \u003cem\u003eAcinetobacter\u003c/em\u003e species and a binomial model with a probit link was fitted to each species. The HMSC models were built separately for ‘floor’ and ‘cow’ samples and the selection or transformation of explanatory variables for each subset was done to optimize the species versus explanatory variable numbers, eliminate inter-correlated variables and minimize categories with a low number of observations. Notably, ‘indoor/outdoor’ category for stabling was grouped with ‘outdoor’, since cows from ‘indoor/outdoor’ stabling stay outdoors most of the year. In addition, the highly intercorrelated heavy metal content data were reduced into two principal components (PCs, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table S11), and antibiotic content data, containing many zero values, were considered as simple presence or absence of antibiotic residues in the samples. The X matrix of explanatory variables of the HMSC model for ‘floor’ samples thus included the following variables that varied between farms: production (dairy/beef), stabling (outdoor, indoor), herd size, log-transformed per-herd antibiotic use, sampling temperature, and total SCFA. The X matrix of the HMSC model for ‘cow’ samples included the above-mentioned variables (except total SCFA) as well as the following variables that varied between cows within farms: age of individual cows, sample pH, C/N ratio, total SCFA, presence of antibiotic residues and PC1 and PC2 for heavy metal content. To account for differences in sequencing depth, the log-transformed number of reads was also included as an explanatory variable for both models. Finally, the ‘floor’ model used a farm-level random effect whereas the ‘cow’ model used farm-level and cow-level random effects. The models were fitted assuming default priors and sampled the posterior distribution by running four Markov Chain Monte Carlo (MCMC) chains, each of which was run for 3,750 iterations with 1,250 discarded as burn-in. We thinned by 10 to obtain a total of 250 posterior samples per chain and 1,000 total posterior samples. To test for MCMC convergence we measured the potential scale reduction factor for the beta (capturing species responses to explanatory variables) parameters and assumed satisfactory convergence when they were close to one. The R scripts used for HMSC analysis are available at the Zenodo repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/\u003c/span\u003e\u003cspan address=\"https://zenodo.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.17426203\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.17426203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eAntibiotic susceptibility, and antibiotic and heavy metal resistance gene analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis of antibiotic susceptibility data was performed for species with a sufficient number of strains – specifically, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e (n = 32) and \u003cem\u003eA. indicus\u003c/em\u003e (n = 45; one strain with \u003cem\u003erpoB\u003c/em\u003e sequence identity \u0026lt; 97% to the type strain was excluded). Analyses were restricted to antibiotics for which these species showed non-wild-type decreased susceptibility (i.e., sulfamethoxazole, cefalotin, and streptomycin in both species, and penicillin in \u003cem\u003eA. pseudolwoffii\u003c/em\u003e). Inhibition zone diameters were compared between strains isolated from antibiotic-using and antibiotic-free farms using the Wilcoxon rank-sum test, or across on-farm antibiotic use categories using the Kruskal–Wallis test, and the \u003cem\u003ep\u003c/em\u003e-values were adjusted for multiple testing using the Benjamini-Hochberg (BH) method. The richness of acquired ARGs or HMRG in the \u003cem\u003eAcinetobacter\u003c/em\u003e enrichment metagenomes was compared between antibiotic-using and antibiotic-free farms with the Wilcoxon rank-sum test. Comparisons were performed for both the raw counts of unique ARG or HMRG types and for counts normalized by assembly length (log-transformed).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFarm and sample description\u003c/h2\u003e \u003cp\u003eThis study included 28 Czech cattle farms sampled from spring to autumn 2022, aiming to encompass diverse farm settings typical of Czechia. The farms thus varied in their production type (17 dairy and 11 beef farms), herd size (ranging from 5 to 1,250 cows), and stabling system (indoor, outdoor, or a combination with indoor overwintering) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). According to farmers' reports, 11 farms had not administered any antibiotics to their cattle in the six months prior to sampling. Of the remaining 17 farms, four used less than 100 g of antibiotics in total, eight used several hundred grams, and five used quantities in the thousands of grams (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The types of antibiotics used varied considerably between farms (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The most commonly reported antibiotics were procaine benzylpenicillin, amoxicillin (both β-lactams), and dihydrostreptomycin (aminoglycoside) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Antibiotic use was primarily associated with dairy cattle, largely due to frequent treatment of mastitis. Dairy herds are also more likely to be housed indoors than beef cattle. This imbalance in the dataset reflects the practical realities of cattle farming in Czechia. Sampling included composite fecal samples collected from farm floors or pastures (one per farm, referred to as \u0026lsquo;floor\u0026rsquo; samples, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), as well as samples from individual cows with known age, breed, and recent antibiotic history, collected on 14 farms (referred to as \u0026lsquo;cow\u0026rsquo; samples, n\u0026thinsp;=\u0026thinsp;93, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Thus, 121 samples were obtained in total.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAntibiotic residues and heavy metals are present in cattle feces\u003c/h2\u003e \u003cp\u003eAntibiotic residues were sporadically detected in cattle feces (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), even on farms reporting high antibiotic usage. The detected antibiotics included β-lactams (procaine benzylpenicillin and penicillin G), fluoroquinolones (marbofloxacin and enrofloxacin), aminoglycosides (dihydrostreptomycin and novobiocin), tetracyclines (oxytetracycline), lincosamides (lincomycin), and rifamycins (rifaximin). These antibiotics generally reflected those reportedly used on the farms. However, the β-lactams amoxicillin and ceftiofur were not detected in any sample, despite their frequent application. Penicillin G was occasionally detected in low quantities on farms or in cows with no recent record of antibiotic treatment.\u003c/p\u003e \u003cp\u003eHeavy metals were present in all samples (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The detected heavy metals were arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), antimony (Sb), selenium (Se), strontium (Sr), and zinc (Zn). Among them, Cu, Zn, and Sr were detected at the highest levels (dozens to hundreds of \u0026micro;g/g), and Cu and Zn levels were positively correlated with on-farm antibiotic use (Rho\u0026thinsp;=\u0026thinsp;0.6) (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003estrains from cattle feces are taxonomically diverse and include putative novel species\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eAcinetobacter\u003c/em\u003e isolates were recovered from all 28 \u0026lsquo;floor\u0026rsquo; fecal samples included in the study. Approximately 800 isolates were analyzed in total, and dereplication resulted in 284 distinct strains. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes their consensus identification/classification and distribution across samples. Detailed results are provided in Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e, and a genus-wide \u003cem\u003erpoB\u003c/em\u003e phylogram is shown in Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA total of 179 strains (63%) were assigned to 16 validly named species. The most frequently recovered species were \u003cem\u003eA. indicus\u003c/em\u003e (46 strains from 20 \u0026lsquo;floor\u0026rsquo; samples), \u003cem\u003eAcinetobacter pseudolwoffii\u003c/em\u003e (32 from 16), \u003cem\u003eAcinetobacter thermotolerans\u003c/em\u003e (24 from 11), \u003cem\u003eAcinetobacter gandensis\u003c/em\u003e (18 from 13), \u003cem\u003eA\u003c/em\u003e. \u003cem\u003ebaumannii\u003c/em\u003e (13 from 6; none of the strains belonged to international clone 1 or 2), \u003cem\u003eAcinetobacter faecalis\u003c/em\u003e (13 from 8), and \u003cem\u003eA. variabilis\u003c/em\u003e (12 from 8). \u003cem\u003eAcinetobacter amyesii\u003c/em\u003e, \u003cem\u003eAcinetobacter pecorum\u003c/em\u003e, and \u003cem\u003eAcinetobacter vivianii\u003c/em\u003e were each represented by five strains, while six additional species were recovered only once (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEighty-one strains (28.5%) were classified into 13 novel taxa, i.e., taxonomically unique groups each comprising at least two strains from different samples with distinct macro-restriction profiles. These taxa most likely represent novel species, as supported by their unique, nearly homogeneous MALDI-TOF MS and \u003cem\u003erpoB\u003c/em\u003e profiles, which reliably separate them from known species and from each other (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Each taxon was designated by the strain number of the reference isolate used for MALDI-TOF MS and \u003cem\u003erpoB\u003c/em\u003e analysis (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). For example, Taxon 7509 was designated by the reference strain ANC 7509. The most frequent taxa were Taxon 7506 (12 strains from 5 samples), Taxon 7655 (12 from 7), Taxon 7209 (10 from 6), Taxon 7509 (9 from 6), Taxon 7384 (7 from 5), and Taxon 7947 (7 from 3) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe remaining 24 strains (8.5%) could not be assigned or classified at the species level. Based on their unique profiles, seven strains most likely represent six additional novel species (including two highly similar strains from a single sample). Eleven strains belonged to \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup, i.e., a phylogenetic lineage encompassing \u003cem\u003eA. lwoffii\u003c/em\u003e, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e, \u003cem\u003eA. pecorum\u003c/em\u003e, and Taxon 7443; however, the available data did not permit conclusive identification or classification. Similar situations were observed for one strain within the phylogroup typified by \u003cem\u003eAcinetobacter terrae\u003c/em\u003e [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] and for two strains related to \u003cem\u003eAcinetobacter schindleri\u003c/em\u003e. Finally, conflicting MALDI-TOF MS and \u003cem\u003erpoB\u003c/em\u003e sequencing results for the last three strains precluded any taxonomic conclusion (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSelective isolation at 30\u0026deg;C yielded 241 strains representing all identified species, taxa, and other taxonomic types, except for Taxon 7947 and two taxonomically unique strains. In contrast, selective isolation at 44\u0026deg;C recovered only 43 strains, belonging to \u003cem\u003eA. baumannii\u003c/em\u003e, \u003cem\u003eA. thermotolerans\u003c/em\u003e, two novel taxa, and two taxonomically unique strains.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003estrains display decreased susceptibility to multiple antibiotics\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the disk diffusion test using a panel of 18 antibiotics, some \u003cem\u003eAcinetobacter\u003c/em\u003e strains displayed no inhibition zones when tested with penicillin (n\u0026thinsp;=\u0026thinsp;27), ampicillin (n\u0026thinsp;=\u0026thinsp;15), cefalotin (n\u0026thinsp;=\u0026thinsp;31), streptomycin (n\u0026thinsp;=\u0026thinsp;28), tetracycline (n\u0026thinsp;=\u0026thinsp;2), sulfamethoxazole (n\u0026thinsp;=\u0026thinsp;19), trimethoprim (n\u0026thinsp;=\u0026thinsp;45), or chloramphenicol (n\u0026thinsp;=\u0026thinsp;9), indicating non-susceptibility to these antibiotics (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). However, interpreting these results requires consideration of the species context to differentiate between intrinsic and acquired resistance. For instance, \u003cem\u003eA. baumannii\u003c/em\u003e is intrinsically resistant to penicillin, ampicillin, cefalotin, and chloramphenicol. Therefore, we further examined the inhibition zone size distribution within species with sufficient strain numbers, where small zones deviating from normal distribution indicate the presence of acquired resistance mechanisms (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u0026ndash;14). This analysis indicated the presence of acquired resistance to cefalotin (n\u0026thinsp;=\u0026thinsp;4 strains), nalidixic acid (n\u0026thinsp;=\u0026thinsp;1), penicillin G (n\u0026thinsp;=\u0026thinsp;2), streptomycin (n\u0026thinsp;=\u0026thinsp;36), sulfamethoxazole (n\u0026thinsp;=\u0026thinsp;13), tetracycline (n\u0026thinsp;=\u0026thinsp;2), and trimethoprim (n\u0026thinsp;=\u0026thinsp;12) across nine \u003cem\u003eAcinetobacter\u003c/em\u003e species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). As several strains showed acquired resistance to more than one antibiotic, the total number of strains with acquired resistance to at least one antibiotic was 57. Notably, three MDR \u003cem\u003eAcinetobacter\u003c/em\u003e strains (i.e. displaying acquired non-susceptibility to at least one agent in three or more antimicrobial categories [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]) were identified within \u003cem\u003eA. faecalis\u003c/em\u003e (strain ANC 7486), \u003cem\u003eA. thermotolerans\u003c/em\u003e (ANC 7955), and Taxon 7209 (ANC 7562). All three strains were isolated from farms that had used several hundred grams of antibiotics within the previous six months.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, all \u003cem\u003eA. baumannii\u003c/em\u003e strains were wild-type susceptible to all tested antibiotics except one strain, which showed reduced susceptibility to streptomycin. Furthermore, \u003cem\u003eA. baumannii\u003c/em\u003e strains were susceptible to an extended panel of clinically relevant antibiotics (Fig. S14). The colistin MIC values of all 284 strains remained\u0026thinsp;\u0026le;\u0026thinsp;2 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, suggesting an overall good susceptibility.\u003c/p\u003e \u003cp\u003eDetailed analysis of streptomycin inhibition zone diameters in \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e (the species with the highest number of strains) revealed a significant reduction in streptomycin susceptibility among strains from antibiotic-using farms compared with those from antibiotic-free farms (Fig.\u0026nbsp;3BC). Moreover, susceptibility differed across the categories of on-farm antibiotic use, with the lowest median inhibition zones observed in isolates from high-antibiotic-use farms (Fig. S15).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcquired antibiotic resistance genes and LowGC-type plasmids are present in\u003c/b\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003estrains\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePCR screening for acquired ARGs initially focused on those frequently found in European farm environments, as well as genes previously identified in a subset of strains with available whole genome sequences obtained in this project [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. These included \u003cem\u003estrA\u003c/em\u003e, \u003cem\u003estrB\u003c/em\u003e, and \u003cem\u003eaadA27\u003c/em\u003e (streptomycin resistance), \u003cem\u003etet\u003c/em\u003e(Y) (tetracycline resistance), and \u003cem\u003esul1\u003c/em\u003e and \u003cem\u003esul2\u003c/em\u003e (sulfamethoxazole resistance) (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Of the 36 strains with reduced susceptibility to streptomycin, 28 carried both \u003cem\u003estrA\u003c/em\u003e and \u003cem\u003estrB\u003c/em\u003e, while five carried \u003cem\u003eaadA27\u003c/em\u003e. Both strains with reduced susceptibility to tetracycline harbored \u003cem\u003etet\u003c/em\u003e(Y). Eight strains with reduced susceptibility to sulfamethoxazole (out of 13) carried \u003cem\u003esul2\u003c/em\u003e, while none was \u003cem\u003esul1\u003c/em\u003e-positive.\u003c/p\u003e \u003cp\u003eScreening was further extended to include strains with small inhibition zones (\u0026le;\u0026thinsp;12 mm), where reduced susceptibility could not be clearly attributed to intrinsic or acquired mechanisms due to low numbers of strains per taxon. This notably raised the number of \u003cem\u003estrA-strB\u003c/em\u003e -positive strains to 53 (out of 65 examined), spanning 10 species or novel taxa, suggesting their wide distribution (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). In addition, these genes occurred in combination with \u003cem\u003esul2\u003c/em\u003e and/or \u003cem\u003etet\u003c/em\u003e(Y) in 11 strains. Notably, two strains, ANC 7562 and ANC 7968, belonging to the novel Taxon 7209 and Taxon 7947, respectively, carried \u003cem\u003estrA-strB\u003c/em\u003e, \u003cem\u003etet\u003c/em\u003e(Y), and \u003cem\u003esul2.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAll strains were additionally screened for the presence of the carbapenemase gene \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e, which was detected in the \u003cem\u003eAcinetobacter\u003c/em\u003e enrichment cultures (see below). This screening revealed the presence of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e in three strains of \u003cem\u003eA. pseudolwoffii\u003c/em\u003e, i.e. ANC 7479, ANC 7490, and ANC 7493. These strains were susceptible to meropenem (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e) and imipenem (MIC values 0.125\u0026ndash;0.5 mg/L) but had decreased susceptibility to streptomycin and harbored \u003cem\u003estrA\u003c/em\u003e and \u003cem\u003estrB\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFinally, PCR screening targeted \u003cem\u003eAcinetobacter\u003c/em\u003e-specific LowGC-type plasmids, known to mediate ARG transfer in livestock settings [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Three strains belonging to \u003cem\u003eA. pecorum\u003c/em\u003e (ANC 7879), \u003cem\u003eA. variabilis\u003c/em\u003e (ANC 7728), and the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup (ANC 7878) were positive and all showed large inhibition zones for all tested antibiotics except trimethoprim.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003eabundance in cattle feces is low, but increases in feces deposited on farm floor\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on qPCR analysis, \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in \u0026lsquo;floor\u0026rsquo; samples accounted for an average of \u0026asymp;\u0026thinsp;1% of the total bacteria (average absolute abundance 4.33 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e 16S rRNA gene copies per g dry weight), whereas nearly 50% of the \u0026lsquo;cow\u0026rsquo; samples fell below the detection limit\u0026thinsp;\u0026asymp;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e 16S rRNA gene copies per g dry weight). Reflecting this, average \u003cem\u003eAcinetobacter\u003c/em\u003e abundance in \u0026lsquo;floor\u0026rsquo; samples was at least an order of magnitude higher than the average per-farm value from \u0026lsquo;cow\u0026rsquo; samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Due to the high proportion of \u0026lsquo;cow\u0026rsquo; samples below the limit of detection, subsequent analyses focused solely on the \u0026lsquo;floor\u0026rsquo; samples. Here, \u003cem\u003eAcinetobacter\u003c/em\u003e abundance in feces from dairy farms was at least 10 times greater than beef farms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), while the differences among the different stabling types remained insignificant. Among the factors tested for correlation with \u003cem\u003eAcinetobacter\u003c/em\u003e abundance in \u0026lsquo;floor\u0026rsquo; samples, on-farm antibiotic use showed no correlation while total SCFA showed a positive correlation (Rho\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (Fig. S16A) and the C/N ratio showed a negative correlation (Rho = -0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (Fig. S16B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003erpoB\u003c/b\u003e \u003cb\u003emetabarcoding confirms the high diversity of\u003c/b\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003especies and indicates the main environmental drivers of species occurrence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMetabarcoding of the taxonomic marker gene \u003cem\u003erpoB\u003c/em\u003e was used to study \u003cem\u003eAcinetobacter\u003c/em\u003e taxonomic diversity and species composition. Due to the low abundance of acinetobacters in most samples, metabarcoding was performed on enrichment cultures, and presence\u0026ndash;absence data were used to minimize potential bias arising from differential strain growth during enrichment. In total, 12,959 \u003cem\u003erpoB\u003c/em\u003e clusters were identified, with an average observed richness of 412 clusters per sample. Given that the 98% sequence identity threshold used for \u003cem\u003erpoB\u003c/em\u003e sequence clustering corresponds to subspecies-level differentiation for most \u003cem\u003eAcinetobacter\u003c/em\u003e species [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], these results indicate a remarkably high \u003cem\u003eAcinetobacter\u003c/em\u003e strain-level diversity in cattle feces. No significant differences in observed richness were detected across sample types, antibiotic usage, production systems, or stabling conditions.\u003c/p\u003e \u003cp\u003eApplying a 97% identity threshold between representative \u003cem\u003erpoB\u003c/em\u003e cluster sequences and their closest reference \u003cem\u003erpoB\u003c/em\u003e sequences (Additional file 1), we classified 6,637 \u003cem\u003erpoB\u003c/em\u003e clusters into 55 \u003cem\u003eAcinetobacter\u003c/em\u003e taxa, comprising 33 validly named species and 22 putative novel taxa or unclassified singletons. Almost the same number of clusters (6,322) remained unclassified at the species level. Clusters assigned to \u003cem\u003eA. pseudolwoffii\u003c/em\u003e, \u003cem\u003eA. pecorum\u003c/em\u003e, \u003cem\u003eA. lwoffii\u003c/em\u003e (all members of the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup), and \u003cem\u003eA. indicus\u003c/em\u003e were detected in more than 80% of \u0026lsquo;cow\u0026rsquo; samples, indicating that these species represent the core \u003cem\u003eAcinetobacter\u003c/em\u003e species in the cattle intestine. In contrast, the next most prevalent species, \u003cem\u003eA. variabilis\u003c/em\u003e, was found in only 52% of \u0026lsquo;cow\u0026rsquo; samples. Clusters assigned to the four core species also occurred in more than 90% of \u0026lsquo;floor\u0026rsquo; samples.\u003c/p\u003e \u003cp\u003eNMDS ordination was used to assess differences in \u003cem\u003eAcinetobacter\u003c/em\u003e community composition (based on all \u003cem\u003erpoB\u003c/em\u003e clusters) between \u0026lsquo;cow\u0026rsquo; and \u0026lsquo;floor\u0026rsquo; samples from the same farm (14 farms included, Fig. S17). \u003cem\u003eAcinetobacter\u003c/em\u003e communities were not clearly clustered according to sample type or farm, showing considerable between-cow variability within certain farms. However, in all but three farms, the \u0026lsquo;floor\u0026rsquo; samples fell within two standard deviations of the individual \u0026lsquo;cow\u0026rsquo; samples\u0026rsquo; distance to the farm-specific centroid. This suggests that, in most cases, the \u0026lsquo;floor\u0026rsquo; samples provided a representative approximation of the average \u003cem\u003eAcinetobacter\u003c/em\u003e community composition among the cows on the same farm.\u003c/p\u003e \u003cp\u003eSeparate NMDS ordinations were subsequently performed for \u0026lsquo;floor\u0026rsquo; and \u0026lsquo;cow\u0026rsquo; samples to explore \u003cem\u003eAcinetobacter\u003c/em\u003e community composition in relation to selected farm and cow-specific factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The \u0026lsquo;floor\u0026rsquo; samples tended to separate along NMDS1 based on stabling type (Fig. S18), with indoor floor samples being exclusively in the right part of the plot. Farms with high per-head antibiotic usage (e.g. F01, F13, F27, F28) also tended to cluster together based on their \u0026lsquo;floor\u0026rsquo; sample profiles, but certain farms with low or no antibiotic usage (but with indoor stabling, e.g. F07 and F24) clustered with them. This indicates that \u003cem\u003eAcinetobacter\u003c/em\u003e community composition in \u0026lsquo;floor\u0026rsquo; samples is likely shaped by a combination of multiple factors, whose individual effects are difficult to disentangle by NMDS. Similarly, NMDS analysis based on \u0026lsquo;cow\u0026rsquo; samples showed trends for clustering according to farm (e.g. F03, F09 and F25), production, stabling, and breed (e.g. Hereford and Holstein), with none of these factors showing a clear independent effect on \u003cem\u003eAcinetobacter\u003c/em\u003e community composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig. S18\u0026ndash;S20).The \u0026lsquo;cow\u0026rsquo; NMDS plots were further correlated with herd size, per-herd antibiotic use, pH, SCFA and heavy metal content, indicating possible effects of these factors on \u003cem\u003eAcinetobacter\u003c/em\u003e community composition. The high between-cow variability could not be clearly explained by recent (within 6 months prior to sampling) antibiotic administration. Though some of the treated cows were distant from their untreated counterparts on the NMDS plots, untreated cows from certain farms displayed high dispersion as well (Fig. S21).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo disentangle the effects of various farm- and cow-specific factors on \u003cem\u003eAcinetobacter\u003c/em\u003e community composition, HMSC analysis was conducted separately on \u0026lsquo;floor\u0026rsquo; and \u0026lsquo;cow\u0026rsquo; samples. This analysis was done at the \u003cem\u003eAcinetobacter\u003c/em\u003e species level (i.e., \u003cem\u003erpoB\u003c/em\u003e clusters classified to the same species were grouped) to facilitate interpretation. Variance partitioning (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) revealed that \u003cem\u003eAcinetobacter\u003c/em\u003e species composition in the \u0026lsquo;floor\u0026rsquo; samples was mainly determined by per-head antibiotic use (explaining on average 19.2% variance), followed by stabling (18.3%), herd size (17.9%) and production type (15.3%). These relatively similar contributions are in line with NMDS results, showing no overriding effect of any single factor. Similarly, production type, per-head antibiotic use, and herd size were the primary factors shaping \u003cem\u003eAcinetobacter\u003c/em\u003e species composition in \u0026lsquo;cow\u0026rsquo; samples, explaining 14.2%, 10.1%, and 8.8% of variance, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In contrast, cow-specific factors such as cow age, SCFA or C/N content contributed relatively little to the explained variance. Notably, the random cow effect accounted for 23.9% variance, indicating that \u003cem\u003eAcinetobacter\u003c/em\u003e community composition might be highly structured at the individual-cow level, potentially due to unmeasured host-specific factors or microenvironmental variation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmaps of species niches based on HMSC β-parameters revealed both common and contrasting responses of the individual \u003cem\u003eAcinetobacter\u003c/em\u003e species to various farm- and cow-level factors (Fig. S22). Most of the \u003cem\u003eAcinetobacter\u003c/em\u003e spp. (as identified with \u003cem\u003erpoB\u003c/em\u003e metabarcoding) exhibited a higher likelihood of occurrence in dairy farms as compared to beef farms, both in \u0026lsquo;floor\u0026rsquo; and individual \u0026lsquo;cow\u0026rsquo; fecal samples. Most species were also positively associated with high levels of SCFA in feces; notably \u003cem\u003eA. gandensis\u003c/em\u003e, \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eA. pecorum\u003c/em\u003e, and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e showed a significant positive relationship (with \u0026gt;\u0026thinsp;90% posterior support) in both \u0026lsquo;floor\u0026rsquo; and \u0026lsquo;cow\u0026rsquo; samples. In addition, the majority of species were positively associated with higher sampling temperatures (related to the summer sampling season).\u003c/p\u003e \u003cp\u003eIn contrast, antibiotic use at the farm level was negatively associated with the occurrence of most \u003cem\u003eAcinetobacter\u003c/em\u003e species in \u0026lsquo;cow\u0026rsquo; and \u0026lsquo;floor\u0026rsquo; fecal samples, with the only exception being Taxon 7384 in \u0026lsquo;floor\u0026rsquo; samples, which showed a significant positive association. Herd size had an overall negative effect on the occurrence of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in \u0026lsquo;cow\u0026rsquo; samples, whereas in \u0026lsquo;floor\u0026rsquo; samples, this effect was species specific, with \u003cem\u003eA. thermotolerans\u003c/em\u003e and \u003cem\u003eA. towneri\u003c/em\u003e showing significant preference for larger herds. Considering stabling, contrasting results were obtained for \u0026lsquo;floor\u0026rsquo; and \u0026lsquo;cow\u0026rsquo; samples. All species detected in the \u0026lsquo;floor\u0026rsquo; samples showed a positive trend towards indoor conditions, while it was generally the opposite for \u0026lsquo;cow\u0026rsquo; samples, with the main exception being \u003cem\u003eA. indicus\u003c/em\u003e. Interestingly, \u003cem\u003eA. pecorum\u003c/em\u003e, \u003cem\u003eA. terrae\u003c/em\u003e, and \u003cem\u003eA. variabilis\u003c/em\u003e present in \u0026lsquo;cow\u0026rsquo; fecal samples were significantly positively associated with metal PC2, representing higher Pb and Cd content.\u003c/p\u003e \u003cp\u003e \u003cb\u003eShotgun sequencing of enrichment cultures provides\u003c/b\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003eMAG and plasmid sequences\u003c/b\u003e\u003c/p\u003e \u003cp\u003eShotgun sequencing of \u003cem\u003eAcinetobacter\u003c/em\u003e enrichment cultures from 28 \u0026lsquo;floor\u0026rsquo; samples yielded 11,250 contigs (287 Mb in total) affiliated with the genus \u003cem\u003eAcinetobacter\u003c/em\u003e (Table S10). Contigs longer than 250,000 bp, representing \u003cem\u003eAcinetobacter\u003c/em\u003e chromosomes or large chromosome fragments, were further classified at the species level using the ANIb approach (Table S12). Of the 207 contigs examined, 116 were successfully classified to known species based on the 96% ANIb threshold [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. They represented \u003cem\u003eA. faecalis\u003c/em\u003e, \u003cem\u003eA. gandensis\u003c/em\u003e, \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eA. pecorum\u003c/em\u003e, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e, and \u003cem\u003eA. schindleri\u003c/em\u003e. Six contigs exhibited\u0026thinsp;\u0026gt;\u0026thinsp;99% completeness and \u0026gt;\u0026thinsp;5% contamination and represented single-contig, circular, high-quality metagenome-assembled genomes (MAGs) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Two of these MAGs were classified as \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eA. pecorum\u003c/em\u003e based on the 96% ANIb threshold, while one showed an ANIb value of 95.7% to \u003cem\u003eA. amyesii\u003c/em\u003e. The three remaining MAGs (F01_chromosome_61, F18_chromosome_3, and F22_chromosome_7) likely represent novel \u003cem\u003eAcinetobacter\u003c/em\u003e taxa, sharing 99.4\u0026ndash;100% identity across a 355-bp fragment of the \u003cem\u003erpoB\u003c/em\u003e with reference strains of Taxon 7683, Taxon 7579, and Taxon 7655, respectively.\u003c/p\u003e \u003cp\u003eIn total, 599 putative plasmid contigs were identified across the \u003cem\u003eAcinetobacter\u003c/em\u003e assemblies, of which 514 showed significant sequence similarity to PLSDB entries (Table S12) and 213 contained homologues of plasmid replication initiation (\u003cem\u003erep\u003c/em\u003e) genes listed in the Acinetobacter Plasmid Typing database (128 of these contigs met both criteria; Table S13). Among the 213 \u003cem\u003erep\u003c/em\u003e homologues, 120 could be confidently assigned to known Rep types (Table S13), belonging to the three major families, i.e. R1, R3, and RP, with R3 predominating. The remaining 93 \u003cem\u003erep\u003c/em\u003e genes shared\u0026thinsp;\u0026lt;\u0026thinsp;95% nucleotide sequence identity with described Rep types and likely represent novel variants. The \u003cem\u003erep\u003c/em\u003e genes corresponding to LowGC-type plasmids (i.e., R3-T20 type), were not detected in the \u003cem\u003eAcinetobacter\u003c/em\u003e assemblies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eShotgun sequencing of enrichment cultures revealed a rich\u003c/b\u003e \u003cb\u003eAcinetobacter\u003c/b\u003e \u003cb\u003eresistome with potential to spread clinically important antibiotic resistance genes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAbricate analysis revealed the presence of 19 distinct ARGs across the \u003cem\u003eAcinetobacter\u003c/em\u003e assemblies, with a total of 116 Abricate hits (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Table S14). These ARGs were predicted to confer resistance to aminoglycosides, amphenicols, β-lactams (including carbapenems), tetracyclines, and sulfonamides. Of these, 78 ARGs were located on plasmid-derived contigs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur further analyses focused on horizontally acquired ARGs, excluding chromosomally encoded intrinsic carbapenemase genes, which do not confer significant carbapenem resistance unless placed under a strong promoter provided by an upstream insertion sequence [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Therefore, we examined the genetic context of all identified carbapenemase genes and excluded those lacking adjacent insertion sequences. These carbapenemase genes (identified by Abricate as \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;235\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;282\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;258\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;537\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;646\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;648\u003c/sub\u003e) were all located between the metalloprotease and molecular chaperone DnaK genes, suggesting a chromosomal origin. Thus, the only carbapenemase genes retained for further analyses were the ones bracketed by IS\u003cem\u003eAba3\u003c/em\u003e and present on contigs classified as \u003cem\u003eAcinetobacter\u003c/em\u003e plasmids; their sequences were 100% identical to the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e sequence of \u003cem\u003eA. baumannii\u003c/em\u003e MAD (GenBank accession number AY665723). All other identified ARGs are either known to be horizontally transferable or were associated with mobile genetic elements. The complete set of horizontally transferable ARGs was then compared between antibiotic-using and antibiotic-free farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig. S23). We detected significantly more horizontally transferable ARG types in antibiotic-using farms, as compared to antibiotic-free farms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), though the effect of antibiotic usage was small (median\u0026thinsp;=\u0026thinsp;0 and 3 ARGs, respectively).\u003c/p\u003e \u003cp\u003eNotably, the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e gene was found only in farms where antibiotics were used. Carbapenems are not administered to cattle, suggesting that these genes may be co-selected with other antibiotic or heavy metal resistance genes in the farms. Supporting this, genetic analyses showed co-localization of the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e gene with the \u003cem\u003estrA-strB\u003c/em\u003e genes on two contigs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), indicating that carbapenem resistance may be co-selected with aminoglycosides, which are frequently applied in cattle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnother important case of co-localization is the presence of \u003cem\u003etet\u003c/em\u003e(X3) and \u003cem\u003efloR\u003c/em\u003e on a single contig (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). While \u003cem\u003etet\u003c/em\u003e(X3) confers resistance to the last-resort antibiotic tigecycline, \u003cem\u003efloR\u003c/em\u003e confers resistance to florfenicol, which is occasionally used on cattle farms. The co-localization of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e and \u003cem\u003etet\u003c/em\u003e(X3) with mobile genetic elements (Fig.\u0026nbsp;8AB) on contigs sharing highly similar regions with \u003cem\u003eAcinetobacter\u003c/em\u003e plasmids suggests their potential for dissemination through horizontal gene transfer. Of note, another contig carrying \u003cem\u003etet\u003c/em\u003e(X3) (F20_1806) showed 100% identity to the LowGC-type plasmid pHH1107 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] over 5,117 bp, but it lacked the \u003cem\u003erep\u003c/em\u003e gene required for reliable classification.\u003c/p\u003e \u003cp\u003eA total of 466 HMRGs hits were obtained across the \u003cem\u003eAcinetobacter\u003c/em\u003e assemblies, representing 19 distinct HMRG types (Table S15). The most frequently (\u0026gt;\u0026thinsp;10 occurrences) detected HMRGs were \u003cem\u003egolT\u003c/em\u003e (gold/copper resistance), \u003cem\u003earsB, arsC\u003c/em\u003e, and \u003cem\u003earsH\u003c/em\u003e (arsenic resistance), \u003cem\u003edpsA\u003c/em\u003e (iron resistance), \u003cem\u003eczcA\u003c/em\u003e and \u003cem\u003eczcD\u003c/em\u003e (cadmium/zinc/cobalt resistance), and \u003cem\u003enreB\u003c/em\u003e (cobalt/nickel). Only 37 HMRGs were localized on plasmid contigs. Co-localization of HMRGs with ARGs was rare, observed on only eight contigs, and primarily involved intrinsic carbapenemase genes on contigs likely representing \u003cem\u003eAcinetobacter\u003c/em\u003e chromosomes. In a single instance, the \u003cem\u003edpsA\u003c/em\u003e gene co-occurred with \u003cem\u003estrA\u003c/em\u003e and \u003cem\u003estrB\u003c/em\u003e, although not in close proximity. Overall, these findings indicate that co-selection of heavy metal and antibiotic resistance genes is uncommon in the studied farms. This is further supported by the finding that HMRG counts did not differ significantly between antibiotic-using and antibiotic-free farms (Fig. S24).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenome sequencing confirms the presence of\u003c/b\u003e \u003cb\u003ebla\u003c/b\u003e\u003csub\u003e\u003cb\u003eOXA-58\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eon\u003c/b\u003e \u003cb\u003eA. pseudolwoffii\u003c/b\u003e \u003cb\u003eANC 7493 plasmid\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe genome assembly of \u003cem\u003eA. pseudolwoffii\u003c/em\u003e ANC 7493 consisted of a single circular chromosome (2,764,499 bp) and one circular plasmid (167,549 bp; designated pANC7493.1). Abricate analysis confirmed the presence of the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e, \u003cem\u003estrA\u003c/em\u003e, and \u003cem\u003estrB\u003c/em\u003e genes in the genome, all located in close proximity on the plasmid pANC7493.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These genes were part of a region spanning positions 93,403\u0026ndash;100,027 bp, which shared\u0026thinsp;\u0026gt;\u0026thinsp;99.9% sequence identity with the metagenomic contig F17_297 (positions 1\u0026ndash;6,322), differing only by the absence of a LysE family translocator gene between \u003cem\u003estrB\u003c/em\u003e and an AraC family transcriptional regulator gene from the contig (Fig S25). The \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e was flanked by IS\u003cem\u003eAba3\u003c/em\u003e insertion sequences, with the upstream copy being truncated (i.e., IS\u003cem\u003eAba3-\u003c/em\u003elike element).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePlasmid pANC7493.1 contained a single \u003cem\u003erep\u003c/em\u003e gene, enabling its classification as the R3-T103 type according to the Acinetobacter Plasmid Typing scheme [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. No relaxase or mating-pair formation genes were detected, suggesting that the plasmid is neither mobilizable nor self-transmissible. In addition, the plasmid encoded several stability and maintenance systems, including restriction\u0026ndash;modification and toxin\u0026ndash;antitoxin modules, as well as metabolic and putative host-adaptive genes. These included the outer membrane protein A gene \u003cem\u003eompA\u003c/em\u003e, a known \u003cem\u003eA. baumannii\u003c/em\u003e virulence factor [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], a large gene encoding Ig-like domain-containing protein typical of biofilm-associated proteins [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], and two siderophore receptor genes, \u003cem\u003efhuE\u003c/em\u003e and \u003cem\u003efatA\u003c/em\u003e. The plasmid also carried several HMRGs, including an arsenic resistance operon, the cadmium/cobalt/zinc resistance gene \u003cem\u003eczcD\u003c/em\u003e, and a mercuric resistance operon regulator gene \u003cem\u003emerR1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAccording to NCBI BLASTn results, the highest sequence coverage between pANC7493.1 and entries in the NCBI nucleotide collection was 52%, indicating that the plasmid represents a novel genetic element. The best BLASTn matches were three \u003cem\u003eA. pseudolwoffii\u003c/em\u003e plasmids: CP084301.1 (52% coverage, 98.35% identity) and CP183900.1 and CP183904.1 (both 46% coverage, 98.79% identity), originating from chicken and bovine samples in China. Mapping of these plasmid sequences onto pANC7493.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) revealed that while all four plasmids shared HMRG regions, the ARG region was unique to pANC7493.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe cattle fecal microbiome is important for both animal and human health, particularly regarding zoonotic transmission and the agricultural application of cattle manure. Multiple studies have characterized its composition and consistently reported a predominance of members of the bacterial phyla \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e, whereas \u003cem\u003eProteobacteria\u003c/em\u003e generally account for less than 5% of the total bacterial community [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. While most studies on \u003cem\u003eProteobacteria\u003c/em\u003e have focused on \u003cem\u003eEscherichia coli\u003c/em\u003e [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], \u003cem\u003eAcinetobacter\u003c/em\u003e species remain underexplored despite their importance in pathogenesis and dissemination of ARGs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To address the low abundance of \u003cem\u003eAcinetobacter\u003c/em\u003e in cattle feces, we combined culturing strains with metabarcoding and metagenomic analysis of enrichment cultures. This approach uncovered a rich diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e species, including putative novel species, and provided insights into their response to antibiotic selection pressure.\u003c/p\u003e \u003cp\u003eBoth pure strain isolation and \u003cem\u003erpoB\u003c/em\u003e metabarcoding revealed a high diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in cattle feces. Of the 284 strains recovered, 63% were assigned to 16 validly named species, whereas the remaining 37% comprised either putative novel species awaiting formal description or unclassified singletons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Strains obtained in this study have already contributed to the delineation of \u003cem\u003eA. amyesii\u003c/em\u003e [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] and \u003cem\u003eA. thermotolerans\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] as well as to the emended description of \u003cem\u003eA. faecalis\u003c/em\u003e [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Thus, the share of strains belonging to described species prior to the start of this project was only\u0026thinsp;\u0026asymp;\u0026thinsp;48%, highlighting that cattle represent a reservoir of largely unexploited \u003cem\u003eAcinetobacter\u003c/em\u003e diversity. It should be noted that some of the novel \u003cem\u003eAcinetobacter\u003c/em\u003e taxa, including \u003cem\u003eA. thermotolerans\u003c/em\u003e, were recovered at the cultivation temperature of 44\u0026deg;C. As growth at elevated temperatures is considered a prerequisite for mammalian pathogenicity (this temperature was used to improve \u003cem\u003eA. baumannii\u003c/em\u003e recovery), these taxa warrant further investigation regarding their potential virulence.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003erpoB\u003c/em\u003e sequences obtained from metabarcoding of enrichment cultures were classified into 33 validly named species and 22 putative novel species or taxonomically unique singletons, comprising the aforementioned taxa and singletons included in our custom \u003cem\u003erpoB\u003c/em\u003e database. The higher species count relative to isolate-based data was anticipated, as strain isolation requires an additional culture step on agar plates, where certain taxa may fail to form colonies on ACE agar, thus limiting the number of isolates that can be examined. In addition, this analysis was conducted across \u0026lsquo;cow\u0026rsquo; and \u0026lsquo;floor\u0026rsquo; samples, whereas only \u0026lsquo;floor\u0026rsquo; samples were used for strain isolation because of the low throughput of the isolate-based approach. Nonetheless, the taxonomic assignments based on \u003cem\u003erpoB\u003c/em\u003e metabarcoding need to be interpreted with caution, as they were not complemented by additional methods such as MALDI-TOF MS profiling or phenotypic characterization, which could be applied only to the strains. Although the number of species detected with either method may appear high given that only 87 \u003cem\u003eAcinetobacter\u003c/em\u003e species are currently validly described (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://szu.gov.cz/wp-content/anemec/Classification.pdf\u003c/span\u003e\u003cspan address=\"https://szu.gov.cz/wp-content/anemec/Classification.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), previous studies have indicated that a substantial portion of the genus\u0026rsquo;s phylogenetic diversity remains undescribed [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. The diversity revealed in this study may thus represent only a small fraction of the genus\u0026rsquo;s taxonomic breadth, highlighting the need for continued exploration of \u003cem\u003eAcinetobacter\u003c/em\u003e in animal-associated environments.\u003c/p\u003e \u003cp\u003eThe core \u003cem\u003eAcinetobacter\u003c/em\u003e species detected in most cattle fecal samples based on \u003cem\u003erpoB\u003c/em\u003e metabarcoding were \u003cem\u003eA. indicus\u003c/em\u003e and members of the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup (\u003cem\u003eA. lwoffii\u003c/em\u003e, \u003cem\u003eA. pecorum\u003c/em\u003e, and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e). Consistently, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e and \u003cem\u003eA. indicus\u003c/em\u003e were also represented by the largest number of isolated strains, whereas strains belonging to other members of the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup were recovered at lower frequencies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The occurrence of \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eA. lwoffii\u003c/em\u003e, and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e in cattle manure has been documented previously [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. In contrast, \u003cem\u003eA. pecorum\u003c/em\u003e was only recently described based on isolates from sheep and chickens [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Our study thus demonstrates the presence of \u003cem\u003eA. pecorum\u003c/em\u003e in cattle feces and provides the first complete MAG of this species from cattle. Compared with the core taxa, \u003cem\u003eA. baumannii\u003c/em\u003e was detected less frequently, occurring in fewer than 5% of \u0026lsquo;cow\u0026rsquo; and \u0026lsquo;floor\u0026rsquo; samples by metabarcoding of enrichment cultures and in 21% of \u0026lsquo;floor\u0026rsquo; samples by strain isolation (using two different growth temperatures). These findings are consistent with previous studies, which suggested that \u003cem\u003eA. baumannii\u003c/em\u003e likely originates from environmental sources rather than being a primary colonizer of cattle [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis raises the question of whether the \u003cem\u003eAcinetobacter\u003c/em\u003e species and taxa detected in this study are capable of stable colonization of the cattle intestine, or whether they represent transient passengers of the intestinal tract. \u003cem\u003eAcinetobacter\u003c/em\u003e species are strict aerobes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and thus appear unlikely to thrive under the largely anaerobic conditions of the cattle gut. However, \u003cem\u003eA. baumannii\u003c/em\u003e has been shown to proliferate in the colonic crypts of mice [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], suggesting the existence of intestinal micro-niches with oxygen levels sufficient to support \u003cem\u003eAcinetobacter\u003c/em\u003e growth. Based on our data, we propose that the \u003cem\u003eAcinetobacter\u003c/em\u003e communities observed in cattle feces consist of both stable colonizers\u0026mdash;represented in particular by the core species identified in this study\u0026mdash;and transient species acquired from environmental sources, such as \u003cem\u003eA. baumannii\u003c/em\u003e and other less frequently detected taxa. This interpretation is further supported by our metabarcoding analysis of species composition in cattle feces, which revealed that farm- and cow-associated variables explained only a limited portion of the variation, while a substantial proportion (on average 24%) remained attributable to a random cow effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). When examined by species, the random cow effect was low for \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eA. pecorum\u003c/em\u003e, and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e (\u0026asymp;\u0026thinsp;2\u0026ndash;7%), supporting their role as stable colonizers, whereas it was substantially higher for \u003cem\u003eA. lwoffii\u003c/em\u003e (47%).\u003c/p\u003e \u003cp\u003eAt the genus level, \u003cem\u003eAcinetobacter\u003c/em\u003e abundance was substantially lower in \u0026lsquo;cow\u0026rsquo; samples (feces collected per rectum or immediately upon defecation) than in \u0026lsquo;floor\u0026rsquo; samples (feces deposited on the farm floor). This observation further supports the notion that growth of \u003cem\u003eAcinetobacter\u003c/em\u003e in the cattle intestine is limited, whereas these bacteria can rapidly proliferate once exposed to external conditions. The increased abundance in deposited feces may result from rapid growth under oxygen-rich conditions, supported by the availability of short-chain fatty acids in cattle feces as a suitable carbon source, and by the competitive advantage over major intestinal taxa that favor anaerobic conditions (e.g., \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]). Additional contributions could arise from colonization by airborne \u003cem\u003eAcinetobacter\u003c/em\u003e \u0026ndash; an aspect that merits further investigation. Observed abundances in deposited feces (typically 10⁷\u0026ndash;10⁹ 16S rRNA gene copies per gram of dry weight, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were comparable to values reported in manure inputs for biogas plants in Germany [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], where 10⁶\u0026ndash;10⁸ copies per gram of fresh material were detected, equivalent to roughly tenfold higher levels on a dry weight basis. Notably, \u003cem\u003eAcinetobacter\u003c/em\u003e abundance was higher in dairy than beef cattle samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with most species showing a general preference for dairy farms (Fig. S22). Previous studies have documented differences in the intestinal microbiome between beef and dairy cattle [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] and variation in \u003cem\u003eA. baumannii\u003c/em\u003e isolation rates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This result is difficult to explain based on the current data, but it may be related to particular cow breeds, types of nutrition or higher levels of human contact in dairy farms.\u003c/p\u003e \u003cp\u003eAnalysis of the antibiotic susceptibility phenotypes of strains supported the hypothesis that ongoing antibiotic use in Czech cattle farming provides selective pressure for resistance acquisition. Based on data from two core species, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e and \u003cem\u003eA. indicus\u003c/em\u003e, strains from antibiotic-using farms displayed lower streptomycin susceptibility than those from antibiotic-free farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, three MDR strains identified in this study originated exclusively from antibiotic-using farms. These strains belonged to the recently described species \u003cem\u003eA. faecalis\u003c/em\u003e [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], \u003cem\u003eA. thermotolerans\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and a novel taxon (Taxon 7209), underscoring the potential role of newly recognized taxa in the dissemination of antimicrobial resistance. Nevertheless, reduced antibiotic susceptibility in these strains was limited to antibiotics not classified as critically important for human medicine (i.e., tetracycline, streptomycin, sulfamethoxazole, and trimethoprim). This contrasts with findings from China, where MDR \u003cem\u003eAcinetobacter\u003c/em\u003e isolates displayed resistance to clinically critical agents such as carbapenems and tigecycline [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consistent with this, all \u003cem\u003eA. baumannii\u003c/em\u003e isolates recovered in this study were susceptible to all clinically relevant antibiotics.\u003c/p\u003e \u003cp\u003eEven though strain-level analyses suggested that the overall health risk associated with Czech cattle farms is low, resistome profiling by shotgun metagenomic sequencing of enrichment cultures revealed the presence of clinically relevant ARGs, which were further corroborated by strain-level data. Notably, the carbapenemase gene \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e was detected on several sequence contigs from antibiotic-using farms and was confirmed in three strains of \u003cem\u003eA. pseudolwoffii\u003c/em\u003e. All three strains were susceptible to carbapenems, but the inconsistency between genotype and phenotype is not uncommon [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], likely owing to insufficient gene expression. The co-localization of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e with \u003cem\u003estrA-strB\u003c/em\u003e (either on the same contig in metagenomic data or within the same strains) suggests that carbapenem resistance may be co-selected by aminoglycoside use in farm settings. In addition, metagenomic data indicated a possible co-selection of the tigecycline resistance gene \u003cem\u003etet\u003c/em\u003e(X) with florfenicol. Both gene clusters are associated with transposable elements and plasmids, indicating their potential for high mobility within and between bacterial hosts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Together, these findings highlight the capacity of cattle-associated \u003cem\u003eAcinetobacter\u003c/em\u003e spp. to serve as reservoirs of clinically relevant resistance determinants that could, under suitable conditions, be mobilized into pathogenic bacteria.\u003c/p\u003e \u003cp\u003eThe co-localization of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e with \u003cem\u003estrA\u003c/em\u003e and \u003cem\u003estrB\u003c/em\u003e on a plasmid was confirmed through whole-genome sequencing of one of the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e-positive strains, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e ANC 7493 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Fig. S25). Since this strain was susceptible to carbapenems, we assume that the IS\u003cem\u003eAba3\u003c/em\u003e-like element located upstream of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e does not provide a strong promoter sufficient for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e expression within this host. The expression of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e in \u003cem\u003eA. baumannii\u003c/em\u003e and other \u003cem\u003eAcinetobacter\u003c/em\u003e spp. may be enhanced by the insertion of IS\u003cem\u003eAba2\u003c/em\u003e, IS\u003cem\u003e18\u003c/em\u003e, and other insertion sequence types within the IS\u003cem\u003eAba3\u003c/em\u003e-like element through the provision of hybrid promoter sequences [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], but these structures were not observed here. The plasmid pANC7493.1 appears to be non-mobilizable and non\u0026ndash;self-transmissible, but since natural competence is widespread among \u003cem\u003eAcinetobacter\u003c/em\u003e spp. [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], horizontal gene transfer via transformation cannot be ruled out. Although the plasmid appears to be novel, similar scaffolds have been recovered from \u003cem\u003eA. pseudolwoffii\u003c/em\u003e isolates obtained from chicken and bovine samples in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), suggesting that related plasmids may circulate globally.\u003c/p\u003e \u003cp\u003ePCR screening of \u003cem\u003eAcinetobacter\u003c/em\u003e strains further identified the hosts of the \u003cem\u003esul2\u003c/em\u003e, \u003cem\u003estrA-strB\u003c/em\u003e, and \u003cem\u003etet\u003c/em\u003e(Y) genes, which have been reported from farm environments in Europe and elsewhere [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. The \u003cem\u003estrA-strB\u003c/em\u003e genes were particularly widespread, occurring in 10 species or novel taxa, likely reflecting the frequent administration of aminoglycosides (e.g., dihydrostreptomycin) in cattle. Regarding aminoglycoside resistance, we also demonstrated that \u003cem\u003eaadA27\u003c/em\u003e, encoding the ANT(3\u0026rsquo;\u0026rsquo;)-II aminoglycoside nucleotidyltransferase and originally described in \u003cem\u003eA. lwoffii\u003c/em\u003e from permafrost [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], is present in at least four species or novel taxa (including \u003cem\u003eA. faecalis\u003c/em\u003e, in which we recently reported the gene [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]). These findings highlight the widespread dissemination of aminoglycoside resistance determinants across diverse \u003cem\u003eAcinetobacter\u003c/em\u003e lineages, suggesting that they may constitute a long-standing component of the environmental resistome.\u003c/p\u003e \u003cp\u003eLowGC-type plasmids, previously thought to mediate the transfer of ARGs from livestock manure to soil and to contribute to the environmental spread of antibiotic resistance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], were scarce in our dataset. They were detected only in three \u003cem\u003eAcinetobacter\u003c/em\u003e strains (i.e., 1%), which were mostly susceptible to antibiotics. Within the scope of this study, therefore, LowGC-type plasmids do not appear to represent a dominant vehicle for ARG dissemination. Nonetheless, our results newly identify their hosts as \u003cem\u003eA. variabilis\u003c/em\u003e and members of the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup, including \u003cem\u003eA. pecorum\u003c/em\u003e\u0026mdash;information that was overlooked in earlier studies, which had recovered such plasmids mainly from manured soil via exogenous plasmid isolation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. By contrast, we detected several other Rep-type plasmids (mostly of the Rep3 family) in \u003cem\u003eAcinetobacter\u003c/em\u003e enrichment cultures from cattle feces, some of which carried ARGs such as \u003cem\u003estrA-strB.\u003c/em\u003e The above-mentioned plasmid pANC7493.1 carrying \u003cem\u003estrA-strB\u003c/em\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e also belonged to the Rep3 family. Reference plasmids of these Rep-types have been reported from diverse geographical regions and sources, including animal feces, clinical isolates, and hospital sewage (Table S12; [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], indicating their broad host range and global circulation. Together, these findings suggest that while LowGC-type plasmids may not be central to ARG spread in cattle, cattle-associated acinetobacters nonetheless harbor plasmid backbones capable of facilitating the dissemination of resistance genes across environments and host species.\u003c/p\u003e \u003cp\u003eLivestock manure represents an environment conducive to horizontal gene transfer of ARGs due to its high bacterial density and diversity, abundant nutrients, and the presence of antibiotic residues exerting selective pressure [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. We assume similar conditions apply to \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in cattle feces, given their high diversity, access to suitable carbon sources such as short-chain fatty acids, and increased abundance in feces deposited on the farm floor, which indicates active growth. Antibiotic residues were also present in both \u0026lsquo;cow\u0026rsquo; and \u0026lsquo;floor\u0026rsquo; samples in our study, although certain commonly used antibiotics, such as some β-lactams, were not detected\u0026mdash;likely reflecting their low stability [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. In addition, heavy metals were consistently present in all fecal samples, representing another selective factor that could potentially drive co-selection of antibiotic resistance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The concentrations of Cu, Zn, Cd, Pb, Cr, and As were within the ranges reported in cattle farms [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], indicating that such metal contamination is likely widespread. Consistently, our shotgun metagenome analysis identified 19 distinct types of heavy metal resistance genes. However, they were infrequently localized on plasmid contigs and, in a single instance only, co-occurred on the same contig with horizontally acquired antibiotic resistance genes. Although the co-selection of heavy metal and antibiotic resistance cannot be entirely excluded \u0026ndash; particularly given the presence of multiple heavy metal resistance genes on plasmid pANC7493.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) \u0026ndash; the overall data indicate that such events are infrequent in the studied farm environments.\u003c/p\u003e \u003cp\u003eThis study demonstrates the value of an integrative approach for characterizing low-abundance members of the animal microbiome. While each of the methods used carries inherent strengths and limitations, together they provided a complementary and comprehensive picture of \u003cem\u003eAcinetobacter\u003c/em\u003e populations in cattle feces. The analysis of total community DNA (i.e., without culturing) enabled accurate assessment of \u003cem\u003eAcinetobacter\u003c/em\u003e abundance. Although this strategy is now widely employed for diversity profiling through metabarcoding [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], it was not feasible in our study because most samples did not yield \u003cem\u003eAcinetobacter\u003c/em\u003e-specific amplicons. Consequently, our diversity assessment relied on enrichment cultures. This approach is, however, inherently biased, as species or strains differ in their growth performance in the liquid ACE medium used for enrichment. To mitigate this bias, we based our analysis strictly on presence\u0026ndash;absence data rather than relative abundances. Still, it must be acknowledged that some slow-growing strains may have remained below the detection threshold, whereas fast-growing strains were more readily detected. Such biases are not unique to our enrichment strategy but also occur in widely used PCR-based community profiling approaches employing universal bacterial primers [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCoupling enrichment cultures with shotgun metagenomics provided valuable insights into the \u003cem\u003eAcinetobacter\u003c/em\u003e resistome without the need to sequence individual strains. Unlike \u003cem\u003erpoB\u003c/em\u003e metabarcoding, this approach did not rely on \u003cem\u003eAcinetobacter\u003c/em\u003e-specific primers, making it necessary to carefully filter the data for non-\u003cem\u003eAcinetobacter\u003c/em\u003e sequences. This precaution was important because non-\u003cem\u003eAcinetobacter\u003c/em\u003e bacteria may also proliferate in liquid ACE medium, particularly when traces of alternative carbon sources from cattle feces are present. As a result, ARGs carried on broad-host-range plasmids or represented by contigs too short for confident taxonomic assignment may have remained undetected in this study. Nevertheless, this approach is very useful for revealing the resistome of low-abundance taxa from complex samples, as previously shown by Marano et al. [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe culture of pure strains offered the advantage of direct phenotypic testing, which could then be linked to genotypic traits through PCR or whole-genome sequencing. Our use of ACE medium for isolation has previously been demonstrated to be effective for recovering a broad range of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. from diverse environments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Combined with MALDI-TOF MS (amended with a custom \u003cem\u003eAcinetobacter\u003c/em\u003e database) and sequencing of a variable region of the \u003cem\u003erpoB\u003c/em\u003e gene [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], this approach allowed for reliable species identification and delineation of novel taxonomic clusters.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study provides considerable insights into the taxonomic diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e and its antimicrobial resistance in cattle feces. Our findings revealed a surprisingly high diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e species, including several putative novel species. We identified \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eA. pseudolwoffii\u003c/em\u003e, and other members of the \u003cem\u003eA. lwoffii\u003c/em\u003e phylogroup as core \u003cem\u003eAcinetobacter\u003c/em\u003e species associated with cattle. Consistent with previous reports, \u003cem\u003eA. baumannii\u003c/em\u003e was rare in cattle feces, and the strains recovered here did not appear to pose an immediate health risk. Nevertheless, we detected clinically relevant resistance genes, including \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e and \u003cem\u003etet\u003c/em\u003e(X), in cattle-associated \u003cem\u003eAcinetobacter\u003c/em\u003e, despite relatively strict antibiotic use regulations on Czech farms. The association of these genes with mobile genetic elements highlights their potential for dissemination under favorable conditions and emphasizes the need for continued improvements in cattle health management to further reduce reliance on antibiotics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations. The study involved only the collection of bovine fecal samples, either from the farm floor or by non-invasive rectal sampling performed by a licensed veterinarian using a sterile examination glove. These procedures did not cause pain, suffering, distress, or lasting harm to the animals and fall below the threshold defined in Article 1(5)(f) of Directive 2010/63/EU, which excludes such non-harmful practices from the scope of animal experimentation legislation. Therefore, in accordance with Czech and EU regulations, no formal animal experiment approval was required for this study. Sampling was conducted with the consent of the farm owners.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eThe nucleotide sequence datasets generated during the current study are available as follows. Partial \u003cem\u003erpoB\u003c/em\u003e sequences from 284 \u003cem\u003eAcinetobacter\u003c/em\u003e strains are available at the NCBI GenBank repository (https://www.ncbi.nlm.nih.gov/nuccore/) under accession numbers PX405702\u0026ndash;PX405985. Partial sequences of \u003cem\u003eaadA27\u003c/em\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA-58\u003c/sub\u003e, \u003cem\u003etet\u003c/em\u003e(Y), \u003cem\u003esul2\u003c/em\u003e, and \u003cem\u003erep\u003c/em\u003e amplified from \u003cem\u003eAcinetobacter\u0026nbsp;\u003c/em\u003estrains are available at NCBI GenBank under accession numbers PX380145\u0026ndash;PX380148, PX359217\u0026ndash;PX359219, PX359220\u0026ndash;PX359221, PX380135\u0026ndash;PX380144, and PX396045\u0026ndash;PX396047, respectively. Raw reads corresponding to \u003cem\u003erpoB\u003c/em\u003e metabarcoding data (Illumina MiSeq) from 118 cattle feces samples are available at NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra/), under accession numbers SRR34872364\u0026ndash;SRR34872481. Raw Illumina NovaSeq and Oxford Nanopore reads from 28 shotgun metagenomes are available at NCBI SRA under accession numbers SRR34902429\u0026ndash;SRR34902456 and SRR34878076\u0026ndash;SRR34878103, respectively, and the corresponding assemblies are available at the Zenodo repository (https://zenodo.org/) under the identifier 17176853. The complete genome \u003cem\u003eA. pseudolwoffii\u003c/em\u003e ANC 7493 is available at the NCBI GenBank repository under the accession number JBSSNL000000000. The R scripts used for HMSC analysis are available at the Zenodo repository under the identifier 17426203. Other data generated or analyzed during this study are included in this published article as supplementary tables or additional files.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Czech Science Foundation (project 22-05373S; contribution: design of the study and collection, analysis, and interpretation of data, and writing the manuscript) and the Ministry of Education, Youth and Sports of the Czech Republic (project CZ.02.01.01/00/22_008/0004635\u0026mdash;AdAgriF; contribution: interpretation of data and writing the manuscript). Anitha Ravi was also supported by the Charles University Grant Agency (project 160125; contribution: interpretation of data and writing the manuscript).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAR performed sample processing, prepared enrichment cultures, isolated DNA, conducted qPCR and metabarcoded PCR, analyzed metabarcoding and shotgun metagenomic data, and was a major contributor to writing the manuscript. VS analyzed the isolates by MALDI-TOF MS. PTD conceptualized and performed bioinformatic analyses, including the processing of raw reads and sequence assemblies. HS contributed substantially to sample processing, enrichment cultures, and DNA isolation. MM isolated and dereplicated the \u003cem\u003eAcinetobacter\u003c/em\u003e strains. JS conceptualized the assessment of sample chemical composition and contributed to the analysis of C, N, and antibiotic content. ANeh analyzed the antibiotic content. MV performed farm sampling and data collection and conducted \u003cem\u003erpoB\u003c/em\u003e sequencing of strains. IO conceptualized and performed the HMSC analysis. HSS carried out PCR screening of strains for antibiotic resistance genes. TV contributed to the conceptualization and execution of shotgun metagenomic data annotation. SM analyzed the antibiotic susceptibility of strains. TC conceptualized the assessment of sample chemical composition and analyzed SCFA content. EP coordinated and performed farm sampling and coordinated isolate \u003cem\u003erpoB\u003c/em\u003e sequencing. ANem coordinated strain isolation, performed taxonomic analysis of strains, analyzed susceptibility data, drafted the corresponding parts of the manuscript, and revised the manuscript. MK designed and coordinated the study, acquired funding, contributed to sample processing and shotgun metagenomic data analysis and interpretation, and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe are grateful to the 28 anonymous farmers for providing access to their farms and farm metadata. We thank Eva Vlkov\u0026aacute;, Kateřina Jochov\u0026aacute;, and Miroslav Joch (Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague) for organization of cattle-farm sampling, and Lenka Michalč\u0026iacute;kov\u0026aacute;, Mirka Petrovov\u0026aacute;, Tereza Michalov\u0026aacute; (Institute of Microbiology of the Czech Academy of Sciences, Prague), Lucie Mal\u0026iacute;kov\u0026aacute;, Ladislav Čerm\u0026aacute;k, \u0026Scaron;těp\u0026aacute;nka Dvoř\u0026aacute;kov\u0026aacute;, and Anna Ma\u0026scaron;lejov\u0026aacute; (Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague) for technical assistance. We gratefully acknowledge Jaroslav Kukla and Filip Křivohlav\u0026yacute; (Laboratory of Environmental Chemistry and Soil Analysis, Institute of Environmental Sciences, Faculty of Science, Charles University) for their invaluable laboratory support in chemical analyses. We further thank Felix Wesener, Gabriele Tosadori (Institute of Microbiology of the Czech Academy of Sciences, Prague), and Petra \u0026Scaron;panělov\u0026aacute; (Centre for Epidemiology and Microbiology, National Institute of Public Health) for valuable discussion and help with data analyses. We are grateful to Kornelia Smalla (Julius-Kuhn Institute, Braunschweig) and Heike Schmitt (Institute for Risk Assessment Sciences, Utrecht) for providing positive controls for antibiotic-resistance gene screening.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWichmann F, Udikovic-Kolic N, Andrew S, Handelsman J. 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Environ Sci Technol. 2021;55:6814\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.est.1c00612\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.1c00612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antibiotic susceptibility, Carbapenemase, Cattle farm, Diversity, Identification, MALDI-TOF MS, Metabarcoding, Metagenomics, rpoB.","lastPublishedDoi":"10.21203/rs.3.rs-8343889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8343889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eAntibiotic resistance poses a major threat to human health, with antibiotic use in livestock contributing to the selection and spread of resistance genes. The genus \u003cem\u003eAcinetobacter\u003c/em\u003e includes human- and animal-associated species capable of acquiring resistance, yet their diversity and resistance potential in livestock remain far less explored than in humans. In this study, we investigated \u003cem\u003eAcinetobacter\u003c/em\u003e in cattle feces from 28 Czech farms with contrasting antibiotic use, aiming to assess species composition, resistance profiles, and the potential for resistance dissemination. We applied an integrative approach combining strain isolation and characterization, enrichment cultures, metabarcoding, and shotgun metagenomics.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eCattle feces harbored diverse \u003cem\u003eAcinetobacter\u003c/em\u003e species with \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e being the core species based on both isolated strains and metabarcoding, while \u003cem\u003eA. baumannii\u003c/em\u003e was less common. \u003cem\u003eAcinetobacter\u003c/em\u003e species occurrence determined by metabarcoding was driven by multiple factors, including production type, herd size, and per-head antibiotic use, while their abundance was mostly influenced by sample type (higher in feces from the farm floor than in rectal samples) and production type (higher in dairy than in beef cattle). Remarkably, 37% of the 284 isolated strains could not be assigned to validly named species and represent at least 19 putative novel species. Decreased susceptibility due to acquired resistance was observed in 57 strains; notably, \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eA. pseudolwoffii\u003c/em\u003e from antibiotic-using farms were less susceptible to streptomycin than those from antibiotic-free farms. Shotgun metagenomics revealed a greater richness of acquired resistance genes in antibiotic-using farms, including the clinically relevant carbapenemase gene \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e. This gene was located on putative plasmid contigs alongside streptomycin resistance determinants \u003cem\u003estrA\u003c/em\u003e-\u003cem\u003estrB\u003c/em\u003e, suggesting horizontal dissemination under streptomycin selection pressure. Strain analysis confirmed the co-localization of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;58\u003c/sub\u003e and \u003cem\u003estrA\u003c/em\u003e-\u003cem\u003estrB\u003c/em\u003e on a large plasmid in \u003cem\u003eA. pseudolwoffii\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eDespite relatively strict regulations, Czech cattle farms constitute a reservoir of antibiotic-resistant \u003cem\u003eAcinetobacter\u003c/em\u003e carrying mobile resistance genes of clinical concern. Commonly applied antibiotics likely co-select for such genes, posing an ongoing public health risk. Our findings reveal an unexpectedly high diversity of \u003cem\u003eAcinetobacter\u003c/em\u003e spp. in cattle, highlighting the research bias toward human-associated species and underscoring the need for integrated One Health monitoring approaches.\u003c/p\u003e","manuscriptTitle":"Cattle feces are a reservoir of diverse Acinetobacter species with potential to spread antibiotic resistance genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 18:11:20","doi":"10.21203/rs.3.rs-8343889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T07:21:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-14T20:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22714994096283494297199762712938579011","date":"2026-03-10T11:41:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T11:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55563709246863805613700863556304341339","date":"2026-02-02T09:42:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T08:41:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-04T11:07:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-13T04:22:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2025-12-12T08:46:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0dd20647-c6cb-418e-a758-2ec620b33da0","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:04:58+00:00","versionOfRecord":{"articleIdentity":"rs-8343889","link":"https://doi.org/10.1186/s42523-026-00568-3","journal":{"identity":"animal-microbiome","isVorOnly":false,"title":"Animal Microbiome"},"publishedOn":"2026-04-22 15:59:06","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2026-02-03 18:11:20","video":"","vorDoi":"10.1186/s42523-026-00568-3","vorDoiUrl":"https://doi.org/10.1186/s42523-026-00568-3","workflowStages":[]},"version":"v1","identity":"rs-8343889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8343889","identity":"rs-8343889","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}