TY - JOUR
AU - Glaeser, Stefanie, P
AB - ABSTRACT Studies considering environmental multidrug-resistant Acinetobacter spp. are scarce. The application of manure on agricultural fields is one source of multidrug-resistant bacteria from livestock into the environment. Here, Acinetobacter spp. were quantified by quantitative polymerase chain reaction in manure applied to biogas plants and in the output of the anaerobic digestion, and Acinetobacter spp. isolated from those samples were comprehensively characterized. The concentration of Acinetobacter 16S ribosomal ribonucleic acid (rRNA) gene copies per g fresh weight was in range of 106–108 in manure and decreased (partially significantly) to a still high concentration (105–106) in digestates. 16S rRNA, gyrB-rpoB and blaOXA51-like gene sequencing identified 17 different Acinetobacter spp., including six A. baumannii strains. Multilocus sequence typing showed no close relation of the six strains with globally relevant clonal complexes; however, they represented five novel sequence types. Comparative genomics and physiological tests gave an explanation how Acinetobacter could survive the anaerobic biogas process and indicated copper resistance and the presence of intrinsic beta-lactamases, efflux-pump and virulence genes. However, the A. baumannii strains lacked acquired resistance against carbapenems, colistin and quinolones. This study provided a detailed characterization of Acinetobacter spp. including A. baumannii released via manure through mesophilic or thermophilic biogas plants into the environment. biogas plant, Acinetobacter, anaerobic digestion, whole genome sequencing, manure, antibiotic resistance INTRODUCTION The genus Acinetobacter represents a heterogeneous group of glucose non-fermentative, catalase-positive, oxidase-negative, aerobic, Gram-negative coccobacilli (Lee et al. 2017). Most of the Acinetobacter species are considered to be ubiquitous in the environment (Towner 2009). In terms of clinical significance, multidrug-resistant Acinetobacter belonging to the Acinetobacter baumannii–Acinetobacter calcoaceticus complex (ACB complex; Towner 2009) is seen as a major current One Health risk. For the clinically relevant species of Acinetobacter, only few data on their environmental niches are available (Berlau et al. 1999; Houang et al. 2001; Huys et al. 2007; Hrenovic et al. 2014; Rafei et al. 2015; Wilharm et al. 2017; Klotz et al. 2019). Currently, Acinetobacter spp. are receiving increasing attention as significant opportunistic pathogens and are associated with infections in critically ill patients (Visca, Seifert and Towner 2011). Of these, multidrug-resistant strains of A. baumannii are among the most troublesome pathogens globally, and represent one of the ESKAPE pathogens (ESKAPE: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, A. baumannii, Pseudomonas aeruginosa and Enterobacter spp.) (Rice 2008). Recently, the World Health Organization (WHO) has for the first time released a list of drug-resistant bacteria that pose the lethal threat to modern medicine and human health—and for which novel antimicrobials are desperately needed. This multidrug-resistant pathogens list is topped by multidrug-resistant phenotype of A. baumannii (Willyard 2017). In three decades, this bacterium has evolved from being completely antimicrobial susceptible to mostly multidrug resistant, in parts by expanding a genomic resistance island encoding resistance to several classes of antibiotics, which were horizontally transferred mostly via plasmids, transposons or integrons from bacteria of the genera Pseudomonas, Escherichia and Salmonella (Fournier et al. 2006). This expeditious emergence and dissemination of multidrug-resistant A. baumannii showed the successful adaptation of this bacterium to selective environmental pressure (Dijkshoorn, Nemec and Seifert 2007; Kempf and Rolain 2012). The resistant strains of A. baumannii produce carbapenemase enzymes that are able to hydrolyze carbapenems (β-lactam antibiotics), which are usually considered as ‘last-line antibiotics’ against resistant pathogens (Perez et al. 2010; Papp-Wallace et al. 2011; Kempf and Rolain 2012). Carbapenem resistance mostly occurs via class D OXA type serine oxacillinases (blaOXA type, e.g. blaOXA-23) and class B metallo-β-lactamases (MBLs, e.g. New Delhi metallo-β-lactamases, NDM-1), which are largely responsible for clinical outbreaks worldwide (Kempf and Rolain 2012; Zarrilli et al. 2013). So far the colistin resistance in A. baumannii is mediated by mutations in the two-component system proteins PmrA/B and lipid A synthetic genes (Adams et al. 2009; Moffatt et al. 2010) and lipid A modification by inclusion of phosphoethanolamine (Qureshi et al. 2015). The advent of carbapenem- and colistin-resistant A. baumannii is of significant attention for global health, as it heralds an age of limited effective antibiotic choices, and multidrug-resistant A. baumannii strains are notorious because of their rapid evolution, increasing prevalence and the ability to cause outbreaks (Perez et al. 2010; Kempf and Rolain 2012). The wide use of antibiotics in humans is one of the primary factors for the rise of resistances (Goossens et al. 2005; Costelloe et al. 2010; Davies and Davies 2010). Apart from clinical use, antibiotics are largely used in animal husbandry at therapeutic level for disease treatment, accounting for a significant proportion of the antibiotics produced globally (Aarestrup 2005; Chee-Sanford et al. 2009; Van Boeckel et al. 2015). It has been estimated that ∼75% of antibiotics administered to animals are excreted (Chee-Sanford et al. 2009). Animal husbandry and manure have been frequently referred to as reservoirs for potentially pathogenic and antibiotic-resistant bacteria and antibiotic resistance genes (Hölzel et al. 2010; Friese et al. 2013; Marti et al. 2013; Zhu et al. 2013; Von Salviati et al. 2015). Thus, the use of manure as fertilizer in soil can result into dissemination of antibiotic-resistant bacteria/antibiotic resistance genes into the environment (Ghosh and LaPara 2007; Heuer, Schmitt and Smalla 2011; Rahube and Yost 2012; Jechalke et al. 2014). Apart from direct field application, manure is also used as carbon source in biogas plants that have been gradually increasing in number throughout Germany. To avoid direct dispense of manure into soil and aquatic ecosystems, anaerobic digestion with partially high process temperatures is considered as sustainable approach resulting in production of biogas and biofertilizers, thereby decreasing the microbial load of the surrounding environments (Bagge, Sahlström and Albihn 2005; Saunders et al. 2012; Manyi-Loh et al. 2013; Resende et al. 2014). Resende et al. (2014) identified multidrug-resistant bacterial isolates from the influent and effluent sources of an anaerobic digestion treatment plant for cattle manure, and pleaded for the implementation of sanitary and microbiological safe treatments of manures to avoid unintended consequences to humans, animals and the environment, respectively. Currently, there are only few studies considering the release of antibiotic resistance genes and culturable antibiotic-resistant bacteria from biogas plants. Schauss et al. (2015, 2016) recently showed that extended spectrum β-lactamase (ESBL)-producing E. coli were transmitted through mesophilic anaerobic biogas plants, and identified other potentially pathogenic bacterial isolates (including few isolates identified as Acinetobacter spp.) from the studied input and output samples. Glaeser et al. (2016) reported vancomycin-resistant enterococci (VRE) from the same input and output materials of the biogas plants, indicating that enterococci with vancomycin resistance genes could be released into the environment by application of biogas plant's treated manure as biofertilizers. Wolters et al. (2014) reported the transmission of mobile antibiotic resistance plasmids through biogas plants that may be taken up by non-resistant bacteria under changing environmental conditions. Although the presence of Acinetobacter spp. resistant to multiple antibiotics had been investigated in animal husbandry (Wang et al. 2012; He et al. 2019) and manure (Hrenovic et al. 2019), the risk associated with their release into the environment after manure processing in biogas plants has not been investigated so far. The present study thus aimed to determine the abundance of Acinetobacter spp. in input (mixed manure) and output (anaerobic digestates) samples of various German biogas plants, and to determine whether or not the anaerobic processing of manure in biogas plants effects the transmission of the aerobic Acinetobacter spp. into the environment. Quantitative polymerase chain reaction (qPCR) was used to quantify Acinetobacter spp. 16S ribosomal ribonucleic acid (rRNA) copies in total deoxyribonucleic acid (DNA) extracts of biogas plant input and output samples. In addition, Acinetobacter spp. isolates cultured from input and output samples by different cultivation efforts (Schauss et al. 2015, 2016) were characterized in detail. The isolates were phylogenetically assigned based on 16S rRNA and rpoB-gyrB gene and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analyses. Isolates identified as A. baumannii were examined by blaOXA-51 typing and multilocus sequence typing (MLST) and comparative genome analysis. Isolates were further characterized with respect to several physiological properties as temperature-dependent growth and anaerobic condition survival tests as well as copper and antibiotic resistance patterns. MATERIALS AND METHODS Biogas plants and sampling Input and output samples of 15 German biogas plants (BGP-1 to 15) located in the north of Hesse, near Aachen, Bremen, and in Bavaria near Munich were examined in 2012 and 2013. The studied biogas plants represented continuous stirred tank reactors (CSTRs) with wet fermentation processes (dry mass <15% w/v). Fourteen of the biogas plants were running under mesophilic (35–40°C) and one, BGP-12, under thermophilic conditions (50°C) (Table 1). More characteristics of the biogas plants, including volumes and retention times of the reception tank, the main and post-fermenters and the storage tank as well as the agitation technologies are given as supplementary material (Table S1, Supporting Information). All biogas plants, except BGP-7 and BGP-12, were fed with both, slurries and manure. BGP-12 was fed with manure only and BGP-7 with food leftovers. An overview of slurry and manure compounds and co-substrates is given in Table 1. Input samples investigated here contained slurry and manure components mixed in a ratio as added to the biogas digester (excluding bioenergy crops since the focus was set on resistant bacteria with potential exposure to veterinary antibiotics) (Table 1). Slurry and manure components were mixed respectively with a sterile stirrer in a sterile 50 L pot at the day of sampling. Output samples were obtained from half-day pre-mixed final storage tanks by randomly sampling a volume of 2 L every 10 min. During sampling, the storage tank was continuously mixed. The retention time was not considered because of identical sampling time points for input and output manure. Samples were taken in 250 mL sterile polyethylene (PE) bottles and stored immediately at 6°C, transported to the laboratory and processed on the same day for the cultivation-dependent approach. Samples used for molecular analysis were directly stored at −80°C until DNA extraction. Table 1. Characteristics of the studied German biogas plants: composition of input samples (slurry—manure), co-substrates applied to the fermentation and digestion layout of the studied BGPs. Modified based on Schauss et al. (2016). All plants are CSTRs. Biogas plants . Input . Co-substrates (t per day) . Digestion process . Operation temperature . BGP-1 75% slurry 8.5 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 2 forage rye 25% manure (6:0.5 cattle, laying hens) BGP-2 50% slurry 8 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 8 forage rye 50% manure (1:1:2 horse, chicken cattle) BGP-3 15% slurry 2.2 corn two-stage, mesophilic ∼40°C (10:1 cattle, fattening pig) 2 forage rye 85% manure 0.4 grass silage (4:1 cattle, horse) 0.3 feed leftover BGP-4 62% cattle slurry 10 corn two-stage, mesophilic ∼40°C 37% cattle manure 2.5 forage rye 1.5 grass silage BGP-5 73% dairy cattle slurry 9 corn one-stage, mesophilic ∼40°C 27% manure 0.6 grass silage (2:1 cattle, fattening chicken) BGP-6 83% slurry (1:1 dairy cattle to breeding sow) 20 corn one-stage, mesophilic ∼40°C 17% dairy cattle manure BGP-7 2% slurry (cattle) 28% flotation tailings None two-stage, mesophilic ∼40°C; two-step hygienization between digester and storage tank step: 70°C 70% food leftover BGP-8 85% dairy cattle slurry 6 corn one-stage, mesophilic 41.5°C 15% mother cow manure 1 forage rye BGP-9 100% organic dairy cowslurry None one-stage, mesophilic 28–40°C BGP-10 80% dairy cattle slurry 9 corn two-stage, mesophilic ∼40°C 20% laying hens manure BGP-11 100% fattening bulls 36, crops are varying two-stage, mesophilic ∼40°C BGP-12 100% chicken manure 25.5 corn two lines, two-stage, thermophilic ∼50°C BGP-13 100% fattening pig slurry 8 corn 1 forage rye two-stage, mesophilic 41°C 1 grass silage BGP-14 53% fattening bull slurry 7.6 corn two-stage, mesophilic ∼40°C 47% fattening bull manure 1.5 grass silage horse manure BGP-15 67% fattening bull slurry 2.5 corn two-stage, mesophilic 42°C 33% turkey hen manure 1 forage rye Additional turkey, hens manure sample 2 grass silage 1 shredded grain Biogas plants . Input . Co-substrates (t per day) . Digestion process . Operation temperature . BGP-1 75% slurry 8.5 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 2 forage rye 25% manure (6:0.5 cattle, laying hens) BGP-2 50% slurry 8 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 8 forage rye 50% manure (1:1:2 horse, chicken cattle) BGP-3 15% slurry 2.2 corn two-stage, mesophilic ∼40°C (10:1 cattle, fattening pig) 2 forage rye 85% manure 0.4 grass silage (4:1 cattle, horse) 0.3 feed leftover BGP-4 62% cattle slurry 10 corn two-stage, mesophilic ∼40°C 37% cattle manure 2.5 forage rye 1.5 grass silage BGP-5 73% dairy cattle slurry 9 corn one-stage, mesophilic ∼40°C 27% manure 0.6 grass silage (2:1 cattle, fattening chicken) BGP-6 83% slurry (1:1 dairy cattle to breeding sow) 20 corn one-stage, mesophilic ∼40°C 17% dairy cattle manure BGP-7 2% slurry (cattle) 28% flotation tailings None two-stage, mesophilic ∼40°C; two-step hygienization between digester and storage tank step: 70°C 70% food leftover BGP-8 85% dairy cattle slurry 6 corn one-stage, mesophilic 41.5°C 15% mother cow manure 1 forage rye BGP-9 100% organic dairy cowslurry None one-stage, mesophilic 28–40°C BGP-10 80% dairy cattle slurry 9 corn two-stage, mesophilic ∼40°C 20% laying hens manure BGP-11 100% fattening bulls 36, crops are varying two-stage, mesophilic ∼40°C BGP-12 100% chicken manure 25.5 corn two lines, two-stage, thermophilic ∼50°C BGP-13 100% fattening pig slurry 8 corn 1 forage rye two-stage, mesophilic 41°C 1 grass silage BGP-14 53% fattening bull slurry 7.6 corn two-stage, mesophilic ∼40°C 47% fattening bull manure 1.5 grass silage horse manure BGP-15 67% fattening bull slurry 2.5 corn two-stage, mesophilic 42°C 33% turkey hen manure 1 forage rye Additional turkey, hens manure sample 2 grass silage 1 shredded grain Open in new tab Table 1. Characteristics of the studied German biogas plants: composition of input samples (slurry—manure), co-substrates applied to the fermentation and digestion layout of the studied BGPs. Modified based on Schauss et al. (2016). All plants are CSTRs. Biogas plants . Input . Co-substrates (t per day) . Digestion process . Operation temperature . BGP-1 75% slurry 8.5 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 2 forage rye 25% manure (6:0.5 cattle, laying hens) BGP-2 50% slurry 8 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 8 forage rye 50% manure (1:1:2 horse, chicken cattle) BGP-3 15% slurry 2.2 corn two-stage, mesophilic ∼40°C (10:1 cattle, fattening pig) 2 forage rye 85% manure 0.4 grass silage (4:1 cattle, horse) 0.3 feed leftover BGP-4 62% cattle slurry 10 corn two-stage, mesophilic ∼40°C 37% cattle manure 2.5 forage rye 1.5 grass silage BGP-5 73% dairy cattle slurry 9 corn one-stage, mesophilic ∼40°C 27% manure 0.6 grass silage (2:1 cattle, fattening chicken) BGP-6 83% slurry (1:1 dairy cattle to breeding sow) 20 corn one-stage, mesophilic ∼40°C 17% dairy cattle manure BGP-7 2% slurry (cattle) 28% flotation tailings None two-stage, mesophilic ∼40°C; two-step hygienization between digester and storage tank step: 70°C 70% food leftover BGP-8 85% dairy cattle slurry 6 corn one-stage, mesophilic 41.5°C 15% mother cow manure 1 forage rye BGP-9 100% organic dairy cowslurry None one-stage, mesophilic 28–40°C BGP-10 80% dairy cattle slurry 9 corn two-stage, mesophilic ∼40°C 20% laying hens manure BGP-11 100% fattening bulls 36, crops are varying two-stage, mesophilic ∼40°C BGP-12 100% chicken manure 25.5 corn two lines, two-stage, thermophilic ∼50°C BGP-13 100% fattening pig slurry 8 corn 1 forage rye two-stage, mesophilic 41°C 1 grass silage BGP-14 53% fattening bull slurry 7.6 corn two-stage, mesophilic ∼40°C 47% fattening bull manure 1.5 grass silage horse manure BGP-15 67% fattening bull slurry 2.5 corn two-stage, mesophilic 42°C 33% turkey hen manure 1 forage rye Additional turkey, hens manure sample 2 grass silage 1 shredded grain Biogas plants . Input . Co-substrates (t per day) . Digestion process . Operation temperature . BGP-1 75% slurry 8.5 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 2 forage rye 25% manure (6:0.5 cattle, laying hens) BGP-2 50% slurry 8 corn two-stage, mesophilic ∼40°C (3:1 fattening pig, dairy cattle) 8 forage rye 50% manure (1:1:2 horse, chicken cattle) BGP-3 15% slurry 2.2 corn two-stage, mesophilic ∼40°C (10:1 cattle, fattening pig) 2 forage rye 85% manure 0.4 grass silage (4:1 cattle, horse) 0.3 feed leftover BGP-4 62% cattle slurry 10 corn two-stage, mesophilic ∼40°C 37% cattle manure 2.5 forage rye 1.5 grass silage BGP-5 73% dairy cattle slurry 9 corn one-stage, mesophilic ∼40°C 27% manure 0.6 grass silage (2:1 cattle, fattening chicken) BGP-6 83% slurry (1:1 dairy cattle to breeding sow) 20 corn one-stage, mesophilic ∼40°C 17% dairy cattle manure BGP-7 2% slurry (cattle) 28% flotation tailings None two-stage, mesophilic ∼40°C; two-step hygienization between digester and storage tank step: 70°C 70% food leftover BGP-8 85% dairy cattle slurry 6 corn one-stage, mesophilic 41.5°C 15% mother cow manure 1 forage rye BGP-9 100% organic dairy cowslurry None one-stage, mesophilic 28–40°C BGP-10 80% dairy cattle slurry 9 corn two-stage, mesophilic ∼40°C 20% laying hens manure BGP-11 100% fattening bulls 36, crops are varying two-stage, mesophilic ∼40°C BGP-12 100% chicken manure 25.5 corn two lines, two-stage, thermophilic ∼50°C BGP-13 100% fattening pig slurry 8 corn 1 forage rye two-stage, mesophilic 41°C 1 grass silage BGP-14 53% fattening bull slurry 7.6 corn two-stage, mesophilic ∼40°C 47% fattening bull manure 1.5 grass silage horse manure BGP-15 67% fattening bull slurry 2.5 corn two-stage, mesophilic 42°C 33% turkey hen manure 1 forage rye Additional turkey, hens manure sample 2 grass silage 1 shredded grain Open in new tab Total community DNA extraction from manure and anaerobic digestates Total community DNA was extracted in triplicates from input and output materials of biogas plants using a phenol chloroform extraction method based on the method established by Carroll et al. (2012). For enzymatic cell lysis, samples were suspended in 750 µL lysis buffer [sodium chloride, 200 mM; ethylenediaminetetraacetic acid (EDTA), 100 mM; Tris base, 200 mM; pH 8.0] including lysozyme (20 mg mL−1; AppliChem, Darmstadt, Germany), vortexed shortly and incubated at 37°C for 30 min. Subsequently, 80 µL of 10% (w/v) sodium dodecyl sulphate and 20 µL of proteinase K (50 mg mL−1 dissolved in water; Sigma-Aldrich, St. Lousi, Missouri, USA) were added. Samples were vortexed and incubated for 30 min at 60°C. To ensure the lysis of Gram-positive bacteria, samples were heated for additional 5 min at 95°C. Thereafter, samples were transferred into sterile 2-mL screw cap tubes containing ∼600 mg of 0.1 mm silica beads and four 2 mm glass beads (both Carl Roth, Karlsruhe, Germany). Screw cap tubes were autoclaved with beads before usage. Mechanical lysis of cells was performed by mixing screw cap tubes for 2 min into a mixer mill (MM2, Retsch, Haan, Germany). After centrifugation at 13 780 × g for 4 min at 4°C, the supernatant was transferred into sterile tubes containing 500 µL phenol/chloroform/isoamyl alcohol (Carl Roth). The tubes were vortexed for 15 s and centrifuged for 4 min at 4°C and 13 780 × g to dissolve humic acid substances; the upper phase containing nucleic acids was transferred to sterile tubes filled with 500 µL chloroform (Merck, Darmstadt, Germany), while the rest of the phenol and humic acid substances remained in the lower chloroform phase. For further purification of the nucleic acids, 1 mL of 100% ethanol and 45 µL of 0.3 M sodium acetate (Carl Roth) were added to the reaction tubes and incubated at −80°C for 1 h. Thereafter, the tubes were thawed and the nucleic acid precipitated by centrifugation for 4 min at 13 780 × g. The supernatant was discarded and tubes were placed into a vacuum concentrator (BaVaCo-M Mini-30, Bachofer, Reutlingen, Germany) for 10 min at room temperature to ensure complete liquid evaporation. The remaining DNA pellets were resuspended in 200 µL DNase and RNase free water (Carl Roth). Extracted nucleic acids were further purified using a QIAamp® DNA Stool Mini Kit (Qiagen, Hilden, Germany), following the manufacturer's instruction, and quantified by absorbance measurement using a NanoDrop spectrophotometer (NanoDrop 1000, Peqlab, Erlangen, Germany). Quantification of Acinetobacter spp. 16S rRNA gene copies from total community DNA Acinetobacter spp. 16S rRNA gene targets were quantified by qPCR using the Acinetobacter specific primer system Ac436f/Ac676r (Vanbroekhoven et al. 2004). In parallel, the total Bacteria 16S rRNA gene targets were quantified using the universal 16S rRNA gene sequence targeting primer system 27F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 1492R (5′-GGYTACCTTGTTACGACTT-3′) (Lane 1991). All qPCR reactions were performed in a CFX96 Touch Real-time PCR detection system (Bio-Rad Laboratories, Hercules, California, USA) with the SsoFast EvaGreen ® Supermix kit (Bio-Rad). Amplification was performed in a total volume of 10 µL including 5 µL of SsoFast EvaGreen ® Supermix, 1 µL of template DNA (5 ng µL−1) and 0.2 µM of each primer. The final volume was adjusted with PCR water. The thermocycler program used for both primer systems was identical: 98°C for 2 min, 45 cycles at 98°C and 60°C for 5 s; a subsequent melting curve was generated by heating from 65°C to 95°C with 0.5°C increments at 5 s time. No primer dimer formation was obtained by melt curve analyses. All reactions were performed in technical duplicates. Pseudomonas libanensis DSM 17149T was used as negative control for the Acinetobacter spp. specific PCR, and PCR grade water as no-template control for both primer systems. For quantification, a standard dilution series (10-fold dilution) of 16S rRNA gene fragment generated with the universal primers 27F/1492R (Lane 1991) from Acinetobacter lwoffii DSM 2403T was used after purification with the QIAquick PCR purification Kit (Qiagen). The concentration of 16S rRNA gene targets was quantified based on DNA concentration (ng µL−1) measured photometrically and the fragment length (in nt) as described by Kolb et al. (2003). The initial target copy number ranged from 101 to 109 16S rRNA gene targets per reaction and was plotted against Ct values to generate a standard curve (r2 > 0.98; amplification efficiencies between 90 and 110%). The relative quantity of 16S rRNA genes of Acinetobacter in biogas plants was calculated by normalization to the bacterial 16S rRNA gene abundance to compensate for variance induced by amplification efficiency across samples. One-tailed pairwise t-tests were performed in SigmaPLOT 12.5 (Systat Software, San Jose, California, USA) to prove significant reduction of Acinetobacter 16S rRNA gene copies in digestates compared with manure. Cultivation of Acinetobacter spp. isolates Acinetobacter spp. isolates were derived from different cultivation approaches including cultivation on R2A (Oxoid, Wesel, Germany), EMB (Merck) and CHROMagar ESBL (CHROMagar, Paris, France) agar plates. Bacteria were detached from 10 g manure and digestate samples using 0.2% filter-sterilized sodium pyrophosphate solution and serially diluted in 0.9% NaCl. All plates were incubated for 24 to 48 h at 37°C under aerobic conditions. For the cultivation of ESBL-producing bacteria, manure samples (0.1, 1 and 10 g) were additionally directly weighted into LB medium (Sigma-Aldrich) containing 1 mg L−1 cephalosporins [Cefotaxime (C16H16N5NaO7S2) + Ceftazidime (C22H22N6O7S); Sigma-Aldrich]. After 24 h of incubation at 37°C, subsamples (10 µL) were streaked on CHROMagar ESBL (CHROMagar; Schauss et al. 2015). Carbapenem-resistant bacteria were cultured in parallel LB medium as enrichment broth containing 1 or 4 mg L−1 meropenem (C17H25N3O5S, Sigma-Aldrich) and CHROMagar KPC (CHROMagar) for selective streaking of enrichment cultures. Isolates were incubated for overnight at 37°C. Morphologically different colonies grown on agar plates were purified by transferring the single colony for multiple times. Fresh biomass of the isolates was suspended in newborn calf serum albumin (Invitrogen, Waltham, Massachusetts, USA) and stored at −20°C or −80°C for long-term preservation. For molecular characterization, cell lysates were generated from two loops of freshly grown biomass as described previously (Schauss et al. 2015). Phylogenetic identification of Acinetobacter based on 16S rRNA gene and rpoB/gyrB Partial 16S rRNA gene sequences of Acinetobacter isolates previously published by Schauss et al. (2015, 2016) were completed by sequencing the 3′ end of the 16 S rRNA gene using primer E786F (5′-GATTAGATACCCTGGTAG-3′; Colquhoun 1997). The 16S rRNA gene of remaining isolates was PCR-amplified using universal primers 27F/1492R as described by Glaeser et al. (2016) and sequenced using primer 27F and E786F by Sanger sequencing method (LGC Genomics, Berlin, Germany). Sequences were corrected and merged manually based on electropherograms by removing unclear 5′ and 3′ ends of the sequences in MEGA7 (Kumar, Stecher and Tamura 2016). Initial phylogenetic identification was performed using the EzBioCloud server (https://www.ezbiocloud.net/; Yoon et al. 2017) including 16S rRNA gene sequences of type strains of species with validated names (http://www.bacterio.net/-allnames.html). RNA polymerase β-subunit (rpoB) and the DNA gyrase subunit B (gyrB) genes were PCR amplified, and more comprehensive phylogenetic analyses were performed as described previously (Nemec et al. 2009; Krizova et al. 2014). The respective gene sequences of all isolates and all type strains and genomic species of Acinetobacter were aligned using ClustalW provided in MEGA7. The alignments of nucleotide sequences of the protein coding genes were performed according to the correct open reading frame (ORF). Phylogenetic trees were constructed in MEGA7 using the maximum-likelihood method based on the general time reversible model (Nei and Kumar 2000) and 100 re-samplings (bootstrap analysis). GenBank accession numbers of nucleotide sequences are MT138751–MT138756 (16S rRNA gene) and MT157622–MT157720 (rpoB/gyrB), respectively. Differentiation of Acinetobacter isolates by genomic fingerprinting All isolates were differentiated at the strain level by genomic DNA fingerprinting using repetitive DNA element PCR with (GTG)5 primer (5′-GTGGTGGTGGTGGTG-3′; Versalovic, Schneider and Bruijn 1994), and enterobacterial repetitive intergenic consensus PCR (ERIC-PCR) with primers ERIC1R (5′-ATGTAAGCTCCTGGGGATTCA-3′) and ERIC2 (5′-AAGTAAGTGACTGGGGTGAGC-3′) published by Versalovic, Koeuth and Lupski (1991). PCR amplification and agarose gel electrophoresis were performed as described by Glaeser et al. (2013, 2016). Cluster analysis of fingerprinting patterns was performed in Gel Compare II (Applied Maths, Sint-Martens-Latem, Belgium) with Unweighted Pair Group Method with Arithmetic mean (UPGMA) clustering based on a similarity matrix calculated using the Pearson correlation. MALDI-TOF mass spectrometry-based identification All isolates were further identified at the level of species by MALDI-TOF MS as described by Eisenberg et al. (2017). Briefly, bacterial isolates were selected from the culture plates and then subjected to steel-targets according to manufacturer's instruction (BrukerBiotyper, BrukerDaltonics, Bremen, Germany). Isolates were prepared using a direct transfer protocol provided by the manufacturer and analyzed on a Bruker Microflex LT system MALDI-TOF MS using Biotyper Version V3.3.1.0. The standard database used (DB 7311, BrukerDaltonics) comprised spectra from different Acinetobacter strains. The Compass software considers MALDI scores >2.3 and >2.0 as secure species and genus identification levels, respectively. The identification was repeated three times to verify the original findings. blaOXA-51-like typing of Acinetobacter isolates The presence of blaOXA-51-like genes was checked for all Acinetobacter spp. isolates. If the gene was detected, a PCR-based mapping of the location of the insertion sequence element (ISAba1) upstream of blaOXA-51-like genes was performed using the primer pair ISAba1F and OXA-51-likeR as described by Turton et al. (2006a). Complete sequence amplification and Sanger sequencing using forward and reverse primers was done according to Zander et al. (2012). All blaOXA-51 variants were identified by BLAST (Basic Local Alignment Search Tool) analysis using the nucleotide BLAST platform. Novel OXA-51 variants were deposited in GenBank (accession numbers given in respective figures) and registered at Lahey database currently hosted at NCBI (https://www.ncbi.nlm.nih.gov/pathogens/beta-lactamasedata-resources/). A maximum likelihood tree based on complete coding regions of the blaOXA-51-like gene was calculated in MEGA7 as described above for rpoB/gyrB analysis. Analysis included all of the international OXA-51 clonal lineages from Zander et al. (2012), variants of class D β-lactamases present in various Acinetobacter species mentioned by Périchon et al. (2014) and Wilharm et al. (2017). Molecular typing for epidemiological and population-related study MLST was performed with A. baumannii isolates using the Pasteur (Diancourt et al. 2010) and Oxford (Bartual et al. 2005) MLST schemes. Forward primers applied by Pasteur scheme were used for Sanger sequencing of all housekeeping gene sequences. Sequenced genes were corrected manually based on electropherograms. Sequences were deposited in GenBank with accession numbers MT157622–MT157720, respectively. Genes used in the Oxford scheme were retrieved from the genome sequences of A. baumannii isolates. Gene sequences were shortened to size required for MLST analysis using MEGA7 and sequence types (STs) were identified using both MLST database (http://pubmlst.org/abaumannii/). All STs represented in the Pasteur MLST database (https://pubmlst.org/bigsdb?db=pubmlst_abaumannii_pasteur_seqdef) were downloaded. The combined population structure of STs from our study and the Pasteur database was evaluated by generating minimum spanning tree (MST) from the Phyloviz (https://online.phyloviz.net/index; Francisco et al. 2012; Ribeiro-Gonçalves et al. 2016) using the goeBURST algorithm (Francisco et al. 2009). Multilocus sequence analysis (MLSA) MLSA based on concatenated sequences of all genes provided in Pasteur MLST scheme was performed with A. baumannii isolates, including avian strains (Wilharm et al. 2017), clinical strains and further type strains of species of the genus Acinetobacter. Nucleotide sequences were aligned according to the respective amino acid sequences and concatenated for analysis. A maximum likelihood tree was calculated as described above using MEGA7. Whole genome sequencing, average nucleotide identity (ANI) and genome analysis of A. baumannii strains Genomic DNA was extracted with the MasterPure DNA purification kit (Epicentre, Madison, Wisconsin, USA). Shotgun libraries were generated using the Nextera XT DNA sample preparation kit following the manufacturer's instructions. The whole genome shotgun libraries were sequenced using the dual index paired-end (v3, 2 × 300 bp) approach for the Illumina MiSeq platform as recommended by the manufacturer (Illumina, San Diego, USA). Comparative genome analyses were performed in EDGAR (Blom et al. 2016). Genome sequences of the six A. baumannii isolates were deposited in NCBI under bioproject ‘PRJEB35515’, respectively. Average nucleotide identity (ANI) values were determined among the six A. baumannii isolates and to A. baumannii ATCC 19606T and A. calcoaceticus DSM 30006T (Table 2). IslandViewer 4 (Bertelli et al. 2017) for the island-like regions and IS Finder (Siguier et al. 2006) for insertion sequence (IS) elements were applied to search for resistance islands and IS elements. Table 2. Overview of isolates and comparison of different investigated German biogas plants. MALDI-TOF score values for species and genus level identification are log(score) ≥2.0 and log(score) 1.7–2.0, respectively. A log score below 1.7 did not enable a genus or species assignment. . . . Phylogenetic calculations . MALDI-TOF MS . . (ANI) % . . Isolates . Biogas plant . 16S rRNA gene-based phylogenetic identification . Organism (best match) . Log score . A. baumannii ATCC 19606T vs BGPs strain . 1 KPC-SM-17a BGP-1 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.22 97.4% 2 552B1–12EESBL BGP-1 (I) 99.9% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 3 571B5–12EESBL BGP-5 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.29 97.64% 4 901B6–12EESBL BGP-6 (I) 99.7% A. baumannii ATCC 19606T A. baumannii 2.36 97.59% 5 KPC-SM-125 BGP-15 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 6 945B12–12AESBL BGP-12 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.31 97.6% 7 551B1–12EESBL BGP-1 (I) 100% A. calcoaceticus DSM 30006T A. calcoaceticus 2.53 n.d. 8 553B1–12EESBL BGP-1 (I) 99.9% A. calcoaceticus DSM 30006T A. calcoaceticus 2.62 n.d. 9 794B1–12ER2A BGP-1 (I) 99.5% A. indicus CIP 110367T n.d. 1.59 n.d. 10 574B5–12EESBL BGP-5 (I) 98.7% A. guillouiae CIP 63.46T n.d. 1.62 n.d. 11 734B5–12EEMB BGP-5 (I) 99.6% A. beijerinckii CIP 110307T Acinetobacter sp. 2.36 n.d. 12 815B5–12ER2A BGP-5 (I) 99.3% A. towneri DSM 14962T A. towneri 2.39 n.d. 13 902B6–12EESBL BGP-6 (I) 99.5% A. bereziniae LMG 1003T A. bereziniae 2.34 n.d. 14 KPC-SM-69 BGP-15 (I) 98.8% A. towneri DSM 14962T A. towneri 1.87 n.d. 15 KPC-SM-21 BGP-1 (O) 97% A. baumannii ATCC 19606T n.d. 1.56 n.d. 16 KPC-SM-24 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.75 n.d. 17 KPC-SM-26 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.76 n.d. . . . Phylogenetic calculations . MALDI-TOF MS . . (ANI) % . . Isolates . Biogas plant . 16S rRNA gene-based phylogenetic identification . Organism (best match) . Log score . A. baumannii ATCC 19606T vs BGPs strain . 1 KPC-SM-17a BGP-1 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.22 97.4% 2 552B1–12EESBL BGP-1 (I) 99.9% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 3 571B5–12EESBL BGP-5 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.29 97.64% 4 901B6–12EESBL BGP-6 (I) 99.7% A. baumannii ATCC 19606T A. baumannii 2.36 97.59% 5 KPC-SM-125 BGP-15 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 6 945B12–12AESBL BGP-12 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.31 97.6% 7 551B1–12EESBL BGP-1 (I) 100% A. calcoaceticus DSM 30006T A. calcoaceticus 2.53 n.d. 8 553B1–12EESBL BGP-1 (I) 99.9% A. calcoaceticus DSM 30006T A. calcoaceticus 2.62 n.d. 9 794B1–12ER2A BGP-1 (I) 99.5% A. indicus CIP 110367T n.d. 1.59 n.d. 10 574B5–12EESBL BGP-5 (I) 98.7% A. guillouiae CIP 63.46T n.d. 1.62 n.d. 11 734B5–12EEMB BGP-5 (I) 99.6% A. beijerinckii CIP 110307T Acinetobacter sp. 2.36 n.d. 12 815B5–12ER2A BGP-5 (I) 99.3% A. towneri DSM 14962T A. towneri 2.39 n.d. 13 902B6–12EESBL BGP-6 (I) 99.5% A. bereziniae LMG 1003T A. bereziniae 2.34 n.d. 14 KPC-SM-69 BGP-15 (I) 98.8% A. towneri DSM 14962T A. towneri 1.87 n.d. 15 KPC-SM-21 BGP-1 (O) 97% A. baumannii ATCC 19606T n.d. 1.56 n.d. 16 KPC-SM-24 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.75 n.d. 17 KPC-SM-26 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.76 n.d. O: output sample; I: input sample; KPC: CHROMagar KPC (CHROMagar, Paris, France); EMB: eosin-methylene blue (Merck); R2A: Oxoid. Isolates with 16S rRNA gene sequence similarity to A. baumannii ATCC 19606T were represented in bold. ‘n.d.’ = not determined by MALDI-TOF MS and ANI. Open in new tab Table 2. Overview of isolates and comparison of different investigated German biogas plants. MALDI-TOF score values for species and genus level identification are log(score) ≥2.0 and log(score) 1.7–2.0, respectively. A log score below 1.7 did not enable a genus or species assignment. . . . Phylogenetic calculations . MALDI-TOF MS . . (ANI) % . . Isolates . Biogas plant . 16S rRNA gene-based phylogenetic identification . Organism (best match) . Log score . A. baumannii ATCC 19606T vs BGPs strain . 1 KPC-SM-17a BGP-1 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.22 97.4% 2 552B1–12EESBL BGP-1 (I) 99.9% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 3 571B5–12EESBL BGP-5 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.29 97.64% 4 901B6–12EESBL BGP-6 (I) 99.7% A. baumannii ATCC 19606T A. baumannii 2.36 97.59% 5 KPC-SM-125 BGP-15 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 6 945B12–12AESBL BGP-12 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.31 97.6% 7 551B1–12EESBL BGP-1 (I) 100% A. calcoaceticus DSM 30006T A. calcoaceticus 2.53 n.d. 8 553B1–12EESBL BGP-1 (I) 99.9% A. calcoaceticus DSM 30006T A. calcoaceticus 2.62 n.d. 9 794B1–12ER2A BGP-1 (I) 99.5% A. indicus CIP 110367T n.d. 1.59 n.d. 10 574B5–12EESBL BGP-5 (I) 98.7% A. guillouiae CIP 63.46T n.d. 1.62 n.d. 11 734B5–12EEMB BGP-5 (I) 99.6% A. beijerinckii CIP 110307T Acinetobacter sp. 2.36 n.d. 12 815B5–12ER2A BGP-5 (I) 99.3% A. towneri DSM 14962T A. towneri 2.39 n.d. 13 902B6–12EESBL BGP-6 (I) 99.5% A. bereziniae LMG 1003T A. bereziniae 2.34 n.d. 14 KPC-SM-69 BGP-15 (I) 98.8% A. towneri DSM 14962T A. towneri 1.87 n.d. 15 KPC-SM-21 BGP-1 (O) 97% A. baumannii ATCC 19606T n.d. 1.56 n.d. 16 KPC-SM-24 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.75 n.d. 17 KPC-SM-26 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.76 n.d. . . . Phylogenetic calculations . MALDI-TOF MS . . (ANI) % . . Isolates . Biogas plant . 16S rRNA gene-based phylogenetic identification . Organism (best match) . Log score . A. baumannii ATCC 19606T vs BGPs strain . 1 KPC-SM-17a BGP-1 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.22 97.4% 2 552B1–12EESBL BGP-1 (I) 99.9% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 3 571B5–12EESBL BGP-5 (I) 99.8% A. baumannii ATCC 19606T A. baumannii 2.29 97.64% 4 901B6–12EESBL BGP-6 (I) 99.7% A. baumannii ATCC 19606T A. baumannii 2.36 97.59% 5 KPC-SM-125 BGP-15 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.33 97.57% 6 945B12–12AESBL BGP-12 (O) 99.8% A. baumannii ATCC 19606T A. baumannii 2.31 97.6% 7 551B1–12EESBL BGP-1 (I) 100% A. calcoaceticus DSM 30006T A. calcoaceticus 2.53 n.d. 8 553B1–12EESBL BGP-1 (I) 99.9% A. calcoaceticus DSM 30006T A. calcoaceticus 2.62 n.d. 9 794B1–12ER2A BGP-1 (I) 99.5% A. indicus CIP 110367T n.d. 1.59 n.d. 10 574B5–12EESBL BGP-5 (I) 98.7% A. guillouiae CIP 63.46T n.d. 1.62 n.d. 11 734B5–12EEMB BGP-5 (I) 99.6% A. beijerinckii CIP 110307T Acinetobacter sp. 2.36 n.d. 12 815B5–12ER2A BGP-5 (I) 99.3% A. towneri DSM 14962T A. towneri 2.39 n.d. 13 902B6–12EESBL BGP-6 (I) 99.5% A. bereziniae LMG 1003T A. bereziniae 2.34 n.d. 14 KPC-SM-69 BGP-15 (I) 98.8% A. towneri DSM 14962T A. towneri 1.87 n.d. 15 KPC-SM-21 BGP-1 (O) 97% A. baumannii ATCC 19606T n.d. 1.56 n.d. 16 KPC-SM-24 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.75 n.d. 17 KPC-SM-26 BGP-01 (O) 98.9% A. lwoffii NCTC 5866T A. lwoffii 1.76 n.d. O: output sample; I: input sample; KPC: CHROMagar KPC (CHROMagar, Paris, France); EMB: eosin-methylene blue (Merck); R2A: Oxoid. Isolates with 16S rRNA gene sequence similarity to A. baumannii ATCC 19606T were represented in bold. ‘n.d.’ = not determined by MALDI-TOF MS and ANI. Open in new tab Temperature-dependent growth and heavy metal ion tolerance tests In order to determine the temperature range for growth, a spot assay technique described previously was applied (Aydogan et al. 2016). Plates were incubated at 4°C, 10°C, 15°C, 20°C, 25°C, 28°C, 30°C, 37°C, 45°C, 50°C and 55°C, respectively. Growth was monitored after two, three and seven days of incubation for all isolates. Copper tolerance was tested by preparing suspension of overnight cultured isolates in NaCl (0.9% w/v) adjusted to 0.5 McFarland turbidity. Serial dilutions up to 10−3 were performed in an empty sterile 96-well plate panel for total volume of 200 µL. Five microliters of each dilution was spotted on Müller-Hinton agar plates (Carl Roth) supplemented with various molar concentrations of CuSO4 × 5H2O (Merck) as described by Romero et al. (2017). The plates were incubated at 37°C aerobically and checked for growth after 24 h. The lowest copper concentration that suppressed growth was considered as MIC value. Survival of Acinetobacter in anaerobic condition All isolates were checked for survival or growth in anaerobic environment in controlled laboratory conditions. The survival of isolates under anaerobic condition was checked by taking isolates pre-grown (overnight aerobically at 25°C) on nutrient agar (Merck) plates, and exposing to the anaerobic conditions in Anaerocult A system (Merck) at the same temperature for seven days. Following anaerobic cultivation, a loop biomass was re-inoculated onto the fresh nutrient agar, and growth was checked after overnight aerobic incubation at 37°C. The ability of isolates to grow in anaerobic conditions was performed by direct exposure of streaked plates to the anaerobic conditions in Anaerocult A system at 25°C for seven days. Antimicrobial susceptibility test of Acinetobacter Antimicrobial susceptibility test for all isolates was performed with standard Micronaut-S test panel (Merlin, Bornheim-Hersel, Germany) including veterinary relevant antibiotics to determine minimum inhibitory concentrations (MICs in mg L−1) using CLSI guidelines M100-S23 (Clinical and Laboratory Standards Institute 2012) as described previously (Schauss et al. 2015; Glaeser et al. 2016). A. baumannii isolates were additionally characterized using the MRGN Micronaut-S System (Merlin) as described by Müller et al. (2018). Classification into sensitive (S), intermediate (I) and resistant (R) against tested antibiotics was done according to EUCAST (http://www.eucast.org/clinical_breakpoints/) and CLSI databases of clinical breakpoints. RESULTS Presence of Acinetobacter spp. 16S rRNA gene targets in input and output samples of German biogas plants The concentrations of Acinetobacter spp. specific 16S rRNA gene copies were in the range of 106 to 108 copies per g fresh weight (FW) in input and 105 to 106 in output materials. The concentration decreased between one and three orders of magnitude by comparing the input and output samples of individual biogas plants. For eight of the biogas plants the decrease was statistically significant (one-tailed pairwise t-test; P < 0.01 to P < 0.05; Fig. 1; Table S2, Supporting Information). In contrast to the concentration of Acinetobacter spp. 16S rRNA gene copies, the concentrations of 16S rRNA gene copies of total Bacteria were in the range of 1010 g−1 FW in input and output samples, respectively. No significant differences were observed (pairwise t-tests; P > 0.05). Figure 1. Open in new tabDownload slide Concentrations of Acinetobacter(A) and Bacteria(B) 16S rRNA gene copies in input and output material of 15 German biogas plants. Mean values and standard deviation of qPCR measurements of three independent extracted total community DNA samples were shown. Asterisks indicated significant difference among input and output samples determined per biogas plant (*P < 0.05; **P < 0.01; pairwise t-test). Figure 1. Open in new tabDownload slide Concentrations of Acinetobacter(A) and Bacteria(B) 16S rRNA gene copies in input and output material of 15 German biogas plants. Mean values and standard deviation of qPCR measurements of three independent extracted total community DNA samples were shown. Asterisks indicated significant difference among input and output samples determined per biogas plant (*P < 0.05; **P < 0.01; pairwise t-test). Phylogenetic identification of Acinetobacter spp. cultured from manure and biogas plant digestates A total of 17 Acinetobacter spp. isolates were cultured; 12 from input and 5 from output samples of various biogas plants (Table 2). Based on the phylogenetic analysis of nearly full-length 16S rRNA gene sequence eight isolates of input and output samples were placed into the ACB cluster, including two isolates with highest 16S rRNA gene sequence similarity to the type strain of A. calcoaceticus (>99.8%; both BGP-1) and six isolates with highest 16S rRNA gene sequence similarity to A. baumannii (>99.6%; two isolates from BGP-1, one each from BGP-5, BGP-6, BGP-12 and BGP-15) (Table 2). The other isolates of the input samples showed highest 16S rRNA gene sequence similarity to A. towneri (>98.7%; two isolates; both BGP-15), A. beijerinckii (99.6%; one isolate; BGP-5), A. bereziniae (99.5%; one isolate; BGP-6), A. guillouiae (98.7%; one isolate; BGP-5) and A. indicus (99.5%; one isolate; BGP-1) (Table 2). Other output isolates were next related to A. lwoffii (98.9% similarity; two isolates; both BGP-1) and A. baumannii but with a 16S rRNA gene sequence similarity of 97% (one isolate; BGP-1) and without clustering into the ACB cluster (Figure S1, Supporting Information). MALDI-TOF data (Table 2) and phylogenetic analysis based on rpoB/gyrB sequences (Fig. 2) confirmed the phylogenetic assignment obtained by the 16S rRNA gene sequencing approach. Most of the isolates were identified by MALDI-TOF with log score hits >2.0 to the respective species mentioned above. There were three exceptions: isolates 794B1–12ER2A, next related to A. indicus (99.5%); 574B5–12EESBL, next related to A. guillouiae (98.7%); and KPC-SM-21, next related to A. baumannii (97.0%). Score values for those three isolates were too low for species assignment. Figure 2. Open in new tabDownload slide Maximum likelihood tree based on concatenated nucleotide sequence of rpoB-gyrB, where rpoB reperesented the Z1 and Z2 regions described by Nemec et al. (2009). GenBank accession numbers were given in parentheses. Bootstrap values >70% (100 resamplings) were indicated. Bar: 0.01 substitutions per nucleotide position. Figure 2. Open in new tabDownload slide Maximum likelihood tree based on concatenated nucleotide sequence of rpoB-gyrB, where rpoB reperesented the Z1 and Z2 regions described by Nemec et al. (2009). GenBank accession numbers were given in parentheses. Bootstrap values >70% (100 resamplings) were indicated. Bar: 0.01 substitutions per nucleotide position. The phylogenetic tree calculated based on rpoB/gryB gene sequences confirmed the placement of the six A. baumannii isolates and the two A calcoaceticus isolates into the ACB cluster and other isolates next to the type strains of the above-mentioned species (Fig. 2). Isolate KPC-SM-21 (97% 16S rRNA gene similarity to A. baumannii) clustered with high bootstrap support next to A. gerneri DSM 14967T. As expected, the six isolates assigned as A. baumannii clustered also with A. baumannii ATCC 19606T in MLSA approach based on seven different housekeeping genes used in the Pasteur MLST scheme (Figure S2, Supporting Information). Comparative genomic fingerprint analyses including BOX- and (GTG)5-PCRs showed distinct genomic fingerprint types for 15 of the isolates, which indicated that they represented different strains (Fig. 3). Only two isolates, 551B1–12EESBL and 553B1–12EESBL, which were isolated from the same sample, shared identical fingerprint patterns indicating clonality. The genetically different A. baumannii-related isolates were considered subsequently as strains. Figure 3. Open in new tabDownload slide Cluster analysis of Acinetobacter spp. isolates based on genomic fingerprints generated by ERIC-PCR and (GTG)5-PCR. Cluster analysis was performed in GelCompare II using the UPGMA clustering method based on the Pearson correlation. BGP: biogas plant; I: input; O: output sample. Intrinsic blaOXA-51-like genes variants; ‘+’: positive; ‘−’: negative. ‘n.d.’: not determined. Figure 3. Open in new tabDownload slide Cluster analysis of Acinetobacter spp. isolates based on genomic fingerprints generated by ERIC-PCR and (GTG)5-PCR. Cluster analysis was performed in GelCompare II using the UPGMA clustering method based on the Pearson correlation. BGP: biogas plant; I: input; O: output sample. Intrinsic blaOXA-51-like genes variants; ‘+’: positive; ‘−’: negative. ‘n.d.’: not determined. Genome sequence-based characterization of A. baumannii strains The six A. baumannii strains were further investigated by genome sequencing. The obtained draft genomes had a size ranging from 3.08 to 4.16 Mbp. Details of all genomes are provided in NCBI under bio project accession number PRJEB35515. Pairwise ANI values among the genomes of the six strains and A. baumannii ATCC 19606T were in the range of 97.28–97.67% (Table 2). Cluster analyses based on pairwise ANI values showed a single unique cluster determined for all A. baumannii strains of this study and respective type strains of A. baumannii (Figure S3, Supporting Information). All values were above the species boundary of 95–96% (Richter and Rosselló-Móra 2009), which confirmed the assignment of the six strains to the species A. baumannii. blaOXA-51 typing of A. baumannii strains All six A. baumannii strains contained a blaOXA-51-like gene; however, no PCR products instead were obtained by blaOXA-51 PCR screening for remaining Acinetobacter isolates. Protein BLAST of the complete blaOXA-51-like gene sequences of the A. baumannii strains was performed to identify their OXA-51-like type. Four known OXA-51-like types, OXA-104, OXA-106, OXA-343 and OXA-91, and two novel variants (OXA-909 and OXA-910) were determined. Phylogenetic analyses based on blaOXA-51-like gene sequences showed a clustering of the A. baumannii strains from manure and digestates with avian and clinical strains of A. baumannii (Fig. 4). The lack of insertion sequence element ISAba1 upstream of blaOXA-51-like genes in all A. baumannii strains was confirmed by PCR and comparative genome analysis (Figure S4, Supporting Information). Figure 4. Open in new tabDownload slide Correlation of complete nucleotide sequences of blaOXA-51-like genes from this study with international clonal lineages, avian and clinical strains of A. baumannii, calculated by maximum likelihood method. GenBank accession numbers were represented in parentheses. Representatives of blaOXA-213 and blaOXA-23 were used as outgroup to root the tree. Bootstrap values >70% (100 re-samplings) were shown. Strains related to this study were given in bold font. International clonal lineages: IC; European Union clonal lineages: EU. Figure 4. Open in new tabDownload slide Correlation of complete nucleotide sequences of blaOXA-51-like genes from this study with international clonal lineages, avian and clinical strains of A. baumannii, calculated by maximum likelihood method. GenBank accession numbers were represented in parentheses. Representatives of blaOXA-213 and blaOXA-23 were used as outgroup to root the tree. Bootstrap values >70% (100 re-samplings) were shown. Strains related to this study were given in bold font. International clonal lineages: IC; European Union clonal lineages: EU. MLST of A. baumannii strains MLST was applied to study the six A. baumannii strains in an epidemiological context. Based on the applied Pasteur MLST scheme five strains represented novel ST types (ST1442P, ST1443P, ST1444P, ST1445P and ST1446P) and one strain represented previously known ST46P (Fig. 5; Table S7, Supporting Information). However, four strains were assigned as novel STs and two strains were determined as ST1210Ox and ST1557Ox based on Oxford scheme, respectively (Table S7, Supporting Information). An MST calculated with the goeBURST algorithm (Francisco et al. 2009) showed the placement of the six strains among the STs provided in the Pasteur MLST database scheme (both clinical and extra-clinical origin), and those obtained from wild and livestock avian samples (Wilharm et al. 2017) and bovine sources (Klotz et al. 2019). Figure 5. Open in new tabDownload slide (A) MST generated by goeBURST algorithm using MLST profile data from Pasteur scheme, avian (Wilharm et al. 2017) and bovine (Klotz et al. 2019) related STs and those obtained from this study. Black and gray indicated avian and bovine STs. Partial snapshot of MST showing population structure of A. baumannii strains in (B) international clonal complex 1 (IC-1) and (C) international clonal complex 2 (IC-2) with STs of this study. Green and blue circles resembled founder and remaining STs from Pasteur database (both clinical and extra-clinical origin). The black arrows point to STs related to this study, which were labeled with red circles. Isolation source and STs were given in parentheses. Novel STs were given in bold font. Figure 5. Open in new tabDownload slide (A) MST generated by goeBURST algorithm using MLST profile data from Pasteur scheme, avian (Wilharm et al. 2017) and bovine (Klotz et al. 2019) related STs and those obtained from this study. Black and gray indicated avian and bovine STs. Partial snapshot of MST showing population structure of A. baumannii strains in (B) international clonal complex 1 (IC-1) and (C) international clonal complex 2 (IC-2) with STs of this study. Green and blue circles resembled founder and remaining STs from Pasteur database (both clinical and extra-clinical origin). The black arrows point to STs related to this study, which were labeled with red circles. Isolation source and STs were given in parentheses. Novel STs were given in bold font. Heavy metal resistance, antibiotic resistance and virulence genes Genome sequences of all six A. baumannii strains and A. baumannii ATCC 19606T (ACQB00000000) were compared in EDGAR, which confirmed the presence of copper-related efflux pump genes (copA and copB) in all strains studied (Figure S5, Supporting Information). Similarly, genome analysis revealed the presence of AmpC ß-lactamases (Class C type) and multiple intrinsic resistance-nodulation-cell division (RND) and multidrug and toxic compound extrusion (MATE) type efflux genes, including the chloramphenicol resistance gene craA and the fosfomycin resistance efflux gene abaF in the genome of all six strains (for details, see Fig. 6; Table S5, Supporting Information), and all six strains lacked insertion sequence and resistance islands. In all strains the transposition of ISAba1 into the adeS gene was absent. Five out of six strains (Table S5 and Figure S8, Supporting Information) in our study lacked the adeC gene. In all six strains, the comM gene was complete and not interrupted by a resistance island. Moreover, genome analysis also showed the presence of genes that code for most virulence factors like type II and type VI secretion systems, outer membrane protein A (OmpA), protein tyrosine kinase, putative polysaccharide export outer membrane protein (EpsA), iron starvation-related protein (Fe/S protein) and type I chaperone usher pili (Csu pili) required by this bacterium to thrive in both biotic and abiotic environments (Fig. 6; Figure S6 and Table S6, Supporting Information); however, all virulence-related genes were located in the core genome. In addition genes involved in processing of polyphosphates were determined in all A. baumannii strains. Among those, genes coding for AMP phosphotransferase and adenylate kinase were determined. Locus tag numbers are provided in Figure S7 (Supporting Information). Figure 6. Open in new tabDownload slide Circular plot of whole genome of six strains of A. baumannii isolated from German biogas plant. The whole genome (size in the middle of plot) of A. baumannii ATCC 19606T was used as reference represented by two outer circles. Circular plot was generated with BioCircos (Cui et al. 2016) implemented in EDGAR (Blom et al. 2016). All labeled genes were found to be intrinsic to the genome of respective strains. Figure 6. Open in new tabDownload slide Circular plot of whole genome of six strains of A. baumannii isolated from German biogas plant. The whole genome (size in the middle of plot) of A. baumannii ATCC 19606T was used as reference represented by two outer circles. Circular plot was generated with BioCircos (Cui et al. 2016) implemented in EDGAR (Blom et al. 2016). All labeled genes were found to be intrinsic to the genome of respective strains. Temperature-dependent growth and survival of Acinetobacter in anaerobic condition All Acinetobacter spp. isolates grew well between 30 and 37°C. The growth of some isolates was slightly reduced between 20–28°C, but growth of all isolates was weak at lower tested temperature (4–15°C). The highest growth temperature for most of the Acinetobacter spp. isolates from both input and output samples was 37°C. The exceptions were the six A. baumannii strains that still grew at 45 and 50°C but not at 55°C and the strain next related to A. baumannii, KPC-SM-21 (97.3% 16S rRNA gene sequence similarity) did also grow at 45°C but not at 50°C and above. The type strain of A. baumannii also grew at 45°C, but not at 50°C and above (Figure S9, Supporting Information). All Acinetobacter isolates were unable to grow under anaerobic conditions. However, all isolates were able to survive in anaerobic conditions on nutrient agar plates for a week at 25°C, and thereafter grew well in aerobic conditions at 37°C (data not shown). Copper tolerance of Acinetobacter spp. isolates All Acinetobacter spp. isolates were tested with respect to their growth in the presence of copper. The six A. baumannii strains including the type strain of A. baumannii and two other isolates grew well in the presence of 4 mM copper and three other isolates showed reduced growth at that copper concentration (Figure S10, Supporting Information). None of the isolates grew in the presence of 8 mM copper and at higher copper concentrations. Besides the six A. baumannii strains, the two isolates assigned to A. calcoaceticus, 553B1–12EESBL and 551B1–12EESBL, and the isolate next related to A. baumannii, KPC-SM-21, were among the isolates that grew in the presence of 4 mM copper. Antimicrobial susceptibility patterns of Acinetobacter spp. isolates All isolates including A. baumannii strains were susceptible to most of the tested antibiotics like cephalosporins (cefotaxime and ceftazidime), carbapenems (meropenem and imipenem), piperacillin, quinolones (levofloxacin and ciprofloxacin), tetracycline, trimethoprim/sulfamethoxazole and polymyxin E (colistin). Data of antibiotic susceptibility patterns are provided in Tables S3 and S4 (Supporting Information). DISCUSSION This study showed a high abundance of 16S rRNA gene copies of Acinetobacter spp. in input and output samples of German biogas plants. Although the copy number was significantly reduced in output materials of several biogas plants, the concentration was still in a high range. Several different Acinetobacter spp. were isolated in the presence of cephalosporins and carbapenems. Isolates were next related to A. baumannii, A. calcoaceticus, A. towneri, A. beijerinckii, A. bereziniae, A. defluvii, A. indicus and A. lwoffii (Table 2). The detailed characterization of the six strains assigned to the species A. baumannii further illustrated a high diversity of A. baumannii released via manure and pretreated digestate from biogas plants into the environment. Even though the genus Acinetobacter is generally regarded as obligate aerobic, its members have been known to occur in different anaerobic or oxygen-limited environments, including anaerobic digesters (Supaphol et al. 2011; Baek, Kim and Lee 2014; Jo et al. 2015). Recently Higgins et al. (2018) reported that A. baumannii survived the activated anaerobic mesophilic sludge digestion in wastewater treatment plants, but were ultimately destroyed in alkaline lime-treated stabilized sludge. The authors illustrated in lab scale tests that A. baumannii were not able to grow under anaerobic conditions but survived an incubation period of four weeks under the same conditions. The authors pointed out that anaerobic treatment also enables the survival and dissemination of this nosocomial obligate aerobic pathogen. Here we confirmed that finding by the determination of Acinetobacter spp. in digestates of anaerobic biogas fermenters. In agreement with findings of Higgins et al. (2018), all Acinetobacter isolates (including A. baumannii strains) of this study were able to survive in anaerobic laboratory test conditions. So far, it is still unclear how Acinetobacter can survive under anaerobic conditions. Earlier studies have shown that Acinetobacter spp. were able to accumulate efficiently intracellular polyphosphates, and thereby contributing to a minor extent to the phosphate elimination in sewage treatment plants (Fuhs and Chen 1975; Deinema et al. 1980; Deinema, Van Loosdrecht and Scholten 1985; Wentzel et al. 1986, 1991; Stephenson 1987; Bark et al. 1992). Van Groenestijn et al. (1987) reported that the accumulated polyphosphates in Acinetobacter cells act as a phosphorus reserve and might be used as energy source by enzymatic processing of the polyphosphates via combined action of polyphosphates: AMP phosphotransferase and an adenylate kinase. Comparative genome analyses revealed the presence of genes that code for these enzymes in all six strain of A. baumannii cultured in this study (Figure S7, Supporting Information). The described biological process can be an explanation for the survival of aerobic organism in anaerobic biogas plant or anaerobic sludge treatment in waste water treatment plants, because the polyphosphate reserve in Acinetobacter cells can be vital under anaerobic environment conditions when these strict aerobic organisms have no other options to generate energy (Kortstee et al. 1994). Survival and transfer of potentially pathogenic culturable antibiotic-resistant bacteria and antibiotic resistance genes in anaerobic treatment processes have been reported in few previous studies (Beneragama et al. 2013; Resende et al. 2014; Schauss et al. 2015; Glaeser et al. 2016; 2016; Wolters et al. 2016). In a recent study, Song et al. (2017) provided insights into the dissemination of antibiotic resistance genes in anaerobic digestion systems, and showed that Acinetobacter and other genera are potential hosts of erythromycin resistance mediating methylase (ermB), sulfonamide resistance due to dihydropteroate synthase (sul1) and trimethoprim resistance by class 1 integron-borne (dfrA7) genes. Intriguingly, there exist several factors that impact the reduction of pathogens in anaerobic digestion, for instance, temperature (Martin, Bostian and Stern 1990), moisture content (Russ and Yanko 1981), biosolids (Sidhu and Toze 2009), efficient mixing and organic matter stabilization (Smith et al. 2005) and composition of indigenous microorganisms (Pietronave et al. 2004). In this study, six strains of A. baumannii were identified, of which two strains originated from effluent samples of two different biogas plants (Table 2). To the best of our knowledge, there are only few more reports regarding the presence of A. baumannii in effluent source after anaerobic digestion processes (Resende et al. 2014; Schauss et al. 2015), and A. baumannii isolates cultured from the anaerobic digesters were not studied in detail yet. In the Oxford scheme based MLST of A. baumannii strains, two previously known and four novel STs were identified. Using the Pasteur MLST, one previously known and five novel STs were determined. The known STs did not represent the major STs of international clonal complex (IC) 1 and 2 (Pasteur based) or CC109Ox and CC92Ox (Oxford based) responsible for outbreak of global epidemiology (Zarrilli et al. 2013). Based on the suggestion of Gaiarsa et al. (2019), STs from Pasteur scheme were used to generate combined population structures through MST calculation, since the Pasteur scheme provides higher precision for discriminating strains among clonal groups and is less influenced by homologous recombination. The antibiotic susceptibility test showed that A. baumannii strains were still susceptible to third- and fifth-generation cephalosporins and their combinations (cefotaxime, ceftaxidime, ceftolozan), cabapenems and colistin (Table S4, Supporting Information). However, with the exception to the fourth-generation cephalosporins (cefquinome) with and without clavulanic acid, amoxicillin, enrofloxacin, tetracycline and trimethoprim/sulfamethoxazole, A. baumannii strains under this study had relatively higher MIC values compared with other non-A. baumannii isolates for tested antibiotics (Table S3, Supporting Information). The genomes of A. baumannii strains contained intrinsic class C type β-lactamase and multiple efflux pump-related genes (Fig. 6; Table S5, Supporting Information). The higher MIC values against oxacillin, the third-generation cephalosporin ceftiofur (with or without clavulanic acid), and florfenicol (Table S3, Supporting Information) and against temocillin, fosfomycin and chloramphenicol (Table S4, Supporting Information) could be due to the basal-level expression of RND type efflux genes like adeABC (regulated by two-component regulatory operon, adeR-adeS) and adeIJK, multidrug and toxic compound extrusion (MATE) type efflux gene, e.g. multidrug efflux abeM, chloramphenicol resistance gene craA (reviewed by Coyne, Courvalin and Périchon 2011), fosfomycin resistance efflux gene abaF (Sharma et al. 2017) and ampC cephalosporinase (Corvec et al. 2003). All these genes were not located in resistance islands but rather were intrinsic to the core genomes (Table S5, Supporting Information). As expected, all A. baumannii strains in our study lacked point mutations in conserved regions of the AdeRS system that were reported by Yoon, Courvalin and Grillot-Courvalin (2013) mostly among multidrug-resistant clinical isolates of A. baumannii. Transposition of ISAba1 into the adeS gene was linked with overexpression of this efflux system in clinical isolates (Ruzin, Keeney and Bradford 2007); however, this gene was intact in all A. baumannii strains of our study (Figure S8, Supporting information). Five out of six strains (Table S5 and Figure S8, Supporting Information) in our study lacked the adeC gene, which has been considered as non-essential for the multidrug-resistant phenotype conferred by the efflux pump based on earlier studies (Marchand et al. 2004; Nemec et al. 2007). Insertion of IS element upstream was reported for hyperexpression of ampC gene in clinical A. baumannii (Corvec et al. 2003); however, all six strains lacked insertion at the upstream of ampC gene. Although A. baumannii is intrinsically resistant to these veterinary (florfenicol and ceftiofur) and clinical (temocillin, fosfomycin and chloramphenicol) antibiotics (Coyne, Courvalin and Périchon 2011), there are some reports on the use of fosfomycin in combination with other drugs, such as colistin, minocycline and polymyxin against Acinetobacter (Zhang et al. 2013b;Sirijatuphat and Thamlikitkul 2014). Similar to our study, A. baumannii derived from dairy cows, beef cattle and calves of German livestock (Klotz et al. 2019) and avian sources (Wilharm et al. 2017) carried much less acquired resistances compared with strains derived from companion animals and human activities influenced sources like wastewater that harbored acquired blaOXA-23, -72 and blaTEM genes (Ewers et al. 2017; Higgins et al. 2018). Similarly, the comparative genome analysis showed the presence of genes that code for most virulence factors like type II and VI secretion systems, OmpA, EpsA, tyrosine kinase, Fe/S protein and Csu pili, which are required by this bacterium to thrive in both biotic and abiotic environments (Weber et al. 2017; Harding, Hennon and Feldman 2018). Apart from clinical settings, A. baumannii has been described as a soil organism, however, without the support of enough specific references (Fournier et al. 2006). The presence of A. baumannii strains outside clinical settings is still an issue of controversial debate. Houang et al. (2001), Vangnai and Petchkroh (2007) and Hamouda et al. (2011) have already reported A. baumannii strains among samples collected from vegetables, fish, meat, soil and agricultural fields. Most recently, Lupo et al. (2014), Rafei et al. (2015), Wilharm et al. (2017), Kittinger et al. (2018) and Klotz et al. (2019) successfully isolated A. baumannii from water, soil, farm animals and their products and from avian samples including wild birds, suggesting that animals and birds might represent reservoirs for A. baumannii with the potential of dissemination of new emerging carbapenemase, such as blaOXA-143 to humans. The influent manure (Table 1) used in biogas plants mentioned in this study was mostly originating from animal husbandries and poultry farms. Therefore, our results are in congruence with the findings of Wilharm et al. (2017) and Klotz et al. (2019) suggesting that livestock and birds could be secondary habitats of this bacterium. In contrast to several other pathogens, the main colonization site of A. baumannii in animals is still unknown. Klotz et al. (2019) cultured A. baumannii mainly from nasal swabs but also from feces samples and determined ST-types, which seemed to be able to colonize cattle and humans as well. Similarly, detailed analysis of virulence proteins of A. baumannii strains isolated from diseased chicken and isolation of this bacterium carrying the highly problematic blaNDM-1 resistance gene from a lung sample of a pig with pneumonia and sepsis might point toward a potential zoonotic role for multidrug-resistant A. baumannii (Zhang et al. 2013a; Liu et al. 2016). All A. baumannii strains of our study harbored the blaOXA-51-like gene, which is considered an intrinsic class D β-lactamase (Turton et al. 2006a). This gene is naturally acting as a weak β-lactamase, nevertheless, the β-lactam hydrolyzing activity can be increased by upstream insertion of ISAba1 (Turton et al. 2006a). Since A. baumannii is one of the members of ACB complex, its identification is equally challenging and time consuming although multiple techniques have been developed (Tjernberg and Ursing 1989; Gerner-Smidt, Tjernberg and Ursing 1991; Peleg, Seifert and Paterson 2008; Nemec et al. 2011). Therefore, our findings support the notion that amplification of intrinsic blaOXA-51-like gene is recommended for preliminary identification of A. baumannii from ACB complex as suggested by Turton et al. (2006b), because this gene has been rarely reported in non-A. baumannii complex species (Lee et al. 2009). Besides the members of ACB complex, A. lwoffii being ubiquitous in nature and considered to belong to normal microbiota known to inhabit human skin is an emerging opportunistic pathogen within the genus Acinetobacter (Bouvet and Grimont 1986; Turton et al. 2010). Several reports of infections and clinical manifestation of this bacterium have been published (Rathinavelu, Zavros and Merchant 2003; Tega et al. 2007; Hu et al. 2011; Singh et al. 2016). The presence of strains of opportunistic pathogens A. baumannii and A. lwoffii in output sources of biogas plants may pose environmental and epidemiological risks, because multidrug-resistant strains of these bacteria are known to spread and take up multiple antibiotic resistance genes in clinical and environmental settings mostly via horizontal gene transfer (Fournier et al. 2006; Visca, Seifert and Towner 2011; Tanner et al. 2017). It was also proven that unleashed pathogenic bacteria from anaerobic digestion could survive in the field sites after application of digestate as manure (Estrada et al. 2004; Johansson et al. 2005). Manure applications have been reported to increase the abundance of clinically relevant antibiotic resistance genes and antibiotic-resistant bacteria in soil (Heuer, Schmitt and Smalla 2011; Zhou et al. 2013; Udikovic-Kolic et al. 2014; Mckinney et al. 2018; Wolters et al. 2018). It is worth mentioning that anthropogenic activities have released antibiotics and heavy metals into the aquatic and soil environment, which in small concentration exert selective pressure to both exogenous and native soil microbiota (Bürgmann et al. 2018; Xie, Shen and Zhao 2018). In this study, the copper tolerance test revealed a high MIC value (8 mM) of A. baumannii strains compared with others (Figure S10, Supporting Information). Genome analysis showed presence of copper-related efflux genes (copA and copB) among all A. baumannii strains of this study (Fig. 6; Figure S5, Supporting Information). The expression of these genes among A. baumannii strains could be the answer behind slightly higher copper tolerance compared with other Acinetobacter isolates. Williams et al. (2016) performed in vitro analysis in the presence of copper and showed the expression of copper-related genes (copA and copB) in clinical A. baumannii strains as well. Recent analysis of large complete genome collections revealed frequent co-occurrence of antibiotic and metal ion resistance genes among human pathogens compared with bacteria less often colonizing humans (Li, Xia and Zhang 2017). Members of Acinetobacter with a plasmid encoding a copper resistance gene (copA) have recently been reported (Irawati, Yuwono and Rusli 2016). Resistance to metal ions is often associated with mobile genetic elements that have been responsible for a wide dissemination of antibiotic resistance genes (Baker-Austin et al. 2006; Hobman and Crossman 2015). Furthermore, self-transferable plasmids conferring resistance toward multiple antibiotics were isolated from animal manure applied as fertilizer (Smalla et al. 2000; Heuer et al. 2002; Heuer and Smalla 2007). Indeed, as long as antibiotic resistance genes exist in soil and manure, the probability for their transfer remains obvious, and since a plethora of antibiotic resistance genes are exchanged in a single horizontal gene transfer event, this facilitates eventual evolution of multidrug-resistant strains from susceptible bacteria under appropriate selection pressure (Thomas and Nielsen 2005; Chee-Sanford et al. 2009; Hughes and Andersson 2015). It is now generally accepted that manured soil and plant phytosphere represent hotspots for horizontal gene transfer, most importantly between organisms associated together, presumably because of rich nutrients of manure (Van Elsas, Turner and Bailey 2003; Heuer and Smalla 2007; Chee-Sanford et al. 2009; Heuer, Schmitt and Smalla 2011; Nesme and Simonet 2015). Recently, Leclercq et al. (2016) reported environmental species of Acinetobacter among the group of genera involved in persistence of antibiotic resistance genes in manure treated soil. The application of effluent manure harboring potential pathogens obtained from anaerobic digestion processes in the agricultural fields might result in colonization of plants. Berg et al. (2002) and Sachdev et al. (2010) successfully isolated A. baumannii (strain LRVP52; GenBank: EU221389, strain LRFN53; GenBank: EU221350, and strain HIRFP40; GenBank: EU921471) from the rhizosphere of agricultural plants. The horizontal gene transfer of antibiotic resistance genes might occur when relevant donor bacteria are present in such niches, thus the movement of antibiotic resistance genes from pathogens into soil likely results in transfer and persistence of antibiotic resistance genes in natural environments once stably acquired in soil microbiota. This may lead to human infections with emerging pathogens, akin to the rise of A. baumannii infections (Chee-Sanford et al. 2009; Forsberg et al. 2012). Members of the genus Acinetobacter mostly have optimum growth temperatures of 33–35°C but especially A. baumannii shows good growth at 42–45°C (Doughari et al. 2011). All six strains of A. baumannii were observed to survive at 50°C (Figure S9, Supporting Information). In a recent study, similar high growth temperatures have been observed by Hrenovic et al. (2014); a multidrug-resistant environmental A. baumannii strain reportedly grew at a high temperature (50°C), and showed 87% similarity (pulsed-field gel electrophoresis based molecular typing) to a clinical isolate of A. baumannii obtained from the general hospital in Pula (Croatia). These isolates represented a cluster within European clone I (IC 1) that had been recognized as a cause of nosocomial infection in Croatia for over 10 years. In addition, Yavankar, Pardesi and Chopade (2007) investigated 39 of 118 Acinetobacter spp. isolates from human epidermis and reported the tolerance of isolates at temperature of 50°C. As suggested by Hrenovic et al. (2014), the extracellular polymeric substances produced by A. baumannii may have an additional protective function under unfavorable environmental conditions (low pH, desiccation and high temperatures). Furthermore, the survival of all six A. baumannii strains and one isolate (namely KPC-SM-21) closely assigned to A. baumannii in high temperature zones (>40°C, mesophilic; originated from output samples) in German biogas plants of this study might be explained by biosynthesis of trehalose as a compatible solute by prokaryotes under environmental stress (McIntyre et al. 2007; Reina-Bueno et al. 2012). Recently, Zeidler et al. (2017) investigated temperature and salt induced solute implication in pathobiology of A. baumannii and reported that high osmolarity and high temperatures are important factors for expression of trehalose-6-phosphate phosphatase (otsB) and biosynthesis of trehalose in osmo- and thermotolerant wild type A. baumannii strains. Currently, there exists only one study regarding the role of Acinetobacter in persistence of antibiotic resistance genes in manure amended soil (Leclercq et al. 2016). We assumed that both opportunistic and environmental strains of Acinetobacter may play a role in dissemination of antibiotic resistance genes and a detailed study is required to cover the total abundance of Acinetobacter spp. that can be released with manure and biogas plants digestate into the environment to understand the role played by Acinetobacter in spread of antibiotic resistance genes. This study provided a first detailed characterization of Acinetobacter spp. released via manure and from biogas plants after anaerobic digestion, transferred through the anaerobic post-fermenter and storage tank into the environment. Because studied biogas plants were CSTR systems with a continuous inflow of fresh slurry and manure material, the given retention time gave just estimates how long the cultured output bacteria were exposed to anaerobic conditions. However, because the output samples were taken from the final storage tank containing the digestates used for field application without further treatments, those samples were the best option to determine the risk of pathogen release from biogas plants. Our study showed that Acinetobacter isolates closely associated to important nosocomial pathogens were present in manure and biogas plant digestates, both fertilizers used in agriculture. The strains of A. baumannii from this study had higher MIC values than other isolates for all tested antibiotics except cefquinome (with and without clavulanic acid), cefotaxime, ceftazidime (with or without avibactam or 3-aminophenylboronic acid), ceftolozan, amoxicillin, amikacin, quinolones (enrofloxacin, levofloxacin and ciprofloxacin), tetracycline, trimethoprim/sulfamethoxazole, tigecycline, carbapenems (meropenem and imipenem) and colistin, respectively (Tables S3 and S4, Supporting Information). Although all A. baumannii strains lacked potent acquired antibiotic resistance genes, the comparative genome analyses showed the presence of multiple efflux-related and virulence or pathogenicity factor-related genes in their genomes, which are necessary for survival of this bacterium in both abiotic and biotic environments. Indeed, the cultivation-dependent method detected the survival of strains of A. baumannii in thermophilic and mesophilic anaerobic biogas digestion processes, which consequently pose significant environmental and epidemiological risks when released in agricultural soil via application of effluent manure. We support the use of both culture-dependent and culture-independent approaches to understand the diversity and role of Acinetobacter in anaerobic digestion plant and soil amended with the resulting digestate in a prospective study. Overall, this study has shown that isolates related to nosocomial A. baumannii were released from manure and digestates of anaerobic German biogas plants into the environment and agricultural land. More detailed cultivation studies with Acinetobacter selective media with and without antibiotics are required to get a better knowledge on the release of (antibiotic)-resistant Acinetobacter from livestock with and without anaerobic manure treatment. It is also not yet clear whether a thermophilic biogas plant process would have a better elimination efficiency than a mesophilic biogas plant process. This is however indicated because all cultured Acinetobacter spp. could not grow at 55°C and above. ACKNOWLEDGEMENTS We acknowledge Sharmishra Mitra (JLU) who was involved in the cultivation of several of the Acinetobacter spp. and thank Evelyn Skiebe (RKI) for excellent technical assistance and colleagues at the MF2 genome sequencing unit (RKI) for Illumina sequencing support. FUNDING This project was funded by two Federal Ministry of Education and Research (BMBF)-funded projects, RiskAGuA (02WRS1274A) and ARMIS (5th JPIAMR Joint Call). DP was funded by the Justus Liebig University Giessen (Ph.D. Grant). Conflicts of interest None declared. REFERENCES Aarestrup FM . Veterinary drug usage and antimicrobial resistance in bacteria of animal origin . Basic Clin Pharmacol Toxicol . 2005 ; 96 : 271 – 81 . 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TI - Acinetobacter baumannii in manure and anaerobic digestates of German biogas plants
JF - FEMS Microbiology Ecology
DO - 10.1093/femsec/fiaa176
DA - 2020-10-01
UR - https://www.deepdyve.com/lp/oxford-university-press/acinetobacter-baumannii-in-manure-and-anaerobic-digestates-of-german-XDMvoqRS91
VL - 96
IS - 10
DP - DeepDyve
ER -