Characterization of the resistome and of antibiotic-resistant bacteria in top soil improvers and irrigation waters devoted to food production: a case-study from Italy

doi:10.21203/rs.3.rs-5232259/v1

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Characterization of the resistome and of antibiotic-resistant bacteria in top soil improvers and irrigation waters devoted to food production: a case-study from Italy

https://doi.org/10.21203/rs.3.rs-5232259/v1

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Biosolids and reclaimed waters are seen as precious resources to reintroduce organic matter into soils used for agriculture and to reduce the water footprint of intensive agricultural food system. While the circular economy is a virtuous exercise, it cannot be excluded that the adoption of such a practice can introduce vulnerabilities in the food chain, by exposing crops to zoonotic agents and antimicrobial resistance determinants. This option is far from being a speculation and evidence start to accumulate indicating that the risk is tangible. In this work we add further evidence that the circular economy practices of reusing biomass and reclaimed waters in agricultural setting may be vectors for the spreading of antimicrobial resistance genes targeting molecules used to treat human bacterial infections and demonstrate that such genes, identified through metagenomics screening of these samples, are present into live bacterial organisms, harbouring multi drug resistance genes clusters. Moreover, we observed that most of the antibiotic-resistant bacteria identified belonged to species with an environmental diffusion, which were not supposed to be exposed to the antimicrobials, suggesting that inter-specie transfer occurred.

AMR

irrigation

reused water

top soil improvers

agriculture

Circular Economy

food safety

resistome

metagenomics

The recycling of waste through composting, including those from municipal Wastewater treatment plants (WWTPs) animal farming and from the food industry, in the primary production of feed and food of vegetable origin is a valuable resource of energy and nutrients, while the use of reclaimed water for irrigation represents a tool to abate the water footprint of the food chain (Barbieri et al. 2023). The use of such organic waste streams is considered as a the most important circular economy practice by the stakeholders in the agrifood chain (James, Millington, and Randall 2022). At the same time WWTPs and animal manure are recognized to be important sources of Antibiotic-Resistant Bacteria (ARBs), and Antibiotic Resistance Genes (ARGs) and have been described to play a role in the dissemination of antimicrobial resistance (AMR) in the environment (Tyrrell et al. 2023). Manure maturation and wastewater treatments can reduce the level of microbial contaminants, but not remove them completely (European Environment Agency. 2020). Moreover, ARGs and ARBs can be enriched during sludge treatment in WWTPs (Ju et al. 2019). As a matter of fact, antibiotic-resistant strains of Enterococcus spp have been demonstrated not to be eliminated in wastewaters originating from a treatment plant receiving domestic and industrial effluents (Ferreira da Silva et al. 2006). Similarly, a significant increase in the prevalence of enterococci resistant to ciprofloxacin, an antibiotic commonly used to treat urinary tract infections in humans, has been observed in treated wastewater compared with the raw wastewater sampled in the same plant before treatment (Ferreira da Silva et al. 2006). An additional report showed that increased resistance to the ampicillin, ciprofloxacin, cefoperazone, sulfamethoxazole, and tetracycline was observed in samples taken in sites downstream the WWTPs receiving the wastewater effluents compared to sites located upstream the WWTPs (Mukherjee et al. 2021).

Beside the ARG conveyed by treated sludges from WWTPs, spreading of genes conferring resistance to clinically important antibiotics, such as the sul1 and sul2 genes, that confer resistance to sulphonamide, the tetM, tetW and tetG genes conferring resistance to tetracyclin and the ermB and ermC genes involved in the macrolide resistance has been traced to the use of land application of animal manure (Lopatto et al. 2019; Macedo et al. 2021; Ruuskanen et al. 2016; Wang et al. 2020). As a matter of fact, the widespread use of antibiotics in cattle and poultry breeding leads to notable concentration of antibiotic residues in animal manure (X. Zhang et al. 2022), whose prolonged agricultural use may seriously contaminate the soil with antibiotics, exerting a selective pressure acting on the maintenance of the of ARGs in the soil environment (Li et al. 2018).

WHO has declared that antimicrobial resistance is currently one of the greatest global public health threats (World Health Organization 2023). As a matter of fact, infections caused by multidrug-resistant (MDR) organisms result in significant increase in patients' mortality (Serra-Burriel et al. 2020) and it has been estimated that at least 700,000 people die each year from drug-resistant infections and that this number could reach 10 million by 2050 (Ragheb et al. 2019), setting the antimicrobial resistance one of the urgent multisectoral actions to achieve the Sustainable Development Goals (SDGs)(World Health Organization 2023).

In a previous work, we demonstrated high loads for sulphonamides, tetracyclines, fluoroquinolones, and macrolides in biowastes from municipal WWTPs, animal manure, and slurry litter and high level of contamination with sulphonamides, tetracyclines, and fluoroquinolones in waters reclaimed and from river and channels used for irrigation in open field lettuce production in Italy (Barola et al. 2024).

Given the importance attributed to the practice of recycling of organic waste streams (James, Millington, and Randall 2022), in this work we present the investigation of the resistome in biosolids at the stage they are ready for the application on land and in irrigation water sampled in an Italian region with a strong agricultural vocation. We used metagenomics to define the resistome and show that the signals identified with the genomic approach had correspondence with the presence of living bacteria isolated from the same samples expressing antimicrobial resistance.

Sampling

Twenty-five biosolid and 14 irrigation water samples were collected in the period 2021–2022 in Emilia-Romagna region, in Italy (Tables 1 and 2).

The biosolids belonged to the following categories:

sludges from wastewater treatment plants with merged inputs from urban and intensive animal farming settlements (n = 6) (SL-WWTP) and from a dairy food plant (n = 1) (SL-FP);

mixed compost from urban organic and green wastes, accounting for declared different contributions from household wastes and green wastes (n = 5) (MCO);

animal waste (bovine manure, pig and bovine slurry and poultry litter) (n = 7) (AW);

digestates, accounting for potential different contributions from household, green, and animal wastes, and WWTP sludges (n = 6) (DIG).

All the biosolids were collected at the end of their production cycle, ready for land application, and sampled in accordance with the UNI 10802/2004 international standard; animal wastes were collected at farm, sludges and composts at production/retailer level. Biosolids with more than 16% moisture were stored in plastic bins at 4°C. Pellet samples were stored in the dark at room temperature.

Reclaimed water samples were collected in 2022 from a WWTP with the authorization to produce reclaimed waters for agriculture based on the EU Regulation 2020/741. In this WWTP, yearly, 5.5 million cubic meters are recovered after combined UV and H₂O₂ treatment and allocated for agricultural use during summer, when the water demand for irrigation is higher. Surface waters were sampled in the drainage area of the Po River basin, in the Emilia-Romagna region, which is characterized by a network of rivers and canals with interconnected water bodies devoted to agricultural practices (network of around 3,500 km serving an area with 120,000 ha extension) and surrounded by farming and slaughterhouse’s activities. Such irrigation waters are routinely checked for legacy microbiological quality parameter (Escherichia coli expressed as MPN/100 ml), according to the regional legislation in force (Galli, Franceschini, and Anna 2022).

The details of the water samples were the following:

reclaimed water collected from a municipal WWTP with tertiary treatment (n = 6), including the incoming (Reused IN, n = 3) and the outcoming water (Reused OUT, n = 3) entering and exiting the facility, respectively;

surface water from irrigation canals (n = 6) connected to plain surface water bodies that also receive urban and livestock discharges, with withdrawals near the intake point for irrigation use;

surface water from rivers (n = 2) collected for irrigation purposes, upstream of significant livestock and/or urban settlements.

DNA extraction and shotgun metagenomic sequencing

DNA was extracted from 0.25 g of each untreated biosolid sample using the DNeasy PowerSoil Pro kit-QIAGEN (Qiagen, CA, USA) following the manufacturer's instructions.

Water samples (5 aliquots of 200 ml, each) were vacuum filtered using 0.45 µm filters. DNA was extracted from filters using the DNeasy PowerWater Kit-QIAGEN (Qiagen, CA, USA) following the manufacturer's instructions.

Sequencing libraries of 400 bp were prepared from 100 ng of the extracted DNA through enzymatic fragmentation by using the NEBNext® Fast DNA Fragmentation & Library Prep Set for Ion Torrent™ Kit (New England BioLabs, MA, USA) according to the manufacturer’s instructions. The libraries were quantitated with the Tape Station System 4200 and the High Sensitivity D1000 Reagents kit (Agilent Technologies, CA, USA) and were diluted reaching the concentration of 100 pM in DNAase/RNAase free water for the preparation of a sequencing pool of three samples. Each pool was diluted to a final concentration of 50 pM, enriched by using the IonChef System and Ion 510™ & 520™ & Ion 530™ Kit (Thermo Fisher Scientific, MA, USA), loaded onto the Ion 530™ Chip sequencing chip and sequenced with the Ion Torrent S5 sequencer (Thermo Fisher Scientific, MA, USA).

Metagenomic analysis

The metagenomic sequences were analyzed using the tools available on the public bioinformatic webserver ARIES (https://aries.iss.it/platform/)(Knijn et al. 2020), and the analyses were visualized using the MEGAN V6 Community Edition software (http://www-ab.informatik.uni-tuebingen.de/software/megan6/) (Huson et al. 2007). In detail, the sequencing reads in fastq format were assembled into scaffolds by using the SPAdes v3.14.1 tool (Bankevich et al. 2012) with default parameters. The scaffolds were aligned against the NCBI-NR (non-redundant) protein database (ftp://ftp.ncbi.nlm.nih.gov/), using the Diamond v2.0.8.1 tool and the DIAMOND-BLASTX algorithm (Buchfink, Xie, and Huson 2015). The parameters used for the database interrogation included the use of the standard genetic code for translation of the contigs before alignment. The resulting translated protein sequences were aligned to the target sequences in the NCBI-NR proteins database without reporting alignments with frameshifts mutations and with the algorithm filtering false positive results based on the composition-based statistics used by BLAST (--comp-based-stats). The scoring matrix used was BLOSUM62 and the maximum expected e-value to report an alignment was 0.001. The alignments obtained, in DAA format, were analyzed and visualized with MEGAN6 software (Huson et al. 2007). For the functional metagenomics analysis of the resistome the SEED subsystems (Overbeek et al. 2005) content was determined using the MEGAN internal Reference sequence maps.

Phenotypic testing of antibiotics resistance in biosolid samples

To confirm the results of the alignments we have used a microbiological strategy to ascertain whether the signals observed were originated by functional genes present in live bacterial strains. This latter strategy was used to analyse the biosolids only, as these were the only samples for which a back-up was available. As a matter of fact, all water samples were filtered and the filters used to extract the DNA, to maximize the alignment signals-to-noise ratio and thus no more samples were available for the microbial cultivation procedure. We selected seven biosolid samples out of the 25 analyzed by metagenomics, chosen to cover the spectrum of matrices studied, and five different antibiotics selected among those with the higher (mupirocin, daptomycin) and lower (bacitracin, tetracyclines and fosfomycin) number of contigs aligning the respective hits on the NCBI-NR database (Figs. 1 and 2). The amount of each antibiotic used in the phenotypic assay was selected based on the published data in the literature on their clinical use (Table 3) (Antonello et al. 2021; Jiang et al. 2022; Manson et al. 2004; Patel, Gorwitz, and Jernigan 2009; Peng et al. 2005; Tran, Munita, and Arias 2015).

One gram of each biosolid sample was enriched in 100 ml of BPW (Buffered Peptone Water) and grown overnight at 37°C. The resulting bacterial cultures were diluted 1:100 in fresh BPW, grown up to OD600nm 0.5 and aliquots were added to cascade dilutions of antibiotic in BPW according to their MICs (Table 3) and eventually incubated overnight at 37°C. To attempt the isolation of resistant bacteria, the cultures grown in presence of the antibiotics were plated on blood-azide and LB agar plates added with the double of the initial concentration of each antibiotic used in each liquid culture reported in Table 3.

Whole genome sequencing and genomic analysis of the resistant bacterial isolates

Genomic DNA was extracted from the isolated bacterial strains with the GRS Genomic DNA Kit Bacteria (GRISP Research Solutions, Porto, Portugal) from two ml of TSB cultures of each resistant strain grown overnight at 37°C in presence of antibiotics.

Library preparation and genomic sequencing was performed as described above for the metagenomics sequencing.

The genomic analyses were performed using the tools available on the ARIES public webserver (https://aries.iss.it/platform/) (Knijn et al. 2020). Raw sequencing reads were subjected to quality check and Trimmomatic tool (Bolger, Lohse, and Usadel 2014) was used to remove the adaptors and to clean the poor quality bases from the raw reads by accepting 20 as the lowest Phred value. The trimmed reads were assembled in contigs using the SPAdes v3.14.1 software (Bankevich et al. 2012) with default parameters. The MLST v2.16.1 and the rMLST tools available on the ARIES (Knijn et al. 2020) (https://w3.iss.it/site/aries/) and PubMLST (Jolley and Maiden 2010) (https://pubmlst.org) webservers, respectively, were used to identify the bacterial species. The AMR genes content of each genome was assessed using the ABRicate v1 software (https://github.com/tseemann/abricate) with the Resfinder database (Zankari et al. 2012).

The serotype of the E. coli isolated strain was determined by aligning the assembled contigs with the reference sequences for the O and H antigen genes (Katrine G. Joensen et al. 2015) using the BLAST software and the blastn algorithm v2.11.0 (Camacho et al. 2009). The virulence genes content was defined by using the Patho_typing tool (https://github.com/B-UMMI/patho_typing) developed by the INNUENDO project (Llarena et al. 2018) using the E. coli virulence genes database (Katrine Grimstrup Joensen et al. 2014).

All the metagenomic and genomic sequences are available at European Nucleotide Archive at EMBL-EBI under the accession number PRJEB80097.

Antibiotic resistance determinants in biosolid and water samples

Functional shotgun metagenomics analysis was used to characterize the resistome of the 25 biosolids and the 14 water samples. The samples as a whole ranged in size between eight and 10 million reads each (400 bp in length). The metagenomics assembly yielded between 200,000 and 500,000 contigs. The latter were aligned onto the NCBI-NR protein database using the BLASTX algorithm (Buchfink, Xie, and Huson 2015). The number of contigs aligning to hits belonging to the "Resistance to antibiotics and toxic compounds" of the SEED class in the NCBI-NR database varied between the different samples and ranged from 84 to 6,508 for the biosolids (2,584 on average) and from 1,274 to 9,363 for the water samples (2,406 on average) (Tables 4 and 5). It’s interesting to observe that two out of the three water samples collected at a WWTP at the incoming and outcoming stage of the tertiary treatment stage, displayed an increase in the number of contigs mapping onto the "Resistance to antibiotics and toxic compounds" of the SEED class in the outcoming samples suggesting that in those samples, the treatment may have caused the enrichment of ARGs (samples 1296 Vs 1297 and sample 1550 Vs 1551 in Table 5).

The investigation of the aligned contigs revealed the presence of several genetic traits encoding resistance to antimicrobials (Figs. 1 and 2). The most represented resistance determinants in biosolid samples were those conferring resistance to daptomycin, with an average of 135 contigs (11–375 in the different biosolids samples) aligning to hits of the "Resistance to antibiotics and toxic compounds" of the SEED class in the NCBI-NR database, followed by mupirocin (85 average contigs mapping; 0-274 in the different biosolids samples), fusidic acid (80 average contigs mapping; 0-293 in the different biosolids samples), and bacitracin (13 average contigs mapping; 0-166 in the different biosolids samples). Determinants conferring resistance to tetracyclines were recognized by a number of contigs ranging between 0 and 90 contigs from the different biosolids samples and those conferring resistance to beta lactamases of all Amber classes displayed a number of aligned contigs between 0 and 19 in the different samples. Other determinants included those conferring resistance to macrolides, fosfomycin, vancomycin and others (Fig. 1).

Regarding water samples, the panel of AMR determinants identified was similar to that displayed by the biosolid samples. The major difference resided in the bacitracin resistance determinants which showed a much lower number of contigs aligning, in favor of polymyxin, not identified in the biosolids and placing among the highest ranks of the AMR determinants identified in the water samples. Interestingly, the beta-lactamases of all Amber classes were more represented in water samples than in biosolids (Fig. 2).

It's noteworthy that determinants conferring resistance to ethionamide and isoniazid, used to treat human tuberculosis, were identified in both the types of samples with a significant number of contigs mapping (0–70 in the different biosolid samples and 18–139 in water samples). It should be underlined the use of isoniazid for the therapy and prophylaxis of bovine tuberculosis is forbidden, in Italy. Apart for those determinants linked to daptomycin, mupirocin, and ethionamide, the other seem reflect the inventoried use of the main antimicrobial classes administered to food producing animals, in Italy (European Medicines Agency. 2023).

Identification of antibiotic-resistant phenotypes of cultured biosolids and isolation of antibiotic-resistant bacterial strains.

The characterization of the AMR-phenotype was carried out on seven of the 25 biosolids samples used for the metagenomics analysis. The samples were chosen to span all the matrices with a definite origin. These samples were incubated with five different antibiotics chosen among those displaying the higher (mupirocin, daptomycin) and lower (bacitracin, tetracyclines and fosfomycin) number of contigs aligning the respective hits in the NCBI-NR database. The couple sample/antibiotic used, together with the concentrations are reported in Table 3. All the cultures tested appeared to contain bacteria resistant to all the selected antibiotics, showing turbidity when incubated with all the antibiotic concentrations used, indicating that bacterial growth occurred. After plating onto solid media, a few colonies were randomly picked from each plate for the following characterization of the ARG asset. Three bacterial isolates were obtained from the samples incubated with daptomycin (2746_D1; 2747_D1; ISS2_D1), four from the samples treated with tetracycline (2746_T1; 2747_T1; 2755_T1; ISS2_T1), two from mupirocin added samples (2751_M1; 2751_M2), two from the samples treated with bacitracin (ISS3_B1; ISS6_B1) and four from samples with fosfomycin (2755_F1_G+; 2755_F2_G+; 2755_F3_G-; 2755_F4_G-) (Tables 6 and 7). Since the isolated strains resulted from random picking of colonies from the solid media, these should be considered as representing only a partial picture of the whole ARBs population present in the original samples. Interestingly, the cultures growing in presence of fosfomycin yielded a more abundant bacterial growth onto plates containing a higher concentration of the antibiotic (256 ng/ul) compared to the plates with the lower concentration (64 ng/ul), with both the solid media used. No isolation was achieved by plating the ISS3 and 2755 onto solid medium supplemented with daptomycin and mupirocin respectively.

Characterization of the antibiotic-resistant bacterial isolates

The WGS of the isolated colonies showed that all the strains belonged to gram positive bacterial species, except for the 2747_T1, which belonged to Escherichia coli species (Table 6).

All the isolates possessed genes conferring resistance to multiple antibiotics. The most represented AMR determinants identified in the isolated strains were those conferring resistance to macrolides, indeed, most of the strains had at least one gene that confer resistance to this class of antibiotics (Table 7). This evidence is in line with the load of macrolides residues previously observed in the biosolids samples (Barola et al. 2024) and with the results of the metagenomics screening of the same samples in this study, where the macrolides resistance determinants where, as a whole, well represented in the samples (Fig. 1).

Similarly, genes associated to the fluoroquinolone resistance were found in the 2747_T1 strain only (Table 7) in accordance with the very low signal observed in the metagenomics screening (0–2 contigs aligning this specific class of antibiotics in the NCBI-NR database) (Fig. 1). This isolate was the only one belonging to an overt bacterial E. coli pathotype causing human disease. As a matter of fact, the genomic characterization of 2747_T1 revealed that this strain was a Shiga toxin-producing E. coli (STEC), positive for the stx2 gene. This isolate, an E. coli strain of O174:H21 serotype, also possessed other virulence-associated virulence of STEC such as iha, lpfa, terC, ompA encoding for the Z1307 outer membrane protein (Sharma et al. 2017), ompT, ycb locus, ent locus, fep locus, factors associated to adherence and adhesion, including the AidA-I adhesin, factors associated to the flagellar system and genes encoding for fimbrial proteins (fim genes) (Naseer et al. 2017).

Interestingly, the four strains grown and isolated in presence of different concentrations of fosfomycin did not show the presence of any of the fosfomycin resistance-associated genes present in the database used (Zankari et al. 2012). An explanation for this result may be the presence in these isolates of genes not included in the database or of point mutations conferring inherent resistance to fosfomycin, such as those of murA gene (Falagas et al. 2019). On the other hand, genes conferring resistance to this antibiotic were found in the genome of five other strains isolated after selection with daptomycin, mupirocin and tetracycline (Table 7).

FAO defined the agricultural intensification in relation to the inputs in the process, which may be land time, fertilizer, seed, feed or cash (Kenmore et al. 2004), paving the way for developing strategies to increase the agricultural production, which has, indeed, more than tripled in the 1960–2015-time span (Food and Agriculture Organization of the United Nations 2017). Such an increase resulted in a heavier water request for the agri-food sector and the need to restore the sources of carbon, nitrogenous and phosphorous to sustain soil productivity. On the other hand, the organic matter from multiple wastes and the reclaimed waters coming from WWTPs may represent a worthy way to satisfy this demand. Treatment of wastewaters is meant to abate the contamination deriving from the human and animal origin of the organic matter and reduce the contamination of the downstream environment. However, it has been described that such treatments may not be completely effective in eliminating ARG (Lee, Beck, and Bürgmann 2022) and that WWTP are important contributors to the pollution of environment with antibiotic-resistant bacteria (Bouki, Venieri, and Diamadopoulos 2013; Rizzo et al. 2013). The environment and the environmental bacteria are natural reservoirs of AMR genes and mobile genetic elements (MGEs), continuously fed by AMR genes from animal and human waste (Graham et al. 2019; He et al. 2020). Therefore, agricultural practices, such as biowaste land spreading and reclaimed waters used for irrigation, may represent effective hotspots for the accumulation and spread of antimicrobial resistant bacteria (ARBs) and ARGs that can eventually reach humans with food. On the other hand, the use of such organic waste streams represents the circular economy practice with the highest rank of importance in the opinion of stakeholders of the whole food/feed chains in Europe (James, Millington, and Randall 2022) but, at the same time, the recognition of the risks associated with this practice fall below the average of the current state of knowledge (James, Millington, and Randall 2022).

In a previous study we have reported preliminary observations that municipal sewage sludges, compost and mixed compost could be a source of antimicrobial resistance genes, representing a possible risk for the diffusion of ARGs to the soil bacterial communities and the crop (Gigliucci et al. 2017).

In the present study, we investigated several products from circular economy used on agricultural soils and sampled at the end of their production cycle, to ascertain whether the processes adopted to prepare waters and biomass for spreading on land were effective in eliminating ARBs and ARGs. The samples included sludges from WWTP and food industry, composts, digestates and animal wastes, as well as irrigation water, both reclaimed waters from WWTP and from rivers and canals and show that these products are indeed a source of Antibiotic-resistant bacteria (ARBs). To the best of our knowledge, this is one of the few papers dealing with the detection of ARGs and ARBs in biosolids at the end of their production cycle (ready-to-be-applied to land) and the first paper demonstrating the presence of antibiotic resistance bacteria (ARBs) in ready-to-use biosolids derived by sludges from WWTP. As a matter of fact, there is a large production of scientific evidence on the presence of ARGs and ARBs in WWTP. However, this is related with the investigation of the raw biomass, which is used as a proxy for determining what circulates in the population insisting on the treatment plants in wastewater-based surveillance programs (Bijlsma et al. 2024; Knight et al. 2024).

We performed the characterization of the resistome in a range of waters and biosolids products using a mixed strategy, based on shotgun metagenomics followed by phenotypic AMR testing and microbiological demonstration of the presence of bacteria carrying ARGs. This analytical strategy was intended to characterise the antibiotic resistance-related hazards in the concerned matrices as the first step of assessing the risks associated with the use of organic waste streams deriving from circular economy in the agrifood sector.

In our study, all the samples displayed signals traceable to resistance to multiple antibiotics and showed turbidity when incubated in presence of the antibiotics. One of the most abundant AMR determinants identified in biosolids was associated with resistance to the daptomycin (Fig. 1). Daptomycin is a lipopeptide antibiotic with activity against Gram-positive pathogens, including methicillin-resistant S. aureus (MRSA) and is considered an important option for the management of multidrug-resistant (MDR) infections due to Gram-positive organisms (Tran, Munita, and Arias 2015). It’s also interesting to note that this antibiotic is used exclusively under prescription in Italy and its use is restricted to the clinical treatment of human infections in hospitals, via endovenous route. Random selection of bacterial colonies onto solid media complemented with this antibiotic, yielded the isolation of an ARB belonging to Clostridium sartagoforme species (2746_D1), a bacterial species identified in the intestinal content of different animal herbivore species, such as horse and rabbit (Gong et al. 2021) and from cow dung compost (J.-N. Zhang et al. 2015). This strain was isolated from a sample of mixed compost (2746_D1, Table 6) not expected to contain inputs from urban sewage. From the same sample we could also isolate a strain of Bacillus cereus resistant to Tetracyclines (2746_T1, Table 6). Other daptomycin-resistant bacteria identified included a strain of Enterococcus faecium from poultry litter and Bacillus licheniformis from a sample of animal waste (ISS2, Table 1).

The spectrum of ARGs identified in the whole samples’ panel included many other molecules of clinical interest such as tetracyclines, mupirocin, bacitracin and fosfomycin. Tetracyclines are broad-spectrum antibiotics widely used to treat human infections, although the use of some molecules belonging to the first generations (e.g. doxycyclin) is being partially abandoned due to the increase of bacterial resistance (Rox et al. 2024). Moreover, in intensive farming systems, they are regularly administered to feedlots via medicated feeds to treat enteric diseases. We found tetracyclines resistant bacteria belonging to Bacillus cereus and Escherichia coli species, in many types of biosolid samples tested (Table 6). Among the other antibiotics identified in the full resistome (Figs. 1 and 2), mupirocin is a drug active against gram positive bacteria and its use is being proposed as a treatment for the decolonization strategies to reduce surgical site infections in orthopaedic surgery patients (Portais et al. 2024), while bacitracin is included in the list of medically important drugs by the WHO (World Health Organization 2019). In animal production, bacitracin only is allowed, limited to rabbit farming, to fight Clostridial enteritis. Finally, fosfomycin is a molecule often prescribed in combination therapy to limit the emergence of resistance, but it’s also being used as single drug to treat complicated urinary tract infections caused by E. coli. Moreover, intravenous fosfomycin administration is gaining interest for the treatment of severe infections caused by multidrug-resistant organisms (Vardakas et al. 2016). We have isolated bacteria resistant to mupirocin belonging to multiple species and strains of species widespread in the environment such as Brevibacillus laterosporus and Niallia circulans resistant to bacitracin, from WWTP sludges. The latter bacterial species is a soil bacterium used for enzyme production (Danilova and Sharipova 2020), which has also been implicated in cases of human disease (Berry et al. 2004; Boyett and Rights 1952; Castagnola et al. 1997; Gatermann et al. 1991; Krause, Gould, and Forty 1999; Logan, Old, and Dick 1985; Tandon et al. 2001; Wilde and Ruth 1988). Finally, the fosfomycin-resistant isolates identified belonged to three bacterial species, Lysinibacillus boronitolerans, Lysinibacillus capsici and Paenibacillus azoreducens, all from WWTP sludges-derived biosolids. The member of the species Lysinibacillum spp are soil bacteria (Ahmed et al. 2007) used in the treatment of certain plant diseases (Melnick et al. 2011) or as probiotics (Zeng et al. 2022), while the Paenibacillus azoreducens is described as a bacterium isolated from textile industry wastewater possibly deriving from its use in industrial applications as azo dye decolorization (Meehan, Bjourson, and McMullan 2001).

In this study we found AMR-associated determinants related with bacterial resistance to all amber classes of Beta lactamases, comprising Extended spectrum β-lactamases (ESBL) and carbapenems (Figs. 1 and 2). ESBLs-producing Escherichia coli are important cause of extraintestinal human infections, which are treated with carbapenems, and carbapenem-resistant Enterobacteriaceae are included in the WHO list of bacterial for which new antibiotics are needed, flagged with critical priority (World Health Organization 2017).

All the isolated ARB strains harboured multiple ARGs, further strengthening the possibility of ARBs spreading using organic waste streams (Table 7). Antibiotic-resistant bacteria with multiple resistance genes have been isolated from cattle manure expected to be spread on land (Abbas et al. 2023) and from Mixed Compost from domestic wastes (Furukawa, Misawa, and Moore 2018). Moreover, ARGs have been described using molecular techniques in several biosolids of different composition, including digestates from green wastes, manure and sludges, and in manure of different animal species (Abbas et al. 2023; Burch et al. 2022; Neher et al. 2023; Tripathi et al. 2023; Wolak et al. 2022). One study also investigated the transmission dynamics of ARGs from poultry litter to soil and to plants, providing indication of the effective transmission of ARGs to crops (Tripathi et al. 2023).

Many of the strains isolated in this study belonged to species of soil bacteria that were not described to cause human infections and therefore be treated with antibiotics or subjected to any antibiotic-mediated selective pressure, suggesting that an active transfer of ARGs occurred and highlighting the importance of the release of ARG in the environment where the latter can spread to multiple bacterial species sustaining their amplification cycle. Previous observations of the presence of antibiotic residues in the samples investigated in this study (Barola et al. 2024) may provide the rationale for the ARGs to be transferred and maintained in the bacterial populations by selective pressure. Our data also support the observation that reclaimed waters and water bodies downstream are recipients of ARGs (Donchev et al. 2024) and that the conventional treatments based on UV irradiation and H₂O₂ addition applied during the production of reclaimed waters, and the aerobic and anaerobic fermentation processes for the stabilization of the considered biosolids ready to be applied on land do not eliminate antimicrobial resistant bacteria, which in turn would be transferred to the amended and/or irrigated soils. In addition, in two of the three couples of reclaimed water samples taken at a WWTP, the number of contigs matching the AMR hits in the NCBI-NR protein database was higher in the outcoming samples than in the input water samples (Table 5), suggesting that ARGs have been apparently concentrated by the treatment applied.

The observed bacterial growth in presence of all the antibiotics tested and the isolation of multiple colonies of ARBs confirm that the functional metagenomics approach used is robust enough to provide a credible picture of the presence of ARBs in the tested samples.

To conclude, in this work we show that the pressures on human and animal health exerted using antimicrobial, reflects on the food safety by determining the presence of ARGs and ARBs in biowastes and reclaimed waters of current use in agricultural practices.

All of this induces the following considerations: based on circular economy-driven sustainable food production systems, the reuse of such biowastes is deemed as essential to guarantee food security and sustainability in outdoor farming. On the other hand, the use of antimicrobials in human therapeutics cannot be substituted/abated, as in the case of veterinary medicine, where a 50% reduction target in food production animals has been proposed within 2030, accounting to the Green Deal and Next Generation goals of the European Commission.

Owing to the above, the potential risk of ARGs via forages and vegetable food to humans and animals, due to the presence of an environmental reservoir may deserve more attention in terms of: a) origin of the biowaste associated with the hazard profile to be used; b) adequate treatments to abate ARGs and ARBs of priority impact on human and animal health in biowastes; and c) to start monitoring plans to describe time-trends of ARGs and ARBs in feed/food commodities, and their transfer to human and animal beings, as already done for contaminants and veterinary drug residues, according to the implementation of the proposed One Health action plan against AMR.

This work was supported by the Italian Ministry of Health RF-2019-12369714 grant, project: “Emerging food safety risks from microbial hazards deriving from anthropogenic pressures in agricultural settings”. The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

The authors have no relevant financial or non-financial interests to disclose.

Ethical Approval: not applicable

Consent to Participate: not applicable

Competing Interests: no competing interest to declare

Consent to Publish: not applicable

Federica Gigliucci performed lab experiments, material preparation, bioinformatics analyses and drafted the manuscript and all authors commented on previous version of the manuscript; Giorgia Barbieri contributed to lab experiments, material preparation and helped in the data analysis; Marie Veyrunes performed lab experiments; Paola Chiani contributed to the lab experiments. Manuela Marra performed genomic and metagenomic sequencing; Maria Carollo performed genomic and metagenomic sequencing; Arnold Knijn developed the ARIES webserver; Gianfranco Brambilla helped in the samples collection and material preparation, contributed to the conceptualization of the study and to the revision of the manuscript; Stefano Morabito conceived the study, performed part of the bioinformatics analyses and strongly contributed to revise the manuscript. All authors read and approved the final manuscript.

Data availability statements

The authors declare that: the sequencing data were deposited into the European Nucleotide Archive at EMBL-EBI under the accession number PRJEB80097; all data supporting the findings of this study are available within the paper and its Supplementary Information.

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Table 1

List of the biosolid samples collected in Italy in the period 2021–2022 and subjected to shotgun metagenomic sequencing.
Sample code	Type	Origin
ISS2	Manure and animal meal	Animal
ISS3	Bovine manure	Animal
223	Bovine manure	Animal
2752	Bovine manure	Animal
2749	Pig slurry	Animal
227	Poultry litter	Animal
2747	Poultry litter	Animal
2751	Sludge from food plant	Food industry
ISS5	Mixed compost	Household wastes, green wastes, and digestates
ISS5.2	Mixed compost	Household wastes, green wastes and digestates
ISS6	Mixed compost	Household wastes, green wastes and digestates
187	Mixed compost	Household wastes, green wastes and digestates
2746	Mixed compost	Household wastes, green wastes and digestates
220	Digestate	Household, green, WWTP sludges, and animal wastes
222	Digestate	Household, green and animal wastes, urban sludges
225	Digestate	Household, green and animal wastes, urban sludges
226	Digestate	Household, green and animal wastes, urban sludges
2753	Digestate	Household, green and animal wastes, urban sludges
2754	Digestate	Household, green and animal wastes, urban sludges
ISS18	Sludge from WWTPs	Inputs from urban and intensive animal farming settlements
188	Sludge from WWTPs	Inputs from urban and intensive animal farming settlements
189	Sludge from WWTPs	Inputs from urban and intensive animal farming settlements
190_22	Sludge from WWTPs	Inputs from urban and intensive animal farming settlements
191	Sludge from WWTPs	Inputs from urban and intensive animal farming settlements
2755	Sludge from WWTPs	Inputs from urban and intensive animal farming settlements

Table 2

List of the irrigation water samples collected in Italy in 2022 and subjected to shotgun metagenomic sequencing.
Sample	Type of water
1296	Incoming water to municipal WWTP (IN)
1550	Incoming water to municipal WWTP (IN)
2210	Incoming water to municipal WWTP (IN)
1297	Outcoming water from municipal WWTP (OUT)
1551	Outcoming water from municipal WWTP (OUT)
2211	Outcoming water from municipal WWTP (OUT)
1299	Surface water from irrigation canals
1301	Surface water from irrigation canals
1552	Surface water from irrigation canals
1554	Surface water from irrigation canals
2212	Surface water from irrigation canals
2213	Surface water from irrigation canals
1298	Surface water from rivers
1553	Surface water from rivers

Table 3

Biosolid samples and antibiotics included in the phenotypic assay of antibiotics resistance. The MIC used for each of the considered antibiotics were derived from the scientific literature.
Sample code	Antibiotics¹	No. contigs²	MICs (µg/ml)	References
2746	DPT	226	4	(Jiang et al. 2022; Peng et al. 2005; Tran, Munita, and Arias 2015)
	TET	66	200
2747	DPT	187	4	(Jiang et al. 2022; Peng et al. 2005; Tran, Munita, and Arias 2015)
	TET	31	200
2751	MUP	109	64; 512	(Patel, Gorwitz, and Jernigan 2009)
2755	TET	27	200
	MUP	146	64; 512	(Antonello et al. 2021; Patel, Gorwitz, and Jernigan 2009; Peng et al. 2005)
	FO	1	64; 256
ISS2	DPT	85	4	(Jiang et al. 2022; Peng et al. 2005; Tran, Munita, and Arias 2015)
	TET	14	200
ISS3	DPT	116	4	(Jiang et al. 2022; Tran, Munita, and Arias 2015)
	BA	51	256	(Manson et al. 2004)
ISS6	BA	137	256

¹DPT= daptomycin, TET = tetracycline, MUP = mupirocin, FO = fosfomycin, BA = bacitracin

²Number of contigs aligning on the hit corresponding to this antibiotic in the NCBI-NR database

Table 4

Number of contigs from the assembly of the different metagenomics sequencing of biosolids samples aligning to hits of the "Resistance to antibiotics and toxic compounds" of the SEED class in the NCBI-NR database.
Sample code	Type of matrix	No. of contigs
ISS_2	Animal waste	856
ISS_3	Animal waste	1515
223	Bovine manure	2395
2752	Bovine manure	2521
222	Digestate	1831
226	Digestate	2032
220	Digestate	2336
2753	Digestate	3026
225	Digestate	6288
2754	Digestate	6508
187_19	Mixed compost	1963
ISS_6	Mixed compost	2105
ISS_5.2	Mixed compost	2148
2746	Mixed compost	3344
ISS_5	Mixed compost	3661
2749	Pig slurry	4894
2747	Poultry litter	1590
227	Poultry litter	1853
2751	Sludge from food plant	2148
190	Sludge from WWTPs	84
191	Sludge from WWTPs	186
189	Sludge from WWTPs	1953
ISS_18	Sludge from WWTPs	2573
2755	Sludge from WWTPs	4220

Table 5

Number of contigs from the assembly of the different metagenomics sequencing of water samples aligning to hits of the "Resistance to antibiotics and toxic compounds" of the SEED class in the NCBI-NR database.
Sample	Type of water	No. of contigs
1296	Incoming water for reuse at municipal WWTP	1274
2210	Incoming water for reuse at municipal WWTP	2719
1550	Incoming water for reuse at municipal WWTP	2730
2211	Reused water from municipal WWTP	1460
1297	Reused water from municipal WWTP	1484
1551	Reused water from municipal WWTP	9363
1299	Surface water from irrigation canal	1404
1554	Surface water from irrigation canal	1438
2212_B	Surface water from irrigation canal	1464
2213_B	Surface water from irrigation canal	1511
1301	Surface water from irrigation canal	1982
1552	Surface water from irrigation canal	2460
1298	Surface water from river	1892
1553	Surface water from river	2503

Table 6

Bacterial species isolated from the biosolid samples: MCO = Mixed Compost; WWTP = WasteWater Treatment Plant.
Bacterial isolate	Sample of isolation	Bacterial Species	Antibiotic concentration used on solid medium (µg/ml)
2746_D1	MCO	Clostridium sartagoforme	4
2747_D1	Poultry Litter	Enterococcus faecium	2
ISS2_D1	Manure + animal meal	Bacillus licheniformis	8
2746_T1	MCO	Bacillus cereus	12.5
2747_T1	Poultry Litter	Escherichia coli	12.5
2755_T1	WWTP sludge	Bacillus cereus	100
ISS2_T1	Manure + animal meal	Paenibacillus odorifer	50
2751_M1	WWTP sludge	Bacillus cereus	64
2751_M2	WWTP sludge	Clostridium amylolyticum	512
ISS3_B1	Bovine and Equine manure + green wastes	Brevibacillus laterosporus	256
ISS6_B1	MCO	Niallia circulans	256
2755_F1_G+	WWTP sludge	Lysinibacillus boronitolerans	128
2755_F2_G+	WWTP sludge	Lysinibacillus capsici	16
2755_F3_G-	WWTP sludge	Lysinibacillus boronitolerans	128
2755_F4_G-	WWTP sludge	Paenibacillus azoreducens	16

Table 7

ARGs identified in the bacterial strains isolated from biosolid samples.
	Antibiotic-Resistance Genes identified in the bacterial genomes*
Sample Codes	MLS	TET	FO	VAN	β-LAC	AGs	CHL	Rmp	BA	TMP	FQ	MDR
2746_D1	mphB	-	fosB	vanRM	bla1	-	catB	-	-	-	-	-
2747_D1	ermB; msrA; lnuB; isaE	-	-	-	-	ant6-la; aac6-li	-	-	-	-	-	efrA; efrB
ISS2_D1	ermD	-	-	-	blaP	-	-	rphB	bcrA, bcrB; bcrC	-	-	-
2746_T1	-	tetL	fosB	vanRM	bla1;	-	-	-	-	-	-	-
2746_T1	-	tetL	fosB	vanRM	bcII	-	-	-	-	-	-	-
2747_T1	lsaA; mphD	tetL; tetM	fosB	vanRM vanZF	bla1; ampC; ampH	-	-	-	bacA	dfrE	patA	efrA; efrB; emrA:emrB; emrR; emeA msbAmdtNmdtOmdtP; mdtE; mdtF;mdtA;mdtB;mdtC
2755_T1	mphA	tetL	fosB	vanRMvanZF	bla1;	-	-	-	-	-	-	-
2755_T1	mphA	tetL	fosB	vanRMvanZF	bcII	-	-	-	-	-	-	-
ISS2_T1	msrD; mefA; lnuB; mphB; ermB; isaE	tetL;	-	-	-	aph3-III; ant9-la	catA	-	-	dfrG	-	-
ISS2_T1	msrD; mefA; lnuB; mphB; ermB; isaE	tetM	-	-	-	aph3-III; ant9-la	catA	-	-	dfrG	-	-
2751_M1	-	-	fosB	vanRM	bla1;	ant6-la	-	-	-	-	-	-
2751_M1	-	-	fosB	vanRM	bcII	ant6-la	-	-	-	-	-	-
2751_M2	msrD; mefA; lsaB; mphbmel	tetL; tetB	-	-	-	-	-	-	-	-	-	-
ISS3_B1	cfrA; lsaB	-	-	vanYF	-	-	-	-	-	-	-	-
ISS6_B1	-	-	-	vanYF; vanSM;vanRM	-	aadk-Pm	-	-	-	-	-	-
2755_F1_G	lsaB	-	-	-	-	-	-	-	-	-	-	-
2755_F2_G	lsaB	-	-	-	-	-	-	-	-	-	-	-
2755_F3_G-	-	tetL	-	-	-	-	-	-	-	-	-	-
2755_F4_G-	lsaB	tetB	-	-	-	-	-	rphB	-	-	-	-

*MLS: Macrolides; TET: Tetracycline; FO: Fosfomycin; VAN: Vancomycin; β-LAC: Beta-lactams; AGs: Aminoglycoside; CHL: Chloramphenicol; RMP: Rifampicin; BA: Bacitracin; TMP: Trimethoprim; FQ: Fluoroquinolone; MDR: Multidrug resistance

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Characterization of the resistome and of antibiotic-resistant bacteria in top soil improvers and irrigation waters devoted to food production: a case-study from Italy

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Abstract

Figures

Introduction

Materials and methods

Sampling

DNA extraction and shotgun metagenomic sequencing

Metagenomic analysis

Phenotypic testing of antibiotics resistance in biosolid samples

Whole genome sequencing and genomic analysis of the resistant bacterial isolates

Results

Antibiotic resistance determinants in biosolid and water samples

Characterization of the antibiotic-resistant bacterial isolates

Discussion

Declarations

References

Tables

Supplementary Files

Status:

Version 1