Recovery of a WWTP fertilizer added with plant growth promoting bacteria (PGPB). Relationship with antibiotic resistance in the "One Health" environment with an oak species: Quercus ilex

Recovery of a WWTP fertilizer added with plant growth promoting bacteria (PGPB). Relationship with antibiotic resistance in the "One Health" environment with an oak species: Quercus ilex | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Recovery of a WWTP fertilizer added with plant growth promoting bacteria (PGPB). Relationship with antibiotic resistance in the "One Health" environment with an oak species: Quercus ilex Vanesa M. Fernández-Pastrana, Daniel González-Reguero, Marina Robas-Mora, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8738943/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Soil degradation is a critical problem in Spain, aggravated by intensive agricultural practices, urbanisation and natural phenomena such as forest fires. This study addresses soil regeneration through the valorisation of waste from wastewater treatment plants (WWTP) and its supplementation with plant growth-promoting bacteria (PGPB). Quercus ilex , a species of holm oak of significant importance in Spain, was used as a model to evaluate the effects of these treatments on plant growth and soil health. In this research, plant growth assays, metabolic and taxonomic diversity analyses, nutritional and antibiotic resistance evaluations were carried out, all providing significant results. Treatments with EDAR waste, both sterilized and non-sterilized, significantly improved the growth of Quercus ilex , showing highly positive effects after PGPB supplementation, evaluating in this study Bacillus pretiosus (C1) and Pseudomonas agronomica (C2) strains, showing improvements in nutrient uptake and stress tolerance. Moreover, understanding what is happening requires considering microbial diversity, which plays a crucial role in understanding soil functions and transformations. The microbial diversity analysis revealed high diversity in all treatments, with changes observed in the composition of the soil microbial community. Additionally, treatments with PGPB did not increase antibiotic resistance in soil microbial communities, which is fundamental within the context of the " One Health " approach pursued throughout this work. Bacillus pretiosus Pseudomonas agronomica Quercus ilex soil regeneration WWTP PGPB waste valorization One Health microbial diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Quercus ilex , Commonly known as holm oak, it is one of the most emblematic trees of the Iberian Peninsula. It plays a fundamental role in the ecosystem of the Iberian Peninsula and in the cultural landscape of the country. This species belongs to the Fagaceae family and is known for its evergreen foliage and resistance to harsh environmental conditions. One of the highlights of the holm oak is its role in the conservation of biodiversity. The dense structure and composition of the holm oak canopy provide shelter and food for mammals, birds, reptiles and invertebrates, many endemic to the Mediterranean region [ 1 ], thus contributing to the rich biological diversity of Mediterranean ecosystems [ 2 ] In addition, the holm oak also plays a crucial role in Spain's rural economy. It has been widely used as a source of multiple resources, such as wood and acorns. It also plays a fundamental role in soil conservation and climate change mitigation [ 3 ]. The holm oak is a species highly adapted to long periods of drought and arid soils [ 4 ]. Its adaptability is due to its characteristic morphology. It has a deep root system that allows it to access underground water sources. It also plays a key role in soil conservation by preventing soil erosion, stabilising the substrate and improving water infiltration, a crucial factor for the sustainable sustainability of holm oak and pasture forests [ 5 ]. Likewise, its physiology allows it to have a high plasticity in the use of water and nutrients [ 6 ]. It is an evergreen tree with tough leathery (sclerophyllous) leaves that are oval in shape and commonly with serrated edges. The adaxial surface of the leaf is dark green, while the abaxial surface is whitish with the presence of trichomes, which helps to reduce water loss through perspiration. The morphology of its roots helps to stabilize the soil and prevent erosion. The holm oak currently faces a series of threats. The loss of forest due to urbanization, intensive agriculture, industrial pollution, pests or forest fires represent a serious concern for the conservation of this species and Mediterranean ecosystems in general [ 1 ]. One of the main problems is soil degradation, experiencing a significant decrease in its quality and ability to sustain plant and animal life. Soil degradation involves the loss of organic matter, nutrients, and the soil structure itself, resulting in a decrease in biodiversity and microbial activity, as well as the soil's ability to retain water and support plant life [ 7 ]. This impact has been increasing over the years, as can be seen in Fig. 1 , posing a much more serious problem over time. As mentioned above, fires are one of the most notorious causes of holm oak loss, which has led to large volume losses in forests around the world. A fire leaves devastating consequences, but one of the most worrying aspects in the ecology of forest fires is their effect on the regeneration of affected soils. It is estimated that around 30% of soil organic biomass is lost during a high-intensity fire, which can lead to a decrease of up to 80% in microbial activity and a loss of biodiversity in the soil. The natural regeneration of burned soils can take decades, and even centuries, depending on various factors such as climate, soil type, and the severity of the fire [ 9 ]. To deal with this problem, different aspects are addressed. One of these approaches is the use of organic waste as fertilizer [ 10 ]. Waste recovery helps to reduce, reuse and reuse products that are initially discarded [ 11 ]. Among the wide variety of fertilizers, we find the so-called biofertilizers. These are organic products that contain live microorganisms that help fix atmospheric nitrogen, solubilize phosphorus, and improve nutrient availability in the soil. These fertilizers improve soil health by improving soil structure and increasing microbial activity, which at the same time improves long-term fertility and contributes to sustainable agriculture [ 12 ]. In addition, biofertilizers reduce the risk of environmental pollution by not releasing harmful chemicals into the environment. On the other hand, chemical fertilizers are synthetic products manufactured through industrial processes, which can have long-term negative effects, such as soil degradation and water pollution due to excess nutrients that are not absorbed by plants [ 13 ]. Biofertilizers stand out for their potential biotechnological application, since the bacteria they carry are the so-called plant growth promoting bacteria (PGPR), microorganisms that colonize the rhizosphere of plants and improve their growth through different mechanisms [ 14 , 15 ]. These mechanisms include the production of phytohormones, phosphorus solubilization, nitrogen fixation, and the production of siderophores that improve iron availability [ 15 – 18 ]. In addition, PGPBs can produce 1-aminocyclopropane-1-carboxylate deaminase (ACCd), an enzyme that helps plants tolerate abiotic stress. In the present study, two strains of PGPB: Bacillus pretiosus (C1) and Pseudomonas agronomica (C2), two bacteria known to use as PGPB [ 19 ]. The present study also seeks to show whether the addition of a bacterial strain to an environment is capable of modulating its response to antibiotic resistance. This phenomenon represents one of the greatest challenges in the agricultural field, in the horizontal dissemination of antibiotic resistance. Bearing in mind that the release of antibiotics not only affects pathogens, but also the microbial communities in the soil, altering their balance and encouraging the emergence of resistance. Antibiotic resistance genes can be transferred between bacteria through horizontal mechanisms such as conjugation, transformation, and transduction, facilitating the rapid spread of these resistances in different environments [ 20 – 22 ]. The approach One Health recognises the interconnectedness between human, animal and environmental health, underlining the need for integrated strategies to combat antibiotic resistance. In this context, it is crucial to develop and implement sustainable agricultural practices that do not contribute to this global problem. The use of biofertilizers, rather than chemical fertilizers and pesticides, represents a promising strategy to reduce the selection pressure of resistant bacteria and promote a healthy microbiological balance in agricultural soils [ 23 ]. A useful tool in soil condition analysis is the cenoantibiogram, a technique that allows the evaluation of antibiotic resistance of bacteria present in soils [ 24 ]. This analysis is essential to identify and monitor the presence of antibiotic resistance genes in soil microbial communities, which is essential to prevent the spread of resistance that can affect human, animal and environmental health [ 22 , 25 , 26 ]. This knowledge helps to ensure that the agricultural practices used do not contribute to the spread of antibiotic resistance, while maintaining the integrity and security of the ecosystem. This analysis allows not only to improve the efficiency of biofertilizers, but also to ensure that interventions in the soil are safe and sustainable, aligning with the objectives of One Health . On the other hand, for the evaluation of the impact of the addition of a bacterial strain to a medium and its follow-up, metagenomics based on amplicon sequencing has been shown to be a robust and useful technique. This tool allows us to carry out population and phylogenetic diversity studies based on the analysis of the coding gene for the 16S rRNA . Its application in the field of ecology has also been called environmental genomics, ecogenomics or community genomics [ 27 – 30 ]. Materials and methods 2.1. Bacterial strains The bacterial strains tested were isolated from the rhizosphere of soil free of and from the rhizosphere of Medicago sativa. They have been chosen from the research team in (GIR - MICROAMB) "Environmental Bacterial Microbiology" of the Faculty of Pharmacy of the San Pablo CEU University. These bacteria have a biotechnological interest due to their plant growth promoting capabilities (PGPB). These strains are also free of transmissible antibiotic resistance genes and virulence determinants [19]. Table 1. PGPR characteristics of the strains tested. AIA: production of 3-indoleacetic acid; ACCd: production of 1-aminocyclopopane-1-carboxyl deaminase; P/A: presence (+) / absence (-). WGS: Whole Genome Dry (Illumina®). Identification (WGS) Insulation AIA (μg/mL10 -1 ) ACCd (p/a) Siderophores (cm) Bacillus pretiosus (C1) Medicago sativa 5.61± 0.03 + + Pseudomonas agronomica (C2) Free floor 5.85± 0.09 + + 3.2. Experimental Design The biological tests were carried out on seedlings of two holm oaks, Quercus ilex (L.), from the shouth of Guadarramma with the register number FS-45/08/28/003. The seedlings were supplied by IMIDRA (Madrid Institute for Rural, Agrarian and Food Research and Development). These seedlings come from seeds of native plants of the Community of Madrid whose purpose is to repopulate forests to avoid genetic contamination. For the growth test under laboratory conditions, 10 cm x 8 cm seedbeds with 35 18 cm high alveoli were used, placed in forest trays or leachate trays. As a substrate for the development of the biological assays, a sample of oak groves was used in the municipality of Navacerrada, in the foothills of the Sierra de Guadarrama (GPS 21º92 ́N 5º03 ́W, Madrid, Spain). Three irrigation matrices were established that included water (W), WWTP organic waste (EDAR) and sterilized WWTP organic waste (EDAR_ST). Three different biological treatments were applied to these matrices: without inoculum (C0 control), supplemented with the C1 strain (Bacillus pretiosus) and with the C2 strain (Pseudomonas agronómica). In total, 9 treatments were carried out resulting from the combination of irrigation matrices and biological treatments. For each treatment, 35 replicates of Quercus ilex seedlings were performed. 3.3 Preparation of Bacterial Suspensions and Biofertilizer 3.3.1. Preparation of bacterial suspensions Starting from pure cultures of the C1 (Bacillus pretiosus) and C2 (Pseudomonas agronomica) strains on nutrient agar from Condalab® (Madrid, Spain), a bacterial culture was prepared in liquid LB medium (Oxoid Limited, Wade Road, Basingstoke, England). After 48h of growth, it was verified by using the semi-quantitative method of submersible paddles UriCult Trio that the bacterial density was approximately 10 8 u.f.c. ml -1 . This process ensured the standardization of the bacterial inoculum for its subsequent application in the final volume of the irrigation matrix. 3.3.2. Preparation of the irrigation chemical matrix The waste from WWTP (Industrias Cárnicas Villar, S.A., Soria, Spain) has shown fertilizing activity, experimentally, at a dilution of 1/512 (V EDAR /V H2O ). For all the trials in this work, this dilution was used as the basis for the formulation of the biofertilizer (incorporation of inoculum). At the same time, and in order to eliminate the microbiota of the WWTP organic waste, an aliquot (EDAR_ST) was sterilized by autoclaving (121ºC, 20 min, 1atm). The physicochemical composition of the WWTP sludge can be consulted in Table 2. To eliminate the microbiota of the WWTP sludge, an aliquot of the same (EDAR_ST) was sterilized by autoclaving at 121ºC for 20 minutes at 1 atmosphere of pressure. Table 2. Physicochemical composition of the WWTP waste (Analysis carried out by LabAqua. Tests covered by accreditation ENAC nº109/LE 28). PARAMETERS METHODS RESULTS UNITS Physicochemical characteristics Conductivity at 20 °C A-F-PE-0015 Electrometry 1454 μS/cm Conductivity at 25 °C A-F-PE-0015 Electrometry 1612 μS/cm Biochemical Oxygen Demand (BOD5) A-F-PE-0002 Gauge method 3200 mgO2/L Chemical Oxygen Demand A-F-PE-0003 Digestion—Colorimetry 6720 mgO2/L Chemical demand for decanted oxygen A-F-PE-0003 Digestion—Colorimetry 4220 mg/L Nitrates A-F-PE-0010 Digestion <0.05 mg/L Kjeldhal Nitrogen A-F-PE-0007 Kjeldhal 296.7 mg/L pH A-F-PE-0010 Electrometry 6.5 U.pH. Suspended solids A-F-PE-0006 Gravimetry 3228 mg/L Toxicity PIT-F/0012 Bioluminescence assay with Vibrio fisheri 14 U.T. Majority cations Potassium A-D-PE-0025-ICP-OES 60.2 mg/L Anions Nitrates A-BV-PE-0001 HPLC—Conductivity <2.5 mg/L Orthophosphates Ca-R-PE-0011 Spectrometry 74.32 mg PO4/L Sulfates A-BV-PE-0001 HPLC—Conductivity 89.0 mg/L Sulfites A-F-PE-0040 Volumetry 4.5 mg/L 3.3.3. Biofertiliser preparation First, the bacterial suspensions of C1 (Bacillus pretiosus) and C2 (Pseudomonas agronomica) were prepared. To do this, they were grown in LB broth (Condalab®, Madrid, Spain) for 48 h (stirring 50 r.p.m., 25ºC ± 2ºC). The biofertilizer was prepared on a weekly basis to avoid non-specific transformations resulting from microbial metabolism (autochthonous microbiota and inoculum). 3 L of each treatment were prepared in each session and stored in refrigeration (4 °C). In order to standardize the microbial density to an equivalent of 0.5 McFarland (approximately 10 8 cfu. mL -1 ), 100 mL of the grown LB medium was added to 900 mL of the irrigation chemical matrix: water or the 1/512 dilution of EDAR/EDAR_ST, as appropriate and tested using Uricult TM (Liofilchem srl, Italy). 3.4 Growth test and conditions The experiment was carried out under controlled laboratory conditions in a phytotron (photoperiod of 11 hours of light and 13 hours of darkness, light intensity of 505 μmoles.m -2 . s -1 (white and yellow light), temperature 25ºC ± 3ºC and relative humidity 30% ± 5%). The irrigation regime was designed in order to emulate the field conditions and was carried out systematically with the treatments described above (EDAR/EDAR_ST, supplemented with strains C1 and/or C2), and water in the case of the control. The objective was to guarantee the relative humidity of the soil, without reaching saturation or waterlogging, which resulted in a weekly irrigation periodicity with experimental average volume of 85mL/alveolus. 3.5 Harvesting and biomass analysis The harvest was carried out one year after (October 2023 – October 2024). It was destructive and involved the extraction of the aerial and radical part of each plant. Additionally, from the root part, the rhizospheric fraction of the soil (2g per seedling) was obtained for the subsequent analysis of the edaphic communities. For the calculation of the total biomass, the crop was left to dry at room temperature (22ºC ± 2ºC) and weighed after one week (dry weight, g). For nutritional analysis, the leaves were separated from the stem. To ensure sufficient biomass for the analysis (15 g), each replicate (n=3) was made up of the leaves or stem of several individuals subjected to the same treatment (replications). It was packaged and stored in refrigeration (4 ºC), for shipment to the analysis laboratory (Rock River Labs Spain, Lalín, Pontevedra, Spain). The nutritional analysis was carried out 24 hours after harvest. 3.6. Analysis of soil microbial communities For the extraction of the rhizospheric communities, 1g of soil was collected from each seedling. For each treatment, in order to make triplicates, the soil of 11 seedlings was joined and homogenized, obtaining three triplicates per treatment of 11g per triplicate. Subsequently, the procedure described in García-Villaraco et al. (2009) was followed, as modified. To do this, 2 g of soil was suspended in 20 mL of sterile saline solution (0.45% NaCl) and homogenized with an Omni-Mixer homogenizer at 16,000 r.p.m. for two minutes. It was then centrifuged at 690g for five minutes with a Hettich Zentrifugen centrifuge model Mikro 22R. 3.6.1. Study of the metabolic functionality of the microbial community: Biolog ECO® From the microbial suspension obtained and the dilutions selected according to the previous results, the 96 wells of the Biolog Eco plates® (31 different carbon sources and one target (water), per tricplice) were loaded with 135 μL per well. The plates were incubated for 94 hours at 25ºC, and their absorbance was measured at 595 nm, 630 nm and 492 nm every 24 hours using the Asys UVM340 plate reading equipment and Micro WinTM V3.5 software. With the results of the absorbance measurements, the values were corrected by subtracting the target (corrected absorbance). Then, the mean of the corrected absorbance of all wells was calculated as the mean of the 31 (Biolog Eco®) for each replicate. The value of AWCD (Average Well Color Development) was represented against the incubation time to obtain the growth curves of the microbial community in the wells of the plate. In these curves, the incubation time at which the growth of the microorganisms was initiating the stationary phase was chosen for subsequent multivariate analyses. Additionally, with the corrected absorbance values of the chosen incubation time, the metabolic (functional) diversity of each sample was calculated using the Shannon-Weaver diversity index. H(m)= -∑ qi log2qi; where qi= n/N; where n is the corrected absorbance (AWCD) of each well and N is the total absorbance of all wells. 3.6.2. Study of community antibiotic resistance: Study of the Cenoantibiogram From the soil extract obtained in saline solution (NaCl 0.45%), it was verified that the density of viable microorganisms was >10 8 cfu mL -1 (optical density (OD) = 0.5 McFar-land). Mueller-Hinton agar (Condalab®, Madrid, Spain) was seeded and MIS was evaluated using antibiotic strips from ε test, in triplicate, for the following antibiotics: amoxicillin (AML), amoxicillin-clavulanic acid (AUG), piperacillin (PP), piperacillin-tazobactam (TZP), imipenem (IMI), imipenem-EDTA (IMD), ciprofloxacin (CIP), nalidixic acid (NA), trimethoprim-sulfomethoxazole (TS), cefotaxime (CTX) and cefpirome (CR) (BioMérieux®, Marcy l'Etoile, France). (BioMérieux®, Marcy l'Etoile, France). The plates were then incubated according to the manufacturer's instructions (25°C ± 2°C). For the quantification of the minimum inhibitory concentration (MIC), the most restrictive halo was used as a reference. 3.6.3 Study of microbial taxonomic diversity: Metagenomics. The composition and structure of the sampled microbial communities was evaluated by amplification and sequencing of the variable regions V3-V4 of the 16S rRNA gene. Amplification was performed after 25 cycles of PCR. In this procedure, positive (CM) and negative (NC) controls were used to ensure quality control. The positive control is a mock community and was processed in the same way as the samples. The libraries obtained were sequenced using Illumina Miseq (300×2). The analysis was commissioned to the company Microomics® S.L. (Barcelona, Spain), who analyzed the taxonomy of the extracts according to the protocol shown in Figure 2. The sequences were deposited in the NCBI repository under the accession number of BioProject PRJNA1425473. 3.7 Statistical analysis To determine the existence of variations in biometrics depending on the type of irrigation (matrix: EDAR, EDAR_ST and water control; and biological treatment: C0, C1 and C2), a one-factor ANOVA test was performed, where the dependent variable was the dry weight of the biomass (g) and the independent variables were irrigation. As a result of this analysis, it is possible to discriminate whether there are statistically significant variations in plant growth (biomass production). To conclude which risk justified such variations, a post-hoc Duncan analysis of pairwise comparison between the means was performed. Following the same procedure, a first ANOVA was carried out to find out, of all the nutritional parameters analyzed, which ones suffered some variation in their mean depending on the type of irrigation. Only for those that showed statistically significant differences (p-value < 0.01), a Duncan graph and post-hoc analysis was performed, which allowed us to know which treatment (irrigation matrix and biological treatment) explained such deviations from the mean. Finally, a multivariate study of principal component analysis (PCA) was carried out as an exploratory technique of trends between the different variables analyzed (biometrics and nutritional) according to the type of biological treatment (PGPB strains and control without inoculum) and chemical (fertigation with EDAR residue and EDAR_ST, and control irrigated with water). In the same way, an PCA was performed using the MICs of the rhizospheric communities in each of the treatments as analysis variables. All these analyses were carried out using the SPSS v.29.0 program (IBM Corp, Armonk, NY, USA). Results The main findings are presented below in order to facilitate the monitoring and interpretation of the most relevant results. Only those variables that, after ANOVA, underwent considerable variations depending on the type of treatment, were graphed. we will see the most relevant findings. 4.1. Length and weight test Figure 3 shows the results of stem length ( Figure 3A ) and dry weight of biomass after harvest ( Figure 3B ). As can be seen, after different treatments, notable trends in growth are revealed. Treatments with WWTP sludge (both sterilized and non-sterilized) exhibit a greater impact on stem length, reaching longer lengths. Treatments, both EDAR and EDAR_ST, with bacterial inoculum (C1 and C2) not only show greater growth in both variables compared to treatments with conventional irrigation (W, water) but also compared to their controls without inoculum They are the ones with the most growth in length. It should be noted that treatment with EDAR_ST inoculated with C2 is the one that presents the greatest significant development compared to the rest of the treatments. This indicates that both the WWTP sludge and its sterilized form can be significantly more effective than treatment with water alone. 4.2 Nutritional analysis 4.2.1 Protein composition in the leaf Figure 4 shows the variables of the group of proteins that varied statistically according to the fertigation treatment: crude protein (%), soluble protein (%), total amino acids (% dry mass, DM), available crude protein (%), histidine, lysine and methionine. Regarding Figure 4, which represents the amount of protein, it is interesting to see how the relative protein content of the leaves is increased in those treatments whose biometric variables were also increased (Figure 3 of biometrics). In this case, it should be noted that treatments irrigated with WWTPs have a statistically significant higher protein content. In the same way, it can be observed that treatments irrigated with EDAT_ST, together with bacterial inoculums (C1 and C2), represent a significant increase in protein content compared to their own control and conventional irrigation with water. 4.2.2 Analysis of the main components of the leaf The two variables that best explain the model were projected in the two-dimensional plane to study possible groupings between treatments: As we can see in Figure 5, the addition of the C2 strain induces changes of nutritional improvement in the plant that hosts them, regardless of the matrix that carries them. The principal component analysis study yielded two components that explain the system with 75.98% of the variance explained. 4.2.3 Protein composition in the stem Figure 6 shows the variables of the group of proteins that varied statistically according to fertigation treatment: crude protein (%), soluble protein (%), total amino acids (% dry mass, DM), available crude protein (%), histidine, lysine and methionine. As in the case of the leaf, the relative amount of protein in the stem is also increased in treatments irrigated with both EDAR and EDAR_ST, highlighting those that combine chemical treatment with biological treatment (C1 and C2). Both with respect to conventional irrigation with water, and intra-treatment with respect to its controls. 4.2.4 Carbohydrate composition in the stem Figure 7 shows the variables of the carbohydrate group that varied statistically according to fertigation treatment: starch (%) and lignin (%). In the ANOVA test performed with Duncan's statistic, we can see how there is a significant positive difference in the three treatments. It is observed how all of them grow with respect to their controls. The results in the composition of lignin are striking, for which there are significant notable differences. 4.2.5 Analysis of the main components of the stem The two variables that best explain the model were projected in the two-dimensional plane to study possible groupings between treatments: Analyzing the principal component analysis, we observed that the samples are aggregated by the influence of the different biological treatments. Therefore, both the addition of C1 ( Bacillus pretiosus ) and C2 ( Pseudomonas agronómica ) induce changes that improve the nutritional quality of the plant that hosts them. The principal component analysis study yielded two components that explain the system with 68.67% of the variance explained. 4.3 Diversity analysis The metabolic diversity of each sample was calculated, using the Shannon-Weaver diversity index, and the differences were analyzed at 144 hours. This index indicates the variability, metabolic in the case of the present work, that occurs within an ecosystem. In most natural ecosystems it varies between 0.5 and 5, although its normal value is between 2 and 3; values below 2 are considered low in diversity and above 3 are high in species diversity. Table 3 . Functional diversity (Shannon index, H ́) of the soil microbial community at 144h of incubation. WC0 WC1 WC2 EDARC0 EDARC1 EDARC2 EDAR_STC0 EDAR_STC1 EDAR_STC2 H' 144h 4.78±0.16 4.60±0.18 4.71±0.17 4.19±0.19 4.75±0.16 4.28±0.18 4.75±0.16 4.54±0.11 4.51±0.19 As can be seen in Table 3, in the analysis of the metabolic diversity of the rhizosphere, all treatments have a high metabolic diversity regardless of the treatment carried out and remain on average at an H' of 4.45, so it is concluded that there are no significant differences between the different treatments. 4.4 Cenoantibiogram The principal component analysis study yielded two components that explain the system with 88.89% of the variance explained. Component 1 represents 77.73% of the variance and component 2 11.16%. Figure 9 shows the analysis of principal components, we observe a clear segregation at the level of biological treatment with strains C1 ( Bacillus pretiosus ) and C2 ( Pseudomonas agronomica ) and on the other hand the aggregation of the control treatment. This means that the incorporation of PGPB strains could be responsible for reducing the MIC of the antibiotics studied, according to the distribution of the cases explained with respect to the distribution of antibiotics in the load factor graph. 4.5 Taxonomic diversity Tables 4 and 5 represent a bioinformatic analysis in relation to the quality of the sampling. After the first sampling, 15,477 phenotypes were detected, demonstrating great diversity, likewise, singlets and doublets were eliminated to avoid noise in the data because these are rare phylotypes (present only once or twice). In addition, they were subsampled to match the sample size ensures that comparisons between samples are not biased by differences in the total number of readings. Table 4. General descriptive metrics of the metagenomic dataset, including the total number of samples, identified features, and overall frequency values. Metrics Value No. of samples 27 No. of features 15477 Total frequency 775742 Table 5. Descriptive statistical summary of frequency distribution across metagenomic samples, including minimum, quartiles, median, maximum, and mean values Type Frequency Frec. Minimum 15541 Q1 24620 Frec. Medium 28715 3 33638 Frec. Maximum 42292 Frec. Media 28731,18 4.5.1 Relative abundances at the gender level. Figure 10 below shows the relative abundances of different microbial genera for different treatments. Figure 10 shows how the treatments have a significant impact on the composition of the microbial community at the genus level, with a high abundance of Pseudomonas in the treatments with EDARStC2, EDARC2 and WC2 that correspond to the treatments that were inoculated with our strain Pseudomonas agronomica (C2). In the case of Bacillus pretiosus (C1), it is more present in treatments with EDARC1, WC1 and, to a lesser extent, also in EDAR_STC1. It is therefore deduced that there is no variation with respect to the taxonomic structure, the bacterium that is added increases the genetic diversity of the same taxon without eliminating any others. 4.5.2 Beta diversity Figure 11 shows a principal component analysis (PCA) based on the Unweighted UniFrac distance, which represents the distribution and variation trends of the taxonomic diversity of the samples according to the chemical and biological treatments applied. Figure 11 shows a grouping by chemical treatment. However, a clear segmentation is also observed by the treatment with the bacterium (C2). So there is a clear influence on the behavior of the soils depending on the bacteria that is added. Gene prediction A functional analysis based on KEGG annotations was carried out to characterize the metabolic potential of the microbial communities present in the different treatments: chemical fertilizer (Chemical_Fertilizer), ORGAON®PK biofertilizer (ORGAON_PK), its sterilized version (ORGAON_PK_ST) and water treatment (Water). Functional profiles were grouped into major metabolic pathways and visualized using relative abundance plots. Overall, the microbiomes of all treatments presented a similar functional profile, characterized by a high representation of pathways associated with amino acid metabolism, carbohydrate metabolism, energy metabolism, and cofactor and vitamin metabolism. To a lesser extent, functions related to the biosynthesis of secondary metabolites, lipid metabolism, nucleotide metabolism and degradation of xenobiotic compounds were observed. A differential analysis of KEGG functions among microbial communities was performed using the nonparametric Kruskal–Wallis test. This analysis revealed the absence of statistically significant functions after correction by multiple tests (FDR < 0.05). In order to reduce the dimensionality of the dataset and prioritize those functions with greater discriminative capacity between treatments, a Random Forest classification model was applied to the KO functions predicted by PICRUSt2. This approach made it possible to identify the most relevant functional genes for differentiation between experimental groups. From the functions noted by COG categories, a relatively homogeneous functional distribution was observed among the treatments. The most abundant functions included those related to amino acid and carbohydrate transport and metabolism, energy production and conversion, cell envelope biogenesis, and signal transduction. The categories "unknown function" and "general prediction of function" were also highlighted, reflecting the presence of genes with incomplete or poorly characterized annotation. The least represented categories were those related to RNA processing and modification, post-translational modification, intracellular trafficking, defense mechanisms and biosynthesis of secondary metabolites. No drastic differences were observed between treatments, although certain functional profiles showed subtle variations in the relative abundance of pathways associated with regulatory and structural processes (such as DNA replication and repair or ribosomal translation). A classification analysis was applied using Random Forest using the KO functions predicted by PICRUSt2, with the aim of identifying the most relevant functional genes for discrimination between treatments. The results show that the model reached an overall classification error (OOB error) of 25.9%, and allowed the identification of a set of KO functions with a high contribution to the accuracy of the model, measured as "Mean Decrease Accuracy". A different genetic functional profile was predicted for each treatment. In the water treatment, functions associated with respiration processes and carbon management were mainly observed, which reflects the basal metabolic activity of the microbiota in the absence of external amendments. These functions suggest a profile adapted to conditions of low nutrient availability, where energy generation mechanisms and optimization in the use of simple compounds predominate. Table 6 . Functions predicted to be of greatest importance in the Random Forest model according to the treatment applied. The KO genes identified as most relevant for the classification of treatments by the Random Forest algorithm are shown, along with their functional function annotated in KEGG. Treatment KO Predicted Function (KEGG) WC0 K05585 NAD(P)H-quinone oxidoreductase subunit N [EC:7.1.1.2] K08698 carbon dioxide concentrating mechanism protein CcmM WC1 K18007 NAD-reducing hydrogenase small subunit [EC:1.12.1.2] EDARC0 K05898 3-oxosteroid 1-dehydrogenase [EC:1.3.99.4] EDARC2 K14742 tRNA threonylcarbamoyladenosine biosynthesis protein TsaB K01921 D-alanine-D-alanine ligase [EC:6.3.2.4] K01761 methionine-gamma-lyase [EC:4.4.1.11] K15514 3,4-dehydroadipyl-CoA semialdehyde dehydrogenase [EC:1.2.1.77] K02613 ring-1,2-phenylacetyl-CoA epoxidase subunit PaaE K00507 stearoyl-CoA desaturase (Delta-9 desaturase) [EC:1.14.19.1] EDARSTC1 K16869 octanoyl-[GcvH]:p rotein N-octanoyltransferase [EC:2.3.1.204] K07219 Putative Molybdopterin Biosynthesis Protein K16781 tetratricopeptide repeat protein 8 EDARSTC2 K18793 beta-lactamase class D OXA-23 [EC:3.5.2.6] K14750 Ethylbenzene dioxygenase ferredoxin component In treatments with WWTP residue, both in its natural and sterilized form, a greater functional diversity linked to the metabolism of complex compounds was detected, including transformation pathways of lipids, amino acids and aromatic compounds. In addition, in the treatments with sterilized WWTPs, functions related to cofactor biosynthesis, protein regulation and antibiotic resistance emerged, suggesting a specific adaptation of the inoculated microbial communities to this environment. Discussion In the present work, the incorporation of a biofertilizer on the growth, nutritional quality and associated soil microbiota of seedlings of Q. ilex In Greenhouse conditions. The holm oak ( Quercus ilex ) is currently facing several threats [9]. Deforestation and desertification of peninsular soils, due to overexploitation of timber, extreme weather events and natural accidents, leads to the loss of forest mass and soil degradation. This results in a significant decrease in soil quality and its ability to sustain plant and animal life, compromising agricultural productivity [31]. Soil degradation involves a significant loss of organic matter, essential nutrients, and the soil structure itself, thus resulting in a decrease in biodiversity and microbial activity, as well as the soil's ability to retain water and support plant life [7]. Therefore, it is necessary to carry out cross-sectional studies, such as the present one, which provide us with information not only on the state of the plant but also on the state of the soil. Therefore, the use of plant growth promoting bacteria (PGPB) that allow better seedling development is of special interest. The present work proposes the use of a WWTP waste, under different conditions, together with two bacterial strains PGPB, C1 ( Bacillus pretiosus ) and C2 ( Pseudomonas agronomica ). The use of PGPBs produces changes that can improve plant growth through the production of phytohormones, nutrient solubilization, and improved soil structure [32–34]. Specifically, the strains used in this work belong to the genera Bacillus and Pseudomonas , widely described for their plant growth-promoting capabilities. The PGPB capacity of these strains has been successfully tested in previous studies on various plant models [19,35–38]. Likewise, the complete sequencing of the genome of both [19], confirms its safety for biotechnological use in agricultural and forestry production. In this study, the waste generated in wastewater treatment plants (WWTP) was used for a dual purpose: to serve as a support for the application of plant growth promoting bacteria (PGPB) and to provide a complex nutritional substrate that stimulates mineralization processes, facilitating the conversion of organic nutrients into inorganic forms more easily assimilated by plants [39,40]. This approach is aligned with what has been described in previous studies, where residual materials such as urban solid waste, by-products of agricultural origin and microalgae biomass have been recovered for reincorporation into the production system under the principles of the circular economy [19,38,39,41]. Considering the results obtained, it was observed that both chemical and biological treatments showed significant differences over controls. Treatment with WWTP residue, both sterilized and non-sterilized, had a significant impact on stem length and weight compared to controls, was highlighted. In particular, treatments with EDAR supplemented with bacterial strains Bacillus pretiosus (C1) and Pseudomonas agronomica (C2) showed a higher growth compared to control treatments irrigated with water, which reflects that biological treatment was also of great importance. The aforementioned strains were tested in different studies where similar results were found, having a positive effect in terms of promoting plant growth, both on their own and conveyed in a vegetable residue for use as a biofertilizer [19,37]. Other authors obtained similar results when applying strains of Bacillus and Pseudomonas on other woody species conveyed in recovered waste as proposed in this work [35,36,38]. Although nutritional analysis was not the main objective of this study, whose purpose was to evaluate the potential of biofertilizers in reforestation processes, their incorporation provides a valuable indirect indicator of the general physiological state of plants. This approach, still rare in agroforestry restoration research, complements the traditional emphasis on growth variables and biomass production. Although there are some works that explore this path for the study of the state of development in woody species [36,38,42]. Similarly, the nutritional analysis revealed that treatments with EDAR and EDAR_ST, especially when supplemented with PGPB, result in the maintenance of the protein content in leaves and stems (Figure 4 and Figure 6), a fact of great relevance since, as previously indicated, an increase in stem length and weight was shown. which may lead to the belief that protein levels may decrease. Studies carried out on plants of Glycine max (soybean), and using WWTP waters as a matrix, shows and in accordance with the results obtained in the present study, how both the WWTP residue and its combination with PGPB strains promote plant growth [43]. However, from an industrial point of view, the first option seems more interesting to avoid cost overruns derived from sterilization treatment. This increase in protein content can be attributed to the improvement in nutrient availability and the ability of PGPBs to stimulate plant metabolism [44]. It also highlights the lignin content in the nutritional variables of the stem, which provides the plant with rigidity and structural resistance, in addition to playing an important role in the sustainability of ecosystems by contributing to the sequestration of carbon in the soil, helping to mitigate climate change [45]. In the evaluation of biofertilizers, a critical consideration is to determine whether inoculation can alter or interfere with resident soil microbial communities [46]. To evaluate the impact of the different treatments on soil microbial activity, an analysis of metabolic diversity was carried out using plate of Biolog EcoPlates and using the Shannon-Weaver index. The results obtained showed a high metabolic diversity in all treatments, with no significant differences between treatments and an average H' of 4.45. This result indicates that the application of PGPB does not modify the microbial metabolic diversity in the soil. Indicative that the treatments carried out do not generate a negative impact on the treated soils, which is of great importance for the long-term sustainability of the ecosystem. Rather than crowding out native microbial groups, inoculated bacteria appear to coexist with them, maintaining the overall balance of the system. Concordant results have been reported in studies with wheat, where inoculation with a consortium of Pseudomonas sp. and Azotobacter sp. left the diversity of the microbial community essentially unchanged [47], as well as oilseed rape, in which inoculation with Pseudomonas sp. increased the abundance of beneficial groups without negatively affecting global diversity [48]. In the present study, diversity indices remained within comparable ranges between the different treatments, suggesting that the application of biofertilizers does not compromise the stability of the soil microbiome. This observation is relevant, as it supports the preservation of pre-existing metabolic interactions and the ecological balance of the soil system. However, contrasting results have been described in the literature; for example, Sierra-García [49] and Ferreira [50] They documented changes in functional and metabolic diversity following the introduction of exogenous microbial strains. Microbial diversity, both in its composition and in its metabolic activity, is essential for maintaining the health and quality of ecosystems, since a wide variety of microorganisms participate in crucial soil functions and transformations [17,51,52]. The diversity of the strains present is crucial to assess how this diversity is affected in arable soils [53]. The results of this work indicate that the introduction of exogenous bacterial strains C1 and C2 increases metabolic diversity in all treatments, both chemical (EDAR and EDAR_ST) and biological (C1 and C2), compared to their respective controls (EDARC0, EDAR_STC0 and WC0). The positive impact of increased diversity must be analysed from other perspectives, such as antibiotic resistance or the consequences on strains that have already colonised the soil, as greater diversity can sometimes pose a biological threat [54]. Therefore, this study also included antibiotic resistance analysis of the microbial community and a metagenomic analysis of 16S amplicons to identify and quantify the taxa present. But it is important to demonstrate that the changes produced in the plants studied are a consequence of the addition of the PGPBs that are studied, so that currently the research carried out in this field has metagenomic analyses. In addition, the interaction of microorganisms with each other and what is the mechanism of action or the pathways involved in improving the properties are checked [55]. As seen in this study, the problem is addressed evaluating the efficacy of a fertilizer derived from WWTPs, supplemented with plant growth promoting bacteria (PGPB), which has been seen in previous research to increase plant benefits and growth [38,41,42,56]. Other authors found that the use of these organic fertilizers with different treatments also resulted in not only improving agricultural yields, but also valorizing waste, thus contributing to the circular economy and the mitigation of soil degradation in a sustainable context. One Health [37,41,56–58]. The use of fertilizers and biofertilizers in modern agriculture is a topic of interest due to their implications for agricultural productivity and environmental sustainability. Different published studies show that biofertilizers, in particular, have shown great potential in improving plant growth and soil health, which is aligned with the objectives of agricultural sustainability and reducing dependence on synthetic chemicals [12]. Taxonomic diversity analyzed through gene sequencing 16S rRNA , showed significant differences in microbial community composition depending on treatment. Treatments with Pseudomonas agronomica (C2) showed a growth in the relative abundance of Pseudomonas , while treatments with Bacillus pretiosus (C1) had a higher abundance of Bacillus (Figure 10). These results indicate that introduced bacteria not only survive in the soil, but can also effectively colonize the rhizosphere of Quercus ilex . In the case of our results, there is no doubt that the addition of the C1 and C2 strains leads to an increase in the population of the species Bacillus sp. and Pseudomonas sp, versus controls. Therefore, it can be stated with genetic evidence that the strains adapted to the ecosystem without producing alterations on the rest of the microbial communities, which was also contrasted in another research where a similar approach was carried out with the addition of Bacillus and Pseudomonas in a biofertilizer obtaining similar results in which Bacillus and the Pseudomonas They are capable of appearing in different locations of the plant, obtaining results similar to those of this study [35,36,42]. For the evaluation of sensitivity to antibiotics, the cenoantibiogram was used, a methodology that allows to address resistance from an ecological perspective by analyzing the behavior of the microbial community as a whole [24]. Unlike conventional methods based on individual isolates, such as disc diffusion, microdilution for the determination of minimum inhibitory concentrations (MICs), or genotypic approaches aimed at detecting resistance genes by PCR, microarrays, or sequencing, this approach provides an integrated view of community resistance [59]. The results showed that antibiotic resistance was modulated both by the type of fertilizer applied and by bacterial inoculation. In particular, the addition of beneficial strains (C1 and C2) led to a decrease in the resistance profile, an effect that was more pronounced when such strains were incorporated into complex matrices such as EDAR and EDAR_ST, possibly associated with lower competitive pressure within the microbial community [60]. Taken together, these findings support the use of beneficial bacteria as a potential strategy to reduce antimicrobial resistance in agricultural and forestry systems. Concordant observations have been described in studies that used Peribacillus frigoritolerans subsp. Mercuritolerans , Pseudomonas mercuritolerans and consortia of Pseudomonas , where the incorporation of isolates with low MIC values allowed to attenuate the resistance of microbial communities subjected to mercury exposure [24,61,62]. This finding is of great importance in the context of our work, which pursues an environment One Health , as it suggests that the use of PGPB in fertilizers does not contribute to the spread of antibiotic resistance in the soil. The findings of this study elevate the importance of integrating biofertilizers into sustainable agricultural practices. Improved plant growth and soil health, coupled with reduced antibiotic resistance, reflects that PGPB-based biofertilizers are a viable and environmentally friendly alternative to conventional chemical fertilizers [12]. The functional outcomes predicted by PICRUSt2 show a conserved pattern between treatments, characterized by central metabolic functions. This suggests that, despite the modifications introduced by the application of the WWTP residue and the inoculated strains, the bacterial communities maintain a stable trophic base. This functional conservation can be explained by the typical redundancy of soil microbiomes, where different microbial groups are able to play equivalent roles in fundamental ecosystem processes. However, clear differences were identified in functions associated with energy metabolism, the transformation of complex compounds and the biosynthesis of metabolites. These variations seem to be related to the presence of compounds derived from meat residue, which exert selective pressure on bacterial communities. In particular, the appearance of pathways linked to antibiotic resistance, the degradation of aromatic compounds and the assembly of cofactors in treatments with sterilized WWTPs, indicates a specific functional adaptation to the residual organic matrix and the absence of native microbiota. Inoculation with Bacillus pretiosus and Pseudomonas agronomica may also have contributed to the emergence of distinctive functions, such as those related to lipid and amino acid processing, active transport, and protein regulation. These functions can be interpreted as reflecting competitive or synergistic interactions with native communities, which amplifies the capacity for transformation of compounds and the release of nutrients potentially usable by seedlings. The use of combined approaches of statistical analysis and classification would allow, in future work, to identify with greater precision the most discriminating functions between treatments and to prioritize those of greater relevance as possible functional biomarkers. Although these are predictions based on 16S sequencing, the patterns detected are consistent with trends described in soils treated with organic waste. In this sense, validation by metatranscriptomics or metabolomics is essential to confirm the real metabolic activity. Conclusions From the results obtained, it is concluded that the chemical treatments EDAR and EDAR_ST significantly promoted plant growth and plant biometrics compared to water control. Similarly, inoculation with Bacillus pretiosus (C1) and Pseudomonas agronomica (C2) stimulated plant growth compared to non-inoculated treatments. Likewise, the combination of chemical and biological treatments generated a synergistic effect that enhanced this growth promotion. Metagenomic analyses showed an increase in the abundance of Bacillus spp. and Pseudomonas spp. in the treatments inoculated with C1 and C2 in relation to the controls, which confirms the survival of the strains after their addition, regardless of the chemical treatment applied, and their direct involvement in the modifications observed in the plants. No inhibitory interactions were detected between the inoculated strains or alterations in the taxonomic structure or diversity of the microbial community, indicating a low ecological impact. On the other hand, the cenoantibiogram proved to be an effective tool to assess the state of the soil, since the inoculated strains modified the resistance profile of the soil community, reducing the minimum inhibitory concentrations of antibiotics for clinical use. Finally, the nutritional analysis revealed significant increases in the content of proteins, carbohydrates and lignin in leaves and stems after bacterial inoculation, highlighting the positive effect of these treatments on plant health and resistance. Declarations Author contributions Conceptualization: M.R.-M., A.P.L., P.A.J.-G. and D.G.-R.; Data Curation: M.R.-M. and V.M.F.-P.; Formal Analysis: M.R.-M., V.M.F.-P. and D.G.-R.; Funding Acquisition: M.R.-M.; Investigation: M.R.-M., V.M.F.-P., D.P.I., D.G.-R.; Methodology: M.R.-M., D.G.-R., A.P.L. and P.A.J.-G.; Project Administration: M.R.-M., A.P.L. and P.A.J.-G.; Resources: A.P.L. and P.A.J.-G.; Software: V.M.F.-P., D.G.R. and D.P.I.; Supervision: M.R.-M., D.G.R., and P.A.J.-G.; Validation: M.R.-M., D.G.-R., A.P.L. and P.A.J.-G.; Visualization: M.R.-M., A.P.L. and P.A.J.-G.; Writing—Original Draft: M.R.-M., D.P.I., V.M.F.-P. and D.G.-R.; Writing—Review and Editing: V.M.F.-P., D.G.-R., P.A.J.-G.. All authors have read and agreed to the published version of the manuscript. Acnowledgments We would like to thank the Madrid Institute for Rural, Agricultural, and Food Research and Development (IMIDRA), of the Government of the Community of Madrid, Ministry of Environment, Agriculture, and the Interior, for the cession of the seedlings of Quercus ilex for the development of this work. Data availability: The sequences were deposited in the NCBI repository under the accession number of BioProject PRJNA1425473. Conflict of interest: The authors declares no conflicts of interest. Funding: This project has been no funding. Clinical trial number: not applicable. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. References Schaffhauser, A.; Pimont, F.; Curt, T.; Cassagne, N.; Dupuy, J.-L.; Tatoni, T. Effects of Fire Recurrence on Fire Behaviour in Cork Oak Woodlands (Quercus Suber L.) and Mediterranean Shrublands over the Last Fifty Years. C. R. 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G31 and Azotobacter Sp. PBC2 Promoted Winter Wheat Growth and Slightly Altered the Native Bacterial Community. Sci. Rep. 2025 , 15 , 3248. Li, R.; Sun, B.; Song, M.; Yan, G.; Hu, Q.; Bai, Z.; Wang, J.; Zhuang, X. Improvement of Saline Soil Properties and Brassica Rapa L. Growth Using Biofertilizers. Sustainability 2024 , 16 , 2196. Sierra-García, I.N.; Ferreira, M.J.; Torres-Ballesteros, A.; Louvado, A.; Gomes, N.; Cunha, A. Brevibacterium EB3 Inoculation Enhances Rhizobacterial Community Interactions Leading to Improved Growth of Salicornia Europaea. Appl. Soil Ecol. 2024 , 196 , 105306, doi:10.1016/j.apsoil.2024.105306. Ferreira, M.J.; Veríssimo, A.C.S.; Pinto, D.C.G.A.; Sierra-Garcia, I.N.; Granada, C.E.; Cremades, J.; Silva, H.; Cunha, Â. Engineering the Rhizosphere Microbiome with Plant Growth Promoting Bacteria for Modulation of the Plant Metabolome. Plants 2024 , 13 , 2309, doi:10.3390/plants13162309. Garbeva, P. van; Van Veen, J.A.; Van Elsas, J.D. MICROBIAL DIVERSITY IN SOIL: Selection of Microbial Populations by Plant and Soil Type and Implications for Disease Suprressiveness. Annu. Rev. Phytopathol. 2004 , 42 , 243. Azaroual, S.E.; Kasmi, Y.; Aasfar, A.; El Arroussi, H.; Zeroual, Y.; El Kadiri, Y.; Zrhidri, A.; Elfahime, E.; Sefiani, A.; Meftah Kadmiri, I. Investigation of Bacterial Diversity Using 16S rRNA Sequencing and Prediction of Its Functionalities in Moroccan Phosphate Mine Ecosystem. Sci. Rep. 2022 , 12 , 1–16. Flores-Rentería, D.; Rincón, A.; Valladares, F.; Yuste, J.C. Agricultural Matrix Affects Differently the Alpha and Beta Structural and Functional Diversity of Soil Microbial Communities in a Fragmented Mediterranean Holm Oak Forest. Soil Biol. Biochem. 2016 , 92 , 79–90. Gupta, A.; Singh, U.B.; Sahu, P.K.; Paul, S.; Kumar, A.; Malviya, D.; Singh, S.; Kuppusamy, P.; Singh, P.; Paul, D. Linking Soil Microbial Diversity to Modern Agriculture Practices: A Review. Int. J. Environ. Res. Public. Health 2022 , 19 , 3141. Brereton, N.; González, E.; Desjardins, D.; Labrecque, M.; Pitre, F. Co-Cropping with Three Phytoremediation Crops Influences Rhizosphere Microbiome Community in Contaminated Soil. Sci. Total Environ. 2020 , 711 , 135067. Awogbemi, O.; Von Kallon, D.V. Valorization of Agricultural Wastes for Biofuel Applications. Heliyon 2022 , 8 . Atinkut, H.B.; Yan, T.; Zhang, F.; Qin, S.; Gai, H.; Liu, Q. Cognition of Agriculture Waste and Payments for a Circular Agriculture Model in Central China. Sci. Rep. 2020 , 10 , 10826. Ajayi, O.O.; Toromade, A.S.; Olagoke, A. Circular Agro Economies (CAE): Reducing Waste and Increasing Profitability in Agriculture. Int. J. Adv. Econ. 2024 , 6 , 598–611. Belkum, A. van; Bachmann, T.T.; Lüdke, G.; Lisby, J.G.; Kahlmeter, G.; Mohess, A.; Becker, K.; Hays, J.P.; Woodford, N.; Mitsakakis, K.; et al. Developmental Roadmap for Antimicrobial Susceptibility Testing Systems. Nat. Rev. Microbiol. 2019 , 17 , 51–62, doi:10.1038/s41579-018-0098-9. Freeland, G.; Hettiarachchy, N.; Atungulu, G.G.; Apple, J.; Mukherjee, S. Strategies to Combat Antimicrobial Resistance from Farm to Table. Food Rev. Int. 2023 , 39 , 27–40, doi:10.1080/87559129.2021.1893744. Razmilic, V.; Nouioui, I.; Karlyshev, A.; Jawad, R.; Trujillo, M.E.; Igual, J.M.; Andrews, B.A.; Asenjo, J.A.; Carro, L.; Goodfellow, M. Micromonospora Parastrephiae Sp. Nov. and Micromonospora Tarensis Sp. Nov., Isolated from the Rhizosphere of a Parastrephia Quadrangularis Plant Growing in the Salar de Tara Region of the Central Andes in Chile. Int. J. Syst. Evol. Microbiol. 2023 , 73 , doi:10.1099/ijsem.0.006189. González-Reguero, D.; Robas-Mora, M.; Alonso, M.R.; Fernández-Pastrana, V.M.; Lobo, A.P.; Gómez, P.A.J. Induction of Phytoextraction, Phytoprotection and Growth Promotion Activities in Lupinus Albus under Mercury Abiotic Stress Conditions by Peribacillus Frigoritolerans Subsp., Mercuritolerans Subsp. Nov. Ecotoxicol. Environ. Saf. 2024 , 285 , 117139, doi:10.1016/j.ecoenv.2024.117139. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 09 Mar, 2026 Editor invited by journal 26 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8738943","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603884302,"identity":"cc5db674-8e30-438d-9d16-28ce207121ad","order_by":0,"name":"Vanesa M. Fernández-Pastrana","email":"","orcid":"","institution":"CEU San Pablo University","correspondingAuthor":false,"prefix":"","firstName":"Vanesa","middleName":"M.","lastName":"Fernández-Pastrana","suffix":""},{"id":603884303,"identity":"0d479146-ad44-44fe-b017-63e7f6ac56a7","order_by":1,"name":"Daniel González-Reguero","email":"data:image/png;base64,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","orcid":"","institution":"CEU San Pablo University","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"","lastName":"González-Reguero","suffix":""},{"id":603884304,"identity":"39c8a651-2536-45cc-bc42-b9cf3491d3c3","order_by":2,"name":"Marina Robas-Mora","email":"","orcid":"","institution":"CEU San Pablo University","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Robas-Mora","suffix":""},{"id":603884305,"identity":"c72a2e09-9962-4c94-9e84-4487684f6229","order_by":3,"name":"Diana Penalba-Iglesias","email":"","orcid":"","institution":"CEU San Pablo University","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Penalba-Iglesias","suffix":""},{"id":603884306,"identity":"f14b4876-d911-4fa2-ac91-e27ec2970b19","order_by":4,"name":"Agustín Probanza Lobo","email":"","orcid":"","institution":"CEU San Pablo University","correspondingAuthor":false,"prefix":"","firstName":"Agustín","middleName":"Probanza","lastName":"Lobo","suffix":""},{"id":603884307,"identity":"0f90409c-da68-403e-bc09-92cd267e2fab","order_by":5,"name":"Pedro A. Jiménez-Gómez","email":"","orcid":"","institution":"CEU San Pablo University","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"A.","lastName":"Jiménez-Gómez","suffix":""}],"badges":[],"createdAt":"2026-01-30 08:25:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8738943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8738943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104498180,"identity":"dde784a7-5a6e-456d-9272-cdfd94a7e04c","added_by":"auto","created_at":"2026-03-12 13:11:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":347083,"visible":true,"origin":"","legend":"\u003cp\u003eDeforestation \u003cem\u003eQuercus ilex\u003c/em\u003e (%). Color coding that reflects from zero deforestation to a totally dry environment, severe deforestation [8]\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/79750571fbca03bb9dfd7eb0.jpeg"},{"id":104498178,"identity":"b602a4c7-afd2-4498-9963-7ebce6900019","added_by":"auto","created_at":"2026-03-12 13:11:55","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":288937,"visible":true,"origin":"","legend":"\u003cp\u003eSequence processing and analysis sequence.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/969887f146b19e4bbf57f940.jpeg"},{"id":104498183,"identity":"dee3a889-e2b6-41b5-906e-a5f0f616c53d","added_by":"auto","created_at":"2026-03-12 13:11:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":181030,"visible":true,"origin":"","legend":"\u003cp\u003eBiometric analysis of A) stem length (cm) and B) stem weight (g). Representation of mean values (n=3). Bars with identical letters indicate that the average values are not significantly different (p-value \u0026lt; 0.05). C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e. C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e. W: watering with water. EDAR: irrigation with WWTP sludge waste EDAR_ST: irrigation with sterilized WWTP sludge.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/725a66fbd8734c65966eebe9.jpeg"},{"id":104498283,"identity":"38d32710-0129-40dc-8a4a-f66ae5a4705d","added_by":"auto","created_at":"2026-03-12 13:12:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":260927,"visible":true,"origin":"","legend":"\u003cp\u003eMean nutritional variables in relation to the quality of the protein leaf. Bars with identical letters indicate that the average values are not significantly different (p-value \u0026lt; 0.05). C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e, C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e, W: irrigation with water, EDAR: wastewater and EDAR_ST: sterile wastewater. A) Levels of crude protein, total amino acids, soluble protein and crude protein available under different irrigation treatments and addition of PGPB. Representation of the different treatments and their respective concentrations %. B) Levels of histidine, lysine and methionine in relation to protein quality. Use of different irrigation treatments and addition of PGPB.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/a3794bf3acf9d92f924bb547.jpeg"},{"id":104498164,"identity":"48bd8950-3a33-4553-853c-52852fd572f9","added_by":"auto","created_at":"2026-03-12 13:11:47","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":449997,"visible":true,"origin":"","legend":"\u003cp\u003eA) Analysis of the main components. Distribution of the cases studied: WC0, WC1, WC2, EDARC0, EDARC1, EDARC2, EDAR_STC0, EDAR_STC1 and EDAR_STC2. The distribution of the cases studied responds to the weight given by each of the load factors in each individual. B) Load factors.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/73265a3c3034ff427b7527ee.jpeg"},{"id":104498202,"identity":"d17ad44a-cfa7-4fe7-93e7-7018309822fe","added_by":"auto","created_at":"2026-03-12 13:12:02","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":277547,"visible":true,"origin":"","legend":"\u003cp\u003eMean nutritional variables in relation to the quality of the protein in the stem. Bars with identical letters indicate that the average values are not significantly different (p-value \u0026lt; 0.05). C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e, C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e, W: irrigation with water, EDAR: wastewater and EDAR_ST: sterile wastewater. A) Levels of crude protein, total amino acids, soluble protein and crude protein available under different irrigation treatments and addition of PGPB. Representation of the different treatments and their respective concentrations %. B) Levels of histidine, lysine and methionine in relation to protein quality. Use of different irrigation treatments and addition of PGPB.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/f4bcabeb866511eb3bb2b4c9.jpeg"},{"id":104498182,"identity":"2a312671-7854-4fcb-aa6b-62bf98e0442c","added_by":"auto","created_at":"2026-03-12 13:11:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":20714,"visible":true,"origin":"","legend":"\u003cp\u003eNutritional data related to carbohydrate content (lignin, starch, water-soluble sugars and ethanol-soluble sugars). Bars with identical letters indicate that the average values are not significantly different (p-value \u0026lt; 0.05). C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e, C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e, W: irrigation with water, EDAR: wastewater and EDAR_ST: sterile wastewater.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/450f708fcdb59ac7a48eec8b.png"},{"id":104498181,"identity":"38efe5a1-62fc-47cb-b073-6cefc00f98ef","added_by":"auto","created_at":"2026-03-12 13:11:57","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":240941,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the main components. Distribution of the cases studied: WC0, WC1, WC2, EDARC0, EDARC1, EDARC2, EDAR_STC0, EDAR_STC1 and EDAR_STC2. The distribution of the cases studied responds to the weight given by each of the load factors in each individual.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/10fa34ae9eec554905ed222d.jpeg"},{"id":104498176,"identity":"29537d81-3eca-4a48-9ea5-7d7f10419027","added_by":"auto","created_at":"2026-03-12 13:11:54","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":158179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e PCA, which represents in the (2D) plan the distribution and variation trends of chemical and biological irrigation treatments, according to the two components (variables: antibiotics), which best explain the model. C0: control without inoculum. C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e. C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e. W: watering with water. Surrounded in blue, grouping of chemical treatments irrigated with fertilizer (EDAR). Surrounded in green, grouping of chemical treatments irrigated with sterilized fertilizer (EDAR_ST). Surrounded in red, a grouping of chemical treatments irrigated with water. \u003cstrong\u003eB)\u003c/strong\u003e Load factors.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/b3a23094f18dc70cd38d3262.jpeg"},{"id":104498296,"identity":"eca7f7d3-8e22-4442-adde-dc3218063752","added_by":"auto","created_at":"2026-03-12 13:12:20","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":170798,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of taxonomic composition at the genus level of rhizospheric samples of Q. suber under the different chemical and biological treatments. C0: control without inoculum. C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e. C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e. W: watering with water.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/26a04ecd6d84286facfcb745.jpeg"},{"id":104498199,"identity":"2ea165ff-782c-4f8c-ab5f-ca8f98e2568f","added_by":"auto","created_at":"2026-03-12 13:12:00","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":89251,"visible":true,"origin":"","legend":"\u003cp\u003eACP that represents in the plane (2D) the distribution and trends of variation of the taxonomic diversity (Unweighted Unifrac) of the samples, depending on the chemical and biological treatment, which best explain the model. C0: control without inoculum. C1: \u003cem\u003eBacillus pretiosus\u003c/em\u003e. C2: \u003cem\u003ePseudomonas agronomica\u003c/em\u003e. W: watering with water. Surrounded in blue, grouping of chemical treatments irrigated with water (W). Surrounded in red, grouping of chemical treatments watered with sterilized fertilizer (EDAR_ST). Surrounded by green, grouping of chemical treatments irrigated with EADAR fertilizer.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/f2246c5814bc4a4ba3566d43.png"},{"id":104498214,"identity":"c235342b-3005-4811-885d-3668dd5150b8","added_by":"auto","created_at":"2026-03-12 13:12:08","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":341503,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) General functional profile of KEGG metabolic pathways predicted by PICRUSt2 in microbiomes associated with water and biofertilizer treatments (sterilized and non-sterilized). The main routes correspond to level 2 categories of the KEGG system. (\u003cstrong\u003eB\u003c/strong\u003e) Functional distribution of metabolic pathways grouped according to KEGG classification at the category level.\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/ee8de3b88c20141a48317ce0.jpeg"},{"id":104498174,"identity":"ff00994f-c5d2-4261-9ec9-0e9dea52a0fb","added_by":"auto","created_at":"2026-03-12 13:11:54","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":91034,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG functions of greatest importance in the Random Forest classification model for discrimination between treatments. The functions represented correspond to the KOs with the highest \"Mean Decrease Accuracy\" values in the model.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/2d8aee4287768759137bbb1b.png"},{"id":104781504,"identity":"c8f16037-1e8f-47ba-be73-17223f11f0e4","added_by":"auto","created_at":"2026-03-17 07:55:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4311077,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8738943/v1/0810aa32-f60c-49bd-b70e-00f3c0d0165e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recovery of a WWTP fertilizer added with plant growth promoting bacteria (PGPB). Relationship with antibiotic resistance in the \"One Health\" environment with an oak species: Quercus ilex","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eQuercus ilex\u003c/em\u003e, Commonly known as holm oak, it is one of the most emblematic trees of the Iberian Peninsula. It plays a fundamental role in the ecosystem of the Iberian Peninsula and in the cultural landscape of the country. This species belongs to the Fagaceae family and is known for its evergreen foliage and resistance to harsh environmental conditions. One of the highlights of the holm oak is its role in the conservation of biodiversity. The dense structure and composition of the holm oak canopy provide shelter and food for mammals, birds, reptiles and invertebrates, many endemic to the Mediterranean region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], thus contributing to the rich biological diversity of Mediterranean ecosystems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In addition, the holm oak also plays a crucial role in Spain's rural economy. It has been widely used as a source of multiple resources, such as wood and acorns. It also plays a fundamental role in soil conservation and climate change mitigation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The holm oak is a species highly adapted to long periods of drought and arid soils [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Its adaptability is due to its characteristic morphology. It has a deep root system that allows it to access underground water sources. It also plays a key role in soil conservation by preventing soil erosion, stabilising the substrate and improving water infiltration, a crucial factor for the sustainable sustainability of holm oak and pasture forests [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Likewise, its physiology allows it to have a high plasticity in the use of water and nutrients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is an evergreen tree with tough leathery (sclerophyllous) leaves that are oval in shape and commonly with serrated edges. The adaxial surface of the leaf is dark green, while the abaxial surface is whitish with the presence of trichomes, which helps to reduce water loss through perspiration. The morphology of its roots helps to stabilize the soil and prevent erosion.\u003c/p\u003e \u003cp\u003eThe holm oak currently faces a series of threats. The loss of forest due to urbanization, intensive agriculture, industrial pollution, pests or forest fires represent a serious concern for the conservation of this species and Mediterranean ecosystems in general [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. One of the main problems is soil degradation, experiencing a significant decrease in its quality and ability to sustain plant and animal life.\u003c/p\u003e \u003cp\u003eSoil degradation involves the loss of organic matter, nutrients, and the soil structure itself, resulting in a decrease in biodiversity and microbial activity, as well as the soil's ability to retain water and support plant life [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This impact has been increasing over the years, as can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, posing a much more serious problem over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs mentioned above, fires are one of the most notorious causes of holm oak loss, which has led to large volume losses in forests around the world. A fire leaves devastating consequences, but one of the most worrying aspects in the ecology of forest fires is their effect on the regeneration of affected soils. It is estimated that around 30% of soil organic biomass is lost during a high-intensity fire, which can lead to a decrease of up to 80% in microbial activity and a loss of biodiversity in the soil. The natural regeneration of burned soils can take decades, and even centuries, depending on various factors such as climate, soil type, and the severity of the fire [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo deal with this problem, different aspects are addressed. One of these approaches is the use of organic waste as fertilizer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Waste recovery helps to reduce, reuse and reuse products that are initially discarded [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among the wide variety of fertilizers, we find the so-called biofertilizers. These are organic products that contain live microorganisms that help fix atmospheric nitrogen, solubilize phosphorus, and improve nutrient availability in the soil. These fertilizers improve soil health by improving soil structure and increasing microbial activity, which at the same time improves long-term fertility and contributes to sustainable agriculture [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, biofertilizers reduce the risk of environmental pollution by not releasing harmful chemicals into the environment. On the other hand, chemical fertilizers are synthetic products manufactured through industrial processes, which can have long-term negative effects, such as soil degradation and water pollution due to excess nutrients that are not absorbed by plants [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBiofertilizers stand out for their potential biotechnological application, since the bacteria they carry are the so-called plant growth promoting bacteria (PGPR), microorganisms that colonize the rhizosphere of plants and improve their growth through different mechanisms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These mechanisms include the production of phytohormones, phosphorus solubilization, nitrogen fixation, and the production of siderophores that improve iron availability [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, PGPBs can produce 1-aminocyclopropane-1-carboxylate deaminase (ACCd), an enzyme that helps plants tolerate abiotic stress. In the present study, two strains of PGPB: \u003cem\u003eBacillus pretiosus\u003c/em\u003e (C1) and \u003cem\u003ePseudomonas agronomica\u003c/em\u003e (C2), two bacteria known to use as PGPB [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study also seeks to show whether the addition of a bacterial strain to an environment is capable of modulating its response to antibiotic resistance. This phenomenon represents one of the greatest challenges in the agricultural field, in the horizontal dissemination of antibiotic resistance. Bearing in mind that the release of antibiotics not only affects pathogens, but also the microbial communities in the soil, altering their balance and encouraging the emergence of resistance. Antibiotic resistance genes can be transferred between bacteria through horizontal mechanisms such as conjugation, transformation, and transduction, facilitating the rapid spread of these resistances in different environments [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe approach \u003cem\u003eOne Health\u003c/em\u003e recognises the interconnectedness between human, animal and environmental health, underlining the need for integrated strategies to combat antibiotic resistance. In this context, it is crucial to develop and implement sustainable agricultural practices that do not contribute to this global problem. The use of biofertilizers, rather than chemical fertilizers and pesticides, represents a promising strategy to reduce the selection pressure of resistant bacteria and promote a healthy microbiological balance in agricultural soils [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA useful tool in soil condition analysis is the cenoantibiogram, a technique that allows the evaluation of antibiotic resistance of bacteria present in soils [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This analysis is essential to identify and monitor the presence of antibiotic resistance genes in soil microbial communities, which is essential to prevent the spread of resistance that can affect human, animal and environmental health [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This knowledge helps to ensure that the agricultural practices used do not contribute to the spread of antibiotic resistance, while maintaining the integrity and security of the ecosystem. This analysis allows not only to improve the efficiency of biofertilizers, but also to ensure that interventions in the soil are safe and sustainable, aligning with the objectives of \u003cem\u003eOne Health\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, for the evaluation of the impact of the addition of a bacterial strain to a medium and its follow-up, metagenomics based on amplicon sequencing has been shown to be a robust and useful technique. This tool allows us to carry out population and phylogenetic diversity studies based on the analysis of the coding gene for the \u003cem\u003e16S rRNA\u003c/em\u003e. Its application in the field of ecology has also been called environmental genomics, ecogenomics or community genomics [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003e2.1.\u0026nbsp;Bacterial strains\u003c/h2\u003e\n\u003cp id=\"_Toc169786018\"\u003eThe bacterial strains tested were isolated from the rhizosphere of soil free of and from the rhizosphere of Medicago sativa. They have been chosen from the research team in (GIR - MICROAMB) \u0026quot;Environmental Bacterial Microbiology\u0026quot; of the Faculty of Pharmacy of the San Pablo CEU University. These bacteria have a biotechnological interest due to their plant growth promoting capabilities (PGPB). These strains are also free of transmissible antibiotic resistance genes and virulence determinants [19].\u003c/p\u003e\n\u003cp id=\"_Toc169713923\"\u003eTable 1. PGPR characteristics of the strains tested. AIA: production of 3-indoleacetic acid; ACCd: production of 1-aminocyclopopane-1-carboxyl deaminase; P/A: presence (+) / absence (-). WGS: Whole Genome Dry (Illumina\u0026reg;).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdentification (WGS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIA (\u0026mu;g/mL10 \u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCd (p/a)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSiderophores (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus pretiosus\u0026nbsp;\u003c/em\u003e(C1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eMedicago sativa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5.61\u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas agronomica\u0026nbsp;\u003c/em\u003e(C2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFree floor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5.85\u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc169786020\"\u003e3.2. Experimental Design\u0026nbsp;\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eThe biological tests were carried out on seedlings of two holm oaks, \u003cem\u003eQuercus ilex\u003c/em\u003e (L.), from the shouth of Guadarramma with the register number FS-45/08/28/003. The seedlings were supplied by IMIDRA (Madrid Institute for Rural, Agrarian and Food Research and Development). These seedlings come from seeds of native plants of the Community of Madrid whose purpose is to repopulate forests to avoid genetic contamination. For the growth test under laboratory conditions, 10 cm x 8 cm seedbeds with 35 18 cm high alveoli were used, placed in forest trays or leachate trays. \u0026nbsp;As a substrate for the development of the biological assays, a sample of oak groves was used in the municipality of Navacerrada, in the foothills of the Sierra de Guadarrama (GPS 21\u0026ordm;92 ́N 5\u0026ordm;03 ́W, Madrid, Spain).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree irrigation matrices were established that included water (W), WWTP organic waste (EDAR) and sterilized WWTP organic waste (EDAR_ST). Three different biological treatments were applied to these matrices: without inoculum (C0 control), supplemented with the C1 strain (Bacillus pretiosus) and with the C2 strain (Pseudomonas agron\u0026oacute;mica). In total, 9 treatments were carried out resulting from the combination of irrigation matrices and biological treatments. For each treatment, 35 replicates of Quercus ilex seedlings were performed.\u003c/p\u003e\n\u003ch2 id=\"_Toc169786021\"\u003e3.3 Preparation of Bacterial Suspensions and Biofertilizer\u003c/h2\u003e\n\u003ch3 id=\"_Toc169786022\"\u003e\u003cem\u003e3.3.1. Preparation of bacterial suspensions\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eStarting from pure cultures of the C1 (Bacillus pretiosus) and C2 (Pseudomonas agronomica) strains on nutrient agar from Condalab\u0026reg; (Madrid, Spain), a bacterial culture was prepared in liquid LB medium (Oxoid Limited, Wade Road, Basingstoke, England). After 48h of growth, it was verified by using the semi-quantitative method of submersible paddles UriCult Trio that the bacterial density was approximately 10\u003csup\u003e8\u003c/sup\u003e u.f.c. ml\u003csup\u003e-1\u003c/sup\u003e. This process ensured the standardization of the bacterial inoculum for its subsequent application in the final volume of the irrigation matrix.\u0026nbsp;\u003c/p\u003e\n\u003ch3 id=\"_Toc169786023\"\u003e\u003cem\u003e3.3.2. Preparation of the irrigation chemical matrix\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe waste from WWTP (Industrias C\u0026aacute;rnicas Villar, S.A., Soria, Spain) has shown fertilizing activity, experimentally, at a dilution of 1/512 (V\u003csub\u003eEDAR\u003c/sub\u003e/V\u003csub\u003eH2O\u003c/sub\u003e). For all the trials in this work, this dilution was used as the basis for the formulation of the biofertilizer (incorporation of inoculum). At the same time, and in order to eliminate the microbiota of the WWTP organic waste, an aliquot (EDAR_ST) was sterilized by autoclaving (121\u0026ordm;C, 20 min, 1atm). The physicochemical composition of the WWTP\u0026nbsp;sludge can be consulted in Table 2. To eliminate the microbiota of the WWTP sludge, an aliquot of the same (EDAR_ST) was sterilized by autoclaving at 121\u0026ordm;C for 20 minutes at 1 atmosphere of pressure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Physicochemical composition of the WWTP waste (Analysis carried out by LabAqua. Tests covered by accreditation ENAC n\u0026ordm;109/LE 28).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"698\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePARAMETERS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMETHODS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUNITS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePhysicochemical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eConductivity at 20 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0015 Electrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026mu;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eConductivity at 25 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0015 Electrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026mu;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eBiochemical Oxygen Demand (BOD5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0002 Gauge method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emgO2/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eChemical Oxygen Demand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0003 Digestion\u0026mdash;Colorimetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e6720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emgO2/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eChemical demand for decanted oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0003 Digestion\u0026mdash;Colorimetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eNitrates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0010 Digestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eKjeldhal Nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0007 Kjeldhal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e296.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0010 Electrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eU.pH.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSuspended solids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0006 Gravimetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eToxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003ePIT-F/0012 Bioluminescence assay with \u003cem\u003eVibrio fisheri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eU.T.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMajority cations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-D-PE-0025-ICP-OES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAnions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eNitrates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-BV-PE-0001 HPLC\u0026mdash;Conductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eOrthophosphates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eCa-R-PE-0011 Spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e74.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg PO4/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSulfates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-BV-PE-0001 HPLC\u0026mdash;Conductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSulfites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003eA-F-PE-0040 Volumetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3 id=\"_Toc169786024\"\u003e\u003cem\u003e3.3.3. Biofertiliser preparation\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eFirst, the bacterial suspensions of C1 (Bacillus pretiosus) and C2 (Pseudomonas agronomica) were prepared. To do this, they were grown in LB broth (Condalab\u0026reg;, Madrid, Spain) for 48 h (stirring 50 r.p.m., 25\u0026ordm;C \u0026plusmn; 2\u0026ordm;C). The biofertilizer was prepared on a weekly basis to avoid non-specific transformations resulting from microbial metabolism (autochthonous microbiota and inoculum). 3 L of each treatment were prepared in each session and stored in refrigeration (4 \u0026deg;C). In order to standardize the microbial density to an equivalent of 0.5 McFarland (approximately 10\u003csup\u003e8\u003c/sup\u003e cfu. mL\u003csup\u003e-1\u003c/sup\u003e), 100 mL of the grown LB medium was added to 900 mL of the irrigation chemical matrix: water or the 1/512 dilution of EDAR/EDAR_ST, as appropriate and tested using Uricult\u003csup\u003eTM\u003c/sup\u003e (Liofilchem srl, Italy).\u003c/p\u003e\n\u003ch2 id=\"_Toc169786025\"\u003e3.4 Growth test and conditions\u003c/h2\u003e\n\u003cp\u003eThe experiment was carried out under controlled laboratory conditions in a phytotron (photoperiod of 11 hours of light and 13 hours of darkness, light intensity of 505 \u0026mu;moles.m\u003csup\u003e-2\u003c/sup\u003e. s\u003csup\u003e-1\u003c/sup\u003e (white and yellow light), temperature 25\u0026ordm;C \u0026plusmn; 3\u0026ordm;C and relative humidity 30% \u0026plusmn; 5%). The irrigation regime was designed in order to emulate the field conditions and was carried out systematically with the treatments described above (EDAR/EDAR_ST, supplemented with strains C1 and/or C2), and water in the case of the control. The objective was to guarantee the relative humidity of the soil, without reaching saturation or waterlogging, which resulted in a weekly irrigation periodicity with experimental average volume of 85mL/alveolus.\u003c/p\u003e\n\u003ch2 id=\"_Toc169786026\"\u003e3.5 Harvesting and biomass analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe harvest was carried out one year after (October 2023 \u0026ndash; October 2024). It was destructive and involved the extraction of the aerial and radical part of each plant. Additionally, from the root part, the rhizospheric fraction of the soil (2g per seedling) was obtained for the subsequent analysis of the edaphic communities. For the calculation of the total biomass, the crop was left to dry at room temperature (22\u0026ordm;C \u0026plusmn; 2\u0026ordm;C) and weighed after one week (dry weight, g). For nutritional analysis, the leaves were separated from the stem. To ensure sufficient biomass for the analysis (15 g), each replicate (n=3) was made up of the leaves or stem of several individuals subjected to the same treatment (replications). It was packaged and stored in refrigeration (4 \u0026ordm;C), for shipment to the analysis laboratory (Rock River Labs Spain, Lal\u0026iacute;n, Pontevedra, Spain). The nutritional analysis was carried out 24 hours after harvest.\u003c/p\u003e\n\u003ch2 id=\"_Toc169786027\"\u003e3.6. Analysis of soil microbial communities\u003c/h2\u003e\n\u003cp\u003eFor the extraction of the rhizospheric communities, 1g of soil was collected from each seedling. For each treatment, in order to make triplicates, the soil of 11 seedlings was joined and homogenized, obtaining three triplicates per treatment of 11g per triplicate. Subsequently, the procedure described in Garc\u0026iacute;a-Villaraco et al. (2009) was followed, as modified. To do this, 2 g of soil was suspended in 20 mL of sterile saline solution (0.45% NaCl) and homogenized with an Omni-Mixer homogenizer at 16,000 r.p.m. for two minutes. It was then centrifuged at 690g for five minutes with a Hettich Zentrifugen centrifuge model Mikro 22R.\u003c/p\u003e\n\u003ch3\u003e\u003cspan id=\"_Toc169786028\"\u003e\u003cem\u003e3.6.1. Study of the metabolic functionality of the microbial community: Biolog ECO\u0026reg;\u003c/em\u003e\u003c/span\u003e\u003c/h3\u003e\n\u003cp\u003eFrom the microbial suspension obtained and the dilutions selected according to the previous results, the 96 wells of the Biolog Eco plates\u0026reg; (31 different carbon sources and one target (water), per tricplice) were loaded with 135 \u0026mu;L per well. The plates were incubated for 94 hours at 25\u0026ordm;C, and their absorbance was measured at 595 nm, 630 nm and 492 nm every 24 hours using the Asys UVM340 plate reading equipment and Micro WinTM V3.5 software. With the results of the absorbance measurements, the values were corrected by subtracting the target (corrected absorbance). Then, the mean of the corrected absorbance of all wells was calculated as the mean of the 31 (Biolog Eco\u0026reg;) for each replicate. The value of AWCD (Average Well Color Development) was represented against the incubation time to obtain the growth curves of the microbial community in the wells of the plate. In these curves, the incubation time at which the growth of the microorganisms was initiating the stationary phase was chosen for subsequent multivariate analyses. Additionally, with the corrected absorbance values of the chosen incubation time, the metabolic (functional) diversity of each sample was calculated using the Shannon-Weaver diversity index.\u003c/p\u003e\n\u003cp\u003eH(m)= -\u0026sum; qi log2qi; where qi= n/N; where n is the corrected absorbance (AWCD) of each well and N is the total absorbance of all wells.\u003c/p\u003e\n\u003ch3 id=\"_Toc169786029\"\u003e\u003cem\u003e3.6.2. Study of community antibiotic resistance: Study of the Cenoantibiogram\u0026nbsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eFrom the soil extract obtained in saline solution (NaCl 0.45%), it was verified that the density of viable microorganisms was \u0026gt;10\u003csup\u003e8\u003c/sup\u003e cfu mL\u003csup\u003e-1\u003c/sup\u003e (optical density (OD) = 0.5 McFar-land). Mueller-Hinton agar (Condalab\u0026reg;, Madrid, Spain) was seeded and MIS was evaluated using antibiotic strips from \u0026epsilon; test, in triplicate, for the following antibiotics: amoxicillin (AML), amoxicillin-clavulanic acid (AUG), piperacillin (PP), piperacillin-tazobactam (TZP), imipenem (IMI), imipenem-EDTA (IMD), ciprofloxacin (CIP), nalidixic acid (NA), trimethoprim-sulfomethoxazole (TS), cefotaxime (CTX) and cefpirome (CR) (BioM\u0026eacute;rieux\u0026reg;, Marcy l\u0026apos;Etoile, France). (BioM\u0026eacute;rieux\u0026reg;, Marcy l\u0026apos;Etoile, France). The plates were then incubated according to the manufacturer\u0026apos;s instructions (25\u0026deg;C \u0026plusmn; 2\u0026deg;C). For the quantification of the minimum inhibitory concentration (MIC), the most restrictive halo was used as a reference.\u003c/p\u003e\n\u003ch3 id=\"_Toc169786030\"\u003e\u003cem\u003e3.6.3 Study of microbial taxonomic diversity: Metagenomics.\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe composition and structure of the sampled microbial communities was evaluated by amplification and sequencing of the variable regions V3-V4 of the 16S rRNA gene. Amplification was performed after 25 cycles of PCR. In this procedure, positive (CM) and negative (NC) controls were used to ensure quality control. The positive control is a mock community and was processed in the same way as the samples. The libraries obtained were sequenced using Illumina Miseq (300\u0026times;2). The analysis was commissioned to the company Microomics\u0026reg; S.L. (Barcelona, Spain), who analyzed the taxonomy of the extracts according to the protocol shown in Figure 2. The sequences were deposited in the NCBI repository under the accession number of BioProject PRJNA1425473.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2 id=\"_Toc169786031\"\u003e\u003cem\u003e3.7 Statistical analysis\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo determine the existence of variations in biometrics depending on the type of irrigation (matrix: EDAR, EDAR_ST and water control; and biological treatment: C0, C1 and C2), a one-factor ANOVA test was performed, where the dependent variable was the dry weight of the biomass (g) and the independent variables were irrigation. As a result of this analysis, it is possible to discriminate whether there are statistically significant variations in plant growth (biomass production). To conclude which risk justified such variations, a post-hoc Duncan analysis of pairwise comparison between the means was performed. Following the same procedure, a first ANOVA was carried out to find out, of all the nutritional parameters analyzed, which ones suffered some variation in their mean depending on the type of irrigation. Only for those that showed statistically significant differences (p-value \u0026lt; 0.01), a Duncan graph and post-hoc analysis was performed, which allowed us to know which treatment (irrigation matrix and biological treatment) explained such deviations from the mean. Finally, a multivariate study of principal component analysis (PCA) was carried out as an exploratory technique of trends between the different variables analyzed (biometrics and nutritional) according to the type of biological treatment (PGPB strains and control without inoculum) and chemical (fertigation with EDAR residue and EDAR_ST, and control irrigated with water). In the same way, an PCA was performed using the MICs of the rhizospheric communities in each of the treatments as analysis variables. All these analyses were carried out using the SPSS v.29.0 program (IBM Corp, Armonk, NY, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe main findings are presented below in order to facilitate the monitoring and interpretation of the most relevant results. Only those variables that, after ANOVA, underwent considerable variations depending on the type of treatment, were graphed. we will see the most relevant findings.\u003c/p\u003e\n\u003ch2 id=\"_Toc169786033\"\u003e4.1. Length and weight test\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e shows the results of stem length (\u003cstrong\u003eFigure 3A\u003c/strong\u003e) and dry weight of biomass after harvest (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). As can be seen, after different treatments, notable trends in growth are revealed. Treatments with WWTP sludge (both sterilized and non-sterilized) exhibit a greater impact on stem length, reaching longer lengths. Treatments, both EDAR and EDAR_ST, with bacterial inoculum (C1 and C2) not only show greater growth in both variables compared to treatments with conventional irrigation (W, water) but also compared to their controls without inoculum\u003c/p\u003e\n\u003cp\u003eThey are the ones with the most growth in length. It should be noted that treatment with EDAR_ST inoculated with C2 is the one that presents the greatest significant development compared to the rest of the treatments. \u0026nbsp;This indicates that both the WWTP sludge and its sterilized form can be significantly more effective than treatment with water alone.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc169786034\"\u003e4.2 Nutritional analysis\u003c/h2\u003e\n\u003ch3 id=\"_Toc169786036\"\u003e\u003cem\u003e4.2.1 Protein composition in the leaf\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e shows the variables of the group of proteins that varied statistically according to the fertigation treatment: crude protein (%), soluble protein (%), total amino acids (% dry mass, DM), available crude protein (%), histidine, lysine and methionine.\u003c/p\u003e\n\u003cp\u003eRegarding Figure 4, which represents the amount of protein, it is interesting to see how the relative protein content of the leaves is increased in those treatments whose biometric variables were also increased (Figure 3 of biometrics). In this case, it should be noted that treatments irrigated with WWTPs have a statistically significant higher protein content. In the same way, it can be observed that treatments irrigated with EDAT_ST, together with bacterial inoculums (C1 and C2), represent a significant increase in protein content compared to their own control and conventional irrigation with water.\u003c/p\u003e\n\u003ch3 id=\"_Toc169786037\"\u003e\u003cem\u003e4.2.2 Analysis of the main components of the leaf\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe two variables that best explain the model were projected in the two-dimensional plane to study possible groupings between treatments:\u003c/p\u003e\n\u003cp\u003eAs we can see in Figure 5, the addition of the C2 strain induces changes of nutritional improvement in the plant that hosts them, regardless of the matrix that carries them. The principal component analysis study yielded two components that explain the system with 75.98% of the variance explained.\u003c/p\u003e\n\u003ch3 id=\"_Toc169786038\"\u003e\u003cem\u003e4.2.3 Protein composition in the stem\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e shows the variables of the group of proteins that varied statistically according to fertigation treatment: crude protein (%), soluble protein (%), total amino acids (% dry mass, DM), available crude protein (%), histidine, lysine and methionine.\u003c/p\u003e\n\u003cp\u003eAs in the case of the leaf, the relative amount of protein in the stem is also increased in treatments irrigated with both EDAR and EDAR_ST, highlighting those that combine chemical treatment with biological treatment (C1 and C2). Both with respect to conventional irrigation with water, and intra-treatment with respect to its controls.\u0026nbsp;\u003c/p\u003e\n\u003ch3 id=\"_Toc169786039\"\u003e\u003cem\u003e4.2.4 Carbohydrate composition in the stem\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 7\u003c/strong\u003e shows the variables of the carbohydrate group that varied statistically according to fertigation treatment: starch (%) and lignin (%).\u003c/p\u003e\n\u003cp\u003eIn the ANOVA test performed with Duncan\u0026apos;s statistic, we can see how there is a significant positive difference in the three treatments. It is observed how all of them grow with respect to their controls. The results in the composition of lignin are striking, for which there are significant notable differences.\u003c/p\u003e\n\u003ch3 id=\"_Toc169786040\"\u003e\u003cem\u003e4.2.5 Analysis of the main components of the stem\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe two variables that best explain the model were projected in the two-dimensional plane to study possible groupings between treatments:\u003c/p\u003e\n\u003cp\u003eAnalyzing the principal component analysis, we observed that the samples are aggregated by the influence of the different biological treatments. Therefore, both the addition of C1 (\u003cem\u003eBacillus pretiosus\u003c/em\u003e) and C2 (\u003cem\u003ePseudomonas agron\u0026oacute;mica\u003c/em\u003e) induce changes that improve the nutritional quality of the plant that hosts them. The principal component analysis study yielded two components that explain the system with 68.67% of the variance explained.\u003c/p\u003e\n\u003ch2 id=\"_Toc169786041\"\u003e4.3 Diversity analysis\u003c/h2\u003e\n\u003cp\u003eThe metabolic diversity of each sample was calculated, using the Shannon-Weaver diversity index, and the differences were analyzed at 144 hours. This index indicates the variability, metabolic in the case of the present work, that occurs within an ecosystem. In most natural ecosystems it varies between 0.5 and 5, although its normal value is between 2 and 3; values below 2 are considered low in diversity and above 3 are high in species diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Functional diversity (Shannon index, H ́) of the soil microbial community at 144h of incubation.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eWC0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eWC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eWC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eEDARC0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eEDARC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eEDARC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEDAR_STC0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEDAR_STC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003eEDAR_STC2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u0026apos; 144h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.78\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.60\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.71\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.19\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.75\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.28\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.75\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.54\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e4.51\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs can be seen in Table 3, in the analysis of the metabolic diversity of the rhizosphere, all treatments have a high metabolic diversity regardless of the treatment carried out and remain on average at an H\u0026apos; of 4.45, so it is concluded that there are no significant differences between the different treatments.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc169786042\"\u003e4.4 Cenoantibiogram\u003c/h2\u003e\n\u003cp\u003eThe principal component analysis study yielded two components that explain the system with 88.89% of the variance explained. Component 1 represents 77.73% of the variance and component 2 11.16%. \u003cstrong\u003eFigure 9\u003c/strong\u003e shows the analysis of principal components, we observe a clear segregation at the level of biological treatment with strains C1 (\u003cem\u003eBacillus pretiosus\u003c/em\u003e) and C2 (\u003cem\u003ePseudomonas agronomica\u003c/em\u003e) and on the other hand the aggregation of the control treatment. This means that the incorporation of PGPB strains could be responsible for reducing the MIC of the antibiotics studied, according to the distribution of the cases explained with respect to the distribution of antibiotics in the load factor graph.\u003c/p\u003e\n\u003ch2 id=\"_Toc169786043\"\u003e4.5 Taxonomic diversity\u003c/h2\u003e\n\u003cp\u003eTables \u003cstrong\u003e4\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;5\u003c/strong\u003e represent a bioinformatic analysis in relation to the quality of the sampling. After the first sampling, 15,477 phenotypes were detected, demonstrating great diversity, likewise, singlets and doublets were eliminated to avoid noise in the data because these are rare phylotypes (present only once or twice). In addition, they were subsampled to match the sample size ensures that comparisons between samples are not biased by differences in the total number of readings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eGeneral descriptive metrics of the metagenomic dataset, including the total number of samples, identified features, and overall frequency values. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMetrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNo. of samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNo. of features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e15477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e775742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eDescriptive statistical summary of frequency distribution across metagenomic samples, including minimum, quartiles, median, maximum, and mean values\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFrec. Minimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e15541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e24620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFrec. Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e33638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFrec. Maximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e42292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFrec. Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28731,18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3 id=\"_Toc169786044\"\u003e\u003cem\u003e4.5.1\u0026nbsp;\u003c/em\u003e\u003cem\u003eRelative abundances at the gender level.\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 10\u003c/strong\u003e below shows the relative abundances of different microbial genera for different treatments.\u003c/p\u003e\n\u003cp\u003eFigure 10 shows how the treatments have a significant impact on the composition of the microbial community at the genus level, with a high abundance of Pseudomonas in the treatments with EDARStC2, EDARC2 and WC2 that correspond to the treatments that were inoculated with our strain \u003cem\u003ePseudomonas agronomica\u003c/em\u003e (C2). In the case of \u003cem\u003eBacillus pretiosus\u003c/em\u003e (C1), it is more present in treatments with EDARC1, WC1 and, to a lesser extent, also in EDAR_STC1. It is therefore deduced that there is no variation with respect to the taxonomic structure, the bacterium that is added increases the genetic diversity of the same taxon without eliminating any others.\u0026nbsp;\u003c/p\u003e\n\u003ch3 id=\"_Toc169786045\"\u003e\u003cem\u003e4.5.2 Beta diversity\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 11\u0026nbsp;\u003c/strong\u003eshows a principal component analysis (PCA) based on the Unweighted UniFrac distance, which represents the distribution and variation trends of the taxonomic diversity of the samples according to the chemical and biological treatments applied.\u003c/p\u003e\n\u003cp\u003eFigure 11 shows a grouping by chemical treatment. However, a clear segmentation is also observed by the treatment with the bacterium (C2). So there is a clear influence on the behavior of the soils depending on the bacteria that is added.\u003c/p\u003e"},{"header":"Gene prediction","content":"\u003cp\u003eA functional analysis based on KEGG annotations was carried out to characterize the metabolic potential of the microbial communities present in the different treatments: chemical fertilizer (Chemical_Fertilizer), ORGAON\u0026reg;PK biofertilizer (ORGAON_PK), its sterilized version (ORGAON_PK_ST) and water treatment (Water). Functional profiles were grouped into major metabolic pathways and visualized using relative abundance plots.\u003c/p\u003e\n\u003cp\u003eOverall, the microbiomes of all treatments presented a similar functional profile, characterized by a high representation of pathways associated with amino acid metabolism, carbohydrate metabolism, energy metabolism, and cofactor and vitamin metabolism. To a lesser extent, functions related to the biosynthesis of secondary metabolites, lipid metabolism, nucleotide metabolism and degradation of xenobiotic compounds were observed.\u003c/p\u003e\n\u003cp\u003eA differential analysis of KEGG functions among microbial communities was performed using the nonparametric Kruskal\u0026ndash;Wallis test. This analysis revealed the absence of statistically significant functions after correction by multiple tests (FDR \u0026lt; 0.05). In order to reduce the dimensionality of the dataset and prioritize those functions with greater discriminative capacity between treatments, a Random Forest classification model was applied to the KO functions predicted by PICRUSt2. This approach made it possible to identify the most relevant functional genes for differentiation between experimental groups.\u003c/p\u003e\n\u003cp\u003eFrom the functions noted by COG categories, a relatively homogeneous functional distribution was observed among the treatments. The most abundant functions included those related to amino acid and carbohydrate transport and metabolism, energy production and conversion, cell envelope biogenesis, and signal transduction. The categories \u0026quot;unknown function\u0026quot; and \u0026quot;general prediction of function\u0026quot; were also highlighted, reflecting the presence of genes with incomplete or poorly characterized annotation.\u003c/p\u003e\n\u003cp\u003eThe least represented categories were those related to RNA processing and modification, post-translational modification, intracellular trafficking, defense mechanisms and biosynthesis of secondary metabolites. No drastic differences were observed between treatments, although certain functional profiles showed subtle variations in the relative abundance of pathways associated with regulatory and structural processes (such as DNA replication and repair or ribosomal translation).\u003c/p\u003e\n\u003cp\u003eA classification analysis was applied using Random Forest using the KO functions predicted by PICRUSt2, with the aim of identifying the most relevant functional genes for discrimination between treatments.\u003c/p\u003e\n\u003cp\u003eThe results show that the model reached an overall classification error (OOB error) of 25.9%, and allowed the identification of a set of KO functions with a high contribution to the accuracy of the model, measured as \u0026quot;Mean Decrease Accuracy\u0026quot;.\u003c/p\u003e\n\u003cp\u003eA different genetic functional profile was predicted for each treatment. In the water treatment, functions associated with respiration processes and carbon management were mainly observed, which reflects the basal metabolic activity of the microbiota in the absence of external amendments. These functions suggest a profile adapted to conditions of low nutrient availability, where energy generation mechanisms and optimization in the use of simple compounds predominate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e. Functions predicted to be of greatest importance in the Random Forest model according to the treatment applied.\u0026nbsp;The KO genes identified as most relevant for the classification of treatments by the Random Forest algorithm are shown, along with their functional function annotated in KEGG.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Function (KEGG)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eWC0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK05585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003eNAD(P)H-quinone oxidoreductase subunit N [EC:7.1.1.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK08698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003ecarbon dioxide concentrating mechanism protein CcmM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eWC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK18007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003eNAD-reducing hydrogenase small subunit [EC:1.12.1.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eEDARC0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK05898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003e3-oxosteroid 1-dehydrogenase [EC:1.3.99.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eEDARC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK14742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003etRNA threonylcarbamoyladenosine biosynthesis protein TsaB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK01921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003eD-alanine-D-alanine ligase [EC:6.3.2.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK01761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003emethionine-gamma-lyase [EC:4.4.1.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK15514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003e3,4-dehydroadipyl-CoA semialdehyde dehydrogenase [EC:1.2.1.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK02613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003ering-1,2-phenylacetyl-CoA epoxidase subunit PaaE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK00507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003estearoyl-CoA desaturase (Delta-9 desaturase) [EC:1.14.19.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eEDARSTC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK16869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003eoctanoyl-[GcvH]:p rotein N-octanoyltransferase [EC:2.3.1.204]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK07219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003ePutative Molybdopterin Biosynthesis Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK16781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003etetratricopeptide repeat protein 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eEDARSTC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK18793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003ebeta-lactamase class D OXA-23 [EC:3.5.2.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eK14750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003eEthylbenzene dioxygenase ferredoxin component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn treatments with WWTP residue, both in its natural and sterilized form, a greater functional diversity linked to the metabolism of complex compounds was detected, including transformation pathways of lipids, amino acids and aromatic compounds. In addition, in the treatments with sterilized WWTPs, functions related to cofactor biosynthesis, protein regulation and antibiotic resistance emerged, suggesting a specific adaptation of the inoculated microbial communities to this environment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present work, the incorporation of a biofertilizer on the growth, nutritional quality and associated soil microbiota of seedlings of \u003cem\u003eQ. ilex\u003c/em\u003e In Greenhouse conditions. The holm oak (\u003cem\u003eQuercus ilex\u003c/em\u003e) is currently facing several threats [9]. Deforestation and desertification of peninsular soils, due to overexploitation of timber, extreme weather events and natural accidents, leads to the loss of forest mass and soil degradation. This results in a significant decrease in soil quality and its ability to sustain plant and animal life, compromising agricultural productivity [31]. Soil degradation involves a significant loss of organic matter, essential nutrients, and the soil structure itself, thus resulting in a decrease in biodiversity and microbial activity, as well as the soil\u0026apos;s ability to retain water and support plant life [7]. Therefore, it is necessary to carry out cross-sectional studies, such as the present one, which provide us with information not only on the state of the plant but also on the state of the soil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, the use of plant growth promoting bacteria (PGPB) that allow better seedling development is of special interest. The present work proposes the use of a WWTP waste, under different conditions, together with two bacterial strains PGPB, C1 (\u003cem\u003eBacillus pretiosus\u003c/em\u003e) and C2 (\u003cem\u003ePseudomonas agronomica\u003c/em\u003e). The use of PGPBs produces changes that can improve plant growth through the production of phytohormones, nutrient solubilization, and improved soil structure [32\u0026ndash;34]. Specifically, the strains used in this work belong to the genera \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, widely described for their plant growth-promoting capabilities. The PGPB capacity of these strains has been successfully tested in previous studies on various plant models\u0026nbsp;[19,35\u0026ndash;38]. Likewise, the complete sequencing of the genome of both\u0026nbsp;[19], confirms its safety for biotechnological use in agricultural and forestry production.\u003c/p\u003e\n\u003cp\u003eIn this study, the waste generated in wastewater treatment plants (WWTP) was used for a dual purpose: to serve as a support for the application of plant growth promoting bacteria (PGPB) and to provide a complex nutritional substrate that stimulates mineralization processes, facilitating the conversion of organic nutrients into inorganic forms more easily assimilated by plants\u0026nbsp;[39,40]. This approach is aligned with what has been described in previous studies, where residual materials such as urban solid waste, by-products of agricultural origin and microalgae biomass have been recovered for reincorporation into the production system under the principles of the circular economy\u0026nbsp;[19,38,39,41].\u003c/p\u003e\n\u003cp\u003eConsidering the results obtained, it was observed that both chemical and biological treatments showed significant differences over controls. Treatment with WWTP residue, both sterilized and non-sterilized, had a significant impact on stem length and weight compared to controls, was highlighted. In particular, treatments with EDAR supplemented with bacterial strains \u003cem\u003eBacillus pretiosus\u003c/em\u003e (C1) and \u003cem\u003ePseudomonas agronomica\u003c/em\u003e (C2) showed a higher growth compared to control treatments irrigated with water, which reflects that biological treatment was also of great importance. The aforementioned strains were tested in different studies where similar results were found, having a positive effect in terms of promoting plant growth, both on their own and conveyed in a vegetable residue for use as a biofertilizer [19,37]. Other authors obtained similar results when applying strains of \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003eon other woody species conveyed in recovered waste as proposed in this work [35,36,38].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough nutritional analysis was not the main objective of this study, whose purpose was to evaluate the potential of biofertilizers in reforestation processes, their incorporation provides a valuable indirect indicator of the general physiological state of plants. This approach, still rare in agroforestry restoration research, complements the traditional emphasis on growth variables and biomass production. Although there are some works that explore this path for the study of the state of development in woody species\u0026nbsp;[36,38,42].\u003c/p\u003e\n\u003cp\u003eSimilarly, the nutritional analysis revealed that treatments with EDAR and EDAR_ST, especially when supplemented with PGPB, result in the maintenance of the protein content in leaves and stems (Figure 4 and Figure 6), a fact of great relevance since, as previously indicated, an increase in stem length and weight was shown. which may lead to the belief that protein levels may decrease. Studies carried out on plants of \u003cem\u003eGlycine max\u003c/em\u003e (soybean), and using WWTP waters as a matrix, shows and in accordance with the results obtained in the present study, how both the WWTP residue and its combination with PGPB strains promote plant growth [43]. However, from an industrial point of view, the first option seems more interesting to avoid cost overruns derived from sterilization treatment. This increase in protein content can be attributed to the improvement in nutrient availability and the ability of PGPBs to stimulate plant metabolism [44]. It also highlights the lignin content in the nutritional variables of the stem, which provides the plant with rigidity and structural resistance, in addition to playing an important role in the sustainability of ecosystems by contributing to the sequestration of carbon in the soil, helping to mitigate climate change [45].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the evaluation of biofertilizers, a critical consideration is to determine whether inoculation can alter or interfere with resident soil microbial communities [46]. To evaluate the impact of the different treatments on soil microbial activity, an analysis of metabolic diversity was carried out using plate of Biolog EcoPlates and using the Shannon-Weaver index. The results obtained showed a high metabolic diversity in all treatments, with no significant differences between treatments and an average H\u0026apos; of 4.45. This result indicates that the application of PGPB does not modify the microbial metabolic diversity in the soil. Indicative that the treatments carried out do not generate a negative impact on the treated soils, which is of great importance for the long-term sustainability of the ecosystem. Rather than crowding out native microbial groups, inoculated bacteria appear to coexist with them, maintaining the overall balance of the system. Concordant results have been reported in studies with wheat, where inoculation with a consortium of \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003esp. and \u003cem\u003eAzotobacter\u0026nbsp;\u003c/em\u003esp. left the diversity of the microbial community essentially unchanged [47], as well as oilseed rape, in which inoculation with \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003esp. increased the abundance of beneficial groups without negatively affecting global diversity\u0026nbsp;[48]. In the present study, diversity indices remained within comparable ranges between the different treatments, suggesting that the application of biofertilizers does not compromise the stability of the soil microbiome. This observation is relevant, as it supports the preservation of pre-existing metabolic interactions and the ecological balance of the soil system. However, contrasting results have been described in the literature; for example, Sierra-Garc\u0026iacute;a\u0026nbsp;[49]\u0026nbsp;and Ferreira\u0026nbsp;[50]\u0026nbsp;They documented changes in functional and metabolic diversity following the introduction of exogenous microbial strains.\u003c/p\u003e\n\u003cp\u003eMicrobial diversity, both in its composition and in its metabolic activity, is essential for maintaining the health and quality of ecosystems, since a wide variety of microorganisms participate in crucial soil functions and transformations\u0026nbsp;[17,51,52]. The diversity of the strains present is crucial to assess how this diversity is affected in arable soils\u0026nbsp;[53]. The results of this work indicate that the introduction of exogenous bacterial strains C1 and C2 increases metabolic diversity in all treatments, both chemical (EDAR and EDAR_ST) and biological (C1 and C2), compared to their respective controls (EDARC0, EDAR_STC0 and WC0). The positive impact of increased diversity must be analysed from other perspectives, such as antibiotic resistance or the consequences on strains that have already colonised the soil, as greater diversity can sometimes pose a biological threat [54]. Therefore, this study also included antibiotic resistance analysis of the microbial community and a metagenomic analysis of 16S amplicons to identify and quantify the taxa present.\u003c/p\u003e\n\u003cp\u003eBut it is important to demonstrate that the changes produced in the plants studied are a consequence of the addition of the PGPBs that are studied, so that currently the research carried out in this field has metagenomic analyses. In addition, the interaction of microorganisms with each other and what is the mechanism of action or the pathways involved in improving the properties are checked [55]. As seen in this study, the problem is addressed evaluating the efficacy of a fertilizer derived from WWTPs, supplemented with plant growth promoting bacteria (PGPB), which has been seen in previous research to increase plant benefits and growth [38,41,42,56]. Other authors found that the use of these organic fertilizers with different treatments also resulted in not only improving agricultural yields, but also valorizing waste, thus contributing to the circular economy and the mitigation of soil degradation in a sustainable context. \u003cem\u003eOne Health\u0026nbsp;\u003c/em\u003e[37,41,56\u0026ndash;58]. The use of fertilizers and biofertilizers in modern agriculture is a topic of interest due to their implications for agricultural productivity and environmental sustainability. Different published studies show that biofertilizers, in particular, have shown great potential in improving plant growth and soil health, which is aligned with the objectives of agricultural sustainability and reducing dependence on synthetic chemicals [12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaxonomic diversity analyzed through gene sequencing \u003cem\u003e16S rRNA\u003c/em\u003e, showed significant differences in microbial community composition depending on treatment. Treatments with \u003cem\u003ePseudomonas agronomica\u003c/em\u003e (C2) showed a growth in the relative abundance of \u003cem\u003ePseudomonas\u003c/em\u003e, while treatments with \u003cem\u003eBacillus pretiosus\u003c/em\u003e (C1) had a higher abundance of \u003cem\u003eBacillus\u003c/em\u003e (Figure 10). These results indicate that introduced bacteria not only survive in the soil, but can also effectively colonize the rhizosphere of \u003cem\u003eQuercus ilex\u003c/em\u003e. In the case of our results, there is no doubt that the addition of the C1 and C2 strains leads to an increase in the population of the species \u003cem\u003eBacillus\u0026nbsp;\u003c/em\u003esp. and \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003esp, versus controls. Therefore, it can be stated with genetic evidence that the strains adapted to the ecosystem without producing alterations on the rest of the microbial communities, which was also contrasted in another research where a similar approach was carried out with the addition of \u003cem\u003eBacillus\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003ein a biofertilizer obtaining similar results in which \u003cem\u003eBacillus\u003c/em\u003e and the \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003eThey are capable of appearing in different locations of the plant, obtaining results similar to those of this study\u0026nbsp;[35,36,42].\u003c/p\u003e\n\u003cp\u003eFor the evaluation of sensitivity to antibiotics, the cenoantibiogram was used, a methodology that allows to address resistance from an ecological perspective by analyzing the behavior of the microbial community as a whole [24]. Unlike conventional methods based on individual isolates, such as disc diffusion, microdilution for the determination of minimum inhibitory concentrations (MICs), or genotypic approaches aimed at detecting resistance genes by PCR, microarrays, or sequencing, this approach provides an integrated view of community resistance [59]. The results showed that antibiotic resistance was modulated both by the type of fertilizer applied and by bacterial inoculation. In particular, the addition of beneficial strains (C1 and C2) led to a decrease in the resistance profile, an effect that was more pronounced when such strains were incorporated into complex matrices such as EDAR and EDAR_ST, possibly associated with lower competitive pressure within the microbial community [60]. Taken together, these findings support the use of beneficial bacteria as a potential strategy to reduce antimicrobial resistance in agricultural and forestry systems. Concordant observations have been described in studies that used \u003cem\u003ePeribacillus frigoritolerans\u0026nbsp;\u003c/em\u003esubsp. \u003cem\u003eMercuritolerans\u003c/em\u003e, \u003cem\u003ePseudomonas mercuritolerans\u003c/em\u003e and consortia of \u003cem\u003ePseudomonas\u003c/em\u003e, where the incorporation of isolates with low MIC values allowed to attenuate the resistance of microbial communities subjected to mercury exposure [24,61,62]. This finding is of great importance in the context of our work, which pursues an environment \u003cem\u003eOne Health\u003c/em\u003e, as it suggests that the use of PGPB in fertilizers does not contribute to the spread of antibiotic resistance in the soil.\u003c/p\u003e\n\u003cp\u003eThe findings of this study elevate the importance of integrating biofertilizers into sustainable agricultural practices. Improved plant growth and soil health, coupled with reduced antibiotic resistance, reflects that PGPB-based biofertilizers are a viable and environmentally friendly alternative to conventional chemical fertilizers\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eThe functional outcomes predicted by PICRUSt2 show a conserved pattern between treatments, characterized by central metabolic functions. This suggests that, despite the modifications introduced by the application of the WWTP residue and the inoculated strains, the bacterial communities maintain a stable trophic base. This functional conservation can be explained by the typical redundancy of soil microbiomes, where different microbial groups are able to play equivalent roles in fundamental ecosystem processes.\u003c/p\u003e\n\u003cp\u003eHowever, clear differences were identified in functions associated with energy metabolism, the transformation of complex compounds and the biosynthesis of metabolites. These variations seem to be related to the presence of compounds derived from meat residue, which exert selective pressure on bacterial communities. In particular, the appearance of pathways linked to antibiotic resistance, the degradation of aromatic compounds and the assembly of cofactors in treatments with sterilized WWTPs, indicates a specific functional adaptation to the residual organic matrix and the absence of native microbiota.\u003c/p\u003e\n\u003cp\u003eInoculation with \u003cem\u003eBacillus pretiosus\u003c/em\u003e and \u003cem\u003ePseudomonas agronomica\u003c/em\u003e may also have contributed to the emergence of distinctive functions, such as those related to lipid and amino acid processing, active transport, and protein regulation. These functions can be interpreted as reflecting competitive or synergistic interactions with native communities, which amplifies the capacity for transformation of compounds and the release of nutrients potentially usable by seedlings.\u003c/p\u003e\n\u003cp\u003eThe use of combined approaches of statistical analysis and classification would allow, in future work, to identify with greater precision the most discriminating functions between treatments and to prioritize those of greater relevance as possible functional biomarkers. Although these are predictions based on 16S sequencing, the patterns detected are consistent with trends described in soils treated with organic waste. In this sense, validation by metatranscriptomics or metabolomics is essential to confirm the real metabolic activity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFrom the results obtained, it is concluded that the chemical treatments EDAR and EDAR_ST significantly promoted plant growth and plant biometrics compared to water control. Similarly, inoculation with \u003cem\u003eBacillus pretiosus\u003c/em\u003e (C1) and \u003cem\u003ePseudomonas agronomica\u003c/em\u003e (C2) stimulated plant growth compared to non-inoculated treatments. Likewise, the combination of chemical and biological treatments generated a synergistic effect that enhanced this growth promotion. Metagenomic analyses showed an increase in the abundance of \u003cem\u003eBacillus\u003c/em\u003e spp. and \u003cem\u003ePseudomonas\u003c/em\u003e spp. in the treatments inoculated with C1 and C2 in relation to the controls, which confirms the survival of the strains after their addition, regardless of the chemical treatment applied, and their direct involvement in the modifications observed in the plants. No inhibitory interactions were detected between the inoculated strains or alterations in the taxonomic structure or diversity of the microbial community, indicating a low ecological impact. On the other hand, the cenoantibiogram proved to be an effective tool to assess the state of the soil, since the inoculated strains modified the resistance profile of the soil community, reducing the minimum inhibitory concentrations of antibiotics for clinical use. Finally, the nutritional analysis revealed significant increases in the content of proteins, carbohydrates and lignin in leaves and stems after bacterial inoculation, highlighting the positive effect of these treatments on plant health and resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: M.R.-M., A.P.L., P.A.J.-G. and D.G.-R.; Data Curation: M.R.-M. and V.M.F.-P.; Formal Analysis: M.R.-M., V.M.F.-P. and D.G.-R.; Funding Acquisition: M.R.-M.; Investigation: M.R.-M., V.M.F.-P., D.P.I., D.G.-R.; Methodology: M.R.-M., D.G.-R., A.P.L. and P.A.J.-G.; Project Administration: M.R.-M., A.P.L. and P.A.J.-G.; Resources: A.P.L. and P.A.J.-G.; Software: V.M.F.-P., D.G.R. and D.P.I.; Supervision: M.R.-M., D.G.R., and P.A.J.-G.; Validation: M.R.-M., D.G.-R., A.P.L. and P.A.J.-G.; Visualization: M.R.-M., A.P.L. and P.A.J.-G.; Writing\u0026mdash;Original Draft: M.R.-M., D.P.I., V.M.F.-P. and D.G.-R.; Writing\u0026mdash;Review and Editing: V.M.F.-P., D.G.-R., P.A.J.-G.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcnowledgments\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Madrid Institute for Rural, Agricultural, and Food Research and Development (IMIDRA), of the Government of the Community of Madrid, Ministry of Environment, Agriculture, and the Interior, for the cession of the seedlings of \u003cem\u003eQuercus ilex\u003c/em\u003e for the development of this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sequences were deposited in the NCBI repository under the accession number of BioProject PRJNA1425473.\u003c/p\u003e\n\u003cp\u003eConflict of interest:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis project has been no funding.\u003c/p\u003e\n\u003cp\u003eClinical trial number:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchaffhauser, A.; Pimont, F.; Curt, T.; Cassagne, N.; Dupuy, J.-L.; Tatoni, T. 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Induction of Phytoextraction, Phytoprotection and Growth Promotion Activities in Lupinus Albus under Mercury Abiotic Stress Conditions by Peribacillus Frigoritolerans Subsp., Mercuritolerans Subsp. Nov. \u003cem\u003eEcotoxicol. Environ. Saf. \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e285\u003c/em\u003e, 117139, doi:10.1016/j.ecoenv.2024.117139. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bacillus pretiosus, Pseudomonas agronomica, Quercus ilex, soil regeneration, WWTP, PGPB, waste valorization, One Health, microbial diversity","lastPublishedDoi":"10.21203/rs.3.rs-8738943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8738943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil degradation is a critical problem in Spain, aggravated by intensive agricultural practices, urbanisation and natural phenomena such as forest fires. This study addresses soil regeneration through the valorisation of waste from wastewater treatment plants (WWTP) and its supplementation with plant growth-promoting bacteria (PGPB). \u003cem\u003eQuercus ilex\u003c/em\u003e, a species of holm oak of significant importance in Spain, was used as a model to evaluate the effects of these treatments on plant growth and soil health. In this research, plant growth assays, metabolic and taxonomic diversity analyses, nutritional and antibiotic resistance evaluations were carried out, all providing significant results. Treatments with EDAR waste, both sterilized and non-sterilized, significantly improved the growth of \u003cem\u003eQuercus ilex\u003c/em\u003e, showing highly positive effects after PGPB supplementation, evaluating in this study \u003cem\u003eBacillus pretiosus\u003c/em\u003e (C1) and \u003cem\u003ePseudomonas agronomica\u003c/em\u003e (C2) strains, showing improvements in nutrient uptake and stress tolerance. Moreover, understanding what is happening requires considering microbial diversity, which plays a crucial role in understanding soil functions and transformations. The microbial diversity analysis revealed high diversity in all treatments, with changes observed in the composition of the soil microbial community. Additionally, treatments with PGPB did not increase antibiotic resistance in soil microbial communities, which is fundamental within the context of the \"\u003cem\u003eOne Health\u003c/em\u003e\" approach pursued throughout this work.\u003c/p\u003e","manuscriptTitle":"Recovery of a WWTP fertilizer added with plant growth promoting bacteria (PGPB). Relationship with antibiotic resistance in the \"One Health\" environment with an oak species: Quercus ilex","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 13:10:09","doi":"10.21203/rs.3.rs-8738943/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"230627932251345907644827717092798368826","date":"2026-05-15T18:49:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48887317012607121811408662281135965257","date":"2026-05-14T16:13:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126309780590539065475063649653971180179","date":"2026-05-04T07:30:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T07:03:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-26T07:35:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T09:58:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T18:11:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-02-19T18:06:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef317232-cce1-4374-9950-c6f36a288430","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"230627932251345907644827717092798368826","date":"2026-05-15T18:49:11+00:00","index":50,"fulltext":""},{"type":"reviewerAgreed","content":"48887317012607121811408662281135965257","date":"2026-05-14T16:13:06+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"126309780590539065475063649653971180179","date":"2026-05-04T07:30:42+00:00","index":43,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T13:10:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 13:10:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8738943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8738943","identity":"rs-8738943","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}