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罗马尼亚 中国林业大学 常用非甾体抗炎药人工暴露的根际微生物群的结构和代谢谱Structural and Metabolic Profiling of Lycopersicon esculentum Rhizosphere Microbiota Artificially Exposed at Commonly Used Non-Steroidal Anti-Inflammatory DrugsAt each established sampling period, 2 g of rhizosphere soil samples were used to extract the microbiota with a 10 mL PBS solution. The extraction was allowed for 30 min through continuous mechanical shaking (LaboShake, Gerhardt Analytical System, Konigswinter, Germany), followed by 1 h of rest.使用格哈特公司振荡器LaboShake振荡2克根际土壤30分钟。LaboShake最高可承载30千克的负载。MDPImicroorganisms 2 of 16Microorganisms 2022, 10, 254 check forupdates Citation: Kovacs,E.D.; Silaghi-Dumitrescu,L.; Roman, C.;Tian, D. Structural and MetabolicProfiling of Lycopersicon esculentumRhizosphere Microbiota ArtificiallyExposed at Commonly UsedNon-Steroidal Anti-InflammatoryDrugs. Microorganisms 2022,10,254.https://doi.org/10.3390/ microorganisms10020254 Academic Editor: Daolin Du Received: 8 December 2021 Accepted: 18 January 2022 Published: 24 January 2022 Publisher's Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations. Copyright: @ 2022 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) Emoke Dalma Kovacs 1,2*, Luminita Silaghi-Dumitrescu2, Cecilia Romanl and Di Tian 3D Research Institute for Analytical Instrumentation, INCDO-INOE 2000, 400293 Cluj-Napoca, Romania;cici_roman@yahoo.com 2 Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University, 400028 Cluj-Napoca, Romania;luminita.silaghi@gmail.com 3 Research Center of Forest Management Engineering of State Forestry and Grassland Administration,College of Forestry, Beijing Forestry University, Beijing 100083, China; tiandi@bjfu.edu.cn Correspondence: dalmaemokekovacs@gmail.com Abstract: In this study, the effect of common non-steroidal anti-inflammatory drugs on Lycopersiconesculentum rhizosphere microbiota was monitored. The experiments were performed with artifi-cially contaminated soil with ibuprofen (0.5 mgkg-), ketoprofen (0.2 mgkg-1) and diclofenac(0.7 mgkg-). The results evidenced that the rhizosphere microbiota abundance decreased especiallyunder exposure to diclofenac (187-201 nmolg- dry weight soil) and ibuprofen (166-183 nmolg-dryweight soil) if compared with control (185-240 nmolg-1 dry weight soil), while the fungal/bacteriaratio changed significantly with exposure to diclofenac (<27%) and ketoprofen (<18%). Comparedwith control samples, the average amount of the ratio of Gram-negative/Gram-positive bacteria washigher in rhizosphere soil contaminated with ibuprofen (>25%) and lower in the case of diclofenac(<46%) contamination. Carbon source consumption increased with the time of assay in case of thecontrol samples (23%) and those contaminated with diclofenac (8%). This suggests that rhizospheremicrobiota under contamination with diclofenac consume a higher amount of carbon, but they donot consume a larger variety of its sources. In the case of contamination with ibuprofen and keto-profen, the consumption of carbon source presents a decreasing tendency after day 30 of the assay.Rhizosphere microbiota emitting volatile organic compounds were also monitored. Volatile com-pounds belonging to alcohol, aromatic compounds, ketone, terpene, organic acids, aldehyde, sulphurcompounds, esters, alkane, nitrogen compounds, alkene and furans were detected in rhizospheresoil samples. Among these, terpene, ketone, alcohol, aromatic compounds, organic acids and alkanewere the most abundant compound classes (>75%), but their percentage changed with exposureto diclofenac, ketoprofen and ibuprofen. Such changes in abundance, structure and the metabolicactivity of Lycopersicon esculentum rhizosphere microbiota under exposure to common non-steroidalanti-inflammatory drugs suggest that there is a probability to also change the ecosystem servicesprovided by rhizosphere microbiota. Keywords: microorganisms; pharmaceuticals; exposure; functioning 1. Introduction Diclofenac, ibuprofen and ketoprofen are common non-steroidal anti-inflammatorydrugs (NSAIDs) that are often reported in environmental assessment studies This is becauseof their high consumption rate [1,2] and improper removal during wastewater treatmentprocesses [3,4]. They reach the soil system either through the reuse of treated municipalwastewater in irrigation purposes [5,6], or from the resulting sludge and biosolids asfertilizers [7,8]. Their common occurrence in soil environments raised high concernsbecause of the continuous input [9], subsequent accumulation potential [5] and their potential ecotoxicological effects on nontargeted living organisms at different trophiclevels [10,11]. Soil microbiota resistance to pharmaceuticals is a global issue and understanding itsfunctional and molecular basis is essential. Bacterial resistance to pharmaceuticals could beeither natural or acquired [12]. Although a natural resistance could be not considered aserious clinical issue, the acquired resistance is a much more severe case when we considerthe high ability of bacteria to capture fragments of DNA and genes of resistance to manycommonly used pharmaceuticals, even from phylogenetically distinct organisms. Throughhorizontal gene transfer and cross-resistance development, bacteria could become resistantto multiple drugs [13]. Soil microbiota are key actors in soil processes, contributing significantly to numerousecosystem services provided by soil. They are involved in the processes of nutrient cyclingand organic matter degradation [14]. Microorganisms are also able to synthesize volatileorganic compounds, such as alcohols, terpenes, ketones, alkanes, etc. [15]. These are sec-ondary metabolites with multiple ecological roles and mechanisms of action. Reports havestarted to highlight that those volatile organic compounds emitted by soil microbiota can actas signalling molecules assuring distance communication between various organisms [16],can induce inhibitory activity against fungal spore germination [17], those changing mi-crobiota structure, and can modulate enzyme activity [18]. In this way, they can directlyinfluence the aboveground biodiversity and productivity. Through the chemicals that themicroorganism cycles or releases, they can stimulate or inhibit plant development [16].Rhizosphere, the interface between plant roots and soil is one of the most abundant anddynamic system inhabited by microorganism [19]. Rhizosphere microbiomes differ fromsoil microbiomes. Although it is well acknowledged that the microbiota of rhizospherehave positive effects on plant development and heath [20,21], there is less knowledge ontheir structure, abundance and function under challenging conditions. Considering thefrequency of NSAIDs' presence in soil environment [1,7,8], studies are essential to unravelthe functions of rhizosphere microbiota under exposure to frequently detected NSAIDs. A major goal in ecology is to assure the development of more stable agrosystemsthat can face current challenges. This could be achieved through the achievement of adeeper knowledge on rhizosphere ecology and the identification of rhizobiome chemicaland biological diagnostics and signatures for identified issues. Microbiota are characterizedas small organisms; therefore, compared to other organisms, they present a high surfacearea-to-volume ratio, which provides a large contact interface that interacts with theirsurrounding environment, respectively, with surrounding contaminants [22]. Under theexposure to pharmaceuticals, soil microbiome biological parameters such as structure,abundance and metabolic activity could suffer changes. Wang et al. [23] assessed the toxiceffects of enrofloxacin on soil enzymatic activities and showed that the activities of sucrasewas inhibited significantly at all incubation periods. In studies on microbial utilisation inthe Biolog plates, they reported that the utilisation of metabolites was inhibited severelyand reached essentially zero after 21 days, although they gradually decreased with theincreasing of time (until day 21 of the assay). In studies reported by Liu et al., [24] it wasfound that soil microbial functional diversity and the capacity of soil microbial communitiesto utilise substrates were sensitive to sulfamethoxazole and chlortetracycline. Similarly,in the triclosan ecotoxicity assessment of soil microbiota, Ramires et al. [25] state that thisantimicrobial agent inhibited the consumption pattern of carboxylic acids. In the studyof the influence of tetracycline presence from cow manure on soil microbiota, Chessaet al. [26] evidenced that tetracycline only transiently influenced the microbiota abundanceand functions. As soil microbiome could be changed in structure, abundance, or metabolic activityonce exposed to different pharmaceuticals, this could result in changes in key ecologicalprocesses of soil. At the best of our knowledge, there is at present minor information onhow the presence of NSAIDs could shape rhizosphere microbiome structure, abundanceand metabolic activity. Moreover, there is no information on how the presence of certain NSAIDs influence or not the secondary metabolites profile produced by rhizosphere mi-crobiome. The main aim of this study was to identify if the presence of common NSAIDssuch as ibuprofen, diclofenac and ketoprofen could shape Lycopersicon esculentum rhizo-sphere microbiota (i) abundance and phenotypic structure, (ii) metabolic activity, and (iii)secondary metabolites profile. 2. Materials and Methods 2.1. Experimental Set-Up Pharmaceuticals such as ibuprofen (C13H19O2), ketoprofen (C16H14O3) and diclofenac(C14H11Cl2NO2), belonging to the class of non-steroidal anti-inflammatory drugs, wereselected for our study. The selection of these NSAIDs was based on their increased con-sumption and widespread occurrence in environment. Argic phaeozem soil samples (0-40 cm), collected in April 2020 from Cojocna, ClujCounty was used in this study. The main physical chemical properties of the soil samplesare listed in Table 1. All soil samples were tested to be free of studied NSAIDs according to the method de-scribed by KoOVvaaccs et al. [27]. The samples were artificially contaminated individually witheach pharmaceutical in part (0.5 mgkg- ibuprofen, 0.2 mgkg-1 ketoprofen, 0.7 mg·kg-1diclofenac) as previously described [27]. For each rhizosphere soil sampling period, indi-vidual pots in triplicate were prepared for each soil type vs. pharmaceutical as presentedin the schematic diagram of the experiment set-up (Figure 1). Fourteen-days-old tomatoseeds (Lycopersicon esculentum) were planted in contaminated soil pots prepared one daybefore and allowed for development in a laboratory climate chamber with the followingday-night cycle conditions: day-14 h of light, 25°C; night-10 h of darkness, 18C. Thesoil water content was adjusted to assure a 58% water holding capacity (WHC) duringthe study. Figure 1. Schematic diagram of experiment set-up Table 1. Average values (n=12) of physical chemical properties of the studied soil samples. Soil Property Argic Phaeozem Clay 27.2±0.94 Sand 16.1±0.20 Silt 56.7±1.37 Texture Silty Clay Loam Moisture (cm/cm) 0.344±0.01 Soil temperature (C) 10.4±0.09 Organic carbon (%) 6.2±0.12 pH 5.9±0.09 2.2. Rhizosphere Microbiota Analysis through PLFA Approach The rhizosphere soil microbiota phenotypic structure and abundance assessment wasperformed considering the phospholipids-derived fatty acids (PLFA) gas chromatographicanalysis. The rhizosphere soils were sampled from pots contaminated with pharmaceuticals(Figure 1) after very short-term exposure (day 1), short-term exposure (day 7), mid-termexposure (day 30) and long-term exposure (day 60). The extraction of PLFA was performed on 1 g of freeze-dried (Labconco FreeZone6 freeze-dry system, Kansas, MO, USA) soil according to the method described by Blighand Dyer [28] and modified by Frostegard et al. [29]. The lipids were fractionated into phos-pholipids, glycolipids and neutral lipids using a silicic acid column (500 mg, Phenomenex,Torrance, CA, USA). After a mild alkaline methanolysis, 150 uL of extracts containingthe fatty acids methyl esters was injected into a gas chromatograph with flame ionizationdetector (7890A GC-FID, Agilent Technologies, Santa Clara, CA, USA). The fatty acidsmethyl esters separation was obtained using a 5% phenyl-methylpolysiloxane column(HP-Ultra 2, J&W Scientific, Folsom, CA, USA) with the following properties: 25 mm× 0.2 mm id., 0.33 um film thickness. The PLFAD1 method from the MIDI SherlockIMMicrobial Identification System (Microbial ID, Inc., Newark, DE, USA) was used for thephospholipids-derived fatty acids separation. The gas chromatograph operation parame-ters are listed in Table 2. Table 2. GC-FID operation parameters for phospholipids-derived fatty acids analysis from rhizo-sphere soils. Parameter Conditions Inlet temperature 280°C Split mode 40:1 170°C, increase with 28°Cmin-until 288°℃,followed by a new increase with 60°Cmin-until 310 °C. This final temperature was maintained constant for 1.25 min1.2mL·min-1 Oven temperature program Detector temperature 300°C For the interpretation of the phospholipids-derived fatty acids data, bacterial fatty acidstandards and software from MIDI SherlockM Microbial Identification System (MicrobialID, Inc., Newark, DE, USA) was used. Saprotrophic fungi were identified using 18:2w6cPLFA biomarker [29], and ectomycorrhizal fungi with 18:2w9c PLFA biomarker [30]. PLFAbiomarkers such as 18:2w6c and 18:3w3 were used for nitrogen-reducing bacteria iden-tification [31], and 17:1w7c,10Me16:0, 17:1w6, 15:1, i17:1w7c,cy18:0w7.8, i15:1w7c andi19:1w7c markers were used for sulphur-reducing bacteria identification [32,33]. 2.3. Rhizosphere Microbiota Responses Evaluation The rhizosphere microbiota response to ibuprofen, diclofenac and ketoprofen wasevaluated considering community-level physiological profile (CLPP) and emitted volatile organic compounds (VOCs). The sampling of the rhizosphere soil samples from the con-taminated pots was performed according to the schematic diagram presented in Figure 1. 2.3.1. Community-Level Physiological Profile (CLPP) The assessment of the metabolic activity of rhizosphere soil microbiota after exposureto NSAIDs was performed using Biolog EcoPlateM containing 31 different carbon sources.At each established sampling period, 2 g of rhizosphere soil samples were used to extractthe microbiota with a 10 mL PBS solution. The extraction was allowed for 30 min throughcontinuous mechanical shaking (LaboShake, Gerhardt Analytical System, Konigswinter,Germany), followed by 1 h of rest. The mixture of soil suspension and supernatant wassubjected to sonication and centrifugation as described by Lindahl and Bakken [34] until weobtained the final soil lixiviate. The soil particles from the obtained lixiviates were removedthrough low-speed centrifugation (1000 rpm for 1 min, LMC-3000 centrifuge, GrantBio,Riga, Latvia). From this final solution, 150 uL was added to each well of EcoPlate andincubated under dark conditions at 25 °C for 3 days (LabCompanion, Billerica, MA, USA).The optical density (OD) of each well was measured at 入= 590 nm using an SpectraMaxiD3 Microplate Reader (Molecular Devices, San Jose, CA, USA) and SoftMax Pro7 software(Molecular Devices) just after inoculation and once a day during the incubation period. 2.3.2. Emitted VOCs The rhizosphere soil-emitted volatile organic compounds content was determinedthrough headspace-solid phase microextraction sampling using 85 um polyacrylate fibre(Supelco Inc., Bellefonte, PA, USA). An amount of 1 g of soil samples was diluted with2 mL of PBS solution in 20 mL headspace glass vials (Agilent Technologies). The headspacevials were tightly capped with Teflon-faced rubber liner caps and subjected for incubationfor 72 h in dark at 25 °C. After the incubation period, the vials were equilibrated for 30 minat 60 °C using a TriPlus RSH autosampler (Thermo Scientific, Austin, TX, USA). The SPMEfibre after activation in the SSL injector was exposed and maintained in the vial headspacesurface for 15 min. The volatile profile analysis was performed on GC-MS/MS (Trace1310, TSQ 9000, Thermo Scientific, Austin, TX, USA) with electron impact ionization (70 eVionization energy). The separation was performed on Agilent HP-5MS capillary column(30 m x 0.25 mm, 0.25 um) using helium as carrier gas with 1.2 mLmin-1 flow. SPME fibrewas injected into the GC injection port and adsorbed volatiles in the fibre were desorbedonto the column at 250 °C for 5 min in splitless mode. The volatile organic compoundswere identified by comparison of their mass spectra with compounds corresponding tomass spectra library (NIST/EPA/NIH, Chromeleon 7.2 CDS Software, Thermo Scientific,Austin, TX,USA).The identified volatile organic compounds were expressed in percentagesas a normalised amount of each volatile organic compound resulted after the division ofpeak areas of identified volatile organic compounds by total peak area of all identifiedvolatile organic compounds. 2.4. Statistical Interpretation of Data The differences in rhizosphere soil microbial community composition were investi-gated by principal component analysis (PCA) using Statistica 10 software (StatSoft, Ham-burg, Germany). For the statistical analysis, the OD of each well after inoculation wassubtracted from the OD after each measurement period during the incubation. The aver-ages and standard deviations corresponding to each carbon source were determined assamples were analysed in triplicate, since Biolog EcoPlates contain three replicates of eachcarbon source. The average well colour development (AWCD), Richness (S), Shannon’sdiversity index (H') and Shannon's evenness index (E) were determined according tothe formulas presented by Sofo and Ricciuti [35]. All these parameters were calculatedseparately for all incubation times. For Richness, a 0.25 value for optical density (OD) wasset as the threshold for a positive response [35]. ANOVA was conducted to assess the effectof the studied pharmaceuticals on the community-level utilisation of carbon sources. The assumption of the homogeneity of variance and the test for normality of distributions wereverified applying Levene’s test and Shapiro-Wilk's test using Statistica 10 software version(StatSoft, Germany). A level of p= 0.05 was considered to assume statistical significance.The UpSetR diagrams were performed according to Khan and Mathelier [36]. 3. Results and Discussions 3.1. Rhizosphere Microbiota Abundance Changes with Contamination of NSAIDs The structure and abundance of microbiota were monitored in Lycopersicon esculentumrhizosphere soils with and without the artificial contamination with commonly consumedNSAIDs during a 60-day assay period. The total microbial biomass was expressed asthe sum of PLFAs with concentrations in control samples (without contamination) andthose contaminated ranged between 165.6 and 240 nmolg-dry weight soil during theassay (Figure 2). The control rhizosphere soil recorded higher values of PLFA (p<0.05)with concentration ranges of 184.8-240 nmolg-1 dry weight soil. In soils contaminatedwith NSAIDs, the microbial biomass in Lycopersicon esculentum rhizosphere soils has thefollowing pattern: 187.9-215.4 (ketoprofen contamination)>186.7-201.2 (diclofenac con-tamination)> 165.6-182.7 (ibuprofen contamination) nmolg-dry weight soil, respectively(Figure 2). 250 Day1 Day 7 Day 30 Day 60 口 Total PLFA ■ Total Bacteria 圆Total Fungi Figure 2. Soil microbiota abundance variation in rhizosphere soil during assay period. The PCA of the 48 PLFAs data (Figure 3) indicated that rhizosphere soil microbialcommunity abundance was markedly affected by soil contamination with NSAIDs,butpoor differentiation between control and contamination with ibuprofen was observed,indicated by their closest scores along the first principal component (PC1) and secondprincipal component (PC2). The first two components, PC1 and PC2 explained 44.92% and27.29% of the total variance in PLFAs abundance. The PC1 axis differentiated contaminationwith diclofenac and ketoprofen but did not differentiate controls by contamination withibuprofen, whereas the PC2 axis did not differentiate well control samples by contaminationwith specific NSAIDs. Figure 3. Principal component analysis (PCA) of rhizosphere PLFAs from control samples and thosecontaminated with different NSAIDs. 3.2. Rhizosphere Microbiota Community Structure Changes in Time with Contaminationof NSAIDs Rhizosphere microbiota structure abundance differed through the assay samplingperiods in all studied cases. Starting from day one until day thirty of the assay, an increasingtendency was observed, followed by a stabilization until day sixty of the assay (Figure 2). Lycopersicon esculentum rhizosphere soils presented a bacterial dominance (Table 3)in all studied assays. The ratios of fungi to bacteria (F/B), Gram-negative bacteria toGram-positive bacteria (G-/G+),aerobes bacteria to anaerobes bacteria (AerB/AnB) andectomycorrhizal fungi/saprotrophic fungi (Ecto/Sapro) are presented in Table 3. Table 3. Microbiota phenotypic structure components ratio variation among contamination. Day Fungi/ Gram (-)/ Aerobes/ Ectomycorrhizal/ NSAIDs Bacteria Gram (+) Anaerobes Saprotrophic 1 0.131 2.585 2.157 0.695 Control 7 0.128 2.422 3.286 0.496 30 0.122 2.151 3.090 0.669 60 0.121 2.518 3.274 0.614 1 0.135 4.926 2.111 0.702 Ibuprofen 7 0.104 3.382 2.351 0.916 30 0.080 3.165 3.211 0.849 60 0.075 3.157 4.820 0.835 1 0.100 2.379 2.805 1.033 Ketoprofen 7 0.090 1.887 3.747 1.441 30 0.095 1.950 3.977 1.594 60 0.099 1.946 4.386 2.360 1 0.094 2.764 4.063 0.722 Diclofenac 7 0.079 2.113 5.825 0.564 30 0.085 1.604 6.740 0.555 60 0.087 1.359 9.143 0.499 According to that, it was observed that among bacterial communities, a higher domi-nance was observed in the case of Gram-negative and aerobic bacteria ones. The averageamount of fungal/bacterial ratio among the contaminated soils with specific NSAIDsand the period of exposure revealed the following pattern: control > contamination withibuprofen > contamination with ketoprofen > contamination with diclofenac. Comparedwith control, the average value of the ratio of Gram-negative/Gram positive bacteriawas higher in the rhizosphere soil contaminated with ibuprofen and lower in the caseof diclofenac contamination (Table 3). Similarly, the ratio of aerobic/anaerobic bacteriapresented an increasing tendency compared with that of control, with the following pattern:control < contamination with ibuprofen
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