Arbuscular mycorrhizal and microbial profiles of an aged phenol–polynuclear aromatic hydrocarbon-contaminated soil

Ecotoxicology and Environmental Safety 192 (2020) 110299

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Arbuscular mycorrhizal and microbial profiles of an aged phenol–polynuclear aromatic hydrocarbon-contaminated soil

T

Monika Malicka∗, Franco Magurno, Zofia Piotrowska-Seget, Damian Chmura Institute of Biology Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia, Jagiellońska 28 Street, 40-032, Katowice, Poland

A R T I C LE I N FO

A B S T R A C T

Keywords: Arbuscular mycorrhizal fungi Glomeromycota Polynuclear aromatic hydrocarbons Phenol Denaturating gradient gel electrophoresis

Arbuscular mycorrhizal fungi (AMF) are ubiquitous, obligatory plant symbionts that have a beneficial influence on plants in contaminated environments. This study focused on evaluating the biomass and biodiversity of the AMF and microbial communities associated with Poa trivialis and Phragmites australis plants sampled at an aged site contaminated with phenol and polynuclear aromatic hydrocarbons (PAHs) and an uncontaminated control site. We analyzed the soil phospholipid fatty acid profile to describe the general structure of microbial communities. PCR-denaturing gradient gel electrophoresis with primers targeting the 18S ribosomal RNA gene was used to characterize the biodiversity of the AMF communities and identify dominant AMF species associated with the host plants in the polluted and control environments. The root mycorrhizal colonization and AMF biomass in the soil were negatively affected by the presence of PAHs and phenol, with no significant differences between the studied plant species, whereas the biodiversity of the AMF communities were influenced by the soil contamination and plant species. Soil contamination was more detrimental to the biodiversity of AMF communities associated with Ph. australis, compared to P. trivialis. Both species favored the development of different AMF species, which might be related to the specific features of their different root systems and soil microbial communities. The contaminated site was dominated by AMF generalists like Funneliformis and Rhizophagus, whereas in the control site Dominikia, Archaeospora, Claroideoglomus, Glomus, and Diversispora were also detected.

1. Introduction The Upper Silesian Industrial Region, located in Southern Poland, is one of the regions in Europe with the highest density of post-industrial contaminated “hot spots”. The region's economy is based on coal and heavy metal mining as well as the processing of toxic organic substances used in the large-scale production of several goods. Therefore, coal mine heaps, toxic waste dumps, and contaminated water courses are common elements of the Silesian landscape. Despite the high toxicity and poor edaphic factors of the soil, these sites are spontaneously and densely vegetated (Gucwa-Przepióra et al., 2016; Markowicz et al., 2015). Numerous studies have shown that plant growth promoting microorganisms (PGPMs) are among the key determinants of plant tolerance

and adaptation to unfavorable environments (Etesami and Maheshwari, 2018; Kumar, 2016; Ma et al., 2019; Pandey and Yarzábal, 2019). One of the most important groups of PGPMs are arbuscular mycorrhizal fungi (AMF), which ubiquitously colonize the roots of more than 80% of vascular plant species (Ma et al., 2019; Schüßler et al., 2001; Smith and Read, 2008). AMF enhance plant growth by increasing the uptake of inorganic nutrients and water, improving the soil structure, and alleviating several biotic and abiotic stresses (Bitterlich et al., 2018; Garg et al., 2017; Rajtor and Piotrowska-Seget, 2016; Shu and Wu, 2017). AMF have received considerable attention for the restoration and phytoremediation of sites affected by toxic recalcitrant pollutants like hydrocarbons and heavy metals, as many experiments have proved their contribution in decreasing the transfer of such pollutants to the shoots by trapping them in the roots and mycorrhizosphere (Garg et al.,

Abbreviations: ANOVA, Analysis of variance; AMF, Arbuscular mycorrhizal fungi; CCA, Canonical correspondance analysis; DBA, Dibenenzo(a,h)anthracene; DGGE, Denaturating gradient gel electrophoresis; EE-GRP, Easily extractable glomalin-related proteins; T-GRP, Total glomalin-related proteins; FLU, Fluoranthene; MOTU, Molecular operational taxonomic unit; N, Nitrogen; NAH, Naphthalene; NLFAs, Neutral fatty acids; PAHs, Polynuclear aromatic hydrocarbons; PGPMs, Plant growth promoting microorganisms; PHE, Phenanthrene; PLFAs, Phospholipid fatty acids; SOM, Soil organic matter; PYR, Pyrene; UPGMA, Unweighted pair group method with arithmetic mean ∗ Corresponding author. E-mail address: [email protected] (M. Malicka). https://doi.org/10.1016/j.ecoenv.2020.110299 Received 9 November 2019; Received in revised form 1 February 2020; Accepted 4 February 2020 0147-6513/ © 2020 Elsevier Inc. All rights reserved.

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Table 1 Soil physicochemical characteristics. Soil parameter

Kalina (contaminated)

pH H2O pH KCl Moisture content [%] Conductivity [μS] SOM content [%] Total N [%] a, b, c

Kokotek (uncontaminated)

P. trivialis Median ± IQR

Ph. australis Median ± IQR

P. trivialis Median ± IQR

Ph. australis Median ± IQR

7.08 ± 0.39 a 7.62 ± 0.59a 7.00 ± 2.00a 742.00 ± 504.50a 5.00 ± 1.38a 0.61 ± 0.05a

7.20 ± 0.21a 7.32 ± 0.93a 7.00 ± 2.00a 825.00 ± 661.00a 4.77 ± 1.69a 0.68 ± 0.05a

6.52 ± 0.68 c 6.65 ± 0.25 b 3.5 ± 2.25 b 90.75 ± 100.82 b 4.14 ± 1.36a 0.61 ± 0.04 b

6.92 ± 0.18 b 6.59 ± 0.25 b 5.5 ± 5.75a 54.15 ± 19.00c 2.44 ± 1.78a 0.61 ± 0.02 b

Different letters indicate significant differences between samples with P < 0.05 (Two-way DBM test followed by Conover test).

were collected. The bulk soil was removed manually by shaking the roots for 1 min. The fresh bulk soil was stored at 4 °C before the estimation of phenol and PAH concentrations and soil moisture content. For analyses of the granulometric composition, pH, soil organic matter (SOM) content, and Kjeldahl N level, part of the bulk soil was air dried, sieved with a 2-mm pore size sieve, and stored at room temperature. Rhizosphere and loosely adhering soil was recovered by shaking the roots in 0.9% NaCl for 30 min (120 rpm) and then centrifuging the soil suspension at 4000×g for 20 min, whereupon the pellet was stored at −20 °C. The roots were thoroughly washed with water and cut into 1cm-long pieces, which were either preserved in 70% ethanol at 4 °C before the evaluation of AMF colonization or stored at −20 °C before DNA extraction.

2017; Gucwa-Przepióra et al., 2016; Zhan et al., 2018). Moreover, AMF may enhance the degradation of hydrocarbons and other organic contaminants. Although AMF lack degradative ability, they actively cooperate with hydrocarbon-degrading bacteria by providing them with photosynthetically derived carbon (Iffis et al., 2016; Rajtor and Piotrowska-Seget, 2016). The identification and selection of the appropriate components of tripartite symbiotic systems (plant – AMF – bacteria) and the knowledge on the environmental variables that may stunt or boost their functions, are therefore mandatory (Liu and Dalpé, 2009; Yu et al., 2011; Zhou et al., 2013). The subject of our study is a preliminary assessment of the AMF biodiversity and their interactions with different plant species and microbiota in an aged soil environment contaminated with phenol and polynuclear hydrocarbons (PAHs). The main aims of the work are to (i) unravel the effects of phenol-PAH contaminants on the AMF root colonization, symbiotic activity, and AMF biomass in soil; (ii) determine the contribution of plant host species and phenol-PAH contamination in affecting the AMF biodiversity; (iii) identify dominant AMF with a potentially high tolerance to the organic contaminants; and (iv) characterize the associations between AMF and bacteria in the contaminated soil.

2.2. Soil physicochemical analysis According to the granulometric analysis (PN-R-04032 standard), the soil was characterized as loamy sand. The soil moisture content was assayed using a MAX50/WH moisture analyzer by drying the soil to a constant dry mass at 105 °C (ISO 16586:2003). The soil pH was measured in distilled water and 1 M KCl solution (1:5, w/v), using a CP-411 pH meter (ISO 10390:2005). The same aqueous solution was used to measure the soil conductivity, with a CC-511 conductometer (ISO 11265:1994). The SOM content was analyzed using the standard losson-ignition method (PN-EN 15935:2013–02), and the concentration of total N was measured using a Kjeldahl apparatus (PN–ISO 11261:2002). The phenol in the soil samples was extracted using Soxhlet apparatus (EPA 3540C method) and the concentration was determined by gas chromatography (EPA 8041 A method) using the 7820 A Gas Chromatograph System (Agilent, Santa Clara, California, USA) equipped with a Restek Rxi®-5 m s (Restek, Bellefonte, Pennsylvania, USA) column and a flame ionization detector. The extraction and determination of the PAHs concentration were performed according to the PN-ISO 13877:2004 protocol, using an LC-20AD high-pressure liquid chromatograph system (Shimadzu, Kyoto, Japan) equipped with a Supelcosil™ LC-PAH column (Supelco, Bellefonte, Pennsylvania, USA) and diode array detector. Each assay was performed in two technical repetitions per sample. All physicochemical properties of the soil and

2. Materials and methods 2.1. Study site and sampling design Samples of roots and polluted soil were collected in July 2016 from the shore of Kalina pond located at Świętochłowice in the Upper Silesian Industrial Region (50°16′49″N, 18°55′38″E). The selected location was aged by contamination with phenol and PAHs present in leakages from a toxic waste dump of an adjacent chemical plant. Notwithstanding, the surrounding area is abundantly inhabited by shoreline plant communities. Control samples were collected from the unpolluted shore of Kokotek pond II located at Lubliniec in the Silesian Upland (50°37′14″N, 18°43′46″E). The control site was chosen as the closest unpolluted pond with similar plant communities (dominated by Ph. australis and P. trivialis) and similar soil structure. For each of the two dominant species, 12 plants with a soil core

Table 2 Concentrations of phenol and PAHs in the contaminated soil samples. No significant differences were found. Concentrations of the contaminants in the soil samples collected from the control site were below detection limit. Concentrations of phenol and PAHs in the contaminated soil [mg kg soil−1]

Poa trivialis Median ± IQR Phragmites australis Median ± IQR

a

Phenola

NAH

PHE

FLU

PYR

DBA

ΣPAHs

1090.20 ± 739.20

3.78 ± 16.64

3.39 ± 9.58

7.28 ± 6.46

8.05 ± 23.34

9.85 ± 10.56

70.86 ± 124.53

1061.56 ± 702.21

3.69 ± 14.17

3.45 ± 9.58

7.58 ± 9.84

7.9 ± 23.88

41.83 ± 10.42

72.91 ± 142.35

Mean ± SD was shown. 2

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2.8. Phospholipid fatty acid (PLFA) and neutral fatty acid (NLFA) analyses

the concentrations of phenol and PAHs are listed in Tables 1 and 2.

Total PLFAs and NLFAs were extracted from the rhizosphere and soil loosely adhering to roots in two technical repetitions according to the method described by Frostegård et al. (1993), with minor modifications as described by Sułowicz and Piotrowska-Seget (2016). The concentrations of specific PLFAs and NLFAs, expressed as nanomoles per gram of dry soil, enabled estimation of the microbial biomass, including gram-negative bacteria (16:1ω7c, cy17:0, 18:1ω7c, cy19:0 PLFAs), gram-positive bacteria (i15:0, a15:0, i16:0, i17:0, a17:0 PLFAs), actinomycetes (10Me16:0, 10Me17:0, 10Me18:0 PLFAs) (Moore-Kucera and Dick, 2008), saprophytic fungi (18:2ω6.9c, 18:1ω9c PLFAs) (Frostegård et al., 2011), and AMF (16:1ω5c NLFAs) (Olsson, 1999).

2.3. Estimation of mycorrhizal root colonization by staining Root pieces were washed with tap water, depigmented in 10% KOH for 24 h, bleached with alkaline H2O2, when necessary, and stained using the ink and vinegar method described by Vierheilig et al. (1998), examinining one hundred root pieces per sample. The intensity of mycorrhizal colonization (M%), frequency of mycorrhiza occurrence (F %), and abundance of arbuscules (A%) and vesicles (V%) in the root system were determined using the method of Trouvelot et al. (1986), and calculated with the Mycocalc program (https://www2.dijon.inra. fr/mychintec/Mycocalc-prg/download.html). 2.4. Estimation of mycorrhizal root colonization by qPCR

2.9. Extraction of total DNA from roots and soil

The portion of the 18 S rDNA gene amplified by the AMF-specific primers AMV4.5NF/AMDGR (Sato et al., 2005) was used as a target for the SYBR Green qPCR. The 10-μl mixture contained 1 × FastStart Essential DNA Green Master (Roche), 0.5 mM of forward and reverse primers, and 1.5 ng of total DNA from the roots or 1 μl of a serial dilution of a standard (plasmid containing the amplicon of Rhizophagus irregularis DAOM, 197198), in two technical repetitions. The reaction was performed in a LightCycler® 96 instrument (Roche, Basel, Switzerland) with the following conditions: initial denaturation at 95 °C (5 min), followed by 40 cycles of denaturation at 95 °C (10 s), annealing at 55 °C (30 s), extension at 72 °C (30 s), and primer dimer removal and signal acquisition at 80 °C (10 s). The 18 S rDNA copy number in each sample was calculated using LightCycler® 96 Application Software according to a calibration curve prepared from a serial dilution of the standard.

Total DNA was extracted from 0.5 g of the rhizosphere and soil loosely adhering to roots using the PowerSoil® DNA Isolation Kit (Mo Bio, Carlsbad, California, USA). DNA was extracted from 0.5 g of the roots using the PowerPlant® DNA Isolation Kit according to the manufacturer's protocol (Mo Bio, Carlsbad, California, USA). The quality and concentration of the DNA samples were evaluated using a NanoDrop™ 2000 UV–Vis spectrophotometer (Thermo Fischer Scientific, Waltham, Massachusetts, USA) and through electrophoresis on 2% agarose gels. The DNA samples were stored at −80 °C before further manipulation. 2.10. PCR-DGGE analysis of the AMF community in roots and soil Using a three-step nested-PCR strategy, a portion of the AMF 18 S rDNA gene was amplified with the universal eukaryotic primers NS1/ NS4 (White et al., 1990), AMF-specific primers AML1/AML2 (Lee et al., 2008), and AMF-specific primers NS31-GC/Glo 1 with GC-clamp (Liang et al., 2008). The PCR on 0.2 μg DNA template per sample was performed with DreamTaq Polymerase according to the manufacturer's protocol (Thermo Fisher Scientific, Waltham, Massachusetts, USA), in a T100™ Thermal Cycler (Bio-Rad, Hercules, California, USA), using the thermal cycling conditions described by Wang et al. (2015). The amplicons were checked for correct size by electrophoresis on a 2% agarose gel. 20 μl of the final PCR product were separated on a 6% polyacrylamide gel (37.5:1 acrylamide:bisacrylamide), with a 30–50% linear horizontal denaturing gradient (100% denaturant agent of 7 M urea and 40% deionized formamide), for 15 h at 60 °C and 70 V, using the DCode™ Universal Mutation Detection System (Bio-Rad, Hercules, California, USA). The gels were stained with SYBR Gold (Invitrogen, Carlsbad, California, USA) and photographed using the Gel Doc™ XR + System with UV transillumination (Bio-Rad, Hercules, California, USA). Detection of the bands and calculation of their relative intensities were performed using Image Lab™ software (Bio-Rad, Hercules, California, USA). The values obtained were used to calculate the species richness (R’ = number of bands), Shannon's diversity index (H′), and Shannon's evenness index (J′). The analysis of similarity between AMF communities was performed in MOTHUR 1.39 (Schloss et al., 2009) using the Jaccard similarity coefficient. The distance matrix obtained was used for cluster analysis using the UPGMA.

2.5. AMF spore counting AMF spores were isolated from the rhizosphere and soil loosely adhering to roots (20 g per sample) using the wet sieving method described by Gerdemann and Nicolson (1963) followed by centrifugation in a 50% sucrose solution (Daniels and Skipper, 1982). The spores were captured on a gridded membrane filter using vacuum filtration and counted under a dissecting microscope. 2.6. Measurement of AMF extramatrical hyphal length The extraction of AMF hyphae from the rhizosphere and soil loosely adhering to roots was performed using the method described by Abbott and Robson (1985). Hyphae isolated on a membrane filter by vacuum filtration were stained with 0.05% trypan blue in lactoglycerol, and the hyphal length was measured using the gridline intersect method described by Tennant (1975), examining 50 squares on the membrane filter per sample. 2.7. Estimation of the concentration of glomalin-related proteins The extraction of EE-GRP (easily extrahable glomalin related proteind) and T-GRP (total pool of glomalin related proteind) from the rhizosphere and soil loosely adhering to roots was performed according to the method described by Wright and Upadhyaya (1998). In brief, the GRP fractions were extracted in three repetitions/sample, autoclaving (EE-GRP: 30 min; T-GRP: 4 extractions, 60 min each) from 1 g of soil suspended in 8 ml of sodium citrate solution (EE-GRP: 20 mM; T-GRP: 50 mM). The extracts were centrifuged and the GRP concentrations in the supernatants were measured using the Bradford total protein assay (Bradford, 1976; Wright et al., 1996). Extractions and the Bradford assay were performed in two technical repetitions per sample.

2.11. Molecular identification of dominant AMF species The most intensive and common bands in root DGGE profiles were selected to identify dominant AMF species in each sampling site. Bands were excised from the polyacrylamide gel and incubated in water overnight at 4 °C to elute the DNA, which was then reamplified with the primers NS31/Glo 1. The PCR products, purified with the Clean-Up Kit (A&A Biotechnology, Gdynia, Poland), were sequenced at Genomed S.A. (Warsaw, Poland). Sequence editing was conducted manually using 3

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Table 3 The values of AMF abundance indicators in roots (R) and soil (S) for different plant species, Poa trivialis (PT) and Phragmites australis (PA), and different sampling sites, Kalina (K) and control (C). AMF ABUNDANCE INDICATOR

Number of 18 S rDNA gene copies [log gene copies g−1 dry soil]

Hyphal length [m g−1 dry soil]

Number of spores g−1 dry soil

16ω5c NLFA [nmol g−1 dry soil]

Easily extrahable GRP [nmol g−1 dry soil]

Total GRP [mg kg−1 dry soil]

a, b, c

Sample

K_PT_R K_PA_R C_PT_R C_PA_R K_PT_S K_PA_S C_PT_S C_PA_S K_PT_S K_PA_S C_PT_S C_PA_S K_PT_S K_PA_S C_PT_S C_PA_S K_PT_S K_PA_S C_PT_S C_PA_S K_PT_S K_PA_S C_PT_S C_PA_S

Mean ± SD

P-value

6.38 ± 0.46b 6.12 ± 0.45b 6.89 ± 0.16a 6.96 ± 0.24a 3.92 ± 1.85a 1.99 ± 1.02b 1.46 ± 1.01b 1.80 ± 0.58b 52.00 ± 24.00a 62.00 ± 28.00a 28.00 ± 4.00b 16.00 ± 5.00b 7.75 ± 6.98b 2.96 ± 2.12b 42.43 ± 23.13a 9.18 ± 9.52b 3.21 ± 1.42c 3.42 ± 1.66c 10.02 ± 3.38a 6.79 ± 2.15b 39.39 ± 4.94bc 24.60 ± 5.79c 59.13 ± 25.38ab 67.08 ± 29.65a

Variance explained by a site factor (%)

Sampling site

Plant species

Site x plant

< 0.001

0.362

0.121

92.8

< 0.001

0.054

< 0.001

50.4

< 0.001

0.363

0.015

87.7

< 0.001

< 0.001

0.001

47.62

< 0.001

0.081

0.030

88.7

< 0.001

0.246

0.007

74.6

Different letters indicate significant differences between samples with P < 0.05 (Two-way ANOVA followed by Tukey's HSD test).

contaminated sites, two groups were compared (differences between two species). Student's test or Wilcoxon sum rank test was applied when data did not fulfill requirements for parametrical tests. Comparison of the biodiversity indices (R′, H′, J′) based on DGGE profiles was performed with three-way ANOVA, with the sampling site, plant species, and sample type (roots or soil) as factors. In the case of identification of the sampling site as the main factor and no interactions between the analyzed factors, two-way ANOVA was performed, with the plant species and sample type as factors, followed by Tukey's HSD test to identify significant differences between the biodiversity indices for plants collected from the same site. The calculation of Spearman's correlation matrix between all of the data analyzed in this study and CCA of the PLFA profiles of the microbial communities and DGGE profiles of the AMF communities were performed using Past 3.14 (Hammer et al., 2001).

Mega 6.06 (Tamura et al., 2013) and Chromas Lite 2.01 (Technelysium Pty Ltd, Brisbane, Queensland, Australia). Non-Glomeromycotina sequences and chimeras were discarded after Blastn analysis. Sequences were clustered in MOTUs at a 97% level of similarity, using Mothur 1.39 (Schloss et al., 2009). Eighteen reference sequences and MOTU representative sequences were aligned through the Cipres web-portal with MAFFT on XSEDE (7.305) (Katoh et al., 2005). The alignment was performed using the G–INS–i method for global alignment, and 20PAM/k = 2 for the nucleic acid matrix selection. Bayesian phylogenetic tree inference was computed with MrBayes 3.2 (Ronquist et al., 2012) using GTR + G as nucleotide substitution model. Four Markov chains were run over one million generations with 1000 trees sampled. The generation of the tree was supported by partial constraints selected from a reference tree with 18 AMF 18 S rDNA reference sequences, approximately 1120 bp long, built using the parameter for bayesian analysis as above. The phylogenetic tree was visualized and edited in Mega 6 (Tamura et al., 2013). The sequences obtained in the present study were deposited at the National Center for Biotechnology Information's GenBank database under the accession numbers MK351205–MK351220.

3. Results and discussion Our study showed that long-term soil contamination with toxic organic pollutants had a strong negative influence on the mycorrhizal root colonization, AMF community biodiversity, and AMF abundance in roots and soil. To the best of our knowledge, this is the first study on AMF communities in soil strongly contaminated with phenol and PAHs; therefore, we compared our results with studies on AMF in environments polluted with crude oil and petroleum hydrocarbons, which also contained a substantial fraction of PAHs. As a control, uncontaminated site with a similar plant community and soil granulometry was selected. The CCA analysis and Spearman's correlations confirmed that the differences in soil physicochemical parameters, particularly the pH, were a consequence of the presence of contaminants. High conductivity was associated with the presence of steel filings from post-industrial wastes. No significant concentrations of heavy metals were detected with the atomic absorption spectrophotometer (data not shown).

2.12. Statistical analyses Statistical tests were performed using the Statistica 12.5 PL software package (StatSoft, Inc., Cracow, Poland) and R software (R Core Team, 2019) with packages asbio (Aho, 2019). The comparison of soil physicochemical characteristics, soil AMF biomass, mycorrhizal root colonization, and soil PLFA profiles was based on two-way ANOVA, with the sampling site and plant species as factors or by means of two way heteroscedastic rank-based permutation test Brunner-Dette-Munk test (BDM test, Brunner et al., 1997) in case when data did not meet assumptions for parametrical tests i. e normality and homogeneity of variance. In the case of significance of interactions between the factors and one of the main effects (site, species), post-hoc comparisons (Tukey's honestly significant difference test, or non-parametrical Conover test) were performed to find significant differences between the contaminated and uncontaminated sites for each plant species. For

3.1. Root colonization The root colonization by AMF was significantly different between 4

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Table 4 The comparison of concentrations of PLFAs specific for different groups of microorganisms between different plant species, Poa trivialis (PT) and Phragmites australis (PA), and different sampling sites, Kalina (K) and control (C). PLFAs [nmol g−1 dry soil]

Total

Bacterial

Gram-positive

Gram-negative

Actino-mycetes

Saprophytic fungi

a, b, c

Sample

K_PT K_PA C_PT C_PA K_PT K_PA C_PT C_PA K_PT K_PA C_PT C_PA K_PT K_PA C_PT C_PA K_PT K_PA C_PT C_PA K_PT K_PA C_PT C_PA

Mean ± SD*

108.07 ± 36.81a 164.26 ± 67.86a 143.16 ± 63.18a 148.70 ± 51.72a 47.77 ± 16.55a 73.72 ± 30.28a 62.50 ± 28.86a 66.89 ± 23.88a 22.99 ± 8.23b 37.59 ± 19.33a 25.01 ± 11.73b 22.34 ± 6.82b 22.92 ± 7.61b 32.80 ± 10.50ab 35.70 ± 17.68ab 43.78 ± 17.78a 1.87 ± 0.75b 3.33 ± 1.13a 1.80 ± 1.18 b 0.76 ± 0.39c 7.93 ± 2.85a 13.24 ± 6.81a 11.61 ± 5.36a 11.77 ± 5.62a

P-value

Variance explained (%)

Sampl. site

Plant species

Site x plant

site factor

plant factor

0.569

0.048

0.146

4.9

62.2

0.647

0.033

0.184

3.1

70.3

0.056

0.084

0.029

31.9

25.9

0.009

0.022

0.730

56.3

42.8

< 0.001

0.767

< 0.001

61.1

0.2

0.417

0.074

0.098

9.7

48.7

Different letters indicate significant differences between samples with P < 0.05 (Two-way ANOVA followed by Tukey's HSD test).

Table 5 The comparison of biodiversity indices based on DGGE profiles of AMF communities in roots (R) and soil (S) between different plant species, Poa trivialis (PT) and Phragmites australis (PA), and different sampling sites, Kalina (K) and control (C). BIODIVERSITY INDEX

Shannon-Wienner [H’]

Species richness [R’]

Species evenness [J’]

Sample

K_PT_S K_PA_S C_PT_S C_PA_S K_PT_R K_PA_R C_PT_R C_PA_R K_PT_S K_PA_S C_PT_S C_PA_S K_PT_R K_PA_R C_PT_R C_PA_R K_PT_S K_PA_S C_PT_S C_PA_S K_PT_R K_PA_R C_PT_R C_PA_R

Mean ± SD

2.69 ± 0.23A 2.66 ± 0.32A 2.94 ± 0.12ab 3.14 ± 0.20a 2.28 ± 0.42B 2.11 ± 0.51B 2.98 ± 0.23a 2.76 ± 0.17b 31.33 ± 4.58A 38.83 ± 5.95A 36.50 ± 4.81b 46.25 ± 4.33a 24.00 ± 6.69B 22.33 ± 6.04B 35.50 ± 5.99b 30.17 ± 3.83c 0.78 ± 0.06A 0.73 ± 0.08AB 0.82 ± 0.02a 0.82 ± 0.04a 0.72 ± 0.12AB 0.65 ± 0.15B 0.81 ± 0.10a 0.81 ± 0.03a

P-value

Variance explained (%)

Sampl. site

Plant species

Sample type

site factor

sample type factor

< 0.001

0.394

< 0.001

62.7

23.5

< 0.001

0.022

< 0.001

29.4

32.5

< 0.001

0.104

0.02

67.2

12.5

A, B

Uppercase letters indicate significant differences between samples collected from different plants species from the contaminated site with P < 0.05 (Three-way ANOVA followed by two-way ANOVA and Tukey's HSD test). a, b Lowercase letters indicate significant differences between samples collected from different plants species from the uncontaminated site with P < 0.05 (Threeway ANOVA followed by two-way ANOVA and Tukey's HSD test).

lower in the samples collected from the contaminated site. The abundance of vesicles in the roots ranged from 0% to 6%. The root colonization parameters showed strong negative correlation with the concentration of soil contaminants (Supplementary Fig. 2). Although the root colonization parameters were slightly higher for P. trivialis than for

the contaminated and control sites. A moderate (M% = 5%–65%) and a low (M% = 0%–25%) intensity of mycorrhizal colonization was determined in the uncontaminated and contaminated sites, respectively (Supplementary Fig. 1). The F%, A% (Supplementary Fig. 1), and AMF 18 S rDNA copy numbers in the roots (Table 3) were also significantly 5

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Fig. 1. CCA showing the relationship between environmental variables and soil (A) and root (B) DGGE profiles of AMF 18S rDNA gene. Dots and diamonds represent samples which represent P. trivialis (grey) and Ph. australis (black) from the contaminated site and uncontaminated site, respectively.

3.2. Quantification of AMF in the rhizosphere and soil loosely adhering to roots

Ph. australis, the differences between two species were not statistically significant. Two-way ANOVA showed no interaction between the plant species and the sampling site, indicating that the sampling site was the main factor affecting the root colonization. In accordance with this result, studies on plants growing in hydrocarbon-contaminated lands in the Amazon jungle (Garcés-Ruiz et al., 2017), Canada (Iffis et al., 2016), Mexico (Franco-Ramírez et al., 2007), and Argentina and Germany (Cabello, 1997) have reported poor AMF colonization and low arbuscular abundance, compared to sites with no or low contamination. The negative effects of PAHs on root colonization were also demonstrated in monoxenic root organ cultures, where despite being poorly colonized, mycorrhizal roots showed better growth and lower oxidative stress than the non-mycorrhizal controls did (Aranda et al., 2013; Calonne et al., 2014b; Debiane et al., 2008, 2009). Additionally, microscopic examination of the roots revealed different types of mycorrhizal colonization in both sampling sites (Supplementary Fig. 3). In the roots collected from the control site, both the Arum and Paris types were observed. In the roots collected from the contaminated site, only the Arum type was present, suggesting the domination of Glomeraceae in the AMF community (Pandey and Garg, 2017).

Beside poor root colonization, in vitro and field studies have also reported impairments in the development of extraradical mycelia and sporulation of AMF in the presence of certain contaminants (Aranda et al., 2013; Debiane et al., 2008, 2009; Franco-Ramírez et al., 2007; Nwoko, 2014), which was associated with decreases in sterol and phospholipid biosyntheses (Calonne et al., 2014a), succinate dehydrogenase activity (Gaspar et al., 2002), and P transport (Calonne et al., 2014b). In our study, two-way ANOVA identified the sampling site as the main factor responsible for the differences in AMF biomass in the soil samples and its significant interaction with the plant species (Table 3). The spore number and hyphal length were significantly higher in the contaminated site than in the control site; however, the vast majority of spores collected from the contaminated site were damaged and dead. More accurate indicators of AMF biomass, such as the concentrations of GRP and AMF-specific 16ω5c NLFA (highly present in vital lipid-rich spores), were lower in the contaminated soil, highlighting the detrimental effects of phenol and PAHs on the biomass and on the development of AMF propagules and extramatrical mycelia in the soil (Bedini et al., 2007; Vestberg et al., 2012). The concentration of T-GRP was higher in the uncontaminated soil, albeit significant differences were found only for Ph. australis. No significant differences were

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Fig. 2. UPGMA clustering of DGGE profiles of AMF 18S rDNA amplicons from soil (A) and roots (B). Node labels represent the values of Jaccard dissimilarity coefficient. C and K mean samples from the control (uncontaminated) and Kalina (contaminated) site, respectively. PT and PA are abbreviations of plant species Poa trivialis and Phragmites australis. The UPGMA dendrogram was visualized using the Figtree 1.4 program (Rambaut, 2010).

positive PLFAs were very high in the soil associated with Ph. australis in the contaminated site, they showed no significant correlations with the soil contaminant concentrations or with the soil physicochemical parameters. In contrast, the concentrations of gram-negative PLFAs were the highest in association with Ph. australis in the uncontaminated site, showing significantly negative correlation with the phenol and PAH concentrations, soil pHKCl, conductivity, SOM content, and total N concentration (Table 4, Supplementary Fig. 2). The highest difference between the sampling sites was recorded for the actinobacterial PLFA markers, the concentration of which was higher in the contaminated site and positively correlated with the soil contaminant concentrations, conductivity, and total N level (Table 4, Supplementary Fig. 2). The actinomycetes include very important degraders of aromatic organic contaminants; for example, Rhodococcus (Pizzul et al., 2006; Płociniczak et al., 2017; Rehfuss and Urban, 2005). The concentrations of total bacterial PLFAs, saprophytic fungal PLFAs, and gram-positive PLFAs showed no correlation with the indicators of AMF root colonization, biomass, and diversity. However, the gram-negative PLFA concentrations were positively correlated with the EE-GRP concentration and AMF H’ index in the roots. Actinobacterial PLFAs showed negative correlations with the F%, EE-GRP, T-GRP, number of AMF 18 S rDNA gene copies in the roots, and AMF biodiversity indices (Supplementary Fig. 2). Although the cooperation between AMF and bacteria in both the protection of plants and rhizodegradation of organic contaminants has often been reported (Rajtor and Piotrowska-Seget, 2016), AMF-suppressive microbial communities may also occur, when Actinobacteria are one of the main microbial components in soil (Svenningsen et al., 2018). Actinobacteria produce a broad spectrum of antibacterial and antifungal agents (Wang et al., 2018).

found in the T-GRP concentrations of the plant species at the same site, whereas significant differences were reported for the EE-GRP concentrations, which were higher in the control site, especially in the soil associated with P. trivialis (3-fold higher than in the contaminated site). Moreover, the concentration of EE-GRP showed strong positive correlation with the concentration of 16ω5c NLFA (Supplementary Fig. 2).

3.3. Microbial biomass in soil and its interaction with AMF biomass Two-way ANOVA showed no significant differences in the concentrations of total, bacterial, and saprophytic fungal PLFAs between the soils collected from the contaminated and uncontaminated sites (Table 4). The concentrations of PLFAs were not correlated with the soil physicochemical parameters, except for the saprophytic fungal PLFAs, the concentration of which was negatively correlated with pHKCl (Supplementary Fig. 2). Although no differences in the concentrations of total bacterial PLFAs between the sites and the plant species were found, there were differences between the PLFA concentrations of the actinomycetes and gram-positive and gram-negative bacteria in the soil associated with Ph. australis. The CCA analysis of soil PLFA profiles confirmed that soil contamination had a stronger influence on the composition of microbial communities associated with Ph. australis than with that of P. trivialis (Supplementary Fig. 4). Ph. australis is known for its high potential in the bioremediation of hydrocarbon-contaminated soils and wetlands (Abed et al., 2018; Gregorio et al., 2014). Its positive influence on hydrocarbon degraders is mediated by the production of root exudates that are rich in phenolic compounds (Toyama et al., 2011), the effective influx of oxygen to deeper parts of the soil or sediments, and an increased redox potential (Gregorio et al., 2014). The lower potential of P. trivialis to stimulate the soil microbiota in harsh conditions is probably due to its shallow root system, which might have a less significant contribution to the rhizosphere priming effect (Shahzad et al., 2015; Zwicke et al., 2015). Shahzad et al. (2015) showed that P. trivialis did not stimulate soil microbial activity, even if it was well colonized by AMF. Although the concentrations of gram-

3.4. Structure and biodiversity of AMF communities based on DGGE profiles Although, the literature data on the DGGE methodology suggest that the “1 band–1 species” assumption results in overestimation of the AMF diversity (Neilson et al., 2013), DGGE is a fast and inexpensive way to 7

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even under high concentrations of hydrocarbons (Huang et al., 2017; Rajtor and Piotrowska-Seget, 2016; Zhan et al., 2018). Glomeraceae are ubiquitous „generalists” with low ecological requirements, characterized by a short life cycle. They rapidly colonize plant roots and invest a lot of carbon and energy resources in sporulation, which enables them to adapt and survive under the pressure of abiotic stress, in the contaminated and disturbed anthropogenic habitats (Fester, 2013; Lenoir i wsp., 2016). In agreement with our findings, these taxa were frequently found to be dominant in environments polluted with hydrocarbons. Funneliformis, which was the only genus associated exclusively with the contaminated site, was reported in an artificial wetland conceived for the phytoremediation of groundwater (Fester, 2013) and in contaminated soils in Argentina and Germany (Cabello, 1997). Hassan et al. (2014) and Iffis et al. (2016) respectively reported an extreme (90%) and a very high (65%) relative abundance of Rhizophagus in hydrocarbon-contaminated soils, which was rapidly decreasing in parallel with the concentration of contaminants. Several studies have demonstrated the potential of Claroideoglomus for rapid root colonization of plants in unfavorable environments if not overdominated by members of the Glomeraceae (Hassan et al., 2014; Iffis et al., 2016; de la Providencia et al., 2015). In the control site, the most abundant MOTU (01), which was present in every root sample, was placed in the Glomeraceae clade without clustering to any known genus clade. Among the other MOTUs, those related to the genera Dominikia (MOTU09, 10, 11), Claroideoglomus (MOTU05, 06), Glomus (MOTU08), and Diversispora-Otospora (MOTU14, 15), and the taxon Glomeraceae sp. (MOTU12) were characteristic of the P. trivialis AMF root communities. The Ph. australis roots were inhabited by Archaeospora sp. (MOTU16, 100% of root samples), Rhizophagus sp. (MOTU03), and Glomeraceae sp. (MOTU13). The preference of Diversisporaceae for unpolluted sites was reported in the studies of Hassan et al. (2014) and Iffis et al. (2016), showing the high ecological requirements of this family. Archaeospora and Dominikia were previously found in uncontaminated sites and sites contaminated with hydrocarbons (Hassan et al., 2014; Garcés-Ruiz et al., 2017; Krüger et al., 2017). However, information about the distribution pattern of Dominikia is limited owing to its recent description as a new genus (Błaszkowski et al., 2015). Whereas the plant identity had no significant effects on the AMF root colonization and AMF biomass in soil, it affected the biodiversity of AMF communities in both sites, especially the uncontaminated site. Ph. australis and P. trivialis represent ruderal perennial Poaceae, characterized by different lifestyles and morphologies. P. trivialis is a common hemicryptophyte of moist grasslands and pastures, is very durable, and is planted frequently in bad quality or polluted soils (Liu et al., 2017). In Silesian Voivodeship, Ph. australis is an invasive helophyte of wetlands, dry coal mine heaps, and zinc-lead heaps contaminated with heavy metals (Talik et al., 2018). The species is known for its high phytoremediation potential, also with AMF assistance (Fester, 2013; Huang et al., 2017). Both plant species were characterized by a low biodiversity of AMF communities in the contaminated site; however, in the control site, without any pressure of stress factors, the AMF biodiversity was higher in Ph. australis than in P. trivialis. Plants were also inhabited by different dominant AMF species in both studied environments. AMF fungi do not form species-specific associations with a host plant, but they can preferentially colonize certain plant species in the same environment (Magurno et al., 2015). In the case of P. trivialis and Ph. australis, it may depend on their different root architecture and potentially the composition of root exudates, which are used by plants to attract or repel different groups of rhizosphere microorganisms (Smet et al., 2015; Marquez-Garcia et al., 2014).

compare the AMF biodiversity between the studied sites. The analysis of the DGGE profiles of AMF 18 S rDNA gene fragments by three-way ANOVA indicated the sampling site and sample type as the main factors affecting the AMF biodiversity (Table 5). The AMF H′ and R’ biodiversity indices were lower in the contaminated site than in the uncontaminated site, showing a negative correlation with the concentration of soil contaminants (Table 5, Supplementary Fig. 2). The highest AMF species richness was detected in the uncontaminated soil, with 45 bands recorded, whereas the number was below 40 in the contaminated soil. Other studies based on high-throughput sequencing have unveiled less than 30 different AMF MOTUs in polluted sites (Garcés-Ruiz et al., 2017; Hassan et al., 2014; Iffis et al., 2016; de la Providencia et al., 2015). Moreover, in both sampling sites, the AMF biodiversity was lower in the roots than in the soil, as reported in previous studies (Balestrini et al., 2010). The biodiversity indices of the root AMF communities were negatively correlated with the soil physicochemical parameters and positively correlated with the root mycorrhizal colonization parameters (F%, M%, A%, and 18 S rDNA gene copies) (Supplementary Fig. 2). The divergence between AMF richness in the roots and soil was larger in the contaminated site, probably due to the extremely low (sometimes absent) root colonization. The differences in AMF biodiversity indices between the plant species were significant only in the uncontaminated site, mainly in the roots, with the highest values in P. trivialis (Table 5). The J’ index of the AMF community was higher in the uncontaminated site than in the contaminated site, with no significant differences between plant species and between the roots and soil (Table 5). CCA analysis of the DGGE profiles (Fig. 1. A, B) and UPGMA cluster analysis (Fig. 2. A, B; Supplementary Fig. 5) demonstrated that the similarity of the AMF communities between the two sampling sites was very low, especially in the soil. The distribution of DGGE profiles associated with the contaminated and uncontaminated sites along the CCA axis 2 was shaped by the concentration of soil contaminants and physicochemical parameters of the soil. The differences in DGGE profiles between P. trivialis and Ph. australis were more pronounced in the uncontaminated site, which was characterized by a higher AMF biodiversity. The UPGMA clustering analysis also revealed a higher similarity between the DGGE profiles of individual soil samples in the uncontaminated site compared with those in the contaminated site. The CCA analysis showed a higher dispersion of individual DGGE profiles in the contaminated soil in relation to the contaminant concentration and soil physicochemistry. In order to identify the dominant AMF species present in the plant roots, 40 DGGE bands (20 bands for each sampling site), characterized by their highest relative abundance, were sequenced. Fourteen nonAMF sequences from the contamimated site and one chimeric sequence from the uncontaminated site were rejected. According to the BLASTn analysis, the non-AMF sequences were related to the pathogenic fungus Olpidium brassicae, the endophytic fungus Piriformospora indica, and Cercozoa (Protista). Only six sequences from the contaminated site were identified as AMF-related rDNA. The AMF-specific sequences were clustered in 4 and 13 MOTUs for the contaminated and uncontaminated sites, respectively. According to the phylogenetic analysis (Supplementary Fig. 6), MOTUs from the contaminated site were associated with Funneliformis caledonium (MOTU02, detected in 90% of Ph. australis root samples), and with Rhizophagus sp. (MOTU03), Funneliformis mosseae (MOTU07), and Claroideoglomus luteum (MOTU04), present in 60%, 40%, and 25% of the P. trivalis root samples, respectively. Pot and in vitro experiments showed that some genera from Glomeraceae, like Rhizophagus and Funneliformis, were negatively affected by the presence of hydrocarbon contaminants (Aranda et al., 2013; Debiane et al., 2009; Nwoko, 2014). Nevertheless, when employed in experiments of AMF-assisted phytoremediation of substrates contaminated with petroleum hydrocarbons, they were able to survive, colonize plants, and improve their growth

4. Conclusions Our results demonstrated the negative influence of phenol and PAHs on the development of arbuscular mycorrhizae. The AMF species 8

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richness was lower in plant roots collected from the contaminated site, indicating a weakened AMF symbiotic activity. In the contaminated environment, only the generalist AMF genera like Rhizophagus and Funneliformis were present. Examination of the AMF communities associated with Ph. australis and P. trivialis showed no influence of the plant identity on the AMF biomass in the roots and soil; however, both plant species and microbial communities associated with them favored the development of different AMF species. Although the DGGE-based study of the AMF community did not provide a detailed characterization of the AMF richness and species composition, it nonetheless demonstrated the general shaping of the mycorrhizal status in the contaminated environment, indicating the AMF species that may potentially show tolerance to the presence of organic contaminants. Thus, such studies can be useful in future experiments on AMF-assisted phytoremediation of polluted soils, as well as for gaining knowledge on the interactions of AMF with different plant species and soil microbial compositions.

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CRediT authorship contribution statement Monika Malicka: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing original draft. Franco Magurno: Formal analysis, Writing - original draft. Zofia Piotrowska-Seget: Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing. Damian Chmura: Formal analysis. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2020.110299. References Abbott, L.K., Robson, A.D., 1985. Formation of external hyphae in soil by four species of vesicular‐arbuscular mycorrhizal fungi. New Phytol. 99, 245–255. https://doi.org/ 10.1111/j.1469-8137.1985.tb03653.x. Abed, R.M.M., Al-Kharusi, S., Gkorezis, P., Prigent, S., Headley, T., 2018. Bacterial communities in the rhizosphere of Phragmites australis from an oil-polluted wetland. Arch. Agron Soil Sci. 64, 360–370. https://doi.org/10.1080/03650340.2017. 1352087. Aho, A., 2019. Asbio: A Collection of Statistical Tools for Biologists. R Package Version 1.5-5. https://CRAN.R-project.org/package=asbio. Aranda, E., Scervino, J.M., Godoy, P., Reina, R., Ocampo, J.A., Wittich, R.M., GarcíaRomera, I., 2013. Role of arbuscular mycorrhizal fungus Rhizophagus custos in the dissipation of PAHs under root-organ culture conditions. Environ. Pollut. 181, 182–189. https://doi.org/10.1016/j.envpol.2013.06.034. Balestrini, R., Magurno, F., Walker, C., Lumini, E., Bianciotto, V., 2010. Cohorts of arbuscular mycorrhizal fungi (AMF) in Vitis vinifera, a typical Mediterranean fruit crop. Environmental Microbiology Reports 2, 594–604. https://doi.org/10.1111/j.17582229.2010.00160.x. Bedini, S., Avio, L., Argese, E., Giovannetti, M., 2007. Effects of long-term land use on arbuscular mycorrhizal fungi and glomalin-related soil protein. Agric. Ecosyst. Environ. 120, 463–466. https://doi.org/10.1016/j.agee.2006.09.010. Bitterlich, M., Sandmann, M., Graefe, J., 2018. Arbuscular mycorrhiza alleviates restrictions to substrate water flow and delays transpiration limitation to stronger drought in tomato. Front. Plant Sci. 9, 154. https://doi.org/10.3389/fpls.2018. 00154. Bradford, M.M., 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254. https://doi.org/10.1016/0003-2697(76)90527-3. Brunner, E., Dette, H., Munk, A., 1997. Box-type approximations in nonparametric factorial designs. J. Am. Stat. Assoc. 92, 1494–1502. https://doi.org/10.1080/ 01621459.1997.10473671. Błaszkowski, J., Chwat, G., Góralska, A., Ryszka, P., Kovacs, G., 2015. Two new genera, Dominikia and Kamienskia, and D. disticha sp. nov. in Glomeromycota. Nova Hedwigia 100, 225–238. https://doi.org/10.1127/nova_hedwigia/2014/0216. Cabello, M.N., 1997. Hydrocarbon pollution: its effect on native arbuscular mycorrhizal fungi (AMF). FEMS (Fed. Eur. Microbiol. Soc.) Microbiol. Ecol. 22, 233–236. https:// doi.org/10.1016/S0168-6496(96)00094-3. Calonne, M., Fontaine, J., Debiane, D., Laruelle, F., Grandmougin-Ferjani, A., LounèsHadj Sahraoui, A., 2014a. The arbuscular mycorrhizal Rhizophagus irregularis activates storage lipid biosynthesis to cope with the benzo[a]pyrene oxidative stress. Phytocchemistry 97, 30–37. https://doi.org/10.1016/j.phytochem.2013.10.014. Calonne, M., Fontaine, J., Tisserant, B., Dupré de Boulois, H., Grandmougin-Ferjani, A.,

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