Earthworms and cadmium – Heavy metal resistant gut bacteria as indicators for heavy metal pollution in soils?

Ecotoxicology and Environmental Safety 171 (2019) 843–853

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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Earthworms and cadmium – Heavy metal resistant gut bacteria as indicators for heavy metal pollution in soils?

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Maja Šruta,1, , Sebastian Menkeb,1, Martina Höcknerc, Simone Sommerb a

University of Zagreb, Faculty of Science, Department of Biology, Rooseveltov trg 6, 10000 Zagreb, Croatia University of Ulm, Institute of Evolutionary Ecology and Conservation Genomics, Helmholtzstr. 10/1, 89081 Ulm, Germany c University of Innsbruck, Institute of Zoology, Technikerstr. 25, 6020 Innsbruck, Austria b

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil pollution Cadmium Gut microbiota Indicator species Earthworm Lumbricus terrestris

Preservation of the soil resources stability is of high importance for ecosystems, particularly in the current era of environmental change, which presents a severe pollution burden (e.g. by heavy metals) to soil fauna. Gut microbiomes are becoming recognized as important players in organism health, with comprehension of their perturbations in the polluted environment offering new insights into the nature and extent of heavy metal effects on the health of soil biota. Our aim was to investigate the effect of environmentally relevant heavy metal concentrations of cadmium (Cd) on the earthworm (Lumbricus terrestris) gut microbiota. Our results revealed that Cd exposure led to perturbations of earthworm gut microbiota with an increase in bacteria previously described as heavy metal resistant or able to bind heavy metals, revealing the potential of the earthworm-gut microbiota system in overcoming human-caused heavy metal pollution. Furthermore, an ‘indicator species analysis’ linked the bacterial genera Paenibacillus, Flavobacterium and Pseudomonas, with Cd treatment, suggesting these bacterial taxa as biomarkers of exposure in earthworms inhabiting Cd-stressed soils. The results of this study help to understand the impact of anthropogenic disturbance on soil fauna health and will have implications for environmental monitoring and protection of soil resources.

1. Introduction Environmental pollution by heavy metals poses a severe risk for soil ecosystems. Cadmium (Cd), one of the dominant heavy metal soil pollutants, appears in soil ecosystems as a side product of the metal and mining industry, from usage of fertilizers containing Cd and through air deposition (Järup and Akesson, 2009). Heavy metals, including Cd, are linked to various toxic effects in exposed organisms, such as the induction of oxidative stress, DNA damage and carcinogenesis, and effects on the immune system (Jin et al., 2017). Metal-polluted soils also strongly influence soil microbiota and the microbiota of soil organisms. Several studies have shown a decrease in microbe biodiversity in metalpolluted soils (Bamborough and Cummings, 2009; Gremion et al., 2003; Singh et al., 2014). For instance, Cd inhibits microbial reproduction in soil (Chen et al., 2014), and other metals have been found to decrease the biomass, species richness and activity of microbial communities in forest and arable soils (Chodak et al., 2013; Frostegård et al., 1993; Gołębiewski et al., 2014; Sheik et al., 2012). Earthworms are dominant members of the soil macrofauna and are

essential species in soil. Because of their burrowing activities and casting, earthworms carry out biological regulation in soil by mixing organic matter and mineral particles inside their gut. Further, they maintain the soil structure, and regulate the water content and availability of nutrients for plants (Brito-Vega and Espinosa-Victoria, 2009). Earthworms accumulate or concentrate heavy metals from soil and Cd is concentrated from the environment in earthworms more than other metals (Hartenstein et al., 1980; Ireland, 1979). Through their activities in soil, earthworms impact the microbial properties of soil, an aspect that is crucial for the stability of soil ecosystems (Pass et al., 2015). The earthworm gut represents a unique ecological niche with stable conditions, in contrast to the surrounding soil conditions (Nechitaylo et al., 2010). Bacterial taxa present in the earthworm gut represent those found in the environment (diet, soil) only to a certain extent which is the result of filtering and specific milieu in their gut, such as a neutral pH, anaerobic conditions, constant moisture, and high amount of carbon substrate (Pass et al., 2015). By ingesting soil bacteria during feeding and burrowing, earthworms act as biological filters for soil microbiotas, enabling the anaerobes to flourish (Aira et al., 2015; Drake

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Corresponding author. E-mail address: [email protected] (M. Šrut). 1 These authors contributed equally. https://doi.org/10.1016/j.ecoenv.2018.12.102 Received 21 October 2018; Received in revised form 26 December 2018; Accepted 29 December 2018 0147-6513/ © 2019 Elsevier Inc. All rights reserved.

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Fig. 1. Schematic representation of the experimental setup. Earthworms were exposed to heavy metal (Cd) and their gut microbiota were sequenced before and after exposure to various Cd concentrations. The respective soil and manure microbiotas were also sequenced. Soil_acc refers to the soil prepared for earthworm acclimation.

2. Materials and methods

and Horn, 2007; Knapp et al., 2009). Additionally, the earthworm gut has the ability to discriminate between beneficial and harmful bacteria (Dishaw et al., 2014). The gut microbiota interacts with ingested heavy metals, acts as an important mediator of metal bioavailability and toxicity, and behaves as a barrier for the uptake of heavy metals (Zhang et al., 2016). For instance, germ-free mice absorb significantly higher levels of Cd and Pb compared with control mice and as a result show different expression profiles of genes involved in metal detoxification and transport (Breton et al., 2013a). Several metals, including Cd, have been linked to alterations in the gut microbiota in vertebrate species (Breton et al., 2013b; Guo et al., 2014; Liu et al., 2014; Wu et al., 2014; Zhai et al., 2017; Zhang et al., 2015). In earthworms, environmental exposure to arsenic and iron caused shifts in the composition of the Lumbricus rubellus microbiota (Pass et al., 2015). A healthy and stable gut microbiota plays an important role in host health and changes in microbiota composition, known as dysbiosis, can have severe consequences. For instance, the microbiota and the host immunity are well known to be in continuous interaction, thereby modifying the host immune response (McPhee and Schertzer, 2015). Gut microbiota changes can lead to a misbalance between the host immune system and the gut microbiota, can trigger immune responses and can lead to immunological diseases (Jin et al., 2017). Germ free mice have deficiencies in the development of lymphoid tissue, reduced innate and adaptive immune response and are more susceptible to infections (Round and Mazmanian, 2009). On the other hand, the immune response of the host can have an impact on the intestinal microbial composition and its function (Palm et al., 2015). The immunological control of the gut microbiota is critical for maintaining intestinal homeostasis and involves a variety of innate and adaptive components (Palm et al., 2015). Therefore, the changes in the host gut microbiota can serve as an important indication of potential misbalance in an organism. Changes in gut microbiota composition have been hypothesized as an important indicator of toxicity for environmental pollution (Jin et al., 2017). Recent studies imply that the changes in gut microbiota structure and function can be used to evaluate the biological effects of pollutants and to define specific microbiota communities as biomarkers of exposure (Gaulke et al., 2016; Zhang et al., 2016). Therefore, an understanding of chemical-specific microbiota alterations in key organisms, should improve our comprehension of the nature and extent of pollution effects and enable better monitoring and preventative measures in the various ecosystems challenged with anthropogenic pollution. The aims of this study were: 1) to investigate microbiota shifts in the earthworm L. terrestris exposed to environmentally relevant concentrations of the heavy metal Cd and 2) to identify bacteria involved in the first response of the earthworm gut microbiota to Cd stress in order to propose potential microbiota biomarkers of exposure.

2.1. Earthworm exposure and sampling L. terrestris specimens and soil (80 g dry soil per earthworm) used for earthworm acclimation and exposure (mixture of peat and humus) were obtained from the company Wurmwelten/Germany (www. wurmwelten.de). The soil type is of adequate quality to keep earthworms in good condition for prolonged periods of time (Šrut et al., 2017). Prior to the exposure, ninety adult earthworms originating from a single population were acclimatized in the temperature chamber at 15 °C in a 12 h/12 h light/dark cycle for 4 weeks. Soil used for the acclimation and exposure was heat-treated (120 °C, 12 h) with a soil water content of 50% and earthworms were fed weekly with horse manure (1.2 g manure per individual). Soil water content was measured by the standard method of weighting the wet soil, drying it overnight in a furnace to reach the constant weight and weighing the dry soil. In the experimental conditions (15 °C), the moisture remains constant over the prolonged period of time. Following the acclimation period, earthworms were divided into three groups of 30 individuals that were exposed either to control soil or to 10 mg/kg or 50 mg/kg CdCl2 for 4 weeks. The concentration of Cd in control soil was 0.06 mg/kg. In our previous research, the comparable nominal concentrations of CdCl2 used in the same soil type were almost equal to real concentrations measured in the soil (Šrut et al., 2017). Soil containing Cd was prepared prior to earthworm introduction and was left for 2 days to be biologically and chemically processed (aged). Exposure was carried out in containers 32 × 24.5 × 20 cm (L × W × H), under the same conditions as during the acclimation (15 °C, 12 h/12 h light/dark cycle, 1.2 g manure per individual per week). The gut content of earthworms for the analysis of intestinal microorganisms (Ma et al., 2017) was sampled prior to and after exposure. Gut samples collected prior to exposure were labeled as ‘Cd-0_pre’, ‘Cd-10_pre’ and ‘Cd-50_pre’, whereas samples collected after the exposure were labeled ‘Cd-0_post’, ‘Cd-10_post’ and ‘Cd-50_post’. The experimental setup is schematically shown in Fig. 1. Fecal matter was collected by placing individual earthworms overnight at 15 °C in the dark on a sterile moist filter paper placed in a Petri dish. The released pellet was collected by using a sterile spatula, placed in a 1.5 ml tube, flash-frozen in liquid nitrogen and stored at −80 °C until further processing. In addition, soil and manure samples (3 samples/replicas from each treatment) were collected in order to investigate their microbiota communities. ‘Soil_acc’ refers to samples of freshly prepared experimental soil for acclimation before the introduction of earthworms, ‘Soil_Cd-0_pre’ refers to soil samples collected after 4 weeks of earthworm acclimation, ‘Soil_Cd-0_post’ refers to soil samples with the treatment ‘Cd-0_post’ and ‘Soil_Cd-10/50_post’ refers to soil samples from ‘Cd-10_post’ and ‘Cd-50_post’ treatments, which were grouped together for analyses, because some of the replicas were not successfully sequenced.

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into a phyloseq-class object by using the ‘phyloseq’ package (McMurdie and Holmes, 2013). For downstream analyses, we removed the ASVs of the confounding factor manure from all earthworm and soil microbiotas. This step was performed in order to obtain earthworm gut microbiota without the influence of the food source. Subsequently, we removed singletons and rarefied our data at 20,000 reads/sample and calculated a species diversity within a sample (alpha (α) diversity) by calculating the phylogenetic diversity (Faith, 1992), Shannon index (Shannon, 1948; Spellerberg and Fedor, 2003), and Simpson diversity (Simpson, 1949)), for all samples of the first and second sampling (earthworm gut, manure, and soil microbiota). Phylogenetic diversity takes in the account phylogenetic difference between species, the Shannon index assumes all species are represented in a sample and that they are randomly sampled and the Simpson index gives more weight to common or dominant species. We tested for significant differences in αdiversities between earthworm, soil and manure samples using Dunn's test of multiple comparisons and adjusted the false discovery rate using the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995; Dinno, 2017). We calculated beta diversity matrices for the creation of PCoA plots based on the Bray-Curtis metric after we had transformed sample counts into relative abundances and had filtered all ASVs that occurred with a mean of less than 1e-05. Because we observed significant differences between control groups from the first (‘Cd-0_pre’) and second (‘Cd-0_post’) sampling (Supplementary Fig. 1), which indicates that the acclimation period of one month was not sufficient to stabilize earthworm gut microbiota, subsequent analyses focused on the comparisons between earthworm gut microbiota of ‘Cd-0_post’, ‘Cd-10_post’, and ‘Cd-50_post’ treatments. To test whether beta diversity distances differed significantly between these treatment groups (Cd-0_post, Cd-10_post, and Cd-50_post), we applied a PERMANOVA approach using the ‘adonis’ function (Dixon, 2003; McMurdie and Holmes, 2013) on the beta diversity matrix calculated for this subset. Beforehand, we tested the assumption of homogeneity using the ‘betadisper’ function (Dixon, 2003). Furthermore, we applied a model that uses a negative binomial distribution (R package ‘DESeq. 2′ (Love et al., 2014)) on the non-rarefied datasets to investigate whether bacterial taxa (at phylum and genus level) differed significantly between earthworm gut microbiota treatments, soils and manure samples (R packages ‘phyloseq’ (McMurdie and Holmes, 2013) and ‘microbiomeSeq’ (Ssekagiri et al., 2018)). We applied the ‘local’ fit for fitting of dispersions (fits a local regression of log dispersions over log base mean) and a significance threshold of 0.05. The difference in abundance of bacterial taxa between treatments is expressed as log2 fold change (L2FC). In order to assess the importance (rank) of differentially abundant taxa in earthworm post treatment groups, we applied random forest classifier to measure the mean decrease in accuracy, whereby taxa with a higher mean decrease are considered more important (Liaw and Wiener, 2002). Finally, bacterial taxa from earthworm microbiotas that were significantly attributable to ‘Cd-0_post’, ‘Cd-10_post’ or ‘Cd-50_post’ were tested by applying an ‘indicator species analysis’ with 999 permutations (R package ‘indicspecies’ (De Cáceres and Legendre, 2009)). With this method, the relative abundance and occurrence of ASVs in samples of the various treatment groups was analysed in order to identify the ASVs that significantly characterized the respective groups. The maximal index of 1 indicated that a certain ASV was present in all individuals of the group of interest. An indicator value less than 1 meant that the ASV was either present in more than one group or that it was not present in all members of this group. This method has been previously suggested for the definition of biomarkers of exposure to chemicals (Gaulke et al., 2016).

2.2. DNA extraction, library preparation and sequencing 200 mg earthworm gut content, soil and manure samples were used for DNA extraction with the NucleoSpin 96 Soil kit (Macherey&Nagel). Mechanical lysis was performed for 2 × 2.5 min at 50 Hz by using the Analytic Jena Homogenizer. For library preparation, the Fluidigm (Access Array™ System for Illumina Sequencing Systems, Fluidigm Corporation) approach and chemistry for simultaneous PCR and barcoding was applied. A 291-bp fragment of the hypervariable V4 region of the 16S rRNA gene was targeted by using the primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Caporaso et al., 2012; Kuczynski et al., 2011). The primers were modified according to the Fluidigm protocol and were tagged with sequences (CS1 forward tag and CS2 reverse tag) that were complementary to the forward or reverse access array barcode primers for Illumina. The reaction was performed in 15 µl and consisted of 10 ng/µl template DNA, 1X FastStart HighFidelity Reaction buffer without MgCl2 (Roche), 4.5 nM MgCl2 (Roche), 5% DMSO (Roche), 200 μM of each PCR grade nucleotide (Roche), 0.05 U/µl FastStart High Fidelity Enzyme blend (Roche), 400 nM access array barcode primers for Illumina (Fluidigm), 200 nM target specific primers and 14% PCR certified water. PCR cycles were performed according to the Fluidigm protocol. Subsequently, the PCR samples were cleaned by using NucleoMag NGS Beads (Machery& Nagel) according to manufacturer's recommendations and the cleaned libraries were quality checked with capillary electrophoresis on the Qiaxcel Advanced system (Qiagen) and quantified by using the QuantiT™ PicoGreen® kit (Invitrogen/Life Technologies). Samples were pooled with equal amounts of 150 ng DNA/sample. Finally, pooled samples were diluted to 8 nM in hybridization buffer and libraries were sequenced as paired-end run on Illumina® MiSeq. 2.3. Bioinformatic analyses with qiime2 Pre-processing of sequencing reads was carried out by using qiime2 (version 2017.10) ((Caporaso et al., 2010), https://qiime2.org) and its plugins. Specifically, the ‘demux’ plugin (https://github.com/qiime2/ q2-demux) was used for the import of the demultiplexed paired-end sequencing reads and the creation of the ‘artifact’ file (i.e. qiime2 data format required for subsequent analyses). Further, the ‘dada2′ plugin (Callahan et al., 2016) was applied for quality filtering (–p-max-ee 2, –p-trunk-q 2), chimera filtering (‘consensus’), to trim primers (–p-trimleft-f 23, –p-trim-left-r 20), to truncate forward and reverse reads (–ptrunc-len-f 200, –p-trunc-len-r 200), and finally to collapse reads into representative sequences, the so-called amplicon sequence variants (ASVs). Taxonomy to these ASVs was assigned against the Greengenes database (version 13_8) by using the ‘feature-classifier’ plugin (https:// github.com/qiime2/q2-feature-classifier) with the ‘fit-classifiersklearn’. Furthermore, we aligned ASVs with MAFFT (Katoh and Standley, 2013), removed non-informative positions in the alignment with the ‘mask’ command (https://github.com/qiime2/q2-alignment) and finally constructed the phylogenetic tree using Fast Tree 2 (Price et al., 2010). In order to be able to undertake further analyses in R (R Development Core Team, 2011), and to prepare figures (‘ggplot2′ (Wickham, 2009)), the non-rarefied ‘feature-table’, the bacterial phylogenetic tree, and the taxonomy were exported from qiime2 ‘artifacts’. The ‘feature-table.biom’ file was converted into a text file, the taxonomy was added in the last column and this text file was reconverted into a ‘feature-table_tax.biom’ file using the ‘biom convert’ command in qiime2 (http://biom-format.org, (McDonald et al., 2012)). 2.4. Analyses in R The ‘feature-table_tax.biom’ file, the bacterial phylogenetic tree, and a text file containing the metadata were imported into R and combined 845

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Fig. 2. α-diversity boxplots representing Shannon and Simpson index and Faith's Phylogenetic Diversity (PD) between treatment groups, soil and manure samples.

p.adj < 0.01) and Actinobacteria (L2FC: −1.59, p.adj < 0.001) and a lower abundance of Bacteroidetes (L2FC: 3.66, p.adj < 0.001), Proteobacteria (L2FC: 1.29, p.adj < 0.001) and Verrucomicrobia (L2FC: 3.30, p.adj < 0.001) compared to ‘Soil_Cd-0_pre’.

3. Results 3.1. Sequencing and ASV assignment After the 4 weeks exposure, the earthworm mortality was 3% for ‘Cd-0_post’, 13% for ‘Cd-10_post’ and 20% for ‘Cd-50_post’. High-throughput amplicon sequencing of the V4-region of the bacterial 16S rRNA gene from soil, manure and earthworm faecal samples (n = 187) resulted in a total of 14,761,482 reads. After quality filtering, 10,247,539 (min: 30,932; max: 91,298; mean: 57,570; median: 57,710) reads remained for the assignment of ASVs. The resulting dataset consisted of 178 samples (9 samples removed due to number of reads ≤ 7) containing 4786 ASVs (min: 1.0; max: 813,792; mean: 2143; median: 52). Analyses on the rarefied data set (20,000 reads) are based on 175 samples, because three samples had insufficient reads.

3.3. Earthworm gut microbiota changes attributable to Cd treatment Microbiota diversity between the three sample groups collected before the exposure, which could be considered as replicates of the control group (‘Cd-0_pre’, ‘Cd-10_pre’, ‘Cd-50_pre’) did not differ significantly (Fig. 2). This indicates consistency between replicates and confirms the uniformity of the analyses. L. terrestris gut microbiota in the control samples ‘Cd-0_post’, consisted of Proteobacteria (38.25%), Bacteroidetes (33.17%), Firmicutes (11.55%), Verrucomicrobia (7.50%), Actinobacteria (5.89%), and Tenericutes (2.26%) (Fig. 3). Among Proteobacteria in the earthworm gut, the most abundant were Gammaproteobacteria (20.49%), followed by Betaproteobacteria (8.96%), and Alphaproteobacteria (8.65%), whereas Deltaproteobacteria were present at very low proportions (0.03%). Our results revealed that Cd exposure caused perturbations in earthworm gut microbiota composition. The treatment group ‘Cd10_post’ showed slight, although insignificant, increase in α diversity compared to the ‘Cd-0_post’ (Dunn's test: PD: Z = -0.19, p.adj = 0.90; Shannon index: Z = -0.28, p.adj = 0.87; Simpson diversity: Z = 0.43, p.adj = 0.75) (Fig. 2). Treatment group ‘Cd-50_post’, however, had significantly higher α-diversity than ‘Cd-0_post’ for Shannon index and Simpson diversity (Dunn's test: Z = -2.64, p.adj = 0.02; Z = -2.75, p.adj = 0.02, respectively), whereas there was no difference for PD (Dunn's test: Z = -1,28, p.adj = 0.30) (Fig. 2). Additionally, Bray-Curtis distances were significantly different between treatment groups (PERMANOVA: F2,73 = 8.07, p = 0.001) (Fig. 4). Especially earthworm microbiotas of the ‘Cd-50_post’

3.2. Soil and manure microbiota All soil samples had higher α-diversities than earthworm gut microbiota samples and manure samples (Fig. 2). Soil sampled before earthworm introductions (Soil_acc) had a slightly higher α-diversity than soil after earthworm acclimation (Soil_Cd-0_pre), but this effect was not significant (Dunn's test: PD: Z = 0.41, p-adj = 0.78; Shannon index: Z = 0.05, p-adj = 0.99; Simpson: Z = 0.02, p-adj = 1). The control soil collected after earthworm exposure (Soil_Cd-0_post) had a lower (Dunn's test: PD: Z = 0.33, p-adj = 0.83; Shannon index: Z = 0.83, p-adj = 0.50; Simpson: Z = 3.62, p-adj =0.005) α-diversity compared with the Cd-exposed soil (Soil_Cd-10/50_post). Dunn's test results for all other combinations are presented in Supplementary Table 1. Neither soil nor manure samples possessed the phylum Tenericutes at proportions seen in the earthworm gut microbiota (Fig. 3). ‘Soil_acc’ had a significantly higher abundance of Firmicutes (L2FC: −1.38, 846

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Fig. 3. Proportions (%) of the most abundant phyla in the various earthworm treatment groups and in the soil and manure samples.

2.54) and Chryseobacterium (L2FC: 2.53). A strong decrease was observed for genera Cohnella (L2FC: −2.67) and Rhizobium (L2FC: −1.96). The bacterial genera that had the highest importance (rank) as obtained by random forest classifier were Dermacoccus (Rank 1), Rhizobium (Rank 2), Symbiobacterium (Rank 3), Rhodobacter (Rank 4), and Rathayibacter (Rank 5) (Supplementary Fig. 2). At the concentration of 50 mg/kg (‘Cd-50_post’) the proportions of the pyhla Actinobacteria (L2FC: −0.86, p.adj < 0.001), Bacteroidetes (L2FC: −0.35, p.adj < 0.05) and Proteobacteria (L2FC: −0.66, p.adj < 0.001) significantly increased in comparison with the ‘Cd0_post’ group (Fig. 3). At the genus level, 43 genera increased, whereas 5 decreased in abundance in relation to ‘Cd-0_post’ (Fig. 6). A strong increase was observed for the genera Rathayibacter (L2FC: 7.31), Dermacoccus (L2FC: 6.83), Nocardioides (L2FC: 4.89), Salinibacterium (L2FC: 4.84), Solirubrobacter (L2FC: 3.53), heteroC45_4W (L2FC: 3.29) Chryseobacterium (L2FC: 3.19), Sporocytophaga (L2FC: 3.15) and

treatment clustered distant to the remaining groups (Fig. 4).

3.4. Cd-sensitive and Cd-resistant taxa In order to identify whether earthworm gut microbial genera differed significantly between control and Cd-treated groups, we applied the DESeq. 2 analysis. Bacterial phyla that differed significantly in abundance between ‘Cd-0_post’ and ‘Cd-10_post’ treatments were Chlamydiae (upregulated in Cd-10_post; L2FC: −2.33, p.adj < 0.001) and Fusobacteria (upregulated in Cd-0_post; L2FC: 2.26, p.adj < 0.001). When looking at the genus level, we found 36 genera to be significantly different in abundance between the two groups. Out of those, 24 genera increased, whereas 12 genera decreased in abundance in relation to ‘Cd-0_post’ (Fig. 5). A strong increase was observed for genera Dermacoccus (L2FC: 5.82), Rathayibacter (L2FC: 3.91), Fluviicola (L2FC: 3.89), Sanguibacter (L2FC: 3.35), Streptomyces (L2FC: 2.69), Solibacillus (L2FC:

Fig. 4. PCoA plot based on the Bray-Curtis distances. 847

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Fig. 5. Results of DESeq. 2 analyses for comparisons of significantly altered bacterial genera between ‘Cd-0_post’ and ‘Cd-10_post’ treatment groups.

Streptomyces, Chryseobacterium, Nocardioides, Paenibacillus, Agrobacterium, Roseococcus and Achromobacter, whereas decrease was observed for genera Symbiobacterium, Thermacetogenium and Ochrobactrum (Figs. 5 and 6).

Streptomyces (L2FC: 2.87). A strong decrease was observed for genera Ochrobactrum (L2FC: −2.28) and Thermacetogenium (L2FC: −1.47). The bacterial genera with the highest importance were Dermacoccus (Rank 1), Rathayibacter (Rank 2), Ammoniphilus (Rank 3), Nocardioides (Rank 4), and Agrobacterium (Rank 5) (Supplementary Fig. 3). Many of the genera overlapped between two treatment groups. In both ‘Cd-10_post’ and ‘Cd-50_post’ significant increase in abundance was observed for genera Dermacoccus, Rathayibacter, Sanguibacter,

3.5. Unique bacterial indicator taxa associated with Cd treatment Indicator species analysis was employed in order to find predictive

Fig. 6. Results of DESeq. 2 analyses for comparisons of significantly altered bacterial genera between ‘Cd-0_post’ and ‘Cd-50_post’ treatment groups. 848

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Fig. 7. Indicator species that significantly characterize treatment groups after earthworm exposure to Cd. An indicator value of 1 indicates that a certain ASV is present in all individuals of a group. An indicator value less than 1 means that the ASV is either present in more than one group or that it is not present in all members of this group.

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microbiota belonged to the phyla Proteobacteria and Actinobacteria (Aira et al., 2015). Similarly, in E. fetida and Perionyx excavatus, the majority of the observed microorganisms were Proteobacteria, followed by Firmicutes, Actinobacteria and Bacteroidetes (Ma et al., 2017; Singh et al., 2015). In our study, we observed significant differences between control and 50 mg/kg Cd treatment group, which clustered separately in PCoA. Additionally, for this treatment group we noticed a significant increase in the proportion of Actinobacteria, Bacteroidetes and Proteobacteria in comparison with the control. Similarly, in mice orally treated with Cd for 8 weeks, the proportion of Actinobacteria in the cecal content changed significantly at a concentration of 100 mg/kg compared with the control (Breton et al., 2013b). Furthermore, the increase in the abundance of Bacteroidetes in the gut was described for mice and fish exposed to a low dose of Cd (Ba et al., 2017; Zhai et al., 2017; Zhang et al., 2015). Bacteria of the phylum Tenericutes, and particularly the class Mollicutes, decreased in abundance following Cd treatment at both concentrations compared with the control. Although this change was not significant, a similar pattern was previously described for Tenericutes in amphibian gut microbiota exposed to long-term heavy metal pollution (Zhang et al., 2016).

patterns of Cd exposure. For the control treatment (‘Cd-0_post’), 16 indicator ASVs were identified, whereas 34 and 78 indicator ASVs were linked to the 10 and 50 mg/kg Cd treatment, respectively. Many of the Cd associated ASVs overlapped between the 10 and 50 mg/kg treatments including mostly members of the genera Paenibacillus, Flavobacterium and Pseudomonas. Other overlaps included the genera Chthoniobacter, Rathayibacter, Agrobacterium, Methanobrevibacter and Rhodobacter. (Fig. 7). In the ‘Cd-50_post’ treatment group strong indicators included also the genera Phenylobacterium, Pimelobacter, Prosthecobacter, Azospirillum, Kaistia and species Candidatus iphinematobacter (Fig. 7). 4. Discussion 4.1. Earthworm gut microbiota differs from soil and manure microbiota The α-diversity of earthworm gut samples was lower in comparison with that of soil and manure samples and, as expected, did not fully represent the microbiota community found in the experimental soil or manure used for feeding. This is in accordance with previous findings showing that the microbiota associated with the earthworm gut has a reduced level of diversity and richness compared with the surrounding soil environment (Gómez-Brandón et al., 2011; Pass et al., 2015). The proportion of Proteobacteria, Bacteroidetes and Verrucomicrobia was significantly lower, whereas the proportion of Firmicutes and Actinobacteria was significantly higher in ‘Soil_acc’, in comparison with ‘Soil_Cd-0_pre’, indicating the profound impact of earthworms on the soil microbial community. Through soil ingestion, earthworms modify microbial composition and activity by depositing casts in the soil in which microbes can either flourish or decline (Aira and Domínguez, 2014). As previously shown, earthworm casts have a similar bacterial composition to the surrounding soil, but differ in their proportions (Furlong et al., 2002). Manure had a higher proportion of Proteobacteria in comparison with ‘Soil_acc’ and, thus, the combined influence of the food source and earthworm activity might have led to the observed changes in the soil microbial composition prior to and after earthworm introduction. The phylum Tenericutes, consisting mostly of undetermined orders of the class Mollicutes, was present in very low numbers in soil and manure samples but was abundant in the earthworm gut. The increase of Mollicutes in the gut after the exposure period (2 months in experimental soil) in comparison with the abundance before exposure (1 month in the experimental soil) indicates that this phylum is strongly linked to the earthworm gut and is not derived from either soil or food. Nevertheless, we cannot totally discard the premise that it might be derived from the soil in which earthworms were originally grown by the breeder. However, bacteria from the Mollicutes class have previously been described in the gut and coelomic fluid of earthworms of the family Lumbricidae, although no conclusion could be made as to whether the earthworm gut is their specific habitat (Nechitaylo et al., 2010, 2009).

4.3. Cd-sensitive and Cd-resistant taxa were determined among earthworm gut microbiota Many of the genera that showed a Cd-dependent increase in abundance belonged to the phylum Actinobacteria. Among this phylum, an increase in both Cd treatment groups was noticed for genera Dermacoccus, Rathayibacter, Nocardioides, Sanguibacter and Streptomyces. This increase could be explained by reported resistance to heavy metal toxicity for several of these genera. For instance, metal tolerant strains of Rathayibacter have been found in soils with historic heavy metal contamination (Ellis et al., 2003; Sułowicz et al., 2011). Similarly, Nocardioides has a high adaptive potential to survive and maintain metabolic activity under extreme conditions (heavy metals, UV, nuclear radiation) (Evtushenko and Ariskina, 2012). Sanguibacter sp. is reported to be Cd-resistant (Mastretta et al., 2009), whereas, Streptomyces strains bio accumulate different heavy metals, including Cd (Joshi and Jaiswal, 2013). The abundance of several other genera, from phyla Bacteroidetes, Proteobacteria and Firmicutes, with a reported resistance to heavy metals increased in our study, namely Chryseobacterium, Agrobacterium, Achromobacter and Paenibacillus. Chryseobacterium solincola as well as some of the Paenibacillus strains can survive and grow in medium containing high heavy metal concentrations and have, therefore, been suggested as candidates for heavy metal bioremediation (Benmalek et al., 2014; Govarthanan et al., 2016; Rawat and Rai, 2012). Agrobacterium tumefaciens is able to survive in regions containing high levels of heavy metals and possesses various transporters involved in Ni, Cu, Zn and Cr resistance (Mosa et al., 2016; Xie et al., 2015) and Achromobacter strains have been described as potent metal adsorbents (Abyar et al., 2012; Subudhi et al., 2016). Genera Ochrobactrum, Symbiobacterium and Thermacetogenium significantly decreased in abundance in both Cd treatment groups. Surprisingly, the genera Ochrobactrum has been described as heavy metal tolerant (Gupta and Diwan, 2017; Rodríguez-Llorente et al., 2010), whereas for genera Symbiobacterium and Thermacetogenium there is no reported sensitivity towards heavy metals. The increase in the abundance of metal tolerant and metal accumulating bacterial genera in the earthworm gut following the exposure to Cd could be the first line of defence in earthworms exposed to heavy metal pollution. Since gastrointestinal tract is the first physical and biological barrier against ingested toxic compounds, it is also the first target for toxicants (Gillois et al., 2018). Once inside the gut, environmental contaminants can interact with the gut microbiota in several ways. Contaminants can be metabolized by the bacteria, cause

4.2. Dose-dependent effect of Cd on microbiota of the earthworm gut The most abundant phylum in the L. terrestris gut samples was Proteobacteria, followed by Bacteroidetes, Firmicutes, Tenericutes, Verrucomicrobia and Actinobacteria. This is similar to the previously described L. terrestris gut microbiota composition consisting predominantly of Proteobacteria, followed by Bacteroidetes and with lower abundances Acidobacteria, Planctomycetes, Verrucomicrobia and Actinobacteria (Nechitaylo et al., 2010). Proteobacteria also seem to be the most abundant phyla for other earthworm species. For instance, in L. rubellus, Proteobacteria comprised more than 50% of the gut microbiota, followed by Actinobacteria, Bacteroidetes and Acidobacteria, whereas Firmicutes, Chloroflexi and Cyanobacteria appeared at lower proportions (Pass et al., 2015). In Eisenia andrei, the majority of 850

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CRediT authorship contribution statement

dysbiosis or infer with the enzymatic activity of the gut microbiota (Claus et al., 2016). Therefore, the gut microbiota is estimated as a major player, determining the toxicity of environmental contaminants (Claus et al., 2016).

Maja Šrut: Conceptualization, Data curation, Writing - original draft, Writing - review & editing. Sebastian Menke: Conceptualization, Formal analysis, Writing - original draft. Martina Höckner: Writing review & editing. Simone Sommer: Conceptualization, Funding acquisition, Supervision, Writing - review & editing.

4.4. Unique bacterial indicators as potential biomarkers of exposure to Cd

Acknowledgments

In order to reveal a unique microbiota footprint in the earthworm gut attributable to Cd exposure, we have identified indicator ASVs by using an ‘indicator species analysis’ (De Cáceres and Legendre, 2009). This analysis has previously been used to characterize microbial taxa in the human gut related to diverse diets (Li et al., 2009), to reveal microbial biomarkers of disease (Galimanas et al., 2014), to define microbial taxa associated with various environments (Kembel et al., 2012) and, most recently, to identify biomarkers of exposure to chemical pollution in fish (Gaulke et al., 2016). The authors of the last-mentioned study suggest that the characterization of indicator ASVs of the microbiota exposed to environmental chemicals may be useful in environmental and health monitoring. Many of the indicator ASVs associated with Cd exposure in our study belonged to the genera Paenibacillus, Flavobacterium and Pseudomonas. Members of the genera Paenibacillus and Flavobacterium are soil-derived denitrifiers or dissimilatory nitrate reducers in the earthworm gut that contribute to the nitrous oxide emissions and help in organic matter digestion by providing hydrolytic enzymes (Horn et al., 2005). Pseudomonas bacteria, which are nitrogen fixing plant growth-promoting and phosphate solubilizing bacteria are ingested by earthworms and therefore found in their alimentary canal (Brito-Vega and Espinosa-Victoria, 2009; Khyade, 2018). Other interesting indicators that were linked to both Cd treatment groups included Chthoniobacter, Rathayibacter, Agrobacterium, Methanobrevibacter and Rhodobacter. Those genera have been found in the soil ecosystems and many of them were also present in the earthworm gut (Aira et al., 2015; Brito-Vega and Espinosa-Victoria, 2009; Kant et al., 2011; Koubová et al., 2012; Panke-Buisse et al., 2014; Park et al., 2017). Evidence has been presented for the metal tolerance for these indicator taxa, which was already discussed for some genera in the previous section. Furthermore, Flavobacterium species show resistance to some heavy metals (Cd, Cu) and their abundance in the gut of Nile tilapia upon exposure to Cd was significantly increased (Rajbanshi, 2009; Zhai et al., 2017). Pseudomonas strains have been found in soils with historic heavy metal contamination and some strains show resistance to various heavy metals (Ellis et al., 2003; Sułowicz et al., 2011). Additionally, Rhodobacter species show high resistance to a variety of heavy metals (Giotta et al., 2006). Defined indicator ASVs of the earthworm gut could serve as biomarkers of exposure to Cd in soils and could be used for future biomonitoring programmes for human-caused environmental pollutants. In conclusion, we were able to demonstrate that environmentally relevant concentrations of Cd in soil altered earthworm gut microbiota by increasing the abundance of bacteria that have previously been described as the heavy metal resistant or to possess heavy metal binding capacities. Bacteria described as indicator amplicon sequence variants in this study could potentially serve as biomarkers of exposure to Cd and have relevance for soil biomonitoring practices. The high extent of chemical exposure to soil organisms and the overall role of the gut microbiota for organism health makes the description and comprehension of the effects of chemical exposure on the microbiota highly important. The overall results of this study should help us to understand the dynamics and the effects of heavy metal pollution on the microbiota of key soil invertebrates and thereby contribute to measures for preserving environmental health.

We are grateful to Kerstin Wilhelm for laboratory guidance of M.S. during her visit at the Institute for Evolutionary Ecology and Conservation Genomics at the University of Ulm and Victoria Drechsel from the University of Innsbruck, for help with earthworm maintenance and feeding. We appreciate the financial support by the BadenWürttemberg Ministry of Science, Research and Art, Germany (Simone Sommer: 31-7717-9-7/1/1). The authors would like to thank anonymous reviewers for useful comments on the manuscript. Data statement Sequencing data are publicly available for download at dryad via link: http://dx.doi.org/10.5061/dryad.950km35. Declarations of interest None. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2018.12.102 References Abyar, H., Safahieh, A., Zolgharnein, H., Zamani, I., 2012. Isolation and identification of Achromobacter denitrificans and evaluation of its capacity in cadmium removal. Pol. J. Environ. Stud. 21, 1523–1527. https://doi.org/10.1002/jobm.201400157. Aira, M., Bybee, S., Pérez-Losada, M., Domínguez, J., 2015. Feeding on microbiomes: effects of detritivory on the taxonomic and phylogenetic bacterial composition of animal manures. FEMS Microbiol. Ecol. 91, 1–10. https://doi.org/10.1093/femsec/ fiv117. Aira, M., Domínguez, J., 2014. Changes in nutrient pools, microbial biomass and microbial activity in soils after transit through the gut of three endogeic earthworm species of the genus Postandrilus Qui and Bouché, 1998. J. Soils Sediment. 14, 1335–1340. https://doi.org/10.1007/s11368-014-0889-1. Ba, Q., Li, M., Chen, P., Huang, C., Duan, X., Lu, L., Li, J., Chu, R., Xie, D., Song, H., Wu, Y., Ying, H., Jia, X., Wang, H., 2017. Sex-dependent effects of cadmium exposure in early life on gut microbiota and fat accumulation in mice. Environ. Health Perspect. 125, 437–446. https://doi.org/10.1289/EHP360. Bamborough, L., Cummings, S.P., 2009. The impact of zinc and lead concentrations and seasonal variation on bacterial and actinobacterial community structure in a metallophytic grassland soil. Folia Microbiol. 54, 327–334. https://doi.org/10.1007/ s12223-009-0042-5. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. https://doi.org/10. 2307/2346101. Benmalek, Y., Halouane, A., Hacene, H., Fardeau, M.L., 2014. Resistance to heavy metals and bioaccumulation of lead and zinc by Chryseobacterium solincola strain 1YB-R12T isolated from soil. Int. J. Environ. Eng. 6, 68–77. https://doi.org/10.1504/IJEE.2014. 057832. Breton, J., Daniel, C., Dewulf, J., Pothion, S., Froux, N., Sauty, M., Thomas, P., Pot, B., Foligné, B., 2013a. Gut microbiota limits heavy metals burden caused by chronic oral exposure. Toxicol. Lett. 222, 132–138. https://doi.org/10.1016/j.toxlet.2013.07. 021. Breton, J., Massart, S., Vandamme, P., De Brandt, E., Pot, B., Foligné, B., 2013b. Ecotoxicology inside the gut: impact of heavy metals on the mouse microbiome. BMC Pharmacol. Toxicol. 14, 1–11. https://doi.org/10.1186/2050-6511-14-62. Brito-Vega, H., Espinosa-Victoria, D., 2009. Bacterial diversity in the digestive tract of earthworms (Oligochaeta). J. Biol. Sci. 9, 192–199. https://doi.org/10.3923/jbs. 2009.192.199. Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K.,

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