Patterns of benthic bacterial diversity in coastal areas contaminated by heavy metals, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs)

Abstract

Prokaryotes in coastal sediments are fundamental players in the ecosystem functioning and regulate processes relevant in the global biogeochemical cycles. Nevertheless, knowledge on benthic microbial diversity patterns across spatial scales, or as function to anthropogenic influence, is still limited. We investigated the microbial diversity in two of the most chemically polluted sites along the coast of Italy. One site is the Po River Prodelta (Northern Adriatic Sea), which receives contaminant discharge from one of the largest rivers in Europe. The other site, the Mar Piccolo of Taranto (Ionian Sea), is a chronically polluted area due to steel production plants, oil refineries, and intense maritime traffic. We collected sediments from 30 stations along gradients of contamination, and studied prokaryotic diversity using Illumina sequencing of amplicons of a 16S rDNA gene fragment. The main sediment variables and the concentration of eleven metals, polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) were measured. Chemical analyses confirmed the high contamination in both sites, with concentrations of PCBs particularly high and often exceeding the sediment guidelines. The analysis of more than 3 millions 16S rDNA sequences showed that richness decreased with higher contamination levels. Multivariate analyses showed that contaminants significantly shaped community composition. Assemblages differed significantly between the two sites, but showed wide within-site variations related with spatial gradients in the chemical contamination, and the presence of a core set of OTUs shared by the two geographically distant sites. A larger importance of PCB-degrading taxa was observed in the Mar Piccolo, suggesting their potential selection in this historically polluted site. Our results indicate that sediment contamination by multiple contaminants significantly alter benthic prokaryotic diversity in coastal areas, and suggests considering the potential contribution of the resident microbes to contaminant bioremediation actions.

Keywords: PCBs; chemical pollution; marine sediments; microbial diversity; next generation sequencing.

FIGURE 1

The study sites. The two sites sampled in Italy: the Po River Prodelta (A) and the Mar Piccolo of Taranto (B).

FIGURE 2

Spatial patterns of contaminants. Spatial patterns of selected chemical contaminants in the two sites. Shown are mercury (A,D), polychlorinated biphenyls (PCBs) (B,E) and polycyclic aromatic hydrocarbons (PAHs) (C,F) in the Po River Prodelta and the Mar Piccolo of Taranto, respectively.

FIGURE 3

Community composition and Unweighted Pair Group Method with Arithmetic mean (UPGMA) dendrogram. Combined panel showing the relative abundance of prokaryotic phyla, or classes (for Proteobacteria), in the sampled stations (Right) and the UPGMA dendrogram, based on unweighted Unifrac distances matrix (Left). On the right panel, phyla or classes showing a mean relative abundance across all stations <1%, as well as all the unassigned sequences, were aggregated into the group reported as “Unassigned; Other.”

FIGURE 4

Core microbiome in contaminated coastal sediments. Relative abundance (expressed as %) of OTUs shared between the Po River Prodelta (Left) and Mar Piccolo of Taranto (Right) sites. The OTU identity is reported the highest level of taxonomic identification obtained using QIIME (which ranged from Family to Species). Letters in the colored boxes in background describe the Classes to which each OTUs are affiliated (α = Alphaproteobacteria; β = Betaproteobacteria, δ = Deltaproteobacteria, 𝜀 = Epsilobacteria, γ = Gammaproteobacteria, A = Actinobacteria, Cl = Clostridia, C = Cytophagia, F = Flavobacteria, Mb = Methanobacteria, Mm = Methanomicrobia).

FIGURE 5

Relationships between environmental variables, contaminants and taxonomic composition across sites. dbRDA ordinations of the distLM model which describe the relationship between the environmental variables (P, % LOI, silt content), the chemical contaminants (Al, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, PAHs, Pb, PCBs, and Zn) and the taxonomic composition at the phylum (Left) and OTU (Right) level. In the phylum plot, the first axis (dbRDA1) captures 57.6% of the fitted and 42.3% of the total variation between the samples’ taxonomic profile at the phylum level, while the second (dbRDA2) captures 26.3% of the fitted and 19.2% of the total variation. In the OTU plot, the first axis (dbRDA1) captures 47% of the fitted and 33.9% of the total variation between the samples’ taxonomic profile at the OTU level, while the second (dbRDA2) captures 14.3% of the fitted and 10.3% of the total variation. Green circles: Po River Prodelta samples. Blue circles: Mar Piccolo of Taranto samples.

FIGURE 6

Relationships between environmental variables, contaminants and taxonomic composition within each site. dbRDA ordinations of the distLM model which describe the relationship between the environmental variables (P, % LOI, silt content), the chemical contaminants (Al, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, PAHs, Pb, PCBs, and Zn) and the taxonomic composition at the OTU level in the Po River Prodelta (Left) and Mar Piccolo of Taranto (Right) sites. In the Po River Prodelta plot, the first axis (dbRDA1) captures 19.1% of the fitted and 17.1% of the total variation between the samples’ taxonomic profile, while the second (dbRDA2) captures 8.1% of the fitted and 7.2% of the total variation. In the Mar Piccolo of Taranto plot, the first axis (dbRDA1) captures 22.4% of the fitted and 18.1% of the total variation between the samples’ taxonomic profile, while the second (dbRDA2) captures 20% of the fitted and 16.2% of the total variation. Green circles: Po River Prodelta samples. Blue circles: Mar Piccolo of Taranto samples.