Distribution, Community Composition, and Potential Metabolic Activity of Bacterioplankton in an Urbanized Mediterranean Sea Coastal Zone

Abstract

Bacterioplankton are fundamental components of marine ecosystems and influence the entire biosphere by contributing to the global biogeochemical cycles of key elements. Yet, there is a significant gap in knowledge about their diversity and specific activities, as well as environmental factors that shape their community composition and function. Here, the distribution and diversity of surface bacterioplankton along the coastline of the Gulf of Naples (GON; Italy) were investigated using flow cytometry coupled with high-throughput sequencing of the 16S rRNA gene. Heterotrophic bacteria numerically dominated the bacterioplankton and comprised mainly Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes Distinct communities occupied river-influenced, coastal, and offshore sites, as indicated by Bray-Curtis dissimilarity, distance metric (UniFrac), linear discriminant analysis effect size (LEfSe), and multivariate analyses. The heterogeneity in diversity and community composition was mainly due to salinity and changes in environmental conditions across sites, as defined by nutrient and chlorophyll a concentrations. Bacterioplankton communities were composed of a few dominant taxa and a large proportion (92%) of rare taxa (here defined as operational taxonomic units [OTUs] accounting for <0.1% of the total sequence abundance), the majority of which were unique to each site. The relationship between 16S rRNA and the 16S rRNA gene, i.e., between potential metabolic activity and abundance, was positive for the whole community. However, analysis of individual OTUs revealed high rRNA-to-rRNA gene ratios for most (71.6% ± 16.7%) of the rare taxa, suggesting that these low-abundance organisms were potentially active and hence might be playing an important role in ecosystem diversity and functioning in the GON.IMPORTANCE The study of bacterioplankton in coastal zones is of critical importance, considering that these areas are highly productive and anthropogenically impacted. Their richness and evenness, as well as their potential activity, are very important to assess ecosystem health and functioning. Here, we investigated bacterial distribution, community composition, and potential metabolic activity in the GON, which is an ideal test site due to its heterogeneous environment characterized by a complex hydrodynamics and terrestrial inputs of varied quantities and quality. Our study demonstrates that bacterioplankton communities in this region are highly diverse and strongly regulated by a combination of different environmental factors leading to their heterogeneous distribution, with the rare taxa contributing to a major proportion of diversity and shifts in community composition and potentially holding a key role in ecosystem functioning.

Keywords: 16S rRNA gene and rRNA; Gulf of Naples; Illumina sequencing; bacterioplankton; potential metabolic activity.

FIG 1

Map of the Gulf of Naples highlighting the location of the stations sampled. Samples for flow cytometry and 16S rRNA gene sequencing were collected at all stations, whereas samples for 16S rRNA sequencing were obtained only from stations PO1, TA1, and VE3 (indicated with red symbols). Red rectangle indicates Sarno River-influenced stations. The map was created with Ocean Data View software (version 4.7.3).

FIG 2

Station clustering based on Bray Curtis dissimilarity (a) and the distance matrix generated by calculating pairwise UniFrac metrics (b). The scale bar in panel b shows the distance between clusters in UniFrac units; if two or more environments have similar lineages, they have a distance of 0. The significance of the cluster nodes was determined using the jackknife analysis. Jackknife significance values are >99.9% = 1, 90 to 99% = 2, 70 to 90% = 3; 50 to 70% = 4, <50% = 5, and higher values indicate a higher adaptation of communities to the existing environmental conditions.

FIG 3

(a) Bacterial community composition at the phylum level at each GON station sampled. The phylum Proteobacteria is split into several classes (Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, and Epsilonproteobacteria). Bacterial OTUs that could not be classified were labeled as “other bacteria.” (b) The relative abundances of the top 12 abundant phylotypes in the GON at a lower taxonomic level (order/family level).

FIG 4

LEfSe analysis, indicating significantly differential distribution of taxa in the different groups of stations as identified by Bray-Curtis dendrogram (Fig. 2a) followed by ANOSIM R statistic (a), and after grouping coastal+river-influenced stations (b). Red asterisks indicate abundant OTUs that were present in all the libraries, while blue asterisks indicate OTUs that shifted between abundant and rare among stations. OTUs without an asterisk are rare (<0.1% of total sequences of the libraries).

FIG 5

CCA ordination plot depicting the relationship between environmental parameters and bacterial community structure, as represented by 16S rRNA gene sequence data.

FIG 6

Correlation between 16S rRNA and 16S rRNA gene frequencies at stations VE3, PO1, and TA1 with slopes for each station (slope for the total data is y = 0.6191x + 0.7343; r² = 0.3845). Individual data points correspond to paired log(rRNA + 1) and log(rRNA gene + 1) for each individual OTU. Data were log transformed in order to eliminate bias. Therefore, correlations are limited to OTUs where both rRNA and rRNA gene sequences were present.

FIG 7

Comparison of the relative abundances and potential activities of different phylogenetic groups within and between different sites in the GON. (A) Maximum likelihood phylogenetic tree of the 50 most abundant OTUs present in all the equally subsampled 16S rRNA and 16S rRNA gene libraries; Methanococcoides burtonii, an archaeal species, was included as an outgroup. (B) Heatmap showing the log abundance [log (observations + 1)] for each 16S rRNA and 16S rRNA gene OTU present in the tree. (C) Specific potential activity relative to abundance of each OTU present at the VE3, PO1, and TA1 stations calculated using the rRNA-to-rRNA gene ratio.