Whole Genome Sequence of Marinobacter salarius Strain SMR5, Shown to Promote Growth in its Diatom Host

Interaction of silver nanoparticles with marine/lake snow in early formation stage

Marine and lake snows play an important ecological role in aquatic systems, and recent researches have also revealed their interactions with various pollutants. In this paper, the interaction of silver nanoparticles (Ag-NPs), a typical nano-pollutant, with marine/lake snow in the early formation stage was investigated by roller table experiments. Results indicated Ag-NPs promoted the accumulation of larger marine snow flocs while inhibited the development of lake snow. The promotion effect of AgNPs might be attributed to their oxidative dissolution into low-toxic silver chloride complexes in seawater, and the subsequent incorporation into marine snow, which would enhance the rigidity and strength of larger flocs and favor the development of biomass. Conversely, Ag-NPs mainly existed in the form of colloidal nanoparticles in lake water and their strong antimicrobial effect suppressed the growths of biomass and lake snow. In addition, Ag-NPs could also affect the microbial community of marine/lake snow, including impact on microbial diversity, and elevation on abundances of extracellular polymeric substances (EPS) synthesis genes and silver resistance genes. This work has deepened our understanding of the fate and ecological effect of Ag-NPs via the interaction with marine/lake snow in aquatic environments.

Assessing the relative importance of stressors to the benthic index, M-AMBI: An example from U.S. estuaries

M-AMBI, a multivariate benthic index, has been used by European and American (U.S.) authorities to assess estuarine and coastal health and has been used in scientific studies throughout the world. It has been shown to be related to multiple pressures and stressors, but the relative importance of individual stressors within a multiple stressor context has not generally been assessed. In this study, we assembled data collected between 1999 and 2015 by the U.S. Environmental Protection Agency using consistent methods. These data included sediment and water quality measures and benthic invertebrate data which were used to calculate M-AMBI. We further assembled watersheds for all US estuaries with benthic data and calculated land use metrics. Random forest (RF) was used to identify those variables most strongly related to M-AMBI. Because RF is a compilation of multiple, nonlinear models, we then assessed which of these variables had a direct relationship with M-AMBI. The resulting variables were then assessed using RF to identify the subsets of variables that produced an effective and parsimonious model. This process was conducted at the national and ecoregional scale and the variables identified as being most important to predict M-AMBI were compared with literature reports of ecological patterns in a given area. At the national scale, better condition was correlated with clearer waters, lower amounts of agriculture in the watershed, and lower carbon and metal concentrations in estuarine sediments. Other stressors were identified as being important at the ecoregional scale, although sediment metal concentrations and watershed agriculture were identified as being important in most ecoregions. Our results suggest that this technique is useful to identify the most important variables impacting M-AMBI at broad spatial scales, even when the percentage of sites in Bad or Poor condition is low. This technique also provides an initial identification of important stressors that can be used to target more intensive local studies.

Reduced representation sequencing accurately quantifies relative abundance and reveals population-level variation in Pseudo-nitzschia spp

Certain species within the genus Pseudo-nitzschia are able to produce the neurotoxin domoic acid (DA), which can cause illness in humans, mass-mortality of marine animals, and closure of commercial and recreational shellfisheries during toxic events. Understanding and forecasting blooms of these harmful species is a primary management goal. However, accurately predicting the onset and severity of bloom events remains difficult, in part because the underlying drivers of bloom formation have not been fully resolved. Furthermore, Pseudo-nitzschia species often co-occur, and recent work suggests that the genetic composition of a Pseudo-nitzschia bloom may be a better predictor of toxicity than prevailing environmental conditions. We developed a novel next-generation sequencing assay using restriction site-associated DNA (2b-RAD) genotyping and applied it to mock Pseudo-nitzschia communities generated by mixing cultures of different species in known abundances. On average, 94% of the variance in observed species abundance was explained by the expected abundance. In addition, the false positive rate was low (0.45% on average) and unrelated to read depth, and false negatives were never observed. Application of this method to environmental DNA samples collected during natural Pseudo-nitzschia spp. bloom events in Southern California revealed that increases in DA were associated with increases in the relative abundance of P. australis. Although the absolute correlation across time-points was weak, an independent species fingerprinting assay (Automated Ribosomal Intergenic Spacer Analysis) supported this and identified other potentially toxic species. Finally, we assessed population-level genomic variation by mining SNPs from the environmental 2bRAD dataset. Consistent shifts in allele frequencies in P. pungens and P. subpacifica were detected between high and low DA years, suggesting that different intraspecific variants may be associated with prevailing environmental conditions or the presence of DA. Taken together, this method presents a potentially cost-effective and high-throughput approach for studies aiming to evaluate both population and species dynamics in mixed samples.

Bacteria Associated With Phaeocystis globosa and Their Influence on Colony Formation

Phaeocystis globosa (P. globosa) is one of the dominant algae during harmful algal blooms (HABs) in coastal regions of Southern China. P. globosa exhibits complex heteromorphic life cycles that could switch between solitary cells and colonies. The ecological success of P. globosa has been attributed to its colony formation, although underlying mechanisms remain unknown. Here, we investigated different bacterial communities associated with P. globosa colonies and their influence on colony formation of two P. globosa strains isolated from coastal waters of Guangxi (GX) and Shantou (ST). Eight operational taxonomic units (OTUs) were observed in ST co-cultures and were identified as biomarkers based on Linear discriminant analysis Effect Size (LEfSe) analysis, while seven biomarkers were identified in P. globosa GX co-cultures. Bacterial communities associated with the P. globosa GX were more diverse than those of the ST strain. The most dominant phylum in the two co-cultures was Proteobacteria, within which Marinobacter was the most abundant genus in both GX and ST co-cultures. Bacteroidota were only observed in the GX co-cultures and Planctomycetota were only observed in the ST co-cultures. Co-culture experiments revealed that P. globosa colony formation was not influenced by low and medium cell densities of Marinobacter sp. GS7, but was inhibited by high cell densities of Marinobacter sp. GS7. Overall, these results indicated that the associated bacteria are selected by different P. globosa strains, which may affect the colony formation and development of P. globosa.