Is TBX agar a suitable medium for monitoring Escherichia coli in bathing water using the membrane filtration method?

The use of a suitable method for the enumeration of indicator microorganisms is of crucial importance for reliable monitoring and assessment of the quality of bathing waters. Among other characteristics, the method should be selective enough and ensure acceptable relative recovery of target microorganisms. This study presents the basic parameters, relative recovery and categorical performance characteristics of Tryptone Bile X-glucuronide (TBX) agar for Escherichia coli (E. coli) enumeration in bathing water samples using the membrane filtration method.The results of the relative recovery study, in which TBX agar was compared against temperature-modified ISO 9308-1:2014, showed that in order to achieve a satisfactory relative recovery of E. coli with TBX agar at 44 ± 0.5 °C, the resuscitation period on a non-selective medium (Minerals Modified Glutamate Agar, MMGA) at 36 ± 2 °C is crucial. Incubation on a double-layer MMGA/TBX medium with a 6-h resuscitation period and alternating incubation on single-layer MMGA and TBX agar with a 4-h resuscitation period resulted in acceptable and very similar relative recovery. The achieved performance characteristics of the tested medium, double-layer MMGA/TBX agar, are acceptable. The selectivity was matrix-dependent and was 60.6% for inland and 69.9% for coastal waters. No significant effect of the resuscitation period on selectivity was recorded. Finally, the results showed that when the resuscitation period on a non-selective medium is included, TBX agar is a suitable medium for E. coli enumeration in bathing water samples using the membrane filtration method and that its use, theoretically, would not have negative effects on the assessment of bathing water quality.

Ecology of aerobic anoxygenic phototrophs on a fine-scale taxonomic resolution in Adriatic Sea unravelled by unsupervised neural network.

BACKGROUND: Aerobic anoxygenic phototrophs are metabolically highly active, diverse and widespread polyphyletic members of bacterioplankton whose photoheterotrophic capabilities shifted the paradigm about simplicity of the microbial food chain. Despite their considerable contribution to the transformation of organic matter in marine environments, relatively little is still known about their community structure and ecology at fine-scale taxonomic resolution. Up to date, there is no comprehensive (i.e. qualitative and quantitative) analysis of their community composition in the Adriatic Sea. RESULTS: Analysis was based on pufM gene metabarcoding and quantitative FISH-IR approach with the use of artificial neural network. Significant seasonality was observed with regards to absolute abundances (maximum average abundances in spring 2.136 ± 0.081 × 104 cells mL-1, minimum in summer 0.86 × 104 cells mL-1), FISH-IR groups (Roseobacter clade prevalent in autumn, other Alpha- and Gammaproteobacteria in summer) and pufM sequencing data agglomerated at genus-level. FISH-IR results revealed heterogeneity with the highest average relative contribution of AAPs assigned to Roseobacter clade (37.66%), followed by Gammaproteobacteria (35.25%) and general Alphaproteobacteria (31.15%). Community composition obtained via pufM sequencing was dominated by Gammaproteobacteria clade NOR5/OM60, specifically genus Luminiphilus, with numerous rare genera present in relative abundances below 1%. The use of artificial neural network connected this community to biotic (heterotrophic bacteria, HNA and LNA bacteria, Synechococcus, Prochlorococcus, picoeukaryotes, heterotrophic nanoflagellates, bacterial production) and abiotic environmental factors (temperature, salinity, chlorophyll a and nitrate, nitrite, ammonia, total nitrogen, silicate, and orthophosphate concentration). A type of neural network, neural gas analysis at order-, genus- and ASV-level, resulted in five distinct best matching units (representing particular environments) and revealed that high diversity was generally independent of temperature, salinity, and trophic status of the environment, indicating a potentially dissimilar behaviour of aerobic anoxygenic phototrophs compared to the general bacterioplankton. CONCLUSION: This research represents the first comprehensive analysis of aerobic anoxygenic phototrophs in the Adriatic Sea on a trophic gradient during a year-round period. This study is also one of the first reports of their genus-level ecology linked to biotic and abiotic environmental factors revealed by unsupervised neural network algorithm, paving the way for further research of substantial contribution of this important bacterial functional group to marine ecosystems.

Picoplankton diversity in an oligotrophic and high salinity environment in the central Adriatic Sea.

By combining qualitative 16S metabarcoding and quantitative CARD-FISH methods with neural gas analysis, different patterns of the picoplankton community were revealed at finer taxonomic levels in response to changing environmental conditions in the Adriatic Sea. We present the results of a one-year study carried out in an oligotrophic environment where increased salinity was recently observed. We have shown that the initial state of community structure changes according to environmental conditions and is expressed as qualitative and quantitative changes. A general pattern of increasing diversity under harsh environmental conditions, particularly under the influence of increasing salinity at the expense of community abundance was observed. Considering the trend of changing seawater characteristics due to climate change, this study helps in understanding a possible structural change in the microbial community of the Adriatic Sea that could affect higher levels of the marine food web.

Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea.

Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.