Prevalence of Actinobacteria in the production of 2-methylisoborneol and geosmin, over Cyanobacteria in a temperate eutrophic reservoir

Multivariate statistical and bioinformatic analyses for the seasonal variations of actinobacterial community structures in a drinking water reservoir

Actinobacterial community is a conspicuous part of aquatic ecosystems and displays an important role in the case of biogeochemical cycle, but little is known about the seasonal variation of actinobacterial community in reservoir ecological environment. In this study, the high-throughput techniques were used to investigate the structure of the aquatic actinobacterial community and its inducing water quality parameters in different seasons. The results showed that the highest diversity and abundance of actinobacterial community occurred in winter, with Sporichthya (45.42%) being the most abundant genus and Rhodococcus sp. (29.32%) being the most abundant species. Network analysis and correlation analysis suggested that in autumn the dynamics of actinobacterial community were influenced by more factors and Nocardioides sp. SX2R5S2 was the potential keystone species which was negatively correlated with temperature (R = -0.72, P < 0.05). Changes in environmental factors could significantly affect the changes in actinobacterial community, and the dynamics of temperature, dissolved oxygen (DO), and turbidity are potential conspicuous factors influencing seasonal actinobacterial community trends. The partial least squares path modeling further elucidated that the combined effects of DO and temperature not only in the diversity of actinobacterial community but also in other water qualities, while the physiochemical parameters (path coefficient = 1.571, P < 0.05) was strong environmental factors in natural mixture period. These results strengthen our understanding of the dynamics and structures of actinobacterial community in the drinking water reservoirs and provide scientific guidance for further water quality management and protection in water sources.

The effect of novel aquaculture mode on phosphorus sorption-release in pond sediment

The emergence of aquaculture modes has brought considerable changes to the aquaculture landscape and profoundly influenced environmental processes. However, there is limited research on nutrient cycling in emerging aquaculture modes. This study investigated the characteristics and mechanisms of sediment phosphorus (P) sorption-release in traditional earthen pond culture (TEP) and pond-tank culture mode (PTC), which represents novel aquaculture modes. The results showed that under higher nutrient load, the PTC did not show significant differences in nutrient concentration in water and sediments compared to TEP. Although there are no significant differences in overlying water P concentration between the modes throughout the entire aquaculture period, the trends of its variation over time are different, which significantly affected the P sorption-release characteristics of sediment. Additionally, correlation analysis suggested that calcium-bound P and hot NaOH-extractable organic P may affect the sorption-release characteristics of sediment as active P fractions. The change in redox condition caused by enzyme-mediated organic matter decomposition (such as protein and lipids) is also an important reason for sediment P release. However, the P fractions and organic matter content showed no significant differences between the two modes. Sediment microbial analysis showed that TEP exhibited a significant dominance of inorganic P-solubilizing bacteria, especially Actinobacteria and Bacilli classes. PTC had a higher proportion of organic P-solubilizing bacteria, primarily in the Bacteroidia class. The quantitative results of the key functional gene phoD in organic P decomposition also showed that the abundance in PTC was significantly higher than that in TEP. This suggested that microbial differences may be another reason for differences in P sorption-release behavior. This study revealed the differences in P sorption-release characteristics and mechanisms between the TEP and PTC, which holds positive implications for water quality and pollution management in novel aquaculture modes.

Volatile sensation: The chemical ecology of the earthy odorant geosmin

Geosmin may be the most familiar volatile compound, as it lends the earthy smell to soil. The compound is a member of the largest family of natural products, the terpenoids. The broad distribution of geosmin among bacteria in both terrestrial and aquatic environments suggests that this compound has an important ecological function, for example, as a signal (attractant or repellent) or as a protective specialized metabolite against biotic and abiotic stresses. While geosmin is part of our everyday life, scientists still do not understand the exact biological function of this omnipresent natural product. This minireview summarizes the current general observations regarding geosmin in prokaryotes and introduces new insights into its biosynthesis and regulation, as well as its biological roles in terrestrial and aquatic environments.

Actinobacteria produce taste and odor in drinking water reservoir: Community composition dynamics, co-occurrence and inactivation models

Taste and odor (T&O) has become a significant concern for drinking water safety. Actinobacteria are believed to produce T&O during the non-algal bloom period; however, this has not been widely investigated. In this study, the seasonal dynamics of the actinobacterial community structure and inactivation of odor-producing actinobacteria were explored. The results indicated that the diversity and community composition of actinobacteria exhibited significant spatiotemporal distribution. Network analysis and structural equation modeling showed that the actinobacterial community occupied a similar environmental niche, and the major environmental attributes exhibited spatiotemporal dynamics, which affected the actinobacterial community. Furthermore, the two genera of odorous actinobacteria were inactivated in drinking water sources using chlorine. Amycolatopsis spp. have a stronger chlorine resistance ability than Streptomyces spp., indicating that chlorine inactivates actinobacteria by first destroying cell membranes and causing the release of intracellular compounds. Finally, we integrated the observed variability in the inactivation rate of actinobacteria into an expanded Chick-Watson model to estimate its effect on inactivation. These findings will deepen our understanding of the seasonal dynamics of actinobacterial community structure in drinking water reservoirs and provide a foundation for reservoir water quality management strategies.

Co-Occurrence of Taste and Odor Compounds and Cyanotoxins in Cyanobacterial Blooms: Emerging Risks to Human Health?

Cyanobacteria commonly form large blooms in waterbodies; they can produce cyanotoxins, with toxic effects on humans and animals, and volatile compounds, causing bad tastes and odors (T&O) at naturally occurring low concentrations. Notwithstanding the large amount of literature on either cyanotoxins or T&O, no review has focused on them at the same time. The present review critically evaluates the recent literature on cyanotoxins and T&O compounds (geosmin, 2-methylisoborneol, β-ionone and β-cyclocitral) to identify research gaps on harmful exposure of humans and animals to both metabolite classes. T&O and cyanotoxins production can be due to the same or common to different cyanobacterial species/strains, with the additional possibility of T&O production by non-cyanobacterial species. The few environmental studies on the co-occurrence of these two groups of metabolites are not sufficient to understand if and how they can co-vary, or influence each other, perhaps stimulating cyanotoxin production. Therefore, T&Os cannot reliably serve as early warning surrogates for cyanotoxins. The scarce data on T&O toxicity seem to indicate a low health risk (but the inhalation of β-cyclocitral deserves more study). However, no data are available on the effects of combined exposure to mixtures of cyanotoxins and T&O compounds and to combinations of T&O compounds; therefore, whether the co-occurrence of cyanotoxins and T&O compounds is a health issue remains an open question.

Allelopathic inhibition effects and mechanism of phenolic acids to Microcystis aeruginosa

Allelochemicals are essential agents for the biological control of harmful blooms. It is crucial to identify efficient algal suppressors and understand their mechanisms. This study reports the inhibition of Microcystis aeruginosa growth by 6 phenolic acids derived from plants' secondary metabolites. The inhibitory effect of phenolic acids was significantly influenced by exposure dose and phenolic acid species. Caffeic acid has the most efficient algal inhibition ability (96 h-EC₅₀ of 5.8 mg/L). In contrast, the other 5 analogs (cinnamic acid, p-coumaric acid, 3-hydroxycinnamic acid, ferulic acid, and isoferulic acid) showed a weak inhibition effect or promotion effect with the exposure dose of 5-100 mg/L. ROS and chlorophyll a content tests combined with metabolomics analysis revealed that caffeic acid could induce the ROS accumulation of M. aeruginosa. They mainly disturbed nucleotide, amino acid, and fatty acid metabolism, leading to the downregulation of most metabolites, including toxins of microcystin LR and cyanopeptolin A, and the precursors of some unpleasant terpenoids. It has been suggested that caffeic acid is an effective agent for controlling M. aeruginosa blooms.

Emission of cyanobacterial volatile organic compounds and their roles in blooms

Cyanobacteria are photosynthetic prokaryotes and one of dominant species in eutrophicated waters, which easily burst blooms in summer with high irradiance and temperature conditions. In response to high irradiance, high temperature, and nutrient conditions, cyanobacteria release abundant of volatile organic compounds (VOCs) by up-regulating related gene expression and oxidatively degrading β-carotene. These VOCs not only increase offensive odor in waters, but also transfer allelopathic signals to algae and aquatic plants, resulting in cyanobacteria dominating eutrophicated waters. Among these VOCs, β-cyclocitral, α-ionone, β-ionone, limonene, longifolene, and eucalyptol have been identified as the main allelopathic agents, which even directly kill algae by inducing programmed cell death (PCD). The VOCs released from cyanobacteria, especially the ruptured cells, exhibit repelling effects on the herbivores, which is beneficial to survival of the population. Cyanobacterial VOCs might transfer aggregating information among homogeneous species, so the acceptors initiate aggregation to resist the coming stresses. It can be speculated that the adverse conditions can promote VOC emission from cyanobacteria, which play important roles in cyanobacteria dominating eutrophicated waters and even bursting blooms.

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning-based gamma test variable selection and empirical wavelet transform

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neural network (ANN), extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) are developed. First, the four models were developed using only water quality variables. Second, based on the results of the first, a new modelling strategy was introduced based on preprocessing signal decomposition. Hence, the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) were used for decomposing the water quality variables into several subcomponents, and the obtained intrinsic mode functions (IMFs) and multiresolution analysis (MRA) components were used as new input variables for the ML models. Results of the present investigation show that (i) using single models, good predictive accuracy was obtained using the RFR model exhibiting an R and NSE values of ≈0.914 and ≈0.833 for the first station, and ≈0.944 and ≈0.884 for the second station, while the others models, i.e., ANN, RVFL, and ELM, have failed to provide a good estimation of the CBGA; (ii) the decomposition methods have contributed to a significant improvement of the individual models performances; (iii) among the thee decomposition methods, the EMD was found to be superior to the VMD and EWT; and (iv) the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances with R and NSE values of approximately ≈0.983, ≈0.967, and ≈0.989 and ≈0.976, respectively.