摘要: Contamination by dexamethasone (DEX) in aquatic environments is expected to rise significantly as it is used in the treatment of inflammation, allergies, and autoimmune disorders, especially COVID-19. However, the underlying effects and mechanisms of DEX in leading to metabolic or infectious diseases have remained largely unexplored in teleosts. Here, we used zebrafish (Danio rerio) as a model to study the effects of DEX exposure on metabolic and infectious diseases. We found that DEX-induced hepatobiliary syndrome significantly increased susceptibility to type II grass carp reovirus (GCRV-II), which causes severe hemorrhagic disease in aquaculture. Comparative transcriptomic analysis demonstrated the shared and disease-specific immunometabolic responses among zebrafish larvae with hepatobiliary syndrome and/or GCRV-II infection. Moreover, compared with those of wild-type zebrafish, zebrafish larvae with DEX-induced hepatobiliary syndrome and/or GCRV-II infection presented increased expression of inflammatory markers (il1b), coagulation markers (fibrinogens and antithrombin III), and genes involved in autophagy, including hsp90aa. In vivo inhibition of autophagy via 3-MA and Hsp90 activity via geldanamycin markedly suppressed hepatic lipid deposition and reactive oxygen species accumulation caused by hepatobiliary syndrome and/or GCRV-II infection, thus significantly reducing the severity of disease and level of mortality induced by DEX and/or GCRV-II infection. In conclusion, our findings establish that the inhibition of autophagy and Hsp90 activity are promising therapeutic targets for DEX-induced hepatobiliary syndrome, GCRV-II infection, and DEX-induced hepatobiliary syndrome complicated with GCRV-II infection.

摘要: Estimating the individual feed intake (FI) for multiple consecutive meals of fish reared at commercial densities has long been a challenge and this difficulty has prevented the genetic improvement of feed efficiency (FE) in fish. We propose an automatic and real-time measurement system for individual FI of fish reared in a group based on computer vision and radio frequency identification fusion technology in large yellow croaker (Larimichthys crocea). To achieve this, we designed a feeding station where only one fish at a time can enter and have their passive integrated transponder (PIT) tag recorded. We then trained a feed pellet detection model based on You Only Look Once v5 using an annotated dataset, which achieved a final precision of nearly 100%. Finally, we utilized the trained feed detection model combined with PIT scanning to accurately and automatically track individual FI of fish with access to the feeding station. In 10 experiments lasting a total of 792 min conducted in the laboratory, the automatic real-time feed counting achieved an average accuracy of 94.5%. In addition, during a 14-day FI measurement period conducted in an indoor farm with 894 fish that received two meals per day, large yellow croaker feed efficiency ratio (FER) was 0.9 f 0.4 with a coefficient of variation of 47%. FER showed a weak positive correlation with initial body weight and a weak negative correlative with FI. There was also a moderate correlation between FER and body weight gain (BWG), with subgroups that had high BWG exhibiting greater FER values. The approach described here demonstrates a method to automatically and accurately investigate FER in fish that can be used to assess the potential for their genetic improvement.

摘要: Because of anthropogenic and natural factors, zooplanktivorous fish often dominate fish assemblages within lake ecosystems along the middle and lower reaches of the Yangtze River Basin (MLYRB), China. Despite their prevalence, the density-dependent effects of zooplanktivorous fish on lake food webs within the MLYRB are poorly understood compared to other omnivorous fish. We conducted a mesocosm experiment to quantify how plankton communities and water quality responded to varying densities of a common zooplanktivorous fish (Thin Sharpbelly Toxabramis swinhonis). During the experiment, the mean weekly total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH4 & thorn;-N), chemical oxygen demand (CODMn), turbidity, and chlorophyll-a (Chl.alpha) values in treatments where Thin Sharpbelly were present (i.e., fish-present) were all significantly higher than those in the control treatment with no Thin Sharpbelly (i.e., fish-absent). In addition, all water quality variables exhibited a significant positive relationship with the density of Thin Sharpbelly. The abundance and biomass of phytoplankton in the fish-present treatments were significantly higher than in the fish-absent treatment, with Pseudanabaena spp., Dolichospermum spp., and Limnothrix spp. dominating the phytoplankton assemblage. The biomass and abundance of small-bodied zooplankton increased with fish density, whereas the relative abundance and biomass of large-bodied zooplankton (cladocerans and copepods) showed the opposite trend. Also, the ratio of zooplankton to phytoplankton biomass declined nonlinearly with fish density, while the ratio of Chl.alpha to TP was significantly positively correlated with fish density. Collectively, experimental results indicated that zooplanktivorous fish increased the concentrations of nutrients and stimulated phytoplankton growth through feeding on large zooplankton and the strength of these effects increase with fish density. Moreover, results indicate that high densities of zooplanktivorous fish can alter plankton assemblage structure by facilitating growth of undesirable cyanobacteria and shifting the composition of zooplankton to smaller-bodied species and forms. Our results demonstrate how Thin Sharpbelly, and potentially other obligate zooplanktivorous fish, can have adverse effects on water quality and plankton assemblages, but these responses are density dependent. Our findings suggest that managing the density of zooplanktivorous fish could be a useful ecological rehabilitation measure for improving water quality in MLYRB lakes.