Tissue-resident trained immunity in hepatocytes protects against septic liver injury in zebrafish.

Trained immunity is classically characterized by long-term functional reprogramming of innate immune cells to combat infectious diseases. Infection-induced organ injury is a common clinical severity phenotype of sepsis. However, whether the induction of trained immunity plays a role in protecting septic organ injury remains largely unknown. Here, through establishing an in vivo ß-glucan training and lipopolysaccharide (LPS) challenge model in zebrafish larvae, we observe that induction of trained immunity could inhibit pyroptosis of hepatocytes to alleviate septic liver injury, with an elevated trimethyl-histone H3 lysine 4 (H3K4me3) modification that targets mitophagy-related genes. Moreover, we identify a C-type lectin domain receptor in zebrafish, named DrDectin-1, which is revealed as the orchestrator in gating H3K4me3 rewiring-mediated mitophagy activation and alleviating pyroptosis-engaged septic liver injury in vivo. Taken together, our results uncover tissue-resident trained immunity in maintaining liver homeostasis at the whole-animal level and offer an in vivo model to efficiently integrate trained immunity for immunotherapies.

Poly(I:C) induces anti-inflammatory response against secondary LPS challenge in zebrafish larvae.

Poly(I:C) is known as an agonist of the TLR3 receptor which could prime inflammation and elicit the host immune response, which is widely applied as adjuvant or antivirus treatment. However, the negative effects of poly(I:C) on regulating immune response to protect the host from inflammatory diseases remain largely unknown. Here, we establish an in vivo model to pre-treat zebrafish larvae with poly(I:C) at 2 dpf, then challenge them with LPS at 6 dpf, and find that poly(I:C) training could significantly alleviate the LPS challenge-induced septic shock and inflammatory phenotypes. Moreover, the poly(I:C)-trained larvae exhibit decreased number of macrophages, but not neutrophils, after secondary LPS challenge. Furthermore, training the larvae with poly(I:C) could elevate the transcripts of mTOR signaling and heighten the H3K4me3-mediated epigenetic modifications. And interestingly, we find that inhibiting the H3K4me3 modification, rather than mTOR signaling, could recover the number of macrophages in poly(I:C)-trained larvae, which is consistent with the observations of inflammatory phenotypes. Taken together, these results suggest that poly(I:C) training could induce epigenetic rewiring to mediate the anti-inflammatory response against secondary LPS challenge-induced septic shock through decreasing macrophages' number in vivo, which might expand our understanding of poly(I:C) in regulating fish immune response.

Classification of heroin, methamphetamine, ketamine and their additives by attenuated total reflection-Fourier transform infrared spectroscopy and chemometrics.

Drug crime is a prominent issue of concern from pole to pole. In order to seek higher profits, drug gangs often add diluents and adulterants to the drugs to disperse drug products Analysis of these additives would be greatly conducive to determine the origin of drug products for law enforcement departments. A method using attenuated total reflectance-Fourier transform infrared spectroscopy and chemometrics methods to classify the heroin hydrochloride, methamphetamine hydrochloride, ketamine hydrochloride and their five additives (caffeine, phenacetin, starch, glucose, and sucrose), was developed. The Baseline correction, multivariate scatter correction, standard normal variate and Savitzky-Golay algorithm were adopted to pre-process the spectral data. Several supervised pattern recognition methods including decision tree, Bayes discriminant analysis, and support vector machine were considered as algorithms of constructing classifiers. The results reveal that, repetitive and interfering data in original spectrum data could be eliminated by principal component analysis and factor analysis. F-measure, as a comprehensive evaluation index of precision rate and recall rate, was more objective than precision rate and recall rate to reflect the ability of model to distinguish samples. It should be used as one of the indicators to evaluate the model. The CHAID classification tree could be identified as priorities in the decision tree model, while the linear kernel could be considered as the optimal kernel in the support vector machine model. The classification ability of three hydrochloride mixtures based on Bayes discriminant analysis was better than that of another models. Bayes discriminant analysis model was the more useful and practical method for classifying the target drugs of abuse than that of decision trees and support vector machine. The designed approach represents a potentially simple, non-destructive, and rapid method of classifying hydrochloride mixtures.