Characterization of DmToll and DmToll7 homologue in Litopenaeus vannamei based on structure analysis.

Toll-like receptors (TLRs) are a family of pattern recognition receptors (PRRs) that recognize invading pathogens and activate downstream signaling pathways. The number of 10 Tolls is found in Litopenaeus vannamei but have not yet been identified as the corresponding Toll homologue of model animal. In this study, we predicted the three-dimensional (3D) structures of 10 LvTolls (LvToll1-10) with AlphaFold2 program. The per-residue local distance difference test (pLDDT) scores of LvTolls showed the predicted structure of LvTolls had high accuracy (pLDDT>70). By structural analysis, 3D structures of LvToll2 and LvToll3 had high similarity with Drosophila melanogaster Toll and Toll7, respectively. 3D structure of LvToll7 and LvToll10 were not similar to that of other LvTolls. Moreover, we also predicted that LvSpätzle4 had high structural similarity to DmSpätzle. There were 9 potential hydrogen bonds in LvToll2-LvSpätzle4 complex. Importantly, co-immunoprecipitation assay showed that LvToll2 could bind with LvSpätzle4. Collectively, this study provides new insight for researching invertebrate immunity by identifying the protein of model animal homologue.

Identification and characterization of a TLR4 homologue in Eriocheir sinensis based on structure analysis.

Toll-like receptor 4 (TLR4) plays an essential role in the activation of innate immunity by recognizing diverse pathogenic components of bacteria. Six Tolls were found in Eriocheir sinensis but have not yet been identified as mammalian TLR4 homolog. For this purpose, we predicted three-dimensional (3D) structures of EsTolls (EsToll1-6) with AlphaFold2. 3D structure of LRRs and TIR most had high accuracy (pLDDT >70). By structure analysis, 3D structures of EsToll6 had a high overlap with HsTLR4. Moreover, we also predicted potential 11 hydrogen bonds and 3 salt bridges in the 3D structure of EsToll6-EsML1 complex. 18 hydrogen bonds and 7 salt bridges were predicted in EsToll6-EsML2 complex. Co-immunoprecipitation assay showed that EsToll6 could interact with EsML1 and EsML2, respectively. Importantly, TAK242 (a mammalian TLR4-specific inhibitor) could inhibit the generation of ROS stimulated by lipopolysaccharides (LPS) in EsToll6-EsML2-overexpression Hela cells. Collectively, these results implied that EsToll6 was a mammalian TLR4 homolog and provided a new insight for researching mammalian homologs in invertebrates.

Bisphenols can promote antibiotic resistance by inducing metabolic adaptations and natural transformation.

Whether bisphenols, as plasticizers, can influence bacterial uptake of antibiotic resistance genes (ARGs) in natural environment, as well as the underlying mechanism remains largely unknown. Our results showed that four commonly used bisphenols (bisphenol A, S, F, and AF) at their environmental relative concentrations can significantly promote transmission of ARGs by 2.97-3.56 times in Acinetobacter baylyi ADP1. Intriguingly, we observed ADP1 acquired resistance by integrating plasmids uptake and cellular metabolic adaptations other than through reactive oxygen species mediated pathway. Metabolic adaptations including upregulation of capsules polysaccharide biosynthesis and intracellularly metabolic enzymes, which enabled formation of thicker capsules for capturing free plasmids, and degradation of accumulated compounds. Simultaneously, genes encoding DNA uptake and translocation machinery were incorporated to enhance natural transformation of antibiotic resistance carrying plasmids. We further exposed aquatic fish to bisphenols for 120 days to monitor their long-term effects in aquatic environment, which showed that intestinal bacteria communities were dominated by a drug resistant microbiome. Our study provides new insight into the mechanism of enhanced natural transformation of ARGs by bisphenols, and highlights the investigations for unexpectedly-elevated antibiotic-resistant risks by structurally related environmental chemicals.

Quantitative structure-activity relationships for joint toxicity of substituted phenols and anilines to Scenedesmus obliquus.

There are often many chemicals coexisting in aquatic ecosystems, and few information on the joint toxicity of a mixture of organic pollutants is available at present. The 48-h toxicity of substituted phenols and anilines and their binary mixtures to Scenedesmus obliquus was determined by the algae inhibition test. The median effective inhibition concentration EC50 values for single compounds and EC50(mix) values for coexistent compounds were obtained. The n-octanol/water partition coefficient (logP(mix)) and the frontier orbital energy gap (deltaE(mix)) for mixtures were calculated. The following two-descriptor quantitative structure-activity relationships (QSARs) models were developed to predict single toxicity and joint toxicity respectively: log(1/EC50) = 0.44510gP - 0.801deltaE + 9.501 (r2 = 0.876) and log (1/EC50(mix)) = 0.338logP(mix) - 0.492deltaE(mix) + 6.928 (r2 = 0.831). The two equations were found to fit well. In addition, the model derived from the structural parameters of single components in binary mixtures log(1/EC50(mix)) = 0.222logP - 0.277deltaE + 5.250 (r2 = 0.879) can be used successfully to predict the toxicity of a mixture.

Joint toxicity of aromatic compounds to algae and QSAR study.

There are often many chemicals coexisting in aquatic ecosystems, and information the joint toxicity of a mixture of organic pollutants on microorganisms is scarce at present. Acute toxicity of aromatic anilines and phenols and their mixtures to alga was determined by the algae inhibition test. The median effective inhibition concentration EC(50) values for single compounds and EC(50mix) values for binary and multiple mixtures were obtained. The joint toxic effects of mixtures were estimated by using mixture toxicity index method. The structural descriptors of the n-octanol/water partition coefficient (log P (mix)) and the frontier orbital energy gap (DeltaE (mix)) for mixtures were calculated. Based on the quantitative structure-activity relationship model for single chemical toxicity log(1/EC(50)) = 0.579log P - 0.783DeltaE + 8.966 (n = 11, r (2) = 0.923), the following two-descriptor model was developed for the toxicity of a mixture: log(1/EC(50mix)) = 0.416log P (mix) - 0.584DeltaE (mix) + 7.530 (n = 27, r (2) = 0.944). This model can be used successfully to predict the toxicity of a mixture, whether binary mixtures in variant toxic ratios (4:1, 2:1, 1:1, 1:2 and 1:4) or multiple mixtures of three or four chemicals at an equitoxic ratio are used as predictors.