Tea Tree Oil Prevents Mastitis-Associated Inflammation in Lipopolysaccharide-Stimulated Bovine Mammary Epithelial Cells

Lipopolysaccharide (LPS), commonly known as endotoxin, is ubiquitous and the most-studied pathogen-associated molecular pattern. A component of Gram-negative bacteria, extracellular LPS is sensed by our immune system via the toll-like receptor (TLR)-4. Given that TLR4 is membrane bound, it recognizes LPS in the extracellular milieu or within endosomes. Whether additional sensors, if any, play a role in LPS recognition within the cytoplasm remained unknown until recently. The last decade has seen an unprecedented unfolding of TLR4-independent LPS sensing pathways. First, transient receptor potential (TRP) channels have been identified as non-TLR membrane-bound sensors of LPS and, second, caspase-4/5 (and caspase-11 in mice) have been established as the cytoplasmic sensors for LPS. Here in this review, we detail the brief history of LPS discovery, followed by the discovery of TLR4, TRP as the membrane-bound sensor, and our current understanding of caspase-4/5/11 as cytoplasmic sensors.

Tea tree oil (TTO) stimulates autolysis in exponential and stationary phase cells of Escherichia coli. Electron micrographs of cells grown in the presence of TTO showed the loss of electron dense material, coagulation of cell cytoplasm and formation of extracellular blebs. Stationary phase cells demonstrated less TTO-stimulated autolysis and also had greater tolerance to TTO-induced cell death, compared to exponentially grown cells. It was also revealed that subpopulation of stationary phase cells demonstrated increased tolerance to TTO-bactericidal effects.

Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug–target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF. Supplementary data are available at Bioinformatics online.