In recent years, a lot of scientific interest has focused on cancer immunotherapy. Although chronic inflammation has been described as one of the hallmarks of cancer, acute inflammation can actually trigger the immune system to fight diseases, including cancer. Toll-like receptor (TLR) ligands have long been used as adjuvants for traditional vaccines and it seems they may also play a role enhancing efficiency of tumor immunotherapy. The aim of this perspective is to discuss the effects of TLR stimulation in cancer, expression of various TLRs in different types of tumors, and finally the role of TLRs in anti-cancer immunity and tumor rejection.
Strategies utilizing Toll-like receptor 4 (TLR4) agonists for treatment of cancer, infectious diseases, and other targets report promising results. Potent TLR4 antagonists are also gaining attention as therapeutic leads. Though some principles for TLR4 modulation by lipid A have been described, a thorough understanding of the structure-activity relationship (SAR) is lacking. Only through a complete definition of lipid A-TLR4 SAR is it possible to predict TLR4 signaling effects of discrete lipid A structures, rendering them more pharmacologically relevant. A limited ‘toolbox’ of lipid A-modifying enzymes has been defined and is largely composed of enzymes from mesophile human and zoonotic pathogens. Expansion of this ‘toolbox’ will result from extending the search into lipid A biosynthesis and modification by bacteria living at the extremes. Here, we review the fundamentals of lipid A structure, advances in lipid A uses in TLR4 modulation, and the search for novel lipid A-modifying systems in extremophile bacteria.
With the success
of the Human Genome Project, large-scale systemic projects became
a reality that enabled rapid development of the systems biology field.
Systems biology approaches to host–pathogen interactions have
been instrumental in the discovery of some specifics of Gram-negative
bacterial recognition, host signal transduction, and immune tolerance.
However, further research, particularly using multi-omics approaches,
is essential to untangle the genetic, immunologic, (post)transcriptional,
(post)translational, and metabolic mechanisms underlying progression
from infection to clearance of microbes. The key to understanding
host–pathogen interactions lies in acquiring, analyzing, and
modeling multimodal data obtained through integrative multi-omics
experiments. In this article, we will discuss how multi-omics analyses
are adding to our understanding of the molecular basis of host–pathogen
interactions and systemic maladaptive immune response of the host
to microbes and microbial products.
With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.