Editorial: Role of Iron in Bacterial Pathogenesis

The collection of articles published in this eBook represent different facets of the interactions between pathogens and their host concerning the battle for iron. Pathogens have developed different strategies to acquire iron from their host. These include the production of siderophores, heme acquisition and ferrous iron uptake

Staphylococcus aureus histone deacteylase-like enzyme is a potential target for adjuvant antibiotic discovery

Background: The rise in antibiotic resistance requires prompt action to reduce the burden of untreatable bacterial diseases1. Staphylococcus aureus is a human commensal and opportunistic pathogen that causes a broad range of diseases, from mild skin infections to infective endocarditis. The World Health Organization has placed S. aureus on the high-priority pathogen list due to its multidrug-resisting nature2. The study aims to identify molecular targets for antibiotic adjuvants to restore antibiotic activity. Methods: Extensive blast search and computational analysis were employed to search published S. aureus genomes. The effect of suggested adjuvants was tested on sensitive and resistant S. aureus strains in-vitro. Bacteria were incubated in the presence of either an HDAC inhibitor (TSA) or an antibiotic (Cefixime), or a combination of both. Results: A gene that encodes a histone deacetylase-like enzyme (SA-HDAC) and shares high 3D-homology to human HDAC2 and HDAC8 was identified3. Using computational modeling, it was found that the SA-HDAC protein has an active catalytic pocket containing the highly conserved zinc-binding constellation, suggesting an HDAC-like activity. I-TASSER analysis revealed that HDAC inhibitors such as TSA, CRI, LLX, NHB, and B3N can bind to the catalytic core. From the growth curves generated using the in-vitro study, it was observed that while Cefixime alone had no effect, TSA had an inhibitory effect, and the combination showed an additive effect on both strains. Further, the effect was more evident in the sensitive strain as compared to MRSA. An extensive bioinformatics blast search showed that this gene is absent in most gut microbiota species but found in many pathogens that carry and spread multidrug resistance in healthcare settings as well as in community-acquired infections. Conclusion: SA-HDAC enzyme, which is absent in most gut microbiota, is a highly druggable target that can be utilized for novel adjuvant antibiotic discovery.qscienc

The Human Host Defense Peptide LL-37 Interacts with Neisseria meningitidis Capsular Polysaccharides and Inhibits Inflammatory Mediators Release

Capsular polysaccharides (CPS) are a major virulence factor in meningococcal infections and form the basis for serogroup designation and protective vaccines. Our work has identified meningococcal CPS as a pro-inflammatory ligand that functions through TLR2 and TLR4-MD2-dependent activation. We hypothesized that human cationic host defense peptides interact with CPS and influence its biologic activity. Accordingly, the interaction of meningococcal CPS with the human-derived cationic peptide LL-37, which is expressed by phagocytic and epithelial cells that interface with meningococci during infection, was investigated. LL-37 neutralized the pro-inflammatory activity of endotoxin-free CPS as assessed by TLR2 and TLR4-MD-2-dependent release of TNFα, IL-6 and IL-8 from human and murine macrophages. The cationic and hydrophobic properties of LL-37 were crucial for this inhibition, which was due to binding of LL-37 to CPS. LL-37 also inhibited the ability of meningococcal CPS to induce nitric oxide release, as well as TNFα and CXCL10 (IP-10) release from TLR4-sufficient and TLR4-deficient murine macrophages. Truncated LL-37 analogs, especially those that retained the antibacterial domain, inhibited vaccine grade CPS and meningococcal CPS prepared from the major serogroups (A, B C, Y and W135). Thus, LL-37 interaction with CPS was independent of specific glucan structure. We conclude that the capacity of meningococcal CPS to activate macrophages via TLR2 and TLR4-MD-2 can be inhibited by the human cationic host defense peptide LL-37 and propose that this impacts CPS-based vaccine responses

RamanNet: A generalized neural network architecture for Raman Spectrum Analysis

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysi

Editorial: Global excellence in children and health

Introduction Cooperation and collaboration within and between countries is an increasingly important component of tackling and overcoming the pressing issues impacting upon children’s health and wellbeing. This Research Topic (RT) aimed to highlight advances and achievements in the health and wellbeing of children across the globe. The seven included manuscripts touched on key endemic issues of diarrhoeal illnesses, micronutrient status and child development

Assessment of In Vitro Immunostimulatory Activity of an Adjuvanted Whole-Cell Inactivated Neisseria gonorrhoeae Microparticle Vaccine Formulation

The emergence of drug-resistant gonorrhea infections worldwide combined with the lack of a vaccine is alarming. We prepared a novel microparticulate (MP) vaccine formulation using whole-cell inactivated Neisseria gonorrhoeae as the vaccine antigen, with Alum and AddaVax™ as vaccine adjuvants. The adjuvanted vaccine MP formulation was assessed for in vitro immunostim-ulatory activity, autophagy, and antigen presentation ability. The data shows that the adjuvanted gonococci vaccine MP enhanced autophagy induction in antigen presenting cells (APCs) compared to gonococci vaccine MP without adjuvants, which is important for enhancing antigen presentation. In addition, the adjuvanted vaccine formulation increased the surface expression of antigen presenting molecules MHCI and MHCII as well as co-stimulatory molecules CD40 and CD86 on the surface of dendritic cells. In addition, the gonococci vaccine microparticles at lower doses did not significantly increase the expression of the death receptor CD95 in APCs, which when elevated leads to suboptimal antigen presentation and reduced immune responses. The adjuvanted whole-cell inactivated gonococci microparticle vaccine formulation enhanced antigen uptake, processing, and antigen presentation.An R15 grant from the National Institute of Health funds this project-1R15AI133473-01A1

Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning

Bacterial endotoxin, a major component of the Gram-negative bacterial outer membrane leaflet, is a lipopolysaccharide shed from bacteria during their growth and infection and can be utilized as a biomarker for bacterial detection. Here, the surface enhanced Raman scattering (SERS) spectra of eleven bacterial endotoxins with an average detection amount of 8.75 pg per measurement have been obtained based on silver nanorod array substrates, and the characteristic SERS peaks have been identified. With appropriate spectral pre-processing procedures, different classical machine learning algorithms, including support vector machine, k-nearest neighbor, random forest, etc., and a modified deep learning algorithm, RamanNet, have been applied to differentiate and classify these endotoxins. It has been found that most conventional machine learning algorithms can attain a differentiation accuracy of >99%, while RamanNet can achieve 100% accuracy. Such an approach has the potential for precise classification of endotoxins and could be used for rapid medical diagnoses and therapeutic decisions for pathogenic infections