Engineered Phage Endolysin Eliminates Gardnerella Biofilm without Damaging Beneficial Bacteria in Bacterial Vaginosis Ex Vivo.

Bacterial vaginosis is characterized by an imbalance of the vaginal microbiome and a characteristic biofilm formed on the vaginal epithelium, which is initiated and dominated by Gardnerella bacteria, and is frequently refractory to antibiotic treatment. We investigated endolysins of the type 1,4-beta-N-acetylmuramidase encoded on Gardnerella prophages as an alternative treatment. When recombinantly expressed, these proteins demonstrated strong bactericidal activity against four different Gardnerella species. By domain shuffling, we generated several engineered endolysins with 10-fold higher bactericidal activity than any wild-type enzyme. When tested against a panel of 20 Gardnerella strains, the most active endolysin, called PM-477, showed minimum inhibitory concentrations of 0.13-8 µg/mL. PM-477 had no effect on beneficial lactobacilli or other species of vaginal bacteria. Furthermore, the efficacy of PM-477 was tested by fluorescence in situ hybridization on vaginal samples of fifteen patients with either first time or recurring bacterial vaginosis. In thirteen cases, PM-477 killed the Gardnerella bacteria and physically dissolved the biofilms without affecting the remaining vaginal microbiome. The high selectivity and effectiveness in eliminating Gardnerella, both in cultures of isolated strains as well as in clinically derived samples of natural polymicrobial biofilms, makes PM-477 a promising alternative to antibiotics for the treatment of bacterial vaginosis, especially in patients with frequent recurrence

The AT04A vaccine against proprotein convertase subtilisin/kexin type 9 reduces total cholesterol, vascular inflammation, and atherosclerosis in APOE*3Leiden.CETP mice

Cardiolog

Characterization of the Oral Fungal Microbiome (Mycobiome) in Healthy Individuals

The oral microbiome–organisms residing in the oral cavity and their collective genome–are critical components of health and disease. The fungal component of the oral microbiota has not been characterized. In this study, we used a novel multitag pyrosequencing approach to characterize fungi present in the oral cavity of 20 healthy individuals, using the pan-fungal internal transcribed spacer (ITS) primers. Our results revealed the “basal” oral mycobiome profile of the enrolled individuals, and showed that across all the samples studied, the oral cavity contained 74 culturable and 11 non-culturable fungal genera. Among these genera, 39 were present in only one person, 16 genera were present in two participants, and 5 genera were present in three people, while 15 genera (including non-culturable organisms) were present in ≥4 (20%) participants. Candida species were the most frequent (isolated from 75% of participants), followed by Cladosporium (65%), Aureobasidium, Saccharomycetales (50% for both), Aspergillus (35%), Fusarium (30%), and Cryptococcus (20%). Four of these predominant genera are known to be pathogenic in humans. The low-abundance genera may represent environmental fungi present in the oral cavity and could simply be spores inhaled from the air or material ingested with food. Among the culturable genera, 61 were represented by one species each, while 13 genera comprised between 2 and 6 different species; the total number of species identified were 101. The number of species in the oral cavity of each individual ranged between 9 and 23. Principal component (PCO) analysis of the obtained data set followed by sample clustering and UniFrac analysis revealed that White males and Asian males clustered differently from each other, whereas both Asian and White females clustered together. This is the first study that identified the “basal mycobiome” of healthy individuals, and provides the basis for a detailed characterization of the oral mycobiome in health and disease

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

Background: Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings: The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDAapproved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the antiinflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses

APEX DNA Microarray for the Identification of Pathogenic Fungi

The identification of fungal pathogens, though continuously improving, is still time-consuming and often inadequate for ensuring an early targeted therapy, which may be crucial for the treatment of invasive mycoses. Here, we describe a DNA-microarray system based on the arrayed-primer extension (APEX) technique for a rapid identification of pathogenic fungi, which represents a critical step in medical practice

Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning

A framework is presented to carry out prediction and classification of Motion Capture (MoCap) multichannel data, based on kernel adaptive filters and multi-kernel learning. To this end, a Kernel Adaptive Filter (KAF) algorithm extracts the dynamic of each channel, relying on the similarity between multiple realizations through the Maximum Mean Discrepancy (MMD) criterion. To assemble dynamics extracted from all MoCap data, center kernel alignment (CKA) is used to assess the contribution of each to the classification tasks (that is, its relevance). Validation is performed on a database of tennis players, performing a good classification accuracy of the considered stroke classes. Besides, we find that the relevance of each channel agrees with the findings reported in the biomechanical analysis. Therefore, the combination of KAF together with CKA allows building a proper representation for extracting relevant dynamics from multiple-channel MoCap dataThis work is supported by the project 36075 and mobility grant 8401 funded by Universidad Nacional de Colombia sede Manizales, by program “Doctorados Nacionales 2014” number 647 funded by COLCIENCIAS, as well as PhD financial support from Universidad Autónoma de Occident