The impact of fixed orthodontic appliances and clear aligners on the oral microbiome and the association with clinical parameters: A longitudinal comparative study.

INTRODUCTION: Orthodontic treatment interferes with oral hygiene and promotes plaque retention, which leads to gingival inflammation and enamel demineralization. Although removable clear aligners (CAs) are designed to improve oral hygiene compared with fixed appliances (FAs), comprehensive studies comparing their respective effects on the oral microbiome are limited. This longitudinal study investigated the microbial changes during orthodontic treatment with FA and CA in correlation with clinical parameters. METHODS: Clinical parameters and supragingival plaque were collected from 12 study participants for the FA or CA treatment groups at baseline and at least twice at the 1, 3, 6, and 12-month follow-up appointments. The plaque was also harvested from the aligner tray for the CA group. Microbiome composition was determined via 16S rRNA gene sequencing, compared between groups, and correlated with clinical parameters. RESULTS: Plaque (PI) and gingival indexes (GI) increased significantly in the FA but not the CA group. Beta but not alpha diversities of the microbial communities were distinct between the 2 treatment groups, even though genus-level differences were not significant except for Leptotrichia. The CA tray harbors a unique plaque community. Elevated PI and GI in the FA group correlated with a higher abundance of disease-related genera. CONCLUSIONS: Orthodontic treatments trigger appliance-related plaque community shifts from baseline, and the CA tray environment attracts distinct microbial communities. In comparison with FA, the use of CA resulted in better oral health index outcomes, which is reflected by the corresponding PI and GI-associated oral microbial communities.

Gem-Diaurated Gold(III) Complexes: Synthesis, Structure, Aurophilic Interaction, and Catalytic Activity.

We present a protocol to synthesize air stable gem-diaurated gold(III) compounds from 1,3-diketones in a single cycloauration step with tetrachloroauric acid. So far related species were only accessible from phosphonium bis(ylide) ligands which hold the two gold atoms in close proximity. Lacking such a constraint, our compounds show the longest Au-Au distances of all gem-diaurated carbons, ranging from 3.26 to 3.32 Å. Modeling based on results of CCSD(T) calculations shows no stabilization by aurophilic interactions for our gold(III) systems, compared to 9.1 kcal/mol for gold(I) gem-diauration. This demonstrates no aurophilic interactions are needed for the isolation of air stable gem-diaurated gold(III) complexes. We show the new gem-diaurated gold(III) compounds are active in the gold-catalyzed phenol synthesis and highly active in the cycloisomerization of an N-propargylcarboxamide; here, we obtained the so far highest known TON of over 2500 per gold atom with respect to the oxazole formation.

An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.

Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm.

Mining integrated semantic networks for drug repositioning opportunities.

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.

BacillOndex: an integrated data resource for systems and synthetic biology.

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism.

Activation of the transcription factor NF-kappaB by Campylobacter jejuni.

Campylobacter jejuni is a food-borne pathogen responsible for infectious enterocolitis. The early-response transcription factor NF-kappa B triggers the expression of genes associated with cellular immune and inflammatory responses. Co-incubation of HeLa cells with viable C. jejuni leads to the activation of the transcription factor NF-kappa B as determined by specific induction of a cellular luciferase-based reporter. Boiled cell-free extracts of C. jejuni are also potent dose-dependent stimulators of NF-kappa B-dependent transcription, the levels of which can reach up to 1000-fold as compared with independent controls. Using both cultured HeLa cells and human colonic epithelial (HCA-7) cells, the activation of NF-kappa B by C. jejuni boiled extract has been monitored through the degradation of IKB alpha and DNA binding of the nuclear translocated p50/p65 heterodimer of NF-kappa B. These events are co-ordinated with elaboration of the pro-inflammatory cytokine interleukin-8. Fractionation of the boiled C. jejuni extract suggests that the majority of the bioactive component has a molecular mass of 3 kDa or less, which is insensitive to proteinase K treatment.