Periodontitis is one of the most common oral inflammatory diseases, and results in connective tissue degradation and gradual tooth loss. It manifests with formation of periodontal pockets, in which anaerobic and Gram-negative bacteria proliferate rapidly. Consequently, alteration of the subgingival microbiota is considered the primary etiologic agent of periodontitis. Previous studies have reported that smokers are at increased risk of periodontal disease, in both prevalence and severity, indicating that smoking is a risk factor for the onset and progression of the pathology. In the present study, 16S rRNA sequencing was employed to assess the subgingival microbiota in 6 smoker patients with chronic periodontitis, 6 non-smoker patients with chronic periodontitis and 8 healthy controls. The results demonstrated significant alterations in the microbial structure of periodontitis patients. High relative abundance of Parvimonans, Desulfubulbus, Paludibacter, Haemophilus, and Sphaerochaeta genera characterized subgingival microbiota of periodontitis patients, both smokers and non-smokers. Due to the high precision and sensitivity of the 16S rRNA sequencing method, analysis for low-abundant genera (including Pedobacter, Granulicatella, Paracoccus, Atopobium, Bifidobacterium, Coprococcus, Oridobacteriu, Peptococcus, Oscillospira and Akkermansia) was feasible, and revealed novel phylotypes associated with periodontitis. Of note, a major microbial community alteration was evident in smoker patients, suggesting an association between smoking and severity of subgingival dysbiosis. The present study confirmed that chronic periodontitis is a polymicrobial disease where changes in the equilibrium of subgingival microbiota contribute to severity of pathology.
Objective A large number of studies have highlighted the important role of the gut microbiota in the pathophysiology of neurological disorders, suggesting that its manipulation might serve as a treatment strategy. We hypothesized that the gut microbiota participates in absence seizure development and maintenance in the WAG/Rij rat model and tested this hypothesis by evaluating potential gut microbiota and intestinal alterations in the model, as well as measuring the impact of microbiota manipulation using fecal microbiota transplantation (FMT). Methods Initially, gut microbiota composition and intestinal histology of WAG/Rij rats (a well‐recognized genetic model of absence epilepsy) were studied at 1, 4, and 8 months of age in comparison to nonepileptic Wistar rats. Subsequently, in a second set of experiments, at 6 months of age, untreated Wistar or WAG/Rij rats treated with ethosuximide (ETH) were used as gut microbiota donors for FMT in WAG/Rij rats, and electroencephalographic (EEG) recordings were obtained over 4 weeks. At the end of FMT, stool and gut samples were collected, absence seizures were measured on EEG recordings, and microbiota analysis and histopathological examinations were performed. Results Gut microbiota analysis showed differences in beta diversity and specific phylotypes at all ages considered and significant variances in the Bacteroidetes/Firmicutes ratio between Wistar and WAG/Rij rats. FMT, from both Wistar and ETH‐treated WAG/Rij donors to WAG/Rij rats, significantly decreased the number and duration of seizures. Histological results indicated that WAG/Rij rats were characterized by intestinal villi disruption and inflammatory infiltrates already at 1 month of age, before seizure occurrence; FMT partially restored intestinal morphology while also significantly modifying gut microbiota and concomitantly reducing absence seizures. Significance Our results demonstrate for the first time that the gut microbiota is modified and contributes to seizure occurrence in a genetic animal model of absence epilepsy and that its manipulation may be a suitable therapeutic target for absence seizure management.
In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors.
BackgroundCpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level.For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation.ResultsHere we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions.ConclusionsampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1380-3) contains supplementary material, which is available to authorized users.
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