Long-term changes in the subgingival microbiota in patients with stage III-IV periodontitis treated by mechanical therapy and adjunctive systemic antibiotics: A secondary analysis of a randomized controlled trial.

AIM: To explore whether adjunctive antibiotics can relevantly influence long-term microbiota changes in stage III-IV periodontitis patients. MATERIALS AND METHODS: This is a secondary analysis of a randomized clinical trial on periodontal therapy with adjunctive 500 mg amoxicillin and 400 mg metronidazole or placebo thrice daily for 7 days. Subgingival plaque samples were taken before and 2, 8, 14 and 26 months after mechanical therapy. The V4-hypervariable region of the 16S rRNA gene was sequenced with Illumina MiSeq 250 base pair paired-end reads. Changes at the ribosomal sequence variant (RSV) level, diversity and subgingival-microbial dysbiosis index (SMDI) were explored with a negative binomial regression model and non-parametric tests. RESULTS: Overall, 50.2% of all raw reads summed up to 72 RSVs (3.0%) that were generated from 163 stage III-IV periodontitis patients. Of those, 16 RSVs, including Porphyromonas gingivalis, Tannerella forsythia and Aggregatibacter actinomycetemcomitans, changed significantly over 26 months because of adjunctive systemic antibiotics. SMDI decreased significantly more in the antibiotic group at all timepoints, whereas the 2-month differences in alpha and beta diversity between groups were not significant at 8 and 14 months, respectively. CONCLUSIONS: Mechanical periodontal therapy with adjunctive antibiotics induced a relevant and long-term sustainable change towards an oral microbiome more associated with oral health.

Gut microbiota dysbiosis in Parkinson disease: A systematic review and pooled analysis.

BACKGROUND AND PURPOSE: The role of the gut microbiome in the pathogenesis of Parkinson disease (PD) is under intense investigation, and the results presented are still very heterogeneous. These discrepancies arise not only from the highly heterogeneous pathology of PD, but also from widely varying methodologies at all stages of the workflow, from sampling to final statistical analysis. The aim of the present work is to harmonize the workflow across studies to reduce the methodological heterogeneity and to perform a pooled analysis to account for other sources of heterogeneity. METHODS: We performed a systematic review to identify studies comparing the gut microbiota of PD patients to healthy controls. A workflow was designed to harmonize processing across all studies from bioinformatics processing to final statistical analysis using a Bayesian random-effects meta-analysis based on individual patient-level data. RESULTS: The results show that harmonizing workflows minimizes differences between statistical methods and reveals only a small set of taxa being associated with the pathogenesis of PD. Increased shares of the genera Akkermansia and Bifidobacterium and decreased shares of the genera Roseburia and Faecalibacterium were most characteristic for PD-associated microbiota. CONCLUSIONS: Our study summarizes evidence that reduced levels of butyrate-producing taxa in combination with possible degradation of the mucus layer by Akkermansia may promote intestinal inflammation and reduced permeability of the gut mucosal layer. This may allow potentially pathogenic metabolites to transit and enter the enteric nervous system.

Richness estimation in microbiome data obtained from denoising pipelines.

The quantification of richness within a sample-either measured as the number of observed species or approximated by estimation-is a common first step in microbiome studies and is known to be highly dependent on sequencing depth, which itself is highly variable between samples. Rarefaction curves serve as a tool to investigate this dependency and it is often argued that after rarefying data-sub-sampling to an equal sequencing depth-richness estimates no longer depend on sequencing depth. However, the estimation of richness from data obtained by high throughput sequencing methods and processed by current bioinformatics pipelines may be subject to various sources of variation related to sequencing depth. Those that may confound inference based on richness estimates and cannot be solved by sub-sampling. We investigated how pipeline settings in DADA2 and deblur affect estimates of richness and showed that the use of rarefaction and sub-sampling is inappropriate when default pipeline settings are applied. Furthermore, we showed how independent sample-wise processing established spurious correlations between sequencing depth and richness estimations in data produced by DADA2 and how this problem can be solved by a pooled processing approach.

Sexual Contact Patterns in High-Income Countries-A Comparative Analysis Using Data From Germany, the United Kingdom, and the United States.

Sexual contact patterns determine the spread of sexually transmitted infections and are a central input parameter for mathematical models in this field. We evaluated the importance of country-specific sexual contact pattern parametrization for high-income countries with similar cultural backgrounds by comparing data from two independent studies (HaBIDS and SBG) in Germany, a country without systematic sexual contact pattern data, with data from the National Survey of Sexual Attitudes and Lifestyles (Natsal) in the UK, and the National Survey of Family Growth (NSFG) in the US, the two longest running sexual contact studies in high-income countries. We investigated differences in the distribution of the reported number of opposite-sex partners, same-sex partners and both-sex partners using weighted negative binomial regression adjusted for age and sex (as well as stratified by age). In our analyses, UK and US participants reported a substantially higher number of lifetime opposite-sex sexual partners compared to both German studies. The difference in lifetime partners was caused by a higher proportion of individuals with many partners in the young age group (<24 years) in the UK and the US. Partner acquisition in older age groups was similar. The number of same-sex partners was similar across countries, while there was heterogeneity in the reported experience with partners from both sexes, consistent with the differences observed for opposite-sex sexual partners. These patterns can lead to substantially different dynamics of sexually transmitted infections across ages, and have strong impact on the results of modeling studies.

Higher Trimethylamine-N-Oxide Plasma Levels with Increasing Age Are Mediated by Diet and Trimethylamine-Forming Bacteria.

The gut microbiota-dependent metabolite trimethylamine-N-oxide (TMAO) is linked to an increased risk for cardiovascular diseases. Trimethylamine (TMA), which is subsequently oxidized to TMAO in the liver, is formed by intestinal bacteria via distinct biochemical routes from dietary precursors that are enriched in animal product-based foods. To get a full picture of the entire process of the diet > gut microbiota > TMAO axis, we quantified potential TMA-forming gut bacteria and plasma metabolites using gene-targeted assays and targeted metabolomics on a subsample (n = 425) of a German population-based cohort study. We specifically compared persons reporting daily meat intake with those that rarely or never consume meat. While meat intake did not predict TMAO plasma levels in our study, two major bacterial TMA-forming pathways were linked to the metabolite's concentration. Furthermore, advancing age was strongly associated with TMAO. Construction of a structural equation model allowed us to disentangle the different routes that promote higher TMAO levels with increasing age, demonstrating, for the first time, a functional role of gut microbiota in the process, where specific food items augmented abundances of TMA-forming bacteria that were associated with higher TMAO plasma concentrations. Analyses stratified by age showed an association between carotid intima-media thickness and TMAO only in individuals >65 of age, indicating that this group is particularly affected by the metabolite. IMPORTANCE Many cohort studies have investigated the link between diet and plasma TMAO levels, reporting incongruent results, while gut microbiota were only recently included into analyses. In these studies, taxonomic data were recorded that are not a good proxy for TMA formation, as specific members of various taxa exhibit genes catalyzing this reaction, demanding function-based technologies for accurate quantification of TMA-synthesizing bacteria. Using this approach, we demonstrated that abundances of the main components leading to TMAO formation, i.e., TMA precursors and TMA-forming bacteria, are uncoupled and not governed by the same (dietary) factors. Results emphasize that all levels leading to TMA(O) formation should be considered for accurate risk assessment, rejecting the simple view that diets rich in TMA precursors directly lead to increased plasma levels of this hazardous compound. The results can assist in developing strategies to reduce TMAO levels, specifically in the elderly, who are prone to TMAO-associated diseases.

Data Analysis Strategies for Microbiome Studies in Human Populations-a Systematic Review of Current Practice.

Reproducibility is a major issue in microbiome studies, which is partly caused by missing consensus about data analysis strategies. The complex nature of microbiome data, which are high-dimensional, zero-inflated, and compositional, makes them challenging to analyze, as they often violate assumptions of classic statistical methods. With advances in human microbiome research, research questions and study designs increase in complexity so that more sophisticated data analysis concepts are applied. To improve current practice of the analysis of microbiome studies, it is important to understand what kind of research questions are asked and which tools are used to answer these questions. We conducted a systematic literature review considering all publications focusing on the analysis of human microbiome data from June 2018 to June 2019. Of 1,444 studies screened, 419 fulfilled the inclusion criteria. Information about research questions, study designs, and analysis strategies were extracted. The results confirmed the expected shift to more advanced research questions, as one-third of the studies analyzed clustered data. Although heterogeneity in the methods used was found at any stage of the analysis process, it was largest for differential abundance testing. Especially if the underlying data structure was clustered, we identified a lack of use of methods that appropriately addressed the underlying data structure while taking into account additional dependencies in the data. Our results confirm considerable heterogeneity in analysis strategies among microbiome studies; increasingly complex research questions require better guidance for analysis strategies.IMPORTANCE The human microbiome has emerged as an important factor in the development of health and disease. Growing interest in this topic has led to an increasing number of studies investigating the human microbiome using high-throughput sequencing methods. However, the development of suitable analytical methods for analyzing microbiome data has not kept pace with the rapid progression in the field. It is crucial to understand current practice to identify the scope for development. Our results highlight the need for an extensive evaluation of the strengths and shortcomings of existing methods in order to guide the choice of proper analysis strategies. We have identified where new methods could be designed to address more advanced research questions while taking into account the complex structure of the data.