Airway Microbiota in Patients With Synchronous Multiple Primary Lung Cancer: The Bacterial Topography of the Respiratory Tract

Microbes and microbiota dysbiosis are correlated with the development of lung cancer; however, the airway taxa characteristics and bacterial topography in synchronous multiple primary lung cancer (sMPLC) are not fully understood. The present study aimed to investigate the microbiota taxa distribution and characteristics in the airways of patients with sMPLC and clarify specimen acquisition modalities in these patients. Using the precise positioning of electromagnetic navigation bronchoscopy (ENB), we analyzed the characteristics of the respiratory microbiome, which were collected from different sites and using different sampling methods. Microbiome predictor variables were bacterial DNA burden and bacterial community composition based on 16sRNA. Eight non-smoking patients with sMPLC in the same pulmonary lobe were included in this study. Compared with other sampling methods, bacterial burden and diversity were higher in surface areas sampled by bronchoalveolar lavage (BAL). Bacterial topography data revealed that the segment with sMPLC lesions provided evidence of specific colonizing bacteria in segments with lesions. After taxonomic annotation, we identified 4863 phylotypes belonging to 185 genera and 10 different phyla. The four most abundant specific bacterial community members detected in the airway containing sMPLC lesions were Clostridium, Actinobacteria, Fusobacterium, and Rothia, which all peaked at the segments with sMPLC lesions. This study begins to define the bacterial topography of the respiratory tract in patients with sMPLC and provides an approach to specimen acquisition for sMPLC, namely BAL fluid obtained from segments where lesions are located.

Salivary Microbial Dysbiosis Is Associated With Peri-Implantitis: A Case-Control Study in a Brazilian Population

Background and Objectives

The aim of this study was to examine the salivary microbiome in healthy peri-implant sites and those with peri-implantitis.

Methods

Saliva samples were collected from 21 participants with healthy peri-implant sites and 21 participants with peri-implantitis. The V4 hypervariable region of the 16S rRNA gene was sequenced using the Ion Torrent PGM System (Ion 318™ Chip v2 400). The NGS analysis and composition of the salivary microbiome were determined by taxonomy assignment. Downstream bioinformatic analyses were performed in QIIME (v 1.9.1).

Results

Clinical differences according to peri-implant condition status were found. Alpha diversity metrics revealed that the bacterial communities of participants with healthy peri-implant sites tended to have a richer microbial composition than individuals with peri-implantitis. In terms of beta diversity, bleeding on probing (BoP) may influence the microbial diversity. However, no clear partitioning was noted between the salivary microbiome of volunteers with healthy peri-implant sites or volunteers with peri-implantitis. The highest relative abundance of Stenotrophomonas, Enterococcus and Leuconostoc genus, and Faecalibacterium prausnitzii, Haemophilus parainfluenzae, Prevotella copri, Bacteroides vulgatus, and Bacteroides stercoris bacterial species was found in participants with peri-implantitis when compared with those with healthy peri-implant sites.

Conclusion

Differences in salivary microbiome composition were observed between patients with healthy peri-implant sites and those with peri-implantitis. BoP could affect the diversity (beta diversity) of the salivary microbiome.

Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.

Comprehensive Compositional Analysis of the Slit Lamp Bacteriota

Slit lamps are routinely used to examine large numbers of patients every day due to high throughput. Previous, cultivation-based results suggested slit lamps to be contaminated with bacteria, mostly coagulase-negative staphylococci, followed by micrococci, bacilli, but also Staphylococcus aureus. Our study aimed at obtaining a much more comprehensive, cultivation-independent view of the slit lamp bacteriota and its hygienic relevance, as regularly touched surfaces usually represent fomites, particularly if used by different persons. We performed extensive 16S rRNA gene sequencing to analyse the bacteriota, of 46 slit lamps from two tertiary care centers at two sampling sites, respectively. 82 samples yielded enough sequences for downstream analyses and revealed contamination with bacteria of mostly human skin, mucosa and probably eye origin, predominantly cutibacteria, staphylococci and corynebacteria. The taxonomic assignment of 3369 ASVs (amplicon sequence variants) revealed 19 bacterial phyla and 468 genera across all samples. As antibiotic resistances are of major concern, we screened all samples for methicillin-resistant Staphylococcus aureus (MRSA) using qPCR, however, no signals above the detection limit were detected. Our study provides first comprehensive insight into the slit lamp microbiota. It underlines that slit lamps carry a highly diverse, skin-like bacterial microbiota and that thorough cleaning and disinfection after use is highly recommendable to prevent eye and skin infections.

The Skin Microbiome of Patients With Atopic Dermatitis Normalizes Gradually During Treatment

Background

Atopic dermatitis (AD) is characterized by an altered skin microbiome dominantly colonized by S. aureus. Standard treatment includes emollients, anti-inflammatory medications and antiseptics.

Objectives

To characterize changes in the skin microbiome during treatment for AD.

Methods

The skin microbiomes of children with moderate-to-severe AD and healthy children were investigated in a longitudinal prospective study. Patients with AD were randomized to receive either standard treatment with emollients and topical corticosteroids or standard treatment with the addition of dilute bleach baths (DBB) and sampled at four visits over a three-month period. At each visit, severity of AD was measured, swabs were taken from four body sites and the composition of the microbiome at those sites was assessed using 16S rRNA amplification.

Results

We included 14 healthy controls and 28 patients. We found high relative abundances of S. aureus in patients, which correlated with AD severity and reduced apparent alpha diversity. As disease severity improved with treatment, the abundance of S. aureus decreased, gradually becoming more similar to the microbiomes of healthy controls. After treatment, patients who received DBB had a significantly lower abundance of S. aureus than those who received only standard treatment.

Conclusions

There are clear differences in the skin microbiome of healthy controls and AD patients that diminish with treatment. After three months, the addition of DBB to standard treatment had significantly decreased the S. aureus burden, supporting its use as a therapeutic option. Further study in double-blinded trials is needed.

Hepatic Branch Vagotomy Modulates the Gut-Liver-Brain Axis in Murine Cirrhosis

BACKGROUND

Cirrhosis and hepatic encephalopathy (HE) are linked with an altered gut-liver-brain axis, however, the relative contribution of hepatic vagal innervation is unclear. We aimed to determine the impact of hepatic vagotomy on the gut microbiome, brain, and liver in murine cirrhosis.

METHODS

10-15-week-old male C57BL/6 mice with and without hepatic vagotomy underwent carbon tetrachloride (CCl4) gavage for 8 weeks. Frontal cortex [inflammation, glial/microglial activation, BDNF (brain-derived neurotrophic factor)], liver [histology including inflammation and steatosis, fatty acid synthesis (sterol-responsive binding protein-1) SREBP-1, insulin-induced gene-2 (Insig2) and BDNF], and colonic mucosal microbiota (16srRNA microbial sequencing) were evaluated on sacrifice. Conventional mice with and without cirrhosis were compared to vagotomized counterparts.

RESULTS

Conventional control vs. cirrhosis: Cirrhosis resulted in dysbiosis, hepatic/neuro-inflammation with glial/microglial activation, and low brain BDNF vs. controls. Conventional control vs. vagotomy controls: Vagotomized control mice had a lower colonic dysbiosis than conventional mice but the rest of the hepatic/brain parameters were similar. Conventional cirrhosis vs. vagotomized cirrhosis: After vagotomy + cirrhosis, we found lower dysbiosis but continuing neuroinflammation in the absence of glial/microglial activation vs. conventional cirrhosis. Vagotomy + Cirrhosis groups showed higher hepatic steatosis due to higher SREBP1 and low Insig2 protein and altered activation of key genes involved in hepatic lipid metabolism and inflammation. BDNF levels in the brain were higher but low in the liver in vagotomy + cirrhosis, likely a protective mechanism.

CONCLUSIONS

Hepatic vagal innervation affects the gut microbial composition, hepatic inflammation and steatosis, and cortical inflammation and BDNF expression and could be a critical modulator of the gut-liver-brain axis with consequences for HE development.

Modulating the Microbiota as a Therapeutic Intervention for Type 2 Diabetes

Mounting evidence suggested that the gut microbiota has a significant role in the metabolism and disease status of the host. In particular, Type 2 Diabetes (T2D), which has a complex etiology that includes obesity and chronic low-grade inflammation, is modulated by the gut microbiota and microbial metabolites. Current literature supports that unbalanced gut microbial composition (dysbiosis) is a risk factor for T2D. In this review, we critically summarize the recent findings regarding the role of gut microbiota in T2D. Beyond these associative studies, we focus on the causal relationship between microbiota and T2D established using fecal microbiota transplantation (FMT) or probiotic supplementation, and the potential underlying mechanisms such as byproducts of microbial metabolism. These microbial metabolites are small molecules that establish communication between microbiota and host cells. We critically summarize the associations between T2D and microbial metabolites such as short-chain fatty acids (SCFAs) and trimethylamine N-Oxide (TMAO). Additionally, we comment on how host genetic architecture and the epigenome influence the microbial composition and thus how the gut microbiota may explain part of the missing heritability of T2D found by GWAS analysis. We also discuss future directions in this field and how approaches such as FMT, prebiotics, and probiotics supplementation are being considered as potential therapeutics for T2D.