DNA extraction protocol impacts ocular surface microbiome profile

Best practices for the experimental design of one health studies on companion animal and owner microbiomes - From data collection to analysis

The relationship between owner and companion animal represents an underestimated opportunity for the studying of One Health relationships between humans, animals, and the environment they share. Microbiome exchanges between owner and pet have been documented for the gut, skin, oral, and nasal microbiomes. These studies give a unique insight into bacterial flows between humans and animals, but come with their specific challenges. This review discusses the data and sample collection challenges, as well as laboratory, bioinformatic and data analysis challenges specific to One Health studies on companion animal and owner microbiomes. We provide an overview of possible data to be collected and pitfalls to avoid during sample collection and conservation, DNA extraction, and library preparation. We present the main bioinformatics pipelines in sequencing-data microbiome analysis, as well as data analysis specific to pet-owner microbiome comparison. We review and compare three beta-diversity measures (Bray-Curtis dissimilarity, unweighted, and weighted UniFrac distances) for pet-owner distances and the tests to compare them. Finally, we propose a framework with key considerations to bear in mind when designing and carrying out owner-companion animal studies, as well as best practices to implement them. Although these studies come with additional difficulties compared to species-specific microbiome studies, they offer the opportunity to identify biomarkers, environmental triggers, and impacts of pet-owner interactions across species.

Novel Techniques to Unravel Causative Bacterial Ecological Shifts in Chronic Urinary Tract Infection

Chronic urinary tract infection (UTI) presents with protracted lower urinary tract symptoms and elevated urinary leukocyte counts, but its bacterial etiological agents remain obscure. In this cross-sectional investigation, we aimed to unravel the role of the bladder microbiota in chronic UTI pathogenesis by studying the host immune response. Urine samples were collected from healthy controls (HT), chronic UTI patients who had not initiated treatment (PT) and those undergoing treatment (OT), then sorted into white blood cell (WBC) and epithelial cell (EPC) fractions. Bacteria associated with both fractions were identified by chromogenic agar culture coupled with mass spectrometry and 16S rRNA sequencing. Distinct WBC-exclusive bacteria were observed in the healthy population, but this pattern was less obvious in patients, plausibly due to epithelial shedding and breaching of the urothelial barrier. We also described a bacterial fingerprint guided by Escherichia that was able to stratify patients based on symptom severity. Clustering analyses of mean rank changes revealed highly statistically significant upward and downward ecological shifts in communities of bacteria between the healthy and diseased populations. Interestingly, many of the most abundant genera identified in sequencing remained stable when compared between the study cohorts. We concluded that reshuffling of the urinary microbiome, rather than the activity of a single known urinary pathogen, could drive chronic UTI.

Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures

Determination of bacterial community composition in synthetic communities is critical for understanding microbial systems. The community composition is typically determined through bacterial plating or through PCR-based methods, which can be labor-intensive, expensive, or prone to bias. Simultaneously, flow cytometry has been suggested as a cheap and fast alternative. However, since the technique captures the phenotypic state of bacterial cells, accurate determination of community composition could be affected when bacteria are co-cultured. We investigated the performance of flow cytometry for quantifying oral synthetic communities and compared it to the performance of strain specific qPCR and 16S rRNA gene amplicon sequencing. Therefore, axenic cultures, mock communities and co-cultures of oral bacteria were prepared. Random forest classifiers trained on flow cytometry data of axenic cultures were used to determine the composition of the synthetic communities, as well as strain specific qPCR and 16S rRNA gene amplicon sequencing. Flow cytometry was shown to have a lower average root mean squared error and outperformed the PCR-based methods in even mock communities (flow cytometry: 0.11 ± 0.04; qPCR: 0.26 ± 0.09; amplicon sequencing: 0.15 ± 0.01). When bacteria were co-cultured, neither flow cytometry, strain-specific qPCR, nor 16S rRNA gene amplicon sequencing resulted in similar community composition. Performance of flow cytometry was decreased compared with mock communities due to changing phenotypes. Finally, discrepancies between flow cytometry and strain-specific qPCR were found. These findings highlight the challenges ahead for quantifying community composition in co-cultures by flow cytometry.IMPORTANCEQuantification of bacterial composition in synthetic communities is crucial for understanding and steering microbial interactions. Traditional approaches like plating, strain-specific qPCR, and amplicon sequencing are often labor-intensive and expensive and limit high-throughput experiments. Recently, flow cytometry has been suggested as a swift and cheap alternative for quantifying communities and has been successfully demonstrated on simple bacterial mock communities. However, since flow cytometry measures the phenotypic state of cells, measurements can be affected by differing phenotypes. Especially, changing phenotypes resulting from co-culturing bacteria can have a profound effect on the applicability of the technique in this context. This research illustrates the feasibility and challenges of flow cytometry for the determination of community structure in synthetic mock communities and co-cultures.

HIF‑1 and macrophage activation signalling pathways are potential biomarkers of invasive aspergillosis

Invasive aspergillosis (IA) is a severe disease, the pathogenesis of which remains unclear. The present study aimed to determine the molecular mechanism of IA and to identify potential biomarkers using bioinformatics analysis. The GSE78000 dataset, which includes data from patients with IA and healthy individuals, was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the IA and control groups were identified with the 'affy' package in R software. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases were then used to analyse the function and pathway enrichment of DEGs. The protein-protein interaction network was analysed with the Search Tool for the Retrieval of Interacting Genes (STRING) website. In addition, DEGs were confirmed using reverse transcription-quantitative PCR and western blotting in samples with IA (n=6) and control samples (n=6) collected from the Department of Respiratory and Critical Care Medicine of the First Affiliated Hospital of Henan University of Science and Technology (Luoyang, China). The present study identified 735 DEGs, including 312 upregulated and 423 downregulated genes. Through GO and KEGG analyses of the DEGs, macrophage activation and hypoxia-inducible factor 1 (HIF-1) signalling pathways were revealed to be significantly upregulated and downregulated, respectively, in patients with IA compared with that of the healthy individuals. Subsequently, correlation analysis of macrophage activation and HIF-1 signalling pathways was revealed using correlation as a distance metric for hierarchical clustering correlation analysis. However, there was no protein-protein interaction between the macrophage activity regulation and HIF-1 signalling pathways based on STRING analysis. In summary, the present study identified candidate genes and associated molecules that may be associated to IA and revealed potential biomarkers and therapeutic targets for IA.

Role of the ocular surface microbiome in allergic eye diseases

PURPOSE OF REVIEW

The purpose of this review is to provide an update on emerging literature on the role of the ocular surface microbiome (OSM) in allergic eye diseases.

RECENT FINDINGS

Findings in the literature suggest that the ocular surface microbiome plays a role in the pathophysiology and course of allergic disease of the ocular surface.

SUMMARY

Knowledge regarding the role of the ocular surface microbiome in allergic disease is important to guide development of targets for future therapeutic interventions.

Challenges and insights in the exploration of the low abundance human ocular surface microbiome

Purpose

The low microbial abundance on the ocular surface results in challenges in the characterization of its microbiome. The purpose of this study was to reveal factors introducing bias in the pipeline from sample collection to data analysis of low-abundant microbiomes.

Methods

Lower conjunctiva and lower lid swabs were collected from six participants using either standard cotton or flocked nylon swabs. Microbial DNA was isolated with two different kits (with or without prior host DNA depletion and mechanical lysis), followed by whole-metagenome shotgun sequencing with a high sequencing depth set at 60 million reads per sample. The relative microbial compositions were generated using the two different tools MetaPhlan3 and Kraken2.

Results

The total amount of extracted DNA was increased by using nylon flocked swabs on the lower conjunctiva. In total, 269 microbial species were detected. The most abundant bacterial phyla were Actinobacteria, Firmicutes and Proteobacteria. Depending on the DNA extraction kit and tool used for profiling, the microbial composition and the relative abundance of viruses varied.

Conclusion

The microbial composition on the ocular surface is not dependent on the swab type, but on the DNA extraction method and profiling tool. These factors have to be considered in further studies about the ocular surface microbiome and other sparsely colonized microbiomes in order to improve data reproducibility. Understanding challenges and biases in the characterization of the ocular surface microbiome may set the basis for microbiome-altering interventions for treatment of ocular surface associated diseases.