DJ-1 (Park7) affects the gut microbiome, metabolites and development of Innate Lymphoid cells (ILCs)

Abstract The proper communication between gut and brain is pivotal for the maintenance of health and, dysregulation of the gut-brain axis can lead to several clinical disorders. In Parkinson’s disease (PD) 85% of all patients experienced constipation many years before showing any signs of motor phenotypes. For differential diagnosis and preventive treatment, there is an urgent need for the identification of biomarkers indicating early disease stages long before the disease phenotype manifests. DJ-1 is a chaperone protein involved in the protection against PD and genetic mutations in this protein have been shown to cause familial PD. However, how the deficiency of DJ-1 influences the risk of PD remains incompletely understood. In the present study, we provide evidence that DJ-1 is implicated in shaping the gut microbiome including; their metabolite production, inflammation and innate immune cells (ILCs) development. We revealed that deficiency of DJ-1 leads to a significant increase in two specific genera/species, namely Alistipes and Rikenella . In DJ-1 knock-out (DJ-1 -/- ) mice the production of fecal calprotectin and MCP-1 inflammatory proteins were elevated. Fecal and serum metabolic profile showed that malonate which influences the immune system was significantly more abundant in DJ-1 −/− mice. DJ-1 appeared also to be involved in ILCs development. Further, inflammatory genes related to PD were augmented in the midbrain of DJ-1 −/− mice. Our data suggest that metabolites and inflammation produced in the gut could be used as biomarkers for PD detection. Perhaps, these metabolites and inflammatory mediators could be involved in triggering inflammation resulting in PD pathology

Statistical, visual and functional analysis of microbiome data

The advancements in next-generation sequencing technologies have revolutionized microbiome research by allowing culture-independent high-throughput profiling of the genetic contents of microbial communities. Nowadays, 16S rRNA based marker gene sequencing is widely used to characterize the taxonomic composition and phylogenetic diversity of complex microbial communities. However, statistical, visual and functional analysis of such data possess great challenges. In addition, many aspects of the current approaches can be improved to get a better understanding of communities. The proper analysis of the resulting large and complicated datasets remains a key bottleneck in current microbiome studies. Over the last decade, powerful computational pipelines and standard protocols have been developed to support efficient raw data processing and annotation of microbiome data. The focus has now shifted towards downstream statistical analysis and functional interpretation. To address this bottleneck, we have developed MicrobiomeAnalyst, a user-friendly web-based tool that incorporates recent progresses in statistics and interactive visualization techniques, coupled with novel knowledge bases, to facilitate comprehensive analysis of common data sets generated from microbiome studies. MicrobiomeAnalyst contains four major components, including i) a module for community diversity profiling, comparative analysis and functional prediction of 16S rRNA marker gene data; ii) a module for exploratory data analysis, functional profiling and metabolic network visualization for shotgun metagenomics or metatranscriptomics data; iii) a module to help users to interpret their taxa of interest via enrichment analysis against ~300 taxon sets manually collected from recent literature and public databases; and iv) a module to allow users to visually explore their data sets within the context of compatible public data (meta-analysis) for pattern discovery and biological insights. The tool is freely accessible at http://www.microbiomeanalyst.ca.Les progrès dans les technologies de séquençage de nouvelle génération ont révolutionné la recherche sur le microbiôme en permettant un profilage à haut débit, indépendamment de la culture du contenu génétique des communautés microbiennes. De nos jours, le séquençage du gène marqueur basé sur l'ARNr 16S est largement utilisé pour caractériser la composition taxonomique et la diversité phylogénétique des communautés microbiennes complexes. Cependant, l'analyse statistique, visuelle et fonctionnelle de ces données présente de grands défis. En outre, de nombreux aspects des approches actuelles peuvent être améliorés pour mieux comprendre les communautés. L'analyse appropriée des données volumineuses et complexes reste un goulot d'étranglement majeur dans les études actuelles sur le microbiôme. Au cours de la dernière décennie, de puissantes méthodes computationnelles et des protocoles standardisés ont été développés pour prendre en charge un traitement et une annotation des données efficacement. Inversement, l'accent a désormais été mis sur l'analyse statistique en aval et l'interprétation fonctionnelle.Pour remédier à ce goulot d'étranglement, nous avons développé MicrobiomeAnalyst, un outil web convivial qui intègre les progrès récents dans les statistiques et les techniques de visualisation interactives, couplées avec de nouvelles bases de connaissances, pour faciliter l'analyse complète des profils taxonomiques et fonctionnels communs issus des études sur le microbiôme. MicrobiomeAnalyst comprend quatre modules majeurs, dont de i), un module pour le profilage de la diversité de la communauté, de l'analyse comparative et de la prédiction fonctionnelle des données du gène marqueur de l'ARNr 16S, de ii), un module pour l'analyse exploratoire des données, le profilage fonctionnel et la visualisation du réseau métabolique pour les données de métagénomique ou de métatranscriptomique « Shotgun », de iii), un module pour aider les utilisateurs à interpréter leurs taxons d'intérêt par l'analyse d'enrichissement contre notre base de données d'environ 300 ensembles de taxons collectés manuellement à partir de la littérature récente et de bases de données publiques, et de iv), un module pour aider les utilisateurs à explorer visuellement leurs données dans le contexte de données publiques (méta-analyse) pour la découverte de modèles et de connaissances biologiques. L'outil est librement accessible à http://www.microbiomeanalyst.ca

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data

Abstract The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no

Impact of narrow-spectrum penicillin V on the oral and faecal resistome in a young child treated for otitis media

Objectives Antibiotic overuse has led to the global emergence of antimicrobial-resistant bacteria, and children are among the most frequent users of antibiotics. Most studies with broad-spectrum antibiotics show a severe impact on resistome development in patients. Although narrow-spectrum antibiotics are believed to have fewer side effects, their impact on the microbiome and resistome is mostly unknown. The aim of this study was to investigate the impact of the narrow-spectrum antibiotic phenoxymethylpenicillin (penicillin V) on the microbiome and resistome of a child treated for acute otitis media. Methods Oral and faecal samples were collected from a 1-year-old child before (Day 0) and after (Days 5 and 30) receiving penicillin V for otitis media. Metagenomic sequencing data were analysed to determine taxonomic profiling using Kraken and Bracken software, and resistance profiling using KMA in combination with the ResFinder database. Results In the oral samples, antimicrobial resistance genes (ARGs) belonging to four classes were identified at baseline. At Day 5, the abundance of some ARGs was increased, whereas some remained unchanged and others could no longer be detected. At Day 30, most ARGs had returned to baseline levels or lower. In the faecal samples, seven ARGs were observed at baseline and five at Day 5. At Day 30, the number of ARGs had increased to 21. Conclusions Following penicillin V, we observed a remarkable enrichment of the aecal resistome, indicating that even narrow-spectrum antibiotics may have important consequences in selecting for a more resistant microbiome

Differential response to prolonged amoxicillin treatment: long-term resilience of the microbiome versus long-lasting perturbations in the gut resistome

ABSTRACTThe collateral impact of antibiotics on the microbiome has attained increasing attention. However, the ecological consequences of long-term antibiotic exposure on the gut microbiome, including antibiotic resistance, are still limited. Here, we investigated long-term exposure effects to amoxicillin on the human gut microbiome and resistome. Fecal samples were collected from 20 patients receiving 3-months of amoxicillin or placebo treatment as part of a Norwegian multicenter clinical trial on chronic low back pain (AIM study). Samples were collected at baseline, last day of treatment, and 9 months after antibiotic cessation. The abundance and diversity of microbial and resistome composition were characterized using whole shotgun and functional metagenomic sequencing data. While the microbiome profiles of placebo subjects were stable over time, discernible changes in diversity and overall microbiome composition were observed after amoxicillin treatment. In particular, health-associated short-chain fatty acid producing species significantly decreased in proportion. However, these changes were short-lived as the microbiome showed overall recovery 9 months post-treatment. On the other hand, exposure to long-term amoxicillin was associated with an increase in total antimicrobial resistance gene load and diversity of antimicrobial resistance genes, with persistent changes even at 9 months post-treatment. Additionally, beta-lactam resistance was the most affected antibiotic class, suggesting a targeted response to amoxicillin, although changes at the gene level varied across individuals. Overall, our results suggest that the impact of prolonged amoxicillin exposure was more explicit and long-lasting in the fecal resistome than in microbiome composition. Such information is relevant for designing rational administration guidelines for antibiotic therapies

Image_3_Microbial DNA extraction of high-host content and low biomass samples: Optimized protocol for nasopharynx metagenomic studies.TIFF

IntroductionLow microbial biomass and high human DNA content in nasopharyngeal aspirate samples hinder comprehensive characterization of microbiota and resistome. We obtained samples from premature infants, a group with increased risk of developing respiratory disorders and infections, and consequently frequent exposure to antibiotics. Our aim was to devise an optimal protocol for handling nasopharyngeal aspirate samples from premature infants, focusing on host DNA depletion and microbiome and resistome characterization.MethodsThree depletion and three DNA extraction protocols were compared, using RT-PCR and whole metagenome sequencing to determine the efficiency of human DNA removal, taxonomic profiling and assignment of antibiotic resistance genes. Protocols were tested using mock communities, as well as pooled and individual patient samples.ResultsThe only extraction protocol to retrieve the expected DNA yield from mock community samples was based on a lytic method to improve Gram positive recovery (MasterPure™). Host DNA content in non-depleted aliquots from pooled patient samples was 99%. Only samples depleted with MolYsis™ showed satisfactory, but varied reduction in host DNA content, in both pooled and individual patient samples, allowing for microbiome and resistome characterisation (host DNA content from 15% to 98%). Other depletion protocols either retrieved too low total DNA yields, preventing further analysis, or failed to reduce host DNA content. By using Mol_MasterPure protocol on aliquots from pooled patient samples, we increased the number of bacterial reads by 7.6 to 1,725.8-fold compared to non-depleted reference samples. PCR results were indicative of achieved microbial enrichment. Individual patient samples processed with Mol_MasterPure protocol varied greatly in total DNA yield, host DNA content (from 40% to 98%), species and antibiotic resistance gene richness.DiscussionDespite high human DNA and low microbial biomass content in nasopharynx aspirates of preterm infants, we were able to reduce host DNA content to levels compatible with downstream shotgun metagenomic analysis, including bacterial species identification and coverage of antibiotic resistance genes. Whole metagenomic sequencing of microbes colonizing the nasopharynx may contribute to explaining the possible role of airway microbiota in respiratory conditions and reveal carriage of antibiotic resistance genes.</p

Image_2_Microbial DNA extraction of high-host content and low biomass samples: Optimized protocol for nasopharynx metagenomic studies.TIFF

IntroductionLow microbial biomass and high human DNA content in nasopharyngeal aspirate samples hinder comprehensive characterization of microbiota and resistome. We obtained samples from premature infants, a group with increased risk of developing respiratory disorders and infections, and consequently frequent exposure to antibiotics. Our aim was to devise an optimal protocol for handling nasopharyngeal aspirate samples from premature infants, focusing on host DNA depletion and microbiome and resistome characterization.MethodsThree depletion and three DNA extraction protocols were compared, using RT-PCR and whole metagenome sequencing to determine the efficiency of human DNA removal, taxonomic profiling and assignment of antibiotic resistance genes. Protocols were tested using mock communities, as well as pooled and individual patient samples.ResultsThe only extraction protocol to retrieve the expected DNA yield from mock community samples was based on a lytic method to improve Gram positive recovery (MasterPure™). Host DNA content in non-depleted aliquots from pooled patient samples was 99%. Only samples depleted with MolYsis™ showed satisfactory, but varied reduction in host DNA content, in both pooled and individual patient samples, allowing for microbiome and resistome characterisation (host DNA content from 15% to 98%). Other depletion protocols either retrieved too low total DNA yields, preventing further analysis, or failed to reduce host DNA content. By using Mol_MasterPure protocol on aliquots from pooled patient samples, we increased the number of bacterial reads by 7.6 to 1,725.8-fold compared to non-depleted reference samples. PCR results were indicative of achieved microbial enrichment. Individual patient samples processed with Mol_MasterPure protocol varied greatly in total DNA yield, host DNA content (from 40% to 98%), species and antibiotic resistance gene richness.DiscussionDespite high human DNA and low microbial biomass content in nasopharynx aspirates of preterm infants, we were able to reduce host DNA content to levels compatible with downstream shotgun metagenomic analysis, including bacterial species identification and coverage of antibiotic resistance genes. Whole metagenomic sequencing of microbes colonizing the nasopharynx may contribute to explaining the possible role of airway microbiota in respiratory conditions and reveal carriage of antibiotic resistance genes.</p