A new approach to describe the taxonomic structure of microbiome and its application to assess the relationship between microbial niches.

BACKGROUND: Data from microbiomes from multiple niches is often collected, but methods to analyse these often ignore associations between niches. One interesting case is that of the oral microbiome. Its composition is receiving increasing attention due to reports on its associations with general health. While the oral cavity includes different niches, multi-niche microbiome data analysis is conducted using a single niche at a time and, therefore, ignores other niches that could act as confounding variables. Understanding the interaction between niches would assist interpretation of the results, and help improve our understanding of multi-niche microbiomes. METHODS: In this study, we used a machine learning technique called latent Dirichlet allocation (LDA) on two microbiome datasets consisting of several niches. LDA was used on both individual niches and all niches simultaneously. On individual niches, LDA was used to decompose each niche into bacterial sub-communities unveiling their taxonomic structure. These sub-communities were then used to assess the relationship between microbial niches using the global test. On all niches simultaneously, LDA allowed us to extract meaningful microbial patterns. Sets of co-occurring operational taxonomic units (OTUs) comprising those patterns were then used to predict the original location of each sample. RESULTS: Our approach showed that the per-niche sub-communities displayed a strong association between supragingival plaque and saliva, as well as between the anterior and posterior tongue. In addition, the LDA-derived microbial signatures were able to predict the original sample niche illustrating the meaningfulness of our sub-communities. For the multi-niche oral microbiome dataset we had an overall accuracy of 76%, and per-niche sensitivity of up to 83%. Finally, for a second multi-niche microbiome dataset from the entire body, microbial niches from the oral cavity displayed stronger associations to each other than with those from other parts of the body, such as niches within the vagina and the skin. CONCLUSION: Our LDA-based approach produces sets of co-occurring taxa that can describe niche composition. LDA-derived microbial signatures can also be instrumental in summarizing microbiome data, for both descriptions as well as prediction.

Host-microbiome interactions in apical periodontitis: The endodontic microbiome in relation to circulatory immunologic markers.

AIM: To explore microbial differences in the endodontic infection of teeth with primary or secondary apical periodontitis (AP), with or without symptomatology. Additionally, to investigate if these differences are depicted in immunologic markers in blood. METHODOLOGY: Twenty-nine teeth with primary or secondary AP were extracted and cryo-pulverized. Blood was drawn from the subjects at three different time-points before and three time-points after the extraction in a time period of four months. The V4 hypervariable region of the 16S rRNA gene was sequenced using Illumina MiSeq. The microbial profiles were ordinated using principal component analysis and tested for differences between groups with permutational multivariate analysis of variance using the Bray-Curtis distance. If significantly different, the microbial profiles were further analysed using the LDA effect size (LEfSe) biomarker discovery tool. A broad panel of inflammatory mediators in blood was examined longitudinally in all subjects during the six visits with mixed models. The Spearman correlation between these mediators and the zOTUs was calculated, and significant correlations (p < .05) were used as input for significant analysis of microarrays (SAM) using MeV. RESULTS: After subsampling, the 467 zOTUs were classified into 9 phyla and 99 genera or higher level taxa. The predominant genus in the entire sample set was Fusobacterium with a relative abundance of 12.3%, followed by Prevotella (9.9%), Actinomyces (7.7%) and Streptococcus (6.7%). The microbiomes of the endodontic infections were significantly associated with endodontic status (primary/secondary infection; p = .015) as well as with the presence or absence of pain (p = .011). There was also a difference in the concentration of inflammatory mediators, namely, C-reactive protein, Interleukin (IL)-8, IL-10, IL-12p70, RANKL and TNF-α, depending on the existence of pain. In addition, the presence of specific bacteria (zOTUs) was correlated, positively or negatively, with the expression of several circulating inflammatory markers. CONCLUSIONS: The microbial profiles and the concentration-time relationship of systemic inflammatory mediators of primary endodontic infection differed from those of secondary, and of symptomatic from those of asymptomatic cases. The fingerprint of associations between the immunological and microbiological profiles differed between asymptomatic and symptomatic patients.

Correlating the succession of microbial communities from Nigerian soils to petroleum biodegradation.

Whilst biodegradation of different hydrocarbon components has been widely demonstrated to occur by specialist oil-degrading bacteria, less is known about the impact on microbial communities as a function of oil composition by comparing the biodegradation of chemically complex fuels to synthetic products. The objectives of this study were (i) to assess the biodegradation capacity and succession of microbial communities isolated from Nigerian soils in media with crude oil or synthetic oil as sole sources of carbon and energy, and (ii) to assess the temporal variability of the microbial community size. Community profiling was done using 16 S rRNA gene amplicon sequencing (Illumina), and oil profiling using gas chromatography. The biodegradation of natural and synthetic oil differed probably due to the content of sulfur that may interfere with the biodegradation of hydrocarbons. Both alkanes and PAHs in the natural oil were biodegraded faster than in the synthetic oil. Variable community responses were observed during the degradation of alkanes and more simple aromatic compounds, but at later phases of growth they became more homogeneous. The degradation capacity and the size of the community from the more-contaminated soil were higher than those from the less-contaminated soil. Six abundant organisms isolated from the cultures were found to biodegrade oil molecules in pure cultures. Ultimately, this knowledge may contribute to a better understanding of how to improve the biodegradation of crude oil by optimizing culturing conditions through inoculation or bioaugmentation of specific bacteria during ex-situ biodegradation such as biodigesters or landfarming.

The microbiome of dental and peri-implant subgingival plaque during peri-implant mucositis therapy: A randomized clinical trial.

AIM: To assess the microbial effects of mechanical debridement in conjunction with a mouthrinse on sites with peri-implant mucositis and gingivitis. MATERIALS AND METHODS: Eighty-nine patients with peri-implant mucositis were included in a double-blinded, randomized, placebo-controlled trial with mechanical debridement and 1-month use of either delmopinol, chlorhexidine (CHX), or a placebo mouthrinse. Submucosal and subgingival plaque samples of implants and teeth were collected at baseline and after 1 and 3 months, processed for 16S V4 rRNA gene amplicon sequencing, and analysed bioinformatically. RESULTS: The sites with peri-implant mucositis presented with a less diverse and less anaerobic microbiome. Exposure to delmopinol or CHX, but not to the placebo mouthrinse resulted in microbial changes after 1 month. The healthy sites around the teeth harboured a more diverse and more anaerobe-rich microbiome than the healthy sites around the implants. CONCLUSIONS: Peri-implant sites with mucositis harbour ecologically less complex and less anaerobic biofilms with lower biomass than patient-matched dental sites with gingivitis while eliciting an equal inflammatory response. Adjunctive antimicrobial therapy in addition to mechanical debridement does affect both dental and peri-implant biofilm composition in the short term, resulting in a less dysbiotic subgingival biofilm.

Comparability of microbiota of swabbed and spit saliva.

In general, saliva is used for microbiota analysis in longitudinal studies, and several collection methods are being used. Using a robust sample collection procedure is important, as it may influence salivary composition. This study explored the comparability of the microbiota of swabbed and spit saliva. Twenty-two females participated in this cross-sectional study. The bacterial composition of the three saliva samples (swab collected by the participant (SW-P), swab collected by the researcher (SW-R), and spit (SP) was assessed by 16S rRNA gene amplicon sequencing. The bacterial composition of the swabbed and the spit saliva was significantly different irrespective of the operator, and Shannon diversity was significantly higher in spit saliva than in SW-P and SW-R. The salivary microbiota of spit and swabbed adult saliva differs significantly. Research on microbial composition therefore requires collection of similar saliva sample types in all study participants.

Effects of Surface Prereacted Glass on Saliva-Derived Polymicrobial Biofilms in an Active Attachment Biofilm Model.

Bioactive restorative materials are being developed to either influence the de/remineralization balance of the dental hard tissues locally or to release components that interact with the oral microbiota. Surface prereacted glass (S-PRG, Shofu, Japan) is a material that may influence both processes. S-PRG releases fluoride, which can interact with the de/remineralization process, and a range of other compounds that may influence the oral microbiota. In the current study, several experiments were performed to investigate the potential of S-PRG to influence both the growth and lactic acid production of saliva-derived polymicrobial biofilms. Biofilm formation was studied using the Amsterdam Active Attachment model. An eluate of the S-PRG particles was tested by adding it to the growth medium or by exposing the biofilms to it for 1 h. The effect of S-PRG particles was tested by adding the particles to the growth medium. The current experiments showed that the presence of S-PRG eluate in the medium influenced biofilm growth and lactic acid production even at low concentrations. The composition of the biofilms changed in the presence of S-PRG eluate, even at concentrations of S-PRG eluate at which biofilm viability was not affected. Treatment of developing biofilms with S-PRG eluate did neither show an effect on biofilm viability nor on lactic acid production. The addition of S-PRG particles to the growth medium resulted in both a lower biofilm viability and lower lactic acid production, indicating that the release of ions from the particles was fast enough to influence biofilm formation. From the current experiments, it can be concluded that S-PRG has the potential to influence biofilm growth, but the presence of the released ions during biofilm formation is required to show an effect.

Manipulation of Saliva-Derived Microcosm Biofilms To Resemble Dysbiotic Subgingival Microbiota.

Periodontitis is a highly prevalent oral inflammatory disease triggered by dysbiotic subgingival microbiota. For the development of microbiome modulators that can reverse the dysbiotic state and reestablish a health-associated microbiota, a high-throughput in vitro multispecies biofilm model is needed. Our aim is to establish a model that resembles a dysbiotic subgingival microbial biofilm by incorporating the major periodontal pathogen Porphyromonas gingivalis into microcosm biofilms cultured from pooled saliva of healthy volunteers. The biofilms were grown for 3, 7, and 10 days and analyzed for their microbial composition by 16S rRNA gene amplicon sequencing as well as measurement of dipeptidyl peptidase IV (DPP4) activity and butyric acid production. The addition of P. gingivalis increased its abundance in saliva-derived microcosm biofilms from 2.7% on day 3 to >50% on day 10, which significantly reduced the Shannon diversity but did not affect the total number of operational taxonomic units (OTUs). The P. gingivalis-enriched biofilms displayed altered microbial composition as revealed by principal-component analysis and reduced interactions among microbial species. Moreover, these biofilms exhibited enhanced DPP4 activity and butyric acid production. In conclusion, by adding P. gingivalis to saliva-derived microcosm biofilms, we established an in vitro pathogen-enriched dysbiotic microbiota which resembles periodontitis-associated subgingival microbiota in terms of increased P. gingivalis abundance and higher DPP4 activity and butyric acid production. This model may allow for investigating factors that accelerate or hinder a microbial shift from symbiosis to dysbiosis and for developing microbiome modulation strategies.IMPORTANCE In line with the new paradigm of the etiology of periodontitis, an inflammatory disorder initiated by dysbiotic subgingival microbiota, novel therapeutic strategies have been proposed targeting reversing dysbiosis and restoring host-compatible microbiota rather than eliminating the biofilms unselectively. Thus, appropriate laboratory models are required to evaluate the efficacy of potential microbiome modulators. In the present study, we used the easily obtainable saliva as an inoculum, spiked the microcosm biofilms with the periodontal pathogen Porphyromonas gingivalis, and obtained a P. gingivalis-enriched microbiota, which resembles the in vivo pathogen-enriched subgingival microbiota in severe periodontitis. This biofilm model circumvents the difficulties encountered when using subgingival plaque as the inoculum and achieves microbiota in a dysbiotic state in a controlled and reproducible manner, which is required for high-throughput and large-scale evaluation of strategies that can potentially modulate microbial ecology.

Optimizing the quality of clinical studies on oral microbiome: A practical guide for planning, performing, and reporting.

With this review, we aim to increase the quality standards for clinical studies with microbiome as an output parameter. We critically address the existing body of evidence for good quality practices in oral microbiome studies based on 16S rRNA gene amplicon sequencing. First, we discuss the usefulness of microbiome profile analyses. Is a microbiome study actually the best approach for answering the research question? This is followed by addressing the criteria for the most appropriate study design, sample size, and the necessary data (study metadata) that should be collected. Next, we evaluate the available evidence for best practices in sample collection, transport, storage, and DNA isolation. Finally, an overview of possible sequencing options (eg, 16S rRNA gene hypervariable regions, sequencing platforms), processing and data interpretation approaches, as well as requirements for meaningful data storage, sharing, and reporting are provided.

Submucosal microbiome of peri-implant sites: A cross-sectional study.

AIM: To study the peri-implant submucosal microbiome in relation to implant disease status, dentition status, smoking habit, gender, implant location, implant system, time of functional loading, probing pocket depth (PPD), and presence of bleeding on probing. MATERIALS AND METHODS: Biofilm samples were collected from the deepest peri-implant site of 41 patients with paper points, and analysed using 16S rRNA gene pyrosequencing. RESULTS: We observed differences in microbial profiles by PPD, implant disease status, and dentition status. Microbiota in deep pockets included higher proportions of the genera Fusobacterium, Prevotella, and Anaeroglobus compared with shallow pockets that harboured more Rothia, Neisseria, Haemophilus, and Streptococcus. Peri-implantitis (PI) sites were dominated by Fusobacterium and Treponema compared with healthy implants and peri-implant mucositis, which were mostly colonized by Rothia and Streptococcus. Partially edentulous (PE) individuals presented more Fusobacterium, Prevotella, and Rothia, whereas fully edentulous individuals presented more Veillonella and Streptococcus. CONCLUSIONS: PPD, implant disease status, and dentition status may affect the submucosal ecology leading to variation in composition of the microbiome. Deep pockets, PI, and PE individuals were dominated by Gram-negative anaerobic taxa.

Short-Chain N-Acylhomoserine Lactone Quorum-Sensing Molecules Promote Periodontal Pathogens in In Vitro Oral Biofilms.

Acylhomoserine lactones (AHLs), the quorum-sensing (QS) signals produced by a range of Gram-negative bacteria, are involved in biofilm formation in many pathogenic and environmental bacteria. Nevertheless, the current paradigm excludes a role of AHLs in dental plaque formation, while other QS signals, such as AI-2 and autoinducer peptides, have been demonstrated to play an important role in biofilm formation and virulence-related gene expression in oral pathogens. In the present work, we have explored the effect of externally added AHLs on in vitro oral biofilm models for commensal, cariogenic, and periodontal dental plaque. While little effect on bacterial growth was observed, some AHLs specifically affected the lactic acid production and protease activity of the biofilms. Most importantly, the analysis of bacterial diversity in the biofilms showed that the addition of C6-homoserine lactone (C6-HSL) results in a shift toward a periodontal bacterial composition profile by increasing the relative presence of the orange-complex bacteria Peptostreptococcus and Prevotella These results point to a relevant role of AHL-mediated QS in dental plaque formation and might be involved in the development of dysbiosis, the mechanism of which should be further investigated. This finding potentially opens new opportunities for the prevention or treatment of the periodontal disease.IMPORTANCE Dental plaque is omnipresent in healthy oral cavities and part of our commensal microbial colonization. At the same time, dental plaque is the cause of the most common human diseases, caries and gum disease. Dental plaque consists of billions of microbes attached to the surface of your teeth. Communication among these microbes is pivotal for development of these complex communities yet poorly studied in dental plaque. In the present study, we show that a specific communication molecule induces changes within the community related to the development of gum disease. This finding suggests that interfering with microbial communication may represent an interesting novel strategy to prevent gum disease that should be further investigated.

The efficacy of whole human genome capture on ancient dental calculus and dentin.

OBJECTIVES: Dental calculus is among the richest known sources of ancient DNA in the archaeological record. Although most DNA within calculus is microbial, it has been shown to contain sufficient human DNA for the targeted retrieval of whole mitochondrial genomes. Here, we explore whether calculus is also a viable substrate for whole human genome recovery using targeted enrichment techniques. MATERIALS AND METHODS: Total DNA extracted from 24 paired archaeological human dentin and calculus samples was subjected to whole human genome enrichment using in-solution hybridization capture and high-throughput sequencing. RESULTS: Total DNA from calculus exceeded that of dentin in all cases, and although the proportion of human DNA was generally lower in calculus, the absolute human DNA content of calculus and dentin was not significantly different. Whole genome enrichment resulted in up to four-fold enrichment of the human endogenous DNA content for both dentin and dental calculus libraries, albeit with some loss in complexity. Recovering more on-target reads for the same sequencing effort generally improved the quality of downstream analyses, such as sex and ancestry estimation. For nonhuman DNA, comparison of phylum-level microbial community structure revealed few differences between precapture and postcapture libraries, indicating that off-target sequences in human genome-enriched calculus libraries may still be useful for oral microbiome reconstruction. DISCUSSION: While ancient human dental calculus does contain endogenous human DNA sequences, their relative proportion is low when compared with other skeletal tissues. Whole genome enrichment can help increase the proportion of recovered human reads, but in this instance enrichment efficiency was relatively low when compared with other forms of capture. We conclude that further optimization is necessary before the method can be routinely applied to archaeological samples.

Fungal mitochondrial oxygen consumption induces the growth of strict anaerobic bacteria.

Fungi are commonly encountered as part of a healthy oral ecosystem. Candida albicans is the most often observed and investigated fungal species in the oral cavity. The role of fungi in the oral ecosystem has remained enigmatic for decades. Recently, it was shown that C. albicans, in vitro, influences the bacterial composition of young oral biofilms, indicating it possibly plays a role in increasing diversity in the oral ecosystem. C. albicans favored growth of strictly anaerobic species under aerobic culture conditions. In the present study, the role of mitochondrial respiration, as mechanism by which C. albicans modifies its environment, was investigated. Using oxygen sensors, a rapid depletion of dissolved oxygen (dO2) was observed. This decrease was not C. albicans specific as several non-albicans Candida species showed similar oxygen consumption. Heat inactivation as well as addition of the specific mitochondrial respiration inhibitor Antimycin A inhibited depletion of dO2. Using 16S rDNA sequencing, it is shown that mitochondrial activity, more than physical presence of C. albicans is responsible for inducing growth of strictly anaerobic oral bacteria in aerobic growth conditions. The described mechanism of dO2 depletion may be a general mechanism by which fungi modulate their direct environment.

metaModules identifies key functional subnetworks in microbiome-related disease.

MOTIVATION: The human microbiome plays a key role in health and disease. Thanks to comparative metatranscriptomics, the cellular functions that are deregulated by the microbiome in disease can now be computationally explored. Unlike gene-centric approaches, pathway-based methods provide a systemic view of such functions; however, they typically consider each pathway in isolation and in its entirety. They can therefore overlook the key differences that (i) span multiple pathways, (ii) contain bidirectionally deregulated components, (iii) are confined to a pathway region. To capture these properties, computational methods that reach beyond the scope of predefined pathways are needed. RESULTS: By integrating an existing module discovery algorithm into comparative metatranscriptomic analysis, we developed metaModules, a novel computational framework for automated identification of the key functional differences between health- and disease-associated communities. Using this framework, we recovered significantly deregulated subnetworks that were indeed recognized to be involved in two well-studied, microbiome-mediated oral diseases, such as butanoate production in periodontal disease and metabolism of sugar alcohols in dental caries. More importantly, our results indicate that our method can be used for hypothesis generation based on automated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment. AVAILABILITY AND IMPLEMENTATION: metaModules is available at https://bitbucket.org/alimay/metamodules/ CONTACT: a.may@vu.nl or s.abeln@vu.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL.

Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.

The mycobiome of root canal infections is correlated to the bacteriome.

OBJECTIVES: Bacterial infection of the root canal system causes apical periodontitis. Less is known about the role of fungi in these infections. This study aimed to assess the fungal prevalence, abundance, and diversity of root canal infections, as well as the relation between fungi and bacteria present in different parts of the root canal. MATERIALS AND METHODS: Twenty-six teeth with primary apical periodontitis were extracted, split in apical and coronal root segments, and cryo-pulverized. Bacteriome profiles of 23 teeth were analyzed based on the V3-V4 hypervariable region of the 16S ribosomal RNA gene. Mycobiome profiles of six teeth were analyzed based on the internal transcribed spacer (ITS) 1 or ITS2 region. Samples were sequenced on the Illumina MiSeq platform. RESULTS: A total of 338 bacterial operational taxonomic units (OTUs), 28 ITS1 OTUs, and 24 ITS2 OTUs were identified. Candida and Malassezia were the most frequently identified fungi. No differences could be found between the bacteriome and mycobiome profiles of the apical and coronal root segments. The bacteriome of fungi-positive root segments contained more Actinomyces, Bifidobacterium, four different Lactobacillus OTUs, Propionibacterium, and Streptococcus. A Spearman correlation matrix between bacteriomes and mycobiomes identified no correlations, but separate clusters could be observed. CONCLUSIONS: A considerable proportion of the root canal infections contain fungi, although fungal diversity is limited. However, when fungi are present, the composition of the bacteriome is clearly different. CLINICAL RELEVANCE: Interaction between bacteria and fungi in root canal infections may complicate the infection and require alternative treatment strategies.

A Single Nucleotide Change in the Promoter mutp Enhances Fluoride Resistance of Streptococcus mutans.

Previously, we identified a single nucleotide mutation in the promoter (mutp) of the fluoride antiporter-coding genes in a naturally fluoride-resistant Streptococcus mutans strain. Here, we studied the role of this mutation in a defined genetic background. The results confirmed that this mutation alone confers fluoride resistance on S. mutans, as shown by growth and lactic acid production assays. This resistance was explained by constitutively higher mutp promoter activity and upregulation of the fluoride antiporter-coding genes.

Nitrate and the Origin of Saliva Influence Composition and Short Chain Fatty Acid Production of Oral Microcosms.

Nitrate is emerging as a possible health benefactor. Especially the microbial conversion of nitrate to nitrite in the oral cavity and the subsequent conversion to nitric oxide in the stomach are of interest in this regard. Yet, how nitrate influences the composition and biochemistry of the oral ecosystem is not fully understood. To investigate the effect of nitrate on oral ecology, we performed a 4-week experiment using the multiplaque artificial mouth (MAM) biofilm model. This model was inoculated with stimulated saliva of two healthy donors. Half of the microcosms (n = 4) received a constant supply of nitrate, while the other half functioned as control (n = 4). Additionally, all microcosms received a nitrate and sucrose pulse, each week, on separate days to measure nitrate reduction and acid formation. The bacterial composition of the microcosms was determined by 16S rDNA sequencing. The origin of the saliva (i.e., donor) showed to be the strongest determinant for the development of the microcosms. The supplementation of nitrate was related to a relatively high abundance of Neisseria in the microcosms of both donors, while Veillonella was highly abundant in the nitrate-supplemented microcosms of only one of the donors. The lactate concentration after sucrose addition was similarly high in all microcosms, irrespective of treatment or donor, while the concentration of butyrate was lower after nitrate addition in the nitrate-receiving microcosms. In conclusion, nitrate influences the composition and biochemistry of oral microcosms, although the result is strongly dependent on the inoculum.

Unraveling the outcome of 16S rDNA-based taxonomy analysis through mock data and simulations.

MOTIVATION: 16S rDNA pyrosequencing is a powerful approach that requires extensive usage of computational methods for delineating microbial compositions. Previously, it was shown that outcomes of studies relying on this approach vastly depend on the choice of pre-processing and clustering algorithms used. However, obtaining insights into the effects and accuracy of these algorithms is challenging due to difficulties in generating samples of known composition with high enough diversity. Here, we use in silico microbial datasets to better understand how the experimental data are transformed into taxonomic clusters by computational methods. RESULTS: We were able to qualitatively replicate the raw experimental pyrosequencing data after rigorously adjusting existing simulation software. This allowed us to simulate datasets of real-life complexity, which we used to assess the influence and performance of two widely used pre-processing methods along with 11 clustering algorithms. We show that the choice, order and mode of the pre-processing methods have a larger impact on the accuracy of the clustering pipeline than the clustering methods themselves. Without pre-processing, the difference between the performances of clustering methods is large. Depending on the clustering algorithm, the most optimal analysis pipeline resulted in significant underestimations of the expected number of clusters (minimum: 3.4%; maximum: 13.6%), allowing us to make quantitative estimations of the bacterial complexity of real microbiome samples.

TaxMan: a server to trim rRNA reference databases and inspect taxonomic coverage.

Amplicon sequencing of the hypervariable regions of the small subunit ribosomal RNA gene is a widely accepted method for identifying the members of complex bacterial communities. Several rRNA gene sequence reference databases can be used to assign taxonomic names to the sequencing reads using BLAST, USEARCH, GAST or the RDP classifier. Next-generation sequencing methods produce ample reads, but they are short, currently ∼100-450 nt (depending on the technology), as compared to the full rRNA gene of ∼1550 nt. It is important, therefore, to select the right rRNA gene region for sequencing. The primers should amplify the species of interest and the hypervariable regions should differentiate their taxonomy. Here, we introduce TaxMan: a web-based tool that trims reference sequences based on user-selected primer pairs and returns an assessment of the primer specificity by taxa. It allows interactive plotting of taxa, both amplified and missed in silico by the primers used. Additionally, using the trimmed sequences improves the speed of sequence matching algorithms. The smaller database greatly improves run times (up to 98%) and memory usage, not only of similarity searching (BLAST), but also of chimera checking (UCHIME) and of clustering the reads (UCLUST). TaxMan is available at http://www.ibi.vu.nl/programs/taxmanwww/.

FAIR compliant database development for human microbiome data samples.

Introduction: Sharing microbiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be standardized, transparent and readily available for researchers. The microbiome data and associated metadata will then be described with regards to composition and origin, in order to maximize the possibilities for application in various contexts of research. Here, we propose a set of tools and protocols to develop a real-time FAIR (Findable. Accessible, Interoperable and Reusable) compliant database for the handling and storage of human microbiome and host-associated data. Methods: The conflicts arising from privacy laws with respect to metadata, possible human genome sequences in the metagenome shotgun data and FAIR implementations are discussed. Alternate pathways for achieving compliance in such conflicts are analyzed. Sample traceable and sensitive microbiome data, such as DNA sequences or geolocalized metadata are identified, and the role of the GDPR (General Data Protection Regulation) data regulations are considered. For the construction of the database, procedures have been realized to make data FAIR compliant, while preserving privacy of the participants providing the data. Results and discussion: An open-source development platform, Supabase, was used to implement the microbiome database. Researchers can deploy this real-time database to access, upload, download and interact with human microbiome data in a FAIR complaint manner. In addition, a large language model (LLM) powered by ChatGPT is developed and deployed to enable knowledge dissemination and non-expert usage of the database.