Bernd W. Brandt

Same Exposure but Two Radically Different Responses to Antibiotics: Resilience of the Salivary Microbiome versus Long-Term Microbial Shifts in Feces

ABSTRACT Due to the spread of resistance, antibiotic exposure receives increasing attention. Ecological consequences for the different niches of individual microbiomes are, however, largely ignored. Here, we report the effects of widely used antibiotics (clindamycin, ciprofloxacin, amoxicillin, and minocycline) with different modes of action on the ecology of both the gut and the oral microbiomes in 66 healthy adults from the United Kingdom and Sweden in a two-center randomized placebo-controlled clinical trial. Feces and saliva were collected at baseline, immediately after exposure, and 1, 2, 4, and 12 months after administration of antibiotics or placebo. Sequences of 16S rRNA gene amplicons from all samples and metagenomic shotgun sequences from selected baseline and post-antibiotic-treatment sample pairs were analyzed. Additionally, metagenomic predictions based on 16S rRNA gene amplicon data were performed using PICRUSt. The salivary microbiome was found to be significantly more robust, whereas the antibiotics negatively affected the fecal microbiome: in particular, health-associated butyrate-producing species became strongly underrepresented. Additionally, exposure to different antibiotics enriched genes associated with antibiotic resistance. In conclusion, healthy individuals, exposed to a single antibiotic treatment, undergo considerable microbial shifts and enrichment in antibiotic resistance in their feces, while their salivary microbiome composition remains unexpectedly stable. The health-related consequences for the gut microbiome should increase the awareness of the individual risks involved with antibiotic use, especially in a (diseased) population with an already dysregulated microbiome. On the other hand, understanding the mechanisms behind the resilience of the oral microbiome toward ecological collapse might prove useful in combating microbial dysbiosis elsewhere in the body. IMPORTANCE Many health care professionals use antibiotic prophylaxis strategies to prevent infection after surgery. This practice is under debate since it enhances the spread of antibiotic resistance. Another important reason to avoid nonessential use of antibiotics, the impact on our microbiome, has hardly received attention. In this study, we assessed the impact of antibiotics on the human microbial ecology at two niches. We followed the oral and gut microbiomes in 66 individuals from before, immediately after, and up to 12 months after exposure to different antibiotic classes. The salivary microbiome recovered quickly and was surprisingly robust toward antibiotic-induced disturbance. The fecal microbiome was severely affected by most antibiotics: for months, health-associated butyrate-producing species became strongly underrepresented. Additionally, there was an enrichment of genes associated with antibiotic resistance. Clearly, even a single antibiotic treatment in healthy individuals contributes to the risk of resistance development and leads to long-lasting detrimental shifts in the gut microbiome. Many health care professionals use antibiotic prophylaxis strategies to prevent infection after surgery. This practice is under debate since it enhances the spread of antibiotic resistance. Another important reason to avoid nonessential use of antibiotics, the impact on our microbiome, has hardly received attention. In this study, we assessed the impact of antibiotics on the human microbial ecology at two niches. We followed the oral and gut microbiomes in 66 individuals from before, immediately after, and up to 12 months after exposure to different antibiotic classes. The salivary microbiome recovered quickly and was surprisingly robust toward antibiotic-induced disturbance. The fecal microbiome was severely affected by most antibiotics: for months, health-associated butyrate-producing species became strongly underrepresented. Additionally, there was an enrichment of genes associated with antibiotic resistance. Clearly, even a single antibiotic treatment in healthy individuals contributes to the risk of resistance development and leads to long-lasting detrimental shifts in the gut microbiome.

Comparing clustering and pre-processing in taxonomy analysis

MOTIVATION Massively parallel sequencing allows for rapid sequencing of large numbers of sequences in just a single run. Thus, 16S ribosomal RNA (rRNA) amplicon sequencing of complex microbial communities has become possible. The sequenced 16S rRNA fragments (reads) are clustered into operational taxonomic units and taxonomic categories are assigned. Recent reports suggest that data pre-processing should be performed before clustering. We assessed combinations of data pre-processing steps and clustering algorithms on cluster accuracy for oral microbial sequence data. RESULTS The number of clusters varied up to two orders of magnitude depending on pre-processing. Pre-processing using both denoising and chimera checking resulted in a number of clusters that was closest to the number of species in the mock dataset (25 versus 15). Based on run time, purity and normalized mutual information, we could not identify a single best clustering algorithm. The differences in clustering accuracy among the algorithms after the same pre-processing were minor compared with the differences in accuracy among different pre-processing steps. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. CONTACT bonder.m.j@gmail.com or b.brandt@acta.nl

PhyloPars: estimation of missing parameter values using phylogeny

A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible. The PhyloPars web server provides a statistically consistent method that combines an incomplete set of empirical observations with the species phylogeny to produce a complete set of parameter estimates for all species. It builds upon a state-of-the-art evolutionary model, extended with the ability to handle missing data. The resulting approach makes optimal use of all available information to produce estimates that can be an order of magnitude more accurate than ad-hoc alternatives. Uploading a phylogeny and incomplete feature matrix suffices to obtain estimates of all missing values, along with a measure of certainty. Real-time cross-validation provides further insight in the accuracy and bias expected for estimated values. The server allows for easy, efficient estimation of metabolic parameters, which can benefit a wide range of fields including systems biology and ecology. PhyloPars is available at: http://www.ibi.vu.nl/programs/phylopars/.

Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification

To date, characterization of ancient oral (dental calculus) and gut (coprolite) microbiota has been primarily accomplished through a metataxonomic approach involving targeted amplification of one or more variable regions in the 16S rRNA gene. Specifically, the V3 region (E. coli 341–534) of this gene has been suggested as an excellent candidate for ancient DNA amplification and microbial community reconstruction. However, in practice this metataxonomic approach often produces highly skewed taxonomic frequency data. In this study, we use non-targeted (shotgun metagenomics) sequencing methods to better understand skewed microbial profiles observed in four ancient dental calculus specimens previously analyzed by amplicon sequencing. Through comparisons of microbial taxonomic counts from paired amplicon (V3 U341F/534R) and shotgun sequencing datasets, we demonstrate that extensive length polymorphisms in the V3 region are a consistent and major cause of differential amplification leading to taxonomic bias in ancient microbiome reconstructions based on amplicon sequencing. We conclude that systematic amplification bias confounds attempts to accurately reconstruct microbiome taxonomic profiles from 16S rRNA V3 amplicon data generated using universal primers. Because in silico analysis indicates that alternative 16S rRNA hypervariable regions will present similar challenges, we advocate for the use of a shotgun metagenomics approach in ancient microbiome reconstructions.

Multi-Harmony: detecting functional specificity from sequence alignment

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein–protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.

The Oral Microbiome of Denture Wearers Is Influenced by Levels of Natural Dentition

Objectives The composition of dental plaque has been well defined, whereas currently there is limited understanding of the composition of denture plaque and how it directly influences denture related stomatitis (DS). The aims of this study were to compare the microbiomes of denture wearers, and to understand the implications of these towards inter-kingdom and host-pathogen interactions within the oral cavity. Methods Swab samples were obtained from 123 participants wearing either a complete or partial denture; the bacterial composition of each sample was determined using bar-coded illumina MiSeq sequencing of the bacterial hypervariable V4 region of 16S rDNA. Sequencing data processing was undertaken using QIIME, clustered in Operational Taxonomic Units (OTUs) and assigned to taxonomy. The dentures were sonicated to remove the microbial flora residing on the prosthesis, sonicate was then cultured using diagnostic colorex Candida media. Samples of unstimulated saliva were obtained and antimicrobial peptides (AMP) levels were measured by ELISA. Results We have shown that dental and denture plaques are significantly distinct both in composition and diversity and that the oral microbiome composition of a denture wearer is variable and is influenced by the location within the mouth. Dentures and mucosa were predominantly made up of Bacilli and Actinobacteria. Moreover, the presence of natural teeth has a significant impact on the overall microbial composition, when compared to the fully edentulous. Furthermore, increasing levels of Candida spp. positively correlate with Lactobacillus spp. AMPs were quantified, though showed no specific correlations. Conclusions This is the first study to provide a detailed understanding of the oral microbiome of denture wearers and has provided evidence that DS development is more complex than simply a candidal infection. Both fungal and bacterial kingdoms clearly play a role in defining the progression of DS, though we were unable to show a defined role for AMPs.

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/.

On the ecosystemic network of saliva in healthy young adults

A dysbiotic state is believed to be a key factor in the onset of oral disease. Although oral diseases have been studied for decades, our understanding of oral health, the boundaries of a healthy oral ecosystem and ecological shift toward dysbiosis is still limited. Here, we present the ecobiological heterogeneity of the salivary ecosystem and relations between the salivary microbiome, salivary metabolome and host-related biochemical salivary parameters in 268 healthy adults after overnight fasting. Gender-specific differences in the microbiome and metabolome were observed and were associated with salivary pH and dietary protein intake. Our analysis grouped the individuals into five microbiome and four metabolome-based clusters that significantly related to biochemical parameters of saliva. Low salivary pH and high lysozyme activity were associated with high proportions of streptococcal phylotypes and increased membrane-lipid degradation products. Samples with high salivary pH displayed increased chitinase activity, higher abundance of Veillonella and Prevotella species and higher levels of amino acid fermentation products, suggesting proteolytic adaptation. An over-specialization toward either a proteolytic or a saccharolytic ecotype may indicate a shift toward a dysbiotic state. Their prognostic value and the degree to which these ecotypes are related to increased disease risk remains to be determined.

Hepatitis C virus NS4B carboxy terminal domain is a membrane binding domain

BackgroundHepatitis C virus (HCV) induces membrane rearrangements during replication. All HCV proteins are associated to membranes, pointing out the importance of membranes for HCV. Non structural protein 4B (NS4B) has been reported to induce cellular membrane alterations like the membranous web. Four transmembrane segments in the middle of the protein anchor NS4B to membranes. An amphipatic helix at the amino-terminus attaches to membranes as well. The carboxy-terminal domain (CTD) of NS4B is highly conserved in Hepaciviruses, though its function remains unknown.ResultsA cytosolic localization is predicted for the NS4B-CTD. However, using membrane floatation assays and immunofluorescence, we now show targeting of the NS4B-CTD to membranes. Furthermore, a profile-profile search, with an HCV NS4B-CTD multiple sequence alignment, indicates sequence similarity to the membrane binding domain of prokaryotic D-lactate dehydrogenase (d-LDH). The crystal structure of E. coli d-LDH suggests that the region similar to NS4B-CTD is located in the membrane binding domain (MBD) of d-LDH, implying analogy in membrane association. Targeting of d-LDH to membranes occurs via electrostatic interactions of positive residues on the outside of the protein with negative head groups of lipids. To verify that anchorage of d-LDH MBD and NS4B-CTD is analogous, NS4B-CTD mutants were designed to disrupt these electrostatic interactions. Membrane association was confirmed by swopping the membrane contacting helix of d-LDH with the corresponding domain of the 4B-CTD. Furthermore, the functionality of these residues was tested in the HCV replicon system.ConclusionTogether these data show that NS4B-CTD is associated to membranes, similar to the prokaryotic d-LDH MBD, and is important for replication.