Biology subjects

Relman, D. A.

Publications and source records attributed to Relman, D. A..

4 recordsLinked to original sources

bioRxiv2018

Treatment-Specific Composition of Gut Microbiota Is Associated with Disease Remission in a Pediatric Crohn’s Disease Cohort

BackgroundThe beneficial effects of antibiotics in Crohns disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD, and the use of microbiota features as classifiers or predictors of disease remission.\n\nMethods16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs. MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct Random Forest models to classify remission or predict treatment response.\n\nResultsBoth MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random Forest models constructed from microbiota profiles pre- and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (AUC of 0.879, 95% CI 0.683, 0.9877; sensitivity 0.7778; specificity 1.000, P < 0.001). A Random Forest model trained on preantibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC of 0.8, P = 0.24).\n\nConclusionsMET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.\n\nSummaryWe investigated the impact of metronidazole and metronidazole plus azithromycin on the gut microbiota in pediatric Crohns disease. Disease remission was associated with a treatment-specific microbiota configuration, and could be predicted based on pre-antibiotic microbiota profiles.

microbiology↗

bioRxiv2018

Early transcriptional responses after dengue vaccination mirror the response to natural infection and predict neutralizing antibody titers

BackgroundSeveral promising live attenuated virus (LAV) dengue vaccines are in development, but information about innate immune responses and early correlates of protection are lacking.\n\nMethodsWe characterized human genome-wide transcripts in whole blood from 10 volunteers at 11 time-points after immunization with the dengue virus type 3 (DENV-3) component of the NIH dengue vaccine candidate TV003 and from 30 hospitalized children with acute primary DENV-3 infection. We compared day-specific gene expression patterns with subsequent neutralizing antibody (NAb) titers.\n\nResultsThe transcriptional response to vaccination was largely confined to days 5-20 and was dominated by an interferon-associated signature and a cell cycle signature that peaked on days 8 and 14, respectively. Changes in transcript abundance were much greater in magnitude and scope in symptomatic natural infection than following vaccination (maximum fold-change >200 versus 21 post-vaccination; 3,210 versus 286 transcripts with significant fold-change), but shared gene modules were induced in the same sequence. The abundance of 131 transcripts on days 8 and 9 post-vaccination was strongly correlated with NAb titers measured 6 weeks post-vaccination.\n\nConclusionsLAV dengue vaccination elicits early transcriptional responses that mirror those found in symptomatic natural infection and provide candidate early markers of protection against DENV infection.\n\nClinical Trial Registration Number: NCT00831012 (available at clinicaltrials.gov)\n\nSummary: Interferon- and cell cycle-associated gene transcript abundance levels in the peripheral blood of dengue vaccine recipients on days 8 and 9 post-vaccination were associated with dengue neutralizing antibody titers on day 42, and mirrored responses in primary dengue infection, suggesting the possibility of predicting protective immunity.

immunology↗

bioRxiv2018

Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome

Recent studies suggest that the microbiome has an impact on gestational health and outcome. However, characterization of the pregnancy-associated microbiome has largely relied on 16S rRNA gene amplicon-based surveys. Here, we describe an assembly-driven, metagenomics-based, longitudinal study of the vaginal, gut, and oral microbiomes in 292 samples from ten subjects sampled every three weeks throughout pregnancy. 1.53 Gb of non-human sequence was assembled into scaffolds, and functional genes were predicted for gene-and pathway-based analyses. Vaginal assemblies were binned into 97 draft quality genomes. Redundancy analysis (RDA) of microbial community composition at all three body sites revealed gestational age to be a significant source of variation in patterns of gene abundance. In addition, health complications were associated with variation in community functional gene composition in the mouth and gut. The diversity of Lactobacillus iners-dominated communities in the vagina, unlike most other vaginal community types, significantly increased with gestational age. The genomes of co-occurring Gardnerella vaginalis strains with predicted distinct functions were recovered in samples from two subjects. In seven subjects, gut samples contained strains of the same Lactobacillus species that dominated the vaginal community of that same subject, and not other Lactobacillus species; however, these within-host strains were divergent. CRISPR spacer analysis suggested shared phage and plasmid populations across body sites and individuals. This work underscores the dynamic behavior of the microbiome during pregnancy and suggests the potential importance of understanding the sources of this behavior for fetal development and gestational outcome.

microbiology↗

bioRxiv2017

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

BackgroundThe accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants -- DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls.\n\nResultsdecontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants.\n\nConclusionsdecontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.

bioinformatics↗