Determination of Effect Sizes for Power Analysis for Microbiome Studies Using Large Microbiome Databases.

Herein, we present a tool called Evident that can be used for deriving effect sizes for a broad spectrum of metadata variables, such as mode of birth, antibiotics, socioeconomics, etc., to provide power calculations for a new study. Evident can be used to mine existing databases of large microbiome studies (such as the American Gut Project, FINRISK, and TEDDY) to analyze the effect sizes for planning future microbiome studies via power analysis. For each metavariable, the Evident software is flexible to compute effect sizes for many commonly used measures of microbiome analyses, including α diversity, ß diversity, and log-ratio analysis. In this work, we describe why effect size and power analysis are necessary for computational microbiome analysis and show how Evident can help researchers perform these procedures. Additionally, we describe how Evident is easy for researchers to use and provide an example of efficient analyses using a dataset of thousands of samples and dozens of metadata categories.

Ecological divergence of syntopic marine bacterial species is shaped by gene content and expression.

Identifying mechanisms by which bacterial species evolve and maintain genomic diversity is particularly challenging for the uncultured lineages that dominate the surface ocean. A longitudinal analysis of bacterial genes, genomes, and transcripts during a coastal phytoplankton bloom revealed two co-occurring, highly related Rhodobacteraceae species from the deeply branching and uncultured NAC11-7 lineage. These have identical 16S rRNA gene amplicon sequences, yet their genome contents assembled from metagenomes and single cells indicate species-level divergence. Moreover, shifts in relative dominance of the species during dynamic bloom conditions over 7 weeks confirmed the syntopic species' divergent responses to the same microenvironment at the same time. Genes unique to each species and genes shared but divergent in per-cell inventories of mRNAs accounted for 5% of the species' pangenome content. These analyses uncover physiological and ecological features that differentiate the species, including capacities for organic carbon utilization, attributes of the cell surface, metal requirements, and vitamin biosynthesis. Such insights into the coexistence of highly related and ecologically similar bacterial species in their shared natural habitat are rare.

The gastrointestinal and microbiome impact of a resistant starch blend from potato, banana, and apple fibers: A randomized clinical trial using smart caps.

The gastrointestinal (GI) impact of fibers including resistant starch (RS) consumption depends on various types and amounts of fibers, the initial microbiome states, and accurate intake measurements. A randomized clinical trial evaluated the GI impact of varying doses of a novel resistant starch blend (RSB) with smart cap monitoring. RSB contained at least 50% RS and was a proprietary mixture of a potato starch, green banana flour, and apple fiber powder (a source of apple pectin, not resistant starch). The study design randomized participants to one of four arms: 10 g/day of potato starch (0 RSB), 10 g/day of RSB, 10 to 20 to 20 g/day of RSB or 10 to 20 to 30 g/day RSB for two-week intervals over 6 weeks. Results confirmed that while resistant starch of approximately 5 g per day improves GI symptoms at 2, 4, and 6 weeks, it did not demonstrate a detectable effect on short chain fatty acids. Increasing doses of the blend (RSB) led to a decrease in the diarrhea score. Using an estimate of total consumption of RSB based on smart cap recordings of container openings and protocol-specified doses of RSB, a reduction in the sleep disturbance score was associated with higher RSB dose. The exploratory microbiome evaluation demonstrated that among the 16S rRNA gene sequences most associated with the consumption of the novel blend RSB, two belong to taxa of notable interest to human health: Faecalibacterium and Akkermansia.

Zebra: Static and Dynamic Genome Cover Thresholds with Overlapping References.

Assigning taxonomy remains a challenging topic in microbiome studies, due largely to ambiguity of reads which overlap multiple reference genomes. With the Web of Life (WoL) reference database hosting 10,575 reference genomes and growing, the percentage of ambiguous reads will only increase. The resulting artifacts create both the illusion of co-occurrence and a long tail end of extraneous reference hits that confound interpretation. We introduce genome cover, the fraction of reference genome overlapped by reads, to distinguish these artifacts. We show how to dynamically predict genome cover by read count and examine our model in Staphylococcus aureus monoculture. Our modeling cleanly separates both S. aureus and true contaminants from the false artifacts of reference overlap. We next introduce saturated genome cover, the true fraction of a reference genome overlapped by sample contents. Genome cover may not saturate for low abundance or low prevalence bacteria. We assuage this worry with examination of a large human fecal data set. By compositing the metric across like samples, genome cover saturates even for rare species. We note that it is a threshold on saturated genome cover, not genome cover itself, which indicates a spurious reference hit or distant relative. We present Zebra, a method to compute and threshold the genome cover metric across like samples, a recurrence to estimate genome cover and confirm saturation, and provide guidance for choosing cover thresholds in real world scenarios. Standalone genome cover and integration into Woltka are available: https://github.com/biocore/zebra_filter, https://github.com/qiyunzhu/woltka. IMPORTANCE Taxonomic assignment, assigning sequences to specific taxonomic units, is a crucial processing step in microbiome analyses. Issues in taxonomic assignment affect interpretation of what microbes are present in each sample and may be associated with specific environmental or clinical conditions. Assigning importance to a particular taxon relies strongly on independence of assigned counts. The false inclusion of thousands of correlated taxa makes interpretation ambiguous, leading to underconstrained results which cannot be reproduced. The importance sometimes attached to implausible artifacts such as anthrax or bubonic plague is especially problematic. We show that the Zebra filter retrieves only the nearest relatives of sample contents enabling more reproducible and biologically plausible interpretation of metagenomic data.

Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater.

Niche theory is a foundational ecological concept that explains the distribution of species in natural environments. Identifying the dimensions of any organism's niche is challenging because numerous environmental factors can affect organism viability. We used serial invasion experiments to introduce Ruegeria pomeroyi DSS-3, a heterotrophic marine bacterium, into a coastal phytoplankton bloom on 14 dates. RNA-sequencing analysis of R. pomeroyi was conducted after 90 min to assess its niche dimensions in this dynamic ecosystem. We identified ~100 external conditions eliciting transcriptional responses, which included substrates, nutrients, metals and biotic interactions such as antagonism, resistance and cofactor synthesis. The peak bloom was characterized by favourable states for most of the substrate dimensions, but low inferred growth rates of R. pomeroyi at this stage indicated that its niche was narrowed by factors other than substrate availability, most probably negative biotic interactions with the bloom dinoflagellate. Our findings indicate chemical and biological features of the ocean environment that can constrain where heterotrophic bacteria survive.

Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom.

Metagenomic and metatranscriptomic time-series data covering a 52-day period in the fall of 2016 provide an inventory of bacterial and archaeal community genes, transcripts, and taxonomy during an intense dinoflagellate bloom in Monterey Bay, CA, USA. The dataset comprises 84 metagenomes (0.8 terabases), 82 metatranscriptomes (1.1 terabases), and 88 16S rRNA amplicon libraries from samples collected on 41 dates. The dataset also includes 88 18S rRNA amplicon libraries, characterizing the taxonomy of the eukaryotic community during the bloom. Accompanying the sequence data are chemical and biological measurements associated with each sample. These datasets will facilitate studies of the structure and function of marine bacterial communities during episodic phytoplankton blooms.

Sulfur metabolites that facilitate oceanic phytoplankton-bacteria carbon flux.

Unlike biologically available nitrogen and phosphorus, which are often at limiting concentrations in surface seawater, sulfur in the form of sulfate is plentiful and not considered to constrain marine microbial activity. Nonetheless, in a model system in which a marine bacterium obtains all of its carbon from co-cultured phytoplankton, bacterial gene expression suggests that at least seven dissolved organic sulfur (DOS) metabolites support bacterial heterotrophy. These labile exometabolites of marine dinoflagellates and diatoms include taurine, N-acetyltaurine, isethionate, choline-O-sulfate, cysteate, 2,3-dihydroxypropane-1-sulfonate (DHPS), and dimethylsulfoniopropionate (DMSP). Leveraging from the compounds identified in this model system, we assessed the role of sulfur metabolites in the ocean carbon cycle by mining the Tara Oceans dataset for diagnostic genes. In the 1.4 million bacterial genome equivalents surveyed, estimates of the frequency of genomes harboring the capability for DOS metabolite utilization ranged broadly, from only 1 out of every 190 genomes (for the C2 sulfonate isethionate) to 1 out of every 5 (for the sulfonium compound DMSP). Bacteria able to participate in DOS transformations are dominated by Alphaproteobacteria in the surface ocean, but by SAR324, Acidimicrobiia, and Gammaproteobacteria at mesopelagic depths, where the capability for utilization occurs in higher frequency than in surface bacteria for more than half the sulfur metabolites. The discovery of an abundant and diverse suite of marine bacteria with the genetic capacity for DOS transformation argues for an important role for sulfur metabolites in the pelagic ocean carbon cycle.

Microdiversity and temporal dynamics of marine bacterial dimethylsulfoniopropionate genes.

Dimethylsulfoniopropionate (DMSP) is an abundant organic sulfur metabolite produced by many phytoplankton species and degraded by bacteria via two distinct pathways with climate-relevant implications. We assessed the diversity and abundance of bacteria possessing these pathways in the context of phytoplankton community composition over a 3-week time period spanning September-October, 2014 in Monterey Bay, CA. The dmdA gene from the DMSP demethylation pathway dominated the DMSP gene pool and was harboured mostly by members of the alphaproteobacterial SAR11 clade and secondarily by the Roseobacter group, particularly during the second half of the study. Novel members of the DMSP-degrading community emerged from dmdA sequences recovered from metagenome assemblies and single-cell sequencing, including largely uncharacterized gammaproteobacteria and alphaproteobacteria taxa. In the DMSP cleavage pathway, the SAR11 gene dddK was the most abundant early in the study, but was supplanted by dddP over time. SAR11 members, especially those harbouring genes for both DMSP degradation pathways, had a strong positive relationship with the abundance of dinoflagellates, and DMSP-degrading gammaproteobacteria co-occurred with haptophytes. This in situ study of the drivers of DMSP fate in a coastal ecosystem demonstrates for the first time correlations between specific groups of bacterial DMSP degraders and phytoplankton taxa.