Qiita: rapid, web-enabled microbiome meta-analysis.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

American Gut: an Open Platform for Citizen Science Microbiome Research.

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Bacterial community changes in an industrial algae production system.

While microalgae are a promising feedstock for production of fuels and other chemicals, a challenge for the algal bioproducts industry is obtaining consistent, robust algae growth. Algal cultures include complex bacterial communities and can be difficult to manage because specific bacteria can promote or reduce algae growth. To overcome bacterial contamination, algae growers may use closed photobioreactors designed to reduce the number of contaminant organisms. Even with closed systems, bacteria are known to enter and cohabitate, but little is known about these communities. Therefore, the richness, structure, and composition of bacterial communities were characterized in closed photobioreactor cultivations of Nannochloropsis salina in F/2 medium at different scales, across nine months spanning late summer-early spring, and during a sequence of serially inoculated cultivations. Using 16S rRNA sequence data from 275 samples, bacterial communities in small, medium, and large cultures were shown to be significantly different. Larger systems contained richer bacterial communities compared to smaller systems. Relationships between bacterial communities and algae growth were complex. On one hand, blooms of a specific bacterial type were observed in three abnormal, poorly performing replicate cultivations, while on the other, notable changes in the bacterial community structures were observed in a series of serial large-scale batch cultivations that had similar growth rates. Bacteria common to the majority of samples were identified, including a single OTU within the class Saprospirae that was found in all samples. This study contributes important information for crop protection in algae systems, and demonstrates the complex ecosystems that need to be understood for consistent, successful industrial algae cultivation. This is the first study to profile bacterial communities during the scale-up process of industrial algae systems.

Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping.

The use of sterile swabs is a convenient and common way to collect microbiome samples, and many studies have shown that the effects of room-temperature storage are smaller than physiologically relevant differences between subjects. However, several bacterial taxa, notably members of the class Gammaproteobacteria, grow at room temperature, sometimes confusing microbiome results, particularly when stability is assumed. Although comparative benchmarking has shown that several preservation methods, including the use of 95% ethanol, fecal occult blood test (FOBT) and FTA cards, and Omnigene-GUT kits, reduce changes in taxon abundance during room-temperature storage, these techniques all have drawbacks and cannot be applied retrospectively to samples that have already been collected. Here we performed a meta-analysis using several different microbiome sample storage condition studies, showing consistent trends in which specific bacteria grew (i.e., "bloomed") at room temperature, and introduce a procedure for removing the sequences that most distort analyses. In contrast to similarity-based clustering using operational taxonomic units (OTUs), we use a new technique called "Deblur" to identify the exact sequences corresponding to blooming taxa, greatly reducing false positives and also dramatically decreasing runtime. We show that applying this technique to samples collected for the American Gut Project (AGP), for which participants simply mail samples back without the use of ice packs or other preservatives, yields results consistent with published microbiome studies performed with frozen or otherwise preserved samples. IMPORTANCE In many microbiome studies, the necessity to store samples at room temperature (i.e., remote fieldwork) and the ability to ship samples without hazardous materials that require special handling training, such as ethanol (i.e., citizen science efforts), is paramount. However, although room-temperature storage for a few days has been shown not to obscure physiologically relevant microbiome differences between comparison groups, there are still changes in specific bacterial taxa, notably, in members of the class Gammaproteobacteria, that can make microbiome profiles difficult to interpret. Here we identify the most problematic taxa and show that removing sequences from just a few fast-growing taxa is sufficient to correct microbiome profiles.

Multiplex growth rate phenotyping of synthetic mutants in selection to engineer glucose and xylose co-utilization in Escherichia coli.

Engineering the simultaneous consumption of glucose and xylose sugars is critical to enable the sustainable production of biofuels from lignocellulosic biomass. In most major industrial microorganisms glucose completely inhibits the uptake of xylose, limiting efficient sugar mixture conversion. In E. coli removal of the major glucose transporter PTS allows for glucose and xylose co-consumption but only after prolonged adaptation, which is an effective process but hard to control and prone to co-evolving undesired traits. Here we synthetically engineer mutants to target sugar co-consumption properties; we subject a PTS- mutant to a short adaptive step and subsequently either delete or overexpress key genes previously suggested to affect sugar consumption. Screening the co-consumption properties of these mutants individually is very laborious. We show we can evaluate sugar co-consumption properties in parallel by culturing the mutants in selection and applying a novel approach that computes mutant growth rates in selection using chromosomal barcode counts obtained from Next-Generation Sequencing. We validate this multiplex growth rate phenotyping approach with individual mutant pure cultures, identify new instances of mutants cross-feeding on metabolic byproducts, and, importantly, find that the rates of glucose and xylose co-consumption can be tuned by altering glucokinase expression in our PTS- background. Biotechnol. Bioeng. 2017;114: 885-893. © 2016 Wiley Periodicals, Inc.

Bacteria from diverse habitats colonize and compete in the mouse gut.

To study how microbes establish themselves in a mammalian gut environment, we colonized germ-free mice with microbial communities from human, zebrafish, and termite guts, human skin and tongue, soil, and estuarine microbial mats. Bacteria from these foreign environments colonized and persisted in the mouse gut; their capacity to metabolize dietary and host carbohydrates and bile acids correlated with colonization success. Cohousing mice harboring these xenomicrobiota or a mouse cecal microbiota, along with germ-free "bystanders," revealed the success of particular bacterial taxa in invading guts with established communities and empty gut habitats. Unanticipated patterns of ecological succession were observed; for example, a soil-derived bacterium dominated even in the presence of bacteria from other gut communities (zebrafish and termite), and human-derived bacteria colonized germ-free bystander mice before mouse-derived organisms. This approach can be generalized to address a variety of mechanistic questions about succession, including succession in the context of microbiota-directed therapeutics.

Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences.

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to "classic" open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, "classic" open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of "classic" open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by "classic" open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME's uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME's OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill.

The Deepwater Horizon (DWH) oil spill in the spring of 2010 resulted in an input of ∼4.1 million barrels of oil to the Gulf of Mexico; >22% of this oil is unaccounted for, with unknown environmental consequences. Here we investigated the impact of oil deposition on microbial communities in surface sediments collected at 64 sites by targeted sequencing of 16S rRNA genes, shotgun metagenomic sequencing of 14 of these samples and mineralization experiments using (14)C-labeled model substrates. The 16S rRNA gene data indicated that the most heavily oil-impacted sediments were enriched in an uncultured Gammaproteobacterium and a Colwellia species, both of which were highly similar to sequences in the DWH deep-sea hydrocarbon plume. The primary drivers in structuring the microbial community were nitrogen and hydrocarbons. Annotation of unassembled metagenomic data revealed the most abundant hydrocarbon degradation pathway encoded genes involved in degrading aliphatic and simple aromatics via butane monooxygenase. The activity of key hydrocarbon degradation pathways by sediment microbes was confirmed by determining the mineralization of (14)C-labeled model substrates in the following order: propylene glycol, dodecane, toluene and phenanthrene. Further, analysis of metagenomic sequence data revealed an increase in abundance of genes involved in denitrification pathways in samples that exceeded the Environmental Protection Agency (EPA)'s benchmarks for polycyclic aromatic hydrocarbons (PAHs) compared with those that did not. Importantly, these data demonstrate that the indigenous sediment microbiota contributed an important ecosystem service for remediation of oil in the Gulf. However, PAHs were more recalcitrant to degradation, and their persistence could have deleterious impacts on the sediment ecosystem.

Amino termini of many yeast proteins map to downstream start codons.

Comprehensive knowledge of proteome complexity is crucial to understanding cell function. Amino termini of yeast proteins were identified through peptide mass spectrometry on glutaraldehyde-treated cell lysates as well as a parallel assessment of publicly deposited spectra. An unexpectedly large fraction of detected amino-terminal peptides (35%) mapped to translation initiation at AUG codons downstream of the annotated start codon. Many of the implicated genes have suboptimal sequence contexts for translation initiation near their annotated AUG, and their ribosome profiles show elevated tag densities consistent with translation initiation at downstream AUGs as well as their annotated AUGs. These data suggest that a significant fraction of the yeast proteome derives from initiation at downstream AUGs, increasing significantly the repertoire of encoded proteins and their potential functions and cellular localizations.

The adjacent positioning of co-regulated gene pairs is widely conserved across eukaryotes.

BACKGROUND: Coordinated cell growth and development requires that cells regulate the expression of large sets of genes in an appropriate manner, and one of the most complex and metabolically demanding pathways that cells must manage is that of ribosome biogenesis. Ribosome biosynthesis depends upon the activity of hundreds of gene products, and it is subject to extensive regulation in response to changing cellular conditions. We previously described an unusual property of the genes that are involved in ribosome biogenesis in yeast; a significant fraction of the genes exist on the chromosomes as immediately adjacent gene pairs. The incidence of gene pairing can be as high as 24% in some species, and the gene pairs are found in all of the possible tandem, divergent, and convergent orientations. RESULTS: We investigated co-regulated gene sets in S. cerevisiae beyond those related to ribosome biogenesis, and found that a number of these regulons, including those involved in DNA metabolism, heat shock, and the response to cellular stressors were also significantly enriched for adjacent gene pairs. We found that as a whole, adjacent gene pairs were more tightly co-regulated than unpaired genes, and that the specific gene pairing relationships that were most widely conserved across divergent fungal lineages were correlated with those genes that exhibited the highest levels of transcription. Finally, we investigated the gene positions of ribosome related genes across a widely divergent set of eukaryotes, and found a significant level of adjacent gene pairing well beyond yeast species. CONCLUSION: While it has long been understood that there are connections between genomic organization and transcriptional regulation, this study reveals that the strategy of organizing genes from related, co-regulated pathways into pairs of immediately adjacent genes is widespread, evolutionarily conserved, and functionally significant.