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

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18

Advanced Computational Tools for Analyzing Microbial Communities for Energy Production Environments

The research objective of this thesis is to develop and apply new computational tools and techniques in order to potentiate and expand the utility of microbiome research efforts, particularly in the application area of biofuels development. Tools for microbiome analysis, such as the Quantitative Insights Into Microbial Ecology (QIIME) package, facilitate the processing of raw data produced by next- generation sequencing instruments and also provide implementations of algorithms that aid in drawing biological conclusions from the data. However, the scope of typical modern analyses has increased significantly in the past five years, making integration of the heterogeneous multi-omic datasets challenging. Additionally, the size of datasets has grown beyond what was historically tractable, especially for meta-analyses that combine multiple studies. Chapter one presents relevant background information about microbial ecology and a summary of the state of existing computational tools and techniques used in its exploration. Chapter two focuses on an in-depth analysis of the microbial community variations associated with algal bioreactors. Chapter three describes a novel computational method that permits broader and deeper microbiome analyses and its application to samples taken from the site of the Deepwater Horizon oil spill. Chapter three also highlights the power of meta- analysis and the need for tools that provide researchers an easier way to analyze their data in the context of comparable publicly available data. Chapter four discusses a variety of data storage models and their suitability for microbiome research, and chapter five describes Qiita, a new platform that takes lessons from chapters three and four and provides an integrated platform for accessing and analyzing data in meta-analyses. Lastly, the appendix presents detailed information on analyzing microbial communities with QIIME

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

Abstract 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.</p

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

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

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