New insights into microbial ecology through subtle nucleotide variation

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Microbiology 7 (2016): 1318, doi:10.3389/fmicb.2016.01318.Characterizing the community structure of naturally occurring microbes through marker gene amplicons has gained widespread acceptance for profiling microbial populations. The 16S ribosomal RNA (rRNA) gene provides a suitable target for most studies since (1) it meets the criteria for robust markers of evolution, e.g., both conserved and rapidly evolving regions that do not undergo horizontal gene transfer, (2) microbial ecologists have identified widely adopted primers and protocols for generating amplicons for sequencing, (3) analyses of both cultivars and environmental DNA have generated well-curated databases for taxonomic profiling, and (4) bioinformaticians and computational biologists have published comprehensive software tools for interpreting the data and generating publication-ready figures. Since the initial descriptions of high-throughput sequencing of 16S rRNA gene amplicons to survey microbial diversity, we have witnessed an explosion of association-based inferences of interactions between microbes and their environment.AME was supported by the University of Chicago and the Marine Biological Laboratory collaboration award

DRISEE overestimates errors in metagenomic sequencing data

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Briefings in Bioinformatics 15 (2014): 783-787, doi:10.1093/bib/bbt010.The extremely high error rates reported by Keegan et al. in ‘A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE’ (PLoS Comput Biol 2012;8:e1002541) for many next-generation sequencing datasets prompted us to re-examine their results. Our analysis reveals that the presence of conserved artificial sequences, e.g. Illumina adapters, and other naturally occurring sequence motifs accounts for most of the reported errors. We conclude that DRISEE reports inflated levels of sequencing error, particularly for Illumina data. Tools offered for evaluating large datasets need scrupulous review before they are implemented.National Institutes of Health [1UH2DK083993 to M.L.S.]; National Science Foundation [BDI- 096026 to S.M.H.]

Genome reconstructions indicate the partitioning of ecological functions inside a phytoplankton bloom in the Amundsen Sea, Antarctica

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Microbiology 6 (2015): 1090, doi:10.3389/fmicb.2015.01090.Antarctica polynyas support intense phytoplankton blooms, impacting their environment by a substantial depletion of inorganic carbon and nutrients. These blooms are dominated by the colony-forming haptophyte Phaeocystis antarctica and they are accompanied by a distinct bacterial population. Yet, the ecological role these bacteria may play in P. antarctica blooms awaits elucidation of their functional gene pool and of the geochemical activities they support. Here, we report on a metagenome (~160 million reads) analysis of the microbial community associated with a P. antarctica bloom event in the Amundsen Sea polynya (West Antarctica). Genomes of the most abundant Bacteroidetes and Proteobacteria populations have been reconstructed and a network analysis indicates a strong functional partitioning of these bacterial taxa. Three of them (SAR92, and members of the Oceanospirillaceae and Cryomorphaceae) are found in close association with P. antarctica colonies. Distinct features of their carbohydrate, nitrogen, sulfur and iron metabolisms may serve to support mutualistic relationships with P. antarctica. The SAR92 genome indicates a specialization in the degradation of fatty acids and dimethylsulfoniopropionate (compounds released by P. antarctica) into dimethyl sulfide, an aerosol precursor. The Oceanospirillaceae genome carries genes that may enhance algal physiology (cobalamin synthesis). Finally, the Cryomorphaceae genome is enriched in genes that function in cell or colony invasion. A novel pico-eukaryote, Micromonas related genome (19.6 Mb, ~94% completion) was also recovered. It contains the gene for an anti-freeze protein, which is lacking in Micromonas at lower latitudes. These draft genomes are representative for abundant microbial taxa across the Southern Ocean surface.This work was performed with financial support from NSF Antarctic Sciences awards ANT-1142095 to AP

Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in mBio 5 (2014): e00682-13, doi:10.1128/mBio.00682-13.Bacteria living on the aerial parts of plants (the phyllosphere) are globally abundant and ecologically significant communities and can have significant effects on their plant hosts. Despite their importance, little is known about the ecological processes that drive phyllosphere dynamics. Here, we describe the development of phyllosphere bacterial communities over time on the model plant Arabidopsis thaliana in a controlled greenhouse environment. We used a large number of replicate plants to identify repeatable dynamics in phyllosphere community assembly and reconstructed assembly history by measuring the composition of the airborne community immigrating to plant leaves. We used more than 260,000 sequences from the v5v6 hypervariable region of the 16S rRNA gene to characterize bacterial community structure on 32 plant and 21 air samples over 73 days. We observed strong, reproducible successional dynamics: phyllosphere communities initially mirrored airborne communities and subsequently converged to a distinct community composition. While the presence or absence of particular taxa in the phyllosphere was conserved across replicates, suggesting strong selection for community composition, the relative abundance of these taxa was highly variable and related to the spatial association of individual plants. Our results suggest that stochastic events in early colonization, coupled with dispersal limitation, generated alternate trajectories of bacterial community assembly within the context of deterministic selection for community membership.Funding was provided by the J. Unger Vetleson Foundation to S.L.S

Gut microbes contribute to variation in solid organ transplant outcomes in mice

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Microbiome 6 (2018): 96, doi:10.1186/s40168-018-0474-8.Solid organ transplant recipients show heterogeneity in the occurrence and timing of acute rejection episodes. Understanding the factors responsible for such variability in patient outcomes may lead to improved diagnostic and therapeutic approaches. Rejection kinetics of transplanted organs mainly depends on the extent of genetic disparities between donor and recipient, but a role for environmental factors is emerging. We have recently shown that major alterations of the microbiota following broad-spectrum antibiotics, or use of germ-free animals, promoted longer skin graft survival in mice. Here, we tested whether spontaneous differences in microbial colonization between genetically similar individuals can contribute to variability in graft rejection kinetics. We compared rejection kinetics of minor mismatched skin grafts in C57BL/6 mice from Jackson Laboratory (Jax) and Taconic Farms (Tac), genetically similar animals colonized by different commensal microbes. Female Tac mice rejected skin grafts from vendor-matched males more quickly than Jax mice. We observed prolonged graft survival in Tac mice when they were exposed to Jax mice microbiome through co-housing or fecal microbiota transplantation (FMT) by gastric gavage. In contrast, exposure to Tac mice did not change graft rejection kinetics in Jax mice, suggesting a dominant suppressive effect of Jax microbiota. High-throughput sequencing of 16S rRNA gene amplicons from Jax and Tac mice fecal samples confirmed a convergence of microbiota composition after cohousing or fecal transfer. Our analysis of amplicon data associated members of a single bacterial genus, Alistipes, with prolonged graft survival. Consistent with this finding, members of the genus Alistipes were absent in a separate Tac cohort, in which fecal transfer from Jax mice failed to prolong graft survival. These results demonstrate that differences in resident microbiome in healthy individuals may translate into distinct kinetics of graft rejection, and contribute to interpersonal variability in graft outcomes. The association between Alistipes and prolonged skin graft survival in mice suggests that members of this genus might affect host physiology, including at sites distal to the gastrointestinal tract. Overall, these findings allude to a potential therapeutic role for specific gut microbes to promote graft survival through the administration of probiotics, or FMT.This work was supported by NIH/NIAID R01 AI115716 to MLA

Minimum entropy decomposition : unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in ISME Journal 9 (2015): 968–979, doi:10.1038/ismej.2014.195.Molecular microbial ecology investigations often employ large marker gene datasets, for example, ribosomal RNAs, to represent the occurrence of single-cell genomes in microbial communities. Massively parallel DNA sequencing technologies enable extensive surveys of marker gene libraries that sometimes include nearly identical sequences. Computational approaches that rely on pairwise sequence alignments for similarity assessment and de novo clustering with de facto similarity thresholds to partition high-throughput sequencing datasets constrain fine-scale resolution descriptions of microbial communities. Minimum Entropy Decomposition (MED) provides a computationally efficient means to partition marker gene datasets into ‘MED nodes’, which represent homogeneous operational taxonomic units. By employing Shannon entropy, MED uses only the information-rich nucleotide positions across reads and iteratively partitions large datasets while omitting stochastic variation. When applied to analyses of microbiomes from two deep-sea cryptic sponges Hexadella dedritifera and Hexadella cf. dedritifera, MED resolved a key Gammaproteobacteria cluster into multiple MED nodes that are specific to different sponges, and revealed that these closely related sympatric sponge species maintain distinct microbial communities. MED analysis of a previously published human oral microbiome dataset also revealed that taxa separated by less than 1% sequence variation distributed to distinct niches in the oral cavity. The information theory-guided decomposition process behind the MED algorithm enables sensitive discrimination of closely related organisms in marker gene amplicon datasets without relying on extensive computational heuristics and user supervision.AME was supported by a G. Unger Vetlesen Foundation grant to the Marine Biological Laboratory and the Alfred P Sloan Foundation

proovframe: frameshift-correction for long-read (meta)genomics

Long-read sequencing technologies hold big promises for the genomic analysis of complex samples such as microbial communities. Yet, despite improving accuracy, basic gene prediction on long-read data is still often impaired by frameshifts resulting from small indels. Consensus polishing using either complementary short reads or to a lesser extent the long reads themselves can mitigate this effect but requires universally high sequencing depth, which is difficult to achieve in complex samples where the majority of community members are rare. Here we present proovframe, a software implementing an alternative approach to overcome frameshift errors in long-read assemblies and raw long reads. We utilize protein-to-nucleotide alignments against reference databases to pinpoint indels in contigs or reads and correct them by deleting or inserting 1-2 bases, thereby conservatively restoring reading-frame fidelity in aligned regions. Using simulated and real-world benchmark data we show that proovframe performs comparably to short-read-based polishing on assembled data, works well with remote protein homologs, and can even be applied to raw reads directly. Together, our results demonstrate that protein-guided frameshift correction significantly improves the analyzability of long-read data both in combination with and as an alternative to common polishing strategies. Proovframe is available from https://github.com/thackl/proovframe

SeaBase : a multispecies transcriptomic resource and platform for gene network inference

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Oxford University Press for personal use, not for redistribution. The definitive version was published in Integrative and Comparative Biology 54 (2014): 250-263, doi: 10.1093/icb/icu065.Marine and aquatic animals are extraordinarily useful as models for identifying mechanisms of development and evolution, regeneration, resistance to cancer, longevity and symbiosis, among many other areas of research. This is due to the great diversity of these organisms and their wide-ranging capabilities. Genomics tools are essential for taking advantage of these “free lessons” of nature. However, genomics and transcriptomics are challenging in emerging model systems. Here, we present SeaBase, a tool for helping to meet these needs. Specifically, SeaBase provides a platform for sharing and searching transcriptome data. More importantly, SeaBase will support a growing number of tools for inferring gene network mechanisms. The first dataset available on SeaBase is a developmental transcriptome profile of the sea anemone Nematostella vectensis (Anthozoa, Cnidaria). Additional datasets are currently being prepared and we are aiming to expand SeaBase to include user-supplied data for any number of marine and aquatic organisms, thereby supporting many potentially new models for gene network studies.2015-06-0