Increasing global temperatures are having a profound impact in the Arctic, including the dramatic loss of multiyear sea ice in 2007 that has continued to the present. The majority of life in the ...Arctic is microbial and the consequences of climate-mediated changes on microbial marine food webs, which are responsible for biogeochemical cycling and support higher trophic levels, are unknown. We examined microbial communities over time by using high-throughput sequencing of microbial DNA collected between 2003 and 2010 from the subsurface chlorophyll maximum (SCM) layer of the Beaufort Sea (Canadian Arctic). We found that overall this layer has freshened and concentrations of nitrate, the limiting nutrient for photosynthetic production in Arctic seas, have decreased. We compared microbial communities from before and after the record September 2007 sea ice minimum and detected significant differences in communities from all three domains of life. In particular, there were significant changes in species composition of Eukarya, with ciliates becoming more common and heterotrophic marine stramenopiles (MASTs) accounting for a smaller proportion of sequences retrieved after 2007. Within the Archaea, Marine Group I Thaumarchaeota, which earlier represented up to 60% of the Archaea sequences in this layer, have declined to <10%. Bacterial communities overall were less diverse after 2007, with a significant decrease of the Bacteroidetes. These significant shifts suggest that the microbial food webs are sensitive to physical oceanographic changes such as those occurring in the Canadian Arctic over the past decade.
Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale ...studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.
Although the multiplication of bacteriophages (phages) has a substantial impact on the biosphere, comparatively little is known about how the external environment affects phage production. Here we ...report that sub-lethal concentrations of certain antibiotics can substantially stimulate the host bacterial cell's production of some virulent phage. For example, a low dosage of cefotaxime, a cephalosporin, increased an uropathogenic Escherichia coli strain's production of the phage PhiMFP by more than 7-fold. We name this phenomenon Phage-Antibiotic Synergy (PAS). A related effect was observed in diverse host-phage systems, including the T4-like phages, with beta-lactam and quinolone antibiotics, as well as mitomycin C. A common characteristic of these antibiotics is that they inhibit bacterial cell division and trigger the SOS system. We therefore examined the PAS effect within the context of the bacterial SOS and filamentation responses. We found that the PAS effect appears SOS-independent and is primarily a consequence of cellular filamentation; it is mimicked by cells that constitutively filament. The fact that completely unrelated phages manifest this phenomenon suggests that it confers an important and general advantage to the phages.
Among dsDNA tailed bacteriophages (Caudovirales), members of the Myoviridae family have the most sophisticated virion design that includes a complex contractile tail structure. The Myoviridae ...generally have larger genomes than the other phage families. Relatively few "dwarf" myoviruses, those with a genome size of less than 50 kb such as those of the Mu group, have been analyzed in extenso. Here we report on the genome sequencing and morphological characterization of a new group of such phages that infect a diverse range of Proteobacteria, namely Aeromonas salmonicida phage 56, Vibrio cholerae phages 138 and CP-T1, Bdellovibrio phage φ1422, and Pectobacterium carotovorum phage ZF40. This group of dwarf myoviruses shares an identical virion morphology, characterized by usually short contractile tails, and have genome sizes of approximately 45 kb. Although their genome sequences are variable in their lysogeny, replication, and host adaption modules, presumably reflecting differing lifestyles and hosts, their structural and morphogenesis modules have been evolutionarily constrained by their virion morphology. Comparative genomic analysis reveals that these phages, along with related prophage genomes, form a new coherent group within the Myoviridae. The results presented in this communication support the hypothesis that the diversity of phages may be more structured than generally believed and that the innumerable phages in the biosphere all belong to discrete lineages or families.
High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic ...units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel "denoising" pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray-Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.
Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have ...mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.
We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.
We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.
Sequence-based approaches to study microbiomes, such as 16S rRNA gene sequencing and metagenomics, are uncovering associations between microbial taxa and a myriad of factors. A drawback of these ...approaches is that the necessary sequencing library preparation and bioinformatic analyses are complicated and continuously changing, which can be a barrier for researchers new to the field. We present three essential components to conducting a microbiome experiment from start to finish: first, a simplified and step-by-step custom gene sequencing protocol that requires limited lab equipment, is cost-effective, and has been thoroughly tested and utilized on various sample types; second, a series of scripts to integrate various commonly used bioinformatic tools that is available as a standalone installation or as a single downloadable virtual image; and third, a set of bioinformatic workflows and tutorials to provide step-by-step guidance and education for those new to the microbiome field. This resource will provide the foundations for those newly entering the microbiome field and will provide much-needed guidance and best practices to ensure that quality microbiome research is undertaken. All protocols, scripts, workflows, tutorials, and virtual images are freely available through the Microbiome Helper website (https://github.com/mlangill/microbiome_helper/wiki).
As the microbiome field continues to grow, a multitude of researchers are learning how to conduct proper microbiome experiments. We outline here a streamlined and custom approach to processing samples from detailed sequencing library construction to step-by-step bioinformatic standard operating procedures. This allows for rapid and reliable microbiome analysis, allowing researchers to focus more on their experiment design and results. Our sequencing protocols, bioinformatic tutorials, and bundled software are freely available through Microbiome Helper. As the microbiome research field continues to evolve, Microbiome Helper will be updated with new protocols, scripts, and training materials.
Little is known about the microbiota shift induced by exacerbation in chronic obstructive pulmonary disease (COPD) patients. The sputa microbiota of COPD patients was evaluated when clinically stable ...and during acute exacerbations of the disease. Sputa microbiota was analyzed using 16S ribosomal RNA gene pyrosequencing and quantitative polymerase chain reaction-based pathogen detection. Nine COPD patients were enrolled. Pyrosequencing of 16S rRNA genes identified 2,267 unique bacterial operational taxonomic units. Principal microbiota shifts during exacerbation were in either Proteobacteria, Firmicutes or Bacteroidetes. Streptococcus and Moraxella levels were detected during exacerbation in severe (Global Initiative for Chronic Obstructive Lung Disease 3) COPD patients. Most of the clinically-important genera found in the sputum with the pyrosequencing of 16S rRNA gene correlated with specific quantitative polymerase chain reactions for bacteria while respiratory viruses were nearly absent. Sputum microbiotas of exacerbated COPD patients are complex. This pilot study shows a clear shift in the microbiota of patients during exacerbation. The nature of this shift varies from patient to patient in such a way that the treatment should be patient-specific. Further studies are needed to establish the impact of microbial exacerbations on the pulmonary microbiota.
Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the ...importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.
In aquatic environments, fungal communities remain little studied despite their taxonomic and functional diversity. To extend the ecological coverage of this group, we conducted an in-depth analysis ...of fungal sequences within our collection of 3.6 million V4 18S rRNA pyrosequences originating from 319 individual marine (including sea-ice) and freshwater samples from libraries generated within diverse projects studying Arctic and temperate biomes in the past decade. Among the ~1.7 million post-filtered reads of highest taxonomic and phylogenetic quality, 23,263 fungal sequences were identified. The overall mean proportion was 1.35%, but with large variability; for example, from 0.01 to 59% of total sequences for Arctic seawater samples. Almost all sample types were dominated by Chytridiomycota-like sequences, followed by moderate-to-minor contributions of Ascomycota, Cryptomycota and Basidiomycota. Species and/or strain richness was high, with many novel sequences and high niche separation. The affinity of the most common reads to phytoplankton parasites suggests that aquatic fungi deserve renewed attention for their role in algal succession and carbon cycling.
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