Qiita: rapid, web-enabled microbiome meta-analysis

González, Antonio G.; Navas-Molina, José A.; Kościółek, Tomasz; McDonald, Daniel; Vázquez-Baeza, Yoshiki; Ackermann, Gail; DeReus, Jeff; Janssen, Stefan; Swafford, Austin D.; Orchanian, Stephanie B.; Sanders, Jon G.; Shorenstein, Joshua; Holste, Hannes; Petrus, Semar; Robbins‐Pianka, Adam; Brislawn, Colin; Wang, Mingxun; Rideout, Jai Ram; Bolyen, Evan; Dillon, Matthew R.; Caporaso, J. Gregory; Dorrestein, Pieter C.; Knight, Rob

doi:10.1038/s41592-018-0141-9

Cited by 473 publications

(388 citation statements)

References 22 publications

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“…The final amplicon pool was processed using the Qiagen PCR cleanup kit following EMP protocols and sequenced on a MiSeq 2×250 bp run (37). Sequencing runs were processed in Qiita (38) using Qiime2 commands (39). Samples were trimmed to 150 bp and then processed through the deblur (40) pipeline which generates unique, single ASVs (amplicon sequence variants).…”

Section: Methodsmentioning

confidence: 99%

Microbial ecology of Atlantic salmon, Salmo salar, hatcheries: impacts of the built environment on fish mucosal microbiota

Minich

Jantawongsri

Johnston³

et al. 2019

Preprint

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ABSTRACTSuccessful rearing of fish in hatcheries is critical for conservation, recreational fishing, and commercial fishing through wild stock enhancements, and aquaculture production. Flow through (FT) hatcheries require more water than Recirculating-Aquaculture-Systems (RAS) which enable up to 99% of water to be recycled thus significantly reducing environmental impacts. Here, we evaluated the biological and physical microbiome interactions of the built environment of a hatchery from three Atl salmon hatcheries (RAS n=2, FT n=1). Six juvenile fish were sampled from tanks in each of the hatcheries for a total of 60 fish across 10 tanks. Water and tank side biofilm samples were collected from each of the tanks along with three salmon body sites (gill, skin, and digesta) to assess mucosal microbiota using 16S rRNA sequencing. The water and tank biofilm had more microbial richness than fish mucus while skin and digesta from RAS fish had 2× the richness of FT fish. Body sites each had unique microbial communities (P<0.001) and were influenced by the various hatchery systems (P<0.001) with RAS systems more similar. Water and especially tank biofilm richness was positively correlated with skin and digesta richness. Strikingly, the gill, skin and digesta communities were more similar to the origin tank biofilm vs. all other experimental tanks suggesting that the tank biofilm has a direct influence on fish-associated microbial communities. The results from this study provide evidence for a link between the tank microbiome and the fish microbiome with the skin microbiome as an important intermediate.IMPORTANCEAtlantic salmon, Salmo salar, is the most farmed marine fish worldwide with an annual production of 2,248 million metric tonnes in 2016. Salmon hatcheries are increasingly changing from flow through towards RAS design to accommodate more control over production along with improved environmental sustainability due to lower impacts on water consumption. To date, microbiome studies on hatcheries have focused either on the fish mucosal microbiota or the built environment microbiota, but have not combined the two to understand interactions. Our study evaluates how water and tank biofilm microbiota influences fish microbiota across three mucosal environments (gill, skin, and digesta). Results from this study highlight how the built environment is a unique source of microbes to colonize fish mucus and furthermore how this can influence the fish health. Further studies can use this knowledge to engineer built environments to modulate fish microbiota for a beneficial phenotype.

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Section: Methodsmentioning

confidence: 99%

Microbial ecology of Atlantic salmon, Salmo salar, hatcheries: impacts of the built environment on fish mucosal microbiota

Minich

Jantawongsri

Johnston³

et al. 2019

Preprint

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“…The differences in microbial compositions between floor and air dust also lead to variation in the downstream bioinformatics analyses. Microbiome Search Engine (MSE) [17] is a recently developed online analysis platform that enables rapid search of query microbiome against more than 200,000 bacterial microbiome samples from Qiita [18]. The online platform can not only find the most compositionally similar microbiome samples in the dataset, but can also report a novelty score which is a quantitatively index assessing the novelty of the query microbiome.…”

Section: Resultsmentioning

confidence: 99%

Distinct microbiome composition of floor and air dust distort richness estimation in university dormitories

Sun

Yuan

et al. 2019

Preprint

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19The number of culture-independent indoor microbiome study has increased 20 remarkably in recent years, but microbial composition among different sampling 21 strategies remains poorly characterized and their impact to downstream microbiome 22 analysis is also not clear. In this study, we reported a case study of microbial 23 composition of floor and air dust in 87 dormitory rooms of Shanxi University, China. 24 Floor and air dust were collected by vacuum cleaner and petri-dish, respectively, and 25 the bacterial composition was characterized by 16S rRNA sequencing. The 26 composition of floor and air dust differed significantly (R 2 = 0.65, p < 0.001, Adonis), 27 and Pseudomonas dominated in floor dust (75.1%) but was less common in air dust 28(1.9%). The top genera in air dust, including Ralstonia (15.6%), Pelomonas (11.3%), 29Anoxybacillus (9.3%) and Ochrobactrum (6.2%), all accounted for < 1% abundance 30 in floor dust. The dominant Pseudomonas in floor dust swamped low frequency 31 organisms, leading to significant lower number of operational taxonomic units (OTUs) 32 compared with air dust in the same sequencing depth. The different microbial 33 composition of floor and air dust can lead to differences in downstream 34 bioinformatics analyses. We searched the dormitory microbiome against ~200,000 35 samples deposited in Microbiome Search Engine (MSE), and found that the 36 compositions of floor dust samples were similar to samples from building 37 environment and human nasopharynx, whereas the compositions of the air dust 38 samples were similar to mosquito tissues.39 40 Importance 41 42Increasing number of indoor microbiome studies has been conducted in recent years, 43 but the impact of sampling strategy is far from clear. In this study, we reported that the 44 floor and air sampling can lead to drastic variation in microbial composition and 45 downstream analyses in university dormitories, including microbial diversity 46 estimation and compositional similarity search. Thus, the bioinformatics analysis 47 48 necessary to collected samples from different sampling strategies to comprehensively 49 characterize the microbial composition and exposure to human occupants in an indoor 50 environment. 51 52 53 54 55 The development and application of the cost-efficient next-generation sequencing 56 technology in the past decade greatly facilitates the culture-independent researches to 57 characterize microbiome composition in various ecological niches, including human 58 gastrointestinal and respiratory tract, skin and indoor environment [1-6]. The first step 59 of microbiome study is to collect samples, and various approaches has been applied 60 for different sample types. The gut microbiome is commonly sampled from stool, 61 which represents well the microbial composition of colonic lumen and mucosa [7], 62but other sampling strategies such as mucosal biopsy, brushing or rectal swab are also 63 applied in various studies [8, 9]. Similarly, swab, spurum, lavage, aspirates and 64 brushing have all been used...

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“…A description of all methods in addition to the R code and data that were used to generate these findings can also be found at: https://purl.stanford.edu/fx440fg9601. Raw sequencing data for this work were deposited into SRA with Qiita …”

Section: Data Availabilitymentioning

confidence: 99%

Microbial biogeography and ecology of the mouth and implications for periodontal diseases

Proctor

Shelef

González

et al. 2019

Periodontology 2000

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In humans, the composition of microbial communities differs among body sites and between habitats within a single site. Patterns of variation in the distribution of organisms across time and space are referred to as “biogeography.” The human oral cavity is a critical observatory for exploring microbial biogeography because it is spatially structured, easily accessible, and its microbiota has been linked to the promotion of both health and disease. The biogeographic features of microbial communities residing in spatially distinct, but ecologically similar, environments on the human body, including the subgingival crevice, have not yet been adequately explored. The purpose of this paper is twofold. First, we seek to provide the dental community with a primer on biogeographic theory, highlighting its relevance to the study of the human oral cavity. We summarize what is known about the biogeographic variation of dental caries and periodontitis and postulate that disease occurrence reflects spatial patterning in the composition and structure of oral microbial communities. Second, we present a number of methods that investigators can use to test specific hypotheses using biogeographic theory. To anchor our discussion, we apply each method to a case study and examine the spatial variation of the human subgingival microbiota in 2 individuals. Our case study suggests that the composition of subgingival communities may conform to an anterior‐to‐posterior gradient within the oral cavity. The gradient appears to be structured by both deterministic and nondeterministic processes, although additional work is needed to confirm these findings. A better understanding of biogeographic patterns and processes will lead to improved efficacy of dental interventions targeting the oral microbiota.

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Qiita: rapid, web-enabled microbiome meta-analysis

Cited by 473 publications

References 22 publications

Microbial ecology of Atlantic salmon, Salmo salar, hatcheries: impacts of the built environment on fish mucosal microbiota

Microbial ecology of Atlantic salmon, Salmo salar, hatcheries: impacts of the built environment on fish mucosal microbiota

Distinct microbiome composition of floor and air dust distort richness estimation in university dormitories

Microbial biogeography and ecology of the mouth and implications for periodontal diseases

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