Balance Trees Reveal Microbial Niche Differentiation

Morton, James T.; Sanders, Jon G.; Quinn, Robert A.; McDonald, Daniel; González, Antonio; Vázquez-Baeza, Yoshiki; Navas-Molina, José A.; Song, Se Jin; Metcalf, Jessica L.; Hyde, Embriette R.; Lladser, Manuel E.; Dorrestein, Pieter C.; Knight, Rob

doi:10.1128/msystems.00162-16

Cited by 266 publications

(280 citation statements)

References 27 publications

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“…After removing Cyanobacteria, there is no apparent difference in diversity or evenness. Removing cyanobacterial reads does not remove their influence on other OTUs, because of the dependence structure of compositional data (Gloor and Reid, 2016;Morton et al, 2017). However, even if removing Cyanobacteria creates a bias in the rest of the data, the same bias is introduced in both bloom and non-bloom samples alike, so the comparison should remain valid.…”

Section: Discussionmentioning

confidence: 99%

Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

et al. 2017

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Cyanobacterial blooms occur in lakes worldwide, producing toxins that pose a serious public health threat. Eutrophication caused by human activities and warmer temperatures both contribute to blooms, but it is still difficult to predict precisely when and where blooms will occur. One reason that prediction is so difficult is that blooms can be caused by different species or genera of cyanobacteria, which may interact with other bacteria and respond to a variety of environmental cues. Here we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterise the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community-including non-cyanobacteria-prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloom events are predictable. We used probabilistic assemblages of OTUs to characterise the bloom-associated community, and to classify samples into bloom or non-bloom categories, achieving up to 92% classification accuracy (86% after excluding cyanobacterial sequences). Finally, using symbolic regression, we were able to predict the start date of a bloom with 78-92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables.

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Section: Discussionmentioning

confidence: 99%

Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

et al. 2017

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“…In addition, they are statistically more appropriate for many analyses (Morton et al. ), and robust to different levels of ability to quantify co‐occurring compounds.…”

Section: Methodsmentioning

confidence: 99%

“…We elected to use absolute concentrations because we felt that they were a more direct reflection of the collected data, possibly more ecologically meaningful for interactions with mutualists and antagonists , Barlow et al 2017, and more relevant to future bioassays that test activity of specific compounds. In addition, they are statistically more appropriate for many analyses (Morton et al 2017), and robust to different levels of ability to quantify co-occurring compounds.…”

Section: Statistical Analysesmentioning

confidence: 99%

Chemistry of floral rewards: intra‐ and interspecific variability of nectar and pollen secondary metabolites across taxa

Palmer‐Young¹,

Farrell

Adler³

et al. 2018

Ecological Monographs

170

188

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Floral chemistry mediates plant interactions with pollinators, pathogens, and herbivores, with major consequences for fitness of both plants and flower visitors. The outcome of such interactions often depends on compound dose and chemical context. However, chemical diversity and intraspecific variation of nectar and pollen secondary chemistry are known for very few species, precluding general statements about their composition. We analyzed methanol extracts of flowers, nectar, and pollen from 31 cultivated and wild plant species, including multiple sites and cultivars, by liquid‐chromatography–mass‐spectrometry. To depict the chemical niche of each tissue type, we analyzed differences in nectar and pollen chemical richness, absolute and proportional concentrations, and intraspecific variability. We hypothesized that pollen would have higher concentrations and more compounds than nectar, consistent with Optimal Defense Theory and pollen's importance as a male gamete. To investigate chemical correlations across and within tissues, which could reflect physiological constraints, we quantified chemical overlap between conspecific nectar and pollen, and phenotypic integration of individual compounds within tissue types. Nectar and pollen were chemically differentiated both across and within species. Of 102 compounds identified, most occurred in only one species. Machine‐learning algorithms assigned samples to the correct species and tissue type with 98.6% accuracy. Consistent with our hypothesis, pollen had 23.8‐ to 235‐fold higher secondary chemical concentrations and 63% higher chemical richness than nectar. The most common secondary compound classes were flavonoids, alkaloids, terpenoids, and phenolics (primarily phenylpropanoids including chlorogenic acid). The most common specific compound types were quercetin and kaempferol glycosides, known to mediate biotic and abiotic effects. Pollens were distinguished from nectar by high concentrations of hydroxycinnamoyl‐spermidine conjugates, which affect plant development, abiotic stress tolerance, and herbivore resistance. Although chemistry was qualitatively consistent within species and tissue types, concentrations varied across cultivars and sites, which could influence pollination, herbivory, and disease in wild and agricultural plants. Analyses of multivariate trait space showed greater overlap across sites and cultivars in nectar than pollen chemistry; this overlap reflected greater within‐site and within‐cultivar variability of nectar. Our analyses suggest different ecological roles of nectar and pollen mediated by chemical concentration, composition, and variability.

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“…In the event that these primer sets miss a few essential taxa, databases such as probeBase or SILVA database provide the ability to design sequencing primers (Klindworth et al, 2013). Knowledge regarding the choice of primers for detection of archaea, fungi and viruses may be retrieved from previously published papers, for archaea: Fischer, Gullert, Neulinger, Streit, and Schmitz (2016) which emphasizes the fact that the results of a microbial investigation provide an overview or image of a microbial community without necessarily providing full accuracy (Morton et al, 2017). A recent study reported on the relative abundance of bacterial taxa from the phylum to the genus level and revealed high reproducibility when the same research centre repeated the analyses (i.e., high intra-centre reproducibility), while low reproducibility of results occurred when the same sample was processed in different laboratories (i.e., low intercentre reproducibility), mainly due to the use of different PCR primers (Hiergeist, Reischl, & Gessner, 2016).…”

Section: "Who Is There?": Profiling the Bacterial Taxa Of A Communitymentioning

confidence: 99%

World Workshop on Oral Medicine VII: Targeting the microbiome for oral medicine specialists—Part 1. A methodological guide

et al. 2019

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Advances in high‐throughput sequencing technologies have allowed for a rapid increase in knowledge about the human microbiome in both healthy and diseased states, which is expected to increase our understanding of multifactorial diseases. The World Workshop on Oral Medicine VII chose the microbiome as one of its topics of focus. Part 1 of this review provides updated knowledge in the field of microbiome research, describes the advantages and disadvantages of currently available sequencing technologies, and proposes a seven‐step “recipe” for designing and performing studies that is supported by contemporary evidence. Part 2 of this review in a companion paper discusses the results of high‐throughput sequencing studies published to date on the microbiota associated with oral mucosal diseases. The goal of this collective enterprise is to encourage more oral medicine specialists to become engaged in multidisciplinary collaborations to investigate the role of the microbiome in relation to oral diseases, which could potentially lead to enhanced diagnosis, risk assessment and treatment of these patients.

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Balance Trees Reveal Microbial Niche Differentiation

Cited by 266 publications

References 27 publications

Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

Chemistry of floral rewards: intra‐ and interspecific variability of nectar and pollen secondary metabolites across taxa

World Workshop on Oral Medicine VII: Targeting the microbiome for oral medicine specialists—Part 1. A methodological guide

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