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. 2018 Dec 5:9:2985.
doi: 10.3389/fmicb.2018.02985. eCollection 2018.

Enriching Beneficial Microbial Diversity of Indoor Plants and Their Surrounding Built Environment With Biostimulants

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Enriching Beneficial Microbial Diversity of Indoor Plants and Their Surrounding Built Environment With Biostimulants

Alexander Mahnert et al. Front Microbiol. .

VSports最新版本 - Abstract

Microbial diversity is suggested as the key for plant and human health VSports手机版. However, how microbial diversity can be enriched is largely unknown but of great interest for health issues. Biostimulants offer the way to directly augment our main living areas by the healthy microbiome of indoor plants. Here, we investigated shifts of the microbiome on leaves of spider plants (Chlorophytum comosum) and its surrounding abiotic surfaces in the built environment after irrigation with a vermicompost-based biostimulant for 12 weeks. The biostimulant could not only promote plant growth, but changed the composition of the microbiome and abundance of intact microbial cells on plant leaves and even stronger on abiotic surfaces in close vicinity under constant conditions of the microclimate. Biostimulant treatments stabilized microbial diversity and resulted in an increase of Bacteroidetes and a surprising transient emerge of new phyla, e. g. , Verrucomicrobia, Acidobacteria, and Thaumarchaeota. The proportion of potentially beneficial microorganisms like Brevibacillus, Actinoallomurus, Paenibacillus, Sphaerisporangium increased relatively; microbial diversity was stabilized, and the built environment became more plant-like. Detected metabolites like indole-3-acetic acid in the biostimulant were potentially contributed by species of Pseudomonas. Overall, effects of the biostimulant on the composition of the microbiome could be predicted with an accuracy of 87%. This study shows the potential of biostimulants not only for the plant itself, but also for other living holobionts like humans in the surrounding environment. .

Keywords: 16S rRNA gene amplicon analysis; LC-MS; biostimulants; built environment; indoor plants; microbiome; qPCR; vermicompost. V体育安卓版.

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Figures

FIGURE 1
FIGURE 1
Experimental setup and workflow of this study.
FIGURE 2
FIGURE 2
Microbial abundance of samples treated with the biostimulant and untreated samples. (A) Steeped biostimulant, (B) control of DNA extraction reagents, (C) tap water control, (D) sterile water control, (E) wipe control, (F) air control, (G) soil of biostimulant treated systems, (H) soil of systems without contact to the biostimulant, (I) plant leaves of biostimulant treated systems, (J) plant leaves of systems without contact to the biostimulant, (K) desiccator surfaces of biostimulant treated systems, and (L) desiccator surfaces of systems without contact to the biostimulant. 16S rRNA gene copy numbers were extrapolated to 1l steeped biostimulant (A), DNA extraction reagents (B), irrigation waters (C,D), or air (F), 1 g soil (G,H), 1 m2 wipe (E), plant leaf area (I,J) or desiccator surface (K,L).
FIGURE 3
FIGURE 3
PCoA of the changing microbial composition over time and after application of the biostimulant. Desiccator surfaces experienced the biggest shift in their microbial composition and became more leaf-like. Samples from biostimulant treated test systems are shown as spheres and samples from test systems treated with sterile and tap water are shown as rings.
FIGURE 4
FIGURE 4
Regression scatterplot to track the rate of change in microbial diversity (Shannon H′) from a baseline through the course of the experiment for samples treated with the biostimulant and untreated samples.
FIGURE 5
FIGURE 5
Volatility plot indicating changes of microbial diversity (Shannon H′) for the main sample categories (controls, biostimulant, soil, plant leaves, desiccator surfaces and air).
FIGURE 6
FIGURE 6
Heatmap of the prediction ability of samples treated with the biostimulant (A), the main sampling categories air, controls, plant leaves, soil, and desiccator surfaces (B), and the day of sampling (C). Machine learning tools based on random Forest classification and regression models were used to train the software to predict a certain metadata category from its ASVs (amplicon sequence variants) profile.
FIGURE 7
FIGURE 7
Most abundant taxa (>10% relative abundance) on highest taxonomic levels per origin of samples (abiotic surfaces = desiccator surfaces).
FIGURE 8
FIGURE 8
Volatility plot of selected taxa on genus and species level showing distinct changes in relative abundance for different type of samples (biostimulant – Bacillus, soil – Paenibacillus, plant leaves – Stenotrophomonas, desiccator surfaces – Methylobacterium radiotolerans).
FIGURE 9
FIGURE 9
Volatility plot of selected taxa on genus and species level showing distinct changes in relative abundance for different type of samples (controls – Ralstonia) and of the transient occurrence of new phyla (Acidobacteria, Thaumarchaeota, and Verrucomicrobia).
FIGURE 10
FIGURE 10
Proportion plots of differential abundance analysis using balances in gneiss according to type of treatment (A), time of sampling (B), soil (C), plant leaves (D), and samples of the desiccator surfaces (E).
FIGURE 11
FIGURE 11
The potential sources of microbes determined with SourceTracker. Change in proportions [%] of microbes from air, controls, the biostimulant, soil and unknown sources for control, plant leaf and desiccator surfaces (abiotic surfaces) samples over time (indicated by an arrow).
FIGURE 12
FIGURE 12
(A) Prediction of potential pathogenic phenotypes for treated samples with the biostimulant and untreated samples. (B) Prediction of potential pathogenic phenotypes over time for samples treated with the biostimulant and (C) for untreated samples. Predictions are based on the Greengenes database reference set clustered at 97% similarity and precalculated files created with the help of PICRUSt, IMG, KEGG and PATRIC. The plot shows the relative proportion [%] of potential pathogenic traits inferred from the 16S rRNA gene profile.

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