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Using a Control to Better Understand Phyllosphere Microbiota

View Article: PubMed Central - PubMed

ABSTRACT

An important data gap in our understanding of the phyllosphere surrounds the origin of the many microbes described as phyllosphere communities. Most sampling in phyllosphere research has focused on the collection of microbiota without the use of a control, so the opportunity to determine which taxa are actually driven by the biology and physiology of plants as opposed to introduced by environmental forces has yet to be fully realized. To address this data gap, we used plastic plants as inanimate controls adjacent to live tomato plants (phyllosphere) in the field with the hope of distinguishing between bacterial microbiota that may be endemic to plants as opposed to introduced by environmental forces. Using 16S rRNA gene amplicons to study bacterial membership at four time points, we found that the vast majority of all species-level operational taxonomic units were shared at all time-points. Very few taxa were unique to phyllosphere samples. A higher taxonomic diversity was consistently observed in the control samples. The high level of shared taxonomy suggests that environmental forces likely play a very important role in the introduction of microbes to plant surfaces. The observation that very few taxa were unique to the plants compared to the number that were unique to controls was surprising and further suggests that a subset of environmentally introduced taxa thrive on plants. This finding has important implications for improving our approach to the description of core phytobiomes as well as potentially helping us better understand how foodborne pathogens may become associated with plant surfaces.

No MeSH data available.


Related in: MedlinePlus

Network relationships in Control and Phyllosphere.Computing Spearman’s correlation coefficients with associated P values, (P< 0.05) after correction using FDR for all pairwise relationships of genera in control and phyllosphere samples (using Cytoscape v3x for visualization www.Cytoscape.org), 21 pairwise correlations were shared (C) between control and phyllosphere samples. A total of 37 correlations were unique to phyllosphere (B) and 23 correlations were unique to controls (A). Correlations unique to the phyllosphere appear increased among members of Bacilli, and Betaproteobacteria, including genera such as Ralstonia, Staphylococcus, and Arthrobacter. For example, Ralstonia has many significant relationships in phyllosphere samples but none in controls.
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pone.0163482.g005: Network relationships in Control and Phyllosphere.Computing Spearman’s correlation coefficients with associated P values, (P< 0.05) after correction using FDR for all pairwise relationships of genera in control and phyllosphere samples (using Cytoscape v3x for visualization www.Cytoscape.org), 21 pairwise correlations were shared (C) between control and phyllosphere samples. A total of 37 correlations were unique to phyllosphere (B) and 23 correlations were unique to controls (A). Correlations unique to the phyllosphere appear increased among members of Bacilli, and Betaproteobacteria, including genera such as Ralstonia, Staphylococcus, and Arthrobacter. For example, Ralstonia has many significant relationships in phyllosphere samples but none in controls.

Mentions: The composition itself (shared incidence of each taxa in each sample) was highly similar for both sample types. The differential abundance of specific groups described above provided the first insight into microbiota that were responding to host plant physiology in contrast to environmental deposition. To further explore bacterial relationships thriving in response to biological and physiological drivers from living leaves, we performed a network analysis by computing Spearman’s correlation coefficients with corresponding P values for all pairwise distances of bacteria in phyllosphere and control samples (Fig 5). Examining significant pairwise correlations (P< 0.05 with False Discovery Rate (FDR) correction[34], a total of 23 unique correlations were identified in control samples. For phyllosphere samples, 37 unique correlations were observed and 21 significant correlations were shared between the two sample types (Fig 5).


Using a Control to Better Understand Phyllosphere Microbiota
Network relationships in Control and Phyllosphere.Computing Spearman’s correlation coefficients with associated P values, (P< 0.05) after correction using FDR for all pairwise relationships of genera in control and phyllosphere samples (using Cytoscape v3x for visualization www.Cytoscape.org), 21 pairwise correlations were shared (C) between control and phyllosphere samples. A total of 37 correlations were unique to phyllosphere (B) and 23 correlations were unique to controls (A). Correlations unique to the phyllosphere appear increased among members of Bacilli, and Betaproteobacteria, including genera such as Ralstonia, Staphylococcus, and Arthrobacter. For example, Ralstonia has many significant relationships in phyllosphere samples but none in controls.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036865&req=5

pone.0163482.g005: Network relationships in Control and Phyllosphere.Computing Spearman’s correlation coefficients with associated P values, (P< 0.05) after correction using FDR for all pairwise relationships of genera in control and phyllosphere samples (using Cytoscape v3x for visualization www.Cytoscape.org), 21 pairwise correlations were shared (C) between control and phyllosphere samples. A total of 37 correlations were unique to phyllosphere (B) and 23 correlations were unique to controls (A). Correlations unique to the phyllosphere appear increased among members of Bacilli, and Betaproteobacteria, including genera such as Ralstonia, Staphylococcus, and Arthrobacter. For example, Ralstonia has many significant relationships in phyllosphere samples but none in controls.
Mentions: The composition itself (shared incidence of each taxa in each sample) was highly similar for both sample types. The differential abundance of specific groups described above provided the first insight into microbiota that were responding to host plant physiology in contrast to environmental deposition. To further explore bacterial relationships thriving in response to biological and physiological drivers from living leaves, we performed a network analysis by computing Spearman’s correlation coefficients with corresponding P values for all pairwise distances of bacteria in phyllosphere and control samples (Fig 5). Examining significant pairwise correlations (P< 0.05 with False Discovery Rate (FDR) correction[34], a total of 23 unique correlations were identified in control samples. For phyllosphere samples, 37 unique correlations were observed and 21 significant correlations were shared between the two sample types (Fig 5).

View Article: PubMed Central - PubMed

ABSTRACT

An important data gap in our understanding of the phyllosphere surrounds the origin of the many microbes described as phyllosphere communities. Most sampling in phyllosphere research has focused on the collection of microbiota without the use of a control, so the opportunity to determine which taxa are actually driven by the biology and physiology of plants as opposed to introduced by environmental forces has yet to be fully realized. To address this data gap, we used plastic plants as inanimate controls adjacent to live tomato plants (phyllosphere) in the field with the hope of distinguishing between bacterial microbiota that may be endemic to plants as opposed to introduced by environmental forces. Using 16S rRNA gene amplicons to study bacterial membership at four time points, we found that the vast majority of all species-level operational taxonomic units were shared at all time-points. Very few taxa were unique to phyllosphere samples. A higher taxonomic diversity was consistently observed in the control samples. The high level of shared taxonomy suggests that environmental forces likely play a very important role in the introduction of microbes to plant surfaces. The observation that very few taxa were unique to the plants compared to the number that were unique to controls was surprising and further suggests that a subset of environmentally introduced taxa thrive on plants. This finding has important implications for improving our approach to the description of core phytobiomes as well as potentially helping us better understand how foodborne pathogens may become associated with plant surfaces.

No MeSH data available.


Related in: MedlinePlus