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Amplicon-based metagenomics identified candidate organisms in soils that caused yield decline in strawberry.

Xu X, Passey T, Wei F, Saville R, Harrison RJ - Hortic Res (2015)

Bottom Line: More than 2000 fungal or bacterial operational taxonomy units (OTUs) were found in these samples.Relative abundance of individual OTUs was statistically compared for differences between samples from sites with or without yield decline.Based on further selection criteria, we focused on 34 bacterial and 17 fungal OTUs and found that yield decline resulted probably from one or more of the following four factors: (1) low abundance of Bacillus and Pseudomonas populations, which are well known for their ability of supressing pathogen development and/or promoting plant growth; (2) lack of the nematophagous fungus (Paecilomyces species); (3) a high level of two non-specific fungal root rot pathogens; and (4) wet soil conditions.

View Article: PubMed Central - PubMed

Affiliation: East Malling Research , East Malling, West Malling, Kent, ME19 6BJ, UK.

ABSTRACT
A phenomenon of yield decline due to weak plant growth in strawberry was recently observed in non-chemo-fumigated soils, which was not associated with the soil fungal pathogen Verticillium dahliae, the main target of fumigation. Amplicon-based metagenomics was used to profile soil microbiota in order to identify microbial organisms that may have caused the yield decline. A total of 36 soil samples were obtained in 2013 and 2014 from four sites for metagenomic studies; two of the four sites had a yield-decline problem, the other two did not. More than 2000 fungal or bacterial operational taxonomy units (OTUs) were found in these samples. Relative abundance of individual OTUs was statistically compared for differences between samples from sites with or without yield decline. A total of 721 individual comparisons were statistically significant - involving 366 unique bacterial and 44 unique fungal OTUs. Based on further selection criteria, we focused on 34 bacterial and 17 fungal OTUs and found that yield decline resulted probably from one or more of the following four factors: (1) low abundance of Bacillus and Pseudomonas populations, which are well known for their ability of supressing pathogen development and/or promoting plant growth; (2) lack of the nematophagous fungus (Paecilomyces species); (3) a high level of two non-specific fungal root rot pathogens; and (4) wet soil conditions. This study demonstrated the usefulness of an amplicon-based metagenomics approach to profile soil microbiota and to detect differential abundance in microbes.

No MeSH data available.


Pairwise plots of the first three principal components from a principal component analysis of the bacterial OTU data (together with % variance accounted by each component) for samples taken in May 2014 from four sites: EM13 (circle), HB12 (triangle), HB13 (square) and PV12 (diamond). For PV12 and HB12 sites where yield decline was observed for non-chloropicrin-treated plants, samples for the chloropicrin-treated plots were not included.
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fig4: Pairwise plots of the first three principal components from a principal component analysis of the bacterial OTU data (together with % variance accounted by each component) for samples taken in May 2014 from four sites: EM13 (circle), HB12 (triangle), HB13 (square) and PV12 (diamond). For PV12 and HB12 sites where yield decline was observed for non-chloropicrin-treated plants, samples for the chloropicrin-treated plots were not included.

Mentions: Table 3 shows the estimated β diversity measures for bacteria and fungi. In general, β diversity among samples was greater for fungi (i.e. lower similarity, high dissimilarity) than for bacteria. However, these diversity estimates did not show consistent patterns regarding their relationship with site and yield decline. For example, EMR site (no yield decline) showed the least similarity to the two sites with the yield-decline phenomenon (HB12 and PV12). However, HB12 was the least similar to PV12. Samples from HB12 and PV12, as a group, were not clearly separated from other samples based on principal component analysis of all bacterial OTUs (Figure 4) or fungal OTUs (Figure 5).


Amplicon-based metagenomics identified candidate organisms in soils that caused yield decline in strawberry.

Xu X, Passey T, Wei F, Saville R, Harrison RJ - Hortic Res (2015)

Pairwise plots of the first three principal components from a principal component analysis of the bacterial OTU data (together with % variance accounted by each component) for samples taken in May 2014 from four sites: EM13 (circle), HB12 (triangle), HB13 (square) and PV12 (diamond). For PV12 and HB12 sites where yield decline was observed for non-chloropicrin-treated plants, samples for the chloropicrin-treated plots were not included.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4595980&req=5

fig4: Pairwise plots of the first three principal components from a principal component analysis of the bacterial OTU data (together with % variance accounted by each component) for samples taken in May 2014 from four sites: EM13 (circle), HB12 (triangle), HB13 (square) and PV12 (diamond). For PV12 and HB12 sites where yield decline was observed for non-chloropicrin-treated plants, samples for the chloropicrin-treated plots were not included.
Mentions: Table 3 shows the estimated β diversity measures for bacteria and fungi. In general, β diversity among samples was greater for fungi (i.e. lower similarity, high dissimilarity) than for bacteria. However, these diversity estimates did not show consistent patterns regarding their relationship with site and yield decline. For example, EMR site (no yield decline) showed the least similarity to the two sites with the yield-decline phenomenon (HB12 and PV12). However, HB12 was the least similar to PV12. Samples from HB12 and PV12, as a group, were not clearly separated from other samples based on principal component analysis of all bacterial OTUs (Figure 4) or fungal OTUs (Figure 5).

Bottom Line: More than 2000 fungal or bacterial operational taxonomy units (OTUs) were found in these samples.Relative abundance of individual OTUs was statistically compared for differences between samples from sites with or without yield decline.Based on further selection criteria, we focused on 34 bacterial and 17 fungal OTUs and found that yield decline resulted probably from one or more of the following four factors: (1) low abundance of Bacillus and Pseudomonas populations, which are well known for their ability of supressing pathogen development and/or promoting plant growth; (2) lack of the nematophagous fungus (Paecilomyces species); (3) a high level of two non-specific fungal root rot pathogens; and (4) wet soil conditions.

View Article: PubMed Central - PubMed

Affiliation: East Malling Research , East Malling, West Malling, Kent, ME19 6BJ, UK.

ABSTRACT
A phenomenon of yield decline due to weak plant growth in strawberry was recently observed in non-chemo-fumigated soils, which was not associated with the soil fungal pathogen Verticillium dahliae, the main target of fumigation. Amplicon-based metagenomics was used to profile soil microbiota in order to identify microbial organisms that may have caused the yield decline. A total of 36 soil samples were obtained in 2013 and 2014 from four sites for metagenomic studies; two of the four sites had a yield-decline problem, the other two did not. More than 2000 fungal or bacterial operational taxonomy units (OTUs) were found in these samples. Relative abundance of individual OTUs was statistically compared for differences between samples from sites with or without yield decline. A total of 721 individual comparisons were statistically significant - involving 366 unique bacterial and 44 unique fungal OTUs. Based on further selection criteria, we focused on 34 bacterial and 17 fungal OTUs and found that yield decline resulted probably from one or more of the following four factors: (1) low abundance of Bacillus and Pseudomonas populations, which are well known for their ability of supressing pathogen development and/or promoting plant growth; (2) lack of the nematophagous fungus (Paecilomyces species); (3) a high level of two non-specific fungal root rot pathogens; and (4) wet soil conditions. This study demonstrated the usefulness of an amplicon-based metagenomics approach to profile soil microbiota and to detect differential abundance in microbes.

No MeSH data available.