Limits...
Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover.

Staley C, Gould TJ, Wang P, Phillips J, Cotner JB, Sadowsky MJ - Front Microbiol (2014)

Bottom Line: Overall inferred functional variation was significantly different (P ≤ 0.035) between two water basins surrounded by agricultural vs. developed land cover, and abundances of bacterial orders that correlated with functional traits by metagenomic analysis were greater where abundances of the trait were inferred to be higher.PICRUSt inferences were significantly correlated (r = 0.147, P = 1.80 × 10(-30)) with metagenomic annotations.Results of this study suggest that a suite of "core functional traits" is conserved throughout the river and distributions of functional traits, rather than specific taxa, may shift in response to environmental heterogeneity.

View Article: PubMed Central - PubMed

Affiliation: BioTechnology Institute, University of Minnesota St. Paul, MN, USA.

ABSTRACT
Taxonomic characterization of environmental microbial communities via high-throughput DNA sequencing has revealed that patterns in microbial biogeography affect community structure. However, shifts in functional diversity related to variation in taxonomic composition are poorly understood. To overcome limitations due to the prohibitive cost of high-depth metagenomic sequencing, tools to infer functional diversity based on phylogenetic distributions of functional traits have been developed. In this study we characterized functional microbial diversity at 11 sites along the Mississippi River in Minnesota using both metagenomic sequencing and functional-inference-based (PICRUSt) approaches. This allowed us to determine how distance and variation in land cover throughout the river influenced the distribution of functional traits, as well as to validate PICRUSt inferences. The distribution and abundance of functional traits, by metagenomic analysis, were similar among sites, with a median standard deviation of 0.0002% among tier 3 functions in KEGG. Overall inferred functional variation was significantly different (P ≤ 0.035) between two water basins surrounded by agricultural vs. developed land cover, and abundances of bacterial orders that correlated with functional traits by metagenomic analysis were greater where abundances of the trait were inferred to be higher. PICRUSt inferences were significantly correlated (r = 0.147, P = 1.80 × 10(-30)) with metagenomic annotations. Discrepancies between metagenomic and PICRUSt taxonomic-functional relationships, however, suggested potential functional redundancy among abundant and rare taxa that impeded the ability to accurately assess unique functional traits among rare taxa at this sequencing depth. Results of this study suggest that a suite of "core functional traits" is conserved throughout the river and distributions of functional traits, rather than specific taxa, may shift in response to environmental heterogeneity.

No MeSH data available.


Percentages of tier 1 KO functions among all shotgun metagenomic (left) and PICRUSt functional predictions (right). Functional categories for organismal systems and human diseases were omitted. Gray diamonds reflect the total number of annotated functional genes or PICRUSt predictions for all functional categories inferred at tier 2 (right y-axis).
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Figure 3: Percentages of tier 1 KO functions among all shotgun metagenomic (left) and PICRUSt functional predictions (right). Functional categories for organismal systems and human diseases were omitted. Gray diamonds reflect the total number of annotated functional genes or PICRUSt predictions for all functional categories inferred at tier 2 (right y-axis).

Mentions: The greatest number of genes (>40%) that were assigned a function encoded proteins involved in “metabolism” among tier 1 KO categories in both metagenomic and PICRUSt datasets (Table 1 and Figure 3). Annotation of the shotgun metagenomic dataset revealed >30% and ~10% of sequences encoded proteins involved in “environmental information processing” and “genetic information processing,” respectively (Figure 3). However, PICRUSt functional inferences revealed approximately the same percentages of genes in both of these functional categories, with 18.01 ± 0.16% and 18.88 ± 0.37% sequence reads per site for environmental and genetic information processing, respectively (Figure 3).


Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover.

Staley C, Gould TJ, Wang P, Phillips J, Cotner JB, Sadowsky MJ - Front Microbiol (2014)

Percentages of tier 1 KO functions among all shotgun metagenomic (left) and PICRUSt functional predictions (right). Functional categories for organismal systems and human diseases were omitted. Gray diamonds reflect the total number of annotated functional genes or PICRUSt predictions for all functional categories inferred at tier 2 (right y-axis).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126211&req=5

Figure 3: Percentages of tier 1 KO functions among all shotgun metagenomic (left) and PICRUSt functional predictions (right). Functional categories for organismal systems and human diseases were omitted. Gray diamonds reflect the total number of annotated functional genes or PICRUSt predictions for all functional categories inferred at tier 2 (right y-axis).
Mentions: The greatest number of genes (>40%) that were assigned a function encoded proteins involved in “metabolism” among tier 1 KO categories in both metagenomic and PICRUSt datasets (Table 1 and Figure 3). Annotation of the shotgun metagenomic dataset revealed >30% and ~10% of sequences encoded proteins involved in “environmental information processing” and “genetic information processing,” respectively (Figure 3). However, PICRUSt functional inferences revealed approximately the same percentages of genes in both of these functional categories, with 18.01 ± 0.16% and 18.88 ± 0.37% sequence reads per site for environmental and genetic information processing, respectively (Figure 3).

Bottom Line: Overall inferred functional variation was significantly different (P ≤ 0.035) between two water basins surrounded by agricultural vs. developed land cover, and abundances of bacterial orders that correlated with functional traits by metagenomic analysis were greater where abundances of the trait were inferred to be higher.PICRUSt inferences were significantly correlated (r = 0.147, P = 1.80 × 10(-30)) with metagenomic annotations.Results of this study suggest that a suite of "core functional traits" is conserved throughout the river and distributions of functional traits, rather than specific taxa, may shift in response to environmental heterogeneity.

View Article: PubMed Central - PubMed

Affiliation: BioTechnology Institute, University of Minnesota St. Paul, MN, USA.

ABSTRACT
Taxonomic characterization of environmental microbial communities via high-throughput DNA sequencing has revealed that patterns in microbial biogeography affect community structure. However, shifts in functional diversity related to variation in taxonomic composition are poorly understood. To overcome limitations due to the prohibitive cost of high-depth metagenomic sequencing, tools to infer functional diversity based on phylogenetic distributions of functional traits have been developed. In this study we characterized functional microbial diversity at 11 sites along the Mississippi River in Minnesota using both metagenomic sequencing and functional-inference-based (PICRUSt) approaches. This allowed us to determine how distance and variation in land cover throughout the river influenced the distribution of functional traits, as well as to validate PICRUSt inferences. The distribution and abundance of functional traits, by metagenomic analysis, were similar among sites, with a median standard deviation of 0.0002% among tier 3 functions in KEGG. Overall inferred functional variation was significantly different (P ≤ 0.035) between two water basins surrounded by agricultural vs. developed land cover, and abundances of bacterial orders that correlated with functional traits by metagenomic analysis were greater where abundances of the trait were inferred to be higher. PICRUSt inferences were significantly correlated (r = 0.147, P = 1.80 × 10(-30)) with metagenomic annotations. Discrepancies between metagenomic and PICRUSt taxonomic-functional relationships, however, suggested potential functional redundancy among abundant and rare taxa that impeded the ability to accurately assess unique functional traits among rare taxa at this sequencing depth. Results of this study suggest that a suite of "core functional traits" is conserved throughout the river and distributions of functional traits, rather than specific taxa, may shift in response to environmental heterogeneity.

No MeSH data available.