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A biomolecular isolation framework for eco-systems biology.

Roume H, Muller EE, Cordes T, Renaut J, Hiller K, Wilmes P - ISME J (2012)

Bottom Line: The methodology was validated by comparison to traditional dedicated and simultaneous biomolecular isolation methods.To prove the broad applicability of the methodology, we applied it to microbial consortia of biotechnological, environmental and biomedical research interest.The developed methodological framework lays the foundation for standardized molecular eco-systematic studies on a range of different microbial communities in the future.

View Article: PubMed Central - PubMed

Affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.

ABSTRACT
Mixed microbial communities are complex, dynamic and heterogeneous. It is therefore essential that biomolecular fractions obtained for high-throughput omic analyses are representative of single samples to facilitate meaningful data integration, analysis and modeling. We have developed a new methodological framework for the reproducible isolation of high-quality genomic DNA, large and small RNA, proteins, and polar and non-polar metabolites from single unique mixed microbial community samples. The methodology is based around reproducible cryogenic sample preservation and cell lysis. Metabolites are extracted first using organic solvents, followed by the sequential isolation of nucleic acids and proteins using chromatographic spin columns. The methodology was validated by comparison to traditional dedicated and simultaneous biomolecular isolation methods. To prove the broad applicability of the methodology, we applied it to microbial consortia of biotechnological, environmental and biomedical research interest. The developed methodological framework lays the foundation for standardized molecular eco-systematic studies on a range of different microbial communities in the future.

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Metabolome heterogeneity within LAO-enriched microbial community samples. (a) Scatter plot of the two first principal components obtained using principal component analysis (PCA) of the normalized metabolomics data derived from the four biological replicates (islets; I1-I4), for each of the four distinct sampling dates (D1-D4). Each microbial community metabolome is indicated by a dot and color-coded according to sampling date. (b) Between-class PCA of the individual technical replicates for each biological replicate (islets; I1-I4). (a and b) The center of gravity for each date/islet cluster is marked by a rectangle and the colored ellipse covers 67% of the samples belonging to the cluster. (c) Hierarchical clustering of the normalized metabolomics data using the Pearson product moment correlation coefficient.
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fig2: Metabolome heterogeneity within LAO-enriched microbial community samples. (a) Scatter plot of the two first principal components obtained using principal component analysis (PCA) of the normalized metabolomics data derived from the four biological replicates (islets; I1-I4), for each of the four distinct sampling dates (D1-D4). Each microbial community metabolome is indicated by a dot and color-coded according to sampling date. (b) Between-class PCA of the individual technical replicates for each biological replicate (islets; I1-I4). (a and b) The center of gravity for each date/islet cluster is marked by a rectangle and the colored ellipse covers 67% of the samples belonging to the cluster. (c) Hierarchical clustering of the normalized metabolomics data using the Pearson product moment correlation coefficient.

Mentions: Using the normalized metabolomics data, extensive sample-to-sample variation is apparent for both biological (islets) and technical replicates. Although most samples from specific dates are distinguishable by their metabolomic profiles (Figure 2a and Supplementary Figure 2), extensive overlap between biological and technical replicates is apparent (Figure 2b), with numerous samples clustering outside of their respective replicate groups (Figure 2c). These results indicate that sample heterogeneity is an important consideration for integrated omic studies of natural microbial consortia.


A biomolecular isolation framework for eco-systems biology.

Roume H, Muller EE, Cordes T, Renaut J, Hiller K, Wilmes P - ISME J (2012)

Metabolome heterogeneity within LAO-enriched microbial community samples. (a) Scatter plot of the two first principal components obtained using principal component analysis (PCA) of the normalized metabolomics data derived from the four biological replicates (islets; I1-I4), for each of the four distinct sampling dates (D1-D4). Each microbial community metabolome is indicated by a dot and color-coded according to sampling date. (b) Between-class PCA of the individual technical replicates for each biological replicate (islets; I1-I4). (a and b) The center of gravity for each date/islet cluster is marked by a rectangle and the colored ellipse covers 67% of the samples belonging to the cluster. (c) Hierarchical clustering of the normalized metabolomics data using the Pearson product moment correlation coefficient.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3526178&req=5

fig2: Metabolome heterogeneity within LAO-enriched microbial community samples. (a) Scatter plot of the two first principal components obtained using principal component analysis (PCA) of the normalized metabolomics data derived from the four biological replicates (islets; I1-I4), for each of the four distinct sampling dates (D1-D4). Each microbial community metabolome is indicated by a dot and color-coded according to sampling date. (b) Between-class PCA of the individual technical replicates for each biological replicate (islets; I1-I4). (a and b) The center of gravity for each date/islet cluster is marked by a rectangle and the colored ellipse covers 67% of the samples belonging to the cluster. (c) Hierarchical clustering of the normalized metabolomics data using the Pearson product moment correlation coefficient.
Mentions: Using the normalized metabolomics data, extensive sample-to-sample variation is apparent for both biological (islets) and technical replicates. Although most samples from specific dates are distinguishable by their metabolomic profiles (Figure 2a and Supplementary Figure 2), extensive overlap between biological and technical replicates is apparent (Figure 2b), with numerous samples clustering outside of their respective replicate groups (Figure 2c). These results indicate that sample heterogeneity is an important consideration for integrated omic studies of natural microbial consortia.

Bottom Line: The methodology was validated by comparison to traditional dedicated and simultaneous biomolecular isolation methods.To prove the broad applicability of the methodology, we applied it to microbial consortia of biotechnological, environmental and biomedical research interest.The developed methodological framework lays the foundation for standardized molecular eco-systematic studies on a range of different microbial communities in the future.

View Article: PubMed Central - PubMed

Affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.

ABSTRACT
Mixed microbial communities are complex, dynamic and heterogeneous. It is therefore essential that biomolecular fractions obtained for high-throughput omic analyses are representative of single samples to facilitate meaningful data integration, analysis and modeling. We have developed a new methodological framework for the reproducible isolation of high-quality genomic DNA, large and small RNA, proteins, and polar and non-polar metabolites from single unique mixed microbial community samples. The methodology is based around reproducible cryogenic sample preservation and cell lysis. Metabolites are extracted first using organic solvents, followed by the sequential isolation of nucleic acids and proteins using chromatographic spin columns. The methodology was validated by comparison to traditional dedicated and simultaneous biomolecular isolation methods. To prove the broad applicability of the methodology, we applied it to microbial consortia of biotechnological, environmental and biomedical research interest. The developed methodological framework lays the foundation for standardized molecular eco-systematic studies on a range of different microbial communities in the future.

Show MeSH
Related in: MedlinePlus