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Automated DNA extraction platforms offer solutions to challenges of assessing microbial biofouling in oil production facilities.

Oldham AL, Drilling HS, Stamps BW, Stevenson BS, Duncan KE - AMB Express (2012)

Bottom Line: The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition.Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach.Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Department of Microbiology and Plant Biology, University of Oklahoma, 770 Van Vleet Oval GLCH #136, Norman, OK 73019, USA. athenia.L.oldham-1@ou.edu.

ABSTRACT
The analysis of microbial assemblages in industrial, marine, and medical systems can inform decisions regarding quality control or mitigation. Modern molecular approaches to detect, characterize, and quantify microorganisms provide rapid and thorough measures unbiased by the need for cultivation. The requirement of timely extraction of high quality nucleic acids for molecular analysis is faced with specific challenges when used to study the influence of microorganisms on oil production. Production facilities are often ill equipped for nucleic acid extraction techniques, making the preservation and transportation of samples off-site a priority. As a potential solution, the possibility of extracting nucleic acids on-site using automated platforms was tested. The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition. Three pipeline biofilm samples were chosen for these comparisons; two contained crude oil and corrosion products and the third transported seawater. Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach. DNA quality was evaluated for amplification by quantitative PCR (qPCR) and end-point PCR to generate 454 pyrosequencing libraries for 16S rRNA microbial community analysis. Microbial community structure, as assessed by DGGE analysis and pyrosequencing, was comparable among the three extraction methods. Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources.

No MeSH data available.


Comparison of microbial communities based on 16S rRNA gene sequencing. Relative abundance of bacterial phyla (class for Proteobacteria) from DNA extracted from a) sample A, b) sample B and c) sample C using the PowerBiofilm (P), QuickGene (Q) and Maxwell (M) extraction methods. Unclassified sequences and phyla (or class for Proteobacteria) with membership < 1% of total sequences were pooled into the classification labeled "Other". d) Non-metric multidimensional scaling (NMDS) plot based on θYC distances between libraries extracted using the PowerBiofilm (purple), QuickGene (green) and Maxwell (black) methods from Sample A (squares), B (triangles) and C (circles). AMOVA: p < 0.001.
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Figure 4: Comparison of microbial communities based on 16S rRNA gene sequencing. Relative abundance of bacterial phyla (class for Proteobacteria) from DNA extracted from a) sample A, b) sample B and c) sample C using the PowerBiofilm (P), QuickGene (Q) and Maxwell (M) extraction methods. Unclassified sequences and phyla (or class for Proteobacteria) with membership < 1% of total sequences were pooled into the classification labeled "Other". d) Non-metric multidimensional scaling (NMDS) plot based on θYC distances between libraries extracted using the PowerBiofilm (purple), QuickGene (green) and Maxwell (black) methods from Sample A (squares), B (triangles) and C (circles). AMOVA: p < 0.001.

Mentions: To identify the major bacterial taxa present in the three samples, 454 pyrosequencing libraries of the V1-V2 region of 16S rRNA gene were generated (Figure 4). The number of sequences analyzed per 16S gene library were: 11907 (P), 12076 (Q), and 1050 (M) for sample A; 9754 (P) 8186 (Q) and 12634 (M) for sample B; and 11319 (P), 14705 (Q), and 13175 (M) for sample C. Although library sizes varied considerably, especially for sample A, the proportion of sequences that classified to the same taxonomic groups (at 97% similarity) was comparable among the DNA extracts (Figure 4). Furthermore, bacterial composition was very different between the three samples. For sample A, dominant phyla were gram-positive members of the Firmicutes (48-56%), and to a lesser extent Thermotogae (22-36%), Thermodesulfobacteria (6-16%) and Synergistetes (6-9%) (Figure 4a). The dominance of gram-positive Firmicutes in all three sample A extracts demonstrated that the three platforms were all capable of lysing these harder-to-lyse microorganisms. For sample B libraries, members of the phylum Synergistetes (46-47%) and the class Deltaproteobacteria (50-52%) were equally dominant among extracts, with minor representation by Thermatogae (0.7-2.0%) (Figure 4b). DNA extracts from the seawater-carrying pipeline sample C appeared more diverse than samples A and B, with dominant taxonomic groups that included members of the Gammaproteobacteria (49-56%), Alphaproteobacteria (7-10%), and Bacteroidetes (19-33%). Less abundant representation by Epsilonproteobacteria and Fusobacteria (2-3%) and the minor group of gram-positive Actinobacteria (0.1-1.8%) was also observed (Figure 4c). An AMOVA performed on a random subsample (1050 sequences) from each library demonstrated that samples clustered together regardless of extraction method and were significantly different from one another (p < 0.001) (Figure 4d). These data support the conclusions drawn from the DGGE analysis demonstrating: 1) microbial communities of the three samples differed from one another and 2) bacterial composition for a given sample was comparable among the three extraction methods. Furthermore, whether a dominant (sample A) or minor (sample C) group, gram-positive bacteria were detected by all three platforms with only minor variation between the three extraction methods.


Automated DNA extraction platforms offer solutions to challenges of assessing microbial biofouling in oil production facilities.

Oldham AL, Drilling HS, Stamps BW, Stevenson BS, Duncan KE - AMB Express (2012)

Comparison of microbial communities based on 16S rRNA gene sequencing. Relative abundance of bacterial phyla (class for Proteobacteria) from DNA extracted from a) sample A, b) sample B and c) sample C using the PowerBiofilm (P), QuickGene (Q) and Maxwell (M) extraction methods. Unclassified sequences and phyla (or class for Proteobacteria) with membership < 1% of total sequences were pooled into the classification labeled "Other". d) Non-metric multidimensional scaling (NMDS) plot based on θYC distances between libraries extracted using the PowerBiofilm (purple), QuickGene (green) and Maxwell (black) methods from Sample A (squares), B (triangles) and C (circles). AMOVA: p < 0.001.
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Related In: Results  -  Collection

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Figure 4: Comparison of microbial communities based on 16S rRNA gene sequencing. Relative abundance of bacterial phyla (class for Proteobacteria) from DNA extracted from a) sample A, b) sample B and c) sample C using the PowerBiofilm (P), QuickGene (Q) and Maxwell (M) extraction methods. Unclassified sequences and phyla (or class for Proteobacteria) with membership < 1% of total sequences were pooled into the classification labeled "Other". d) Non-metric multidimensional scaling (NMDS) plot based on θYC distances between libraries extracted using the PowerBiofilm (purple), QuickGene (green) and Maxwell (black) methods from Sample A (squares), B (triangles) and C (circles). AMOVA: p < 0.001.
Mentions: To identify the major bacterial taxa present in the three samples, 454 pyrosequencing libraries of the V1-V2 region of 16S rRNA gene were generated (Figure 4). The number of sequences analyzed per 16S gene library were: 11907 (P), 12076 (Q), and 1050 (M) for sample A; 9754 (P) 8186 (Q) and 12634 (M) for sample B; and 11319 (P), 14705 (Q), and 13175 (M) for sample C. Although library sizes varied considerably, especially for sample A, the proportion of sequences that classified to the same taxonomic groups (at 97% similarity) was comparable among the DNA extracts (Figure 4). Furthermore, bacterial composition was very different between the three samples. For sample A, dominant phyla were gram-positive members of the Firmicutes (48-56%), and to a lesser extent Thermotogae (22-36%), Thermodesulfobacteria (6-16%) and Synergistetes (6-9%) (Figure 4a). The dominance of gram-positive Firmicutes in all three sample A extracts demonstrated that the three platforms were all capable of lysing these harder-to-lyse microorganisms. For sample B libraries, members of the phylum Synergistetes (46-47%) and the class Deltaproteobacteria (50-52%) were equally dominant among extracts, with minor representation by Thermatogae (0.7-2.0%) (Figure 4b). DNA extracts from the seawater-carrying pipeline sample C appeared more diverse than samples A and B, with dominant taxonomic groups that included members of the Gammaproteobacteria (49-56%), Alphaproteobacteria (7-10%), and Bacteroidetes (19-33%). Less abundant representation by Epsilonproteobacteria and Fusobacteria (2-3%) and the minor group of gram-positive Actinobacteria (0.1-1.8%) was also observed (Figure 4c). An AMOVA performed on a random subsample (1050 sequences) from each library demonstrated that samples clustered together regardless of extraction method and were significantly different from one another (p < 0.001) (Figure 4d). These data support the conclusions drawn from the DGGE analysis demonstrating: 1) microbial communities of the three samples differed from one another and 2) bacterial composition for a given sample was comparable among the three extraction methods. Furthermore, whether a dominant (sample A) or minor (sample C) group, gram-positive bacteria were detected by all three platforms with only minor variation between the three extraction methods.

Bottom Line: The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition.Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach.Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Department of Microbiology and Plant Biology, University of Oklahoma, 770 Van Vleet Oval GLCH #136, Norman, OK 73019, USA. athenia.L.oldham-1@ou.edu.

ABSTRACT
The analysis of microbial assemblages in industrial, marine, and medical systems can inform decisions regarding quality control or mitigation. Modern molecular approaches to detect, characterize, and quantify microorganisms provide rapid and thorough measures unbiased by the need for cultivation. The requirement of timely extraction of high quality nucleic acids for molecular analysis is faced with specific challenges when used to study the influence of microorganisms on oil production. Production facilities are often ill equipped for nucleic acid extraction techniques, making the preservation and transportation of samples off-site a priority. As a potential solution, the possibility of extracting nucleic acids on-site using automated platforms was tested. The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition. Three pipeline biofilm samples were chosen for these comparisons; two contained crude oil and corrosion products and the third transported seawater. Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach. DNA quality was evaluated for amplification by quantitative PCR (qPCR) and end-point PCR to generate 454 pyrosequencing libraries for 16S rRNA microbial community analysis. Microbial community structure, as assessed by DGGE analysis and pyrosequencing, was comparable among the three extraction methods. Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources.

No MeSH data available.