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. 2011 Jul;77(13):4527-38.
doi: 10.1128/AEM.02317-10. Epub 2011 May 20.

Culture-independent analysis of bacterial fuel contamination provides insight into the level of concordance with the standard industry practice of aerobic cultivation

Judith White  1 , Jack Gilbert, Graham Hill, Edward Hill, Susan M Huse, Andrew J Weightman, Eshwar Mahenthiralingam
Affiliations

Culture-independent analysis of bacterial fuel contamination provides insight into the level of concordance with the standard industry practice of aerobic cultivation

Judith White et al. Appl Environ Microbiol. 2011 Jul.

Abstract

Bacterial diversity in contaminated fuels has not been systematically investigated using cultivation-independent methods. The fuel industry relies on phenotypic cultivation-based contaminant identification, which may lack accuracy and neglect difficult-to-culture taxa. By the use of industry practice aerobic cultivation, 16S rRNA gene sequencing, and strain genotyping, a collection of 152 unique contaminant isolates from 54 fuel samples was assembled, and a dominance of Pseudomonas (21%), Burkholderia (7%), and Bacillus (7%) was demonstrated. Denaturing gradient gel electrophoresis (DGGE) of 15 samples revealed Proteobacteria and Firmicutes to be the most abundant phyla. When 16S rRNA V6 gene pyrosequencing of four selected fuel samples (indicated by "JW") was performed, Betaproteobacteria (42.8%) and Gammaproteobacteria (30.6%) formed the largest proportion of reads; the most abundant genera were Marinobacter (15.4%; JW57), Achromobacter (41.6%; JW63), Burkholderia (80.7%; JW76), and Halomonas (66.2%; JW78), all of which were also observed by DGGE. However, the Clostridia (38.5%) and Deltaproteobacteria (11.1%) identified by pyrosequencing in sample JW57 were not observed by DGGE or aerobic culture. Genotyping revealed three instances where identical strains were found: (i) a Pseudomonas sp. strain recovered from 2 different diesel fuel tanks at a single industrial site; (ii) a Mangroveibacter sp. strain isolated from 3 biodiesel tanks at a single refinery site; and (iii) a Burkholderia vietnamiensis strain present in two unrelated automotive diesel samples. Overall, aerobic cultivation of fuel contaminants recovered isolates broadly representative of the phyla and classes present but lacked accuracy by overrepresenting members of certain groups such as Pseudomonas.

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Figures

Fig. 1.
Fig. 1.
Gram-negative bacteria isolated from contaminated fuel samples. Partial 16S rRNA gene sequences (550 bp) were aligned, and the evolutionary history was inferred using the neighbor-joining method with a bootstrap value of 1,000 and the Jukes-Cantor algorithm. Sequences with a “JW” prefix represent fuel contaminants; the most closely related sequences were included as references. To simplify the tree, multiple isolates of the same genus/family were compressed at certain nodes; however, numbers of isolates in each group are shown. The tree was rooted by the Gram-positive species Bacillus cereus (EU048539).
Fig. 2.
Fig. 2.
Gram-positive fuel-contaminating bacterial isolates. The phylogenetic tree was constructed as described for Fig. 1 and was rooted by the Gram-negative species Pseudomonas stutzeri (U22427).
Fig. 3.
Fig. 3.
Influence of growth media on the DGGE profile of cultivable versus noncultivable bacteria. DNA was directly extracted from sample JW78 (lane 1; see Table S1 in the supplemental material). The fuel was also cultivated on TSA (lane 2), BSM (lane 3), and BH (lane 4) media, and plate-wash DNA was subjected to DGGE analysis. Excised bands are labeled with arrows, and the identities of these OTUs were as follows: A, B, C, H, and I represent Halomonas sp., D, E, G, K, L, M, N, O, P, and Q represent Pseudomonas sp., and F and J produced poor sequence reads. Lane M contains a marker consisting of OTUs from Pseudomonas sp., Staphylococcus sp., Bacillus sp., and Arthrobacter sp. (47). The image represents a composite of lanes taken from a single DGGE gel.
Fig. 4.
Fig. 4.
Class distributions of 16S V6 reads recovered from four contaminated fuel samples. Only classes with >0.1% of amplicons in a single sample are identified (see the color coded key below the bar chart).
Fig. 5.
Fig. 5.
Comparison of class distributions of OTUs and isolates identified via pyrosequencing, DGGE analysis, and cultivation. The summative class distribution data from the entire study as revealed using pyrosequencing (4 samples), DGGE (15 samples), and cultivation (54 samples) are presented (see the color coded key below the bar chart).
Fig. 6.
Fig. 6.
Cluster analysis of RAPD profiles of identical strains identified in different fuel samples. A Pearson correlation similarity coefficient was used to draw the UPGMA dendrogram of fingerprints as shown on the left (the percentages of similarity are indicated on the scale bar). The fingerprint of each isolate is shown, with the isolate number (the first four characters correspond to the fuel sample number) and genus shown on the right. Identical strains are indicated by the brackets; the fingerprints of isolates JW25.1a, JW57.3a, and JW72.7a are shown as representative nonidentical controls.
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