Dataset Information

Quantification of burkholderia coxL genes in Hawaiian volcanic deposits.

ABSTRACT: Isolation of multiple carbon monoxide (CO)-oxidizing Burkholderia strains and detection by culture-independent approaches suggest that Burkholderia may be an important component of CO-oxidizing communities in Hawaiian volcanic deposits. The absolute and relative abundance of the bacteria in these communities remains unknown, however. In this study, a quantitative PCR (Q-PCR) approach has been developed to enumerate Burkholderia coxL genes (large subunit of carbon monoxide dehydrogenase). This represents the first attempt to enumerate coxL genes from CO oxidizers in environmental samples. coxL copy numbers have been determined for samples from three sites representing a vegetation gradient on a 1959 volcanic deposit that included unvegetated cinders (bare), edges of vegetated sites (edge), and sites within tree stands (canopy). Q-PCR has also been used to estimate copy numbers of Betaproteobacteria 16S rRNA gene copy numbers and total Bacteria 16S rRNA. coxL genes could not be detected in the bare site (detection limit, > or = 4.7 x 10(3) copies per reaction) but average 1.0 x 10(8) + or - 2.4 x 10(7) and 8.6 x 10(8) + or - 7.6 x 10(7) copies g(-1) (dry weight) in edge and canopy sites, respectively, which differ statistically (P = 0.0007). Average Burkholderia coxL gene copy numbers, expressed as a percentage of total Bacteria 16S rRNA gene copy numbers, are 6.2 and 0.7% for the edge and canopy sites, respectively. Although the percentage of Burkholderia coxL is lower in the canopy site, significantly greater gene copy numbers demonstrate that absolute abundance of coxL increases in vegetated sites and contributes to the expansion of CO oxidizer communities during biological succession on volcanic deposits.

SUBMITTER: Weber CF

PROVIDER: S-EPMC2849267 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Quantification of burkholderia coxL genes in Hawaiian volcanic deposits.

Weber C F CF King G M GM

Applied and environmental microbiology 20100205 7

Isolation of multiple carbon monoxide (CO)-oxidizing Burkholderia strains and detection by culture-independent approaches suggest that Burkholderia may be an important component of CO-oxidizing communities in Hawaiian volcanic deposits. The absolute and relative abundance of the bacteria in these communities remains unknown, however. In this study, a quantitative PCR (Q-PCR) approach has been developed to enumerate Burkholderia coxL genes (large subunit of carbon monoxide dehydrogenase). This re ...[more]

PMID: 20139318

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Project description:BackgroundBasalt is the most common igneous rock on the Earth's surface covering. Basalt-associated microorganisms drive the cycling and sequestration of different elements such as nitrogen, carbon and other nutrients, which facilitate subsequent pioneer and plant development, impacting long-term regulation of the Earth's temperature and biosphere. The initial processes of colonization and subsequent rock weathering by microbial communities are still poorly understood and relatively few data are available on the diversity and richness of the communities inhabiting successive and chronological lava flows. In this study, the bacterial communities present on lava deposits from different eruptions of the 1975-84 Krafla Fires (32-, 35- and 39-year old, respectively) at the Krafla, Iceland, were determined.ResultsThree sites were sampled for each deposit (32-, 35- and 39-year old), two proximal sites (at 10 m distance) and one more distant site (at 100 m from the two other sites). The determined chemical composition and metal concentrations were similar for the three basalt deposits. No significant differences were observed in the total number of cells in each flow. 16S rRNA gene amplicon sequencing showed that the most abundant classified phylum across the 3 flows was Proteobacteria, although predominance of Acidobacteria, Actinobacteria and Firmicutes was observed for some sampling sites. In addition, a considerable fraction of the operational taxonomic units remained unclassified. Alpha diversity (Shannon, inverse Simpson and Chao), HOMOVA and AMOVA only showed a significant difference for Shannon between the 32- and 39-year old flow (p < 0.05). Nonmetric multidimensional scaling (NMDS) analysis showed that age significantly (p = 0.026) influenced the leftward movement along NMDS axis 1.ConclusionsAlthough NMDS indicated that the (relatively small) age difference of the deposits appeared to impact the bacterial community, this analysis was not consistent with AMOVA and HOMOVA, indicating no significant difference in community structure. The combined results drive us to conclude that the (relatively small) age differences of the deposits do not appear to be the main factor shaping the microbial communities. Probably other factors such as spatial heterogeneity, associated carbon content, exogenous rain precipitations and wind also affect the diversity and dynamics.

Project description:Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF. Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then applied] to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). TWS machine learning closely correlates with the gold-standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.

Dataset Information

Quantification of burkholderia coxL genes in Hawaiian volcanic deposits.

Publications

Quantification of burkholderia coxL genes in Hawaiian volcanic deposits.

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