PLFA analysis of the microbial community structure at the points of the temporary localization of radioactive waste

Aim. PTLRW are the trenches and bursts for the localization of radioactive waste that were created during the first priority measures for elimination of the Chornobyl accident. The aim of the presented work was to characterize the microbial community structure on PTLRW. Methods. To describe the influence of environmental factors on the soil microflora, the agrochemical parameters of soil (pH, carbon, nitrogen, mobile potassium and phosphorus) were evaluated. Dose loading was calculated using the ERICA tool software package. The total lipid fraction was extracted with a modified Bligh-Dyer method. Results. The pH of the soil ranged from 3.0 to 3.9. The carbon content ranged from 0.95% to 2.11%. The exception was Red Forest from the trench/outside the trench where the carbon content was 2.52 and 1.98% and with a pH 4.5. Nitrogen content ranged from 33.6 mg / kg to 74.2 mg / kg. The PLFA content ranged from 15 μg / g to 18.9 μg / g, except Novoshepelychi and Zalissia (33.3 μg / g and 23 μg / g). Conclusions. In terms of the structural composition of the microorganisms, the PTLRW points were more homogeneous compared to the contaminated radionuclide ecosystems. In natural ecosystems, gram-positive bacteria were the main dominant group, unlike PTLRW where there were several groups. Keywords: PTLRW, microbial community structure, PLFA, biomarkers, ERICA tool.

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PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure

mSphere ◽

10.1128/msphere.00530-20 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Peter Rubbens ◽

Ruben Props ◽

Frederiek-Maarten Kerckhof ◽

Nico Boon ◽

Willem Waegeman

Keyword(s):

Flow Cytometry ◽

Community Structure ◽

Microbial Community ◽

Microbial Community Structure ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Community Diversity ◽

Data Sets ◽

Natural Ecosystems ◽

Flow Cytometry Data

ABSTRACT Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics. IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

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