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When biosurfactants compete with pollutants: rhamnolipid production and moisture-dependent performance in hydrocarbon-degrading environments

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DataCite Commons2026-03-09 更新2026-05-04 收录
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https://repod.icm.edu.pl/citation?persistentId=doi:10.18150/NS5OMA
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Rhamnolipids are widely studied for bioremediation of hydrocarbon-contaminated soils, yet previous studies report inconsistent effects. To investigate this, we analyzed rhamnolipid production in 20 Pseudomonas aeruginosa strains. All strains produced significantly more rhamnolipids on hydrophilic carbon sources than on hydrophobic diesel oil, and synthesis increased under nitrogen limitation. These findings challenge the assumption that rhamnolipids are mainly produced to facilitate hydrocarbon uptake.In sand microcosms, exogenously added rhamnolipids accelerated hydrocarbon degradation only under low moisture conditions (permanent wilting point, PWP). When water was sufficient, their effectiveness declined because soil microbiota used them as an easily available carbon source. Microbial community analysis confirmed that the strongest rhamnolipid-induced shifts occurred under PWP conditions. Overall, the results highlight the importance of water availability in determining the role of rhamnolipids in hydrocarbon degradation and suggest their particular relevance in dry soils.This dataset contains experimental data used to generate the figures presented in the associated publication investigating the role of rhamnolipids in hydrocarbon biodegradation under different soil moisture conditions. The dataset includes raw measurements, processed datasets used for statistical analyses, and outputs from kinetic modeling and multivariate analysis.Files related to Figure 1 contain measurements surface tension caused by rhamnolipid production by Pseudomonas aeruginosa strains cultivated under different growth media conditions. Figure 2 - dataset includes total rhamnolipid concentrations and the proportion of mono-rhamnolipids to di-rhamnolipids for cultures grown with glucose or hydrocarbons as the carbon source and with either low or high nitrogen availability.Files corresponding to Fig. 4 include time-series measurements of rhamnolipid concentrations obtained from soil microcosm experiments conducted under three soil moisture regimes: permanent wilting point (PWP), field capacity (FC), and saturation (SAT). These data were used to fit bi-exponential kinetic models describing rhamnolipid dissipation and to estimate degradation rate constants (k₁, k₂) and half-life values.Files associated with Fig. 6 contain concentration data for different hydrocarbon groups (straight-chain alkanes, branched alkanes, monocyclic aromatic compounds, polycyclic aromatic compounds, and cycloalkanes) measured during incubation experiments in soil microcosms with and without rhamnolipid addition under the three soil moisture conditions. These datasets were used to estimate first-order biodegradation kinetics, including rate constants (k) and half-lives (t1/2).Files related to Fig. s5 include data regarding microbial community structure (relative abundance) after 60 days of incubation in soils maintained at different moisture regimes (PWP, FC, SAT) with and without rhamnolipid addition. These data were used for principal component analysis (PCA) to evaluate shifts in microbial community composition.For this raw data statistical analyses were performed using standard statistical procedures, including one-way ANOVA to evaluate differences between experimental variants. Kinetic parameters describing rhamnolipid dissipation and hydrocarbon biodegradation were estimated by fitting bi-exponential or first-order kinetic models to the experimental data. Principal component analysis (PCA) was performed on scaled T-RFLP profiles to visualize differences in microbial community structure between treatments.Raw experimental measurements were used to generate processed datasets for statistical analyses and kinetic modeling. These processed datasets were subsequently used to create the figures presented in the publication. The file organization reflects this workflow, linking experimental measurements to statistical outputs and graphical representations.
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RepOD
创建时间:
2026-02-19
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