Biology subjects

Lichtenberg, M.

Publications and source records attributed to Lichtenberg, M..

5 recordsLinked to original sources

bioRxiv2025

Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms

Standard antibiotic susceptibility tests (ASTs) often fail to accurately predict treatment outcomes because they do not account for biofilm-specific mechanisms of reduced susceptibility. In the present study, we explored alternative approaches to predict tobramycin susceptibility of Pseudomonas aeruginosa biofilms that were experimentally evolved in physiologically relevant conditions. To this end, we used four analytical methods - whole-genome sequencing (WGS), matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), isothermal microcalorimetry (IMC) and multi-excitation Raman spectroscopy (MX-Raman). Machine learning models were trained on data outputs from these methods to predict tobramycin susceptibility of our evolved strains and subsequently validated with a collection of clinical isolates. For minimal inhibitory concentration (MIC) predictions of the evolved strains, the highest accuracy{+/-}1 was achieved with MALDI-TOF MS (97.83 %), while for biofilm prevention concentration (BPC) predictions, Raman spectroscopy performed best with an accuracy{+/-}1 of 80.43 %. Overall, all analytical methods demonstrated comparable predictive performance, showing their potential for improving biofilm AST.

microbiology↗

bioRxiv2024

A method for separating bacterial cultures into single cells and defined size fractions of biofilm aggregates

In vitro microbiological experiments that aim to describe differences between planktonic and biofilm aggregate populations are challenging since liquid batch cultures contain a mix of both. Here, we present a simple method for fractioning a bacterial liquid batch culture into aggregates and single cells. Stackable cell strainers with mesh sizes of 30 m and 10 m were used to filtrate 6 day old batch cultures of Pseudomonas aeruginosa to produce size fractions of 0-10m and >30m. By confocal laser scanning microscopy measurements, we show that 95.5% of the total biomass was <10 m in the "0-10m size fraction" and that 92.5% of the total biomass was >30m in the ">30m size fraction". Furthermore, the adjustment of bacterial concentration using CFU/ml was validated by quantifying the total DNA of viable bacteria in the two size fractions after DNase treatment to deplete eDNA and DNA from dead bacteria. Surprisingly, this showed that adjusting the bacterial concentration using CFU/ml was a valid method with no significant differences in total DNA from viable bacteria.

microbiology↗

bioRxiv2020

Metabolic flux fingerprinting differentiates planktonic and biofilm states of Pseudomonas aeruginosa and Staphylococcus aureus

The challenges of defining the biofilm phenotype has been clear for decades. Many biomarkers for biofilm are known, but methods for identifying these are often invasive and/or complicated. These methods often rely on disrupting the biofilm matrix or examining virulence factors and compounds, which may only be expressed under certain conditions. We used microcalorimetric measurements of metabolic energy release to investigate whether unchallenged, planktonic Pseudomonas aeruginosa displayed differences in metabolism compared to surface-bound and non-attached biofilms. The pattern of energy release observed in the recorded microcalorimetric thermograms clearly depended on growth state, though the total energy expenditure was not different between growth states. To characterize these differences, we developed a classification pipeline utilizing machine learning algorithms to classify growth state, based on the observed patterns of energy release. With this approach, we could with high accuracy detect the growth form of blinded samples. To challenge the algorithm, we attempted to limit the amount of training data. By training the algorithm with only a single data point from each growth form, we obtained a mean accuracy of 90.5% using two principal components. Further validation of the classification pipeline showed that the approach was not limited to P. aeruginosa but could also be used for detection of gram-positive Staphylococcus aureus biofilm. We propose that microcalorimetric measurements, in combination with this new quantitative framework, can be used as a non-invasive biomarker to detect the presence of biofilm. These results could have a significant potential in clinical settings where the detection of biofilms in infections often means a different outcome and treatment regime for the patient.

microbiology↗

bioRxiv2017

Radiative energy budgets of phototrophic surface-associated microbial communities and their photosynthetic efficiency under diffuse and collimated light

We investigated the radiative energy budgets of a heterogeneous photosynthetic coral reef sediment and a compact uniform cyanobacterial biofilm on top of coastal sediment. By combining electrochemical, thermocouple and fiber-optic microsensor measurements of O2, temperature and light, we could calculate the proportion of the absorbed light energy that was either dissipated as heat or conserved by photosynthesis. We show, across a range of different incident light regimes, that such radiative energy budgets are highly dominated by heat dissipation constituting up to 99.5% of the absorbed light energy. Highest photosynthetic energy conservation efficiency was found in the coral sediment under light-limiting conditions and amounted to ~13% of the absorbed light energy. Additionally, the effect of light directionality, i.e., diffuse or collimated light, on energy conversion efficiency was tested on the two surface-associated systems. The effects of light directionality on the radiative energy budgets of these phototrophic communities were not unanimous but, resulted in local spatial differences in heat-transfer, gross photosynthesis and light distribution. The light acclimation index, Ek was >2 times higher in the coral sediment compared to the biofilm and changed the pattern of photosynthetic energy conservation under light-limiting conditions. At moderate to high incident 45 irradiances, the photosynthetic conservation of absorbed energy was highest in collimated light; a tendency that changed in the biofilm under sub-saturating incident irradiances, where higher photosynthetic efficiencies were observed under diffuse light. Our results suggest that the optical properties and the structural organization of phytoelements are important traits affecting the photosynthetic efficiency of biofilms and sediments.

ecology↗

bioRxiv2016

Diffusion or advection? Mass transfer and diffusive boundary layer landscapes of the brown alga Fucus vesiculosus

The role of hyaline hairs on the thallus of brown-algae in the genus Fucus is long debated and several functions have been proposed. We used a novel motorized setup for 2D-and 3D-mapping with O2-microsensors to investigate the spatial heterogeneity of the diffusive boundary layer (DBL) and O2 flux around single and multiple tufts of hyaline hairs on the thallus of Fucus vesiculosus. Flow was a major determinant of DBL thickness, where higher flow decreased DBL thickness and increased O2 flux between algal thallus and the surrounding seawater. However, the topography of the DBL varied and did not directly follow the contour of the underlying thallus. Areas around single tufts of hyaline hairs exhibited both increased and decreased DBL thickness as compared to areas over smooth thallus surfaces. Over thallus areas with several hyaline hair tufts, the overall effect was a local increase in the DBL thickness. We also found indications for advective O2 transport driven by pressure gradients or vortex-shedding downstream from dense tufts of hyaline hairs alleviating local mass-transfer resistance imposed by thickened DBL. Mass-transfer dynamics around hyaline hair tufts are thus more complex than hitherto assumed and may have important implications for algal physiology and plant-microbe interactions.

biophysics↗