Differential degradation of RNA species by autophagy-related pathways in Arabidopsis.

The plant vacuole recycles proteins and RNA delivered to it by autophagy. In this study, by isolating intact vacuoles from Arabidopsis plants, followed by subsequent RNA purification, and deep sequencing, we provide a comprehensive characterization of Arabidopsis vacuolar RNAome. In the vacuolar RNAome, we detected ribosomal RNAs, transfer RNAs, including those of chloroplast origin, and in addition small RNA types. As autophagy is a main mechanism for the transport of RNA to the vacuole, atg5-1 mutants deficient in autophagy were included in our analysis. We observed severely reduced amounts of most chloroplast-derived RNA species in these mutants. Comparisons with cellular RNA composition provided an indication of possible up-regulation of alternative RNA breakdown pathways. By contrast, vacuolar RNA processing and composition in plants lacking vacuolar ribonuclease 2, involved in cellular RNA homeostasis, only showed minor alterations, possibly because of the presence of further so far unknown vacuolar RNase species. Among the small RNA types, we detected mature miRNAs in all vacuolar preparations but at much lower frequency in atg5-1, raising the possibility of a biological role for vacuolar miRNAs.

Improved FRAP Measurements on Biofilms.

We expand the standard fluorescence recovery after photobleaching (FRAP) model introduced by Axelrod et al. in 1976. Our goal is to capture some of the following common artifacts observed in the fluorescence measurements obtained with a confocal laser scanning microscope in biofilms: 1) linear drift, 2) exponential decrease (due to bleaching during the measurements), 3) stochastic Gaussian noise, and 4) uncertainty in the exact time point of the onset of fluorescence recovery. To fit the resulting stochastic model to data from FRAP measurements and to estimate all unknown model parameters, we apply a suitably adapted Metropolis-Hastings algorithm. In this way, a more accurate estimation of the diffusion coefficient of the fluorophore is achieved. The method was tested on data obtained from FRAP measurements on a cultivated biofilm.

Diffusion profiles in L. lactis biofilms under different conditions.

Despite being an important topic in biofilm research, we still know little about diffusion in biofilms. Emerging biofilms of Lactococcus lactis growing in custom-made flow-cells were monitored and diffusion constants across the height of the biofilms recorded. The biofilms showed different diffusional behavior with regard to flow rate and pH variations, despite growing to similar thickness. At a higher flow rate, the biofilm exhibits slower diffusion compared to the reference cultivation at lower flow rate. By increasing pH, the biofilm exhibited fast growth and little difference in diffusion compared to the reference cultivation. Furthermore, the diffusion inside of the biofilms differed depending on the position in the flow-cell. The present study reveals new insights in how external factors can affect structure and density of biofilms. The method can be reliably used for L. lactis biofilms with a thickness up to 120 µm.

A continuous-time adaptive particle filter for estimations under measurement time uncertainties with an application to a plasma-leucine mixed effects model.

BACKGROUND: When mathematical modelling is applied to many different application areas, a common task is the estimation of states and parameters based on measurements. With this kind of inference making, uncertainties in the time when the measurements have been taken are often neglected, but especially in applications taken from the life sciences, this kind of errors can considerably influence the estimation results. As an example in the context of personalized medicine, the model-based assessment of the effectiveness of drugs is becoming to play an important role. Systems biology may help here by providing good pharmacokinetic and pharmacodynamic (PK/PD) models. Inference on these systems based on data gained from clinical studies with several patient groups becomes a major challenge. Particle filters are a promising approach to tackle these difficulties but are by itself not ready to handle uncertainties in measurement times. RESULTS: In this article, we describe a variant of the standard particle filter (PF) algorithm which allows state and parameter estimation with the inclusion of measurement time uncertainties (MTU). The modified particle filter, which we call MTU-PF, also allows the application of an adaptive stepsize choice in the time-continuous case to avoid degeneracy problems. The modification is based on the model assumption of uncertain measurement times. While the assumption of randomness in the measurements themselves is common, the corresponding measurement times are generally taken as deterministic and exactly known. Especially in cases where the data are gained from measurements on blood or tissue samples, a relatively high uncertainty in the true measurement time seems to be a natural assumption. Our method is appropriate in cases where relatively few data are used from a relatively large number of groups or individuals, which introduce mixed effects in the model. This is a typical setting of clinical studies. We demonstrate the method on a small artificial example and apply it to a mixed effects model of plasma-leucine kinetics with data from a clinical study which included 34 patients. CONCLUSIONS: Comparisons of our MTU-PF with the standard PF and with an alternative Maximum Likelihood estimation method on the small artificial example clearly show that the MTU-PF obtains better estimations. Considering the application to the data from the clinical study, the MTU-PF shows a similar performance with respect to the quality of estimated parameters compared with the standard particle filter, but besides that, the MTU algorithm shows to be less prone to degeneration than the standard particle filter.