Chemoselective umpolung of thiols to episulfoniums for cysteine bioconjugation.

Cysteine conjugation is an important tool in protein research and relies on fast, mild and chemoselective reactions. Cysteinyl thiols can either be modified with prefunctionalized electrophiles, or converted into electrophiles themselves for functionalization with selected nucleophiles in an independent step. Here we report a bioconjugation strategy that uses a vinyl thianthrenium salt to transform cysteine into a highly reactive electrophilic episulfonium intermediate in situ, to enable conjugation with a diverse set of bioorthogonal nucleophiles in a single step. The reactivity profile can connect several nucleophiles to biomolecules through a short and stable ethylene linker, ideal for introduction of infrared labels, post-translational modifications or NMR probes. In the absence of reactive exogenous nucleophiles, nucleophilic amino acids can react with the episulfonium intermediate for native peptide stapling and protein-protein ligation. Ready synthetic access to isotopologues of vinyl thianthrenium salts enables applications in quantitative proteomics. Such diverse applications demonstrate the utility of vinyl-thianthrenium-based bioconjugation as a fast, selective and broadly applicable tool for chemical biology.

Use of random forest algorithm for predictive modelling of transfer factor soil-plant for radiocaesium: A feasibility study.

A German dataset with soil-plant transfer factors for radiocaesium including many co-variables was analysed and prepared for the application of the Random Forest (RF) algorithm using the R libraries 'party', and 'caret'. A RF predictive model for soil-plant transfer factor was created based on 10 co-variables. These are, for example, taxonomic plant family, plant part, soil type and the exchangeable potassium concentration in the soil. The RF model results were compared with the results of two (semi-)mechanistic models. Of the more than 3000 entries in the original dataset, only about 1200 could be used, as this was the largest complete dataset with the largest number of co-variables available. The obtained RF predictive model can reproduce the experimental observations better than the two (semi)-mechanistic models, which are based on many assumptions and fixed parameter values. Model performance was quantified using the metrics of Root Mean Square Error (rmse) and Mean Absolute Error (mae). The RF model was able to reproduce the variability of the data by up to 6 orders of magnitude. The categorical co-predictors, especially taxonomic plant family and plant part, have a greater influence than the numerical co-predictors, such as pH and exchangeable soil potassium concentration. This feasibility study shows that RF is a promising tool to obtain predictive models for transfer factors. However, to build a widely applicable predictive model, a dataset is needed that contains at least thousands of entries for transfer factors and for the most important co-variables and considers a large parameter space.

Contribution of serum lipids and cholesterol cellular metabolism in lung cancer development and progression.

Neoplasms of the lungs are the leading cause of cancer incidence and mortality worldwide. Although immunotherapy has increased the overall survival of patients with lung cancer, there is the need to improve this treatment. At this regard, blood lipid levels are thought to be linked to cancer risk and thus a preventive intervention through regulation of the nutrition of patients with lung cancer is gaining much attention. In this study, we therefore asked about the contribution of serum lipids and cholesterol cellular metabolism in lung cancer development and progression. We measured different serum lipids and analyzed cholesterol synthesis enzymes 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR) and acetyl-coenzyme A cholesterol acetyltransferase 1 (ACAT1) as well as the cholesterol cellular export protein ATP-binding cassette (ABC) A-1 mRNA by quantitative PCR (qPCR) in the control and tumoral regions of post-surgery lung tissues to analyze the accumulation of cholesterol in cancer cells in a cohort of patients with lung adenocarcinoma (LUAD). We found that triglycerides in serum directly correlated with the body mass index (BMI) in patients with LUAD. By contrast, we found that high-density lipoprotein (HDL) cholesterol inversely correlated with the BMI, C-reactive protein (CRP) and overall survival and total cholesterol inversely correlated with the tumor diameter, serum CRP and overall survival in these LUAD patients. Functionally, the role of cholesterol is indispensable for the growth and development of normal animal cells where it is tightly regulated. Excess of cellular cholesterol regulated by HMGCR is converted to cholesteryl esters by the enzyme ACAT1 and exported extracellularly by the cholesterol transporter ABCA1. Here we found HMGCR and ACAT1 upregulated and ABCA1 downregulated in the lung's tumoral region of our LUAD cohort, indicating cholesterol dysregulated cellular export in lung tumor cells.

Quantification of Conceptual Model Uncertainty in the Modeling of Wet Deposited Atmospheric Pollutants.

Conceptual model uncertainty and parameter uncertainty are dominant contributors to the total uncertainty of a radioecological model output. In the present study the focus is on conceptual model uncertainty, which is often not acknowledged. Conceptual model uncertainty is assessed by subtracting from the total uncertainty of the model output the propagated parameter uncertainty, obtained by means of Bayesian inference analysis. The conceptual model uncertainty is quantified for two process-based models, which describe the interception of wet deposited pollutants under equilibrium and kinetic conditions, respectively. The natural variability due the chemical valence of the elements considered is accounted for in both models. Quantitative evidence has been obtained that the conceptual model uncertainty can contribute to the total uncertainty budget of the models for interception of wet deposited pollutants at least as much as, if not more than, parameter uncertainty.

How do microalgae perceive light in a high-rate pond? Towards more realistic Lagrangian experiments.

Hydrodynamics in a high-rate production reactor for microalgae cultivation affects the light history perceived by cells. The interplay between cell movement and medium turbidity leads to a complex light pattern, whose forcing effects on photosynthesis and photoacclimation dynamics are non-trivial. Hydrodynamics of high density algal ponds mixed by a paddle wheel has been studied recently, although the focus has never been on describing its impact on photosynthetic growth efficiency. In this multidisciplinary downscaling study, we first reconstructed single cell trajectories in an open raceway using an original hydrodynamical model offering a powerful discretization of the Navier-Stokes equations tailored to systems with free surfaces. The trajectory of a particular cell was selected and the associated high-frequency light pattern was computed. This light pattern was then experimentally reproduced in an Arduino-driven computer controlled cultivation system with a low density Dunaliella salina culture. The effect on growth and pigment content was recorded for various frequencies of the light pattern, by setting different paddle wheel velocities. Results show that the frequency of this realistic signal plays a decisive role in the dynamics of photosynthesis, thus revealing an unexpected photosynthetic response compared to that recorded under the on/off signals usually used in the literature. Indeed, the light received by a single cell contains signals from low to high frequencies that nonlinearly interact with the photosynthesis process and differentially stimulate the various time scales associated with photoacclimation and energy dissipation. This study highlights the need for experiments with more realistic light stimuli to better understand microalgal growth at high cell densities. An experimental protocol is also proposed, with simple, yet more realistic, step functions for light fluctuations.

Administration of 17ß-Estradiol Improves Motoneuron Survival and Down-regulates Inflammasome Activation in Male SOD1(G93A) ALS Mice.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease manifested by the progressive loss of upper and lower motoneurons. The pathomechanism of ALS is complex and not yet fully understood. Neuroinflammation is believed to significantly contribute to disease progression. Inflammasome activation was recently shown in the spinal cord of human sporadic ALS patients and in the SOD1(G93A) mouse model for ALS. In the present study, we investigated the neuroprotective and anti-inflammatory effects of 17ß-estradiol (E2) treatment in pre-symptomatic and symptomatic male SOD1(G93A) mice. Symptomatic mice with E2 substitution exhibited improved motor performance correlating with an increased survival of motoneurons in the lumbar spinal cord. Expression of NLRP3 inflammasome proteins and levels of activated caspase 1 and mature interleukin 1 beta were significantly reduced in SOD1(G93A) mice supplemented with E2.

Dynamic coupling of photoacclimation and photoinhibition in a model of microalgae growth.

The development of mathematical models that can predict photosynthetic productivity of microalgae under transient conditions is crucial for enhancing large-scale industrial culturing systems. Particularly important in outdoor culture systems, where the light irradiance varies greatly, are the processes of photoinhibition and photoacclimation, which can affect photoproduction significantly. The former is caused by an excess of light and occurs on a fast time scale of minutes, whereas the latter results from the adjustment of the light harvesting capacity to the incoming irradiance and takes place on a slow time scale of days. In this paper, we develop a dynamic model of microalgae growth that simultaneously accounts for the processes of photoinhibition and photoacclimation, thereby spanning multiple time scales. The properties of the model are analyzed in connection to PI-response curves, under a quasi steady-state assumption for the slow processes and by neglecting the fast dynamics. For validation purposes, the model is calibrated and compared against multiple experimental data sets from the literature for several species. The results show that the model can describe the difference in photosynthetic unit acclimation strategies between Dunaliella tertiolecta (n-strategy) and Skeletonema costatum (s-strategy).

Long-term adaptive response to high-frequency light signals in the unicellular photosynthetic eukaryote Dunaliella salina.

Productivity of microalgal cultivation processes is tightly related to photosynthetic efficiency, and therefore to light availability at the cell scale. In an agitated, highly turbid suspension,the light signal received by a single phytoplankton cell moving in a dense culture is a succession of flashes. The growth characteristics of microalgae under such dynamic light conditions are thus fundamental information to understand nonlinear properties of the photosynthetic process and to improve cultivation process design and operation. Studies of the long term consequences of dynamic illumination regime on photosynthesis require a very specific experimental set-up where fast varying signals are applied on the long term. In order to investigate the growth response of the unicellular photosynthetic eukaryote Dunaliella salina (Chlorophyceae) to intermittent light exposure, different light regimes using LEDs with the same average total light dose were applied in continuous cultures. Flashing light with different durations of light flashes (△t of 30 s, 15 s, 2 s and 0.1 s) followed by dark periods of variable length (0.67 ≤ L:D ≤ 2) yielding flash frequencies in the range 0.017-5 Hz, were compared to continuous illumination. Specific growth rate, photosynthetic pigments, lipid productivity and elemental composition were measured on two duplicates for each irradiance condition. The different treatments of intermittent light led to specific growth rates ranging from 0.25 to 0.93 day(-1) . While photosynthetic efficiency was enhanced with increased flash frequency, no significant differences were observed in the particular carbon and chlorophyll content. Pigment analysis showed that within this range of flash frequency, cells progressively photoacclimated to the average light intensity.

The effect of photosynthesis time scales on microalgae productivity.

Microalgae are often seen as a potential biofuel producer. In order to predict achievable productivities in the so called raceway culturing system, the dynamics of photosynthesis has to be taken into account. In particular, the dynamical effect of inhibition by an excess of light (photoinhibition) must be represented. We propose a model considering both photosynthesis and growth dynamics. This model involves three different time scales. We study the response of this model to fluctuating light with different frequencies by slow/fast approximations. Therefore, we identify three different regimes for which a simplified expression for the model can be derived. These expressions give a hint on productivity improvement which can be expected by stimulating photosynthesis with a faster hydrodynamics.