The C-terminal domain of p53 orchestrates the interplay between non-covalent and covalent poly(ADP-ribosyl)ation of p53 by PARP1.

The post-translational modification poly(ADP-ribosyl)ation (PARylation) plays key roles in genome maintenance and transcription. Both non-covalent poly(ADP-ribose) binding and covalent PARylation control protein functions, however, it is unknown how the two modes of modification crosstalk mechanistically. Employing the tumor suppressor p53 as a model substrate, this study provides detailed insights into the interplay between non-covalent and covalent PARylation and unravels its functional significance in the regulation of p53. We reveal that the multifunctional C-terminal domain (CTD) of p53 acts as the central hub in the PARylation-dependent regulation of p53. Specifically, p53 bound to auto-PARylated PARP1 via highly specific non-covalent PAR-CTD interaction, which conveyed target specificity for its covalent PARylation by PARP1. Strikingly, fusing the p53-CTD to a protein that is normally not PARylated, renders this a target for covalent PARylation as well. Functional studies revealed that the p53-PAR interaction had substantial implications on molecular and cellular levels. Thus, PAR significantly influenced the complex p53-DNA binding properties and controlled p53 functions, with major implications on the p53-dependent interactome, transcription, and replication-associated recombination. Remarkably, this mechanism potentially also applies to other PARylation targets, since a bioinformatics analysis revealed that CTD-like regions are highly enriched in the PARylated proteome.

In vitro hepatotoxicity of Petasites hybridus extract (Ze 339) depends on the concentration, the cytochrome activity of the cell system, and the species used.

Ze 339, a CO2 extract prepared from the leaves of Petasites hybridus, possesses antispasmodic and anti-inflammatory effects and is proven to be effective in the treatment of allergic rhinitis. To study possible hepatotoxic effects of Ze 339, its main constituents and metabolites, a series of in vitro investigations were performed. Furthermore, different reconstituted fractions of extract (petasins and fatty acid fraction) were examined in three in vitro test systems using hepatocytes: Two human cell lines, with lower and higher activity of cytochrome P450 enzymes (HepG2, HepaRG) as well as a rodent cell line with high cytochrome P450 activity (H-4-II-E), were used. Metabolic activity, assessed by the WST-1 assay, was chosen as indicator of cytotoxicity. To assess potential bioactivation of Ze 339 compounds, metabolic experiments using S9 fractions from rats, dogs, and humans and isolated cytochromes (human/rat) were performed, and the formation of reactive metabolites was assessed by measuring cellular concentrations of glutathione and glutathione disulphide. Our data revealed that the cytotoxicity of Ze 339, its single constituents, and main metabolites depends on the concentration, the cytochrome activity of the cell system, and the species used.

Making a difference in multi-data-set crystallography: simple and deterministic data-scaling/selection methods.

Phasing by single-wavelength anomalous diffraction (SAD) from multiple crystallographic data sets can be particularly demanding because of the weak anomalous signal and possible non-isomorphism. The identification and exclusion of non-isomorphous data sets by suitable indicators is therefore indispensable. Here, simple and robust data-selection methods are described. A multi-dimensional scaling procedure is first used to identify data sets with large non-isomorphism relative to clusters of other data sets. Within each cluster that it identifies, further selection is based on the weighted ΔCC1/2, a quantity representing the influence of a set of reflections on the overall CC1/2 of the merged data. The anomalous signal is further improved by optimizing the scaling protocol. The success of iterating the selection and scaling steps was verified by substructure determination and subsequent structure solution. Three serial synchrotron crystallography (SSX) SAD test cases with hundreds of partial data sets and one test case with 62 complete data sets were analyzed. Structure solution was dramatically simplified with this procedure, and enabled solution of the structures after a few selection/scaling iterations. To explore the limits, the procedure was tested with much fewer data than originally required and could still solve the structure in several cases. In addition, an SSX data challenge, minimizing the number of (simulated) data sets necessary to solve the structure, was significantly underbid.

Identification of rogue datasets in serial crystallography.

Advances in beamline optics, detectors and X-ray sources allow new techniques of crystallographic data collection. In serial crystallography, a large number of partial datasets from crystals of small volume are measured. Merging of datasets from different crystals in order to enhance data completeness and accuracy is only valid if the crystals are isomorphous, i.e. sufficiently similar in cell parameters, unit-cell contents and molecular structure. Identification and exclusion of non-isomorphous datasets is therefore indispensable and must be done by means of suitable indicators. To identify rogue datasets, the influence of each dataset on CC1/2 [Karplus & Diederichs (2012 ▸). Science, 336, 1030-1033], the correlation coefficient between pairs of intensities averaged in two randomly assigned subsets of observations, is evaluated. The presented method employs a precise calculation of CC1/2 that avoids the random assignment, and instead of using an overall CC1/2, an average over resolution shells is employed to obtain sensible results. The selection procedure was verified by measuring the correlation of observed (merged) intensities and intensities calculated from a model. It is found that inclusion and merging of non-isomorphous datasets may bias the refined model towards those datasets, and measures to reduce this effect are suggested.