Role of mutations and post-translational modifications in systemic AL amyloidosis studied by cryo-EM.

Systemic AL amyloidosis is a rare disease that is caused by the misfolding of immunoglobulin light chains (LCs). Potential drivers of amyloid formation in this disease are post-translational modifications (PTMs) and the mutational changes that are inserted into the LCs by somatic hypermutation. Here we present the cryo electron microscopy (cryo-EM) structure of an ex vivo λ1-AL amyloid fibril whose deposits disrupt the ordered cardiomyocyte structure in the heart. The fibril protein contains six mutational changes compared to the germ line and three PTMs (disulfide bond, N-glycosylation and pyroglutamylation). Our data imply that the disulfide bond, glycosylation and mutational changes contribute to determining the fibril protein fold and help to generate a fibril morphology that is able to withstand proteolytic degradation inside the body.

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a time-consuming procedure. With the rise of deep learning in computer vision tasks, the automation of such problems has become more and more applicable. However, because of insufficient quality of raw data and missing labels, neural networks alone cannot be applied successfully to target the given problem. Thus, we propose an approach combining conventional computer vision techniques and deep learning to automatically detect fibril crossovers in two-dimensional cryo-electron microscopy image data and apply it to murine amyloid protein A fibrils, where we first use direct image processing methods to simplify the image data such that a convolutional neural network can be applied to the remaining segmentation problem. LAY DESCRIPTION: The ability of protein to form fibrillary structures underlies important cellular functions but can also give rise to disease, such as in a group of disorders, termed amyloid diseases. These diseases are characterised by the formation of abnormal protein filaments, so-called amyloid fibrils, that deposit inside the tissue. Many amyloid fibrils are helically twisted, which leads to periodic variations in the apparent width of the fibril, when observing amyloid fibrils using microscopy techniques like cryogenic electron microscopy (cryo-EM). Due to the two-dimensional projection, parts of the fibril orthogonal to the projection plane appear narrower than parts parallel to the plane. The parts of small width are called crossovers. The distance between two adjacent crossovers is an important characteristic for the analysis of amyloid fibrils, because it is informative about the fibril morphology and because it can be determined from raw data by eye. A given protein can typically form different fibril morphologies. The morphology can vary depending on the chemical and physical conditions of fibril formation, but even when fibrils are formed under identical solution conditions, different morphologies may be present in a sample. As the crossovers allow to define fibril morphologies in a heterogeneous sample, detecting crossovers is an important first step in the sample analysis. In the present paper, we introduce a method for the automated detection of fibril crossovers in cryo-EM image data. The data consists of greyscale images, each showing an unknown number of potentially overlapping fibrils. In a first step, techniques from image analysis and pattern detection are employed to detect single fibrils in the raw data. Then, a convolutional neural network is used to find the locations of crossovers on each single fibril. As these predictions may contain errors, further postprocessing steps assess the quality and may slightly alter or reject the predicted crossovers.

Characterization of the 3D microstructure of Ibuprofen tablets by means of synchrotron tomography.

A new methodology to segment the three-dimensional (3D) internal structure of Ibuprofen tablets from synchrotron tomography is presented, introducing a physically coherent trinarization for greyscale images of Ibuprofen tablets consisting of three phases: microcrystalline cellulose, Ibuprofen and pores. For this purpose, a hybrid approach is developed combining a trinarization by means of statistical learning with a trinarization based on a watershed algorithm. This hybrid approach allows us to compute microstructure characteristics of tablets using methods of statistical image analysis. A comparison with experimental results shows that there is a significant amount of pores which is below the resolution limit. At the same time, results from image analysis let us conjecture that these pores constitute the great majority of the surface between pores and solid. Furthermore, we compute microstructure characteristics, which are experimentally not accessible such as local percolation probabilities and chord length distribution functions. Both characteristics are meaningful in order to quantify the influence of tablet compaction on its microstructure. The presented approach can be used to get better insight into the relationship between production parameters and microstructure characteristics based on 3D image data of Ibuprofen tablets manufactured under different conditions and elucidate key effects on the strength and solubility kinetics of the final  formulation. LAY DESCRIPTION: A typical formulation of uniaxial compacted Ibuprofen tablets consist of a mixture of an excipient (microcrystalline cellulose) with an active ingredient (a ground fraction of Ibuprofen). The final mechanical strength of the tablet as well as the release kinetics are strongly influenced by the underlying microstructure, i.e. the spatial arrangement of the microcrystalline cellulose and Ibuprofen within the tablet. In order to optimize the performance of the tablet, it is important to investigate the relationship between its microstructure and the corresponding production parameters. For this purpose, 3D imaging is a powerful tool as it allows computing microstructural properties such as the internal arrangement, interconnectivity and pore location and distribution, characteristics that cannot be computed by experimental characterization techniques. In the present study, a new algorithm for an accurate trinarization of 3D image data obtained by synchrotron tomography is presented. Trinarization means that we reconstruct microcrystalline cellulose, Ibuprofen and pores on the basis of the 3D images, where one can only observe different greyscale values, but not the different constituents themselves. For this purpose, a hybrid approach combining a trinarization by means of artificial intelligence with a trinarization based on a geometrically motivated algorithm is developed. This hybrid approach allows to compute microstructure characteristics of tablets using image analysis. A comparison with experimental results shows that there is a significant amount of pores below the resolution limit. At the same time results from image analysis lead to the conjecture that these pores constitute the major part of the surface between pores and solid. Moreover, characteristics are computed by image analysis, which are meaningful in order to quantify the influence of tablet compaction parameters on its microstructure. The presented novel approach can be used to elucidate the relationship between production parameters and microstructure characteristics based on 3D image data of Ibuprofen tablets manufactured under different mixing, loading and processing conditions.

Physical basis of amyloid fibril polymorphism.

Polymorphism is a key feature of amyloid fibril structures but it remains challenging to explain these variations for a particular sample. Here, we report electron cryomicroscopy-based reconstructions from different fibril morphologies formed by a peptide fragment from an amyloidogenic immunoglobulin light chain. The observed fibril morphologies vary in the number and cross-sectional arrangement of a structurally conserved building block. A comparison with the theoretically possible constellations reveals the experimentally observed spectrum of fibril morphologies to be governed by opposing sets of forces that primarily arise from the ß-sheet twist, as well as peptide-peptide interactions within the fibril cross-section. Our results provide a framework for rationalizing and predicting the structure and polymorphism of cross-ß fibrils, and suggest that a small number of physical parameters control the observed fibril architectures.

Land flatworms (Platyhelminthes, Geoplanidae) of São Tomé: a first account on their diversity, with the description of five new species.

The present contribution provides the first faunistic and taxonomic account of six species of land flatworm from the island of São Tomé, including five new species of the genus Othelosoma Gray, 1869 and the introduced Bipalium kewense Moseley, 1878. One of the new species represents the first African land flatworm that has specks on its dorsal body surface, instead of stripes or a more or less uniform colouration. At least two of the new species were observed to prey on snails. The study details the fourth record of a sclerotic spermatophore in a species of land flatworm, and discusses the definition and homology of double female genital canals in African and Indian species of the genus Othelosoma.

Hydrostatic Pressure Increases the Catalytic Activity of Amyloid Fibril Enzymes.

We studied the combined effects of pressure (0.1-200 MPa) and temperature (22, 30, and 38 °C) on the catalytic activity of designed amyloid fibrils using a high-pressure stopped-flow system with rapid UV/Vis absorption detection. Complementary FT-IR spectroscopic data revealed a remarkably high pressure and temperature stability of the fibrillar systems. High pressure enhances the esterase activity as a consequence of a negative activation volume at all temperatures (about -14 cm(3) mol(-1) ). The enhancement is sustained in the whole temperature range covered, which allows a further acceleration of the enzymatic activity at high temperatures (activation energy 45-60 kJ mol(-1) ). Our data reveal the great potential of using both pressure and temperature modulation to optimize the enzyme efficiency of catalytic amyloid fibrils.

3D Microstructure Effects in Ni-YSZ Anodes: Prediction of Effective Transport Properties and Optimization of Redox Stability.

This study investigates the influence of microstructure on the effective ionic and electrical conductivities of Ni-YSZ (yttria-stabilized zirconia) anodes. Fine, medium, and coarse microstructures are exposed to redox cycling at 950 °C. FIB (focused ion beam)-tomography and image analysis are used to quantify the effective (connected) volume fraction (Φeff), constriction factor (ß), and tortuosity (τ). The effective conductivity (σeff) is described as the product of intrinsic conductivity (σ0) and the so-called microstructure-factor (M): σeff= σ0*M. Two different methods are used to evaluate the M-factor: (1) by prediction using a recently established relationship, Mpred= ε߰.36/τ5.17, and (2) by numerical simulation that provides conductivity, from which the simulated M-factor can be deduced (Msim). Both methods give complementary and consistent information about the effective transport properties and the redox degradation mechanism. The initial microstructure has a strong influence on effective conductivities and their degradation. Finer anodes have higher initial conductivities but undergo more intensive Ni coarsening. Coarser anodes have a more stable Ni phase but exhibit lower YSZ stability due to lower sintering activity. Consequently, in order to improve redox stability, it is proposed to use mixtures of fine and coarse powders in different proportions for functional anode and current collector layers.

Application of microflow conditions to visible light photoredox catalysis.

Applications of microflow conditions for visible light photoredox catalysis have successfully been developed. Operationally simple microreactor and FEP (fluorinated ethylene propylene copolymer) tube reactor systems enable significant improvement of several photoredox reactions using different photocatalysts such as [Ru(bpy)(3)](2+) and Eosin Y. Apart from rate acceleration, this approach facilitates previously challenging transformations of nonstabilized intermediates. Additionally, the productivity of the synergistic, catalytic enantioselective photoredox α-alkylation of aldehydes was demonstrated to be increased by 2 orders of magnitude.

Risk factors related to a hospital-associated cluster of Clostridium difficile PCR ribotype 027 infections in Germany During 2007.

In 2007, Clostridium difficile polymerase chain reaction (PCR) ribotype 027 emerged in Germany. We conducted a hospital-based case-control study to identify specific risk factors for infection with this strain. Logistic regression analysis involving 15 case patients and 31 control patients revealed that exposure to fluoroquinolones (matched odds ratio, 36.2; P < .01) or cephalosporins (matched odds ratio, 19.1; P < .01) was independently related to C. difficile PCR ribotype 027 infection.