Sup35NMp morphology evaluation on Au, Si, formvar and mica surfaces using AFM, SEM and TEM

Amyloid fibril length distribution from dynamic light scattering data

The study of the aggregation of amyloid proteins is challenging. A new approach to processing dynamic light scattering data was developed and tested using aggregates of the well-known model Sup35NM amyloid. After filtering and calculating the moving averages of autocorrelation functions to reduce impacts of noise, each averaged autocorrelation function is converted to the fibril length distribution via numerical modeling. The processing results were verified using atomic force and scanning electron microscopy data. Analysis of fibril length distribution changes over time gives valuable information about the aggregation process.

Design of a New [PSI +]-No-More Mutation in SUP35 With Strong Inhibitory Effect on the [PSI +] Prion Propagation

A number of [PSI⁺]-no-more (PNM) mutations, eliminating [PSI⁺] prion, were previously described in SUP35. In this study, we designed and analyzed a new PNM mutation based on the parallel in-register β-structure of Sup35 prion fibrils suggested by the known experimental data. In such an arrangement, substitution of non-charged residues by charged ones may destabilize the fibril structure. We introduced Q33K/A34K amino acid substitutions into the Sup35 protein, corresponding allele was called sup35-M0. The mutagenized residues were chosen based on ArchCandy in silico prediction of high inhibitory effect on the amyloidogenic potential of Sup35. The experiments confirmed that Sup35-M0 leads to the elimination of [PSI⁺] with high efficiency. Our data suggested that the elimination of the [PSI⁺] prion is associated with the decreased aggregation properties of the protein. The new mutation can induce the prion with very low efficiency and is able to propagate only weak [PSI⁺] prion variants. We also showed that Sup35-M0 protein co-aggregates with the wild-type Sup35 in vivo. Moreover, our data confirmed the utility of the strategy of substitution of non-charged residues by charged ones to design new mutations to inhibit a prion formation.

The tumor cell-secreted matricellular protein WISP1 drives pro-metastatic collagen linearization

Collagen linearization is a hallmark of aggressive tumors and a key pathogenic event that promotes cancer cell invasion and metastasis. Cell-generated mechanical tension has been proposed to contribute to collagen linearization in tumors, but it is unknown whether other mechanisms play prominent roles in this process. Here, we show that the secretome of cancer cells is by itself able to induce collagen linearization independently of cell-generated mechanical forces. Among the tumor cell-secreted factors, we find a key role in this process for the matricellular protein WISP1 (CCN4). Specifically, WISP1 directly binds to type I collagen to promote its linearization in vitro (in the absence of cells) and in vivo in tumors. Consequently, WISP1-induced type I collagen linearization facilitates tumor cell invasion and promotes spontaneous breast cancer metastasis, without significantly affecting gene expression. Furthermore, higher WISP1 expression in tumors from cancer patients correlates with faster progression to metastatic disease and poor prognosis. Altogether, these findings reveal a conceptually novel mechanism whereby pro-metastatic collagen linearization critically depends on a cancer cell-secreted factor.

Automated image segmentation-assisted flattening of atomic force microscopy images

Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.