Biology subjects

Berger, B. M.

Publications and source records attributed to Berger, B. M..

2 recordsLinked to original sources

bioRxiv2023

Pre-gelation staining expansion microscopy (PS-ExM) for visualization of the Plasmodium liver stage

Fluorescence and light microscopy are important tools in the history of natural science. However, the resolution of microscopes is limited by the diffraction of light. One possible method to circumvent this physical restriction is the recently developed expansion microscopy (ExM). By physically expanding the sample in a swellable hydrogel, biomolecules are separated in space allowing to image molecules that are otherwise difficult to study due to their size or complexity. Furthermore, the improved resolution is useful to study protein localization and interactions. However, the original ultrastructure ExM (U-ExM) protocol is very time-consuming, and some epitopes are lost during the process. In this study, we developed a shortened pre-gelation staining ExM (PS-ExM) protocol and tested it to investigate the Plasmodium liver stage. The protocol presented in this study allows expanding pre-stained samples, which results in shorter incubation times, better preservation of some epitopes, and the advantage that non-expanded controls can be performed alongside using the same staining protocol. The protocol applicability was accessed throughout the Plasmodium liver stage showing isotropic five-fold expansion. Furthermore, we used PS-ExM to visualize the association of lysosomes to the parasitophorous vacuole membrane (PVM) as an example of visualizing host-pathogen interaction. We are convinced that this new tool will be helpful for a deeper understanding of the biology of the Plasmodium liver stage.

cell biology↗

bioRxiv2021

Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters

Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a1 and a2 positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification.

biochemistry↗