Biology subjects

Wohlgemuth, S.

Publications and source records attributed to Wohlgemuth, S..

2 recordsLinked to original sources

bioRxiv2018

Kinetochore recruitment of CENP-F illustrates how paralog divergence shapes kinetochore composition and function

The metazoan proteins CENP-E and CENP-F are components of a fibrous layer of mitotic kinetochores named the corona. Several features suggest that CENP-E and CENP-F are paralogs: they are very large (approximately 2700 and 3200 residues, respectively), rich in predicted coiled-coil structure, C-terminally prenylated, and endowed with microtubule-binding sites at their termini. In addition, CENP-E contains an ATP-hydrolyzing motor domain that promotes microtubule plus-end directed motion. Here, we show that CENP-E and CENP- F are recruited to mitotic kinetochores independently of the Rod-Zwilch-ZW10 (RZZ) complex, the main corona constituent. We identify selective interactions of CENP-E and CENP-F respectively with BubR1 and Bub1, paralogous proteins involved in mitotic checkpoint control and chromosome alignment. While BubR1 is dispensable for kinetochore localization of CENP-E, Bub1 is stringently required for CENP-F localization. Through biochemical reconstitution, we demonstrate that the CENP-E:BubR1 and CENP-F:Bub1 interactions are direct and require similar determinants, a dimeric coiled-coil in CENP-E or CENP-F and a kinase domain in BubR1 or Bub1. Our findings are consistent with the existence of pseudo-symmetric, paralogous Bub1:CENP-F and BubR1:CENP-E axes, supporting evolutionary relatedness of CENP-E and CENP-F.

cell biology↗

bioRxiv2017

Predicting Ligand-Dependent Tumors From Multi-Dimensional Signaling Features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using a novel approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.\n\nSummaryPrediction of ligand-induced growth of cancer cell lines, which correlates with ligand-blocking antibody efficacy, could be significantly improved by learning from features of a mechanistic signaling model, and was applied to reveal a correlation between growth factor expression and predicted response in patient samples.

systems biology↗