Cellular adhesion is critical for tissue organization and integrity, but the full complement of proteins required for proper adhesion remains unresolved. Here, we define the requirements for cell-substrate adhesion in cultured human cells using orthogonal, large-scale functional genetic approaches. Using mechanical assays to test the maintenance (“shake-off”) or formation of adhesion and parallel large-scale assays of cell morphology, we identify dozens of gene targets with roles in adhesion. Our analyses reveal dynamic requirements for adhesion across timepoints and cell lines. We additionally conduct targeted downstream mechanistic analyses to resolve the molecular basis for altered adhesion. Collectively, we identify established and uncharacterized regulators of adhesion, including genes involved in mitosis, focal adhesions, actin regulation, and membrane trafficking. Unexpectedly, we find that cells that fail cytokinesis display impaired adhesion, with strongly altered actin organization and nuclear dynamics. Together, this work provides a comprehensive view of the genetic requirements for cell-substrate adhesion.
Kaitlyn Manzer, Kuan-Chung Su, Matteo Di Bernardo et al.· bioRxiv· 0 citations
The regulation of protein stability is essential for cellular homeostasis and is determined by a combination of intrinsic sequence motifs and extrinsic recognition enzymes. Despite growing knowledge of the protein degradation machinery, the ability to predict a protein’s stability from its amino acid sequence remains challenging. Here we develop a machine learning model to predict protein stability from N-terminal amino acid sequences. Using our model and experimental validation, we identify known and novel sequence motifs governing protein stability. We additionally use this model to predict the stability of alternative translational isoforms with distinct N-termini produced from the same mRNA. Despite differing by a limited number of amino acids, we identify N-terminal isoforms with drastically different stabilities relative to their annotated counterparts, highlighting the potential of N-terminal extensions and truncations to regulate protein function. Together, this model provides a valuable tool for evaluating additional protein datasets and protein design strategies.
Océane Marescal, Iain M. Cheeseman· bioRxiv· 0 citations
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