Jackson, E. L. | bioRxiv Science

bioRxiv2020

G9a methyltransferase governs cell identity in the lung and is required for KRAS G12D tumor development and propagation

Lung development, integrity and repair rely on precise Wnt signaling, which is corrupted in diverse diseases, including cancer. Here, we discover that G9a methyltransferase regulates Wnt signaling in the lung by controlling the transcriptional activity of chromatin-bound {beta}-catenin, through a non-histone substrate. Inhibition of G9a induces transcriptional, morphologic, and molecular changes consistent with alveolar type 2 (AT2) lineage commitment. Mechanistically, G9a activity functions to support regenerative properties of KrasG12D tumors and normal AT2 cells - the predominant cell of origin of this cancer. Consequently, G9a inhibition prevents KrasG12D lung adenocarcinoma tumor formation and propagation,and disrupts normal AT2 cell trans-differentiation. Consistent with these findings, low G9a expression in human lung adenocarcinoma correlates with enhanced AT2 gene expression and improved prognosis. These data reveal G9a as a critical regulator of Wnt signaling, implicating G9a as a potential target in lung cancer and other AT2-mediated lung pathologies.

cancer biology↗

bioRxiv2017

Beyond thermodynamic constraints: Evolutionary history shapes protein sequence variation

Biological evolution generates a surprising amount of site-specific variability in protein sequences. Yet attempts at modeling this process have been only moderately successful, and current models based on protein structural metrics explain, at best, 60% of the observed variation. Surprisingly, simple measures of protein structure, such as solvent accessibility, are often better predictors of site-specific variability than more complex models employing all-atom energy functions and detailed structural modeling. We suggest here that these more complex models perform poorly because they lack consideration of the evolutionary process that is in part captured by the simpler metrics. We compare protein sequences that are computationally designed to sequences that are computationally evolved using the same protein-design energy function and to homologous natural sequences. We find that by a wide variety of metrics, evolved sequences are much more similar to natural sequences than are designed sequences. In particular, designed sequences are too conserved on the protein surface relative to natural sequences whereas evolved sequences are not. Our results suggest that evolutionary simulation produces a realistic sampling of sequence space. By contrast, protein design--at least as currently implemented--does not. Existing energy functions seem to be sufficiently accurate to correctly describe the key thermodynamic constraints acting on protein sequences, but they need to be paired with realistic sampling schemes to generate realistic sequence alignments.

evolutionary biology↗