Schmelzle, T. | bioRxiv Science

bioRxiv2020

A new perspective on the evolution of the interaction between the Vg/VGLL1-3 proteins and the TEAD transcription factors

The most downstream elements of the Hippo pathway, the TEAD transcription factors, are regulated by several cofactors, such as Vg/VGLL1-3. Earlier findings on human VGLL1 and here on human VGLL3 show that these proteins interact with TEAD via a conserved amino acid motif called the TONDU domain. Surprisingly, our studies reveal that the TEAD-binding domain of Drosophila Vg and of human VGLL2 is more complex and contains an additional structural element, an {Omega}-loop, that contributes to TEAD binding and in vivo function. To explain this unexpected structural difference between proteins from the same family, we propose that, after the genome-wide duplications at the origin of vertebrates, the {Omega}-loop present in an ancestral VGLL gene has been lost in some VGLL variants. These findings illustrate how structural and functional constraints can guide the evolution of transcriptional cofactors to preserve their ability to compete with other cofactors for binding to transcription factors.

biochemistry↗

bioRxiv2017

Correction of copy number induced false positives in CRISPR screens

Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70-80% decrease of false positive hits in regions of high copy number with either method.

bioinformatics↗