Determination of Ubiquitin Fitness Landscapes Under Different Chemical Stresses in a Classroom Setting

Abstract

Ubiquitin is essential for eukaryotic life and varies in only 3 amino acid positions between yeast and humans. However, recent deep sequencing studies indicate that ubiquitin is highly tolerant to single mutations. We hypothesized that this tolerance would be reduced by chemically induced physiologic perturbations. To test this hypothesis, a class of first year UCSF graduate students employed deep mutational scanning to determine the fitness landscape of all possible single residue mutations in the presence of five different small molecule perturbations. These perturbations uncover 'shared sensitized positions' localized to areas around the hydrophobic patch and the C-terminus. In addition, we identified perturbation specific effects such as a sensitization of His68 in HU and a tolerance to mutation at Lys63 in DTT. Our data show how chemical stresses can reduce buffering effects in the ubiquitin proteasome system. Finally, this study demonstrates the potential of lab-based interdisciplinary graduate curriculum.

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

“…In combination with biological competition assays, recent advances in gene sequencing have enabled “deep mutational scanning” of structure/function outcomes for large libraries of amino acid variants (Fowler & Fields, ; Gray, Hause, & Fowler, ; Roscoe, Thayer, Zeldovich, Fushman, & Bolon, ). Although the assay output is (i) a combination of all possible changes in stability and function, and (ii) sensitive to the threshold of the biological competition assay (Mavor et al., ), data for a given position usually report results for all possible amino acid substitutions and thus may provide information about rheostat positions. Here, we have used RheoScale to analyze results for three TIM barrel isozymes that were exhaustively substituted in the central β barrel and parts of the flanking loops (Figure A and B) (Chan et al., ).…”

Recent insights into the genotype–phenotype relationship from massively parallel genetic assays

“…Such quantitative models are limited by the precision of the large data sets informing them (Rubin et al, ). Great strides have been made here—while early studies were limited in numbers of replicates due to their expense, genetic barcoding strategies now enable each genotype to be represented by multiple independent lineages in a single experiment, allowing characterization of biological and experimental noise and even error correction (Fowler et al, ; Mavor et al, ). Further, a critical decision for any deep‐sequencing‐based assay is how best to make use of a defined number of sequencing reads ( e.g .…”

“…In proteins, whether the focal phenotype is biochemical functionality (Lagator, Sarikas, Acar, Bollback, & Guet, ; McLaughlin et al, ; Sarkisyan et al, ) or a highly integrated trait like fitness (Bank, Hietpas, Jensen, & Bolon, ; Bank, Hietpas, Wong, Bolon, & Jensen, ; Chan et al, ; Diss & Lehner, ; Firnberg et al, ; Hietpas et al, ; Jacquier et al, ; Jiang et al, ; Jiang, Mishra, Hietpas, Zeldovich, & Bolon, ; Klesmith et al, ; Mavor et al, ; Melamed et al, ; Melnikov et al, ; Roscoe et al, ; Wrenbeck et al, ), the DME appears to be universally multimodal, typically with near‐neutral and highly deleterious modes and a vanishing fraction of beneficial effects (Figure ). An almost trivial exception to this is the DME of repressor proteins on expression (Lagator, Sarikas, et al, ), or more generally when a decrease in one phenotype leads to an increase in a downstream phenotype.…”

“…Studies examining environmental effects therefore tend to have the ambition of proof of principle, rather than exhaustive sampling, and these have produced myriad examples of the environmental dependence of both mutation effects and epistasis. It remains extremely difficult to form any general conclusions, as “environment” may refer to the concentration of small molecules (gene expression inducers, enzyme substrates, cofactors, antibiotics) which have some specific role in the system of study (Dean, ; Lagator et al, ; Lagator, Paixão, et al, ; Melnikov et al, ; Nghe et al, ; Shultzaberger et al, ; de Vos, Dawid, Sunderlikova, & Tans, ; de Vos, Poelwijk, Battich, Ndika, & Tans, ; Wrenbeck et al, ), precise physicochemical parameters known to matter in in vitro studies (Hayden, Ferrada, & Wagner, ; Hayden & Wagner, ), or more general “pleiotropic” factors such as temperature, chemical stresses, complex nutrients or even host organism (Bank et al, ; Caudle et al, ; Dandage et al, ; Flynn et al, ; Fragata et al, ; Hietpas, Bank, Jensen, & Bolon, ; Jagdishchandra Joshi & Prasad, ; Lalić, Cuevas, & Elena, ; Li & Zhang, ; Mavor et al, ). It will be important going forward to develop a more systematic approach to the environment, focussing either on environments likely to provide mechanistic insight into mutation effects, or simply on those most relevant to industry or nature.…”

Mapping the growth effect of previously hidden ubiquitin alleles using an overexpression based mutational scan

“…Termed Deep Mutational Scanning (DMS), these studies have elucidated the functional impact of individual point mutations via either a direct functional readout (1) or more commonly by using growth rate as a functional proxy for essential genes (2,3,4,5,6,7). While generating point mutant libraries has been possible for years (8), DMS offers a systematic approach quickly to determine the functional consequence of thousands alleles in parallel opening up exciting opeterninites to study genetic interactions (9) and chemical genetics (10,11).…”