Deep mutational scanning of Pneumocystis jirovecii dihydrofolate reductase reveals allosteric mechanism of resistance to an antifolate.

Pneumocystis jirovecii is a fungal pathogen that causes pneumocystis pneumonia, a disease that mainly affects immunocompromised individuals. This fungus has historically been hard to study because of our inability to grow it in vitro. One of the main drug targets in P. jirovecii is its dihydrofolate reductase (PjDHFR). Here, by using functional complementation of the baker's yeast ortholog, we show that PjDHFR can be inhibited by the antifolate methotrexate in a dose-dependent manner. Using deep mutational scanning of PjDHFR, we identify mutations conferring resistance to methotrexate. Thirty-one sites spanning the protein have at least one mutation that leads to resistance, for a total of 355 high-confidence resistance mutations. Most resistance-inducing mutations are found inside the active site, and many are structurally equivalent to mutations known to lead to resistance to different antifolates in other organisms. Some sites show specific resistance mutations, where only a single substitution confers resistance, whereas others are more permissive, as several substitutions at these sites confer resistance. Surprisingly, one of the permissive sites (F199) is without direct contact to either ligand or cofactor, suggesting that it acts through an allosteric mechanism. Modeling changes in binding energy between F199 mutants and drug shows that most mutations destabilize interactions between the protein and the drug. This evidence points towards a more important role of this position in resistance than previously estimated and highlights potential unknown allosteric mechanisms of resistance to antifolate in DHFRs. Our results offer unprecedented resources for the interpretation of mutation effects in the main drug target of an uncultivable fungal pathogen.

Compensatory mutations potentiate constructive neutral evolution by gene duplication.

Protein functions generally depend on their assembly into complexes. During evolution, some complexes have transitioned from homomers encoded by a single gene to heteromers encoded by duplicate genes. This transition could occur without adaptive evolution through intermolecular compensatory mutations. Here, we experimentally duplicate and evolve an homodimeric enzyme to examine if and how this could happen. We identify hundreds of deleterious mutations that inactivate individual homodimers but produce functional enzymes when co-expressed as duplicated proteins that heterodimerize. The structure of one such heteromer reveals how both losses of function are buffered through the introduction of asymmetry in the complex that allows them to subfunctionalize. Constructive neutral evolution can thus occur by gene duplication followed by only one deleterious mutation per duplicate.

Epistasis between promoter activity and coding mutations shapes gene evolvability.

The evolution of protein-coding genes proceeds as mutations act on two main dimensions: regulation of transcription level and the coding sequence. The extent and impact of the connection between these two dimensions are largely unknown because they have generally been studied independently. By measuring the fitness effects of all possible mutations on a protein complex at various levels of promoter activity, we show that promoter activity at the optimal level for the wild-type protein masks the effects of both deleterious and beneficial coding mutations. Mutations that are deleterious at low activity but masked at optimal activity are slightly destabilizing for individual subunits and binding interfaces. Coding mutations that increase protein abundance are beneficial at low expression but could potentially incur a cost at high promoter activity. We thereby demonstrate that promoter activity in interaction with protein properties can dictate which coding mutations are beneficial, neutral, or deleterious.

Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use.

Antimicrobial resistance is an emerging threat for public health. The success of resistance mutations depends on the trade-off between the benefits and costs they incur. This trade-off is largely unknown and uncharacterized for antifungals. Here, we systematically measure the effect of all amino acid substitutions in the yeast cytosine deaminase Fcy1, the target of the antifungal 5-fluorocytosine (5-FC, flucytosine). We identify over 900 missense mutations granting resistance to 5-FC, a large fraction of which appear to act through destabilization of the protein. The relationship between 5-FC resistance and growth sustained by cytosine deamination is characterized by a sharp trade-off, such that small gains in resistance universally lead to large losses in canonical enzyme function. We show that this steep relationship can be explained by differences in the dose-response functions of 5-FC and cytosine. Finally, we observe the same trade-off shape for the orthologue of FCY1 in Cryptoccocus neoformans, a human pathogen. Our results provide a powerful resource and platform for interpreting drug target variants in fungal pathogens as well as unprecedented insights into resistance-function trade-offs.

Deep Mutational Scanning of Protein-Protein Interactions Between Partners Expressed from Their Endogenous Loci In Vivo.

Deep mutational scanning (DMS) generates mutants of a protein of interest in a comprehensive manner. CRISPR-Cas9 technology enables large-scale genome editing with high efficiency. Using both DMS and CRISPR-Cas9 therefore allows us to investigate the effects of thousands of mutations inserted directly in the genome. Combined with protein-fragment complementation assay (PCA), which enables the quantitative measurement of protein-protein interactions (PPIs) in vivo, these methods allow for the systematic assessment of the effects of mutations on PPIs in living cells. Here, we describe a method leveraging DMS, CRISPR-Cas9, and PCA to study the effect of point mutations on PPIs mediated by protein domains in yeast.

High-Throughput Gene Mutagenesis Screening Using Base Editing.

Base editing is a CRISPR-Cas9 genome engineering tool that allows programmable mutagenesis without the creation of double-stranded breaks. Here, we describe the design and execution of large-scale base editing screens using the Target-AID base editor in yeast. Using this approach, thousands of sites can be mutated simultaneously. The effects of these mutations on fitness can be measured using a pooled growth competition assay followed by DNA sequencing of gRNAs as barcodes.

Barcode fusion genetics-protein-fragment complementation assay (BFG-PCA): tools and resources that expand the potential for binary protein interaction discovery.

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein-protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Protein context shapes the specificity of SH3 domain-mediated interactions in vivo.

Protein-protein interactions (PPIs) between modular binding domains and their target peptide motifs are thought to largely depend on the intrinsic binding specificities of the domains. The large family of SRC Homology 3 (SH3) domains contribute to cellular processes via their ability to support such PPIs. While the intrinsic binding specificities of SH3 domains have been studied in vitro, whether each domain is necessary and sufficient to define PPI specificity in vivo is largely unknown. Here, by combining deletion, mutation, swapping and shuffling of SH3 domains and measurements of their impact on protein interactions in yeast, we find that most SH3s do not dictate PPI specificity independently from their host protein in vivo. We show that the identity of the host protein and the position of the SH3 domains within their host are critical for PPI specificity, for cellular functions and for key biophysical processes such as phase separation. Our work demonstrates the importance of the interplay between a modular PPI domain such as SH3 and its host protein in establishing specificity to wire PPI networks. These findings will aid understanding how protein networks are rewired during evolution and in the context of mutation-driven diseases such as cancer.

Perturbing proteomes at single residue resolution using base editing.

Base editors derived from CRISPR-Cas9 systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput. Here, we test the ability of the Target-AID base editor to systematically modify genes genome-wide by targeting yeast essential genes. We mutate around 17,000 individual sites in parallel across more than 1500 genes. We identify over 700 sites at which mutations have a significant impact on fitness. Using previously determined and preferred Target-AID mutational outcomes, we find that gRNAs with significant effects on fitness are enriched in variants predicted to be deleterious based on residue conservation and predicted protein destabilization. We identify key features influencing effective gRNAs in the context of base editing. Our results show that base editing is a powerful tool to identify key amino acid residues at the scale of proteomes.

Competition experiments in a soil microcosm reveal the impact of genetic and biotic factors on natural yeast populations.

The ability to measure microbial fitness directly in natural conditions and in interaction with other microbes is a challenge that needs to be overcome if we want to gain a better understanding of microbial fitness determinants in nature. Here we investigate the influence of the natural microbial community on the relative fitness of the North American populations SpB, SpC and SpC* of the wild yeast Saccharomyces paradoxus using DNA barcodes and a soil microcosm derived from soil associated with oak trees. We find that variation in fitness among these genetically distinct groups is influenced by the microbial community. Altering the microbial community load and diversity with an irradiation treatment significantly diminishes the magnitude of fitness differences among populations. Our findings suggest that microbial interactions could affect the evolution of yeast lineages in nature by modulating variation in fitness.

beditor: A Computational Workflow for Designing Libraries of Guide RNAs for CRISPR-Mediated Base Editing.

CRISPR-mediated base editors have opened unique avenues for scar-free genome-wide mutagenesis. Here, we describe a comprehensive computational workflow called beditor that can be broadly adapted for designing guide RNA libraries with a range of CRISPR-mediated base editors, Protospacer Adjacent Motif (PAM) recognition sequences, and genomes of many species. Additionally, to assist users in selecting the best sets of guide RNAs for their experiments, a priori estimates of editing efficiency, called beditor scores, are calculated. These beditor scores are intended to select guide RNAs that conform to requirements for optimal base editing: the editable base falls within maximum activity window of the CRISPR-mediated base editor and produces nonconfounding mutational effects with minimal predicted off-target effects. We demonstrate the utility of the software by designing guide RNAs for base editing to model or correct thousands of clinically important human disease mutations.

Double Selection Enhances the Efficiency of Target-AID and Cas9-Based Genome Editing in Yeast.

CRISPR-Cas9 loss of function (LOF) and base editing screens are powerful tools in genetics and genomics. Yeast is one of the main models in these fields, but has only recently started to adopt this new toolkit for high throughput experiments. We developed a double selection strategy based on co-selection that increases LOF mutation rates using the Target-AID base editor. We constructed the pDYSCKO vector, which is amenable to high throughput double selection experiments, and show that the improvement in Target-AID efficiency generalizes across loci. Using modeling, we show that this improvement in efficiency provides the required increased in detection power to measure the fitness effects of thousands of mutations in typical yeast pooled screens. We show that double selection can also improve Cas9 mediated LOF rates, but that this multiplex genome editing causes programmable chromosomal translocations at high frequency. This suggests that multiplex LOF editing should be performed with caution and that base-editors could be preferable tools for some screens in yeast. Base editing using double selection is simple and straightforward and provides an alternative to homology directed repair based high throughput variant strain construction methods.

Extended Linkers Improve the Detection of Protein-protein Interactions (PPIs) by Dihydrofolate Reductase Protein-fragment Complementation Assay (DHFR PCA) in Living Cells.

Understanding the function of cellular systems requires describing how proteins assemble with each other into transient and stable complexes and to determine their spatial relationships. Among the tools available to perform these analyses on a large scale is Protein-fragment Complementation Assay based on the dihydrofolate reductase (DHFR PCA). Here we test how longer linkers between the fusion proteins and the reporter fragments affect the performance of this assay. We investigate the architecture of the RNA polymerases, the proteasome and the conserved oligomeric Golgi (COG) complexes in living cells and performed large-scale screens with these extended linkers. We show that longer linkers significantly improve the detection of protein-protein interactions and allow to measure interactions further in space than the standard ones. We identify new interactions, for instance between the retromer complex and proteins related to autophagy and endocytosis. Longer linkers thus contribute an enhanced additional tool to the existing toolsets for the detection and measurements of protein-protein interactions and protein proximity in living cells.