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The ability to design a protein to bind specifically to a target RNA enables numerous applications, with the modular architecture of the PUF domain lending itself to new RNA-binding specificities. For each repeat of the Pumilio-1 PUF domain, we generate a library that contains the 8,000 possible combinations of amino acid substitutions at residues critical for RNA contact. We carry out yeast three-hybrid selections with each library against the RNA recognition sequence for Pumilio-1, with any possible base present at the position recognized by the randomized repeat. We use sequencing to score the binding of each variant, identifying many variants with highly repeat-specific interactions. From these data, we generate an RNA binding code specific to each repeat and base. We use this code to design PUF domains against 16 RNAs, and find that some of these domains recognize RNAs with two, three or four changes from the wild type sequence., The PUF RNAbinding domain has an eight-repeat structure with the ability to bind an eight-base RNA sequence. Here the authors generate a comprehensive library of variants of a PUF domain to find an RNA binding code and design PUF domains that recognize an arbitrary RNA sequence.

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements—notably the TATA box—as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features. A massively parallel reporter assay was used to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum in two assay systems, uncovering the sequence features affecting promoter strength and facilitating promoter strength prediction and synthetic design.

The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell’s regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development., Existing studies of the chromatin accessibility, the primary mark of regulatory DNA, in Arabidopsis are based mainly on bulk samples. Here, the authors report the regulatory landscape of Arabidopsis thaliana roots at single-cell resolution.

DNA sequencing has led to the discovery of millions of mutations that change the encoded protein sequences, but the impact of nearly all of these mutations on protein function is unknown. We addressed this scarcity of functional data by developing Miro, a proteomic technology that uses mistranslation to introduce amino acid substitutions and biochemical assays to quantify functional differences of thousands of protein variants by mass spectrometry. We apply this technology to the proteome of yeast to reveal amino acid substitutions that impact protein structure, ligand binding, protein-protein interactions, protein post-translational modifications, and protein thermal stability. Adapting Miro to human cells will provide a means to efficiently accelerate our mechanistic interpretation of genomic mutations to predict disease risk.

Genetic engineering of cis-regulatory elements in crop plants is a promising strategy to ensure food security. However, such engineering is currently hindered by our limited knowledge of plant cis-regulatory elements. Here, we adapted STARR-seq — a technology for the high-throughput identification of enhancers — for its use in transiently transformed tobacco leaves. We demonstrate that the optimal placement in the reporter construct of enhancer sequences from a plant virus, pea and wheat was just upstream of a minimal promoter, and that none of these four known enhancers was active in the 3′-UTR of the reporter gene. The optimized assay sensitively identified small DNA regions containing each of the four enhancers, including two whose activity was stimulated by light. Furthermore, we coupled the assay to saturation mutagenesis to pinpoint functional regions within an enhancer, which we recombined to create synthetic enhancers. Our results describe an approach to define enhancer properties that can be performed in potentially any plant species or tissue transformable by Agrobacterium and that can use regulatory DNA derived from any plant genome.One-sentence summaryWe developed a high-throughput assay in transiently transformed tobacco leaves that can identify enhancers, characterize their functional elements and detect condition-specific enhancer activity.

Here, Zhou et al. took advantage of Saccharomyces cerevisiae and its well-characterized mating and invasion pathways to explain how transcription factor variants alter motif recognition and gene expression, and ultimately, organismal phenotypes..., Amino acid substitutions are commonly found in human transcription factors, yet the functional consequences of much of this variation remain unknown, even in well-characterized DNA-binding domains. Here, we examine how six single-amino acid variants in the DNA-binding domain of Ste12—a yeast transcription factor regulating mating and invasion—alter Ste12 genome binding, motif recognition, and gene expression to yield markedly different phenotypes. Using a combination of the “calling-card” method, RNA sequencing, and HT-SELEX (high throughput systematic evolution of ligands by exponential enrichment), we find that variants with dissimilar binding and expression profiles can converge onto similar cellular behaviors. Mating-defective variants led to decreased expression of distinct subsets of genes necessary for mating. Hyper-invasive variants also decreased expression of subsets of genes involved in mating, but increased the expression of other subsets of genes associated with the cellular response to osmotic stress. While single-amino acid changes in the coding region of this transcription factor result in complex regulatory reconfiguration, the major phenotypic consequences for the cell appear to depend on changes in the expression of a small number of genes with related functions.

Antibiotic resistance is a growing threat to public health, making the development of antibiotics of critical importance. One promising class of potential new antibiotics are ribosomally-synthesized and post-translationally modified peptides (RiPPs), which include klebsidin, a lasso peptide from Klebsiella pneumoniae that inhibits certain bacterial RNA polymerases. We develop a high-throughput assay based on growth inhibition of Escherichia coli to analyze the mutational tolerance of klebsidin. We transform a library of klebsidin variants into E. coli and use next-generation DNA sequencing to count the frequency of each variant before and after its expression, thereby generating functional scores for 320 of 361 single amino acid changes. We identify multiple positions in the macrocyclic ring and the C-terminal tail region of klebsidin that are intolerant to mutation, as well as positions in the loop region that are highly tolerant to mutation. Characterization of selected peptide variants scored as active reveals that each adopts a threaded lasso conformation; active loop variants applied extracellularly as peptides slow the growth of E. coli and K. pneumoniae. We generate an E. coli strain with a mutation in RNA polymerase that confers resistance to klebsidin and similarly carry out a selection with the klebsidin library. We identify a single variant, klebsidin F9Y, that maintains activity against the resistant E. coli when expressed intracellularly. This finding supports the utility of this method and suggests that comprehensive mutational analysis of lasso peptides can identify unique and potentially improved variants.

Massively-parallel measurements of dominant negative inhibition by protein fragments have been used to map protein interaction sites and discover peptide inhibitors. However, the underlying principles governing fragment-based inhibition have thus far remained unclear. Here, we adapt a high-throughput inhibitory fragment assay for use inEscherichia coli, applying it to a set of ten essential proteins. This approach yielded single amino acid-resolution maps of inhibitory activity, with peaks localized to functionally important interaction sites, including oligomerization interfaces and folding contacts. Leveraging these data, we perform a systematic analysis to uncover principles of fragment-based inhibition. We determine a robust negative correlation between susceptibility to inhibition and cellular protein concentration, demonstrating that inhibitory fragments likely act primarily by titrating native protein interactions. We also characterize a series of trade-offs related to fragment length, showing that shorter peptides allow higher-resolution mapping but suffer from lower inhibitory activity. We employ an unsupervised statistical analysis to show that the inhibitory activities of protein fragments are largely driven not by generic properties such as charge, hydrophobicity, and secondary structure, but by the more specific characteristics of their bespoke macromolecular interactions. AlphaFold computational modeling of peptide complexes with one protein shows that the inhibitory activity of peptides is associated with their predicted ability to form native-like interactions. Overall, this work demonstrates fundamental characteristics of inhibitory protein fragment function and provides a foundation for understanding and controlling protein interactionsin vivo.Significance StatementPeptide fragments derived from protein sequences can inhibit interactions of their parental proteins, providing a promising avenue for drug development. Here we employ a massively-parallel assay to measurein vivoinhibition by fragments that tile the full sequences of ten essential bacterial proteins. We leverage these data to decipher principles of fragment-based inhibition, showing how parental protein concentration drives activity and how protein fragment length interplays with activity and specificity. We employ statistical analysis to parse the roles of biophysical properties in fragment-to-fragment variation, and AlphaFold modeling to determine the relationship between measured inhibitory activity and predicted native-like binding. These results provide a path towards rational design of peptide inhibitors and broader principles of protein-protein interactions in living cells.

Background The 3′ untranslated region (UTR) plays critical roles in determining the level of gene expression through effects on activities such as mRNA stability and translation. Functional elements within this region have largely been identified through analyses of native genes, which contain multiple co-evolved sequence features. Results To explore the effects of 3′ UTR sequence elements outside of native sequence contexts, we analyze hundreds of thousands of random 50-mers inserted into the 3′ UTR of a reporter gene in the yeast Saccharomyces cerevisiae. We determine relative protein expression levels from the fitness of transformants in a growth selection. We find that the consensus 3′ UTR efficiency element significantly boosts expression, independent of sequence context; on the other hand, the consensus positioning element has only a small effect on expression. Some sequence motifs that are binding sites for Puf proteins substantially increase expression in the library, despite these proteins generally being associated with post-transcriptional downregulation of native mRNAs. Our measurements also allow a systematic examination of the effects of point mutations within efficiency element motifs across diverse sequence backgrounds. These mutational scans reveal the relative in vivo importance of individual bases in the efficiency element, which likely reflects their roles in binding the Hrp1 protein involved in cleavage and polyadenylation. Conclusions The regulatory effects of some 3′ UTR sequence features, like the efficiency element, are consistent regardless of sequence context. In contrast, the consequences of other 3′ UTR features appear to be strongly dependent on their evolved context within native genes.

Single cell transcriptomic profiling can map developmental trajectories and assess cell-type–specific changes during heat shock at single cell resolution., Single cell RNA sequencing can yield high-resolution cell-type–specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach to Arabidopsis (Arabidopsis thaliana) root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type–specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat-shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

SummaryMultiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating ‘variant effect maps’, which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets, and enable tracking and sharing of progress on ongoing MAVE projects.Availability and implementationhttps://registry.varianteffect.orgContactfritz.roth@utoronto.ca

In plants, chromatin accessibility – the primary mark of regulatory DNA – is relatively static across tissues and conditions. This scarcity of accessible sites that are dynamic or tissue-specific may be due in part to tissue heterogeneity in previous bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to A. thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. However, even this vast increase relative to bulk studies in the number of dynamic sites does not resolve the poor correlation at individual loci between accessibility and expression. Instead, we find that the entirety of a cell’s regulatory landscape and its transcriptome each capture cell type identity independently. We leverage this shared information on cell identity to integrate accessibility and transcriptome data in order to characterize developmental progression, endoreduplication and cell division in the root. We further use the combined data to characterize cell type-specific motif enrichments of large transcription factor families and to link the expression of individual family members to changing accessibility at specific loci, taking the first steps toward resolving the direct and indirect effects that shape gene expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

As hosts acquire resistance to viruses, viruses must overcome that resistance to re-establish infectivity, or go extinct. Despite the significant hurdles associated with adapting to a resistant host, viruses are evolutionarily successful and maintain stable coevolutionary relationships with their hosts. To investigate the factors underlying how pathogens adapt to their hosts, we performed a deep mutational scan of the region of the λ tail fiber tip protein that mediates contact with the λ host,E. coli. Phages harboring amino acid substitutions were subjected to selection for infectivity on wild typeE. coli, revealing a highly restrictive fitness landscape, in which most substitutions completely abrogate function. By comparing this lack of mutational tolerance to evolutionary diversity, we highlight a set of mutationally intolerant and diverse positions associated with host range expansion. Imposing selection for infectivity on three λ-resistant hosts, each harboring a different missense mutation in the λ receptor, reveals hundreds of adaptive variants in λ. We distinguish λ variants that confer promiscuity, a general ability to overcome host resistance, from those that drive host-specific infectivity. Both processes may be important in driving adaptation to a novel host.

Response to heat stress is mediated by heat shock transcription factors (HSFs), which possess conserved DNA-binding and oligomerization domains. The oligomerization domain is required for HSF1 to transition under heat stress from a monomer to a homotrimer, which alters DNA-binding specificity and affinity. Sequence variation in the oligomerization domain affects HSF1 activity, although this link is poorly understood. We performed a deep mutational scan of >400,000 variants of the oligomerization domain of Saccharomyces cerevisiae Hsf1 and measured fitness under stress and non-stress conditions. We identify mutations that confer temperature-specific phenotypes; some exceptional Hsf1variants lead to enhanced growth under heat stress and changes to in vivo DNA-binding and transcriptional programs. The link between Hsf1 oligomerization and DNA-binding domain is evolutionarily conserved, with co-evolving residues between these domains found among fungi. Mutation of transcription factor oligomerization domains may represent a path toward re-wiring transcriptional programs without mutation of DNA-binding domains.

Bacteria can evade cohabiting phages through mutations in phage receptors, but these mutations may come at a cost if they disrupt the receptor’s native cellular function. To investigate the relationship between these two conflicting activities, we generated sequence–function maps of Escherichia coli LamB with respect to sensitivity to phage λ and transport of maltodextrin. By comparing 413 missense mutations whose effect on both traits could be analysed, we find that these two phenotypes were correlated, implying that most mutations affect these phenotypes through a common mechanism such as loss of protein stability. However, individual mutations could be found that specifically disrupt λ-sensitivity without affecting maltodextrin transport. We identify and individually assay nine such mutations, whose spatial positions implicate loop L6 of LamB in λ binding. Although missense mutations that lead to λ-resistance are rare, they were approximately as likely to be maltodextrin-utilizing (Mal+) as not (Mal-), implying that E. coli can adapt to λ while conserving the receptor’s native function. We propose that in order for E. coli and λ to stably cohabitate, selection for λ-resistance and maltose transport must be spatially or temporally separated.

Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms., Dimensionality reduction is often used to visualize expression profiling data in order to find relationships among cells. Here, the authors use Uniform Manifold Approximation and Projection (UMAP) on published expression data of gene deletions of S. cerevisiae to find novel protein interactions.

Loss-of-function pathogenic variants in BRCA1 confer a predisposition to breast and ovarian cancer. Genetic testing for sequence changes in BRCA1 frequently reveals a missense variant for which the impact on cancer risk and on the molecular function of BRCA1 is unknown. Functional BRCA1 is required for the homology-directed repair (HDR) of double-strand DNA breaks, a critical activity for maintaining genome integrity and tumor suppression. Here, we describe a multiplex HDR reporter assay for concurrently measuring the effects of hundreds of variants of BRCA1 for their role in DNA repair. Using this assay, we characterized the effects of 1,056 amino acid substitutions in the first 192 residues of BRCA1. Benchmarking these results against variants with known effects on DNA repair function or on cancer predisposition, we demonstrate accurate discrimination of loss-of-function versus benign missense variants. We anticipate that this assay can be used to functionally characterize BRCA1 missense variants at scale, even before the variants are observed in results from genetic testing.

Biosensors are important components of many synthetic biology and metabolic engineering applications. Here, we report a second generation of Saccharomyces cerevisiae digoxigenin and progesterone biosensors based on destabilized dimeric ligand-binding domains that undergo ligand-induced stabilization. The biosensors, comprising one ligand-binding domain monomer fused to a DNA-binding domain and another fused to a transcriptional activation domain, activate reporter gene expression in response to steroid binding and receptor dimerization. The introduction of a destabilizing mutation to the dimer interface increased biosensor dynamic range by an order of magnitude. Computational redesign of the dimer interface and functional selections were used to create heterodimeric pairs with further improved dynamic range. A heterodimeric biosensor built from the digoxigenin and progesterone ligand- binding domains functioned as a synthetic “AND”-gate, with 20-fold stronger response to the two ligands in combination than to either one alone. We also identified mutations that increase the sensitivity or selectivity of the biosensors to chemically similar ligands. These dimerizing biosensors provide additional flexibility for the construction of logic gates and other applications.

To develop a system for conditional amino acid misincorporation, we engineered tRNAs in the yeast Saccharomyces cerevisiae to be substrates of the rapid tRNA decay (RTD) pathway, such that they accumulate when RTD is turned off. We used this system to test the effects on growth of a library of tRNASer variants with all possible anticodons, and show that many are lethal when RTD is inhibited and the tRNA accumulates. Using mass spectrometry, we measured serine misincorporation in yeast containing each of six tRNA variants, and for five of them identified hundreds of peptides with serine substitutions at the targeted amino acid sites. Unexpectedly, we found that there is not a simple correlation between toxicity and the level of serine misincorporation; in particular, high levels of serine misincorporation can occur at cysteine residues without obvious growth defects. We also showed that toxic tRNAs can be used as a tool to identify sequence variants that reduce tRNA function. Finally, we generalized this method to another tRNA species, and generated conditionally toxic tRNATyr variants in a similar manner. This method should facilitate the study of tRNA biology and provide a tool to probe the effects of amino acid misincorporation on cellular physiology.

Although synonymous mutations can affect gene expression, they have generally not been considered in genomic studies that focus on mutations that increase the risk of cancer. However, mounting evidence implicates some synonymous mutations as driver mutations in cancer. Here, a massively parallel assay, based on cell sorting of a reporter containing a segment of p53 fused to GFP, was used to measure the effects of nearly all synonymous mutations in exon 6 of TP53. In this reporter context, several mutations within the exon caused strong expression changes including mutations that may cause potential gain or loss of function. Further analysis indicates that these effects are largely attributed to errors in splicing, including exon skipping, intron inclusion, and exon truncation, resulting from mutations both at exon–intron junctions and within the body of the exon. These mutations are found at extremely low frequencies in healthy populations and are enriched a few-fold in cancer genomes, suggesting that some of them may be driver mutations in TP53. This assay provides a general framework to identify previously unknown detrimental synonymous mutations in cancer genes. Implications: Using a massively parallel assay, this study demonstrates that synonymous mutations in the TP53 gene affect protein expression, largely through their impact on splicing. Visual Overview: http://mcr.aacrjournals.org/content/molcanres/15/10/1301/F1.large.jpg. Mol Cancer Res; 15(10); 1301–7. ©2017 AACR.

In the yeast Saccharomyces cerevisiae, beneficial mutations selected during sulfate-limited growth are typically amplifications of the SUL1 gene, which encodes the high-affinity sulfate transporter, resulting in fitness increases of >35% . Cis-regulatory mutations have not been observed at this locus; however, it is not clear whether this absence is due to a low mutation rate such that these mutations do not arise, or they arise but have limited fitness effects relative to those of amplification. To address this question directly, we assayed the fitness effects of nearly all possible point mutations in a 493-base segment of the gene’s promoter through mutagenesis and selection. While most mutations were either neutral or detrimental during sulfate-limited growth, eight mutations increased fitness >5% and as much as 9.4%. Combinations of these beneficial mutations increased fitness only up to 11%. Thus, in the case of SUL1, promoter mutations could not induce a fitness increase similar to that of gene amplification. Using these data, we identified functionally important regions of the SUL1 promoter and analyzed three sites that correspond to potential binding sites for the transcription factors Met32 and Cbf1. Mutations that create new Met32- or Cbf1-binding sites also increased fitness. Some mutations in the untranslated region of the SUL1 transcript decreased fitness, likely due to the formation of inhibitory upstream open reading frames. Our methodology—saturation mutagenesis, chemostat selection, and DNA sequencing to track variants—should be a broadly applicable approach.

Single-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

Dominant negative polypeptides can act as inhibitors by binding to the wild type protein or by titrating an essential ligand. Here, we use high-throughput sequencing of DNA libraries composed of fragments of yeast genes to identify dominant negative polypeptides based on their depletion during cell growth. The method uncovers numerous inhibitory polypeptides for a protein and thus is capable of defining interacting domains with exquisite resolution, even pinpointing individual residues that contact ligands.

The engineering of microorganisms to monitor environmental chemicals or to produce desirable bioproducts is often reliant on the availability of a suitable biosensor. However, the conversion of a ligand-binding protein into a biosensor has been difficult. Here, we report a general strategy for generating biosensors in Escherichia coli that act by ligand-dependent stabilization of a transcriptional activator and mediate ligand concentration-dependent expression of a reporter gene. We constructed such a biosensor by using the lac repressor, LacI, as the ligand-binding domain and fusing it to the Zif268 DNA-binding domain and RNA polymerase omega subunit transcription-activating domain. Using error-prone PCR mutagenesis of lacI and selection, we identified a biosensor with multiple mutations, only one of which was essential for biosensor behavior. By tuning parameters of the assay, we obtained a response dependent on the ligand isopropyl β-D-1-thiogalactopyranoside (IPTG) of up to a seven-fold increase in the growth rate of E. coli. The single destabilizing mutation combined with a lacI mutation that expands ligand specificity to D-fucose generated a biosensor with improved response both to D-fucose and to IPTG. However, a mutation equivalent to the one that destabilized LacI in either of two structurally similar periplasmic binding proteins did not confer ligand-dependent stabilization. Finally, we demonstrated the generality of this method by using mutagenesis and selection to engineer another ligand-binding domain, MphR, to function as a biosensor. This strategy may allow many natural proteins that recognize and bind to ligands to be converted into biosensors.

Few mechanisms are known that explain how transcription factors can adjust phenotypic outputs to accommodate differing environments. In Saccharomyces cerevisiae, the decision to mate or invade relies on environmental cues that converge on a shared transcription factor, Ste12. Specificity toward invasion occurs via Ste12 binding cooperatively with the cofactor Tec1. Here, we determine the range of phenotypic outputs (mating vs. invasion) of thousands of DNA-binding domain variants in Ste12 to understand how preference for invasion may arise. We find that single amino acid changes in the DNA-binding domain can shift the preference of yeast toward either mating or invasion. These mutations define two distinct regions of this domain, suggesting alternative modes of DNA binding for each trait. We characterize the DNA-binding specificity of wild-type Ste12 to identify a strong preference for spacing and orientation of both homodimeric and heterodimeric sites. Ste12 mutants that promote hyperinvasion in a Tec1-independent manner fail to bind cooperative sites with Tec1 and bind to unusual dimeric Ste12 sites composed of one near-perfect and one highly degenerate site. We propose a model in which Ste12 alone may have evolved to activate invasion genes, which could explain how preference for invasion arose in the many fungal pathogens that lack Tec1.

Loss-of-function mutations in BRCA1 confer a predisposition to breast and ovarian cancer. Genetic testing for mutations in the BRCA1 gene frequently reveals a missense variant for which the impact on the molecular function of the BRCA1 protein is unknown. Functional BRCA1 is required for homology directed repair (HDR) of double-strand DNA breaks, a key activity for maintaining genome integrity and tumor suppression. Here we describe a multiplex HDR reporter assay to simultaneously measure the effect of hundreds of variants of BRCA1 on its role in DNA repair. Using this assay, we measured the effects of ~1,700 amino acid substitutions in the first 302 residues of BRCA1. Benchmarking these results against variants with known effects, we demonstrate accurate discrimination of loss-of-function versus benign variants. We anticipate that this assay can be used to functionally characterize BRCA1 missense variants at scale, even before the variants are observed in results from genetic testing.

Microorganisms have universally adapted their RNAs and proteins to survive at a broad range of temperatures and growth conditions. However, for RNAs, there is little quantitative understanding of the effects of mutations on function at high temperatures. To understand how variant tRNA function is affected by temperature change, we used the tRNA nonsense suppressor SUP4oc of the yeast Saccharomyces cerevisiae to perform a high-throughput quantitative screen of tRNA function at two different growth temperatures. This screen yielded comparative values for 9243 single and double variants. Surprisingly, despite the ability of S. cerevisiae to grow at temperatures as low as 15°C and as high as 39°C, the vast majority of variants that could be scored lost half or more of their function when evaluated at 37°C relative to 28°C. Moreover, temperature sensitivity of a tRNA variant was highly associated with its susceptibility to the rapid tRNA decay (RTD) pathway, implying that RTD is responsible for most of the loss of function of variants at higher temperature. Furthermore, RTD may also operate in a met22Δ strain, which was previously thought to fully inhibit RTD. Consistent with RTD acting to degrade destabilized tRNAs, the stability of a tRNA molecule can be used to predict temperature sensitivity with high confidence. These findings offer a new perspective on the stability of tRNA molecules and their quality control at high temperature.

Genes associated with hereditary breast and ovarian cancer (HBOC) are often sequenced in search of mutations that are predictive of susceptibility to these cancer types, but the sequence results are frequently ambiguous because of the detection of missense substitutions for which the clinical impact is unknown. The BARD1 protein is the heterodimeric partner of BRCA1 and is included on clinical gene panels for testing for susceptibility to HBOC. Like BRCA1, it is required for homology-directed DNA repair (HDR). We measured the HDR function of 29 BARD1 missense variants, 27 culled from clinical test results and two synthetic variants. Twenty-three of the assayed variants were functional for HDR; of these, four are known neutral variants. Three variants showed intermediate function, and three others were defective in HDR. When mapped to BARD1 domains, residues crucial for HDR were located in the N- and C- termini of BARD1. In the BARD1 RING domain, critical residues mapped to the zinc-coordinating amino acids and to the BRCA1-BARD1 binding interface, highlighting the importance of interaction between BRCA1 and BARD1 for HDR activity. Based on these results, we propose that the HDR assay is a useful complement to genetic analyses to classify BARD1 variants of unknown clinical significance.

Random mutagenesis methods only partially cover the mutational space and are constrained by DNA synthesis length limitations. Here we demonstrate programmed allelic series (PALS), a single-volume, site-directed mutagenesis approach using microarray-programmed oligonucleotides. We created libraries including nearly every missense mutation as singleton events for the yeast transcription factor Gal4 (99.9% coverage) and human tumor suppressor p53 (93.5%). PALS-based comprehensive missense mutational scans may aid structure-function studies, protein engineering, and the interpretation of variants identified by clinical sequencing.

Human genetics has historically depended on the identification of individuals whose natural genetic variation underlies an observable trait or disease risk. Here we argue that new technologies now augment this historical approach by allowing the use of massively parallel assays in model systems to measure the functional effects of genetic variation in many human genes. These studies will help establish the disease risk of both observed and potential genetic variants and to overcome the problem of “variants of uncertain significance.”