A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 7,700 CRISPRi perturbations targeting 1,712 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays

The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.

A universal sequencing read interpreter

Massively parallel DNA sequencing has led to the rapid growth of highly multiplexed experiments in biology. These experiments produce unique sequencing results that require specific analysis pipelines to decode highly structured reads. However, no versatile framework that interprets sequencing reads to extract their encoded information for downstream biological analysis has been developed. Here, we report INTERSTELLAR (interpretation, scalable transformation, and emulation of large-scale sequencing reads) that decodes data values encoded in theoretically any type of sequencing read and translates them into sequencing reads of another structure of choice. We demonstrated that INTERSTELLAR successfully extracted information from a range of short- and long-read sequencing reads and translated those of single-cell (sc)RNA-seq, scATAC-seq, and spatial transcriptomics to be analyzed by different software tools that have been developed for conceptually the same types of experiments. INTERSTELLAR will greatly facilitate the development of sequencing-based experiments and sharing of data analysis pipelines.

Bioinformatic Analysis of Yeast Two-Hybrid Next-Generation Interaction Screen Data

Yeast two-hybrid next-generation interaction screening (Y2H-NGIS) uses the output of next-generation sequencing to mine for novel protein-protein interactions. Here, we outline the analytics underlying Y2H-NGIS datasets. Different systems, libraries, and experimental designs comprise Y2H-NGIS methodologies. We summarize the analysis in several layers that comprise the characterization of baits and preys, quantification, and identification of true interactions for subsequent secondary validation. We present two software designed for this purpose, NGPINT and Y2H-SCORES, which are used as front-end and back-end tools in the analysis. Y2H-SCORES software can be used and adapted to analyze different datasets not only from Y2H-NGIS but from other techniques ruled by similar biological principles.

Complex genetics cause and constrain fungal persistence in different parts of the mammalian body

Determining how genetic polymorphisms enable certain fungi to persist in mammalian hosts can improve understanding of opportunistic fungal pathogenesis, a source of substantial human morbidity and mortality. We examined the genetic basis of fungal persistence in mice using a cross between a clinical isolate and the lab reference strain of the budding yeast Saccharomyces cerevisiae. Employing chromosomally encoded DNA barcodes, we tracked the relative abundances of 822 genotyped, haploid segregants in multiple organs over time and performed linkage mapping of their persistence in hosts. Detected loci showed a mix of general and antagonistically pleiotropic effects across organs. General loci showed similar effects across all organs, while antagonistically pleiotropic loci showed contrasting effects in the brain vs the kidneys, liver, and spleen. Persistence in an organ required both generally beneficial alleles and organ-appropriate pleiotropic alleles. This genetic architecture resulted in many segregants persisting in the brain or in nonbrain organs, but few segregants persisting in all organs. These results show complex combinations of genetic polymorphisms collectively cause and constrain fungal persistence in different parts of the mammalian body.

The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross

In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids.

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information

Protein-protein interactions (PPIs) provide a physical basis of molecular communications for a wide range of biological processes in living cells. Establishing the PPI network has become a fundamental but essential task for a better understanding of biological events and disease pathogenesis. Although many machine learning algorithms have been employed to predict PPIs, with only protein sequence information as the training features, these models suffer from low robustness and prediction accuracy. In this study, a new deep-learning-based framework named the Structural Gated Attention Deep (SGAD) model was proposed to improve the performance of PPI network reconstruction (PINR). The improved predictive performances were achieved by augmenting multiple protein sequence descriptors, the topological features and information flow of the PPI network, which were further implemented with a gating mechanism to improve its robustness to noise. On 11 independent test data sets and one combined data set, SGAD yielded area under the curve values of approximately 0.83-0.93, outperforming other models. Furthermore, the SGAD ensemble can learn more characteristics information on protein pairs through a two-layer neural network, serving as a powerful tool in the exploration of PPI biological space.

OUP accepted manuscript

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.

Fluctuating Environments Maintain Genetic Diversity through Neutral Fitness Effects and Balancing Selection

Genetic variation is the raw material upon which selection acts. The majority of environmental conditions change over time and therefore may result in variable selective effects. How temporally fluctuating environments impact the distribution of fitness effects and in turn population diversity is an unresolved question in evolutionary biology. Here, we employed continuous culturing using chemostats to establish environments that switch periodically between different nutrient limitations and compared the dynamics of selection to static conditions. We used the pooled Saccharomyces cerevisiae haploid gene deletion collection as a synthetic model for populations comprising thousands of unique genotypes. Using barcode sequencing, we find that static environments are uniquely characterized by a small number of high-fitness genotypes that rapidly dominate the population leading to dramatic decreases in genetic diversity. By contrast, fluctuating environments are enriched in genotypes with neutral fitness effects and an absence of extreme fitness genotypes contributing to the maintenance of genetic diversity. We also identified a unique class of genotypes whose frequencies oscillate sinusoidally with a period matching the environmental fluctuation. Oscillatory behavior corresponds to large differences in short-term fitness that are not observed across long timescales pointing to the importance of balancing selection in maintaining genetic diversity in fluctuating environments. Our results are consistent with a high degree of environmental specificity in the distribution of fitness effects and the combined effects of reduced and balancing selection in maintaining genetic diversity in the presence of variable selection.

Protein-fragment complementation assays for large-scale analysis of protein-protein interactions

Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

The genotype-phenotype landscape of an allosteric protein

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here, we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.

Measurements drive progress in directed evolution for precise engineering of biological systems

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.

A large accessory protein interactome is rewired across environments

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

Genome-Wide Dynamic Evaluation of the UV-Induced DNA Damage Response

Genetic screens in Saccharomyces cerevisiae have allowed for the identification of many genes as sensors or effectors of DNA damage, typically by comparing the fitness of genetic mutants in the presence or absence of DNA-damaging treatments. However, these static screens overlook the dynamic nature of DNA damage response pathways, missing time-dependent or transient effects. Here, we examine gene dependencies in the dynamic response to ultraviolet radiation-induced DNA damage by integrating ultra-high-density arrays of 6144 diploid gene deletion mutants with high-frequency time-lapse imaging. We identify 494 ultraviolet radiation response genes which, in addition to recovering molecular pathways and protein complexes previously annotated to DNA damage repair, include components of the CCR4-NOT complex, tRNA wobble modification, autophagy, and, most unexpectedly, 153 nuclear-encoded mitochondrial genes. Notably, mitochondria-deficient strains present time-dependent insensitivity to ultraviolet radiation, posing impaired mitochondrial function as a protective factor in the ultraviolet radiation response.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model

Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein-protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs.

Perspective of Molecular Diagnosis in Healthcare: From Barcode to Pattern Recognition

Barcode technology has a broad spectrum of applications including healthcare, food security, and environmental monitoring, due to its ability to encode large amounts of information. With the rapid development of modern molecular research, barcodes are utilized as a reporter with different molecular combinations to label many biomolecular targets, including genomic and metabolic elements, even with multiplex targeting. Along with the advancements in barcoded bioassay, the improvements of various designs of barcode components, encoding and decoding strategies, and their portable adoption are indispensable in satisfying multiple purposes, such as medical confirmation and point-of-care (POC) testing. This perspective briefly discusses the current direction and progress of barcodes development and provides a hypothesis for barcoded bioassay in the near future.

iSeq 2.0: A Modular and Interchangeable Toolkit for Interaction Screening in Yeast

We developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae, which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3AI, KanMXAI, HphMXAI, and NatMXAI), and an array of ∼24,000 "double-barcoder" strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.

Improved discovery of genetic interactions using CRISPRiSeq across multiple environments

Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient-sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.

An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information

Protein-protein interactions (PPIs) perform a very important function in many cellular processes, including signal transduction, post-translational modifications, apoptosis, and cell growth. Deregulation of PPIs results in many diseases, including cancer and pernicious anemia. Although many high-throughput methods have been applied to generate a large amount of PPIs data, they are generally expensive, inefficient and labor-intensive. Hence, there is an urgent need for developing a computational method to accurately and rapidly detect PPIs. In this article, we proposed a highly efficient approach to predict PPIs by integrating a new protein sequence substitution matrix feature representation and ensemble weighted sparse representation model classifier. The proposed method is demonstrated on Saccharomyces cerevisiae dataset and achieved 99.26% prediction accuracy with 98.53% sensitivity at precision of 100%, which is shown to have much higher predictive accuracy than current state-of-the-art algorithms. Extensive experiments are performed with the benchmark data set from Human and Helicobacter pylori that the proposed method achieves outstanding better success rates than other existing approaches in this problem. Experiment results illustrate that our proposed method presents an economical approach for computational building of PPI networks, which can be a helpful supplementary method for future proteomics researches.

Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies

Standard practice for phenotyping complex cell pools is to measure the fold enrichment of genotype-specific amplicons after a period of competitive growth. Here, we show that fold-enrichment measures cannot be compared across genotype pools with different fitness distributions. We develop a method to calculate an unbiased estimate of relative fitness by tracking abundances over several time points and show how to optimize experimental protocols to minimize fitness measurement error.

Bartender: a fast and accurate clustering algorithm to count barcode reads

Motivation

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.

Results

In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods.

Availability and implementation

Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.

Contact

sasha.levy@stonybrook.edu or song.wu@stonybrook.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mapping DNA damage-dependent genetic interactions in yeast via party mating and barcode fusion genetics

Condition‐dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State‐of‐the‐art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double‐mutant strains, does not scale readily to multi‐condition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFG‐GI), by which double‐mutant strains generated via en masse “party” mating can also be monitored en masse for growth to detect genetic interactions. By using site‐specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG‐GI enables multiplexed quantitative tracking of double mutants via next‐generation sequencing. We applied BFG‐GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4‐nitroquinoline 1‐oxide (4NQO), bleomycin, zeocin, and three other DNA‐damaging environments. BFG‐GI recapitulated known genetic interactions and yielded new condition‐dependent genetic interactions. We validated and further explored a subnetwork of condition‐dependent genetic interactions involving MAG1,SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53.

Genomics of cellular proliferation in periodic environmental fluctuations

Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.