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Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics. Synopsis A multiplexed assay measures abundance of 1,379 binary protein complexes in 14 environments. Many environment‐dependent changes were found, enabling exploration of the extent to which network dynamics can be explained by mRNA levels. A DNA‐barcode‐based multiplexed protein interaction assay measured in vivo abundance of 1,379 binary protein complexes under 14 diverse environments in Saccharomyces cerevisiae . More than half of binary complexes were found to be environment‐dependent, especially those among transmembrane transporters. Many binary complexes changed in a concerted, protein‐centric manner, and under a “diauxic” shift in carbon source from glucose to ethanol, mRNA levels predicted many of the observed changes. Graphical Abstract A multiplexed assay measures abundance of 1,379 binary protein complexes in 14 environments. Many environment‐dependent changes were found, enabling exploration of the extent to which network dynamics can be explained by mRNA levels.

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. Synopsis A new method, Barcode Fusion Genetics to Map Genetic Interactions (BFG‐GI) allows generating double mutants and measuring condition‐dependent genetic interactions en masse . Application of BFG‐GI to DNA repair genes reveals a new function for the Shu complex. BFG‐GI involves generating double‐mutant‐specific fused barcodes, enabling to measure the abundance of double mutants en masse by next generation sequencing. Once a double mutant BFG‐GI pool has been generated genetic interactions can be tested in new growth conditions. BFG‐GI is applied to 26 genes related to DNA damage repair in nine different conditions, including seven DNA‐damaging agents. A novel relationship is reported between the Shu complex and the checkpoint protein kinase Rad53. Graphical Abstract A new method, Barcode Fusion Genetics to Map Genetic Interactions (BFG‐GI) allows generating double mutants and measuring condition‐dependent genetic interactions en masse . Application of BFG‐GI to DNA repair genes reveals a new function for the Shu complex.

Two approaches for understanding biological systems and gene function at a large scale are the mapping of protein-protein interactions (PPIs) and genetic interactions (GIs). PPIs map the physical connectivity structure of proteins, while GIs map dependence in phenotype when two or more genes are perturbed simultaneously. Current large-scale maps of PPIs and GIs are subject to various limitations, such as being measured in a single environment, and in the case of GI maps, a lack of exploration of complex dependencies beyond double knockouts. Here, I describe the analysis and development of emerging methods in PPIs and GIs to overcome these limitations. Analysis of a dynamic PPI assay (barcoded protein complementation assay; BC-PCA), finds that the majority of complexes (55%) change in abundance between conditions, and reveals a striking relationship between relative complex levels and relative mRNA expression during a diauxic shift from growth in glucose compared to ethanol (explaining an estimated 47% of the observed changes). A new method to analyze arbitrarily complex GIs in multiple environments is developed, and its ability to uncover new phenotypes for ABC transporters is demonstrated. Amongst the complex GIs, a four-knockout phenotype was found which confers unexpected resistance to the antifungal drug fluconazole. These methods and resulting analyzes demonstrate the need for an expansion of the scope of PPI and GI interaction mapping, and motivate their further development towards understanding living systems and gene function at a large scale.

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 and 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 a decrease in the activation or activity of the checkpoint protein kinase Rad53. Footnotes * Manuscript has been accepted for publication in Molecular Systems Biology. This is the accepted version. In response to reviewers we completely revisited our analysis methodology, which had an impact on the resulting genetic interaction map (primarily increasing the number of positive interactions, and correspondence to a previous dataset). We have also carried out more experiments related to Rad53 and the Shu complex. Main findings remained unchanged.

MicroRNAs and RNA binding proteins are crucial elements of post-transcriptional gene regulation, which governs the fate of mRNA molecules in the cell. However, the landscape of these regulatory interactions, particularly across different mammalian cell types, remains underexplored. We describe REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation directly from RNA sequence. REPRESS was trained on AGO2-CLIP, miR-eCLIP and Degradome-Seq data profiling millions of microRNA binding and mRNA degradation sites across multiple cell types in human and mouse. It reveals biology that other state-of-the-art methods did not, such as identifying repressive non-canonical miRNA target sites and decoding the regulatory effects of sequence context and miRNA binding site multiplicity. REPRESS outperforms other advanced methods and neural architectures on a comprehensive suite of seven orthogonal tasks, including identifying genetic variants that affect microRNA binding, predicting out-of-distribution data from massively parallel reporter assays, and predicting canonical and non-canonical miRNA mediated repression. To demonstrate the general utility of REPRESS, we show that it provides insights into novel biology and the design of RNA therapeutics. Code is available at : https://github.com/deepgenomics/repress

Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics. We describe a foundation model for RNA biology, “BigRNA”, which was trained on thousands of genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence. Unlike approaches that are restricted to missense variants, BigRNA can identify pathogenic non-coding variant effects across diverse mechanisms, including polyadenylation, exon skipping and intron retention. BigRNA accurately predicted the effects of steric blocking oligonucleotides (SBOs) on increasing the expression of 4 out of 4 genes, and on splicing for 18 out of 18 exons across 14 genes, including those involved in Wilson disease and spinal muscular atrophy. We anticipate that BigRNA and foundation models like it will have widespread applications in the field of personalized RNA therapeutics.