Quantitative Genetic Interaction Research Articles

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice.

Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL–QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.

A Quantitative Genetic Interaction Map of HIV Infection

We have developed a platform for quantitative genetic interaction mapping using viral infectivity as a functional readout and constructed a viral host-dependency epistasis map (vE-MAP) of 356 human genes linked to HIV function, comprising >63,000 pairwise genetic perturbations. The vE-MAP provides an expansive view of the genetic dependencies underlying HIV infection and can be used to identify drug targets and study viral mutations. We found that the RNA deadenylase complex, CNOT, is a central player in the vE-MAP and show that knockout of CNOT1, 10, and 11 suppressed HIV infection in primary Tcells by upregulating innate immunity pathways. This phenotype was rescued by deletion of IRF7, a transcription factor regulating interferon-stimulated genes, revealing a previously unrecognized host signaling pathway involved in HIV infection. The vE-MAP represents a generic platform that can be used to study the global effects of howdifferent pathogens hijack and rewire the host during infection.

Tdp1 processes chromate-induced single-strand DNA breaks that collapse replication forks.

Hexavalent chromium [Cr(VI)] damages DNA and causes cancer, but it is unclear which DNA damage responses (DDRs) most critically protect cells from chromate toxicity. Here, genome-wide quantitative functional profiling, DDR measurements and genetic interaction assays in Schizosaccharomyces pombe reveal a chromate toxicogenomic profile that closely resembles the cancer chemotherapeutic drug camptothecin (CPT), which traps Topoisomerase 1 (Top1)-DNA covalent complex (Top1cc) at the 3’ end of single-stand breaks (SSBs), resulting in replication fork collapse. ATR/Rad3-dependent checkpoints that detect stalled and collapsed replication forks are crucial in Cr(VI)-treated cells, as is Mus81-dependent sister chromatid recombination (SCR) that repairs single-ended double-strand breaks (seDSBs) at broken replication forks. Surprisingly, chromate resistance does not require base excision repair (BER) or interstrand crosslink (ICL) repair, nor does co-elimination of XPA-dependent nucleotide excision repair (NER) and Rad18-mediated post-replication repair (PRR) confer chromate sensitivity in fission yeast. However, co-elimination of Tdp1 tyrosyl-DNA phosphodiesterase and Rad16-Swi10 (XPF-ERCC1) NER endonuclease synergistically enhances chromate toxicity in top1Δ cells. Pnk1 polynucleotide kinase phosphatase (PNKP), which restores 3’-hydroxyl ends to SSBs processed by Tdp1, is also critical for chromate resistance. Loss of Tdp1 ameliorates pnk1Δ chromate sensitivity while enhancing the requirement for Mus81. Thus, Tdp1 and PNKP, which prevent neurodegeneration in humans, repair an important class of Cr-induced SSBs that collapse replication forks.

Quantitative Yeast Genetic Interaction Profiling of Bacterial Effector Proteins Uncovers a Role for the Human Retromer in Salmonella Infection.

Intracellular bacterial pathogens secrete a repertoire of effector proteins into host cells that are required to hijack cellular pathways and cause disease. Despite decades of research, the molecular functions of most bacterial effectors remain unclear. To address this gap, we generated quantitative genetic interaction profiles between 36 validated and putative effectors from three evolutionarily divergent human bacterial pathogens and 4,190 yeast deletion strains. Correlating effector-generated profiles with those of yeast mutants, we recapitulated known biology for several effectors with remarkable specificity and predicted previously unknown functions for others. Biochemical and functional validation in human cells revealed a role for an uncharacterized component of the Salmonella SPI-2 translocon, SseC, in regulating maintenance of the Salmonella vacuole through interactions with components of the host retromer complex. These results exhibit the power of genetic interaction profiling to discover and dissect complex biology at the host-pathogen interface.

A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance

SUMMARYChemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy. This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA-repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map helps navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.

High-Throughput Quantitative Genetic Interaction Mapping in the Fission Yeast Schizosaccharomyces pombe.

Epistasis mapping, in which the phenotype that emerges from combining pairs of mutations is measured quantitatively, is a powerful tool for unbiased study of gene function. When performed at a large scale, this approach has been used to assign function to previously uncharacterized genes, define functional modules and pathways, and study their cross talk. These experiments rely heavily on methods for rapid sampling of binary combinations of mutant alleles by systematic generation of a series of double mutants. Epistasis mapping technologies now exist in various model systems. Here we provide an overview of different epistasis mapping technologies, including the pombe epistasis mapper (PEM) system designed for the collection of quantitative genetic interaction data in fission yeast Schizosaccharomyces pombe Comprising a series of high-throughput selection steps for generation and characterization of double mutants, the PEM system has provided insight into a wide range of biological processes as well as facilitated evolutionary analysis of genetic interactomes across different species.

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner.

The Genetic Interactions Structured Terminology (GIST): A modular approach to classifying and curating quantitative genetic interactions

The Genetic Interactions Structured Terminology (GIST): A modular approach to classifying and curating quantitative genetic interactions

Conditional Epistatic Interaction Maps Reveal Global Functional Rewiring of Genome Integrity Pathways in Escherichia coli.

As antibiotic resistance is increasingly becoming a public health concern, an improved understanding of the bacterial DNA damage response (DDR), which is commonly targeted by antibiotics, could be of tremendous therapeutic value. Although the genetic components of the bacterial DDR have been studied extensively in isolation, how the underlying biological pathways interact functionally remains unclear. Here, we address this by performing systematic, unbiased, quantitative synthetic genetic interaction (GI) screens and uncover widespread changes in the GI network of the entire genomic integrity apparatus of Escherichia coli under standard and DNA-damaging growth conditions. The GI patterns of untreated cultures implicated two previously uncharacterized proteins (YhbQ and YqgF) as nucleases, whereas reorganization of the GI network after DNA damage revealed DDR roles for both annotated and uncharacterized genes. Analyses of pan-bacterial conservation patterns suggest that DDR mechanisms and functional relationships are near universal, highlighting a modular and highly adaptive genomic stress response.

Sleep Duration and Area-Level Deprivation in Twins.

We used quantitative genetic models to assess whether area-level deprivation as indicated by the Singh Index predicts shorter sleep duration and modifies its underlying genetic and environmental contributions. Participants were 4,218 adult twin pairs (2,377 monozygotic and 1,841 dizygotic) from the University of Washington Twin Registry. Participants self-reported habitual sleep duration. The Singh Index was determined by linking geocoding addresses to 17 indicators at the census-tract level using data from Census of Washington State and Census Tract Cartographic Boundary Files from 2000 and 2010. Data were analyzed using univariate and bivariate genetic decomposition and quantitative genetic interaction models that assessed A (additive genetics), C (common environment), and E (unique environment) main effects of the Singh Index on sleep duration and allowed the magnitude of residual ACE variance components in sleep duration to vary with the Index. The sample had a mean age of 38.2 y (standard deviation [SD] = 18), and was predominantly female (62%) and Caucasian (91%). Mean sleep duration was 7.38 h (SD = 1.20) and the mean Singh Index score was 0.00 (SD = 0.89). The heritability of sleep duration was 39% and the Singh Index was 12%. The uncontrolled phenotypic regression of sleep duration on the Singh Index showed a significant negative relationship between area-level deprivation and sleep length (b = -0.080, P < 0.001). Every 1 SD in Singh Index was associated with a ∼4.5 min change in sleep duration. For the quasi-causal bivariate model, there was a significant main effect of E (b(0E) = -0.063; standard error [SE] = 0.30; P < 0.05). Residual variance components unique to sleep duration were significant for both A (b(0Au) = 0.734; SE = 0.020; P < 0.001) and E (b(0Eu) = 0.934; SE = 0.013; P < 0.001). Area-level deprivation has a quasi-causal association with sleep duration, with greater deprivation being related to shorter sleep. As area-level deprivation increases, unique genetic and nonshared environmental residual variance in sleep duration increases.

Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms.

The lagging annotation of bacterial genomes and the inherent genetic complexity of many phenotypes is hindering the discovery of new drug targets and the development of new antimicrobial agents and vaccines. This unit presents Tn-seq, a method that has made it possible to quantitatively determine fitness for most genes in a microorganism and to screen for quantitative genetic interactions on a genome-wide scale and in a high-throughput fashion. Tn-seq can thus direct studies on the annotation of genes and untangle complex phenotypes. The method is based on the construction of a saturated transposon insertion library. After library selection, changes in the frequency of each insertion mutant are determined by sequencing flanking regions en masse. These changes are used to calculate each mutant's fitness. The method was originally developed for the Gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis, but has now been applied to several different microbial species.

Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms.

The lagging annotation of bacterial genomes and the inherent genetic complexity of many phenotypes is hindering the discovery of new drug targets and the development of new antimicrobial agents and vaccines. This unit presents Tn-seq, a method that has made it possible to quantitatively determine fitness for most genes in a microorganism and to screen for quantitative genetic interactions on a genome-wide scale and in a high-throughput fashion. Tn-seq can thus direct studies on the annotation of genes and untangle complex phenotypes. The method is based on the construction of a saturated transposon insertion library. After library selection, changes in the frequency of each insertion mutant are determined by sequencing flanking regions en masse. These changes are used to calculate each mutant's fitness. The method was originally developed for the Gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis, but has now been applied to several different microbial species.

Sleep Duration and Depressive Symptoms: A Gene-Environment Interaction

We used quantitative genetic models to assess whether sleep duration modifies genetic and environmental influences on depressive symptoms. Participants were 1,788 adult twins from 894 same-sex twin pairs (192 male and 412 female monozygotic [MZ] pairs, and 81 male and 209 female dizygotic [DZ] pairs] from the University of Washington Twin Registry. Participants self-reported habitual sleep duration and depressive symptoms. Data were analyzed using quantitative genetic interaction models, which allowed the magnitude of additive genetic, shared environmental, and non-shared environmental influences on depressive symptoms to vary with sleep duration. Within MZ twin pairs, the twin who reported longer sleep duration reported fewer depressive symptoms (ec = -0.17, SE = 0.06, P < 0.05). There was a significant gene × sleep duration interaction effect on depressive symptoms (a'c = 0.23, SE = 0.08, P < 0.05), with the interaction occurring on genetic influences that are common to both sleep duration and depressive symptoms. Among individuals with sleep duration within the normal range (7-8.9 h/night), the total heritability (h2) of depressive symptoms was approximately 27%. However, among individuals with sleep duration within the low (< 7 h/night) or high (≥ 9 h/night) range, increased genetic influence on depressive symptoms was observed, particularly at sleep duration extremes (5 h/night: h2 = 53%; 10 h/night: h2 = 49%). Genetic contributions to depressive symptoms increase at both short and long sleep durations.

From Structure to Systems: High-Resolution, Quantitative Genetic Analysis of RNA Polymerase II

RNA polymerase II (RNAPII) lies at the core of dynamic control of gene expression. Using 53 RNAPII point mutants, we generated a point mutant epistatic miniarray profile (pE-MAP) comprising ∼60,000 quantitative genetic interactions in Saccharomyces cerevisiae. This analysis enabled functional assignment of RNAPII subdomains and uncovered connections between individual regions and other protein complexes. Using splicing microarrays and mutants that alter elongation rates in vitro, we found an inverse relationship between RNAPII speed and in vivo splicing efficiency. Furthermore, the pE-MAP classified fast and slow mutants that favor upstream and downstream start site selection, respectively. The striking coordination of polymerization rate with transcription initiation and splicing suggests that transcription rate is tuned to regulate multiple gene expression steps. The pE-MAP approach provides a powerful strategy to understand other multifunctional machines at amino acid resolution.

A Lipid E-MAP Identifies Ubx2 as a Critical Regulator of Lipid Saturation and Lipid Bilayer Stress

Biological membranes are complex, and the mechanisms underlying their homeostasis are incompletely understood. Here, we present a quantitative genetic interaction map (E-MAP) focused on various aspects of lipid biology, including lipid metabolism, sorting, and trafficking. This E-MAP contains ∼250,000 negative and positive genetic interaction scores and identifies a molecular crosstalk of protein quality control pathways with lipid bilayer homeostasis. Ubx2p, a component of the endoplasmic-reticulum-associated degradation pathway, surfaces as a key upstream regulator of the essential fatty acid (FA) desaturase Ole1p. Loss of Ubx2p affects the transcriptional control of OLE1, resulting in impaired FA desaturation and a severe shift toward more saturated membrane lipids. Both the induction of the unfolded protein response and aberrant nuclear membrane morphologies observed in cells lacking UBX2 are suppressed by the supplementation of unsaturated FAs. Our results point toward the existence of dedicated bilayer stress responses for membrane homeostasis.

Approaching the Functional Annotation of Fungal Virulence Factors Using Cross-Species Genetic Interaction Profiling

In many human fungal pathogens, genes required for disease remain largely unannotated, limiting the impact of virulence gene discovery efforts. We tested the utility of a cross-species genetic interaction profiling approach to obtain clues to the molecular function of unannotated pathogenicity factors in the human pathogen Cryptococcus neoformans. This approach involves expression of C. neoformans genes of interest in each member of the Saccharomyces cerevisiae gene deletion library, quantification of their impact on growth, and calculation of the cross-species genetic interaction profiles. To develop functional predictions, we computed and analyzed the correlations of these profiles with existing genetic interaction profiles of S. cerevisiae deletion mutants. For C. neoformans LIV7, which has no S. cerevisiae ortholog, this profiling approach predicted an unanticipated role in the Golgi apparatus. Validation studies in C. neoformans demonstrated that Liv7 is a functional Golgi factor where it promotes the suppression of the exposure of a specific immunostimulatory molecule, mannose, on the cell surface, thereby inhibiting phagocytosis. The genetic interaction profile of another pathogenicity gene that lacks an S. cerevisiae ortholog, LIV6, strongly predicted a role in endosome function. This prediction was also supported by studies of the corresponding C. neoformans null mutant. Our results demonstrate the utility of quantitative cross-species genetic interaction profiling for the functional annotation of fungal pathogenicity proteins of unknown function including, surprisingly, those that are not conserved in sequence across fungi.

A gene ontology inferred from molecular networks

Ontologies have proven very useful for capturing knowledge as a hierarchy of terms and their interrelationships. In biology a major challenge has been to construct ontologies of gene function given incomplete biological knowledge and inconsistencies in how this knowledge is manually curated. Here we show that large networks of gene and protein interactions in Saccharomyces cerevisiae can be used to infer an ontology whose coverage and power are equivalent to those of the manually curated Gene Ontology (GO). The network-extracted ontology (NeXO) contains 4,123 biological terms and 5,766 term-term relations, capturing 58% of known cellular components. We also explore robust NeXO terms and term relations that were initially not cataloged in GO, a number of which have now been added based on our analysis. Using quantitative genetic interaction profiling and chemogenomics, we find further support for many of the uncharacterized terms identified by NeXO, including multisubunit structures related to protein trafficking or mitochondrial function. This work enables a shift from using ontologies to evaluate data to using data to construct and evaluate ontologies.

Multiple Genetic Interaction Experiments Provide Complementary Information Useful for Gene Function Prediction

Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

Differential analysis of high-throughput quantitative genetic interaction data.

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.