Time-resolved mapping of genetic interactions to model rewiring of signaling pathways

Parallel control of cold-triggered adipocyte thermogenesis by UCP1 and CKB

That uncoupling protein 1 (UCP1) is the sole mediator of adipocyte thermogenesis is a conventional viewpoint that has primarily been inferred from the attenuation of the thermogenic output of mice genetically lacking Ucp1 from birth (germline Ucp1-/-). However, germline Ucp1-/- mice harbor secondary changes within brown adipose tissue. To mitigate these potentially confounding ancillary changes, we constructed mice with inducible adipocyte-selective Ucp1 disruption. We find that, although germline Ucp1-/- mice succumb to cold-induced hypothermia with complete penetrance, most mice with the inducible deletion of Ucp1 maintain homeothermy in the cold. However, inducible adipocyte-selective co-deletion of Ucp1 and creatine kinase b (Ckb, an effector of UCP1-independent thermogenesis) exacerbates cold intolerance. Following UCP1 deletion or UCP1/CKB co-deletion from mature adipocytes, moderate cold exposure triggers the regeneration of mature brown adipocytes that coordinately restore UCP1 and CKB expression. Our findings suggest that thermogenic adipocytes utilize non-paralogous protein redundancy-through UCP1 and CKB-to promote cold-induced energy dissipation.

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations

MOTIVATION

High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language.

RESULTS

Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets.

AVAILABILITY AND IMPLEMENTATION

The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Morphological profiling of human T and NK lymphocytes by high-content cell imaging

The immunological synapse is a complex structure that decodes stimulatory signals into adapted lymphocyte responses. It is a unique window to monitor lymphocyte activity because of development of systematic quantitative approaches. Here we demonstrate the applicability of high-content imaging to human T and natural killer (NK) cells and develop a pipeline for unbiased analysis of high-definition morphological profiles. Our approach reveals how distinct facets of actin cytoskeleton remodeling shape immunological synapse architecture and affect lytic granule positioning. Morphological profiling of CD8⁺ T cells from immunodeficient individuals allows discrimination of the roles of the ARP2/3 subunit ARPC1B and the ARP2/3 activator Wiskott-Aldrich syndrome protein (WASP) in immunological synapse assembly. Single-cell analysis further identifies uncoupling of lytic granules and F-actin radial distribution in ARPC1B-deficient lymphocytes. Our study provides a foundation for development of morphological profiling as a scalable approach to monitor primary lymphocyte responsiveness and to identify complex aspects of lymphocyte micro-architecture.

Emergence and propagation of epistasis in metabolic networks

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

TheCellVision.org: A Database for Visualizing and Mining High-Content Cell Imaging Projects

Advances in genome engineering and high throughput imaging technologies have enabled genome-scale screens of single cells for a variety of phenotypes, including subcellular morphology and protein localization. We constructed TheCellVision.org, a freely available and web-accessible image visualization and data browsing tool that serves as a central repository for fluorescence microscopy images and associated quantitative data produced by high-content screening experiments. Currently, TheCellVision.org hosts ∼575,590 images and associated analysis results from two published high-content screening (HCS) projects focused on the budding yeast Saccharomyces cerevisiae TheCellVision.org allows users to access, visualize and explore fluorescence microscopy images, and to search, compare, and extract data related to subcellular compartment morphology, protein abundance, and localization. Each dataset can be queried independently or as part of a search across multiple datasets using the advanced search option. The website also hosts computational tools associated with the available datasets, which can be applied to other projects and cell systems, a feature we demonstrate using published images of mammalian cells. Providing access to HCS data through websites such as TheCelllVision.org enables new discovery and independent re-analyses of imaging data.

Fundamental control of grade-specific colorectal cancer morphology by Src regulation of ezrin-centrosome engagement

The phenotypic spectrum of colorectal cancer (CRC) is remarkably diverse, with seemingly endless variations in cell shape, mitotic figures and multicellular configurations. Despite this morphological complexity, histological grading of collective phenotype patterns provides robust prognostic stratification in CRC. Although mechanistic understanding is incomplete, previous studies have shown that the cortical protein ezrin controls diversification of cell shape, mitotic figure geometry and multicellular architecture, in 3D organotypic CRC cultures. Because ezrin is a substrate of Src tyrosine kinase that is frequently overexpressed in CRC, we investigated Src regulation of ezrin and morphogenic growth in 3D CRC cultures. Here we show that Src perturbations disrupt CRC epithelial spatial organisation. Aberrant Src activity suppresses formation of the cortical ezrin cap that anchors interphase centrosomes. In CRC cells with a normal centrosome number, these events lead to mitotic spindle misorientation, perturbation of cell cleavage, abnormal epithelial stratification, apical membrane misalignment, multilumen formation and evolution of cribriform multicellular morphology, a feature of low-grade cancer. In isogenic CRC cells with centrosome amplification, aberrant Src signalling promotes multipolar mitotic spindle formation, pleomorphism and morphological features of high-grade cancer. Translational studies in archival human CRC revealed associations between Src intensity, multipolar mitotic spindle frequency and high-grade cancer morphology. Collectively, our study reveals Src regulation of CRC morphogenic growth via ezrin-centrosome engagement and uncovers combined perturbations underlying transition to high-grade CRC morphology. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability

Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

Mapping genetic interactions in cancer: a road to rational combination therapies

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.

Why and how to study genetic changes with context-dependent effects

The phenotypic impacts of a genetic change can depend on genetic background (e.g. epistasis), as well as other contexts including environment, developmental stage, cell type, disease state, and higher-order combinations thereof. Recent advances in high-throughput phenotyping are uncovering examples of context dependence faster than genotype-phenotype maps and other core concepts are changing to reflect the dynamic nature of biological systems. Here, we review several approaches to study context dependence and their findings. In our opinion, these findings encourage more studies that examine the spectrum of effects a genetic change may have, as opposed to studies that exclusively measure the impact of a genetic change in a particular context. Studies that elucidate the mechanisms that cause the effects of genetic change to vary with context are of special interest. Previous studies of the mechanisms underlying context dependence have improved predictions of phenotype from genotype and have provided insight about how biological systems function and evolve.

Changes in gene expression predictably shift and switch genetic interactions

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often—but not always—be predicted by hierarchical mechanistic models.

Non-additive genetic interactions are plastic and can complicate genetic prediction. Here, using deep mutagenesis of the lambda repressor, Li et al. reveal that changes in gene expression can alter the strength and direction of genetic interactions between mutations in many genes and develop mathematical models for predicting them.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

An EGFR-Induced Drosophila Lung Tumor Model Identifies Alternative Combination Treatments

Lung cancer is the leading cause of cancer-associated mortality. Mutations in the EGFR gene are among the most important inducers of lung tumor development, but success of personalized therapies is still limited because of toxicity or developing resistances. We expressed constitutively active EGFR (EGFR^CA) exclusively in the airway system of Drosophila melanogaster and performed comprehensive phenotyping. Ectopic expression of EGFR^CA induced massive hyper- and metaplasia, leading to early death. We used the lethal phenotype as a readout and screened a library of FDA-approved compounds and found that among the 1,000 compounds, only the tyrosine kinase inhibitors (TKI) afatinib, gefitinib, and ibrutinib rescued lethality in a whole-animal screening approach. Furthermore, we screened the library in the presence of a subtherapeutic afatinib dose and identified bazedoxifene as a synergistically acting compound that rescues EGFR-induced lethality. Our findings highlight the potential of Drosophila-based whole-animal screening approaches not only to identify specific EGFR inhibitors but also to discover compounds that act synergistically with known TKIs. Moreover, we showed that targeting the EGFR together with STAT-signaling is a promising strategy for lung tumor treatment.

Large-scale datasets uncovering cell signalling networks in cancer: context matters

Cell signaling pathways control the responses of cells to external perturbations. Depending on the cell's internal state, genetic background and environmental context, signaling pathways rewire to elicit the appropriate response. Such rewiring also can lead to cancer development and progression or cause resistance to therapies. While there exist static maps of annotated pathways, they do not capture these rewired networks. As large-scale datasets across multiple contexts and patients are becoming available the doors to infer and study context-specific signaling network have also opened. In this review, we will highlight the most recent approaches to study context-specific signaling networks using large-scale omics and genetic perturbation datasets, with a focus on studies of cancer and cancer-related pathways.

Context-dependent genetic interactions in cancer

Genetic co-dependencies have been found in many contexts, from processes during the development of organisms to many diseases in man, including cancer. Genetic interactions - and in particular synthetic lethal phenotypes - have provided fundamental insights into the genetic architecture of cells and identified potential new opportunities for therapeutic interventions. However, recent studies also demonstrated that genetic interactions are highly context dependent and synthetic lethal interactions in one tumor context might not be translatable to others. Therefore, to better define and understand contexts will be a key challenge for future studies to fully exploit genetic interaction networks for target identification and cancer therapy. In this review, we summarize recent developments in mapping context-specific genetic interaction networks with a particular focus on conceptual and experimental advances in the past years. We then discuss genetic and environmental contexts that influence genetic interaction networks. Finally, we outline challenges of putting genetic interaction networks into context and give an outlook on future directions.

Genetic interaction networks in cancer cells

The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual's environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.

Time-resolved mapping of genetic interactions to model rewiring of signaling pathways

Context-dependent changes in genetic interactions are an important feature of cellular pathways and their varying responses under different environmental conditions. However, methodological frameworks to investigate the plasticity of genetic interaction networks over time or in response to external stresses are largely lacking. To analyze the plasticity of genetic interactions, we performed a combinatorial RNAi screen in Drosophila cells at multiple time points and after pharmacological inhibition of Ras signaling activity. Using an image-based morphology assay to capture a broad range of phenotypes, we assessed the effect of 12768 pairwise RNAi perturbations in six different conditions. We found that genetic interactions form in different trajectories and developed an algorithm, termed MODIFI, to analyze how genetic interactions rewire over time. Using this framework, we identified more statistically significant interactions compared to end-point assays and further observed several examples of context-dependent crosstalk between signaling pathways such as an interaction between Ras and Rel which is dependent on MEK activity.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).