Quantitative Genetic Interactions Reveal Biological Modularity

The scanning probe microscopies provide a unique view of biological and biomedical systems at a nanoscale appropriate to appreciate molecular events. The advent of these methods has brought the ability to acquire quantitative information at the molecular level. Given the proliferation of microscopes and associated methods, the probability for important discoveries is high. If tempered with an appreciation for the potential for artifacts, the SPMs may revolutionize our view of biological systems and biomaterials interactions with those systems.

Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)—a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving ∼4500 genes, and is available at http://drygin.ccbr.utoronto.ca

Genetic interaction screens have long been used to map gene functions and cellular pathways in model organisms and more recently in human cells. However, due to the technical challenges of these maps, most studies in human cells have focused on a single cell line, limiting the ability to determine how different genetic and cellular contexts influence genetic interactions. Here, we engineered a 110,728-element combinatorial CRISPR-Cas9 library to systematically map 12,282 genetic interactions among tumor suppressor genes and druggable targets. We mapped the landscape of genetic interactions in cell lines from breast cancer, head and neck squamous carcinoma, and non-small cell lung cancer as well as in non-cancerous, epithelial cells. The resulting genetic interaction maps emphasize the context dependency of these interactions. Of the interactions that are conserved across multiple cell lines, many include frequently essential genes, suggesting that these interactions may be critical for fundamental cellular functions. We found that genetic interaction maps from cancer cell lines are more similar to one another than non-cancer interaction maps, even between cancer and normal cells from the same tissue. Subsequent targeted chemogenetic screens confirmed interaction hubs across multiple cell lines while highlighting the robustness of some of the strongest interactions. Many of the identified genetic interactions suggest therapeutic interventions, such as the combination of CDK4 and MAPK inhibitors. Using the tissue-specific genetic interaction networks, we also built a deep learning model that accurately predicts patient outcomes, outperforming models that use accepted biomarkers such as MammaPrint. In summary, these genetic interaction maps provide a broad resource to investigate context dependency in cancer signaling pathways and in cancer drug response. Citation Format: Samson H. Fong, Brent M. Kuenzi, John Lee, Kyle Sanchez, Ana Bojorquez-Gomez, Kyle Ford, Brenton P. Munson, Katherine Licon, Jeff Hager, John Paul Shen, Jason F. Kreisberg, Prashant Mali, Trey Ideker. Systematic mapping of genetic interactions in human cancer cells reveals context dependent cancer signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2133.

BackgroundHigh-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms.ResultsUsing large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.ConclusionsWe have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.

Chemical tools are transforming our understanding of biomolecules and living systems. Included in this group are bioorthogonal reagents-functional groups that are inert to most biological species, but can be selectively ligated with complementary probes, even in live cells and whole organisms. Applications of these tools have revealed fundamental new insights into biomolecule structure and function-information often beyond the reach of genetic approaches. In many cases, the knowledge gained from bioorthogonal probes has enabled new questions to be asked and innovative research to be pursued. Thus, the continued development and application of these tools promises to both refine our view of biological systems and facilitate new discoveries. Despite decades of achievements in bioorthogonal chemistry, limitations remain. Several reagents are too large or insufficiently stable for use in cellular environments. Many bioorthogonal groups also cross-react with one another, restricting them to singular tasks. In this Account, we describe our work to address some of the voids in the bioorthogonal toolbox. Our efforts to date have focused on small reagents with a high degree of tunability: cyclopropenes, triazines, and cyclopropenones. These motifs react selectively with complementary reagents, and their unique features are enabling new pursuits in biology. The Account is organized by common themes that emerged in our development of novel bioorthogonal reagents and reactions. First, natural product structures can serve as valuable starting points for probe design. Cyclopropene, triazine, and cyclopropenone motifs are all found in natural products, suggesting that they would be metabolically stable and compatible with a variety of living systems. Second, fine-tuning bioorthogonal reagents is essential for their successful translation to biological systems. Different applications demand different types of probes; thus, generating a collection of tools that span a continuum of reactivities and stabilities remains an important goal. We have used both computational analyses and mechanistic studies to guide the optimization of various cyclopropene and triazine probes. Along the way, we identified reagents that are chemoselective but best suited for in vitro work. Others are selective and robust enough for use in living organisms. The last section of this Account highlights the need for the continued pursuit of new reagents and reactions. Challenges exist when bioorthogonal chemistries must be used in concert, given that many exploit similar mechanisms and cannot be used simultaneously. Such limitations have precluded certain multicomponent labeling studies and other biological applications. We have relied on mechanistic and computational insights to identify mutually orthogonal sets of reactions, in addition to exploring unique genres of reactivity. The continued development of mechanistically distinct, biocompatible reactions will further diversify the bioorthogonal reaction portfolio for examining biomolecules.

Functional Data Analysis: Omics for Environmental Risk Assessment

Animals move in and interact with complex environments that can be characterised by a set of spatial layers containing environmental data. Spatial databases can manage these different data sets in a unified framework, defining spatial and non-spatial relationships that simplify the analysis of the interaction between animals and their habitat. A large set of analyses can be performed directly in the database with no need for dedicated GIS or statistical software. Such an approach moves the information content managed in the database from a ‘geographical space’ to an ‘animal’s ecological space’. This more comprehensive model of the animals’ movement ecology reduces the distance between physical reality and the way data are structured in the database, filling the semantic gap between the scientist’s view of biological systems and its implementation in the information system. This chapter shows how vector and raster layers can be included in the database and how you can handle them using (spatial) SQL. The database built so far in Chaps. 2, 3, 4 and 5 is extended with environmental ancillary data sets and with an automated procedure to intersect these layers with GPS positions.

Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.

In the last decades our view of biological systems has changed dramatically. One reason is an increasing awareness of molecular crowding in virtually all living cells. An example for a crowded system is photosynthesis. At the first glance, for many years the riddle of photosynthesis and the involved flow of electrons seemed to be solved since long ago. Nearly all involved proteins were known as well as most mechanisms of electron transfer within them. Between the photosynthetic proteins electrons were assumed to be transported via free diffusion of electron carriers. However, the diffusion of these carriers within the photosynthetic membrane may be strongly influenced by molecular crowding, which might nearly completely restrict it. Nevertheless, effects of molecular crowding are only sparsely investigated in the available literature although they show again that “the whole is more than the sum of its parts” (Aristotle). Even if all single components of a process are known, this does not mean that their interplay is really understood. Apart from diffusion many other important parameters determining the metabolism in a cell or within a membrane, like e. g. reaction equilibria, aggregation, self organisation or reaction rates, are also influenced by molecular crowding. Hence, molecular crowding is an important but underestimated phenomenon that is worth to be investigated in more detail already because of its omnipresence.

The genome revolution represents a complete change on our view of biological systems. The quantitative determination of changes in all major molecular components of the living cells, the "omics" approach, opened whole new fields for all health sciences. Genomics, transcriptomics, proteomics, metabolomics, and others, together with appropriate prediction and modeling tools, will mark the future of developmental toxicity assessment both for wildlife and humans. This is especially true for disciplines, like teratology, which rely on studies in model organisms, as studies at lower levels of organization are difficult to implement. Rodents and frogs have been the favorite models for studying human reproductive and developmental disorders for decades. Recently, the study of the development of zebrafish embryos (ZE) is becoming a major alternative tool to adult animal testing. ZE intrinsic characteristics makes this model a unique system to analyze in vivo developmental alterations that only can be studied applying in toto approaches. Moreover, under actual legislations, ZE is considered as a replacement model (and therefore, excluded from animal welfare regulations) during the first 5days after fertilization. Here we review the most important components of the zebrafish toolbox available for analyzing early stages of embryotoxic events that could eventually lead to teratogenesis.

Gene function on a genomic scale

The genome revolution has brought about a complete change on our view of biological systems. The quantitative determination of changes in all the major molecular components of the living cells, the "omics" approach, opened whole new fields for all health sciences, including toxicology. Endocrine disruption,i.e., the capacity of anthropogenic pollutants to alter the hormonal balance of the organisms, is one of the fields of Ecotoxicology in which omics has a relevant role. In the first place, the discovery of scores of potential targets in the genome of almost any Metazoan species studied so far, each of them being a putative candidate for interaction with endocrine disruptors. In addition, the understanding that ligands, receptors, and their physiological functions suffered fundamental variations during animal evolution makes it necessary to assess disruption effects separately for each major taxon. Fortunately, the same deal of knowledge on genes and genomes powered the development of new high-throughput techniques and holistic approaches. Genomics, transcriptomics, proteomics, metabolomics, and others, together with appropriate prediction and modeling tools, will mark the future of endocrine disruption assessment both for wildlife and humans.

Chapter 2 - Genes and Pathways Contributing to Obesity: A Systems Biology View

To understand the behavior of biological systems we need to consider that they are made of populations of individual cells. The behavior of these cells is also dependent on the interactions of multiple organelles and protein complexes. Every response is dependent on interactions of cells across multiple scales from the molecular through the level of the organelle, the cell and then to cell-cell interactions. To look at these cells across different biological scales, with a systems approach, we need to develop mathematical models and simulations that allow us to consider biological interactions at different scales and across scales. Creating such multi scale models of molecular and cellular dynamics was the heart of my thesis. To do so I pursued 3 aims: 1) I conducted statistical and model-based analysis of imaging experiments at both the cellular and organelle biological scales 2) I developed TIPS, a stochastic modeling framework for tracking of individual agent kinematics through complex binding to model and simulate cellular behavior at the molecular scale. I applied TIPS to simulate Calcium dependent STIM and Orai binding and motion. 3) I developed MiGHT a multi-scalar stochastic modeling framework for the simulation of cellular adaptation and utilized this framework to model the interconnected networks of cellular behavior across multiple biological scales. I used MiGHT to model and simulate the macrophage response to lipopolysaccharide, specifically modeling how the macrophage response adapts to the cytokine environment through the modeling of cellular behavior across the molecular, cellular, and systemic biological scales. Here, my thesis provided a way to approach taking experimental data to single scale models and simulations to interconnected multi-scalar models and simulations. In doing so, this work has provided a way in which we can model and simulate how perturbations at molecular scales, such as genetic alterations or drug administration, can impact cellular and systemic behavior.