Mapping of molecular networks across diseases, cell states and species. The Ideker laboratory has long promoted strategies to experimentally map and analyze the molecular networks that encode biological function. This interest began during Dr. Ideker’s PhD, at which time he and Leroy Hood outlined the “Systems Biology” approach to the study of biological systems by “perturbing them systematically; monitoring the global response at multiple levels (gene, protein, metabolite); and formulating network models that describe the structure of the system and its response to perturbation” (Ideker et al. Science 2001; Ideker et al. Reviews of Human Genetics and Genomics 2001). Network mapping has continually driven the research agenda of my laboratory via our interest in complementary mapping approaches: mapping epistatic genetic interactions by combinatorial gene knockout (Shen et al. Nat. Methods 2017; Bandyopadhyay et al. Science 2010); mapping protein interaction networks with affinity purification mass spectrometry or yeast-two-hybrid (Ravasi et al. Cell 2010; Suthram et al. Nature 2005; Kelley et al. PNAS 2003); or mapping transcriptional regulatory networks with chromatin IP (Workman et al. Science 2006).
In recent years, we have mapped DNA damage response networks (Silva et al. G3 2020; Silva et al. DNA Repair 2019) and the protein networks by which HPV interacts with human host proteins in cervical cancer cells (Eckhardt et al. Cancer Discovery 2018). We created a comprehensive catalog of cancer pathways and networks published in literature (Kuenzi et al. Nat. Rev. Cancer 2020), and we participated in large team efforts to map the physical and functional interaction landscapes of SARS-CoV-2 (Martin-Sancho et al. Mol. Cell 2021; Gordon et al. Nature 2020; Gordon et al. Science 2020).
DNA damage response: Genetic interaction view
[Bandyopadhyay et al. Science 2010]
A major testbed for our network assembly approaches has been to experimentally map and functional validate models of DNA damage response, which involves many tumor suppressor genes in which loss-of-function mutations cause cancer. Failure of cells to respond to DNA damage is also a major means by which environmental toxins diminish health. Consequently, cells have evolved complex repair and stress responses that are highly conserved across the eukaryotic kingdom, from yeast to humans. Our work in this area has focused on elucidation of the network of genetic (synthetic-lethal and epistatic) interactions underlying the DNA damage response, in budding yeast and in human cancer cells. To elucidate this genetic network we are using the technique of differential interaction mapping, as discussed in [Bandyopadhyay et al. Science 2010]. We then generated comprehensive differential interaction maps among tumor suppressors and druggable genes in both humans and yeast, giving rise to a network of many conserved cross-species interactions across a panel of DNA damaging agents and stressors [Srivas et al. Molecular Cell 2016; Shen et al. Oncotarget 2015].
We also published a series of technological advances on the genetic interaction mapping platform. First, together with the laboratory of Dr. Prashant Mali we developed a CRISPR/Cas9-based screening method that systematically maps genetic interactions impacting cellular growth in cancer cell lines [Shen et al. Nature Methods 2017]. In CRISPR/Cas9-based systems, a guide-RNA (gRNA) in complex with the Cas9 protein targets genomic sequences homologous to the gRNA. Notably, this approach also enables multiplex targeting via delivery of multiple gRNAs per cell. We combined this combinatorial strategy with array-based oligonucleotide synthesis to create dual-gRNA libraries covering all pairs among 73 sample cancer genes. This library comprised 2,628 double gene knockouts; testing each across three cell lines yielded 152 synthetic-lethal interactions, 16 (10.5%) of which were identified in more than one line indicating the importance of future screens in multiple contexts. Genetic interactions identified by this systematic CRISPR/Cas9-based approach could be reproduced in small-scale drug-drug interaction assays, demonstrating that we have an established integrated pipeline that enables assay of up to 105 genetic interactions per high-throughput experiment. A second technological development has enabled us to identify genes and gene combinations not only that affect cell viability, the usual readout in high-throughput screening, but also those that affect the activity of target pathways, marked by fluorescent reporters [Jaeger et al. Molecular Cell 2018].
Systematic Gene-to-Phenotype Arrays (SGPAs)
[Jaeger et al. Molecular Cell 2018]
Most recently, we mapped DNA damage response networks (Silva et al. G3 2020; Silva et al. DNA Repair 2019) and the protein networks by which HPV interacts with human host proteins in cervical cancer cells (Eckhardt et al. Cancer Discovery 2018). We created a comprehensive catalog of cancer pathways and networks published in literature (Kuenzi et al. Nat. Rev. Cancer 2020), and we participated in large team efforts to map the physical and functional interaction landscapes of SARS-CoV-2 (Martin-Sancho et al. Mol. Cell 2021; Gordon et al. Nature 2020; Gordon et al. Science 2020).