scholarly journals A map of directional genetic interactions in a metazoan cell

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Bernd Fischer ◽  
Thomas Sandmann ◽  
Thomas Horn ◽  
Maximilian Billmann ◽  
Varun Chaudhary ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Alterations ◽  
Genetic Interactions ◽  
Gene Interactions ◽  
Human Cancer Cells ◽  
Multiple Phenotypes ◽  
Interaction Map

Gene–gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene–gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

scholarly journals Decoding directional genetic dependencies through orthogonal CRISPR/Cas screens

2017 ◽  
Author(s):  
Michael Boettcher ◽  
Ruilin Tian ◽  
James Blau ◽  
Evan Markegard ◽  
David Wu ◽  
...  
Keyword(s):  
Genetic Information ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Interactions ◽  
Combinatorial Screening ◽  
Human Cancer Cells ◽  
Functional Interactions ◽  
Powerful Approach ◽  
Screening Platform ◽  
Interaction Approach

SummaryGenetic interaction studies are a powerful approach to identify functional interactions between genes. This approach can reveal networks of regulatory hubs and connect uncharacterised genes to well-studied pathways. However, this approach has previously been limited to simple gene inactivation studies. Here, we present an orthogonal CRISPR/Cas-mediated genetic interaction approach that allows the systematic activation of one gene while simultaneously knocking out a second gene in the same cell. We have developed this concept into a quantitative and scalable combinatorial screening platform that allows the parallel interrogation of hundreds of thousands of genetic interactions. We demonstrate that the established platform works robustly to uncover genetic interactions in human cancer cells and to interpret the direction of the flow of genetic information.

scholarly journals Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways

2020 ◽  
Vol 295 (50) ◽  
pp. 16906-16919
Author(s):  
Jae-Hong Kim ◽  
Yeojin Seo ◽  
Myungjin Jo ◽  
Hyejin Jeon ◽  
Young-Seop Kim ◽  
...  
Keyword(s):  
Signal Transduction ◽  
Cell Migration ◽  
Intracellular Signaling ◽  
Mammalian Cells ◽  
Drug Targets ◽  
Large Scale ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Signal Transduction Pathways ◽  
Global View

Kinases are critical components of intracellular signaling pathways and have been extensively investigated with regard to their roles in cancer. p21-activated kinase-1 (PAK1) is a serine/threonine kinase that has been previously implicated in numerous biological processes, such as cell migration, cell cycle progression, cell motility, invasion, and angiogenesis, in glioma and other cancers. However, the signaling network linked to PAK1 is not fully defined. We previously reported a large-scale yeast genetic interaction screen using toxicity as a readout to identify candidate PAK1 genetic interactions. En masse transformation of the PAK1 gene into 4,653 homozygous diploid Saccharomyces cerevisiae yeast deletion mutants identified ∼400 candidates that suppressed yeast toxicity. Here we selected 19 candidate PAK1 genetic interactions that had human orthologs and were expressed in glioma for further examination in mammalian cells, brain slice cultures, and orthotopic glioma models. RNAi and pharmacological inhibition of potential PAK1 interactors confirmed that DPP4, KIF11, mTOR, PKM2, SGPP1, TTK, and YWHAE regulate PAK1-induced cell migration and revealed the importance of genes related to the mitotic spindle, proteolysis, autophagy, and metabolism in PAK1-mediated glioma cell migration, drug resistance, and proliferation. AKT1 was further identified as a downstream mediator of the PAK1-TTK genetic interaction. Taken together, these data provide a global view of PAK1-mediated signal transduction pathways and point to potential new drug targets for glioma therapy.

scholarly journals Selective vulnerability of aneuploid human cancer cells to inhibition of the spindle assembly checkpoint

2020 ◽  
Author(s):  
Yael Cohen-Sharir ◽  
James M. McFarland ◽  
Mai Abdusamad ◽  
Carolyn Marquis ◽  
Helen Tang ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cellular Response ◽  
Large Scale ◽  
Spindle Assembly Checkpoint ◽  
Human Cancer ◽  
Spindle Assembly ◽  
Selective Vulnerability ◽  
Human Cancer Cell Lines ◽  
Human Cancer Cells ◽  
Diploid Cells

AbstractSelective targeting of aneuploid cells is an attractive strategy for cancer treatment. Here, we mapped the aneuploidy landscapes of ~1,000 human cancer cell lines and classified them by their degree of aneuploidy. Next, we performed a comprehensive analysis of large-scale genetic and chemical perturbation screens, in order to compare the cellular vulnerabilities between near-diploid and highly-aneuploid cancer cells. We identified and validated an increased sensitivity of aneuploid cancer cells to genetic perturbation of core components of the spindle assembly checkpoint (SAC), which ensures the proper segregation of chromosomes during mitosis. Surprisingly, we also found highly-aneuploid cancer cells to be less sensitive to short-term exposures to multiple inhibitors of the SAC regulator TTK. To resolve this paradox and to uncover its mechanistic basis, we established isogenic systems of near-diploid cells and their aneuploid derivatives. Using both genetic and chemical inhibition of BUB1B, MAD2 and TTK, we found that the cellular response to SAC inhibition depended on the duration of the assay, as aneuploid cancer cells became increasingly more sensitive to SAC inhibition over time. The increased ability of aneuploid cells to slip from mitotic arrest and to keep dividing in the presence of SAC inhibition was coupled to aberrant spindle geometry and dynamics. This resulted in a higher prevalence of mitotic defects, such as multipolar spindles, micronuclei formation and failed cytokinesis. Therefore, although aneuploid cancer cells can overcome SAC inhibition more readily than diploid cells, the proliferation of the resultant aberrant cells is jeopardized. At the molecular level, analysis of spindle proteins identified a specific mitotic kinesin, KIF18A, whose levels were drastically reduced in aneuploid cancer cells. Aneuploid cancer cells were particularly vulnerable to KIF18A depletion, and KIF18A overexpression restored the sensitivity of aneuploid cancer cells to SAC inhibition. In summary, we identified an increased vulnerability of aneuploid cancer cells to SAC inhibition and explored its cellular and molecular underpinnings. Our results reveal a novel synthetic lethal interaction between aneuploidy and the SAC, which may have direct therapeutic relevance for the clinical application of SAC inhibitors.

scholarly journals System-Wide Analysis of Protein Acetylation and Ubiquitination Reveals a Diversified Regulation in Human Cancer Cells

Biomolecules ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 411 ◽  
Author(s):  
Hiroko Kozuka-Hata ◽  
Aya Kitamura ◽  
Tomoko Hiroki ◽  
Aiko Aizawa ◽  
Kouhei Tsumoto ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cancer Cell ◽  
Translational Control ◽  
Large Scale ◽  
Human Cancer ◽  
Initiation Factor ◽  
Protein Acetylation ◽  
Human Cancer Cell Lines ◽  
Human Cancer Cells ◽  
Lysine Modification

Post-translational modifications are known to be widely involved in the regulation of various biological processes, through the extensive diversification of each protein function at the cellular network level. In order to unveil the system-wide function of the protein lysine modification in cancer cell signaling, we performed global acetylation and ubiquitination proteome analyses of human cancer cells, based on high-resolution nanoflow liquid chromatography–tandem mass spectrometry, in combination with the efficient biochemical enrichment of target modified peptides. Our large-scale proteomic analysis enabled us to identify more than 5000 kinds of ubiquitinated sites and 1600 kinds of acetylated sites, from representative human cancer cell lines, leading to the identification of approximately 900 novel lysine modification sites in total. Very interestingly, 236 lysine residues derived from 141 proteins were found to be modified with both ubiquitination and acetylation. As a consequence of the subsequent motif extraction analyses, glutamic acid (E) was found to be highly enriched at the position (−1) for the lysine acetylation sites, whereas the same amino acid was relatively dispersed along the neighboring residues of the lysine ubiquitination sites. Our pathway analysis also indicated that the protein translational control pathways, such as the eukaryotic initiation factor 2 (EIF2) and the ubiquitin signaling pathways, were highly enriched in both of the acetylation and ubiquitination proteome data at the network level. This report provides the first integrative description of the protein acetylation and ubiquitination-oriented systematic regulation in human cancer cells.

Protein Interaction Networks

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Large Scale ◽  
Scientific Literature ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Protein Protein Interactions ◽  
Expression Array ◽  
Biological Responses ◽  
Altered Gene

Genes and proteins interact with each other in many complicated ways. For example, proteins can interact directly with each other to form complexes or to modify each other so that their function is altered. Gene expression can be repressed or induced by transcription factor proteins. In addition there are countless other types of interactions. They constitute the key physiological steps in regulating or initiating biological responses. For example the binding of transcription factors to DNA triggers the assembly of the RNA assembly machinery that transcribes the mRNA that then is used as the template for protein production. Interactions such as these have been carefully elucidated and have been described in great detail in the scientific literature. Modern assays such as yeast-2-hybrid screens offer rapid means to ascertain many of the potential protein–protein interactions in an organism in a large-scale approach. In addition, other experimental modalities such as gene-expression array assays offer indirect clues about possible genetic interactions. One area that has been greatly explored in the bioinformatics literature is the possibility of learning genetic or protein networks, both from the scientific literature and from large-scale experimental data. Indeed, as we get to know more and more genes, it will become increasingly important to appreciate their interactions with each other. An understanding of the interactions between genes and proteins in a network allows for a meaningful global view of the organism and its physiology and is necessary to better understand biology. In this chapter we will explore methods to either (1) mine the scientific literature to identify documented genetic interactions and build networks of genes or (2) to confirm protein interactions that have been proposed experimentally. Our focus here is on direct physical protein–protein interactions, though the techniques described could be extended to any type of biological interaction between genes or proteins. There are multiple steps that must be addressed in identifying genetic interaction information contained within the text. After compiling the necessary documents and text, the first step is to identify gene and protein names in the text.

Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping

2013 ◽  
Vol 10 (5) ◽  
pp. 427-431 ◽  
Author(s):  
Christina Laufer ◽  
Bernd Fischer ◽  
Maximilian Billmann ◽  
Wolfgang Huber ◽  
Michael Boutros
Keyword(s):  
Cancer Cells ◽  
Human Cancer ◽  
Genetic Interactions ◽  
Human Cancer Cells

scholarly journals Systemic properties of Carcinogenesis: Lessons from studies on the Earth and in the Space

2020 ◽  
Author(s):  
Serhiy Souchelnytskyi
Keyword(s):  
Cancer Cells ◽  
Cell Biology ◽  
Regulatory Networks ◽  
Human Cancer ◽  
Cell Interaction ◽  
Compensatory Mechanisms ◽  
Cancer Hallmarks ◽  
Human Cancer Cells ◽  
Systemic Analysis ◽  
The Earth

proteins and genes act in coordinated ways, and their relations are visualized as networks. Networks are more accurate descriptions of cancer regulatory mechanisms, in comparison to lists of oncogenes and tumor suppressors. To extract essential regulators (nodes) and connections (edges), interrogations of these networks are performed, e.g. cancer cells are subjected to different treatments. Interrogations force cancer cells to engage nodes and edges essential for maintaining cancer properties, i.e. drivers, and nonessential followers. The challenge is to discriminate which of the mechanisms drive tumorigenesis, and which are followers. Interrogation of cancer cells under variable g-forces is the treatment to which cancer cells are not normally exposed. Therefore, low (weightlessness) and high (acceleration) g-forces may trigger responses, which may differ in part of followers from responses on the Earth, but still engage carcinogenesis-essential drivers nodes and edges. Methodology: Experimental interrogation of human cancer cells to generate carcinogenesis-related regulatory networks was performed by using proteomics, cell biology, biochemistry, immunohistochemistry and bioinformatics tools. We used also reported datasets deposited in various databases. These networks were analyzed with algorithms to extract drivers of carcinogenesis. Results: Systemic analysis of human breast carcinogenesis has shown mechanisms of engagement of all known cancer hallmarks. Moreover, novel hallmarks have emerged, e.g. involvement of mechanisms of virus-cell interaction and RNA/miR processing. The breast cancer networks are rich, with >6,000 involved proteins and genes. The richness of the networks may explain many clinical observations, e.g. personalized response to treatments. Systemic analysis highlighted novel opportunities for treatment of cancer, by identifying key nodes of known and novel hallmark mechanisms. Systemic properties of the cancer network provides an opportunity to study compensatory mechanisms. These compensatory mechanisms frequently contribute to development of resistance to treatment. These mechanisms will be discussed. Cancer cells are not “wired” to function in weightlessness. The cells would have to adapt. This adaptation will include preserving mechanisms driving carcinogenesis, in addition to the space-only-related adaptation. Key carcinogenesis regulators in the space would be the same as on the Earth, while “passenger”-mechanisms would differ. Systems biology allows integration of a space- and the Earth-data, and would extract key regulators, and, subsequently lead to better diagnostic. Conclusion: Systemic analysis of carcinogenesis studies with different ways of interrogation delivered better diagnostic and novel modalities of treatment.

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