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Principles of Systems Biology, No. 29
Cell Systems
Cell Systems Call Principles of Systems Biology, No. 29 This month: in silico labeling of microscopy images (Christiansen/Finkbeiner), single-cell lineage trees and data integration (Rajewsky, Satija), gene expression (Weinberger/Simpson, Tavazoie, Ameres/Zuber), and signalling networks (Mercer/Wollscheid, Fussenegger). Seeing More with Deep Learning
Whole Animal Cellular Lineage Tree
Eric Christiansen, Google; and Steven Finkbeiner, UCSF Gladstone Institutes
Mireya Plass, Christine Kocks, and Nikolaus Rajewsky, Berlin Institute for Medical Systems Biology, MDC; Jordi Solana, Oxford Brookes University
Principles Many popular microscopy methods use fluorescent molecules to label specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency, limitations in the number of simultaneous labels because of spectral overlap, and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. In our recent publication (Christiansen et al., Cell, published online April 12, 2018. https://doi. org/10.1016/j.cell.2018.03.040) and animated blog post (https://ai.googleblog.com/2018/04/ seeing-more-with-in-silico-labeling-of.html), we show that a computational machine-learning approach, which we call ‘‘in silico labeling,’’ reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. In silico labeling predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment.
.a computational machinelearning approach . reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples.
Principles Can single-cell transcriptomics improve our understanding of how stem cells differentiate in vivo? To address this question, we used planarian flatworms, which contain a large pool of adult stem cells that constantly differentiate to progenitor and mature cell types, and predicted a consensus lineage tree for the whole animal. We accomplished this by combining perturbation experiments with a newly developed method for lineage reconstruction, PAGA, and other independent computational approaches, such as gene expression dynamics and a method that predicts future gene expression from transcriptomic changes, velocyto. This tree reflects known gene expression changes and includes all identified cell types arranged in 23 lineages rooted to a single stem cell group (Plass et al., Science, published online on April 19, 2018. https://doi.org/10.1126/ science.aaq1723). Moreover, we exploited this tree to identify gene sets that likely contain genes driving the differentiation of stem cells to specific cell types and identified cell types important for regeneration.
This tree.includes all identified cell types arranged in 23 lineages rooted to a single stem cell group.
What’s Next?
What’s Next?
Users of existing microscopes will be able to perform longitudinal studies which were previously impossible, predicting labels and gleaning more information from their data than was previously feasible. We can also imagine radically new microscopes, as designers are freed from the constraint of producing human-interpretable data. The types of unlabeled images we used for this study are familiar to many scientists, and many thought they could ‘‘see’’ everything in the image that could be seen. But this study shows that even these unlabeled images contain more information than can be easily appreciated by a human. What other information could be uncovered by deep learning in otherwise overlooked imaging modalities?
Our study describes a roadmap to study cell differentiation relying only on high-throughput single-cell gene expression data. We provide a tutorial for the new lineage prediction tool PAGA that will enable researchers to use our approach to study developmental and regenerative processes in many other organisms. Editor’s Note: see also 3 papers published online April 26, 2018: Wagner et al., Science, https://doi.org/10.1126/science.aar4362; Briggs et al., Science, https://doi.org/10.1126/science. aar5780; Farrell, et al., Science, https://doi.org/ 10.1126/science.aar3131.
Integrating Single-Cell Sequencing Data Andrew Butler and Rahul Satija, New York University and the New York Genome Center
Principles Recent improvements in technology and computation now enable routine analysis of individual single-cell RNA-seq (scRNA-seq) datasets, but jointly analyzing multiple experiments remains challenging. To address this, we developed a computational method for integrating scRNAseq datasets by identifying sources of shared variation (Butler, et al., Nat. Biotechnol., published online on April 2, 2018. https://doi.org/ 10.1038/nbt.4096). Implemented in our R toolkit, Seurat, the approach leverages canonical correlation analysis and dynamic time warping to ‘‘align’’ shared cell types across experiments. By integrating single-cell datasets, we can systematically compare how different cell types respond to perturbation, disease, and evolution. To demonstrate, we integrated scRNA-seq data from stimulated and resting immune cells to identify 13 shared cell types, and their specific transcriptional response. Additionally, we pool datasets generated by multiple sequencing technologies, and find that an integrated analysis boosts statistical power to identify rare cell states. Finally, we jointly cluster pancreatic islet datasets from human and mouse, identifying ten common cell types, alongside shared genetic markers.
By integrating single-cell datasets, we can systematically compare how different cell types respond to perturbation, disease, and evolution. What’s Next? We expect that our methods will enable largescale collaborations and consortia, including the Human Cell Atlas, to integrate samples originating across multiple individuals, labs, and technologies. More generally, the comparative analysis of samples across disease states, the alignment of developmental trajectories across species, and the integration of sequencing and imaging single-cell datasets, represent exciting directions for the future.
Cell Systems 6, May 23, 2018 ª 2018 Published by Elsevier Inc. 533
Cell Systems
Cell Systems Call Crowding Tunes Up the Noise S. Elizabet Norred, University of Tennessee, Knoxville; Leor Weinberger, Gladstone Center for Cell Circuitry, Departments of Pharmaceutical Chemistry and Biophysics & Biochemistry, University of California, San Francisco; and Michael L. Simpson, Oak Ridge National Laboratory.
Principles Historically, two largely disparate bodies of literature have developed in cellular biophysics. One literature examined molecular crowding of biomolecules and the effects on cellular diffusion (Zhou et al., Annu. Rev. Biophys. 37, 375–397). The second literature quantified fluctuations in gene expression (noise) and sources of episodic bursts in gene expression (Sanchez and Golding, Science, 342, 1188–1193). Little was known about how the former affects the latter. Recently, we tuned macromolecular crowding to measure the effect on transcriptional and translational expression bursting (Norred et al., ACS Synth. Biol., published online on April 24, 2018. https://pubs.acs.org/ doi/abs/10.1021/acssynbio.8b00139). Surprisingly, in cell-free experiments, crowding decoupled the well-established relationship between protein and mRNA noise by creating spatial heterogeneity. Imaging expression bursts in cell-sized environments demonstrated that crowding generated spatially distinct mRNA populations with different translational activities, and this spatial variability significantly amplified noise in the protein population.
.crowding decoupled the well-established relationship between protein and mRNA noise by creating spatial heterogeneity. What’s Next? Notably, these cell-free studies examined prokaryotic transcription and translation machinery. However, crowding-induced spatial heterogeneity of mRNA may provide a mechanistic basis for recently discovered amplification in protein translation noise in eukaryotic cells (Hansen et al. https://doi. org/10.1101/222901). It will be interesting to see if molecules that modulate molecular crowding could be used to alter nuclear phase transitions to regulate transcription (Hnisz et al., Cell, 169, 13–23) or to therapeutically tune transcriptional noise (Dar et al., Science, 344, 1392–1396).
534 Cell Systems 6, May 23, 2018
Regulation of Gene Expression by Trial and Error
Defining Direct Transcriptional Functions of Genes and Drugs
Peter L. Freddolino, Jamie Yang, Amir Momen-Roknabadi, and Saeed Tavazoie, Columbia University
Johannes Zuber, Research Institute of Molecular Pathology (IMP), ViennaBioCenter (VBC), Vienna, Austria; and Stefan L. Ameres, Institute of Molecular Biotechnology (IMBA), VBC, Vienna, Austria
Principles Since the foundational work of Jacob and Monod, cellular adaptation has been viewed as a genetically encoded regulatory program where perception of specific changes in the environment leads to genetically predefined adaptive changes in gene expression. We have discovered an alternative adaptation mechanism whereby cells randomly and epigenetically modulate the expression of individual genes, reinforcing those changes that lead to improvement in their overall health. We have termed this phenomenon stochastic tuning (Freddolino et al., eLife 2018;7:e31867). Using budding yeast, we have shown that a process consistent with stochastic tuning operates to establish adaptive gene expression states in the absence of genetically encoded regulatory circuitry. As such, stochastic tuning provides a powerful optimization mechanism that enables cells to adapt to novel or extreme conditions where their conventional gene regulatory systems are inadequate.
.cells randomly . modulate the expression of individual genes, reinforcing those changes that lead to improvement in their overall health. What’s next? Stochastic tuning may also function as a gene regulatory mechanism in the context of multicellular development, physiology, and disease. In particular, it may contribute to adaptation of cancer cells to tumor micro-environmental challenges, immune evasion, and therapeutic resistance. We found that the histone acetyltransferase GCN5 plays an essential role in stochastic tuning. Identifying other key effectors and developing a detailed molecular understanding are critical next steps.
Principles The diversity of mRNA and protein half-lives poses a challenge for deciphering primary transcriptional responses to cell perturbations. At early time points, changes in cellular mRNA abundance are inevitably biased for effects on short-lived transcripts, while later time points preclude the distinction of direct from secondary effects. Therefore, direct transcriptional responses to any cell perturbation should be defined based on changes in mRNA production rather than total mRNA levels. To this end, thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq) provides a simple and scalable method for measuring gene expression dynamics, including direct transcriptional output, within the total mRNA population (Herzog et al., Nat. Methods, 14, 1198–1204). In a recent study (Muhar et al., Science, published online on April 5, 2018, https:// doi.org/10.1126/science.aao2793) we have pioneered the use of SLAM-seq for defining direct transcriptional responses to pharmacologic and chemical-genetic protein perturbation. SLAM-seq profiling following auxin-inducible degradation of BRD4 and MYC, two important regulators implicated in cancer, characterized BRD4 as a global co-factor of Pol2-dependent transcription, while MYC acts as a selective activator of genes involved in basic metabolic processes.
SLAM-seq opens new opportunities for probing responses to drugs and. for defining the primary function of regulatory genes and pathways. What’s Next? By providing a simple and scalable method for directly quantifying specific and global changes in mRNA synthesis rates, SLAMseq opens new opportunities for probing responses to drugs and other cell perturbations. Its combination with emerging protein degradation technologies establishes an experimental workflow for defining the primary function of regulatory genes and pathways.
Cell Systems
Cell Systems Call Unmasking Proteotype-PhenotypeGenotype Relationships in Viral Signaling Networks Jason Mercer, University College London; and Bernd Wollscheid, ETH Zurich Principles The reversible post-translational modification, phosphorylation, is indispensable for many fundamental cellular processes. However, a systems-level understanding of viral phospho-networks and how they drive infection phenotypes and assure virion infectivity is lacking. Vaccinia virus (VACV) encodes two dual-specificity enzymes: F10 kinases and H1 phosphatase. F10 drives virus assembly, and H1 assures virion transcriptional competence. In our recent work (Novy et al., Nat. Microbiol. 3, 588–599) we defined the vaccinia virus F10/H1 signaling network by applying a combination of proteotype analysis using quantitative mass-spectrometry and mutant F10(-) and H1(-) viruses. We found that H1-deficient virions harbour a hidden hypercleavage phenotype driven by reversible phosphorylation of the virus protease I7 (S134). Quantitative phosphoproteomic profiling further revealed that the phosphorylation-dependent activity of the viral early transcription factor, A7 (Y367), underlies the transcription-deficient phenotype of H1 mutant virions. Integrated data analysis including complementation experiments enabled us to uncover a complex viral signaling network that controls virus assembly and transcriptional competence.
A systems-level understanding of viral phospho-networks and how they drive infection phenotypes and assure virion infectivity is lacking. What’s Next? Our results reveal the underlying complexity and implicit spatial and temporal regulation of the F10/H1 signaling network required to assure the formation of infectious poxvirus particles. By combining mutant viruses and proteotype profiling, we have demonstrated that one can go beyond virus genotype-phenotype relationships to define viral signaling networks, uncover masked phenotypes, and define new links between mutant virus phenotypes and their causal proteotypes.
Tailoring Synthetic Receptors to Specific Ligands Leo Scheller and Martin Fussenegger, ETH Zurich, Basel Principles Predictable input-output behavior of cellular systems is one of the key features of synthetic biology. To date, most such systems rely on natural receptors that respond to a molecule of interest. However, molecules for which there are no natural receptors, such as synthetic compounds, require engineered receptors with altered ligand specificity. In a recent publication (Scheller et al., Nat. Chem. Biol., published online April 23, 2018. https://dx.doi.org/10.1038/ nchembio.2498), we describe a modular receptor platform that activates endogenous signaling pathways in response to custom inputs. This approach improves the dynamic range of the sensors and accelerates the design and engineering of novel receptors. The great versatility of the platform was illustrated by applying it to sense a synthetic azo dye, nicotine, a peptide tag, and the biomarker PSA (prostate-specific antigen). Input specificity is determined by antibody fragment-mediated dimerization of extracellular receptor domains to activate a set of intracellular signaling domains. These receptors were used to induce transgene expression with high signal-to-noise ratios, to construct two-input/two-output systems, and to modulate cytokine expression of immune cells.
.we describe a modular receptor platform that activates endogenous signaling pathways in response to custom inputs. What’s Next? Engineered receptors for soluble biomarkers that have previously been invisible to cell-based systems are likely to find applications in cell-based therapies and various other synthetic biology applications. It should become possible to engineer cells with programmable behavior in the tumor microenvironment or to construct novel therapeutic implants that sense a biomarker of choice and produce a therapeutic protein(s) in response.
Cell Systems 6, May 23, 2018 535
Cell Systems Call Principles of Systems Biology, No. 29 This month: in silico labeling of microscopy images (Christiansen/Finkbeiner), single-cell lineage trees and data integration (Rajewsky, Satija), gene expression (Weinberger/Simpson, Tavazoie, Ameres/Zuber), and signalling networks (Mercer/Wollscheid, Fussenegger). Seeing More with Deep Learning
Whole Animal Cellular Lineage Tree
Eric Christiansen, Google; and Steven Finkbeiner, UCSF Gladstone Institutes
Mireya Plass, Christine Kocks, and Nikolaus Rajewsky, Berlin Institute for Medical Systems Biology, MDC; Jordi Solana, Oxford Brookes University
Principles Many popular microscopy methods use fluorescent molecules to label specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency, limitations in the number of simultaneous labels because of spectral overlap, and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. In our recent publication (Christiansen et al., Cell, published online April 12, 2018. https://doi. org/10.1016/j.cell.2018.03.040) and animated blog post (https://ai.googleblog.com/2018/04/ seeing-more-with-in-silico-labeling-of.html), we show that a computational machine-learning approach, which we call ‘‘in silico labeling,’’ reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. In silico labeling predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment.
.a computational machinelearning approach . reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples.
Principles Can single-cell transcriptomics improve our understanding of how stem cells differentiate in vivo? To address this question, we used planarian flatworms, which contain a large pool of adult stem cells that constantly differentiate to progenitor and mature cell types, and predicted a consensus lineage tree for the whole animal. We accomplished this by combining perturbation experiments with a newly developed method for lineage reconstruction, PAGA, and other independent computational approaches, such as gene expression dynamics and a method that predicts future gene expression from transcriptomic changes, velocyto. This tree reflects known gene expression changes and includes all identified cell types arranged in 23 lineages rooted to a single stem cell group (Plass et al., Science, published online on April 19, 2018. https://doi.org/10.1126/ science.aaq1723). Moreover, we exploited this tree to identify gene sets that likely contain genes driving the differentiation of stem cells to specific cell types and identified cell types important for regeneration.
This tree.includes all identified cell types arranged in 23 lineages rooted to a single stem cell group.
What’s Next?
What’s Next?
Users of existing microscopes will be able to perform longitudinal studies which were previously impossible, predicting labels and gleaning more information from their data than was previously feasible. We can also imagine radically new microscopes, as designers are freed from the constraint of producing human-interpretable data. The types of unlabeled images we used for this study are familiar to many scientists, and many thought they could ‘‘see’’ everything in the image that could be seen. But this study shows that even these unlabeled images contain more information than can be easily appreciated by a human. What other information could be uncovered by deep learning in otherwise overlooked imaging modalities?
Our study describes a roadmap to study cell differentiation relying only on high-throughput single-cell gene expression data. We provide a tutorial for the new lineage prediction tool PAGA that will enable researchers to use our approach to study developmental and regenerative processes in many other organisms. Editor’s Note: see also 3 papers published online April 26, 2018: Wagner et al., Science, https://doi.org/10.1126/science.aar4362; Briggs et al., Science, https://doi.org/10.1126/science. aar5780; Farrell, et al., Science, https://doi.org/ 10.1126/science.aar3131.
Integrating Single-Cell Sequencing Data Andrew Butler and Rahul Satija, New York University and the New York Genome Center
Principles Recent improvements in technology and computation now enable routine analysis of individual single-cell RNA-seq (scRNA-seq) datasets, but jointly analyzing multiple experiments remains challenging. To address this, we developed a computational method for integrating scRNAseq datasets by identifying sources of shared variation (Butler, et al., Nat. Biotechnol., published online on April 2, 2018. https://doi.org/ 10.1038/nbt.4096). Implemented in our R toolkit, Seurat, the approach leverages canonical correlation analysis and dynamic time warping to ‘‘align’’ shared cell types across experiments. By integrating single-cell datasets, we can systematically compare how different cell types respond to perturbation, disease, and evolution. To demonstrate, we integrated scRNA-seq data from stimulated and resting immune cells to identify 13 shared cell types, and their specific transcriptional response. Additionally, we pool datasets generated by multiple sequencing technologies, and find that an integrated analysis boosts statistical power to identify rare cell states. Finally, we jointly cluster pancreatic islet datasets from human and mouse, identifying ten common cell types, alongside shared genetic markers.
By integrating single-cell datasets, we can systematically compare how different cell types respond to perturbation, disease, and evolution. What’s Next? We expect that our methods will enable largescale collaborations and consortia, including the Human Cell Atlas, to integrate samples originating across multiple individuals, labs, and technologies. More generally, the comparative analysis of samples across disease states, the alignment of developmental trajectories across species, and the integration of sequencing and imaging single-cell datasets, represent exciting directions for the future.
Cell Systems 6, May 23, 2018 ª 2018 Published by Elsevier Inc. 533
Cell Systems
Cell Systems Call Crowding Tunes Up the Noise S. Elizabet Norred, University of Tennessee, Knoxville; Leor Weinberger, Gladstone Center for Cell Circuitry, Departments of Pharmaceutical Chemistry and Biophysics & Biochemistry, University of California, San Francisco; and Michael L. Simpson, Oak Ridge National Laboratory.
Principles Historically, two largely disparate bodies of literature have developed in cellular biophysics. One literature examined molecular crowding of biomolecules and the effects on cellular diffusion (Zhou et al., Annu. Rev. Biophys. 37, 375–397). The second literature quantified fluctuations in gene expression (noise) and sources of episodic bursts in gene expression (Sanchez and Golding, Science, 342, 1188–1193). Little was known about how the former affects the latter. Recently, we tuned macromolecular crowding to measure the effect on transcriptional and translational expression bursting (Norred et al., ACS Synth. Biol., published online on April 24, 2018. https://pubs.acs.org/ doi/abs/10.1021/acssynbio.8b00139). Surprisingly, in cell-free experiments, crowding decoupled the well-established relationship between protein and mRNA noise by creating spatial heterogeneity. Imaging expression bursts in cell-sized environments demonstrated that crowding generated spatially distinct mRNA populations with different translational activities, and this spatial variability significantly amplified noise in the protein population.
.crowding decoupled the well-established relationship between protein and mRNA noise by creating spatial heterogeneity. What’s Next? Notably, these cell-free studies examined prokaryotic transcription and translation machinery. However, crowding-induced spatial heterogeneity of mRNA may provide a mechanistic basis for recently discovered amplification in protein translation noise in eukaryotic cells (Hansen et al. https://doi. org/10.1101/222901). It will be interesting to see if molecules that modulate molecular crowding could be used to alter nuclear phase transitions to regulate transcription (Hnisz et al., Cell, 169, 13–23) or to therapeutically tune transcriptional noise (Dar et al., Science, 344, 1392–1396).
534 Cell Systems 6, May 23, 2018
Regulation of Gene Expression by Trial and Error
Defining Direct Transcriptional Functions of Genes and Drugs
Peter L. Freddolino, Jamie Yang, Amir Momen-Roknabadi, and Saeed Tavazoie, Columbia University
Johannes Zuber, Research Institute of Molecular Pathology (IMP), ViennaBioCenter (VBC), Vienna, Austria; and Stefan L. Ameres, Institute of Molecular Biotechnology (IMBA), VBC, Vienna, Austria
Principles Since the foundational work of Jacob and Monod, cellular adaptation has been viewed as a genetically encoded regulatory program where perception of specific changes in the environment leads to genetically predefined adaptive changes in gene expression. We have discovered an alternative adaptation mechanism whereby cells randomly and epigenetically modulate the expression of individual genes, reinforcing those changes that lead to improvement in their overall health. We have termed this phenomenon stochastic tuning (Freddolino et al., eLife 2018;7:e31867). Using budding yeast, we have shown that a process consistent with stochastic tuning operates to establish adaptive gene expression states in the absence of genetically encoded regulatory circuitry. As such, stochastic tuning provides a powerful optimization mechanism that enables cells to adapt to novel or extreme conditions where their conventional gene regulatory systems are inadequate.
.cells randomly . modulate the expression of individual genes, reinforcing those changes that lead to improvement in their overall health. What’s next? Stochastic tuning may also function as a gene regulatory mechanism in the context of multicellular development, physiology, and disease. In particular, it may contribute to adaptation of cancer cells to tumor micro-environmental challenges, immune evasion, and therapeutic resistance. We found that the histone acetyltransferase GCN5 plays an essential role in stochastic tuning. Identifying other key effectors and developing a detailed molecular understanding are critical next steps.
Principles The diversity of mRNA and protein half-lives poses a challenge for deciphering primary transcriptional responses to cell perturbations. At early time points, changes in cellular mRNA abundance are inevitably biased for effects on short-lived transcripts, while later time points preclude the distinction of direct from secondary effects. Therefore, direct transcriptional responses to any cell perturbation should be defined based on changes in mRNA production rather than total mRNA levels. To this end, thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq) provides a simple and scalable method for measuring gene expression dynamics, including direct transcriptional output, within the total mRNA population (Herzog et al., Nat. Methods, 14, 1198–1204). In a recent study (Muhar et al., Science, published online on April 5, 2018, https:// doi.org/10.1126/science.aao2793) we have pioneered the use of SLAM-seq for defining direct transcriptional responses to pharmacologic and chemical-genetic protein perturbation. SLAM-seq profiling following auxin-inducible degradation of BRD4 and MYC, two important regulators implicated in cancer, characterized BRD4 as a global co-factor of Pol2-dependent transcription, while MYC acts as a selective activator of genes involved in basic metabolic processes.
SLAM-seq opens new opportunities for probing responses to drugs and. for defining the primary function of regulatory genes and pathways. What’s Next? By providing a simple and scalable method for directly quantifying specific and global changes in mRNA synthesis rates, SLAMseq opens new opportunities for probing responses to drugs and other cell perturbations. Its combination with emerging protein degradation technologies establishes an experimental workflow for defining the primary function of regulatory genes and pathways.
Cell Systems
Cell Systems Call Unmasking Proteotype-PhenotypeGenotype Relationships in Viral Signaling Networks Jason Mercer, University College London; and Bernd Wollscheid, ETH Zurich Principles The reversible post-translational modification, phosphorylation, is indispensable for many fundamental cellular processes. However, a systems-level understanding of viral phospho-networks and how they drive infection phenotypes and assure virion infectivity is lacking. Vaccinia virus (VACV) encodes two dual-specificity enzymes: F10 kinases and H1 phosphatase. F10 drives virus assembly, and H1 assures virion transcriptional competence. In our recent work (Novy et al., Nat. Microbiol. 3, 588–599) we defined the vaccinia virus F10/H1 signaling network by applying a combination of proteotype analysis using quantitative mass-spectrometry and mutant F10(-) and H1(-) viruses. We found that H1-deficient virions harbour a hidden hypercleavage phenotype driven by reversible phosphorylation of the virus protease I7 (S134). Quantitative phosphoproteomic profiling further revealed that the phosphorylation-dependent activity of the viral early transcription factor, A7 (Y367), underlies the transcription-deficient phenotype of H1 mutant virions. Integrated data analysis including complementation experiments enabled us to uncover a complex viral signaling network that controls virus assembly and transcriptional competence.
A systems-level understanding of viral phospho-networks and how they drive infection phenotypes and assure virion infectivity is lacking. What’s Next? Our results reveal the underlying complexity and implicit spatial and temporal regulation of the F10/H1 signaling network required to assure the formation of infectious poxvirus particles. By combining mutant viruses and proteotype profiling, we have demonstrated that one can go beyond virus genotype-phenotype relationships to define viral signaling networks, uncover masked phenotypes, and define new links between mutant virus phenotypes and their causal proteotypes.
Tailoring Synthetic Receptors to Specific Ligands Leo Scheller and Martin Fussenegger, ETH Zurich, Basel Principles Predictable input-output behavior of cellular systems is one of the key features of synthetic biology. To date, most such systems rely on natural receptors that respond to a molecule of interest. However, molecules for which there are no natural receptors, such as synthetic compounds, require engineered receptors with altered ligand specificity. In a recent publication (Scheller et al., Nat. Chem. Biol., published online April 23, 2018. https://dx.doi.org/10.1038/ nchembio.2498), we describe a modular receptor platform that activates endogenous signaling pathways in response to custom inputs. This approach improves the dynamic range of the sensors and accelerates the design and engineering of novel receptors. The great versatility of the platform was illustrated by applying it to sense a synthetic azo dye, nicotine, a peptide tag, and the biomarker PSA (prostate-specific antigen). Input specificity is determined by antibody fragment-mediated dimerization of extracellular receptor domains to activate a set of intracellular signaling domains. These receptors were used to induce transgene expression with high signal-to-noise ratios, to construct two-input/two-output systems, and to modulate cytokine expression of immune cells.
.we describe a modular receptor platform that activates endogenous signaling pathways in response to custom inputs. What’s Next? Engineered receptors for soluble biomarkers that have previously been invisible to cell-based systems are likely to find applications in cell-based therapies and various other synthetic biology applications. It should become possible to engineer cells with programmable behavior in the tumor microenvironment or to construct novel therapeutic implants that sense a biomarker of choice and produce a therapeutic protein(s) in response.
Cell Systems 6, May 23, 2018 535