center for systems

& synthetic biology

an NIGMS national systems biology center



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Optimized yeast fluorescent proteins: Our Center collaborated with the UCSF Nikon Imaging Center to generate optimized yeast fluorescent proteins. The results and recommendations for use have been published in PLoS ONE.

Improved Blue, Green, and Red Fluorescent Protein Tagging Vectors for S. cerevisiae
Lee et al., 2013
PLoS ONE 8(7): e67902

Fluorescent protein fusions are a powerful tool to monitor the localization and trafficking of proteins. Such studies are particularly easy to carry out in the budding yeast Saccharomyces cerevisiae due to the ease with which tags can be introduced into the genome by homologous recombination. However, the available yeast tagging plasmids have not kept pace with the development of new and improved fluorescent proteins. Here, we have constructed yeast optimized versions of 19 different fluorescent proteins and tested them for use as fusion tags in yeast. These include two blue, seven green, and seven red fluorescent proteins, which we have assessed for brightness, photostability and perturbation of tagged proteins. We find that EGFP remains the best performing green fluorescent protein, that TagRFP-T and mRuby2 outperform mCherry as red fluorescent proteins, and that mTagBFP2 can be used as a blue fluorescent protein tag. Together, the new tagging vectors we have constructed provide improved blue and red fluorescent proteins for yeast tagging and three color imaging.
The 41 optimized blue, green, and red fluorescent protein tagging vectors generated and their detailed descriptions are available via Addgene.

Cellular Optogenetics: We have developed optically gated protein interaction modules, using the plant derived phytochrome system (Phy-PIF). These interactions can be switched on in living cells by illuminating with 650 nm light, and switched off by illuminating with 750 nm light. Both switching on and off occurs within seconds, and thus the Phy-PIF system is ideal for rapidly perturbing the dynamics of molecular interactions in living cells. Recruitment of catalytic signaling proteins to the membrane, to sites of substrates (or conversely sequestration away from substrates) can be used to rapidly control key signaling events.



Toettcher, J.E., Weiner, O.D., and Lim, W.A. "Using optogenetics to interrogate the dynamic control of signal transmission by the ras/erk module" Cell 155(6):1422-34 (2013). (Abstract)

Toettcher, J.E., Gong, D., Lim, W.A., and Weiner, O.D. "Light-based feedback for controlling intracellular signaling dynamics." Nature Methods 8, 837-839 (2011). (Abstract)

Toettcher, J.E., Gong, D., Lim, W.A., and Weiner, O.D. "Light control of plasma membrane recruitment using the Phy-PIF system." Methods in Enzymology 497, 409-23 (2011). (Abstract)

Toettcher, J.E., Voigt, C.A., Weiner, O.D., and Lim, W.A. "The promise of optogenetics in cell biology: interrogating molecular circuits in space and time." Nature Methods 1, 35-38 (2011). (Abstract)

Levskaya A1, Weiner OD, Lim WA, Voigt CA. “Spatiotemporal control of cell signalling using a light-switchable protein interaction.”Nature. 2009 Oct 15;461(7266):997-1001. doi: 10.1038/nature08446. Epub 2009 Sep 13.


Phy-PIF optogenetic recruitment plasmids can be obtained from Addgene at the following links:

Levskaya, et al, Nature. 2009

  • Phy-PIF recruitment modules
  • Rac, Cdc42, Rho activation

Toettcher, et al, Nature Methods, 2011

  • PI3K activation

Yang, et al, Mol Biol Cell. 2013

  • organelle targeting system

Toettcher, et al, Cell, 2013

  • opto-Sos (Ras activation)


CRISPRi technology:We have recently repurposed the bacterial immune system, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pathway, as an RNA-guided DNA binding platform, to repress expression of arbitrary genes in bacteria or human cells. This CRISPR interfering system (CRISPRi), works independently of host cellular machineries, requiring only a nuclease-deficient Cas9 (dCas9) protein and a customized single guide RNA (sgRNA) designed with a 20-basepair complementary region to any gene of interest. Co-expression of dCas9 and sgRNA can efficiently block transcription (in bacteria, ~300-fold repression) by interfering with transcriptional elongation, RNA polymerase binding, or transcription factor binding.

The binding specificity is determined jointly by 20-bp matching region on the sgRNA and a short DNA motif (protospacer adjacent motif or PAM, sequence: NGG) juxtaposed to the DNA complementary region. The uniqueness of CRISPRi, as compared to several recently published works on applying the wild-type CRISPR system for genome mutagenesis (Cong et al., Mali et al., Jiang et al., Hwang et al.), is that the nuclease-deficient mutant could silence transcription on the gene expression level without genetically altering the target loci. Thus, CRISPRi is a system that can regulate a genome instead of modifying a genome.

1. CRISPRi system for bacterial gene knockdown

he dCas9 fusion plasmids contain a human codon optimized dcas9 gene that is fused to different effector domains, under the control of either a spleen focus-forming virus (SFFV) or a murine Stem cell retrovirus LTR promoter. The sgRNA plasmids contain a murine U6 promoter controlled sgRNA cassette, wherein the GN19 can be custom designed to target sequences in the genome. The sgRNA plasmid also contains a CMV-puro-t2A-mCherry expression cassette, for selection or fluorescent gating of transiently transfected cells. .

2. CRISPR-mediated transcription regulation system in eukaryotes

CRISPRi transcription regulation system in human cells

The first plasmid (pdCas9_humanized) contains a human codon optimized dcas9 gene under the control of Murine Stem Cell retroVirus LTR promoter. The second plasmid (pgRNA_humanized) contains a murine U6 promoter controlled sgRNA cassette, wherein the GN19 can be custom designed to target sequences in the genome. The pgRNA_humanized also contains a CMV-puro-t2A-mCherry expression cassette, useful for selection or fluorescent gating of transiently transfected cells. Co-expression of both plasmids in HEK293 cells could cause up to 2~3-fold repress on targeted fluorescent genes. We also provide a dCas9 construct that is fused to 2 copies of NLS and a tagBFP gene (pdCas9_BFP-Humanized).

CRISPRi transcription regulation system in yeast

The dCas9 fusion CEN/ARS plasmids contain a human codon optimized dCas9 fused with two C-terminal SV40 nuclear localization signal sequences and an Mxi1 repressive domain. The sgRNA CEN/ARS plasmids contain a SNR52 promoter, sgRNA, and SUP4 terminator 3’ flanking sequence.

The reagents used in these studies, along with additional descriptions, are available via Addgene, here and here.


Multicellular systems biology minicourse: Multicellular organisms utilize populations of interacting cells to accomplish development, homeostasis, repair, and reproduction.  In all these tasks, individual cells must collect and exchange information to guide their collective behaviors. In the engineering sciences, similar phenomena are formulated as problems of distributed control. However, solutions to these engineering problems are particularly challenging when the system lacks a single organizing center, as is the case for most tissues. Further complicating distributed control in the context of tissues is the probabilistic nature of the cellular components along with the amazing mix of time and length scales governing their interactions. In this minicourse, led by Matt Thomson and Zev Gartner, we explored the mechanisms and algorithms used by tissues that allow coordination of individual cellular components to accomplish complex tasks. Here are a sample of some of the modules from the course.

Module1: The objective was to explore how noisy morphogen gradients affect cell differentiation and patterning with regard to time. The provided code creates a french flag model with a noisy morphogen gradient and different cellular factors. The equilibrium function monitors how different combinations of cellular and morphogen variables affect the total pattern equilibration time. Click here for the code and associated files.

Module 2: Morphogen gradients are one mechanism by which cell patterning occurs in a developing organism. However, such gradients are often noisy, and how cells interpret these noisy signals to form defined tissues is unclear. Our simple one-dimensional model represents single cells, spread out in space, that are capable of directed, differential motility depending on their intrinsic “state.” Each state depends on the concentration of morphogen that the cell detects at a given point. We model how various degrees of differential motility can resolve even highly noisy morphogen gradients to form defined patterns. Click here for the code and associated files.

CRNSimulator Mathematica Package: Many mathematical models in biology are described by ordinary differential equations, often derived from mass-action or Michaelis-Menten chemical kinetics. This is a Mathematica package for the syntactic manipulation of chemical reaction networks as Mathematica expressions, and for the simulation of such systems. It is particularly well-suited for engineered chemical systems, in which chemical reaction networks can be used as a kind of "programming language".  This tool was developed by David Soloveichik.

MATLAB toolbox for microscope control (via Micro-Manager) and the feedback controllerThis software is a set of MATLAB functions that allows the user to control a specific microscope - the Nikon TI with a Mosaic II DMD, a 650 nm and 750 nm LED light source, and an LMM5 laser launch with at least 405, 514 and 561 lasers.  This tool was developed by Jared Toettcher.

PTMfunc: Recent advances in mass spectrometry have permitted the identification of thousands of posttranslational modifications (PTMs) including phosphorylation and ubiquitylation. It has been difficult, however, to determine which modifications are biologically meaningful. Center investigators Pedro Beltrao, Nevan Krogan, Al Burlingame, Wendell Lim and colleagues developed a method of prioritizing PTMs, leading to predictors of functional relevance, and identification of regulatory hot spots.

Pathway Linker:Biomedical research often focuses on altering the functions of selected proteins. These changes can unexpectedly perturb signaling pathways and non-specifically affect several cellular processes. PathwayLinker can assist experimental work by linking the queried proteins to signaling pathways through protein-protein and/or genetic interactions. PathwayLinker identifies and visualizes the first neighbor interactor network of the queried proteins, analyzes the signaling pathway memberships of the proteins in this subnet, and provides links to further online resources.

From Structure to Systems: High-Resolution, Quantitative Genetic Analysis of RNA Polymerase II
Braberg et al., 2013
Cell. 2013 Aug 15;154(4):775-88. doi:10.1016/j.cell.2013.07.033.


RNA polymerase II (RNAPII) lies at the core of dynamic control of gene expression. Using 53 RNAPII point mutants, we generated a point mutant epistatic miniarray profile (pE-MAP) comprising ∼60,000 quantitative genetic interactions in Saccharomyces cerevisiae. This analysis enabled functional assignment of RNAPII subdomains and uncovered connections between individual regions and other protein complexes. Using splicing microarrays and mutants that alter elongation rates in vitro, we found an inverse relationship between RNAPII speed and in vivo splicing efficiency. Furthermore, the pE-MAP classified fast and slow mutants that favor upstream and downstream start site selection, respectively. The striking coordination of polymerization rate with transcription initiation and splicing suggests that transcription rate is tuned to regulate multiple gene expression steps. The pE-MAP approach provides a powerful strategy to understand other multifunctional machines at amino acid resolution.



Supplementary Dataset 1: pE-MAP Clustered in Two Dimensions


Using optogenetics to interrogate the dynamic control of signal transmission by the ras/erk module
Toettcher et al., 2013
Cell 155(6):1422-34 (2013).


The complex, interconnected architecture of cell-signaling networks makes it challenging to disentangle how cells process extracellular information to make decisions. We have developed an optogenetic approach to selectively activate isolated intracellular signaling nodes with light and use this method to follow the flow of information from the signaling protein Ras. By measuring dose and frequency responses in single cells, we characterize the precision, timing, and efficiency with which signals are transmitted from Ras to Erk. Moreover, we elucidate how a single pathway can specify distinct physiological outcomes: by combining distinct temporal patterns of stimulation with proteomic profiling, we identify signaling programs that differentially respond to Ras dynamics, including a paracrine circuit that activates STAT3 only after persistent (>1 hr) Ras activation. Optogenetic stimulation provides a powerful tool for analyzing the intrinsic transmission properties of pathway modules and identifying how they dynamically encode distinct outcomes.



RPPA Data: Reverse-phase protein arrays (RPPA), a high-throughput proteomic technique for measuring 180 phospho- and total protein levels


Quantitative genetic-interaction mapping in mammalian cells
Roguev et al., 2013
Nat Methods 2013 May; 10(5):432-7.


Mapping genetic interactions (GIs) by simultaneously perturbing pairs of genes is a powerful tool for understanding complex biological phenomena. Here we describe an experimental platform for generating quantitative GI maps in mammalian cells using a combinatorial RNA interference strategy. We performed 11,000 pairwise knockdowns in mouse fibroblasts, focusing on 130 factors involved in chromatin regulation to create a GI map. Comparison of the GI and protein-protein interaction (PPI) data revealed that pairs of genes exhibiting positive GIs and/or similar genetic profiles were predictive of the corresponding proteins being physically associated. The mammalian GI map identified pathways and complexes but also resolved functionally distinct submodules within larger protein complexes. By integrating GI and PPI data, we created a functional map of chromatin complexes in mouse fibroblasts, revealing that the PAF complex is a central player in the mammalian chromatin landscape.




Table S2: Significant genetic interactions (GIs) observed (−2 ≤ S ≥ 2)
Supplementary Dataset 1: Raw data before scoring GIs
Supplementary Dataset 2: Scored and clustered GI data in Treeview format


Activity-dependent protein dynamics define interconnected cores of co-regulated postsynaptic proteins
Trinidad et al., 2013
Mol Cell Proteomics 2013 Jan;12(1):29-41


Synapses are highly dynamic structures that mediate cell-cell communication in the central nervous system. Their molecular composition is altered in an activity-dependent fashion, which modulates the efficacy of subsequent synaptic transmission events. Whereas activity-dependent trafficking of individual key synaptic proteins into and out of the synapse has been characterized previously, global activity-dependent changes in the synaptic proteome have not been studied. To test the feasibility of carrying out an unbiased large-scale approach, we investigated alterations in the molecular composition of synaptic spines following mass stimulation of the central nervous system induced by pilocarpine. We observed widespread changes in relative synaptic abundances encompassing essentially all proteins, supporting the view that the molecular composition of the postsynaptic density is tightly regulated. In most cases, we observed that members of gene families displayed coordinate regulation even when they were not known to physically interact. Analysis of correlated synaptic localization revealed a tightly co-regulated cluster of proteins, consisting of mainly glutamate receptors and their adaptors. This cluster constitutes a functional core of the postsynaptic machinery, and changes in its size affect synaptic strength and synaptic size. Our data show that the unbiased investigation of activity-dependent signaling of the postsynaptic density proteome can offer valuable new information on synaptic plasticity


Table S1: Raw iTRAQ signal areas for all peptides used in the analysis
Table S2: Protein expression data of all 893 proteins used in the analysis
Table S4:
Pairwise Pearson coefficient listed for correlations between PSD110 proteins with a PCC>0.7034
Table S5:List for Figure 5 (correlation network of PSD110 protein and near neighbors)
Table S6:List proteins in clusters I and II of Figure 5
Table S7:Order of individual proteins within the dendrogram and colorbar shown in Figure 6 (weighted expression network analysis of the entire PSD dataset<

Global Identification and Characterization of Both O-GlcNAcylation and Phosphorylation at the Murine Synapse

Trinidad et al., 2012
Mol Cell Proteomics. 2012 Aug;11(8):215-29

O-linked N-acetylglucosamine (O-GlcNAc) is a dynamic, reversible monosaccharide modifier of serine and threonine residues on intracellular protein domains. Crosstalk between O-GlcNAcylation and phosphorylation has been hypothesized. Here, we identified over 1750 and 16,500 sites of O-GlcNAcylation and phosphorylation from murine synaptosomes, respectively. In total, 135 (7%) of all O-GlcNAcylation sites were also found to be sites of phosphorylation. Although many proteins were extensively phosphorylated and minimally O-GlcNAcylated, proteins found to be extensively O-GlcNAcylated were almost always phosphorylated to a similar or greater extent, indicating the O-GlcNAcylation system is specifically targeting a subset of the proteome that is also phosphorylated. Both PTMs usually occur on disordered regions of protein structure, within which, the location of O-GlcNAcylation and phosphorylation is virtually random with respect to each other, suggesting that negative crosstalk at the structural level is not a common phenomenon. As a class, protein kinases are found to be more extensively O-GlcNAcylated than proteins in general, indicating the potential for crosstalk of phosphorylation with O-GlcNAcylation via regulation of enzymatic activity.


Table S1:  Mass spectrometry identification of proteins at the murine synapse
Table S2:  Mass spectrometry identification of O-GlcNAcylated peptides at the murine synapse
Table S3:  Mass spectrometry identification of phosphorylated peptides at the murine synapse
Table S4:  Mass spectrometry identification of N-GlcNAcylated peptides at the murine synapse
Table S5:  Mass spectrometry identification of potentially O-GalNAcylated peptides at the murine synapse
Table S6:  Mass spectrometry identification of peptides simultaneously modified by both O-GlcNAc and phosphate at the murine synapse
Table S7:  Analysis of overrepresented motifs in the phosphorylation dataset identified at the murine synapse as revealed by Motif-X

Hierarchical modularity and the evolution of genetic interactomes across species

Ryan et al 2012
Mol Cell. 2012 Jun 8;46(5):691-704

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional crosstalk between biological processes, suggesting general design principles of genetic interactomes.

Table S1:  The unaveraged S. pombe E-MAP
Table S2:  The averaged S. pombe E-MAP
Table S3:  The similarity scores for every gene pair in the S. pombe E-MAP
Table S4:  The scaled and merged E-MAP and SGA S.cerevisiae Data