Noise genetics: inferring protein function by correlating phenotype with protein levels and localization in individual human cells.

To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes.

Dynamic proteomics of human protein level and localization across the cell cycle.

Regulation of proteins across the cell cycle is a basic process in cell biology. It has been difficult to study this globally in human cells due to lack of methods to accurately follow protein levels and localizations over time. Estimates based on global mRNA measurements suggest that only a few percent of human genes have cell-cycle dependent mRNA levels. Here, we used dynamic proteomics to study the cell-cycle dependence of proteins. We used 495 clones of a human cell line, each with a different protein tagged fluorescently at its endogenous locus. Protein level and localization was quantified in individual cells over 24h of growth using time-lapse microscopy. Instead of standard chemical or mechanical methods for cell synchronization, we employed in-silico synchronization to place protein levels and localization on a time axis between two cell divisions. This non-perturbative synchronization approach, together with the high accuracy of the measurements, allowed a sensitive assay of cell-cycle dependence. We further developed a computational approach that uses texture features to evaluate changes in protein localizations. We find that 40% of the proteins showed cell cycle dependence, of which 11% showed changes in protein level and 35% in localization. This suggests that a broader range of cell-cycle dependent proteins exists in human cells than was previously appreciated. Most of the cell-cycle dependent proteins exhibit changes in cellular localization. Such changes can be a useful tool in the regulation of the cell-cycle being fast and efficient.

Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels.

The cell cycle is a temporal program that regulates DNA synthesis and cell division. When we compared the codon usage of cell cycle-regulated genes with that of other genes, we discovered that there is a significant preference for non-optimal codons. Moreover, genes encoding proteins that cycle at the protein level exhibit non-optimal codon preferences. Remarkably, cell cycle-regulated genes expressed in different phases display different codon preferences. Here, we show empirically that transfer RNA (tRNA) expression is indeed highest in the G2 phase of the cell cycle, consistent with the non-optimal codon usage of genes expressed at this time, and lowest toward the end of G1, reflecting the optimal codon usage of G1 genes. Accordingly, protein levels of human glycyl-, threonyl-, and glutamyl-prolyl tRNA synthetases were found to oscillate, peaking in G2/M phase. In light of our findings, we propose that non-optimal (wobbly) matching codons influence protein synthesis during the cell cycle. We describe a new mathematical model that shows how codon usage can give rise to cell-cycle regulation. In summary, our data indicate that cells exploit wobbling to generate cell cycle-dependent dynamics of proteins.

Using bleach-chase to measure protein half-lives in living cells.

Protein removal has a central role in numerous cellular processes. Obtaining systematic measurements of multiple protein removal rates is necessary to understand the principles that govern these processes, but it is currently a major technical challenge. To address this, we developed 'bleach-chase', a noninvasive method for measuring the half-lives of multiple proteins at high temporal resolution in living cells. The method uses a library of annotated human reporter cell clones, each with a unique fluorescently tagged protein expressed from its native chromosomal location. In this protocol, we detail a simple procedure that bleaches the cells and uses time-lapse fluorescence microscopy and automated image analysis to systematically measure the half-life dynamics of multiple proteins. The duration of the protocol is 4-5 d. The method may be applicable to a wide range of fluorescently tagged proteins and cell lines.

'Multi-epitope-targeted' immune-specific therapy for a multiple sclerosis-like disease via engineered multi-epitope protein is superior to peptides.

Antigen-induced peripheral tolerance is potentially one of the most efficient and specific therapeutic approaches for autoimmune diseases. Although highly effective in animal models, antigen-based strategies have not yet been translated into practicable human therapy, and several clinical trials using a single antigen or peptidic-epitope in multiple sclerosis (MS) yielded disappointing results. In these clinical trials, however, the apparent complexity and dynamics of the pathogenic autoimmunity associated with MS, which result from the multiplicity of potential target antigens and "epitope spread", have not been sufficiently considered. Thus, targeting pathogenic T-cells reactive against a single antigen/epitope is unlikely to be sufficient; to be effective, immunospecific therapy to MS should logically neutralize concomitantly T-cells reactive against as many major target antigens/epitopes as possible. We investigated such "multi-epitope-targeting" approach in murine experimental autoimmune encephalomyelitis (EAE) associated with a single ("classical") or multiple ("complex") anti-myelin autoreactivities, using cocktail of different encephalitogenic peptides vis-a-vis artificial multi-epitope-protein (designated Y-MSPc) encompassing rationally selected MS-relevant epitopes of five major myelin antigens, as "multi-epitope-targeting" agents. Y-MSPc was superior to peptide(s) in concomitantly downregulating pathogenic T-cells reactive against multiple myelin antigens/epitopes, via inducing more effective, longer lasting peripheral regulatory mechanisms (cytokine shift, anergy, and Foxp3+ CTLA4+ regulatory T-cells). Y-MSPc was also consistently more effective than the disease-inducing single peptide or peptide cocktail, not only in suppressing the development of "classical" or "complex EAE" or ameliorating ongoing disease, but most importantly, in reversing chronic EAE. Overall, our data emphasize that a "multi-epitope-targeting" strategy is required for effective immune-specific therapy of organ-specific autoimmune diseases associated with complex and dynamic pathogenic autoimmunity, such as MS; our data further demonstrate that the "multi-epitope-targeting" approach to therapy is optimized through specifically designed multi-epitope-proteins, rather than myelin peptide cocktails, as "multi-epitope-targeting" agents. Such artificial multi-epitope proteins can be tailored to other organ-specific autoimmune diseases.

Proteome half-life dynamics in living human cells.

Cells remove proteins by two processes: degradation and dilution due to cell growth. The balance between these basic processes is poorly understood. We addressed this by developing an accurate and noninvasive method for measuring protein half-lives, called "bleach-chase," that is applicable to fluorescently tagged proteins. Assaying 100 proteins in living human cancer cells showed half-lives that ranged between 45 minutes and 22.5 hours. A variety of stresses that stop cell division showed the same general effect: Long-lived proteins became longer-lived, whereas short-lived proteins remained largely unaffected. This effect is due to the relative strengths of degradation and dilution and suggests a mechanism for differential killing of rapidly growing cells by growth-arresting drugs. This approach opens a way to understand proteome half-life dynamics in living cells.

Generation of double-labeled reporter cell lines for studying co-dynamics of endogenous proteins in individual human cells.

Understanding the dynamic relationship between components of a system or pathway at the individual cell level is a current challenge. To address this, we developed an approach that allows simultaneous tracking of several endogenous proteins of choice within individual living human cells. The approach is based on fluorescent tagging of proteins at their native locus by directed gene targeting. A fluorescent tag-encoding DNA is introduced as a new exon into the intronic region of the gene of interest, resulting in expression of a full-length fluorescently tagged protein. We used this approach to establish human cell lines simultaneously expressing two components of a major antioxidant defense system, thioredoxin 1 (Trx) and thioredoxin reductase 1 (TrxR1), labeled with CFP and YFP, respectively. We find that the distributions of both proteins between nuclear and cytoplasmic compartments were highly variable between cells. However, the two proteins did not vary independently of each other: protein levels of Trx and TrxR1 in both the whole cell and the nucleus were substantially correlated. We further find that in response to a stress-inducing drug (CPT), both Trx and TrxR1 accumulated in the nuclei in a manner that was highly temporally correlated. This accumulation considerably reduced cell-to-cell variability in nuclear content of both proteins, suggesting a uniform response of the thioredoxin system to stress. These results indicate that Trx and TrxR1 act in concert in response to stress in regard to both time course and variability. Thus, our approach provides an efficient tool for studying dynamic relationship between components of systems of interest at a single-cell level.

Protein dynamics in drug combinations: a linear superposition of individual-drug responses.

Drugs and drug combinations have complex biological effects on cells and organisms. Little is known about how drugs affect protein dynamics that determine these effects. Here, we use a dynamic proteomics approach to accurately follow 15 protein levels in human cells in response to 13 different drugs. We find that protein dynamics in response to combinations of drugs are described accurately by a linear superposition (weighted sum) of their response to individual drugs. The weights in this superposition describe the relative impact of each drug on each protein. Using these weights, we show that one can predict the dynamics in a three-drug or four-drug combination on the basis of the dynamics in drug pairs. Our approach might eliminate the need to increase the number of experiments exponentially with the number of drugs and suggests that it might be possible to rationally control protein dynamics with specific drug combinations.

Dynamic Proteomics: a database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells.

Recent advances allow tracking the levels and locations of a thousand proteins in individual living human cells over time using a library of annotated reporter cell clones (LARC). This library was created by Cohen et al. to study the proteome dynamics of a human lung carcinoma cell-line treated with an anti-cancer drug. Here, we report the Dynamic Proteomics database for the proteins studied by Cohen et al. Each cell-line clone in LARC has a protein tagged with yellow fluorescent protein, expressed from its endogenous chromosomal location, under its natural regulation. The Dynamic Proteomics interface facilitates searches for genes of interest, downloads of protein fluorescent movies and alignments of dynamics following drug addition. Each protein in the database is displayed with its annotation, cDNA sequence, fluorescent images and movies obtained by the time-lapse microscopy. The protein dynamics in the database represents a quantitative trace of the protein fluorescence levels in nucleus and cytoplasm produced by image analysis of movies over time. Furthermore, a sequence analysis provides a search and comparison of up to 50 input DNA sequences with all cDNAs in the library. The raw movies may be useful as a benchmark for developing image analysis tools for individual-cell dynamic-proteomics. The database is available at http://www.dynamicproteomics.net/.

Anatomy of T cell autoimmunity to myelin oligodendrocyte glycoprotein (MOG): prime role of MOG44F in selection and control of MOG-reactive T cells in H-2b mice.

Myelin oligodendrocyte glycoprotein (MOG) is an important myelin target antigen, and MOG-induced EAE is now a widely used model for multiple sclerosis. Clonal dissection revealed that MOG-induced EAE in H-2(b) mice is associated with activation of an unexpectedly large number of T cell clones reactive against the encephalitogenic epitope MOG35-55. These clones expressed extremely diverse TCR with no obvious CDR3alpha/CDR3beta motif(s). Despite extensive TCR diversity, the cells required MOG40-48 as their common core epitope and shared MOG44F as their major TCR contact. Fine epitope-specificity analysis with progressively truncated peptides suggested that the extensive TCR heterogeneity is mostly related to differential recognition of multiple overlapping epitopes nested within MOG37-52, each comprised of a MOG40-48 core flanked at the N- and/or the C-terminus by a variable number of residues important for interaction with different TCR. Abrogation of both the encephalitogenic potential of MOG and T cell reactivity against MOG by a single mutation (MOG44F/MOG44A), together with effective down-regulation of MOG-induced EAE by MOG37-44A-52, confirmed in vivo the primary role for MOG44F in the selection/activation of MOG-reactive T cells. We suggest that such a highly focused T cell autoreactivity could be a selective force that offsets the extensive TCR diversity to facilitate a more "centralized control" of pathogenic MOG-related T cell autoimmunity.