Why do seemingly identical cells respond differently to a drug? To address this, we studied the dynamics and variability of the protein response of human cancer cells to a chemotherapy drug, camptothecin. We present a dynamic-proteomics approach that measures the levels and locations of nearly 1000 different endogenously tagged proteins in individual living cells at high temporal resolution. All cells show rapid translocation of proteins specific to the drug mechanism, including the drug target (topoisomerase-1), and slower, wide-ranging temporal waves of protein degradation and accumulation. However, the cells differ in the behavior of a subset of proteins. We identify proteins whose dynamics differ widely between cells, in a way that corresponds to the outcomes—cell death or survival. This opens the way to understanding molecular responses to drugs in individual cells.
Article28 February 2012Open Access Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels Milana Frenkel-Morgenstern Corresponding Author Milana Frenkel-Morgenstern Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel Present address: Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain Search for more papers by this author Tamar Danon Tamar Danon Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Thomas Christian Thomas Christian Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Takao Igarashi Takao Igarashi Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Lydia Cohen Lydia Cohen Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Ya-Ming Hou Ya-Ming Hou Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Lars Juhl Jensen Lars Juhl Jensen Disease Systems Biology, Novo Nordisk Foundation for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Milana Frenkel-Morgenstern Corresponding Author Milana Frenkel-Morgenstern Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel Present address: Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain Search for more papers by this author Tamar Danon Tamar Danon Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Thomas Christian Thomas Christian Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Takao Igarashi Takao Igarashi Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Lydia Cohen Lydia Cohen Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Ya-Ming Hou Ya-Ming Hou Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Lars Juhl Jensen Lars Juhl Jensen Disease Systems Biology, Novo Nordisk Foundation for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Author Information Milana Frenkel-Morgenstern 1,2, Tamar Danon1, Thomas Christian3, Takao Igarashi3, Lydia Cohen1, Ya-Ming Hou3 and Lars Juhl Jensen4 1Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel 2Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel 3Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA 4Disease Systems Biology, Novo Nordisk Foundation for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark *Corresponding author. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel. Tel.: +34 601046898; Fax: +34 912945037; E-mail: [email protected] or E-mail: [email protected] Molecular Systems Biology (2012)8:572https://doi.org/10.1038/msb.2012.3 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info 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. Synopsis Most cell cycle-regulated genes adopt non-optimal codon usage, namely, their translation involves wobbly matching codons. Here, the authors show that tRNA expression is cyclic and that codon usage, therefore, can give rise to cell-cycle regulation of proteins. Most cell cycle-regulated genes adopt non-optimal codon usage. Non-optimal codon usage can give rise to cell-cycle dynamics at the protein level. The high expression of transfer RNAs (tRNAs) observed in G2 phase enables cell cycle-regulated genes to adopt non-optimal codon usage, and conversely the lower expression of tRNAs at the end of G1 phase is associated with optimal codon usage. The protein levels of aminoacyl-tRNA synthetases oscillate, peaking in G2/M phase, consistent with the observed cyclic expression of tRNAs. Introduction The cell cycle is a fundamental cellular process that allows cells to multiply and faithfully transfer their genetic information to their offspring (Csikász-Nagy, 2009). The full complexity of this process became apparent a decade ago with the first genome-wide microarray studies of the mitotic cell cycle of budding yeast (Cho et al, 1998; Spellman et al, 1998). During the eukaryotic cell cycle, gene expression is regulated at different levels, including through the translation of mRNAs into proteins (Sonenberg and Hinnebusch, 2009). Accurate translation is a complex event coordinated by essential components of the cell, such as the ribosome, messenger RNAs, aminoacylated (charged) transfer RNAs (tRNAs), and a host of additional protein and RNA factors (Francklyn et al, 2002; Lackner and Bähler, 2008). The tRNAs have a central role in translation as they are adaptor molecules that link the nucleotide sequence of the mRNA and the amino-acid sequence of a protein (Lowe and Eddy, 1997; Percudani et al, 1997; Schattner et al, 2005; Goodenbour and Pan, 2006). The expression of tRNAs is tissue specific and it varies in distinct cellular conditions (Dittmar et al, 2006). Recent studies demonstrate that the redundancy of the genetic code allows a choice to be made between 'synonymous' codons for the same amino acid, which may have dramatic effects on the rate of translation due to the tRNA recycling and channeling into the ribosome (Cannarozzi et al, 2010; Weygand-Durasevic and Ibba, 2010; Brackley et al, 2011; Gingold and Pilpel, 2011; Plotkin and Kudla, 2011). Moreover, mRNAs usually start by using the codons corresponding to rarer tRNAs, undergoing a slower phase of elongation, which is then followed by a faster phase (Tuller et al, 2010). The 'redundancy' in the genetic code implies that 61 codons are translated requiring fewer than 61 tRNAs according to the 'wobble' base-pairing rules (isoaccepting codons; Crick, 1966). This is especially true when the base at the 5′ end of the anticodon is inosine (abbreviated as I), which deviates from the standard base-pairing rules. The four main wobble base pairs are guanine-uracil, inosine-uracil, inosine-adenine, and inosine-cytosine (G:U, I:U, I:A, I:C; Lander et al, 2001). Finally, the Percudani rules state that tRNAs only wobble with a synonymous codon if there is no better tRNA for that codon (Percudani et al, 1997). Due to the degeneracy of the genetic code, all amino acids except methionine and tryptophan are encoded by multiple, synonymous codons. The usage of synonymous codons is far from uniform and there is a strong preference toward certain codons in highly expressed genes when compared with other genes (Sharp et al, 1986; Lavner and Kotlar, 2005; Goodenbour and Pan, 2006). Indeed, codon usage preferences are closely correlated with the abundance of corresponding tRNAs in bacteria and yeast (Grantham et al, 1981; Ikemura, 1981, 1982; Futcher et al, 1999), which maximizes the speed and accuracy of protein translation (Gouy and Gautier, 1982; Ikemura, 1985; Akashi and Eyre-Walker, 1998; Duret and Mouchiroud, 1999; Coghlan and Wolfe, 2000; Duret, 2000; Wright et al, 2004; Drummond et al, 2006). However, charging level of some tRNAs matches some anomalous codon usage patterns for different groups of genes in bacteria (Liljenström et al, 1985; Dittmar et al, 2005). Moreover, the correspondence between codon adaptation and gene expression makes translation efficient at a global level rather than at the level of specific genes (Kudla et al, 2009). More specifically, the first 30–50 codons of most mRNA sequences are less efficiently translated than the following part of their sequences (Tuller et al, 2010). The optimal correlation between tRNA levels and their corresponding codon frequencies are dependent on the total amount of tRNAs, ribosomes (Kudla et al, 2009), and the aminoacyl-tRNA synthetases (aaRSs) that charge tRNAs through a two-step aminoacylation reaction using ATP (Orfanoudakis et al, 1987). Finally, changes in the ATP availability in cells influence the concentration of charged tRNAs during a cell cycle (Ibba and Söll, 2004). Non-optimal codons adapt wobble codon–anticodon base pairing with a low binding affinity. Recent studies revealed that synonymous changes for non-optimal codons can alter the expression of human genes (Kimchi-Sarfaty et al, 2007). Moreover, the codons with the least amount of tRNAs and, thus, the lowest rate of translation, do not necessarily have the lowest genome frequency (Parmley and Huynen, 2009), and they may fulfill a role in translation 'pausing' between protein domains (Makhoul and Trifonov, 2002). However, the function of non-optimal codons, in general, and of wobble codon–anticodon base pairing, in particular, in regulating the temporal aspects of protein translation remains unclear in eukaryotes. We have studied translation regulation of cell cycle-dependent genes through comparative analyses of codon preferences, dynamic quantitative proteomics (Sigal et al, 2006a; Cohen et al, 2008) and mathematical modeling. We discovered that in four distant eukaryotes, proteins encoded by cell cycle-regulated mRNAs have similar preferences in terms of non-optimal codon usage and wobble codon–anticodon base pairing. The dynamics of the charged tRNA pool is expected to vary during the cell cycle as a result of the variations in the ATP availability (Orfanoudakis et al, 1987). In addition, we found experimentally that the levels of glycyl-, threonyl-, and glutamyl-prolyl-aminoacyl-tRNA synthetases oscillate during the human cell cycle, and that tRNA expression levels increase in the G2/M phase of the yeast cell cycle. Moreover, tRNAs are most weakly expressed toward the end of G1 phase. Similarly, we found that genes expressed in different phases of the cell cycle adopt different codon preferences. We show that about 15% of the cell cycle-regulated genes expressed in the G1 phase adopt relatively optimal codon usage, even at the beginning of their coding sequences. All other cell cycle-regulated genes prefer non-optimal codons for their coding sequences. Finally, we developed a mathematical model based on a competitive mechanism in which the cycling of charged tRNAs leads to oscillations in the rate of translation for mRNAs containing non-optimal codons. Results Codon preferences of cell cycle-regulated genes In unicellular prokaryotes and eukaryotes, the abundance of certain tRNAs correlates with the codon preferences of genes encoding highly expressed proteins, for example, ribosomal proteins (Percudani et al, 1997; Kanaya et al, 1999; Bernstein et al, 2002; Lavner and Kotlar, 2005; Kotlar and Lavner, 2006). Thus, codons that perfectly match the anticodons of the tRNAs are preferentially used in highly expressed genes (Grosjean and Fiers, 1982). The mRNAs coding for rare proteins also have selective codon usage, albeit much weaker than the mRNAs coding abundant proteins (Liljenström and von Heijne, 1987). We hypothesized that cell cycle-regulated genes should also exhibit a preference for certain codons and thus, we analyzed the codon usage preferences for synonymous codons in three sets of human cell cycle-regulated genes, B1, B2, top-600, from an earlier study (Jensen et al, 2006; see Materials and methods). Although the B1 set of genes is the most reliable group of cycling genes, it includes highly expressed genes that are strongly biased in terms of their codon usage, a situation which is undesirable for our purposes. By contrast, highly expressed genes are not so abundant in the B2 and top-600, although they are of somewhat less reliable. The three sets of cell cycle-regulated genes gave consistent results, either all showing positive or negative preferences for a given codon (Table I). To evaluate the statistical significance of this result, P-values were calculated from 10 000 bootstrap samples with the same codon adaptation index (CAI) distribution as cell cycle-regulated genes (see Materials and methods and Table I). The codon preference was considered as significant when P-value <0.01 for at least two of the three sets of cycling genes (Table I). In fact, the codon usage is confounded by the local GC content (Drummond and Wilke, 2008) and thus, we produced an additional bootstrap procedure preserving the GC content instead of the CAI distribution of the cell cycle-regulated genes. The P-values obtained by this procedure did not alter the final conclusions (see Supplementary information). Table 1. The codon preferences for the sets of human cell cycle-regulated genes: B1, B2 and top-600 sets (Jensen et al, 2006) Aa Codon 5′ → 3′ Preferences human P-values human Anticodon 3′ → 5′ Binding at third position Affinity Organisma B1 B2 Top-600 B1 B2 Top-600 S.p. S.c. A.t. Ala GCA 0.04 0.05 0.03 0.05 0.09 0.14 CGI I:A Low • Ala GCC −0.1 −0.07 −0.04 0.0001 0.01 0.16 CGI I:C High • Ala GCG −0.01 −0.03 −0.02 0.58 0.01 0.03 CGC C:G High • • • Ala GCT 0.07 0.05 0.03 0.0001 0.0001 0.05 CGI I:T Low • • • Arg AGA 0.07 0.05 0.04 0.02 0.14 0.13 UCU U:A Low • Arg AGG −0.02 −0.02 −0.01 0.17 0.0001 0.02 UCC C:G High • • Arg CGA 0 0.03 0.02 0.19 0.0001 0.0001 GCI I:A Low Arg CGC −0.01 −0.04 −0.03 0.75 0.09 0.06 GCI I:C High Arg CGG −0.06 −0.04 −0.03 0.0001 0.2 0.19 GCC C:G High • • • Arg CGT 0.02 0.02 0.01 0.07 0.0001 0.04 GCI I:T Low • • Asn AAC −0.13 −0.11 −0.08 0.0001 0.0001 0.0001 UUG G:C High • Asn AAT 0.13 0.11 0.08 0.0001 0.0001 0.0001 UUG G:T Low • Asp GAC −0.1 −0.1 −0.07 0.01 0.0001 0.01 CUG G:C High • Asp GAT 0.1 0.1 0.07 0.01 0.0001 0.01 CUG G:T Low • Cys TGC −0.15 −0.12 −0.04 0.0001 0.0001 0.37 UCG G:C High • • • Cys TGT 0.15 0.12 0.04 0.0001 0.0001 0.37 UCG G:T Low • • • Gln CAA 0.1 0.06 0.05 0.0001 0.42 0.09 GUU U:A Low Gln CAG −0.1 −0.06 −0.05 0.0001 0.43 0.1 GUC C:G High Glu GAA 0.13 0.1 0.08 0.0001 0.03 0.04 CUU U:A Low • • Glu GAG −0.13 −0.1 −0.08 0.0001 0.04 0.04 CUC C:G High • • Gly GGA 0.04 0.05 0.04 0.35 0.15 0.29 CCU U:A Low • Gly GGC −0.04 −0.06 −0.04 0.17 0.01 0.21 CCG G:C High • Gly GGG −0.05 −0.03 −0.03 0.02 0.09 0.0001 CCC C:G High • Gly GGT 0.05 0.04 0.03 0.01 0.0001 0.0001 CCG G:T Low • • His CAC −0.14 −0.13 −0.07 0.0001 0.0001 0.05 GUG G:C High • • His CAT 0.14 0.13 0.07 0.0001 0.0001 0.05 GUG G:T Low • • Ile ATA 0.05 0.05 0.04 0.03 0.12 0.08 UAI I:A Low Ile ATC −0.12 −0.12 −0.08 0.01 0.0001 0.02 UAI I:C High • Ile ATT 0.07 0.07 0.04 0.02 0.0001 0.06 UAI I:T Low • • • Leu CTA 0.02 0.01 0.01 0.0001 0.03 0.02 GUI I:A Low • Leu CTC −0.05 −0.04 −0.03 0.0001 0.0001 0.0001 GUI I:C High • • Leu CTG −0.1 −0.08 −0.06 0.0001 0.04 0.02 GUC C:G High Leu CTT 0.03 0.04 0.03 0.06 0.01 0.05 GUI I:T Low • • Leu TTA 0.06 0.04 0.03 0.0001 0.03 0.0001 AAU U:A Low • Leu TTG 0.04 0.03 0.02 0.0001 0.0001 0.0001 AAC C:G High • • Lys AAA 0.04 0.09 0.06 0.43 0.01 0.11 UUU U:A Low Lys AAG −0.04 −0.09 −0.06 0.44 0.01 0.11 UUC C:G High Met ATG 0 0 0 1 1 1 UAC C:G High • • • Phe TTC −0.13 −0.1 −0.07 0.0001 0.0001 0.0001 AAG G:C High • Phe TTT 0.13 0.1 0.07 0.0001 0.0001 0.0001 AAG G:T Low • Pro CCA 0.07 0.04 0.04 0.01 0.06 0.02 GGI I:A Low • • • Pro CCC −0.1 −0.06 −0.06 0.0001 0.02 0.0001 GGI I:C High • • Pro CCG −0.02 −0.03 −0.02 0.19 0.02 0.07 GGC C:G High • • • Pro CCT 0.05 0.05 0.04 0.01 0.0001 0.0001 GGI I:T Low Ser AGC −0.05 −0.05 −0.03 0.0001 0.0001 0.02 UCG G:C High • • Ser AGT 0.03 0.04 0.03 0.03 0.0001 0.0001 UCG G:T Low Ser TCA 0.03 0.03 0.02 0.1 0.02 0.34 AGI I:A Low • Ser TCC −0.05 −0.04 −0.03 0.0001 0.0001 0.0001 AGI I:C High • Ser TCG −0.02 −0.02 −0.02 0.0001 0.01 0.57 AGC C:G High • • Ser TCT 0.06 0.04 0.03 0.0001 0.0001 0.01 AGI I:T Low • • • Thr ACA 0.01 0.03 0.02 0.63 0.29 0.72 UGI I:A Low • Thr ACC −0.05 −0.07 −0.05 0.09 0.0001 0.1 UGI I:C High • Thr ACG −0.05 −0.03 −0.02 0.0001 0.0001 0.11 UGC C:G High • • • Thr ACT 0.09 0.07 0.05 0.0001 0.0001 0.0001 UGI I:T Low • • • Trp TGG 0 0 0 1 1 1 ACC G:C High • • • Tyr TAC −0.08 −0.1 −0.06 0.03 0.0001 0.05 AUG G:C High • Tyr TAT 0.08 0.1 0.06 0.04 0.0001 0.05 AUG G:T Low • Val GTA 0.07 0.05 0.03 0.0001 0.0001 0.0001 CUI I:A Low Val GTC −0.05 −0.05 −0.03 0.0001 0.0001 0.0001 CUI I:C High • Val GTG −0.09 −0.06 −0.05 0.01 0.15 0.03 CUC C:G High • Val GTT 0.07 0.06 0.05 0.01 0.01 0.01 CUI I:T Low • • We found that cell cycle-regulated genes prefer non-optimal codons, which are recognized by wobble base pairing, and thus have a low codon–anticodon binding affinity (Table I). For instance, TTT was overrepresented among cycling genes when we consider the TTT and TTC codons of phenylalanine (Table I). While no tRNA genes exist for the corresponding AAA anticodon, a tRNA gene does exists with the GAA anticodon. In addition, asparagine, aspartic acid, cysteine, histidine, and tyrosine were similarly seen to display a preference for the non-optimal codons (Table I). Using accurate thermodynamic data for binding affinities of all possible wobble base-pairing cases (I:C, I:A, I:T, G:T, G:C, C:G, U:A) (Watkins and SantaLucia, 2005), we found that for all amino acids cell cycle-regulated genes have a strong, significant (P<0.01) preference for codons with a low codon–anticodon binding affinity (Table I). To assess the biological importance of the codon preferences observed, we tested whether they are evolutionarily conserved. To this end, we analyzed sets of cell cycle-regulated genes in Schizosaccharomyces pombe, Saccharomyces cerevisiae, and Arabidopsis thaliana (Jensen et al, 2006). For both yeasts species, these genes show significant and consistent preferences for non-optimal codons of amino acids, which use the inosine modification at the wobble position. There are eight such amino acids in Schizosaccharomyces pombe (as in higher eukaryotes) and seven in S. cerevisiae (Supplementary Tables 1 and 2). For Arabidopsis thaliana, a significant preference for non-optimal codons was found for amino acids encoded by two or more codons, also consistent with the trend in humans (Supplementary Table 3). Although the GC content of genes appears to influence the codon preferences of cell cycle-regulated genes in yeast (Supplementary Tables 4–7), the trends are nonetheless consistent with that observed for human genes. Together, these results show that the preference for using non-optimal codons to encode cell cycle-regulated proteins is conserved across distantly related eukaryotes (see Table I). To study if the cell cycle-regulated genes expressed in different phases of the cell cycle adopt the same codon preferences, we used the top-600 sets of genes. Notably, non-optimal codon usage was observed for genes expressed in all phases except the G1 phase (see Supplementary information). In this phase of the cell cycle, both ATP and charged tRNA concentrations are likely to be low (Orfanoudakis et al, 1987), as is the total tRNA pool, which we found to be lowest toward the G1 phase in yeast S. cerevisiae (Table II; Figure 1). As a result, relatively optimal codon preferences were observed in human and yeast genes expressed in G1 phase (Supplementary Table 8). Finally, we found that the level of aaRSs is also likely to be low in the G1 phase, while augmented in the G2/M phase of the human cell cycle (Figure 2A; Supplementary Figure 1). Taken together, these findings indicate that genes may use synonymous codons to adjust their expression pattern during a cell cycle. Figure 1.The tRNA concentration during the cell cycle of S. cerevisiae. The concentration was calculated as an average of the different points in the same phases of the cell cycle according to Table II. Download figure Download PowerPoint Figure 2.Total fluorescence as a function of the time during two cell cycles for YFP-tagged proteins, glycyl-tRNA synthetase (GARS), threonyl-tRNA synthetase (TARS), tryptophanyl-tRNA synthetase (WARS), and glutamyl-prolyl-tRNA synthetase (EPRS), when compared with GAPDH and ARGLU1 . (A) The lines represent the average fluorescence (±standard error) from >15 individual cells during two generations for the synthetases that show significant cell cycle-dependent protein dynamics. ARGLU1 is used as a positive control. (B) The total fluorescence (±standard error) for WARS and GAPDH as a negative control. WARS and GAPDH do not show the cell cycle-dependent protein dynamics. Source data is available for this figure in the Supplementary Information. (Source data for Figure 2A) Protein dynamics of cell-cycle-regulated proteins during two cell-cycles [msb20123-sup-0001-SourceData-S1.xls] (Source data for Figure 2B) Protein dynamics of non cell-cycle-regulated proteins during two cell-cycles [msb20123-sup-0002-SourceData-S2.xls] Download figure Download PowerPoint Table 2. The concentration of tRNA during the cell cycle in the yeast S. cerevisiae Time points (min) tRNA concentration (mg/ml) Estimated cell-cycle phase 0 10.0 Synchronized in M phase 30 7.8 M 60 14.9 G1 90 13.7 G1 120 4.1 G1 150 10.8 G1 180 7.9 S 210 11.5 S 240 21.3 G2 270 21.5 G2 300 9.7 M 330 8.9 M 360 11.1 M Protein dynamics of aaRSs aaRSs covalently attach amino acids to tRNAs and consequently, they have a fundamental role in controlling the amount of charged tRNAs available for protein synthesis (Ibba and Söll, 2004; Francklyn et al, 2008). Thus, we systematically measured the aaRSs available during the cell cycle of individual human cells. We used time-lapse microscopy to measure the dynamics of four aaRSs found in the LARC library (Sigal et al, 2006a, 2006b, 2007; Cohen et al, 2008; see Supplementary information), namely glycyl-tRNA synthetase (GARS), threonyl-tRNA synthetase (TARS), tryptophanyl-tRNA synthetase (WARS) and glutamyl-prolyl-tRNA synthetase (EPRS). In these studies, we also measured the dynamics of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a negative control and that of the arginine-glutamate-rich protein-1 (ARGLU1) as a positive control, the expression of which is regulated through the cell cycle at the protein and mRNA levels (Sigal et al, 2006a; Supplementary Figure 2). Each synthetase was tagged with the yellow fluorescent protein (eYFP) at its endogenous chromosomal location in the H1299 cell line (see Supplementary information), and the resulting videos (recorded over 72 h) were analyzed to quantify the accumulation of the proteins at each time point as described previously (Sigal et al, 2006a). Cell-cycle regulation was defined on the basis of a criterion of at least two-fold difference in the rate of accumulation over the cell cycle, and a difference of at least eight-fold standard errors between the highest and lowest protein accumulation rate (Sigal et al, 2006a). Based on these criteria, the protein dynamics of GARS, TARS, EPRS, and ARGLU1 were clearly cell cycle dependent, whereas WARS and GAPDH could not be considered to have cell cycle-dependent protein dynamics (Figure 2; Supplementary Figure S1). Interestingly, glycine, threonine, and proline are encoded by four different codons and glutamic acid is by two codons. Therefore, cell cycle-dependent protein levels of GARS, TARS, and EPRS may be a source for the cell cycle-regulated behavior of charged tRNAGly, tRNAThr, tRNAGlu, and tRNAPro, as evident in our mathematical model described below. Tryptophan is only encoded by one codon, which leaves no margin for gene-specific, cell cycle-dependent translation rates through the use of suboptimal codons, and which would explain why WARS does not exhibit cell cycle-dependent protein dynamics (Figure 2B). In general, changes in the concentration of aaRSs are not necessary for all the corresponding amino acids to be cell cycle dependent because the ATP pool oscillates during the human cell cycle (Orfanoudakis et al, 1987), and because tRNA levels also rise and fall during the cell cycle (Table II; Figure 1). Thus, in steady-state circumstance, the cycling of ATP and aaRSs levels together provides a mechanism to generate oscillating levels of charged tRNAs (aa-tRNAs) synthesized by steady-state levels of aaRSs. Taken together, these observations indicate that the availability of charged tRNAs during a cell cycle may regulate the expression of genes with regard to their codon usage preferences. Codon usage of proteins with cell cycle-dependent protein dynamics To evaluate the translational regulation of proteins that do not cycle at the mRNA, but do cycle at protein levels, we used the protein data set studied previously (Sigal et al, 2006a) but extended with the five additional proteins (Figure 2). Thus, 11 proteins were found to have cycling protein levels but non-cycling mRNA levels (Whitfield et al, 2002; Gauthier et al, 2008, 2010): DDX5, USP7, TOP1, ANP32B, H2AFV, GTF2F2, RBBP7, SFRS10, GARS, TARS, and EPRS, which were determined as cell cycle regulated in means of protein dynamics in human cells. ARGLU1 cycles at the mRNA level and was excluded from that analysis. As a negative set, we used the 11 proteins that were found to not cycle at the protein level despite the mRNA cycling (Whitfield et al, 2002): SAE1, SET, HMGA2, YPEL1, DDX46, LMNA, HMGA1, ZNF433, KIAA1937, GAPDH, and WARS. The cell-cycle codon scores (CCCS) (see Materials and methods) were calculated for all the proteins analyzed (Supplementary Table 9) and consistent with our hypothesis, we found a significant difference between median distributions of the two groups (Wilcoxon's test; P-value<1E−3) (Figure 3). All of the 11 cycling proteins had a positive CCCS, while the non-cycling proteins had both negative and positive scores (Figure 3). Taken together, these observations indicate that the presence of many non-optimal codons in a gene is not sufficient to cause large-amplitude oscillations at the protein level. Figure 3.Comparison of CCCS for proteins with cell cycle-dependent protein dynamics versus proteins with non-cell cycle-dependent protein dynamics. The CCCS evaluates the proportion of wobble codon–anticodon base pairing similar to that of the top-600 genes. A red line represents the distribution mean. Download figure Download PowerPoint Mathematical model To describe how temporal changes in the tRNA pool can lead to the translational regulation mathematically (Figure 4), we concentrated only on two processes: amino-acid charging of tRNAs by aaRSs (producing aminoacyl-tRNAs or 'aa-tRNAs'); and cognate or 'wobble' aa-tRNA binding to mRNAs. The rate of transport of aa-tRNAs species to a ribosomal A site, the intrinsic kinetics of peptidyl transfer, ribosome concentration and their translocation were not considered in this model. Figure 4.A schematic presentation of the additional level of protein translation regulation via the tRNA pool. (A) The translation of poly-TTC and poly-TTT chains (used as an example) when the pool of charged tRNAs includes many TTC-tRNAPhe. (B) Changes in the translation rate of poly-TTC and poly-TTT chains if few TTC-tRNAPhe are available. (C) The oscillating tRNA pool may produce cell cycle-dependent translation of genes, which use wobble codon–anticodon base pairing. The translation rate of proteins using optimal codons stays constant. Download figure Download PowerPoint The aminoacylation reaction is achieved in two steps (Ibba and Söll, 2004).
Abstract 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 CDR3α/CDR3β 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.
The ability of antipili antibody to prevent ascending urinary tract infection was investigated in rats. One group of rats was immunized passively with rabbit antisera to purified pili and challenged by intravesicular inoculation of 5 x 10(7) heavily piliated Escherichia coli. Only 2 of 14 immunized animals developed cortical abscesses as compared to 13 of 15 control rats given normal rabbit serum (P equals 0.0001). The mean log titer of bacteria in the kidneys of the immunized rats was 0.85 vs. 6.08 in the controls (P less than 0.005). A second group was actively immunized with pili. 3 of 16 immunized animals became infected as compared to 10 of 15 controls (P equals 0.01). The mean log titers were 2.13 and 4.54, respectively (P less than 0.01). A third group was passively immunized and challenged with a strain that had different O, K, and H antigens but shared pili antigens. Abscesses occurred in 4 of 15 immunized animals as compared to 13 of 15 controls (P equals 0.001). The mean log titers were 2.37 and 5.63, respectively (P less than 0.005). These results indicate that antipili antibody protects rats against ascending urinary tract infections.
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.
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.
We previously found that the peripheral blood (PB) mononuclear cells (MCs) (PBMCs) of a patient with chronic neutropenia contained an expanded population of cytotoxic CD8+ T cells using a variable (V) region delta1 gene product in the T-cell receptor-alpha (TCR-alpha) polypeptide [Vdelta1-constant(C)alpha+ T cells]. Sequencing of polymerase chain reaction (PCR) amplification products have now revealed a productive Vdelta1/joining (J)alphaIGRJa03/Calpha rearrangement of the TCR-alpha gene, predominantly associated with a Vbeta16/Dbeta2.1/Jbeta2.1/Cbeta2 TCR-beta gene, in these cells. Furthermore, we detected a markedly deficient proliferative response of the patient PBMCs to triggering with monoclonal antibodies (MoAbs) to the CD3 molecule, contrasting with a substantial response to the Vbeta3, 12, 14, 15, 17 and 20-specific staphylococcal enterotoxin B (SEB) superantigen, suggesting defective TCR-mediated activation of the Vdelta1+/Vbeta16+ clone. Moreover, whereas triggering of Vdelta1- T cells cultured with interleukin-2 (IL-2) by MoAb to the CD3 molecule enhanced proliferation, Vdelta1-Calpha+ T cells were inhibited by MoAbs to either CD3 or Vdelta1. Vdelta1-Calpha+ T-cell clones spontaneously secrete interferon-gamma (IFN-gamma) and were further induced to release tumour necrosis factor (TNF-alpha) when triggered by anti-CD3 plus phorbol ester. Aberrant signalling by the clonotypic TCR together with the functional properties of the CD8+ Vdelta1+/Vbeta16+ clone may thus contribute to the immunohaematological abnormalities observed in this patient.
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.
