Evaluation and Improvement of Quantification Accuracy in Isobaric Mass Tag-Based Protein Quantification Experiments

Ahrné, Erik; Glatter, Timo; Viganó, Cristina; Schubert, Conrad von; Nigg, Erich A.; Schmidt, Alexander

doi:10.1021/acs.jproteome.6b00066

Cited by 150 publications

(180 citation statements)

References 38 publications

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“…The setup of the μRPLC-MS system was as described previously (59) . Chromatographic separation of peptides was carried out using an EASY nano-LC 1000 system (Thermo Fisher Scientific), equipped with a heated RP-HPLC column (75 μm x 37 cm) packed in-house with 1.9 μm C18 resin (Reprosil-AQ Pur, Dr. Maisch).…”

Section: Analysis Of Protein Synthesis Ratesmentioning

confidence: 99%

“…The database search results were filtered using the ion score to set the false discovery rate (FDR) to 1% on the peptide and protein level, respectively, based on the number of reverse protein sequence hits in the datasets. The relative quantitative data obtained were normalized and statistically analyzed using our in-house script (SafeQuant) as above (59) .…”

Section: Analysis Of Protein Synthesis Ratesmentioning

confidence: 99%

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Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates

Riba

Nanni

Mittal

et al. 2018

Preprint

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Although protein synthesis dynamics has been studied both with theoretical models and by profiling ribosome footprints, the determinants of ribosome flux along open reading frames (ORFs) are not fully understood. Combining measurements of protein synthesis rate with ribosome footprinting data, we here inferred translation initiation and elongation rates for over a thousand ORFs in exponentially-growing wildtype yeast cells. We found that the amino acid composition of synthesized proteins is as important a determinant of translation elongation rate as parameters related to codon and tRNA adaptation. We did not find evidence of ribosome collisions curbing the protein output of yeast transcripts, either in high translation conditions associated with exponential growth, or in strains in which deletion of individual ribosomal protein genes leads to globally increased or decreased translation. Slow translation elongation is characteristic of RP-encoding transcripts, which have markedly lower protein output than other transcripts with equally high ribosome densities. Significance StatementAlthough sequencing of ribosome footprints has uncovered new aspects of mRNA translation, the determinants of ribosome flux remain incompletely understood. Combining ribosome footprint data with measurements of protein synthesis rates, we here inferred translation initiation and elongation rates for over a thousand ORFs in yeast strains with varying translation capacity. We found that the translation elongation rate varies up to ~20-fold among yeast transcripts, and is significantly correlated with the rate of translation initiation. Furthermore, the amino acid composition of synthesized proteins impacts the rate of translation elongation to the same extent as measures of codon and tRNA adaptation. Transcripts encoding ribosomal proteins are translated especially slow, having markedly lower protein output than other transcripts with equally high ribosome densities.

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Section: Analysis Of Protein Synthesis Ratesmentioning

confidence: 99%

Section: Analysis Of Protein Synthesis Ratesmentioning

confidence: 99%

Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates

Riba

Nanni

Mittal

et al. 2018

Preprint

Self Cite

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“…Results from the database search were imported into Progenesis QI and the final peptide measurement list containing the peak areas of all identified peptides, respectively, was exported. This list was further processed and statically analyzed using our in-house developed SafeQuant R script (SafeQuant, https://github.com/eahrne/SafeQuant, 37 ). The peptide and protein false discovery rate (FDR) was set to 1% using the number of reverse hits in the dataset.…”

Section: Flow Cytometrymentioning

confidence: 99%

Precise transcription timing by a second-messenger drives a bacterial G1/S cell cycle transition

Kaczmarczyk

Hempel

Arx

et al. 2019

Preprint

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29Bacteria adapt their growth rate to their metabolic status and environmental 30 conditions by modulating the length of their quiescent G1 period. But the molecular 31 mechanisms controlling G1 length and exit from G1 are poorly understood. Here we 32 identify a key role for the second messenger c-di-GMP, and demonstrate that a gradual 33 increase in c-di-GMP concentration determines precise gene expression during G1/S in 34 Caulobacter crescentus. We show that c-di-GMP strongly stimulates the kinase ShkA, 35activates the TacA transcription factor, and initiates a G1/S-specific transcription 36 program leading to cell morphogenesis and S-phase entry. C-di-GMP activates ShkA by 37 binding to its central pseudo-receiver domain uncovering this wide-spread domain as a 38 novel signal input module of bacterial kinases. Activation of the ShkA-dependent 39 genetic program also causes c-di-GMP to reach peak levels, which triggers S-phase 40 entry and, in parallel, promotes proteolysis of ShkA and TacA. Thus, a gradual 41 increase of c-di-GMP results in a precisely tuned ShkA-TacA activity window enabling 42 G1/S specific gene expression before cells commit to replication initiation. By defining 43 a regulatory mechanism for G1/S control, this study contributes to understanding 44 bacterial growth control at the molecular level. cell division (D or G2) 1 . Since chromosome replication and cell division (C and D 49 periods) remain constant over a wide range of growth rates 2,3 , the step committing 50 cells to initiate chromosome replication largely determines bacterial proliferation rates. 51Bacteria like Escherichia coli or Bacillus subtilis can increase their growth rate by 52 bypassing the B period and by initiating replication multiple times per division cycle 2 . 53In contrast, Caulobacter crescentus strictly separates its cell cycle stages. An 54 asymmetric division generates a sessile stalked (ST) cell, which directly re-enters S-55 phase, and a motile swarmer (SW) cell that remains in G1 for a variable time 56 depending on nutrient availability 4,5 . Coincident with G1/S transition, motile SW cells 57 undergo morphogenesis to gain sessility ( Fig. 1a). But what determines the length of 58 G1 in this organism has remained unclear. 59In C. crescentus, replication initiation is regulated by the cell cycle kinase CckA. 60CckA is a bifunctional enzyme that acts as a kinase for the replication initiation 61 inhibitor CtrA in G1, but switches to being a phosphatase in S phase, resulting in the 62 inactivation of CtrA and clearance of the replication block 6 (Fig. 1a). The CckA switch 63 is governed by two response regulators, DivK and PleD. While DivK controls CckA 64 activity through protein-protein interactions 7-9 , PleD is a diguanylate cyclase, which is 65 responsible for the characteristic oscillation of c-di-GMP during the cell cycle 10 . The 66 concentration of c-di-GMP, below the detection limit in G1, increases during G1/S to 67 reach peak levels at the onset of S-phase 11 where it allosterically stimulates Cc...

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“…Results from the database search were imported into Progenesis QI and the final peptide measurement list containing the peak areas of all identified peptides, respectively, was exported. This list was further processed and statically analyzed using our in-house developed SafeQuant R script 70 . The peptide and protein false discovery rate (FDR) were set to 1% using the number of reverse hits in the dataset.…”

Section: Quantification Of Cggr and Mcherry Levelsmentioning

confidence: 99%

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Monteiro

Hubmann

Norder

et al. 2019

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Metabolic heterogeneity between individual cells of a population harbors offers significantchallenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence requires tools to zoom into metabolism of individual cells.While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e. the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells, by drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), and by transplanting the B. subtilis FBP-binding transcription factor CggR into yeast. As proof of principle, using fluorescence microscopy, we applied the sensor to identify metabolic subpopulations in yeast cultures. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations. perform flux-dependent regulation 29,30 . Biosensors for such metabolites, such as recently accomplished for E. coli 31 , would in principle allow for measurement of metabolic fluxes in single cells, in combination with microscopy or flow cytometry.Here, drawing on glycolytic flux-signalling metabolite fructose-1,6-bisphosphate (FBP) levels in yeast 30 and using the B. subtilis FBP-binding transcription factor CggR 32,33 , we developed a biosensor, which allows to measure glycolytic flux in single yeast cells. To this end, we used computational protein design, biochemical, proteome and metabolome analyses, for (i) the development of a synthetic yeast promoter regulated by the bacterial transcriptional factor CggR, (ii) the engineering of the transcription factors' FBP binding site towards increasing the sensor's dynamic range, and (iii) the establishment of growthindependent CggR expression levels. We demonstrate the applicability of the biosensor for flow cytometry and time-lapse fluorescence microscopy. We envision that the biosensor will open new avenues for both fundamental and applied metabolic research, not only for monitoring glycolytic flux, but also for engineering control circuits with glycolytic flux as input variable. Results Design of biosensor conceptFor our biosensor, we exploited the fact that the level of the glycolytic intermediate fructose-1,6-biphosphate (FBP) in yeast 30,34 , similar to other organisms 27 , strongly correlates with the glycolysis flux 28,34,35 , and that changing FBP levels exert fluxdependent regulation. In B. subtilis, for instance, FBP binds to the transcription factor (TF) CggR 33 , which when bound to its target DNA forms a tetrameric assembly of two dimers, through which transcription gets inhibited 36 . Upon binding of FBP to the CggR-DNA complex, the dimer-dimer contacts of CggR are disrupted, and the promoter is derepressed 37 .Here, we aimed to transplant the B...

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Evaluation and Improvement of Quantification Accuracy in Isobaric Mass Tag-Based Protein Quantification Experiments

Cited by 150 publications

References 38 publications

Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates

Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates

Precise transcription timing by a second-messenger drives a bacterial G1/S cell cycle transition

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

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