The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index; 6 of the 63 sources its claims cite were checked for support.
Dynamic profiling of the protein life cycle in response to pathogens
Open accessAbstract
How the immune system readies for battle: Although gene expression is tightly controlled at both the RNA and protein levels, the quantitative contribution of each step, especially during dynamic responses, remains largely unknown. Indeed, there has been much debate whether changes in RNA level contribute substantially to protein-level regulation. Jovanovic et al. built a genome-scale model of the temporal dynamics of differential protein expression during the stimulation of immunological dendritic cells (see the Perspective by Li and Biggin). Newly stimulated functions involved the up-regulation of specific RNAs and concomitant increases in the levels of the proteins they encode, whereas housekeeping functions were regulated posttranscriptionally at the protein level. Science , this issue 10.1126/science.1259038 ; see also p. 1066
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24 Own result · 18 Not checked yet · 14 Sources not found · 11 Own interpretation · 3 Own method · 1 Partly supported · accuracy not yet measured
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels.”
- Also stated in: § mRNA abundance dynamics dominate protein changes post stimulation · plain
“We found that mRNA levels explain ~87–92%, per-mRNA translation rates ~4–7%, and degradation rates ~3–6% of protein fold changes after 12 hours (Figs. 3C, S11B, (29)).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Abstract
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- Also stated in: § mRNA abundance dynamics dominate protein changes post stimulation · plain
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In the paper
“the fact that strong early changes are all in immune response proteins (fig. S4A)”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
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In the paper
“Fold changes in induced immune response proteins (29) were particularly dominated by mRNA level changes (Figs. 2B, 3E, table S6).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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“the increase in the calculated ‘degradation rate’ – likely reflecting depletion by secretion, or “decreased cellular half life” – of proteins from the recently-characterized secretome of LPS stimulated mouse macrophages (33) (P < 10−10, LPS vs. MOCK; Wilcoxon rank-sum Test, fig. S9C).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
What the sources say
- Not checked yetcites [33] Eichelbaum K Winter M Diaz MB Herzig S Krijgsveld J Selective enrichment of newly synthesized proteins for quantitative secretome analysis Nat Biotechnol 2012 10.1038/nbt.2356 23000932 source
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In the paper
“Considering all 3 variables together, we account for nearly 79% of the variance of the independently measured protein levels (figs. S10, S11A, (29)).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA levels contribute the most to protein expression levels before stimulation
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
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In the paper
“For example, transient up-regulation of the mRNA encoding the negative immune regulator Trafd1 (34) (Fig. 3F) is the main cause of a strong increase in its protein.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
What the sources say
- Not checked yetcites [34] Sanada T FLN29 deficiency reveals its negative regulatory role in the Toll-like receptor (TLR) and retinoic acid-inducible gene I (RIG-I)-like helicase signaling pathway J Biol Chem 283 33858 33864 2008 18849341 PMC2662213 source
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In the paper
“mRNA fold changes contributed at least eight times as much as the protein life cycle combined for both induced and repressed proteins (fig. S12, table S5, (29)).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Using TE values instead of our pulsed-SILAC derived translation rates, we estimate a comparable contribution of protein synthesis (Figs. 3B, S10).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA levels contribute the most to protein expression levels before stimulation
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In the paper
“Indeed, we found that changes in translation and degradation rates together explain more of absolute protein changes than changes in mRNA levels (mRNA: ~32% to 43% of the fit value; per-mRNA protein production rate: ~22–41%; degradation rates: ~19–36%, Figs. 3D, S11C).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > Protein life cycle changes primarily affect proteins performing basic cellular functions
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In the paper
“Third, the correlation between our protein translation at baseline (t=0h), as estimated by pulsed-SILAC data or by TE values, is comparable when considering only the lowest expressed 25% (Pearson r~0.52), the highest expressed 25% (r~0.58), or all modeled proteins (r ~ 0.5) (figs. S16A, S16B).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
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- It may state two separate claims as one. 28
- Probably not a substantial claim. 2
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“only a few studies (5, 12, 17–20) have attempted to shed light on how changes in each of these processes determine differential protein expression – either relative (ratios) or absolute (differences) – during dynamic responses and only one recent report (20) has attempted to quantitate each process. However, none of these has comprehensively accounted for experimental measurement errors, nor have”
§ Dynamic profiling of the protein life cycle in response to pathogens > Introduction
What the sources say
- Partly supportedcites [5] Vogel C Marcotte EM Insights into the regulation of protein abundance from proteomic and transcriptomic analyses Nat Rev Genet 13 227 232 2012 22411467 PMC3654667 sourceExperimental — accuracy not yet measured
The section “mRNA and protein levels in response to perturbation” says dynamic studies were limited until recently partly by measurement noise and concludes that perturbed systems need further analysis. “A model for understanding regulation of protein abundance” says interdependencies complicate assessing regulatory contributions. Neither passage establishes that no dynamic-response study comprehensively accounted for measurement error or disentangled those interdependencies; the copy also does not substantiate the specific count of studies. (LLM, conf=0.84)
- Partly supportedcites [12] Marguerat S Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells Cell 151 671 683 2012 23101633 PMC3482660 sourceExperimental — accuracy not yet measured
The Results and Discussion sections on coordinated expression and quiescence compare mRNA and protein abundances during a changing cellular state and discuss possible roles for translation and protein turnover. But the paper quantifies RNA and protein levels, not each underlying process separately. Its quantification section estimates measurement errors; the passages do not show a comprehensive treatment of those errors or disentangling of interdependencies. (LLM, conf=0.82)
- Partly supportedcites [17] Vogel C Silva GM Marcotte EM Protein Expression Regulation under Oxidative Stress Mol Cell Proteomics 10 2011 10.1074/mcp.M111.009217 PMC3237073 21933953 sourceExperimental — accuracy not yet measured
The Conclusions (page not provided) call for more time-resolved proteome measurements, supporting the claim that such studies were limited. The paper compares mRNA and protein dynamics and discusses translation and degradation, but it does not state that prior studies failed to comprehensively account for measurement error or disentangle interdependencies. Its LC-MS/MS Analysis section discusses measurement accuracy for its own data, not that assessment of prior work. (LLM, conf=0.82)
- Partly supportedcites [18] Lee MV A dynamic model of proteome changes reveals new roles for transcript alteration in yeast Mol Syst Biol 7 2011 10.1038/msb.2011.48 PMC3159980 21772262 sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says that all but a few earlier mRNA–protein studies neglected temporal changes or biological replicates. The Results section, “Dynamic modeling suggests alternate roles of transcript reduction” (page unavailable), describes this paper’s model of mRNA changes, protein synthesis, and degradation during a dynamic response. Our copy does not establish the broader claim that no prior study comprehensively accounted for measurement error or disentangled interdependencies. (LLM, conf=0.78)
- Partly supportedcites [19] Maier T Quantification of mRNA and protein and integration with protein turnover in a bacterium Mol Syst Biol 7 511 2011 21772259 PMC3159969 sourceExperimental — accuracy not yet measured
The Results sections on protein turnover and mRNA–protein dynamics show that this study estimated translation-efficiency and protein-degradation rates and examined protein changes during growth and stress responses. The rate estimates concern protein homeostasis, however; the cited passages do not establish that all three processes were quantified during dynamic responses. They also do not establish the broader claims about how few studies existed or whether those studies accounted for measurement error and data interdependencies. (LLM, conf=0.82)
- Partly supportedcites [20] Kristensen AR Gsponer J Foster LJ Protein synthesis rate is the predominant regulator of protein expression during differentiation Mol Syst Biol 9 2013 10.1038/msb.2013.47 PMC3792347 24045637 sourceExperimental — accuracy not yet measured
The Introduction says much less was known about how these processes contribute to protein-expression changes after perturbation, and the Results (‘Modeling the control of protein expression’) quantify contributions from transcriptional change, synthesis and degradation during differentiation. The cited paper does not establish the citing sentence’s broader assessment that none of the studies comprehensively accounted for measurement error or disentangled interdependencies. (LLM, conf=0.83)
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In the paper
“Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for over half of the absolute change in protein molecules in the cell.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Abstract
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In the paper
“We quantified 6,079 proteins by LC-MS/MS in at least one sample and 2,288 proteins in all samples (time points, conditions and replicates; Fig. 2A, table S1).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A pulsed-SILAC strategy to measure protein dynamics
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In the paper
“we found good reproducibility of the LPS/MOCK ratios of key fitted values (Figs. 2B, S4) and of the relative differences in per-mRNA translation rates (e.g., ΔTi(12h) = Ti(12h)LPS/Ti(12h)MOCK, Pearson r = 0.68, fig. S5A) or degradation rates (e.g., ΔDi(12h) = Di(12h)LPS/Di(12h)MOCK, r = 0.62, fig. S5B).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“In Trafd1 and hundreds of other genes, a transient, strong, spiked change in mRNA, combined with a time-constant protein half life, much longer than the 12h time course, result in a monotonous increase in protein levels, such that global protein fold changes at 12h post LPS correlate best to mRNA changes at 5h (fig. S13).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
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In the paper
“Of these 79%, mRNA levels explained 59–68%, per-mRNA translation rates 18–26%, and degradation rates 8–22%. (Figs. 3A, S11A, (29)).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA levels contribute the most to protein expression levels before stimulation
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“However, changes in per-mRNA translation rates contributed more substantially to protein level induction, whereas changes in protein degradation rates mostly contributed to protein level repression (fig. S12, table S5, (29)).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We found that mRNA levels explain ~87–92%, per-mRNA translation rates ~4–7%, and degradation rates ~3–6% of protein fold changes after 12 hours (Figs. 3C, S11B, (29)).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Only a handful of proteins (e.g., Tnfaip2 (26, 35, 36)), show peaked, transient protein expression within our time scale; all have very high basal degradation rates, which typically do not increase further.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
What the sources say
- Not checked yetcites [26] Chevrier N Systematic Discovery of TLR Signaling Components Delineates Viral-Sensing Circuits Cell 147 853 867 2011 22078882 PMC3809888 source
- Not checked yetcites [35] Kuan AP Genetic control of autoimmune myocarditis mediated by myosin-specific antibodies Immunogenetics 49 79 85 1999 9887344 source
- Not checked yetcites [36] Burton PR Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls Nature 447 661 678 2007 17554300 PMC2719288 source
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In the paper
“There was no significant change in the ratio of mitochondrial-to-nuclear DNA at 12h post LPS stimulation, but a significant (~25%; P=0.016, t-test) reduction at 24h post stimulation (Fig. 4C).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > An increase in degradation rates of mitochondrial proteins is associated with mitophagy
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In the paper
“Both structural mitochondrial proteins and enzymes in key mitochondrial metabolic pathways have increased degradation.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > An increase in degradation rates of mitochondrial proteins is associated with mitophagy
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In the paper
“we saw a decrease in the M/L ratios (a proxy for increased degradation) of ~80% of annotated mitochondrial proteins in LPS vs. MOCK samples (Fig. 4D)”
- Also stated in: § An increase in degradation rates of mitochondrial proteins is associated with mitophagy · plain
“in nearly all mitochondrial proteins with a higher mitochondrial localization prediction score (from MitoCarta (43) – over 95% of the 156 proteins with a score > 20 of the 472 measured mitochondrial proteins) (fig. S17).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > An increase in degradation rates of mitochondrial proteins is associated with mitophagy
What the sources say
- Not checked yetcites [43] Pagliarini DJ A mitochondrial protein compendium elucidates complex I disease biology Cell 134 112 123 2008 18614015 PMC2778844 source
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- Also stated in: § An increase in degradation rates of mitochondrial proteins is associated with mitophagy · plain
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In the paper
“First, while the 3,147 modeled genes are somewhat enriched for higher expressed genes (fig. S14), we do model a substantial number of lowly expressed mRNAs (fig. S14).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
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In the paper
“Upon LPS stimulation, a substantial decrease in the level of mitochondrial proteins is associated with increased degradation rates, although these proteins are among the most stable in unstimulated DCs (Figs. 2B, 3E, 4B, table S6).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > An increase in degradation rates of mitochondrial proteins is associated with mitophagy
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In the paper
“protein synthesis and degradation rates do change significantly for 357 proteins (~11% of consistently detected proteins, tables S7, S8, (29)), and in particular for proteins performing essential cellular functions (‘housekeeping proteins’, Figs. 2B, 3E, table S6), including cytoskeletal, metabolic, ribosomal (Fig. 4A) and mitochondrial proteins (Fig. 4B).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > Protein life cycle changes primarily affect proteins performing basic cellular functions
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Finally, a few key regulators of DCs and the LPS response (e.g., Cebpb, a pioneer transcription factor whose mRNA is already very highly expressed pre-stimulation, and Rela, Fig. 3F) are considerably dynamically regulated at the protein level, such that increased protein degradation rates (Rela) and/or increased per-mRNA translation rates (Rela and Cebpb) are main drivers for protein change.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Indeed, whereas from raw data mRNA explains 27% of the gene-to-gene variation in protein levels at baseline (t=0), using modeled expression values it explains 42%, and, once correcting for data reproducibility (29), it explains 52%.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Discussion
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The model assumes that per-mRNA translation rates and degradation rates change linearly.
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In the paper
“the quantitative contribution of each process to changes in protein expression levels (Fig. 1A) remains largely unknown and much debated (5, 6).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Introduction
What the sources say
- Not checked yetcites [5] Vogel C Marcotte EM Insights into the regulation of protein abundance from proteomic and transcriptomic analyses Nat Rev Genet 13 227 232 2012 22411467 PMC3654667 source
- Not checked yetcites [6] Li JJ Bickel PJ Biggin MD System wide analyses have underestimated protein abundances and the importance of transcription in mammals PeerJ 2 e270 2014 24688849 PMC3940484 source
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Probably not a substantial claim.
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In the paper
“Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Abstract
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In the paper
“This ensures a low rate of false positives (calling a change where none exists), but may result in false negatives, and hence in some underestimation of the contribution of protein synthesis and degradation.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“a good correlation between our per-mRNA translation rates and our independent measurement of TE values in DCs using ribosome profiling at t=0h (r = 0.5, table S4, fig. S8A), comparable to the correlation between TE values in mouse DCs and mouse fibroblasts (r = 0.54, fig. S8B)”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
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In the paper
“DCs are mostly post-mitotic, and LPS synchronizes them (27) and causes dramatic regulatory changes from the expression of thousands of transcripts (22, 24, 25) to protein phosphorylation (26).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A pulsed-SILAC strategy to measure protein dynamics
What the sources say
- Not checked yetcites [27] Shalek AK Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells Nature 498 236 240 2013 23685454 PMC3683364 source
- Not checked yetcites [22] Rabani M Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells Nat Biotechnol 2011 10.1038/nbt.1861 PMC3114636 21516085 source
- Not checked yetcites [24] Amit I Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses Science 326 257 263 2009 19729616 PMC2879337 source
- Not checked yetcites [25] Garber M A High-Throughput Chromatin Immunoprecipitation Approach Reveals Principles of Dynamic Gene Regulation in Mammals Mol Cell 47 810 822 2012 22940246 PMC3873101 source
- Not checked yetcites [26] Chevrier N Systematic Discovery of TLR Signaling Components Delineates Viral-Sensing Circuits Cell 147 853 867 2011 22078882 PMC3809888 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“our most differential and reliable parameter estimates correspond to the well-quantified genes (29), whereas proteins with less reliable measurements are not associated with reliable changes.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“the fair correlation of our translation and protein degradation rate estimates in resting cells (table S3, fig. S6) with previous estimates in mouse fibroblasts (NIH3T3) based on a similar pulsed-SILAC approach (16) (r(Ti(0))= 0.35; r(Di(0)) = 0.58; fig. S7A, S7B)”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > A model-based estimation of protein synthesis and degradation rates
What the sources say
- Not checked yetcites [16] Schwanhausser B Global quantification of mammalian gene expression control Nature 473 337 342 2011 21593866 source
- Not checked yetcites [30] Subtelny AO Eichhorn SW Chen GR Sive H Bartel DP Poly(A)-tail profiling reveals an embryonic switch in translational control Nature 508 66 71 2014 24476825 PMC4086860 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“These dynamic changes cannot be observed solely from total protein and transcript levels, but the corresponding rate changes are readily apparent (Fig. 3F).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > mRNA abundance dynamics dominate protein changes post stimulation
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“suggesting decreased production of new mitochondrial proteins and increased destruction of old ones.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Results > An increase in degradation rates of mitochondrial proteins is associated with mitophagy
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“We find similar patterns of contributions when we use the Spearman rank correlation rather than Pearson correlation (fig. S20, (29)), suggesting that our conclusions are robust to outliers with particularly strong changes.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Discussion
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- The authors state this as an assumption: The model assumes that per-mRNA translation rates and degradation rates change linearly.
- Not matched
In the paper
“Furthermore, our analysis corrected for RNA-Seq expression reproducibility, intra-library protein expression reproducibility, and library-dependent protein expression biases ((29), fig. S21), all essential to avoid inadvertent attribution of measurement errors to modeled translation and protein degradation rates.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Discussion
What the sources say
- Not matchedcites [29] Supplementary Online Material (SOM).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, the increased role we observed for protein degradation, in contrast to prior studies (16, 20), may be needed by postmitotic cells that cannot simply renew their protein pool by division-coupled passive dilution.”
§ Dynamic profiling of the protein life cycle in response to pathogens > Discussion
What the sources say
- Not checked yetcites [16] Schwanhausser B Global quantification of mammalian gene expression control Nature 473 337 342 2011 21593866 source
- Not checked yetcites [20] Kristensen AR Gsponer J Foster LJ Protein synthesis rate is the predominant regulator of protein expression during differentiation Mol Syst Biol 9 2013 10.1038/msb.2013.47 PMC3792347 24045637 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“While our study does not directly address which steps in mRNA regulation account for this, our related work on the RNA life cycle during the first 3 hours in LPS stimulated DCs suggests that transcriptional changes may in turn dominate differential mRNA expression, whereas dynamic changes in RNA processing or degradation affect only a minority of genes, albeit with important functions (46).”
§ Dynamic profiling of the protein life cycle in response to pathogens > Discussion
What the sources say
- Not checked yetcites [46] Rabani M High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies Cell 159 1698 1710 2014 25497548 PMC4272607 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
The model assumes that per-mRNA translation rates and degradation rates change linearly.
“Nevertheless, we make some simplifying assumptions in our model (e.g., linear changes in per-mRNA translation rates and degradation rates) that may be refined in the future (e.g., with sigmoidal functions (22, 52, 53)), allowing us to estimate additional valuable parameters (e.g., time point of rate change).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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In the citing paper
“However, the quantitative contribution of mRNA abundance to protein abundance remains controversial (Cheng et al., 2016, Jovanovic et al., 2015, Kristensen et al., 2013, Li et al., 2014, Liu et al., 2016, Robles et al., 2014, Schwanhäusser et al., 2011, Vogel et al., 2010, Vogel and Marcotte, 2012).”
From Impact of Alternative Splicing on the Human Proteome (2017) · cites this paper as Jovanovic et al., 2015
In the citing paper
“Although substantial protein expression changes were observed in both studies, RNA‐level regulation appeared to be stronger than that of protein‐level changes, fueling the debate on the relative importance of transcription, translation, and degradation.”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Jovanovic et al, 2015
In the citing paper
“Although a few other computational approaches can quantitate the rate parameters based on first‐order differential equations (Lee et al, 2011; Jovanovic et al, 2015; Omranian et al, 2015), PECA is the first approach that introduced a probabilistic model for statistical inference of regulatory parameters.”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Jovanovic et al, 2015
In the citing paper
“This finding contrasts the reports from steady‐state systems in yeast (Li et al, 2014; Csardi et al, 2015), but is consistent with a recent study on a dynamic system of mammalian cells responding to lipopolysaccharide (LPS) treatment (Jovanovic et al, 2015).”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Jovanovic et al, 2015
In the citing paper
“This re‐analysis confirmed that the LPS response is driven by substantial RNA regulation immediately after stimulation (Jovanovic et al, 2015).”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Jovanovic et al, 2015
In the citing paper
“In this work, we have demonstrated that protein misfolding stress places much greater weights on the importance of protein‐level regulation than the recent observations that characterized transcriptomics changes as the main driver of phenotypic adaptation (Lee et al, 2011; Jovanovic et al, 2015).”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Jovanovic et al, 2015
In the citing paper
“In particular, once haplotypes are resolved188–190, single-cell epigenomic profiles should allow correlation of events such as the methylation patterns of a gene’s promoters and enhancers, or of enhancers targeted by the same transcription factor191.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [191]
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Metadata
- Authors
- Jovanovic, M., Rooney, M. S., Mertins, P., Przybylski, D., Chevrier, N., Satija, R., Rodriguez, E. H., Fields, A. P., Schwartz, S., Raychowdhury, R., Mumbach, M. R., Eisenhaure, T., Rabani, M., Gennert, D., Lu, D., Delorey, T., Weissman, J. S., Carr, S. A., Hacohen, N., & Regev, A.
- Affiliations
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
- Broad Institute (US)
- University of California, San Francisco (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Rooney, Michael S.
- Satija, Rahul
- Rabani, Michal
- Hacohen, Nir
- Regev, Aviv
- ORCID
- Venue
- Science
- Published
- 2015-03-06
- Volume
- 347
- Issue
- 6226
- Pages
- 1259038-1259038
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4506746
- OpenAlex
- W2079279399
- Cited by
- 460
- References
- 74
- Citations by year
- 2026: 31 · 2025: 34 · 2024: 33 · 2023: 30 · 2022: 42 · 2021: 34 · 2020: 39 · 2019: 36 · 2018: 45 · 2017: 59 · 2016: 52 · 2015: 24
- FWCI
- 26.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH
- HHMI
- Swiss National Science Foundation
- National Human Genome Research Institute Centers of Excellence in Genomics Science (P50 HG006193)
- Broad Institute Funds
- Klarman Cell Observatory
- Marie Sklodowska-Curie International Outgoing Fellowships
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein synthesis, protein degradation, mRNA dynamics, gene expression regulation, lipopolysaccharide, dendritic cells, pathogen response, proteome remodeling, transcriptional induction
- MeSH terms
- Amino Acids, Animals, Bone Marrow Cells, Dendritic Cells, Isotope Labeling, Lipopolysaccharides, RNA, Messenger, Protein Biosynthesis, Sequence Analysis, RNA, Cell Culture Techniques, Mitochondrial Proteins, Mice, Host-Pathogen Interactions, Molecular Dynamics Simulation, Proteolysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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