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Cited by 460Advanced Proteomics Techniques and ApplicationsGene Regulatory Network AnalysisRNA and protein synthesis mechanisms

Dynamic profiling of the protein life cycle in response to pathogens

Open access
Jovanovic, Marko, Rooney, Michael S., Mertins, Philipp, Przybylski, Dariusz, Chevrier, Nicolas, Satija, Rahul, Rodriguez, Edwin H., Fields, Alexander P. +12 more·2015·Science
DOI 10.1126/science.1259038PMC PMC4506746
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Abstract

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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Crossref · 10.1126/science.1259038

Cites

74 of 74 references verified0 citation errors · 0 not checkable

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Nothing to recompute.

Claims the paper makes

0 of 63 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    In LPS-stimulated mouse dendritic cells, changes in mRNA abundance are the dominant determinant of most dynamic fold changes in protein levels.

    “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)).”
    Quote in the paperSubstantial claim
  • Findingown data

    Strong early changes in the data are all in immune response proteins.

    “the fact that strong early changes are all in immune response proteins (fig. S4A)”
    Quote in the paperNot a substantial claim
  • Findingown data

    Fold changes in induced immune response proteins were particularly dominated by mRNA level changes.

    “Fold changes in induced immune response proteins (29) were particularly dominated by mRNA level changes (Figs. 2B, 3E, table S6).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Findingcitation

    The calculated degradation rate of proteins from the secretome of LPS-stimulated mouse macrophages increases (P < 10−10, LPS vs MOCK).

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

      Not checked in the free run.

  • Findingown data

    Combining mRNA level, per-mRNA translation rate, and degradation rate accounts for nearly 79% of the variance of independently measured protein levels in resting DCs.

    “Considering all 3 variables together, we account for nearly 79% of the variance of the independently measured protein levels (figs. S10, S11A, (29)).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims. 28
  • Classified as probably not a substantial claim. 2
  • The quote only partly states this claim. 1

  • May combine two claims.

    Findingown data

    The preexisting proteome of proteins that carry out basic cellular functions is remodeled mainly through changes in protein production or degradation, and this accounts for more than half of the absolute change in protein molecules in the cell.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The LC-MS/MS analysis quantified 6,079 proteins in at least one sample and 2,288 proteins in all samples.

    “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).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Fitting parameters for 3,147 genes separately for each replicate gave good reproducibility of LPS/MOCK ratios of key fitted values and of relative differences in per-mRNA translation rates (Pearson r = 0.68) and degradation rates (r = 0.62).

    “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).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingown data

    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 results in a monotonous increase in protein levels, such that global protein fold changes at 12h post LPS correlate best to mRNA changes at 5h.

    “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).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Of the 79% variance explained, mRNA levels explain 59-68%, per-mRNA translation rates 18-26%, and degradation rates 8-22%.

    “Of these 79%, mRNA levels explained 59–68%, per-mRNA translation rates 18–26%, and degradation rates 8–22%. (Figs. 3A, S11A, (29)).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingcitation

    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.

    “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)).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingown data

    mRNA levels explain ~87-92%, per-mRNA translation rates ~4-7%, and degradation rates ~3-6% of protein fold changes after 12 hours.

    “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)).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingown data

    Only a handful of proteins (e.g., Tnfaip2) show peaked, transient protein expression within the time scale; all have very high basal degradation rates, which typically do not increase further.

    “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.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [26] Chevrier N Systematic Discovery of TLR Signaling Components Delineates Viral-Sensing Circuits Cell 147 853 867 2011 22078882 PMC3809888 source

      Not checked in the free run.

    • cites [35] Kuan AP Genetic control of autoimmune myocarditis mediated by myosin-specific antibodies Immunogenetics 49 79 85 1999 9887344 source

      Not checked in the free run.

    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    There was no significant change in the mitochondrial-to-nuclear DNA ratio at 12h post LPS stimulation, but a significant (~25%; P=0.016, t-test) reduction at 24h post stimulation.

    “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).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Both structural mitochondrial proteins and enzymes in key mitochondrial metabolic pathways have increased degradation.

    “Both structural mitochondrial proteins and enzymes in key mitochondrial metabolic pathways have increased degradation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In pulsed SILAC data at 24h post LPS and mock stimulation, the M/L ratios decreased for ~80% of annotated mitochondrial proteins in LPS vs MOCK samples, and for nearly all mitochondrial proteins with a higher mitochondrial localization prediction score (over 95% of the 156 proteins with a score >20 of the 472 measured mitochondrial proteins).

    “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).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [43] Pagliarini DJ A mitochondrial protein compendium elucidates complex I disease biology Cell 134 112 123 2008 18614015 PMC2778844 source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    The 3,147 modeled genes are somewhat enriched for higher expressed genes, but the model includes a substantial number of lowly expressed mRNAs.

    “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).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    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.

    “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).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Protein synthesis and degradation rates change significantly for 357 proteins (~11% of consistently detected proteins), particularly for proteins performing essential cellular functions (housekeeping proteins), including cytoskeletal, metabolic, ribosomal, and mitochondrial proteins.

    “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).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingown data

    A few key regulators of DCs and the LPS response (e.g., Cebpb and Rela) are considerably dynamically regulated at the protein level, with increased protein degradation rates (Rela) and/or increased per-mRNA translation rates (Rela and Cebpb) as main drivers of protein change.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    At baseline, mRNA explains 27% of gene-to-gene variation in protein levels from raw data, 42% with modeled expression values, and 52% after correcting for data reproducibility.

    “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%.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • The authors state this as an assumption: The model assumes that per-mRNA translation rates and degradation rates change linearly.

  • Classified as probably not a substantial claim.

    Premisecitation

    How much each step of RNA and protein turnover contributes to shifts in protein expression is still unclear and disputed.

    “the quantitative contribution of each process to changes in protein expression levels (Fig. 1A) remains largely unknown and much debated (5, 6).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [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 in the free run.

    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Few studies have examined how mRNA, translation, and degradation changes shape differential protein expression during dynamic responses, and none has fully accounted for measurement error or disentangled interdependencies in the data.

    “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”
    Quote in the paperSubstantial claim0 of 6 sources checked
    • cites [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 in the free run.

    • cites [12] Marguerat S Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells Cell 151 671 683 2012 23101633 PMC3482660 source

      Not checked in the free run.

    • cites [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 source

      Not checked in the free run.

    • cites [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 source

      Not checked in the free run.

    • cites [19] Maier T Quantification of mRNA and protein and integration with protein turnover in a bacterium Mol Syst Biol 7 511 2011 21772259 PMC3159969 source

      Not checked in the free run.

    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    The proteome response to LPS is regulated by transcriptional induction of novel cellular functions and by remodeling of preexisting functions through the protein life cycle.

    “Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductreasoning

    The analysis has a low false-positive rate for detecting changes but may produce false negatives and underestimate the contribution of protein synthesis and degradation.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Per-mRNA translation rates correlate well with independent ribosome profiling TE measurements in DCs at t=0h (r=0.5), comparable to the correlation between TE values in mouse DCs and mouse fibroblasts (r=0.54).

    “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)”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Mouse bone marrow-derived dendritic cells are mostly post-mitotic, and LPS synchronizes them and triggers broad regulatory changes.

    “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).”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [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 in the free run.

    • cites [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 in the free run.

    • cites [24] Amit I Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses Science 326 257 263 2009 19729616 PMC2879337 source

      Not checked in the free run.

    • cites [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 in the free run.

    • cites [26] Chevrier N Systematic Discovery of TLR Signaling Components Delineates Viral-Sensing Circuits Cell 147 853 867 2011 22078882 PMC3809888 source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductreasoning

    The model's most differential and reliable parameter estimates correspond to well-quantified genes, while proteins with less reliable measurements are not linked to reliable changes.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims. The quote only partly states this claim.

    Study design and conductown data

    Translation and protein degradation rate estimates in resting cells correlate fairly with previous estimates in mouse fibroblasts from a similar pulsed-SILAC approach (r(Ti(0))=0.35; r(Di(0))=0.58) and with translation efficiency values from ribosome profiling in mouse fibroblasts (r(Ti(0))=0.37).

    “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)”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [16] Schwanhausser B Global quantification of mammalian gene expression control Nature 473 337 342 2011 21593866 source

      Not checked in the free run.

    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    These dynamic changes cannot be observed solely from total protein and transcript levels, but the corresponding rate changes are readily apparent.

    “These dynamic changes cannot be observed solely from total protein and transcript levels, but the corresponding rate changes are readily apparent (Fig. 3F).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The decreases in mRNA and translation rates suggest decreased production of new mitochondrial proteins and increased destruction of old ones.

    “suggesting decreased production of new mitochondrial proteins and increased destruction of old ones.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Using Spearman rank correlation instead of Pearson correlation gives similar contribution patterns, which suggests the conclusions are robust to outliers with strong changes.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • The authors state this as an assumption: The model assumes that per-mRNA translation rates and degradation rates change linearly.

  • May combine two claims.

    Study design and conductcitation

    The analysis adjusted for reproducibility in RNA-Seq expression, reproducibility within protein libraries, and biases in protein expression that depend on the library.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Supplementary Online Material (SOM).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The larger role we observed for protein degradation, unlike prior studies, may be required by postmitotic cells that cannot renew their protein pool through division-coupled passive dilution.

    “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.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [16] Schwanhausser B Global quantification of mammalian gene expression control Nature 473 337 342 2011 21593866 source

      Not checked in the free run.

    • cites [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

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Related work on the RNA life cycle in the first three hours after LPS stimulation of DCs suggests that transcriptional changes dominate differential mRNA expression, while RNA processing or degradation changes affect only a minority of genes.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [46] Rabani M High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies Cell 159 1698 1710 2014 25497548 PMC4272607 source

      Not checked in the free run.

30 of 30

  • 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).”
1 of 1

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?

  • The quantitative contribution of mRNA abundance to protein abundance remains controversial.

    “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 reported time-resolved analyses of mammalian mRNA and corresponding protein expression changes, RNA-level regulation appeared to be stronger than protein-level changes.

    “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

  • PECA is the first approach that introduced a probabilistic model for statistical inference of regulatory parameters.

    “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

  • This finding contrasts the reports from steady-state systems in yeast, but is consistent with a recent study on a dynamic system of mammalian cells responding to LPS treatment.

    “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

  • Re-analysis of the published LPS dataset with PECA confirmed that the LPS response is driven by substantial RNA regulation immediately after stimulation.

    “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

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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
Publisher
American Association for the Advancement of Science (AAAS)
Published
2015-03-06
Volume
347
Issue
6226
Pages
1259038-1259038
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.1259038
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 · 2014: 1
FWCI
26.70
Type
journal article
Language
en
Open access
Licence
www.sciencemag.org/about/science-licenses-journal-article-reuse (vor, 366 d)
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

About these checks

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-05.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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