The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index; none of the 63 sources its claims cite is checked for support yet.
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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 63 cited sources checked · 0 supported · 0 flagged
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § mRNA abundance dynamics dominate protein changes post stimulation · plain
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
“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
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
In the paper
“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
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn 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).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
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
“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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 28
- Probably not a substantial claim. 2
- The quote only partly states this claim. 1
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § An increase in degradation rates of mitochondrial proteins is associated with mitophagy · plain
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
None of the sources cited for this claim have been checked yet.
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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
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
- 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 [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 yetcites [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 yetcites [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 yetcites [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 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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?
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]
Citations
Cites
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 8×
“Because these are among themost abundant in the cell (13, 16,37,38), we reasoned that although mRNA changes may dominate the relative (fold) changes in protein levels after LPS stimulation, changes in the protein life cycle could contribute substantiallymore to differences in absolute cellular protein abundance than t…”
BackgroundResultInfluentialRappsilber, J., Mann, M., & Ishihama, Y. (2007) · Nature Protocols · doi:10.1038/nprot.2007.261 · Cited by 4,641
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses
Vogel, C., & Marcotte, E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3185 · Cited by 4,195
Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., & Weissman, J. S. (2009) · Science · doi:10.1126/science.1168978 · Cited by 3,853
Self-citationCorrelation between Protein and mRNA Abundance in Yeast
Gygi, S. P., Rochon, Y., Franza, B. R., & Aebersold, R. (1999) · Molecular and Cellular Biology · doi:10.1128/mcb.19.3.1720 · Cited by 3,819
The impact of microRNAs on protein output
Baek, D., Villén, J., Shin, C., Camargo, F. D., Gygi, S. P., & Bartel, D. P. (2008) · Nature · doi:10.1038/nature07242 · Cited by 3,592
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 456
Specialised ribosomes as versatile regulators of gene expression
Joo, M., Yeom, J., Choi, Y., Jun, H., Song, W., Kim, H.-L., Lee, K., & Shin, E. (2022) · RNA Biology · doi:10.1080/15476286.2022.2135299 · Cited by 25
Ilicic, M., Butler, T. A. J., Zakár, T., & Paúl, J. (2017) · Journal of Smooth Muscle Research · doi:10.1540/jsmr.53.73 · Cited by 22
Abraham, P. E., Garcia, B. J., Gunter, L. E., Jawdy, S. S., Engle, N. L., Yang, X., Jacobson, D., Hettich, R. L., Tuskan, G. A., & Tschaplinski, T. J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190019 · Cited by 22
Ly, T., Endo, A., Brenes, A. J., Gierliński, M., Afzal, V., Pawellek, A., & Lamond, A. I. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14392.1 · Cited by 21
Dupuis, S., Ojeda, V., Gallaher, S. D., Purvine, S. O., Glaesener, A. G., Ponce, R., Nicora, C., Bloodsworth, K., Lipton, M. S. W., Niyogi, K., et al. (2025) · The Plant Cell · doi:10.1093/plcell/koaf086 · Cited by 21
Factors affecting the rapid changes of protein under short-term heat stress
Wu, B., Qiao, J., Wang, X., Liu, M., Xu, S., & Sun, D. (2021) · BMC Genomics · doi:10.1186/s12864-021-07560-y · Cited by 21
Making sense of mRNA landscapes: Translation control in neurodevelopment
Park, Y., Page, N. F., Salamon, I., Li, D., & Rašin, M. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1674 · Cited by 22
Викторович, П. Д., Makhnovskii, P. A., Zgoda, V. G., Gazizova, G. R., Vepkhvadze, T. F., Lednev, E. M., Motanova, E. S., Lysenko, E. A., Orlov, O. I., & Tomilovskaya, E. (2023) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00048.2023 · Cited by 21
Fan, D., Cong, Y., Liu, J., Zhang, H., & Du, Z. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113928 · Cited by 21
Zhao, J. T., Lv, Y., Kang, M., Wang, K., & Xiang, Y. (2015) · The Analyst · doi:10.1039/c5an01742d · Cited by 21
Mandad, S., Rahman, R., Centeno, T. P., Vidal, R. O., Wildhagen, H., Rammner, B., Keihani, S., Opazo, F., Urban, I., Ischebeck, T., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-35277-8 · Cited by 21
Jiang, H., Zhao, N., Zhang, Q., Gao, J., Liu, L., Wu, T.-F., Wang, Y., Huang, Q.-H., Gou, Q., Chen, W., et al. (2015) · International Journal for Parasitology · doi:10.1016/j.ijpara.2015.08.007 · Cited by 20
New horizons in the stormy sea of multimodal single-cell data integration
Jackson, C. A., & Vogel, C. (2022) · Molecular Cell · doi:10.1016/j.molcel.2021.12.012 · Cited by 21
Mylvaganam, S., & Freeman, S. A. (2023) · Immunological Reviews · doi:10.1111/imr.13260 · Cited by 23
Zulfiqar, M., Singh, V. P., Steinbeck, C., & Sorokina, M. (2024) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2024.2306465 · Cited by 20
Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network
Martins, A. J., Narayanan, M., Prüstel, T., Fixsen, B. R., Park, K., Gottschalk, R. A., Lu, Y. C., Andrews-Pfannkoch, C. M., Lau, W. W., Wendelsdorf, K. V., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.002 · Cited by 20
Kovalenko, A. A., Zakharova, M. V., Schwarz, A. P., Dyomina, A. V., Zubareva, O. E., & Zaitsev, A. V. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052752 · Cited by 19
Magnusson, R., Rundquist, O., Kim, M. J., Hellberg, S., Na, C. H., Benson, M., Gómez-Cabrero, D., Kockum, I. S., Tegner, J. N., Piehl, F., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.916128 · Cited by 19
Postnikova, T. Y., Amakhin, D. V., Trofimova, A. M., Tumanova, N. L., Dubrovskaya, N. M., Kalinina, D. S., Kovalenko, A. A., Shcherbitskaia, A. D., Vasilev, D. S., & Zaitsev, A. V. (2022) · Cells · doi:10.3390/cells12010058 · Cited by 19
Modulation of Progesterone Receptor Isoform Expression in Pregnant Human Myometrium
Ilicic, M., Zakár, T., & Paúl, J. (2017) · BioMed Research International · doi:10.1155/2017/4589214 · Cited by 19
Differential stoichiometry among core ribosomal proteinsPreprint
Slavov, N., Semrau, S., Airoldi, E. M., Budnik, B., & van Oudenaarden, A. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/005553 · Cited by 19
Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance
Marchi, T. D., Pyl, P. T., Sjöström, M., Klasson, S., Sartor, H., Tran, L., Pekár, G., Malmström, J. A., Malmström, L., & Niméus, E. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00243 · Cited by 18
Exploring the pan-cancer landscape of posttranscriptional regulation
Ghoshdastider, U., & Sendoel, A. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113172 · Cited by 18
ALKBH4 promotes tumourigenesis with a poor prognosis in non-small-cell lung cancer
Jingushi, K., Aoki, M., Ueda, K., Kogaki, T., Tanimoto, M., Monoe, Y., Ando, M., Matsumoto, T., Minami, K., Ueda, Y., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87763-1 · Cited by 18
Millimeter waves alter DNA secondary structures and modulate the transcriptome in human fibroblasts
Lawler, N. B., Evans, C. W., Romanenko, S., Chaudhari, N., Fear, M. W., Wood, F. M., Smith, N. M., Wallace, V. P., & Iyer, K. S. (2022) · Biomedical Optics Express · doi:10.1364/boe.458478 · Cited by 18
Ereful, N. C., Liu, L.-Y. D., Tsai, E., Kao, S., Dixit, S., Mauleon, R., Malabanan, K., Thomson, M. J., Laurena, A. C., Lee, D., et al. (2016) · Rice · doi:10.1186/s12284-016-0123-4 · Cited by 18
Read, J. F., Serralha, M., Mok, D., Holt, B. J., Cruickshank, M. N., Karpievitch, Y. V., Broadhurst, D., Sly, P. D., Strickland, D. H., Reinke, S. N., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.876654 · Cited by 17
Kito, K., Ito, H., Nohara, T., Ohnishi, M., Ishibashi, Y., & Takeda, D. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.051854 · Cited by 18
Wang, H., Lan, P., & Shen, R. F. (2016) · PROTEOMICS · doi:10.1002/pmic.201500265 · Cited by 17
Protein Turnover Measurements in Human Serum by Serial Immunoaffinity LC-MS/MS
Farrokhi, V., Chen, X., & Neubert, H. (2017) · Clinical Chemistry · doi:10.1373/clinchem.2017.272922 · Cited by 17
Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing
Manes, N. P., Angermann, B., Koppenol-Raab, M., An, E., Sjoelund, V. H., Sun, J., Ishii, M., Germain, R. N., Meier‐Schellersheim, M., & Nita‐Lazar, A. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.048918 · Cited by 17
Physical Cues Controlling Seasonal Immune Allocation in a Natural Piscine Model
Stewart, A., Hablützel, P. I., Watson, H. V., Brown, M. J., Friberg, I. M., Cable, J., & Jackson, J. A. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00582 · Cited by 17
Regulation of protein abundance in normal human tissuesPreprint
Fang, H., Jiang, L., Leprevost, F. D. V., Jian, R., Chan, J., Glinos, D. A., Lappalainen, T., Nesvizhskii, A. I., Reiner, A. P., Snyder, M. P., et al. (2025) · medRxiv · doi:10.1101/2025.01.10.25320181 · Cited by 17
Resource allocation in mammalian systems
Baghdassarian, H. M., & Lewis, N. E. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108305 · Cited by 17
Tian, X., Wang, R., Liu, Z., Lu, S., Chen, X., Zhang, Z., Liu, F., Li, H., Zhang, X., & Wang, M. (2025) · Genome Biology · doi:10.1186/s13059-025-03534-5 · Cited by 16
The Effect of Interferons on Presentation of Defective Ribosomal Products as HLA Peptides
Komov, L., Kadosh, D. M., Barnea, E., & Admon, A. (2021) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2021.100105 · Cited by 16
Network potential identifies therapeutic miRNA cocktails in Ewing sarcoma
Weaver, D. T., Pishas, K. I., Williamson, D. F. K., Scarborough, J. A., Lessnick, S. L., Dhawan, A., & Scott, J. G. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008755 · Cited by 16
A transcriptome-based model of central memory CD4 T cell death in HIV infection
Olvera-García, G., Aguilar-García, T., Gutiérrez-Jasso, F., Imaz-Rosshandler, I., Rangel‐Escareño, C., Orozco, L., Aguilar-Delfín, I., Vázquez-Pérez, J. A., Zúñiga, J., Pérez-Patrigeón, S., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-3308-8 · Cited by 16
Dynamic post-transcriptional regulation during embryonic stem cell differentiationPreprint
van den Berg, P. R., Budnik, B., Slavov, N., & Semrau, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/123497 · Cited by 17
Wang, Y., Perez‐Rosello, T., Smukowski, S. N., Surmeier, D. J., & Savas, J. N. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02493-w · Cited by 15
Surkova, S. Y., Sokolkova, A., Kozlov, K. N., Nuzhdin, S. V., & Samsonova, M. G. (2019) · Developmental Biology · doi:10.1016/j.ydbio.2019.01.006 · Cited by 15
Jahn, S. K., Hennicke, T., Kassack, M. U., Drews, L., Reichert, A. S., & Fritz, G. (2020) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2020.118711 · Cited by 17
Pearl, D., Katsumura, S., Amiri, M., Tabatabaei, N., Zhang, X., Vinette, V., Pang, X., Beug, S. T., Kim, S.-H., Jones, L., et al. (2020) · The Journal of Immunology · doi:10.4049/jimmunol.1900538 · Cited by 15
Kovalenko, A. A., Zakharova, M. V., Zubareva, O. E., Schwarz, A. P., Postnikova, T. Y., & Zaitsev, A. V. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.05.035 · Cited by 15
AMPK/HuR-Driven IL-20 Post-Transcriptional Regulation in Psoriatic Skin
Garcin, G., Guiraud, I., Lacroix, M., Genthon, C., Rialle, S., Joujoux, J., Meunier, L., Lavabre‐Bertrand, T., Stoebner, P., & Gallic, L. L. (2015) · Journal of Investigative Dermatology · doi:10.1038/jid.2015.282 · Cited by 15
Islam, D., Lombardini, E., Ruamsap, N., Im‐Erbsin, R., Khantapura, P., Teo, I. A., Neesanant, P., Gonwong, S., Yongvanitchit, K., Swierczewski, B., et al. (2015) · Immunology · doi:10.1111/imm.12549 · Cited by 15
Ricci, C. A., Kamal, A. H. M., Chakrabarty, J. K., Fuess, L. E., Mann, W. T., Jinks, L. R., Brinkhuis, V., Chowdhury, S. M., & Mydlarz, L. D. (2019) · Integrative and Comparative Biology · doi:10.1093/icb/icz107 · Cited by 16
Ghandhi, S. A., Chao Sima, A., Weber, W., Melo, D. R., Rudqvist, N.-P., Morton, S. R., Turner, H. C., & Amundson, S. A. (2020) · Radiation Research · doi:10.1667/rade-20-00041 · Cited by 14
Pinneh, E. C., van Dolleweerd, C. J., Göritzer, K., Drake, P. M. W., K‐C., J., & Teh, A. Y. (2022) · Biotechnology and Bioengineering · doi:10.1002/bit.28073 · Cited by 14
Teng, J., Cui, M. Y., Zhao, Y., Chen, H. J., Du, W. J., Xue, L. Y., & Ji, X. S. (2022) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2022.104576 · Cited by 14
Integration of a multi-omics stem cell differentiation dataset using a dynamical model
van den Berg, P. R., Bérenger-Currias, N. M. L. P., Budnik, B., Slavov, N., & Semrau, S. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010744 · Cited by 15
Liberti, A., Pollastro, C., Pinto, G., Illiano, A., Marino, R., Amoresano, A., Spagnuolo, A., & Sordino, P. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1217077 · Cited by 15
A novel method for quantitative measurements of gene expression in single living cells
Mandic, A., Strebinger, D., Regali, C., Phillips, N. E., & Suter, D. M. (2017) · Methods · doi:10.1016/j.ymeth.2017.04.008 · Cited by 15
Hecker, N., Seemann, S. E., Silahtaroglu, A., Ruzzo, W. L., & Gorodkin, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-06200-4 · Cited by 14
Cameron, S. A., White, S. M., Arrollo, D., Shulman, S. T., & Rowley, A. H. (2017) · Clinical & Experimental Immunology · doi:10.1111/cei.13010 · Cited by 14
Jia, H., Sun, W., Li, M., & Zhang, Z. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00586 · Cited by 14
Big data in yeast systems biology
Yu, R., & Nielsen, J. (2019) · FEMS Yeast Research · doi:10.1093/femsyr/foz070 · Cited by 17
Contribution of Mass Spectrometry-Based Proteomics to the Understanding of TNF-α Signaling
Ciuffa, R., Caron, É., Leitner, A., Uliana, F., Gstaiger, M., & Aebersold, R. H. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00728 · Cited by 14
Recent methodological advances towards single-cell proteomics
Kim, S., Kamarulzaman, L., & Taniguchi, Y. (2023) · Proceedings of the Japan Academy Series B · doi:10.2183/pjab.99.021 · Cited by 15
Torri, A., Mongelli, V. C., Mondotte, J. A., & Saleh, M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00362 · Cited by 13
Hun, L. V., Cheung, K. W., Brooks, E., Zudekoff, R., Luckhart, S., & Riehle, M. A. (2021) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2021.103669 · Cited by 13
Fast Maturation of Splenic Dendritic Cells Upon TBI Is Associated With FLT3/FLT3L Signaling
Zhang, J., Li, Z., Chandrasekar, A., Li, S., Ludolph, A. C., Boeckers, T. M., Huber-Lang, M. S., Roselli, F., & Heuvel, F. O. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.824459 · Cited by 14
Dumaine, J. E., & Ashley, N. T. (2018) · PeerJ · doi:10.7717/peerj.4423 · Cited by 14
Mi, D., Manuel, M. N., Huang, Y.-T., Mason, J. O., & Price, D. J. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00419 · Cited by 13
Sensitive and specific detection of proteins based on target-responsive DNA polymerase activity
Jung, Y., Lee, C. Y., Park, K. S., & Park, H. G. (2019) · Analytica Chimica Acta · doi:10.1016/j.aca.2019.01.025 · Cited by 13
Timing effciency in small-RNA-regulated post-transcriptional processes
Biswas, K., & Ghosh, A. (2020) · Physical review. E · doi:10.1103/physreve.101.022418 · Cited by 13
Occhipinti, A., Hamadi, Y., Kugler, H., Wintersteiger, C. M., Yordanov, B., & Angione, C. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2973386 · Cited by 13
Regulation of Phaeodactylum plastid gene transcription by redox, light, and circadian signals
Kayanja, G. E., Ibrahim, I. M., & Puthiyaveetil, S. (2021) · Photosynthesis Research · doi:10.1007/s11120-020-00811-1 · Cited by 13
MYC Induces Immunotherapy and IFNγ Resistance Through Downregulation of JAK2
Markovits, E., Harush, O., Baruch, E. N., Shulman, E. D., Debby, A., Itzhaki, O., Anafi, L., Danilevsky, A., Shomron, N., Ben‐Betzalel, G., et al. (2023) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-22-0184 · Cited by 15
Immune Responses – Transcriptional and Post-Transcriptional Networks Pass the Baton
Lichti, J., Gallus, C., & Glasmacher, E. (2017) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2017.11.003 · Cited by 12
Virgin, E. E., & Rosvall, K. A. (2018) · General and Comparative Endocrinology · doi:10.1016/j.ygcen.2018.08.008 · Cited by 12
Large-scale informatic analysis to algorithmically identify blood biomarkers of neurological damage
O’Connell, G. C., Alder, M. L., Smothers, C. G., & Chang, J. H. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007719117 · Cited by 12
Analyzing Ribosome Remodeling in Health and Disease
Petelski, A. A., & Slavov, N. (2020) · PROTEOMICS · doi:10.1002/pmic.202000039 · Cited by 13
The crosstalk between SUMOylation and immune system in host-pathogen interactions
Zhu, G., Tong, N., Zhu, Y., Wang, L., & Wang, Q. (2024) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2024.2339259 · Cited by 11
Brocard, M., Lu, J. Q., Hall, B. S., Borah, K., Möller‐Levet, C. S., Georgana, I., Sorgeloos, F., Beste, D. J. V., Goodfellow, I., & Locker, N. (2021) · Journal of Virology · doi:10.1128/jvi.01134-21 · Cited by 11
Reid, D. W., Xu, D., Chen, P., Yang, H., & Sun, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-06077-3 · Cited by 11
Functional genomics of parental care of insects
Cunningham, C. B. (2020) · Hormones and Behavior · doi:10.1016/j.yhbeh.2020.104756 · Cited by 11
Fishing for the genetic basis of migratory behavior
Harringmeyer, O. S., Woolfolk, M. L., & Hoekstra, H. E. (2021) · Cell · doi:10.1016/j.cell.2020.12.037 · Cited by 11
Chemerin Impact on Alternative mRNA Transcription in the Porcine Luteal Cells
Makowczenko, K. G., Jastrzebski, J., Paukszto, Ł., Dobrzyń, K., Kieżun, M., Smolińska, N., & Kamiński, T. (2022) · Cells · doi:10.3390/cells11040715 · Cited by 11
Unique gene program of rat small resistance mesenteric arteries as revealed by deep RNA sequencing
Reho, J. J., Shetty, A. C., Dippold, R. P., Mahurkar, A., & Fisher, S. A. (2015) · Physiological Reports · doi:10.14814/phy2.12450 · Cited by 11
Teo, G., Zhang, Y. B., Vogel, C., & Choi, H. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0040-1 · Cited by 11
Schira‐Heinen, J., Czapla, A., Hendricks, M., Kloetgen, A., Wruck, W., Adjaye, J., Kögler, G., Müller, H. W., Stühler, K., & Trompeter, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-60065-8 · Cited by 11
HydraRNA: a hybrid architecture based full-length RNA language model
Li, G., Jiang, F., Zhu, J., Cui, H., Wang, Z., & Chen, W. (2025) · Genome Biology · doi:10.1186/s13059-025-03853-7 · Cited by 10
Di, Y., Li, W., Šalovská, B., Ba, Q., Hu, Z., Wang, S., & Liu, Y. (2023) · Biophysics Reports · doi:10.52601/bpr.2023.230007 · Cited by 11
Martin, L. E., Ruiz, M., & Hillyer, J. F. (2024) · Journal of Experimental Biology · doi:10.1242/jeb.248149 · Cited by 10
Ma, J., & Li, X. (2017) · Toxin Reviews · doi:10.1080/15569543.2017.1318403 · Cited by 10
Variable protein homeostasis in housekeeping and non-housekeeping pathways under mycotoxins stress
Sun, Y., Wen, J., Chen, R., & Deng, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-44305-0 · Cited by 10
Signaling pathways that control mRNA translation initiation in macrophages
Tabatabaei, N., Hou, S., Kim, K.-W., & Tahmasebi, S. (2020) · Cellular Signalling · doi:10.1016/j.cellsig.2020.109700 · Cited by 10
Lymphoblastoid Cell Lines as a Tool to Study Inter-Individual Differences in the Response to Glucose
Grassi, M. A., Rao, V. R., Chen, S., Cao, D., Gao, X., Cleary, P. A., Huang, R. S., Paterson, A. D., Natarajan, R. D., Rehman, J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160504 · Cited by 11
Whiting, J. R., Magalhaes, I. S., Singkam, A. R., Robertson, S., D’Agostino, D., Bradley, J. E., & MacColl, A. D. C. (2018) · Molecular Ecology · doi:10.1111/mec.14772 · Cited by 10
Integration of large-scale multi-omic datasets: A protein-centric view
Rendleman, J., Choi, H., & Vogel, C. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.09.001 · Cited by 10
The correlation between proteoglycan 2 and neuropsychiatric systemic lupus erythematosus
Qiao, X., Lu, L., Zhou, K., Tan, L., Liu, X., Ni, J., Hou, Y., Liang, J. F., & Dou, H. (2022) · Clinical Immunology · doi:10.1016/j.clim.2022.109042 · Cited by 10
Improved surfaceome coverage with a label‐free nonaffinity‐purified workflow
Glisovic‐Aplenc, T., Gill, S. I., Spruce, L. A., Smith, I. R. L., Fazelinia, H., Shestova, O. E., Ding, H., Tasian, S. K., Aplenc, R., & Seeholzer, S. H. (2017) · PROTEOMICS · doi:10.1002/pmic.201600344 · Cited by 10
Impact of protein degradation and cell growth on mammalian proteomesPreprint
Leduc, A., Zheng, S., Saxena, P., & Slavov, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637566 · Cited by 10
A Multi-Parameter Analysis of Cellular Coordination of Major Transcriptome Regulation Mechanisms
Jiang, W., Guo, Z., Lages, N., Zheng, W. J., Feliers, D., Zhang, F., & Wang, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-24039-1 · Cited by 10
Andrieux, G., Chakraborty, S., Das, T., & Boerries, M. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.586479 · Cited by 9
Spleen Tyrosine Kinase Contributes to Müller Glial Expression of Proangiogenic Cytokines in Diabetes
Yerlikaya, E. I., Toro, A. L., Sunilkumar, S., VanCleave, A. M., Leung, M., Kawasawa, Y. I., Kimball, S. R., & Dennis, M. D. (2022) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.63.11.25 · Cited by 9
Using Deep Learning to Extrapolate Protein Expression Measurements
Barzine, M., Freivalds, K., Wright, J. C., Opmanis, M., Rituma, D., Ghavidel, F. Z., Jarnuczak, A. F., Celms, E., Čerāns, K., Jonassen, I., et al. (2020) · PROTEOMICS · doi:10.1002/pmic.202000009 · Cited by 10
Peroxiredoxin 4 directly affects the male fertility outcome in porcine
Ryu, D., Pang, W., Rahman, M. S., Park, Y. J., & Pang, M. (2021) · Theriogenology · doi:10.1016/j.theriogenology.2021.05.020 · Cited by 9
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
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.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
Are you an author, or do you see a problem with this page? Request a review
