Evidence map›Paper›PMID 27923875›Full record

ArticleMolecular & cellular proteomics : MCP2017

Accounting for Protein Subcellular Localization: A Compartmental Map of the Rat Liver Proteome.

Michel Jadot, Marielle Boonen, Jaqueline Thirion, Nan Wang, Jinchuan Xing, Caifeng Zhao, Abla Tannous, Meiqian Qian, Haiyan Zheng, John K Everett and 3 more

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

  1. Article
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  15. SNAT7 regulates mTORC1 via macropinocytosis.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  16. Review
  17. Review
  18. Review
  19. Subcellular proteomics.Nature reviews. Methods primers · 2021
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Michel JadotFrom the ‡URPhyM-Laboratoire de Chimie Physiologique, Université de Namur, 61 rue de Bruxelles, Namur 5000, Belgium; michel.jadot@unamur.be sleat@cabm.rutgers.edu lobel@cabm.rutgers.edu.
Marielle BoonenFrom the ‡URPhyM-Laboratoire de Chimie Physiologique, Université de Namur, 61 rue de Bruxelles, Namur 5000, Belgium.
Jaqueline ThirionFrom the ‡URPhyM-Laboratoire de Chimie Physiologique, Université de Namur, 61 rue de Bruxelles, Namur 5000, Belgium.
Nan Wang§Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
Jinchuan Xing§Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
Caifeng Zhao¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854.
Abla Tannous¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854.
Meiqian Qian¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854.
Haiyan Zheng¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854.
John K Everett¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854.
Dirk F Moore‖Department of Biostatistics, School of Public Health, Rutgers Biomedical and Health Sciences, 683 Hoes Lane West, Piscataway, New Jersey 08854.
David E Sleat¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854; michel.jadot@unamur.be sleat@cabm.rutgers.edu lobel@cabm.rutgers.edu.
Peter Lobel¶Center for Advanced Biotechnology and Medicine, Rutgers Biomedical and Health Sciences, 679 Hoes Lane West, Piscataway, New Jersey 08854; michel.jadot@unamur.be sleat@cabm.rutgers.edu lobel@cabm.rutgers.edu.

Funding

Rutgers Mass Spectrometry Core for Integrative Neuroscience ResearchP30NS046593 · NINDS · UNIV OF MED/DENT OF NJ-NJ MEDICAL SCHOOL · PI LOBEL, PETER · 2004 to 2018
$9.9M
NOVEL LYSOSOMAL ENZYME DEFICIENT IN BATTEN DISEASER01NS037918 · NINDS · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI SLEAT, DAVID EDWARD · 1998 to 2023
$7.3M
LYSOSOMAL ENZYMES AND ASSOCIATED HUMAN GENETIC DISEASESR01DK054317 · NIDDK · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI LOBEL, PETER · 1999 to 2012
$4.3M
High Resolution LC-MS/MS SystemS10RR024584 · NCRR · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI LOBEL, PETER · 2009 to 2009
$500k
NCRR NIH HHS S10 RR024584NIDDK NIH HHS R01 DK054317NINDS NIH HHS P30 NS046593NINDS NIH HHS R01 NS037918
6 · The paper itself

Abstract

Accurate knowledge of the intracellular location of proteins is important for numerous areas of biomedical research including assessing fidelity of putative protein-protein interactions, modeling cellular processes at a system-wide level and investigating metabolic and disease pathways. Many proteins have not been localized, or have been incompletely localized, partly because most studies do not account for entire subcellular distribution. Thus, proteins are frequently assigned to one organelle whereas a significant fraction may reside elsewhere. As a step toward a comprehensive cellular map, we used subcellular fractionation with classic balance sheet analysis and isobaric labeling/quantitative mass spectrometry to assign locations to >6000 rat liver proteins. We provide quantitative data and error estimates describing the distribution of each protein among the eight major cellular compartments: nucleus, mitochondria, lysosomes, peroxisomes, endoplasmic reticulum, Golgi, plasma membrane and cytosol. Accounting for total intracellular distribution improves quality of organelle assignments and assigns proteins with multiple locations. Protein assignments and supporting data are available online through the Prolocate website (http://prolocate.cabm.rutgers.edu). As an example of the utility of this data set, we have used organelle assignments to help analyze whole exome sequencing data from an infant dying at 6 months of age from a suspected neurodegenerative lysosomal storage disorder of unknown etiology. Sequencing data was prioritized using lists of lysosomal proteins comprising well-established residents of this organelle as well as novel candidates identified in this study. The latter included copper transporter 1, encoded by SLC31A1, which we localized to both the plasma membrane and lysosome. The patient harbors two predicted loss of function mutations in SLC31A1, suggesting that this may represent a heretofore undescribed recessive lysosomal storage disease gene.

Indexed as

Amino Acid Transport Systems, BasicAmino Acid Transport Systems, NeutralAnimalsDatabases, ProteinHumansInfantLiverLysosomal Storage DiseasesLysosomesMass SpectrometryMutationNeurodegenerative DiseasesProteomeProteomicsRatsSequence Analysis, DNAAmino Acid Transport Systems, BasicAmino Acid Transport Systems, NeutralProteomeSLC3A1 protein, human

Identifiers

PMID27923875
PMCPMC5294208

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.