Evidence map›Paper›PMID 24465277›Full record

ArticleTheranostics2014

Quantitative liver-specific protein fingerprint in blood: a signature for hepatotoxicity.

Zhiyuan Hu, Christopher Lausted, Hyuntae Yoo, Xiaowei Yan, Amy Brightman, Jiankui Chen, Weizhi Wang, Xiangli Bu, Leroy Hood

Open access · goldAbstract read
In one paragraph

Article in Theranostics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
8.2field-weighted citation impact, top 3% of its field
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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.

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  15. Protective Effects of Aqueous Extracts ofOxidative medicine and cellular longevity · 2018
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  19. Hepatoprotective Effect of Citral on Acetaminophen-Induced Liver Toxicity in Mice.Evidence-based complementary and alternative medicine : eCAM · 2017
    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

9 authors at 4 institutions in 2 countries.

Zhiyuan Hu1. National Center for NanoScience and Technology, 11 BeiYiTiao, ZhongGuanCun, Beijing, 100190, China. ; 2. Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, USA. ; 3. State Key Laboratory of Proteomics, Beijing Proteomics Research Center, Beijing 100850, China.
Christopher Lausted2. Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, USA.
Hyuntae Yoo2. Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, USA. ; 4. University of Texas at Dallas, Department of Molecular and Cell Biology, FO 31, 800 West Campbell Road, Richardson, Texas 75080, USA.
Xiaowei Yan2. Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, USA.
Amy Brightman2. Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, USA.
Jiankui Chen5. Beijing 307 Hospital, Department of Laboratory Medicine, Beijing 100071, China.
Weizhi Wang1. National Center for NanoScience and Technology, 11 BeiYiTiao, ZhongGuanCun, Beijing, 100190, China.
Xiangli Bu1. National Center for NanoScience and Technology, 11 BeiYiTiao, ZhongGuanCun, Beijing, 100190, China.
Leroy Hood2. Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, USA.
Institute for Systems Biology · USNational Center for Nanoscience and Technology · CN307th Hospital of Chinese People’s Liberation Army · CNThe University of Texas at Dallas · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We discuss here a new approach to detecting hepatotoxicity by employing concentration changes of liver-specific blood proteins during disease progression. These proteins are capable of assessing the behaviors of their cognate liver biological networks for toxicity or disease perturbations. Blood biomarkers are highly desirable diagnostics as blood is easily accessible and baths virtually all organs. Fifteen liver-specific blood proteins were identified as markers of acetaminophen (APAP)-induced hepatotoxicity using three proteomic technologies: label-free antibody microarrays, quantitative immunoblotting, and targeted iTRAQ mass spectrometry. Liver-specific blood proteins produced a toxicity signature of eleven elevated and four attenuated blood protein levels. These blood protein perturbations begin to provide a systems view of key mechanistic features of APAP-induced liver injury relating to glutathione and S-adenosyl-L-methionine (SAMe) depletion, mitochondrial dysfunction, and liver responses to the stress. Two markers, elevated membrane-bound catechol-O-methyltransferase (MB-COMT) and attenuated retinol binding protein 4 (RBP4), report hepatic injury significantly earlier than the current gold standard liver biomarker, alanine transaminase (ALT). These biomarkers were perturbed prior to onset of irreversible liver injury. Ideal markers should be applicable for both rodent model studies and human clinical trials. Five of these mouse liver-specific blood markers had human orthologs that were also found to be responsive to human hepatotoxicity. This panel of liver-specific proteins has the potential to effectively identify the early toxicity onset, the nature and extent of liver injury and report on some of the APAP-perturbed liver networks.

Indexed as

Blood Chemical AnalysisPeptide MappingAcetaminophenAdultAgedAnimalsBiomarkersChemical and Drug Induced Liver InjuryFemaleHumansImmunoblottingLiverMaleMass SpectrometryMiceMice, Inbred C57BLAcetaminophenBiomarkersBHMT.biomarkerCOMTCPS1liver injuryRBP4toxicity

Identifiers

PMID24465277
PMCPMC3900804
OpenAlexW2150892054

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.