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Record W2015997966 · doi:10.1039/c3mt00186e

Analysis of the biological response of mouse liver (Mus musculus) exposed to As2O3 based on integrated -omics approaches

2013· article· en· W2015997966 on OpenAlexfundno aff
M. A. García-Sevillano, Tamara García‐Barrera, Francisco Navarro, José Luis Gómez‐Ariza

Bibliographic record

VenueMetallomics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsnot available
FundersNational Research Council CanadaMinisterio de Economía y Competitividad
KeywordsComputational biologyBiologyOmicsBioinformatics

Abstract

fetched live from OpenAlex

Organic and inorganic mass spectrometries were used to investigate the biochemical response of mice (Mus musculus) to inorganic arsenic exposure using liver as the target organ. The toxicological effects of trivalent inorganic arsenic after oral administration (3 mg kg(-1) body weight and per day) were investigated over a period of 7 days using metallomics, metabonomics and redox proteomics approaches. Size-exclusion chromatography (SEC) with ICP-MS detection was combined with anion exchange chromatography (AEC) to characterize the biological response of the exposed mice. On the other hand, direct infusion mass spectrometry (DI-ESI-QTOF-MS) of polar and lipophilic extracts using positive and negative modes of acquisition (ESI+/ESI-) provided information about time-dependent changes in endogenous metabolites identified by Partial Least Square-Discriminant Analysis (PLS-DA). Finally, the study has been complemented with the evaluation of up/down-regulation of enzymes related to oxidative stress such as superoxide dismutase (SOD), glutathione reductase (GR), catalase (CAT) and peroxidases in connection with metal toxicity issues. The results show that the inorganic arsenic methylation in the liver may reach the saturation point upon chronic exposure to the element. On the other hand, SEC-ICP-MS coupling provided information about metal containing-proteins and metabolites related to arsenic exposure (metallomics) which has been correlated with the changes in the global metabolism (metabonomics), also considering their consequences on the redox status of protein and protein expression (redox proteomics). Our study shows that arsenic causes biochemical pathway alterations, such as energy metabolism (e.g. glycolysis, Krebs cycle), amino acid metabolism, choline metabolism and degradation of membrane phospholipids (apoptosis). This work illustrates the high reliability of the integrated use of organic mass spectrometry for the metabonomic study of biochemical effects induced by As2O3, with inorganic mass spectrometry for metallomic and speciation assessment of arsenic biomethylation in the liver of exposed mice, and redox proteomics to evaluate inhibition of enzymatic activity in different proteins such as superoxide dismutase (SOD), catalase (CAT) and glutathione reductase (GR) caused by this element. In conclusion, the integration of metallomics, metabolomics and redox proteomics results provides a more comprehensive evaluation about the biological response in experiments dealing with exposure to toxic metals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.228
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2013
Admission routes1
Has abstractyes

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