Evidence map›Paper›PMID 37415082›Full record

ArticleJournal of microbiology and biotechnology2023

Comprehensive Evaluation System for Post-Metabolic Activity of Potential Thyroid-Disrupting Chemicals.

Yurim Jang, Ji Hyun Moon, Byung Kwan Jeon, Ho Jin Park, Hong Jin Lee, Do Yup Lee

Open access · bronzeAbstract read
In one paragraph

Article in Journal of microbiology and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 47% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 3 institutions in 1 country.

Yurim JangInterdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul 08826, Republic of Korea.
Ji Hyun MoonDepartment of Agricultural Biotechnology, Seoul National University, Seoul 08826, Republic of Korea.
Byung Kwan JeonDepartment of Bio and Fermentation Convergence Technology, Kookmin University, Seoul 02707, Republic of Korea.
Ho Jin ParkDepartment of Food Science and Biotechnology, Chung-Ang University, Anseong 17546, Republic of Korea.
Hong Jin LeeDepartment of Food Science and Biotechnology, Chung-Ang University, Anseong 17546, Republic of Korea.
Do Yup LeeInterdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul 08826, Republic of Korea.
Seoul National University · KRChung-Ang University · KRKookmin University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endocrine-disrupting chemicals (EDCs) are compounds that disturb hormonal homeostasis by binding to receptors. EDCs are metabolized through hepatic enzymes, causing altered transcriptional activities of hormone receptors, and thus necessitating the exploration of the potential endocrine-disrupting activities of EDC-derived metabolites. Accordingly, we have developed an integrative workflow for evaluating the post-metabolic activity of potential hazardous compounds. The system facilitates the identification of metabolites that exert hormonal disruption through the integrative application of an MS/MS similarity network and predictive biotransformation based on known hepatic enzymatic reactions. As proof-of-concept, the transcriptional activities of 13 chemicals were evaluated by applying the in vitro metabolic module (S9 fraction). Identified among the tested chemicals were three thyroid hormone receptor (THR) agonistic compounds that showed increased transcriptional activities after phase I+II reactions (T3, 309.1 ± 17.3%; DITPA, 30.7 ± 1.8%; GC-1, 160.6 ± 8.6% to the corresponding parents). The metabolic profiles of these three compounds showed common biotransformation patterns, particularly in the phase II reactions (glucuronide conjugation, sulfation, GSH conjugation, and amino acid conjugation). Data-dependent exploration based on molecular network analysis of T3 profiles revealed that lipids and lipid-like molecules were the most enriched biotransformants. The subsequent subnetwork analysis proposed 14 additional features, including T4 in addition to 9 metabolized compounds that were annotated by prediction system based on possible hepatic enzymatic reaction. The other 10 THR agonistic negative compounds showed unique biotransformation patterns according to structural commonality, which corresponded to previous in vivo studies. Our evaluation system demonstrated highly predictive and accurate performance in determining the potential thyroid-disrupting activity of EDC-derived metabolites and for proposing novel biotransformants.

Indexed as

Tandem Mass SpectrometryThyroid GlandBiotransformationbiotransformant predictionmetabolomicsmolecular networkingreporter gene assayThyroid-disrupting chemical

Identifiers

PMID37415082
PMCPMC10619556
OpenAlexW4383484066

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.