Cited by 106Pharmacological Effects and Toxicity StudiesCarcinogens and Genotoxicity AssessmentDevelopmental Biology and Gene Regulation

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Design Principles of Concentration-Dependent Transcriptome Deviations in Drug-Exposed Differentiating Stem Cells

Open access

Abstract

Information on design principles governing transcriptome changes upon transition from safe to hazardous drug concentrations or from tolerated to cytotoxic drug levels are important for the application of toxicogenomics data in developmental toxicology. Here, we tested the effect of eight concentrations of valproic acid (VPA; 25-1000 μM) in an assay that recapitulates the development of human embryonic stem cells to neuroectoderm. Cells were exposed to the drug during the entire differentiation process, and the number of differentially regulated genes increased continuously over the concentration range from zero to about 3000. We identified overrepresented transcription factor binding sites (TFBS) as well as superordinate cell biological processes, and we developed a gene ontology (GO) activation profiler, as well as a two-dimensional teratogenicity index. Analysis of the transcriptome data set by the above biostatistical and systems biology approaches yielded the following insights: (i) tolerated (≤25 μM), deregulated/teratogenic (150-550 μM), and cytotoxic (≥800 μM) concentrations could be differentiated. (ii) Biological signatures related to the mode of action of VPA, such as protein acetylation, developmental changes, and cell migration, emerged from the teratogenic concentrations range. (iii) Cytotoxicity was not accompanied by signatures of newly emerging canonical cell death/stress indicators, but by catabolism and decreased expression of cell cycle associated genes. (iv) Most, but not all of the GO groups and TFBS seen at the highest concentrations were already overrepresented at 350-450 μM. (v) The teratogenicity index reflected this behavior, and thus differed strongly from cytotoxicity. Our findings suggest the use of the highest noncytotoxic drug concentration for gene array toxicogenomics studies, as higher concentrations possibly yield wrong information on the mode of action, and lower drug levels result in decreased gene expression changes and thus a reduced power of the study.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/tx400402j

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Although cytotoxicity measures are fundamental in assessing a drug's potential for hepatotoxicity in vitro, gene expression and various phenotypic changes can occur at much lower concentrations.

    “although cytotoxicity measures are fundamental in the assessment of a drug’s potential for hepatotoxicity in vitro33,34, gene expression and various phenotypic changes can occur at much lower concentrations35,36.”

    From Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology (2022) · cites this paper as [36]

    The Discussion reports altered gene expression and functional-process signatures at 350 μM VPA, below the 800 μM threshold for cytotoxicity. The paper studies differentiating stem cells and infers developmental disturbances from transcriptomic analyses; it does not establish that various measured phenotypic changes occur at those lower concentrations. The hepatotoxicity clause has separate citations and is not attributed to this source. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 43

  1. Pallocca G., 2013, Cell. Biol. Toxicol., 29, 239, 10.1007/s10565-013-9250-5 · DOI
  2. Bal-Price A. K., 2012, Altex, 29, 202, 10.14573/altex.2012.2.202 · DOI
  3. Vojnits K., 2012, Curr. Med. Chem., 19, 6224
  4. Klaric M., 2013, Front. Biosci., 5, 118, 10.2741/S361 · DOI
  5. Ramirez T., 2013, Altex, 30, 209, 10.14573/altex.2013.2.209 · DOI
  6. Thomas R. S., 2011, Toxicol. Sci., 120, 194, 10.1093/toxsci/kfq355 · DOI
  7. Thomas R. S., 2013, Toxicol. Sci., 134, 180, 10.1093/toxsci/kft094 · DOI
  8. van Thriel C., 2012, Neurotoxicology, 33, 911, 10.1016/j.neuro.2011.10.002 · DOI
  9. Adler S., 2011, Arch. Toxicol., 85, 367, 10.1007/s00204-011-0693-2 · DOI
  10. Basketter D. A., 2012, Altex, 29, 3, 10.14573/altex.2012.1.003 · DOI
  11. Mol P. G., 2013, Drug Saf., 36, 1105, 10.1007/s40264-013-0094-y · DOI
  12. Andersen M. E., 2011, Altex, 28, 175, 10.14573/altex.2011.3.175 · DOI
  13. Krewski D., 2011, Annu. Rev. Public Health, 32, 161, 10.1146/annurev-publhealth-031210-101153 · DOI
  14. Leist M., 2008, Altex, 25, 103, 10.14573/altex.2008.2.103 · DOI
  15. Leist M., 2008, Altex, 25, 163, 10.14573/altex.2008.3.163 · DOI
  16. Weng M. K., 2012, PLoS One, 7, e36708, 10.1371/journal.pone.0036708 · DOI
  17. Stiegler N. V., 2011, Toxicol. Sci., 121, 73, 10.1093/toxsci/kfr034 · DOI
  18. Zimmer B., 2012, Environ. Health Perspect., 120, 1116, 10.1289/ehp.1104489 · DOI
  19. Kuegler P. B., 2012, Glia, 60, 218, 10.1002/glia.21257 · DOI
  20. Zimmer B., 2011, Toxicol. Sci., 121, 357, 10.1093/toxsci/kfr054 · DOI
  21. Zimmer B., 2011, Cell Death Differ., 18, 383, 10.1038/cdd.2010.109 · DOI
  22. Kuegler P. B., 2010, Altex, 27, 17
  23. Meganathan K., 2012, PLoS One, 7, e44228, 10.1371/journal.pone.0044228 · DOI
  24. Krug A. K., 2013, Arch. Toxicol., 87, 123, 10.1007/s00204-012-0967-3 · DOI
  25. Whyte W. A., 2013, Cell, 153, 307, 10.1016/j.cell.2013.03.035 · DOI · Paper page
  26. Balmer N. V., 2012, Hum. Mol. Genet., 21, 4104, 10.1093/hmg/dds239 · DOI
  27. Theunissen P. T., 2012, Toxicol. Sci., 125, 430, 10.1093/toxsci/kfr293 · DOI
  28. Jergil M., 2009, Toxicol. Sci., 108, 132, 10.1093/toxsci/kfp002 · DOI
  29. Theunissen P. T., 2011, Toxicol. Sci., 122, 437, 10.1093/toxsci/kfr134 · DOI
  30. Chambers S. M., 2011, Methods Mol. Biol., 793, 87, 10.1007/978-1-61779-328-8_6 · DOI
  31. Smyth G. K., 2005, Bioinformatics, 21, 2067, 10.1093/bioinformatics/bti270 · DOI
  32. Benjamini Y., 2001, Ann. Stat., 29, 1165, 10.1214/aos/1013699998 · DOI · Paper page
  33. Chow, S. and Rodgers, P. (2005) Construction Area-Proportional Venn and Euler Diagrams with Three Circles, inEuler Diagrams Workshop,Paris.http://kar.kent.ac.uk/id/eprint/14285. · DOI
  34. Minguez P., 2006, Genome Inf., 17, 57
  35. Benjamini Y., 2006, Biometrika, 93, 491, 10.1093/biomet/93.3.491 · DOI · Paper page
  36. Elkon R., 2003, Genome Res., 13, 773, 10.1101/gr.947203 · DOI
  37. Ulitsky I., 2010, Nat. Protoc., 5, 303, 10.1038/nprot.2009.230 · DOI
  38. Leist M., 2010, Altex, 27, 309, 10.14573/altex.2010.4.309 · DOI
  39. Dietrich D. R., 2013, Altex, 30, 381, 10.14573/altex.2013.3.381 · DOI
  40. Hengstler J. G., 2003, Annu. Rev. Pharmacol. Toxicol., 43, 485, 10.1146/annurev.pharmtox.43.100901.140219 · DOI
  41. Krug A. K., 2013, Arch. Toxicol., 87, 2215, 10.1007/s00204-013-1072-y · DOI
  42. Wiltse J., 2005, Crit. Rev. Toxicol., 35, 727, 10.1080/10408440591007403 · DOI
  43. Sztajnkrycer M. D., 2002, J. Toxicol. Clin. Toxicol., 40, 789, 10.1081/CLT-120014645 · DOI

Cited by

Sorted by citations

Metadata

Authors
Waldmann, T., Rempel, E., Balmer, N. V., König, A., Kolde, R., Gaspar, J. A., Henry, M., Hescheler, J., Sachinidis, A., Rahnenführer, J., Hengstler, J. G., & Leist, M.
Affiliations
  • University of Konstanz (DE)
  • TU Dortmund University (DE)
  • Quretec (Estonia) (EE)
  • University of Cologne (DE)
Corresponding author
Waldmann, Tanja
Venue
Chemical Research in Toxicology
Published
2014-01-21
Volume
27
Issue
3
Pages
408-420
ISSN
  • 0893-228X (ISSN-L)
  • 1520-5010
OpenAlex
W2085421312
Cited by
106
References
3
Citations by year
2025: 3 · 2024: 1 · 2023: 1 · 2022: 7 · 2021: 7 · 2020: 12 · 2019: 8 · 2018: 12 · 2017: 8 · 2016: 12 · 2015: 17 · 2014: 18
FWCI
17.25
Type
journal article
Language
en
Open access
Locations
  • Chemical Research in Toxicology (publishedVersion, other-oa)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmacological Effects and Toxicity Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
Keywords
valproic acid, developmental toxicity, transcriptome, human embryonic stem cells, neuroectoderm differentiation, cytotoxicity, transcription factor binding sites, mechanism of action, protein acetylation, cell migration, cell cycle genes, drug concentration dependence, toxicogenomics
MeSH terms
Binding Sites, Cell Differentiation, Cells, Cultured, Humans, Transcription Factors, Valproic Acid, Down-Regulation, Up-Regulation, Cluster Analysis, Principal Component Analysis, Embryonic Stem Cells, Neural Plate, Transcriptome

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.

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