Advanced search
1 file | 565.32 KB Add to list

Gene co-expression networks drive and predict reproductive effects in Daphnia in response to environmental disturbances

Author
Organization
Abstract
Increasing effects of anthropogenic stressors and those of natural origin on aquatic ecosystems have intensified the need for predictive and functional models of their effects. Here, we use gene expression patterns in combination with weighted gene coexpression networks and generalized additive models to predict effects on reproduction in the aquatic microcrustacean Daphnia. We developed models to predict effects on reproduction upon exposure to different cyanobacteria, different insecticides and binary mixtures of cyanobacteria and insecticides. Models developed specifically for groups of stressors (e.g., either cyanobacteria or insecticides) performed better than general models developed on all data. Furthermore, models developed using in silico generated mixture gene expression profiles from single stressor data were able to better predict effects on reproduction compared to models derived from the mixture exposures themselves. Our results highlight the potential of gene expression data to quantify effects of complex exposures at higher level organismal effects without prior mechanistic knowledge or complex exposure data.
Keywords
ADVERSE OUTCOME PATHWAYS, DAPHNIA-MAGNA, RISK-ASSESSMENT, MICROCYSTIS-AERUGINOSA, TOXICITY, PULEX, IDENTIFICATION, EXPOSURE, ECOTOXICOLOGY, POLLUTANTS

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 565.32 KB

Citation

Please use this url to cite or link to this publication:

MLA
Asselman, Jana, Michael E Pfrender, Jacqueline A Lopez, et al. “Gene Co-expression Networks Drive and Predict Reproductive Effects in Daphnia in Response to Environmental Disturbances.” ENVIRONMENTAL SCIENCE & TECHNOLOGY 52.1 (2018): 317–326. Print.
APA
Asselman, J., Pfrender, M. E., Lopez, J. A., Shaw, J. R., & De Schamphelaere, K. (2018). Gene co-expression networks drive and predict reproductive effects in Daphnia in response to environmental disturbances. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 52(1), 317–326.
Chicago author-date
Asselman, Jana, Michael E Pfrender, Jacqueline A Lopez, Joseph R Shaw, and Karel De Schamphelaere. 2018. “Gene Co-expression Networks Drive and Predict Reproductive Effects in Daphnia in Response to Environmental Disturbances.” Environmental Science & Technology 52 (1): 317–326.
Chicago author-date (all authors)
Asselman, Jana, Michael E Pfrender, Jacqueline A Lopez, Joseph R Shaw, and Karel De Schamphelaere. 2018. “Gene Co-expression Networks Drive and Predict Reproductive Effects in Daphnia in Response to Environmental Disturbances.” Environmental Science & Technology 52 (1): 317–326.
Vancouver
1.
Asselman J, Pfrender ME, Lopez JA, Shaw JR, De Schamphelaere K. Gene co-expression networks drive and predict reproductive effects in Daphnia in response to environmental disturbances. ENVIRONMENTAL SCIENCE & TECHNOLOGY. 2018;52(1):317–26.
IEEE
[1]
J. Asselman, M. E. Pfrender, J. A. Lopez, J. R. Shaw, and K. De Schamphelaere, “Gene co-expression networks drive and predict reproductive effects in Daphnia in response to environmental disturbances,” ENVIRONMENTAL SCIENCE & TECHNOLOGY, vol. 52, no. 1, pp. 317–326, 2018.
@article{8540569,
  abstract     = {Increasing effects of anthropogenic stressors and those of natural origin on aquatic ecosystems have intensified the need for predictive and functional models of their effects. Here, we use gene expression patterns in combination with weighted gene coexpression networks and generalized additive models to predict effects on reproduction in the aquatic microcrustacean Daphnia. We developed models to predict effects on reproduction upon exposure to different cyanobacteria, different insecticides and binary mixtures of cyanobacteria and insecticides. Models developed specifically for groups of stressors (e.g., either cyanobacteria or insecticides) performed better than general models developed on all data. Furthermore, models developed using in silico generated mixture gene expression profiles from single stressor data were able to better predict effects on reproduction compared to models derived from the mixture exposures themselves. Our results highlight the potential of gene expression data to quantify effects of complex exposures at higher level organismal effects without prior mechanistic knowledge or complex exposure data.},
  author       = {Asselman, Jana and Pfrender, Michael E and Lopez, Jacqueline A and Shaw, Joseph R and De Schamphelaere, Karel},
  issn         = {0013-936X},
  journal      = {ENVIRONMENTAL SCIENCE & TECHNOLOGY},
  keywords     = {ADVERSE OUTCOME PATHWAYS,DAPHNIA-MAGNA,RISK-ASSESSMENT,MICROCYSTIS-AERUGINOSA,TOXICITY,PULEX,IDENTIFICATION,EXPOSURE,ECOTOXICOLOGY,POLLUTANTS},
  language     = {eng},
  number       = {1},
  pages        = {317--326},
  title        = {Gene co-expression networks drive and predict reproductive effects in Daphnia in response to environmental disturbances},
  url          = {http://dx.doi.org/10.1021/acs.est.7b05256},
  volume       = {52},
  year         = {2018},
}

Altmetric
View in Altmetric
Web of Science
Times cited: