Response of gene expression in zebrafish exposed to pharmaceutical mixtures: Implications for environmental risk.

Ecotoxicol Environ Saf

School of Biomedical and Biological Science, 411 Davy Building, University of Plymouth, Drake Circus, Plymouth PL4 8AA, United Kingdom; School of Life Sciences, Heriot-Watt University, 3.05 William Perkin Building, Edinburgh EH14 4AS, United Kingdom; Center for Environmental Biotechnology, University of Tennessee, Knoxville TN 37996, USA; Department of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, TN 37996, USA. Electronic address:

Published: August 2017

Complex mixtures of pharmaceutical chemicals in surface waters indicate potential for mixture effects in aquatic organisms. The objective of the present study was to evaluate whether effects on target gene expression and enzymatic activity of individual substances at environmentally relevant concentrations were additive when mixed. Expression of zebrafish cytochrome P4501A (cyp1a) and vitellogenin (vtg) genes as well as activity of ethoxyresorufin-O-deethylase (EROD) were analyzed after exposure (96h) to caffeine-Caf, ibuprofen-Ibu, and carbamazepine-Cbz (0.05 and 5µM), tamoxifen-Tmx (0.003 and 0.3µM), and after exposure to pharmaceutical mixtures (low mix: 0.05µM of Caf, Ibu, Cbz and 0.003µM of Tmx, and high mix: 5µM of Caf, Ibu, Cbz and 0.3µM of Tmx). Pharmaceuticals tested individually caused significant down regulation of both cyp1a and vtg, but EROD activity was not affected. Exposure to low mix did not cause a significant change in gene expression; however, the high mix caused significant up-regulation of cyp1a but did not affect vtg expression. Up-regulation of cyp1a was consistent with induction of EROD activity in larvae exposed to high mix. The complex mixture induced different responses than those observed by the individual substances. Additive toxicity was not supported, and results indicate the need to evaluate complex mixtures rather than models based on individual effects, since in environment drugs are not found in isolation and the effects of their mixtures is poorly understood.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ecoenv.2017.04.038DOI Listing

Publication Analysis

Top Keywords

gene expression
12
high mix
12
expression zebrafish
8
pharmaceutical mixtures
8
complex mixtures
8
individual substances
8
low mix
8
caf ibu
8
ibu cbz
8
erod activity
8

Similar Publications

TRIAGE: an R package for regulatory gene analysis.

Brief Bioinform

November 2024

Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.

Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions.

View Article and Find Full Text PDF

Deep learning in integrating spatial transcriptomics with other modalities.

Brief Bioinform

November 2024

State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.

View Article and Find Full Text PDF

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.

View Article and Find Full Text PDF

Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships.

View Article and Find Full Text PDF

Background: Extracellular matrix (ECM) proteins play a crucial role in regulating the biological properties of adherent cells. For cryopreserved fibroblasts, a favourable ECM environment can help restore their natural morphology and function more rapidly, minimizing post-thaw stress responses.

Methods And Results: This study explored the functional responses of cryopreserved enriched caprine adult dermal fibroblast (cadFibroblast) cells to structural [collagen-IV and rat tail collagen (RTC)] and adhesion ECM proteins (laminin, fibronectin, and vitronectin) under in vitro culture conditions.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!