Evaluation of water quality threats to the endangered Okaloosa darter (Etheostoma okaloosae) in East Turkey Creek on Eglin Air Force Base

A Self-Organizing Map of the Fathead Minnow Liver Transcriptome to Identify Consistent Toxicogenomic Patterns across Chemical Fingerprints

Lack of consistent findings in different experimental settings remains a major challenge in toxicogenomics. The present study investigated whether consistency between findings of different microarray experiments can be improved when the analysis is based on a common reference frame ("toxicogenomic universe"), which can be generated using the machine learning algorithm of the self-organizing map (SOM). This algorithm arranges and clusters genes on a 2-dimensional grid according to their similarity in expression across all considered data. In the present study, 19 data sets, comprising of 54 different adult fathead minnow liver exposure experiments, were retrieved from Gene Expression Omnibus and used to train a SOM. The resulting toxicogenomic universe aggregates 58 872 probes to 2500 nodes and was used to project, visualize, and compare the fingerprints of these 54 different experiments. For example, we could identify a common pattern, with 14% of significantly regulated nodes in common, in the data sets of an interlaboratory study of ethinylestradiol exposures. Consistency could be improved compared with the 5% total overlap in regulated genes reported before. Furthermore, we could determine a specific and consistent estrogen-related pattern of differentially expressed nodes and clusters in the toxicogenomic universe by applying additional clustering steps and comparing all obtained fingerprints. Our study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments. Environ Toxicol Chem 2020;39:526-537. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Blood Transcriptomics Analysis of Fish Exposed to Perfluoro Alkyls Substances: Assessment of a Non-Lethal Sampling Technique for Advancing Aquatic Toxicology Research

In contrast to mammals, the blood from other vertebrates such as fish contains nucleated red cells. Using a fathead minnow ( Pimephales promelas) oligonucleotide microarray, we compared altered transcripts in the liver and whole blood after exposure to environmentally relevant concentrations of perfluorooctanesulfonic acid (PFOS) and a mixture of seven types of perfluoro alkyl substances (PFAS), including perfluorooctanoic acid (PFOA). We used quantitative polymerase chain reactions and cell-based assays to confirm the main effects and found that blood responded with a greater number of altered genes than the liver. The exposure to PFAS altered similar genes with central roles in a cellular pathway in both tissues, including estrogen receptor α and peroxisome proliferator activator β and γ, indicating that the genes previously associated with PFAS exposure are differentially expressed in blood and liver. The altered transcripts are involved with cholesterol metabolism and mitochondrial function. Our data confirmed that PFAS are weak xenoestrogens and exert effects on DNA integrity. Gene expression profiling from blood samples not related with the immune system, including very-low-density lipoprotein, vitellogenin, estrogen receptor, and thyroid hormone receptor, demonstrated that blood is a useful tissue for assessing endocrine disruption in non-mammalian vertebrates. We conclude that the use of blood for non-lethal sampling in genomics studies is informative and particularly useful for assessing the effects of pollution in endangered species. Further, using blood will reduce animal use and widen the experimental design options for studying the effects of contaminant exposure on wildlife.

Assessing gene network stability and individual variability in the fathead minnow (Pimephales promelas) transcriptome

Transcriptomics is increasingly used to assess biological responses to environmental stimuli and stressors such as aquatic pollutants. However, fundamental studies characterizing individual variability in mRNA levels are lacking, which currently limits the use of transcriptomics in environmental monitoring assessments. To address individual variability in transcript abundance, we performed a meta-analysis on 231 microarrays that were conducted in the fathead minnow (FHM), a widely used toxicological model. The mean variability for gene probes was ranked from most to least variable based upon the coefficient of variation. Transcripts that were the most variable in individual tissues included NADH dehydrogenase flavoprotein 1, GTPase IMAP family member 7-like and v-set domain-containing T-cell activation inhibitor 1-like while genes encoding ribosomal proteins (rpl24 and rpl36), basic transcription factor 3, and nascent polypeptide-associated complex alpha subunit were the least variable in individuals across a range of microarray experiments. Gene networks that showed high variability (based upon the variation in expression of individual members within the network) included cell proliferation, metabolism (steroid, lipids, and glucose), cell adhesion, vascularization, and regeneration while those that showed low variability (more stability) included mRNA and rRNA processing, regulation of translational fidelity, RNA splicing, and ribosome biogenesis. Real-time PCR was conducted on a subset of genes for comparison of variability collected from the microarrays. There was a significant positive relationship between the two methods when measuring individual variability, suggesting that variability detected in microarray data can be used to guide decisions on sample sizes for measuring transcripts in real-time PCR experiments. A power analysis revealed that measuring estrogen receptor ba (esrba) requires fewer biological replicates than that of estrogen receptor bb (esrbb) in the gonad and samples sizes required to detect a 50% change for reproductive-related transcripts is between 12 and 20. Characterizing individual variability at the molecular level will prove necessary as efforts are made toward integrating molecular tools into environmental risk assessments.