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Per- and polyfluoroalkyl substances (PFAS) pose a significant health threat due to their environmental persistence and toxicity. While PFAS contamination is widespread in Florida, the state currently lacks fish consumption advisories (FCAs) for these compounds, despite existing FCAs for legacy pollutants. This study quantified 40 PFAS in edible muscle tissue from 264 fish (16 species) across four estuaries along Florida's Atlantic coast to assess ecological and human health risks. The highest concentrations of total PFAS were found in Red Drum (Sciaenops ocellatus; 0.209 - 51.6 ng/g wet weight) and Spotted Seatrout (Cynoscion nebulosus; 2.01 - 24.3 ng/g ww) in the Indian River Lagoon, where up to 68% of Red Drum and 75% of Spotted Seatrout exceeded ecological quality standards, indicating potential impacts on predators. PFOS was the predominant PFAS, driving ecological and human health risks. Estimated daily intakes (EDIs) of PFOS exceeded the EPA reference dose (RfD) by up to 3 orders of magnitude, with the highest exposures concentrated in Red Drum and Spotted Seatrout from the Indian River Lagoon. These findings highlight that PFAS monitoring of tissue concentrations in fish across Florida's freshwater and marine systems would be needed for accurately quantifying exposure risks. Such efforts are fundamental to informing regulatory frameworks regarding FCAs and ensuring the long-term protection of aquatic ecosystems and human health.
We conducted a Phenome-Wide Association Study (PheWAS) to investigate whether alleles previously shown to be associated with problem behaviors in Labrador Retrievers from the U.S. Transportation Security Administration’s (TSA) odor detection program also show behavioral associations in other populations. The original TSA cohort (2002–2013) consisted of dogs from a former breeding program that drew from U.S. commercial sources and the Australian Customs Service. While those data included TSA testing results from the foster period, detailed behavior profiles and reasons for elimination from the program were not recorded. To extend and validate these genetic associations in populations with richer behavioral data, we analyzed three additional Labrador Retriever cohorts with both genotype and C-BARQ behavioral questionnaire data: (1) Australian pet dogs, (2) UK dogs from a mixed pet and working background (primarily gamebird retrieving), and (3) U.S. working guide dogs. This analysis identified a total of 15 associations between 12 behavioral traits and 8 markers at 6 genome loci. Notably, we found four types of aggression and one type of fear that are directed at familiar humans or dogs, but none directed at unfamiliar ones. Other problem traits identified include separation-related behaviors, excitability, and chasing small animals. Furthermore, we utilized whole genome sequencing to identify a functional candidate associated with “aggression when approached by a household dog at a favorite resting place”. We propose this variant in an ADAMTSL1 intron results in the loss of TCF7L1 protein binding, and we highlight the evolutionary history of that conserved element, including the fixation of two mutations in the human lineage. Our PheWAS findings suggest relevance to working dog selection, breeding, and training, presenting opportunities to reduce costs while improving performance and resilience.
Class-based cumulative risk assessment approaches have been applied to high-priority environmental contaminants such as polycyclic aromatic compounds (PACs), yet uncertainties remain in their application. In this study, we evaluated the influence of inactive chemicals on mixture modeling outcomes and explored strategies for predicting the aryl hydrocarbon receptor (AhR)-mediated toxicity of PAC mixtures. Using an in vitro AhR reporter gene assay, we tested seven defined mixtures composed of six active and seven inactive PACs. Observed concentration-response curves were compared to predictions from three established mixture models, concentration addition (CA), independent action (IA), and generalized concentration addition (GCA), using both effective concentration eliciting 10% response (EC10) and benchmark concentration (BMC10) approaches. Including inactive chemicals without scaling led to consistent overestimation of potency, especially in models assuming equal efficacy. Predictive accuracy improved across all models when mixtures were limited to active chemicals and contributions were scaled to 100%, excluding inactives. Among approaches, GCA consistently produced the best agreement with measured responses, particularly when paired with BMC modeling. BMC10 values better accommodate partial agonists. Our findings support a pragmatic, mechanism-based framework for modeling environmental mixtures, one that prioritizes active components, scales their contributions, and adopts BMC-based methods to estimate potency.
