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Tire tread particles are microplastics (< 5 mm) and leach organic chemicals into aquatic environments. It is important to understand the behavior of tire wear compounds in sunlight-exposed waters in terms of their persistence, removal, and transformation. Therefore, we conducted photolysis experiments with leachates from laboratory-generated tire tread particles (TTP) over 72 h in a solar simulator to evaluate the behavior of leached compounds and fluorescent components over time. Compared to initial leachates, simulated sunlight exposure resulted in ~12 % decrease in dissolved organic carbon, 11 % reduction in the total fluorescence of leachates, and ~30 % removal of the 213 chromatographic features detected by nontargeted analysis (NTA) using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A decrease in total chemical abundance determined by NTA was observed, with normalized peak areas decreasing by 36.4% in the 72 h photoirradiated samples and by 13.6% in the dark samples. Fifty-three compounds were tentatively identifiable based on mass spectral matching and among them, 12 compounds were confirmed with authentic standards. Among the 53 compounds, 19 compounds were photo-labile, 27 were photo-resistant, and 7 were photo-transformation products. NTA also identified compounds previously unreported as tire-related compounds. Parallel factor analysis (PARAFAC) modeling of three-dimensional excitation-emission-matrix (EEM) data identified five fluorescent components. PARAFAC component C4 (excitation/emission peak at 285/445 nm) was found to be a fluorescent analog for 6PPD. Rapid double exponential decay kinetics were observed for the 6PPD-like component during photoirradiation. Similarly, the peak fluorescence of commercially available 6PPD exposed to simulated sunlight was reduced by >90 % in the first 0.5 h of photoirradiation. 6PPD photodegradation resulted in the production of a fluorescent transformation product resembling PARAFAC Component C2 (with emission at 360 nm). These results prove that EEM fluorescence analyses can serve as a rapid method for kinetics analysis of 6PPD, and may be combined with NTA compound tentative identification to track the behavior of other TTP-derived compounds in experimental studies. 

Abstract SummaryUnderstanding the pathways and biological processes underlying differential gene expression is fundamental for characterizing gene expression changes in response to an experimental condition. Zebrafish, with a transcriptome closely mirroring that of humans, are frequently utilized as a model for human development and disease. However, a challenge arises due to the incomplete annotations of zebrafish pathways and biological processes, with more comprehensive annotations existing in humans. This incompleteness may result in biased functional enrichment findings and loss of knowledge. danRerLib, a versatile Python package for zebrafish transcriptomics researchers, overcomes this challenge and provides a suite of tools to be executed in Python including gene ID mapping, orthology mapping for the zebrafish and human taxonomy, and functional enrichment analysis utilizing the latest updated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. danRerLib enables functional enrichment analysis for GO and KEGG pathways, even when they lack direct zebrafish annotations through the orthology of human-annotated functional annotations. This approach enables researchers to extend their analysis to a wider range of pathways, elucidating additional mechanisms of interest and greater insight into experimental results. Availability and implementationdanRerLib, along with comprehensive documentation and tutorials, is freely available. The source code is available at https://github.com/sdsucomptox/danrerlib/ with associated documentation and tutorials at https://sdsucomptox.github.io/danrerlib/. The package has been developed with Python 3.9 and is available for installation on the package management systems PIP (https://pypi.org/project/danrerlib/) and Conda (https://anaconda.org/sdsu_comptox/danrerlib) with additional installation instructions on the documentation website.