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Development of a Flame Retardant and an Organohalogen Flame Retardant Chemical Inventory

ABSTRACT: There have been many attempts to compile comprehensive lists of flame retardants. However, this goal has proven challenging due to the heterogeneity of compounds that can be used as flame retardants coupled with changes in formulation chemistry over time. Flame retardants have been the focus of many recent existing hazard, exposure, and risk assessments. These assessments have been class-based or for individual chemical substances. Here, diverse sets of publicly available data sources from governmental organizations and the open literature were compiled to develop an inventory of chemicals used as flame retardants and organohalogen flame retardants. The chemical substances from these data sources were mapped to appropriate chemical identifiers via manual curation and deduplicated. Despite different data sources containing a large number of overlapping chemical substances, compiling information from multiple data sources was found to increase the breadth of potential flame retardant chemistries. The flame retardant and organohalogen flame retardant inventories were developed as a resource for scientists interested in better understanding properties of flame retardant and organohalogen flame retardant classes. Measurement(s)chemical use informationTechnology Type(s)manual and automated literature collection

SUBMITTER: Bevington C

PROVIDER: S-EPMC9192637 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose: The goals of this study are to examine mRNA and miR networks on exposures to flame retardant chemicals (FRCs), an ubiquitous group of chemicals in the environment Methods: Four biological replicates were created by pooling eight embryos per replicate from individual wells which were placed into an Eppendorf Safelock Tube and excess solution removed. 0.5 mM zirconium oxide beads were added along with 500 µL of RNAzol (Molecular Research Center, Inc.) and the tubes were immediately placed into a bullet blender (Next Advance), using settings recommended by the manufacturer. The RNA was purified using the Direct-zol MiniPrep kit (Zymo Research), including an optional DNase-1 digestion treatment for 15 minutes. RNA integrity (RIN) was assessed using an Agilent Bioanalyzer (Santa Clara, CA), and RNA samples with RIN values >8 were processed for library preparation and sequencing at the Oregon State University Center for Genome Research and Biocomputing. Total RNA was used as input for both mRNA and miRNA sequencing. For mRNA sequencing, mRNA was poly A selected, libraries were prepared with the PrepX™ mRNA and Illumina sequencing workflow (Wafergen Biosystems). For miRNA seq, the Illumina TruSeq Small RNA library kit was used to generate small RNA libraries from total RNA. For sequencing, an Illumina HiSeq 3000 sequencer (Illumina, San Diego) was used for mRNA and small RNA single-end sequencing at 100 and 50 base pairs, respectively. Bioinformatics analysis of sequencing data was performed on an R platform. Briefly, reads were evaluated by FastQC v0.11.3 to detect major sequencing problems, and then trimmed for quality control with Skewer v0.2.2 . RNA-seq alignment and quantification proceeded with Bowtie2 v2.2.3 being used to build HISAT2 (Kim et al., 2013) genome index files from the Genome Reference Consortium Zebrafish Build 10 (GRCz10) genome. For miR identification and quantification, a combination of miRDeep2 v2.0.0.8, miRBase release 22 and Bowtie v1.2.1.1 were used. Differential expression between experimental and control samples was determined with functions from the Bioconductor package, edgeR; mRNAs with a log2 fold change ≥1.5 and Benjamini-Hochberg (BH) adjusted p ≤ 0.05 were considered differentially expressed while an adjusted p ≤ 0.05 was applied to miRs without any fold change cutoffs. Heatmap clustering of differentially expressed genes were generated in R based on their log2 fold changes using the ggplot2 package. Results: We found widespread disruption of mRNA and miR expression. Neurodevelopment was a key disrupted biological process across multiple FRCs and was corroborated by behavioral deficits. Several mRNAs (eg: osbpl2a) and miRs (eg- mir-125b-5p), showed differential expression common to multiple FRCs (10 and 7 respectively). These common miRs were also predicted to regulate a network of differentially expressed genes with diverse functions, including apoptosis, neurodevelopment, lipid regulation and inflammation. Commonly disrupted transcription factors (TFs) such as RXR, RAR and VDR were predicted to regulate a wide network of differentially expressed mRNAs across a majority of the FRCs. Many of the differential mRNA-TF and mRNA-miR pairs were predicted to play important roles in development as well as cancer signaling. Specific comparisons between TBBPA and its derivative TBBPA-DBPE showed contrasting gene expression patterns that corroborated with their phenotypic profiles. The newer generation FRCs such as IPP and TCEP produced distinct gene expression changes compared to the legacy FRC BDE-47. Conclusions: Our study is the first to establish a mRNA-miR-TF regulatory network across a large group of structurally diverse FRCs and diverse phenotypic responses. The purpose was to discover common and unique biological targets that will help us understand mechanisms of action for these important chemicals.

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Development of a Flame Retardant and an Organohalogen Flame Retardant Chemical Inventory

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