In vitro development of resistance against antipseudomonal agents: comparison of novel ß-lactam/ß-lactamase inhibitor combinations and other ß-lactam agents.

We subjected seven P. aeruginosa isolates to a 10-day serial passaging against five antipseudomonal agents to evaluate resistance levels post-exposure and putative resistance mechanisms in terminal mutants were analyzed by whole-genome sequencing analysis. Meropenem (mean, 38-fold increase), cefepime (14.4-fold), and piperacillin-tazobactam (52.9-fold) terminal mutants displayed high minimum inhibitory concentration (MIC) values compared to those obtained after exposure to ceftolozane-tazobactam (11.4-fold) and ceftazidime-avibactam (5.7-fold). Fewer isolates developed elevated MIC values for other ß-lactams and agents belonging to other classes when exposed to meropenem in comparison to other agents. Alterations in nalC and nalD, involved in the upregulation of the efflux pump system MexAB-OprM, were common and observed more frequently in isolates exposed to ceftazidime-avibactam and meropenem. These alterations, along with ones in mexR and amrR, provided resistance to most ß-lactams and levofloxacin but not imipenem. The second most common gene altered was mpl, which is involved in the recycling of the cell wall peptidoglycan. These alterations were mainly noted in isolates exposed to ceftolozane-tazobactam and piperacillin-tazobactam but also in one cefepime-exposed isolate. Alterations in other genes known to be involved in ß-lactam resistance (ftsI, oprD, phoP, pepA, and cplA) and multiple genes involved in lipopolysaccharide biosynthesis were also present. The data generated here suggest that there is a difference in the mechanisms selected for high-level resistance between newer ß-lactam/ß-lactamase inhibitor combinations and older agents. Nevertheless, the isolates exposed to all agents displayed elevated MIC values for other ß-lactams (except imipenem) and quinolones tested mainly due to alterations in the MexAB-OprM regulators that extrude these agents.

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease.

Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. Here, we define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. While islets from the majority of donors exhibited the expected relative response magnitudes, with glucose being highest, amino acid moderate, and fatty acid small, 9% of islets stimulated with amino acid and 8% of islets stimulated with fatty acids had larger responses compared with high glucose. We leveraged this insulin response heterogeneity and used transcriptomics and proteomics to identify molecular correlates of specific nutrient responsiveness, as well as those proteins and mRNAs altered in type 2 diabetes. We also examine nutrient-responsiveness in stem cell-derived islet clusters and observe that they have dysregulated fuel sensitivity, which is a hallmark of functionally immature cells. Our study now represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people's islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition. ONE-SENTENCE SUMMARY: Deep phenotyping and multi-omics reveal individualized nutrient-specific insulin secretion propensity.

Web-based multi-omics integration using the Analyst software suite.

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Hepatic Transcriptomic Responses to Ethinylestradiol in Embryonic Japanese Quail and Double-Crested Cormorant.

Understanding species differences in sensitivity to toxicants is a critical issue in ecotoxicology. We recently established that double-crested cormorant (DCCO) embryos are more sensitive than Japanese quail (JQ) to the developmental effects of ethinylestradiol (EE2). We explored how this difference in sensitivity between species is reflected at a transcriptomic level. The EE2 was dissolved in dimethyl sulfoxide and injected into the air cell of eggs prior to incubation at nominal concentrations of 0, 3.33, and 33.3 µg/g egg weight. At midincubation (JQ 9 days; DCCO 16 days), livers were collected from five embryos/treatment group for RNA sequencing. Data were processed and analyzed using EcoOmicsAnalyst and ExpressAnalyst. The EE2 exposure dysregulated 238 and 1,987 genes in JQ and DCCO, respectively, with 78 genes in common between the two species. These included classic biomarkers of estrogen exposure such as vitellogenin and apovitellenin. We also report DCCO-specific dysregulation of Phase I/II enzyme-coding genes and species-specific transcriptional ontogeny of vitellogenin-2. Twelve Kyoto Encyclopedia of Genes and Genomes pathways and two EcoToxModules were dysregulated in common in both species including the peroxisome proliferator-activated receptor (PPAR) signaling pathway and fatty acid metabolism. Similar to previously reported differences at the organismal level, DCCO were more responsive to EE2 exposure than JQ at the gene expression level. Our description of differences in transcriptional responses to EE2 in early life stage birds may contribute to a better understanding of the molecular basis for species differences. Environ Toxicol Chem 2024;43:772-783. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Comparing Transcriptomic Responses to Chemicals Across Six Species Using the EcoToxChip RNASeq Database.

The EcoToxChip project includes RNA-sequencing data from experiments involving model (Japanese quail, fathead minnow, African clawed frog) and ecological (double-crested cormorant, rainbow trout, northern leopard frog) species at multiple life stages (whole embryo and adult) exposed to eight chemicals of environmental concern known to perturb a wide range of biological systems (ethinyl estradiol, hexabromocyclododecane, lead, selenomethionine, 17ß trenbolone, chlorpyrifos, fluoxetine, and benzo[a]pyrene). The objectives of this short communication were to (1) present and make available this RNA-sequencing database (i.e., 724 samples from 49 experiments) under the FAIR principles (FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability), while also summarizing key meta-data attributes and (2) use ExpressAnalyst (including the Seq2Fun algorithm and EcoOmicsDB) to perform a comparative transcriptomics analysis of this database focusing on baseline and differential transcriptomic changes across species-life stage-chemical combinations. The database is available in NCBI GEO under accession number GSE239776. Across all species, the number of raw reads per sample ranged between 13 and 58 million, with 30% to 79% of clean reads mapped to the "vertebrate" subgroup database in EcoOmicsDB. Principal component analyses of the reads illustrated separation across the three taxonomic groups as well as some between tissue types. The most common differentially expressed gene was CYP1A1 followed by CTSE, FAM20CL, MYC, ST1S3, RIPK4, VTG1, and VIT2. The most common enriched pathways were metabolic pathways, biosynthesis of cofactors and biosynthesis of secondary metabolites, and chemical carcinogenesis, drug metabolism, and metabolism of xenobiotics by cytochrome P450. The RNA-sequencing database in the present study may be used by the research community for multiple purposes, including, for example, cross-species investigations, in-depth analyses of a particular test compound, and transcriptomic meta-analyses. Environ Toxicol Chem 2024;00:1-6. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms.

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

Genome-Resolved Metagenomics and Metatranscriptomics Reveal Insights into the Ecology and Metabolism of Anaerobic Microbial Communities in PCB-Contaminated Sediments.

Growth of organohalide-respiring bacteria such as Dehalococcoides mccartyi on halogenated organics (e.g., polychlorinated biphenyls (PCBs)) at contaminated sites or in enrichment culture requires interaction and support from other microbial community members. To evaluate naturally occurring interactions between Dehalococcoides and key supporting microorganisms (e.g., production of H2, acetate, and corrinoids) in PCB-contaminated sediments, metagenomic and metatranscriptomic sequencing was conducted on DNA and RNA extracted from sediment microcosms, showing evidence of both Dehalococcoides growth and PCB dechlorination. Using a genome-resolved approach, 160 metagenome-assembled genomes (MAGs), including three Dehalococcoides MAGs, were recovered. A novel reductive dehalogenase gene, distantly related to the chlorophenol dehalogenase gene cprA (pairwise amino acid identity: 23.75%), was significantly expressed. Using MAG gene expression data, 112 MAGs were assigned functional roles (e.g., corrinoid producers, acetate/H2 producers, etc.). A network coexpression analysis of all 160 MAGs revealed correlations between 39 MAGs and the Dehalococcoides MAGs. The network analysis also showed that MAGs assigned with functional roles that support Dehalococcoides growth (e.g., corrinoid assembly, and production of intermediates required for corrinoid synthesis) displayed significant coexpression correlations with Dehalococcoides MAGs. This work demonstrates the power of genome-resolved metagenomic and metatranscriptomic analyses, which unify taxonomy and function, in investigating the ecology of dehalogenating microbial communities.

MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.

Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, and potential activities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through joint analysis of paired microbiome and metabolomics datasets; and (iii) a Statistical Meta-Analysis module to help identify consistent signatures by integrating datasets across multiple studies. Other important improvements include added support for multi-factor differential analysis and interactive visualizations for popular graphical outputs, updated methods for functional prediction and correlation analysis, and expanded taxon set libraries based on the latest literature. These new features are demonstrated using a multi-omics dataset from a recent type 1 diabetes study. MicrobiomeAnalyst 2.0 is freely available at microbiomeanalyst.ca.

ExpressAnalyst: A unified platform for RNA-sequencing analysis in non-model species.

The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst ( www.expressanalyst.ca ), a web-based platform for processing, analyzing, and interpreting RNA-sequencing data from any eukaryotic species. ExpressAnalyst contains a series of modules that cover from processing and annotation of FASTQ files to statistical and functional analysis of count tables or gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst allows researchers to obtain global expression profiles and gene-level insights from raw RNA-sequencing reads within 24 h. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-sequencing data from multiple non-model salamander species, including two that do not have a reference transcriptome.

Transcriptomic points of departure calculated from human intestinal cells exposed to dietary nanoparticles.

Use of nanoparticles (NPs) in the food industry raises dietary health concerns. Assessing dietary NPs remains challenged due the vast number of products and the resource-intensive nature of toxicity testing. Advancements in high-throughput transcriptomics, coupled with benchmark dose (BMD) analysis are poised to modernize chemical safety assessments. The study objective was to derive transcriptomic point of departure (tPOD) values for common dietary NPs through dose-response analysis of 3'RNA-sequencing data. Two intestinal cell lines (Caco-2, HIEC-6) were exposed to 9 forms of Ag, SiO2, and TiO2, and expression of L1000 landmark genes was characterized. In Caco-2 cells, tPODmode concentrations were 0.4-0.6, 21-32, and 17-59 ppm for NPs of Ag, SiO2, and TiO2, respectively; in HIEC-6 cells, the respective tPOD values were 6-7, 7-9, and 3-13 ppm. Pathway BMDs across cases identified, for example, osteoclast and Th1/Th2 cell differentiation, and cell cycle, signaling, and senescence pathways. In all cases, the tPOD and pathway BMD values were lower than concentrations associated with cellular changes (e.g., generation of reactive oxygen species and proinflammatory cytokines, and cytotoxicity). These results demonstrate that transcriptomics dose-response analysis using in vitro models can help to increase understanding of a NP's mechanisms of action and derive quantitative information for dietary risk assessment.

A Systematic Review of Mercury Exposures from Skin-Lightening Products.

BACKGROUND: The Minamata Convention on Mercury (Article 4) prohibits the manufacture, import, or export of skin-lightening products containing mercury concentrations above 1 ppm. However, there is a lack of knowledge surrounding the global prevalence of mercury-added skin-lightening products. OBJECTIVE: The objective of this study was to increase our understanding of worldwide human mercury exposure from skin-lightening products. METHODS: A systematic search of peer-reviewed scientific literature was performed for relevant articles in four databases (PubMed, Web of Science Core Collection, Scopus, and TOXLINE). The search strategy, eligibility criteria, and data-extraction methods were established a priori. The search identified 2,303 unique scientific articles, of which 41 were ultimately deemed eligible for inclusion after iterative screens at the title, abstract, and whole-text levels. To facilitate data extraction and synthesis, all papers were organized according to four data groups a) "Mercury in products," b) "Usage of products," c) "Human biomarkers of exposure," and d) "Health impacts." RESULTS: This review was based on data contained in 41 peer-reviewed scientific papers from 22 countries worldwide published between 2000 and 2022. In total, we captured mercury concentration values from 787 skin-lightening product samples [overall pooled central median mercury level was 0.49µg/g; interquartile range (IQR): 0.02-5.9] and 1,042 human biomarker measurements from 863 individuals. We also synthesized usage information from 3,898 individuals and self-reported health impacts associated with using mercury-added products from 832 individuals. DISCUSSION: This review suggests that mercury widely exists as an active ingredient in many skin-lightening products worldwide and that users are at risk of variable and often high exposures. These synthesized findings identify data gaps and help increase our understanding of the health risks associated with the use of these products. https://doi.org/10.1289/EHP10808.

Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling.

Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 µg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.

Aerobic Bioaugmentation to Decrease Polychlorinated Biphenyl (PCB) Emissions from Contaminated Sediments to Air.

We conducted experiments to determine whether bioaugmentation with aerobic, polychlorinated biphenyl (PCB)-degrading microorganisms can mitigate polychlorinated biphenyl (PCB) emissions from contaminated sediment to air. Paraburkholderia xenovorans strain LB400 was added to bioreactors containing PCB-contaminated site sediment. PCB mass in both the headspace and aqueous bioreactor compartments was measured using passive samplers over 35 days. Time-series measurements of all 209 PCB congeners revealed a 57% decrease in total PCB mass accumulated in the vapor phase of bioaugmented treatments relative to non-bioaugmented controls, on average. A comparative congener-specific analysis revealed preferential biodegradation of lower-chlorinated PCBs (LC-PCBs) by LB400. Release of the most abundant congener (PCB 4 [2,2'-dichlorobiphenyl]) decreased by over 90%. Simulations with a PCB reactive transport model closely aligned with experimental observations. We also evaluated the effect of the phytogenic biosurfactant, saponin, on PCB bioavailability and biodegradation by LB400. Time-series qPCR measurements of biphenyl dioxygenase (bphA) genes showed that saponin better maintained bphA abundance, compared to the saponin-free treatment. These findings indicate that an active population of bioaugmented, aerobic PCB-degrading microorganisms can effectively lower PCB emissions and may therefore contribute to minimizing PCB inhalation exposure in communities surrounding PCB-contaminated sites.

Combined read- and assembly-based metagenomics to reconstruct a Dehalococcoides mccartyi genome from PCB-contaminated sediments and evaluate functional differences among organohalide-respiring consortia in the presence of different halogenated contaminants.

Microbial communities that support respiration of halogenated organic contaminants by Dehalococcoides sp. facilitate full-scale bioremediation of chlorinated ethenes and demonstrate the potential to aid in bioremediation of halogenated aromatics like polychlorinated biphenyls (PCBs). However, it remains unclear if Dehalococcoides-containing microbial community dynamics observed in sediment-free systems quantitatively resemble that of sediment environments. To evaluate that possibility we assembled, annotated, and analyzed a Dehalococcoides sp. metagenome-assembled genome (MAG) from PCB-contaminated sediments. Phylogenetic analysis of reductive dehalogenase gene (rdhA) sequences within the MAG revealed that pcbA1 and pcbA4/5-like rdhA were absent, while several candidate PCB dehalogenase genes and potentially novel rdhA sequences were identified. Using a compositional comparative metagenomics approach, we quantified Dehalococcoides-containing microbial community structure shifts in response to halogenated organics and the presence of sediments. Functional level analysis revealed significantly greater abundances of genes associated with cobamide remodeling and horizontal gene transfer in tetrachloroethene-fed cultures as compared to halogenated aromatic-exposed consortia with or without sediments, despite little evidence of statistically significant differences in microbial community taxonomic structure. Our findings support the use of a generalizable comparative metagenomics workflow to evaluate Dehalococcoides-containing consortia in sediments and sediment-free environments to eludicate functions and microbial interactions that facilitate bioremediation of halogenated organic contaminants.

Metagenomes, Metagenome-Assembled Genomes, and Metatranscriptomes from Polychlorinated Biphenyl-Contaminated Sediment Microcosms.

We present a comprehensive data set that describes an anaerobic microbial consortium native to polychlorinated biphenyl (PCB)-contaminated sediments. Obtained from sediment microcosms incubated for 200 days, the data set includes 4 metagenomes, 4 metatranscriptomes (in duplicate), and 62 metagenome-assembled genomes and captures microbial community interactions, structure, and function relevant to anaerobic PCB biodegradation.

Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of 'omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new 'omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography-mass spectrometry (LC-MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

Transcriptomic Points of Departure Calculated from Rainbow Trout Gill, Liver, and Gut Cell Lines Exposed to Methylmercury and Fluoxetine.

Ethical and resource limitation concerns are pushing chemicals management to develop alternatives to animal testing strategies. The objective of our study was to determine whether transcriptomic point of departure (tPOD) values could be derived from studies that followed Organisation for Economic Co-operation and Development (OECD) Test No. 249 (rainbow trout gill cell line), as well as from studies on trout liver and gut cells. Gill, liver, and gut cell lines were exposed to methylmercury and fluoxetine. Concentrations causing 50% cytotoxicity (LC50) were derived, the whole transcriptome was sequenced, and gene tPOD and pathway benchmark dose (BMD) values were derived from transcriptomic dose-response analysis. Differences in LC50 and transcriptomic responses across the cell lines were noted. For methylmercury, the tPODmode values were 14.5, 20.5, and 17.8 ppb for the gill, liver, and gut cells, respectively. The most sensitive pathway (pathway BMDs in parentheses) was ferroptosis in the gill (3.1 ppb) and liver (3.5 ppb), and glutathione metabolism in the gut (6.6 ppb). For fluoxetine, the tPODmode values were 109.4, 108.4, and 97.4 ppb for the gill, liver, and gut cells, respectively. The most sensitive pathway was neurotrophin signaling in the gill (147 ppb) and dopaminergic signaling in the gut (86.3 ppb). For both chemicals, the gene tPOD and pathway BMD values were lower than cytotoxic concentrations in vitro, and within 10-fold below the in vivo LC50s. By bringing together transcriptomics and dose-response analysis with an OECD test method in three cell lines, the results help to establish an in vitro method yielding tPOD values that are hypothesized to be protective of in vivo concentrations associated with adverse outcomes, and also give insights into mechanisms of action. Environ Toxicol Chem 2022;41:1982-1992. © 2022 SETAC.

Natural Biodegradation of Vinyl Chloride and cis-Dichloroethene in Aerobic and Suboxic Conditions.

Chlorinated ethene (CE) groundwater contamination is commonly treated through anaerobic biodegradation (i.e., reductive dechlorination) either as part of an engineered system or through natural attenuation. Aerobic biodegradation has also been recognized as a potentially significant pathway for the removal of the lower CEs cis-1,2-dichloroethene (cDCE) and vinyl chloride (VC). However, the role of aerobic biodegradation under low oxygen conditions typical of contaminated groundwater is unclear. Bacteria capable of aerobic VC biodegradation appear to be common in the environment, while aerobic biodegradation of cDCE is less common and little is known regarding the organisms responsible. In this study, we investigate the role of aerobic cDCE and VC biodegradation in a mixed contaminant plume (including CEs, BTEX, and ketones) at Naval Air Station North Island, Installation Restoration Site 9. Sediment and groundwater collected from the plume source area, mid-plume, and shoreline were used to prepare microcosms under fully aerobic (8 mg/L dissolved oxygen (DO)) and suboxic (< 1 mg/L DO) conditions. In the shoreline microcosms, VC and cDCE were rapidly degraded under suboxic conditions (100% and 77% removal in < 62 days). In the suboxic VC microcosms, biodegradation was associated with a > 5 order of magnitude increase in the abundance of functional gene etnE, part of the aerobic VC utilization pathway. VC and cDCE were degraded more slowly under fully aerobic conditions (74% and 30% removal) in 110 days. High-throughput 16S rRNA and etnE sequencing suggest the presence of novel VC- and cDCE-degrading bacteria. These results suggest that natural aerobic biodegradation of cDCE and VC is occurring at the site and provide new evidence that low (< 1 mg/L) DO levels play a significant role in natural attenuation of cDCE and VC.