Evaluating batch correction methods for image-based cell profiling.

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmark ten high-performing single-cell RNA sequencing (scRNA-seq) batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, JUMP. We focus on five scenarios with varying complexity, ranging from batches prepared in a single lab over time to batches imaged using different microscopes in multiple labs. We find that Harmony and Seurat RPCA are noteworthy, consistently ranking among the top three methods for all tested scenarios while maintaining computational efficiency. Our proposed framework, benchmark, and metrics can be used to assess new batch correction methods in the future. This work paves the way for improvements that enable the community to make the best use of public Cell Painting data for scientific discovery.

Inconsistent Transcriptomic Responses to Hexabromocyclododecane in Japanese Quail: A Comparative Analysis of Results From Four Different Study Designs.

Efforts to use transcriptomics for toxicity testing have classically relied on the assumption that chemicals consistently produce characteristic transcriptomic signatures that are reflective of their mechanism of action. However, the degree to which transcriptomic responses are conserved across different test methodologies has seldom been explored. With increasing regulatory demand for New Approach Methods (NAMs) that use alternatives to animal models and high-content approaches such as transcriptomics, this type of comparative analysis is needed. We examined whether common genes are dysregulated in Japanese quail (Coturnix japonica) liver following sublethal exposure to the flame retardant hexabromocyclododecane (HBCD), when life stage and test methodologies differ. The four exposure scenarios included one NAM: Study 1-early-life stage (ELS) exposure via a single egg injection, and three more traditional approaches; Study 2-adult exposure using a single oral gavage; Study 3-ELS exposure via maternal deposition after adults were exposed through their diet for 7 weeks; and Study 4-ELS exposure via maternal deposition and re-exposure of nestlings through their diet for 17 weeks. The total number of differentially expressed genes (DEGs) detected in each study was variable (Study 1, 550; Study 2, 192; Study 3, 1; Study 4, 3) with only 19 DEGs shared between Studies 1 and 2. Factors contributing to this lack of concordance are discussed and include differences in dose, but also quail strain, exposure route, sampling time, and HBCD stereoisomer composition. The results provide a detailed overview of the transcriptomic responses to HBCD at different life stages and routes of exposure in a model avian species and highlight certain challenges and limits of comparing transcriptomics across different test methodologies. Environ Toxicol Chem 2024;00:1-11. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

HumanIslets: An integrated platform for human islet data access and analysis.

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

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

Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.

A de novo variant in PAK2 detected in an individual with Knobloch type 2 syndrome.

P21-activated kinase 2 (PAK2) is a serine/threonine kinase essential for a variety of cellular processes including signal transduction, cellular survival, proliferation, and migration. A recent report proposed monoallelic PAK2 variants cause Knobloch syndrome type 2 (KNO2)-a developmental disorder primarily characterized by ocular anomalies. Here, we identified a novel de novo heterozygous missense variant in PAK2, NM_002577.4:c.1273G>A, p.(D425N), by whole genome sequencing in an individual with features consistent with KNO2. Notable clinical phenotypes include global developmental delay, congenital retinal detachment, mild cerebral ventriculomegaly, hypotonia, FTT, pyloric stenosis, feeding intolerance, patent ductus arteriosus, and mild facial dysmorphism. The p.(D425N) variant lies within the protein kinase domain and is predicted to be functionally damaging by in silico analysis. Previous clinical genetic testing did not report this variant due to unknown relevance of PAK2 variants at the time of testing, highlighting the importance of reanalysis. Our findings also substantiate the candidacy of PAK2 variants in KNO2 and expand the KNO2 clinical spectrum.

Maternal gastrointestinal nematode infection alters hippocampal neuroimmunity, promotes synaptic plasticity, and improves resistance to direct infection in offspring.

The developing brain is vulnerable to maternal bacterial and viral infections which induce strong inflammatory responses in the mother that are mimicked in the offspring brain, resulting in irreversible neurodevelopmental defects, and associated cognitive and behavioural impairments. In contrast, infection during pregnancy and lactation with the immunoregulatory murine intestinal nematode, Heligmosomoides bakeri, upregulates expression of genes associated with long-term potentiation (LTP) of synaptic networks in the brain of neonatal uninfected offspring, and enhances spatial memory in uninfected juvenile offspring. As the hippocampus is involved in spatial navigation and sensitive to immune events during development, here we assessed hippocampal gene expression, LTP, and neuroimmunity in 3-week-old uninfected offspring born to H. bakeri infected mothers. Further, as maternal immunity shapes the developing immune system, we assessed the impact of maternal H. bakeri infection on the ability of offspring to resist direct infection. In response to maternal infection, we found an enhanced propensity to induce LTP at Schaffer collateral synapses, consistent with RNA-seq data indicating accelerated development of glutamatergic synapses in uninfected offspring, relative to those from uninfected mothers. Hippocampal RNA-seq analysis of offspring of infected mothers revealed increased expression of genes associated with neurogenesis, gliogenesis, and myelination. Furthermore, maternal infection improved resistance to direct infection of H. bakeri in offspring, correlated with transfer of parasite-specific IgG1 to their serum. Hippocampal immunohistochemistry and gene expression suggest Th2/Treg biased neuroimmunity in offspring, recapitulating peripheral immunoregulation of H. bakeri infected mothers. These findings indicate maternal H. bakeri infection during pregnancy and lactation alters peripheral and neural immunity in uninfected offspring, in a manner that accelerates neural maturation to promote hippocampal LTP, and upregulates the expression of genes associated with neurogenesis, gliogenesis, and myelination.

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.

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.

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.