Biology subjects

Tanguay, R. L.

Publications and source records attributed to Tanguay, R. L..

7 recordsLinked to original sources

bioRxiv2024

Clearing the air on pollutant disruptions of the gut-brain axis: Developmental exposure to Benzopyrene disturbs zebrafish behavior and the gut microbiome in adults and subsequent generations

Developmental exposure to benzo[a]pyrene (BaP), a ubiquitous environmental pollutant, has been linked to various toxic effects, including neurodegenerative disorders and, most recently, multigenerational behavioral impairment. While the specific mechanisms driving BaP neurotoxicity are not fully understood, recent work highlights two important determinants of developmental BaP neurotoxicity: (1) the aryl hydrocarbon receptor (AHR), which is responsible for inducing host metabolism of BaP, and (2) the gut microbiome, which may interact with BaP to affect its metabolism, or be perturbed directly by BaP to yield disruptions to the gut-brain axis. To explore the role of AHR, the gut microbiome, and their interaction on BaP-induced neurotoxicity, we utilized the zebrafish model. We sought to determine (1) how exposure to BaP and developmental expression of AHR2, a key gene in the zebrafish AHR pathway, link to adult zebrafish behavior, (2) how these same variables associated with the structure and function of the adult zebrafish gut metagenome, and (3) whether these associations were multigenerational. This finding revealed a reticulated axis of association between BaP exposure, developmental AHR2 expression, the zebrafish gut metagenome, and behavior. Our results also indicate that AHR2 is a key mediator of how BaP elicits neurotoxicity and microbiome dysbiosis. Additionally, this axis of association manifests inter- and transgenerationally, suggesting that exposure to BaP may yield dysbiotic and neurodevelopmental impacts on subsequent generations. These findings demonstrate the power of utilizing the zebrafish model to study pollutant-metagenome interactions and elucidate the role of specific host genes in the neurotoxicity and dysbiosis. ImportanceBenzo[a]pyrene (BaP) is a toxic chemical that is especially ubiquitous in industrialized nations, as it is produced in large quantities by burning coal, oil, and other organic compounds. Early-life and in utero exposure to this chemical can induce adverse behavior changes in model animals, like mice and zebrafish. In humans, developmental exposure is linked to symptoms of ADHD, anxiety, and depression. This study found BaP affects zebrafish behavior and the gut microbiome throughout their life and across subsequent generations. We further discovered that a host regulatory pathway called the aryl hydrocarbon receptor is a key component of how benzo[a]pyrene, the gut microbiome, and behavior interconnect. The aryl hydrocarbon receptor binds many toxicants, as well as microbial metabolites associated with maintaining gut homeostasis, and regulates the gut-brain connection. This receptor presents a new potential mechanism of how gut microbiota relate to BaP exposure and behavior for future studies to investigate.

microbiology↗

bioRxiv2024

Single cell sequencing of zebrafish kidney marrows reveals AHR2-dependent endogenous regulation of hematopoiesis

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates a wide range of biological and toxicological responses. While largely studied in ligand-activated toxicant responses, AHR also plays important roles in endogenous physiological processes. We leveraged single cell sequencing and an AHR2 knockout zebrafish line to investigate the role of AHR2 in regulating hematopoiesis (production and differentiation of red and white blood cells from hematopoietic stem cells). Our objectives were to determine if absence of AHR2-1) alters proportions of immune cell populations and/or 2) impacts gene expression within individual immune cell types. We dissected kidney marrow (organ of hematopoiesis in zebrafish) from adult wildtype and AHR2 knockout zebrafish (N=3/genotype), isolated single cells and sequenced [~] 5000 cells/sample (10X Genomics). We identified 14 cell clusters representing the expected major blood (erythrocytes, thrombocytes), immune (B cells, macrophages, lymphoid cells, granulocytes, etc), progenitors and kidney cell populations. We focused our analyses only on the progenitor and mature immune cell types. While there were no genotype-specific differences in proportion of individual cell types, gene expression differences were observed within several cell types. For known genes, such as rrm2, changes were up to 2000-fold, signifying their importance in AHR2-hematopoesis interaction. Several of the known genes are also identified as markers of carcinoma cells for an array of cancer types. However, many of the dysregulated genes are poorly annotated, limiting our ability to examine biological processes and pathways dysregulated on AHR2 mutation. Nevertheless, our study indicates that AHR2 plays an important endogenous role in hematopoiesis. Future work will focus on better characterizing anatomy of dysregulated genes and their functions in hematopoiesis.

immunology↗

bioRxiv2023

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies

Zebrafish have become an essential tool in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems. Author SummaryWe demonstrate that a deep autoencoder using raw behavioral tracking data from zebrafish toxicity screens outperforms conventional statistical methods, resulting in a comprehensive evaluation of behavioral data. Our models can accurately distinguish between normal and abnormal behavior with near-complete overlap with existing statistical approaches, with many chemicals detectable at lower concentrations than with conventional statistical tests; this is a crucial finding for the protection of public health. Our deep learning models enable the identification of new substances capable of inducing aberrant behavior, and we generated new data to demonstrate the reproducibility of these results. Thus, neurodevelopmentally active chemicals identified by our deep autoencoder models may represent previously undetectable signals of subtle individual response differences. Our method elegantly accounts for the high degree of behavioral variability associated with the genetic diversity found in a highly outbred population, as is typical for zebrafish research, thereby making it applicable to multiple laboratories. Utilizing the vast quantities of control data generated during high-throughput screening is one of the most innovative aspects of this study and to our knowledge is the first study to explicitly develop a deep autoencoder model for anomaly detection in large-scale toxicological behavior studies.

pharmacology and toxicology↗

bioRxiv2020

Leveraging High-Throughput Screening Data and Conditional Generative Adversarial Networks to Advance Predictive Toxicology

There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional Generative Adversarial Network (cGAN) and leveraging this large set of toxicity data, plus chemical structure information, we could efficiently predict toxic outcomes of untested chemicals. CAS numbers for each chemical were used to generate textual files containing three-dimensional structural information for each chemical. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first used regression (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train a generator to produce toxicity data. Our results showed that both Go-ZT and GAN-ZT models produce similar results, but the cGAN achieved a higher sensitivity (SE) value of 85.7% vs 71.4%. Conversely, Go-ZT attained higher specificity (SP), positive predictive value (PPV), and Kappa results of 67.3%, 23.4%, and 0.21 compared to 24.5%, 14.0%, and 0.03 for the cGAN, respectively. By combining both Go-ZT and GAN-ZT, our consensus model improved the SP, PPV, and Kappa, to 75.5%, 25.0%, and 0.211, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.663. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into untested areas of the chemical space to prioritize compounds for HT testing. SummaryA conditional Generative Adversarial Network (cGAN) can leverage a large chemical set of experimental toxicity data plus chemical structure information to predict the toxicity of untested compounds.

bioinformatics↗

bioRxiv2020

Nitrate and nitrite exposure increases anxiety-like behavior and alters brain metabolomic profile in zebrafish

IntroductionDietary nitrate lowers blood pressure and improves athletic performance in humans, yet data supporting observations that it may increase cerebral blood flow and improve cognitive performance are mixed. Here we tested the hypothesis that nitrate and nitrite treatment would improve indicators of learning and cognitive performance in a zebrafish (Danio rerio) model. We also explored the extent to which nitrate and nitrite treatment affected the brain metabolome in order to understand how nitrate and nitrite supplementation may affect indices of cognitive function. MethodsFish were exposed to sodium nitrate (606.9 mg/L), sodium nitrite (19.5 mg/L), or control water for 2-4 weeks and free swim, startle response, innate predator avoidance, social cohesion, and shuttle box assays were performed. ResultsNitrate and nitrite treatment did not change fish weight, length, predator avoidance, or distance and velocity traveled in an unstressed environment. Nitrate- and nitrite-treated fish initially experienced more negative reinforcement and increased time to decision in the shuttle box assay, which is consistent with a decrease in associative learning or executive function however, over multiple trials, all treatment groups demonstrated behaviors associated with learning. Nitrate and nitrite treatment significantly increased anxiety-like behavior but did not alter epinephrine, norepinephrine or dopamine levels. Targeted LC-MS/MS analysis revealed no significant increase in brain nitrate or nitrite concentrations with treatment. An untargeted metabolomics analysis found 47 metabolites whose abundance was significantly altered in the brain with nitrate and nitrite treatment including an 18-19% reduction in the neurotransmitter {gamma}-aminobutyric acid (GABA), and 17-22% reduction in its precursor, glutamine, which may contribute to the increased anxiety-like behavior. ConclusionNitrate and nitrite treatment did not adversely affect multiple parameters of zebrafish health but was associated with mild anxiety-like behavior, changes in the brain metabolome, and caused a short-term decrease in executive function or associative learning.

animal behavior and cognition↗

bioRxiv2020

Interspecies comparative metagenomics reveals correlated gut microbiome functional capacities among vertebrates

While recent research reveals that the gut microbiome drives vertebrate health, little is known about whether the mechanisms these microbes employ to interact with physiology are consistent across host species. To help close this knowledge gap, we compared gut metagenomes across 10 vertebrate species, including biomedical animal models, to define the inter-species variation in the biochemical pathways encoded by gut microbiota. Doing so revealed gut-enriched pathways conserved across vertebrates, as well as pathways that vary concordantly with host evolutionary history. Overall, the functional capacity of the non-human gut microbiome generally reflects that of humans, though a subset of the pathways encoded by human gut microbiota are not well represented in non-human microbiomes. Collectively, these results support the use of animal models to study the mechanisms through which gut microbes impact human health, but suggest that researchers should cautiously consider which model will optimally represent a specific mechanism of interest. SignificanceEfforts to understand how the gut microbiome interacts with human physiology frequently relies on the use of animal models. However, it is generally not understood if the biochemical pathways encoded in gut microbiomes of these different animal models - which define the routes of interaction between gut microbes and their hosts - reflect those found in the human gut. To address this question, we compared gut metagenomes generated 10 different vertebrate lineages. In so doing, our study revealed that non-human gut metagenomes generally encode a set of pathways that are consistent with those found in the human gut. However, some human metagenome pathways are poorly represented in non-human guts, including pathways implicated in disease. Moreover, our analysis identified pathways that appear to be conserved across vertebrates, as well as pathways that are linked to the evolutionary history of their hosts, observations that hold potential to clarify the basis for phylosymbiosis.

microbiology↗

bioRxiv2020

An integrated gene catalog of the zebrafish gut microbiome reveals significant homology with mammalian microbiomes

Gut microbiome research increasingly utilizes zebrafish (Danio rerio) given their amenability to high-throughput experimental designs. However, the utility of zebrafish for discerning translationally relevant host-microbiome interactions is constrained by a paucity of knowledge about the biological functions that zebrafish gut microbiota can execute, how these functions associate with zebrafish physiology, and the degree of homology between the genes encoded by the zebrafish and human gut microbiomes. To address this knowledge gap, we generated a foundational catalog of zebrafish gut microbiome genomic diversity consisting of 1,569,102 non-redundant genes from twenty-nine individual fish. We identified hundreds of novel microbial genes as well as dozens of biosynthetic gene clusters of potential clinical interest. The genomic diversity of the zebrafish gut microbiome varied significantly across diets and this variance associated with altered expression of intestinal genes involved in inflammation and immune activation. Zebrafish, mouse, and human fecal microbiomes shared > 50% of their total genomic diversity and the vast majority of gene family abundance for each individual metagenome ([~]99%) was accounted for by genes that comprised this shared fraction. These results indicate that the zebrafish gut houses a functionally diverse microbial community that manifests extensive homology to that of humans and mice despite substantial disparities in taxonomic composition. We anticipate that the gene catalog developed here will enable future mechanistic study of host-microbiome interactions using the zebrafish model. ImportanceZebrafish have emerged as an important model system for defining host-microbiome interactions. However, the utility of this model is blunted by limited insight into the functions that are carried by zebrafish gut microbiota, their relationship with zebrafish physiology, and their consistency with the functions carried by human gut microbiota. To address these limitations, we constructed the first genomic database of zebrafish gut microbiome diversity. We use this novel resource to demonstrate that the genomic diversity of the zebrafish gut microbiome varies with diet and this variance links with altered intestinal gene expression. We also identify substantial homology between zebrafish, human, and mouse metagenomic diversity, indicating that these microbiomes may operate similarly.

microbiology↗