Biology subjects

Tanguay, R. L.

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

4 recordsLinked to original sources

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↗