Biology subjects

Allard, P.

Publications and source records attributed to Allard, P..

10 recordsLinked to original sources

bioRxiv2026

Dopaminergic neuronal dysfunction induced by newer generation systemic insecticides in Caenorhabditis elegans

While a growing number of studies have linked environmental exposures and Parkinsons disease (PD)1-3, the impact of many pesticides remains understudied4,5; for example, neonicotinoids are the most used insecticides in the world, but research into their contribution to PD is limited to a handful of studies6-8. Newer pesticides, such as the butenolide flupyradifurone (FPF), specifically developed to overcome increased pest resistance9 and spurred on by tighter restrictions on neonicotinoids such as imidacloprid (IMI)10, are even less studied. New approach methodologies (NAMs) that allow for rapid evaluation of pesticide exposures are needed to evaluate potential links between the growing number of pesticides and PD11. To this end, we exposed the model nematode Caenorhabditis elegans12 to IMI and FPF. Due to its high degree of tractability, and conservation of many genetic, neuronal, and toxic mode of action processes, C. elegans has been invaluable in both elucidating mechanisms and novel therapeutic targets for PD that can be validated in other models13, and as a complementary tool for early toxicity screening14. Along this line, we found that exposure to IMI, and to a greater extent FPF, in young adult animals causes significant dendritic blebbing, an early sign of neurodegeneration, exclusively in dopaminergic neurons. Blebbing was accompanied by impairment of dopamine-mediated behaviors, changes in neuronal mitochondrial morphology, and elevation of pathways related to reactive oxygen species (ROS). We were able to reduce the blebbing caused by IMI and FPF two ways: 1) pharmacologically via administration of the antioxidant N-acetyl cysteine (NAC); 2) genetically via knockout of a MAP kinase (MAPK) stress response pathway. This suggests that oxidative stress is a key mediator of this insecticide-induced dopaminergic neurodegeneration.

neuroscience↗

bioRxiv2025

Cell villages and Dirichlet modeling map human cell fitness genetics

The capacity of cells to proliferate and survive is central to development and disease. Assays that measure cell fitness are therefore a cornerstone of biology, but traditional techniques lack donor diversity and have high technical variability that impedes scale and reproducibility. To overcome these barriers, we designed and validated a "cell village"-based fitness screening approach using pooled cultures of 12-39 genetically distinct human neural progenitor cell (NPC) lines. We also developed Townlet to establish a foundational statistical framework based on Dirichlet regression for analyzing proportional data from cell villages. Applying these systems, we identified hyperproliferation in NPCs harboring the autism risk factor chromosome 16p11.2 deletion, mapped common genetic variants near ZFHX3 associated with NPC proliferation rate, and discovered genetic modifiers of lead (Pb) sensitivity implicating ARNT2. Together, these experimental and analytical tools advance a scalable, genetically diverse in vitro platform for dissecting human variation in cell fitness and gene-environment interactions.

genetics↗

bioRxiv2024

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95% CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95% CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95% CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance the predictive value of these assays in future rapid screening efforts.

developmental biology↗

bioRxiv2024

Screening and characterization of 133 physiologically-relevant environmental chemicals for reproductive toxicity

Reproduction is a functional outcome that relies on complex cellular, tissue, and organ interactions that span the developmental period to adulthood. Thus, the assessment of its disruption by environmental chemicals is remarkably painstaking in conventional toxicological animal models and does not scale up to the number of chemicals present in our environment and requiring testing. We adapted a previously described low-throughput in vivo chromosome segregation assay using C. elegans predictive of reproductive toxicity and leveraged available public data sources (ToxCast, ICE) to screen and characterize 133 physiologically-relevant chemicals in a high-throughput manner. The screening outcome was further validated in a second, independent in vivo assay assessing embryonic viability. In total, 13 chemicals were classified as reproductive toxicants with the two most active chemicals belonging to the large family of Quaternary Ammonium Compounds (QACs) commonly used as disinfectants but with limited available reproductive toxicity data. We compared the results from the C. elegans assay with ToxCast in vitro data compiled from 700+ cell response assays and 300+ signaling pathways-based assays. We did not observe a difference in the bioactivity or in average potency (AC50) between the top and bottom chemicals. However, the intended target categories were significantly different between the classified chemicals with, in particular, an over-representation of steroid hormone targets for the high Z-score chemicals. Taken together, these results point to the value of in vivo models that scale to high-throughput level for reproductive toxicity assessment and to the need to prioritize the assessment of QACs impacts on reproduction.

pharmacology and toxicology↗

bioRxiv2023

Exploiting fluctuations in gene expression to detect causal interactions between genes

Characterizing and manipulating cellular behaviour requires a mechanistic understanding of the causal interactions between cellular components. We present an approach to detect causal interactions between genes without the need to perturb the physiological state of cells. This approach exploits naturally occurring cell-to-cell variability which is experimentally accessible from static population snapshots of genetically identical cells without the need to follow cells over time. Our main contribution is a simple mathematical relation that constrains the propagation of gene expression noise through biochemical reaction networks. This relation allows us to rigorously interpret fluctuation data even when only a small part of a complex gene regulatory process can be observed. We show how this relation can, in theory, be exploited to detect causal interactions by synthetically engineering a passive reporter of gene expression, akin to the established "dual reporter assay". While the focus of our contribution is theoretical, we also present an experimental proof-of-principle to demonstrate the real-world applicability of our approach in certain circumstances. Our experimental data suggest that the method can detect causal interactions in specific synthetic gene regulatory circuits in E. coli confirming our theoretical result in a narrow set of controlled experimental settings. Further work is needed to show that the approach is practical on a large scale, with naturally occurring gene regulatory networks, or in organisms other than E. coli.

systems biology↗

bioRxiv2023

Metabolic memory of Δ9-tetrahydrocannabinol exposure in pluripotent stem cells and primordial germ cells-like cells

Cannabis, the most consumed illicit psychoactive drug in the world, is increasingly used by pregnant women. However, while cannabinoid receptors are expressed in the early embryo, the impact of phytocannabinoids exposure on early embryonic processes is lacking. Here, we leverage a stepwise in vitro differentiation system that captures early embryonic developmental cascade to investigate the impact of exposure to the most abundant phytocannabinoid, {Delta}9-tetrahydrocannabinol ({Delta}9-THC). We demonstrate that {Delta}9-THC increases the proliferation of naive mouse embryonic stem cells (ESCs) but not of their primed counterpart. Surprisingly, this increased proliferation, dependent on the CB1 receptor binding, is only associated with moderate transcriptomic changes. Instead, {Delta}9-THC capitalizes on ESCs metabolic bivalence by increasing their glycolytic rates and anabolic capabilities. A memory of this metabolic rewiring is retained throughout differentiation to Primordial Germ Cell-Like Cells in the absence of direct exposure and is associated with an alteration of their transcriptional profile. These results represent the first in-depth molecular characterization of the impact of {Delta}9-THC exposure on early stages of germline development.

developmental biology↗

bioRxiv2022

Single-nucleus resolution mapping of the adult C. elegans and its application to elucidate inter- and trans-generational response to alcohol.

Single-cell RNA transcriptomic platforms have significantly contributed to our understanding of tissue heterogeneity as well as of developmental and cellular differentiation trajectories. They also provide an opportunity to map an organisms response to environmental cues with high resolution and unbiasedly identify the cell types that are the most transcriptionally sensitive to exposures. Here, we applied single nucleus RNA-seq experimental and computational approaches to C. elegans to establish the transcriptome of the adult nematode and comprehensively characterize the transcriptional impact of ethanol as a model environmental exposure on the entire organism at cell type-resolution over several generations. Clustering, tissue and phenotype enrichment, and gene ontology analyses identified 31 clusters representing a diverse number of adult cell types, including those from syncytial and multi-nucleated tissues which are difficult to assess by single cell RNA-seq, such as the mitotic and meiotic germline, hypodermal cells, and the intestine. We applied this method to identify the impact of inter- and trans-generational exposure to two human-relevant doses of alcohol. Cell type proportions were not significantly altered by ethanol. However, Euclidean distance analysis identified several germline, striated muscle, and neuronal clusters as being major transcriptional targets of ethanol at both the F1 and F3 generations although the relative order of clusters changed between generations. The impact on germline clusters was further confirmed by phenotypic enrichment analysis as well as functional validation, namely a remarkable inter- and trans-generational increase in germline apoptosis, aneuploidy, and embryonic lethality. Together, snRNA-seq of the adult C. elegans represents a powerful approach for the detailed examination of an adult organisms response to environmental cues.

genetics↗

bioRxiv2022

A high-throughput approach to identify reproductive toxicants among environmental chemicals using an in vivo evaluation of gametogenesis in budding yeast Saccharomyces cerevisiae

BackgroundEnvironmental chemical exposures are likely making important contributions to current levels of infertility and its increasing incidence. Yet the US produces high volumes of industrial chemicals for which there is limited data on their potential human reproduction toxicity. Current assays typically used in policy and regulatory settings involve costly and timeconsuming whole-animal rodent tests which limit the rapidity with which one can assess the thousands of chemicals yet to be tested. ObjectiveOur aim was to develop a fast and reliable strategy to evaluate a large number of chemicals for reproductive toxicity by developing a high-throughput toxicity assessment using the yeast S. cerevisiae. MethodsYeast are chronically exposed to each environmental chemical at two doses, 30 M and 100 M, in a 96-well plate-based format throughout gametogenesis. Non-gametes are removed and chemicals are washed away before gamete viability is measured using absorbance at 600 nm to produce growth curves. The difference in time at half-maximal saturation with and without exposure is used to determine the extent of reproductive toxicity. ResultsWe validated our assay using bisphenol A (BPA), a well-established mammalian reproductive toxicant. We find that BPA in yeast has similar detrimental effects in meiosis as shown in worms and mammals. Competition assays with BPA analogs reveal that two of out of 19 BPA analogs examined (bisphenol E and 17{beta}-estradiol) show synergistic effects with BPA at doses tested and none show antagonistic effects. Out of 179 additional environmental chemicals, we designated 57 chemicals as reproductively toxic. Finally, by comparing chemicals in our cohort that have been evaluated for reproductive toxicity in mammalian studies, we find a statistically significant association between toxic chemicals in yeast and mammals. ConclusionWe show that a high-throughput assay using yeast may be a useful approach for rapidly and reliably identifying chemicals that pose a reproductive risk.

pharmacology and toxicology↗

bioRxiv2019

PharmOmics: A Species- and Tissue-specific Drug Signature Database and Online Tool for Toxicity Prediction and Drug Repurposing

Drug development has been hampered by a high failure rate in clinical trials due to efficacy or safety issues not predicted by preclinical studies in model systems. A key contributor is our incomplete understanding of drug functions across organ systems and species. Therefore, elucidating species- and tissue-specific actions of drugs can provide systems level insights into therapeutic efficacy, potential adverse effects, and interspecies differences that are necessary for more effective translational medicine. Here, we present a comprehensive drug knowledgebase and analytical tool, PharmOmics, comprised of genomic footprints of drugs in individual tissues from human, mouse, and rat transcriptome data from GEO, ArrayExpress, TG-GATEs, and DrugMatrix. Using multi-species and multi-tissue gene expression signatures as molecular indicators of drug functions, we developed gene network-based approaches for drug repositioning. We demonstrate the potential of PharmOmics to predict drugs for new disease indications and validated two predicted drugs for non-alcoholic fatty liver disease in mice. We also examined the potential of PharmOmics to identify drugs related to hepatoxicity and nephrotoxicity. By combining tissue- and species-specific in vivo drug signatures with biological networks, PharmOmics serves as a complementary tool to support drug characterization.

systems biology↗

bioRxiv2018

Prenatal Bisphenol A Exposure in Mice Induces Multi-tissue Multi-omics Disruptions Linking to Cardiometabolic Disorders

The health impacts of endocrine disrupting chemicals (EDCs) remain debated and their tissue and molecular targets are poorly understood. Here we leveraged systems biology approaches to assess the target tissues, molecular pathways, and gene regulatory networks associated with prenatal exposure to the model EDC Bisphenol A (BPA). Prenatal BPA exposure led to scores of transcriptomic and methylomic alterations in the adipose, hypothalamus, and liver tissues in mouse offspring, with cross-tissue perturbations in lipid metabolism as well as tissue-specific alterations in histone subunits, glucose metabolism and extracellular matrix. Network modeling prioritized main molecular targets of BPA, including Pparg, Hnf4a, Esr1, and Fasn. Lastly, integrative analyses identified the association of BPA molecular signatures with cardiometabolic phenotypes in mouse and human. Our multi-tissue, multi-omics investigation provides strong evidence that BPA perturbs diverse molecular networks in central and peripheral tissues, and offers insights into the molecular targets that link BPA to human cardiometabolic disorders.\n\nAuthor summaryThe inability to pinpoint the mechanistic underpinnings of environmentally-induced diseases likely stems from the pleiotropic effects of chemicals such as BPA on diverse tissues and molecular space (transcriptome, epigenome, etc.). This makes it challenging to fully dissect their health impact and merits a call for modern big data approaches to examine environmental factors. Our data-driven study is the first unbiased, multi-tissue multiomic systems biology investigation of the molecular circuitry and mechanisms underlying offspring response to prenatal BPA exposure. Importantly, the incorporation of network-based modeling allows us to capture novel players in the regulation of BPA activities in vivo, and the integration with human disease association datasets helps bridge the molecular pathways affected by BPA with diverse human diseases. In doing so, our study provides compelling molecular evidence that developmental BPA exposure significantly perturbs metabolic and endocrine systems in the offspring, and supports BPA as one of the environmental factors involved in the developmental origins of health and disease (DOHaD).

systems biology↗