Identification of in vitro and in vivo disconnects using transcriptomic data

Otava, Martin; Shkedy, Ziv; Talloen, Willem; Verheyen, Geert R.; Kasim, Adetayo

doi:10.1186/s12864-015-1726-7

Cited by 17 publications

(13 citation statements)

References 51 publications

(44 reference statements)

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“…Extensive efforts have been made to understand the frequently poor correspondence of experimental results between different species or between in vitro and in vivo studies [10,[29][30][31][32]. We have previously used our toxicogenomic approach to predict rat in vivo toxicity endpoints from rat in vitro RNA-seq data with good concordance [25].…”

Section: Discussionmentioning

confidence: 99%

Concordance between Thioacetamide-Induced Liver Injury in Rat and Human In Vitro Gene Expression Data

Schyman

Printz

Estes

et al. 2020

IJMS

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The immense resources required and the ethical concerns for animal-based toxicological studies have driven the development of in vitro and in silico approaches. Recently, we validated our approach in which the expression of a set of genes is uniquely associated with an organ-injury phenotype (injury module), by using thioacetamide, a known liver toxicant. Here, we sought to explore whether RNA-seq data obtained from human cells (in vitro) treated with thioacetamide-S-oxide (a toxic intermediate metabolite) would correlate across species with the injury responses found in rat cells (in vitro) after exposure to this metabolite as well as in rats exposed to thioacetamide (in vivo). We treated two human cell types with thioacetamide-S-oxide (primary hepatocytes with 0 (vehicle), 0.125 (low dose), or 0.25 (high dose) mM, and renal tubular epithelial cells with 0 (vehicle), 0.25 (low dose), or 1.00 (high dose) mM) and collected RNA-seq data 9 or 24 h after treatment. We found that the liver-injury modules significantly altered in human hepatocytes 24 h after high-dose treatment involved cellular infiltration and bile duct proliferation, which are linked to fibrosis. For high-dose treatments, our modular approach predicted the rat in vivo and in vitro results from human in vitro RNA-seq data with Pearson correlation coefficients of 0.60 and 0.63, respectively, which was not observed for individual genes or KEGG pathways.

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Section: Discussionmentioning

confidence: 99%

Concordance between Thioacetamide-Induced Liver Injury in Rat and Human In Vitro Gene Expression Data

Schyman

Printz

Estes

et al. 2020

IJMS

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“…This technique is widely used to screen the toxicity of related RNA molecules and to elucidate the toxicity mechanisms ( 53 , 54 ). Biological analytes from environmental, industrial, and drug-induced toxic exposures have been analyzed using transcriptomics ( 54 – 61 ). Moreover, transcriptomics contributes to the comprehensive investigation of cellular responses induced by NPs using bioinformatics software ( 22 ).…”

Section: Recent Approaches For Metabolomics and Transcriptomics In Namentioning

confidence: 99%

Integration of metabolomics and transcriptomics in nanotoxicity studies

Shin

Lee

et al. 2018

BMB Rep.

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Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The ‘-omics’ analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The ‘omics’ approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the ‘omics’ approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed “metabotranscriptomics”.

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“…For instance, in vivo, ex vivo and in vitro studies have found differing effects of stress on NK cell cytotoxicity [27]. Elsewhere, differing transcriptomics results between in vitro and in vivo toxicology studies pose a challenge for the pharmaceutical industry [28]. Lastly, cell migration patterns can differ substantially between in vivo and in vitro contexts [29].…”

Section: Diverse Sources For Model Parameter Values and Mechanicsmentioning

confidence: 99%

Strategies for calibrating models of biology

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Alden

Timmis

et al. 2018

Briefings in Bioinformatics

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Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.

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Identification of in vitro and in vivo disconnects using transcriptomic data

Cited by 17 publications

References 51 publications

Concordance between Thioacetamide-Induced Liver Injury in Rat and Human In Vitro Gene Expression Data

Concordance between Thioacetamide-Induced Liver Injury in Rat and Human In Vitro Gene Expression Data

Integration of metabolomics and transcriptomics in nanotoxicity studies

Strategies for calibrating models of biology

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