Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury
Ontology highlight
ABSTRACT:
SUBMITTER: Steven Wink
PROVIDER: S-BSST117 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-?B-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.
| S-EPMC5962642 | biostudies-literature
Project description:ObjectiveMachine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.MethodsSuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under the curve was compared to select among predictors. Clinical variables responsible for driving outcomes were estimated at each time point.ResultsSuperLearner fits demonstrated excellent cross-validated prediction of death (overall AUC 0.94-0.97), multi-organ failure (overall AUC 0.84-0.90), and transfusion (overall AUC 0.87-0.9) across multiple post-injury time points, and good prediction of Acute Respiratory Distress Syndrome (overall AUC 0.84-0.89) and venous thromboembolism (overall AUC 0.73-0.83). Outcomes with inferior data quality included coagulopathic trajectory (AUC 0.48-0.88). Key clinical predictors evolved over the post-injury timecourse and included both anticipated and unexpected variables. Non-random missingness of data was identified as a predictor of multiple outcomes over time.ConclusionsMachine learning algorithms can be used to generate dynamic prediction after injury while avoiding the risk of over- and under-fitting inherent in ad hoc statistical approaches. SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support.
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Project description:Stress is a conserved physiological response in mammals. Whereas moderate stress strengthens memory to improve reactions to previously experienced difficult situations, too much stress is harmful.We used specific ?-adrenergic agonists, as well as ?2-adrenergic receptor (?2AR) and arrestin knockout models, to study the effects of adaptive ?2AR activation on cognitive function using Morris water maze and object recognition experiments. We used molecular and cell biological approaches to elucidate the signaling subnetworks.We observed that the duration of the adaptive ?2AR activation determines its consequences on learning and memory. Short-term formoterol treatment, for 3 to 5 days, improved cognitive function; however, prolonged ?2AR activation, for more than 6 days, produced harmful effects. We identified the activation of several signaling networks downstream of ?2AR, as well as an essential role for arrestin and lactate metabolism in promoting cognitive ability. Whereas Gs-protein kinase A-cyclic adenosine monophosphate response element binding protein signaling modulated monocarboxylate transporter 1 expression, ?-arrestin-1 controlled expression levels of monocarboxylate transporter 4 and lactate dehydrogenase A through the formation of a ?-arrestin-1/phospho-mitogen-activated protein kinase/hypoxia-inducible factor-1? ternary complex to upregulate lactate metabolism in astrocyte-derived U251 cells. Conversely, long-term treatment with formoterol led to the desensitization of ?2ARs, which was responsible for its decreased beneficial effects.Our results not only revealed that ?-arrestin-1 regulated lactate metabolism to contribute to ?2AR functions in improved memory formation, but also indicated that the appropriate management of one specific stress pathway, such as through the clinical drug formoterol, may exert beneficial effects on cognitive abilities.
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Project description:Lapatinib and trastuzumab (Herceptin) are targeted therapies designed for patients with HER2+ breast tumors. Although these therapies improved survival rates of patients with this tumor type, not all the patients harboring HER2 amplification respond to these drugs. The NeoALTTO clinical trial was designed to test whether a higher response rate can be achieved by combining lapatinib and trastuzumab. Although the combination therapy showed almost double the response rate compared to the monotherapies, 40% of the patients did not respond to the treatment. In this study, we sought to identify biomarkers of HER2+ breast cancer patients' response to drugs relying on gene expression profiles of tumors. We show that univariate gene expression-based biomarkers are significant but weak predictors of drug response. We further show that pathway activities, estimated from gene expression patterns quantified using the recent transcriptional similarity coefficient (TSC) between the tumor samples, yield high predictive value for therapy response (concordance index >0.8, p < 0.05). Moreover, machine learning models, built using multiple algorithms including logistic regression, naive Bayes, random forest, k-nearest neighbor, and support vector machine, for predicting drug response in the NeoALTTO clinical trial, resulted in lower performance compared to our pathway-based approach. Our results indicate that transcriptional similarity of biological pathways can be used to predict lapatinib and trastuzumab response in HER2+ breast cancer.
| S-EPMC7509171 | biostudies-literature
Project description:Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute "network profiles" for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.
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Project description:Quantitative phase imaging (QPI) with its high-contrast images of optical phase delay (OPD) maps is often used for label-free single-cell analysis. Contrary to other imaging methods, sensitivity improvement has not been intensively explored because conventional QPI is sensitive enough to observe the surface roughness of a substrate that restricts the minimum measurable OPD. However, emerging QPI techniques that utilize, for example, differential image analysis of consecutive temporal frames, such as mid-infrared photothermal QPI, mitigate the minimum OPD limit by decoupling the static OPD contribution and allow measurement of much smaller OPDs. Here, we propose and demonstrate supersensitive QPI with an expanded dynamic range. It is enabled by adaptive dynamic range shift through a combination of wavefront shaping and dark-field QPI techniques. As a proof-of-concept demonstration, we show dynamic range expansion (sensitivity improvement) of QPI by a factor of 6.6 and its utility in improving the sensitivity of mid-infrared photothermal QPI. This technique can also be applied for wide-field scattering imaging of dynamically changing nanoscale objects inside and outside a biological cell without losing global cellular morphological image information.
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Project description:In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom-a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements.
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Project description:Activation of the hexosamine pathway (HP) through gain-of-function mutations in its rate-limiting enzyme glutamine fructose-6-phosphate amidotransferase (GFAT-1) ameliorates proteotoxicity and increases lifespan in Caenorhabditis elegans. Here, we investigate the role of the HP in mammalian protein quality control. In mouse neuronal cells, elevation of HP activity led to phosphorylation of both PERK and eIF2? as well as downstream ATF4 activation, identifying the HP as a modulator of the integrated stress response (ISR). Increasing uridine 5'-diphospho-N-acetyl-D-glucosamine (UDP-GlcNAc) levels through GFAT1 gain-of-function mutations or supplementation with the precursor GlcNAc reduces aggregation of the polyglutamine (polyQ) protein Ataxin-3. Blocking PERK signaling or autophagy suppresses this effect. In C. elegans, overexpression of gfat-1 likewise activates the ISR. Consistently, co-overexpression of gfat-1 and proteotoxic polyQ peptides in muscles reveals a strong protective cell-autonomous role of the HP. Thus, the HP has a conserved role in improving protein quality control through modulation of the ISR.
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Project description:In the damaged heart, cardiac adaptation relies primarily on cardiomyocyte hypertrophy. The recent discovery of cardiac stem cells in the postnatal heart, however, suggests that these cells could participate in the response to stress via their capacity to regenerate cardiac tissues. Using models of cardiac hypertrophy and failure, we demonstrate that components of the Notch pathway are up-regulated in the hypertrophic heart. The Notch pathway is an evolutionarily conserved cell-to-cell communication system, which is crucial in many developmental processes. Notch also plays key roles in the regenerative capacity of self-renewing organs. In the heart, Notch1 signaling takes place in cardiomyocytes and in mesenchymal cardiac precursors and is activated secondary to stimulated Jagged1 expression on the surface of cardiomyocytes. Using mice lacking Notch1 expression specifically in the heart, we show that the Notch1 pathway controls pathophysiological cardiac remodeling. In the absence of Notch1, cardiac hypertrophy is exacerbated, fibrosis develops, function is altered, and the mortality rate increases. Therefore, in cardiomyocytes, Notch controls maturation, limits the extent of the hypertrophic response, and may thereby contribute to cell survival. In cardiac precursors, Notch prevents cardiogenic differentiation, favors proliferation, and may facilitate the expansion of a transient amplifying cell compartment.
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Project description:Nucleotide-binding oligomerization domain 1 (NOD1) is an intracellular pattern recognition receptor (PRR) responsible for sensing bacterial peptidoglycan fragments. Stimulation of NOD1 leads to a robust innate immune response via activation of the major transcription factor NF-κB. In addition to peptidoglycan sensing, NOD1 and the closely related PRR NOD2 have been linked to inflammation by responding to the endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR). Here we show that differential ER stress induction renders cells more susceptible to Salmonella enterica serovar Typhimurium infection in a NOD1-dependent manner, measured by increased NF-κB activation and cytokine expression. In HeLa57A cells stably transfected with an NF-κB::luciferase reporter, we show that cells undergoing ER stress induced by thapsigargin display a significant increase in NF-κB activation in response to NOD1 stimulation by C12-iE-DAP (acylated derivative of the iE-DAP dipeptide [gamma-d-glutamyl-meso-diaminopimelic acid]) and the S Typhimurium effector protein SopE. Tunicamycin-induced ER stress had no effect on NOD1-stimulated NF-κB activation. We further show that the mouse intestinal epithelial cell line MODE-K and RAW264.7 macrophages are more responsive to Salmonella infection when treated with thapsigargin but not with tunicamycin. These profound differences between thapsigargin- and tunicamycin-treated cells upon inflammation suggest that different components downstream of the UPR contribute to NOD1 activation. We found that the NOD1-induced inflammatory response is dependent on protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) activation in conjunction with stimulation of the inositol triphosphate receptor (IP3R). Together, these results suggest that differential UPR activation makes cells more responsive to bacterial infections in a NOD1-dependent manner.
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