Transcriptomic Analysis in Human 3D Skin Model Injected with Resorbable Hyaluronic Acid Fillers Reveals Foreign Body Response.

Usage of injectable dermal fillers applied for aesthetic purposes has extensively increased over the years. As such, the number of related adverse reactions has increased, including patients showing severe complications such as product migration, topical swelling and inflammatory reactions of the skin. In order to understand the underlying molecular events of these adverse reactions we performed a genome-wide gene expression study on the multi-cell type human Phenion® Full-Thickness Skin Model exposed to five experimental hyaluronic acid (HA) preparations with increasing cross-linking degree, four commercial fillers from Perfectha®, and non-resorbable filler Bio-Alcamid®. In addition, we evaluated whether cross-linking degree or particle size of the HA-based fillers could be associated with the occurrence of adverse effects. In all cases, exposure to different HA fillers resulted in a clearly elevated gene expression of cytokines and chemokines related to acute inflammation as part of the foreign body response. Furthermore, for one experimental filler genes of OXPHOS complexes I-V were significantly down-regulated (adjusted p-value < 0.05), resulting in mitochondrial dysfunction which can be linked to over-expression of pro-inflammatory cytokines TNFα and IL-1ß and chemokine CCL2. Our hypothesis that cross-linking degree or particle size of the HA-based fillers is related to the biological responses induced by these fillers could only partially be confirmed for particle size. In conclusion, our innovative approach resulted in gene expression changes from a human 3D skin model exposed to dermal fillers that mechanistically substantiate aforementioned adverse reactions, and thereby adds to the weight of evidence that these fillers may induce inflammatory and fibrotic responses.

Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds.

The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic doses: 2-8-24-72-168-240-336 h; toxic doses 0-2-8-24-72-168-240 h). Transcriptomics (RNA sequencing & microRNA sequencing), proteomics (LC-MS), epigenomics (MeDIP sequencing) and metabolomics (LC-MS & NMR) data were obtained from these samples. Furthermore, functional endpoints (ATP content, Caspase3/7 and O2 consumption) were measured in exposed microtissues. Additionally, multi-omics data from human biopsies from patients are available. This data is now being released to the scientific community through the BioStudies data repository ( https://www.ebi.ac.uk/biostudies/ ).

Gene expression responses reflecting 5-FU-induced toxicity: Comparison between patient colon tissue and 3D human colon organoids.

Capecitabine is a chemotherapeutic drug that is widely used as a monotherapy option in advanced cancer patients. After administration, it is converted into its active metabolite 5-fluorouracil (5-FU), a cytotoxic compound that may also induce adverse side effects in the gastrointestinal (GI) tract. Although these side effects can interfere with the continuation of the chemotherapy, diagnostic tools to detect early onset and prevention strategies are not available. In this explorative case study, we aim to identify differentially expressed genes (DEGs) that provide insight into the molecular mechanisms of toxicity induced by 5-FU in healthy colon tissue of breast cancer patients receiving capecitabine. Gene expression responses observed in patients were compared with those established in an in vitro model of healthy colon organoids. Colon biopsies from two patients with advanced breast cancer were collected before and after the treatment with capecitabine and used for RNA sequencing to determine transcriptomic responses. Differential expression analysis resulted in 31 affected genes, showing that the most affected pathways were transport of small molecules, cellular responses to stress, folate metabolism, NF-kB signalling pathway and immune system responses. The most biologically relevant genes were haemoglobin subunits encoding genes, involved in several processes; ATP12A, SLC26A3 and AQP8, involved in the transport of ions and water; TRIM31, a regulator of NF-kB signalling pathway; MST1P2 and MST1L, stimulators of macrophages. Comparison of human in vitro and in vivo responses showed that the gene expression of TRIM31 was similarly altered in the colon organoids exposed to 5-FU. Therefore, this gene constitutes a potential biomarker of colon toxicity that might be used in future in vitro drug safety design and screening.

MicroRNA-4732-3p Is Dysregulated in Breast Cancer Patients with Cardiotoxicity, and Its Therapeutic Delivery Protects the Heart from Doxorubicin-Induced Oxidative Stress in Rats.

Anthracycline-induced cardiotoxicity is the most severe collateral effect of chemotherapy originated by an excess of oxidative stress in cardiomyocytes that leads to cardiac dysfunction. We assessed clinical data from patients with breast cancer receiving anthracyclines and searched for discriminating microRNAs between patients that developed cardiotoxicity (cases) and those that did not (controls), using RNA sequencing and regression analysis. Serum levels of 25 microRNAs were differentially expressed in cases versus controls within the first year after anthracycline treatment, as assessed by three different regression models (elastic net, Robinson and Smyth exact negative binomial test and random forest). MiR-4732-3p was the only microRNA identified in all regression models and was downregulated in patients that experienced cardiotoxicity. MiR-4732-3p was also present in neonatal rat cardiomyocytes and cardiac fibroblasts and was modulated by anthracycline treatment. A miR-4732-3p mimic was cardioprotective in cardiac and fibroblast cultures, following doxorubicin challenge, in terms of cell viability and ROS levels. Notably, administration of the miR-4732-3p mimic in doxorubicin-treated rats preserved cardiac function, normalized weight loss, induced angiogenesis, and decreased apoptosis, interstitial fibrosis and cardiac myofibroblasts. At the molecular level, miR-4732-3p regulated genes of TGFß and Hippo signaling pathways. Overall, the results indicate that miR-4732-3p is a novel biomarker of cardiotoxicity that has therapeutic potential against anthracycline-induced heart damage.

Omics technologies to understand drug toxicity mechanisms.

Drug side effects are an important study subject in pharmacology. Recent omics technologies provide a range of omics data and help to understand the biological mechanisms involved in drug effects. These modern technologies provide significant support to all biological disciplines, including drug toxicology. In this review, we provide an overview the use of omics applications to understand drug side effects at the molecular level. We discuss by available omics technologies, their possible uses, as well as their advantages and limitations.

Evaluation of Adverse Effects of Resorbable Hyaluronic Acid Fillers: Determination of Macrophage Responses.

Resorbable tissue fillers for aesthetic purposes can induce severe complications including product migration, late swelling, and inflammatory reactions. The relation between product characteristics and adverse effects is not well understood. We hypothesized that the degree of cross-linking hyaluronic acid (HA) fillers was associated with the occurrence of adverse effects. Five experimental HA preparations similar to HA fillers were synthesized with an increasing degree of cross-linking. Furthermore, a series of commercial fillers (Perfectha®) was obtained that differ in degradation time based on the size of their particulate HA components. Cytotoxic responses and cytokine production by human THP-1-derived macrophages exposed to extracts of the evaluated resorbable HA fillers were absent to minimal. Gene expression analysis of the HA-exposed macrophages revealed the responses related to cell cycle control and immune reactivity. Our results could not confirm the hypothesis that the level of cross-linking in our experimental HA fillers or the particulate size of commercial HA fillers is related to the induced biological responses. However, the evaluation of cytokine induction and gene expression in macrophages after biomaterial exposure presents promising opportunities for the development of methods to identify cellular processes that may be predictive for biomaterial-induced responses in patients.

Epirubicin Alters DNA Methylation Profiles Related to Cardiotoxicity.

BACKGROUND: Epirubicin (EPI) is an important anticancer drug that is well-known for its cardiotoxic side effect. Studying epigenetic modification such as DNA methylation can help to understand the EPI-related toxic mechanisms in cardiac tissue. In this study, we analyzed the DNA methylation profile in a relevant human cell model and inspected the expression of differentially methylated genes at the transcriptome level to understand how changes in DNA methylation could affect gene expression in relation to EPI-induced cardiotoxicity. METHODS: Human cardiac microtissues were exposed to either therapeutic or toxic (IC20) EPI doses during 2 weeks. The DNA and RNA were collected from microtissues in triplicates at 2, 8, 24, 72, 168, 240, and 336 hours of exposure. Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) analysis was used to detect DNA methylation levels in EPI-treated and control samples. The MeDIP-seq data were analyzed and processed using the QSEA package with a recently published workflow. RNA sequencing (RNA-seq) was used to measure global gene expression in the same samples. RESULTS: After processing the MeDIP-seq data, we detected 35, 37, 15 candidate genes which show strong methylated alterations between all EPI-treated, EPI therapeutic and EPI toxic dose-treated samples compared to control, respectively. For several genes, gene expressions changed compatibly reflecting the DNA methylation regulation. CONCLUSIONS: The observed DNA methylation modifications provide further insights into the EPI-induced cardiotoxicity. Multiple differentially methylated genes under EPI treatment, such as SMARCA4, PKN1, RGS12, DPP9, NCOR2, SDHA, POLR2A, and AGPAT3, have been implicated in different cardiac dysfunction mechanisms. Together with other differentially methylated genes, these genes can be candidates for further investigations of EPI-related toxic mechanisms. Data Repository: The data has been generated by the HeCaToS project (http://www.ebi.ac.uk/biostudies) under accession numbers S-HECA433 and S-HECA434 for the MeDIP-seq data and S-HECA11 for the RNA-seq data. The R code is available on Github (https://github.com/NhanNguyen000/MeDIP).

Transcriptomics analysis of human iPSC-derived dopaminergic neurons reveals a novel model for sporadic Parkinson's disease.

Parkinson's disease (PD) is a progressive, neurodegenerative disease affecting over 1% of the population beyond 65 years of age. Although some PD cases are inheritable, the majority of PD cases occur in a sporadic manner. Risk factors comprise next to heredity, age, and gender also exposure to neurotoxins from for instance pesticides and herbicides. As PD is characterized by a loss of dopaminergic neurons in the substantia nigra, it is nearly impossible to access and extract these cells from patients for investigating disease mechanisms. The emergence of induced pluripotent stem (iPSC) technology allows differentiating and growing human dopaminergic neurons, which can be used for in vitro disease modeling. Here, we differentiated human iPSCs into dopaminergic neurons, and subsequently exposed the cells to increasing concentrations of the neurotoxin MPP+. Temporal transcriptomics analysis revealed a strong time- and dose-dependent response with genes over-represented across pathways involved in PD etiology such as "Parkinson's Disease", "Dopaminergic signaling" and "calcium signaling". Moreover, we validated this disease model by showing robust overlap with a meta-analysis of transcriptomics data from substantia nigra from post-mortem PD patients. The overlap included genes linked to e.g. mitochondrial dysfunction, neuron differentiation, apoptosis and inflammation. Our data shows, that MPP+-induced, human iPSC-derived dopaminergic neurons present molecular perturbations as observed in the etiology of PD. Therefore we propose iPSC-derived dopaminergic neurons as a foundation for a novel sporadic PD model to study the pathomolecular mechanisms of PD, but also to screen for novel anti-PD drugs and to develop and test new treatment strategies.

Transcriptome analysis of long noncoding RNAs reveals their potential roles in anthracycline-induced cardiotoxicity.

Aims: Anthracyclines (ANTs) are essential chemotherapeutic agents; however, their adverse effects can lead to heart failure in cancer survivors. While long non-coding RNAs (lncRNAs) have become new players in cellular processes, there is limited knowledge on lncRNA expression related to anthracyclines-induced cardiotoxicity. This study investigates the lncRNA profiles in human cardiac microtissues exposed to 3 popular ANTs, namely doxorubicin, epirubicin, and idarubicin, as well as in heart biopsies from ANT-treated patients. Methods and results: The in vitro microtissues were exposed to each ANT at 2 doses over 2 weeks; the transcriptome data was collected at 7 time points. The human biopsies were collected from heart failure patients who underwent ANT treatment and control subjects. Over 100 lncRNAs were differentially expressed in each in vitro ANT treatment condition compared to control samples; 16 of them were differentially expressed across all ANT-treated conditions. The lncRNA databases and literature revealed insight on how these lncRNAs relate to heart failure and cellular functions. For instance, H19 and RMRP are involved in heart failure progression, while BDNF-AS is a cardiomyocyte damage-associated gene; SNHG7 is a cardiac hypertrophy regulator. PCAT19 can promote the miR-182/PDK4 axis and modulate p53 expression, whereas SNHG29 can regulate the Wnt/ß-catenin signaling pathway via the miR-223-3p/CTNND1 axis. Other lncRNAs, which were only differentially expressed in particular ANT-treated conditions, are also involved in cardiomyocyte damage and heart failure disease. The alterations of these lncRNA expressions in the in vitro cardiac tissue were also affirmed by similar changes in the human biopsies. Conclusion: This study revealed several lncRNAs that can be potential biomarkers or targets for further ANT-induced cardiotoxicity investigation, according to the transcriptome in both human cardiac microtissues expose to ANTs as well as in heart biopies form ANT-treated patients. Especially, H19 lncRNA showed its contribution to on-target toxicity, in which it is involved in both chemoresistance and cardiotoxic mechanism.

Unravelling Mechanisms of Doxorubicin-Induced Toxicity in 3D Human Intestinal Organoids.

Doxorubicin is widely used in the treatment of different cancers, and its side effects can be severe in many tissues, including the intestines. Symptoms such as diarrhoea and abdominal pain caused by intestinal inflammation lead to the interruption of chemotherapy. Nevertheless, the molecular mechanisms associated with doxorubicin intestinal toxicity have been poorly explored. This study aims to investigate such mechanisms by exposing 3D small intestine and colon organoids to doxorubicin and to evaluate transcriptomic responses in relation to viability and apoptosis as physiological endpoints. The in vitro concentrations and dosing regimens of doxorubicin were selected based on physiologically based pharmacokinetic model simulations of treatment regimens recommended for cancer patients. Cytotoxicity and cell morphology were evaluated as well as gene expression and biological pathways affected by doxorubicin. In both types of organoids, cell cycle, the p53 signalling pathway, and oxidative stress were the most affected pathways. However, significant differences between colon and SI organoids were evident, particularly in essential metabolic pathways. Short time-series expression miner was used to further explore temporal changes in gene profiles, which identified distinct tissue responses. Finally, in silico proteomics revealed important proteins involved in doxorubicin metabolism and cellular processes that were in line with the transcriptomic responses, including cell cycle and senescence, transport of molecules, and mitochondria impairment. This study provides new insight into doxorubicin-induced effects on the gene expression levels in the intestines. Currently, we are exploring the potential use of these data in establishing quantitative systems toxicology models for the prediction of drug-induced gastrointestinal toxicity.

A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids.

Gefitinib is a tyrosine kinase inhibitor (TKI) that selectively inhibits the epidermal growth factor receptor (EGFR), hampering cell growth and proliferation. Due to its action, gefitinib has been used in the treatment of cancers that present abnormally increased expression of EGFR. However, side effects from gefitinib therapy may occur, among which diarrhoea is most common, that can lead to interruption of the planned therapy in the more severe cases. The mechanisms underlying intestinal toxicity induced by gefitinib are not well understood. Therefore, this study aims at providing insight into these mechanisms based on transcriptomic responses induced in vitro. A 3D culture of healthy human colon and small intestine (SI) organoids was exposed to 0.1, 1, 10 and 30 µM of gefitinib, for a maximum of three days. These drug concentrations were selected using physiologically-based pharmacokinetic simulation considering patient dosing regimens. Samples were used for the analysis of viability and caspase 3/7 activation, image-based analysis of structural changes, as well as RNA isolation and sequencing via high-throughput techniques. Differential gene expression analysis showed that gefitinib perturbed signal transduction pathways, apoptosis, cell cycle, FOXO-mediated transcription, p53 signalling pathway, and metabolic pathways. Remarkably, opposite expression patterns of genes associated with metabolism of lipids and cholesterol biosynthesis were observed in colon versus SI organoids in response to gefitinib. These differences in the organoids' responses could be linked to increased activated protein kinase (AMPK) activity in colon, which can influence the sensitivity of the colon to the drug. Therefore, this study sheds light on how gefitinib induces toxicity in intestinal organoids and provides an avenue towards the development of a potential tool for drug screening and development.

Integrating SNPs-based genetic risk factor with blood epigenomic response of differentially arsenic-exposed rural subjects reveals disease-associated signaling pathways.

Arsenic (As) contamination in groundwater is responsible for numerous adverse health outcomes among millions of people. Epigenetic alterations are among the most widely studied mechanisms of As toxicity. To understand how As exposure alters gene expression through epigenetic modifications, a systematic genome-wide study was designed to address the impact of multiple important single nucleotide polymorphisms (SNPs) related to As exposure on the methylome of drinking water As-exposed rural subjects from Pakistan. Urinary As levels were used to stratify subjects into low, medium and high exposure groups. Genome-wide DNA methylation was investigated using MeDIP in combination with NimbleGen 2.1 M Deluxe Promotor arrays. Transcriptome levels were measured using Agilent 8 × 60 K expression arrays. Genotyping of selected SNPs (As3MT, DNMT1a, ERCC2, EGFR and MTHFR) was measured and an integrated genetic risk factor for each respondent was calculated by assigning a specific value to the measured genotypes based on known risk allele numbers. To select a representative model related to As exposure we compared 9 linear mixed models comprising of model 1 (including the genetic risk factor), model 2 (without the genetic risk factor) and models with individual SNPs incorporated into the methylome data. Pathway analysis was performed using ConsensusPathDB. Model 1 comprising the integrated genetic risk factor disclosed biochemical pathways including muscle contraction, cardio-vascular diseases, ATR signaling, GPCR signaling, methionine metabolism and chromatin modification in association with hypo- and hyper-methylated gene targets. A unique pathway (direct P53 effector) was found associated with the individual DNMT1a polymorphism due to hyper-methylation of CSE1L and TRRAP. Most importantly, we provide here the first evidence of As-associated DNA methylation in relation with gene expression of ATR, ATF7IP, TPM3, UBE2J2. We report the first evidence that integrating SNPs data with methylome data generates a more representative epigenome profile and discloses a better insight in disease risks of As-exposed individuals.

Manganese-Induced Neurotoxicity through Impairment of Cross-Talk Pathways in Human Neuroblastoma Cell Line SH-SY5Y Differentiated with Retinoic Acid.

Manganese (Mn) is an important element; yet acute and/or chronic exposure to this metal has been linked to neurotoxicity and neurodegenerative illnesses such as Parkinson's disease and others via an unknown mechanism. To better understand it, we exposed a human neuroblastoma cell model (SH-SY5Y) to two Mn chemical species, MnCl2 and Citrate of Mn(II) (0-2000 µM), followed by a cell viability assay, transcriptomics, and bioinformatics. Even though these cells have been chemically and genetically modified, which may limit the significance of our findings, we discovered that by using RA-differentiated cells instead of undifferentiated SH-SY5Y cell line, both chemical species induce a similar toxicity, potentially governed by disruption of protein metabolism, with some differences. The MnCl2 altered amino acid metabolism, which affects RNA metabolism and protein synthesis. Citrate of Mn(II), however, inhibited the E3 ubiquitin ligases-target protein degradation pathway, which can lead to the buildup of damaged/unfolded proteins, consistent with histone modification. Finally, we discovered that Mn(II)-induced cytotoxicity in RA-SH-SY5Y cells shared 84 percent of the pathways involved in neurodegenerative diseases.

A Model-Based Workflow to Benchmark the Clinical Cholestasis Risk of Drugs.

We present a generic workflow combining physiology-based computational modeling and in vitro data to assess the clinical cholestatic risk of different drugs systematically. Changes in expression levels of genes involved in the enterohepatic circulation of bile acids were obtained from an in vitro assay mimicking 14 days of repeated drug administration for 10 marketed drugs. These changes in gene expression over time were contextualized in a physiology-based bile acid model of glycochenodeoxycholic acid. The simulated drug-induced response in bile acid concentrations was then scaled with the applied drug doses to calculate the cholestatic potential for each compound. A ranking of the cholestatic potential correlated very well with the clinical cholestasis risk obtained from medical literature. The proposed workflow allows benchmarking the cholestatic risk of novel drug candidates. We expect the application of our workflow to significantly contribute to the stratification of the cholestatic potential of new drugs and to support animal-free testing in future drug development.

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis. METHODS: To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models. RESULTS: From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development. CONCLUSION: Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury.

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream signaling networks deregulated in drug-induced liver injury (DILI) from gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to DILI from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induced the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally.

Metabolomic analysis to discriminate drug-induced liver injury (DILI) phenotypes.

Drug-induced liver injury (DILI) is an adverse toxic hepatic clinical reaction associated to the administration of a drug that can occur both at early clinical stages of drug development, as well after normal clinical usage of approved drugs. Because of its unpredictability and clinical relevance, it is of medical concern. Three DILI phenotypes (hepatocellular, cholestatic, and mixed) are currently recognized, based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values. However, this classification lacks accuracy to distinguish among the many intermediate mixed types, or even to estimate the magnitude and progression of the injury. It was found desirable to have additional elements for better evaluation criteria of DILI. With this aim, we have examined the serum metabolomic changes occurring in 79 DILI patients recruited and monitored using established clinical criteria, along the course of the disease and until recovery. Results revealed that free and conjugated bile acids, and glycerophospholipids were among the most relevant metabolite classes for DILI phenotype characterization. Using an ensemble of PLS-DA models, metabolomic information was integrated into a ternary diagram to display the disease phenotype, the severity of the liver damage, and its progression. The modeling implemented and the use of such compiled information in an easily understandable and visual manner facilitates a straightforward DILI phenotyping and allow to monitor its progression and recovery prediction, usefully complementing the concise information drawn out by the ALT and ALP classification.

Translational Proteomics Analysis of Anthracycline-Induced Cardiotoxicity From Cardiac Microtissues to Human Heart Biopsies.

Anthracyclines, including doxorubicin, idarubicin, and epirubicin, are common antitumor drugs as well as well-known cardiotoxic agents. This study analyzed the proteomics alteration in cardiac tissues caused by these 3 anthracyclines analogs. The in vitro human cardiac microtissues were exposed to drugs in 2 weeks; the proteomic data were measured at 7 time points. The heart biopsy data were collected from heart failure patients, in which some patients underwent anthracycline treatment. The anthracyclines-affected proteins were separately identified in the in vitro and in vivo dataset using the WGCNA method. These proteins engage in different cellular pathways including translation, metabolism, mitochondrial function, muscle contraction, and signaling pathways. From proteins detected in 2 datasets, a protein-protein network was established with 4 hub proteins, and 7 weighted proteins from both cardiac microtissue and human biopsies data. These 11 proteins, which involve in mitochondrial functions and the NF-κB signaling pathway, could provide insights into the anthracycline toxic mechanism. Some of them, such as HSPA5, BAG3, and SH3BGRL, are cardiac therapy targets or cardiotoxicity biomarkers. Other proteins, such as ATP5F1B and EEF1D, showed similar responses in both the in vitro and in vivo data. This suggests that the in vitro outcomes could link to clinical phenomena in proteomic analysis.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids.

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 µM. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development.