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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.

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.

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. Supplementary Information The online version contains supplementary material available at 10.1007/s00204-021-03092-2.

Summary Typical RNA sequencing (RNA-Seq) analyses are performed either at the gene level by summing all reads from the same locus, assuming that all transcripts from a gene make a protein or at the transcript level, assuming that each transcript displays unique function. However, these assumptions are flawed, as a gene can code for different types of transcripts and different transcripts are capable of synthesizing similar, different or no protein. As a consequence, functional changes are not well illustrated by either gene or transcript analyses. We propose to improve RNA-Seq analyses by grouping the transcripts based on their similar functions. We developed FuSe to predict functional similarities using the primary and secondary structure of proteins. To estimate the likelihood of proteins with similar functions, FuSe computes two confidence scores: knowledge (KS) and discovery (DS) for protein pairs. Overlapping protein pairs exhibiting high confidence are grouped to form ‘similar function protein groups’ and expression is calculated for each functional group. The impact of using FuSe is demonstrated on in vitro cells exposed to paracetamol, which highlight genes responsible for cell adhesion and glycogen regulation which were earlier shown to be not differentially expressed with traditional analysis methods. Availability and implementation The source code is available at https://github.com/rajinder4489/FuSe. Data for APAP exposure are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) under accession numbers S-HECA143, S-HECA(158) and S-HECA139. Supplementary information Supplementary data are available at Bioinformatics online.

Disruption of persistent, stress-associated memories is relevant for treating posttraumatic stress disorder (PTSD) and related syndromes, which develop in a subset of individuals following a traumatic event. We previously developed a stress-enhanced fear learning (SEFL) paradigm in inbred mice that produces PTSD-like characteristics in a subset of mice, including persistently enhanced memory and heightened cFos in the basolateral amygdala complex (BLC) with retrieval of the remote (30-day-old) stress memory. Here, the contribution of BLC microRNAs (miRNAs) to stress-enhanced memory was investigated because of the molecular complexity they achieve through their ability to regulate multiple targets simultaneously. We performed small-RNA sequencing (smRNA-Seq) and quantitative proteomics on BLC tissue collected from mice 1 month after SEFL and identified persistently changed microRNAs, including mir-135b-5p, and proteins associated with PTSD-like heightened fear expression. Viral-mediated overexpression of mir-135b-5p in the BLC of stress-resilient animals enhanced remote fear memory expression and promoted spontaneous renewal 14 days after extinction. Conversely, inhibition of BLC mir-135b-5p in stress-susceptible animals had the opposite effect, promoting a resilient-like phenotype. mir-135b-5p is highly conserved across mammals and was detected in post mortem human amygdala, as well as human serum samples. The mir-135b passenger strand, mir-135b-3p, was significantly elevated in serum from PTSD military veterans, relative to combat-exposed control subjects. Thus, miR-135b-5p may be an important therapeutic target for dampening persistent, stress-enhanced memory and its passenger strand a potential biomarker for responsivity to a mir-135-based therapeutic.

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

Correction to: Arch Toxicol (2014) 88:2261–2287 https://doi.org/10.1007/s00204-014-1400-x

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.

Purpose Identify differentially expressed microRNAs (miRNAs) in aqueous humor (AH) and blood of primary open-angle glaucoma (POAG) patients by using small RNA sequencing. These may provide insight into POAG pathophysiology or serve as diagnostic biomarker. Methods AH and plasma of nine POAG patients and 10 cataract control patients were small RNA sequenced on Illumina NovaSeq 6000. Identification of gene transcripts targeted by differentially expressed miRNAs was done with miRWalk and MirPath. These targets were used for pathway analysis and Gene Ontology enrichment. Diagnostic potential was evaluated by receiver operating characteristics analysis. Results We identified 715 miRNAs in plasma and 62 miRNAs in AH. Plasma miRNA profile did not differ between POAG and control. In contrast, in AH, seven miRNAs were differentially expressed. Hsa-miR-30a-3p, hsa-miR-143-3p, hsa-miR-211-5p, and hsa-miR-221-3p were upregulated, whereas hsa-miR-92a-3p, hsa-miR-451a, and hsa-miR-486-5p were downregulated in POAG. Compared to previous studies, hsa-mir-143-3p, hsa-miR-211-5p, and hsa-miR-221-3p were reported previously, strengthening their involvement in POAG whereas hsa-miR-30a-3p, hsa-miR-92a-3p, and hsa-miR-486-5p are implicated in POAG for the first time. Identified gene transcripts were involved in several pathways, some implicated in glaucoma before (e.g., TGF-β and neurotrophin signaling), whereas others are new (e.g., prolactin and apelin signaling). In respect to diagnostics, AH concentration of hsa-mir-143-3p had an area under the curve (AUC) of 0.889. Combined with hsa-miR-221-3p, AUC improved to 0.96. Conclusions Small RNA sequencing identified seven differentially expressed miRNAs in AH of POAG patients. The differentially expressed miRNAs may be useful as POAG biomarkers or could become targets for new therapeutic strategies.

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.

Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene–gene and gene—DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers.

Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. Results Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. Conclusions DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.

Four complementary approaches were used to investigate acetaminophen overdose as a risk factor for Parkinson's disease (PD). Circulating microRNAs (miRNAs) serum profiles from acetaminophen-overdosed patients were compared with patients with terminal PD, revealing four shared miRNAs. Similarities were found among molecular structures of dopamine (DA), acetaminophen, and two known PD inducers indicating affinity for dopaminergic transport. Potential interactions between acetaminophen and the human DA transporter were confirmed by molecular docking modeling and binding free energy calculations. Thus, it is plausible that acetaminophen is taken up by the dopaminergic transport system into the substantia nigra (SN). A ChEMBL query identified proteins that are similarly targeted by DA and acetaminophen. Here, we highlight CYP3A4, present in the SN, a predominant metabolizer of acetaminophen into its toxic metabolite N-acetyl-p-benzoquinone imine and shown to be regulated in PD. Overall, based on our results, we hypothesize that overdosing of acetaminophen is a potential risk factor for parkinsonism.

One of the major complications that patients experience during pharmacological treatment is the occurrence of adverse drug reactions (ADRs). The most affected organs are the liver, kidney, heart and the gastrointestinal-immune system. In comparison to the other organs, less progress has been made on human-relevant prediction of drug-induced intestinal toxicity, evidencing current large data gaps. The most widely used drugs that are associated with intestinal damage include chemotherapeutics, such as 5-Fluorouracil or Tyrosine Kinase Inhibitors (TKIs), as well as non-steroidal anti-inflammatory drugs (NSAIDs). Chemotherapeutics are regarded as inducers of acute intestinal toxicity whereas NSAIDs are associated with chronic inflammation of the intestine. In view of the fact that only a few studies have been dedicated to studying cellular and genomic responses in relation to drug-induced intestinal ADRs, little is known about how intestinal toxicity develops after exposure to such drugs or which molecular mechanisms are involved. Therefore, new models and experiments are required to establish transcriptomic responses and alterations of molecular markers induced by different medicines. This review summarizes the available information about transcriptomic responses and biomarkers of toxicity induced by 5-FU, NSAIDS or TKIs in different experimental models. Future investigation should address the challenges in predicting intestinal toxicity induced by drugs and unveil specific gene expression profiles that can be applied in the development of safer drugs.

Cellular adaptation is important to cope with various stresses induced by altered environmental conditions. By controlling mRNA translation rates cells may adapt to stress to promote survival. Phosphorylation of eIF2 alpha at serine 51 is one of the pathways controlling mRNA translation. Here we investigated the role of phosphorylated eIF2 alpha during exposure to the environmental carcinogen benzo(a)pyrene (BaP). For our study we used mouse embryonic fibroblasts with a wild type eIF2 alpha (MEF WT) and mouse embryonic fibroblasts with an eIF2 alpha S51A knock-in mutation that cannot be phosphorylated. Here, we show that eIF2 alpha phosphorylation occurs in MEF WT cells but not in MEF S51A cells. Survival of MEF S51A cells is profoundly reduced compared to MEF WT controls after BaP exposure. No differences in DNA damage or ROS production were observed between MEF WT and S51A cells. Disruption of eIF2 alpha phosphorylation caused increased levels of apoptosis in response to BaP. This work demonstrates that eIF2 alpha phosphorylation is important for reducing apoptosis and promoting cell survival in order to adapt to BaP exposure.

Background: One of the potential mechanisms linking air pollution to health effects is through changes in DNA-methylation, which so far has mainly been analyzed globally or at candidate sites. Objective: We investigated the association of personal and ambient air pollution exposure measures with genome-wide DNA-methylation changes. Methods: We collected repeated 24-hour personal and ambient exposure measurements of particulate matter (PM2.5), PM2.5 absorbance, and ultrafine particles (UFP) and peripheral blood samples from a panel of 157 healthy non-smoking adults living in four European countries. We applied univariate mixed-effects models to investigate the association between air pollution and genome-wide DNA-methylation perturbations at single CpG (cytosine-guanine dinucleotide) sites and in Differentially Methylated Regions (DMRs). Subsequently, we explored the association of air pollution-induced methylation alterations with gene expression and serum immune marker levels measured in the same subjects. Results: Personal exposure to PM2.5 was associated with methylation changes at 13 CpG sites and 69 DMRs. Two of the 13 identified CpG sites (mapped to genes KNDC1 and FAM50B) were located within these DMRs. In addition, 42 DMRs were associated with personal PM2.5 absorbance exposure, 16 DMRs with personal exposure to UFP, 4 DMRs with ambient exposure to PM2.5, 16 DMRs with ambient PM2.5 absorbance exposure, and 15 DMRs with ambient UFP exposure. Correlation between methylation levels at identified CpG sites and gene expression and immune markers was generally moderate. Conclusion: This study provides evidence for an association between 24-hour exposure to air pollution and DNA-methylation at single sites and regional clusters of CpGs. Analysis of differentially methylated regions provides a promising avenue to further explore the subtle impact of environmental exposures on DNA-methylation. Keywords: DNA-methylation, Air pollution, Fine particles, Ultrafine particles, Immune markers

The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue. Electronic supplementary material The online version of this article (10.1007/s00204-020-02937-6) contains supplementary material, which is available to authorized users.

International audience; Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Alzheimer's disease (AD) is a neurodegenerative disease which is manifested by a progressive and irreversible decline of cognition, memory loss, a shortened attention span, and changes in personality. Aging and genetic pre-dispositions, particularly the presence of a specific form of apolipoprotein E (APOE), are main risk factors of sporadic AD; however, a large body of evidence has shown that multiple environmental factors, including exposure to toxic metals, increase the risk for late onset AD. Lead (Pb) and cadmium (Cd) are ubiquitous toxic metals with a wide range of applications resulting in global distribution in the environment and exposure of all living organisms on earth. In addition to being classified as carcinogenic (Cd) and possibly carcinogenic (Pb) to humans by the International Agency for Research on Cancer, both compounds disrupt metal homeostasis and can cause toxic responses at the cellular and organismal levels. Pb toxicity targets the central nervous system and evidence for that has emerged also for Cd. Recent epidemiological studies show that both metals possibly are etiological factors of multiple neurodegenerative diseases, including Alzheimer's disease (AD). To further explore the association between metal exposure and AD risk we applied whole transcriptome gene expression analysis in peripheral blood leukocytes (PBLs) from 632 subjects of the general population, taken from the EnviroGenomarkers project. We used linear mixed effect models to associate metal exposure to gene expression after adjustment for gender, age, BMI, smoking, and alcohol consumption. For Pb exposure only few associations were identified, including a downregulation of the human eukaryotic translation initiation factor 5 (eIF5). In contrast, Cd exposure, particularly in males, revealed a much stronger transcriptomic response, featuring multiple pathways related to pathomolecular mechanisms of AD, such as endocytosis, neutrophil degranulation, and Interleukin-7 signaling. A gender stratified analysis revealed that the Cd responses were male-specific and included a downregulation of the APOE gene in men. This exploratory study revealed novel hypothetical findings which might contribute to the understanding of the neurotoxic effects of chronic Pb and Cd exposure and possibly improve our knowledge on the molecular mechanisms linking metal exposure to AD risk.

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data., Using a network propagation approach with integrated multi-omic data, Selevsek et al. develop a reproducible workflow for identifying drug toxicity effects in cellular systems. This is demonstrated with the analysis of anthracycline cardiotoxicity in cardiac microtissues under the effect of multiple drugs.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer's disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD.Subsequent gene regulatory network-based analysis identified 3 candidate genes, that is, SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.(c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Patient: Female, 44-year-old Final Diagnosis: Acute liver failure Symptoms: APAP overdose Medication: Acetaminophen Clinical Procedure: — Specialty: Molecular Biology Objective: Unusual clinical course Background: Acetaminophen overdose is the most common cause of acute liver failure. Nevertheless, new biomarker approaches enabling early prediction of the outcome of the acetaminophen overdose are needed. Recently, using next-generation sequencing analysis of serum from human study participants we uncovered injury-specific signatures of circulating microRNAs (miRNAs) that represented underlying molecular mechanisms of toxicity. This case study is first to show the application of miRNA profiling to assess prognosis of acetaminophen poisoning. Case Report: The patient was admitted to the hospital following supra therapeutic acetaminophen ingestion. The patient showed elevated levels of biomarkers of hepatocellular injury alanine aminotransferase, aspartate transaminase, and glutamate dehydrogenase. Even though treatment with N-acetyl cysteine was initiated 24 hours post-ingestion, levels of alanine-aminotransferase and aspartate transaminase peaked at about 40 hours post ingestion of acetaminophen. We analyzed global circulating miRNA levels from 24 consecutive serum samples from this study participant covering the period from admission to time of death. Conclusions: The resulting global miRNA profiles were compared with profiles from study participants with non-lethal acetaminophen poisoning and healthy controls. At the admission, the miRNA profiles of both lethal and non-lethal acetaminophen poisoning showed induction of cellular stress and oxidative damage. Later, the miRNA profiles of the lethal poisoning featured fibrosis and coagulation pathways while profiles of non-lethal cases resembled those of healthy study participants. Although additional confirmatory studies are needed, our case study is first to indicate that global miRNA profiles to be used as liquid biopsies have potential to facilitate the assessment of acetaminophen poisoning.

Within Toxicology, Toxicogenomics stands out as a unique research field aiming at the investigation of molecular alterations induced by chemical exposure. Toxicogenomics comprises a wide range of technologies developed to measure and quantify the '-omes (transcriptome, (epi)genome, proteome and metalobome), offering a human-based approach in contrast to traditional animal-based toxicity testing. With the growing acceptance and continuous improvements in high-throughput technologies, we observed a fast increase in the generation of ‘omics outputs. As a result, Toxicogenomics entered a new, challenging era facing the characteristic 4 Vs of Big Data: volume, velocity, variety and veracity. This chapter addresses these challenges by focusing on computational methods and Toxicoinformatics in the scope of Big ‘omics Data. First, we provide an overview of current technologies and the steps involved in storage, pre-processing and integration of high-throughput datasets, describing databases, standard pipelines and routinely used tools. We show how data mining, pattern recognition and mechanistic/pathway analyses contribute to elucidate mechanisms of adverse effects to build knowledge in Systems Toxicology. Finally, we present the recent progress in tackling current computational and biological limitations. Throughout the chapter, we also provide relevant examples of successful applications of Toxicoinformatics in predicting toxicity in the Big Data era.

We live in an era of 'big data', where the volume, velocity, and variety of the data being generated is increasingly influencing the way toxicological sciences are practiced. With this in mind, a workgroup was formed for the 2017 International Workshops on Genotoxicity Testing (IWGT) to consider the use of high information content data in genetic toxicology assessments. Presentations were given on adductomics, global transcriptional profiling, error-reduced single-molecule sequencing, and cellular phenotype-based assays, which were identified as methodologies that are relevant to present-day genetic toxicology assessments. Presenters and workgroup members discussed the state of the science for these methodologies, their potential use in genetic toxicology, current limitations, and the future work necessary to advance their utility and application. The session culminated with audience-assisted SWOT (strength, weakness, opportunities, and threats) analyses. The summary report described herein is structured similarly. A major conclusion of the workgroup is that while conventional regulatory genetic toxicology testing has served the public well over the last several decades, it does not provide the throughput that has become necessary in modern times, and it does not generate the mechanistic information that risk assessments ideally take into consideration. The high information content assay platforms that were discussed in this session, as well as others under development, have the potential to address aspect(s) of these issues and to meet new expectations in the field of genetic toxicology.

We performed a multiple ‘omics study by integrating data on epigenomic, transcriptomic, and proteomic perturbations associated with mitochondrial dysfunction in primary human hepatocytes caused by the liver toxicant valproic acid (VPA), to deeper understand downstream events following epigenetic alterations in the mitochondrial genome. Furthermore, we investigated persistence of cross-omics changes after terminating drug treatment. Upon transient methylation changes of mitochondrial genes during VPA-treatment, increasing complexities of gene-interaction networks across time were demonstrated, which normalized during washout. Furthermore, co-expression between genes and their corresponding proteins increased across time. Additionally, in relation to persistently decreased ATP production, we observed decreased expression of mitochondrial complex I and III–V genes. Persistent transcripts and proteins were related to citric acid cycle and β-oxidation. In particular, we identified a potential novel mitochondrial-nuclear signaling axis, MT-CO2–FN1–MYC–CPT1. In summary, this cross-omics study revealed dynamic responses of the mitochondrial epigenome to an impulse toxicant challenge resulting in persistent mitochondrial dysfunctioning. Moreover, this approach allowed for discriminating between the toxic effect of VPA and adaptation., Toxicology Letters, 289, ISSN:0378-4274, ISSN:1879-3169

Purpose: The study assessed whether diet and adherence to cancer prevention guidelines during pregnancy were associated with micronucleus (MN) frequency in mothers and newborns. MN is biomarkers of early genetic effects that have been associated with cancer risk in adults. Methods: A total of 188 mothers and 200 newborns from the Rhea cohort (Greece) were included in the study. At early-mid pregnancy, we conducted personal interviews and a validated food frequency questionnaire was completed. With this information, we constructed a score reflecting adherence to the World Cancer Research Fund/American Institute for Cancer Research cancer prevention guidelines on diet, physical activity and body fatness. At delivery, maternal and/or cord blood was collected to measure DNA and hemoglobin adducts of dietary origin and frequencies of MN in binucleated and mononucleated T lymphocytes (MNBN and MNMONO). Results: In mothers, higher levels of red meat consumption were associated with increased MNBN frequency [2nd tertile IRR = 1.34 (1.00, 1.80), 3rd tertile IRR = 1.33 (0.96, 1.85)] and MNMONO frequency [2nd tertile IRR = 1.53 (0.84, 2.77), 3rd tertile IRR = 2.69 (1.44, 5.05)]. The opposite trend was observed for MNBN in newborns [2nd tertile IRR = 0.64 (0.44, 0.94), 3rd tertile IRR = 0.68 (0.46, 1.01)], and no association was observed with MNMONO. Increased MN frequency in pregnant women with high red meat consumption is consistent with previous knowledge. Conclusions: Our results also suggest exposure to genotoxics during pregnancy might affect differently mothers and newborns. The predictive value of MN as biomarker for childhood cancer, rather than adulthood, remains unclear. With few exceptions, the association between maternal carcinogenic exposures during pregnancy and childhood cancer or early biologic effect biomarkers remains poorly understood. The Rhea cohort was funded by the following European projects NewGeneris (FP-6-FOOD-CT-2005-016320), ESCAPE (FP7-2007-211250), HiWATE (FP-6-FOOD-CT-2006-036224), Envirogenomarkers (FP7-2008-ENV-1.2.1.4), CHICOS (FP7-2009-GA 241604), and ENRIECO (FP7-2008-GA 226285). COG holds a Sara Borrell postdoctoral fellowship awarded from the Carlos III National Institute of Health (CD13/00072). MP held a Juan de la Cierva postdoctoral fellowship awarded from the Spanish Ministry of Ministry of Economy and Competitiveness (JCI-2011-09479). DFM received support from the Italian Ministry of Health, 5x1000 Grant-2011.

Valproic acid (VPA) is a very potent anti-cancer and neuro-protective drug probably by its HDAC inhibiting properties, which may cause steatosis in the liver. The present study investigates the effect of repetitive VPA treatment of primary human hepatocytes (PHH) on whole genome gene expression-, DNA methylation-, and miRNA changes, using microarrays and integrated data analyses. PHH were exposed to a non-cytotoxic dose of VPA for 5 days daily which induced lipid accumulation. Part of the PHH was left untreated for 3 days for studying the persistence of 'omics' changes. VPA treatment appeared to inhibit the expression of the transcription factors HNF1A and ONECUTI. HNF1A interacted with 41 differentially expressed genes of which 12 were also differentially methylated. None of the genes present in this network were regulated by a DE-miR. The subnetwork of ONECUTI consisted of 44 differentially expressed genes of which 15 were differentially methylated, and 3 were regulated by a DE-miR. A number of genes in the networks are involved in fatty acid metabolism, and may contribute to the development of steatosis by increasing oxidative stress thereby causing mitochondrial dysfunction, and by shifting metabolism of VPA towards beta-oxidation due to reduced glucuronidation. Part of the changes remained persistent after washing out of VPA, like PMAIP1 which is associated with cellular stress in liver of patients with NASH. The MMP2 gene showed the highest number of interactions with other persistently expressed genes, among which LCN2 which is a key modulator of lipid homeostasis. Furthermore, VPA modulated the expression and DNA methylation level of nuclear receptors and their target genes involved in the adverse outcome pathway of steatosis, thereby expanding our current knowledge of the pathway. In particular, VPA modulated PPARy, and PPARa, AHR and CD36 on both the gene expression and the DNA methylation level, thereby inhibiting beta-oxidation and increasing uptake of fatty acid into the hepatocytes, respectively. Overall, our integrative data analyses identified novel genes modulated by VPA, which provide more insight into the mechanisms of repeated dose toxicity of VPA, leading to steatosis.

There is an increased need to develop novel alternative approaches to the two-year rodent bioassay for the carcinogenicity assessment of substances where the rodent bioassay is still a basic requirement, as well as for those substances where animal use is banned or limited or where information gaps are identified within legislation. The current progress in this area was addressed in a EURL ECVAM- ESTIV workshop held in October 2016, in Juan les Pins. A number of initiatives were presented and discussed, including data-driven, technology-driven and pathway-driven approaches. Despite a seemingly diverse range of strategic developments, commonalities are emerging. For example, providing insight into carcinogenicity mechanisms is becoming an increasingly appreciated aspect of hazard assessment and is suggested to be the best strategy to drive new developments. Thus, now more than ever, there is a need to combine and focus efforts towards the integration of available information between sectors. Such cross-sectorial harmonisation will aid in building confidence in new approach methods leading to increased implementation and thus a decreased necessity for the two-year rodent bioassay., Highlights • Need to develop novel alternative approaches to the two-year rodent cancer bioassay • Insight into carcinogenicity mechanisms is essential to drive new developments. • Direct efforts towards the integration of all available information, also across endpoints. • Cross-sector harmonisation to build confidence in new approach methods

The mechanisms of idiosyncratic drug-induced hepatotoxicity remain largely unclear. It has demonstrated that the drug idiosyncrasy is potentiated in the context of inflammation and intracellular ceramides may play a role in this process.To study the mechanisms, HepG2 cells were co-treated with high and low doses of three idiosyncratic (I) and three non-idiosyncratic (N) compounds, with (I+ and N+) or without (I- and N-) a cytokine mix. Microarray, lipidomics and flow cytometry were performed to investigate the genome-wide expression patterns, the intracellular ceramide levels and the induction of apoptosis.We found that all I + treatments significantly influenced the immune response- and response to stimulus-associated gene ontology (GO) terms, but the induction of apoptotic pathways, which was confirmed by flow cytometry, only appeared to be induced after the high-dose treatment. The ceramide signaling-, ER stress-, NF-kB activation- and mitochondrial activity-related pathways were biologically involved in apoptosis induced by the high-dose I+. Additionally, genes participating in ceramide metabolism were significantly altered resulting in a measurable increase in ceramide levels. The increases in ceramide concentrations may induce ER stress and activate the JNK pathway by affecting the expression of the related genes, and eventually trigger the mitochondria independent apoptosis in hepatocytes. Overall, our study provides a potential mechanism to explain the role of inflammation in idiosyncratic drug reactions. (C) 2017 Elsevier Inc. All rights reserved.

The inflammatory stress has been associated with an increase in susceptibility to idiosyncratic drug-induced liver injury (DILI). However, the molecular mechanisms of this inflammation-associated idiosyncratic drug hepatotoxicity remain unknown. We exposed HepG2 cells with high and low doses of three idiosyncratic (I) and three non-idiosyncratic (N) compounds, in the presence (I+ and N+) or absence (I- and N-) of a cytokine mix for 6, 12 and 24 h. To investigate the genome-wide expression patterns, microarray was performed using the Agilent 4×44K Whole Human Genome chips. The data presented in this DIB include the expression of genes participating in the ceramide metabolism, ER stress, apoptosis and cell survival pathways. The functions of these genes were illustrated in our associated article (Jiang et al., 2017) [1]. Raw and normalized gene expression data are available through NCBI GEO (accession number GSE102006).

Transcription factors (TFs) are important modulators of the inducible portion of the transcriptome, and therefore relevant in the context of exposure to exogenous compounds. Current approaches to predict the activity of TFs in biological systems are usually restricted to a few entities at a time due to low-throughput techniques targeting a limited fraction of annotated human TFs. Therefore, high-throughput alternatives may help to identify new targets of mechanistic and predictive value in toxicological investigations. In this study, we inferred the activity multiple TFs using publicly available microarray data from primary human hepatocytes exposed to hundreds of chemicals and evaluated these molecular profiles using multiple correspondence analysis. Our results demonstrate that the lowest dose and latest exposure time (24 h) in a subset of chemicals generates a signature indicative of carcinogenicity possibly due to DNA-damaging properties. Furthermore, profiles from the earliest exposure time (2 h) and highest dose creates clusters of chemicals implicated in the development of diverse forms of drug-induced liver injury (DILI). Both approaches yielded a number of TFs with similar activity across groups of chemicals, including TFs known in toxicological responses such as AhR, NFE2L2 (Nrf2), NF-kappa B and PPARG. FOXM1, IRF1 and E2F4 were some of the TFs identified that may be relevant in genotoxic carcinogenesis. SMADs (SMAD1, SMAD2, SMAD5) and KLF5 were identified as some of potentially new TFs whose inferred activities were linked to acute and progressive outcomes in DILI. In conclusion this study offers a novel mechanistic approach targeting TF activity during chemical exposure.

Valproic acid (VPA) is one of the most widely prescribed antiepileptic drugs in the world. Despite its pharmacological importance, it may cause liver toxicity and steatosis through mitochondrial dysfunction. The aim of this study is to further investigate VPA-induced mechanisms of steatosis by analyzing changes in patterns of methylation in nuclear DNA (nDNA) and mitochondrial DNA (mtDNA). Therefore, primary human hepatocytes (PHHs) were exposed to an incubation concentration of VPA that was shown to cause steatosis without inducing overt cytotoxicity. VPA was administered daily for 5 days, and this was followed by a 3 day washout (WO). Methylated DNA regions (DMRs) were identified by using the methylated DNA immunoprecipitation-sequencing (MeDIP-seq) method. The nDNA DMRs after VPA treatment could indeed be classified into oxidative stress- and steatosis-related pathways. In particular, networks of the steatosis-related gene EP300 provided novel insight into the mechanisms of toxicity induced by VPA treatment. Furthermore, we suggest that VPA induces a crosstalk between nDNA hypermethylation and mtDNA hypomethylation that plays a role in oxidative stress and steatosis development. Although most VPA-induced methylation patterns appeared reversible upon terminating VPA treatment, 31 nDNA DMRs (including 5 zinc finger protein genes) remained persistent after the WO period. Overall, we have shown that MeDIP-seq analysis is highly informative in disclosing novel mechanisms of VPA-induced toxicity in PHHs. Our results thus provide a prototype for the novel generation of interesting methylation biomarkers for repeated dose liver toxicity in vitro.

BACKGROUND & AIMS: Vitamin D deficiency is common among pregnant women and may be associated with several adverse health outcomes including cancer. Micronuclei frequency is a biomarker of early genetic effects and has been used to examine the association between genotoxic exposures and cancer. We examined maternal vitamin D levels during pregnancy in associations with micronuclei frequency in maternal blood and in cord blood. METHODS: 173 mothers and 171 newborns born between 2007 and 2008 in Heraklion (Crete, Greece) were included in the study. Between 14th and 18th weeks of gestation we collected information on maternal diet using food frequency questionnaires (FFQs). We measured maternal serum concentrations of 25-hydroxyvitamin D [25(OH)D] between the first and second trimester of pregnancy. We estimated dietary vitamin D intake using information from FFQ. After delivery we collected cord blood and maternal peripheral blood. We used the cytokinesis-block micronucleus (CBMN) assay to assess the frequencies of micronucleated cells in binucleated T lymphocytes (MNBN). RESULTS: Maternal insufficient serum levels of 25(OH)D (

Unravelling gene regulatory networks (GRNs) influenced by chemicals is a major challenge in systems toxicology. Because toxicant-induced GRNs evolve over time and dose, the analysis of global gene expression data measured at multiple time points and doses will provide insight in the adverse effects of compounds. Therefore, there is a need for mathematical methods for GRN identification from time-over-dose-dependent data. One of the current approaches for GRN inference is Time Series Network Identification (TSNI). TSNI is based on ordinary differential equations (ODE), describing the time evolution of the expression of each gene, which is assumed to be dependent on the expression of other genes and an external perturbation (i.e. chemical exposure). Here, we present Dose-Time Network Identification (DTNI), a method extending TSNI by including ODE describing how the expression of each gene evolves with dose, which is supposed to depend on the expression of other genes and the exposure time. We also adapted TSNI in order to enable inclusion of time-over-dose-dependent data from multiple compounds. Here, we show that DTNI outperforms TSNI in inferring a toxicant-induced GRN. Moreover, we show that DTNI is a suitable method to infer a GRN dose- and time-dependently induced by a group of compounds influencing a common biological process. Applying DTNI on experimental data from TG-GATEs, we demonstrate that DTNI provides in-depth information on the mode of action of compounds, in particular key events and potential molecular initiating events. Furthermore, DTNI also discloses several unknown interactions which have to be verified experimentally. Electronic supplementary material The online version of this article (doi:10.1007/s00204-016-1922-5) contains supplementary material, which is available to authorized users.

The exposome encompasses an individual's exposure to exogenous chemicals, as well as endogenous chemicals that are produced or altered in response to external stressors. While the exposome concept has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. Here, we explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept that structures and organizes the sequence of biological events from an initial molecular interaction of a chemical with a biological target to an adverse outcome. Complementing exposome research with the AOP concept may facilitate a mechanistic understanding of stress-induced adverse effects, examine the relative contributions from various components of the exposome, determine the primary risk drivers in complex mixtures, and promote an integrative assessment of chemical risks for both human and environmental health. Keywords: Exposome, AOP, Systems toxicology, Systems biology, Systems chemistry, Risk assessment

Cyclosporine A (CsA) is an undecapeptide with strong immunosuppressant activities and is used a lot after organ transplantation. Furthermore, it may induce cholestasis in the liver. In general, the drug-induced cholestasis (DIC) pathway includes genes involved in the uptake, synthesis, conjugation, and secretion of bile acids. However, whether CsA-induced changes in the cholestasis pathway in vitro are persistent for repeated dose toxicity has not yet been investigated. To explore this, primary human hepatocytes (PHH) were exposed to a subcytotoxic dose of 30 itM CsA daily for 3 and 5 days. To investigate the persistence of induced changes upon terminating CsA exposure after 5 days, a subset of PHH was subjected to a washout period (WO-period) of 3 days. Multiple-omics analyses, comprising whole genome analysis of DNA methylation, gene expression, and microRNA expression, were performed. The CsA-treatment resulted after 3 and 5 days, respectively, in 476 and 20 differentially methylated genes (DMGs), 1353 and 1481 differentially expressed genes (DEGs), and in 22 and 29 differentially expressed microRNAs (DE-miRs). Cholestasis-related pathways appeared induced during CsA-treatment. Interestingly, 828 persistent DEGs and 6 persistent DE-miRs but no persistent DMGs were found after the WO-period. These persistent DEGs and DE-miRs showed concordance for 22 genes. Furthermore, 29 persistent DEGs changed into the same direction as observed in livers from cholestasis patients. None of those 29 DEGs which among others relate to oxidative stress and lipid metabolism are yet present in the DIC pathway or cholestasis adverse outcome pathway (AOP) thus presenting novel findings. In summary, we have demonstrated for the first time a persistent impact of repeated dose administration of CsA on genes and microRNAs related to DIC in the gold standard human liver in vitro model with PHH.

Circulating microRNAs (cimiRNAs) are a class of non-encoding RNAs found in bodily fluids such as blood, cerebrospinal fluid (CSF) and tears. CimiRNAs have been implicated as promising biomarkers for central nervous system (CNS) disorders because they are actively secreted as messengers and are profoundly involved in fine-tuning of developmental and differentiation processes. Furthermore, they are attractive biomarkers because they are extremely stable, tissue enriched and can be determined in a quantitative manner. This review aims to provide a comprehensive assessment on the current progress regarding the potential value of cimiRNAs as CNS biomarkers. Within this framework five CNS disorders are explored which share a common pathological hallmark namely cognitive impairment. The CNS disorders include Major depression disorder (MDD), Bipolar disorder (BD), Schizophrenia (SZ), Alzheimer's disease (AD) and Parkinson disease (PD). The similarities and differences between altered cimiRNAs in the different disorders are described. The miR-29 family, miR-34a-5p and miR-132-3p are discussed as common dysregulated cimiRNAs found in the CNS disorders. Furthermore, it is shown that the type of bodily fluid used for measuring cimiRNAs is important as inconsistencies in cimiRNAs expression directions are found when comparing CSF, blood cell-free and blood cell-bound samples.