CODA: a combo-Seq data analysis workflow.

The analysis of the combined mRNA and miRNA content of a biological sample can be of interest for answering several research questions, like biomarkers discovery, or mRNA-miRNA interactions. However, the process is costly and time-consuming, separate libraries need to be prepared and sequenced on different flowcells. Combo-Seq is a library prep kit that allows us to prepare combined mRNA-miRNA libraries starting from very low total RNA. To date, no dedicated bioinformatics method exists for the processing of Combo-Seq data. In this paper, we describe CODA (Combo-seq Data Analysis), a workflow specifically developed for the processing of Combo-Seq data that employs existing free-to-use tools. We compare CODA with exceRpt, the pipeline suggested by the kit manufacturer for this purpose. We also evaluate how Combo-Seq libraries analysed with CODA perform compared with conventional poly(A) and small RNA libraries prepared from the same samples. We show that using CODA more successfully trimmed reads are recovered compared with exceRpt, and the difference is more dramatic with short sequencing reads. We demonstrate how Combo-Seq identifies as many genes and fewer miRNAs compared to the standard libraries, and how miRNA validation favours conventional small RNA libraries over Combo-Seq. The CODA code is available at https://github.com/marta-nazzari/CODA.

IsoTools: a flexible workflow for long-read transcriptome sequencing analysis.

MOTIVATION: Long-read transcriptome sequencing (LRTS) has the potential to enhance our understanding of alternative splicing and the complexity of this process requires the use of versatile computational tools, with the ability to accommodate various stages of the workflow with maximum flexibility. RESULTS: We introduce IsoTools, a Python-based LRTS analysis framework that offers a wide range of functionality for transcriptome reconstruction and quantification of transcripts. Furthermore, we integrate a graph-based method for identifying alternative splicing events and a statistical approach based on the beta-binomial distribution for detecting differential events. To demonstrate the effectiveness of our methods, we applied IsoTools to PacBio LRTS data of human hepatocytes treated with the histone deacetylase inhibitor valproic acid. Our results indicate that LRTS can provide valuable insights into alternative splicing, particularly in terms of complex and differential splicing patterns, in comparison to short-read RNA-seq. AVAILABILITY AND IMPLEMENTATION: IsoTools is available on GitHub and PyPI, and its documentation, including tutorials, CLI, and API references, can be found at https://isotools.readthedocs.io/.

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.

Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity.

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.

FuSe: a tool to move RNA-Seq analyses from chromosomal/gene loci to functional grouping of mRNA transcripts.

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.

R-ODAF: Omics data analysis framework for regulatory application.

Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.

Quantitative Prediction of Steatosis in Patients with Non-Alcoholic Fatty Liver by Means of Hepatic MicroRNAs Present in Serum and Correlating with Hepatic Fat.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent form of chronic liver disease worldwide, but a reliable non-invasive method to quantify liver steatosis in primary healthcare is not available. Circulating microRNAs have been proposed as biomarkers of severe/advanced NAFLD (steatohepatitis and fibrosis). However, the use of circulating miRNAs to quantitatively assess the % of liver fat in suspected NAFLD patients has not been investigated. We performed global miRNA sequencing in two sets of samples: human livers from organ donors (n = 20), and human sera from biopsy-proven NAFLD patients (n = 23), both with a wide range of steatosis quantified in their liver biopsies. Partial least squares (PLS) regression combined with recursive feature elimination (RFE) was used to select miRNAs associated with steatosis. Moreover, regression models with only 2 or 3 miRNAs, with high biological relevance, were built. Comprehensive microRNA sequencing of liver and serum samples resulted in two sets of abundantly expressed miRNAs (418 in liver and 351 in serum). Pearson correlation analyses indicated that 18% of miRNAs in liver and 14.5% in serum were significantly associated with the amount of liver fat. PLS-RFE models demonstrated that 50 was the number of miRNAs providing the lowest error in both liver and serum models predicting steatosis. Comparison of the two miRNA subsets showed 19 coincident miRNAs that were ranked according to biological significance (guide/passenger strand, relative abundance in liver and serum, number of predicted lipid metabolism target genes, correlation significance, etc.). Among them, miR-10a-5p, miR-98-5p, miR-19a-3p, miR-30e-5p, miR-32-5p and miR-145-5p showed the highest biological relevance. PLS regression models with serum levels of 2−3 of these miRNAs predicted the % of liver fat with errors <5%.

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.

Comparing in vitro human liver models to in vivo human liver using RNA-Seq.

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.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

RATEmiRs: the rat atlas of tissue-specific and enriched miRNAs for discerning baseline expression exclusivity of candidate biomarkers.

MicroRNAs (miRNAs) are small RNAs that regulate mRNA expression and have been targeted as biomarkers of organ damage and disease. To explore the utility of miRNAs to assess injury to specific tissues, a tissue atlas of miRNA abundance was constructed. The Rat Atlas of Tissue-specific and Enriched miRNAs (RATEmiRs) catalogues miRNA sequencing data from 21 and 23 tissues in male and female Sprague-Dawley rats, respectively. RATEmiRs identifies tissue-enriched (TE), tissue-specific (TS), or organ-specific (OS) miRNAs via comparisons of one or more tissue or organ vs others. We provide a brief overview of RATEmiRs and present how to use it to detect miRNA expression abundance of candidate biomarkers as well as to compare the expression of miRNAs between rat and human. The database is available at https://www.niehs.nih.gov/ratemirs/.

Towards the development of an omics data analysis framework.

The use of various omics techniques for scientific research is increasing. While toxicogenomics studies have already produced substantial data on diverse omics platforms, to date there has been little routine application in regulatory toxicology. This is despite the promises and excitement of 20 years ago when it was widely speculated that omics methods would reduce or even replace animal use and allow a much enhanced understanding of hazard and susceptibility. One of the reasons for this has been a trepidation about relying on the produced data. It has been argued that omics outputs might not be sufficiently reliable for regulatory application because the techniques, bioinformatics and interpretation can vary. For these reasons the robustness of the obtained results is questioned. This reticence to trust omics data is further magnified by the lack of internationally agreed upon guidelines and protocols for both the generation and processing of omics data. One way forward would be to reach a consensus on an omics data analysis framework (ODAF) for regulatory application (R-ODAF) based on rigorous data analysis. The authors of this article are involved in a Long-Range Research Initiative (LRI) project that will propose an R-ODAF for transcriptomics data. The R-ODAF will then be reviewed and evaluated by the main regulatory agencies and consensus forums such as the Organization for Economic Co-operation and Development (OECD). This work builds on The MicroArray Quality Control work that developed standards for the generation of data from microarrays and sequencing but not for reporting or analysis.

SCREENED: A Multistage Model of Thyroid Gland Function for Screening Endocrine-Disrupting Chemicals in a Biologically Sex-Specific Manner.

Endocrine disruptors (EDs) are chemicals that contribute to health problems by interfering with the physiological production and target effects of hormones, with proven impacts on a number of endocrine systems including the thyroid gland. Exposure to EDs has also been associated with impairment of the reproductive system and incidence in occurrence of obesity, type 2 diabetes, and cardiovascular diseases during ageing. SCREENED aims at developing in vitro assays based on rodent and human thyroid cells organized in three different three-dimensional (3D) constructs. Due to different levels of anatomical complexity, each of these constructs has the potential to increasingly mimic the structure and function of the native thyroid gland, ultimately achieving relevant features of its 3D organization including: 1) a 3D organoid based on stem cell-derived thyrocytes, 2) a 3D organoid based on a decellularized thyroid lobe stromal matrix repopulated with stem cell-derived thyrocytes, and 3) a bioprinted organoid based on stem cell-derived thyrocytes able to mimic the spatial and geometrical features of a native thyroid gland. These 3D constructs will be hosted in a modular microbioreactor equipped with innovative sensing technology and enabling precise control of cell culture conditions. New superparamagnetic biocompatible and biomimetic particles will be used to produce "magnetic cells" to support precise spatiotemporal homing of the cells in the 3D decellularized and bioprinted constructs. Finally, these 3D constructs will be used to screen the effect of EDs on the thyroid function in a unique biological sex-specific manner. Their performance will be assessed individually, in comparison with each other, and against in vivo studies. The resulting 3D assays are expected to yield responses to low doses of different EDs, with sensitivity and specificity higher than that of classical 2D in vitro assays and animal models. Supporting the "Adverse Outcome Pathway" concept, proteogenomic analysis and biological computational modelling of the underlying mode of action of the tested EDs will be pursued to gain a mechanistic understanding of the chain of events from exposure to adverse toxic effects on thyroid function. For future uptake, SCREENED will engage discussion with relevant stakeholder groups, including regulatory bodies and industry, to ensure that the assays will fit with purposes of ED safety assessment. In this project review, we will briefly discuss the current state of the art in cellular assays of EDs and how our project aims at further advancing the field of cellular assays for EDs interfering with the thyroid gland.

RATEmiRs: the rat atlas of tissue-specific and enriched miRNAs database.

BACKGROUND: MicroRNAs (miRNAs) regulate gene expression and have been targeted as indicators of environmental/toxicologic stressors. Using the data from our deep sequencing of miRNAs in an extensive sampling of rat tissues, we developed a database called RATEmiRs for the Rat Atlas of Tissue-specific and Enriched miRNAs to allow users to dynamically determine mature-, iso- and pre-miR expression abundance, enrichment and specificity in rat tissues and organs. RESULTS: Illumina sequencing count data from mapped reads and meta data from the miRNA body atlas consisting of 21 and 23 tissues (14 organs) of toxicologic interest from 12 to 13 week old male and female Sprague Dawley rats respectively, were managed in a relational database with a user-friendly query interface. Data-driven pipelines are available to tailor the identification of tissue-enriched (TE) and tissue-specific (TS) miRNAs. Data-driven organ-specific (OS) pipelines reveal miRNAs that are expressed predominately in a given organ. A user-driven approach is also available to assess the tissue expression of user-specified miRNAs. Using one tissue vs other tissues and tissue(s) of an organ vs other organs, we illustrate the utility of RATEmiRs to facilitate the identification of candidate miRNAs. As a use case example, RATEmiRs revealed two TS miRNAs in the liver: rno-miR-122-3p and rno-miR-122-5p. When liver is compared to just the brain tissues for example, rno-miR-192-5p, rno-miR-193-3p, rno-miR-203b-3p, rno-miR-3559-5p, rno-miR-802-3p and rno-miR-802-5p are also detected as abundantly expressed in liver. As another example, 55 miRNAs from the RATEmiRs query of ileum vs brain tissues overlapped with miRNAs identified from the same comparison of tissues in an independent, publicly available dataset of 10 week old male rat microarray data suggesting that these miRNAs are likely not age-specific, platform-specific nor pipeline-dependent. Lastly, we identified 10 miRNAs that have conserved tissue/organ-specific expression between the rat and human species. CONCLUSIONS: RATEmiRs provides a new platform for identification of TE, TS and OS miRNAs in a broad array of rat tissues. RATEmiRs is available at: https://www.niehs.nih.gov/ratemirs.

Nuclear and Mitochondrial DNA Methylation Patterns Induced by Valproic Acid in Human Hepatocytes.

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.

High-throughput data integration of RNA-miRNA-circRNA reveals novel insights into mechanisms of benzo[a]pyrene-induced carcinogenicity.

The chain of events leading from a toxic compound exposure to carcinogenicity is still barely understood. With the emergence of high-throughput sequencing, it is now possible to discover many different biological components simultaneously. Using two different RNA libraries, we sequenced the complete transcriptome of human HepG2 liver cells exposed to benzo[a]pyrene, a potent human carcinogen, across six time points. Data were integrated in order to reveal novel complex chemical-gene interactions. Notably, we hypothesized that the inhibition of MGMT, a DNA damage response enzyme, by the over-expressed miR-181a-1_3p induced by BaP, may lead to liver cancer over time.

The Rat microRNA body atlas; Evaluation of the microRNA content of rat organs through deep sequencing and characterization of pancreas enriched miRNAs as biomarkers of pancreatic toxicity in the rat and dog.

BACKGROUND: MicroRNAs (miRNA) are ~19-25 nucleotide long RNA molecules that fine tune gene expression through the inhibition of translation or degradation of the mRNA through incorporation into the RNA induced silencing complex (RISC). MicroRNAs are stable in the serum and plasma, are detectable in a wide variety of body fluids, are conserved across veterinary species and humans and are expressed in a tissue specific manner. They can be detected at low concentrations in circulation in animals and humans, generating interest in the utilization of miRNAs as serum and/or plasma based biomarkers of tissue injury. MicroRNA tissue profiling in rodents has been published, but sample an insufficient number of organs of toxicologic interest using microarray or qPCR technologies for miRNA detection. Here we impart an improved rat microRNA body atlas consisting of 21 and 23 tissues of toxicologic interest from male and female Sprague Dawley rats respectively, using Illumina miRNA sequencing. Several of the authors created a dog miRNA body atlas and we collaborated to test miRNAs conserved in rat and dog pancreas in caerulein toxicity studies utilizing both species. RESULTS: A rich data set is presented that more robustly defines the tissue specificity and enrichment profiles of previously published and undiscovered rat miRNAs. We generated 1,927 sequences that mapped to mature miRNAs in rat, mouse and human from miRBase and discovered an additional 1,162 rat miRNAs as compared to the current number of rat miRNAs in miRBase version 21. Tissue specific and enriched miRNAs were identified and a subset of these miRNAs were validated by qPCR for tissue specificity or enrichment. As an example of the power of this approach, we have conducted rat and dog pancreas toxicity studies and examined the levels of some tissue specific and enriched miRNAs conserved between rat and dog in the serum of each species. The studies demonstrate that conserved tissue specific/enriched miRs-216a-5p, 375-3p, 148a-3p, 216b-5p and 141-3p are candidate biomarkers of pancreatic injury in the rat and dog. CONCLUSIONS: A microRNA body atlas for rat and dog was useful in identifying new candidate miRNA biomarkers of organ toxicity in 2 toxicologically relevant species.

diXa: a data infrastructure for chemical safety assessment.

MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep sequencing reveals abundant noncanonical retroviral microRNAs in B-cell leukemia/lymphoma.

Viral tumor models have significantly contributed to our understanding of oncogenic mechanisms. How transforming delta-retroviruses induce malignancy, however, remains poorly understood, especially as viral mRNA/protein are tightly silenced in tumors. Here, using deep sequencing of broad windows of small RNA sizes in the bovine leukemia virus ovine model of leukemia/lymphoma, we provide in vivo evidence of the production of noncanonical RNA polymerase III (Pol III)-transcribed viral microRNAs in leukemic B cells in the complete absence of Pol II 5'-LTR-driven transcriptional activity. Processed from a cluster of five independent self-sufficient transcriptional units located in a proviral region dispensable for in vivo infectivity, bovine leukemia virus microRNAs represent ∼40% of all microRNAs in both experimental and natural malignancy. They are subject to strong purifying selection and associate with Argonautes, consistent with a critical function in silencing of important cellular and/or viral targets. Bovine leukemia virus microRNAs are strongly expressed in preleukemic and malignant cells in which structural and regulatory gene expression is repressed, suggesting a key role in tumor onset and progression. Understanding how Pol III-dependent microRNAs subvert cellular and viral pathways will contribute to deciphering the intricate perturbations that underlie malignant transformation.

A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep.

Texel sheep are renowned for their exceptional meatiness. To identify the genes underlying this economically important feature, we performed a whole-genome scan in a Romanov x Texel F2 population. We mapped a quantitative trait locus with a major effect on muscle mass to chromosome 2 and subsequently fine-mapped it to a chromosome interval encompassing the myostatin (GDF8) gene. We herein demonstrate that the GDF8 allele of Texel sheep is characterized by a G to A transition in the 3' UTR that creates a target site for mir1 and mir206, microRNAs (miRNAs) that are highly expressed in skeletal muscle. This causes translational inhibition of the myostatin gene and hence contributes to the muscular hypertrophy of Texel sheep. Analysis of SNP databases for humans and mice demonstrates that mutations creating or destroying putative miRNA target sites are abundant and might be important effectors of phenotypic variation.