This suggests that even more non-coding DEGs may be detected with an alternative library prep kit. The tested hepatotoxicants mainly impacted the expression of lncRNAs, pseudogenes and miRNAs and these have a potential for use as toxicity biomarkers and may offer additional mechanistic insight in some cases Esteller, ; Ling et al.
These non-coding RNAs are generally less stable and are expressed at lower levels compared to the protein coding mRNAs. Moreover, lncRNA expression is highly restricted to certain tissue types such as testis, heart, and liver Derrien et al. The process of quantification of these low abundant tissue-specific lncRNA transcripts remains a challenging and on-going task.
Recent studies suggest that lncRNAs bind to chromatin, chromatin modifying proteins, certain transcription factors, and miRNAs. This binding event significantly regulates a wide range of mechanisms like epigenetic signaling, disrupting polymerase activities and altering miRNA stability Baumgart et al.
Additionally, it is now also well-accepted that lncRNAs are connected with various biological processes Kung et al. Thus, lncRNAs have been recognized as potential markers for liver injury Takahashi et al. Our study uniquely identified a total of differentially regulated lncRNAs across all toxicants combined. Although the biological function of these highly modulated lncRNAs is unclear, there have been a number of reports Zhu et al.
Increased expression of ccnE1 has been reported in human and mouse liver fibrosis Nevzorova et al. While the majority of miRNAs are located within the cell, some miRNAs, commonly known as circulating or extracellular miRNAs, have also been found in the extracellular environment, including various biological fluids Wang et al.
During the past decade, miRNAs have generated a high level of interest in toxicology Clarke et al. Our study identified a total of 21 differentially regulated miRNAs across all toxicants combined. ANIT treatment in mice for 48 h has been shown to reduce the expression of hepatocyte nuclear factor 1-alpha Hnf1a Tanaka et al. Interestingly, miR down-regulation correlates with Hnf1a gene down-regulation Coulouarn et al.
Psesudogenes are generally produced through a wide range of mechanisms Zhang et al. A spontaneous mutation in a protein-coding gene can generally prevent either transcription or translation of the gene, resulting in the formation of unitary pseudogene.
Additionally, duplicated pseudogenes are also generated through a tandem doubling of certain sequences. These duplicated and unitary pseudogenes lose their protein-coding capability due to either the loss of promoters or mutations that create premature stop codons Mighell et al. The co-expression of pseudogenes and their cognate protein-coding genes have not been looked at thoroughly within the context of toxicity assessment in a single experiment, as pseudogenes probes are generally absent from typical microarray chips.
Our RNA-Seq data identified a total of pseudogenes with altered expression from all toxicants combined. Altogether, although it is premature to draw conclusions, it appears that measurement of non-protein-coding transcripts lncRNAs, miRNAs, and pseudogenes may provide some useful insights regarding mechanisms of liver toxicity.
Future in vitro and in vivo studies are clearly necessary to further understand the utility for mechanistic molecular toxicology of these non-protein-coding diagnostic and prognostic transcripts. Microarrays measure the expression of only pre-defined probes genes and typical arrays are designed to cover only a portion of protein-coding genes.
Thus, it is currently impossible to detect regulation of non-coding genes i. Furthermore, hybridization can result in mismatch between probes and target molecules, leading to increased noise and higher likelihood of misidentified DEGs.
Because of its added advantages, RNA-Seq is progressively replacing microarray technology for many transcriptomic applications Lowe et al. However, microarrays still offer some advantages. In the present study, we generated 39 and 0. Even for this simple prototype study, this massive amount of data introduced data management and analysis challenges.
Secondly, the overall computation time, data storage and management time for a microarray experiment are much lower. Based on our experience, to completely process and summarize the DEGs from a set of microarray-generated gene expression data generally take hours, depending on the amount of transcriptional change in the experiment.
These datasets complemented by public databases such as GEO, DrugMatrix and Array Express have created easily accessible and analyzable databases, which serve as a critical reference for new toxicogenomic data analytics and interpretation. In contrast, there are no such reference databases available for RNA-Seq data, which currently limits toxicogenomic data interpretation.
Fourthly, data processing and analyses are well-established with microarrays; in contrast, as RNA-Seq is still new and evolving, there is not yet a single standardized computational approach for performing an RNA-Seq data analysis. However, with the recent advancement in computing power, hardware and dedicated computational workflows, this limitation will become rapidly obsolete. Finally, cost has probably been an important consideration for not switching from microarrays to RNA-Seq for toxicogenomic studies.
However, based on the current study, RNA-Seq data generation was about 1. Taken together, these findings suggest that RNA-Seq should provide a comprehensive picture of protein-coding and non-coding DEGs as well as a more complete list of impacted canonical pathways at a lesser cost than microarrays.
The present study indicates that RNA-Seq is a good alternative to microarrays for toxicogenomic studies of rat liver. In addition of detecting the majority of trancriptomic perturbations observed with microarrays, RNA-Seq captured additional DEGs and canonical pathways relevant to liver toxicity.
The wider dynamic range offered by RNA-Seq provides a higher level of sensitivity and accuracy, as well as the ability to detect expression changes in non-coding genes that may offer important new insights into xenobiotic-induced liver toxicity.
Given the critical role of databases for the accurate interpretation of toxicogenomics studies and the fact that institutional and public databases are largely based on microarray data, generation of RNA-Seq-based databases and better translation of microarray databases for comparison to and interpretation of RNA-Seq data are needed.
However, the improved sensitivity, accuracy and ability to evaluate non-coding genes of RNA-Seq may prove valuable for studies designed to investigate mechanisms of toxicity. All authors are aware of the manuscript and have contributed significantly to its completion. In addition, all authors are employed at AbbVie and the appropriate disclosures are included on the title page along with keywords.
All authors are employees of AbbVie. The design, study conduct, and financial support for this research was provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication. We gratefully acknowledge expert histological technical support from Christina Dunn in Preclinical Safety. Neutrophils are particularly prominent in MDA toxicity, with many neutrophils in the bile duct lumina.
The inset images demonstrate the cytologic features of bile duct epithelial hypertrophy. C CCl 4 administration resulted in macrovesicular and microvesicular hepatocellular steatosis with a centrilobular distribution. The inset image demonstrates a vacuolated hepatocyte.
D Diclofenac and E APAP, administration under the dosing regimen described did not result in histopathological evidence of hepatocellular or bile duct injury. The extent of vacuolization observed in some hepatocytes was not significantly different from untreated control. Insets for D,E demonstrate individual hepatocytes with cytoplasmic features not different from vehicle control.
The red color line shows the genes that are highly regulated in RNA-Seq compared to microarray. The green line shows genes with comparable expression in both platforms. X-axis denotes cis -gene name and Y-axis is log 2 transformed fold change value. The RNA-Seq and microarray specific pathways are shown in italics.
B APAP impacted canonical pathways. C MDA impacted canonical pathways. D CCl 4 impacted canonical pathways. The computed activation score and p -value for microarray and RNA-Seq are given in columns 2—3 and 4—6, respectively. Abascal, F. Loose ends: almost one in five human genes still have unresolved coding status. Nucleic Acids Res. Baumgart, B. MicroRNA as biomarkers of mitochondrial toxicity. Bisgin, H. Evaluation of bioinformatics approaches for next-generation sequencing analysis of microRNAs with a toxicogenomics study design.
Bohman, K. Misuse of booster cushions among children and adults in Shanghai-an observational and attitude study during buckling up. Traffic Inj.
Bolstad, B. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, — Bottomly, D. PLoS One 6:e Bray, N. Near-optimal probabilistic RNA-seq quantification. Buck, W. Use of traditional end points and gene dysregulation to understand mechanisms of toxicity: toxicogenomics in mechanistic toxicology. Methods Mol. Cerami, E. Pathway commons, a web resource for biological pathway data.
Chandramohan, R. Benchmarking RNA-Seq quantification tools. IEEE Eng. Chen, M. A decade of toxicogenomic research and its contribution to toxicological science. Clarke, J. Circulating microRNA in the methionine and choline-deficient mouse model of non-alcoholic steatohepatitis. Conesa, A. A survey of best practices for RNA-seq data analysis. Genome Biol. Coulouarn, C.
In This Section. This test helps to find out if your child has a medical condition caused by: a small missing piece of chromosome, called a deletion a small extra piece of chromosome, called a duplication the entire chromosome pair being passed down from one parent large parts of multiple chromosomes being the same This test does not check for every possible genetic disease or give information about a specific gene.
Chromosomes The body is made up of billions of cells. How the Test is Done A blood sample is preferred for microarray analysis. When a person has a missing or an extra piece of chromosome: It can cause health problems, such as birth defects, seizures, delays in development, learning problems and autism.
Sometimes there are no problems. When chromosome part s are alike or the entire chromosome pair is the same: It can increase the chance of certain genetic disorders that cause health problems.
Types of Test Results Normal: There are no missing or extra pieces of chromosomes, and no large parts of the chromosomes are the same. Likely pathogenic: There is a missing or extra piece of chromosome that may cause health or learning problems.
Variant of unknown significance VUS : There is a missing or extra piece of chromosome, but it is not clear if it will cause any health or learning problems. Finding a VUS is common. The data gathered through microarrays can be used to create gene expression profiles, which show simultaneous changes in the expression of many genes in response to a particular condition or treatment. Related Concepts 6. You have authorized LearnCasting of your reading list in Scitable.
Do you want to LearnCast this session? This article has been posted to your Facebook page via Scitable LearnCast. A general approach to performing gene expression profiling experiments is indicated as a flow diagram in Figure 1. Having performed the experiment, quality control checks, statistical analysis, and data-mining are performed. More and more, investigators are interested not just in asking how large the magnitude of an expression difference is, but whether it is significant, given the other sources of variation in the experiment.
Similarly, we might want to evaluate whether some subset of genes show similar expression profiles and so form natural clusters of functionally related genes. Or we may combine expression studies with genotyping and surveys of regulatory sequences to investigate the mechanisms that are responsible for similar profiles of gene expression. Finally, all of the expression inferences must be integrated with everything else that is known about the genes, culled from text databases and proteomic experiments and from the investigator's own stores of biological insight.
The ability to survey transcript abundance across an ever-increasing range of conditions gives geneticists a fresh look at their cellular systems, in many cases providing a more holistic view of the biology, but at the same time feeding back into the classical hypothetico-deductive scientific framework.
The technology has rapidly advanced beyond the simple application of fishing for candidate genes and now sees applications as diverse as clinical prediction, ecosystem monitoring, quantitative mapping, and dissection of evolutionary mechanisms.
Two of the better-known examples of the interplay between microarray profiling and hypothesis testing are provided by the studies of Ideker et al. The latter authors profiled the difference in expression between strains of flies that had been divergently selected for positive and negative geotaxis, a supposedly complex behavior relating to whether flies prefer to climb or stay close to the ground.
They identified two dozen differentially expressed genes, several of which were represented by mutant or transgenic stocks that allowed tests of the effect of gene dosage on behavior. At least four of the candidate genes indeed quantitatively affect geotaxis. Ideker et al. They showed how thoughtful experimentation can considerably enhance our understanding of genetic regulatory pathways such as the yeast galactose response.
Much excitement has been generated recently by the potential for clinical applications of gene expression profiling in relation to complex diseases such as cancer, diabetes, aging, and response to toxins.
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