Charles Wang

Rat toxicogenomic study reveals analytical consistency across microarray platforms

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R20.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Dedifferentiation and Proliferation of Mammalian Cardiomyocytes

Background It has long been thought that mammalian cardiomyocytes are terminally-differentiated and unable to proliferate. However, myocytes in more primitive animals such as zebrafish are able to dedifferentiate and proliferate to regenerate amputated cardiac muscle. Methodology/Principal Findings Here we test the hypothesis that mature mammalian cardiomyocytes retain substantial cellular plasticity, including the ability to dedifferentiate, proliferate, and acquire progenitor cell phenotypes. Two complementary methods were used: 1) cardiomyocyte purification from rat hearts, and 2) genetic fate mapping in cardiac explants from bi-transgenic mice. Cardiomyocytes isolated from rodent hearts were purified by multiple centrifugation and Percoll gradient separation steps, and the purity verified by immunostaining and RT-PCR. Within days in culture, purified cardiomyocytes lost their characteristic electrophysiological properties and striations, flattened and began to divide, as confirmed by proliferation markers and BrdU incorporation. Many dedifferentiated cardiomyocytes went on to express the stem cell antigen c-kit, and the early cardiac transcription factors GATA4 and Nkx2.5. Underlying these changes, inhibitory cell cycle molecules were suppressed in myocyte-derived cells (MDCs), while microRNAs known to orchestrate proliferation and pluripotency increased dramatically. Some, but not all, MDCs self-organized into spheres and re-differentiated into myocytes and endothelial cells in vitro. Cell fate tracking of cardiomyocytes from 4-OH-Tamoxifen-treated double-transgenic MerCreMer/ZEG mouse hearts revealed that green fluorescent protein (GFP) continues to be expressed in dedifferentiated cardiomyocytes, two-thirds of which were also c-kit+. Conclusions/Significance Contradicting the prevailing view that they are terminally-differentiated, postnatal mammalian cardiomyocytes are instead capable of substantial plasticity. Dedifferentiation of myocytes facilitates proliferation and confers a degree of stemness, including the expression of c-kit and the capacity for multipotency.

Detecting and correcting systematic variation in large-scale RNA sequencing data

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models

High-throughput DNA microarray provides an effective approach to the monitoring of expression levels of thousands of genes in a sample simultaneously. One promising application of this technology is the molecular diagnostics of cancer, e.g. to distinguish normal tissue from tumor or to classify tumors into different types or subtypes. One problem arising from the use of microarray data is how to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples). There is a need to develop reliable classification methods to make full use of microarray data and to evaluate accurately the predictive ability and reliability of such derived models. In this paper, discriminant partial least squares was used to classify the different types of human tumors using four microarray datasets and showed good prediction performance. Four different cross-validation procedures (leave-one-out versus leave-half-out; incomplete versus full) were used to evaluate the classification model. Our results indicate that discriminant partial least squares using leave-half-out cross-validation provides a more realistic estimate of the predictive ability of a classification model, which may be overestimated by some of the cross-validation procedures, and the information obtained from different cross-validation procedures can be used to evaluate the reliability of the classification model.

COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis

COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data produced by either an Illumina methylation array or targeted bisulfite sequencing. The goal of the COHCAP algorithm is to identify CpG islands that show a consistent pattern of methylation among CpG sites. COHCAP is currently the only DNA methylation package that provides integration with gene expression data to identify a subset of CpG islands that are most likely to regulate downstream gene expression, and it can generate lists of differentially methylated CpG islands with ∼50% concordance with gene expression from both cell line data and heterogeneous patient data. For example, this article describes known breast cancer biomarkers (such as estrogen receptor) with a negative correlation between DNA methylation and gene expression. COHCAP also provides visualization for quality control metrics, regions of differential methylation and correlation between methylation and gene expression. This software is freely available at https://sourceforge.net/projects/cohcap/.

Overview of available methods for diverse RNA-Seq data analyses

RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction.

Small RNA sequencing reveals microRNAs that modulate angiotensin II effects in vascular smooth muscle cells

Background: Role of microRNAs in angiotensin II-mediated vascular smooth muscle cell (VSMC) dysfunction is unclear. Results: Angiotensin II up-regulates miR-132 in VSMC. miR-132 induces MCP-1 partly via targeting PTEN, activates CREB, and regulates genes related to cell-cycle and motility. Conclusion: miR-132/212 is a novel modulator of Ang II actions. Significance: miRNAs may serve as new drug targets for Ang II-mediated cardiovascular diseases. Angiotensin II (Ang II)-mediated vascular smooth muscle cell dysfunction plays a critical role in cardiovascular diseases. However, the role of microRNAs (miRNAs) in this process is unclear. We used small RNA deep sequencing to profile Ang II-regulated miRNAs in rat vascular smooth muscle cells (VSMC) and evaluated their role in VSMC dysfunction. Sequencing results revealed several Ang II-responsive miRNAs, and bioinformatics analysis showed that their predicted targets can modulate biological processes relevant to cardiovascular diseases. Further studies with the most highly induced miR-132 and miR-212 cluster (miR-132/212) showed time- and dose-dependent up-regulation of miR-132/212 by Ang II through the Ang II Type 1 receptor. We identified phosphatase and tensin homolog (PTEN) as a novel target of miR-132 and demonstrated that miR-132 induces monocyte chemoattractant protein-1 at least in part via PTEN repression in rat VSMC. Moreover, miR-132 overexpression enhanced cyclic AMP-response element-binding protein (CREB) phosphorylation via RASA1 (p120 Ras GTPase-activating protein 1) down-regulation, whereas miR-132 inhibition attenuated Ang II-induced CREB activation. Furthermore, miR-132 up-regulation by Ang II required CREB activation, demonstrating a positive feedback loop. Notably, aortas from Ang II-infused mice displayed similar up-regulation of miR-132/212 and monocyte chemoattractant protein-1, supporting in vivo relevance. In addition, microarray analysis and reverse transcriptase-quantitative PCR validation revealed additional novel miR-132 targets among Ang II-down-regulated genes implicated in cell cycle, motility, and cardiovascular functions. These results suggest that miR132/212 can serve as a novel cellular node to fine-tune and amplify Ang II actions in VSMC.

C-terminal-truncated apolipoprotein (apo) E4 inefficiently clears amyloid-β (Aβ) and acts in concert with Aβ to elicit neuronal and behavioral deficits in mice

Apolipoprotein (apo) E4 is the major known genetic risk factor for Alzheimers disease (AD). We have shown in vitro and in vivo that apoE4 preferentially undergoes aberrant cleavage in neurons, yielding neurotoxic C-terminal-truncated fragments. To study the effect of these fragments on amyloid-β (Aβ) clearance/deposition and their potential synergy with Aβ in eliciting neuronal and behavioral deficits, we cross-bred transgenic mice expressing apoE3, apoE4, or apoE4(Δ272–299) with mice expressing human amyloid protein precursor (APP) harboring familial AD mutations (hAPPFAD). At 6–8 mo of age, hAPPFAD mice expressing apoE3 or apoE4 had lower levels of hippocampal Aβ (94% and 89%, respectively) and less Aβ deposition (89% and 87%) than hAPPFAD mice without apoE, whereas hAPPFAD mice expressing mouse apoE had higher Aβ levels. Thus, human apoE stimulates Aβ clearance, but mouse apoE does not. Expression of apoE4(Δ272–299) reduced total Aβ levels by only 63% and Aβ deposition by 46% compared with hAPPFAD mice without apoE. Unlike apoE3 and apoE4, the C-terminal-truncated apoE4 bound poorly with Aβ peptides, leading to decreased Aβ clearance and increased Aβ deposition. Despite their lower levels of Aβ and Aβ deposition, hAPPFAD/apoE4(Δ272–299) mice accumulated pathogenic Aβ oligomers and displayed neuronal and behavioral deficits similar to or more severe than those in hAPPFAD mice. Thus, the C-terminal-truncated apoE4 fragment inefficiently clears Aβ peptides and acts in concert with low levels of Aβ to elicit neuronal and behavioral deficits in mice.