Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats. We catalogue the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView. We find that organ-enriched, differentially expressed genes reflect the known organ-specific biological activities. A large number of transcripts show organ-specific, age-dependent or sex-specific differential expression patterns. We create a web-based, open-access rat BodyMap database of expression profiles with crosslinks to other widely used databases, anticipating that it will serve as a primary resource for biomedical research using the rat model.

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.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly spread worldwide and has affected more than 10 million individuals. A typical feature of COVID-19 is the suppression of type I and III interferon (IFN)-mediated antiviral immunity. However, the molecular mechanism by which SARS-CoV-2 evades antiviral immunity remains elusive. Here, we reported that the SARS-CoV-2 membrane (M) protein inhibits the production of type I and III IFNs induced by the cytosolic dsRNA-sensing pathway mediated by RIG-I/MDA-5–MAVS signaling. In addition, the SARS-CoV-2 M protein suppresses type I and III IFN induction stimulated by SeV infection or poly (I:C) transfection. Mechanistically, the SARS-CoV-2 M protein interacts with RIG-I, MAVS, and TBK1, thus preventing the formation of the multiprotein complex containing RIG-I, MAVS, TRAF3, and TBK1 and subsequently impeding the phosphorylation, nuclear translocation, and activation of IRF3. Consequently, ectopic expression of the SARS-CoV-2 M protein facilitates the replication of vesicular stomatitis virus. Taken together, these results indicate that the SARS-CoV-2 M protein antagonizes type I and III IFN production by targeting RIG-I/MDA-5 signaling, which subsequently attenuates antiviral immunity and enhances viral replication. This study provides insight into the interpretation of SARS-CoV-2-induced antiviral immune suppression and illuminates the pathogenic mechanism of COVID-19.

Graphene, a single-atom-thick carbon nanosheet, has attracted great interest as a promising nanomaterial for a variety of bioapplications because of its extraordinary properties. However, the potential for widespread human exposure raises safety concerns about graphene and its derivatives, referred to as graphene-family nanomaterials. This review summarizes recent findings on the toxicological effects and the potential toxicity mechanisms of graphene-family nanomaterials in bacteria, mammalian cells, and animal models. Graphene, graphene oxide, and reduced graphene oxide elicit toxic effects both in vitro and in vivo, whereas surface modifications can significantly reduce their toxic interactions with living systems. Standardization of terminology and the fabrication methods of graphene-family nanomaterials are warranted for further investigations designed to decrease their adverse effects and explore their biomedical applications.

Background: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.

The suppression of types I and III interferon (IFN) responses by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) contributes to the pathogenesis of coronavirus disease 2019 (COVID‐19). The strategy used by SARS‐CoV‐2 to evade antiviral immunity needs further investigation. Here, we reported that SARS‐CoV‐2 ORF9b inhibited types I and III IFN production by targeting multiple molecules of innate antiviral signaling pathways. SARS‐CoV‐2 ORF9b impaired the induction of types I and III IFNs by Sendai virus and poly (I:C). SARS‐CoV‐2 ORF9b inhibited the activation of types I and III IFNs induced by the components of cytosolic dsRNA‐sensing pathways of RIG‐I/MDA5‐MAVS signaling, including RIG‐I, MDA‐5, MAVS, TBK1, and IKKε, rather than IRF3‐5D, which is the active form of IRF3. SARS‐CoV‐2 ORF9b also suppressed the induction of types I and III IFNs by TRIF and STING, which are the adaptor protein of the endosome RNA‐sensing pathway of TLR3‐TRIF signaling and the adaptor protein of the cytosolic DNA‐sensing pathway of cGAS–STING signaling, respectively. A mechanistic analysis revealed that the SARS‐CoV‐2 ORF9b protein interacted with RIG‐I, MDA‐5, MAVS, TRIF, STING, and TBK1 and impeded the phosphorylation and nuclear translocation of IRF3. In addition, SARS‐CoV‐2 ORF9b facilitated the replication of the vesicular stomatitis virus. Therefore, the results showed that SARS‐CoV‐2 ORF9b negatively regulates antiviral immunity and thus facilitates viral replication. This study contributes to our understanding of the molecular mechanism through which SARS‐CoV‐2 impairs antiviral immunity and provides an essential clue to the pathogenesis of COVID‐19.