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The Sequencing Quality Control (SEQC) consortium shows that junction discovery and differential gene expression profiling with RNA-seq can be robust but transcript-level and absolute measurements remain challenging. We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US 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 all examined platforms, including 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.

Colorectal cancers (CRCs) can be divided into four gene expression-based biologically distinct consensus molecular subtypes (CMS). This classification provides a potential framework for stratified treatment, but to identify novel CMS-drug associations, translation of the subtypes to pre-clinical models is essential. The currently available classifier is dependent on gene expression signals from the immune and stromal compartments of tumors and fails to identify the poor-prognostic CM S4 -mesenchymal group in immortalized cell lines, patient-derived organoids and xenografts. To address this, we present a novel CMS classifier based on a filtered set of cancer cell-intrinsic, subtype-enriched gene expression markers. This new classifier, referred to as CMScaller, recapitulated the subtypes in both in vitro and in vivo models (551 in total). Importantly, by analyzing public drug response data from patient-derived xenografts and cell lines, we show that the subtypes are predictive of response to standard CRC drugs. CMScaller is available as an R package.

Circular RNAs (circRNAs), a kind of non-coding RNAs, have shown large capabilities in gene regulation. However, the mechanisms underlying circRNAs remain largely unknown so far. Recent studies demonstrated that circRNAs play miRNA sponge effects and regulate gene expression by microRNA response elements. Here, we screened circRNA expression profiles of bladder carcinoma using microarray assay. A total of 469 dysregulated circular transcripts are found in bladder cancer compared with normal tissues, among which 285 were up-regulated and 184 were down-regulated. Six circRNAs were identified to have significant differences by qRT-PCR. We speculated that circRNAs might involve in cancer-related pathways via interactions with miRNA by multiple bioinformatical approaches. Therefore, we further predicted that circTCF25 could sequester miR-103a-3p/miR-107, which potentially lead to the up-regulation of thirteen targets related to cell proliferation, migration and invasion. Subsequently, we demonstrated that over-expression of circTCF25 could down-regulate miR-103a-3p and miR-107, increase CDK6 expression and promote proliferation and migration in vitro and vivo . This is the first study to exploit circRNA profiling and circRNA/miRNA interactions in bladder cancer. Our work laid the foundation to investigate the functions of circRNAs in cancers. The data also suggest that circTCF25 might be a new promising marker for bladder cancer.

Hepatic steatosis is a multifactorial condition that is often observed in obese patients and is a prelude to non-alcoholic fatty liver disease. Here, we combine shotgun sequencing of fecal metagenomes with molecular phenomics (hepatic transcriptome and plasma and urine metabolomes) in two well-characterized cohorts of morbidly obese women recruited to the FLORINASH study. We reveal molecular networks linking the gut microbiome and the host phenome to hepatic steatosis. Patients with steatosis have low microbial gene richness and increased genetic potential for the processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid metabolism. We demonstrated that fecal microbiota transplants and chronic treatment with phenylacetic acid, a microbial product of aromatic amino acid metabolism, successfully trigger steatosis and branched-chain amino acid metabolism. Molecular phenomic signatures were predictive (area under the curve = 87%) and consistent with the gut microbiome having an effect on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies. Metabolic activity of specific human gut microorganisms contributes to liver steatosis in obese women.

Electrochemically active bacteria (EAB) receive considerable attention for their utility in bioelectrochemical processes. Although electrode potentials are known to affect the metabolic activity of EAB, it is unclear whether EAB are able to sense and respond to electrode potentials. Here, we show that, in the presence of a high-potential electrode, a model EAB Shewanella oneidensis MR-1 can utilize NADH-dependent catabolic pathways and a background formate-dependent pathway to achieve high growth yield. We also show that an Arc regulatory system is involved in sensing electrode potentials and regulating the expression of catabolic genes, including those for NADH dehydrogenase. We suggest that these findings may facilitate the use of EAB in biotechnological processes and offer the molecular bases for their ecological strategies in natural habitats. Whether electrochemically active bacteria (EAB) can gain energy according to electrode potentials is still unclear. Here, the authors show through transcriptome and deletion mutant analyses that EAB can sense electrode potentials by the Arc system and activate NADH-dependent catabolic pathway to generate ATP.

Transcriptome profiling is widely used to infer functional states of specific cell types, as well as their responses to stimuli, to define contributions to physiology and pathophysiology. Focusing on microglia, the brain’s macrophages, we report here a side-by-side comparison of classical cell-sorting-based transcriptome sequencing and the ‘RiboTag’ method, which avoids cell retrieval from tissue context and yields translatome sequencing information. Conventional whole-cell microglial transcriptomes were found to be significantly tainted by artifacts introduced by tissue dissociation, cargo contamination and transcripts sequestered from ribosomes. Conversely, our data highlight the added value of RiboTag profiling for assessing the lineage accuracy of Cre recombinase expression in transgenic mice. Collectively, this study indicates method-based biases, reveals observer effects and establishes RiboTag-based translatome profiling as a valuable complement to standard sorting-based profiling strategies. Isolating tissue-resident cells runs the risk of altering their expression profile. Jung and colleagues use a RiboTagging approach to describe the microglial translatome and demonstrate that macrophage extraction from their tissue environment results in significant transcriptional alterations.

Cytotoxic T cells are necessary and sufficient for the development of alopecia areata in mice, while JAK inhibition promotes regrowth of hair in patients and mice with established disease. Alopecia areata (AA) is a common autoimmune disease resulting from damage of the hair follicle by T cells. The immune pathways required for autoreactive T cell activation in AA are not defined limiting clinical development of rational targeted therapies 1 . Genome-wide association studies (GWAS) 2 implicated ligands for the NKG2D receptor (product of the KLRK1 gene) in disease pathogenesis. Here, we show that cytotoxic CD8 + NKG2D + T cells are both necessary and sufficient for the induction of AA in mouse models of disease. Global transcriptional profiling of mouse and human AA skin revealed gene expression signatures indicative of cytotoxic T cell infiltration, an interferon-γ (IFN-γ) response and upregulation of several γ-chain (γ c ) cytokines known to promote the activation and survival of IFN-γ–producing CD8 + NKG2D + effector T cells. Therapeutically, antibody-mediated blockade of IFN-γ, interleukin-2 (IL-2) or interleukin-15 receptor β (IL-15Rβ) prevented disease development, reducing the accumulation of CD8 + NKG2D + T cells in the skin and the dermal IFN response in a mouse model of AA. Systemically administered pharmacological inhibitors of Janus kinase (JAK) family protein tyrosine kinases, downstream effectors of the IFN-γ and γ c cytokine receptors, eliminated the IFN signature and prevented the development of AA, while topical administration promoted hair regrowth and reversed established disease. Notably, three patients treated with oral ruxolitinib, an inhibitor of JAK1 and JAK2, achieved near-complete hair regrowth within 5 months of treatment, suggesting the potential clinical utility of JAK inhibition in human AA.

Microglia are resident myeloid cells of the central nervous system integral for neuroprotective and neurodegenerative processes. Here the authors describe a unique TGF-β dependent molecular and functional microglia signature that distinguishes these cells from other immune and glial cells in the periphery and brain. Microglia are myeloid cells of the CNS that participate both in normal CNS function and in disease. We investigated the molecular signature of microglia and identified 239 genes and 8 microRNAs that were uniquely or highly expressed in microglia versus myeloid and other immune cells. Of the 239 genes, 106 were enriched in microglia as compared with astrocytes, oligodendrocytes and neurons. This microglia signature was not observed in microglial lines or in monocytes recruited to the CNS, and was also observed in human microglia. We found that TGF-β was required for the in vitro development of microglia that express the microglial molecular signature characteristic of adult microglia and that microglia were absent in the CNS of TGF-β1–deficient mice. Our results identify a unique microglial signature that is dependent on TGF-β signaling and provide insights into microglial biology and the possibility of targeting microglia for the treatment of CNS disease.

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis — the ontology and the annotations — evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be a strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

Key Points Structural variation was originally defined as insertions, deletions and inversions greater than 1 kb in size, but with the sequencing of human genomes now becoming routine, the operational spectrum of structural variants has widened to include events >50 bp in length. The main focus of structural variant (SV) studies should be accurate characterization of the copy, content and structure of genomic variants. Methods to discover and genotype structural variation can be divided into two main types: experimental and computational. Experimental methods for discovering SVs include hybridization-based approaches (SNP microarrays and array comparative genomic hybridization) and single-molecule analysis (optical mapping). In addition, PCR-based techniques can be used to genotype SVs. Computational methods use genome sequencing data to discover and genotype SVs. There are four main computational approaches: read-pair, read-depth, split-read and sequence-assembly methods. All existing platforms and methods have different biases and limitations. Accurate characterization of the full spectrum of structural variation remains a challenge. Structural variation in the genome can influence disease, complex traits and evolution, but comprehensive characterization of variants is challenging. This Review compares current methods — particularly microarray platforms and sequencing-based computational analysis — and considers future research strategies. Comparisons of human genomes show that more base pairs are altered as a result of structural variation — including copy number variation — than as a result of point mutations. Here we review advances and challenges in the discovery and genotyping of structural variation. The recent application of massively parallel sequencing methods has complemented microarray-based methods and has led to an exponential increase in the discovery of smaller structural-variation events. Some global discovery biases remain, but the integration of experimental and computational approaches is proving fruitful for accurate characterization of the copy, content and structure of variable regions. We argue that the long-term goal should be routine, cost-effective and high quality de novo assembly of human genomes to comprehensively assess all classes of structural variation.