Gut microbial carbohydrate metabolism contributes to insulin resistance.

Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes1,2. Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance3-9. In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host's overall energy extraction10, thereby playing a role in the pathogenesis of obesity and prediabetes3,4,6. Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host-microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance.

Fungus-Derived Neoechinulin B as a Novel Antagonist of Liver X Receptor, Identified by Chemical Genetics Using a Hepatitis C Virus Cell Culture System.

UNLABELLED: Cell culture systems reproducing virus replication can serve as unique models for the discovery of novel bioactive molecules. Here, using a hepatitis C virus (HCV) cell culture system, we identified neoechinulin B (NeoB), a fungus-derived compound, as an inhibitor of the liver X receptor (LXR). NeoB was initially identified by chemical screening as a compound that impeded the production of infectious HCV. Genome-wide transcriptome analysis and reporter assays revealed that NeoB specifically inhibits LXR-mediated transcription. NeoB was also shown to interact directly with LXRs. Analysis of structural analogs suggested that the molecular interaction of NeoB with LXR correlated with the capacity to inactivate LXR-mediated transcription and to modulate lipid metabolism in hepatocytes. Our data strongly suggested that NeoB is a novel LXR antagonist. Analysis using NeoB as a bioprobe revealed that LXRs support HCV replication: LXR inactivation resulted in dispersion of double-membrane vesicles, putative viral replication sites. Indeed, cells treated with NeoB showed decreased replicative permissiveness for poliovirus, which also replicates in double-membrane vesicles, but not for dengue virus, which replicates via a distinct membrane compartment. Together, our data suggest that LXR-mediated transcription regulates the formation of virus-associated membrane compartments. Significantly, inhibition of LXRs by NeoB enhanced the activity of all known classes of anti-HCV agents, and NeoB showed especially strong synergy when combined with interferon or an HCV NS5A inhibitor. Thus, our chemical genetics analysis demonstrates the utility of the HCV cell culture system for identifying novel bioactive molecules and characterizing the virus-host interaction machinery. IMPORTANCE: Hepatitis C virus (HCV) is highly dependent on host factors for efficient replication. In the present study, we used an HCV cell culture system to screen an uncharacterized chemical library. Our results identified neoechinulin B (NeoB) as a novel inhibitor of the liver X receptor (LXR). NeoB inhibited the induction of LXR-regulated genes and altered lipid metabolism. Intriguingly, our results indicated that LXRs are critical to the process of HCV replication: LXR inactivation by NeoB disrupted double-membrane vesicles, putative sites of viral replication. Moreover, NeoB augmented the antiviral activity of all known classes of currently approved anti-HCV agents without increasing cytotoxicity. Thus, our strategy directly links the identification of novel bioactive compounds to basic virology and the development of new antiviral agents.

Transcriptome analysis of long non-coding RNAs in Mycobacterium avium complex-infected macrophages.

Mycobacterium avium complex (MAC) is a non-tuberculous mycobacterium widely distributed in the environment. Even though MAC infection is increasing in older women and immunocompromised patients, to our knowledge there has been no comprehensive analysis of the MAC-infected host-cell transcriptome-and particularly of long non-coding RNAs (lncRNAs). By using in vitro-cultured primary mouse bone-marrow-derived macrophages (BMDMs) and Cap analysis of gene expression, we analyzed the transcriptional and kinetic landscape of macrophage genes, with a focus on lncRNAs, during MAC infection. MAC infection of macrophages induced the expression of immune/inflammatory response genes and other genes similar to those involved in M1 macrophage activation, consistent with previous reports, although Nos2 (M1 activation) and Arg1 (M2 activation) had distinct expression profiles. We identified 31 upregulated and 30 downregulated lncRNA promoters corresponding respectively to 18 and 26 lncRNAs. Upregulated lncRNAs were clustered into two groups-early and late upregulated-predicted to be associated with immune activation and the immune response to infection, respectively. Furthermore, an Ingenuity Pathway Analysis revealed canonical pathways and upstream transcription regulators associated with differentially expressed lncRNAs. Several differentially expressed lncRNAs reported elsewhere underwent expressional changes upon M1 or M2 preactivation and subsequent MAC infection. Finally, we showed that expressional change of lncRNAs in MAC-infected BMDMs was mediated by toll-like receptor 2, although there may be other mechanisms that sense MAC infection. We identified differentially expressed lncRNAs in MAC-infected BMDMs, revealing diverse features that imply the distinct roles of these lncRNAs in MAC infection and macrophage polarization.

Analysis of Enhancer-Promoter Interactions using CAGE and RADICL-Seq Technologies.

Regulation of gene expression is a key feature for higher eukaryotes and how chromatin topology relates to gene activation is an intense area of research. Enhancer-promoter interactions are believed to mediate activation of target genes. Bidirectional transcription represents one hallmark of active enhancers that can be measured using transcriptome technologies such as Cap analysis of gene expression (CAGE). Recently, we have developed RNA and DNA interacting complexes ligated and sequenced (RADICL-Seq) a novel methodology to map genome-wide RNA-chromatin interactions in intact nuclei. Here, we describe how CAGE and RADICL-Seq data can be used to characterize enhancer elements and identify their target genes.

Development of p53 knockout U87MG cell line for unbiased drug delivery testing system using CRISPR-Cas9 and transcriptomic analysis.

Antibody-drug conjugates offers many advantages as a drug delivery platform that allows for highly specific targeting of cell types and genes. Ideally, testing the efficacy of these systems requires two cell types to be different only in the gene targeted by the drug, with the rest of the cellular machinery unchanged, in order to minimize other potential differences from obscuring the effects of the drug. In this study, we created multiple variants of U87MG cells with targeted mutation in the TP53 gene using the CRISPR-Cas9 system, and determined that their major transcriptional differences stem from the loss of p53 function. Using the transcriptome data, we predicted which mutant clones would have less divergent phenotypes from the wild type and thereby serve as the best candidates to be used as drug delivery testing platforms. Further in vitro and in vivo assays of cell morphology, proliferation rate and target antigen-mediated uptake supported our predictions. Based on the combined analysis results, we successfully selected the best qualifying mutant clone. This study serves as proof-of-principle of the approach and paves the way for extending to additional cell types and target genes.

An era of single-cell genomics consortia.

The human body consists of 37 trillion single cells represented by over 50 organs that are stitched together to make us who we are, yet we still have very little understanding about the basic units of our body: what cell types and states make up our organs both compositionally and spatially. Previous efforts to profile a wide range of human cell types have been attempted by the FANTOM and GTEx consortia. Now, with the advancement in genomic technologies, profiling the human body at single-cell resolution is possible and will generate an unprecedented wealth of data that will accelerate basic and clinical research with tangible applications to future medicine. To date, several major organs have been profiled, but the challenges lie in ways to integrate single-cell genomics data in a meaningful way. In recent years, several consortia have begun to introduce harmonization and equity in data collection and analysis. Herein, we introduce existing and nascent single-cell genomics consortia, and present benefits to necessitate single-cell genomic consortia in a regional environment to achieve the universal human cell reference dataset.

C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution.

Single-cell transcriptomic profiling is a powerful tool to explore cellular heterogeneity. However, most of these methods focus on the 3'-end of polyadenylated transcripts and provide only a partial view of the transcriptome. We introduce C1 CAGE, a method for the detection of transcript 5'-ends with an original sample multiplexing strategy in the C1TM microfluidic system. We first quantifiy the performance of C1 CAGE and find it as accurate and sensitive as other methods in the C1 system. We then use it to profile promoter and enhancer activities in the cellular response to TGF-ß of lung cancer cells and discover subpopulations of cells differing in their response. We also describe enhancer RNA dynamics revealing transcriptional bursts in subsets of cells with transcripts arising from either strand in a mutually exclusive manner, validated using single molecule fluorescence in situ hybridization.