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 controlled and therapy-resistant childhood asthma reveals distinct gene expression profiles.

BACKGROUND: Children with problematic severe asthma have poor disease control despite high doses of inhaled corticosteroids and additional therapy, leading to personal suffering, early deterioration of lung function, and significant consumption of health care resources. If no exacerbating factors, such as smoking or allergies, are found after extensive investigation, these children are given a diagnosis of therapy-resistant (or therapy-refractory) asthma (SA). OBJECTIVE: We sought to deepen our understanding of childhood SA by analyzing gene expression and modeling the underlying regulatory transcription factor networks in peripheral blood leukocytes. METHODS: Gene expression was analyzed by using Cap Analysis of Gene Expression in children with SA (n = 13), children with controlled persistent asthma (n = 15), and age-matched healthy control subjects (n = 9). Cap Analysis of Gene Expression sequencing detects the transcription start sites of known and novel mRNAs and noncoding RNAs. RESULTS: Sample groups could be separated by hierarchical clustering on 1305 differentially expressed transcription start sites, including 816 known genes and several novel transcripts. Ten of 13 tested novel transcripts were validated by means of RT-PCR and Sanger sequencing. Expression of RAR-related orphan receptor A (RORA), which has been linked to asthma in genome-wide association studies, was significantly upregulated in patients with SA. Gene network modeling revealed decreased glucocorticoid receptor signaling and increased activity of the mitogen-activated protein kinase and Jun kinase cascades in patients with SA. CONCLUSION: Circulating leukocytes from children with controlled asthma and those with SA have distinct gene expression profiles, demonstrating the possible development of specific molecular biomarkers and supporting the need for novel therapeutic approaches.

The Limited Incorporation and Role of Fluorine in Mn-rich Disordered Rocksalt Cathodes.

Disordered rocksalt oxide (DRX) cathodes are promising candidates for next-generation Co- and Ni-free Li-ion batteries. While fluorine substitution for oxygen has been explored as an avenue to enhance their performance, the amount of fluorine incorporated into the DRX structure is particularly challenging to quantify and impedes our ability to relate fluorination to electrochemical performance. Herein, an experimental-computational method combining 7Li and 19F solid-state nuclear magnetic resonance, and ab initio cluster expansion Monte Carlo simulations, is developed to determine the composition of DRX oxyfluorides. Using this method, the synthesis of Mn- and Ti-containing DRX via standard high temperature sintering and microwave heating is optimized. Further, the upper fluorination limit attainable using each of these two synthesis routes is established for various Mn-rich DRX compounds. A comparison of their electrochemical performance reveals that the capacity and capacity retention mostly depend on the Mn content, while fluorination plays a secondary role.

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.

Molecular profiling of high-level athlete skeletal muscle after acute endurance or resistance exercise - A systems biology approach.

OBJECTIVE: Long-term high-level exercise training leads to improvements in physical performance and multi-tissue adaptation following changes in molecular pathways. While skeletal muscle baseline differences between exercise-trained and untrained individuals have been previously investigated, it remains unclear how training history influences human multi-omics responses to acute exercise. METHODS: We recruited and extensively characterized 24 individuals categorized as endurance athletes with >15 years of training history, strength athletes or control subjects. Timeseries skeletal muscle biopsies were taken from M. vastus lateralis at three time-points after endurance or resistance exercise was performed and multi-omics molecular analysis performed. RESULTS: Our analyses revealed distinct activation differences of molecular processes such as fatty- and amino acid metabolism and transcription factors such as HIF1A and the MYF-family. We show that endurance athletes have an increased abundance of carnitine-derivates while strength athletes increase specific phospholipid metabolites compared to control subjects. Additionally, for the first time, we show the metabolite sorbitol to be substantially increased with acute exercise. On transcriptional level, we show that acute resistance exercise stimulates more gene expression than acute endurance exercise. This follows a specific pattern, with endurance athletes uniquely down-regulating pathways related to mitochondria, translation and ribosomes. Finally, both forms of exercise training specialize in diverging transcriptional directions, differentiating themselves from the transcriptome of the untrained control group. CONCLUSIONS: We identify a "transcriptional specialization effect" by transcriptional narrowing and intensification, and molecular specialization effects on metabolomic level Additionally, we performed multi-omics network and cluster analysis, providing a novel resource of skeletal muscle transcriptomic and metabolomic profiling in highly trained and untrained individuals.

Validation of skeletal muscle cis-regulatory module predictions reveals nucleotide composition bias in functional enhancers.

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions.

HER2/neu kinase-dependent modulation of androgen receptor function through effects on DNA binding and stability.

Given the role of the EGFR/HER2 family of tyrosine kinases in breast cancer, we dissected the molecular basis of EGFR/HER2 kinase signaling in prostate cancer. Using the small molecule dual EGFR/HER2 inhibitor PKI-166, we show that the biologic effects of EGFR/HER-2 pathway inhibition are caused by reduced AR transcriptional activity. Additional genetic and pharmacologic experiments show that this modulation of AR function is mediated by the HER2/ERBB3 pathway, not by EGFR. This HER2/ERBB3 signal stabilizes AR protein levels and optimizes binding of AR to promoter/enhancer regions of androgen-regulated genes. Surprisingly, the downstream signaling pathway responsible for these effects appears to involve kinases other than Akt. These data suggest that the HER2/ERBB3 pathway is a critical target in hormone-refractory prostate cancer.

JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner. Its fourth major release is the largest expansion of the core database to date: the database now holds 457 non-redundant, curated profiles. The new entries include the first batch of profiles derived from ChIP-seq and ChIP-chip whole-genome binding experiments, and 177 yeast TF binding profiles. The introduction of a yeast division brings the convenience of JASPAR to an active research community. As binding models are refined by newer data, the JASPAR database now uses versioning of matrices: in this release, 12% of the older models were updated to improved versions. Classification of TF families has been improved by adopting a new DNA-binding domain nomenclature. A curated catalog of mammalian TFs is provided, extending the use of the JASPAR profiles to additional TFs belonging to the same structural family. The changes in the database set the system ready for more rapid acquisition of new high-throughput data sources. Additionally, three new special collections provide matrix profile data produced by recent alternative high-throughput approaches.

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.

oPOSSUM: integrated tools for analysis of regulatory motif over-representation.

The identification of over-represented transcription factor binding sites from sets of co-expressed genes provides insights into the mechanisms of regulation for diverse biological contexts. oPOSSUM, an internet-based system for such studies of regulation, has been improved and expanded in this new release. New features include a worm-specific version for investigating binding sites conserved between Caenorhabditis elegans and C. briggsae, as well as a yeast-specific version for the analysis of co-expressed sets of Saccharomyces cerevisiae genes. The human and mouse applications feature improvements in ortholog mapping, sequence alignments and the delineation of multiple alternative promoters. oPOSSUM2, introduced for the analysis of over-represented combinations of motifs in human and mouse genes, has been integrated with the original oPOSSUM system. Analysis using user-defined background gene sets is now supported. The transcription factor binding site models have been updated to include new profiles from the JASPAR database. oPOSSUM is available at http://www.cisreg.ca/oPOSSUM/

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.

oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-Seq datasets.

oPOSSUM-3 is a web-accessible software system for identification of over-represented transcription factor binding sites (TFBS) and TFBS families in either DNA sequences of co-expressed genes or sequences generated from high-throughput methods, such as ChIP-Seq. Validation of the system with known sets of co-regulated genes and published ChIP-Seq data demonstrates the capacity for oPOSSUM-3 to identify mediating transcription factors (TF) for co-regulated genes or co-recovered sequences. oPOSSUM-3 is available at http://opossum.cisreg.ca.

Development of a second-generation antiandrogen for treatment of advanced prostate cancer.

Metastatic prostate cancer is treated with drugs that antagonize androgen action, but most patients progress to a more aggressive form of the disease called castration-resistant prostate cancer, driven by elevated expression of the androgen receptor. Here we characterize the diarylthiohydantoins RD162 and MDV3100, two compounds optimized from a screen for nonsteroidal antiandrogens that retain activity in the setting of increased androgen receptor expression. Both compounds bind to the androgen receptor with greater relative affinity than the clinically used antiandrogen bicalutamide, reduce the efficiency of its nuclear translocation, and impair both DNA binding to androgen response elements and recruitment of coactivators. RD162 and MDV3100 are orally available and induce tumor regression in mouse models of castration-resistant human prostate cancer. Of the first 30 patients treated with MDV3100 in a Phase I/II clinical trial, 13 of 30 (43%) showed sustained declines (by >50%) in serum concentrations of prostate-specific antigen, a biomarker of prostate cancer. These compounds thus appear to be promising candidates for treatment of advanced prostate cancer.

Progression of prostate cancer by synergy of AKT with genotropic and nongenotropic actions of the androgen receptor.

Classic work by Huggins and Hodges demonstrated that human prostate cancer regresses dramatically during antihormonal therapy but recurs frequently with androgen independence. Perturbations in the androgen receptor (AR) and PTEN-AKT signaling axes are significantly correlated with the progression of prostate cancer. Genetic alterations of the AR cause receptor hypersensitivity, promiscuity, and androgen-independent receptor transactivation. Prostate cancers maintain an elevated AKT activity through the loss of PTEN function or the establishment of autocrine signaling by growth factors and cytokines. We used an in vivo prostate regeneration system to investigate the biological potency of the potential crosstalk between these two signal transduction pathways. We demonstrate a direct synergy between AKT and AR signaling that is sufficient to initiate and progress naïve adult murine prostatic epithelium to frank carcinoma and override the effect of androgen ablation. Both genotropic and nongenotropic signals mediated by AR are essential for this synergistic effect. However, phosphorylation of AR by AKT at Ser-213 and Ser-791 is not critical for this synergy. These results suggest that more efficient therapeutics for advanced prostate cancer may need to target simultaneously AR signaling and AKT or the growth factor receptor tyrosine kinases that activate AKT.

Inference of transcriptional regulation relationships from gene expression data.

MOTIVATION: In order to find gene regulatory networks from microarray data, it is important to first find direct regulatory relationships between pairs of genes. RESULTS: We propose a new method for finding potential regulatory relationships between pairs of genes from microarray time series data and apply it to expression data for cell-cycle related genes in yeast. We compare our algorithm, dubbed the event method, with the earlier correlation method and the edge detection method by Filkov et al. When tested on known transcriptional regulation genes, all three methods are able to find similar numbers of true positives. The results indicate that our algorithm is able to identify true positive pairs that are different from those found by the two other methods. We also compare the correlation and the event methods using synthetic data and find that typically, the event method obtains better results. AVALIABILITY: software is available upon request.