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.

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.

xcore: an R package for inference of gene expression regulators.

BACKGROUND: Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types. RESULTS: Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq "signatures" directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression. CONCLUSIONS: xcore provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases, xcore can identify meaningful molecular signatures and relevant ChIP-seq experiments.

PCGF1-PRC1 links chromatin repression with DNA replication during hematopoietic cell lineage commitment.

Polycomb group proteins (PcG), polycomb repressive complexes 1 and 2 (PRC1 and 2), repress lineage inappropriate genes during development to maintain proper cellular identities. It has been recognized that PRC1 localizes at the replication fork, however, the precise functions of PRC1 during DNA replication are elusive. Here, we reveal that a variant PRC1 containing PCGF1 (PCGF1-PRC1) prevents overloading of activators and chromatin remodeling factors on nascent DNA and thereby mediates proper deposition of nucleosomes and correct downstream chromatin configurations in hematopoietic stem and progenitor cells (HSPCs). This function of PCGF1-PRC1 in turn facilitates PRC2-mediated repression of target genes such as Hmga2 and restricts premature myeloid differentiation. PCGF1-PRC1, therefore, maintains the differentiation potential of HSPCs by linking proper nucleosome configuration at the replication fork with PcG-mediated gene silencing to ensure life-long hematopoiesis.

Distinctive exercise-induced inflammatory response and exerkine induction in skeletal muscle of people with type 2 diabetes.

Mechanistic insights into the molecular events by which exercise enhances the skeletal muscle phenotype are lacking, particularly in the context of type 2 diabetes. Here, we unravel a fundamental role for exercise-responsive cytokines (exerkines) on skeletal muscle development and growth in individuals with normal glucose tolerance or type 2 diabetes. Acute exercise triggered an inflammatory response in skeletal muscle, concomitant with an infiltration of immune cells. These exercise effects were potentiated in type 2 diabetes. In response to contraction or hypoxia, cytokines were mainly produced by endothelial cells and macrophages. The chemokine CXCL12 was induced by hypoxia in endothelial cells, as well as by conditioned medium from contracted myotubes in macrophages. We found that CXCL12 was associated with skeletal muscle remodeling after exercise and differentiation of cultured muscle. Collectively, acute aerobic exercise mounts a noncanonical inflammatory response, with an atypical production of exerkines, which is potentiated in type 2 diabetes.

Subcutaneous adipose tissue expansion mechanisms are similar in early and late onset overweight/obesity.

BACKGROUND/OBJECTIVE: The development of overweight/obesity associates with alterations in white adipose tissue (WAT) cellularity (fat cell size/number) and lipid metabolism, in particular lipolysis. If these changes differ between early/juvenile (EOO < 18 years of age) or late onset overweight/obesity (LOO) is unknown and was presently examined. SUBJECTS/METHODS: We included 439 subjects with validated information on body mass index (BMI) at 18 years of age. Using this information and current BMI, subjects were divided into never overweight/obese (BMI < 25 kg/m2), EOO and LOO. Adipocyte size, number, morphology (size in relation to body fat) and lipolysis were determined in subcutaneous abdominal WAT. Body composition and WAT distribution was assessed by dual-X-ray absorptiometry. RESULTS: Compared with never overweight/obese, EOO and LOO displayed larger WAT amounts in all examined depots, which in subcutaneous WAT was explained by a combination of increased size and number of fat cells in EOO and LOO. EOO had 40% larger subcutaneous fat mass than LOO (p < 0.0001). Visceral WAT mass, WAT morphology and lipolysis did not differ between EOO and LOO except for minor differences in men between the two obesity groups. On average, the increase in BMI per year was 57% higher in subjects with EOO compared to LOO (p < 0.0001). CONCLUSION: Early onset overweight/obesity causes a more rapid and pronounced accumulation of subcutaneous WAT than adult onset. However, fat mass expansion measures including WAT cellularity, morphology and fat cell lipolysis do not differ in an important way suggesting that similar mechanisms of WAT growth operate in EOO and LOO.

Inducing human retinal pigment epithelium-like cells from somatic tissue.

Regenerative medicine relies on basic research outcomes that are only practical when cost effective. The human eyeball requires the retinal pigment epithelium (RPE) to interface the neural retina and the choroid at large. Millions of people suffer from age-related macular degeneration (AMD), a blinding multifactor genetic disease among RPE degradation pathologies. Recently, autologous pluripotent stem-cell-derived RPE cells were prohibitively expensive due to time; therefore, we developed a faster reprogramming system. We stably induced RPE-like cells (iRPE) from human fibroblasts (Fibs) by conditional overexpression of both broad plasticity and lineage-specific transcription factors (TFs). iRPE cells displayed critical RPE benchmarks and significant in vivo integration in transplanted retinas. Herein, we detail the iRPE system with comprehensive single-cell RNA sequencing (scRNA-seq) profiling to interpret and characterize its best cells. We anticipate that our system may enable robust retinal cell induction for basic research and affordable autologous human RPE tissue for regenerative cell therapy.

DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.

Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.

ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.

Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.

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.

Automatic identification of small molecules that promote cell conversion and reprogramming.

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although transcription factors are able to promote cell reprogramming and transdifferentiation, methods based on their upregulation often show low efficiency. Small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here, we present DECCODE, an unbiased computational method for identification of such molecules based on transcriptional data. DECCODE matches a large collection of drug-induced profiles for drug treatments against a large dataset of primary cell transcriptional profiles to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive validation in the context of human induced pluripotent stem cells shows that DECCODE is able to prioritize drugs and drug combinations enhancing cell reprogramming. We also provide predictions for cell conversion with single drugs and drug combinations for 145 different cell types.

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.

Comparative transcriptomics of primary cells in vertebrates.

Gene expression profiles in homologous tissues have been observed to be different between species, which may be due to differences between species in the gene expression program in each cell type, but may also reflect differences in cell type composition of each tissue in different species. Here, we compare expression profiles in matching primary cells in human, mouse, rat, dog, and chicken using Cap Analysis Gene Expression (CAGE) and short RNA (sRNA) sequencing data from FANTOM5. While we find that expression profiles of orthologous genes in different species are highly correlated across cell types, in each cell type many genes were differentially expressed between species. Expression of genes with products involved in transcription, RNA processing, and transcriptional regulation was more likely to be conserved, while expression of genes encoding proteins involved in intercellular communication was more likely to have diverged during evolution. Conservation of expression correlated positively with the evolutionary age of genes, suggesting that divergence in expression levels of genes critical for cell function was restricted during evolution. Motif activity analysis showed that both promoters and enhancers are activated by the same transcription factors in different species. An analysis of expression levels of mature miRNAs and of primary miRNAs identified by CAGE revealed that evolutionary old miRNAs are more likely to have conserved expression patterns than young miRNAs. We conclude that key aspects of the regulatory network are conserved, while differential expression of genes involved in cell-to-cell communication may contribute greatly to phenotypic differences between species.

Functional annotation of human long noncoding RNAs via molecular phenotyping.

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

The Transcriptional Network That Controls Growth Arrest and Macrophage Differentiation in the Human Myeloid Leukemia Cell Line THP-1.

The response of the human acute myeloid leukemia cell line THP-1 to phorbol esters has been widely studied to test candidate leukemia therapies and as a model of cell cycle arrest and monocyte-macrophage differentiation. Here we have employed Cap Analysis of Gene Expression (CAGE) to analyze a dense time course of transcriptional regulation in THP-1 cells treated with phorbol myristate acetate (PMA) over 96 h. PMA treatment greatly reduced the numbers of cells entering S phase and also blocked cells exiting G2/M. The PMA-treated cells became adherent and expression of mature macrophage-specific genes increased progressively over the duration of the time course. Within 1-2 h PMA induced known targets of tumor protein p53 (TP53), notably CDKN1A, followed by gradual down-regulation of cell-cycle associated genes. Also within the first 2 h, PMA induced immediate early genes including transcription factor genes encoding proteins implicated in macrophage differentiation (EGR2, JUN, MAFB) and down-regulated genes for transcription factors involved in immature myeloid cell proliferation (MYB, IRF8, GFI1). The dense time course revealed that the response to PMA was not linear and progressive. Rather, network-based clustering of the time course data highlighted a sequential cascade of transient up- and down-regulated expression of genes encoding feedback regulators, as well as transcription factors associated with macrophage differentiation and their inferred target genes. CAGE also identified known and candidate novel enhancers expressed in THP-1 cells and many novel inducible genes that currently lack functional annotation and/or had no previously known function in macrophages. The time course is available on the ZENBU platform allowing comparison to FANTOM4 and FANTOM5 data.

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions.

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure.

A framework for identification of on- and off-target transcriptional responses to drug treatment.

Owing to safety concerns or insufficient efficacy, few drug candidates are approved for marketing. Drugs already on the market may be withdrawn due to adverse effects (AEs) discovered after market introduction. Comprehensively investigating the on-/off-target effects of drugs can help expose AEs during the drug development process. We have developed an integrative framework for systematic identification of on-/off-target pathways and elucidation of the underlying regulatory mechanisms, by combining promoter expression profiling after drug treatment with gene perturbation of the primary drug target. Expression profiles from statin-treated cells and HMG-CoA reductase knockdowns were analyzed using the framework, allowing for identification of not only reported adverse effects but also novel candidates of off-target effects from statin treatment, including key regulatory elements of on- and off-targets. Our findings may provide new insights for finding new usages or potential side effects of drug treatment.

MicroRNA-27a/b-3p and PPARG regulate SCAMP3 through a feed-forward loop during adipogenesis.

MicroRNAs (miRNA) modulate gene expression through feed-back and forward loops. Previous studies identified miRNAs that regulate transcription factors, including Peroxisome Proliferator Activated Receptor Gamma (PPARG), in adipocytes, but whether they influence adipogenesis via such regulatory loops remain elusive. Here we predicted and validated a novel feed-forward loop regulating adipogenesis and involved miR-27a/b-3p, PPARG and Secretory Carrier Membrane Protein 3 (SCAMP3). In this loop, expression of both PPARG and SCAMP3 was independently suppressed by miR-27a/b-3p overexpression. Knockdown of PPARG downregulated SCAMP3 expression at the late phase of adipogenesis, whereas reduction of SCAMP3 mRNA levels increased PPARG expression at early phase in differentiation. The latter was accompanied with upregulation of adipocyte-enriched genes, including ADIPOQ and FABP4, suggesting an anti-adipogenic role for SCAMP3. PPARG and SCAMP3 exhibited opposite behaviors regarding correlations with clinical phenotypes, including body mass index, body fat mass, adipocyte size, lipolytic and lipogenic capacity, and secretion of pro-inflammatory cytokines. While adipose PPARG expression was associated with more favorable metabolic phenotypes, SCAMP3 expression was linked to increased fat mass and insulin resistance. Together, we identified a feed-forward loop through which miR-27a/b-3p, PPARG and SCAMP3 cooperatively fine tune the regulation of adipogenesis, which potentially may impact whole body metabolism.

Detailed Functional Characterization of a Waist-Hip Ratio Locus in 7p15.2 Defines an Enhancer Controlling Adipocyte Differentiation.

We combined CAGE sequencing in human adipocytes during differentiation with data from genome-wide association studies to identify an enhancer in the SNX10 locus on chromosome 7, presumably involved in body fat distribution. Using reporter assays and CRISPR-Cas9 gene editing in human cell lines, we characterized the role of the enhancer in adipogenesis. The enhancer was active during adipogenesis and responded strongly to insulin and isoprenaline. The allele associated with increased waist-hip ratio in human genetic studies was associated with higher enhancer activity. Mutations of the enhancer resulted in less adipocyte differentiation. RNA sequencing of cells with disrupted enhancer showed reduced expression of established adipocyte markers, such as ADIPOQ and LPL, and identified CHI3L1 on chromosome 1 as a potential gene involved in adipocyte differentiation. In conclusion, we identified and characterized an enhancer in the SNX10 locus and outlined its plausible mechanisms of action and downstream targets.