Long non-coding RNAs (lncRNAs) belong to a recently discovered class of molecules proposed to regulate various cellular processes. Here, we systematically analyzed their expression in human subcutaneous white adipose tissue (WAT) and found that a limited set was differentially expressed in obesity and/or the insulin resistant state. Two lncRNAs herein termed adipocyte-specific metabolic related lncRNAs, ASMER-1 and ASMER-2 were enriched in adipocytes and regulated by both obesity and insulin resistance. Knockdown of either ASMER-1 or ASMER-2 by antisense oligonucleotides in in vitro differentiated human adipocytes revealed that both genes regulated adipogenesis, lipid mobilization and adiponectin secretion. The observed effects could be attributed to crosstalk between ASMERs and genes within the master regulatory pathways for adipocyte function including PPARG and INSR. Altogether, our data demonstrate that lncRNAs are modulators of the metabolic and secretory functions in human fat cells and provide an emerging link between WAT and common metabolic conditions.

26Single-cell transcriptomic profiling is a powerful tool to explore cellular heterogeneity. However, 27 most of these methods focus on the 3'-end of polyadenylated transcripts and provide only a 28 partial view of the transcriptome. We introduce C1 CAGE, a method for the detection of 29 transcript 5'-ends with an original sample multiplexing strategy in the C1 TM microfluidic system. 30We first quantified the performance of C1 CAGE and found it as accurate and sensitive as other 31 methods in C1 system. We then used it to profile promoter and enhancer activities in the cellular 32 response to TGF-β of lung cancer cells and discovered subpopulations of cells differing in their 33 response. We also describe enhancer RNA dynamics revealing transcriptional bursts in subsets 34 of cells with transcripts arising from either strand within a single-cell in a mutually exclusive 35 manner, which was validated using single molecule fluorescence in-situ hybridization. 37Single-cell transcriptomic profiling can be used to uncover the dynamics of cellular states and 38 gene regulatory networks within a cell population (Trapnell, 2015; Wagner, Regev and Yosef, 39 2016). Most available single-cell methods capture the 3'-end of transcripts and are unable to 40 identify where transcription initiates. Instead, capturing the 5'-end of transcripts allows the 41 identification of transcription start sites (TSS) and thus the inference of the activities of their 42 regulatory elements. Cap analysis gene expression (CAGE), which captures the 5'-end of 43 transcripts, is a powerful tool to identify TSS at single nucleotide resolution (Shiraki et al., 2003; 44 Carninci et al., 2006). Using this technique, the FANTOM consortium has built an atlas of TSS 45 across major human cell-types and tissues (Forrest et al., 2014), analysis of which has led to the 46 identification of promoters as well as enhancers in the human genome (Andersson et al., 2014; 47 Hon et al., 2017). Enhancers have been implicated in a variety of biological processes (Lam et 48 al., 2014; Li, Notani and Rosenfeld, 2016), including the initial activation of responses to 49 stimuli (Arner et al., 2015) and chromatin remodeling for transcriptional activation (Mousavi et al., 50 2013). In addition, over 60% of the fine-mapped causal noncoding variants in autoimmune 51 disease lay within immune-cell enhancers (Farh et al., 2015), suggesting the relevance of 52 enhancers in pathogenesis of complex diseases. Enhancers have been identified by the 53 presence of balanced bidirectional transcription producing enhancer RNAs (eRNAs), which are 54 generally short, unstable and non-polyadenylated (non-polyA) (Andersson et al., 2014). Single 55 molecule fluorescence in situ hybridization (smFISH) studies have suggested that eRNAs are 56 induced with similar kinetics to their target mRNAs but that co-expression at individual alleles 57 was infrequent (Rahman et al., 2016). However, the majority of enhancer studies have been 58 conducted using bulk populations of cells...

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31Background: Osteoarthritis (OA) is a common joint disorder with increasing impact 32 in an aging society; however, there is no cure or effective treatments so far due to lack 33 of sufficient understanding of its pathogenesis. While genome-wide association studies 34 (GWAS) and DNA methylation profiling identified many non-coding loci associated 35 to OA, the interpretation of them remains challenging. 36Methods: Here, we employed Assay for Transposase-Accessible Chromatin with high 37 throughput sequencing (ATAC-seq) to map the accessible chromatin landscape in 38 articular knee cartilage of OA patients and to identify the chromatin signatures relevant 39 to OA. 40 Results:We identified 109,215 accessible chromatin regions in cartilage and 71% of 41 these regions were annotated as enhancers. We found these accessible chromatin 42 regions are enriched for OA GWAS single nucleotide polymorphisms (SNPs) and OA 43 differentially methylated loci, implying their relevance to OA. By linking these 44 enhancers to their potential target genes, we have identified a list of candidate enhancers 45 that may be relevant to OA. Through integration of ATAC-seq data with RNA-seq data, 46we identified genes that are altered both at epigenomic and transcriptomic levels. These 47 enriched for mesenchymal stem cell-specific enhancers and motifs of transcription 50 factor families involved in osteoblast differentiation (e.g. bZIP and ETS). 51 Conclusions: This study marks the first investigation of accessible chromatin 52 landscape on clinically relevant hard tissues and demonstrates how accessible 53 chromatin profiling can provide comprehensive epigenetic information of a disease. 54 Our analyses provide supportive evidence towards the model of endochondral 55 ossification-like cartilage-to-bone conversion in OA knee cartilage, which is consistent 56 with the OA characteristic of thicker subchondral bone. The identified OA-relevant 57 genes and their enhancers may have a translational potential for diagnosis or drug 58 targets. 59 60 Keywords 61 osteoarthritis, epigenomics, enhancer, ATAC-seq, endochondral ossification. 62 63 Background 64Osteoarthritis (OA) is a degenerative joint disease [1,2] that is one of the most common 65 causes of chronic disability in the world [3,4], of which the knee OA is the most 66 common. Main features of OA include cartilage degradation, subchondral bone 67 thickening, joint space narrowing and osteophytes formation [5], resulting in stiffness, 68 swelling, and pain in the joint. Currently available treatments are either pain relief or 69 joint function improvement by strengthening the supporting muscles. However, OA 70 progression ultimately leads to costly total joint replacement surgery, making it a 71 growing global health burden. 72Although the causes of OA are not well understood, risk factors such as age, 73 weight, gender, and genetic factors have been identified [4]. Several models for OA 74 initiation, such as mechanical injury, inflammatory mediators from synovium, defects 75 4 in meta...

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including disease. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 100,000 coding and non-coding genes as described by the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analysis across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, FC-R2 will empower other researchers to investigate the roles of both known genes and recently described lncRNAs. Access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.