Human Pancreatic β Cell lncRNAs Control Cell-Specific Regulatory Networks

1Transcriptome surveys have uncovered tens of thousands of mammalian transcripts longer than 200 nucleotides that have low protein-coding potential. A small fraction of these long non-coding RNAs (lncRNAs) have been shown to control gene expression by modulating chromosomal structure, transcription, splicing, mRNA transport, stability, or translation. Specific lncRNAs have thus been implicated in various key processes, including random X chromosome inactivation, imprinting, the cell cycle, organogenesis, differentiation, pluripotency, and cancer progression. Despite these wide-ranging biological roles, the fraction of lncRNAs that is genuinely functional and the true impact of lncRNAs in human biology and disease remain poorly understood.

2Pancreatic β cells regulate glucose homeostasis by secreting insulin and play a central role in the pathogenesis of major forms of diabetes mellitus. Recently, more than 1,100 lncRNAs were identified in human pancreatic islets and purified β cells as well as in mouse pancreatic islet cells. A large fraction of human β cell lncRNAs are cell-specific, and several are known to be activated during β cell differentiation. This cellular specificity has also been noted for lncRNAs in other cell types and points to the possibility that lncRNAs may regulate genetic programs important for lineage-specific differentiation or specialized cellular functions. Further, several β cell lncRNAs were shown to be regulated by extracellular glucose concentrations, suggesting a potential role of lncRNAs in the functional adaptation of β cells to increased insulin secretory demands. Some islet lncRNAs map to loci that contain polygenic or Mendelian defects associated with human diabetes, whereas selected lncRNAs show deregulation in islets from organ donors with human type 2 diabetes (T2D). Collectively, these properties define a newly identified class of candidate regulators of β cell differentiation and function, with potential implications for human diabetes mellitus. However, the true relevance of β cell lncRNAs depends on whether they elicit a physiological function in human β cells, which remains to be addressed systematically.

3In the current study, we have focused on a set of lncRNAs that show restricted expression in human pancreatic β cells and have tested the hypothesis that they regulate β cell gene expression. Our studies have uncovered a regulatory network in which lineage-specific lncRNAs and transcription factors (TFs) control common genes. Furthermore, we show that lncRNAs frequently regulate genes associated with clusters of islet enhancers, which have previously been shown to be the primary functional targets of islet-specific TFs. We performed a detailed analysis of a specific lncRNA named PLUTO, which controls PDX1, a master regulator of pancreas development and β cell differentiation and, thereby, modulates the PDX1-dependent transcriptional program. Finally, we show that PLUTO and PDX1 are downregulated in islets from organ donors with type 2 diabetes or impaired glucose tolerance, suggesting a potential role in human diabetes.

4To directly test the regulatory function of pancreatic β cell lncRNAs, we carried out loss-of-function experiments in a glucose-responsive human islet β cell line, EndoC-βH1. We chose a human model because only some human lncRNAs are evolutionary conserved, and we perturbed the function of lncRNAs through RNAi-based transcript knockdowns rather than genomic deletions because deletions could potentially disrupt cis-regulatory elements. We thus designed lentiviral vectors that contain RNA polymerase II-transcribed artificial microRNAs (hereafter referred to as amiRNA) with perfect homology to the target sequence to elicit target cleavage. The amiRNAs contain an artificial stem sequence targeting our lncRNA of choice as well as flanking and loop sequences from an endogenous miRNA to allow their processing as pre-miRNA by the RNAi pathway (Figure S1A). As a reference, we used the same strategy to knock down TFs that are well known to regulate gene expression in pancreatic islets as well as five different non-targeting amiRNA sequences as controls.

5The lncRNAs selected for knockdown were derived from a short list of 25 lncRNAs that showed (1) a markedly enriched expression in human islets and fluorescence-activated cell sorting (FACS)-purified β cells relative to the exocrine pancreas and a panel of non-pancreatic tissues, (2) expression in the EndoC-βH1 β cell line, and (3) a chromatin profile in human islets that was consistent with an active promoter (Figures S1C and S1D). Of these 25 lncRNAs, 12 were shortlisted because they were near a protein-coding gene that has an important function in β cells. The lncRNAs had variable subcellular enrichment patterns (Figure S1B), and eight of the 12 lncRNAs had detectable transcripts in orthologous or syntenic mouse regions (Table S1; ). We then screened four amiRNA sequences for each of the 12 lncRNAs and identified two efficient (>50% knockdown) amiRNAs for seven lncRNAs and one efficient amiRNA sequence for the other five lncRNAs (Figure S1E). Two efficient amiRNAs were also obtained for five essential islet TFs (HNF1A, GLIS3, MAFB, NKX2.2, and PDX1). We thus transduced EndoC-βH1 cells with lentiviruses expressing each amiRNA. This was done in duplicate or in triplicate for lncRNAs that only had one efficient amiRNA. 80 hr post-transduction, RNA was harvested and hybridized to oligonucleotide microarrays (Figure 1A). For each target gene, we combined expression data from all knockdowns and compared them to the control transductions with five different control amiRNAs to identify genes that were differentially expressed at a significance level of p < 10−3 (ANOVA) (Figure 1B).

6As expected, knockdown of islet TFs consistently produced transcriptional phenotypes (Figure 1B). Remarkably, knockdown of 9 of the 12 islet lncRNAs also caused transcriptional changes (Figure 1B; Figure S1F). A more detailed analysis showed that some of the lncRNAs that presented knockdown phenotypes had visible effects on a neighboring gene, suggesting a possible cis-regulatory mechanism, although other such lncRNAs did not appear to affect neighboring genes and may thus function through trans-regulatory mechanisms (Figure 1E; Figure S1G). These loss-of-function experiments with selected lncRNAs therefore suggested that lncRNAs can regulate the expression of pancreatic β cell genes.

7Gene silencing using the RNAi pathway can theoretically lead to nonspecific gene deregulation. In our experimental model, a significant nonspecific result would occur when two unrelated amiRNAs elicited changes in a common set of genes that were not observed in the panel of control non-targeting amiRNAs. To assess the likelihood that two unrelated amiRNA sequences elicit such an effect, we studied the five sets of control (non-targeting) amiRNAs, compared all ten possible combinations of two versus three control amiRNAs, and determined the number of differentially expressed genes (Figure 1C). Likewise, for each TF or lncRNA that had two valid amiRNAs, we compared the two target-specific amiRNAs against all possible combinations of three control amiRNAs (Figure 1C). As seen in Figure 1D, control versus control comparisons generated a median of 16 (IQR = 15–22) differentially expressed genes, whereas all five TFs and six of the seven lncRNA knockdowns led to a significantly higher number of differentially expressed genes (Mann-Whitney test, p < 10−4 for all lncRNA/TF versus control comparisons except HI-LNC75, p = 0.004, and HI-LNC76, p > 0.5). These results show that the observed phenotypes are unlikely to be caused by unspecific effects of amiRNAs and indicate that the sequence-specific inhibition of selected islet lncRNAs can result in transcriptional changes comparable in magnitude to the inhibition of well-established islet transcriptional regulators.

8The primary function of β cells is to synthesize and secrete insulin in response to changes in glucose concentrations. Among the genes that showed functional dependence on lncRNAs, we identified numerous genes that are known to regulate transcription or secretion in β cells, including RFX6, PDX1, CACNA1D, ATP2A3, ROBO1 and 2, PDE8A, ATP6AP1, KCNJ15, TRPM3, ERO1LB, and HADH (Figure 2A;,,,,,,,,, ). We therefore measured insulin content and glucose-stimulated insulin secretion (GSIS) in T antigen-excised EndoC-βH3 cells after knocking down four lncRNAs that showed the strongest transcriptional phenotypes (HI-LNC12, HI-LNC78, HI-LNC80, and HI-LNC71). Congruent with the broad transcriptional phenotype, we observed reduced insulin content and, consequently, impaired glucose-stimulated insulin secretion for HI-LNC12, HI-LNC78, and HI-LNC71 knockdowns (Figure 2B). For HI-LNC78, a glucose-regulated islet transcript that is orthologous to mouse Tunar and zebrafish megamind (linc-birc6) lncRNAs, there was a reduction in GSIS after correcting for the reduction in insulin content (p = 0.002) (Figure S2A). To further validate these effects, the same lncRNAs were downregulated using antisense locked nucleic acid (LNA GapmeRs, Exiqon) GapmeRs, which also led to impaired insulin secretion after knockdown of HI-LNC12 and HI-LNC78 (Figure S2B). Taken together, lncRNA knockdown studies identified lncRNAs that modulate gene expression and, consequently, insulin secretion in a human β cell line.

9To gain insight into the expression programs that are regulated by islet-specific lncRNAs and TFs, we compared their knockdown gene expression phenotypes. We first assessed changes in gene expression occurring after knockdown of the different islet TFs and found high Pearson correlation values for all pairwise comparisons (r = 0.4–0.8, p < 10−27) (Figure 3A; Figure S3). This finding is consistent with the notion that islet-specific TFs often bind to common genomic targets and function in a combinatorial manner. Interestingly, the transcriptional changes that occurred after the inhibition of several lncRNAs significantly correlated with those observed following inhibition of TFs (Figure 3A; Figure S3; see also a cluster analysis of TF- and lncRNA-dependent changes in Figure 3B). Some pairwise comparisons that illustrate this finding include HI-LNC78-dependent gene expression changes, which correlated highly with HNF1A- and MAFB-dependent changes (Pearson’s r = 0.87 and 0.89, respectively, p < 10−71), and HI-LNC15-dependent changes, which correlated with those occurring after knockdown of NKX2-2 (r = 0.67, p = 10−32) (Figure 3C). The results from these gene knockdown experiments therefore indicate that selected islet-specific lncRNAs and TFs can regulate common gene expression programs.

10Recent studies have revealed that islet TFs regulate cell-specific transcription by targeting clusters of enhancers and, in particular, clusters with enhancers that are bound by multiple islet TFs. Enhancer clusters share many features with regulatory domains that have otherwise been defined as “stretch enhancers” or “superenhancers”. Given that knockdown of islet lncRNAs and TFs suggested that they regulate similar genes, we asked whether islet lncRNAs also regulate enhancer cluster-associated genes. As expected, gene set enrichment analysis (GSEA) showed that genes with islet-enriched expression, genes associated with enhancer clusters, or genes associated with enhancers that are bound by multiple TFs were downregulated after knockdown of all five TFs, whereas this was not observed for ten control sets of genes expressed at similar levels (Figure 4; Figures S4A and S4B). Likewise, genes associated with enhancer clusters and those showing islet-specific expression were also enriched among genes that were downregulated after knockdown of HI-LNC12, 15, 30, 78, 80, 85, and 71 (Figure 4; Figures S4A and S4B). These results therefore indicate that islet-specific TFs and lncRNAs often co-regulate genes that are associated with enhancer clusters.

11We next used an independent experimental approach to validate the observation that human β cell lncRNAs and TFs regulate common gene expression programs. This involved the analysis of gene modules that show co-expression across a panel of human islet RNA samples. Analogous approaches have been employed to reveal sets of genes that share functional relationships. We implemented this analysis using weighted gene co-expression analysis (WGCNA) of RNA sequencing (RNA-seq) profiles from 64 human pancreatic islet samples. This identified 25 major gene modules containing more than 100 genes, named M1–M25, that showed highly significant co-expression across human islet samples (Figure 5A; Table S2). We next determined which co-expression modules contained islet lncRNAs. Rather than using our previously defined set of lncRNAs, this analysis was performed with a set of 2,373 β cell lncRNAs that was newly annotated using ∼5 billion stranded RNA-seq reads pooled from 41 islet samples (Table S3; Figure S5A). β Cell lncRNAs were found to be enriched in seven pancreatic islet co-expression modules (M3, M7, M12, M13, M18, M20, and M21) (Figure 5B).

12We next characterized the nature of these seven lncRNA-enriched co-expression modules. Five of these (M3, M7, M12, M18, and M20) were enriched in genes associated with pancreatic islet enhancer clusters (Figures 5A–5C, marked in blue). Two other modules (M13 and M21) were enriched for ubiquitously expressed genes involved in mRNA translation and metabolic pathways (Figure S5B). Among the modules enriched in lncRNAs and enhancer clusters, three (M3, M7, and M18) were also enriched in islet-specific TF genes (Figure 5D), and two of these modules (M3 and M7) contained nine of the 12 lncRNAs that had been knocked down in EndoC-βH1 cells. Module M3, the largest of the seven lncRNA-enriched modules, featured gene ontology (GO) terms associated with prototypical islet cell functions and contained several islet TFs and lncRNAs (Figure 5E). In keeping with these findings, we found numerous instances of islet lncRNAs and known cell-specific TFs that showed a tight correlation of gene expression levels across human islet samples (Figure 5F; Figure S5C). These findings thus indicated that β cell-specific lncRNAs, TFs, and genes associated with islet enhancer clusters form part of common expression programs.

13Further analysis is consistent with the notion that lncRNAs play a functional role in driving gene expression variation in the lncRNA-enriched co-expression modules. First, the subset of lncRNAs that were shown to regulate an adjacent gene in knockdown studies also exhibited a particular high co-regulation with the adjacent gene across islet samples (Figure S1G). This observation was extended to define 292 lncRNAs that displayed a highly significant (p < 10−7) correlation of expression with an adjacent protein-coding gene in the panel of human islet samples and are thus candidate cis-regulatory lncRNAs (Table S6). Second, we analyzed all genes that were significantly downregulated in EndoC-βH1 cells after knocking down HI-LNC12, 71, 78, and 80 and found that they were also enriched among genes in human islet modules M3, M7, and M18 but not in size-controlled modules (Figure S5D). In summary, co-expression analysis of native human islets corroborated the findings observed with amiRNA-based perturbations in EndoC-βH1 cells and indicated that a group of islet lncRNAs and TFs form part of common transcriptional networks that target clusters of pancreatic islet enhancers (Figure 5G).

14The identification of functional lncRNAs led us to explore whether some lncRNAs are abnormally expressed in human T2D and might thus be relevant to the pathogenesis of this disease. We therefore analyzed our new set of 2,373 lncRNAs in a recently reported gene expression dataset that includes human islet samples from donors diagnosed with T2D or impaired glucose tolerance (IGT). Our results showed that, despite the fact that gene expression across human islet donors is highly variable, the expression of 15 and 100 lncRNAs was significantly altered in islets from T2D and IGT versus non-diabetic donors respectively (adjusted p < 0.05) (Figure S6A; see Table S7 for a complete list). This finding suggests a potential role of functional β cell lncRNAs in driving some of the β cell gene expression changes that are associated with T2D.

15To explore how β cell lncRNAs can regulate cell-specific transcriptional networks, we focused on HI-LNC71, a nuclearly enriched transcript (Figure S1B) that is transcribed from a promoter that is located ∼3 kb upstream of PDX1, in an antisense orientation (Figure S6B). PDX1 is an essential transcriptional regulator of pancreas development and β cell function that has been implicated in genetic mechanisms underlying Mendelian and type 2 diabetes. Based on this genomic location, we renamed HI-LNC71 PLUTO, for PDX1 locus upstream transcript.

16The potential importance of PLUTO was strengthened by the observation that PLUTO was among the most markedly downregulated lncRNAs in islets from T2D or IGT donors (adjusted p value = 0.07 and 0.005, respectively; Figure 6A; Figure S6B). Interestingly, PDX1 was also downregulated in islets from donors with T2D and IGT (Figure 6A).

17PLUTO is a multi-isoform transcript that contains five major exons that span nearly 100 kb, encompassing a cluster of enhancers that make 3D contacts with the PDX1 promoter in human islets and in EndoC-βH1 cells (Figure 6B; Figure S6A). This observation suggested that PLUTO could affect cis regulation of the PDX1 gene.

18To test whether PLUTO regulates PDX1, we first examined EndoC-βH1 cells after amiRNA-mediated knockdown of PLUTO RNA and found reduced PDX1 mRNA and protein levels (Figure 6C). Similarly, knockdown of PLUTO RNA in dispersed primary human islet cells caused decreased PDX1 mRNA (Figure 6D). To validate these experiments through a complementary approach, we used CRISPR interference (CRISPRi), which involves targeting guide RNAs (gRNAs) downstream of a gene’s transcriptional initiation site to block its transcription. Two independent gRNAs that targeted a region downstream of the PLUTO initiation site efficiently reduced PLUTO RNA levels relative to non-targeting gRNAs, and, in both cases, this led to decreased PDX1 mRNA expression (Figure 6E). Therefore, perturbing either PLUTO RNA levels or its transcription leads to the same inhibitory effect on PDX1 mRNA.

19The mouse Pdx1 locus also has an islet lncRNA (Pluto) that shows only limited sequence homology with human PLUTO. Pluto is also transcribed from the opposite strand of Pdx1 but is initiated from a promoter within the first intron of Pdx1 and, like PLUTO, spans a broad regulatory domain upstream of Pdx1 (Figure S6C). Knockdown of Pluto RNA in the mouse β cell line MIN6 also led to decreased Pdx1 mRNA levels (Figure S6E). These experiments therefore indicated that PLUTO regulates PDX1 mRNA in human β cell lines and primary islet cells, and an analogous effect was observed for the mouse lncRNA ortholog.

20Consistent with this regulatory relationship, PLUTO and PDX1 RNA levels are highly correlated across islet samples (Pearson’s r = 0.86, p = 10−15; Figure 6F), and knockdown of PDX1 and PLUTO in EndoC-βH1 cells resulted in the deregulation of a shared set of genes (Figures 6G–6J). Furthermore, Pluto and Pdx1 were found to be regulated with nearly identical dynamics in response to a shift in glucose concentration (4–11 mM) in mouse pancreatic islets (Figure S6D). PLUTO and PDX1 therefore regulate a common program in pancreatic islets, and this is at least in part explained by the fact that PLUTO regulates PDX1.

21To assess the mechanisms underlying the function of PLUTO, we first examined whether PLUTO controls the stability or transcription of PDX1. Transcriptional inhibition experiments using Actinomycin D showed no significant differences in the stability of PDX1 mRNA upon PLUTO knockdown (Figure 7A). By contrast, intronic PDX1 RNA was reduced upon PLUTO knockdown, suggesting that PLUTO regulates PDX1 transcription (Figure 7B).

22Because PLUTO spans an enhancer cluster, we hypothesized that it could regulate the chromatin state of active enhancers. We thus knocked down PLUTO in β cells and measured H3K27 acetylation as well as H3K4 mono- and tri-methylation levels at several enhancers within the cluster. Our results indicate no significant changes in these characteristic active chromatin marks (Figure S7).

23We next determined whether PLUTO affects the 3D contacts between the enhancer cluster and the PDX1 promoter. Examination of the PDX1 locus using quantitative chromatin conformation capture (3C) assays revealed that two far upstream enhancers (Figure 7C) showed reduced contacts with the PDX1 promoter after PLUTO knockdown (Figure 7D). These findings therefore show that PLUTO regulates the transcription of PDX1, a key pancreatic β cell transcriptional regulator, and that this is associated with its ability to promote contacts between the PDX1 promoter and its enhancer cluster (Figure 7E).

24In the current study, we have tested the hypothesis that lncRNAs play a role in cell-specific gene regulation in pancreatic β cells, a cell type that is central in the pathogenesis of human diabetes. We have thus carried out, for the first time, a systematic analysis of the function of a set of human β cell-specific lncRNAs. Our experiments revealed several examples of β cell lncRNAs in which sequence-specific perturbation causes transcriptional and functional phenotypes. We have further shown that β cell-specific lncRNAs and TFs regulate a common transcriptional network. Finally, we have demonstrated that β cell-specific lncRNAs directly or indirectly participate in the regulation of human enhancer clusters, which are the major functional targets of islet-specific transcription factors and key cis-regulatory determinants of islet cell transcriptional programs. Importantly, these conclusions are supported by concordant results from co-expression network analysis and loss of function experiments. These studies should be interpreted in light of previous evidence indicating that a significant fraction of lncRNAs show lineage-specific expression. Our study extends previous findings by demonstrating a functional role of lncRNAs in lineage-specific TF networks.

25Our findings invite the question of what molecular mechanisms underlie the regulatory effects of β cell lncRNAs. LncRNAs have been proposed to control gene expression through diverse molecular mechanisms, including the formation of protein-specific interactions and scaffolds, RNA-DNA or RNA-RNA hybrids, the titration of miRNAs, and the modulation of 3D chromosomal structures, whereas some transcripts currently defined as lncRNAs can theoretically encode for atypical small peptide sequences. Our knockdown and co-expression analyses have identified a subset of functional lncRNAs that appear to regulate a nearby gene, suggesting a lncRNA-based cis-regulatory mechanism, whereas others are likely to exert trans-regulatory effects. We focused on one functional nuclear-enriched β cell lncRNA, PLUTO, and found that its function in β cell networks is at least in part due to its ability to elicit an effect on the transcription of its adjacent gene, PDX1, which encodes a key β cell transcription factor. Importantly, this was observed for both the mouse and human orthologs, and similar effects were obtained through RNAi suppression or through CRISPR-induced transcriptional interference of PLUTO. Our studies further showed that PLUTO promotes 3D interactions between the PDX1 promoter and its upstream enhancer cluster, which is contained within the body of the PLUTO gene. We thus propose that PLUTO regulates the 3D architecture of the enhancer cluster at the PDX1 locus. This finding is reminiscent but distinct from earlier examples of non-coding RNA genes that modulate 3D chromosomal structure. Given that a significant number of lncRNAs are co-expressed with adjacent lineage-specific protein-coding genes, it is possible that the general regulatory paradigm described here is relevant to analogous lncRNA-protein coding gene pairs.

26Taken together, our data implicate cell-specific lncRNAs in human β cell transcriptional programs. Given the importance of TFs in the pathophysiology of human diabetes and their role in β cell programming strategies, it now seems reasonable to explore whether β cell lncRNAs also play analogous roles. The findings reported here therefore strengthen earlier suggestions that defects in β cell lncRNAs might contribute to the pathogenesis of human diabetes and warrant an assessment of whether they can be harnessed to promote β cell differentiation, function, or cellular mass.

27Human islets used for RNA-seq and chromatin immunoprecipitation sequencing (ChIP-seq) were cultured with CMRL 1066 medium containing 10% fetal calf serum (FCS) before shipment, after which they were cultured for 3 days with RPMI 1640 medium containing 11 mM glucose and supplemented with 10% FCS.

28Glucose-stimulated insulin release was assayed in EndoC-βH1 or EndoC-βH3 cells as described previously.

29RNA was isolated with Tripure (Roche) and treated with DNase I (Sigma). qPCR was performed with SYBR green or Taqman probe detection. See Table S4 for oligonucleotide and probe sequences.

30Lentiviral vectors carrying amiRNAs targeting TFs, lncRNAs, and non-targeting control sequences were transduced into the EndoC-βH1 human β cell line as described previously.

31Figure S1A illustrates the vector design. Oligonucleotide sequences are shown in Table S4. Non-transduced cells were assayed in parallel. Cells were harvested 80 hr post transduction for RNA extraction. For transduction of human islets, islets were first dispersed using trypsin-EDTA and gentle agitation. CRISPRi experiments were performed with two gRNAs designed to target PLUTO exon 1 or two unrelated intergenic control regions and transfected in EndoC-βH3 cells (Table S4).

32RNA was hybridized onto HTA2.0 Affymetrix arrays. RMA normalization was carried out using Expression Console (Affymetrix). Gene-based differential expression analysis was done using Transcriptome Analysis Console (TAC, Affymetrix). Enhancer cluster genes were defined by genes that were associated with clustered islet enhancers that show top 50 percentile binding by TFs (PDX1, FOXA2, NKX2-2, NKX6.1, and MAFB) as defined previously. Pancreatic islet gene sets used for enrichment analysis are shown in Table S5. A list of islet-enriched genes was generated as those with more than two SDs higher expression in human islets than the average expression in 16 human tissues (Table S5).

33RNA-seq data have been described previously. The samples were aligned to the hg19 genome using STAR aligner version 2.3.0 as described in the Supplemental Experimental Procedures, quantification was carried out with HTseq-Count 0.6.1, and differential expression analysis of lncRNA genes was done using DEseq2 1.10 (Table S3) using an adjusted p value threshold of 0.05.

343C and 4C-seq was carried out as described previously For real-time PCR quantification, readings were normalized to a control region within the PDX1 intron. Normalized values are expressed as a fraction of non-targeting amiRNA control sample. See Table S4 for oligonucleotide sequences.

35LncRNAs were annotated through de novo assembly of ∼5 billion stranded paired-end RNA-seq reads from 41 human islet samples, filtered for expression in FACS-purified β cell cells, lack of enrichment in the pancreatic exocrine fraction to exclude acinar contaminants, and the presence of H3K4me3 enrichment in the vicinity of the 5′ end. A more detailed description of the annotation process is provided in the Supplemental Information. Annotations are available in Table S3 and can be accessed on a UCSC Genome Browser (GRCh37/hg19) session by selecting “track hubs” and “Human Islet lncRNAs.” Alternatively, the track hub can be directly visualized in the UCSC Genome Browser using the following link: http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&hubUrl=http://www.imperial.ac.uk/medicine/beta-cell-genome-regulation-laboratory/data/HILNCs/HILNCs.txt&hgS_loadUrlName=http://www.imperial.ac.uk/medicine/beta-cell-genome-regulation-laboratory/data/.

36The WGCNA(v2) tool was used to build a co-transcriptional network based on mRNAs from 64 human islet RNA-seq samples.

37J.F. and I.A. conceived the idea, designed the experiments, and wrote the manuscript. J.F. supervised and I.A. coordinated the project. I.A., Z.T., H.W., J.Y., C.A., E.S., A.S., L. Pasquali, and D.M.Y.R. contributed to data analysis. I.A., A.B., M.B., C.S.C., R.G.F., J.G.H., and N.C. performed the experiments. D.M.Y.R., I.M., and N.N. annotated lncRNAs. L. Piemonti, T.B., C.B., J.K.C., and F.P. provided samples. I.A., J.F., Z.T., A.B., D.M.Y.R, L.G., C.B., J.K.C., F.P., P.R., A.S., L.G., C.A., and E.S. discussed the results. All authors read and approved the manuscript. Z.T., A.B., D.M.Y.R., C.S.C., and N.N. contributed equally.