Long intergenic noncoding RNAs (lincRNAs) are derived from thousands of loci in mammalian genomes and are frequently enriched in transposable elements (TEs). Although families of TE-derived lincRNAs have recently been implicated in the regulation of pluripotency, little is known of the specific functions of individual family members. Here we characterize three new individual TE-derived human lincRNAs, human pluripotency-associated transcripts 2, 3 and 5 (HPAT2, HPAT3 and HPAT5). Loss-of-function experiments indicate that HPAT2, HPAT3 and HPAT5 function in preimplantation embryo development to modulate the acquisition of pluripotency and the formation of the inner cell mass. CRISPR-mediated disruption of the genes for these lincRNAs in pluripotent stem cells, followed by whole-transcriptome analysis, identifies HPAT5 as a key component of the pluripotency network. Protein binding and reporter-based assays further demonstrate that HPAT5 interacts with the let-7 microRNA family. Our results indicate that unique individual members of large primate-specific lincRNA families modulate gene expression during development and differentiation to reinforce cell fate.
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We thank members of the Reijo Pera laboratory for thoughtful feedback and comments regarding manuscript preparation. We thank Z. Siprashvili for assistance in the protein microarray experiments. We thank S. Marro for the neural transdifferentiation experiments and L.B. Torrez for the siRNA experiments. We thank G. Glinsky for thoughtful feedback regarding data assessment. This work was funded by grants U54-1U54HD068158-01 (Stanford University Center for Reproductive and Stem Cell Biology), U01-1U01HL100397-01 (Basic and Translational Research of iPSC-based Hematologic and Vascular Therapies), R01HG006018 (US National Institutes of Health) and RB3-2209 (California Institute of Regenerative Medicine). No federal funding was used for human embryo studies.
The authors declare no competing financial interests.
Integrated supplementary information
(a) The different classes of TEs are color-coded; corresponding colors are used in b–e. (b,c) Coverage of different TE classes on the genome (exons + introns) and transcript (only exons) levels. The percentage of total length for each TE is represented for all 23 HPATs. Three control genes are included. (d) The 23 HPATs with embedded TEs and genomic length. Displayed are the most highly expressed isoforms for each HPAT gene. Genomic DNA is represented as a black line, and exons are represented by gray boxes. TEs are represented by the colored boxes underneath. Each exon is exonized with TEs (exons overlap TEs). The length of each genomic locus is not to scale.
Supplementary Figure 2 Molecular and functional analysis of HPAT2, HPAT3 and HPAT5 during preimplantation development.
(a) Magnified view of the ICM in human blastocyst demonstrates a specific staining pattern in the ICM of human blastocysts. Stars depict HPAT3 signal. Arrows depict HPAT5 signal. (b) HPAT2, HPAT3 and HPAT5 are significantly downregulated in human blastocysts injected with siRNAs compared to siScrambled controls (n = 3 blastocysts; data are shown with s.e.m.). (c) Blastomeres with knockdown of HPAT2, HPAT3 and HPAT5 during human embryo development did not contribute to ICM. The presence of ICM was validated with OCT4 and SOX2 staining. The ICM is highlighted by a yellow dashed circle. (d) Fluorescence-positive ICM in blastocysts with HPAT knockdown and control blastocysts.
(a) Histological sections stained with hematoxylin and eosin from teratomas derived from established iPSCs (iPSCs that resulted from derivation from BJ fibroblasts are termed fully established iPSCs and were used as the last time point for collection (see b). (b) Tracking of morphological changes of BJ fibroblasts during mRNA reprogramming with the Yamanaka factors. Depicted are the days at which cells were collected. Fibroblasts transfected with GFP only for five consecutive days are shown as well with GFP signal (images are representative). (c) Representative example of a dilution series for all 96 assays. Ct values were plotted as a function of the dilution factors (1:2) on a log scale. Linear regression analysis is depicted by the red line. Eight assays with R2 <0.97 were excluded, thus leaving 88 assays. (d) Distribution histogram of calculated primer efficiencies for 88 DELTAgene assays estimated from the slopes of standard curve plots. The average efficiency is 1.02 with s.d. = 0.06. (e) Quantile-quantile plot with experimentally estimated efficiencies (y axis) and the values expected for a normal distribution with mean efficiency = 1.02 and s.d. = 0.06 (x axis). The black line indicates the values expected for a normal distribution (y = x). Efficiency values that were derived from plots with three points in the standard curve are depicted in blue. Values derived from plots with >3 points in the standard curve are depicted in red. (f) Microscopic view of two capture sides on the C1 Single-Cell Auto Prep System microfluidic chip. The left capture side has no cell, and the right capture side has one captured cell (red arrow). (g) Representative example of primer specificity evaluation using melting curve analysis (here with HPAT2). The graph shows the relative change in fluorescence signal (EvaGreen) over the temperature range for all 96 cells on a single array. The area in red depicts false positive signals with incorrect melting curve temperatures (determined with bulk RNA and based on the melting curve temperature provided by Fluidigm). The area in green depicts the correct melting curves. Data outside the correct melting curve were set to 0. (h) Correlation analysis of mean Ct values generated 96 cells of three dynamic IFC arrays (single cells of (i) BJ fibroblasts, (ii) BJ fibroblasts transfected with mRNA encoding GFP for 2 d, (iii) BJ fibroblasts transfected with mRNA encoding GFP for 5 d). Genes that were detected in at least 20% of the 96 cells for each dynamic IFC array are considered. Shown are all three comparisons. Outliers are shown in green (GFP) and red. The assays in red (total of six) were excluded from subsequent analysis due to a non-correlative pattern among the arrays, leaving the 82 assays that are listed in Supplementary Table 2. i, Schematic overview of the quality assessment before normalization of single-cell gene expression. Nine dynamic IFC arrays (96.96 Fluidigm chips) were used for gene expression analysis. Two GFP control chips along with one fibroblast chip were used for correlation analysis (h) followed by initial quality assessment. Processed chips were used for a second round of quality assessment, resulting in 578 normalized single cells.
Supplementary Figure 4 Single-cell gene expression analysis during nuclear reprogramming and reactivation of HPAT expression during in vitro transdifferentiation from fibroblasts into neurons.
(a–c) Heat map plot of single-cell gene expression of different markers during nuclear reprogramming. Single cells are in rows. Genes are in columns. Fibroblast markers decrease over time, and pluripotency-specific markers, including selected HPATs, increase over time as cell progress toward iPSCs (n = 87, 85, 72, 70, 86, 83 and 95 for fibroblasts, day 2, day 5, day 7, day 10, day 12 and iPSCs, respectively). Normalization was performed accordingly (the Supplementary Note provides details). White color indicates no expression. (d) PCA of 578 single cells collected at different time points during nuclear reprogramming. (e) Heat map and unsupervised clustering for 578 single-cell gene expression values resulted in clustering of novel genes implicating a similar biological context during reprogramming. Samples are color-coded according to the specific gene groups (horizontal) and the day at which single cells were collected (vertical). (f) The pluripotency marker POU5F1 (red) and HPAT2 (red; representative of all HPATs in this study) were exclusively expressed in H9 cells (hESCs) but not in (i) cDNA from colon, liver and lung (endoderm) and (ii) during neuronal transdifferentiation from fibroblasts (gray; samples collected at day 5 and day 30 are labeled iN-D5 and iN-D30, respectively) or cDNA from brain) (all ectoderm). EN2 and PAX6, included as ectoderm control markers, were detected during neuron differentiation and in brain samples (n = 3; data shown with s.e.m.). (g) Heat map of bicluster analysis illustrating a different bicluster within each plot (Supplementary Table 3). Three different algorithms for bicluster calculation were applied, resulting in the identification of five clusters, four clusters and 16 clusters.
Supplementary Figure 5 Overexpression and silencing constructs for HPAT2, HPAT3 and HPAT5, NANOG ChIP-seq and regulation of HPAT5 during hESC differentiation.
(a) Validation of siRNAs targeting HPAT2, HPAT3 and HPAT5, respectively, in hESCs. Gene expression and P values were measured relative to siGlo control 48 h after transfection (n = 9). Orange color depicts expected gene downregulation. (b) Validation of the overexpression vectors. BJ fibroblasts were transfected with HPAT2, HPAT3 and HPAT5. Gene expression and P values were measured 48 h after transfection relative to those in GFP-transfected fibroblasts (n = 9). Blue color depicts expected gene upregulation. (c) ChIP-qPCR analysis in H9 cells (hESCs) using NANOG. Signals were quantified using primer sets specific to a subset of HPATs or two ‘negative’ intergenic, non-repetitive regions. Two enhancers around SOX2 are included as positive controls (n = 3; data are shown with s.e.m.). (d) Three snapshots of the UCSC browser (genome location indicated) aligned with the NANOG-binding region for HPAT2, HPAT3 and HPAT5 from ChIP-seq analysis. (e,f) Overexpression constructs and validation of the HPAT5-OE and mCherry-OE lines. HPAT5 was significantly upregulated in hESC-OE cells compared to control cells. mCherry protein expression was also confirmed. n = 3; data are sgiwb with s.e.m. (g) Increase in differentiation markers representing all three germ layers significantly repressed in HPAT5-OE cells. P values are calculated for comparison of the mCherry and HPAT5-OE lines on the same days.
(a) Formaldehyde agarose RNA gel of the Cy5-labeled lincRNAs before hybridization to the protein array. (b) Representative image of a ProtoArray and fluorescence intensity for HPAT2 and HPAT3 (positive) and HPAT5 (negative) on OCT4 protein in duplicate. (c) Heat map of HPAT2-, HPAT3- and HPAT5-binding proteins with RISC proteins and OCT4 highlighted (z score > 2.5). (d) Total number of candidate proteins identified with the three HPATs (with and without common RNA-binding proteins). (e) Validation of the findings by Lu et al. that HERV-H–derived lincRNAs (HPAT2 and HPAT3) bind to specific OCT4, coactivators and mediators.
(a) Predicted let-7 binding sites in HPAT5 transcript. Shown is HPAT5 with embedded TEs along the genomic length (black line). Exons are shown as gray boxes. TEs are shown as colored boxes underneath. let-7 binding sites are within a SINE element (Alu). Bases in red are point mutations and confer HPAT5 specificity. (b) Gene expression analysis of endogenous pre-let-7 and mature let-7 in fibroblasts. n = 3; data are shown with s.e.m. (c) Schematic overview of the HPAT5 locus in genomic DNA from subcloned hESCs that were treated with CRISPR pairs 2 and 5 (gRNA2/5). Forward and reverse primers (in red) were designed to amplify a region of genomic DNA that is inside the deleted HPAT5 locus. (d) Agarose gel illustrating successful derivation of the HPAT5-knockout hESC line. Genomic DNA from hESCs (passage 4 after subcloning) did not result in specific amplification. The controls included negative control (treatment only with one CRISPR arm, gRNA2), wild-type hESCs and no-template control (NT). (e) Gene expression analysis of endogenous HPAT5 in hESCs. n = 3; data are shown with s.e.m. (f) Endogenous let-7 levels do not reach the levels in differentiated cells during 48 h of hESC differentiation. Endogenous let-7 levels are significantly increased 48 h after differentiation with bFGF removal (tenfold). The levels of endogenous let-7 are still significantly higher in human fibroblasts (100-fold) compared to differentiated hESCs. HPAT5 knockout increases endogenous let-7 levels to ones similar to those found in hESCs differentiated for 24 h. Overexpression of let-7 in hESCs results in a -50-fold increase compared to human fibroblasts. let-7 levels were normalized to Hs-RNU6-2. n = 3; data are shown with s.e.m. (g) Differentiation of hESCs into secondary fibroblasts followed by episomal reprogramming into iPSCs. (h) Percentage of AP- and TRA-1-81–positive cells in HPAT5-WT and HPAT5-KO cells 25 d after reprogramming. n = 3; data are shown with s.e.m. (i) Endogenous let-7 and HPAT5 levels during nuclear reprogramming at day 10. n = 3; data are shwon with s.e.m.
(a) Heat map of differentially expressed genes (P < 0.05) after let-7 overexpression in four different samples). (b–d) Enrichment of let-7 seed sits in transcripts that were downregulated in hESC-HPAT5-KO cells. Overexpression from HPAT5-WT transcript rescued let-7–mediated differentiation. The Word cluster plot shows sequences in genes ranked by differential expression, after let-7 transfection. Each dot represents a word, summarizing z scores, and enrichment specificity indices of the enrichment profiles of negatively correlated 6- and 7-mer words. Triangles annotate known seed sites of human miRNAs. (i) A zoomed-in view (top) from the cluster plot. (e) Endogenous HPAT2, HPAT3 and HPAT5 expression in hESCs with let-7 overexpressed. n = 3; data are shown with s.e.m. (f) Immunoblot confirming specific AGO2 pulldown. OE, overexpression. n = 3 samples; data are shown with s.e.m.
Supplementary Figures 1–8, Supplementary Tables 1–4 and Supplementary Note. (PDF 14217 kb)
ChIP analysis with NANOG. (XLSX 798 kb)
Protein microarray analysis. (XLSX 16543 kb)
Prediction of miRNA binding sites. (XLSX 44 kb)
Microarray and cWords analyses. (XLSX 20024 kb)
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Durruthy-Durruthy, J., Sebastiano, V., Wossidlo, M. et al. The primate-specific noncoding RNA HPAT5 regulates pluripotency during human preimplantation development and nuclear reprogramming. Nat Genet 48, 44–52 (2016). https://doi.org/10.1038/ng.3449
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