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Genome-wide screening of NEAT1 regulators reveals cross-regulation between paraspeckles and mitochondria

Nature Cell Biology volume 20, pages 1145–1158 (2018)Cite this article

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Abstract

The long noncoding RNA NEAT1 (nuclear enriched abundant transcript 1) nucleates the formation of paraspeckles, which constitute a type of nuclear body with multiple roles in gene expression. Here we identify NEAT1 regulators using an endogenous NEAT1 promoter-driven enhanced green fluorescent protein reporter in human cells coupled with genome-wide RNAi screens. The screens unexpectedly yield gene candidates involved in mitochondrial functions as essential regulators of NEAT1 expression and paraspeckle formation. Depletion of mitochondrial proteins and treatment of mitochondrial stressors both lead to aberrant NEAT1 expression via ATF2 as well as altered morphology and numbers of paraspeckles. These changes result in enhanced retention of mRNAs of nuclear-encoded mitochondrial proteins (mito-mRNAs) in paraspeckles. Correspondingly, NEAT1 depletion has profound effects on mitochondrial dynamics and function by altering the sequestration of mito-mRNAs in paraspeckles. Overall, our data provide a rich resource for understanding NEAT1 and paraspeckle regulation, and reveal a cross-regulation between paraspeckles and mitochondria.

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Fig. 1: Genome-wide screening uncovers mitochondrial proteins as NEAT1 regulators.
Fig. 2: Loss of mitochondrial proteins leads to generation of elongated paraspeckles.
Fig. 3: Mitochondrial signals regulate NEAT1 expression via ATF2.
Fig. 4: Increased production of NEAT1_2 promotes the formation of elongated paraspeckles and the capability of mRNA sequestration.
Fig. 5: Sequestration of mito-mRNAs by NEAT1 and paraspeckles.
Fig. 6: Enhanced nuclear retention of mito-RNAs in paraspeckles in response to mitochondrial detects.
Fig. 7: Altered NEAT1 expression leads to mitochondria defects.
Fig. 8: NEAT1 protects cells from apoptosis induced by mitochondrial stressor SA and a proposed model of mito-paraspeckle communication.

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Data availability

RNAi screening data reported in this Article have been deposited at PubChem BioAssay with AID 1259429. All sequencing data, including RNA–seq and CHART–RNA–seq, have been deposited at the Gene Expression Omnibus (GEO) under accession no. GSE110775. Source data for statistics in Figs. 1–8 and Supplementary Figs. 2, 3 and 5–7 are provided in Supplementary Table 6. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Change history

  • 05 October 2018

    In the version of this Article originally published, Supplementary Table 1 was incorrectly linked to Supplementary Table 2, Supplementary Table 2 was incorrectly linked to Supplementary Table 3, Supplementary Table 3 was incorrectly linked to Supplementary Table 4, Supplementary Table 4 was incorrectly linked to Supplementary Table 5, Supplementary Table 5 was incorrectly linked to Supplementary Table 6, and Supplementary Table 6 was incorrectly linked to Supplementary Table 1. The files have now been replaced to rectify this.

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Acknowledgements

The authors thank Y. Liu for critical reading of this manuscript, G. Carmichael and S. Nakagawa for discussions, Z. Wu for suggestions on mitochondrial assays, Y. He from the Core Facility Centre of the Institute of Plant Physiology and Ecology for technical support on SIM, and the Core of Molecular Biology of the Institute of Biochemistry and Cell Biology for screens and Seahorse assays. This work was supported by the Ministry of Science and Technology of China (2016YFA0100701), the Chinese Academy of Sciences (XDB19020104), the National Natural Science Foundation of China (31725009, 31730111, 91440202) and the Howard Hughes Medical Institute (55008728).

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Author notes

  1. Shi-Bin Hu

    Present address: Department of Genetics, Stanford University, Stanford, CA, USA

  2. These authors contributed equally: Yang Wang, Shi-Bin Hu, Meng-Ran Wang.

Authors and Affiliations

  1. State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Yang Wang, Shi-Bin Hu, Run-Wen Yao, Di Wu & Ling-Ling Chen

  2. Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Meng-Ran Wang & Li Yang

  3. School of Life Science and Technology, ShanghaiTech University, Shanghai, China

    Li Yang & Ling-Ling Chen

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Contributions

L.-L.C. conceived of the study. Y.W., S.-B.H. designed the experiments. Y.W., S.-B.H., R.-W.Y. and D.W. performed experiments. M.-R.W. and L.Y. preformed bioinformatics analyses of RNAi screenings and RNA–seq data. L.-L.C., Y.W., S.-B.H. and M.-R.W analysed the data. L.-L.C. wrote the manuscript with input from the other authors.

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Correspondence to Ling-Ling Chen.

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Integrated supplementary information

Supplementary Figure 1 Genome-wide RNAi screenings to identify factors involved in NEAT1 regulation (related to Fig. 1).

(a) A schematic drawing shows the reporter cell line used for RNAi screens. This endogenous-NEAT1 promoter-driven EGFP reporter has the potential to identify direct and indirect regulators that have an impact on NEAT1 transcription. (b) Validation of EGFP expression from the endogenous NEAT1 promoter and mCherry expression by EF1α promoter in one NEAT1G-HeLa-R clone. Scale bar, 50 μm (n = 3 independent experiments). (c) A workflow of RNAi screenings used in this study. (d) Scatter plot shows the correlation between two independent RNAi screening replicates including 18,104 siRNA-targeted genes in each assay (P value is calculated to evaluate the significance of Pearson Correlation Coeffcient used two-sided Student’s t-test between replicates). RNAi screening data have been deposited at PubChem BioAssay with AID 1259429. (e) A representative picture shows the EGFP fluorescence of a 384 plate for the genome-wide RNAi screening form two independent experiments. Images were taken 96 hours post-transfection of siRNA libraries. (f) Overview of data analysis workflow of RNAi screening replicates. (g) Summary of targets validated by two sets of shRNAs; their positive or negative effects on NEAT1 expression were shown. (h) A table summary of nuclear encoded genes having mitochondrial functions that have been identified as potential NEAT1 regulators. Blue ones are genes randomly selected and validated by shRNA-mediated knockdown.

Supplementary Figure 2 SIM observation and analysis of paraspeckles in multiple cell lines. (related to Fig. 2).

(a) Representative SIM images of paraspeckles in cells treated with another shRNA targeting the same individual genes as shown in Figure 2b. Scale bar, 500 nm. Data shown represent 2 independent experiments. (b) Statistics of paraspeckles number (top) (n = 164, 109, 90, 47, 92, 80, 87, 48 cells randomly selected under each condition) and bar graph showed the distribution of paraspeckle morphology (bottom) from 2 independent experiments in panel (a). (c) Un-cropped images shown in Fig. 2b and panel (a). Dashed box in each panel shows the outline of cropping. Scale bar, 2 μm. Data shown represent 3 independent experiments. (d) Representative cropped (left) and corresponding uncropped (right) pictures of paraspeckles in cells treated with shRNAs that target different nuclear genes encoding mitochondrial proteins under SIM. Dashed box in each panel shows the outline of cropping. Scale bar, 2 μm (uncropped) and 500 nm (cropped). Data shown represent 3 independent experiments. (e) Bar graph showed the distribution of paraspeckle morphology from 2 independent experiments in panel (d). (f) Uncropped pictures shown in Fig. 2d. Dashed box in each panel shows the outline of cropping. Scale bar, 2 μm. Data shown represent 3 independent experiments. Data in panel (b) are shown as the mean ± s.d. P values are calculated using two-sided unpaired Student’s t-test; **P < 0.01, ***P < 0.001. Statistical source data including the precise P value are provided in Supplementary Table 6.

Supplementary Figure 3 Mitochondrial defects regulate NEAT1 expression via ATF2 (related to Fig. 2, 3 and 4).

(a, b) Knockdown of mitochondrial protein BCL2 reduced the expression of atf2 mRNA in HEK293 (a) and U2OS (b) cells; ATF2 and BCL2 knockdown increased the proportion of NEAT1_2 in HEK293 (a) and U2OS (b) cells (n = 3 independent experiments). (c, d) Knockdown of mitochondrial protein PPRC1 (c) or ER protein ERLIN2 (d) had little effect on ATF2 expression. Left panels in (c, d), RT-qPCR validated the knockdown efficiency and their effect on NEAT1 expression (n = 3 independent experiments). Right panels, PPRC1 (c) and ERLIN2 (d) knockdown had little effect on ATF2 expression, as shown by WB. Data shown represent 2 independent experiments. (e) CRE sites deletion reduced the NEAT1 promoter activity, the expression of luciferase mRNA was normalized to mCherry mRNA (n = 3 independent experiments). (f) Rotenone (1μM, 6h; DMSO as control) and Doxycycline (5 μg/mL, 24h; PBS as control) treatments induced NEAT1 expression (n = 3 independent experiments). (g, h) SA (500 μM, 12 h) (g), FCCP and Oligomycin (2 μM, 24 h) (h) treatments induced NEAT1 expression in U2OS cells (n = 3 independent experiments). (i) FCCP (2 μM, 24h) and Oligomycin (2 μM, 24h) treatments induced atf2 mRNA expression in U2OS cells (n = 3 independent experiments). (j) Un-cropped images shown in Fig. 4d. Dashed box in each panel shows the outline of cropping. Scale bar, 2 μm. Data shown represent 3 independent experiments. Data in panel (a)-(i) are shown as the mean ± s.d. P values are calculated using two-sided unpaired Student’s t-test; **P < 0.01, ***P < 0.001. Statistical source data including the precise P value are provided in Supplementary Table 6. The unprocessed blots for panel (c) and (d) are shown in Supplementary Fig. 8.

Supplementary Figure 4 Nuclear-encoded mRNAs of having mitochondrial functions are sequestered in paraspeckles (related to Fig. 5).

(a) A schematic drawing shows probes used for NEAT1 CHART followed by RNA-seq (CHART-RNA-seq). (b) Overview of data analysis workflow of NEAT1 CHART-RNA. (c) MEME identified AG-rich and U-rich sequence motifs enriched in mRNAs captured by NEAT1 CHART-RNA-seq. (d) Representative wiggle tracks of the NEAT1-enriched mRNAs with mitochondrial functions in one RNA-seq form 3 independent experiments. Red bars indicate the location of inverted repeat Alus (IRAlus). (e) Protein networks associated with mitochondrial functions and diseases of mRNAs enriched by NEAT1 CHART-RNA-seq.

Supplementary Figure 5 Enhanced nuclear retention of mito-mRNAs in elongated paraspeckles in response to mitochondrial detects (related to Fig. 6).

(a) Linear regression between paraspeckle volumes and elongation rate (Er, which is defined to distinguish globular and elongated paraspeckles, see Methods for details) in scramble shRNA-treated and BCL2 KD cells (n = 168 and 150 parapseckles, respectively) shows a positive correlation. The linear regression was fitted by Method of Least Squares, P value is calculated using two-sided Student’s t-test. (b) Representative pictures of the co-localization of NEAT1 and cycs, revealed by smFISH in scramble shRNA-treated and BCL2 KD cells. Dashed box in each panel shows the outline of cropping. Data shown represent 3 independent experiments. Scale bar, 5 μm (uncropped) and 500 nm (cropped). (c) Relative fluorescence intensity of cycs in globular and elongated paraspeckles (n = 50 for each type of paraspeckles and were randomly selected from 3 independent smFISH experiments). (d, e) Enhanced association of NONO with NEAT1 (d) and IRAlus-mRNAs (e) in FCCP (2 μM, 24 h) treated cells. Bar graphs represent fold enrichments of RNAs immunoprecipitated by anti-NONO and anti-mouse IgG2b over the same amount of input from equal amounts of cells across different samples. 18s rRNA is a control (n = 3 independent experiments). (f) Representative pictures of paraspeckles in cells treated with control DMSO and FCCP (2 μM, 24h). Scale bar, 500 nm. Data shown represent 2 independent experiments. (g) RT-qPCR revealed the relative ratio of NEAT1_2 to total NEAT1 upon FCCP treatment (n = 3 independent experiments). (h, i) Representative pictures of the co-localization between NEAT1 and mito-RNAs (h) or actin mRNA (i) shown by smFISH in DMSO, FCCP or Oligomycin treated cells. Scale bar, 5 μm (uncropped) and 500 nm (cropped). Dashed box in each panel shows the outline of cropping. Data shown represent 3 independent experiments. Data in all bar graphs are shown as the mean ± s.d. P values are calculated using two-sided unpaired Student’s t-test; *P < 0.05, **P < 0.01, ***P < 0.001. Statistical source data including the precise P value are provided in Supplementary Table 6.

Supplementary Figure 6 NEAT1 regulates mitochondria homeostasis (related to Fig. 7).

(a) A schematic drawing shows ASOs and sgRNAs used for NEAT1 KD, KO and in cis activation. SgRNA5-10 used to generate NEAT1 KO cell lines. SgRNA7,11 and 12 used for in cis activation of NEAT1. (b) Total NEAT1 and NEAT1_2 expression in WT and CRISPR-Cas9-mediated NEAT1 KO HeLa cells, revealed by RT-qPCR (n = 3 independent experiments). (c) Morphological changes of mitochondria in NEAT1 KO HeLa cells observed by DeltaVision (top) or Structured Illumination Microscopy (SIM) (bottom). Scale bar, 10 μm (DeltaVision) and 5 μm (SIM). Data shown represent 3 independent experiments. (d) RT-qPCR revealed ASO-mediated (48 h) NEAT1 KD in HeLa cells (n = 3 independent experiments). (e) RT-qPCR revealed NEAT1 KD reduced mtDNA abundance compared with control. (f) Bar graph showed the distribution of mitochondrial morphology in control and NEAT1 KD HeLa cells from 3 independent experiments. (g) Morphological changes of mitochondria in control and NEAT1 KD HeLa cells under DeltaVision. Scale bar, 10 μm. Data shown represent 3 independent experiments. (h) NEAT1 KD led to impaired mitochondrial respiration in HeLa cells, shown by seahorse assays (n = 3 independent experiments). (i) OCR and ECAR in control and NEAT1 KD HeLa cells (n = 3 independent experiments). (j) ASO-mediated (48 h) NEAT1 KD in HEK293 cells (n = 3 independent experiments). (k) Morphological changes of mitochondria in NEAT1 KD HEK293 cells under DeltaVision. Scale bar, 10 μm. Data shown represent 3 independent experiments. (l) Bar graph showed the distribution of mitochondrial morphology in control and NEAT1 KD HEK293 cells from 3 independent experiments. (m) NEAT1 KD led to impaired mitochondrial respiration in HEK293 cells detected by seahorse assays (n = 3 independent experiments). (n) OCR and ECAR in the control and NEAT1 KD HEK293 cells (n = 3 independent experiments). Data in all graphs are shown as the mean ± s.d. P values are calculated using two-sided unpaired Student’s t-test; *P < 0.05, **P < 0.01. Statistical source data including precise P values are provided in Supplementary Table 6.

Supplementary Figure 7 Differential gene analysis in NEAT1 KO cells and NEAT1 protects cells from apoptosis induced by SA.

(a) Total RNA sequencing of two individual NEAT1 KO HeLa cell clones, and the distribution of each gene according to its FPKM in WT or KO cells was plotted. Genes up- or down-regulated in each pair of RNA-seq were defined by FPKM KO/FPKM WT > 2 (red) or FPKM KO/FPKM WT < 0.5 (blue). (b) Global gene expression analysis in NEAT1 KO and WT HeLa cells. DEGs, differentially expressed genes. Mito-genes, nuclear encoded genes having mitochondrial functions. (c) Validation of altered expression of mito-genes upon NEAT1 KO by RT-qPCR (n = 3 independent experiments).(d) Gene ontology of differentially expressed mito-genes.(e) An example showing the gating strategy for analyzing the Flow Cytometry data in apoptosis detection assay induced by SA. (f) NEAT1 deleted cells exhibited increased proportion of SA-induced apoptosis. WT and different NEAT1 KO HeLa cell clones were treated with or without 500 μM SA for 24 hours followed by Annexin V staining and Flow Cytometry analyses of apoptosis. Data shown represent 3 independent experiments. (g) The statistical analyses of apoptosis from 3 independent experiments shown in panel (f). (h) Cyt. c expression upon SA stimulation (500 μM, 24h) in HeLa cells with or without NEAT1 detected by WB. Data shown represent 3 independent experiments. (i) Altered transcription lead to uncoupled NEAT1 processing. Data in panel (c) and (g) are shown as the mean ± s.d. P values are calculated using two-sided unpaired Student’s t-test; *P < 0.05, **P < 0.01. Statistical source data including precise P value are provided in Supplementary Table 6. The unprocessed blots for panel (c) and (d) are shown in Supplementary Fig. 8.

Supplementary Figure 8 Uncropped scans of immunoblots.

Boxes indicate the cropped sections used in the corresponding figures. Of note, for some immunoblotting assays membranes were cut into several pieces to incubate with different antibodies, and therefore the raw images of these membranes are of small size.

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Wang, Y., Hu, SB., Wang, MR. et al. Genome-wide screening of NEAT1 regulators reveals cross-regulation between paraspeckles and mitochondria. Nat Cell Biol 20, 1145–1158 (2018). https://doi.org/10.1038/s41556-018-0204-2

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