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Aging-associated lncRNAs are evolutionarily conserved and participate in NFκB signaling

Abstract

The transcriptome undergoes global changes during aging, including both protein-coding and noncoding RNAs. Using comparative genomics, we identify aging-associated long noncoding RNAs (lncRNAs) that are under evolutionary constraint and are more conserved than lncRNAs that do not change with age. Aging-associated lncRNAs are enriched for functional elements, including binding sites for RNA-binding proteins and transcription factors, in particular nuclear factor kappa B (NFκB). Using CRISPR screening, we discovered that 13 of the aging-associated lncRNAs were regulators of the NFκB pathway, and we named this family ‘NFκB modulating aging-related lncRNAs (NFKBMARLs)’. Further characterization of NFκBMARL-1 reveals it can be traced to 29 Ma before humans and is induced by NFκB during aging, inflammation and senescence. Reciprocally, NFκBMARL-1 directly regulates transcription of the NFκB inhibitor NFKBIZ in cis within the same topologically associated domain by binding to the NFKBIZ enhancer and recruiting RELA to the NFKBIZ promoter. These findings reveal many aging-associated lncRNAs are evolutionarily conserved components of the NFκB pathway.

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Fig. 1: Cross-species comparison of aging-associated lncRNAs shows the signature of evolutionary and functional constraints.
Fig. 2: Aging-associated lncRNAs regulate, and are regulated by, the NFκB pathway.
Fig. 3: NFΚBMARL-1 is activated during aging, inflammation and senescence by the NFκB pathway.
Fig. 4: NFΚBMARL-1 regulates NFKBIZ expression under TNF stimulation in cis.
Fig. 5: NFΚBMARL-1 recruits RELA to NFKBIZ by binding on the 3′ end enhancer of NFKBIZ.
Fig. 6: NFΚBMARL-1 regulates phase separation of the NFKBIZ chromatin region.

Data availability

The transcriptome, histone modification, transcription factor, genomic structure data from the Gene Expression Omnibus (GEO) and NODE database were used for analyzing evolutionary conservation, gene expression or epigenetic profiles for NFΚBMARL-1 or NFKBIZ under different treatments. All the publicly available datasets used in this study are labeled in the figures. The datasets used for evolutionary conservation analysis (Supplementary Fig. 1a with 11 species transcriptomes) included GSE80440, GSE101964, GSE108282, GSE66362, GSE107049, GSE85377, GSE52462, GSE66715, GSE123590, GSE106670, OEP001041, GSE75192 and GSE106670. The gene expression and epigenetic profiles of NFΚBMARL-1 or NFKBIZ under different treatments were from GSE43070, GSE72534, GSM996197, GSE109700, GSE74328, GSE130306, GSE99074, GSE100382, GSE64233, GSE91020, GSE121522, GSE55105 and GSE99544. The public databases applied in this study were REMAP (http://remap.univ-amu.fr/), DAVID (https://david.ncifcrf.gov/) and POSTAR2 (http://lulab.life.tsinghua.edu.cn/postar/).

The sequencing data of CRISPR screening sequencing, ribo-strand-specific RNA-seq and ChIRP-seq have been deposited in the GEO under accession number GSE167511. Statistics for corresponding figures are available as Supplementary Information. Any other relevant data or information about this study are available from the corresponding author upon reasonable request.

Code availability

Software and parameters used to perform the analyses are described in Supplementary Software.

References

  1. 1.

    Jordan, I. K., Rogozin, I. B., Wolf, Y. I. & Koonin, E. V. Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res. 12, 962–968 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Sarropoulos, I., Marin, R., Cardoso-Moreira, M. & Kaessmann, H. Developmental dynamics of lncRNAs across mammalian organs and species. Nature 571, 510–541 (2019).

    Google Scholar 

  3. 3.

    Iyer, M. K. et al. The landscape of long noncoding RNAs in the human transcriptome. Nat. Genet. 47, 199–208 (2015).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Necsulea, A. et al. The evolution of lncRNA repertoires and expression patterns in tetrapods. Nature 505, 635–640 (2014).

    CAS  PubMed  Google Scholar 

  5. 5.

    Li, X. et al. GRID-seq reveals the global RNA-chromatin interactome. Nat. Biotechnol. 35, 940–950 (2017).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Kopp, F. & Mendell, J. T. Functional classification and experimental dissection of long noncoding RNAs. Cell 172, 393–407 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Mele, M. & Rinn, J. L. ‘Cat’s cradling’ the 3D genome by the act of lncRNA transcription. Mol. Cell 62, 657–664 (2016).

    CAS  PubMed  Google Scholar 

  8. 8.

    Ulitsky, I. Evolution to the rescue: using comparative genomics to understand long noncoding RNAs. Nat. Rev. Genet. 17, 601–614 (2016).

  9. 9.

    Liu, S. J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355, aah7111 (2017).

  10. 10.

    Joung, J. et al. Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood. Nature 548, 343–346 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Zhu, S. Y. et al. Genome-scale deletion screening of human long noncoding RNAs using a paired-guide RNA CRISPR–Cas9 library. Nat. Biotechnol. 34, 1279–1286 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Cheng, H. et al. Repression of human and mouse brain inflammaging transcriptome by broad gene-body histone hyperacetylation. Proc. Natl Acad. Sci. USA 115, 7611–7616 (2018).

    CAS  PubMed  Google Scholar 

  13. 13.

    Glass, C. K., Saijo, K., Winner, B., Marchetto, M. C. & Gage, F. H. Mechanisms underlying inflammation in neurodegeneration. Cell 140, 918–934 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Kawahara, T. L. A. et al. SIRT6 links histone H3 lysine 9 deacetylation to NFκB-dependent gene expression and organismal life span. Cell 136, 62–74 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Zhang, G. et al. Hypothalamic programming of systemic ageing involving IKK-β, NFκB and GnRH. Nature 497, 211–216 (2013).

    PubMed Central  Google Scholar 

  16. 16.

    Gorgoulis, V. et al. Cellular senescence: defining a path forward. Cell 179, 813–827 (2019).

    CAS  PubMed  Google Scholar 

  17. 17.

    Guttman, M. & Rinn, J. L. Modular regulatory principles of large non-coding RNAs. Nature 482, 339–346 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Bao, Z. Y. et al. LncRNADisease 2.0: an updated database of long noncoding RNA-associated diseases. Nucleic Acids Res. 47, D1034–D1037 (2019).

    CAS  PubMed  Google Scholar 

  19. 19.

    Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    CAS  PubMed  Google Scholar 

  20. 20.

    Chen, W. et al. CRISPRlnc: a manually curated database of validated sgRNAs for lncRNAs. Nucleic Acids Res. 47, D63–D68 (2019).

    CAS  PubMed  Google Scholar 

  21. 21.

    Amaral, P. P. et al. Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci. Genome Biol. 19, 32 (2018).

  22. 22.

    Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).

    CAS  PubMed  Google Scholar 

  23. 23.

    Green, C. D. et al. Impact of dietary interventions on noncoding RNA networks and mRNAs encoding chromatin-related factors. Cell Rep. 18, 2957–2968 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

    De Cecco, M. et al. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature 566, 73–78 (2019).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Fanucchi, S. et al. Immune genes are primed for robust transcription by proximal long noncoding RNAs located in nuclear compartments. Nat. Genet. 51, 138–150 (2019).

    PubMed  Google Scholar 

  26. 26.

    Beerman, I. et al. Functionally distinct hematopoietic stem cells modulate hematopoietic lineage potential during aging by a mechanism of clonal expansion. Proc. Natl Acad. Sci. USA 107, 5465–5470 (2010).

    CAS  Google Scholar 

  27. 27.

    Imamura, K. et al. Long noncoding RNA NEAT1-dependent SFPQ relocation from promoter region to paraspeckle mediates IL8 expression upon immune stimuli. Mol. Cell 53, 393–406 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Xia, X. et al. Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat. Metab. 2, 946–957 (2020).

    CAS  PubMed  Google Scholar 

  29. 29.

    Shchukina, I. et al. Enhanced epigenetic profiling of classical human monocytes reveals a specific signature of healthy aging in the DNA methylome. Nat. Aging 1, 124–141 (2020).

  30. 30.

    Miyake, T. et al. IκBζ is essential for natural killer cell activation in response to IL-12 and IL-18. Proc. Natl Acad. Sci. USA 107, 17680–17685 (2010).

    CAS  PubMed  Google Scholar 

  31. 31.

    Okamoto, K. et al. IκBζ regulates TH17 development by cooperating with ROR nuclear receptors. Nature 464, 1381–U13 (2010).

    CAS  PubMed  Google Scholar 

  32. 32.

    Muller, A. et al. IκBζ is a key transcriptional regulator of IL-36-driven psoriasis-related gene expression in keratinocytes. Proc. Natl Acad. Sci. USA 115, 10088–10093 (2018).

    PubMed  Google Scholar 

  33. 33.

    Alexander, E. et al. IκBζ is a regulator of the senescence-associated secretory phenotype in DNA damage- and oncogene-induced senescence. J. Cell Sci. 126, 3738–3745 (2013).

    CAS  PubMed  Google Scholar 

  34. 34.

    Koelman, L., Pivovarova-Ramich, O., Pfeiffer, A. F. H., Grune, T. & Aleksandrova, K. Cytokines for evaluation of chronic inflammatory status in ageing research: reliability and phenotypic characterisation. Immun. Ageing 16, 11 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    de Gonzalo-Calvo, D. et al. Differential inflammatory responses in aging and disease: TNF-α and IL-6 as possible biomarkers. Free Radic. Biol. Med. 49, 733–737 (2010).

    PubMed  Google Scholar 

  36. 36.

    Rea, I. M. et al. Age and age-related diseases: role of inflammation triggers and cytokines. Front. Immunol. 9, 586 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Park, S. H. et al. Type I interferons and the cytokine TNF cooperatively reprogram the macrophage epigenome to promote inflammatory activation. Nat. Immunol. 18, 1104–1116 (2017).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Wright, H. L., Thomas, H. B., Moots, R. J. & Edwards, S. W. RNA-seq reveals activation of both common and cytokine-specific pathways following neutrophil priming. PLoS ONE 8, e58598 (2013).

  39. 39.

    Young, A. R. J. et al. Autophagy mediates the mitotic senescence transition. Genes Dev. 23, 798–803 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Jin, F. L. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Schmidt, S. F. et al. Acute TNF-induced repression of cell identity genes is mediated by NFκB-directed redistribution of cofactors from super-enhancers. Genome Res. 25, 1281–1294 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Higashijima, Y. et al. Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells. EMBO J. 39, e103949 (2020).

  43. 43.

    Borghini, L., Lu, J., Hibberd, M. & Davila, S. Variation in genome-wide NFκB RELA binding sites upon microbial stimuli and identification of a virus response profile. J. Immunol. 201, 1295–1305 (2018).

    CAS  PubMed  Google Scholar 

  44. 44.

    Tasdemir, N. et al. BRD4 connects enhancer remodeling to senescence immune surveillance. Cancer Discov. 6, 612–629 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Furman, D. et al. Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states. Nat. Med. 23, 174–184 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Zannas, A. S. et al. Epigenetic upregulation of FKBP5 by aging and stress contributes to NFκB-driven inflammation and cardiovascular risk. Proc. Natl Acad. Sci. USA 116, 11370–11379 (2019).

    CAS  PubMed  Google Scholar 

  47. 47.

    Kakiuchi, N. et al. Frequent mutations that converge on the NFKBIZ pathway in ulcerative colitis. Nature 577, 260–265 (2020).

    PubMed  Google Scholar 

  48. 48.

    Kumar, M., Gromiha, M. M. & Raghava, G. P. Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins 71, 189–194 (2008).

    CAS  PubMed  Google Scholar 

  49. 49.

    Kim, O. T., Yura, K. & Go, N. Amino acid residue doublet propensity in the protein–RNA interface and its application to RNA interface prediction. Nucleic Acids Res. 34, 6450–6460 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    He, S., Zhang, H., Liu, H. & Zhu, H. LongTarget: a tool to predict lncRNA DNA-binding motifs and binding sites via Hoogsteen base-pairing analysis. Bioinformatics 31, 178–186 (2015).

    CAS  PubMed  Google Scholar 

  51. 51.

    Nair, S. J. et al. Phase separation of ligand-activated enhancers licenses cooperative chromosomal enhancer assembly. Nat. Struct. Mol. Biol. 26, 193–203 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Boija, A. et al. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell 175, 1842–1855 (2018).

    CAS  PubMed  Google Scholar 

  53. 53.

    Cui, R. F. et al. Relaxed selection limits life span by increasing mutation load. Cell 178, 385–399 (2019).

    Google Scholar 

  54. 54.

    Oates, M. E. et al. D2P2: database of disordered protein predictions. Nucleic Acids Res. 41, D508–D516 (2013).

    CAS  PubMed  Google Scholar 

  55. 55.

    He, J., Tao, H. & Huang, S. Y. Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models. Bioinformatics 35, 4994–5002 (2019).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (91749205, 92049302, 32088101), China Ministry of Science and Technology (2020YFA0804000, 2016YFE0108700) and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) to J.-D.J.H. We thank J. Sedivy at Brown University, Y. Sun at Shanghai Institute of Nutrition and Health and F. Lan at Fudan University for invaluable suggestions and discussions, and the analysis and technology platform at Shanghai Institute of Nutrition and Health for FACS and confocal microscope support.

Author information

Affiliations

Authors

Contributions

J.-D.J.H. and D.C. conceived and designed the study, analyses and experiments, and wrote the manuscript. D.C. performed the analyses and experiments.

Corresponding author

Correspondence to Jing-Dong J. Han.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks Shinichi Nakagawa, Yousin Suh, Gian Gaetano Tartaglia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Identification of novel lncRNAs and evolutionarily conserved lncRNAs.

a, The public RNA-seq data of 11 species for evolutionary associated lncRNAs analysis. b, Pipeline for de novo assembly of transcripts and annotation of lncRNAs. c, Schematic presentation of pipeline for detecting one-to-one orthologous lncRNA families. d, Distribution of one-to-one orthologous protein-coding genes (red) and ncRNAs (blue) in 11 species. e, Distribution of one-to-one ortholog annotated novel lncRNAs (red) and Ensembl annotated lncRNAs (blue) in 11 species.

Extended Data Fig. 2 Features of aging-associated lncRNAs in evolutionary ages.

a, Fraction of the lncRNA orthologous conserved to other species. b, Fraction of human-associated lncRNAs conserved to other species in different evolutionary ages. c, Density plot of transcripts length of human brain and blood aging-associated lncRNAs and non-aging-associated lncRNAs. d, Fraction of transcripts exon numbers of human brain and blood aging-associated lncRNAs and non-aging-associated lncRNAs. e, PhasCons20 scores in aging-associated and non-aging-associated lncRNAs. Wilcoxon rank-sum test. Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima. P values were calculated by two-sided Wilcoxon rank-sum test with correction. f, Enrichment analysis of aging-associated protein-coding genes in different evolutionary ages by two-sided Fisher’s exact test. 2000 random non-aging-associated protein-coding genes serve as control. g, Distribution of physical proximity with protein-coding genes for aging-associated lncRNAs. SAS is defined by antisense transcript overlapping with protein-coding genes. CIS is defined by the distance between lncRNA and protein-coding gene <2 kb. Inter is defined by the distance to protein-coding genes >2 kb. h, Fraction of brain aging-associated lncRNAs with different physical proximity to protein-coding genes are conserved to different evolutionary ages. i, Fraction of blood aging-associated lncRNAs with different physical proximity to protein-coding genes are conserved to different evolutionary ages. j, Enrichment analysis of functional lncRNAs in different evolutionary ages by two-sided Fisher’s exact test. 2000 random non-feature lncRNAs serve as control. k, Survival associated and aging-associated lncRNAs enriched in different cancer types in TCGA dataset. l, Motif enrichment heatmap in TSS regions of aging-associated lncRNAs by Homer. The heatmap shows the motifs significantly enrich in all species (p < 0.05). m, Identity percentage of RELA protein sequence from humans to other species by BLASTP.

Extended Data Fig. 3 Regulatory elements in aging-associated lncRNAs.

a, Binding sites enrichment of RNA binding proteins (up) and repeats (down) distribute on the protein-coding genes, aging-associated lncRNAs and non-aging-associated lncRNAs. P values were calculated by two-sided Wilcoxon rank-sum test with correction. Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima; dots are outliers. b, Fraction of different types of transposon elements in lncRNAs conserved to different evolutionary ages. c, Density distribution of the Pearson correlation coefficients between closest protein-coding genes to aging-associated lncRNAs in same TAD region. TAD regions are annotated from Hi-C dataset of dorsolateral prefrontal cortex (GSE87112). P values were calculated by two-sided Wilcoxon rank-sum test with correction. d, Functional enrichment of closest protein-coding genes in the same TAD region with aging-associated lncRNAs. e, Fraction of conserved lncRNAs in aging-associated lncRNAs within TAD. P values were calculated by two-sided Fisher’s exact test. f, Density distribution of maximal Jensen Shannon specific score for fold change of each protein-coding genes and lncRNA including lincRNA and antisense. g, Heatmap of the |log2FC| of protein-coding genes and lncRNAs in 11 tissues. The abbreviations are as follows: BM for bone marrow; CLN for cervical lymph nodes; CP for choroid plexus; HC for hippocampus; ILN for inguinal lymph nodes; MLN for mesenteric lymph nodes; SP for spleen; TH for thymus.

Extended Data Fig. 4 Enriched functions of blood aging-associated lncRNAs and the general information of CRISPR screen.

a, TF enrichment in brain aging up or down lncRNAs in REMAP ChIP-seq dataset. b, Heatmap showing NES from guilt-by-association to annotate the function of blood aging-associated lncRNAs. Each column is lncRNA and each row is GO term. The other 2 columns are age correlations and expression levels of blood aging-associated lncRNAs c, Motif enrichment of promoter regions of blood aging up lncRNAs by Homer. Equal number of random background regions serve as control. d, GFP fluorescent intensity of serial concentrations of TNFα stimulated NFκB pathway reporter cells, revealed by FACS. e, GFP fluorescent intensity of TNFα stimulated NFκB pathway reporter cells after knocking down NFKB1 or RELA by shRNAs. f, Gating strategy: (1) singlets were selected by FSC-H against FSC-A; (2) singlets were gated for GFP high/low populations. g, Boxplot showing the pgRNAs distribution after selected GFP high and low under TNFα stimulation from n = 3 replicates. Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima; dots are outliers. h, The Pearson correlation coefficient of CRISPR sequence normalized reads between 3 independent replicates of GFP high and GFP low sorted cells. i, Mean read counts of pgRNAs targeting the AAVS1 loci. j, Mean read counts of pgRNAs targeting the non-target controls. k, GFP fluorescent intensity of TNFα stimulated NFκB pathway reporter cells after knocking out 18 GFP low selected lncRNAs by each pgRNAs. All the Fluorescent intensity measurements including WT were performed in the same batch. The MFI in (d, e, k) were analyzed by FlowJo. Data in (d, e, k) are presented as mean ± s.d. in n = 3 independent experiments. All p values in (d, e, k) were calculated using two-sided unpaired Student’s t-test.

Source data

Extended Data Fig. 5 General feature and gene expression of NFΚBMARL-1 in aging and inflammation.

a, The genome browser overview of GRO-seq, RNA-seq and polyA-seq in NFKBMARL-1. Purple mark is the DNA binding region, green mark is RELA binding region. b, Gel imaging of PCR product and Sanger sequencing of 5’ end and 3’ end RACE. c, Copy number of NFΚBMARL-1 in IMR90, revealed by RT-qPCR. d, Expression level of NFΚBMARL-1 in different tissue and cell from GTEx analysis release V8 database. e, Top, syntenic region comparison from human to mouse and macaca in NFΚBMARL-1 genomic region from Ensembl database. Bottom, alignment of transcripts from monkey to human. f, Genome browser view of RNA-seq profile of NFΚBMARL-1 in aging PBMC. g, The expression level of NFKBMARL-1 in aging monocytes with two normalization methods, log2qnorm and Deseq2norm. h, The expression level of NFKBMARL-1 in GTEX database for 7 organs of blood vessel, brain, breast, heart, lung, pituitary, thyroid. All tissues have no significant changes and are thus not shown. The young samples are ages <30, and old samples are ages >60. Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima; dots are expression value (g, h). i, Expression of NFΚBMARL-1, CXCL1, IL6 and IL8 in NFKBIZ shRNA knockdown and TNFα stimulated 293 T, revealed by RT-qPCR. j, Expression of NFΚBMARL-1, CXCL1, IL6 and IL8 in control and NFKBIZ overexpressed TNFα stimulated 293 T cells, revealed by RT-qPCR. k, Gel image of PCR validating the deletion of NFKBIZ by 2 pgRNAs. The MFI were analyzed by FlowJo. l, Fluorescent intensity of NFκB pathway reporter cell, after knocking out NFKBIZ and TNFα stimulation, revealed by FACS. The MFI were analyzed by FlowJo. m, Fluorescent intensity of NFκB pathway reporter cell, after knocking out NFKBIZ and TNFα, LPS and IFNα co-treatment for FACS. The MFI were analyzed by FlowJo. n, Up: The overlap of TNFα up- and down-regulated genes between THP1 and monocytes, shown by Venn diagram. The significance of overlap is determined by Fisher’s exact test. Down: Bar plot showing the expression of NFΚBMARL-1 after TNFα (10 ng/ml), LPS (10 ng/ml) and IFNα (25 ng/ml) treatment for 3hrs in CD14 + monocyte, revealed by RT-qPCR. o, Expression of NFΚBMARL-1 and NFKBIZ in TNFα (10 ng/ml) treated neutrophils. Data in (i, j, l, m) are presented as mean ± s.d. in n = 3 independent experiments. All p values in (g-j, l-n) were calculated using two-sided unpaired Student’s t-test.

Extended Data Fig. 6 NFΚBMARL-1 is up-regulated during cellular senescence and upon NFκB pathway activation.

a, The expression of senescent marker gene CDKN2A (p16INK4A) in aging brain and blood. The RCC and PCC between ages and expression of CDKN2A and their p values are shown. b, Up: Expression level of NFΚBMARL-1 and NFKBIZ after treated by bleomycin (10ug/ml), etoposide (10uM), 4OHT (100 nM) induced HRAS overexpress and replicative senescence in IMR90 or 293 T, revealed by RT-qPCR. Down: The expression levels of senescent markers including p16 and p21 and SASP factors including CXCL1, IL6 and IL8 in 4 different senescence models. c, Expression of NFKBIZ and NFΚBMARL-1 in early and late senescent IMR90 cells revealed by RNA-seq. d, 4OHT (100 nM) induced HRAS overexpressing IMR90 cells with HRAS and p16-INK4A protein level measured by Western blot. e, smFISH for NFΚBMARL-1 in bleomycin-induced senescent IMR90 (left). Statistics of fluorescent intensity of NFΚBMARL-1 in senescent IMR90 (right). Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima; dots are each fluorescent intensity. The results were analyzed by ImageJ. f, Global expression profile of RNA-seq for RELA knockdown in oncogene-induced senescent IMR90. The arrows point out NFKBIZ and NFΚBMARL-1. g, Genome browser view for the H3K27ac modification and expression level of NFΚBMARL-1 and NFKBIZ under cellular senescence process. h, The genome browser view of histone modification H3K18ac, H3K122ac, H3K23ac, H4K5ac and H3K4me1 in senescent IMR90. i, Dot plot of super-enhancer scores in senescent H3K27ac identified peak. The score in the super-enhancer covered NFKBIZ and NFΚBMARL-1 is 0.25 (Chromosome region is chr3:101809216-101964911). j, Global expression profile of RNA-seq for oncogene-induced senescent IMR90 and treated by JQ1. The arrows point out NFKBIZ and NFΚBMARL-1. k, Left, genomic contact matrices of proliferating and senescent IMR90 in chromosome 3. Right, subtraction heatmap of senescent vs proliferating. l, The TNFα induced change of Pol II ChIA-PET signal in HUVEC within each of the 2407 TADs obtained from the HUVEC HiC dataset (GSE63525). The dotted line indicates p = 0.05. The number of TADs with significantly increased and decreased Pol II ChIA-PET signals are indicated by the red and blue labels. Under TNFα treatment, 41 and 3 of the annotated TADs show significantly increased and decreased Pol II in HUVEC cells, respectively. Importantly, the ChIA-PET signals in the TAD enclosing NFKBIZ and NFKBMARL-1 is strongly increased after TNFα treatment, and the Pol II PET signals ranks at the 10th of the ChIA-PET signal up-regulated TAD among a total 2407 TADs. P values were defined and adjusted by edgeR. Data in (b, c) are presented as mean ± s.d. in n = 3 independent experiments. All p values in (b, c) were calculated using two-sided unpaired Student’s t-test.

Source data

Extended Data Fig. 7 The chromatin binding profile of NFΚBMARL-1.

a, Genome browser view showing the transcription factor binding profile of RELA and the epigenetic status of H3K27ac after in TNFα stimulated HUVEC. b, Genome browser view showing the transcription factor binding profile of RELA and the epigenetic status of H3K27ac, H4ac and ATAC after TNFα treatment in CD14 + monocyte. c, Genome browser view showing the transcription factor binding profile of RELA, MED1 and BRD4 and the epigenetic status of H3K27ac after TNFα treatment in adipocyte. d, Genome browser view showing RELA binding profile on NFKBIZ and NFΚBMARL-1 under LPS, TNFα, Pam2CSK4, PolyIC and MtriDAP treatment in D562. e, Genome browser view showing 5 NFκB pathway family members binding on NFKBIZ and NFΚBMARL-1 in lymphoblastoid B cells. f, Cellular fraction distribution of NFΚBMARL-1 in TNFα (10 ng/ml) induced 293 T, THP1 and IMR90, revealed by RT-qPCR.

Extended Data Fig. 8 NFΚBMARL-1 regulates NFKBIZ expression in cis.

a, Gel image of PCR products for validating the genomic deletion of NFΚBMARL-1 in 293 T cells. b, Gel image of PCR validates the genomic deletion of NFΚBMARL-1 in THP1 cells. c, Gel image of PCR validated insertion of EF1 promoter in 293 T cells. d, Immunofluorescence (IF) of NFKBIZ in control and NFΚBMARL-1 KD TNFα stimulated IMR90 cells (yellow circles are identified by DAPI for nucleus). The right panel is the statistics of fluorescent intensity of NFKBIZ within the yellow circle by ImageJ. e, Expression level of NFKBIZ, CXCL1, IL6 and IL8 in control and NFΚBMARL-1 KD bleomycin-induced senescent IMR90 cells, revealed by RT-qPCR. f, Western blot of IL1B and p16-INK4A in NFΚBMARL-1 KD and bleomycin-induced senescent IMR90 cells. g, Image of SAHF dyed by DAPI and BrdU in control and NFΚBMARL-1 KD bleomycin-induced senescent IMR90 cells. h, IF images of IL1B in control and NFΚBMARL-1 KD bleomycin-induced senescent IMR90 cells. The left panel of statistics of fluorescent intensity of IL1B in IMR90. Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima; dots are each fluorescent intensity (d, h). The results were analyzed by ImageJ. i, WT and NFΚBMARL-1 KO THP1 cell line was induced into M0 stage by PMA (100 ng/ml) and induced into M1 stage by LPS (15 ng/ml), the expression level of NFΚBMARL-1, NFKBIZ, CXCL1, IL6, IL8, CD14 and CSF1R were revealed by RT-qPCR. j, Western blot of IL1B in WT and NFΚBMARL-1 KO THP1 cells. k, FACS analysis of CD14-FITC in WT and NFΚBMARL-1 KO THP1 cells induced into M0 and M1 macrophage. The MFI were analyzed by FlowJo. l, Left, schematic of coculture of M1 stage macrophage from WT and NFΚBMARL-1 KO THP1 with Hela cells. Middle, image of 0.5% crystal violet dyeing the Hela cells. Right, quantification of the colony number of Hela cells. Data in (e, i, k) are presented as mean ± s.d. in n = 3 independent experiments. All p values in (d, e, h, i, k, l) were calculated using two-sided unpaired Student’s t-test.

Source data

Extended Data Fig. 9 The mechanism of NFΚBMARL-1 regulates NFKBIZ.

a, Top: NFΚBMARL-1 RNA pull-down assay for RELA, HNRNPU and HNRNPA2B1 in THP1, revealed by western blot. Bottom: RELA binding in tRSA-RNA pull-down for NFΚBMARL-1, quantified by Western blot. b, Native RIP assay by anti-p65 and anti-BRD4 in Bleomycin-induced senescent IMR90, followed by RT-qPCR for NFΚBMARL-1 and NFKBIZ. c, The EMSA of 1ug NFΚBMARL-1 binds to serial concentrations of his tag RELA, RELA-N or RELA-C. d, Left, 3D modeling the interaction structure of RELA (1NFI) and NFΚBMARL-1. The docking 3D structure was visualized by Pymol with the interaction region between RNA and protein within 3 Å colored. The docking nucleotides from RNA are blue. The docking amino acid from RELA is shown with the yellow peak region, the red valley region and orange flat region. Right, the interacting nucleotides in each fragment of NFΚBMARL-1. e, 1μg NFΚBMARL-1 F4 binds to serial concentrations of his tag RELA, revealed by EMSA. f, Expression quantification of NFKBIZ, CXCL1, IL6 and IL8 by RT-qPCR after mutating F4 or ChIRP peak region, induced senescence by bleomycin. g, The genomic interaction from NFΚBMARL-1 and ChIRP binding region to the long and short promoter region of NFKBIZ in bleomycin treated or NFΚBMARL-1 KD IMR90, revealed by 3C-qPCR. The Data in (b, f) are presented as mean ± s.d. in n = 3 independent experiments. All p values in (b, f) were calculated using two-sided unpaired Student’s t-test.

Source data

Extended Data Fig. 10 The evidences of RELA forming droplet foci.

a, Top, amino acid composition of RELA. Each row is a kind of amino acid in protein sequence. Bottom, intrinsic disorder score of RELA (PONDR VSL2). The Rel homology domain (RHD) and activation domain (AD) are indicated in the bottom of intrinsic disorder score. b, Immunofluorescence (IF) imaging of RELA and MED1 in TNFα stimulated 293 T cells. Fluorescence signal is shown left and merged with DAPI stain shown right. c, Live imaging of TNF-α induced endogenous tagged EGFP-p65 293 T cells. d, Images of FRAP experiments performed on the TNFα stimulated endogenous tagged EGFP-p65 293 T cell line. Right, quantification of fluorescent intensity of EGFP-p65 after photobleaching. e, In vitro droplet formation of MED1-RFP mixed with GFP or RELA-GFP in droplet formation buffer. The statistics of relative area of RELA-GFP. Centerline indicates median value; box limits are the interquartile range of 25th and 75th percentiles; whiskers each extend 1.5 times of the interquartile range of maxima and minima; dots are each relative area. The results were analyzed by ImageJ. f, Images of smFISH for NFΚBMARL-1 and IF for RELA and MED1 in TNFα stimulated 293 T. Middle, enlarged selected region of smFISH. Right, quantification of fluorescent signal of the dash line. g, Coomassie blue dyed SDS-PAGE gel of proteins from RNA pull-down with biotin labeled NFΚBMARL-1 in IMR90. M represents marker, WC represents whole cell, NU represents nuclear fraction, S represents sense strand pull-down fraction, AS represents antisense strand pull-down fraction. The piece of gel in sense strand pull-down proteins was cut for MS. h, RNA IP by anti-HNRNPA2B1 and anti-HNRNPU and reveal the enrichment level of NFΚBMARL-1, ACTIN and GAPDH by RT-qPCR. i, Graph showing intrinsic disorder score of HNRNPU (PONDR VSL2). j, Expression of NFΚBMARL-1 and NFKBIZ by RT-qPCR after knocking down HNRNPU by shRNA in TNFα stimulated IMR90. k, Genome browser view of NFKBIZ expression after knocking down HNRNPU in Hela RNA-seq. l, Graph showing intrinsic disorder score of HNRNPA2B1 (PONDR VSL2). m, Expression of NFΚBMARL-1 and NFKBIZ by RT-qPCR after knocking down HNRNPA2B1 by shRNA in TNFα treated IMR90. n, Fluorescent intensity of GFP by FACS after knocking down HNRNPA2B1 by shRNA in NFκB reporter 293 T cells. The MFI was analyzed by Flowjo. Data in (h, j, m, n) are presented as mean ± s.d. in n = 3 independent experiments. All p values in (e, h, j, m, n) were calculated using two-sided unpaired Student’s t-test.

Source data

Supplementary information

Supplementary Information

Descriptions of Supplementary Tables 1–9 and Supplementary Software.

Reporting Summary

Supplementary Table 1

The evolutionary age of aging-associated lncRNAs.

Supplementary Table 2

CRISPR screening library.

Supplementary Table 3

Differentially expressed genes in NFΚBMARL-1 KD RNA-seq compared to control.

Supplementary Table 4

Peaks in NFΚBMARL-1 ChIRP-seq.

Supplementary Table 5

Mass spectrometry of proteins pulled down by NFΚBMARL-1.

Supplementary Table 6

List of designed oligonucleotides.

Supplementary Table 7

Statistical source data.

Supplementary Table 8

The guilt-by-association (GBA) analysis of aging-associated lncRNAs to NFκB pathway.

Supplementary Table 9

The positive hits of lncRNAs and protein-coding genes from CRISPR screening.

Supplementary Software

Software with parameters used to perform analyses.

Source data

Source Data Fig. 4

Uncropped images of immunoblot.

Source Data Fig. 5

Uncropped images of immunoblot.

Source Data Extended Data Fig. 4

Gel image of PCR product validating the knockout region of 18 positive-hit lncRNAs by pgRNAs.

Source Data Extended Data Fig. 6

Uncropped images of immunoblot.

Source Data Extended Data Fig. 8

Uncropped images of immunoblot.

Source Data Extended Data Fig. 9

Uncropped images of immunoblot.

Source Data Extended Data Fig. 10

Uncropped images of immunoblot.

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Cai, D., Han, JD.J. Aging-associated lncRNAs are evolutionarily conserved and participate in NFκB signaling. Nat Aging 1, 438–453 (2021). https://doi.org/10.1038/s43587-021-00056-0

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