The long non-coding RNA MIR31HG regulates the senescence associated secretory phenotype

Oncogene-induced senescence provides a barrier against malignant transformation. However, it can also promote cancer through the secretion of a plethora of factors released by senescent cells, called the senescence associated secretory phenotype (SASP). We have previously shown that in proliferating cells, nuclear lncRNA MIR31HG inhibits p16/CDKN2A expression through interaction with polycomb repressor complexes and that during BRAF-induced senescence, MIR31HG is overexpressed and translocates to the cytoplasm. Here, we show that MIR31HG regulates the expression and secretion of a subset of SASP components during BRAF-induced senescence. The SASP secreted from senescent cells depleted for MIR31HG fails to induce paracrine invasion without affecting the growth inhibitory effect. Mechanistically, MIR31HG interacts with YBX1 facilitating its phosphorylation at serine 102 (p-YBX1S102) by the kinase RSK. p-YBX1S102 induces IL1A translation which activates the transcription of the other SASP mRNAs. Our results suggest a dual role for MIR31HG in senescence depending on its localization and points to the lncRNA as a potential therapeutic target in the treatment of senescence-related pathologies.

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information. Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative. The data and reagents that support the findings of this study are available from the corresponding author upon reasonable request. Source data is available online for Figs. 1-6 and Supplementary Figs. 1-6.
The majority of the experiments were done in three-four replicates according to standars. No previous study was used to determine the number of samples.
No data was excluded from the analysis The majority of the experiments were done at least in 3 independent replicates. RNAseq and Mass Spectomerty experiments were validated by qRT-PCR and Immunoblot.
There was no randomization in the allocation of samples because proper controls were used. All samples (controls and treatments) were treated in the exact same manner for all the experiments.
Researchers were not blinded during experiments and data analysis because proper controls were used.