Posttranslational modifications of epigenetic modifiers provide a flexible and timely mechanism for rapid adaptations to the dynamic environment of cancer cells. SIRT1 is an NAD⁺-dependent epigenetic modifier whose activity is classically associated with healthy aging and longevity, but its function in cancer is not well understood. Here, we reveal that 1a,25-dihydroxyvitamin D₃ (1,25(OH)₂D₃, calcitriol), the active metabolite of vitamin D (VD), promotes SIRT1 activation through auto-deacetylation in human colon carcinoma cells, and identify lysine 610 as an essential driver of SIRT1 activity. Remarkably, our data show that the post-translational control of SIRT1 activity mediates the antiproliferative action of 1,25(OH)₂D₃. This effect is reproduced by the SIRT1 activator SRT1720, suggesting that SIRT1 activators may offer new therapeutic possibilities for colon cancer patients who are VD deficient or unresponsive. Moreover, this might be extrapolated to inflammation and other VD deficiency-associated and highly prevalent diseases in which SIRT1 plays a prominent role.

For Immunofluorescences, datasets were collected with ImageJ software as fluorescence intensity corrected by cell number. For each experiment (named in the excel n1 to nx), 3 different fields were evaluated per slide. Statistical methods are detailed in the Excel file.

For western blots, bands were measured by densitometry and analyzed with ImageJ software. Statistical methods are detailed in the Excel file.

For Deacetylase assays and NAD+ levels, datasets were collected from the luminometer. Statistical methods are detailed in the Excel file.

For TMAs in Figure 3C-E: immunoreactivity was quantified blind with a Histoscore (H score) that considers both the intensity and percentage of cells stained for each intensity (low, medium, or high) following this algorithm (range 0–300): H score = (low%) × 1 + (medium%) × 2 + (high %) × 3. Quantification for each patient biopsy was calculated blindly by 2 investigators. Statistical analysis was performed with Chi-square test.

For the TNM plots, data was collected from Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO)  (https://www.ncbi.nlm.nih.gov/geo/) repository for datasets containing “cancer” samples. Only datasets utilizing the Affymetrix HGU133, HGU133A_2, and HGU133A platforms were considered.

For cell cycle, data were collected and analyzed using CXP software (Becton-Dickinson). Statistical methods are detailed in the Excel file.

For proliferation curves datasets were collected with Spectra FLUOR (Tecan) at 542 nm and analyzed in Excel. Statistical methods are detailed in the Excel file.

Datasets are presented in Excel.

For TNM plots we used this link: https://tnmplot.com/analysis/

Rawdata: original data are presented in PowerPoint

No additional software is required.