**Reporting summary**

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

**Data availability**

All sequencing data are deposited under the following Gene Expression Omnibus accessions: GSE154149, microbial genome analysis from OSKM and WT mice pretreatment and after 7 d of 1 mg ml⁻¹ treatment with doxycycline in the drinking water; GSE200578, ChIP-seq of H3k36me3 samples of OSKM reprogramming MEFs treated with B₁₂; GSE200579, RNA-seq samples of OSKM reprogrammable MEFs treated with or without vitamin B₁₂; GSE232382, RNA-seq samples of OSKM reprogrammable MEFs treated with vitamin B₁₂ and/or various compounds to modulate SAM or histone methylation. Previously published datasets that were used for analysis in the current study are: GSE131032, RNA-seq of time-course analysis of repairing murine epithelium after DSS injury; GSE109142, RNA-seq of human paediatric ulcerative colitis and normal tissue controls; GSE102518, RNA-seq of murine in vitro reprogramming in MEFs of varying genotypes; GSE77722, RNA-seq of murine in vivo reprogramming in mice of varying genotypes. Source data are provided with this paper.

**References**

1. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. *Cell* **126**, 663–676 (2006).

2. Taguchi, J. & Yamada, Y. In vivo reprogramming for tissue regeneration and organismal rejuvenation. *Curr. Opin. Genet. Dev.* **46**, 132–140 (2017).

3. Abad, M. et al. Reprogramming in vivo produces teratomas and iPS cells with totipotency features. *Nature* **502**, 340–345 (2013).

4. Ocampo, A. et al. In vivo amelioration of age-associated hallmarks by partial reprogramming. *Cell* **167**, 1719–1733 (2016).

5. Doeser, M. C., Schöler, H. R. & Wu, G. Reduction of fibrosis and scar formation by partial reprogramming in vivo. *Stem Cells* **36**, 1216–1225 (2018).

6. Lu, Y. et al. Reprogramming to recover youthful epigenetic information and restore vision. *Nature* **588**, 124–129 (2020).

7. Chen, Y. et al. Reversible reprogramming of cardiomyocytes to a fetal state drives heart regeneration in mice. *Science* **373**, 1537–1540 (2021).

8. Browder, K. C. et al. In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice. *Nat. Aging* **2**, 243–253 (2022).

9. Chondronasiou, D. et al. Multi-omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming. *Aging Cell* **21**, e13578 (2022).

10. Ohnishi, K. et al. Premature termination of reprogramming in vivo leads to cancer development through altered epigenetic regulation. *Cell* **156**, 663–677 (2014).

11. Shibata, H. et al. In vivo reprogramming drives Kras-induced cancer development. *Nat. Commun.* **9**, 2081 (2018).

12. Shyh-Chang, N. et al. Influence of threonine metabolism on S-adenosylmethionine and histone methylation. *Science* **339**, 222–226 (2013).

13. Fernández-Arroyo, S. et al. Activation of the methylation cycle in cells reprogrammed into a stem cell-like state. *Oncoscience* **2**, 958–967 (2016).

14. Degnan, P. H., Taga, M. E. & Goodman, A. L. Vitamin B12 as a modulator of gut microbial ecology. *Cell Metab.* **20**, 769–778 (2014).

15. Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. *Science* **336**, 1262–1267 (2012).

16. Mosteiro, L. et al. Tissue damage and senescence provide critical signals for cellular reprogramming in vivo. *Science* **354**, aaf4445 (2016).

17. Melendez, E. et al. Natural killer cells act as an extrinsic barrier for in vivo reprogramming. *Development* https://doi.org/10.1242/dev.200361 (2022).

18. Kennedy, E. A., King, K. Y. & Baldridge, M. T. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. *Front. Physiol.* **9**, 1534 (2018).

19. Chondronasiou, D. et al. Deciphering the roadmap of in vivo reprogramming toward pluripotency. *Stem Cell Rep.* **17**, 2501–2517 (2022).

20. Levy, M. et al. Microbiota-modulated metabolites shape the intestinal microenvironment by regulating NLRP6 inflammasome signaling. *Cell* **163**, 1428–1443 (2015).

21. Chang, C. S. et al. Identification of a gut microbiota member that ameliorates DSS-induced colitis in intestinal barrier enhanced Dusp6-deficient mice. *Cell Rep.* **37**, 110016 (2021).

22. Yui, S. et al. YAP/TAZ-dependent reprogramming of colonic epithelium links ECM remodeling to tissue regeneration. *Cell Stem Cell* **2**, 35–49 (2018).

23. Moschen, A. R. et al. Lipocalin 2 protects from inflammation and tumorigenesis associated with gut microbiota alterations. *Cell Host Microbe* **19**, 455–469 (2016).

24. Degnan, P. H., Barry, N. A., Mok, K. C., Taga, M. E. & Goodman, A. L. Human gut microbes use multiple transporters to distinguish vitamin B₁₂ analogs and compete in the gut. *Cell Host Microbe* **15**, 47–57 (2014).

25. Martens, H., Barg, M., Warren, D. & Jah, J.-H. Microbial production of vitamin B12. *Appl. Microbiol. Biotechnol.* **58**, 275–285 (2002).

26. Green, R. et al. Vitamin B₁₂ deficiency. *Nat. Rev. Dis. Prim.* **3**, 17040 (2017).

27. Kornerup, L. S. et al. Tissue distribution of oral vitamin B₁₂ is influenced by B₁₂ status and B₁₂ form: an experimental study in rats. *Eur. J. Nutr.* **57**, 1459–1469 (2018).

28. Shin, O.-H. et al. Methyl-group donors cannot prevent apoptotic death of rat hepatocytes induced by choline-deficiency. *J. Cell. Biochem.* **64**, 196–208 (1997).

29. Birn, H. et al. Megalin is essential for renal proximal tubule reabsorption and accumulation of transcobalamin-B₁₂. *Am. J. Physiol. Ren. Physiol.* **282**, F408–F416 (2002).

30. Scott, J. S. D., Treston, A. M., Bowman, E. P. W., Owens, J. A. & Cooksley, W. G. The regulatory roles of liver and kidney in cobalamin (vitamin B₁₂) metabolism in the rat: the uptake and intracellular binding of cobalamin and the activity of the cobalamin-dependent enzymes in response to varying cobalamin supply. *Clin. Science* **67**, 299–306 (1984).

31. Okuda, K. Relationship between intake of vitamin B₁₂ and its storage by the kidney in the rat. *J. Nutr.* **77**, 131–136 (1962).

32. Rossi, M., Amaretti, A. & Raimondi, S. Folate production by probiotic bacteria. *Nutrients* **3**, 118–134 (2011).

33. Sanderson, S. M., Gao, X., Dai, Z. & Locasale, J. W. Methionine metabolism in health and cancer: a nexus of diet and precision medicine. *Nat. Rev. Cancer* **19**, 625–637 (2019).

34. Shiraki, N. et al. Methionine metabolism regulates maintenance and differentiation of human pluripotent stem cells. *Cell Metab.* **19**, 780–794 (2014).

35. Wang, J. et al. Dependence of mouse embryonic stem cells on threonine catabolism. *Science* **325**, 435–439 (2009).

36. Bravo, A. C. et al. Analysis of S-adenosylmethionine and S-adenosylhomocysteine: method optimisation and profiling in healthy adults upon short-term dietary intervention. *Metabolites* **12**, 373 (2022).

37. Lildballe, D. L., Mutti, E., Birn, H. & Nexo, E. Maximal load of the vitamin B12 transport system: a study on mice treated for four weeks with high-dose vitamin B₁₂ or cobinamide. *PLoS ONE* **7**, e46657 (2012).
**Reporting summary**

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

**Data availability**

All sequencing data are deposited under the following Gene Expression Omnibus accessions: GSE154149, microbial genome analysis from OSKM and WT mice pretreatment and after 7 d of 1 mg ml⁻¹ treatment with doxycycline in the drinking water; GSE200578, ChIP-seq of H3k36me3 samples of OSKM reprogramming MEFs treated with B₁₂; GSE200579, RNA-seq samples of OSKM reprogrammable MEFs treated with or without vitamin B₁₂; GSE232382, RNA-seq samples of OSKM reprogrammable MEFs treated with vitamin B₁₂ and/or various compounds to modulate SAM or histone methylation. Previously published datasets that were used for analysis in the current study are: GSE131032, RNA-seq of time-course analysis of repairing murine epithelium after DSS injury; GSE109142, RNA-seq of human paediatric ulcerative colitis and normal tissue controls; GSE102518, RNA-seq of murine in vitro reprogramming in MEFs of varying genotypes; GSE77722, RNA-seq of murine in vivo reprogramming in mice of varying genotypes. Source data are provided with this paper.

**References**

1. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. *Cell* **126**, 663–676 (2006).

2. Taguchi, J. & Yamada, Y. In vivo reprogramming for tissue regeneration and organismal rejuvenation. *Curr. Opin. Genet. Dev.* **46**, 132–140 (2017).

3. Abad, M. et al. Reprogramming in vivo produces teratomas and iPS cells with totipotency features. *Nature* **502**, 340–345 (2013).

4. Ocampo, A. et al. In vivo amelioration of age-associated hallmarks by partial reprogramming. *Cell* **167**, 1719–1733 (2016).

5. Doeser, M. C., Schöler, H. R. & Wu, G. Reduction of fibrosis and scar formation by partial reprogramming in vivo. *Stem Cells* **36**, 1216–1225 (2018).

6. Lu, Y. et al. Reprogramming to recover youthful epigenetic information and restore vision. *Nature* **588**, 124–129 (2020).

7. Chen, Y. et al. Reversible reprogramming of cardiomyocytes to a fetal state drives heart regeneration in mice. *Science* **373**, 1537–1540 (2021).

8. Browder, K. C. et al. In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice. *Nat. Aging* **2**, 243–253 (2022).

9. Chondronasiou, D. et al. Multi-omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming. *Aging Cell* **21**, e13578 (2022).

10. Ohnishi, K. et al. Premature termination of reprogramming in vivo leads to cancer development through altered epigenetic regulation. *Cell* **156**, 663–677 (2014).

11. Shibata, H. et al. In vivo reprogramming drives Kras-induced cancer development. *Nat. Commun.* **9**, 2081 (2018).

12. Shyh-Chang, N. et al. Influence of threonine metabolism on S-adenosylmethionine and histone methylation. *Science* **339**, 222–226 (2013).

13. Fernández-Arroyo, S. et al. Activation of the methylation cycle in cells reprogrammed into a stem cell-like state. *Oncoscience* **2**, 958–967 (2016).

14. Degnan, P. H., Taga, M. E. & Goodman, A. L. Vitamin B12 as a modulator of gut microbial ecology. *Cell Metab.* **20**, 769–778 (2014).

15. Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. *Science* **336**, 1262–1267 (2012).

16. Mosteiro, L. et al. Tissue damage and senescence provide critical signals for cellular reprogramming in vivo. *Science* **354**, aaf4445 (2016).

17. Melendez, E. et al. Natural killer cells act as an extrinsic barrier for in vivo reprogramming. *Development* https://doi.org/10.1242/dev.200361 (2022).

18. Kennedy, E. A., King, K. Y. & Baldridge, M. T. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. *Front. Physiol.* **9**, 1534 (2018).

19. Chondronasiou, D. et al. Deciphering the roadmap of in vivo reprogramming toward pluripotency. *Stem Cell Rep.* **17**, 2501–2517 (2022).

20. Levy, M. et al. Microbiota-modulated metabolites shape the intestinal microenvironment by regulating NLRP6 inflammasome signaling. *Cell* **163**, 1428–1443 (2015).

21. Chang, C. S. et al. Identification of a gut microbiota member that ameliorates DSS-induced colitis in intestinal barrier enhanced Dusp6-deficient mice. *Cell Rep.* **37**, 110016 (2021).

22. Yui, S. et al. YAP/TAZ-dependent reprogramming of colonic epithelium links ECM remodeling to tissue regeneration. *Cell Stem Cell* **2**, 35–49 (2018).

23. Moschen, A. R. et al. Lipocalin 2 protects from inflammation and tumorigenesis associated with gut microbiota alterations. *Cell Host Microbe* **19**, 455–469 (2016).

24. Degnan, P. H., Barry, N. A., Mok, K. C., Taga, M. E. & Goodman, A. L. Human gut microbes use multiple transporters to distinguish vitamin B₁₂ analogs and compete in the gut. *Cell Host Microbe* **15**, 47–57 (2014).

25. Martens, H., Barg, M., Warren, D. & Jah, J.-H. Microbial production of vitamin B12. *Appl. Microbiol. Biotechnol.* **58**, 275–285 (2002).

26. Green, R. et al. Vitamin B₁₂ deficiency. *Nat. Rev. Dis. Prim.* **3**, 17040 (2017).

27. Kornerup, L. S. et al. Tissue distribution of oral vitamin B₁₂ is influenced by B₁₂ status and B₁₂ form: an experimental study in rats. *Eur. J. Nutr.* **57**, 1459–1469 (2018).

28. Shin, O.-H. et al. Methyl-group donors cannot prevent apoptotic death of rat hepatocytes induced by choline-deficiency. *J. Cell. Biochem.* **64**, 196–208 (1997).

29. Birn, H. et al. Megalin is essential for renal proximal tubule reabsorption and accumulation of transcobalamin-B₁₂. *Am. J. Physiol. Ren. Physiol.* **282**, F408–F416 (2002).

30. Scott, J. S. D., Treston, A. M., Bowman, E. P. W., Owens, J. A. & Cooksley, W. G. The regulatory roles of liver and kidney in cobalamin (vitamin B₁₂) metabolism in the rat: the uptake and intracellular binding of cobalamin and the activity of the cobalamin-dependent enzymes in response to varying cobalamin supply. *Clin. Science* **67**, 299–306 (1984).

31. Okuda, K. Relationship between intake of vitamin B₁₂ and its storage by the kidney in the rat. *J. Nutr.* **77**, 131–136 (1962).

32. Rossi, M., Amaretti, A. & Raimondi, S. Folate production by probiotic bacteria. *Nutrients* **3**, 118–134 (2011).

33. Sanderson, S. M., Gao, X., Dai, Z. & Locasale, J. W. Methionine metabolism in health and cancer: a nexus of diet and precision medicine. *Nat. Rev. Cancer* **19**, 625–637 (2019).

34. Shiraki, N. et al. Methionine metabolism regulates maintenance and differentiation of human pluripotent stem cells. *Cell Metab.* **19**, 780–794 (2014).

35. Wang, J. et al. Dependence of mouse embryonic stem cells on threonine catabolism. *Science* **325**, 435–439 (2009).

36. Bravo, A. C. et al. Analysis of S-adenosylmethionine and S-adenosylhomocysteine: method optimisation and profiling in healthy adults upon short-term dietary intervention. *Metabolites* **12**, 373 (2022).

37. Lildballe, D. L., Mutti, E., Birn, H. & Nexo, E. Maximal load of the vitamin B12 transport system: a study on mice treated for four weeks with high-dose vitamin B₁₂ or cobinamide. *PLoS ONE* **7**, e46657 (2012).
38. Beedholm-Ebsen, R. et al. Identification of multidrug resistance protein 1 (MRP1/ABCC1) as a molecular gate for cellular export of cobalamin. *Blood* **115**, 1632-1639 (2010).

39. Kokkinakis, D. M. et al. Mitotic arrest, apoptosis, and sensitization to chemotherapy of melanomas by methionine deprivation stress. *Mol. Cancer Res.* **4**, 575-589 (2006).

40. Zviran, A. et al. Deterministic somatic cell reprogramming involves continuous transcriptional changes governed by myc and epigenetic-driven modules. *Cell Stem Cell* **24**, 328-341 (2019).

41. National Research Council (US) Subcommittee on Laboratory Animal Nutrition. *Nutrient Requirements of Laboratory Animals* (National Academies Press, 1995). https://doi.org/10.17226/4758

42. Mentch, S. J. et al. Histone methylation dynamics and gene regulation occur through the sensing of one-carbon metabolism. *Cell Metab.* **22**, 861-873 (2015).

43. Ye, C., Sutter, B. M., Wang, Y., Kuang, Z. & Tu, B. P. A metabolic function for phospholipid and histone methylation. *Mol. Cell* **66**, 180-193 (2017).

44. Chronis, C. et al. Cooperative binding of transcription factors orchestrates reprogramming. *Cell* **168**, 442-459 (2017).

45. van den Hurk, M. et al. Transcriptional and epigenetic mechanisms of cellular reprogramming to induced pluripotency. *Epigenomics* **8**, 1131-1149 (2016).

46. Chu, C. H. et al. KDM4B as a target for prostate cancer: structural analysis and selective inhibition by a novel inhibitor. *J. Med. Chem.* **57**, 5975-5985 (2014).

47. Edmunds, J. W., Mahadevan, L. C. & Clayton, A. L. Dynamic histone H3 methylation during gene induction: HYPB/Setd2 mediates all H3K36 trimethylation. *EMBO J.* **27**, 406-420 (2008).

48. Schwarz, B. A. et al. Prospective isolation of poised iPSC intermediates reveals principles of cellular reprogramming. *Cell Stem Cell* **23**, 289-305 (2018).

49. Li, B. et al. Infrequently transcribed long genes depend on the Set2/Rpd3S pathway for accurate transcription. *Genes Dev.* **21**, 1422-1430 (2007).

50. Neri, F. et al. Intragenic DNA methylation prevents spurious transcription initiation. *Nature* **543**, 72-77 (2017).

51. Barral, A. et al. SETDB1/NSD-dependent H3K9me3/H3K36me3 dual heterochromatin maintains gene expression profiles by bookmarking poised enhancers. *Mol. Cell* **82**, 816-832 (2022).

52. McCauley, B. S. et al. Altered chromatin states drive cryptic transcription in aging mammalian stem cells. *Nat. Aging* **1**, 684-697 (2021).

53. Huang, C. & Zhu, B. Roles of H3K36-specific histone methyltransferases in transcription: antagonizing silencing and safeguarding transcription fidelity. *Biophys. Rep.* **4**, 170-177 (2018).

54. Pu, M. et al. Trimethylation of Lys36 on H3 restricts gene expression change during aging and impacts life span. *Genes Dev.* **29**, 718-731 (2015).

55. Sen, P. et al. H3K36 methylation promotes longevity by enhancing transcriptional fidelity. *Genes Dev.* **29**, 1362-1376 (2015).

56. Carrozza, M. J. et al. Histone H3 methylation by Set2 directs deacetylation of coding regions by Rpd3S to suppress spurious intragenic transcription. *Cell* **123**, 581-592 (2005).

57. Ang, Y.-S. et al. Wdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network. *Cell* **145**, 183-197 (2011).

58. lismaa, S. E. et al. Comparative regenerative mechanisms across different mammalian tissues. *npj Regen. Med.* **3**, 6 (2018).

59. Ayyaz, A. et al. Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell. *Nature* https://doi.org/10.1038/s41586-019-1154-y (2019).

60. Nusse, Y. M. et al. Parasitic helminths induce fetal-like reversion in the intestinal stem cell niche. *Nature* **559**, 109-113 (2018).

61. Perše, M. & Cerar, A. Dextran sodium sulphate colitis mouse model: traps and tricks. *J. Biomed. Biotechnol.* **2012**, 718617 (2012).

62. Czarnewski, P. et al. Conserved transcriptomic profile between mouse and human colitis allows unsupervised patient stratification. *Nat. Commun.* **10**, 2892 (2019).

63. Haberman, Y. et al. Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response. *Nat. Commun.* **10**, 38 (2019).

64. Mentch, S. J. & Locasale, J. W. One-carbon metabolism and epigenetics: understanding the specificity. *Ann. N. Y. Acad. Sci.* https://doi.org/10.1111/nyas.12956 (2016).

65. Boon, R. Metabolic fuel for epigenetic: nuclear production meets local consumption. *Front. Genet.* **12**, 768996 (2021).

66. Ye, C. & Tu, B. P. Sink into the epigenome: histones as repositories that influence cellular metabolism. *Trends Endocrinol. Metab.* **29**, 626-637 (2018).

67. Eram, M. S. et al. Kinetic characterization of human histone H3 lysine 36 methyltransferases, ASH1L and SETD2. *Biochim. Biophys. Acta* **1850**, 1842-1848 (2015).

68. Lurz, E. et al. Vitamin B₁₂ deficiency alters the gut microbiota in a murine model of colitis. *Front. Nutr.* **7**, 83 (2020).

69. Benight, N. M. et al. B-vitamin deficiency is protective against DSS-induced colitis in mice. *Am. J. Physiol. Gastrointest. Liver Physiol.* **301**, G249-G259 (2011).

70. Tamura, J. et al. Immunomodulation by vitamin B₁₂: augmentation of CD8+ T lymphocytes and natural killer (NK) cell activity in vitamin B₁₂-deficient patients by methyl-B₁₂ treatment. *Clin. Exp. Immunol.* **116**, 28-32 (1999).

71. Liu, M. et al. The histone methyltransferase SETD2 modulates oxidative stress to attenuate experimental colitis. *Redox Biol.* **43**, 102004 (2021).

72. Ward, M. G. et al. Prevalence and risk factors for functional vitamin B₁₂ deficiency in patients with Crohn's disease. *Inflamm. Bowel Dis.* **21**, 2839-2847 (2015).

73. Zheng, S. et al. Association of ulcerative colitis with transcobalamin II gene polymorphisms and serum homocysteine, vitamin B₁₂, and folate levels in Chinese patients. *Immunogenetics* **69**, 421-428 (2017).

74. Leinenkugel, G. et al. Sca-1 is a marker for cell plasticity in murine pancreatic epithelial cells and induced by IFN-β in vitro. *Pancreatology* **22**, 294-303 (2022).

75. Dekel, B. et al. Isolation and characterization of nontubular sca-1+lin- multipotent stem/progenitor cells from adult mouse kidney. *J. Am. Soc. Nephrol.* **17**, 3300-3314 (2006).

76. Camarata, T. D., Weaver, G. C., Vasilyev, A. & Arnaout, M. A. Negative regulation of TGFβ signaling by stem cell antigen-1 protects against ischemic acute kidney injury. *PLoS ONE* **10**, e0129561 (2015).

77. Bender Kim, C. F. et al. Identification of bronchioalveolar stem cells in normal lung and lung cancer. *Cell* **121**, 823-835 (2005).

78. Louie, S. M. et al. Progenitor potential of lung epithelial organoid cells in a transplantation model. *Cell Rep.* **39**, 110662 (2022).

79. Zhang, W. et al. Fluorinated N, N-dialkylaminostilbenes repress colon cancer by targeting methionine S-adenosyltransferase 2A. *ACS Chem. Biol.* **8**, 796-803 (2013).

80. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. *Bioinformatics* **29**, 15-21 (2013).

81. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. *Nat. Methods* **13**, 581-583 (2016).

82. Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. *Nat. Commun.* **7**, 11257 (2016).

83. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. *Genome Biol.* **15**, 550 (2014).
38. Beedholm-Ebsen, R. et al. Identification of multidrug resistance protein 1 (MRP1/ABCC1) as a molecular gate for cellular export of cobalamin. *Blood* **115**, 1632–1639 (2010).

39. Kokkinakis, D. M. et al. Mitotic arrest, apoptosis, and sensitization to chemotherapy of melanomas by methionine deprivation stress. *Mol. Cancer Res.* **4**, 575–589 (2006).

40. Zviran, A. et al. Deterministic somatic cell reprogramming involves continuous transcriptional changes governed by myc and epigenetic-driven modules. *Cell Stem Cell* **24**, 328–341 (2019).

41. National Research Council (US) Subcommittee on Laboratory Animal Nutrition. *Nutrient Requirements of Laboratory Animals* (National Academies Press, 1995). https://doi.org/10.17226/4758

42. Mentch, S. J. et al. Histone methylation dynamics and gene regulation occur through the sensing of one-carbon metabolism. *Cell Metab.* **22**, 861–873 (2015).

43. Ye, C., Sutter, B. M., Wang, Y., Kuang, Z. & Tu, B. P. A metabolic function for phospholipid and histone methylation. *Mol. Cell* **66**, 180–193 (2017).

44. Chronis, C. et al. Cooperative binding of transcription factors orchestrates reprogramming. *Cell* **168**, 442–459 (2017).

45. van den Hurk, M. et al. Transcriptional and epigenetic mechanisms of cellular reprogramming to induced pluripotency. *Epigenomics* **8**, 1131–1149 (2016).

46. Chu, C. H. et al. KDM4B as a target for prostate cancer: structural analysis and selective inhibition by a novel inhibitor. *J. Med. Chem.* **57**, 5975–5985 (2014).

47. Edmunds, J. W., Mahadevan, L. C. & Clayton, A. L. Dynamic histone H3 methylation during gene induction: HYPB/Setd2 mediates all H3K36 trimethylation. *EMBO J.* **27**, 406–420 (2008).

48. Schwarz, B. A. et al. Prospective isolation of poised iPSC intermediates reveals principles of cellular reprogramming. *Cell Stem Cell* **23**, 289–305 (2018).

49. Li, B. et al. Infrequently transcribed long genes depend on the Set2/Rpd3S pathway for accurate transcription. *Genes Dev.* **21**, 1422–1430 (2007).

50. Neri, F. et al. Intragenic DNA methylation prevents spurious transcription initiation. *Nature* **543**, 72–77 (2017).

51. Barral, A. et al. SETDB1/NSD-dependent H3K9me3/H3K36me3 dual heterochromatin maintains gene expression profiles by bookmarking poised enhancers. *Mol. Cell* **82**, 816–832 (2022).

52. McCauley, B. S. et al. Altered chromatin states drive cryptic transcription in aging mammalian stem cells. *Nat. Aging* **1**, 684–697 (2021).

53. Huang, C. & Zhu, B. Roles of H3K36-specific histone methyltransferases in transcription: antagonizing silencing and safeguarding transcription fidelity. *Biophys. Rep.* **4**, 170–177 (2018).

54. Pu, M. et al. Trimethylation of Lys36 on H3 restricts gene expression change during aging and impacts life span. *Genes Dev.* **29**, 718–731 (2015).

55. Sen, P. et al. H3K36 methylation promotes longevity by enhancing transcriptional fidelity. *Genes Dev.* **29**, 1362–1376 (2015).

56. Carrozza, M. J. et al. Histone H3 methylation by Set2 directs deacetylation of coding regions by Rpd3S to suppress spurious intragenic transcription. *Cell* **123**, 581–592 (2005).

57. Ang, Y.-S. et al. Wdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network. *Cell* **145**, 183–197 (2011).

58. lismaa, S. E. et al. Comparative regenerative mechanisms across different mammalian tissues. *npj Regen. Med.* **3**, 6 (2018).

59. Ayyaz, A. et al. Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell. *Nature* https://doi.org/10.1038/s41586-019-1154-y (2019).

60. Nusse, Y. M. et al. Parasitic helminths induce fetal-like reversion in the intestinal stem cell niche. *Nature* **559**, 109–113 (2018).

61. Perše, M. & Cerar, A. Dextran sodium sulphate colitis mouse model: traps and tricks. *J. Biomed. Biotechnol.* **2012**, 718617 (2012).

62. Czarnewski, P. et al. Conserved transcriptomic profile between mouse and human colitis allows unsupervised patient stratification. *Nat. Commun.* **10**, 2892 (2019).

63. Haberman, Y. et al. Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response. *Nat. Commun.* **10**, 38 (2019).

64. Mentch, S. J. & Locasale, J. W. One-carbon metabolism and epigenetics: understanding the specificity. *Ann. N. Y. Acad. Sci.* https://doi.org/10.1111/nyas.12956 (2016).

65. Boon, R. Metabolic fuel for epigenetic: nuclear production meets local consumption. *Front. Genet.* **12**, 768996 (2021).

66. Ye, C. & Tu, B. P. Sink into the epigenome: histones as repositories that influence cellular metabolism. *Trends Endocrinol. Metab.* **29**, 626–637 (2018).

67. Eram, M. S. et al. Kinetic characterization of human histone H3 lysine 36 methyltransferases, ASH1L and SETD2. *Biochim. Biophys. Acta* **1850**, 1842–1848 (2015).

68. Lurz, E. et al. Vitamin B₁₂ deficiency alters the gut microbiota in a murine model of colitis. *Front. Nutr.* **7**, 83 (2020).

69. Benight, N. M. et al. B-vitamin deficiency is protective against DSS-induced colitis in mice. *Am. J. Physiol. Gastrointest. Liver Physiol.* **301**, G249–G259 (2011).

70. Tamura, J. et al. Immunomodulation by vitamin B₁₂: augmentation of CD8+ T lymphocytes and natural killer (NK) cell activity in vitamin B₁₂-deficient patients by methyl-B₁₂ treatment. *Clin. Exp. Immunol.* **116**, 28–32 (1999).

71. Liu, M. et al. The histone methyltransferase SETD2 modulates oxidative stress to attenuate experimental colitis. *Redox Biol.* **43**, 102004 (2021).

72. Ward, M. G. et al. Prevalence and risk factors for functional vitamin B₁₂ deficiency in patients with Crohn's disease. *Inflamm. Bowel Dis.* **21**, 2839–2847 (2015).

73. Zheng, S. et al. Association of ulcerative colitis with transcobalamin II gene polymorphisms and serum homocysteine, vitamin B₁₂, and folate levels in Chinese patients. *Immunogenetics* **69**, 421–428 (2017).

74. Leinenkugel, G. et al. Sca-1 is a marker for cell plasticity in murine pancreatic epithelial cells and induced by IFN-β in vitro. *Pancreatology* **22**, 294–303 (2022).

75. Dekel, B. et al. Isolation and characterization of nontubular sca-1+lin- multipotent stem/progenitor cells from adult mouse kidney. *J. Am. Soc. Nephrol.* **17**, 3300–3314 (2006).

76. Camarata, T. D., Weaver, G. C., Vasilyev, A. & Arnaout, M. A. Negative regulation of TGFβ signaling by stem cell antigen-1 protects against ischemic acute kidney injury. *PLoS ONE* **10**, e0129561 (2015).

77. Bender Kim, C. F. et al. Identification of bronchioalveolar stem cells in normal lung and lung cancer. *Cell* **121**, 823–835 (2005).

78. Louie, S. M. et al. Progenitor potential of lung epithelial organoid cells in a transplantation model. *Cell Rep.* **39**, 110662 (2022).

79. Zhang, W. et al. Fluorinated N, N-dialkylaminostilbenes repress colon cancer by targeting methionine S-adenosyltransferase 2A. *ACS Chem. Biol.* **8**, 796–803 (2013).

80. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. *Bioinformatics* **29**, 15–21 (2013).

81. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. *Nat. Methods* **13**, 581–583 (2016).

82. Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. *Nat. Commun.* **7**, 11257 (2016).

83. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. *Genome Biol.* **15**, 550 (2014).
84. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. *Bioinformatics* **31**, 1674–1676 (2015).

85. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. *BMC Bioinformatics* **11**, 119 (2010).

86. Huerta-Cepas, J. et al. EggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. *Nucleic Acids Res.* **47**, D309–D314 (2019).

87. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. *Nat. Methods* **9**, 357–359 (2012).

88. Carbon, S. et al. AmiGO: online access to ontology and annotation data. *Bioinformatics* **25**, 288–289 (2009).

89. Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. *Nat. Commun.* **10**, 2719 (2019).

90. Viltard, M. et al. The metabolomic signature of extreme longevity: naked mole rats versus mice. *Aging* **11**, 4783–4800 (2019).

91. Pang, Z., Chong, J., Li, S. & Xia, J. MetaboAnalystR 3.0: toward an optimized workflow for global metabolomics. *Metabolites* **10**, 186 (2020).

92. Peng, X. et al. Inhibition of phosphoinositide 3-kinase ameliorates dextran sodium sulfate-induced colitis in mice. *J. Pharmacol. Exp. Ther.* **332**, 46–56 (2010).

93. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. *Sci. Rep.* **7**, 16878 (2017).

94. Li, H. et al. The Ink4/Arf locus is a barrier for iPS cell reprogramming. *Nature* **460**, 1136–1139 (2009).

95. Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. *Proc. Natl Acad. Sci. USA* **102**, 15545–15550 (2005).

96. R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

97. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. *Bioinformatics* **31**, 2032–2034 (2015).

98. Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. *Nucleic Acids Res.* **47**, e47 (2019).

99. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. the Gene Ontology Consortium. *Nat. Genet.* **25**, 25–29 (2000).

100. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. *Genome Biol.* **10**, R25 (2009).

101. Li, H. et al. The sequence alignment/map format and SAMtools. *Bioinformatics* **25**, 2078–2079 (2009).

102. Lawrence, M. et al. Software for computing and annotating genomic ranges. *PLoS Comput. Biol.* **9**, e1003118 (2013).

103. Gu, Z., Eils, R., Schlesner, M. & Ishaque, N. EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations. *BMC Genomics* **19**, 234 (2018).

104. Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. *Brief. Bioinform.* **14**, 178–192 (2013).

105. Robinson, J. T. et al. Integrative genomics viewer. *Nat. Biotechnol.* https://doi.org/10.1038/nbt.1754 (2011).

## Acknowledgements

We are grateful to M. I. Muñoz for assistance with the animal protocols, to the IRB Functional Genomics Unit and CRG/CNAG for library preparation and genomic sequencing, and to the IRB (Functional

Genomics, Biostatistics/Bioinformatics, and Histopathology) and PCB (Animal House) core facilities for general research support. M.K. was funded by the Barcelona Institute of Science and technology (BIST) and Asociación Española Contra el Cáncer (AECC; POSTD18020SERR) and supported by the European Molecular Biology Organization (EMBO). F.P. is supported by a Karolinska Institute Starting Grant, Starting Grant from the Swedish Research Council (2019_02050_3). G.K. is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR) – Projets blancs; AMMICa US23/CNRS UMS3655; Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Fondation pour la Recherche Médicale (FRM); a donation by Elior; Equipex Onco-Pheno-Screen; European Joint Programme on Rare Diseases (EJPRD); European Research Council Advanced Investigator Award (ERC-2021-ADG, ICD-Cancer, grant no. 101052444), European Union Horizon 2020 Projects Oncobiome, Prevalung (grant no. 101095604) and Crimson; Fondation Carrefour; Institut National du Cancer (INCa); Institut Universitaire de France; LabEx Immuno-Oncology (ANR-18-IDEX-0001); a Cancer Research ASPIRE Award from the Mark Foundation; the RHU Immunolife; Seerave Foundation; SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and SIRIC Cancer Research and Personalized Medicine (CARPEM). This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. Work in the laboratory of M.S. was funded by the IRB, 'laCaixa' Foundation, and by grants from the Milky Way Research Foundation, the Spanish Ministry of Science co-funded by the European Regional Development Fund (ERDF; SAF2017-82613-R), the European Research Council (ERC-2014-AdG/669622) and the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de Catalonia (Grup de Recerca consolidat 2017 SGR 282).

## Author contributions

M.K. designed and performed most experiments, contributed to bioinformatic data analysis, performed quantification of immunohistochemistry and co-wrote the paper. E.M., D.C., F.P., R.B., A.H.-H. and M.R. provided general experimental support. A.C. and C.S.-O.A. designed and performed bacterial bioinformatic analysis. C.S.-O.A. designed and performed RNA-seq and ChIP-seq bioinformatics analysis. A.J., J.C., S.D., O.Y., E.M. and G.K. performed metabolomics experiments. E.M., D.C., F.P., A.J., S.D., O.Y. and G.K. helped design and analyse metabolomics experiments. N.P. performed the histopathological study and supervised the histopathological techniques. M.S. designed and supervised the study, secured funding, analysed the data and co-wrote the paper. All authors discussed the results and commented on the paper.

## Competing interests

G.K. holds research contracts with Daiichi Sankyo, Eleor, Kaleido, Lytix Pharma, PharmaMar, Osasuna Therapeutics, Samsara Therapeutics, Sanofi, Tollys and Vascage. G.K. is on the Board of Directors of the Bristol Myers Squibb Foundation France. G.K. is a scientific cofounder of everImmune, Osasuna Therapeutics, Samsara Therapeutics and Therafast Bio. G.K. is on the scientific advisory boards of Evolution, Institut Servier and Longevity Vision Funds. G.K. is the inventor of patents covering therapeutic targeting of aging, cancer, cystic fibrosis and metabolic disorders. G.K.'s wife, L. Zitvogel, has held research contracts with Glaxo Smyth Kline, Incyte, Lytix, Kaleido, Innovate Pharma, Daiichi Sankyo, Pilege, Merus, Transgene, 9m, Tusk and Roche, was on the Board of Directors of Transgene, is a cofounder of everImmune, and holds patents covering the treatment of cancer and the therapeutic manipulation of the microbiota. G.K.'s brother, R. Kroemer, was an employee of Sanofi and now consults for Boehringer-Ingelheim. M.K. has ongoing or completed research contracts with Galapagos, Rejuveron Senescence Therapeutics and Mesoestetic. D.C. is an employee of Fenix Group International.
84. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. *Bioinformatics* **31**, 1674–1676 (2015).

85. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. *BMC Bioinformatics* **11**, 119 (2010).

86. Huerta-Cepas, J. et al. EggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. *Nucleic Acids Res.* **47**, D309–D314 (2019).

87. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. *Nat. Methods* **9**, 357–359 (2012).

88. Carbon, S. et al. AmiGO: online access to ontology and annotation data. *Bioinformatics* **25**, 288–289 (2009).

89. Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. *Nat. Commun.* **10**, 2719 (2019).

90. Viltard, M. et al. The metabolomic signature of extreme longevity: naked mole rats versus mice. *Aging* **11**, 4783–4800 (2019).

91. Pang, Z., Chong, J., Li, S. & Xia, J. MetaboAnalystR 3.0: toward an optimized workflow for global metabolomics. *Metabolites* **10**, 186 (2020).

92. Peng, X. et al. Inhibition of phosphoinositide 3-kinase ameliorates dextran sodium sulfate-induced colitis in mice. *J. Pharmacol. Exp. Ther.* **332**, 46–56 (2010).

93. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. *Sci. Rep.* **7**, 16878 (2017).

94. Li, H. et al. The Ink4/Arf locus is a barrier for iPS cell reprogramming. *Nature* **460**, 1136–1139 (2009).

95. Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. *Proc. Natl Acad. Sci. USA* **102**, 15545–15550 (2005).

96. R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

97. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. *Bioinformatics* **31**, 2032–2034 (2015).

98. Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. *Nucleic Acids Res.* **47**, e47 (2019).

99. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. the Gene Ontology Consortium. *Nat. Genet.* **25**, 25–29 (2000).

100. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. *Genome Biol.* **10**, R25 (2009).

101. Li, H. et al. The sequence alignment/map format and SAMtools. *Bioinformatics* **25**, 2078–2079 (2009).

102. Lawrence, M. et al. Software for computing and annotating genomic ranges. *PLoS Comput. Biol.* **9**, e1003118 (2013).

103. Gu, Z., Eils, R., Schlesner, M. & Ishaque, N. EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations. *BMC Genomics* **19**, 234 (2018).

104. Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. *Brief. Bioinform.* **14**, 178–192 (2013).

105. Robinson, J. T. et al. Integrative genomics viewer. *Nat. Biotechnol.* https://doi.org/10.1038/nbt.1754 (2011).

## Acknowledgements

We are grateful to M. I. Muñoz for assistance with the animal protocols, to the IRB Functional Genomics Unit and CRG/CNAG for library preparation and genomic sequencing, and to the IRB (Functional

Genomics, Biostatistics/Bioinformatics, and Histopathology) and PCB (Animal House) core facilities for general research support. M.K. was funded by the Barcelona Institute of Science and technology (BIST) and Asociación Española Contra el Cáncer (AECC; POSTD18020SERR) and supported by the European Molecular Biology Organization (EMBO). F.P. is supported by a Karolinska Institute Starting Grant, Starting Grant from the Swedish Research Council (2019_02050_3). G.K. is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR) – Projets blancs; AMMICa US23/CNRS UMS3655; Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Fondation pour la Recherche Médicale (FRM); a donation by Elior; Equipex Onco-Pheno-Screen; European Joint Programme on Rare Diseases (EJPRD); European Research Council Advanced Investigator Award (ERC-2021-ADG, ICD-Cancer, grant no. 101052444), European Union Horizon 2020 Projects Oncobiome, Prevalung (grant no. 101095604) and Crimson; Fondation Carrefour; Institut National du Cancer (INCa); Institut Universitaire de France; LabEx Immuno-Oncology (ANR-18-IDEX-0001); a Cancer Research ASPIRE Award from the Mark Foundation; the RHU Immunolife; Seerave Foundation; SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and SIRIC Cancer Research and Personalized Medicine (CARPEM). This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. Work in the laboratory of M.S. was funded by the IRB, 'laCaixa' Foundation, and by grants from the Milky Way Research Foundation, the Spanish Ministry of Science co-funded by the European Regional Development Fund (ERDF; SAF2017-82613-R), the European Research Council (ERC-2014-AdG/669622) and the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de Catalonia (Grup de Recerca consolidat 2017 SGR 282).

## Author contributions

M.K. designed and performed most experiments, contributed to bioinformatic data analysis, performed quantification of immunohistochemistry and co-wrote the paper. E.M., D.C., F.P., R.B., A.H.-H. and M.R. provided general experimental support. A.C. and C.S.-O.A. designed and performed bacterial bioinformatic analysis. C.S.-O.A. designed and performed RNA-seq and ChIP-seq bioinformatics analysis. A.J., J.C., S.D., O.Y., E.M. and G.K. performed metabolomics experiments. E.M., D.C., F.P., A.J., S.D., O.Y. and G.K. helped design and analyse metabolomics experiments. N.P. performed the histopathological study and supervised the histopathological techniques. M.S. designed and supervised the study, secured funding, analysed the data and co-wrote the paper. All authors discussed the results and commented on the paper.

## Competing interests

G.K. holds research contracts with Daiichi Sankyo, Eleor, Kaleido, Lytix Pharma, PharmaMar, Osasuna Therapeutics, Samsara Therapeutics, Sanofi, Tollys and Vascage. G.K. is on the Board of Directors of the Bristol Myers Squibb Foundation France. G.K. is a scientific cofounder of everImmune, Osasuna Therapeutics, Samsara Therapeutics and Therafast Bio. G.K. is on the scientific advisory boards of Evolution, Institut Servier and Longevity Vision Funds. G.K. is the inventor of patents covering therapeutic targeting of aging, cancer, cystic fibrosis and metabolic disorders. G.K.'s wife, L. Zitvogel, has held research contracts with Glaxo Smyth Kline, Incyte, Lytix, Kaleido, Innovate Pharma, Daiichi Sankyo, Pilege, Merus, Transgene, 9m, Tusk and Roche, was on the Board of Directors of Transgene, is a cofounder of everImmune, and holds patents covering the treatment of cancer and the therapeutic manipulation of the microbiota. G.K.'s brother, R. Kroemer, was an employee of Sanofi and now consults for Boehringer-Ingelheim. M.K. has ongoing or completed research contracts with Galapagos, Rejuveron Senescence Therapeutics and Mesoestetic. D.C. is an employee of Fenix Group International.
M.S. is shareholder of Senolytic Therapeutics, Life Biosciences, Rejuveron Senescence Therapeutics, and Altos Labs. M.S. was consultant until the end of 2022 of Rejuveron Senescence Therapeutics and Altos Labs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The other authors declare no competing interests.

**Additional information**

**Extended data** is available for this paper at
https://doi.org/10.1038/s42255-023-00916-6.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s42255-023-00916-6.

**Correspondence and requests for materials** should be addressed to Marta Kovatcheva or Manuel Serrano.

**Peer review information** *Nature Metabolism* thanks Helmuth Gehart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the *Nature Metabolism* team.

**Reprints and permissions information** is available at
www.nature.com/reprints.

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

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2023
M.S. is shareholder of Senolytic Therapeutics, Life Biosciences, Rejuveron Senescence Therapeutics, and Altos Labs. M.S. was consultant until the end of 2022 of Rejuveron Senescence Therapeutics and Altos Labs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The other authors declare no competing interests.

**Additional information**

**Extended data** is available for this paper at
https://doi.org/10.1038/s42255-023-00916-6.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s42255-023-00916-6.

**Correspondence and requests for materials** should be addressed to Marta Kovatcheva or Manuel Serrano.

**Peer review information** *Nature Metabolism* thanks Helmuth Gehart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the *Nature Metabolism* team.

**Reprints and permissions information** is available at
www.nature.com/reprints.

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

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2023
a

D7 serum doxycycline
(corrected peak area)

<figure><img src="image_33.png" /></figure>

b

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

d

% change body weight
(D7 vs. D0)

<figure><img src="image_38.png" /></figure>

c

H/E

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

SCA1

KRT14

<figure><img src="image_42.png" /></figure>

doxy:

ABX:

WT

OSKM

H/E

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

SCA1

KRT14

<figure><img src="image_46.png" /></figure>

no relevant
histological changes
in any group

H/E

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

SCA1

KRT14

<figure><img src="image_50.png" /></figure>

doxy:

ABX:

WT

OSKM

Extended Data Fig. 1 | See next page for caption.
a

D7 serum doxycycline (corrected peak area)

<figure><img src="image_29.png" /></figure>

b

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

d

<figure><img src="image_34.png" /></figure>

c

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

no relevant
histological changes
in any group

<figure><img src="image_39.png" /></figure>

Extended Data Fig. 1 | See next page for caption.
**Extended Data Fig. 1 | Broad-spectrum antibiotic treatment inhibits in vivo reprogramming.** (a-d) Mice were treated with doxycycline (doxy) and antibiotics (ABX) as described in Fig. 1a. (a) Serum was harvested from OSKM mice after 7 days of doxycycline treatment (OSKM n = 14; 5 M 9 F), or after ABX + doxycycline treatment (ABX + OSKM n = 12; 3 M 9 F) and doxycycline levels were analyzed by mass spectrometry. The total area of the doxycycline peak corrected for background levels for each mouse is shown. (b-d) Representative subsets of mice from Fig. 1b n = 4 mice (WT; 3 M 1 F), n = 8 (OSKM + doxy; 4 M 4 F), n = 11 (OSKM + doxy + ABX 4 M 7 F) were used for all analyses (b) Tissues were harvested

on day 7 post doxycycline initiation and the expression of the OSKM transgene was assessed by RT-qPCR. (c) Representative histology images of the indicated treatments and markers are shown (left) and quantified (right). Histologic score for stomach dysplasia and inflammation was assigned by a blinded pathologist. No relevant morphologic changes were observed in the kidney cortex and medulla in any of the mice. (d) Body weights of mice from the indicated treatment groups, expressed as a percent change on day 7 post-doxycycline as compared to pre-doxycycline (day 0). Scale bars are 100 µm. Graphs represent average ± SD; ****p < 0.0001 by two-tailed Student's t-test.
**Extended Data Fig. 1 | Broad-spectrum antibiotic treatment inhibits in vivo reprogramming.** (a-d) Mice were treated with doxycycline (doxy) and antibiotics (ABX) as described in Fig. 1a. (a) Serum was harvested from OSKM mice after 7 days of doxycycline treatment (OSKM n = 14; 5 M 9 F), or after ABX + doxycycline treatment (ABX + OSKM n = 12; 3 M 9 F) and doxycycline levels were analyzed by mass spectrometry. The total area of the doxycycline peak corrected for background levels for each mouse is shown. (b-d) Representative subsets of mice from Fig. 1b n = 4 mice (WT; 3 M 1 F), n = 8 (OSKM + doxy; 4 M 4 F), n = 11 (OSKM + doxy + ABX 4 M 7 F) were used for all analyses (b) Tissues were harvested

on day 7 post doxycycline initiation and the expression of the OSKM transgene was assessed by RT-qPCR. (c) Representative histology images of the indicated treatments and markers are shown (left) and quantified (right). Histologic score for stomach dysplasia and inflammation was assigned by a blinded pathologist. No relevant morphologic changes were observed in the kidney cortex and medulla in any of the mice. (d) Body weights of mice from the indicated treatment groups, expressed as a percent change on day 7 post-doxycycline as compared to pre-doxycycline (day 0). Scale bars are 100 µm. Graphs represent average ± SD; ****p < 0.0001 by two-tailed Student's t-test.
<figure><img src="image_51.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_53.png" /></figure>

Extended Data Fig. 2 | See next page for caption.
<figure><img src="image_40.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_42.png" /></figure>

Extended Data Fig. 2 | See next page for caption.
**Extended Data Fig. 2 | Metagenomic analysis of fecal bacteria during in vivo reprogramming.** (a-c) Mice were treated with doxycycline for 7 days as described in Fig. 1a. Stool from subset of WT (n = 4; 2 M 2 F) and OSKM (n = 4 2 M 2 F) mice was analyzed before (pre; day 0) and after seven days (post; day 7) of doxycycline (stool was collected repeatedly from the same mice). (a) The change in the Shannon diversity index on day 7 as compared to day 0 in the indicated treatment groups. Negative values indicate reduced diversity post-doxycyline. Graph represents average ± SD; ns p>0.05 by two-tailed Student's t-test. (b) Microbial stool diversity at the genus level, as categorized

by 16 S rRNA sequencing. The 17 genera with the highest abundance are shown. "Unassigned" refers to bacterial species that could not be assigned a specific genus. "Others" includes assigned bacteria with low counts, as well as archaea. See Supplementary Table 1 for complete list. Mouse 8, marked with a dot (●), had a large proportion of host (murine) DNA contamination in the stool sample. (c) Normalized abundance of genera capable of de novo synthesis of vitamin B12 in the indicated conditions. Genera are ordered by significance in the day7/day0 (OSKM) vs day7/day0 (WT) interaction, and raw p-values (by two-tailed Wald test (DESeq2)) for this score are shown.
**Extended Data Fig. 2 | Metagenomic analysis of fecal bacteria during in vivo reprogramming.** (a-c) Mice were treated with doxycycline for 7 days as described in Fig. 1a. Stool from subset of WT (n = 4; 2 M 2 F) and OSKM (n = 4 2 M 2 F) mice was analyzed before (pre; day 0) and after seven days (post; day 7) of doxycycline (stool was collected repeatedly from the same mice). (a) The change in the Shannon diversity index on day 7 as compared to day 0 in the indicated treatment groups. Negative values indicate reduced diversity post-doxycyline. Graph represents average ± SD; ns p>0.05 by two-tailed Student's t-test. (b) Microbial stool diversity at the genus level, as categorized

by 16 S rRNA sequencing. The 17 genera with the highest abundance are shown. "Unassigned" refers to bacterial species that could not be assigned a specific genus. "Others" includes assigned bacteria with low counts, as well as archaea. See Supplementary Table 1 for complete list. Mouse 8, marked with a dot (●), had a large proportion of host (murine) DNA contamination in the stool sample. (c) Normalized abundance of genera capable of de novo synthesis of vitamin B12 in the indicated conditions. Genera are ordered by significance in the day7/day0 (OSKM) vs day7/day0 (WT) interaction, and raw p-values (by two-tailed Wald test (DESeq2)) for this score are shown.
<figure><img src="image_54.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_62.png" /></figure>

Extended Data Fig. 3 | See next page for caption.
<figure><img src="image_43.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_51.png" /></figure>

Extended Data Fig. 3 | See next page for caption.
**Extended Data Fig. 3** | Vitamin B12 deficiency limits in vivo reprogramming.

(a) Abundance of phosphatidylcholine (PC) species in the serum during reprogramming. WT and OSKM mice (n = 6 per group; 3 M 3 F) were treated with doxycycline for 7 days and paired serum samples were taken pre- (D0) and post (D7) doxyclyline for metabolic analysis by mass spectrometry. The relative abundance of the indicated PC moieties are given as a log2 fold change (FC) as compared to WT on D7. P-values represent the comparison between WT and OSKM groups by two-tailed Student's t-test for each PC moiety. (b) Vitamin B12 immunohistochemistry in the kidney. WT and OSKM mice were treated with doxycycline with or without vitamin B12 as indicated for 7 days. Representative images of kidneys stained for vitamin B12 are shown (left) and total staining is quantified (right). WT doxy (n = 3; 2 M 1 F); OSKM doxy (n = 5; 5 M) and OSKM doxy + B12 (n = 5; 1 M 4 F). (c-h) Analysis of reprogramming in mice treated as described in Fig. 1a, with treatments as indicated. (c) Histologic score of reprogramming was assigned by a blinded histopathologist in the colon (left) and stomach (right). n = 5 mice (OSKM 2 M 3 F and OSKM + ABX, stomach 5 F),

n = 10 (OSKM + B12 4 M 6 F and OSKM + ABX, colon 10 F), n = 8 (OSKM + ABX + B12 3 M 5 F), (d-e) Immunohistochemistry of SCA1 and KRT14 in the colon (d) and stomach (e). Representative images and quantification (average ± SD; inset) from a subset of n = 5 mice per condition are shown. Asterisks represent comparison to the OSKM condition by one-way ANOVA; **p < 0.01, ***p < 0.001, ****p < 0.0001. (f-h) Analysis of mice from Fig. 1e treated with doxycycline, with or without vitamin B12 supplementation as indicated. (f) NANOG immunohistochemistry in the stomach of n = 5 representative mice per group. (g) Expression of the OSKM transgene assessed by RT-qPCR in the indicated tissues on D7 post doxy. n = 5 mice (OSKM; 2 M 3 F), n = 10 mice (OSKM + B12; 4 M 6 F). (h) Representative images of H/E staining of the kidney. No relevant histologic changes were observed in the kidney of any of the mice. Scale bars are 100 µm. Mice marked by an open circle received both B12 and folate supplementation (n.s. difference for B12 vs B12 + folate; see text for details). Graphs represent average ± SD; p-values are by two-tailed Student's t-test unless otherwise indicated.
**Extended Data Fig. 3 | Vitamin B12 deficiency limits in vivo reprogramming.**

(a) Abundance of phosphatidylcholine (PC) species in the serum during reprogramming. WT and OSKM mice (n = 6 per group; 3 M 3 F) were treated with doxycycline for 7 days and paired serum samples were taken pre- (D0) and post (D7) doxyclyline for metabolic analysis by mass spectrometry. The relative abundance of the indicated PC moieties are given as a log2 fold change (FC) as compared to WT on D7. P-values represent the comparison between WT and OSKM groups by two-tailed Student's t-test for each PC moiety. (b) Vitamin B12 immunohistochemistry in the kidney. WT and OSKM mice were treated with doxycycline with or without vitamin B12 as indicated for 7 days. Representative images of kidneys stained for vitamin B12 are shown (left) and total staining is quantified (right). WT doxy (n = 3; 2 M 1 F); OSKM doxy (n = 5; 5 M) and OSKM doxy + B12 (n = 5; 1 M 4 F). (c-h) Analysis of reprogramming in mice treated as described in Fig. 1a, with treatments as indicated. (c) Histologic score of reprogramming was assigned by a blinded histopathologist in the colon (left) and stomach (right). n = 5 mice (OSKM 2 M 3 F and OSKM + ABX, stomach 5 F),

n = 10 (OSKM + B12 4 M 6 F and OSKM + ABX, colon 10 F), n = 8 (OSKM + ABX + B12 3 M 5 F), (d-e) Immunohistochemistry of SCA1 and KRT14 in the colon (d) and stomach (e). Representative images and quantification (average ± SD; inset) from a subset of n = 5 mice per condition are shown. Asterisks represent comparison to the OSKM condition by one-way ANOVA; **p < 0.01, ***p < 0.001, ****p < 0.0001. (f-h) Analysis of mice from Fig. 1e treated with doxycycline, with or without vitamin B12 supplementation as indicated. (f) NANOG immunohistochemistry in the stomach of n = 5 representative mice per group. (g) Expression of the OSKM transgene assessed by RT-qPCR in the indicated tissues on D7 post doxy. n = 5 mice (OSKM; 2 M 3 F), n = 10 mice (OSKM + B12; 4 M 6 F). (h) Representative images of H/E staining of the kidney. No relevant histologic changes were observed in the kidney of any of the mice. Scale bars are 100 µm. Mice marked by an open circle received both B12 and folate supplementation (n.s. difference for B12 vs B12 + folate; see text for details). Graphs represent average ± SD; p-values are by two-tailed Student's t-test unless otherwise indicated.
<figure><img src="image_63.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_68.png" /></figure>

**Extended Data Fig. 4** | Vitamin B12 and 1-carbon (1 C) metabolism markers during in vivo reprogramming. (a) Schematic of methylmalonyl-coA mutase (MUT) enzymatic activity in the mitochondria, Figure adapted from ref. 26, Springer Nature Limited. TCA: tricarboxylic acid. (b) Metabolites of the Gly/Ser/ Thr metabolism pathway (KEGG map00260) identified in the serum of WT and OSKM mice (n = 6 mice per genotype; 3 M 3 F) during a time course of doxycycline administration. Fold change (FC) of the peak area of the indicated metabolites as detected by mass spectrometry is shown relative to (D0) for the respective groups (that is WT and OSKM). P-values represent significant difference between OSKM and WT mice on the indicated day. Serum was collected repeatedly from the same mice. (c) WT and OSKM mice (n = 5 per group; 5 M) were treated with doxycycline and given a bolus of vitamin B12 on day 6, as schematized in Fig. 2f. Serum (repeated measurements) was analyzed by mass spectrometry prior to

doxycycline (day 0), prior to the B12 bolus (day 6), and 24 hours after the bolus (day 7). The ratio of SAM/SAH abundance (upper) and total abundance of homocysteine (Hcy, lower) are shown. P-value represents comparison of WT vs. OSKM by two-way ANOVA. (d) Expression of Bhmt and Cbs in previously published RNA sequencing data from n = 2 paired parental MEFs and induced pluripotent stem cells (iPSC), generated by doxycycline-induced OSKM expression. Data source: 40. (e) Serum analysis of betaine in the mice described in panel (b). (f) Expression of methylmalonyl-coA mutase (Mmut) in WT (n = 4) and OSKM (n = 8) mice from Fig. 1b. Mice were treated with doxycycline for 7 days and RT-qPCR was performed in tissues that do (pancreas, stomach, colon; green) and do not (kidney; orange) exhibit histologic signs of reprogramming. Bar graphs represent average ± SD; p-values by two-tailed Student's t-test unless otherwise indicated.
<figure><img src="image_52.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_53.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_57.png" /></figure>

**Extended Data Fig. 4** | Vitamin B12 and 1-carbon (1 C) metabolism markers during in vivo reprogramming. (a) Schematic of methylmalonyl-coA mutase (MUT) enzymatic activity in the mitochondria, Figure adapted from ref. 26, Springer Nature Limited. TCA: tricarboxylic acid. (b) Metabolites of the Gly/Ser/ Thr metabolism pathway (KEGG map00260) identified in the serum of WT and OSKM mice (n = 6 mice per genotype; 3 M 3 F) during a time course of doxycycline administration. Fold change (FC) of the peak area of the indicated metabolites as detected by mass spectrometry is shown relative to (D0) for the respective groups (that is WT and OSKM). P-values represent significant difference between OSKM and WT mice on the indicated day. Serum was collected repeatedly from the same mice. (c) WT and OSKM mice (n = 5 per group; 5 M) were treated with doxycycline and given a bolus of vitamin B12 on day 6, as schematized in Fig. 2f. Serum (repeated measurements) was analyzed by mass spectrometry prior to

doxycycline (day 0), prior to the B12 bolus (day 6), and 24 hours after the bolus (day 7). The ratio of SAM/SAH abundance (upper) and total abundance of homocysteine (Hcy, lower) are shown. P-value represents comparison of WT vs. OSKM by two-way ANOVA. (d) Expression of Bhmt and Cbs in previously published RNA sequencing data from n = 2 paired parental MEFs and induced pluripotent stem cells (iPSC), generated by doxycycline-induced OSKM expression. Data source: 40. (e) Serum analysis of betaine in the mice described in panel (b). (f) Expression of methylmalonyl-coA mutase (Mmut) in WT (n = 4) and OSKM (n = 8) mice from Fig. 1b. Mice were treated with doxycycline for 7 days and RT-qPCR was performed in tissues that do (pancreas, stomach, colon; green) and do not (kidney; orange) exhibit histologic signs of reprogramming. Bar graphs represent average ± SD; p-values by two-tailed Student's t-test unless otherwise indicated.
a

<figure><img src="image_69.png" /></figure>

b

<figure><img src="image_70.png" /></figure>

c

<figure><img src="image_71.png" /></figure>

d

<figure><img src="image_72.png" /></figure>

e

<figure><img src="image_73.png" /></figure>

**Extended Data Fig. 5** | Reprogramming induces a methionine deprivation signature that is relieved by vitamin B12. (a) Immunoblot (left) and RT-qPCR (right) to measure levels of methionine synthase (encoded by Mtr) in the colon and stomach of WT (n = 4, 3 M1F) and OSKM (n = 8, 4 M4F) mice from Fig. 1b treated with doxycycline for 7 days. Representative mice are shown in the immunoblot. (b) Expression of Cd320 measured by RT-qPCR in the indicated tissues from WT and OSKM mice described in panel (a). (c) Previously published RNAseq data of iPS cells and their parental MEFs (data source: 40) was used to perform gene set enrichment analysis against a published signature of methionine deprivation (msigdb: M13537; ref. 39). (d) RT-qPCR analysis in the indicated tissues from a subset of mice from Fig. 1. The genes were derived from the methionine deprivation signature shown in (c); see Methods for details.

(e-f) Analysis of vitamin B12 levels in the WT and OSKM mice receiving doxycycline and the vitamin B12 bolus, as schematized in Fig. 2f. (e) Cyanocobalamin (CN-cbl) levels in the stool measured by mass spectrometry. Mice were individualized immediately after the bolus and all the fecal matter was collected from the cage 24 hours later. Untreated mice received neither the B12 bolus nor doxycycline. n = 5 mice (untreated), n = 3 (WT doxy), n = 5 (OSKM doxy), all males. (f) Vitamin B12 levels in the serum (holotranscobalamin, holoTC) measured by ADVIA Immunoassay on day 7, 24 hours after the B12 bolus, n = 5 mice (all males) per group. Color of labels indicate tissues that are prone (pancreas, colon, stomach; green) and refractory (kidney; orange) to reprogramming. Bar graphs represent average ± SD; p-values by two-tailed Student's t-test.
a

<figure><img src="image_58.png" /></figure>

b

<figure><img src="image_59.png" /></figure>

c

<figure><img src="image_60.png" /></figure>

d

<figure><img src="image_61.png" /></figure>

e

<figure><img src="image_62.png" /></figure>

**Extended Data Fig. 5** | Reprogramming induces a methionine deprivation signature that is relieved by vitamin B12. (a) Immunoblot (left) and RT-qPCR (right) to measure levels of methionine synthase (encoded by Mtr) in the colon and stomach of WT (n = 4, 3 M1F) and OSKM (n = 8, 4 M4F) mice from Fig. 1b treated with doxycycline for 7 days. Representative mice are shown in the immunoblot. (b) Expression of Cd320 measured by RT-qPCR in the indicated tissues from WT and OSKM mice described in panel (a). (c) Previously published RNAseq data of iPS cells and their parental MEFs (data source: 40) was used to perform gene set enrichment analysis against a published signature of methionine deprivation (msigdb: M13537; ref. 39). (d) RT-qPCR analysis in the indicated tissues from a subset of mice from Fig. 1. The genes were derived from the methionine deprivation signature shown in (c); see Methods for details.

(e-f) Analysis of vitamin B12 levels in the WT and OSKM mice receiving doxycycline and the vitamin B12 bolus, as schematized in Fig. 2f. (e) Cyanocobalamin (CN-cbl) levels in the stool measured by mass spectrometry. Mice were individualized immediately after the bolus and all the fecal matter was collected from the cage 24 hours later. Untreated mice received neither the B12 bolus nor doxycycline. n = 5 mice (untreated), n = 3 (WT doxy), n = 5 (OSKM doxy), all males. (f) Vitamin B12 levels in the serum (holotranscobalamin, holoTC) measured by ADVIA Immunoassay on day 7, 24 hours after the B12 bolus, n = 5 mice (all males) per group. Color of labels indicate tissues that are prone (pancreas, colon, stomach; green) and refractory (kidney; orange) to reprogramming. Bar graphs represent average ± SD; p-values by two-tailed Student's t-test.
<figure><img src="image_74.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_75.png" /></figure>

OSKM

<figure><img src="image_76.png" /></figure>

OSKM + B12

<figure><img src="image_77.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_79.png" /><figcaption>b</figcaption></figure>

OSKM

<figure><img src="image_80.png" /></figure>

day 7

OSKM + B12

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

day 10

<figure><img src="image_83.png" /><figcaption>f</figcaption></figure>

log₂FC histone modification
(OSKM B12 vs. OSKM)

<figure><img src="image_84.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_85.png" /></figure>

Setd2

<figure><img src="image_86.png" /><figcaption>h</figcaption></figure>

colon

stomach

Extended Data Fig. 6 | See next page for caption.
<figure><img src="image_63.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_64.png" /></figure>

OSKM

<figure><img src="image_65.png" /></figure>

OSKM + B12

<figure><img src="image_66.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>b</figcaption></figure>

OSKM

<figure><img src="image_69.png" /></figure>

day 7

OSKM + B12

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

day 10

<figure><img src="image_72.png" /><figcaption>f</figcaption></figure>

log₂FC histone modification
(OSKM B12 vs. OSKM)

<figure><img src="image_73.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_74.png" /></figure>

Setd2

<figure><img src="image_75.png" /><figcaption>h</figcaption></figure>

Extended Data Fig. 6 | See next page for caption.
**Extended Data Fig. 6** | **The impact of vitamin B12 administration on in vitro reprogramming.** (a) Changes in gene expression of the vitamin B12 uptake receptor Cd320 and Mtr (encoding MS) during a previously published time course of iPSC generation by OSKM expression. iPSCs were derived from MEFs of two different genetic backgrounds (Gatad2a-/- and Mbd3-/) that both yield high-efficiency reprogramming. Each row is an independent embryo of the indicated genotype. Data source: 40 (b) Vitamin B12 supplementation does not accelerate iPSC formation. OSKM MEFs were treated with doxycycline with or without vitamin B12 supplementation as indicated, and iPSC colony formation was assessed by alkaline phosphatase staining after 7 and 10 days of treatment. Representative images are shown of MEFs derived from a single embryo; n = 3 independent embryos were tested. (c) Vitamin B12 enhances doxycycline-free retroviral reprogramming. Wild type MEFs were transduced with retroviral vectors encoding Oct4, Sox2, Klf4, and cMyc. Samples were collected on day 5 post-transduction for histone extraction and immunoblot (lower). iPSC colony formation was evaluated by alkaline phosphatase (AP) staining on day 14 (upper). Immunoblot and AP images are representative from n = 3 independent MEFs. (d-e) Stable isotope labeling (SIL) with fully labeled 13C-serine during in vitro reprogramming. (d) Schematic of 1 C metabolism indicating how fully labeled 13C-serine contributes to methyl m + 1 labeling of SAM. Orange points indicate 13 C atoms. Figure adapted from ref. 33, Springer Nature Limited. (e) SIL does not affect reprogramming efficiency. Doxycycline-inducible OSKM MEFs were cultured in normal iPS media with doxycycline without or without vitamin B12 supplementation as indicated. 13C-serine labeled media was administered as described in Methods and reprogramming was quantified by iPS colonies evaluated by AP staining on day 10. Quantification of colonies is shown for the indicated treatments. Each point represents an individual MEF clone. P-values

represent comparison of the OSKM vs OSKM + B12 condition for each media type. (f) Changes in histone methylation during in vitro reprogramming with vitamin B12 supplementation. Histone extracts were prepared from OSKM MEFs treated with doxycycline for 3 and 10 days, with or without vitamin B12 supplementation. Levels of the indicated histone methylation modifications were measured by colorimetric multiplex assay and the relative abundance (fold change, FC) in cells reprogrammed in the presence as compared to absence of vitamin B12 is shown. Note that MEFs derived from independent embryos were used for day 3 and day 10 analyses. (g) Expression of Setd2, which encodes the H3K36 trimethyl-transferase in the colon (left) and stomach (right) from WT and OSKM mice after 7 days of doxycycline treatment. Mice are from Fig. 1b n = 4 mice (WT), n = 8 (OSKM colon), n = 6 (OSKM stomach). (h) H3K36me3 during in vivo reprogramming in the colon and stomach of OSKM mice with and without B12 supplementation. Representative images are shown (left) and quantified (right) in a subset (n = 5 for each group) of mice from Fig. 1e. Scale bar is 100 µm. Mice that received folate in addition to B12 are represented by open points. Graphs represent average ± SD. P-values by two-tailed Student's t-test. used for day 3 and day 10 analyses. (g) Expression of Setd2, which encodes the H3K36 trimethyl-transferase in the colon (left) and stomach (right) from WT and OSKM mice after 7 days of doxycycline treatment. Mice are from Fig. 1b n = 4 mice (WT), n = 8 (OSKM colon), n = 6 (OSKM stomach). (h) H3K36me3 during in vivo reprogramming in the colon and stomach of OSKM mice with and without B12 supplementation. Representative images are shown (left) and quantified (right) in a subset (n = 5 for each group) of mice from Fig. 1e. Scale bar is 100 µm. Mice that received folate in addition to B12 are represented by open points. Graphs represent average ± SD. P-values by two-tailed Student's t-test.
**Extended Data Fig. 6** | **The impact of vitamin B12 administration on in vitro reprogramming.** (a) Changes in gene expression of the vitamin B12 uptake receptor Cd320 and Mtr (encoding MS) during a previously published time course of iPSC generation by OSKM expression. iPSCs were derived from MEFs of two different genetic backgrounds (Gatad2a-/- and Mbd3-/) that both yield high-efficiency reprogramming. Each row is an independent embryo of the indicated genotype. Data source: 40 (b) Vitamin B12 supplementation does not accelerate iPSC formation. OSKM MEFs were treated with doxycycline with or without vitamin B12 supplementation as indicated, and iPSC colony formation was assessed by alkaline phosphatase staining after 7 and 10 days of treatment. Representative images are shown of MEFs derived from a single embryo; n = 3 independent embryos were tested. (c) Vitamin B12 enhances doxycycline-free retroviral reprogramming. Wild type MEFs were transduced with retroviral vectors encoding Oct4, Sox2, Klf4, and cMyc. Samples were collected on day 5 post-transduction for histone extraction and immunoblot (lower). iPSC colony formation was evaluated by alkaline phosphatase (AP) staining on day 14 (upper). Immunoblot and AP images are representative from n = 3 independent MEFs. (d-e) Stable isotope labeling (SIL) with fully labeled 13C-serine during in vitro reprogramming. (d) Schematic of 1 C metabolism indicating how fully labeled 13C-serine contributes to methyl m + 1 labeling of SAM. Orange points indicate 13 C atoms. Figure adapted from ref. 33, Springer Nature Limited. (e) SIL does not affect reprogramming efficiency. Doxycycline-inducible OSKM MEFs were cultured in normal iPS media with doxycycline without or without vitamin B12 supplementation as indicated. 13C-serine labeled media was administered as described in Methods and reprogramming was quantified by iPS colonies evaluated by AP staining on day 10. Quantification of colonies is shown for the indicated treatments. Each point represents an individual MEF clone. P-values

represent comparison of the OSKM vs OSKM + B12 condition for each media type. (f) Changes in histone methylation during in vitro reprogramming with vitamin B12 supplementation. Histone extracts were prepared from OSKM MEFs treated with doxycycline for 3 and 10 days, with or without vitamin B12 supplementation. Levels of the indicated histone methylation modifications were measured by colorimetric multiplex assay and the relative abundance (fold change, FC) in cells reprogrammed in the presence as compared to absence of vitamin B12 is shown. Note that MEFs derived from independent embryos were used for day 3 and day 10 analyses. (g) Expression of Setd2, which encodes the H3K36 trimethyl-transferase in the colon (left) and stomach (right) from WT and OSKM mice after 7 days of doxycycline treatment. Mice are from Fig. 1b n = 4 mice (WT), n = 8 (OSKM colon), n = 6 (OSKM stomach). (h) H3K36me3 during in vivo reprogramming in the colon and stomach of OSKM mice with and without B12 supplementation. Representative images are shown (left) and quantified (right) in a subset (n = 5 for each group) of mice from Fig. 1e. Scale bar is 100 µm. Mice that received folate in addition to B12 are represented by open points. Graphs represent average ± SD. P-values by two-tailed Student's t-test. used for day 3 and day 10 analyses. (g) Expression of Setd2, which encodes the H3K36 trimethyl-transferase in the colon (left) and stomach (right) from WT and OSKM mice after 7 days of doxycycline treatment. Mice are from Fig. 1b n = 4 mice (WT), n = 8 (OSKM colon), n = 6 (OSKM stomach). (h) H3K36me3 during in vivo reprogramming in the colon and stomach of OSKM mice with and without B12 supplementation. Representative images are shown (left) and quantified (right) in a subset (n = 5 for each group) of mice from Fig. 1e. Scale bar is 100 µm. Mice that received folate in addition to B12 are represented by open points. Graphs represent average ± SD. P-values by two-tailed Student's t-test.
a

MEF 1

<figure><img src="image_87.png" /></figure>

Efficiency of reprogramming: High

MEF 2

<figure><img src="image_88.png" /></figure>

Med

MEF 3

<figure><img src="image_89.png" /></figure>

Low

b

<figure><img src="image_90.png" /></figure>

c

<figure><img src="image_91.png" /></figure>

Efficiency of reprogramming: High

d

Effect of B12 on CT

<figure><img src="image_92.png" /></figure>

Efficiency of reprogramming: High

e

<figure><img src="image_93.png" /></figure>

f

<figure><img src="image_94.png" /></figure>

Extended Data Fig. 7 | See next page for caption.
<figure><img src="image_76.png" /><figcaption>a</figcaption></figure>

Efficiency of reprogramming: High

Med

Low

<figure><img src="image_77.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_78.png" /></figure>

Efficiency of reprogramming: High

Med

Low

<figure><img src="image_79.png" /><figcaption>d</figcaption></figure>

Efficiency of reprogramming: High

Med

Low

<figure><img src="image_80.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_81.png" /></figure>

Vitamin B12:

MEF
(no doxy)

OSKM
(+ doxy)

Extended Data Fig. 7 | See next page for caption.
**Extended Data Fig. 7** | **Reprogramming and transcriptional dynamics across MEF clones.** (a) The reprogramming efficiency of three independent MEFs was measured by the formation of iPS colonies as stained with alkaline phosphatase 10 days after continuous addition of doxycycline to the media. (b) The Reprogramming Score was calculated using transcriptional data from MEFs expressing doxycycline-inducible OSKM for 72 h, whose cell surface markers indicated they were poised for high-efficiency reprogramming<sup>48</sup>. See Methods for details. (c) H3K36me3 chromatin immunoprecipitation (ChIP) sequencing was performed in Clone 1, 72 h after the addition of doxycycline and/or vitamin B12, as indicated. The normalized ChIP reads in gene bodies relative to all aligned reads are shown for the middle and bottom tercile in terms of gene expression (as determined by RNA-seq RPKM levels across all condition) are shown (lower). The trace of the ChIP signal is shown, where the y-axis represents average coverage across genes (upper; blue = bottom tercile; grey = middle tercile; red = top tercile, shown for reference; see Fig. 4a for top tercile). TSS – transcription start site; TES – transcription end site. (d-e) The ratio of cryptic transcription (CT). (d) CT in B12-treated cells as compared to non-B12 is shown for the median

CT ratio for all genes for which a score could be calculated is plotted. Individual MEFs (n = 3 total) are shown, separated into MEF (no doxycycline) and OSKM (72 h of doxycycline treatment to induce OSKM expression) conditions.

Asterisks represent the comparison between the MEF and OSKM bars. (e) OSKM cells were treated with doxycycline and the indicated compounds for 72 h, as described in Fig. 4c. The ratio of CT is plotted as a ratio of the CT in the B12 condition for each treatment, in n = 2 independent MEFs. Asterisks represent the comparison between the indicated treatment and the MAT2Ai treatment. The lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). The upper (lower) whisker extends from the hinge to the largest (smallest) value no further than 1.5 * IQR from the hinge. P-values were determined by two-tailed unpaired Wilcoxon test no multiple comparisons adjustment (f) Genes whose CT increases upon reprogramming (top 25% comparing OSKM vs. MEF) and decrease with vitamin B12 supplementation (top 25% comparing OSKM B12 vs. OSKM) in Clone 1, used for the hypergeometric analysis in Fig. 4d. The CT ratio of individual genes is shown for each condition.
**Extended Data Fig. 7 | Reprogramming and transcriptional dynamics across MEF clones.** (a) The reprogramming efficiency of three independent MEFs was measured by the formation of iPS colonies as stained with alkaline phosphatase 10 days after continuous addition of doxycycline to the media. (b) The Reprogramming Score was calculated using transcriptional data from MEFs expressing doxycycline-inducible OSKM for 72 h, whose cell surface markers indicated they were poised for high-efficiency reprogramming<sup>48</sup>. See Methods for details. (c) H3K36me3 chromatin immunoprecipitation (ChIP) sequencing was performed in Clone 1, 72 h after the addition of doxycycline and/or vitamin B12, as indicated. The normalized ChIP reads in gene bodies relative to all aligned reads are shown for the middle and bottom tercile in terms of gene expression (as determined by RNA-seq RPKM levels across all condition) are shown (lower). The trace of the ChIP signal is shown, where the y-axis represents average coverage across genes (upper; blue = bottom tercile; grey = middle tercile; red = top tercile, shown for reference; see Fig. 4a for top tercile). TSS – transcription start site; TES – transcription end site. (d-e) The ratio of cryptic transcription (CT). (d) CT in B12-treated cells as compared to non-B12 is shown for the median

CT ratio for all genes for which a score could be calculated is plotted. Individual MEFs (n = 3 total) are shown, separated into MEF (no doxycycline) and OSKM (72 h of doxycycline treatment to induce OSKM expression) conditions.

Asterisks represent the comparison between the MEF and OSKM bars. (e) OSKM cells were treated with doxycycline and the indicated compounds for 72 h, as described in Fig. 4c. The ratio of CT is plotted as a ratio of the CT in the B12 condition for each treatment, in n = 2 independent MEFs. Asterisks represent the comparison between the indicated treatment and the MAT2Ai treatment. The lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). The upper (lower) whisker extends from the hinge to the largest (smallest) value no further than 1.5 * IQR from the hinge. P-values were determined by two-tailed unpaired Wilcoxon test no multiple comparisons adjustment (f) Genes whose CT increases upon reprogramming (top 25% comparing OSKM vs. MEF) and decrease with vitamin B12 supplementation (top 25% comparing OSKM B12 vs. OSKM) in Clone 1, used for the hypergeometric analysis in Fig. 4d. The CT ratio of individual genes is shown for each condition.
<figure><img src="image_95.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_96.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_97.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_98.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_99.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_100.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

Extended Data Fig. 8 | See next page for caption.
<figure><img src="image_82.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_83.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_84.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_85.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_86.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_87.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>

Extended Data Fig. 8 | See next page for caption.
**Extended Data Fig. 8** | Recovery from DSS colitis is improved by OSKM or vitamin B12. (a-c) Previously published time course of bulk colonic RNAseq during DSS injury and recovery Data source: GSE131032⁶³. Mice were treated with 2.5% DSS for 7 days as indicated. (a) CT analysis for all genes for which a score could be calculated are shown. Each boxplot represents one biologic replicate (that is, mouse), with the cages indicated as described by the authors of the original study. Dashed line represents median CT value at D0. (b) RNA-sequencing expression of the indicated genes. Values represent the scaled log2FC as compared to D0 across all samples as computed by the authors of the original study. (c) Gene set enrichment analysis of a published signature of methionine deprivation (msigdb: M13537 ref. 39) in samples from day 6 vs day 0 of DSS treatment as computed by the authors of the original study. (d-g) Recovery from DSS injury of mice in Fig. 5, with treatment as schematized in Fig. 5a; a representative subset of mice was used for analyses. (d) Periodic acid–Schiff

(PAS) staining, which marks mucous produced by differentiated goblet cells, in colonic sections of mice on day 14. n = 5 mice from each group were analyzed with similar results; representative images are shown. (e) Intestinal barrier permeability as measured by FITC-dextran leak assay. Fluorescence intensity was measured in the serum of mice with the indicated treatments on day 9. n = 5 mice (control), n = 8 (DSS), n = 10 (DSS + B12). (f) Ki67 was quantified within the colonic epithelium (right) and representative immunohistochemistry images are shown (left). P-values indicate comparison to the untreated control. Scale bar is 100 µm. n = 6 mice (DSS D14), n = 5 all other groups. (g) Analysis of Lgr5 by single molecule in situ hybridization (ISH). The number of Lgr5+ clusters per 100 cells in the colonic crypts was quantified from at least 20 crypts per mouse (right) from n = 3 mice and representative images are shown (left). Asterisks indicate comparison to the untreated control. Scale bar is 50 µm. Bar graphs represent average ± SD; ****p < 0.0001 by one-way ANOVA.
**Extended Data Fig. 8** | Recovery from DSS colitis is improved by OSKM or vitamin B12. (a-c) Previously published time course of bulk colonic RNAseq during DSS injury and recovery Data source: GSE131032⁶³. Mice were treated with 2.5% DSS for 7 days as indicated. (a) CT analysis for all genes for which a score could be calculated are shown. Each boxplot represents one biologic replicate (that is, mouse), with the cages indicated as described by the authors of the original study. Dashed line represents median CT value at D0. (b) RNA-sequencing expression of the indicated genes. Values represent the scaled log2FC as compared to D0 across all samples as computed by the authors of the original study. (c) Gene set enrichment analysis of a published signature of methionine deprivation (msigdb: M13537 ref. 39) in samples from day 6 vs day 0 of DSS treatment as computed by the authors of the original study. (d-g) Recovery from DSS injury of mice in Fig. 5, with treatment as schematized in Fig. 5a; a representative subset of mice was used for analyses. (d) Periodic acid–Schiff

(PAS) staining, which marks mucous produced by differentiated goblet cells, in colonic sections of mice on day 14. n = 5 mice from each group were analyzed with similar results; representative images are shown. (e) Intestinal barrier permeability as measured by FITC-dextran leak assay. Fluorescence intensity was measured in the serum of mice with the indicated treatments on day 9. n = 5 mice (control), n = 8 (DSS), n = 10 (DSS + B12). (f) Ki67 was quantified within the colonic epithelium (right) and representative immunohistochemistry images are shown (left). P-values indicate comparison to the untreated control. Scale bar is 100 µm. n = 6 mice (DSS D14), n = 5 all other groups. (g) Analysis of Lgr5 by single molecule in situ hybridization (ISH). The number of Lgr5+ clusters per 100 cells in the colonic crypts was quantified from at least 20 crypts per mouse (right) from n = 3 mice and representative images are shown (left). Asterisks indicate comparison to the untreated control. Scale bar is 50 µm. Bar graphs represent average ± SD; ****p < 0.0001 by one-way ANOVA.
## Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our [Editorial Policies](#) and the [Editorial Policy Checklist](#).

## Statistics

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**

Histology: NanoZoomer-2.0 HT C9600 digital scanner (Hamamatsu)

bacterial sequencing: HiSeq 2500 (Illumina)

transcobalamin levels: ADVIA Centuar Immunoassay System (SIEMENS)

serum metabolomics: 7890B gas chromatography, RRLC 1260 system, 6490 triple-quadrupole mass spectrometer (Agilent Technologies),

Dionex Ultimate 3000 UHPLC system, Orbitrap ID-X Tribrid Mass Spectrometer (Thermo Scientific) as specified in methods

immunoblots: LiCor Odyssey FC

qPCR: LifeTechnologies Quant Studio 6 Flex System

C13 stable isotope labeling metabolomics: UHPLC 1290 Infinity II Series coupled to a QqQ/MS 6490 Series from Agilent Technologies

ChIP-seq: NextSeq550 (Illumina)

RNA-seq: NovaSeq6000 S2 (Illumina) and NextSeq550 (Illumina)

FITC-dextran leak assay: ioTek Synergy H1 Microplate Reader

**Data analysis**

General statistical analyses: GraphPad Prism 9.0.0 with the statistical tests as indicated in each figure.

bacterial analysis: Microbiome data analyses were performed in the R programming language version 4.0.5. Reads that aligned to the mm10 genome, STAR (v2.7.0a), were filtered out. Taxonomic assignments were carried out through Kaiju (v1.7.0). Annotations were extracted from the microbial subset of the NCBI BLAST non-redundant protein database (nr). Cleaned sequences were assembled into contigs using megahit (v1.2.4). Open reading frames from the obtained contigs were predicted with prodigal (v2.6.3). Protein mapping as well as Kegg and COG annotations were obtained using the eggnog mapper (v2.0.0). Alignment to the assembled transcriptome was performed with Bowtie2 (v2.2.2). Sam files were sorted and converted to BAM format using sambamba (v0.6.7).

Serum metabolic pathway impact: Global ANOVA pathway enrichment and Out-degree Centrality Topology analysis through the MetaboAnalyst 4.0 software, using KEGG library (2019) as reference.
# Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our [Editorial Policies](#) and the [Editorial Policy Checklist](#).

## Statistics

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**

Histology: NanoZoomer-2.0 HT C9600 digital scanner (Hamamatsu)

bacterial sequencing: HiSeq 2500 (Illumina)

transcobalamin levels: ADVIA Centuar Immunoassay System (SIEMENS)

serum metabolomics: 7890B gas chromatography, RRLC 1260 system, 6490 triple-quadrupole mass spectrometer (Agilent Technologies),

Dionex Ultimate 3000 UHPLC system, Orbitrap ID-X Tribrid Mass Spectrometer (Thermo Scientific) as specified in methods

immunoblots: LiCor Odyssey FC

qPCR: LifeTechnologies Quant Studio 6 Flex System

C13 stable isotope labeling metabolomics: UHPLC 1290 Infinity II Series coupled to a QqQ/MS 6490 Series from Agilent Technologies

ChIP-seq: NextSeq550 (Illumina)

RNA-seq: NovaSeq6000 S2 (Illumina) and NextSeq550 (Illumina)

FITC-dextran leak assay: ioTek Synergy H1 Microplate Reader

**Data analysis**

General statistical analyses: GraphPad Prism 9.0.0 with the statistical tests as indicated in each figure.

bacterial analysis: Microbiome data analyses were performed in the R programming language version 4.0.5. Reads that aligned to the mm10 genome, STAR (v2.7.0a), were filtered out. Taxonomic assignments were carried out through Kaiju (v1.7.0). Annotations were extracted from the microbial subset of the NCBI BLAST non-redundant protein database (nr). Cleaned sequences were assembled into contigs using megahit (v1.2.4). Open reading frames from the obtained contigs were predicted with prodigal (v2.6.3). Protein mapping as well as Kegg and COG annotations were obtained using the eggnog mapper (v2.0.0). Alignment to the assembled transcriptome was performed with Bowtie2 (v2.2.2). Sam files were sorted and converted to BAM format using sambamba (v0.6.7).

Serum metabolic pathway impact: Global ANOVA pathway enrichment and Out-degree Centrality Topology analysis through the MetaboAnalyst 4.0 software, using KEGG library (2019) as reference.
All RNASeq analyses were performed in the R programming language version 4.0.5. Reads were aligned to the mm10 mouse genome using STAR (v2.7.10a). Annotations were extracted from ENSEMBL (vGRC138.97).

ChIPSeq data was aligned using Bowtie (v0.12.9). Sam files were sorted and converted to BAM format using sambamba (v0.6.7). IGVtools (v2.9.4) was used to generate alignment visualizations.

Histology was quantified using QuPath 0.4.3.

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 and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio 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 description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our policy

All sequencing data are deposited under the following GEO accessions:

GSE154149 Microbial genome analysis from OSKM and WT mice pre-treatment and after 7 days of 1 mg/mL treatment with doxycycline in the drinking water.

GSE200578 ChIP-Seq of H3k36me3 samples of OSKM reprogramming mouse embryonic fibroblasts treated with B12

GSE200579 RNA-Seq samples of OSKM reprogrammable MEFs treated with or without vitamin B12

GSE232382 RNA-Seq samples of OSKM reprogrammable MEFs treated with vitamin B12 and/or various compounds to modulate SAM or histone methylation

Previously published datasets that were used for analysis in this study are:

GSE131032 RNA-Seq of time course of repairing murine epithelium after DSS injury

GSE109142 RNA-Seq of human pediatric ulcerative colitis and normal tissue controls

GSE102518 RNA-Seq of murine in vitro reprogramming in MEFs of varying genotypes

GSE77722 RNA-Seq of murine in vivo reprogramming in mice of varying genotypes

## Human research participants

### Policy information about studies involving human research participants and Sex and Gender in Research.

Reporting on sex and gender n/a

Population characteristics n/a

Recruitment n/a

Ethics oversight n/a

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## 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.

<figure><img src="image_103.png" /></figure>

Life sciences

<figure><img src="image_104.png" /></figure>

Behavioural & social sciences

<figure><img src="image_105.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://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.

**Sample size** No statistical methods were used to predetermine sample size in the mouse studies. As requested by Reviewer 2, for the revision, we conducted a power analysis using the calculator published by the Chinese University of Hong Kong (http://www.lasec.cuhk.edu.hk/sample-size-calculation.html) to assess and appropriately power the analysis of the data presented in Fig. 1d

**Data exclusions** In our ChIPseq (and corresponding RNAseq), we used n=3 independent MEFs. As clearly explained in the text and shown in the figures, two clones had low-to-no inherent reprogramming capacity (biologic variability) so we selected the n=1 clone with high efficiency for ChIP.

**Replication** All replicate values (as indicated in Figure Legends) represent biological replicates. For qPCR experiments, biological replicates (i.e. independent mice, MEFs) were used and indicated in the n for each figure, and each sample was run in technical triplicate. For in vitro experiments, replicates were generally performed on the same day with n=2-3 cell lines as indicated, each derived from independent mouse embryos; independent experiments always included OSKM (doxy) and OSKM + B12 controls. For the fecal bacterial analysis, stool samples from biologic replicates (i.e. independent mice) were collected on the same days. For the serum metabolomic analysis, two independent
All RNASeq analyses were performed in the R programming language version 4.0.5. Reads were aligned to the mm10 mouse genome using STAR (v2.7.10a). Annotations were extracted from ENSEMBL (vGRC138.97).

ChIPSeq data was aligned using Bowtie (v0.12.9). Sam files were sorted and converted to BAM format using sambamba (v0.6.7). IGVtools (v2.9.4) was used to generate alignment visualizations.

Histology was quantified using QuPath 0.4.3.

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 and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio 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 description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our policy

All sequencing data are deposited under the following GEO accessions:

GSE154149 Microbial genome analysis from OSKM and WT mice pre-treatment and after 7 days of 1 mg/mL treatment with doxycycline in the drinking water.

GSE200578 ChIP-Seq of H3k36me3 samples of OSKM reprogramming mouse embryonic fibroblasts treated with B12

GSE200579 RNA-Seq samples of OSKM reprogrammable MEFs treated with or without vitamin B12

GSE232382 RNA-Seq samples of OSKM reprogrammable MEFs treated with vitamin B12 and/or various compounds to modulate SAM or histone methylation

Previously published datasets that were used for analysis in this study are:

GSE131032 RNA-Seq of time course of repairing murine epithelium after DSS injury

GSE109142 RNA-Seq of human pediatric ulcerative colitis and normal tissue controls

GSE102518 RNA-Seq of murine in vitro reprogramming in MEFs of varying genotypes

GSE77722 RNA-Seq of murine in vivo reprogramming in mice of varying genotypes

## Human research participants

### Policy information about studies involving human research participants and Sex and Gender in Research.

Reporting on sex and gender n/a

Population characteristics n/a

Recruitment n/a

Ethics oversight n/a

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## 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.

<figure><img src="image_90.png" /></figure>

Life sciences

<figure><img src="image_91.png" /></figure>

Behavioural & social sciences

<figure><img src="image_92.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://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.

**Sample size** No statistical methods were used to predetermine sample size in the mouse studies. As requested by Reviewer 2, for the revision, we conducted a power analysis using the calculator published by the Chinese University of Hong Kong (http://www.lasec.cuhk.edu.hk/sample-size-calculation.html) to assess and appropriately power the analysis of the data presented in Fig. 1d

**Data exclusions** In our ChIPseq (and corresponding RNAseq), we used n=3 independent MEFs. As clearly explained in the text and shown in the figures, two clones had low-to-no inherent reprogramming capacity (biologic variability) so we selected the n=1 clone with high efficiency for ChIP.

**Replication** All replicate values (as indicated in Figure Legends) represent biological replicates. For qPCR experiments, biological replicates (i.e. independent mice, MEFs) were used and indicated in the n for each figure, and each sample was run in technical triplicate. For in vitro experiments, replicates were generally performed on the same day with n=2-3 cell lines as indicated, each derived from independent mouse embryos; independent experiments always included OSKM (doxy) and OSKM + B12 controls. For the fecal bacterial analysis, stool samples from biologic replicates (i.e. independent mice) were collected on the same days. For the serum metabolomic analysis, two independent
experiments were performed (i.e. untargeted metabolomics and targeted 1C metabolomics); samples for each experiment were collected on the same days/times from independent mice, and time was maintained across experiments. In vivo reprogramming experiments were done in the span of months-to-years as the project evolved; in general, independent panels represent cohorts of mice that were treated together and sacrificed together. For the DSS experiments, results represent data from pooled experiments as indicated in the figure legend, with each experiment having at least n=3 DSS and DSS + B12 control mice. All attempts at replication were successful except as indicated regarding the variability of reprogramming in the ChIP experiment.

Randomization

Mice were randomized into experimental groups based on age (i.e. always trying to maintain consistent 8-16 week age) and, when possible, initial body mass.

Blinding

All histological scoring was conducted by a blinded histopathologist.

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

### Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☐</td><td>☑ Antibodies</td></tr><tr><td>☐</td><td>☑ Eukaryotic cell lines</td></tr><tr><td>☑</td><td>☐ Palaeontology and archaeology</td></tr><tr><td>☐</td><td>☑ Animals and other organisms</td></tr><tr><td>☑</td><td>☐ Clinical data</td></tr><tr><td>☑</td><td>☐ Dual use research of concern</td></tr></tbody></table>

### Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☐</td><td>☑ ChIP-seq</td></tr><tr><td>☑</td><td>☐ Flow cytometry</td></tr><tr><td>☑</td><td>☐ MRI-based neuroimaging</td></tr></tbody></table>

### Antibodies

Antibodies used

IHC: NANOG D2A3 (8822, CellSignaling), Sca1/Ly6A/E [E13 161-7] (ab51317, Abcam), H3K36me3 (Lys36) (D5A7) XP (4909, CellSignaling), Ki67 (ab15580, Abcam), Vitamin B12 (Sigma, V9505). Primary antibodies were diluted 1:100 for NANOG, 1:2000 for Ki67, 1:1000 for Ly6a/Sca1, 1:400 for H3K36me3, 1:2000 for Keratin 14 and 1:750 for Vit B12.

ChIP: H3K36me3 (Lys36) (D5A7) XP (4909, CellSignaling)

Immunoblot: H3K36me3 (Lys36) (D5A7) XP (4909, CellSignaling) 1:1000, methionine synthase (Abcam#ab66039) 1:1000, vinculin (SCBT#sc-73614) 1:5000, total histone H3 (CellSignaling #3638) 1:1000; LI-COR fluorescent secondary reagents (IRDye 800 CW anti mouse #926-32210; IRDye 680 CW anti mouse #926-68070; IRDye 800 CW anti rabbit #926-32211; IRDye 680 CW anti mouse #926-68071) all used at 1:10 000

Validation

all IHC antibodies were validated by our histopathology facility, and IHC protocols include a secondary-only control. ChIP antibody has been validated by the manufacturer for use in mouse cells.

### Eukaryotic cell lines

Policy information about [cell lines and Sex and Gender in Research](#)

Cell line source(s)

all primary mouse embryonic fibroblasts (MEFs) were generated in-house from E13.5 embryos. Cells were genotyped to determine whether they were OSKM transgenic. Cells were not sexed. HEK293T cells were purchased from the ATCC.

Authentication

all MEFs were made in-house from fresh embryos and were not authenticated further. 293T were purchased from the ATCC and come with a certificate of analysis.

Mycoplasma contamination

all MEFs were derived from embryos in-house and were used in experiments as P1; thus, no mycoplasma testing was carried out on these cells. Mycoplasma testing on all cell lines in culture is carried out once per month, and any positive cells are discarded so that no cell lines within the lab are mycoplasma positive.

Commonly misidentified lines
(See [ICLAC](#) register)

HEK293T do not appear in the ICLAC register

### Animals and other research organisms

Policy information about [studies involving animals](#); [ARRIVE guidelines](#) recommended for reporting animal research, and [Sex and Gender in Research](#)

Laboratory animals

C57/BL6 mice and OSKM mice (which are on a C57/BL6 background) aged 8-16 weeks of mixed gender were used in all experiments.
experiments were performed (i.e. untargeted metabolomics and targeted 1C metabolomics); samples for each experiment were collected on the same days/times from independent mice, and time was maintained across experiments. In vivo reprogramming experiments were done in the span of months-to-years as the project evolved; in general, independent panels represent cohorts of mice that were treated together and sacrificed together. For the DSS experiments, results represent data from pooled experiments as indicated in the figure legend, with each experiment having at least n=3 DSS and DSS + B12 control mice. All attempts at replication were successful except as indicated regarding the variability of reprogramming in the ChIP experiment.

Randomization

Mice were randomized into experimental groups based on age (i.e. always trying to maintain consistent 8-16 week age) and, when possible, initial body mass.

Blinding

All histological scoring was conducted by a blinded histopathologist.

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

### Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☐</td><td>Antibodies</td></tr><tr><td>☐</td><td>Eukaryotic cell lines</td></tr><tr><td>☒</td><td>Palaeontology and archaeology</td></tr><tr><td>☐</td><td>Animals and other organisms</td></tr><tr><td>☒</td><td>Clinical data</td></tr><tr><td>☒</td><td>Dual use research of concern</td></tr></tbody></table>

### Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☐</td><td>ChIP-seq</td></tr><tr><td>☒</td><td>Flow cytometry</td></tr><tr><td>☒</td><td>MRI-based neuroimaging</td></tr></tbody></table>

### Antibodies

**Antibodies used**

IHC: NANOG D2A3 (8822, CellSignaling), Sca1/Ly6A/E [E13 161-7] (ab51317, Abcam), H3K36me3 (Lys36) (D5A7) XP (4909, CellSignaling), Ki67 (ab15580, Abcam), Vitamin B12 (Sigma, V9505). Primary antibodies were diluted 1:100 for NANOG, 1:2000 for Ki67, 1:1000 for Ly6a/Sca1, 1:400 for H3K36me3, 1:2000 for Keratin 14 and 1:750 for Vit B12.

ChIP: H3K36me3 (Lys36) (D5A7) XP (4909, CellSignaling)

Immunoblot: H3K36me3 (Lys36) (D5A7) XP (4909, CellSignaling) 1:1000, methionine synthase (Abcam#ab66039) 1:1000, vinculin (SCBT#sc-73614) 1:5000, total histone H3 (CellSignaling #3638) 1:1000; LI-COR fluorescent secondary reagents (IRDye 800 CW anti mouse #926-32210; IRDye 680 CW anti mouse #926-68070; IRDye 800 CW anti rabbit #926-32211; IRDye 680 CW anti mouse #926-68071) all used at 1:10 000

**Validation**

all IHC antibodies were validated by our histopathology facility, and IHC protocols include a secondary-only control. ChIP antibody has been validated by the manufacturer for use in mouse cells.

### Eukaryotic cell lines

Policy information about [cell lines and Sex and Gender in Research](#)

**Cell line source(s)**

all primary mouse embryonic fibroblasts (MEFs) were generated in-house from E13.5 embryos. Cells were genotyped to determine whether they were OSKM transgenic. Cells were not sexed. HEK293T cells were purchased from the ATCC.

**Authentication**

all MEFs were made in-house from fresh embryos and were not authenticated further. 293T were purchased from the ATCC and come with a certificate of analysis.

**Mycoplasma contamination**

all MEFs were derived from embryos in-house and were used in experiments as P1; thus, no mycoplasma testing was carried out on these cells. Mycoplasma testing on all cell lines in culture is carried out once per month, and any positive cells are discarded so that no cell lines within the lab are mycoplasma positive.

**Commonly misidentified lines**

(See [ICLAC register](#))

HEK293T do not appear in the ICLAC register

### Animals and other research organisms

Policy information about [studies involving animals](#); [ARRIVE guidelines](#) recommended for reporting animal research, and [Sex and Gender in Research](#)

**Laboratory animals**

C57/BL6 mice and OSKM mice (which are on a C57/BL6 background) aged 8-16 weeks of mixed gender were used in all experiments.
<table><tr><td>Laboratory animals</td><td>OSKM mice refer to the i4F-B strain in Abad et al Science 2013.</td></tr><tr><td>Wild animals</td><td>study did not involve wild animals</td></tr><tr><td>Reporting on sex</td><td>Sex was not considered in study design and animals of both sexes were used in all experiments, keeping as close to 50/50 as possible, as litters allowed; specific male (M) and female (F) ratios for each in vivo experiment are indicated in the figure legends next to the total n (ex. n=5; 3M 2F). We have not done sex-based analysis, as separating sex would limit the sample size.</td></tr><tr><td>Field-collected samples</td><td>study did not involve field collected sample</td></tr><tr><td>Ethics oversight</td><td>Animal experimentation at the IRB Barcelona was performed according to protocols approved by the Science Park of Barcelona (PCB) Ethics Committee for Research and Animal Welfare.</td></tr></table>

Note that full information on the approval of the study protocol must also be provided in the manuscript.

# ChIP-seq

## Data deposition

<figure><img src="image_106.png" /></figure>

Confirm that both raw and final processed data have been deposited in a public database such as GEO.

<figure><img src="image_107.png" /></figure>

Confirm that you have deposited or provided access to graph files (e.g. BED files) for the called peaks.

### Data access links

May remain private before publication.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200578

### Files in database submission

GSM6038017_0142_2022_0071_2022_MACS2_broad_peaks_peaks.txt.gz
GSM6038018_0143_2022_0072_2022_MACS2_broad_peaks_peaks.txt.gz
GSM6038021_0144_2022_0073_2022_MACS2_broad_peaks_peaks.txt.gz
GSM6038022_0145_2022_0074_2022_MACS2_broad_peaks_peaks.txt.gz

RAW data has been deposited to the Sequence Read Archive (SRA) and will be made public after publication

### Genome browser session
(e.g. UCSC)

http://genome.ucsc.edu/s/yocamille/MKovatcheva_2022

## Methodology

### Replicates

Only one of three MEFs was analyzed in ChIP due to poor inherent reprogramming efficiency of others, as described in the paper. Input and IP samples were generated for each condition.

### Sequencing depth

reads processed 331910925
uniquely aligned 233201616
failed to align 32857274
multialigned 65852035

Reads were 75bps long and single end

### Antibodies

H3K36me3 (D5A7) (CellSignaling #4909)

### Peak calling parameters

MACS2 v. 2.2.6 was used to call peaks using parameters: callpeak --broad -g 3209286105 with INPUT sample as control

### Data quality

MACS2peaks (qvalue < 0.1)
dox_noB12 116853
MEF_noB12 91216
MEF_B12 106819
dox_noB12 118386

### Software

Aligner: bowtie v1.2.3 with parameters: -n 2 -p 30 -m 1 --phred33-quals
Sam to bam and sorting: sambamba v0.6.7
Peak caller: MACS2 v. 2.2.6 with parameters: callpeak --broad -g 3209286105
<table><tr><td>Laboratory animals</td><td>OSKM mice refer to the i4F-B strain in Abad et al Science 2013.</td></tr><tr><td>Wild animals</td><td>study did not involve wild animals</td></tr><tr><td>Reporting on sex</td><td>Sex was not considered in study design and animals of both sexes were used in all experiments, keeping as close to 50/50 as possible, as litters allowed; specific male (M) and female (F) ratios for each in vivo experiment are indicated in the figure legends next to the total n (ex. n=5; 3M 2F). We have not done sex-based analysis, as separating sex would limit the sample size.</td></tr><tr><td>Field-collected samples</td><td>study did not involve field collected sample</td></tr><tr><td>Ethics oversight</td><td>Animal experimentation at the IRB Barcelona was performed according to protocols approved by the Science Park of Barcelona (PCB) Ethics Committee for Research and Animal Welfare.</td></tr></table>

Note that full information on the approval of the study protocol must also be provided in the manuscript.

# ChIP-seq

## Data deposition

[x] Confirm that both raw and final processed data have been deposited in a public database such as GEO.

[x] Confirm that you have deposited or provided access to graph files (e.g. BED files) for the called peaks.

**Data access links**
May remain private before publication.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200578

**Files in database submission**
GSM6038017_0142_2022_0071_2022_MACS2_broadPeaks.txt.gz
GSM6038018_0143_2022_0072_2022_MACS2_broadPeaks.txt.gz
GSM6038021_0144_2022_0073_2022_MACS2_broadPeaks.txt.gz
GSM6038022_0145_2022_0074_2022_MACS2_broadPeaks.txt.gz
RAW data has been deposited to the Sequence Read Archive (SRA) and will be made public after publication

**Genome browser session**
(e.g. UCSC)
http://genome.ucsc.edu/s/yocamille/MKovatcheva_2022

## Methodology

**Replicates**
Only one of three MEFs was analyzed in ChIP due to poor inherent reprogramming efficiency of others, as described in the paper. Input and IP samples were generated for each condition.

**Sequencing depth**
reads processed 331910925
uniquely aligned 233201616
failed to align 32857274
multialigned 65852035
Reads were 75bps long and single end

**Antibodies**
H3K36me3 (D5A7) (CellSignaling #4909)

**Peak calling parameters**
MACS2 v. 2.2.6 was used to call peaks using parameters: callpeak --broad -g 3209286105 with INPUT sample as control

**Data quality**
MACS2peaks (qvalue < 0.1)
dox_noB12 116853
MEF_noB12 91216
MEF_B12 106819
dox_noB12 118386

**Software**
Aligner: bowtie v1.2.3 with parameters: -n 2 -p 30 -m 1 --phred33-quals
Sam to bam and sorting: sambamba v0.6.7
Peak caller: MACS2 v. 2.2.6 with parameters: callpeak --broad -g 3209286105