Supplementary Data

SUPPLEMENTARY DATA

Lactate promotes cardiomyocyte dedifferentiation through metabolic reprogramming

Jesús Ordoño1,2, Soledad Pérez-Amodio1,2,3, Kristen Ball4,5, Aitor Aguirre4,5, Elisabeth Engel1,2,3*

1 Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, Spain

2 CIBER Bioengineering, Biomaterials and Nanotechnology, Spain

3 Dpt. Material Science and Metallurgical Engineering, Universitat Politecnica de Catalunya (UPC), Spain

4 Regenerative biology and cell reprogramming laboratory, Institute for Quantitative Health Sciences and Engineering (IQ), Michigan State University, MI, USA

5 Department of Biomedical Engineering, Michigan State University, MI, USA

*Corresponding author. Dpt. Material Science and Metallurgical Engineering, Universitat Politecnica de Catalunya. Av. Eduard Maristany, 16. 08019 Barcelona. Email: [email protected].

SUPPLEMENTARY METHODS

Cell cycle PCR array. An RT2 Profiler PCR Array (Qiagen, PAMM-020ZC-2) was used for the detection of 84 genes related to cell cycle regulation following manufacturer’s instructions. Only genes with a fold change above 1.2 or below 0.8 compared to control sample are shown.

Cellular activity. Mouse cardiomyocytes were cultured with different lactate concentrations and their activity was measured using Alamar Blue™ (Thermo Scientific). After 3 days of culture, cell media was removed and cells were incubated with 10% Alamar Blue™ in culture media for 2.5 hours, avoiding direct contact with light. Samples were collected in three replicates in a 96-well plate and the fluorescent signal (Ex: 528nm and Em: 590nm) was measured in a TECAN Infinite M200 Pro microplate reader. Sample values were normalized to the signal value of cells incubated without lactate (0mM).

EdU incorporation. EdU (5-ethynyl-2’-deoxyuridine) incorporation was used to evaluate cell proliferation, following EdU-Click 488 (Baseclick, BCK-EdU488-1) instructions. Briefly, cells were seeded at a cell density of around 40,000 cells/well in an μ-slide 8 well chamber (Ibidi) and incubated with 0, 5, 10 or 20mM of L- (+)-lactic acid solution (Sigma). After 24 hours, cell culture media from the chamber (in contact with cells) was mixed with the same volume of a 20μM solution of EdU solution in fresh medium (for a final concentration of 10μM of EdU) and added to cells. Cells were cultured in this new media (where lactate concentration was also diluted 1:2) for additional 24 hours. Cells were then fixed in 4% paraformaldehyde (EMS) for 15 minutes and washed twice in 3% BSA in PBS. Samples were permeabilized in 0.5% Triton X- 100 in PBS for 20 minutes at room temperature. Afterwards, a reaction cocktail containing 6-FAM-Azide (prepared according to manufacturer’s instructions) was added to samples, which were incubated for 30 minutes protected from light. Cells were finally washed in 3% BSA in PBS and counterstained with DAPI. Images were taken using a Leica DMIRBE microscope.

Flow cytometry. Cells were collected with trypsin-EDTA (Sigma) and fixed in 4% paraformaldehyde (EMS) for 10 minutes. Samples were then permeabilized with 0.05% TritonTM X-100 (Sigma) for 10 minutes and blocked with 3% BSA for 30 minutes. Conjugated primary antibodies anti-vimentin- AlexaFluor® 647 (Abcam ab194719) and anti-alpha-smooth muscle actin-FITC (Abcam ab188498) were used. A Beckman Coulter Gallios flow cytometer was used, where 13000 events were gated and analyzed from 2 biological replicates.

SUPPLEMENTARY FIGURES AND LEGENDS

Figure S1. (A) Live/death assay of cardiomyocyte-enriched population of cells after 6 days of incubation with different concentrations of supplemented L-lactate. Green: alive cells; red: dead cells; scale bar 100µm. (B) Relative cellular activity of cardiomyocytes cultured with different concentrations of L-lactate (mM) for 3 days. One-way ANOVA, *p-value < 0.05. (C) Quantification of ki67-positive cardiomyocytes after 3 days of culture with (+) or without (-) glucose or lactate. Data are represented as % of ki67+cTnT+ within all cTnT+ cells. One-way ANOVA, ***p < 0.001, ****p < 0.0001; 10 random images were analyzed for each condition. (D) Ki67-positive cells quantification from a cardiomyocyte population after 7 days of culture with 0mM (Control) or 20mM (Lactate) of lactate. In addition, lactate supplementation was removed from cell media after 3 days of culture (Lactate removal). Data are represented as % of ki67+ within all cells. One-way ANOVA, *p < 0.05; at least 6 random images were analyzed for each condition.

Figure S2. (A) EdU labelling of non-myocyte cells after 2 days of incubation with 5mM of L-lactate. Blue: nuclei, red: EdU; scale bar: 150μm. (B) Quantification of EdU-positive cells after 2 days of culture with different concentrations of L-lactate (mM). One-way ANOVA. 4 random images were analyzed for each condition. (C) Flow cytometry plots showing the (log) intensity of α-smooth muscle actin (α-sma) and vimentin of non-myocyte cells incubated with 0mM (Control) and 20mM (Lactate) of supplemented L- lactate for 4 days. The table shows the fraction of α-sma-positive cells among vimentin-positive cells calculated from 2 technical replicates of a single population of cells (Mean ± SD).

Figure S3. (A-B) Relative expression of progenitor (A) or maturation (B) genes in cardiomyocytes cultured with 20mM lactate, compared to the expression of cardiomyocytes without lactate at every timepoint (dashed line). The data summarizes different N isolations of mouse cardiomyocytes, being N ≤ 4. Student’s T-test compared to control without lactate, *p < 0.05. (C) Relative expression of top up- and downregulated genes from a cell cycle array of cardiomyocytes incubated with 20mM of L-lactate for 6 days. Dashed line indicates the expression of genes from control cells without lactate.

Figure S4. (A) Detailed image of a cell nuclei in karyokinesis. Grey: nuclei; scale bar: 20μm. (B) Karyokinesis of cardiomyocytes (cTnT+) quantified from DAPI staining after 24 hours. Approximately 3,100 cardiomyocytes were evaluated for each condition. (C) Aurora B quantification of positive cardiomyocytes after 2 days of incubation with different concentrations of lactate. Data are represented as % of AurB+cTnT+ within all cTnT+ cells. One-way ANOVA. 7 random images were analyzed per each condition.

Figure S1

A 0mM 5mM 15mM

B C D

Figure S2

A B

C Control Lactate

Vimentin

α-sma

Figure S3

Figures S4

A B C

SUPPLEMENTARY TABLES AND LEGENDS

Table S1. Inflammatory cytokines array. Relative expression of inflammatory cytokines corrected by total protein amount and normalized to internal positive (100) and negative (0) controls. The mean and standard deviation (SD) was calculated from two independent cell isolations. Statistically significant differences (Student’s T-test, p-value < 0.05) are indicated by an asterisk (*).

Table S2. Transcription factors for upregulated genes. Top common transcription factor binding sites for upregulated genes from RNA sequencing analysis. The up and downregulated genes presenting binding sites for the transcription factors on its main regulatory elements are shown, together with its relationship with hypoxia pathways. GeneCards and Qiagen databases were used.

Table S3. Transcription factors for downregulated genes. Top common transcription factor binding sites for top downregulated genes from RNA sequencing analysis. The up and downregulated genes presenting binding sites for the transcription factors on its main regulatory elements are shown, together with its relationship with hypoxia pathways. GeneCards and Qiagen databases were used.

Table S1

Cytokine Control mean Control SD Lactate mean Lactate SD BLC 3.60 1.74 3.65 2.02 CD30 3.32 1.63 2.52 1.46 Eotaxin1 2.69 1.03 1.03 1.42 Eotaxin2 4.59 1.17 1.81 1.48 Fas 2.78* 0.27 -0.81* 0.88 Fractalkine 5.40* 0.81 0.26* 0.53 GCSF 3.13 1.76 -1.54 0.57 GM-CSF -1.18 1.33 0.29 0.16 IFN-g -0.24 0.00 0.71 0.55 IL1a 7.44 1.80 8.53 1.91 IL1b 1.16 0.85 2.03 1.17 IL2 2.04 0.95 2.92 1.40 IL3 2.57 1.70 3.23 1.63 IL4 5.53 2.50 5.84 2.24 IL6 13.07 5.50 15.11 5.85 IL9 5.64 1.52 4.22 1.39 IL10 2.32* 0.22 0.08* 0.28 IL12 p40/p70 3.40* 0.81 -0.09* 0.03 IL12 p70 9.39 1.96 2.81 1.43 IL13 0.48* 0.68 3.53* 1.03 IL17A -0.14 0.14 1.21 0.84 I-TAC 2.04 0.95 2.42 1.11 KC 6.23 2.35 7.80 1.29 Leptin 2.04 0.95 3.01 0.29 LIX 14.95 5.33 18.85 6.19 XCL1 5.44 1.80 5.17 0.87 MCP1 28.94 11.52 33.76 4.98 MCSF 14.70 3.85 13.76 0.19 MIG 3.37 0.57 2.11 0.06 MIP1a 6.54 0.24 3.91 1.32 MIP1g 91.67 3.76 73.06 22.62 RANTES 1.33 0.89 3.88 1.53 SDF1a 1.09* 0.46 4.67* 1.41 I309 9.10 2.45 13.46 2.89 TECK 2.94 1.67 4.83 1.01 TIMP1 29.63 6.55 35.45 0.77 TIMP2 2.21 1.20 2.67 0.40 TNFa 1.44 0.96 1.78 0.62 TNF RI 14.30 1.58 11.00 3.89 TNF RII 12.89 2.42 11.58 3.06

Table S2

(Top) Relation to Transcription factors Upregulated Genes Downregulated genes hypoxia BMP10, LIN28A, TCIM, CLDN7, SLC26A10, GRIK1, SEC14L5, KCNH7, PLD5, FOS SYBU, FER1L4, RFX6, SARDH, LINC00342, [1,2] EPN3 ADAMTS18 BMP10, LIN28A, TCIM, CLDN7, SLC26A10, GRIK1, FXYD1, SEC14L5, PLD5, JUND [1–4] SYBU, FER1L4, RFX6, SARDH, ADAMTS18 TRPC3, EPN3 LIN28A, TCIM, CLDN7, SLC26A10, SYBU, GRIK1, FXYD1, SEC14L5, GLP1R, CTCF FER1L4, RFX6, SARDH, LINC00342, [5] PLD5, TRPC3, EPN3 ADAMTS18 BMP10, LIN28A, TCIM, CLDN7, SLC26A10, GRIK1, FXYD1, SEC14L5, GLP1R, RAD21 [6] SYBU, FER1L4, RFX6, SARDH, ADAMTS18 PLD5, TRPC3, EPN3 BMP10, LIN28A, TCIM, CLDN7, SLC26A10, GRIK1, FXYD1, SEC14L5, GLP1R, REST [7] SYBU, FER1L4, RFX6, SARDH, ADAMTS18 PLD5, TRPC3, EPN3 LIN28A, TCIM, CLDN7, SLC26A10, SYBU, DGKK, GRIK1, FXYD1, SEC14L5, SP1 FER1L4, RFX6, SARDH, LINC00342, [2,8,9] GLP1R, PLD5, TRPC3, EPN3 ADAMTS18 LIN28A, TCIM, CLDN7, SLC26A10, SYBU, GRIK1, FXYD1, SEC14L5, PLD5, CREB1 [2,10–12] FER1L4 EPN3 GATA3 LIN28A, TCIM, SLC26A10, SYBU, SEC14L5, GLP1R, KCNH7, EPN3 [13] aMEF-2 CLDN7, FER1L4, ADAMTS18 GLP1R MEF-2A CLDN7, FER1L4, RFX6, ADAMTS18 GLP1R [14–16]

Table S3

(Top) Relation to Transcription factors Upregulated genes Downregulated Genes hypoxia LIN28A, TCIM, CLDN7, SLC26A10, DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, CREM SYBU, FER1L4, SARDH, [17] PTGER3, PLD5, TRPC3, EPN3 LINC00342, ADAMTS18 BMP10, LIN28A, TCIM, CLDN7, DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, ELF1 SLC26A10, SYBU, FER1L4, SARDH, [18] PTGER3, PLD5, TRPC3, EPN3 LINC00342 LIN28A, TCIM, CLDN7, SLC26A10, DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, KDM1A SYBU, FER1L4, RFX6, SARDH, [19] PTGER3, PLD5, TRPC3, EPN3 ADAMTS18 GRIK1, FXYD1, SEC14L5, GLP1R, KCNH7, LIN28A, TCIM, CLDN7, SLC26A10, CTBP1 [20] PTGER3, PLD5, TRPC3, EPN3 SYBU, FER1L4 BMP10, LIN28A, CLDN7, GRIK1, FXYD1, SEC14L5, GLP1R, KCNH7, DPF2 SLC26A10, SYBU, FER1L4, RFX6, PTGER3, PLD5, TRPC3, EPN3 LINC00342, LIN28A, TCIM, SLC7A3, CLDN7, DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, POLR2A SLC26A10, SYBU, FER1L4, SARDH, KCNH7, PTGER3, PLD5, TRPC3, EPN3 LINC00342 DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, LIN28A, CLDN7, SLC26A10, SYBU, NRF1 [21,22] PTGER3, PLD5, TRPC3, EPN3 FER1L4, SARDH DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, LIN28A, CLDN7, SLC26A10, SYBU, SP2 PTGER3, PLD5, TRPC3, EPN3 SARDH DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, LIN28A, CLDN7, SLC26A10, SYBU, SP3 [2,23,24] PTGER3, PLD5, TRPC3, EPN3 FER1L4, RFX6, SARDH BMP10, LIN28A, CLDN7, DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, ZFHX2 SLC26A10, SYBU, FER1L4, SARDH, PTGER3, PLD5, TRPC3, EPN3 LINC00342 DGKK, GRIK1, FXYD1, SEC14L5, GLP1R, LIN28A, CLDN7, SLC26A10, SYBU, ZNF335 KCNH7, PTGER3, PLD5, TRPC3, EPN3 FER1L4, RFX6 AML1a GRIK1, FXYD1, GLP1R, TRPC3 SLC7A3 [25] POU2F1 GRIK1, SEC14L5, PLD5 SLC7A3 [26] Pax-5 GLP1R, EPN3 FER1L4, ADAMTS18 [27]

SUPPLEMENTARY REFERENCES

[1] Fox C, Walsh P, Mulhall KJ. Molecular Mechanism of Ischaemic Preconditioning of Skeletal Muscle In Vitro. Cureus 2018. doi:10.7759/cureus.3763.

[2] Cummins EP, Taylor CT. Hypoxia-responsive transcription factors. Pflugers Arch Eur J Physiol 2005. doi:10.1007/s00424-005-1413-7.

[3] Gerald D, Berra E, Frapart YM, Chan DA, Giaccia AJ, Mansuy D, et al. JunD Reduces Tumor Angiogenesis by Protecting Cells from Oxidative Stress. Cell 2004;118:781–794. doi:10.1016/j.cell.2004.08.025.

[4] Abdul-Hafez A, Shu R, Uhal BD. JunD and HIF-1α mediate transcriptional activation of angiotensinogen by TGF-β1 in human lung fibroblasts. FASEB J 2009. doi:10.1096/fj.08-114611.

[5] Wang J, Wang Y, Lu L. De-SUMOylation of CCCTC binding factor (CTCF) in hypoxic stress-induced human corneal epithelial cells. J Biol Chem 2012. doi:10.1074/jbc.M111.286641.

[6] Sook Kim M, Hyen Baek J, Bae M-K, Kim K-W. Human rad21 Gene, hHR21SP, Is Downregulated by Hypoxia in Human Tumor Cells. Biochem Biophys Res Commun 2001;281:1106–1112. doi:10.1006/bbrc.2001.4488.

[7] Cavadas MAS, Mesnieres M, Crifo B, Manresa MC, Selfridge AC, Scholz CC, et al. REST mediates resolution of HIF-dependent gene expression in prolonged hypoxia. Sci Rep 2015. doi:10.1038/srep17851.

[8] Li R, Geng H, Xiao J, Qin X, Wang F, Xing J, et al. miR-7a/b attenuates post-myocardial infarction remodeling and protects H9c2 cardiomyoblast against hypoxia-induced apoptosis involving Sp1 and PARP-1. Sci Rep 2016;6:29082. doi:10.1038/srep29082.

[9] Guo ZX, Zhou FZ, Song W, Yu LL, Yan WJ, Yin LH, et al. Suppression of microRNA-101 attenuates hypoxia-induced myocardial H9c2 cell injury by targeting DIMT1-Sp1/survivin pathway. Eur Rev Med Pharmacol Sci 2018. doi:10.26355/eurrev_201810_16167.

[10] Nakayama K. cAMP-response Element-binding Protein (CREB) and NF-κB Transcription Factors Are Activated during Prolonged Hypoxia and Cooperatively Regulate the Induction of Matrix Metalloproteinase MMP1. J Biol Chem 2013;288:22584–22595. doi:10.1074/jbc.M112.421636.

[11] Abramovitch R, Tavor E, Jacob-Hirsch J, Zeira E, Amariglio N, Pappo O, et al. A Pivotal Role of Cyclic AMP-Responsive Element Binding Protein in Tumor Progression. Cancer Res 2004. doi:10.1158/0008-5472.CAN-03-2089.

[12] Lim B, Jung K, Gwon Y, Oh JG, Roh J il, Hong SH, et al. Cardioprotective role of APIP in myocardial infarction through ADORA2B. Cell Death Dis 2019. doi:10.1038/s41419-019-1746-3.

[13] Lin MC, Lin JJ, Hsu CL, Juan HF, Lou PJ, Huang MC. GATA3 interacts with and stabilizes HIF-1α to enhance cancer cell invasiveness. Oncogene 2017. doi:10.1038/onc.2017.8.

[14] Silva JLT, Giannocco G, Furuya DT, Lima GA, Moraes PAC, Nachef S, et al. NF-κB, MEF2A, MEF2D and HIF1-a involvement on insulin- and contraction-induced regulation of GLUT4 gene expression in soleus muscle. Mol Cell Endocrinol 2005;240:82–93. doi:10.1016/j.mce.2005.05.006.

[15] Wang Q, Lu G, Chen Z. MALAT1 promoted cell proliferation and migration via MALAT1/miR- 155/MEF2A pathway in hypoxia of cardiac stem cells. J Cell Biochem 2019;120:6384–6394. doi:10.1002/jcb.27925.

[16] Sellathurai J, Nielsen J, Hejbøl EK, Jørgensen LH, Dhawan J, Nielsen MFB, et al. Low Oxygen Tension Enhances Expression of Myogenic Genes When Human Myoblasts Are Activated from G 0 Arrest. PLoS One 2016. doi:10.1371/journal.pone.0158860.

[17] Oikawa M, Wu M, Lim S, Knight WE, Miller CL, Cai Y, et al. Cyclic nucleotide phosphodiesterase 3A1 protects the heart against ischemia-reperfusion injury. J Mol Cell Cardiol 2013;64:11–19. doi:10.1016/j.yjmcc.2013.08.003.

[18] Rowell J, Koitabashi N, Kass DA, Barth AS. Dynamic gene expression patterns in animal models of early and late heart failure reveal biphasic-bidirectional transcriptional activation of signaling pathways. Physiol Genomics 2014;46:779–787. doi:10.1152/physiolgenomics.00054.2014.

[19] Saccà CD, Gorini F, Ambrosio S, Amente S, Faicchia D, Matarese G, et al. Inhibition of lysine- specific demethylase LSD1 induces senescence in Glioblastoma cells through a HIF-1α-dependent pathway. Biochim Biophys Acta - Gene Regul Mech 2019;1862:535–546. doi:10.1016/j.bbagrm.2019.03.004.

[20] Dorman K, Shen Z, Yang C, Ezzat S, Asa SL. CtBP1 Interacts with Ikaros and Modulates Pituitary Tumor Cell Survival and Response to Hypoxia. Mol Endocrinol 2012;26:447–457. doi:10.1210/me.2011- 1095.

[21] Wang D, Zhang J, Lu Y, Luo Q, Zhu L. Nuclear respiratory factor-1 (NRF-1) regulated hypoxia- inducible factor-1α (HIF-1α) under hypoxia in HEK293T. IUBMB Life 2016. doi:10.1002/iub.1537.

[22] Niu N, Li Z, Zhu M, Sun H, Yang J, Xu S, et al. Effects of nuclear respiratory factor-1 on apoptosis and mitochondrial dysfunction induced by cobalt chloride in H9C2 cells. Mol Med Rep 2019. doi:10.3892/mmr.2019.9839.

[23] Duval E, Bouyoucef M, Leclercq S, Baugé C, Boumédiene K. Hypoxia inducible factor 1 alpha down-regulates type i collagen through Sp3 transcription factor in human chondrocytes. IUBMB Life 2016;68:756–763. doi:10.1002/iub.1539.

[24] Formisano L, Guida N, Valsecchi V, Cantile M, Cuomo O, Vinciguerra A, et al. Sp3/REST/HDAC1/HDAC2 complex represses and Sp1/HIF-1/p300 complex activates ncx1 gene transcription, in brain ischemia and in ischemic brain preconditioning, by Epigenetic mechanism. J Neurosci 2015. doi:10.1523/JNEUROSCI.2174-14.2015.

[25] Lee SH, Manandhar S, Lee YM. Roles of RUNX in hypoxia-induced responses and angiogenesis. Adv. Exp. Med. Biol., 2017. doi:10.1007/978-981-10-3233-2_27.

[26] Hwang-Verslues WW, Chang PH, Jeng YM, Kuo WH, Chiang PH, Chang YC, et al. Loss of corepressor PER2 under hypoxia up-regulates OCT1-mediated EMT gene expression and enhances tumor malignancy. Proc Natl Acad Sci U S A 2013. doi:10.1073/pnas.1222684110.

[27] Harquail J, LeBlanc N, Ouellette RJ, Robichaud GA. miRNAs 484 and 210 regulate Pax-5 expression and function in breast cancer cells. Carcinogenesis 2019;40:1010–1020. doi:10.1093/carcin/bgy191.