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Lactylome analysis suggests lactylation-dependent mechanisms of metabolic adaptation in hepatocellular carcinoma

Nature Metabolism volume 5, pages 61–79 (2023)Cite this article

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Abstract

Enhanced glycolysis and accumulation of lactate is a common feature in various types of cancer. Intracellular lactate drives a recently described type of posttranslational modification, lysine lactylation (Kla), on core histones. However, the impact of lactylation on biological processes of tumour cells remains largely unknown. Here we show a global lactylome profiling on a prospectively collected hepatitis B virus-related hepatocellular carcinoma (HCC) cohort. Integrative lactylome and proteome analysis of the tumours and adjacent livers identifies 9,275 Kla sites, with 9,256 sites on non-histone proteins, indicating that Kla is a prevalent modification beyond histone proteins and transcriptional regulation. Notably, Kla preferentially affects enzymes involved in metabolic pathways, including the tricarboxylic acid cycle, and carbohydrate, amino acid, fatty acid and nucleotide metabolism. We further verify that lactylation at K28 inhibits the function of adenylate kinase 2, facilitating the proliferation and metastasis of HCC cells. Our study therefore reveals that Kla plays an important role in regulating cellular metabolism and may contribute to HCC progression.

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Fig. 1: Characteristics and prognostic significance of protein lysine lactylation in HCC.
Fig. 2: Multiomics association of global Kla and lactate source for Kla.
Fig. 3: Integrative multiomics analysis of Kla and its association with liver pathophysiology.
Fig. 4: Protein lysine lactylation broadly involves in metabolism-related pathways in HCC.
Fig. 5: Pathway alterations and differential expression of lactylome and proteome according to clinical traits.
Fig. 6: Pathway alterations of lactylome according to driver mutations.
Fig. 7: Lactylation of AK2 predicts poor prognosis of HCC.
Fig. 8: AK2-K28la weakens AK2 enzymatic activity and contributes to HCC malignancy.

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Data availability

The processed data of this research, including genome, transcriptome, proteome and lactylome data, and their corresponding raw data, are all publicly available in the NODE database (The National Omics Data Encyclopedia) with accession code OEP002852 (https://www.biosino.org/node/project/detail/OEP002852). The CPTAC-HCC proteome data are available at the Proteomic Data Commons (PDC, https://pdc.cancer.gov/pdc/; accession node: PDC000198). Gene sets are available in Msigdb database (http://www.gsea-msigdb.org/gsea/msigdb/; C2 CP collection). Source data are provided with this paper.

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Acknowledgements

We acknowledge the assistance of Y. Weng (PTM BIO) for developing and evaluating method of mass spectrometry, L. Dong (Zhongshan Hospital, Fudan University) for the construction of TMAs and J. Shi (Zhongshan Hospital, Fudan University) for the supply of materials in multiplexed immunohistochemistry. This work was supported by the National Natural Science Foundation of China (grant Nos. 81961128025 to Q.G., 81973164 to H.H. and 81872321 to J.S.) and by Research Projects from the Science and Technology Commission of Shanghai Municipality (grant Nos. 19XD1420700 to Q.G., 21JC1401200 to Q.G. and 21JC1410100 to J.F.), Shanghai Municipal Key Clinical Specialty and Shanghai Municipal Science and Technology Major Project.

Author information

Author notes

  1. These authors contributed equally: Zijian Yang, Cong Yan, Jiaqiang Ma, Panpan Peng.

Authors and Affiliations

  1. Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China

    Zijian Yang, Jiaqiang Ma, Yingcheng Wu, Shu Zhang, Xiaoying Wang, Shuangjian Qiu, Jian Zhou, Jia Fan & Qiang Gao

  2. Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Cong Yan, Panpan Peng, Xuelian Ren & He Huang

  3. University of Chinese Academy of Sciences, Beijing, China

    Cong Yan, Panpan Peng, Xuelian Ren & He Huang

  4. The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China

    Jiaqiang Ma

  5. Medical Department, Burning Rock Biotech, Guangdong, China

    Shangli Cai

  6. Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, China

    Xia Shen & Shu Zhang

  7. Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, China

    Qiang Gao

  8. State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China

    Qiang Gao

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Contributions

Q.G., H.H. and J.F. conceived and designed the research. Z.Y., J.M., S.Z. and S.Q. coordinated acquisition, distribution and quality evaluation of HCC tumour, and adjacent liver tissue samples. H.H., Z.Y., C.Y., X.R. and X.S. directed and performed analyses and quality control of mass spectrometry data. Z.Y., X.R., H.H., S.C. and Y.W. analysed genome and transcriptome data. J.M. and Z.Y. performed the tissue microarray immunohistochemistry experiments. C.Y., P.P., X.R. and Z.Y. performed HCC cell line experiments. Q.G., J.F., X.W. and J.Z. interpreted data in context of HCC biology and provided clinical guidance. Z.Y., C.Y. and X.R. wrote the manuscript. Q.G., H.H. and J.F. revised the manuscript and supervised the study.

Corresponding authors

Correspondence to He Huang or Qiang Gao.

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S.C. is an employee of Burning Rock Biotech. The other authors declare no competing interests.

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Nature Metabolism thanks Sabine Colnot and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alfredo Giménez-Cassina, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Overview of the research pipeline and quality control for the mass spectrometry platform.

a, Schematic description of research pipeline. b, Illustration of 52 cases of HCC patients included in the individual omics profiling. The genome, transcriptome, proteome and lactylome experiments are shown, respectively. The main clinical information of each patient is shown. Tumor2: Two tumour samples are obtained from each patient with multifocal HCC (n = 6). c, Distribution of score and mass error of differently charged peptides. d, Spearman correlation coefficients calculated from the proteome (left) and lactylome (right) of each biological replicate used for quality control. e, The number of identified Kla sites from all 110 samples from 52 HCC patients.

Source data

Extended Data Fig. 2 Basic properties of Kla in HCC and lactylprotein complex.

a, Distribution of tumour/adjacent ratio of Kla sites. The displayed Kla sites all satisfy p < 0.05 in the comparison between the tumours and adjacent livers. b, Intersection of Kla and Kac sites from five cohorts (all sites, left; sites on metabolism proteins, right). c, Number of Kla sites per protein in the tumours and adjacent livers. d, Subcellular location of all lactylproteins in the tumours and adjacent livers. e, Protein complexes enriched in the lactylproteins differentially lactylated between tumours and adjacent livers. The size of nodes represents the number of Kla sites identified in each protein subunit. The edge represents the interaction between one subunit and the other or itself recorded in the CORUM database. f, Number of detected Kla sites on different histones. N = 58 and 52 for tumour and adjacent liver samples. g, Number of detected histone Kla sites in tumour or adjacent normal tissues. ****P < 0.0001. h, Kla intensity on different histones in tumour or adjacent normal tissues. Tumour versus Adjacent: ***P = 0.00014, ***P = 0.00015. i, Differentially lactylated Kla sites on histones detected. Tumour versus Adjacent: *P = 0.02, ****P < 0.0001, ***P = 0.00054, ***P = 0.00037, ***P = 0.00054, *P = 0.027, **P = 0.0011, **P = 0.013. Box plot centres represent the median and lower and upper hinges represent the first and third quartiles (25th and 75th percentiles), the whiskers extend to the largest and smallest value (g–i). P value is calculated by two-sided Wilcoxon rank-sum test (g–i).

Source data

Extended Data Fig. 3 Cellular Kla can be affected by multiple factors.

a, Protein expression of GLUT1/SLC2A1, MCT1/SLC16A1, MCT4/SLC16A3 in Fudan-Kla and CPTAC-HCC cohort. Tumour versus Adjacent (Fudan-Kla cohort): *P = 0.019, ****P < 0.014; Tumour versus Adjacent (CPTAC-HCC cohort): *P = 0.048, ****P < 0.0001. b, SLC2A1, SLC16A1 and SLC16A3 were knocked down using shRNA in HepG2 cells, respectively. Cellular Kla intensity was inspected by immunoblots. c, Protein expression of indicated genes in tumour and adjacent tissue derived from Fudan-Kla cohort and CPTAC-HCC cohort. Tumour versus Adjacent (Fudan-Kla cohort): ****P < 0.0001, ****P < 0.0001, ****P < 0.0001, ****P < 0.0001. Tumour versus Adjacent (CPTAC-HCC cohort): ****P < 0.0001, ****P < 0.0001, ****P < 0.0001, ****P < 0.0001, **P = 0.0091, ***P = 0.00015, ****P < 0.0001, ****P < 0.0001, ****P < 0.0001, *P = 0.043. d, Spearman correlation coefficients between Kla intensity and expression of different proteins. x axis represents the protein expression of EP300, y axis represents the protein expression of indicated erasers. Number at four corners represents the number of Kla sites on metabolism/non-metabolism proteins. e, HepG2 cells were treated with 10 μM A485 for 48 h, followed by Kla detection. f, HepG2 cells were transfected with shRNA-HDAC1, shRNA-HDAC2 and shRNA-HDAC3, followed by Kla detection. N = 58 and 52 for tumour and adjacent liver samples (Fudan-Kla cohort), N = 159 and 159 for tumour and adjacent liver samples (CPTAC-HCC cohort) (a,c). Box plot centres represent the median and lower and upper hinges represent the first and third quartiles (25th and 75th percentiles), the whiskers extend to the largest and smallest value (a,c). P value is calculated by two-sided Wilcoxon rank-sum test (a,c). Experiments were performed three times (b,e,f).

Source data

Extended Data Fig. 4 Pathway overrepresentation of Kla sites in each quadrant.

a, Pathway enrichment of lactylproteins. Pathways with adjusted P value < 0.01 are shown. All pathways are composed of upregulated Kla in tumours compared with adjacent livers. There are no pathways for downregulated ones in the tumours compared with the adjacent livers that meet the need of adjusted P value < 0.01. b, Barplots showing the highly represented pathways with adjusted P value < 0.01. The quadrants marked on top correspond to Fig. 2c, representing the number of Kla sites on differentially expressed Oncogene/TSG between tumours and adjacent livers. c, Overlap among mRNA, protein, lactylprotein for the Cancer Gene Census (CGC). The schematic diagram of the quadrants in lactylprotein corresponds to the quadrants in Fig. 2a. P value is calculated by hypergeometric test and adjusted by Benjamini–Hochberg method (a,b).

Source data

Extended Data Fig. 5 Lactylation of AK2-K28 is associated with unfavourable prognosis in HCC.

a, Transcriptomic level of AK2 at the pan-cancer level. Data is derived from PCAWG database. N = 187, 533, 139, 79, 265, 183, 550, 91, 196, 323, 168, 1218, 450, 553, 122, 572, 87, 555, 45, 80, 171, 566, 427, 57, 576, 606, 492, 424, 309, 474, 48 and 310 for PCPG, LGG, TGCT, ACC, SARC, PAAD, PRAD, KICH, ESCA, KIRP, GBM, BRCA, STAD, LUSC, THYM, THCA, MESO, UCEC, CHOL, UVM, READ, HNSC, BLCA, UCS, LUAD, KIRC, COAD, LIHC, OV, SKCM, DLBC, CESC samples. b, Kaplan–Meier curve of overall survival for AK2 protein expression in tumours of CPTAC-HCC patients (AK2 + high, n = 80; GPC3 + panKla+AK2 + low, n = 79). c, Quantitative analysis of differential expression of AK2 between tumours and adjacent livers (left) and the western blot validation (right). N = 58 and 52 for tumour and adjacent liver samples. d, Quantitative analysis of differentially lactylated Kla sites on AK2 between tumours and adjacent livers. N = 58 and 52 for tumour and adjacent liver samples. e, The MS/MS spectra (left) and extracted ion chromatograms (right) of the synthetic AK2-K28la peptide (top) corresponding to the sequence of the in vivo peptide (AVLLGPPGAGKlaGTQAPR) (bottom). f, Associations of AK2 protein expression with the corresponding pathway score, clinicopathologic factors, and multiomics profiling. g, Associations of AK2-K28la with driver mutation, pathway scores, clinicopathologic factors and multiomics profiling. Box plot centres represent the median and lower and upper hinges represent the first and third quartiles (25th and 75th percentiles), the whiskers extend to the largest and smallest value (a,c,d). The P value is obtained from the two-sided Wilcoxon rank-sum test (c,d,f,g).

Source data

Extended Data Fig. 6 AK2-K28la weakens AK2 enzymatic activity and Kla of several important metabolic enzymes.

a, HepG2 and Hep3B cells ectopically expressing Flag-tagged AK2 were stimulated with 10 mM lactate for 24 h. Cell lysates were subjected to immunoprecipitation with Flag-M2 beads. Inputs and eluates were analysed by immunoblots. b, HepG2 ectopically expressing Flag-tagged AK2 were stimulated with 10 mM lactate for 24 h. Cell lysates were subjected to immunoprecipitation with Flag-M2 beads. Inputs and eluates were analysed by immunoblots. Time course of Kla of AK2 in HepG2 cells was revealed by immunoprecipitation. c, Kinetic measurements were performed for the activities of Flag-AK2-WT, K28R, K28Q, K28E and K28A mutants in HepG2 cells. d, Depletion and rescue efficiency of AK2 in HepG2 cells were detected by Western blot. e, Metabolome quantification of ADP and AMP in different groups indicated. The horizontal line represents the median. f, Lactylation of AK2 results in enhanced cell proliferation in Hep3B cells. Error bars represent ±s.d. for triplicate experiments. Data are presented as mean ± s.e.m.; *P < 0.05. g, Transwell assay on migration capability of AK2-lactylation. HepG2 cells transfected with Flag-AK2 with or without treatment of 10 mM Lactate for 24 h. After 48 h, cells that migrated to the inferior membrane were stained. h, Expression of genes showing statistical difference in the comparison of WT_nontreated and WT_treated (two-sided Student’s t test, P < 0.05) but not in K28R_nontreated and K28R_treated (two-sided Student’s t test, P ≥ 0.05). i, Gene enrichment analysis results for genes upregulated in the comparison of WT_nontreated and WT_treated but not in the comparison of K28R_nontreated and K28R_treated. j, Transcriptomic expression of Caspases8, 9, 3 and 7 in different groups of HepG2 cells as indicated. N = 3 biological independent samples for each group. k, Lactylation levels of HADH, ADH5, DLD and ALDH7A1 proteins are elevated upon 10 mM lactate treatment for 24 hours in the indicated cells. Mutation of Lys93 and Lys528 to Arg leads to remarkably reduced lactylation level of ALDH7A1 after treatment with 10 mM lactate. Experiments were performed three times (a,b,d,k). P value is calculated with two-sided Wilcoxon rank-sum test (e,j), two-tailed Student’s t test (f), or hypergeometric test and adjusted by Benjamini–Hochberg method (i). Box plot centres represent the median and lower and upper hinges represent the first and third quartiles (25th and 75th percentiles), the whiskers extend to the largest and smallest value (j).

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

The liver samples used in this study were obtained from the Zhongshan Hospital, Fudan University in China. There is no bias in the selection of patients. Every patient offers one non-tumour adjacent tissue sample and one to two tumour tissue samples. See Methods for details.

Supplementary Table 2

Identification information of Kla sites.

Supplementary Table 3

Gene list of capture-based genome sequencing.

Supplementary Table 4

Sequence data of AK2-K28R mutant was shown.

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Unprocessed western blots.

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Yang, Z., Yan, C., Ma, J. et al. Lactylome analysis suggests lactylation-dependent mechanisms of metabolic adaptation in hepatocellular carcinoma. Nat Metab 5, 61–79 (2023). https://doi.org/10.1038/s42255-022-00710-w

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