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Metabolic remodelling during early mouse embryo development

Nature Metabolism volume 3, pages 1372–1384 (2021)Cite this article

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Abstract

During early mammalian embryogenesis, changes in cell growth and proliferation depend on strict genetic and metabolic instructions. However, our understanding of metabolic reprogramming and its influence on epigenetic regulation in early embryo development remains elusive. Here we show a comprehensive metabolomics profiling of key stages in mouse early development and the two-cell and blastocyst embryos, and we reconstructed the metabolic landscape through the transition from totipotency to pluripotency. Our integrated metabolomics and transcriptomics analysis shows that while two-cell embryos favour methionine, polyamine and glutathione metabolism and stay in a more reductive state, blastocyst embryos have higher metabolites related to the mitochondrial tricarboxylic acid cycle, and present a more oxidative state. Moreover, we identify a reciprocal relationship between α-ketoglutarate (α-KG) and the competitive inhibitor of α-KG-dependent dioxygenases, l-2-hydroxyglutarate (l-2-HG), where two-cell embryos inherited from oocytes and one-cell zygotes display higher l-2-HG, whereas blastocysts show higher α-KG. Lastly, increasing 2-HG availability impedes erasure of global histone methylation markers after fertilization. Together, our data demonstrate dynamic and interconnected metabolic, transcriptional and epigenetic network remodelling during early mouse embryo development.

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Fig. 1: Metabolomics profiling of two-cell and blastocyst-stage embryos.
Fig. 2: Genomic and metabolic analysis reveals dynamic metabolic network integrated in the developmental TF network in early embryogenesis.
Fig. 3: A decrease of l-2-HG facilitates global erasure of histone methylation during early embryo development.
Fig. 4: l-2-HG supplementation impedes early embryo development which is rescuable by α-KG.
Fig. 5: L2hgdh deficiency impedes H3K4me3 methylation erasure during mouse early embryo development.

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Data availability

RNA-seq data have been deposited in the NCBI Gene Expression Omnibus under accession code GSE181648. Previously published RNA-seq data that were reanalysed here are available under accessions GSE45719, GSE98150 and GSE33923. Published ChIP–seq data for TFs are available under accession GSE11431. Published ATAC–seq data were downloaded from GSE66390. Source data are provided with this paper. The remaining data that support the findings of this study and uncropped versions of blots are available from the corresponding authors upon reasonable request.

Code availability

All the analysis in this study was made based on custom Perl (v5.30.0), Python (v2.7.16) and R (v4.0.2) codes and is available upon reasonable request.

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Acknowledgements

We thank J. Sheng, L. Shen, D. Ye, Y. Ni, X. Huang, M. Guan, Y. Yang, C. Navdeep and A. Intlekofer for helpful discussion and sharing of facilities. We thank G. Daley and M. Teitell for their long-term support. J. Zhang is supported by the National Key Research and Development Program of China (2018YFA0107100). Z.H. is supported by grants from National Natural Science Foundation of China (92057209). J. Zhang is also supported by the National Key Research and Development Program of China (2018YFA0107103 and 2018YFC1005002), the National Natural Science Foundation projects of China (31871453 and 91857116), Zhejiang Innovation Team grant (2019R01004) and the Zhejiang Natural Science Foundation projects of China (LR19C120001). Z.H. is also supported by grants from National Key R&D Program of China (2019YFA0802102), Tsinghua-Peking Center for Life Sciences (100084) and Beijing Frontier Research Center for Biological Structure.

Author information

Author notes

  1. These authors contributed equally: Jing Zhao, Ke Yao, Hua Yu, Ling Zhang, Yuyan Xu.

Authors and Affiliations

  1. Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China

    Jing Zhao, Hua Yu, Ling Zhang, Yuyan Xu, Lang Chen, Zhen Sun, Yuqing Zhu, Hongru Pan, Min Zhang, Jie Chen, Xudong Fu & Jin Zhang

  2. School of Pharmaceutical Sciences, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China

    Ke Yao & Zeping Hu

  3. Zhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, Hangzhou, China

    Ling Zhang, Cheng Zhang, Xudong Fu & Jin Zhang

  4. Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Yuli Qian & Dan Zhang

  5. Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, THU-PKU Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China

    Shuyan Ji & Wei Xie

  6. Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, NY, USA

    Cristina Correia, Taylor Weiskittel & Hu Li

  7. Center of Computational Medicine and Bioinformatics, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

    Da-Wei Lin & Sriram Chandrasekaran

  8. Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, Research Unit of Chinese Academy of Medical Sciences, East China University of Science and Technology, Shanghai, China

    Yuzheng Zhao

  9. Life Sciences Institute, Zhejiang University, Hangzhou, China

    Heng-Yu Fan

  10. Institute of Hematology, Zhejiang University, Hangzhou, China

    Jin Zhang

Authors
  1. Jing Zhao
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  2. Ke Yao
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  3. Hua Yu
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Contributions

J. Zhang and Z.H. designed and supervised the study. J. Zhao, K.Y., L.Z., Y.X., L.C., Z.S., Y.Z., Y.Q., S.J., H.P., M.Z. and J.C. performed the experiments. J. Zhao and L.Z. performed sample preparation and metabolomics results data analysis. K.Y. and Z.H. developed the metabolomics method, performed the metabolomics experiments and data analysis. H.-Y.F., J. Zhang, Z.H., C.Z., C.C., W.T. and D.-W.L. performed the bioinformatics analysis. J. Zhang, Z.H., H.L., W.X., H.-Y.F., D.Z., X.F., S.C. and Y.Z. contributed to writing and discussing the manuscript.

Corresponding authors

Correspondence to Zeping Hu or Jin Zhang.

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The authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Navdeep Chandel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Ashley Castellanos-Jankiewicz; Pooja Jha.

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

Extended data

Extended Data Fig. 1 Metabolomics sample gradient titration, and metabolomics profiling for in vivo derived embryos and in vitro cultured embryo stem cells.

(a, b) Metabolites from 2.5 K to 80 K mouse embryonic stem cells or 15 to 240 zygotes were extracted and titrated for targeted metabolomics. Heatmap showing relative metabolite abundance normalized with MetaboAnalyst 4.0. Zy: zygote. (c) Representative metabolite SAM was shown for the correlation between the ES cell number used and relative mass spectrometry intensity obtained. R is the Pearson correlation coefficient. Data in c are from n = 3 biological replicates. Data are mean ± SAM. SAM:S-Adenosyl-methionine. (d) A plot showing R2 and -log (p-value) of each detected metabolite in the ES cell titration experiment. The significance level or p-value of Pearson correlation coefficients was obtained using cor.test function in R. (e) Representative metabolites were shown for correlation between the number of embryos and relative mass spectrometry intensity obtained. R is the correlation coefficient. (f) Flow cytometry showing tdTomato positive 2C-like cells (2CLC) and GFP positive ES cells (ESC). (g) The GSH/GSSG ratio in 2-cell and BC embryos. Data in g are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test. BC: blastocyst. (h) Relative methionine levels (signal peak areas normalized to total metabolites) in oocytes and 2 C embryos. Data in h are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test. (i) The SAM/SAH ratio in 2-cell and BC embryos. Data in i are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed paired t-test.

Source data

Extended Data Fig. 2 Analysis of metabolites and the corresponding metabolic enzyme genes in the TCA cycle in 2-cell and blastocyst embryos.

(a-e) Abundance of metabolites in the TCA cycle. Data in a-e are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test. (f) The TCA cycle pathway with differential metabolites and genes indicated by the arrows. (g-m) Expression of the TCA cycle metabolic genes in 2-cell and ICM of blastocyst. Data in g-m are from n = 4 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test. ICM: Inner Cell Mass.

Source data

Extended Data Fig. 3 Abundance of metabolites and expression the corresponding metabolic enzyme genes in the purine metabolism pathway in 2-cell and blastocyst embryos.

(a-j) Abundance of metabolites in the purine metabolism pathway. Data in a-j are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test. (k) The purine metabolism pathways with differential metabolites and genes indicated by the arrows. (l-n) Expression of the purine metabolism genes in the 2-cell embryos and blastocyst ICM. Data in l-n are from n = 4 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test.

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Extended Data Fig. 4 Metabolite abundance and metabolic gene expression in one carbon metabolism and redox metabolism-related pathways in 2-cell and blastocyst embryos.

(a-g) Abundance of metabolites in one carbon metabolism, glutathione metabolism and polyamine metabolism pathways. Data in a-g are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test. (h) The above metabolism pathways with differential metabolites and genes indicated by the arrows. (i-j) Expression of the above pathway metabolism genes in the 2-cell embryos and ICM of blastocyst. Data in i-j are from n = 4 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test.

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Extended Data Fig. 5 Metabolic network analysis and metabolomics analysis with 2CLCs.

(a, b) Metabolic network of 2 C and BC embryos constructed from metabolomics data as the method before21. Deletions of metabolic enzyme genes were simulated, and the differential sensitivity analysis was presented in b. (c) The PCA analysis of metabolomics from 2-cell, blastocyst (BC) embryos, 2-cell like cells (2CLC) and ES cells. (d) Schematics for obtaining the tdTomato+ 2CLCs from the 2 C transcription factor Dux inducible ES cell line (Dox-iDux ESC) and the tdTomato- ESCs. (e) Flow cytometry analysis showing the induced tdTomato+ 2CLCs upon addition of 1 μg/ml doxycycline(dox) for 24 hours. (f) qRT-PCR showing higher expression of 2 C genes in 2CLCs obtained above compared with ESCs. Data in f are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-way(ANOVA) with Sidak’s multiple comparisons post-test. (g) The PCA analysis of metabolomics from 2-cell embryos, blastocyst embryos (BC), 2CLCs from the inducible Dux line or 2CLC (iDux), and ESCs. (h) Heatmap of the metabolomics analysis of the 2CLC and ESC samples. (i) Relative abundance of GSH, GSSG, and spermidine from the 2CLC and ESC samples described above. Data in i are from n = 3 biological replicates; Data are mean ± SEM. Statistical significance was determined by two-way ANOVA with Sidak’s multiple comparisons post-test. (j) The ratio of GSH/GSSG from the 2CLC and ESC samples described above. Data in j are from n = 3 biological experiments; Data are mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test.

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Extended Data Fig. 6 Single cell RNA-seq analysis, epigenetics analysis of metabolic genes, and anabolic metabolism analysis during pre-implantation embryo development.

(a) Genome-wide KEGG pathway analysis showing dynamic gene expression in different developmental stages using publicly available single cell RNA-seq data34. Blast: blastocyst. KEGG secondary category pathways are shown. (b) All metabolic genes are clustered into 6 clusters (C1-C6) by k-means analysis using single-cell RNA-seq data as in a. (c) Enriched KEGG terms for Cluster 1 (blastocyst stage), Cluster 5 (2-cell stage) and Cluster 6 (zygote stage) as defined in b. (d) GSEA analysis of the TCA cycle and Oxidative Phosphorylation between 2CLCs (tomato positive) and ESCs (tomato negative). (e) Boxplots showing expression levels of all OxPhos genes in different mouse embryo development stages analyzed from the same bulk RNA-seq data as in a. Data in e are from n = 4 biological replicates; In e, the center line is the median, the bottom of the box is the 25th percentile boundary, the top of the box the 75th, and the whiskers define the bounds of the data that are not considered outliers, with outliers defined as greater/lesser than ± 1.5 x IQR, where IQR = inter quartile range.v. Statistical significance was determined by Wilcox signed rank test. (f) Left: Representative images showing the NADH/NAD + ratio in the developing embryos. Scale bar: 50 µm. Right: Normalized F405 nm/F488 nm of different stages of embryos. Data are mean ± SEM. (g) Boxplots showing expression levels of all Ribosomal genes in different mouse embryo development stages analyzed from the same bulk RNA-seq data as above. Data in e are from n = 4 biological replicates; In g, the center line is the median, the bottom of the box is the 25th percentile boundary, the top of the box the 75th, and the whiskers define the bounds of the data that are not considered outliers, with outliers defined as greater/lesser than ± 1.5 x IQR, where IQR = inter quartile range.v. Statistical significance was determined by Wilcox signed rank test. (h) Translation activity of different stage embryos stained with OP-Puromycin. Representative images of full z-series confocal max projections of embryos are shown. Data in h are from n = 2 biological experiments with similar results; Scale bar: 50 μm. (i) Analysis of publicly available ATAC-seq data for the developmental stage-specific metabolic gene clusters defined in Fig. 2b. (j) Schematic diagram for constructing transcription factor-metabolic genes (TF-MG) regulatory network in each developmental stage. (k) Heatmap showing gene expression patterns of a few hub TFs identified in early embryo development as in Fig. 2d. (l) Gene regulatory networks between TFs or epigenetic factors and metabolic genes for 2-cell, four-cell, 8-cell/morula embryos and ICM in blastocyst embryos.

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Extended Data Fig. 7 Absolute concentration of L-2-HG, and concentration titration for treating embryos.

(a) The extracted-ion chromatogram of L-2-HG and D-2-HG standards (concentration 100 nM). It shows clear separation between these two enantiomers. (b) The absolute concentration of L-2-HG and D-2-HG in MII, zygote, 2-cell and blastocyst embryos. (c) Reported absolute concentration of 2-HG in various cell lines, tumor and tissues. (d) Schematic illustration of Tunel experimental approach. Zygotes were collected 16 hours after injection of hCG and cultured in KSOM with different concentration L-2-HG. (e) Tunel assay was performed at different stage embryos with different concentration. Embryos were cultured in KSOM with L-2-HG at 0 mM, 0.15 mM, 0.3 mM, 0.45 mM and 0.6 mM. Representative images of full z-series confocal max projections of embryos in three independent experiments are shown. The scale bar is 50 µm.

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Extended Data Fig. 8 Expression of Ldh, Mdh and L2hgdh/D2hgdh mRNA and their protein during early embryo development.

(a) mRNA expression of Ldh, Mdh and L2hgdh/D2hgdh genes from RNA-seq results of early embryos. Data are from n = 4 biological replicates; Data are mean ± SEM. (b) qRT-PCR of Ldhb expression in early embryos. Data are from n = 2 biological experiments with similar results; Data are mean ± SEM. (c) Western blotting of LDHB and L2HGHD in early embryos. Equal microgram of protein from protein lysate extracted from equal number of embryos were used for loading.

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Supplementary information

Reporting Summary

Supplementary Table 1

Sensitivity of reaction.

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qPCR primers.

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siRNA sequence.

Supplementary Data 1–3

2C and BC metabolism analysis, 2C (idux+), ES cell metabolism analysis and gene transcripts per million reads of RNA-seq with 2-HG and α-KG.

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Zhao, J., Yao, K., Yu, H. et al. Metabolic remodelling during early mouse embryo development. Nat Metab 3, 1372–1384 (2021). https://doi.org/10.1038/s42255-021-00464-x

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