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Aberrant NAD+ metabolism underlies Zika virus–induced microcephaly

Nature Metabolism volume 3, pages 1109–1124 (2021)Cite this article

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Abstract

Zika virus (ZIKV) infection during pregnancy can cause microcephaly in newborns, yet the underlying mechanisms remain largely unexplored. Here, we reveal extensive and large-scale metabolic reprogramming events in ZIKV-infected mouse brains by performing a multi-omics study comprising transcriptomics, proteomics, phosphoproteomics and metabolomics approaches. Our proteomics and metabolomics analyses uncover dramatic alteration of nicotinamide adenine dinucleotide (NAD+)-related metabolic pathways, including oxidative phosphorylation, TCA cycle and tryptophan metabolism. Phosphoproteomics analysis indicates that MAPK and cyclic GMP–protein kinase G signaling may be associated with ZIKV-induced microcephaly. Notably, we demonstrate the utility of our rich multi-omics datasets with follow-up in vivo experiments, which confirm that boosting NAD+ by NAD+ or nicotinamide riboside supplementation alleviates cell death and increases cortex thickness in ZIKV-infected mouse brains. Nicotinamide riboside supplementation increases the brain and body weight as well as improves the survival in ZIKV-infected mice. Our study provides a comprehensive resource of biological data to support future investigations of ZIKV-induced microcephaly and demonstrates that metabolic alterations can be potentially exploited for developing therapeutic strategies.

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Fig. 1: Schematic overview and multi-omics summary of the study.
Fig. 2: Transcriptomics and proteomics analyses of ZIKV-infected brains.
Fig. 3: Proteomics analysis reveals altered metabolic pathways in ZIKV-infected brains.
Fig. 4: Metabolomics analysis reveals NAD+ depletion in ZIKV-infected brains.
Fig. 5: Phosphoproteomics analysis reveals disturbed signaling pathways in ZIKV-infected brains.
Fig. 6: NAD+ supplementation potentially suppresses ZIKV-induced cell death in mouse brains.
Fig. 7: NR supplementation alleviates cell death, protects the brain and extends lifespan in ZIKV-infected mice.

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Data availability

The RNA-seq data are published22,54 and can be downloaded from the Sequence Read Archive database, BioProjectID PRJNA358758. The MS proteomics data have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository86 with dataset identifier PXD026814. Raw metabolomics data are included in Supplementary Data 1. Source data are provided with this paper.

Code availability

Codes for data analysis are available at https://github.com/pang2021/ZIKV_NMet.

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Acknowledgements

We thank members of the Hu laboratory for critiquing the manuscript. We thank W. Zeng and G. Wang at Tsinghua for providing NMNAT2 and NAMPT antibodies. We thank Q. Ding at Tsinghua for providing advice on virology. We thank Y. Shi at Institute of Microbiology and C. Qin at Beijing Institute of Microbiology and Epidemiology for providing ZIKV stock. Some illustrations were created with BioRender.com. Z.H. is supported by grants from National Key R&D Program of China (2019YFA0802100-02), National Natural Science Foundation of China (92057209), National Science and Technology Major Project for ‘Significant New Drugs Development’ (2017ZX09304015), Tsinghua University (53332200517), Tsinghua-Peking Joint Center for Life Sciences and Beijing Frontier Research Center for Biological Structure. Z.X. is supported by grants from the National Natural Science Foundation of China (NSFC) (31730108, 31921002, 32061143026) and Chinese Academy of Science (QYZDJ-SSW-SMC007, XDB32020100, YJKYYQ20200052).

Author information

Author notes

  1. These authors contributed equally: Huanhuan Pang, Yisheng Jiang, Jie Li, Yushen Wang, Meng Nie.

Authors and Affiliations

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

    Huanhuan Pang, Jie Li, Meng Nie, Nan Xiao, Ke Yao, LiAng Yao & Zeping Hu

  2. State Key Laboratory of Molecular Developmental Biology, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Yisheng Jiang, Shuo Wang, Yu Zheng & Zhiheng Xu

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

    Yisheng Jiang, Shuo Wang & Zhiheng Xu

  4. Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China

    Jie Li, Zhihong Song, Fansen Ji & Xun Lan

  5. School of Life Sciences, Tsinghua University, Beijing, China

    Yushen Wang

  6. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (the PHOENIX Center), Beijing, China

    Yushen Wang & Lei Song

  7. Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

    Yafei Chang

  8. Institute of TCM-X, MOE Key Laboratory of Bioinformatics / Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China

    Shao Li

  9. School of Life Sciences, Tsinghua University, Beijing, China

    Peng Li

  10. Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China

    Peng Li

  11. Shanghai Qi Zhi Institute, Shanghai, China

    Peng Li

  12. Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China

    Xun Lan

  13. Parkinson’s Disease Center, Beijing Institute for Brain Disorders, Beijing, China

    Zhiheng Xu

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  1. Huanhuan Pang
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Contributions

H.P., Z.X. and Z.H. conceived the project and designed the study. H.P., M.N. and Z.H. wrote the paper. N.X., Y.J., J.L., Y.W., L.S., X.L. and Z.X. contributed to paper writing. H.P. and Z.H. designed and performed metabolomics, analyzed multi-omics data and interpreted results. Y.J. and H.P. designed and performed cell and animal experiments. J.L. and X.L. analyzed RNA-seq and multi-omics data. Y.W. and L.S. performed proteomics and phosphoproteomics and analyzed data. N.X. and K.Y. assisted in metabolomics data analysis. L.Y. assisted in cell and animal experiments. S.W. and Y.Z. assisted in animal experiments. Z.S. and F.J. assisted in RNA-seq data analysis. S.L. and P.L. assisted in multi-omics data analysis and interpretation. Y.C. and Z.X. provided the RNA-seq dataset. Z.H. supervised the project.

Corresponding authors

Correspondence to Lei Song, Xun Lan, Zhiheng Xu or Zeping Hu.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Patricia C. B. Beltrão-Braga, Charles Brenner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Pooja Jha; Isabella Samuelson.

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

Extended data

Extended Data Fig. 1 Multi-omics workflow and quality assessments for transcriptomics, proteomics, phosphoproteomics, and metabolomics data.

a, General workflow of multi-omics experiments and data analysis. b-c, Number of detected proteins (b) and phosphorylated peptides (c) in mock- and ZIKV-infected brains. d, Abundance distributions of transcriptomics data in mock- and ZIKV-infected brains, with expression levels transformed to log10 (FPKM) (n = 3 per group). Data are shown as box plot, the bottom and top of the box are the first and third quartiles, and the band inside the box is the median of the log10 (FPKM). e-g, Pearson correlations of transcriptomics (e), proteomics (f), and phosphoproteomics (g) data between samples from mock- and ZIKV-infected brains. h, Overlap of detected proteins and phosphoproteins. 1,346 proteins were identified with 4,395 highly reliable phosphosites. 3,734 proteins were identified with non-phosphorylated forms (73.5% of all proteins). 581 proteins were identified with only their phosphorylated forms (30.1% of all phosphoproteins). i, Pearson correlations of metabolomics data between samples from mock- and ZIKV-infected brains.

Source data

Extended Data Fig. 2 Transcriptomics and proteomics analyses of mock- and ZIKV-infected brains.

a, Principal component analysis for the detected proteins in mock- and ZIKV-infected brains. b-c, Top 10 most significant GO terms enriched by significantly upregulated (red) and downregulated (blue) proteins (FDR < 0.05 and fold change > 2) in ZIKV-infected brains, respectively. d-e, Heatmap of significantly altered proteins (FDR < 0.05 and fold change > 2) related to cytokine response (d) and axon development (e). f, Inflammatory cytokine alterations induced by ZIKV infection at mRNA level (data from our transcriptomics datasets, n = 3 mice per group). Data are shown as mean ± s.e.m. Two-tailed unpaired t-test was used for statistical analysis. Exact P values are indicated. g, Cell type enrichment scores (ES) calculated by GSVA using proteomics data of ZIKV-infected brains and mock-infected controls. Enrichment scores demonstrating the relative abundance of distinct cell types were shown in orange (increased) and green (decreased).

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Extended Data Fig. 3 Protein-protein interaction in distinct cell types and proteomic changes in ZIKV-infected brains on E18.5 and P3.

a, Correlations between cell type marker proteins and metabolic related proteins. Cell type specific KEGG metabolism pathways were enriched based on protein pairs with spearman correlation coefficient > 0.8 (P < 0.05). Cell type and related marker proteins were listed in the figure. b, Proteomic changes of OXPHOS and TCA cycle in E18.5 and P3 models upon ZIKV infection. Ratios of relative protein levels between ZIKV-infected (ZIKV) and mock-infected (Ctrl) mouse brains in E18.5 model (infected on E13.5 and analyzed on E18.5, left) and P3 model (infected on E15.5 and analyzed on P3, right) were shown. Altered proteins between mock- and ZIKV-infected mouse brains in either model were shown in the figure (p < 0.05).

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Extended Data Fig. 4 Metabolic differences between mock- and ZIKV-infected brains on P3.

a, Hierarchical cluster analysis of metabolite abundance in ZIKV-infected brains (n = 8) and mock-infected controls (n = 8). b-c, Transcriptomic, proteomic, and metabolic analyses of purine (b) and pyrimidine metabolism (c). Protein and mRNA changes of metabolic enzymes in mock- and ZIKV-infected mouse brains were indicated. Metabolites significantly upregulated and downregulated in ZIKV-infected mouse brains were marked in red or blue color, respectively (FDR < 0.05).

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Extended Data Fig. 5 Metabolic changes of mock- and ZIKV-infected brains on E18.5 and P3.

a, Hierarchical cluster analysis of metabolite abundance in ZIKV-infected brains (n = 7) and mock-infected controls (n = 8) on E18.5. b, Altered KEGG metabolic pathways in ZIKV-infected brains on E18.5 compared with mock-infected brains enriched by significantly altered metabolites (FDR < 0.05). c, Changes of NAD+ metabolism, tryptophan metabolism, and TCA cycle between E18.5 and P3 models upon ZIKV infection. Ratios of relative metabolite levels between ZIKV-infected (ZIKV) and mock-infected (Ctrl) mouse brains in E18.5 (left) and ratios in P3 model (right) were shown. Altered metabolites between mock- and ZIKV-infected mouse brains in either model were shown (P < 0.05).

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Extended Data Fig. 6 Nominating potential druggable phosphoproteins for ZIKV-induced microcephaly.

a, iFOT values of altered phosphopeptides and corresponding proteins in MAPK pathway in ZIKV-infected (ZIKV) and mock-infected (Ctrl) brain samples (p < 0.05 and fold change > 2); linear regression model is used to fit scatter between ZIKV and Ctrl groups; shadow area represents 95% confidence interval of iFOT values in individual group. b, Expression of phosphorylated p38 and JNK in mock- and ZIKV-infected mouse brains (n = 3 per group). Data are representative of three independent experiments. Data are shown as mean ± s.e.m. Two-tailed unpaired t-test was used for statistical analysis. Exact P values are indicated. c, Major upregulated phosphoproteomic pathways and FDA-approved drugs targeting these signaling pathways. Significantly altered phosphoproteins (P < 0.05 and fold change > 2) in either E18.5 or P3 model involved in MAPK, calcium, cGMP-PKG, and cAMP signaling pathways were shown. d, Significantly altered kinases detected in phosphoproteomics data (P < 0.05 and fold change > 2). e, Kinase specific predictions in phosphoproteomics data. Sequence logos, credible motif and predictive kinases for significantly upregulated (left) or downregulated (right) phosphopeptides (FDR < 0.05 and fold change > 2) were shown. f, Expression levels of predicted kinases in phosphoproteomics data (P < 0.05 and fold change > 2).

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Extended Data Fig. 7 Effects of NAD+ and NAM supplementation on ZIKV-induced microcephaly.

a-b, Brain weight (a) and body weight (b) of mock-infected and ZIKV-infected mice with or without NAD+ supplementation (n = 10 - 12 mice, each dot represents one mouse). c, Immunostaining images of ZIKV (green), DAPI (blue) and Cleaved caspase-3 (Cleaved Cas3, red) on ZIKV-infected brains with (ZIKV + NAM) or without (ZIKV + Veh.) NAM supplementation. Veh., Vehicle. Scale bar: 200 μm. Data are representative of two independent experiments. d-e, ZIKV intensity (d) and cell death (e) in cortex (left in each panel) and hippocampus (right in each panel) of ZIKV-infected brains with (ZIKV + NAM, n = 4) or without (ZIKV + Veh., n = 3) NAM supplementation, respectively. Three slices for each brain. f-g. Brain weight (f) and body weight (g) of mock- and ZIKV-infected mice with or without NAM supplementation (n = 4 - 6 mice, each dot represents one mouse). All data are shown as mean ± s.e.m. Two-tailed unpaired t-test (a-b, d-g) was used for statistical analysis. Exact P values are indicated in the figure.

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Extended Data Fig. 8 Absolute quantification of NAD+ and its precursors (NAM, NR and NMN) in the brains, primary neurons, and culture media after NAD+ or NAM supplementation.

a, NAD+ concentrations in the homogenous extract of cortex and hippocampus of ZIKV-infected mouse at indicated time points post vehicle (RPMI medium 1640 basic + 2% FBS) or NAD+ (500 μM, 2 μL) injection into the λ point (n = 4 mice for 0 h, 1 h, 2 h, 12 h time points, n = 3 mice for 6 h time point). b, NAD+ and NAM concentrations in the homogenous extract of cortex and hippocampus of ZIKV-infected mouse at indicated time points post vehicle (RPMI medium 1640 basic + 2% FBS) or NAM (500 μM, 2 μL) injection into the λ point (n = 5 mice for 0 h, 2 h, 6 h, 12 h time points; n = 4 mice for 1 h time point). c, Concentrations of NAD+ and its precursors in primary neurons before (zero time point) and after being cultured with 1 mM NAD+ for 0.5 h to 12 h (n = 5 biological replicates per time point). d, Concentrations of NAD+ and its precursors in culture media at indicated time points (n = 5 biological replicates per time point). Fresh media at zero time point contains 1 mM NAD+. Data are shown as mean ± s.e.m. (a-d). Statistical analysis was performed using One-way ANOVA followed by Benjamini and Hochberg multiple comparisons (a-d). Each time point was compared with zero time point. Exact P values are indicated.

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Extended Data Fig. 9 NR supplementation alleviates metabolic disturbances in the ZIKV-infected mice.

a, Heatmap of metabolites detected in mock- and ZIKV-infected mouse brains after vehicle or NR supplementation (n = 4 – 5 mice, each square represents a mouse). b, Partial least squares discriminant analysis (PLS-DA) of ZIKV-infected mouse brains after vehicle (n = 5) or NR (n = 4) supplementation. c, Altered metabolic pathways in ZIKV-infected mouse brains after NR supplementation enriched by differential metabolites between vehicle and NR supplementation groups (Variable importance in the projection, VIP > 1). d-g, Absolute concentrations of NAD+ and its precursors in nicotinamide and nicotinamide metabolism – NAD+ (d), NR (e), NMN (f), and NAM (g) – in mock- and ZIKV-infected mouse brains. h-i, Relative levels of selected metabolites in mock- and ZIKV-infected mouse brains. Only metabolites that were both significantly altered by ZIKV-infection (Ctrl + Veh. group vs ZIKV + Veh group) and NR treatment (ZIKV + Veh. group vs ZIKV + NR group) were selected and shown. The average levels of each metabolite in control group (Ctrl + Veh.) were considered as 1, a.u. (arbitrary unit). n = 5 mice for Ctrl + NR and ZIKV + Veh. groups, n = 4 mice for Ctrl + Veh. and ZIKV + NR groups (d-i). j, Relative expression levels of transcripts, Nmrk and Nampt, in mock- and ZIKV-infected mouse brains from transcriptomics dataset (infected at E18.5 and inspected at P3, n = 3). Expression levels of Nmrk and Nampt in mock-infected brains were considered as 1, respectively. Data are shown as mean ± s.e.m. (d-j). Two-tailed unpaired t-test (d-j) was used for statistical analysis. Exact P values are indicated in the figure (d-g, j) or in the source data (h and i), *P < 0.05.

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Pang, H., Jiang, Y., Li, J. et al. Aberrant NAD+ metabolism underlies Zika virus–induced microcephaly. Nat Metab 3, 1109–1124 (2021). https://doi.org/10.1038/s42255-021-00437-0

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