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Maternal immune activation in mice disrupts proteostasis in the fetal brain

Nature Neuroscience volume 24, pages 204–213 (2021)Cite this article

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Abstract

Maternal infection and inflammation during pregnancy are associated with neurodevelopmental disorders in offspring, but little is understood about the molecular mechanisms underlying this epidemiologic phenomenon. Here, we leveraged single-cell RNA sequencing to profile transcriptional changes in the mouse fetal brain in response to maternal immune activation (MIA) and identified perturbations in cellular pathways associated with mRNA translation, ribosome biogenesis and stress signaling. We found that MIA activates the integrated stress response (ISR) in male, but not female, MIA offspring in an interleukin-17a-dependent manner, which reduced global mRNA translation and altered nascent proteome synthesis. Moreover, blockade of ISR activation prevented the behavioral abnormalities as well as increased cortical neural activity in MIA male offspring. Our data suggest that sex-specific activation of the ISR leads to maternal inflammation-associated neurodevelopmental disorders.

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Fig. 1: Single-cell sequencing of the fetal brain at E14.5 and E18.5 in the setting of MIA.
Fig. 2: Single-cell DGE changes at E14.5.
Fig. 3: MIA inhibits protein translation in the fetal brain via the ISR.
Fig. 4: Alterations in the translational landscape of the MIA fetal brain.
Fig. 5: Genetic ablation of ISR protects against excessive neural activity in MIA offspring.
Fig. 6: Genetic ablation of ISR protects MIA offspring against neurobehavioral abnormalities.

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Data availability

All the sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus with the accession code GSE148237. Publicly available datasets from PANTHER (http://pantherdb.org/) were used for the GO analysis. Source data are provided with this paper.

Code availability

All the custom code are available at https://github.com/bkalishneuro/Maternal-Immune-Activation-Project/blob/main/README.md.

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Acknowledgements

B.T.K. was supported by the Pediatric Scientist Development Program and the March of Dimes. E.K. was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A3A03010693). M.E.G. and B.T.K. were supported by R01 NS115965 from the National Institute of Neurological Disorders and Stroke. R.J.K. was supported by NIH grants R01CA198103-04, R01DK113171-03 and R01AG062190-02. E.E.D. was supported by the Damon Runyon Foundation. G.B.C. and J.R.H. were supported by the Jeongho Kim Neurodevelopmental Research Fund, the Simons Foundation Autism Research Initiative and National Institute of Mental Health grants (R01MH115037 and R01MH119459, respectively). J.R.H. was also supported by the PEW Scholars Program, the N of One: Autism Research Foundation and the Burroughs Wellcome Fund. Figures 1a, 3b and 4a were created using BioRender (https://biorender.com).

Author information

Author notes

  1. These authors contributed equally: Brian T. Kalish, Eunha Kim.

Authors and Affiliations

  1. Department of Neurobiology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Brian T. Kalish, Benjamin Finander, Erin E. Duffy, Eric C. Griffith & Michael E. Greenberg

  2. Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA

    Brian T. Kalish, Benjamin Finander & Lilin Tong

  3. Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Eunha Kim, Hyunju Kim, Casey K. Gilman & Jun R. Huh

  4. The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yeong Shin Yim & Gloria B. Choi

  5. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yeong Shin Yim & Gloria B. Choi

  6. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Lilin Tong

  7. Degenerative Disease Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA

    Randal J. Kaufman

  8. Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    Jun R. Huh

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Contributions

B.T.K., G.B.C., M.E.G. and J.R.H. conceptualized the study. B.T.K. and E.K. designed and performed the single-cell sequencing experiments. B.T.K., B.F. and E.E.D. performed and analyzed the next-generation sequencing data. E.K. performed the immunoblotting and behavioral analyses. E.K., H.K. and Y.S.Y. bred mice for the experiments. C.K.G., L.T. and B.F. prepared tissues for histology and performed immunohistochemistry and microscopy. B.T.K., E.K., E.C.G., J.R.H. and M.E.G. wrote the manuscript. G.B.C., E.C.G., J.R.H. and M.E.G. provided guidance on the design of experiments and interpretation of results. R.J.K. provided eIF2αS51A mice.

Corresponding authors

Correspondence to Brian T. Kalish, Michael E. Greenberg or Jun R. Huh.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Mauro Costa-Mattioli, John Lukens, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Quality Control and E14.5 Sub-clustering.

a, Total number of genes per condition and total number of unique molecular identifiers (UMIs) per condition in the final E14.5 and E18.5 single cell sequencing datasets. Data from n = 2 mice per group. b, UMAP of sub-clustering of mature and immature (SVZ, cortical plate, cortical subplate) neurons at E14.5. Data from n = 2 mice per group. c, UMAP of sub-clustering of radial glia (RG) at E14.5. Data from n = 2 mice per group. d, UMAP of sub-clustering of interneurons (Int), ganglionic eminence (GE), and striatal neurons at E14.5. Data from n = 2 mice per group. e, Dot plot of marker genes associated with all cells at E14.5. Data from n = 2 mice per group. f, Dot plot of marker genes associated with the cells in (b): mature and immature (SVZ, cortical plate, cortical subplate) neurons at E14.5. Data from n = 2 mice per group. g, Dot plot of marker genes associated with the cells in (d): interneurons (Int), ganglionic eminence (GE), and striatal neurons at E14.5. Data from n = 2 mice per group. h, Dot plot of marker genes associated with the cells in (c): radial glia (RG) at E14.5. Data from n = 2 mice per group.

Extended Data Fig. 2 E18.5 sub-clustering.

a, UMAP of sub-clustering of mature and immature (SVZ) neurons at E18.5. Data from n = 2 mice per group. b, UMAP of sub-clustering of radial glia (RG), ganglionic eminence (GE), oligodendrocytes (Olig), and neural stem cells (NSC) at E18.5. Data from n = 2 mice per group. c, UMAP of sub-clustering of striatal neurons (Str) and interneurons (Int) at E18.5. Data from n = 2 mice per group. d, Dot plot of marker genes associated with cells in (a): mature and immature (SVZ) neurons at E18.5. Data from n = 2 mice per group. e, (left) Dot plot of marker genes associated with cells in (b): radial glia (RG), ganglionic eminence (GE), oligodendrocytes (Olig), and neural stem cells (NSC) at E18.5; and (right) Dot plot of marker genes associated with cells in (c): striatal neurons (Str) and interneurons (Int) at E18.5. Data from n = 2 mice per group.

Extended Data Fig. 3 Differential gene expression strip plots.

a, Strip plot displaying differential gene expression between MIA male offspring and PBS male offspring at E14.5. Colored dots represent significant genes (FDR < 0.05). The x axis displays select cortical cell types. Cell groups (left to right): (1) mature and immature (SVZ, cortical plate, cortical subplate) neurons, (2) radial glia (RG), and (3) interneurons (Int), ganglionic eminence (GE), and striatal neurons. Data from n = 2 mice per group. b, Strip plot displaying sex-dependent gene expression in MIA offspring at E14.5. Colored dots represent significant genes (FDR < 0.05). The x axis displays select cortical cell types. Cell groups (left to right): (1) mature and immature (SVZ, cortical plate, cortical subplate) neurons, (2) radial glia (RG), and (3) interneurons (Int), ganglionic eminence (GE), and striatal neurons. Data from n = 2 mice per group. c, Strip plot displaying differential gene expression between MIA male offspring and PBS male offspring at E18.5. Colored dots represent significant genes (FDR < 0.05). The x axis displays select cortical cell types. Cell groups (left to right): (1) mature and immature (SVZ) neurons, (2) radial glia (RG), ganglionic eminence (GE), oligodendrocytes (Olig), and neural stem cells (NSC), and (3) striatal neurons (Str) and interneurons (Int). Data from n = 2 mice per group. d, Strip plot displaying sex-dependent gene expression in MIA offspring at E18.5. Colored dots represent significant genes (FDR < 0.05). The x axis displays select cortical cell types. Cell groups (left to right): (1) mature and immature (SVZ) neurons, (2) radial glia (RG), ganglionic eminence (GE), oligodendrocytes (Olig), and neural stem cells (NSC), and (3) striatal neurons (Str) and interneurons (Int). Data from n = 2 mice per group.

Extended Data Fig. 4 Single cell differential gene expression changes.

a, Bubble plot of highly variable genes between MIA and PBS male offspring at E18.5. All significant genes FDR < 0.05. Blue indicates an increase in MIA males relative to PBS males. Data from n = 2 mice per group. b, Bubble plot of sex-dependent genes in MIA offspring at E18.5. All significant genes FDR < 0.05. Blue indicates an increase in Δ(MIA males – PBS males) relative to females. Data from n = 2 mice per group. c, Bubble plot of ribosome subunit genes between MIA male and PBS male offspring at E14.5, demonstrating a widespread decrease in expression of multiple ribosomal subunits in MIA male offspring. All significant genes FDR < 0.05. Blue indicates an increase in PBS males relative to MIA males. Data from n = 2 mice per group. d, Bubble plot of ribosome subunit genes between MIA female and PBS female offspring at E14.5, demonstrating an increase in expression of multiple ribosomal subunits in MIA female offspring. All significant genes FDR < 0.05. Blue indicates an increase in PBS females relative to MIA females. Data from n = 2 mice per group.

Extended Data Fig. 5 Sex-specific comparisons in control (PBS) conditions.

a, Bubble plot of ribosome subunit genes between PBS male and PBS female offspring at E14.5. All significant genes FDR < 0.05. Data from n = 2 mice per group. b, Bubble plot of ribosome subunit genes between PBS male and PBS female offspring at E18.5. All significant genes FDR < 0.05. Data from n = 2 mice per group.

Extended Data Fig. 6 Upstream regulators of phospho-eIF2α and UPR sensors in E18.5 PBS and MIA fetal cortices.

a, Immunoblot analysis measuring phospho-S6K and quantification in E18 PBS and MIA fetal cortices. The y axis represents relative blot intensity to PBS male control (Two-way ANOVA sex (male or female) × stimulus (PBS or MIA) interaction F1,12 = 0.08413, P = 0.7767; effect of sex F1,12 = 1.188, P = 0.2971; effect of stimulus F1,12 = 0.04729, P = 0.8315 followed by Tukey multiple comparisons test; n = 4 pups from 2 litters). b, Immunoblot analysis measuring upstream regulators of phospho-eIF2α and quantification in E18.5 PBS and MIA fetal cortices. The y axis represents relative blot intensity to PBS control for each sex (Two-tailed unpaired t-test male: pPERK t = 5.485, df = 10, ***P = 0.0003, n = 6 from 3 litters; pGCN2 t = 0.1503, df = 6, n = 4 pups from 2 litters; pPKR t = 0.9501, df = 6, n = 4 pups from 2 litters; Two-tailed unpaired t-test female: pPERK t = 0.5761, df = 6; pGCN2 t = 0.5204, df = 6; pPKR t = 1.545, df = 6; n = 4 pups from 2 litters). c, Immunoblot analysis measuring UPR sensors (IRE1α, ATF6 and PERK) and quantification in E18.5 PBS and MIA male fetal cortices. The y axis represents relative blot intensity to PBS control (Two-tailed unpaired t-test: pIRE t = 2.745, df = 6, *P = 0.0335; ATF6 t = 0.6822, df = 6; pPERK t = 3.516, df = 6, * P = 0.0126; n = 4 pups from 2 litters). Data are mean ± s.e.m.; see Supplementary Table 20 for detailed statistics. Unprocessed blots are provided as a Source Data file.

Source data

Extended Data Fig. 7 ISR activation of E18.5 PBS and MIA male fetus in SFB-negative dams.

Immunoblot analysis measuring phospho-eIF2α and quantification in E18.5 PBS and MIA male fetal cortices, either from SFB-positive or SFB-negative B6 dams. SFB-positive dams were obtained from Taconic Biosciences (Tac) whereas SFB-negative dams from Jackson laboratory (Jax) and maintained in SFB-positive and negative conditions, respectively. The y axis represents relative blot intensity to each PBS control (Two-tailed unpaired t-test: Tac t = 4.659, df = 6, **P = 0.0035; Jax t = 0.9013, df = 6; n = 4 pups from 2 litters). Data are mean ± s.e.m.; see Supplementary Table 20 for detailed statistics. Unprocessed blots are provided as a Source Data file.

Source data

Extended Data Fig. 8 Female MIA offspring neither show ISR activation nor neurodevelopmental abnormalities.

a, Representative images of 8–10 weeks old MIA and PBS female offspring brain tissue immunostained for c-Fos. Scale bar 100 μm. Quantification indicates c-Fos puncta/mm2 (Two-tailed unpaired t-test PBS female versus MIA female: t = 0.1829, df = 5, P = 0.8621; n = 3 for PBS female, n = 4 for MIA female mice). b, Percentage of interaction in the three-chamber sociability test of adult PBS and MIA female offspring littermates (Two-way ANOVA group (PBS female or MIA female) x preference to the target (social target or inanimate) interaction F1,22= 0.986, P = 0.3315; effect of preference to the target F1,22 = 45.24, P = 9.2454 × 10−7 followed by Sidak multiple comparisons test-within group: PBS female ** P = 0.0015, MIA female **** P = 2.06 × 10−5; Two-tailed unpaired t-test social score between PBS female versus MIA female: t = 0.7021, df = 11, P = 0.4972; n = 6 for PBS female, n = 7 for MIA female mice; 2 independent experiments). c, Marble burying index of adult PBS and MIA female offspring littermates (Two-tailed unpaired t-test PBS female versus MIA female: t = 0.9278, df = 15, P = 0.3682; n = 8 for PBS female, n = 9 for MIA female mice; 2 independent experiments). Data are mean ± s.e.m.; see Supplementary Table 20 for detailed statistics.

Extended Data Fig. 9 Pharmacological inhibition of ISR protects MIA offspring from neurobehavioral abnormalities.

a, Percentage of interaction in the three-chamber sociability test of vehicle and ISRIB-treated adult PBS and MIA offspring littermates (Two-way ANOVA group (PBS vehicle, MIA vehicle, PBS ISRIB or MIA ISRIB) x preference to the target (social target or inanimate) interaction F3,62 = 7.401, P = 0.003; effect of preference to the target F1,62 = 57.62, P = 2 × 10−10 followed by Bonferroni multiple comparisons test-within group: PBS vehicle **** P = 4.9 × 10−10, PBS ISRIB * P = 0.0119, MIA vehicle P > 0.9999, MIA ISRIB **** P = 2.76 × 10−5; two-way ANOVA stimulus (PBS or MIA) x treatment (vehicle or ISRIB) interaction F1,31 = 7.111, P = 0.0121; effect of stimulus F1,31 = 4.109, P = 0.0513; effect of treatment F1,31 = 0.003618, P = 0.9524 followed by Dunnett multiple comparisons test: PBS vehicle versus PBS ISRIB P = 0.19, MIA vehicle versus PBS vehicle **P = 0.0071, PBS vehicle versus MIA ISRIB P = 0.3031; n = 10 for PBS vehicle, n = 7 for MIA vehicle, n = 7 for PBS ISRIB, n = 11 for MIA ISRIB; 2 independent experiments). b, Marble burying index of vehicle and ISRIB-treated adult PBS and MIA offspring littermates (Two-way ANOVA stimulus (PBS or MIA) x treatment (vehicle or ISRIB) interaction F1,13 = 4.549, P = 0.0526; effect of stimulus F1,13 = 8.829, P = 0.0108; effect of treatment F1,13 = 4.341, P = 0.0575 followed by Tukey multiple comparisons test: PBS vehicle versus MIA vehicle *P = 0.0147, PBS ISRIB versus MIA vehicle *P = 0.0255, MIA vehicle vs. MIA ISRIB *P = 0.0275; n = 4 for PBS vehicle, n = 4 for MIA vehicle, n = 3 for PBS ISRIB, n = 6 for MIA ISRIB mice; 2 independent experiments). Data are mean ± s.e.m.; see Supplementary Table 20 for detailed statistics.

Supplementary information

Reporting Summary

Supplementary Table 1

DGE between MIA male and PBS male offspring in E14 scRNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(counts per million) (log(CPM)), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 2

Supplementary Table 2 DGE between MIA female and PBS female offspring in E14 scRNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 3

DGE between PBS male and PBS female offspring in E14 scRNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 4

DGE between MIA male and MIA female offspring in E14 scRNA-seq using interaction term analysis. Data analyzed using edgeR with quasi-likelihood F test. Data include log2 (FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 5

DGE between MIA male and PBS male offspring in E18 scRNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 6

DGE between MIA female and PBS female offspring in E18 scRNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 7

DGE between PBS male and PBS female offspring in E18 scRNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 8

DGE between MIA male and MIA female offspring in E18 scRNA-seq using interaction term analysis. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 9

Quality control metrics for ribosome-profiling data.

Supplementary Table 10

DGE between MIA male and PBS male offspring in E18 bulk RNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 11

DGE between MIA female and PBS female offspring in E18 bulk RNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2 (FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 12

DGE between PBS male and PBS female offspring in E18 bulk RNA-seq. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 13

DGE between MIA male and MIA female offspring in E18 bulk RNA-seq using interaction term analysis. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 14

DGE between MIA male and MIA female offspring in E18 bulk RNA-seq using a pairwise comparison. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 15

Differential translation analysis between MIA male and PBS male offspring in E18 ribosome profiling. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 16

Differential translation analysis between MIA female and PBS female offspring in E18 ribosome profiling. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM, F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 17

Differential translation analysis between PBS male and PBS female offspring in E18 ribosome profiling. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 18

Differential translation analysis between MIA male and MIA female offspring in E18 ribosome profiling using interaction term analysis. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 19

Differential translation analysis between MIA male and MIA female offspring in E18 ribosome profiling using a pairwise comparison. Data analyzed using edgeR with quasi-likelihood F test. Data include log2(FC), log(CPM), F statistics, P values (unadjusted) and FDR-adjusted values for multiple comparisons.

Supplementary Table 20

Supporting statistical data for Figs. 3, 5 and 6 and Extended Data Figs. 6–9.

Supplementary Table 21

Supporting data for Fig. 4c.

Source data

Source Data Fig. 3

Uncropped western blots.

Source Data Fig. 5

Uncropped western blots.

Source Data Extended Data Fig. 6

Uncropped western blots.

Source Data Extended Data Fig. 7

Uncropped western blots.

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Kalish, B.T., Kim, E., Finander, B. et al. Maternal immune activation in mice disrupts proteostasis in the fetal brain. Nat Neurosci 24, 204–213 (2021). https://doi.org/10.1038/s41593-020-00762-9

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