Epidemiological association between maternal infection and neurodevelopmental disorders (NDDs) has been found for autism spectrum disorder (ASD), schizophrenia (SZ), bipolar disorder (BPD), anxiety, and major depressive disorder (MDD) (Brown, 2011; Estes and McAllister, 2015; Parboosing et al., 2013; Patterson, 2009). Indeed, maternal immune activation (MIA) itself is sufficient to produce NDD-relevant outcomes in offspring in animal models (Canetta et al., 2014; Machado et al., 2015). Offspring from female mice exposed to the toll-like receptor 3 (TLR3) agonist and viral mimic polyinosinic:polycytidylic acid [poly(I:C)], and offspring in maternal immune activation models using the bacterial mimic lipopolysaccharide (LPS) (Depino, 2015; O'Loughlin et al., 2017; Simões et al., 2018), recapitulate neuropathologies and aberrant behaviors seen in offspring born to flu-infected dams (Shi et al., 2003; Smith et al., 2007). MIA in mice produces behavioral and cognitive abnormalities with relevance across multiple NDD diagnostic boundaries (Estes and McAllister, 2016; Malkova et al., 2012; Richetto et al., 2014).

MIA-induced outcomes in adult offspring have been well characterized; however, mechanisms of initiation and progression of NDD-related brain pathology remain poorly defined. Poly(I:C) increases proinflammatory cytokines such as IL-1β, IL-6, CXCL1, and TNF-α in the maternal circulation (Ballendine et al., 2015; Dahlgren et al., 2006; Hsiao and Patterson, 2011; Meyer et al., 2006b; Meyer et al., 2006a; Smith et al., 2007). Downstream pathological changes in the brains of offspring have been reported across cell types, from neurons to microglia, and these changes have been linked to perturbations in proliferation, migration, and maturation in the developing brain (Haida et al., 2019; Shin Yim et al., 2017; Soumiya et al., 2011; Zhang et al., 2019; Zhao et al., 2019). Recent studies have identified acute transcriptomic changes in fetal brain in mice and rats at single time-points (3–4 hr) following MIA, with significant overlap between transcriptome changes in the cortices of MIA-exposed offspring and those altered in the brains of children with ASD (Lombardo et al., 2018). Although MIA could cause a dynamic sequence of transcriptional changes in the fetal brain following acute exposure, no integrated picture of MIA-associated transcriptomic pathology exists that maps changes spanning from the initial acute response to MIA through birth. Here, we combined time-course transcriptomics with neuroanatomy to map changes in prenatal mouse cortical development following MIA induced by poly(I:C) injection at E12.5.

We used paired transcriptomic and neuroanatomical analyses to map changes in the fetal brain following MIA in a poly(I:C) mouse model (Figure 1, Figure 1—figure supplement 1, Figure 1—figure supplement 2 and Figure 1—figure supplement 3). Induction of MIA via poly(I:C) injection at E12.5 produces relevant phenotypes in mice (Choi et al., 2016; Shin Yim et al., 2017; Smith et al., 2007) and the specific implementation of this model was recently validated in our hands to produce sufficient levels of MIA to mimic viral infection and cause aberrant behavioral outcomes in offspring (Estes et al., 2020). The dosage of 30 mg/ml poly(I:C) elicited substantial elevations in maternal serum IL-6 (on average 2200 pg/ml) 4 hr following injection (Figure 1—figure supplement 1). Pregnant mice were injected with saline or poly(I:C) at E12.5, and dorsal telencephalon (E12.5 + 6 hr, E14.5, E17.5, and birth (P0)) was microdissected (Figure 1a). RNA-seq datasets were generated from male and female embryos from 28 independent litters across control and MIA groups, typically with one to three embryos represented per independent litter (Supplementary file 20, Supplementary file 5, File source data 1). For control and MIA, 7 vs. 7 samples at E12.5, 13 vs. 13 samples at E14.5, 6 vs. 6 samples at E17.5, and 10 vs. 12 samples at P0, were compared in a differential expression (DE) analysis. For transcriptomic analysis, we used a strategy of first defining DE genes at each time-point using the general linear model (GLM) approach implemented in edgeR, followed by mapping DE signatures to systems-level expression patterns via module assignment using weighted gene co-expression network analysis (WGCNA).

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Principal component analysis (PCA) of MIA and control RNA-seq samples showed developmental age accounts for the majority of variance across samples, with some additional separation between MIA and control groups (Figure 1b–c). We performed stage-specific differential expression analysis for male and female samples and for both sexes with sex as a covariate. For each time-point, DE genes were defined using a stringent false discovery rate (FDR) < 0.05 threshold and a more inclusive p < 0.05 threshold (Figure 1d,e, Supplementary file 1–5). Sex-stratified analysis indicated generally concordant DE differences between male and female samples following MIA, although DE effect sizes appeared stronger in females at E14.5 and E17.5, leading to a larger number of DE genes identified independently in females compared to males (Figure 1—figure supplement 2, Supplementary files 6–15). Overall, sex-stratified differences in DE genes were generally subtle, as demonstrated by high DE correlation and shared DE gene sets between sexes, with consistent findings across sexes among key DE genes (Figure 1—figure supplement 3). Samples from both male and female offspring were used for overall DE analysis models, with sex included as a covariate.

For the DE gene set passing the stringent FDR < 0.05 threshold, there were varying numbers of DE genes across time-points (Figure 1d). The strongest transcriptional signature was observed at E17.5 (2621 up and 3058 down), suggesting dramatic impact of MIA on cortical development at this time-point. P0 represented the subtlest transcriptomic signature, with no genes passing the stringent FDR < 0.05 threshold, and with the p < 0.05 DE genes showing a strong upregulation bias. We tested for overlap between DE RNA-seq genes and high confidence autism-associated genes in the Simons Foundation Autism Research Initiative (SFARI) gene database (Basu et al., 2009). The 82 mouse orthologs of SFARI ASD genes that were expressed at measurable levels were significantly enriched among E17.5 DE genes, with 25 upregulated and 19 downregulated DE genes at FDR < 0.05 (p=3.9e-04, hypergeometric test), and 28 upregulated and 28 downregulated DE genes at p < 0.05 (p = 2.8e-06, hypergeometric test) (Figure 1e, Supplementary files 16–17). This enrichment during peak transcriptomic dysregulation suggests involvement of ASD-relevant pathways in MIA etiology here and demonstrates general NDD relevance of our model. Detailed results of DE analysis are reported in Supplementary files 1–15 and can be visualized using our interactive online browser.

We next sought to capture MIA-induced gene regulatory changes at the systems and network level across embryonic cortex development using WGCNA (Langfelder and Horvath, 2008; Zhang and Horvath, 2005; Figure 2, Figure 2—figure supplement 1, Figure 2—figure supplement 2). In this approach, genes with correlated expression patterns are assigned into modules, enabling identification of gene sets with shared expression and function in neurodevelopment. WGCNA co-expression modules were arbitrarily named after colors, with Grey reserved for genes with no strongly correlated expression patterns (Figure 2a). Our initial analysis identified 10 modules (Figure 2—figure supplement 1a), of which correlated modules were combined to produce the final set of five modules and the unassigned Grey set. Modules were tested for association with sample age, MIA versus saline condition, and sex (Figure 2b). The modules captured dynamic trajectories of neurodevelopmental gene expression (Figure 2c). The Blue and Turquoise modules represented genes that increased or decreased in expression respectively, during neurodevelopment (Blue: p=9e-34; Turquoise: p=1e-29, Figure 2—figure supplement 1b). The other modules showed more complex patterns of expression. Each WGCNA module included DE genes, with differences in what time-point had the most extensive DE, indicating module-specific timing of perturbation associated with MIA (Figure 2—figure supplement 1c). DE genes were enriched for module-specific GO terms, with GO enrichment findings similar using all module genes and using an alternative rank-based gene set enrichment that is not dependent on FDR or p-value (Mootha et al., 2003; Subramanian et al., 2005; Figure 2d, Supplementary file 18; Figure 2—figure supplement 2, Supplementary file 19).

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The WGCNA-resolved modules enabled mapping of DE signatures to neurodevelopmental processes that were acutely induced by MIA (Figure 3, Figure 3—figure supplement 1). There was significant association between genes in Grey (p = 0.007) and Green (p = 0.03) modules and MIA treatment, and marginal significance with MIA for the YeMaBl (p = 0.05) module (Figure 2—figure supplement 1b). The acute, initiating signaling pathways, found 6 hr after poly(I:C) injection captured by the Green module and by a subset of genes within the Grey module, show a strong signature of upregulated DE genes from E12.5 to E14.5 (Figure 3a). Analysis of the GO BP enrichment of upregulated DE genes in these modules identified activation of immune-related pathways, including defense response to virus, as well as angiogenesis and Vascular Endothelial Growth Factor A (VEGFA) signaling (Figures 2c and 3b, Figure 2—figure supplement 2a,c). We tested for protein-protein association networks among the acute E12.5 DE (FDR < 0.05) genes using STRING (Szklarczyk et al., 2019), and found significantly more interactions than expected by chance (observed edges = 161, expected edges = 46, enrichment = 3.5, STRING p < 1.0e-16) (Figure 3c). This intersecting protein network was associated with metabolism, hypoxia, and stress (Figure 3b). The network includes a highly interacting core gene set with hub nodes, such as Vegfa, that have many spokes and may be signaling factors that direct the MIA response (Figure 3b,c). Developmental expression profiles for DE genes that were associated with this interaction network, are shown in Figure 3d, and stratified by sex in Figure 3—figure supplement 1a. These signatures capture a complex but coherent transcriptional response involving hypoxia, immune, metabolic, and angiogenesis pathways that are strongly and transiently induced in fetal brain within 6 hr following mid-gestational MIA.

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The most pervasive WGCNA-resolved DE signatures following MIA impacted a large proportion of genes in the Blue and Turquoise modules by E14.5 and peaked at E17.5 (Figure 1c). GO terms related to proliferation and cell cycle were enriched in the Blue module among downregulated DE genes, and terms related to neuronal differentiation and synapses were enriched in the Turquoise module among upregulated DE genes (Figure 2c). Considering the changes in proliferative and lamination markers associated with Blue and Turquoise genes, we sought to validate such changes at the protein level in independent samples (Figure 4, Figure 4—figure supplement 1). Four out of five DE genes tested validated with concordant changes at the protein level as determined by western blot (WB) analysis at E17.5, showing either statistical significance or trends in the same direction as observed in our transcriptomic analysis (Figure 4a–b, Figure 4—figure supplement 1). The DE signatures associated with Blue and Turquoise modules were evidenced by separation between MIA and saline samples in PCA plots at E14.5 and E17.5, with E17.5 MIA samples clustered closer to the P0 samples than saline age-matched counterparts (Figure 1b). To test for MIA-associated perturbation to temporal full transcriptome expression patterns, we generated a linear regression model using RNA-seq data principal components 1–5 and RNA-seq covariates to model age in saline samples. We then used this model to predict age in MIA samples (Figure 4c). E17.5 MIA samples exhibited older predicted age values relative to E17.5 saline controls (p = 4.5e-06, T-test), and E14.5 MIA samples showed a similar trend (p = 0.07, T-test). These findings suggest that mid-gestational MIA at E12.5 perturbs the timing of major downstream neurodevelopmental processes at E14.5 and E17.5 in embryonic cerebral cortex.

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To characterize the neuroanatomical specificity of Blue and Turquoise dysregulation transcriptional signatures (Figure 4d), we performed a static immunofluorescent labeling comparison of late neurogenesis, cortical lamination and cell specification at E17.5 in an independent MIA cohort. E17.5 represents the end of the peak of neurogenesis during cortical development, before the neuronal-glial transition that occurs around E18.5. At this stage, it is expected that progenitor markers, such as PAX6, SOX9 and more specifically SOX2, will identify a small population of progenitors that are maintained and retain the ability to produce neurons, as well as subpopulations of proliferative cells commencing gliogenesis (Martynoga et al., 2012). PAX6+, SOX9+ and SOX2+ cell distribution patterns showed that progenitors localized correctly around the cortical ventricular zone in coronal brain sections; all analyzed cell populations were significantly reduced in MIA brains compared to controls (Figure 4e–f). The PAX6 finding in particular is consistent with our WB analysis at E17.5 (PAX6, p=0.039, two tailed Student’s t-test) (Figure 4b, Figure 4—figure supplement 1a), with protein levels that either resolved or were too subtle to be distinguishable by birth (p=0.766, two tailed Student’s t-test) (Figure 4a, Figure 4—figure supplement 1b–c,i). MIA brains showed a decrease in SOX9+ cells in the ventricular zone (VZ), which is expressed in progenitors (Scott et al., 2010). We also observed increased SOX9+ cells above the VZ in the neocortex, which can remain expressed in a subset of cells, including oligodendrocyte progenitors and maturing astrocytes (Klum et al., 2018; Sun et al., 2017) (SOX9 VZ, p=0.0198; nCtx, p=0.0058, two tailed Student’s t-test). Reduced expression of SOX2, a progenitor marker that is restricted to the VZ, was also observed in MIA brains (SOX2 VZ, p=0.0006 two tailed Student’s t-test) (Figure 4f). Overall, these data are consistent with the transcriptomic downregulation signature of proliferating cells in MIA brains.

To determine if the decrease in progenitor markers might coincide with decreased proliferation, we next labeled cells undergoing proliferation, using the marker KI67 (any stage of cell proliferation) and the Phospho Histone 3 (PH3) marker, which labels a subset of cycling cells in mitosis (M) phase (Hendzel et al., 1997; Walton et al., 2019). We also examined the post-mitotic intermediate progenitor marker TBR2. Analysis of KI67+ and PH3+ cells showed reduced numbers of labeled cells along with a qualitative reduction in numbers and ectopic positioning of postmitotic intermediate progenitor cells in the MIA group (TBR2+). Reduction in Ki67+ cells was observed in several regions of the developing cortex, including zones defined as S1 and S1DZ (Choi et al., 2016; Shin Yim et al., 2017), as well as the ganglionic eminences (Figure 4e). Quantification of Ki67+ cells and PH3+ cells in the neocortex confirmed the MIA-induced reduction in progenitor and mitotic populations at E17.5 (Ki67, p=0.0198; PH3, p=0.0003, two tailed Student’s t-test). The ratio of PH3+ (mitosis) cells to Ki67+ (all stages of cell division) cells was then used to determine whether cell cycle kinetics or the number of actively dividing cells in M phase was different in the MIA-exposed group. Ki67/PH3 ratios indicated a reduced proportion of progenitor cells in M phase in MIA brains at this specific time-point (p=0.0005 Chi-squared) (Figure 4f). These results provide evidence that the reduced proliferation signatures following MIA that were observed in the transcriptomic profiles are associated with changes at the protein level. Moreover, within the reduced progenitor cell populations at E17.5, the number of cells in the mitosis stage of the cell cycle are decreased to a greater extent.

MIA has been associated with altered cortical thickness and reported to cause cortical dysplasia in young adult MIA offspring (Smith et al., 2007; Soumiya et al., 2011; Tsukada et al., 2015), with stereotypic dysplasia impacting lamination localized to the somatosensory cortex reported in one MIA model (Choi et al., 2016; Shin Yim et al., 2017). A number of cortical layer markers were DE at E17.5 in RNA, with validated changes at the protein level (Figure 4a–b, Figure 3—figure supplement 1b). To examine cortical lamination, distribution of cortical layer-specific markers was analyzed via IHC in the same samples as above (Figure 5, Figure 4—figure supplement figure 1 h-i).

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Examination of the distribution of the lamination markers TBR1 (layer 6), CTIP2 (layer five and a subset of cells in layer 6), SATB2 (layers 2–4, and a subset of cells in layer 5), and CUX1 (layers 2 and 3), revealed MIA-induced cortical layer-specific alterations in lamination, thickness, and cell density at E17.5 (Figure 5a–b,e–f). Overall, MIA brains appeared to be at a more mature stage of lamination at E17.5, showing a full separation of cortical deep layers more typical of later ages and in contrast to the partial layer overlap found in control offspring at E17.5 (Figure 5a–b). Similarly, SATB2 and TBR1 co-staining suggested differences in lamination and migration state between the two groups. Oval cell body shapes were observed, which resembled radially migrating cells (Cooper, 2013) were observed in both SATB2 and TBR1 cells in control tissue, whereas these cells showed a more rounded/post-migratory cell body shape in the MIA brains (Figure 5c–d). The number of TBR1+ and CTIP2+ cells, but not SATB2+ cells, were also significantly reduced in the neocortex from MIA offspring (TBR1+ cells, p=0.0138; CTIP2+ cells, p=0.0119; SATB2+ cells, p=0.306, two tailed Student t-test) (Figure 5e). The thickness of these layers and also a more external layer represented by CUX1, were measured by comparing the radial spread of stained cells within the neocortex. We found a significant reduction in CTIP2 layer thickness and a similar trend in TBR1 layer thickness in the MIA brains. In contrast, SATB2 showed an MIA-induced significant increase in layer thickness with no change in the more external layer represented by CUX1 (TBR1 thickness, p=0.081; CTIP2 thickness, 0.024; SATB2 thickness, p=0.039; CUX1, p=0.430, two tailed Student’s t-test). (Figure 5f). These findings are consistent with individual trajectories of DE genes and WB analyses (Figure 4a–b, Figure 4—figure supplement 1h). Finally, to assess cortical gross anatomy, we looked for MIA-associated dysplasic alterations across the brain (Choi et al., 2016) and measured the thickness of the developing somatosensory cortex at two anteroposterior regions at E17.5. In contrast to previous reports (Choi et al., 2016), we did not find obvious cortical dysplasia or gross alterations in brain anatomy in the analyzed rostral and caudal regions, including somatosensory cortex and S1DZ (Figure 5e–f).

To investigate other cell populations that may be impacted by MIA during development, we analyzed astrocytes, oligodendrocytes and GABAergic interneuron cell populations (Figure 6, Figure 4—figure supplement 1k). Concordant with RNA-seq results, the number of cells expressing GFAP, a protein found in radial glia and a subset of astrocytes (Eng et al., 2000), was increased in MIA offspring, mapping specifically to cells that seemed to emerge ventral from the neocortical VZ (Figure 6a–b,q, Figure 4—figure supplement 1k). Since astrocytes also express SOX9 (Klum et al., 2018; Sun et al., 2017) and radial glia also express PAX6, which is decreased by MIA, this finding is consistent with the possibility that the increased SOX9+ cells in the dorsal regions of the neocortex, described earlier, may be indicative of early differentiated astrocytes (Figure 4f). OLIG2, which is expressed in oligodendrocyte progenitors and mature cells as well as transiently in astrocyte progenitors (Dimou et al., 2008), was also upregulated at E17.5 in the MIA group. OLIG2+ cells were increased in MIA brains compared to controls (n = 3 per group, p=0.0134, Student’s t-test) (Figure 6c–d,k, Figure 4—figure supplement 1k). PDGFRα, a marker of early developing oligodendrocytes, was also increased in MIA brains compared to controls (n = 7 per group, zone 1 p<0.0001, zone 2 p=0.0063, zone 3 p=0.0125, Student’s t-test) (Figure 6e–f,l). Thus, these experiments provide evidence that MIA leads to increased numbers of additional cell types (putative astrocytes and oligodendrocytes) that are typically born at later ages during corticogenesis, reinforcing findings of accelerated cell development that is supported by an increased representation of later-born cell types at E17.5 in brains exposed to MIA.

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Finally, we examined DLX2 and GAD67, markers of committed and maturing cortical interneurons. Although Dlx2 expression was downregulated in the MIA group from bulk tissue dissections at E17.5, the number of cells positive for DLX2 protein in the neocortex were comparable when assessed via IHC in the MIA and control groups (n = 3 per group, total DLX2 p=0.5494; by zones DLX2, zone 1 P=0.9848, zone 2 P=0.6903, zone 3 P=0.2714, Student’s t-test) (Figure 6g–h and m,o). In contrast, GAD protein in this DLX2+ subpopulation, assessed by the pan-GABAergic marker GAD67+, was increased across the neocortex of MIA brains, and was not restricted to any specific zones of the neocortex (n = 3 per group, total DLX2+/GAD67 p=0.0004; by zones DLX2+/GAD67, zone 1 P=0.2277, zone 2 P=0.1352, zone 3 P=0.2862, Student’s t-test) (Figure 6i–j and n,p). Since GAD67 expression increases in developing cortical interneurons, the decrease in Dlx2 transcript expression but no difference in cells expressing DLX2 protein could indicate increased interneuron maturation but no change in interneuron numbers at this age. While neuroanatomical characterization here was limited to E17.5 and interrogated a subset of genes and processes of interest, our IHC findings correlate with transcriptional profiles, providing validation of MIA-associated DE in developing cortex.

Studies leveraging MIA models in mice have demonstrated the role of maternal immune signaling as a risk factor for cellular, anatomical, and behavioral outcomes in offspring. Nonetheless, a systems-level perspective of the early neurodevelopmental trajectory of pathology associated with MIA has been lacking. Our results offer a time course resolved map of molecular targets aberrantly transcribed in developing cerebral cortex following MIA. MIA-associated transcriptional signatures were robust, changed greatly across the surveyed time- points, and revealed novel pathways and processes involved in pathology. Among MIA-associated DE up-regulated and down-regulated genes, there was strong enrichment for ASD-relevant loci, indicating the general translational relevance of our MIA model to ASD. Both male and female samples were examined, and we did not find evidence for strong sex-specific DE signatures or impacts on neuroanatomy. Elucidating the trajectory of transcriptional changes across embryonic cortical development following mid-gestational MIA represents a valuable step toward understanding the progression of MIA-induced pathology in the developing brain of offspring.

Our results suggest two waves of transcriptional pathology following MIA. First, we found initial induction of immune, metabolic, and angiogenesis gene expression modules specific to MIA exposure and largely restricted to the days following MIA. There is overlap in the acute induction signatures identified here and acute changes reported in other mid-gestational MIA models, including those using a range of injection gestational ages and rat models of LPS (Carpentier et al., 2013; Meyer, 2014; Meyer et al., 2006a; O'Loughlin et al., 2017; Richetto et al., 2014; Smith et al., 2007; Wu et al., 2017). Previous studies linked maternal immune challenge, prenatal brain hypoxia, and impacts on proliferation (Stolp et al., 2011), neurogenesis, and cortical lamination (Carpentier et al., 2013). Thus, it is possible that the acute signatures identified here and mapped to the Green and YeMaBl modules as well as a subset of genes in the unassigned Grey module, capture a generalized response in fetal brain across mid-gestational MIA models. Notably, induction of hypoxia, cellular metabolism and neuroimmune transcriptional signatures, particularly captured here by the Green module, persist at reduced levels at least through P0, and may indicate lasting perturbation to these pathways. Significant association of the Grey unassigned module with the MIA phenotype was driven by a subset of genes that showed strong acute induction at E12.5 and E14.5. By altering WGCNA parameters, we could assign the Grey module genes to co-expression modules but those alterations increased splitting, resulting in a high number of modules and reduced module coherence without clarifying additional MIA-specific modules made up by the relevant Grey module MIA-associated genes. Thus, we opted for reduced splitting to capture major co-expression trends.

Identifying the early biological processes triggered by MIA are of critical interest for understanding how MIA initiates pathology. Our analysis identified potential drivers of these induced signaling pathways, including targets of substantial interest such as Vegfa, that link hypoxia, neurogenesis and cortical angiogenesis (Argaw et al., 2012; Takahashi and Shibuya, 2005). Vegfa was upregulated in MIA-exposed animal at all time-points and was among the strongest DE genes at E12.5 and P0. Vegfa is critical for cortical development promoting neurogenesis and neurite outgrowth, as well as angiogenesis (Bayless et al., 2016; Mackenzie and Ruhrberg, 2012; Rosenstein et al., 2010). Upregulation of Vegfa is found in several NDDs and psychiatric disorders, including SZ and depression, and was also reported in a different MIA mouse model (Arrode-Brusés and Brusés, 2012; Hohman et al., 2015; Iga et al., 2007; Isung et al., 2012; Kahl et al., 2009; Lee and Kim, 2012; Lizano et al., 2018; Xie et al., 2017). Induction of angiogenesis in the developing cortex also independently causes a transition from proliferative state to neuronal maturation (Bayless et al., 2016; Javaherian and Kriegstein, 2009; Tan et al., 2016). Other genes of interest include Pdk1 (pyruvate dehydrogenase kinase 1), which regulates glucose metabolism (Newington et al., 2012), Ldha (lactate dehydrogenase A), which facilitates glycolysis and participates in brain angiogenesis (Lin et al., 2018), Bnip3 (BCL2 interacting protein 3), which is involved in cell death and mitochondrial autophagy (Liu and Frazier, 2015), and Il20rb (interleukin 20 receptor subunit beta), a receptor subunit for pro-inflammatory interleukins IL-19, IL-20 and IL-24 (Akdis et al., 2016). The early perturbed pathways observable within hours of MIA induction may be critical initiating steps leading to the altered general timing of fetal brain development described here. Our findings expand understanding of the molecular basis of these MIA-induced initial perturbations and reveal potentially key drivers of these changes.

The second component of the neurodevelopmental MIA signature defined here was perturbation to normal patterns of both proliferative and maturational neurodevelopmental modules that manifest several days after MIA induction. Initially, at six hours after MIA, there is a shift in expression of differentiation and neuronal maturation processes (e.g. synaptogenesis) to increased proliferative processes (e.g. positive regulation of cell cycle). This pattern parallels results described for the acute MIA response from other mouse and rat MIA models, where similar pathways were down- or up-regulated in fetal brain in the hours following MIA (Money et al., 2018; Oskvig et al., 2012). At this developmental stage, our data suggest that MIA induces an acute expenditure of the neocortical, and potentially other, progenitor pools, which in turn leads to an expedited maturation of cells that would normally be generated later during development. Specifically, by E17.5, multiple progenitor and cell cycle markers were significantly decreased in the MIA brains while concordantly, we observed an increase in maturational markers and laminar position that resembled a more developed cortex. Finally, astrocytes, oligodendrocytes, GABAergic interneurons and radially migrating excitatory neuron precursors were effected, suggesting an unbiased impact on progenitors of multiple brain cell types. Although these findings are indicative of overall decreased neurogenesis by E17.5, specific birth-dating and time-course experiments are needed to test whether progenitor cells exit the cell cycle differently and if overall neurogenesis is perturbed beyond the E17.5 time-point reported here. Nonetheless, our time course RNA-seq design revealed that the transcriptional changes following MIA can be separated into discrete biological and temporal expression modules that support altered proliferation and maturation of cell types following MIA.

Our findings of disrupted cortical lamination following the acute MIA signature are consistent with, but not identical to previous studies showing cortical dysplasia associated with MIA-induced aberrant behavior in offspring (Choi et al., 2016; Shin Yim et al., 2017). In those studies, dysplasia resolved at later postnatal stages but defects in lamination persisted into adulthood. Although we did not find evidence of aberrant cortical patches in the somatosensory cortex, secondary motor cortex or S1 and S1DZ, as previously reported, our results do show altered cortical lamination at E17.5. Our findings suggest that perturbation to neuronal migration, potentially due to differences in an early exit of pyramidal neuron precursors from the cell cycle that led to cells further along in their radial migration/lamination, developmental progression, at least in part contribute to MIA-associated lamination differences identified at E17.5 here. Thus, results from multiple studies using MIA models to date are consistent in showing a detrimental impact of MIA on cortical patterning; however, we also acknowledge that our and other studies may have differential variables, including onset of MIA, dose, mouse background, and endpoint measurements, which will be important factors to assess in future studies. Finally, bulk tissue RNA-seq is limited in cell type-specific resolution and signatures could additionally be in part driven by cortex-adjacent structures, such as primordial hippocampus, that may have also been captured at the youngest age. Future work using single-cell approaches is needed to resolve the impacted cell types and compare transcriptional pathology following MIA.

While significantly subtler in overall effects compared to earlier time-points, we also see evidence for altered neuronal transcription and lasting immune activation at P0. By P0, these signatures were reduced, which suggests a transient or attenuating effect on cortical patterning. It is also possible that biological variability in neonatal mice or technical variability impacted detection of gene expression changes in these P0 samples. Nonetheless, the subtle changes in gene expression at P0 are consistent with deficits in synapse formation that require changes in expression of immune molecules in neurons from newborn MIA offspring (Elmer et al., 2013).

Consistent with links between NDDs and developmental timing, atypical neurodevelopmental gene expression networks have been reported in a cortical neurodevelopmental model using autism-patient-derived induced pluripotent stem cells (Bayless et al., 2016; Schafer et al., 2019). The majority of the ASD-relevant genes that were DE following MIA (Figure 1e) were associated with perturbation to the modules associated with neurodevelopmental processes. These ASD-relevant genes map to functional and biological pathways generally associated with ASD and NDDs, including gene regulation, neurogenesis, and synapse development, structure, and function. The broad pathway overlap and presence of up- and down-regulation among DE ASD-relevant genes supports their generalized relevance. These overlapping genes represent targets of interest for future studies investigating parallel mechanisms between genetic and MIA-associated NDD risk. Our findings suggest some aspects of the transition from neurogenesis to astrogenesis appear to occur at an earlier stage following MIA based on similar signatures of SOX9+ and GFAP+ cells reported in literature (Bansod et al., 2017), and increased OLIG2+ cells and more mature GAD67+ DLX2+ interneurons in the cortex following MIA. These findings are additionally consistent with recent reports using poly(I:C) MIA models that identified perturbed GABAergic interneuron development (Thion et al., 2019), and maturation of GAD+ neuroblast after birth (Vasistha et al., 2020). Future work is needed to determine if and how MIA-associated dynamic transcriptional changes manifest postnatally and whether perturbation to embryonic development impacts cortical cytoarchitecture and function at later postnatal stages.

Altogether, our analyses indicate that MIA drives acute and lasting transcriptional changes that impact specific cell populations during forebrain development, disrupting normal developmental timing and causing increased numbers of astrocytes, oligodendrocytes, and altered molecular properties of GABAergic interneurons. The timing of MIA during gestation is known to cause a range of phenotypes in offspring, and thus the findings here regarding mid-gestational MIA at E12.5 may not generalize to other models with different timing of MIA induction, especially for later gestational MIA after neurogenesis is largely complete. Further, the strong embryonic signatures we observe were generally consistent across sexes, raising the question of whether secondary insults or sex-specific protective effects contribute to some reports of sex differences in pathology following MIA (Haida et al., 2019; Naviaux et al., 2013; Smith et al., 2007). Nonetheless, the overlapping results between our study and published single time-point studies of the acute MIA fetal brain transcriptional response suggest some consistency across bacterial and viral mimics, and rat and mouse models. Our study substantially expands previous work, revealing new molecular pathways and providing a temporal map of MIA-induced changes across fetal neurodevelopment that will be relevant to understanding the pathology of, and informing future treatments for, NDDs.

Raw and gene-count RNA-seq sequencing data are deposited in GEO under accession number GSE166376. Data analysis code is available at: https://github.com/NordNeurogenomicsLab/Publications/tree/master/Canales_eLife_2021, copy archived at https://archive.softwareheritage.org/swh:1:rev:28836c8758908e130f1f6d8bfb3b6a112c6cbd1b/ Detailed results of differential gene expression analysis are included in the manuscript and supporting files (Supplementary files 1-15), and can be visualized using our interactive online browser at: https://nordlab.shinyapps.io/MIA_RPKM_plots/.

1. 1. Cichewicz K
   2. Nord AS

   (2021) NCBI Gene Expression Omnibus

   ID GSE166376. A temporal map of maternal immune activation-induced changes reveals a shift in neurodevelopmental timing and perturbed cortical development in mice.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166376
2. 1. Cichewicz K
   2. Nord AS

   (2021) NordNeurogenomicsLab

   ID eLife_2021. A temporal map of maternal immune activation-induced changes reveals a shift in neurodevelopmental timing and perturbed cortical development in mice.

   https://github.com/NordNeurogenomicsLab/Publications/tree/master/Canales_eLife_2021

1. 1. Akdis M
   2. Aab A
   3. Altunbulakli C
   4. Azkur K
   5. Costa RA
   6. Crameri R
   7. Duan S
   8. Eiwegger T
   9. Eljaszewicz A
   10. Ferstl R
   11. Frei R
   12. Garbani M
   13. Globinska A
   14. Hess L
   15. Huitema C
   16. Kubo T
   17. Komlosi Z
   18. Konieczna P
   19. Kovacs N
   20. Kucuksezer UC
   21. Meyer N
   22. Morita H
   23. Olzhausen J
   24. O'Mahony L
   25. Pezer M
   26. Prati M
   27. Rebane A
   28. Rhyner C
   29. Rinaldi A
   30. Sokolowska M
   31. Stanic B
   32. Sugita K
   33. Treis A
   34. van de Veen W
   35. Wanke K
   36. Wawrzyniak M
   37. Wawrzyniak P
   38. Wirz OF
   39. Zakzuk JS
   40. Akdis CA

   (2016) Interleukins (from IL-1 to IL-38), interferons, transforming growth factor β, and TNF-α: receptors, functions, and roles in diseases

   Journal of Allergy and Clinical Immunology 138:984–1010.

   https://doi.org/10.1016/j.jaci.2016.06.033

   • PubMed
   • Google Scholar
2. Software

   1. Alexa A
   2. Rahnenfuhrer J

   (2019) topGO: Enrichment Analysis for Gene Ontology, version 2.38.1

   R package.

   https://bioconductor.org/packages/release/bioc/html/topGO.html
3. Software

   1. Andrews S

   (2010) FastQC A Quality Control tool for High Throughput Sequence Data, version  GPL v3

   Babraham Bioinformatics.

   https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
4. 1. Argaw AT
   2. Asp L
   3. Zhang J
   4. Navrazhina K
   5. Pham T
   6. Mariani JN
   7. Mahase S
   8. Dutta DJ
   9. Seto J
   10. Kramer EG
   11. Ferrara N
   12. Sofroniew MV
   13. John GR

   (2012) Astrocyte-derived VEGF-A drives blood-brain barrier disruption in CNS inflammatory disease

   Journal of Clinical Investigation 122:2454–2468.

   https://doi.org/10.1172/JCI60842

   • PubMed
   • Google Scholar
5. 1. Arrode-Brusés G
   2. Brusés JL

   (2012) Maternal immune activation by poly I:c induces expression of cytokines IL-1β and IL-13, chemokine MCP-1 and colony stimulating factor VEGF in fetal mouse brain

   Journal of Neuroinflammation 9:83.

   https://doi.org/10.1186/1742-2094-9-83

   • PubMed
   • Google Scholar
6. 1. Ballendine SA
   2. Greba Q
   3. Dawicki W
   4. Zhang X
   5. Gordon JR
   6. Howland JG

   (2015) Behavioral alterations in rat offspring following maternal immune activation and ELR-CXC chemokine receptor antagonism during pregnancy: implications for neurodevelopmental psychiatric disorders

   Progress in Neuro-Psychopharmacology and Biological Psychiatry 57:155–165.

   https://doi.org/10.1016/j.pnpbp.2014.11.002

   • Google Scholar
7. 1. Bansod S
   2. Kageyama R
   3. Ohtsuka T

   (2017) Hes5 regulates the transition timing of neurogenesis and gliogenesis in mammalian neocortical development

   Development 144:3156–3167.

   https://doi.org/10.1242/dev.147256

   • Google Scholar
8. 1. Basu SN
   2. Kollu R
   3. Banerjee-Basu S

   (2009) AutDB: a gene reference resource for autism research

   Nucleic Acids Research 37:D832–D836.

   https://doi.org/10.1093/nar/gkn835

   • Google Scholar
9. 1. Bayless NL
   2. Greenberg RS
   3. Swigut T
   4. Wysocka J
   5. Blish CA

   (2016) Zika virus infection induces cranial neural crest cells to produce cytokines at levels detrimental for neurogenesis

   Cell Host & Microbe 20:423–428.

   https://doi.org/10.1016/j.chom.2016.09.006

   • PubMed
   • Google Scholar
10. 1. Brown AS

    (2011) The environment and susceptibility to schizophrenia

    Progress in Neurobiology 93:23–58.

    https://doi.org/10.1016/j.pneurobio.2010.09.003

    • PubMed
    • Google Scholar
11. Software

    1. Canales C

    (2021) Publications, version swh:1:rev:28836c8758908e130f1f6d8bfb3b6a112c6cbd1b

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:5f2ff57077946f69fd4fb99a2df240c5b5411a1e;origin=https://github.com/NordNeurogenomicsLab/Publications/;visit=swh:1:snp:afd3044f9dbd07b9cb7620b8ea08b8928c8fde6f;anchor=swh:1:rev:28836c8758908e130f1f6d8bfb3b6a112c6cbd1b/
12. 1. Canetta SE
    2. Bao Y
    3. Co MD
    4. Ennis FA
    5. Cruz J
    6. Terajima M
    7. Shen L
    8. Kellendonk C
    9. Schaefer CA
    10. Brown AS

    (2014) Serological documentation of maternal influenza exposure and bipolar disorder in adult offspring

    American Journal of Psychiatry 171:557–563.

    https://doi.org/10.1176/appi.ajp.2013.13070943

    • PubMed
    • Google Scholar
13. 1. Carpentier PA
    2. Haditsch U
    3. Braun AE
    4. Cantu AV
    5. Moon HM
    6. Price RO
    7. Anderson MP
    8. Saravanapandian V
    9. Ismail K
    10. Rivera M
    11. Weimann JM
    12. Palmer TD

    (2013) Stereotypical alterations in cortical patterning are associated with maternal illness-induced placental dysfunction

    The Journal of Neuroscience 33:16874–16888.

    https://doi.org/10.1523/JNEUROSCI.4654-12.2013

    • PubMed
    • Google Scholar
14. 1. Choi GB
    2. Yim YS
    3. Wong H
    4. Kim S
    5. Kim H
    6. Kim SV
    7. Hoeffer CA
    8. Littman DR
    9. Huh JR

    (2016) The maternal interleukin-17a pathway in mice promotes autism-like phenotypes in offspring

    Science 351:933–939.

    https://doi.org/10.1126/science.aad0314

    • PubMed
    • Google Scholar
15. 1. Cooper JA

    (2013) Mechanisms of cell migration in the nervous system

    Journal of Cell Biology 202:725–734.

    https://doi.org/10.1083/jcb.201305021

    • Google Scholar
16. 1. Dahlgren J
    2. Samuelsson AM
    3. Jansson T
    4. Holmäng A

    (2006) Interleukin-6 in the maternal circulation reaches the rat fetus in mid-gestation

    Pediatric Research 60:147–151.

    https://doi.org/10.1203/01.pdr.0000230026.74139.18

    • PubMed
    • Google Scholar
17. 1. Depino AM

    (2015) Early prenatal exposure to LPS results in anxiety- and depression-related behaviors in adulthood

    Neuroscience 299:56–65.

    https://doi.org/10.1016/j.neuroscience.2015.04.065

    • PubMed
    • Google Scholar
18. 1. Dimou L
    2. Simon C
    3. Kirchhoff F
    4. Takebayashi H
    5. Götz M

    (2008) Progeny of Olig2-expressing progenitors in the gray and white matter of the adult mouse cerebral cortex

    Journal of Neuroscience 28:10434–10442.

    https://doi.org/10.1523/JNEUROSCI.2831-08.2008

    • PubMed
    • Google Scholar
19. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
20. 1. Durinck S
    2. Moreau Y
    3. Kasprzyk A
    4. Davis S
    5. De Moor B
    6. Brazma A
    7. Huber W

    (2005) BioMart and bioconductor: a powerful link between biological databases and microarray data analysis

    Bioinformatics 21:3439–3440.

    https://doi.org/10.1093/bioinformatics/bti525

    • Google Scholar
21. 1. Durinck S
    2. Spellman PT
    3. Birney E
    4. Huber W

    (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt

    Nature Protocols 4:1184–1191.

    https://doi.org/10.1038/nprot.2009.97

    • PubMed
    • Google Scholar
22. 1. Elmer BM
    2. Estes ML
    3. Barrow SL
    4. McAllister AK

    (2013) MHCI requires MEF2 transcription factors to negatively regulate synapse density during development and in disease

    Journal of Neuroscience 33:13791–13804.

    https://doi.org/10.1523/JNEUROSCI.2366-13.2013

    • PubMed
    • Google Scholar
23. 1. Eng LF
    2. Ghirnikar RS
    3. Lee YL

    (2000) Glial fibrillary acidic protein: gfap-thirty-one years (1969-2000)

    Neurochemical Research 25:1439–1451.

    https://doi.org/10.1023/a:1007677003387

    • PubMed
    • Google Scholar
24. Preprint

    1. Estes ML
    2. Farrelly K
    3. Cameron S
    4. Aboubechara JP
    5. Haapanen L
    6. Schauer JD
    7. Horta A
    8. Prendergast K
    9. MacMahon JA
    10. Shaffer CI
    11. Ct L
    12. Kincheloe GN
    13. Tan DJ
    14. Van der LD
    15. Bauman MD
    16. Carter CS
    17. de WJV
    18. McAllister AK

    (2019) Enhancing rigor and reproducibility in maternal immune activation models: practical considerations and predicting resilience and susceptibility using baseline immune responsiveness before pregnancy

    bioRxiv.

    https://doi.org/10.1101/699983

    • Google Scholar
25. 1. Estes ML
    2. Prendergast K
    3. MacMahon JA
    4. Cameron S
    5. Aboubechara JP
    6. Farrelly K
    7. Sell GL
    8. Haapanen L
    9. Schauer JD
    10. Horta A
    11. Shaffer IC
    12. Le CT
    13. Kincheloe GN
    14. Tan DJ
    15. van der List D
    16. Bauman MD
    17. Carter CS
    18. Van de Water J
    19. McAllister AK

    (2020) Baseline immunoreactivity before pregnancy and poly(I:c) dose combine to dictate susceptibility and resilience of offspring to maternal immune activation

    Brain, Behavior, and Immunity 88:619–630.

    https://doi.org/10.1016/j.bbi.2020.04.061

    • PubMed
    • Google Scholar
26. 1. Estes ML
    2. McAllister AK

    (2015) Immune mediators in the brain and peripheral tissues in autism spectrum disorder

    Nature Reviews Neuroscience 16:469–486.

    https://doi.org/10.1038/nrn3978

    • PubMed
    • Google Scholar
27. 1. Estes ML
    2. McAllister AK

    (2016) Maternal immune activation: implications for neuropsychiatric disorders

    Science 353:772–777.

    https://doi.org/10.1126/science.aag3194

    • PubMed
    • Google Scholar
28. 1. Gompers AL
    2. Su-Feher L
    3. Ellegood J
    4. Copping NA
    5. Riyadh MA
    6. Stradleigh TW
    7. Pride MC
    8. Schaffler MD
    9. Wade AA
    10. Catta-Preta R
    11. Zdilar I
    12. Louis S
    13. Kaushik G
    14. Mannion BJ
    15. Plajzer-Frick I
    16. Afzal V
    17. Visel A
    18. Pennacchio LA
    19. Dickel DE
    20. Lerch JP
    21. Crawley JN
    22. Zarbalis KS
    23. Silverman JL
    24. Nord AS

    (2017) Germline Chd8 haploinsufficiency alters brain development in mouse

    Nature Neuroscience 20:1062–1073.

    https://doi.org/10.1038/nn.4592

    • PubMed
    • Google Scholar
29. 1. Haida O
    2. Al Sagheer T
    3. Balbous A
    4. Francheteau M
    5. Matas E
    6. Soria F
    7. Fernagut PO
    8. Jaber M

    (2019) Sex-dependent behavioral deficits and neuropathology in a maternal immune activation model of autism

    Translational Psychiatry 9:e0457.

    https://doi.org/10.1038/s41398-019-0457-y

    • Google Scholar
30. 1. Hendzel MJ
    2. Wei Y
    3. Mancini MA
    4. Van Hooser A
    5. Ranalli T
    6. Brinkley BR
    7. Bazett-Jones DP
    8. Allis CD

    (1997) Mitosis-specific phosphorylation of histone H3 initiates primarily within pericentromeric heterochromatin during G2 and spreads in an ordered fashion coincident with mitotic chromosome condensation

    Chromosoma 106:348–360.

    https://doi.org/10.1007/s004120050256

    • PubMed
    • Google Scholar
31. 1. Hohman TJ
    2. Bell SP
    3. Jefferson AL
    4. Alzheimer’s Disease Neuroimaging Initiative

    (2015) The role of vascular endothelial growth factor in neurodegeneration and cognitive decline: exploring interactions with biomarkers of alzheimer disease

    JAMA Neurology 72:520–529.

    https://doi.org/10.1001/jamaneurol.2014.4761

    • PubMed
    • Google Scholar
32. 1. Hsiao EY
    2. Patterson PH

    (2011) Activation of the maternal immune system induces endocrine changes in the placenta via IL-6

    Brain, Behavior, and Immunity 25:604–615.

    https://doi.org/10.1016/j.bbi.2010.12.017

    • PubMed
    • Google Scholar
33. 1. Iga J
    2. Ueno S
    3. Yamauchi K
    4. Numata S
    5. Tayoshi-Shibuya S
    6. Kinouchi S
    7. Nakataki M
    8. Song H
    9. Hokoishi K
    10. Tanabe H
    11. Sano A
    12. Ohmori T

    (2007) Gene expression and association analysis of vascular endothelial growth factor in major depressive disorder

    Progress in Neuro-Psychopharmacology & Biological Psychiatry 31:658–663.

    https://doi.org/10.1016/j.pnpbp.2006.12.011

    • PubMed
    • Google Scholar
34. 1. Isung J
    2. Aeinehband S
    3. Mobarrez F
    4. Mårtensson B
    5. Nordström P
    6. Asberg M
    7. Piehl F
    8. Jokinen J

    (2012) Low vascular endothelial growth factor and interleukin-8 in cerebrospinal fluid of suicide attempters

    Translational Psychiatry 2:e196.

    https://doi.org/10.1038/tp.2012.123

    • PubMed
    • Google Scholar
35. 1. Javaherian A
    2. Kriegstein A

    (2009) A stem cell niche for intermediate progenitor cells of the embryonic cortex

    Cerebral Cortex 19:i70–i77.

    https://doi.org/10.1093/cercor/bhp029

    • Google Scholar
36. 1. Kahl KG
    2. Bens S
    3. Ziegler K
    4. Rudolf S
    5. Kordon A
    6. Dibbelt L
    7. Schweiger U

    (2009) Angiogenic factors in patients with current major depressive disorder comorbid with borderline personality disorder

    Psychoneuroendocrinology 34:353–357.

    https://doi.org/10.1016/j.psyneuen.2008.09.016

    • PubMed
    • Google Scholar
37. 1. Klum S
    2. Zaouter C
    3. Alekseenko Z
    4. Björklund AK
    5. Hagey DW
    6. Ericson J
    7. Muhr J
    8. Bergsland M

    (2018) Sequentially acting SOX proteins orchestrate astrocyte‐ and oligodendrocyte‐specific gene expression

    EMBO Reports 19:e46635.

    https://doi.org/10.15252/embr.201846635

    • Google Scholar
38. Software

    1. Kolde R

    (2018)

    pheatmap, version Pretty Heatmaps version 1.0.12

    pheatmap.
39. 1. Langfelder P
    2. Horvath S

    (2008) WGCNA: an R package for weighted correlation network analysis

    BMC Bioinformatics 9:559.

    https://doi.org/10.1186/1471-2105-9-559

    • PubMed
    • Google Scholar
40. 1. Langfelder P
    2. Horvath S

    (2012) Fast R functions for robust correlations and hierarchical clustering

    Journal of Statistical Software 46:1–17.

    https://doi.org/10.18637/jss.v046.i11

    • PubMed
    • Google Scholar
41. 1. Lee BH
    2. Kim YK

    (2012) Increased plasma VEGF levels in major depressive or manic episodes in patients with mood disorders

    Journal of Affective Disorders 136:181–184.

    https://doi.org/10.1016/j.jad.2011.07.021

    • PubMed
    • Google Scholar
42. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

    Bioinformatics 30:923–930.

    https://doi.org/10.1093/bioinformatics/btt656

    • PubMed
    • Google Scholar
43. 1. Lin H
    2. Muramatsu R
    3. Maedera N
    4. Tsunematsu H
    5. Hamaguchi M
    6. Koyama Y
    7. Kuroda M
    8. Ono K
    9. Sawada M
    10. Yamashita T

    (2018) Extracellular lactate dehydrogenase A release from damaged neurons drives central nervous system angiogenesis

    EBioMedicine 27:71–85.

    https://doi.org/10.1016/j.ebiom.2017.10.033

    • PubMed
    • Google Scholar
44. 1. Liu KE
    2. Frazier WA

    (2015) Phosphorylation of the BNIP3 C-Terminus inhibits mitochondrial damage and cell death without blocking autophagy

    PLOS ONE 10:e0129667.

    https://doi.org/10.1371/journal.pone.0129667

    • PubMed
    • Google Scholar
45. 1. Lizano P
    2. Lutz O
    3. Ling G
    4. Padmanabhan J
    5. Tandon N
    6. Sweeney J
    7. Tamminga C
    8. Pearlson G
    9. Ruaño G
    10. Kocherla M
    11. Windemuth A
    12. Clementz B
    13. Gershon E
    14. Keshavan M

    (2018) VEGFA GENE variation influences hallucinations and frontotemporal morphology in psychotic disorders: a B-SNIP study

    Translational Psychiatry 8:215.

    https://doi.org/10.1038/s41398-018-0271-y

    • PubMed
    • Google Scholar
46. 1. Lombardo MV
    2. Moon HM
    3. Su J
    4. Palmer TD
    5. Courchesne E
    6. Pramparo T

    (2018) Maternal immune activation dysregulation of the fetal brain transcriptome and relevance to the pathophysiology of autism spectrum disorder

    Molecular Psychiatry 23:1001–1013.

    https://doi.org/10.1038/mp.2017.15

    • PubMed
    • Google Scholar
47. 1. Machado CJ
    2. Whitaker AM
    3. Smith SE
    4. Patterson PH
    5. Bauman MD

    (2015) Maternal immune activation in nonhuman primates alters social attention in juvenile offspring

    Biological Psychiatry 77:823–832.

    https://doi.org/10.1016/j.biopsych.2014.07.035

    • PubMed
    • Google Scholar
48. 1. Mackenzie F
    2. Ruhrberg C

    (2012) Diverse roles for VEGF-A in the nervous system

    Development 139:1371–1380.

    https://doi.org/10.1242/dev.072348

    • PubMed
    • Google Scholar
49. 1. Malkova NV
    2. Yu CZ
    3. Hsiao EY
    4. Moore MJ
    5. Patterson PH

    (2012) Maternal immune activation yields offspring displaying mouse versions of the three core symptoms of autism

    Brain, Behavior, and Immunity 26:607–616.

    https://doi.org/10.1016/j.bbi.2012.01.011

    • PubMed
    • Google Scholar
50. 1. Martynoga B
    2. Drechsel D
    3. Guillemot F

    (2012) Molecular control of neurogenesis: a view from the mammalian cerebral cortex

    Cold Spring Harbor Perspectives in Biology 4:a008359.

    https://doi.org/10.1101/cshperspect.a008359

    • PubMed
    • Google Scholar
51. 1. McFarlane L
    2. Truong V
    3. Palmer JS
    4. Wilhelm D

    (2013) Novel PCR assay for determining the genetic sex of mice

    Sexual Development 7:207–211.

    https://doi.org/10.1159/000348677

    • PubMed
    • Google Scholar
52. 1. Meyer U
    2. Feldon J
    3. Schedlowski M
    4. Yee BK

    (2006a) Immunological stress at the maternal-foetal interface: a link between neurodevelopment and adult psychopathology

    Brain, Behavior, and Immunity 20:378–388.

    https://doi.org/10.1016/j.bbi.2005.11.003

    • PubMed
    • Google Scholar
53. 1. Meyer U
    2. Nyffeler M
    3. Engler A
    4. Urwyler A
    5. Schedlowski M
    6. Knuesel I
    7. Yee BK
    8. Feldon J

    (2006b) The time of prenatal immune challenge determines the specificity of inflammation-mediated brain and behavioral pathology

    Journal of Neuroscience 26:4752–4762.

    https://doi.org/10.1523/JNEUROSCI.0099-06.2006

    • PubMed
    • Google Scholar
54. 1. Meyer U

    (2014) Prenatal poly(i:c) exposure and other developmental immune activation models in rodent systems

    Biological Psychiatry 75:307–315.

    https://doi.org/10.1016/j.biopsych.2013.07.011

    • PubMed
    • Google Scholar
55. 1. Money KM
    2. Barke TL
    3. Serezani A
    4. Gannon M
    5. Garbett KA
    6. Aronoff DM
    7. Mirnics K

    (2018) Gestational diabetes exacerbates maternal immune activation effects in the developing brain

    Molecular Psychiatry 23:1920–1928.

    https://doi.org/10.1038/mp.2017.191

    • PubMed
    • Google Scholar
56. 1. Mootha VK
    2. Lindgren CM
    3. Eriksson KF
    4. Subramanian A
    5. Sihag S
    6. Lehar J
    7. Puigserver P
    8. Carlsson E
    9. Ridderstråle M
    10. Laurila E
    11. Houstis N
    12. Daly MJ
    13. Patterson N
    14. Mesirov JP
    15. Golub TR
    16. Tamayo P
    17. Spiegelman B
    18. Lander ES
    19. Hirschhorn JN
    20. Altshuler D
    21. Groop LC

    (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

    Nature Genetics 34:267–273.

    https://doi.org/10.1038/ng1180

    • PubMed
    • Google Scholar
57. 1. Naviaux RK
    2. Zolkipli Z
    3. Wang L
    4. Nakayama T
    5. Naviaux JC
    6. Le TP
    7. Schuchbauer MA
    8. Rogac M
    9. Tang Q
    10. Dugan LL
    11. Powell SB

    (2013) Antipurinergic therapy corrects the autism-like features in the poly(IC) mouse model

    PLOS ONE 8:e57380.

    https://doi.org/10.1371/journal.pone.0057380

    • PubMed
    • Google Scholar
58. 1. Newington JT
    2. Rappon T
    3. Albers S
    4. Wong DY
    5. Rylett RJ
    6. Cumming RC

    (2012) Overexpression of pyruvate dehydrogenase kinase 1 and lactate dehydrogenase A in nerve cells confers resistance to amyloid β and other toxins by decreasing mitochondrial respiration and reactive oxygen species production

    Journal of Biological Chemistry 287:37245–37258.

    https://doi.org/10.1074/jbc.M112.366195

    • PubMed
    • Google Scholar
59. 1. O'Loughlin E
    2. Pakan JMP
    3. Yilmazer-Hanke D
    4. McDermott KW

    (2017) Acute in utero exposure to lipopolysaccharide induces inflammation in the pre- and postnatal brain and alters the glial cytoarchitecture in the developing amygdala

    Journal of Neuroinflammation 14:212.

    https://doi.org/10.1186/s12974-017-0981-8

    • PubMed
    • Google Scholar
60. 1. Oskvig DB
    2. Elkahloun AG
    3. Johnson KR
    4. Phillips TM
    5. Herkenham M

    (2012) Maternal immune activation by LPS selectively alters specific gene expression profiles of interneuron migration and oxidative stress in the fetus without triggering a fetal immune response

    Brain, Behavior, and Immunity 26:623–634.

    https://doi.org/10.1016/j.bbi.2012.01.015

    • PubMed
    • Google Scholar
61. 1. Parboosing R
    2. Bao Y
    3. Shen L
    4. Schaefer CA
    5. Brown AS

    (2013) Gestational influenza and bipolar disorder in adult offspring

    JAMA Psychiatry 70:677–685.

    https://doi.org/10.1001/jamapsychiatry.2013.896

    • PubMed
    • Google Scholar
62. 1. Patterson PH

    (2009) Immune involvement in schizophrenia and autism: etiology, pathology and animal models

    Behavioural Brain Research 204:313–321.

    https://doi.org/10.1016/j.bbr.2008.12.016

    • PubMed
    • Google Scholar
63. Software

    1. R Development Core Team

    (2015) R: a language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org/
64. 1. Richetto J
    2. Calabrese F
    3. Riva MA
    4. Meyer U

    (2014) Prenatal immune activation induces maturation-dependent alterations in the prefrontal GABAergic transcriptome

    Schizophrenia Bulletin 40:351–361.

    https://doi.org/10.1093/schbul/sbs195

    • PubMed
    • Google Scholar
65. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • PubMed
    • Google Scholar
66. 1. Rosenstein JM
    2. Krum JM
    3. Ruhrberg C

    (2010) VEGF in the nervous system

    Organogenesis 6:107–114.

    https://doi.org/10.4161/org.6.2.11687

    • PubMed
    • Google Scholar
67. 1. Schafer ST
    2. Paquola ACM
    3. Stern S
    4. Gosselin D
    5. Ku M
    6. Pena M
    7. Kuret TJM
    8. Liyanage M
    9. Mansour AA
    10. Jaeger BN
    11. Marchetto MC
    12. Glass CK
    13. Mertens J
    14. Gage FH

    (2019) Pathological priming causes developmental gene network heterochronicity in autistic subject-derived neurons

    Nature Neuroscience 22:243–255.

    https://doi.org/10.1038/s41593-018-0295-x

    • PubMed
    • Google Scholar
68. 1. Scott CE
    2. Wynn SL
    3. Sesay A
    4. Cruz C
    5. Cheung M
    6. Gomez Gaviro MV
    7. Booth S
    8. Gao B
    9. Cheah KS
    10. Lovell-Badge R
    11. Briscoe J

    (2010) SOX9 induces and maintains neural stem cells

    Nature Neuroscience 13:1181–1189.

    https://doi.org/10.1038/nn.2646

    • PubMed
    • Google Scholar
69. 1. Shi L
    2. Fatemi SH
    3. Sidwell RW
    4. Patterson PH

    (2003) Maternal influenza infection causes marked behavioral and pharmacological changes in the offspring

    The Journal of Neuroscience 23:297–302.

    https://doi.org/10.1523/JNEUROSCI.23-01-00297.2003

    • PubMed
    • Google Scholar
70. 1. Shin Yim Y
    2. Park A
    3. Berrios J
    4. Lafourcade M
    5. Pascual LM
    6. Soares N
    7. Yeon Kim J
    8. Kim S
    9. Kim H
    10. Waisman A
    11. Littman DR
    12. Wickersham IR
    13. Harnett MT
    14. Huh JR
    15. Choi GB

    (2017) Reversing behavioural abnormalities in mice exposed to maternal inflammation

    Nature 549:482–487.

    https://doi.org/10.1038/nature23909

    • PubMed
    • Google Scholar
71. 1. Simões LR
    2. Sangiogo G
    3. Tashiro MH
    4. Generoso JS
    5. Faller CJ
    6. Dominguini D
    7. Mastella GA
    8. Scaini G
    9. Giridharan VV
    10. Michels M
    11. Florentino D
    12. Petronilho F
    13. Réus GZ
    14. Dal-Pizzol F
    15. Zugno AI
    16. Barichello T

    (2018) Maternal immune activation induced by lipopolysaccharide triggers immune response in pregnant mother and fetus, and induces behavioral impairment in adult rats

    Journal of Psychiatric Research 100:71–83.

    https://doi.org/10.1016/j.jpsychires.2018.02.007

    • PubMed
    • Google Scholar
72. 1. Smith SE
    2. Li J
    3. Garbett K
    4. Mirnics K
    5. Patterson PH

    (2007) Maternal immune activation alters fetal brain development through interleukin-6

    Journal of Neuroscience 27:10695–10702.

    https://doi.org/10.1523/JNEUROSCI.2178-07.2007

    • PubMed
    • Google Scholar
73. 1. Soumiya H
    2. Fukumitsu H
    3. Furukawa S

    (2011) Prenatal immune challenge compromises the normal course of neurogenesis during development of the mouse cerebral cortex

    Journal of Neuroscience Research 89:1575–1585.

    https://doi.org/10.1002/jnr.22704

    • PubMed
    • Google Scholar
74. 1. Stolp HB
    2. Turnquist C
    3. Dziegielewska KM
    4. Saunders NR
    5. Anthony DC
    6. Molnár Z

    (2011) Reduced ventricular proliferation in the foetal cortex following maternal inflammation in the mouse

    Brain 134:3236–3248.

    https://doi.org/10.1093/brain/awr237

    • PubMed
    • Google Scholar
75. 1. Subramanian A
    2. Tamayo P
    3. Mootha VK
    4. Mukherjee S
    5. Ebert BL
    6. Gillette MA
    7. Paulovich A
    8. Pomeroy SL
    9. Golub TR
    10. Lander ES
    11. Mesirov JP

    (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

    PNAS 102:15545–15550.

    https://doi.org/10.1073/pnas.0506580102

    • PubMed
    • Google Scholar
76. 1. Sun W
    2. Cornwell A
    3. Li J
    4. Peng S
    5. Osorio MJ
    6. Aalling N
    7. Wang S
    8. Benraiss A
    9. Lou N
    10. Goldman SA
    11. Nedergaard M

    (2017) SOX9 is an Astrocyte-Specific nuclear marker in the adult brain outside the neurogenic regions

    The Journal of Neuroscience 37:4493–4507.

    https://doi.org/10.1523/JNEUROSCI.3199-16.2017

    • PubMed
    • Google Scholar
77. 1. Szklarczyk D
    2. Gable AL
    3. Lyon D
    4. Junge A
    5. Wyder S
    6. Huerta-Cepas J
    7. Simonovic M
    8. Doncheva NT
    9. Morris JH
    10. Bork P
    11. Jensen LJ
    12. Mering CV

    (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

    Nucleic Acids Research 47:D607–D613.

    https://doi.org/10.1093/nar/gky1131

    • PubMed
    • Google Scholar
78. 1. Takahashi H
    2. Shibuya M

    (2005) The vascular endothelial growth factor (VEGF)/VEGF receptor system and its role under physiological and pathological conditions

    Clinical Science 109:227–241.

    https://doi.org/10.1042/CS20040370

    • PubMed
    • Google Scholar
79. 1. Tan X
    2. Liu WA
    3. Zhang XJ
    4. Shi W
    5. Ren SQ
    6. Li Z
    7. Brown KN
    8. Shi SH

    (2016) Vascular influence on ventral telencephalic progenitors and neocortical interneuron production

    Developmental Cell 36:624–638.

    https://doi.org/10.1016/j.devcel.2016.02.023

    • PubMed
    • Google Scholar
80. Software

    1. Team R

    (2018) RStudio: Integrated Development Environment for R, version 1.4

    RStudio: Integrated Development Environment for R.

    https://rstudio.com/products/rstudio/
81. 1. Thion MS
    2. Mosser CA
    3. Férézou I
    4. Grisel P
    5. Baptista S
    6. Low D
    7. Ginhoux F
    8. Garel S
    9. Audinat E

    (2019) Biphasic impact of prenatal inflammation and macrophage depletion on the wiring of neocortical inhibitory circuits

    Cell Reports 28:1119–1126.

    https://doi.org/10.1016/j.celrep.2019.06.086

    • PubMed
    • Google Scholar
82. 1. Tsukada T
    2. Simamura E
    3. Shimada H
    4. Arai T
    5. Higashi N
    6. Akai T
    7. Iizuka H
    8. Hatta T

    (2015) The suppression of maternal-fetal leukemia inhibitory factor signal relay pathway by maternal immune activation impairs brain development in mice

    PLOS ONE 10:e0129011.

    https://doi.org/10.1371/journal.pone.0129011

    • PubMed
    • Google Scholar
83. 1. Vasistha NA
    2. Pardo-Navarro M
    3. Gasthaus J
    4. Weijers D
    5. Müller MK
    6. García-González D
    7. Malwade S
    8. Korshunova I
    9. Pfisterer U
    10. von Engelhardt J
    11. Hougaard KS
    12. Khodosevich K

    (2020) Maternal inflammation has a profound effect on cortical interneuron development in a stage and subtype-specific manner

    Molecular Psychiatry 25:2313–2329.

    https://doi.org/10.1038/s41380-019-0539-5

    • PubMed
    • Google Scholar
84. 1. Walton CC
    2. Zhang W
    3. Patiño-Parrado I
    4. Barrio-Alonso E
    5. Garrido JJ
    6. Frade JM

    (2019) Primary neurons can enter M-phase

    Scientific Reports 9:4594.

    https://doi.org/10.1038/s41598-019-40462-4

    • PubMed
    • Google Scholar
85. 1. Wang L
    2. Wang S
    3. Li W

    (2012) RSeQC: quality control of RNA-seq experiments

    Bioinformatics 28:2184–2185.

    https://doi.org/10.1093/bioinformatics/bts356

    • PubMed
    • Google Scholar
86. Book

    1. Wickham H

    (2009) Ggplot2, Elegant Graphics for Data Analysis, Ggplot2

    Springer.

    https://doi.org/10.1007/978-0-387-98141-3

    • Google Scholar
87. 1. Wu WL
    2. Hsiao EY
    3. Yan Z
    4. Mazmanian SK
    5. Patterson PH

    (2017) The placental interleukin-6 signaling controls fetal brain development and behavior

    Brain, Behavior, and Immunity 62:11–23.

    https://doi.org/10.1016/j.bbi.2016.11.007

    • PubMed
    • Google Scholar
88. 1. Xie T
    2. Stathopoulou MG
    3. de Andrés F
    4. Siest G
    5. Murray H
    6. Martin M
    7. Cobaleda J
    8. Delgado A
    9. Lamont J
    10. Peñas-LIedó E
    11. LLerena A
    12. Visvikis-Siest S

    (2017) VEGF-related polymorphisms identified by GWAS and risk for major depression

    Translational Psychiatry 7:e1055.

    https://doi.org/10.1038/tp.2017.36

    • PubMed
    • Google Scholar
89. 1. Zhang J
    2. Jing Y
    3. Zhang H
    4. Bilkey DK
    5. Liu P

    (2019) Maternal immune activation altered microglial immunoreactivity in the brain of postnatal day 2 rat offspring

    Synapse 73:e22072.

    https://doi.org/10.1002/syn.22072

    • PubMed
    • Google Scholar
90. 1. Zhang B
    2. Horvath S

    (2005) A general framework for weighted gene co-expression network analysis

    Statistical Applications in Genetics and Molecular Biology 4:Article17.

    https://doi.org/10.2202/1544-6115.1128

    • PubMed
    • Google Scholar
91. 1. Zhao Q
    2. Wang Q
    3. Wang J
    4. Tang M
    5. Huang S
    6. Peng K
    7. Han Y
    8. Zhang J
    9. Liu G
    10. Fang Q
    11. You Z

    (2019) Maternal immune activation-induced PPARγ-dependent dysfunction of microglia associated with neurogenic impairment and aberrant postnatal behaviors in offspring

    Neurobiology of Disease 125:1–13.

    https://doi.org/10.1016/j.nbd.2019.01.005

    • PubMed
    • Google Scholar

Author details

1. Cesar P Canales

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Myka L Estes and Karol Cichewicz

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2505-8367

2. Myka L Estes

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Conceptualization, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Cesar P Canales and Karol Cichewicz

   Competing interests

   No competing interests declared

3. Karol Cichewicz

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Cesar P Canales and Myka L Estes

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5926-3663

4. Kartik Angara

   Department of Pediatrics & Human Development, Michigan State University, East Lansing, United States

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

5. John Paul Aboubechara

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

6. Scott Cameron

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

7. Kathryn Prendergast

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

8. Linda Su-Feher

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

9. Iva Zdilar

   Center for Neuroscience, UC Davis, Davis, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

10. Ellie J Kreun

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

11. Emma C Connolly

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

12. Jin Myeong Seo

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

13. Jack B Goon

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Data curation, Methodology

    Competing interests

    No competing interests declared

14. Kathleen Farrelly

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

15. Tyler W Stradleigh

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

16. Deborah van der List

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

17. Lori Haapanen

    Division of Rheumatology, Allergy and Clinical Immunology, UC Davis, Davis, United States

    Contribution

    Methodology

    Competing interests

    No competing interests declared

18. Judy Van de Water

    Division of Rheumatology, Allergy and Clinical Immunology, UC Davis, Davis, United States

    Contribution

    Conceptualization, Resources, Supervision, Investigation, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

19. Daniel Vogt

    Department of Pediatrics & Human Development, Michigan State University, East Lansing, United States

    Contribution

    Formal analysis, Supervision, Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

20. A Kimberley McAllister

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Project administration, Writing - review and editing

    Contributed equally with

    Alex S Nord

    For correspondence

    kmcallister@ucdavis.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9177-9889

21. Alex S Nord

    Center for Neuroscience, UC Davis, Davis, United States

    Contribution

    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    A Kimberley McAllister

    For correspondence

    asnord@ucdavis.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4259-7514

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