NF-κB activation in astrocytes impairs wound healing after traumatic brain injury in male mice

Domain: chemistry
Published: 2026-03-06
Editorial outcome: major_revision

Abstract
Traumatic brain injury (TBI) is a complex condition in which multiple pathophysiological mechanisms influence the course of the disease. After the initial mechanical impact, neuroinflammatory reactions of glial cells along with infiltrating peripheral immune cells determine the overall clinical outcome. However, these secondary processes and their molecular determinants promoting either beneficial or detrimental consequences are not well-defined. Here, we show that TBI-mediated NF-κB activation in astrocytes impairs their homeostatic functions, amplifies the post-traumatic neuroimmune response and disturbs the multicellular CNS scar development in a male mouse model of TBI. Our results further demonstrate a specific deficit in the formation of the glial limitans border and establish that paracrine signaling pathways induced by NF-κB-activated astrocytes can prevent a beneficial restoration of the CNS integrity after TBI. These findings enhance our understanding on the NF-κB-mediated post-traumatic pathophysiology and provide information on future targeted therapies to improve TBI outcome.

Introduction
Traumatic brain injury (TBI) is a leading cause of death and represents a major health burden in survivors. TBI can initiate the development of long-term neurocognitive and neuropsychiatric morbidities and has been associated with an increased risk of late-onset neurodegeneration1,2. After the initial mechanical insult directly causing the primary brain lesion, biochemical and neuroimmune reactions set up a secondary injury phase persisting for days or even months and years. Together with other confounding issues, such as age, sex, and comorbidities, these multidimensional secondary processes determine the overall clinical outcome with or without long-term consequences and represent a challenge for the development of effective therapies3,4. Therefore, it has to be clarified whether individual post-traumatic reactions can be guided in a way that allows efficient tissue regeneration while suppressing chronic neurodetrimental inflammation and degeneration.

Astrocytes, the most frequent glial cells in the mammalian brain, execute essential tasks in maintaining CNS homeostasis. Upon injury, astrocytes rapidly respond and adapt diverse functional states aiming to restrict tissue damage and promote neural tissue repair5,6. Reactive astrocytes take an integral part in the CNS scar formation process and—depending on the context—can initiate neurotoxic effects upon traumatic insults. Distinct functional polarization states of astrocytes have been observed in stroke, trauma, and various neurodegenerative diseases, such as Alzheimer’s disease. Specific, phenotype-defining signaling pathways and transcriptional regulators, however, are not well-defined7–10.

The IKK/NF-κB signaling pathway regulates immune and stress responses but also mediates neuroprotection in a context-dependent manner11–13. NF-κB-induced neuroinflammation is increasingly recognized as a central pathogenic mechanism in neurodegeneration14–16, and recent studies identified NF-κB signaling in astrocytes as a critical module triggering pathogenic reactive states17–21. In TBI, multiple danger signals (DAMPs) and stimuli activating innate immunity22,23 activate NF-κB in both neurons and glial cells. While NF-κB in neurons confers neuroprotection after TBI24, it remains to be tested whether and which NF-κB-regulated processes in glial cells promote a beneficial or detrimental post-traumatic outcome.

Here, we use a male mouse model of TBI to determine the transcriptomic and cellular changes in transgenic mice with NF-κB modulation in astrocytes25 that underwent closed-head, traumatic brain injury (CHI), the most common form of TBI found in human patients26. We identified prominent NF-κB-regulated gene expression programs in astrocytes and microglia cells after TBI, and especially a dynamic NF-κB activation pattern in astrocytes surrounding the insult area was found integral for adequate wound healing.

Results
TBI induces a prominent NF-κB-related gene expression signature
To gain insight into the regulation of gene expression after closed head injury, RNA sequencing was conducted with bulk tissue obtained from the insult area of CHI and sham-treated wildtype mice. We focused on the early secondary injury phase 3 days post-TBI, which is critical for the development of long-lasting consequences. Principal component analysis (PCA) revealed two distinct clusters representing sham-treated and TBI-induced mice, which show strong differential gene expression (Supplementary Fig. 1a, b). GO term analysis revealed multiple biological processes, molecular functions and cellular components (Supplementary Table 1) mainly associated with inflammatory processes, immune and stress responses, as well as extracellular matrix (ECM) organization, which were enriched 3 days post-TBI. The KEGG pathway analysis identified several top pathways enriched, which are associated with IKK/NF-κB signaling (Fig. 1a), including the “NF-kappaB signaling pathway” itself (Fig. 1a), that was further supported by the increased expression of NF-κB pathway components (Supplementary Table 2). Diverse gene expression signatures tightly connected to NF-κB signaling and the regulation of neuroimmune responses like cytokines and chemokines (Fig. 1b), as well as interferon-associated genes (Supplementary Fig. 1c), were significantly induced by TBI. Furthermore, the top-upregulated genes showed a prominent enrichment of genes (11 out of 30) regulated by or related to the IKK/NF-κB signaling pathway (Supplementary Table 3). Importantly, a temporal expression analysis of representative NF-κB-regulated target genes starting from 6 h until day 30 after TBI revealed that those genes (e.g., Tgm1, Lgals3, Spp1, among others) peak between day 3 and 7 and reach basal levels at day 30 post-TBI (Fig. 1c–j), indicating transient NF-κB activation. Of note, TBI also decreased the expression of several genes (Supplementary Table 4) and processes, which mainly refer to synaptic signaling, neurotransmitter receptor and ion channel activity (Supplementary Table 5) (GO terms: e.g., “gated channel activity”; “synaptic signaling”; “chemical synaptic transmission”; and “neurotransmitter receptor activity”).

TBI-induced NF-κB activation is restricted to the insult area and follows a cell-type-specific pattern
To characterize the spatio-temporal and cell-type-specific NF-κB activation pattern after CHI/TBI in situ, we used NF-κB reporter mice (kappa-eGFP)27,28. These analyses revealed a highly selective NF-κB activation pattern (EGFP+ cells) that is restricted to the immediate vicinity of the impact area. NF-κB activation follows a dynamic course (Fig. 2a, b) and is visible already 1 day post-injury, gets significantly increased at day 3 and stayed elevated until day 7, returning to the level of day 1 by day 15 post-TBI (Fig. 2a, b). Immunostainings with cell identity markers GFAP and ALDH1L1 for astrocytes, IBA1 for myeloid/microglia cells (Supplementary Fig. 1d), and NG2 for oligodendrocyte progenitor cells, allowed to determine the cell-type-specific NF-κB activation pattern. The majority of EGFP+ cells with activated NF-κB signaling represent IBA1+ macrophage/microglia cells, followed by astrocytes, with NG2-glia acting as minor contributors at day 3 and 7 post-TBI (Fig. 2c). The number of IBA1+ cells with active NF-κB did not change from day 3 to 7 (Fig. 2d), whereas almost 70% of the lesion-located astrocytes show activated NF-κB at day 3 after TBI. This portion declined to around 40% on day 7 (Fig. 2e), indicating a highly dynamic NF-κB activation kinetic in astrocytes. CHI/TBI mimics the nature and heterogeneity of human TBI and can lead to different degrees of injury severity, including fractures and bleedings24,26. Interestingly, NF-κB activation can only be detected when post-traumatic bleedings also take place. Thus, the expression strength of astrocyte marker genes like Gfap, Aqp4, and Aldh1l1 positively correlated with hematoma formation (Supplementary Fig. 1e–g).

To further define the cell-type-specific NF-κB activation after TBI, we conducted flow cytometry using isolated cells from the wider insult area (Fig. 2f). Here, ACSA-2+ astrocytes showed pronounced dynamics in NF-κB activation, presenting with a major increase between day 1 and 3 to day 7 post-TBI, reaching residual activity at day 30 post-TBI (Fig. 2g and Supplementary Fig. 1h). O4+ oligodendrocyte lineage cells represent only a minor fraction with activated NF-κB (Fig. 2h and Supplementary Fig. 1h), while CD45+/CD11b+ myeloid/microglia cells already showed elevated basal NF-κB activity which significantly increases until day 7 post TBI and returning to starting levels at day 30 (Fig. 2i). To better differentiate between resident microglia and myeloid infiltrates besides IBA1 and CD11b expression, the status of the prototypical leukocyte marker CD45 was determined (Supplementary Fig. 1i, j). CD45low microglia were identified as the main contributors of post-traumatic NF-κB activation, while CD45high myeloid infiltrates only featured a comparably mild EGFP signal until day 30 post-TBI (Fig. 2j). Cells of the lymphoid lineage (CD11b−/CD45high) (Fig. 2j) do not show significant NF-κB activation. Among the myeloid subsets, post-traumatic NF-κB was prominently activated in CD45low/CD11c+ DAM cells at day 1 post TBI while CD45high CD11c+ cells show a transient downregulation of basal activity at day 3 (Supplementary Fig. 1k). Ly6G+ neutrophils exhibited only minimal post-traumatic NF-κB activation, while CD206+ CNS-associated macrophages (CAM) presented with marked NF-κB activity at sham and day 1 post TBI, a level continuously decreasing until day 30 (Supplementary Fig. 1k). CD3+ T-cells were almost free of NF-κB activity whereas B220+/MHC-II+ B lymphocytes show mild signs of NF-κB activation (Supplementary Fig. 1l).

Amplified NF-κB signaling in astrocytes impairs neurological recovery and compromises wound closure and reactive gliosis after TBI
To test the specific astrocytic NF-κB activation pattern on TBI outcome, we performed TBI with previously established loss-of-function (LoF; IKK2-DNGFAP) and gain-of-function (GoF; IKK2-CAGFAP) mouse models for NF-κB modulation in astrocytes25,29. These models use the tetracycline-regulated gene expression system (tet-off), where transgene expression was suppressed until the age of 3 and 5 weeks in the case of LoF and GoF animals via doxycycline application to avoid any developmental side effects. After doxycycline withdrawal, the IKK2-CAGFAP and IKK2-DNGFAP mouse models have been functionally validated and confirmed in detail prior to TBI induction (Supplementary Figs. 2 and 3).

After TBI, changes in body weight and motoric behavior were assessed as these parameters are frequently affected in human TBI patients30–32. Here, IKK2-CAGFAP animals experienced a persisting weight loss after TBI (Fig. 3a). Six hours after the mechanical insult, all groups, including sham-treated animals, showed a nonsignificant weight loss. While the sham cohort remained at this level for 1 day, both TBI-treated groups featured a further significant weight loss at this time point. At day 3, 7, and 30, TBI-treated controls regained weight to almost sham levels, whereas IKK2-CAGFAP animals exhibited a reduced body weight persisting until 30 days post-TBI, indicating an impaired recovery process in IKK2-CAGFAP animals.

A 10-point neurological severity score (NSS) was used to assess posttraumatic neurological impairments24,33. We also analyzed gait and locomotion utilizing an automated system (CatWalk™ XT) to test changes in voluntary movement with high temporal and spatial resolution34,35. The NSS revealed a transient increase in neurological impairments—mainly motor dysfunction during beam walking and beam/round stick balancing—6 h after TBI in both control littermates and IKK2-CAGFAP mice compared to corresponding sham animals (Fig. 3b). After 24 h, the NSS already decreased in both controls and IKK2-CAGFAP, however, the NSS stayed significantly higher in IKK2-CAGFAP mice at this time point and persisted until day 3 post TBI. Later on, no significant differences could be detected between groups (Fig. 3b).

While several of the gait parameters investigated by CatWalk™ XT showed only minor changes upon TBI compared to sham mice, the mean intensity of the maximal contact area of left and right front and hind paws was altered after TBI but without significant differences between genotypes (Supplementary Fig. 4a, b). Changes in the contact area can indicate an altered use of affected limbs. Indeed, the mechanical insult on the left hemisphere led to several impairments of the corresponding right paws in the IKK2-CAGFAP, such as length, width, area, and maximal contact area of the right front limb (Supplementary Fig. 4c). For the right hind paw, print positioning (Supplementary Fig. 4d) and print area (Supplementary Fig. 4e) were significantly altered in the IKK2-CAGFAP cohort. Overall, IKK2-CAGFAP animals showed significant differences, especially early after the initial trauma, but recovered until 30 days post-TBI.

To define the cellular and molecular changes influencing the TBI outcome of IKK2-CAGFAP mice, we first performed detailed histopathological analyses of the insult area at various time points after TBI. We found the formation of an initial astroglial border of GFAP-positive cells at day 3 post TBI in control mice, which evolves into a clearly defined glial scar encapsulating the core of the injury area in controls at day 7 and day 15 (Fig. 3c). In IKK2-CAGFAP mice, however, GFAP/IKK2 co-staining revealed a strongly increased and widespread expression pattern of GFAP already under sham conditions (Supplementary Fig. 5a). In response to TBI, the resulting wound borders in IKK2-CAGFAP mice were poorly defined and appeared with a vague and disorganized GFAP staining pattern, possibly indicating an impaired development of the glial limitans border (Fig. 3c, d). In control animals, the damaged trauma area gets slightly increased until day 3 post TBI, thereafter, remodeling of the tissue damage was steadily progressing, leading to a marked reduction in the overall lesion size at day 15 post TBI. In contrast, IKK2-CAGFAP animals showed a strong increase of the wound lesion size at day 3 with only limited reduction of the lesion size at day 7 and 15, thereby revealing a significant wound healing deficit over time until day 15. IKK2-DNGFAP animals do not develop obvious differences and behaved like controls with a clearly defined glial scar formation (Fig. 3c, d).

To further define TBI lesion progression, we also monitored the post-traumatic wound healing process via longitudinal, in-vivo MRI analysis at days 1, 15, and 30 post-TBI. In comparison to control littermates, GoF animals show a profound deficit in wound closure, exhibiting a significantly larger lesion size at day 15, which stayed enlarged until day 30 (Fig. 3e, f). Lesion size reduction in IKK2-DNGFAP animals again progresses similar to the control cohort (Fig. 3e, f).

To evaluate the changes in the glia scar formation of GoF mice in more detail, we performed NG2/CSPG4 and GFAP co-staining of the insult area (Fig. 4a and Supplementary Fig. 5) from day 1 to 15 after TBI. Here, the spread of the scar border zone, which is characterized by very strong NG2 and GFAP staining, was always on a lower level over time in the GoF model compared to controls (Fig. 4b, d). They develop a significantly thicker scar border, which then declines between day 7 and 15 post-TBI (Fig. 4b, d). The deposition of NG2/CSPG4 around the wound core was altered in the IKK2-CAGFAP model, showing a decreased presence over 15 days, while controls exhibit a dynamic course with a significant enrichment at day 7 (Fig. 4b). Reactivity of the NG2 glia was not affected as measured on the degree of hypertrophic NG2-glia (Fig. 4c). The GFAP expression in GoF mice always extends through the entire cortex due to high NF-κB activity, whereas controls again develop a distinctly dynamic course (Fig. 4e). Together, the ratio of injury size to the total area covered by an intense GFAP signal, was significantly lower for the IKK2-CAGFAP group at day 7 and 15 post TBI (Fig. 4f). Also, fibronectin staining revealed a locally restricted signal at the lesion side of controls, which disappeared until day 30 post TBI (Supplementary Fig. 6a, b). In the IKK2-CAGFAP cohort, however, a rather widespread fibronectin expression was observed, which peaked at day 3 post-TBI and declines afterwards until day 30 (Supplementary Fig. 6a, b).

Furthermore, distinct differences in the dynamic expression pattern of ECM-associated genes were detected over the course of 30 days in the bulk insult area of control and GoF animals (Supplementary Fig. 6c). While several mediators (Fn1, Dcn, Chst11, Vcan, Tgfb1, and Cstb) showed a stronger expression in GoF animals specifically at day 3 post TBI, only Chst11 and Tgfb1 stay elevated until day 7 post TBI, whereas Mmp12 exhibited a lower level of expression in GoF mice compared to controls. Expression in Col1a2 showed no significant difference. Overall, this suggests a disturbance in the temporal progression of the post-traumatic wound tissue remodeling and ECM formation in mice with NF-κB hyperactivation in astrocytes.

NF-κB modulation in astrocytes disturbs their homeostatic maintenance and reactive function
To decipher the underlying molecular mechanisms for the wound closure deficit in the GoF model, transcriptomic profiling of primary astrocytes (ACSA-2+) and CD11b+ cells (myeloid infiltrates/microglia) isolated from the ipsilateral hemispheres of sham and TBI treated IKK2-CAGFAP, IKK2-DNGFAP and control animals was conducted (Supplementary Fig. 7a). PCA analysis showed a variable, but genotype-dependent clustering upon TBI (Supplementary Fig. 7b) indicating selective TBI responses when NF-κB is either repressed or pre-activated in astrocytes. Intriguingly, the transcriptome signature of TBI-treated IKK2-CAGFAP and sham-treated IKK2-CAGFAP astrocytes did not cluster differentially in PCA, suggesting that NF-κB activation in astrocytes is sufficient to trigger a TBI-like gene expression response (Supplementary Fig. 7b). Volcano plots of DEGs revealed specific genes differently regulated by TBI in GoF and LoF versus control astrocytes (Supplementary Fig. 7c, d). While a great number of DEGs overlapped (Supplementary Fig. 7e, f), TBI GoF and TBI LoF astrocytes showed several candidates (Top 30) significantly up- or downregulated as compared to the TBI control cohort. In the GoF model, the differential upregulation mostly included genes associated with immune functions, while in the LoF astrocytes, induction was mainly found for structural or non classified genes (Supplementary Tables S6 and 7). In case of GO term analysis, TBI induces an enrichment of processes associated with immune response/inflammation e.g., “positive regulation of cytokine production” and “regulation of leukocyte activation” (Supplementary Fig. 8a) in controls, a reaction which was significantly pronounced in TBI IKK2-CAGFAP astrocytes (Supplementary Fig. 8b). Importantly, a direct comparison of TBI IKK2-CAGFAP vs. TBI control group further confirmed hyperactivation of immune reactions (Fig. 5a) and increased expression of cytokines and chemokines (Supplementary Fig. 8c) but also revealed enrichment of biological processes related to cell cycle progression (Fig. 5a). When TBI IKK2-DNGFAP astrocytes were similarly compared to the TBI control group, the over-represented GO terms showed obvious differences and included “mitochondrial protein complex”, “cofactor metabolic process”, and “antioxidant activity” (Fig. 5b). However, cytokine expression was not affected in IKK2-DNGFAP astrocytes (Supplementary Fig. 8c).

In line with the enriched proliferation processes in GoF astrocytes after TBI (Fig. 5a), we detected an overall increase in the cell number of GFAP+ astrocytes (Fig. 5c, d) and IBA1+ (Fig. 5c, e) cells around the lesion core of IKK2-CAGFAP mice compared to corresponding controls. Reactive astrocytes further increased until day 15 post-TBI, whereas IBA+ cells were elevated only until day 3. To further characterize the post-traumatic gliosis, proliferation of astrocytes and microglia was assessed by BrdU incorporation over time after trauma (Fig. 5f–h). Interestingly, astrocyte proliferation already peaked at day 3 in the IKK2-CAGFAP model, while controls reached maximal proliferation at day 7 (Fig. 5f, g). In IKK2-DNGFAP animals, astrocyte proliferation showed a mild attenuation without a clearly defined peak. A similar observation was made for IBA1+, where a significantly higher cell number and proliferation was detected at day 3 in IKK2-CAGFAP animals, while proliferation in the control group peaked again at day 7 post-TBI (Fig. 5f, h). Microgliosis and proliferation was significantly milder in the IKK2-DNGFAP model at day 7. These data indicate that astrocytes and microglia from IKK2-CAGFAP animals experience an accelerated post-traumatic proliferation response, leading to a specific reactive gliosis phenotype most likely promoting an irregular post-traumatic scar formation. In IKK2-DNGFAP animals, reactive gliosis seemed dampened but without significant effects on wound healing.

Genetic programs driving post-traumatic wound healing and regeneration are disrupted in IKK2-CAGFAP animals
To characterize the functional status of astrocytes and microglia cells in the IKK2-CAGFAP model, we assessed different transcriptomic signatures previously described in literature to be related to wound healing. Healthy astrocytes initiate specific genetic programs important for wound healing and regeneration as early as 2 days after spinal cord trauma36. When we tested this panel of cAEG genes (consensus healthy astrocyte-enriched genes)36 in our TBI model, we found them almost similarly downregulated in control and LoF astrocytes 3 days post-TBI. In contrast, astrocytes with activated NF-κB (sham GoF) already showed a prominent decrease in cAEG gene expression, which was further aggravated after TBI (Fig. 6a and Supplementary Fig. 8d). In addition, genes associated with homeostatic astrocyte functions, like ion channels and neurotransmitter receptors, were more strongly affected in the IKK2-CAGFAP model after TBI (Supplementary Fig. 9a, b). The epithelial-to-mesenchymal-transition (EMT)36, a signature associated with wound repair and regeneration36, gets significantly induced in controls, while IKK2-CAGFAP astrocytes did not mount this specific response (Fig. 6b), possibly resulting in an impaired border-forming capacity. GSEA of the GO-BP term “wound healing” (GO: 0042060), on the other hand, was enriched in both TBI control and TBI IKK2-CAGFAP astrocytes without significant differences (Fig. 6c), indicating a general capability of the IKK2-CAGFAP model to initiate wound healing.

Previous studies identified different subtypes of reactive astrocytes induced by either inflammatory insults producing a neurotoxic A1 phenotype or a rather neuroprotective and regenerative A2 phenotype initiated by ischemic injury37,38. As A1 neuroinflammatory reactive astrocytes are proposed to be induced by NF-κB, we found a significant enrichment of the A1 signature in astrocytes of IKK2-CAGFAP animals at sham and TBI conditions whereas astrocytes of control and IKK2-DNGFAP mice express lower levels of A1 genes (Fig. 6d). Interestingly, only IKK2-DNGFAP astrocytes develop a prominent upregulation in pan-reactive and neuroprotective A2 genes (Fig. 6d).

Importantly, we determined a negative, post-traumatic enrichment in various matrix-associated and regenerative GO terms in TBI IKK2-CAGFAP animals compared to the TBI controls, affecting both ACSA-2+ astrocytes and CD11b+ myeloid/microglia cells (Fig. 6e). Accordingly, the TBI-induced expression of multiple ECM component genes (GO: 0044420), such as Tgfb2, Col4a3, Lama or Ptn was significantly decreased in both cell populations derived from the IKK2-CAGFAP model (Supplementary Fig. 9c, d). Independent of the deficits in ECM remodeling, IKK2-CAGFAP astrocytes exhibited a negative enrichment in processes associated with protective and metabolic functions (Fig. 6f). This included glutathione peroxidase activity (Fig. 6g), important for the ROS-detoxifying processes. Furthermore, we could identify a SASP gene expression profile (senescence-associated secretory phenotype)39,40 after TBI (Supplementary Fig. 10a) previously linked to age-dependent wound repair deficits41. This signature was already present in non-lesioned IKK2-CAGFAP and further cemented by TBI (TBI IKK2-CAGFAP; Supplementary Fig. 10a). Notably, IKK2-DNGFAP animals develop a positive enrichment in those GO terms associated with ROS protection, such as “antioxidant activity” as well as an upregulation in components associated with their detoxification (Fig. 5b and Supplementary Table 8).

Astrocytic NF-κB activation exacerbates neuroinflammatory immune responses after TBI
Traumatic brain injury causes disruption and increased permeability of the blood-brain barrier (BBB), which supports easier entry of peripheral leukocytes into the brain. Immunoglobulin IgG staining in brain samples at different time points post TBI revealed a widespread parenchymal staining in IKK2-CAGFAP compared to control mice suggesting an increased BBB leakage in the GoF model (Supplementary Fig. 10b). Thus, we performed a detailed analysis of the post-traumatic neuroimmune response using flow cytometry to immunophenotype brain resident and infiltrating cells after TBI (gating strategies see Supplementary Fig. 11a, b). TBI induced a significant infiltration of myeloid cells (CD45high CD11b+) in all groups at day 3 of the post-traumatic immune response (Fig. 7a, b). Myeloid CD11b+ infiltrates further contained a population stained positive for CD11c, composed of CD45high/CD11c+ (Fig. 7c, e and Supplementary Fig. 11c) and CD45low CD11c+ (Fig. 7d, e) subsets. A minor population of CD45high CD206+ macrophages (Fig. 7f) is present in the lesioned hemisphere as well. The frequency of the overall myeloid infiltration was significantly higher in the IKK2-CAGFAP cohort (Fig. 7b). This also applies to CD11b+/Ly6C+ infiltrates, which mainly include classical pro-inflammatory monocytes and Ly6G+ neutrophils (Fig. 7f, g). A detailed subtyping revealed a heterogeneous mixture of mainly CD45low/intermediate/CD11c+ microglia, a CD45high/CD11clow/MHC-II+/−/Ly6C+ population and CD45high CD11chigh MHC-IIhigh dendritic cells (Supplementary Fig. 12a–c). In the microglia population (CD45low/intermediate CD11b+), expression of MHC class II and CD11c+ was significantly increased in the IKK2-CAGFAP cohort as well, thereby acting as an indicator of phenotypical changes (Supplementary Fig. 12d). All CD45high/CD11b+ cells co-expressed the migration marker CD44, which has previously been identified as an indicator of a brain-external origin42,43 (Supplementary Fig. 13a). Expression of this migration marker was also still present in residual infiltrates at day 14 post TBI, while expression density of the microglia marker P2RY12 further assists discrimination between resident and peripheral immune cells (Supplementary Fig. 13b). Remarkably, the peripheral CD45high/CD11b+ infiltrates in control and LoF mice were composed of CD11c+ cells and Ly6G+ neutrophils in equal parts, together with a smaller subset of mainly Ly6C+ cells and a minor population of CD206+ CAMs (Fig. 7h). GoF animals, however show an altered composition of peripheral infiltrates, featuring a marked preference of CD11c+ cells, while the proportion of Ly6G+ neutrophils was strongly decreased (Fig. 7h). Following up the qualitative and quantitative progression of the post-traumatic infiltration, we analyzed frequencies and total cell number of CD45high/CD11b- lymphoid (Fig. 7i, j), CD11b+/CD11c+ dendritic/DAM (Fig. 7k, l) and CD11b+/Ly6C+ (Fig. 7m, n) myeloid cells. Frequency and total cell number of lymphoid cells showed a mild increase in the course of 30 days post-TBI in both control and LoF mice. In contrast, GoF animals already presented with a significantly increased level of lymphoid cells (frequency and total number) at sham conditions, which gets prominently upregulated between day 3 and 14 post-TBI and stays elevated until day 30 post-TBI (Fig. 7i, j). CD11b+/CD11c+ cells exhibit a small peak in frequency and numbers at day 3 post-TBI in controls and LoF mice, which then declines to almost sham values at day 30. Again, GoF mice show an elevated basal level of CD11b+/CD11c+ cells and develop an obvious peak in total cell number from day 3 to 7, which then decreases again to basal levels at day 30 (Fig. 7k, l). Infiltration of CD11b+/Ly6C+ cells—primarily including monocytes and granulocytes—shows the highest frequencies at day 3 post-TBI in all three genotypes that then return to sham levels. This also applies to the absolute cell number, especially in GoF animals, which already start with a high basal level at sham conditions (Fig. 7m, n). The absolute cell counts clearly indicate a higher abundance of infiltrates in the GoF model. LoF animals do not exhibit significantly different population frequencies and cell counts but might exhibit a slight delay in the temporal organization of the post-traumatic leukocyte infiltration (Fig. 7i–n). Taking a closer look into the lymphoid compartment in the CNS, upregulation of astrocytic IKK/NF-κB signaling shows a profound effect on this population. The frequency of lymphocytes was sparse in controls at day 3 post trauma, while the GoF model already presents with changes in frequency and composition of the T cells (Supplementary Fig. 13c–f). Here, a significant increase in CD3+ T lymphocytes (Supplementary Fig. 13c) as well an altered CD4/CD8a cell ratio was determined, showing an increased frequency of CD3+/CD8a+ cytotoxic T lymphocytes (CTL) at day 3 post TBI (Supplementary Fig 13d–f). Interestingly, proportion of CD8a+ CTLs increases in the control cohort until day 14 post TBI, indicating that an adaption to the IKK2-CAGFAP group is taking place (Supplementary Fig. 13e). In contrast, there was no difference in the presence of B lymphocytes (B220+/MHC-II+) (Supplementary Fig. 13g). Overall, pre-existing neuroinflammation in IKK2-CAGFAP mice critically affects persistence and infiltration of different myeloid cell populations thereby interfering with regular post-traumatic wound healing.

According to the TBI-induced changes in myeloid immune cell infiltration, we determined differential gene expression profiles in purified CD11b+ cells derived from TBI control, sham IKK2-CAGFAP, TBI IKK2-CAGFAP and TBI IKK2-DNGFAP at day 3 post-TBI TBI as shown by PCA (Supplementary Fig. 14a, b). NF-κB pre-activation in astrocytes exerts a profound effect on the CD11b+ myeloid compartment at day 3 post-TBI in IKK2-CAGFAP mice, whereas controls and IKK2-DNGFAP only showed slightly separated clusters at this time point. The transcriptomic changes in CD11b+ cells were reflected in a positive enrichment of immune cell activation terms that again were more pronounced in TBI IKK2-CAGFAP mice, such as responses to IFN-γ and pronounced cytokine signaling (Supplementary Fig. 14c). IFN-γ has a unique function in microglia activation, promoting their proliferation and driving them towards neurotoxic phenotypes44. Correspondingly, components of the Interferon-gamma mediated signaling pathway (GO:0060333), such as Jak2, Stat1, and Irf1, were significantly upregulated in the IKK2-CAGFAP myeloid population (Supplementary Fig. 14d).

Another important component of the innate immune response in the CNS is inflammasome signaling45,46, and especially the NLRP3 inflammasome reacts on PAMPs and DAMPs released upon TBI47. We detected an increased expression of NLRP3 inflammasome components in the ACSA-2+ population of TBI IKK2-CAGFAP (Fig. 8a) and determined a strong expression of NLRP3 and the associated ASC protein in the ipsilateral (ip) TBI insult area compared to the uninjured contralateral (co) site (Fig. 8b–d). AIM2 (absent in melanoma 2), another inflammasome factor responsive to double-stranded DNA, was downregulated after TBI (Fig. 8b, e). Interestingly, the TBI-induced NLRP3 and ASC levels were significantly increased in IKK2-CAGFAP mice, corresponding to their elevated neuroinflammatory conditions and wound healing deficits (Fig. 8c, d).

Factors determining TBI outcome get deregulated by NF-κB activation in astrocytes
Finally, we determined the protein expression status of top-regulated factors associated with disease progression and regeneration at day 3 post-TBI, including TGM1 (Tgm1), HO-1 (Hmox1), and OPN (Spp1), which are known to be involved in protective and remodeling processes after brain injury48–50. Furthermore, marker proteins indicative for inflammation and reactive gliosis, like MAC2, LCN2, and IBA1, were addressed, as well as NRF2 levels to get information on oxidative stress and glutamate homeostasis after TBI (Fig. 8f). Immunoblot analyses validated robust IKK2-CA transgene expression in IKK2-CAGFAP mice. TBI leads to a significant downregulation, especially in IKK2-CAGFAP animals, while NRF2 is only expressed upon TBI (ipsilateral) but without differences between controls and IKK2-CAGFAP mice. Importantly, all other factors tested were found with altered protein levels on the ipsilateral side, indicating an immediate response at the direct insult area (for quantification see Fig. 8g–m). TGM1 expression is stronger expressed in the IKK2-CAGFAP insult area, whereas OPN is significantly stronger upregulated in IKK2-CAGFAP mice after TBI. In the IKK2-DNGFAP model, however, OPN expression is significantly stronger (Supplementary Fig. 15a, b) while other mediators did not differ from the TBI control cohort (Supplementary Fig. 15a–e), indicating a specific regulation by IKK/NF-κB signaling after TBI. Notably, analysis of Spp1 mRNA expression in purified astrocytes (Supplementary Fig. 15g) and microglia (Supplementary Fig. 15h) revealed a cell-type-specific regulation pattern after TBI. While Spp1 expression was prominently induced in both cell types, the overall expression level was much more pronounced in microglia compared to astrocytes, suggesting that microglia are the main contributors of OPN protein after TBI. Remarkably, this upregulation was significantly lower in microglia of IKK2-CAGFAP animals. HO-1, MAC2, LCN2, and IBA1 protein expression are highly responsive to trauma, while their baseline expression (contralateral side) is low or at the detection limit. In the case of HO-1, IBA1, and LCN2, the TBI-induced protein level was more abundant in IKK2-CAGFAP animals. As osteopontin and lipocalin are proposed critical markers for CNS injury and astrocyte reactivity8,51,52 we also calculated the ratio between OPN/LCN2 protein levels at the insult area to get a scoring of TBI progression/outcome (Fig. 8n). Indeed, there was a clear correlation between beneficial wound healing progression in controls showing an OPN/LCN2 ratio of around one and the detrimental outcome in the IKK2-CAGFAP model with a significantly reduced ratio (mean value=0.279). Interestingly, the OPN/LCN2 ratio correlated inversely with IKK2-CA transgene expression in astrocytes (Fig. 8f, n), meaning that the higher NF-κB activity after TBI, the lower the OPN/LCN2 ratio. Albeit the OPN/LCN2 ratio in the IKK2-DNGFAP model was found higher than 1, the increase was not significant according to the mild phenotype change (Supplementary Fig. 15f). However, at day 14 post-TBI, the IKK2-DNGFAP cohort still showed an enriched expression of OPN, HO-1 and LCN2 protein, suggesting an improved TBI outcome (Supplementary Fig. 15i).

Methods
Transgenic mice
All animal experiments were performed in compliance with the institutional animal care guidelines at Ulm University (TFZ, Ulm) and the German animal protection law and approved by the Regierungspräsidium Tübingen (RP Tübingen, Germany; TVA 1192).

Mice conditionally expressing either a constitutively active IKK2 allele (IKK2-CA) or a dominant-negative acting allele (IKK2-DN) in astrocytes (IKK2-CAGFAP, IKK2-DNGFAP) were previously generated and reported25,29,105. In brief, a transdominant (VSV-IKK2-DN; D145N) and a constitutively active version (CA-IKK2; IKK2-EE; S177E, S181E) of human IKK2 were cloned into the bidirectional tetracycline-dependent expression vector pBL5 to create the plasmids pBI5-VSV-IKK2-DN and pBL5-CA-IKK2. A bidirectional minigene was subsequently released by AatII-AseI, both fragments microinjected into the pronuclei of fertilized oocytes (NMRI outbred) and transplanted to pseudopregnant foster mothers106. To generate IKK2-CAGFAP and IKK2-DNGFAP transgenic mice, GFAP.tTA mice were crossed with (tetO)7.IKK2-CA) or (tetO)7.IKK2-DN)107. For transgene inactivation during complete development, doxycycline (0.5 g/L; MP Biomedicals) in 1% sucrose was given in the drinking water to the mothers during pregnancy from conception to weaning until the age of five weeks, then transgene expression was induced by Dox withdrawal. IKK2-CAGFAP and IKK2-DNGFAP mice were bred on the NMRI background, and single-transgenic and transgene-negative littermates were always used as controls. To monitor cell-type-specific NF-κB activation in response to TBI, we used an NF-κB-responsive reporter mouse line (kappa-EGFP) that uses EGFP as a read-out for in vivo NF-κB activity27,28. These mice were bred on pure 129/P2Ola background.

Male mice were used in experiments if not otherwise stated. Mice were maintained under specific pathogen-free conditions in the animal facility of Ulm University (TFZ, Ulm). Animals were kept in groups of 3–5 mice in a 12-h light-dark cycle and were given ad libitum access to food and water.

CHI model
Adult male mice (12–20 weeks old) were subjected to experimental CHI with a standardized weight-drop device24,26. The animals were anesthetized with ketamine (Pfizer Pharma, Karlsruhe, Germany), with an intraperitoneal dose of 100 mg/kg body weight, and 2% xylazine (Bayer HealthCare, Monheim, Germany), with an i.p. dose of 16 mg/kg body weight. Afterward, the skull was exposed by a longitudinal incision of the skin, and a focal blunt injury was induced in the left hemisphere by dropping a 330 g metal rod on the skull from a height of 2.7 cm. Only animals with a visible imprint of the falling rod on the skull were included for further analyses. After trauma, the mice received supporting oxygenation with 100% O2, the wound was sutured, and the animals were placed into a warmed recovery cage with ad libitum access to food and water. Buprenorphine analgesia (Temgesic; Essex Pharma, Munich, Germany) was administered subcutaneously (0.03 mg/kg body weight) immediately after trauma and again at 6 h until 24 h. Sham-procedure mice underwent anesthesia, scalp incision, suturing of the wound, and analgesia, but no experimental head trauma.

Gait analysis
Mouse gait analysis was performed using the CatWalk XT Automated Gait Analysis System (Noldus Technology, Wageningen, NL). Setup of the walkway area was done according to the manufacturer’s recommendations and adjusted for mice. Three compliant individual runs needed to be performed in 1–10 s for one successful trial. Prior to the experiments, mice were acclimated to the testing setup to ensure an uninterrupted performance. For the trial, home cages with food and nesting material were placed in the goal box, functioning as motivators for a successful run. Gait analysis was conducted by the provided software following acquisition of each trial, using a combination of automatic analysis and manual examination in slow motion. Runs displaying abnormalities, such as interruptions or sniffing by the analysis, were excluded. Trails were conducted using animals before and at day 2, 7, and 30 post-trauma or sham-treatment.

Primary cell isolation
Primary astrocytes and microglia from adult mice were isolated using the Adult Brain Dissociation Kit, gentleMACS Octo dissociator with heaters and MS columns in a magnetic field (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s protocol. In brief, brains were dissected, enzymatically digested, and the debris was removed. Four to 6 hemispheres were pooled per sample. The remaining single cell suspension was first Fc-blocked and labeled with anti-ACSA-2 microbeads (130-097-679, Miltenyi Biotec), washed, and applied to an MS column. Purified ACSA-2-positive astrocytes were pelleted and snap-frozen in liquid nitrogen. The flowthrough was subsequently labeled with anti-CD11b microbeads (130-049, 601, Miltenyi Biotec), washed, and purified using an MS column. The cell pellet containing CD11b positive cells was snap-frozen in liquid nitrogen. ACSA-2 and CD11b isolates were stored at −80 °C until further analysis.

For primary astrocyte cell culture, purified ACSA-2+ astrocytes were plated on Poly-D-Lysin-coated plates using serum-free astrocyte medium. After 24 h, cells were left untreated or treated with a cocktail of LPS (10 µg/ml)/TNF-α (100 pg/ml)/PolyI:C (2 µg/ml) for 16 h to stimulate the IKK/NF-κB signaling pathway. Subsequently, cells were harvested and RNA isolated.

RNA extraction, cDNA synthesis, and qRT-PCR
RNA of cortical tissue and primary cells was isolated using a PeqGOLD® TriFast™ kit (peQlab, Darmstadt, Germany) or, for the day 15 and 30 cohort, Qiagen RNeasy Kits (Qiagen) according to the manufacturer’s instructions. For cDNA synthesis, the transcriptor high fidelity cDNA synthesis kit (Roche, Penzberg, Germany) was used according to the manufacturer’s instructions, but with only 0.5 µL instead of 1 µL reverse transcriptase mix and oligo-dT primers alone. To perform qRT-PCR, we used the LightCycler® 480 Instrument (Roche) according to the manufacturer’s instructions with intron-spanning primers and mono-color hydrolysis probes designed by Roche’s Universal Probe Library UPL assay design center. As a reference gene, the housekeeping gene hypoxanthine-guanine phosphoribosyl transferase (Hprt1) was used. Primer sequences and UPLs are available upon request.

RNA sequencing of primary cells
RNA sequencing was conducted by Novogene (London, UK). In summary, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. After the fragmentation step, random hexamer primers were used for first-strand cDNA synthesis, which was followed by the second-strand cDNA synthesis using dUTP for the directional library. The library was afterward checked by Qubit and real-time PCR for quantification, for size distribution detection, a bioanalyzer was used. Index-coded samples were clustered according to the manufacturer’s instructions. After cluster generation, the library preparations were subsequently sequenced on an Illumina platform. Paired-end reads were generated, and raw data (raw reads) of FASTQ format were processed through fastq. Reads containing adapter and poly-N sequences, as well as reads with low quality, were removed in this step to obtain clean data (clean reads). Q20, Q30, and GC content of the clean data were calculated, and for all downstream analyses, clean data of high quality were used. GRCm39 reference genome as well as gene model annotation files were directly downloaded from the genome website browser (NCBI/UCSC/Ensembl). Paired-end clean reads were aligned to the reference genome using the spliced transcript alignment to a reference (STAR) software. This software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays, followed by seed clustering and stitching procedure. Compared to other RNA sequencing aligners, STAR exhibits better alignment precision and sensitivity in both experimental and simulated data. FeatureCounts was used to count the read numbers mapped of each gene, reads per kilobase of exon model per million mapped reads (RPKM) were calculated based on the gene length and reads count mapped to this gene. With RPKM, the effects of sequencing depth and gene length for the read count are considered at the same time. Currently, it is the most commonly used method for estimation of gene expression levels108. DEGS were calculated by DESeq2, heatmaps of DEGs were generated using R (Version 3.0.3) ggplot2 package, pheatmap package. For gene set enrichment analysis, GSEA 4.3.3 by UC San Diego and Broad Institute (https://www.gsea-msigdb.org) was utilized109,110.

RNA sequencing for bulk tissue
RNA sequencing was conducted by BGI (Shenzen, China). RNA isolated from bulk tissue of ipsilateral and contralateral insult tissue (Wildtype sham n=7, wildtype TBI ipsi n=7 and contra n=7) with a visible hematoma was subjected to 100-bp single-end sequencing with a BGISEQ-500 platform. For each sample, a sequencing depth of at least 48 million clean reads was applied. In brief, poly-A-containing mRNA molecules were purified using poly-T oligo-attached magnetic beads. Afterward, mRNA is fragmented into small pieces and copied into first-strand cDNA using reverse transcriptase and random primers, which is followed by second-strand cDNA synthesis via DNA Polymerase I and RNase H. Fragments were subsequently ligated to the adaptor, the products purified and enriched and pooled to generate a final library containing single-strand DNA circles (ssDNA circles). These ssDNA circles were used to generate DNA nanoballs, loaded into patterned nanoarrays, and read through the sequencing platform. Reads were filtered and cleaned using the SOAPnuke software, for genome mapping, HISAT was applied. Novel coding transcripts were merged with reference transcripts and mapped using Bowtie2, gene expression levels for each sample were calculated with RSEM. Data analysis was conducted using DESeq2 algorithms to detect DEGs. DEGs were used to perform Gene Ontology (GO) and KEGG pathway classification. GO analysis was conducted with PANTHER overrepresentation test, FDR correction, and raw p < 0.05 (release 24.02.2021).

Flow cytometry
Living cells were transferred into a fixed volume, and numbers were determined using Neubauer improved counting chamber. Staining for flow cytometric phenotyping was performed in PBS+5% FCS+ 0.05% Sodium Azide (FACS staining buffer). Dissociation of brains was performed using the Multi Tissue Dissociation Kit 1 (Miltenyi Biotec, Germany) according to the manufacturer’s protocol, Fc receptors were blocked for 15 min (FcR Blocking Reagent mouse, 130-092-575, Miltenyi Biotec, Bergisch Gladbach, Germany), and cells incubated with the respective conjugated antibodies for 20 min. Flow cytometry was performed with the BD FACSCanto II analysis was performed using FlowJo™ 10.7.1 (FlowJo LLC, BD). Living cells were gated by a combination of forward/side scatter and DAPI. Total counts of lymphoid, CD11c+ and Ly6C+ myeloid populations were calculated using the gated frequencies and the priorly determined number of living cells.

The following antibodies were used for flow cytometry analysis:
APC anti-mouse ACSA-2 (REAfinity™; Miltenyi Biotec #130-116-249), PE anti-mouse CD3e (clone: 145-2C11; BioLegend #100308), APC anti-mouse CD4 (clone: GK1.5; BioLegend #100412), FITC anti-mouse CD8a (clone: 53-6.7; BioLegend #100706), BV711 anti-mouse/human CD11b (clone M1/70; BioLegend #101242), PE anti-mouse/human CD11b (clone: M1/70.15.11.5; Miltenyi Biotec #130-113-797) APC anti-mouse CD11b (M1/70; BioLegend #101212), APC/Fire™ 750 anti-mouse/human CD11b (BioLegend #101261), FITC anti-mouse CD11c (N418; BioLegend #117306), PE anti-mouse CD11c (clone: N418; BioLegend #117308), APC/Cy7 anti-mouse CD14 (Sa14-2; BioLegend #123318), PE anti-mouse CD44 (IM-7; BioLegend #103007), APC/Cyanine7 anti-mouse CD44 (IM7; BioLegend #103028), APC anti-mouse CD45 (30-F11; BioLegend #103112), BV605 anti-mouse CD45 (clone: 30-F11; BioLegend #103140), APC anti-mouse CD45R/B220 (clone: RA3-6B2; BioLegend #103212), PE anti-mouse CD206 (MMR) (clone: C068C2; BioLegend #141706), APC/Cyanine7 anti-mouse I-A/I-E (M5/114.15.2; BioLegend #107627), PE anti-mouse Ly-6C (HK1.4; BioLegend #128007), FITC anti-mouse Ly-6G (clone: 1A8; BioLegend #127606), APC anti-mouse Ly-6G (clone: 1A8; BioLegend #127614), PE anti-human/mouse/rat O4 (REAfinity™, Miltenyi Biotec, 130-117-711), PE human IgG1 (REAfinity™, Miltenyi Biotec #130-113-450), PE Rat IgG2b (RTK4530; kappa, BioLegend #400636), PE Rat IgG2c (RTK4174; kappa, BioLegend #400707), FITC Armenian Hamster IgG (HTK888; BioLegend #400906), APC Rat IgG2b (RTK4530; kappa, BioLegend #400612), APC/Cyanin7 Rat IgG1 (RTK2071; kappa, BioLegend #107627)

Immunoblot analysis
Tissue samples (cortical impact area) were snap frozen in liquid nitrogen, homogenized with a mortar and pestle, and lysed in KA-lysis buffer [25 mM Tris-HCl, 150 mM NaCl, 25 mM sodium pyrophosphate, 50 mM β-glycerophosphate, 50 mM NaF, 2 mM EGTA, 2 mM EDTA, 1 mM DTT, 10% glycerol, 1% Triton X-100 (pH 8.0)] supplemented with protease inhibitors (1 mM PMSF and complete mini tablet; Roche Diagnostics, Mannheim, Germany). After centrifugation (30 min, 16,000×g), the supernatant was used as the total protein extract. Equal amounts of protein (30 µg) were separated by SDS-PAGE and transferred to nitrocellulose membranes. After blocking with 5% nonfat dry milk in TBS buffer for 1 h at room temperature, primary antibodies were incubated in blocking solution overnight at 4 °C or for 2 h at room temperature. After 3 washing steps, membranes were incubated with horseradish peroxidase–coupled secondary antibody for 1 h at room temperature. Membranes were exposed to ECL detection reagent (Thermo Fisher Scientific) and developed by ECL.

The following antibodies were used for immunoblot stainings:
Anti-TGM1 rabbit pAb (1:500, Abcam #ab103814), anti-Lipocalin-2 goat pAb (1:2000, R&D Systems #AF1857), anti-Osteopontin goat pAb (1:1000, R&D Systems #AF808), anti-Heme Oxygenase 1 rabbit pAb (1:2000, Abcam #ab13243), anti-NRF2 mouse mAb (1:1000, Santa Cruz Biotechnology #sc-365949), anti-Galectin-3 rabbit pAb (Santa Cruz Biotechnology #sc-20157), anti-IBA1 rabbit pAb (1:2000, FUJIFILM Wako #019-19741), anti-IKKβ rabbit mAb (1:1000, Cell Signaling Technology #8943), β-Actin rabbit mAb (1:500, Cell Signaling Technology #8457), HRP-linked anti-rabbit IgG (1:2000, Cell Signaling Technology #7074), anti-EAAT1 rabbit mAb (1:1000, Cell Signaling Technology #56845).

In-vivo MRI analysis
Longitudinal in vivo MRI was performed at 1, 15, and 30 days post injury (dpi) using a Bruker BioSpec 11.7 T small animal MRI system. Animals were anesthetized with 1.5–2% isoflurane in oxygen, administered via a nosecone, and positioned securely on an MRI-compatible animal bed during imaging. Axial and coronal T2-weighted images were acquired using rapid acquisition with relaxation enhancement (RARE) sequences. For axial imaging, the parameters were: repetition time (TR) of 250 ms, echo time (TE) of 2.2 ms, slice thickness of 0.5 mm, 33 slices, flip angle of 15°, and a field of view (FOV) of 17×17 mm² with a matrix size of 260×260. For coronal imaging, the TR was 193 ms, TE was 5.0 ms, slice thickness remained 0.5 mm, 15 slices were acquired, the flip angle was 17.5°, and the FOV and matrix size were the same as for axial imaging. Four signal averages were collected for each scan to improve the signal-to-noise ratio. The bandwidth for image acquisition was set at 96 KHz. Images from these sequences were used for longitudinal lesion volume quantification, analysis and assessment of wound healing in IKK2-CAGFAP (n=4), IKK2-DNGFAP (n=3) and control (n=6) cohorts. For evaluation of the wound closure process, lesion volumes were first quantified using the RadiAnt DICOM Viewer 2025.2 (Medixant). For that, areas which showed an obvious loss of tissue, tissue perturbations or formation of white spots were measured and used for calculations of the total volume. The lesion volume of day 1 was set to 100%, volumes of day 15 and day 30 were calculated as % of the remaining lesion volumes compared to day 1.

Comparison between the cohorts within each time point is carried out with 2-way ANOVA and Tukey’s multiple comparison test.

Post-mortem sample preparation
For the analysis of EGFP expression and glial reaction to trauma, mice were sacrificed 1, 3, 7, or 15 days after the CHI or sham procedure (at least 3 mice per group). Only mice showing a visible imprint (or fracture) and hematoma formation after CHI were included in the study.

For post-mortem analyses, animals were anesthetized with an IP injection of a mixture of ketamine (100 mg/kg) and xylazine (10 mg/kg) diluted in a 0.9% NaCl solution. For tissue fixation, transcardial perfusion was performed, first with 25 ml of ice-cold PBS to allow blood removal, followed by 50 ml of ice-cold 4% PFA in PBS. Brains were extracted and further postfixed in 4% PFA for 2 h, transferred to a solution of 30% saccharose in PBS at 4 °C overnight for cryoprotection, and stored at 4 °C until use.

Histology and immunofluorescence staining
For immunohistochemistry, 20 µm-thick cryosections were used, and only the slides that showed a visible trauma core were included in the study. Twenty-micrometer-thick cryosectioned brain slices were permeabilized and blocked in a solution of 5% BSA in PBS with 0.5% Triton and incubated with primary antibodies in blocking solution overnight at 4 °C. After washing with PBS, slices were incubated with secondary antibodies in blocking solution for 2 h at room temperature. For nuclear antibodies, heat-mediated antigen retrieval was needed and was performed with sodium citrate (10 mM, pH 6.0, 0.05% Tween 20) before the incubation with primary antibody. When both nuclear and non nuclear antibodies were used at the same time, only non nuclear antibodies were incubated at first and were then fixed with 4% PFA before antigen retrieval. After antigen retrieval, the slices were incubated with both nuclear and non nuclear antibodies. Following primary antibodies were used: chicken α-GFP (1:250, Aves Lab #GFP-1020), rabbit α-NG2 (1:250, Merck #AB5320), rat α-BrdU (1:250, Abcam #ab6326), guinea pig α-Iba1 (1:500, Synaptic Systems #234004), mouse α-GFAP (1:500, Sigma-Aldrich #G3893), rabbit α-ALDH1L1 (1:250, Abcam #ab87117). The following secondary antibodies were used for immunodetection of the primary antibodies: α-chicken Alexa Fluor® 488 (1:500, Thermo Fisher #A11039), α-rat Alexa Fluor® 488 (1:500, Thermo Fisher #A21208), α-mouse IgG Alexa Fluor® 647 (1:500, Dianova #115-606-072), α-guinea pig IgG Alexa Fluor® 647 (1:500, Thermo Fisher #A21450), α-rabbit Cy® 3 (1:500, Dianova #711-165-152). Afterward, slices were washed with PBS and incubated with DAPI at a 1:1000 dilution for 10 min for nuclear detection and then mounted on a slide with aqua-poly/mount for imaging.

Image acquisition and cellular quantification analyses
For the study of EGFP-expressing cells, overview images of the trauma area were acquired with the Keyence BZ-9000 BioRevo microscope (Keyence, Neu-Isenburg, Germany) with filters for DAPI, FITC/Alexa Fluor 488, Texas Red/Alexa Fluor 568/594 and Cy5, in a ×20 magnification and 3 mice per timepoint were included in the study. For the quantification of EGFP-expressing cells, image acquisition was performed with a Leica SPE confocal microscope with ×40 magnification (area: 275,27×275,27 µm per image) and analyzed through the Fiji software (ImageJ). For each immunostaining quantified, at least 6 different images from 2 to 3 consecutive slides containing the trauma core were used for quantification. Cells were quantified if positive for EGFP and a cellular marker (NG2 for NG2-glia, GFAP or Aldh1l1 for astrocytes, Iba1 for microglia), and their nucleus was positive for DAPI counterstaining. The total number of individuated cells per mm2 was reported.

For the quantification of the trauma area, 3 images per mouse were acquired with the Keyence BZ-9000 BioRevo microscope at a ×10 or ×20 magnification, and the area of trauma was visible as an area with DAPI staining (although nuclei were smaller, indicating possible cell death), but no glial markers. Similarly, overviews of the ipsilateral hemisphere were imaged at a ×20 magnification with the Keyence BZ-9000 BioRevo microscope for the analysis of the fibronectin and immunoglobulin stainings, as well as for the quantification of the GFAP/CSPG4 scar.

For the quantification of gliosis, mages were acquired in the area adjacent to the CHI, defined as an area with morphologically different nuclei and without clear cellular staining. The border of the CHI is where gliosis takes place, and the scar is forming. For this study, three different images per immunostaining were quantified, and 3–5 different mice per timepoint were analyzed. Image acquisition was performed with a Leica SPE confocal microscope with ×40 magnification (area: 275,27×275,27 µm per image) and analyzed through the Fiji software (ImageJ). Glial cells were identified by the presence of DAPI-positive nuclei and positivity to a cell-specific marker (NG2 for NG2-glia, GFAP for astrocytes, Iba1 for microglia). For the quantification of glial proliferation, a cell-specific marker was used for cellular identification and nuclear positivity for BrdU was double-checked with DAPI. To distinguish NG2-glia from NG2+ pericytes, the presence of cellular processes and distance from a blood vessel was assessed. All data were analyzed in a blinded manner, and quantifications are reported as mean ± SEM. No statistical method was used to determine the sample size, and they were purely based on previous studies in the field.

Statistical analysis
Analysis of RNA seq data was conducted using NovoMagic, a free online RNA seq tool provided by Novogene (Version of April 2024). DEGs were defined as p < 0.05, |log2 FC| > 0, for GSEA and GO term analysis, significant hits were defined as FDR < 0.05. Immunoblot densitometry was analyzed with ImageJ. Statistical analyses were performed with Prism software (GraphPad, San Diego, CA, USA) and indicated in the specific figure legend. Normality and Gaussian distribution were assumed and not tested separately. Unless indicated otherwise, one- or 2-way ANOVA with subsequent Tukey’s multiple comparison test was used to compare independent measurements at one or different time points, respectively. Data were considered significant at p < 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript entitled, NFkB activation in astrocytes critically affects wound regeneration after TBI, the authors use a weight-drop model of closed-skull brain injury (in mice) to explore gain of functon(GoF) and loss of functon(LoF) of NFkB signaling (IKK2) in astrocytes and focus on wound healing and inflammation. This closed skull injury model induces a significant wound in the cortex of mice. Here, the authors report that TBI induces a prominent NFkB-related gene expression signature within the cortex after TBI. This corresponds with proliferation of astrocytes, cortical inflammation, and induction of a wound healing/glial scar response. Based on the initial analyses, the authors predict that astrocytic responses in NFkB signaling are important in the inflammatory response to TBI. As such, they use astrocytic-IKK2 gain of functon(GoF) and astrocytic-IKK2 loss of function transgenic lines. They report that TBI-WT, TBI-CA (gain of function), and TBI-DN (loss of function) all result in different transcriptomic signatures in astrocytes. In general, the astrocytic- IKK2 gain of function mice had worse cortical lesions and increased myeloid cell infiltration compared to the WT-TBI mice. Nonetheless, the astrocytic-IKK2 (GoF) had major effects on transcriptional profiles, independent of TBI. While, astrocyte-NFkB GoF increased the size of the lesion initially (up to 15 dpi), the wound sizes were similar by 30 dpi. The dominant negative of IKK2 in astrocytes did not have much of an influence on the parameters tested. This may reduce the relevance of the data using the IKK2 overexpression in astrocytes. The overall conclusion is that over expression of IKK2 in astrocytes disrupts wound healing and this is associated with more inflammation and recruitment of myeloid cells.\n1. One major concern is that in that the gain of function experiments using astrocytic-IKK2 show that there is a robust effect of genotype, independent of TBI. This effect is clear in the data from figures 4 and 5. In figure 5, the GoF in astrocytes induces a TBI-like response in the context of the transcriptional analyses. While the authors acknowledge this, it is still problematic for the study. One issue is that the gain of function of IKK2 (or overexpression) in astrocytes seems artificial (based on the results with the dominant negative version) and therefore, less biologically relevant. In addition, the study focuses only with the lesion area of the cortex after TBI. The gain of function problem in the astrocytes will be widespread throughout the brain. Furthermore, the potential confounds of this main effect of genotype is obscured throughout the paper. The authors switch from showing sham-controls to showing all TBIs groups with comparisons of the ipsilateral vs contralateral sides of the injured cortex (Figs.7-9). It is important to note that the authors show an interaction effect between astrocytic-IKK2 and TBI in the context of lesion size (up to 15 dpi), myeloid cell infiltration in the lesion, and with the transcriptional analyses. For the lesion, however, it is the same between TBI and TBI-astrocytic-IKK2 by 30 dpi. It is also unclear how the data in Fig.8 supports the author’s claim on wound healing. These data are difficult to interpret and they lack sham controls. For these reasons, the primary conclusion of the paper are not convincingly supported by the data presented.\n2. Another related concern is that astrocytic-IKK2 dominant negative (IKK2-DNGFAP) does not affect TBI induced CNS scar formation or myeloid cell infiltration. The transcriptional effects in the astrocytic-IKK2, seem different (from the PCA plot) but were not explored in the manuscript. Based on the data in Fig.1 and the assumptions of the introduction, the astrocytic-IKK2 dominant negative should reduce the cortical inflammation after TBI, but it does not. Because it does not seem to have much of a role normally, the overexpression of IKK2 in astrocytes has reduced biological significance.\n3. There are also some concerns on novelty and relevance of the study. For example, it is well established that TBI causes inflammation and increases in NFkB mediated pathways in the brain of rodents. Moreover, myriad neurotrauma (SCI and TBI) studies in rodents show that astrocytes are critical for glia scar formation, are essential for wound healing, and influence immune cell infiltration in the brain and spinal cord. Here, the main findings is that a gain of function/overexpression of NFKB signaling in astrocytes impairs the wound healing response at the lesion site.\n4. Another concern is that the description for the generation of the dominant negative (LoF) and constitutively activated (GoF) cell specific mouse lines are not included within the manuscript. Also, the authors don’t provide any validation that the NFKB pathway is overexpressed in astrocytes and that the NFKB pathway is non-functional in astrocytes. Validation of the models is an important element of rigor and reproducibility.\n5. In the assessment BrdU+ IBA1 and GFAP at 3dpi, there are no sham groups included.\n6. In Fig.2 the authors only show representative images at 3 dpi. Authors should show all time points, not just 3dpi, especially because it seems to peak at 7dpi.\n7. Concern that in Fig.3 the text does not match the data/representative images. Authors state that controls increase from day 1 to day 3, but it seems to decrease in the image and not really change in the graph. As they say, the lesion decreases in controls from day 3 to day 7 – 15 but increases and then stays the same in the GoF. In Fig.3b, there is a key for all three groups but then they do not include the LoF group. It also looks like the representative image do not match the graph for 1dpi. It is also unclear why Fig.3c separates all the groups and what time point this graph represents.\n8. Fig. 6. shows volcano plots for TBI-WT v sham and TBI-CA v sham but not direct comparison, even though that is the group of most interest.\n9. Concern that the data in Fig.7 are disjointed. It starts with data from fig 6 and then moves to blots of ipsi/contra tissue and then ending with more astrocyte transcriptional data from before. It is unclear if control is contralateral tissue and CA is ipsi or if this is just a genotype comparison. Also unclear why in Fig.7d-f, it switches from including sham controls to contralateral controls.\n10. Fig. 9 shows western blots of proteins they say are important for wound healing and inflammation. Nonetheless, no citations supporting this idea are provided. The author’s use of the ratio of osteopontin (protective) to lipocalin-2 (inflammatory) is arbitrary.", "reviewer_2": "In the submitted manuscript, Hein et al use astrocyte-specific strategies to modulate NfKB activation through loss of functon(IKK2-DN GFAP) of gain of functon(IKK2-CA GFAP) mouse models and investigate the how this affects various outcome in a closed head injury model of TBI.\nAn important role for NfKB following traumatic injury is long established, though the attempt to look at this from an astrocyte-specific perspective is interesting and novel.\nA large proportion of the data in the paper is from bulk transcriptomic analysis of either whole tissue or isolated cell populations. Much of this can be confusing as direct comparisons are often made between groups that are of secondary interest and then descriptive comparisons of these comparisons are made (see below).\nThe main finding of the paper is that IKK2-CA GFAP exacerbates many readouts after TBI including behaviour, tissue lesion size and, most interestingly, immune cell recruitment.\nThe title of the paper talks about ‘wound regeneration’ but the paper doesn’t really get into what ‘regeneration’ means and it data to show actual wound regeneration is lacking. It would be good if the authors could clarify what they mean by ‘wound healing’ and ‘wound closure’ and what this means in the brain?\nAlso, a major theme of the paper is scar formation and tissue remodelling, and there is transcriptional and some protein analysis to support this, though a much more thorough analysis of the scar and tissue in tissue sections would be needed to support this (see below).\nThe major interesting finding in this paper is that astrocyte-specific activation of NfKB is sufficient to massively induce a peripheral infiltration of immune cells that are likely responsible of increase tissue damage and lack of resolution of inflammation. This has not been shown before and is of interest to the field.\nLastly, the authors largely focus on gain of functon(IKK2-CAGFAP) rather than loss of function IKK2-DNGFAP as the latter had little to no effect on the model. How does this fit with the authors view that NfKB could be an effective therapeutic target?\n(Note: On multiple occasions figures are not referred to in the text.)\nI have multiple comments that would also need addressing, in figure order:\nFigure 2\nFigure 2e authors describe microglia maintaining NfKB expression but an alternative explanation could be that Iba1 positive recruited monocyte-derived macrophages are taking their place.\nThe authors need to do experiments to show definitively microglia are changing, or tone down microglial-specific conclusions\nFigure 3\nFig. 3b measures ‘Trauma dimension’ and 3c is ‘TBI area’. Fig 3c certainly is underpowered if the authors wont to claim a no change.\nFig. 3c shows control vs IKK2-DNGFAP but this is not referred to in the text at all.\nFig. 3d How do the authors account for what appears to be other lesion sites across the brain when quantifying lesion volume? Ex vivo imaging clearly shows major cavities proximal and distal to the arrow to where they indicate lesion was measured.\nA reduction in lesion volume at 30 days does not equate to tissue regeneration. Is a collapsed cavity or tissue swelling/reorganisation quantified as ‘healing’ by this measure?\nThe authors also discuss ‘clearly defined border’ of GFAP surrounding the core that is lost in IKK-CA mice. This is not clear in these images, partly due to the lines drawn over the border. Higher magnification and potentially some quantification is needed if this is to be claimed.\nFigure 4\nFig. 4e is really interesting in that a chronically primed astrocytes are still responsive. They are not tolerised but primed and become much more activated with TBI.\nWhat is even more interesting is that astrocyte activation does not tolerise microglia but again primes them. This would be the opposite to if a microglial cell was to receive exogenous stimuli, there responses likely dampened see (PMID: 29643512) and many other examples of innate immune tolerance.\nOne caveat here is that it is extremely difficult to quantify microglia in these traumatic lesions as many of the macrophages (microglia or monocyte-derived) will indeed be monocyte-derived cells.\nIt would be very interesting to know if the chronic activation of astrocytes can prime resident microglia or whether the microglia are tolerant to TBI but there increase in Iba1 is in fact due to extra recruited monocyte-derived macrophages. (see below, but I think the data here shows evidence for recruited immune cell changes, not microglia changes)\nThe behavioural data nicely corroborates the lesion data and is unsurprising that the animals recover to control levels considering how well animals recover/compensate motor function deficits.\nFigure 5\nIt is very hard to understand what the take home message is here.\nMost of this Figure (Figure 5 b/g/h) suggests that TBI has no additional effect on the astrocytes that already have NFkB activation (TBI IKK2-CAGFAP vs TBI control).\nHowever, something must be driving the large lesion and behavioural changes between these groups. Indeed, supplemental Fig 3 d and e do suggest what is responsible.\nThe authors are right to highlight the similarity in profiles but I suggest should focus on supp data 3 d and e (move them to the main Figure) as a potential explanation to what is driving the histological lesion and behavioural changes between TBI IKK2-CAGFAP vs TBI control.\nMost of the other comparisons appear to confuse this message i.e. fig 5a, c, d, e and maybe even g and h. Figures Supplemental 3d, e in supplemental three are much more compelling.\nThis then very nicely moves on to the flow cytometry data, Figure 6.\nFigure 6\nThese data are very compelling, though it could be much clearer to which cell populations are being gated/quantified. The authors have gone to the trouble of sub gating different myeloid populations in supp Figure 4 but have not quantified them, this would be of interest to the reader.\nFigure 6a shows major increase of myeloid cells (CD11b+ cells) but that specific gate is not shown either in the figure or the supplementals.\nFigure 6b shows major increase of Ly6C+ monocytes, yet the only gate shown is for Ly6C+CD44+ cells, is this what is quantified in 6b?\nFigure 6c-e shows an increase in what appear to be dendritic cells. This would be a very interesting finding. However, due to the bright staining, and what appears to be a lack of a live/dead stain in the samples, the authors would need to show either an IgG or FMO control for CD11c to show their staining is specific.\nIf they are DCs, what do the authors think they are doing there? Are they presenting antigen to the increased lymphoid cell populations? It would be very interesting to know the make-up of this lymphoid population considering the role that Th1/Th2 or Tregs can play in exacerbating or resolving TBI-associated injury.\nThe PCA plots show nice difference in CD11b cells between the key group of interest TBI IKK2-CAGFAP vs TBI control.\nWhy do the authors then show volcano plots from TBI IKK2-CAGFAP vs sham? It would be much more beneficial for the reader to understand what are the transcripts that are changed between TBI IKK2-CAGFAP vs TBI control.\nIn Fig 6k and l the groups ‘sham’ is not even represented in the PCA plot. Is this because they sham CD11b+ cells are almost entirely microglia, whereas the TBI conditions will have many infiltrating cells? If so, is there much point comparing them in this way?\nThese data are extremely interesting and show that astrocyte-specific activation of NFkB drives immune cell recruitment and retention in the TBI brain. They also suggest that data in Figure 4 should be revised as it is not possible to say that these astrocyte effects in TBI are on microglia, but just as likely recruited monocyte-derived macrophages from the blood.\nFigure 7\nFigure 7a and b are not referred at all in the main text, if they are in the main figure they should be described. Again, however, the interesting finding is that IKK2-CAGFAP exacerbates TBI. Can the authors show this comparison instead?\nThose that they show are homeostatic microglia vs microglia/monocytes/DCs/neutrophils and do not inform us of what the factors are in IKK2-CAGFAP mice that are exacerbating injury.\nFigure 8.\nThe authors want to show that tissue remodelling and scar formation are affected by astrocyte NFkB manipulation. They show that many ECM genes are downregulated in astrocytes and myeloid cells.\nThen, again, the authors start to make comparisons not directly between TBI IKK2-CAGFAP vs TBI control but between TBI vs sham and TBI IKK2-CAGFAP vs sham then comparing (not directly) their outputs, for example Figure 8f. regarding a an EMT gene signature. It is difficult to make conclusions this way.\nTo make this much stronger the authors should go back to the tissue and look at the scar formation with GFAP, col4, CSPGs or other ECM proteins are measure what the border forming capacity is. This alluded in Figure 3, but to claim scar formation and or tissue regeneration is altered, they need to show this at the tissue and protein level, on sections, ideally across time.\nMethods\nMuch more information is needed on the model. Were animals excluded for fractures? What was the mortality rate? Were there any other exclusion criteria?", "reviewer_3": "This is an interesting preclinical study that uses elegant reporter and astrocytic loss of function and gain of function models to investigate NFkB activation in the context of acute TBI, wound healing and functional recovery. Overall, the preclinical studies are rigorous and well designed, and the conclusion are quite well supported by the data. However, additional evidence for deficits in wound healing in the IKK-CAGFAP model are needed, and more quantitative analysis of the glial scar dynamics would strengthen the findings of the paper.\nThe author should address the following points related to these new studies:\n1) The opening figure is limited by solely focusing on bulk RNAseq data from a single timepoint (3dpi). It might be useful to add the temporal data for NFkB regulated genes (SFig 1) to the figure to highlight the dynamic response of IKK/NFkB signaling following CHI.\n2) The use of the NFkB reporter model in Figure 2 is novel and elegant. It provides clear visualization of activation of NFkB signaling in astrocytes and microglia in the acute phase post-injury. To improve the descriptive opening of the manuscript the authors may wish to consider combining tissue level bulk RNAseq/qPCR analysis of IKK/NFkB signaling with the EYFP-reporter model for NFkB activation that details cell-specific responses in astroctyes and microglia. I believe this will lead to a stronger foundation to the research article.\n3) The IKK2-DNGFAP and IKK2-CAGFAP loss and gain of function models for NFkB activation in astrocytes are nice.\nAstrocyte NFkB activation (IKK2-CAGFAP) results in lesion expansion and delayed resolution following CHI, when compared to control CHI mice. The temporal ex vivo MRI lesion assessments are interesting and support the notion of delayed wound healing response acutely following CHI, which ultimately reaches same level of injury resolution at 30 days post-injury. It is a pity that these are not in vivo longitudinal MRI assessments in the same mice because that would be more rigorous and biologically insightful. Are other DTI metrics for white matter damage across the time course available to investigate effects of IKK2-CAGFAP on TBI pathophysiology?\n4) The glial scar in figure 3 needs to be more clearly defined and quantified in control CHI mice in order to compare lack of scar formation in IKK2-CAGFAP CHI mice. There is a robust increase in GFAP reactivity in the IKK2-CAGFAP mice under sham conditions, so it is difficult evaluate the glial scar in CHI mice. Additional quantitative measures are needed.\n5) Glial proliferation studies in Figure 4 are interesting and indicate increased Brdu incorporation in astrocytes and microglia rapidly after CHI in the IKK2-CAGFAP mice. It would be helpful to include a schematic diagram for the region of analysis in Fig 4e and 4f. Is proliferation only occurring at the lesion boundary/glial scar, or does it also occur in contralateral regions distant from the lesion epicenter? There is a much higher level of proliferation in microglia compared with astrocytes. How do the authors interpret this early injury proliferative response and immune interactions between astrocytes and microglia?\n6) Neurological outcomes: The CatWalk gait analysis can measure >100 specific parameters of motor function, coordination, and gait, and so it is relatively easy to show differences between groups using this analysis. How they are related to fine motor coordination impacted by TBI are less clear, and this is hotly debated in the neurotrauma field, especially when assessing motor function in injured mice. No sham mice are included in the current CatWalk analysis… Therefore, it would be important to use more established motor function tests such as accelerating rotarod and/or beam walk to examine fine motor coordination. Furthermore, cognitive function is chronically altered after CHI/TBI, so inclusion of cognitive outcomes (e.g. MWM or novel object recognition) would strengthen the analysis of post-traumatic neurological impairments in IKK2-CAGFAP mice.\n7) RNAseq of isolated astrocytes: The important comparison of TBI IKK2-CAGFAP and TBI control has been relegated to supplemental material (SFig 3c). It would be better suited in the main figure.\n8) NLRP3 inflammasome activation is implicated in the heightened inflammatory response in TBI IKK2-CAGFAP mice. Is this specific to microglia or astrocytes? Perhaps RNAseq data can be examined to address this open question? Also, it would be useful to analyze cleaved caspase-1, IL-1b, and IL-18 in this sample to provide a more robust analysis of NLRP3 inflammasome activation in the model.\n9) There is a need for more concrete evidence to support deficits in wound healing in TBI IKK2-CAGFAP mice. Protein level and in situ analyses (imaging) of ECM and wound healing pathways are needed to support the transcriptomics data in Figure 8.\n10) A much deeper analysis of osteopontin (OPN) is needed, and a rescue experiment using OPN in TBI IKK2-CAGFAP mice or inhibition of LCN2 should be considered to nail the mechanisms that impair wound healing following in TBI due to astrocytic NFkB activation (related to figure 9).\n11) The title needs to be tempered based on existing data, and ‘wound regeneration’ following TBI needs to be more conclusively demonstrated with empirical evidence."}, "author_response": "Response: We would like to thank the reviewer for critically questioning our study and\nexpressing the doubts about the general biological relevance of a gain-of-function\napproach due to the limited effects of the corresponding loss-of-function approach. To\naddress these fundamental issues, we performed several new experiments and added\nnow additional data to strengthen both the relevance of data derived from the gain-of-\nfunction model and to characterize the post-traumatic-consequences in the LoF model\nin more detail. On transcriptomic levels, the IKK2-LoF model shows an upregulation\nof GO terms associated with ROS protection, mitochondrial activity and metabolism\n(Fig. 5g), which might be more relevant for the long-term outcome of TBI. On cellular\nlevel, we found a faster clearance of CD11c+ and lymphoid cells in the later stages of\nTBI (Supplementary Fig. 10i/j), while immunoblot analyses showed persistently\nelevated levels of osteopontin, a factor known to improve TBI outcome\n(Supplementary Fig. 11i). There is also some reduction in microglia and astrocyte\nproliferation (Figure 6) in the LoF model, which however did not significantly affect the\noverall wound healing process.\nWe now also performed longitudinal MRI analysis until 30 dpi to follow lesion size\ndevelopment in the same animal over time in more detail validating the deficient wound\nhealing process in the GoF model, showing a significant difference at day 15 and day\n30 (Figure 3).\nRegarding the overt relevance of the IKK2-CAGFAP model, it became clear in the last\nyears that IKK/ NF-kB signaling increases with advancing age and in the context of\nneurodegenerative disorders such as AD or PD (Kaltschmidt et al., 2022,\nPMID: 35983068). As the incidence of TBI increases in the elderly, TBI in the context\nof elevated NF-kB activity is a relevant clinical scenario affecting TBI outcome thus\nsupporting the biological and clinical relevance of the GoF model for the utilization in\npreclinical TBI research. Accordingly, our IKK2-CAGFAP mice clearly recapitulate\nfeatures usually observed in mice of advanced age, such as polarization to an\nastrocytic A1 phenotype (Supplementary Fig. 7d/e), expression of SASP factors\n(Supplementary Fig. 7f), reactive gliosis (Figure 6) as well as a profound leukocyte\ninfiltration (Figure 8) as indicated by the GoF model in the non-traumatized state.\nConcerning the wound healing deficit in IKK2-CAGFAP mice (depicted and referenced\nin Figures 3, 4, 6, 7 and 10), we examined the transcriptomic profile of both ACSA2+\nastrocytes and CD11b+ myeloid/microglia cells at day 3 post TBI, and found a\nsignificant downregulation of genes which are associated with tissue remodeling/ECM\norganization in the GoF model (Figure 7; Supplementary Fig. 6f). This illustrates the\nprominent deficit in the regulation of genes involved in wound healing after TBI in IKK2-\nCAGFAP animals already at an early time point. We also included Sham controls as\nindicated (Supplementary Fig. 11i).\nAltogether, this suggests that the GoF model recapitulates aging-related deficits in\nastrocyte function, including impaired wound healing in response to TBI.\n2. Another related concern is that astrocytic-IKK2 dominant negative (IKK2-DNGFAP) does\nnot affect TBI induced CNS scar formation or myeloid cell infiltration. The transcriptional\neffects in the astrocytic-IKK2, seem different (from the PCA plot) but were not explored in the\nmanuscript. Based on the data in Fig.1 and the assumptions of the introduction, the astrocytic-\nIKK2 dominant negative should reduce the cortical inflammation after TBI, but it does not.\nBecause it does not seem to have much of a role normally, the overexpression of IKK2 in\nastrocytes has reduced biological significance.\nResponse: We understand the reviewer’s concerns regarding the obvious lack of\nconsequences on TBI induced CNS scar formation or myeloid cell infiltration in the\nIKK2-DNGFAP model. Therefore, we performed additional experiments and following\nthe reviewer’s suggestion also explored the transcriptional profile of the IKK2-DNGFAP\nmodel in isolated cells and in bulk tissue in more detail (Supplementary Data 1;\nSupplementary Fig. 3). Overall, this reveals i) selective suppression in inflammatory\ngene expression and ii) pronounced changes in genes and processes linked to ROS\nprotection and mitochondrial processes as indicated by the enrichment chart in Figure\n5g. In addition, western blot analysis revealed upregulation of osteopontin at 3 dpi\n(Supplementary Fig. 11a/c), which was still detectable at 14 dpi in the IKK2-DNGFAP\nmodel but not in controls. (Supplementary Fig. 11i). Furthermore, the infiltration of\nperipheral immune cells was resolved faster as shown in Supplementary Fig. 10i/j.\n3. There are also some concerns on novelty and relevance of the study. For example, it is well\nestablished that TBI causes inflammation and increases in NFkB mediated pathways in the\nbrain of rodents. Moreover, myriad neurotrauma (SCI and TBI) studies in rodents show that\nastrocytes are critical for glia scar formation, are essential for wound healing, and influence\nimmune cell infiltration in the brain and spinal cord. Here, the main findings is that a gain of\nfunction/overexpression of NFKB signaling in astrocytes impairs the wound healing response\nat the lesion site.\nResponse: We agree with the reviewer that NF-kB activation following traumatic brain\ninjury is long established, and it is also well-studied that astrocytes are the cells critical\nfor glia scar formation, they are essential for wound healing, and they are able to\ninfluence immune cell infiltration. However, to our knowledge there is no genetic\nevidence so far that NF-kB activation in astrocytes is functionally relevant for scar\nformation/wound healing in TBI. Furthermore, the specific role of NF-kB in the post-\ntraumatic remodeling/infiltration process has not been well investigated as of now.\nThus, characterization of the mechanistic consequences of NF-kB activation and\ninhibition in astrocytes for the regulation of TBI outcome is not established and novel.\nWe also show here that astrocyte-specific activation of IKK/ NF-kB signaling is\nsufficient to phenocopy aging-associated changes including age-dependent infiltration\nof peripheral immune cells. As a consequence, specific pre-existing astrocyte\nactivation/reactivity conditions are created which impair the ability of astrocytes to limit\ntissue damage and to promote repair thus recapitulating wound healing deficiencies\nobserved in the aged brain. Our findings also support the view that IKK/ NF-kB-\nmediated pre-activation of astrocytes gives rise to paracrine signaling that promotes\nglobal neuroinflammation possibly “overwriting” the local signals required for\ninstructing the localized protective scar formation finally worsening TBI outcome.\nHowever, selective repression of NF-kB in astrocytes using IKK2-DN expression\nseems not to be sufficient to efficiently counteract TBI-induced global\nneuroinflammation and to provide conditions which result in an obvious benefit for the\nCNS scar formation process.\n4. Another concern is that the description for the generation of the dominant negative (LoF)\nand constitutively activated (GoF) cell specific mouse lines are not included within the\nmanuscript. Also, the authors don’t provide any validation that the NFKB pathway is\noverexpressed in astrocytes and that the NFKB pathway is non-functional in astrocytes.\nValidation of the models is an important element of rigor and reproducibility.\nResponse: We agree with the reviewer that validation of the models is an important\nelement of rigor and reproducibility. For this, we now added additional information on\nthe generation of the conditional mouse models in the method section, and importantly\nincluded additional data to demonstrate the continued functionality of our animal\nmodels (Supplementary Fig. 2/3).\nTransgene expression in the GoF and LoF model is coupled to a luciferase reporter\ngene and robust luciferase activity was validated by IVIS prior to TBI and protein assay\napproaches (Supplementary Fig. 2a/3a) showing that postnatal doxycycline\nwithdrawal results in robust IKK2-CA/IKK2-DN transgene expression. As a\nconsequence, upregulation of the IKK/ NF-kB pathway in the GoF model induces an\nelevated baseline expression of several NF-kB target genes, which we show in cortical\nand cerebellar tissue. For IKK2-CA transgene function on protein level, we would also\nlike to refer to the increased presence of phospho-p65 levels, which are indicative for\nan activated IKK/ NF-kB pathway (Supplementary Fig. 2e/f).\nFurthermore, we isolated primary astrocytes from adult GoF and LoF animals, took\nthem in culture and performed gene expression analyses by qRT-PCR. Here, basal\nNF-kB dependent gene expression is significantly elevated in the GoF model whereas\nthese genes are repressed in the LoF model. When we induced NF-kB in LoF\nastrocytes by the application of a stimulation cocktail consisting of LPS/TNF/Poly I:C,\nwe observed a prominent repression but not a complete blockade of NF-kB target\ngene expression. This indicates, that the dominant-negative IKK2-DN allele is\nfunctional but it is not capable to completely render the NF-kB pathway non-functional\nin IKK2-DNGFAP astrocytes (Supplementary Fig. 3d).\n5. In the assessment BrdU+ IBA1 and GFAP at 3dpi, there are no sham groups included.\nResponse: We thank the Reviewer for pointing out the lack of sham controls. We now\nincluded the respective sham controls, which show a similar behavior to the day 1\ncohort in Figure 6.\n6. In Fig.2 the authors only show representative images at 3 dpi. Authors should show all time\npoints, not just 3dpi, especially because it seems to peak at 7dpi.\nResponse: We thank the reviewer for this comment and therefore included additional\ndata to this part in the revised manuscript. Using a flow cytometric approach, we also\ncharacterized the EGFP/ NF-kB activity up to day 30 post TBI. Both methods revealed\nsimilar NF-kB activation kinetics for astrocytes and microglia concerning day 3 and\nday 7. Furthermore, we now also monitored the post-traumatic NF-kB activation kinetic\nin O4+ oligodendrocytes (Figure 2h) as well as several leukocyte subpopulations\n(Figure 2i/j; Supplementary Fig. 1g-i).\n7. Concern that in Fig.3 the text does not match the data/representative images. Authors state\nthat controls increase from day 1 to day 3, but it seems to decrease in the image and not really\nchange in the graph. As they say, the lesion decreases in controls from day 3 to day 7 – 15\nbut increases and then stays the same in the GoF. In Fig.3b, there is a key for all three groups\nbut then they do not include the LoF group. It also looks like the representative image do not\nmatch the graph for 1dpi. It is also unclear why Fig.3c separates all the groups and what time\npoint this graph represents\nResponse: We thank the reviewer for pointing out this issue and included a\nquantification and representative images of the LoF model in Figure 3a/b.\nFurthermore, we changed several representative images of the TBI GoF wound lesion\narea to highlight the disturbed glial scar formation in Figure 3a/b. In addition, we\nperformed now longitudinal MRI analysis by 30 dpi to follow lesion size development\nin the same animal over time in more detail as depicted in Figure 3c/d.\n8. Fig. 6. shows volcano plots for TBI-WT v sham and TBI-CA v sham but not direct\ncomparison, even though that is the group of most interest.\nResponse: We thank the reviewer for this suggestion and included the direct\ncomparison in the main Figure 5d/e.\n9. Concern that the data in Fig.7 are disjointed. It starts with data from fig 6 and then moves\nto blots of ipsi/contra tissue and then ending with more astrocyte transcriptional data from\nbefore. It is unclear if control is contralateral tissue and CA is ipsi or if this is just a genotype\ncomparison. Also unclear why in Fig.7d-f, it switches from including sham controls to\ncontralateral controls.\nResponse: We thank the review for pointing out the confusing description and\nrearranged Figure 7. We removed Figure 7a,b,g and i due to redundancy and moved\nFigure 7h to the new Figure 7 as Figure 7g. The new Figure 7, designated as Figure\n9 is now characterizing the post-traumatic immune response and inflammasome\nactivity. Regarding the western blot data, we decided to include the contralateral tissue\n(co) as an internal control for both control (Ctrl) and IKK2-CAGFAP animals, as protein\nexpression in non-lesioned tissue did not show any differences to the sham cohort at\nthis time point. See also a representative sham cohort in Supplementary Fig. 11i.\n10. Fig. 9 shows western blots of proteins they say are important for wound healing and\ninflammation. Nonetheless, no citations supporting this idea are provided. The author’s use of\nthe ratio of osteopontin (protective) to lipocalin-2 (inflammatory) is arbitrary.\nResponse: We thank the reviewer for this note and included now the corresponding\nreferences to this issue in the result section and not only in the discussion part. Overall,\nwe also further elaborate on the protective role of osteopontin as well as the\ndetrimental findings on lipocalin-2 in the discussion section.\nReviewer #2: In the submitted manuscript, Hein et al use astrocyte-specific strategies to modulate NfKB\nactivation through loss of function (IKK2-DN GFAP) of gain of function (IKK2-CA GFAP)\nmouse models and investigate the how this affects various outcome in a closed head injury\nmodel of TBI. An important role for NfKB following traumatic injury is long established, though\nthe attempt to look at this from an astrocyte-specific perspective is interesting and novel. A\nlarge proportion of the data in the paper is from bulk transcriptomic analysis of either whole\ntissue or isolated cell populations. Much of this can be confusing as direct comparisons are\noften made between groups that are of secondary interest and then descriptive comparisons\nof these comparisons are made (see below). The main finding of the paper is that IKK2-CA\nGFAP exacerbates many readouts after TBI including behavior, tissue lesion size and, most\ninterestingly, immune cell recruitment.\nThe title of the paper talks about ‘wound regeneration’ but the paper doesn’t really get into\nwhat ‘regeneration’ means and it data to show actual wound regeneration is lacking. It would\nbe good if the authors could clarify what they mean by ‘wound healing’ and ‘wound closure’\nand what this means in the brain? Also, a major theme of the paper is scar formation and\ntissue remodeling, and there is transcriptional and some protein analysis to support this,\nthough a much more thorough analysis of the scar and tissue in tissue sections would be\nneeded to support this (see below).\nThe major interesting finding in this paper is that astrocyte-specific activation of NfKB is\nsufficient to massively induce a peripheral infiltration of immune cells that are likely responsible\nof increase tissue damage and lack of resolution of inflammation. This has not been shown\nbefore and is of interest to the field.\nLastly, the authors largely focus on gain of function (IKK2-CAGFAP) rather than loss of\nfunction IKK2-DNGFAP as the latter had little to no effect on the model. How does this fit with\nthe authors view that NfKB could be an effective therapeutic target?\nI have multiple comments that would also need addressing, in figure order:\nFigure 2\nFigure 2e authors describe microglia maintaining NfKB expression but an alternative\nexplanation could be that Iba1 positive recruited monocyte-derived macrophages are taking\ntheir place. The authors need to do experiments to show definitively microglia are changing,\nor tone down microglial-specific conclusions\nResponse: We thank the reviewer for mentioning this very valid concern. To get a\nmore comprehensive view in the post-traumatic NF-kB activation kinetic and the\nparticipating players, we included now detailed flow cytometry analyses and stained\ncells obtained from the insult area of kappa-EGFP reporter mice post TBI for several\nleukocytic subsets as depicted in Figure 2 and Supplementary Fig. 1. We defined\nmicroglia as CD45lowCD11b+ and confirmed the EGFP expression kinetics observed\nin the histological sections (Figure 2j; Supplementary Fig. 1g). Co-staining with CD44\nin previous experiments and also in the new infiltration data showed a strong staining\nfor the CD45high population which can be used as a marker of cells with peripheral\norigin (Supplementary Fig. S10c) as previously described in literature (Korin et al.,\n2017; PMID: 28758994). This CD45highCD11b+CD44+ cell population, which includes\nmonocytes and macrophages, did not show a major contribution to the post-traumatic\nNF-kB activation pattern, only exhibiting a more noticeable upregulation for the later\nstages. In contrast to the histological analysis, flow cytometry showed a baseline\nexpression of NF-kB for the CD45lowCD11b microglia, which visibly increases until\n7dpi. These additional stainings indicate, that the CD45low myeloid cells are the main\ncontributors of the EGFP signal and, therefore, further support our initial statement\nconcerning the post-traumatic NF-kB activation in microglia (Figure 2j)\nFigure 3\nFig. 3b measures ‘Trauma dimension’ and 3c is ‘TBI area’. Fig 3c certainly is underpowered\nif the authors wont to claim a no change.\nResponse: We thank the reviewer for pointing out this inconsistency. We now\nrearranged the data in Figure 3 and included a quantification of the lesion area in mm2\nas ‘trauma dimension’ for the IKK2-DNGFAP model until 15 dpi. While we certainly agree\nwith the Reviewer’s concern regarding the statistical power, the follow-up of the scar\nformation process in the IKK2-DNGFAP model showed no significant differences to the\ncontrol cohort in neither the histological sections nor in the new longitudinal MRI\nanalysis. Therefore, we conclude that downregulation of the IKK/ NF-kB pathway in\nastrocytes has no significant effects on the parameters we did measure for lesion\nprogression over time.\nFig. 3c shows control vs IKK2-DNGFAP but this is not referred to in the text at all.\nResponse: We have now corrected this mistake and included the description of the\nfindings made in the IKK2-DNGFAP model in the result part.\nFig. 3d How do the authors account for what appears to be other lesion sites across the\nbrain when quantifying lesion volume? Ex vivo imaging clearly shows major cavities proximal\nand distal to the arrow to where they indicate lesion was measured.\nResponse: The cavities seen in the former Figure 3d are artifacts. For ex-vivo\nmeasurement, the fixed brains are transferred into Fluorinert solution. This solution\nshould be free of bubbles as these may interfere with the MRI measurements. In case\nof the image shown for day 30, there were almost certainly some bubbles present, as\nthe Fluorinert solution slightly changes its consistency in dependence of the ambient\ntemperature. However, for better clarity, we now removed the previous ex-vivo MRI\nresults from our study and, in turn, included now the results of a longitudinal in-vivo\nstudy for the same time points shown Figure 3c/d.\nFigure 4\nFig. 4e is really interesting in that a chronically primed astrocytes are still responsive. They\nare not tolerized but primed and become much more activated with TBI.\nWhat is even more interesting is that astrocyte activation does not tolerize microglia but again\nprimes them. This would be the opposite to if a microglial cell was to receive exogenous\nstimuli, there responses likely dampened see (PMID: 29643512) and many other examples of\ninnate immune tolerance. One caveat here is that it is extremely difficult to quantify microglia\nin these traumatic lesions as many of the macrophages (microglia or monocyte-derived) will\nindeed be monocyte-derived cells.\nIt would be very interesting to know if the chronic activation of astrocytes can prime resident\nmicroglia or whether the microglia are tolerant to TBI but there increase in Iba1 is in fact due\nto extra recruited monocyte-derived macrophages. (see below, but I think the data here shows\nevidence for recruited immune cell changes, not microglia changes)\nResponse: We agree with the reviewer’s concern that infiltrating monocytes and\nmacrophages might dilute the TBI effect on microglia. Classification of microglia and\nperipheral infiltrates was conducted by evaluation of the IBA1 intensity, as\nmacrophages were defined as IBA1dim. However, to strengthen our data, we\nconducted additional flow-cytometric analysis, as this method allows a better and\nclearer resolution of receptor expression densities (Figure 2j; Supplementary Fig.\n1). We identified peripheral infiltrates as CD45highCD11b+CD44+, as expression of\nCD44 has been associated with leukocytic migration into the CNS (Korin et al., 2017;\nPMID: 28758994; Böttcher et al., 2019; PMID: 30559476). Co-staining with the\nmicroglial marker P2RY12 also showed an absence on the CD45high myeloid\npopulation. According to this gating strategy, myeloid cells with high CD45 expression\ndensity were identified as peripheral infiltrates. However, while we agree with the\nreviewer’s observation that many of the neuroinflammatory effects found in this study\nare caused by peripheral infiltrates, we also saw an upregulation of CD11c and MHC\nclass II in CNS-resident CD45lowCD11b+CD44- microglia in the GoF mouse model\n(Supplementary Fig. 10b). This finding also implies a paracrine effect of NF-kB -\nactivated astrocytes which may lead to a specific activation status of microglia cells.\nFigure 5\nIt is very hard to understand what the take home message is here.\nMost of this Figure (Figure 5 b/g/h) suggests that TBI has no additional effect on the astrocytes that already have NFkB\nactivation (TBI IKK2-CAGFAP vs TBI control).\nHowever, something must be driving the large lesion and behavioural changes between these groups. Indeed,\nsupplemental Fig 3 d and e do suggest what is responsible.\nThe authors are right to highlight the similarity in profiles but I suggest should focus on supp\ndata 3 d and e (move them to the main Figure) as a potential explanation to what is driving\nthe histological lesion and behavioral changes between TBI IKK2-CAGFAP vs TBI control.\nMost of the other comparisons appear to confuse this message i.e. fig 5a, c, d, e and maybe\neven g and h. Figures Supplemental 3d, e in supplemental three are much more compelling.\nResponse: We appreciate the reviewer’s suggestion for improvement and\nimplemented this suggestion. We now rearranged the figure and main text accordingly\nto convey our message more clearly. The data are now presented in Figure 5 that\ndepicts the PCA (Figure 5c) as well as volcano plots (Figure 5d/e) and enrichment\ncharts (Figure 5f/g) directly comparing TBI IKK2-CAGFAP or TBI IKK2-DNGFAP to TBI\nControl. We removed the original comparisons to the Sham control group or moved\nthem into the Supplement (Supplementary Fig. 6), as we agree completely with the\nReviewer’s assessment.\nFigure 6\nThese data are very compelling, though it could be much clearer to which cell populations are\nbeing gated/quantified. The authors have gone to the trouble of sub gating different myeloid\npopulations in supp Figure 4 but have not quantified them, this would be of interest to the\nreader.\nResponse: We appreciate the reviewer’s comment and thank for the suggestion. We\nnow included the quantification of markers expressed in the CD11c+ population of\nIKK2-CAGFAP animals in Supplementary Fig. 9c. Furthermore, we also present the\ngating strategies of several leukocytic subsets in Figure 8a/b, Supplementary Fig.\n9a/b and Supplementary Fig. 10a.\nFigure 6a shows major increase of myeloid cells (CD11b+ cells) but that specific gate is not\nshown either in the figure or the supplementals.\nResponse: We thank the reviewer for pointing out the missing gating strategy and\nincluded this information in Figure 9a.\nFigure 6b shows major increase of Ly6C+ monocytes, yet the only gate shown is for\nLy6C+CD44+ cells, is this what is quantified in 6b?\nResponse: We thank the reviewer for this suggestion and included the respective\ngating strategies. All of our CD45high myeloid cells do co-express CD44 as depicted in\nSupplementary Fig. 10c. To make the qualitative composition of the CD45high\ninfiltrates clearer and more comprehensive, we now also included a diagram showing\nthe proportion of the populations relative to the total infiltration found at day 3 post TBI\nin controls, LoF and GoF animal models as shown in the main Figure 8f.\nFigure 6c-e shows an increase in what appear to be dendritic cells. This would be a very\ninteresting finding. However, due to the bright staining, and what appears to be a lack of a\nlive/dead stain in the samples, the authors would need to show either an IgG or FMO control\nfor CD11c to show their staining is specific.\nResponse: We conducted a live/dead staining using DAPI, and we also included\nIsotype controls for our gating. A representative image is now depicted in\nSupplementary Fig. 9b.\nIf they are DCs, what do the authors think they are doing there? Are they presenting antigen\nto the increased lymphoid cell populations? It would be very interesting to know the make-up\nof this lymphoid population considering the role that Th1/Th2 or Tregs can play in exacerbating\nor resolving TBI-associated injury.\nResponse: We appreciate the reviewer’s interest in the dendritic cell data and on their\nrole in the regulation of the TBI outcome (former Figure 6, now Figure 8). Overall, the\nrole of dendritic cells in TBI is not intensively studied so far. We observed a strong\npreference of CD11c+ immune cell infiltration in the lesioned brain area of IKK2-\nCAGFAP animals. Subtyping of this cell population revealed a quite heterogeneous\nmake-up of CD11c+/MHC class IIhigh/CD44+/Ly6Clo/hi cells probably including both\nconventional (cDC) and monocyte-derived (moDC) dendritic cells (DCs). In this\ncontext, the enrichment of IFN-g-associated pathways and elevated Csf2 expression\nin the myeloid transcriptome induced by NF-kB activation in astrocytes may create\nconditions similar to EAE promoting monocyte to moDC differentiation (e.g. Mundt et\nal., 2022; PMID: 36327896) and may explain elevated DC levels already under sham\nconditions in the IKK2-CAGFAP model. As these cells can terminally differentiate into\npro-inflammatory macrophages (Amann et al., 2023; PMID: 36759712), they may\nfurther aggravate the neuroinflammatory response in GoF animals.\nConcerning the lymphoid compartment, we included now additional data in\nSupplementary Figure 10e-g. We found that frequencies of CD4 and CD8 cells are\nsignificantly different between control and IKK2-CAGFAP animals at 3 days post TBI,\nexhibiting a preference for CD8+ CTLs in the GoF model. Interestingly, these\ndifferences in ratio are equalized at 14 days post TBI, showing a significant increase\nin CD8a+ and a subsequent decrease in CD4+ T Cell frequencies, similar to the\nsituation in the GoF model at 3 dpi. B220+MHCII+ cells, which we assigned as B cells,\ndid not show any significant differences between control and GoF. The increased\nfrequency of CD8a+ T cells may aggravate post-traumatic tissue damage, as these\ncells can potently activated by DCs and may trigger inflammatory neurodegeneration\nas well as neurological manifestations after TBI. We have now further elaborated on\nthis issue and on the role of DCs in mounting autoimmune responses in the discussion\npart.\nThe PCA plots show nice difference in CD11b cells between the key group of interest TBI\nIKK2-CAGFAP vs TBI control. Why do the authors then show volcano plots from TBI\nIKK2-CAGFAP vs sham? It would be much more beneficial for the reader to understand what\nare the transcripts that are changed between TBI IKK2-CAGFAP vs TBI control. In Fig 6k and\nl the groups ‘sham’ is not even represented in the PCA plot. Is this because they sham\nCD11b+ cells are almost entirely microglia, whereas the TBI conditions will have many\ninfiltrating cells? If so, is there much point comparing them in this way?\nThese data are extremely interesting and show that astrocyte-specific activation of NFkB\ndrives immune cell recruitment and retention in the TBI brain. They also suggest that data in\nFigure 4 should be revised as it is not possible to say that these astrocyte effects in TBI are\non microglia, but just as likely recruited monocyte-derived macrophages from the blood\nResponse: The reviewer’s concerns are very valid and therefore we removed the\nsham comparisons and only compare the TBI groups directly to each other to further\nhighlight the transcriptomic changes between the different genotype groups.\nWe also agree with the reviewer’s observation that several changes are due to the\npresence of blood-derived leukocytes, however we could also identify changes in the\nmicroglia population (CD45lowCD11b+CD44-) themselves, such as an upregulation of\nMHC class II and CD11c receptor expression as shown in Supplementary Fig. 10b.\nFigure 7\nFigure 7a and b are not referred at all in the main text, if they are in the main figure they\nshould be described. Again, however, the interesting finding is that IKK2-CAGFAP\nexacerbates TBI. Can the authors show this comparison instead?\nThose that they show are homeostatic microglia vs microglia/monocytes/DCs/neutrophils and do not inform us of what the factors are in IKK2-CAGFAP mice that are exacerbating injury.\nResponse: We thank the reviewer for pointing out this important issue. We have\nupdated now all of the figure references and reorganized the paper as suggested. We\nfurther removed the sham comparison and moved the comparison between TBI\nControl vs TBI IKK2-CAGFAP into the main Figure 8g/h and 9a.\nFigure 8.\nThe authors want to show that tissue remodelling and scar formation are affected by\nastrocyte NFkB manipulation. They show that many ECM genes are downregulated in\nastrocytes and myeloid cells.\nThen, again, the authors start to make comparisons not directly between TBI IKK2-CAGFAP\nvs TBI control but between TBI vs sham and TBI IKK2-CAGFAP vs sham then comparing\n(not directly) their outputs, for example Figure 8f. regarding a an EMT gene signature. It is\ndifficult to make conclusions this way.\nTo make this much stronger the authors should go back to the tissue and look at the scar\nformation with GFAP, col4, CSPGs or other ECM proteins are measure what the border\nforming capacity is. This alluded in Figure 3, but to claim scar formation and or tissue\nregeneration is altered, they need to show this at the tissue and protein level, on sections,\nideally across time.\nResponse: We thank the reviewer for this suggestion and rearranged the figure\naccordingly. However, while we agree that direct comparison between TBI GoF and\nTBI controls is a more suitable approach and therefore also now included the\ncorresponding GSEA plot (Figure 7b), we decided to not remove the comparison\nagainst Sham for the EMT in order to emphasize the inability of the GoF astrocytes to\nadopt this type of transcriptomic signature.\nWe also conducted and included more thorough analyses of factors involved in the\nrepair and remodeling of injured brain tissue. To further investigate the scar formation\non a cellular level, we included fibronectin staining over the time of 30 days – as shown\nin the Supplementary Fig. 5b/c, as well as a deeper characterization of the glial scar\nstructure and thickness seen in the new Figure 4. In this context, the GFAP scar is\nquantified using the intensity of the GFAP staining around the wound core, while the\nGFAP thickness describes the distance from the TBI lesion core until the first signal\nwas observed. Furthermore, we also stained for NG2/CSGP4 to evaluate the\ndeposition around the lesion core as well as the reactivity of the NG2 glia. While\nreactivity was unchanged, deposition was significantly lower in the IKK2-GoF cohort\nat day 7 post TBI.\nMethods\nMuch more information is needed on the model. Were animals excluded for fractures? What\nwas the mortality rate? Were there any other exclusion criteria?\nWe have now added the relevant information and expanded upon it in our method\nsection. Mortality of the mice was quite low (~5%) due to our optimized post-\noperational care. Due to the experimental procedure, the generation of a relevant\ninsult was estimated right after trauma induction due to formation and extent of visible\nbleedings beneath the exposed skull. We therefore excluded animals without\nimmediately visible or very minor lesions. Fractures– if present – were usually\nsuperficial and animals with fractures were kept in the analysis.\nReviewer #3: This is an interesting preclinical study that uses elegant reporter and astrocytic loss of function\nand gain of function models to investigate NFkB activation in the context of acute TBI, wound\nhealing and functional recovery. Overall, the preclinical studies are rigorous and well\ndesigned, and the conclusion are quite well supported by the data. However, additional\nevidence for deficits in wound healing in the IKK-CAGFAP model are needed, and more\nquantitative analysis of the glial scar dynamics would strengthen the findings of the paper.\nThe author should address the following points related to these new studies:\n1) The opening figure is limited by solely focusing on bulk RNAseq data from a single timepoint\n(3dpi). It might be useful to add the temporal data for NFkB regulated genes (SFig 1) to the\nfigure to highlight the dynamic response of IKK/NFkB signaling following CHI.\nResponse: We thank the reviewer for this suggestion and rearranged Figure 1\naccordingly. The longitudinal expression kinetic of selected NF-kB regulated genes\nmeasured by qRT-PCR is now assigned as Figure 1f-m.\n2) The use of the NFkB reporter model in Figure 2 is novel and elegant. It provides clear\nvisualization of activation of NFkB signaling in astrocytes and microglia in the acute phase\npost-injury. To improve the descriptive opening of the manuscript the authors may wish to\nconsider combining tissue level bulk RNAseq/qPCR analysis of IKK/NFkB signaling with the\nEYFP-reporter model for NFkB activation that details cell-specific responses in astroctyes and\nmicroglia. I believe this will lead to a stronger foundation to the research article.\nResponse: We appreciate the remark of the reviewer and rearranged Figure 1 and\nFigure 2 as we conducted now a more thorough analysis of the post-traumatic NF-kB\nactivation kinetics using the NF-kB reporter model. To get detailed information on the\nidentity of the cell populations responsive to TBI-mediated NF-kB induction, we now\nperformed flow cytometric analysis at day 1, 3, 7, 14 and 30 post TBI and stained for\nastrocytes (ACSA-2), oligodendrocytes (O4) (Figure 2g-i) and leukocytic subsets\nincluding resident microglia (CD45lowCD11b+CD44-), myeloid\n(CD45highCD11b+CD44+) and lymphoid (CD45highCD11b-) infiltrates (Figure 2j,\nSupplementary Fig. 1g-i). This approach also allowed as to confirm microglia as main\ncontributors of the post-traumatic IKK/NF-kB activation among the myeloid cells.\n3) The IKK2-DNGFAP and IKK2-CAGFAP loss and gain of function models for NFkB\nactivation in astrocytes are nice. Astrocyte NFkB activation (IKK2-CAGFAP) results in lesion\nexpansion and delayed resolution following CHI, when compared to control CHI mice. The\ntemporal ex vivo MRI lesion assessments are interesting and support the notion of delayed\nwound healing response acutely following CHI, which ultimately reaches same level of injury\nresolution at 30 days post-injury. It is a pity that these are not in vivo longitudinal MRI\nassessments in the same mice because that would be more rigorous and biologically\ninsightful. Are other DTI metrics for white matter damage across the time course available to\ninvestigate effects of IKK2-CAGFAP on TBI pathophysiology?\nResponse: We appreciated the reviewer’s comment and suggestion. To strengthen\nour data, we now performed a longitudinal in-vivo MRI (T2-weighted) analysis of\ncontrol, IKK2-CAGFAP and IKK2-DNGFAP at day 1, 15 and 30 post TBI, which we\nincluded as Figure 3c/d in replacement of the previous ex-vivo MRI data. These results\nfurther support our findings, clearly showing a deficit in the post-traumatic wound\nhealing in the GoF model that was significantly deficient at day 15 and still impaired\nby tendency at day 30 in comparison to the control cohort. Quantification of the IKK2-\nDNGFAP model showed no obvious differences in the wound lesion progression.\nUnfortunately, we could not determine additional DTI metrics.\n4) The glial scar in figure 3 needs to be more clearly defined and quantified in control CHI\nmice in order to compare lack of scar formation in IKK2-CAGFAP CHI mice. There is a robust\nincrease in GFAP reactivity in the IKK2-CAGFAP mice under sham conditions, so it is difficult\nevaluate the glial scar in CHI mice. Additional quantitative measures are needed.\nResponse: We agree with the reviewer’s concerns and performed additional\nquantitative measures of the scar area. We replaced the original images with ones\ndepicting a clearer and better-defined glial limitans border, also including data form\nthe IKK2-DNGFAP model (Figure 3a/b). The quantitative measures revealed a\ndecreased deposition of NG2/CSPG4, an unchanged reactivity of NG2 glia\n(determined by hypertrophy), as well as a decreased expression but - at day 7 post\nTBI - unaffected thickness of GFAP area (Figure 4) in the IKK2-CAGFAP model. We\nalso identified significantly broader expression of fibronectin in the GoF model\n(Supplementary Fig. 5) overall validating the aberrant scar formation in these\nanimals.\n5) Glial proliferation studies in Figure 4 are interesting and indicate increased BrdU\nincorporation in astrocytes and microglia rapidly after CHI in the IKK2-CAGFAP mice. It would\nbe helpful to include a schematic diagram for the region of analysis in Fig 4e and 4f. Is\nproliferation only occurring at the lesion boundary/glial scar, or does it also occur in\ncontralateral regions distant from the lesion epicenter? There is a much higher level of\nproliferation in microglia compared with astrocytes. How do the authors interpret this early\ninjury proliferative response and immune interactions between astrocytes and microglia?\nResponse: We now included a schematic diagram concerning the region of analysis\nin the TBI insult area (Figure 6a) as well as explanations regarding the approach of\nquantification (Supplementary Fig. 5a). In the control and LoF cohort, proliferation\nand gliosis was restricted to the lesion area while in the GoF group, baseline\nastrogliosis – but not proliferation - was present in the whole brain (Figure 6c). The\nhigher level of proliferation in IBA1+ microglia may be in parts due to infiltrating\nmonocytic/dendritic cells, which also stain positive for IBA1 (Zhang et al., 2021; PMID:\n33582751). In line with this, microglia as the resident immune cells of the brain\nparenchyma, do react strongly to DAMPs and PAMPs, and therefore may show a\nstronger proliferation in comparison to other resident cell types.\n6) Neurological outcomes: The CatWalk gait analysis can measure >100 specific\nparameters of motor function, coordination, and gait, and so it is relatively easy to\nshow differences between groups using this analysis. How they are related to fine\nmotor coordination impacted by TBI are less clear, and this is hotly debated in the\nneurotrauma field, especially when assessing motor function in injured mice. No sham\nmice are included in the current CatWalk analysis. Therefore, it would be important to\nuse more established motor function tests such as accelerating rotarod and/or beam\nwalk to examine fine motor coordination. Furthermore, cognitive function is chronically\naltered after CHI/TBI, so inclusion of cognitive outcomes (e.g. MWM or novel object\nrecognition) would strengthen the analysis of post-traumatic neurological impairments\nin IKK2-CAGFAP mice.\nResponse: The CatWalk system has been developed to automatically and in particular\nobserver independently assess gait and motor function in rodents. It has been used in\na variety of preclinical models of traumatic and non-traumatic neurologic conditions\nsuch as stroke, peripheral nerve injury, spinal cord injury as well as traumatic brain\ninjury.\nWe agree with the Reviewer’s concerns that it is less clear how the >100 specific\nparameters of motor function, coordination, and gait recorded by the CatWalk system\nare exactly related to TBI-induced deficits in motor performance. However, there a\nseveral parameters for instance the mean of max intensity at (%, relative to stand time)\nthat were consistently reported as relevant in TBI and stroke models, implying that\nthey could potentially serve as main alerting parameters of gait dysfunction in these\nmodels (PMID: 37351154). We also included now sham mice in the CatWalk analysis\nto strengthen the impact of TBI on motor coordination.\nFurthermore, we included a neurological severity scoring (Supplementary Fig. 4b)\nwhich includes beam walking and beam/round stick balancing. The NSS revealed an\nincrease in neurological impairments 6 h post TBI in both control littermates (median\nNSS = 5) and IKK2-CAGFAP mice (median NSS = 6) compared to corresponding sham\nanimals (median NSS = 2 and 2.25, respectively). These impairments mainly include\nmotor dysfunction during beam walking and beam/round stick balancing. After 24 h,\nthe NSS is already decreasing again in both controls and IKK2-CAGFAP, however, the\ndeficits of IKK2-CAGFAP mice are significantly increased compared to controls at this\ntime point and also present until day 3 post TBI. Later on, no significant differences\ncan be detected between groups. Therefore, both CatWalk and NSS analysis indicate\na transient deficit in motor performance in the GoF model.\nWe agree that the inclusion of cognitive outcomes (e.g., MWM or novel object\nrecognition) would strengthen the analysis of post-traumatic neurological impairments\nin IKK2-CAGFAP mice. However, this type of behavioral tests needs a comprehensive\nset of new experiments, an issue that is not manageable in this research study, in\nparticular as these tests need lengthy additional animal testing approvals.\n7) RNAseq of isolated astrocytes: The important comparison of TBI IKK2-CAGFAP and TBI\ncontrol has been relegated to supplemental material (SFig 3c). It would be better suited in the\nmain figure.\nResponse: We thank the reviewer for this remark and moved the TBI IKK2-CAGFAP vs\nTBI control comparison to the main figure (Figure 5d-g). We removed most of the\nsham comparisons or moved them into the Supplementary Fig. 6, as they do not\ncontribute critically to the overall message and conclusion of our study. Expression of\nDEGs and enrichment plots in comparison to the Sham Control cohort can now be\nfound in Supplementary Fig. 6a-d.\n8) NLRP3 inflammasome activation is implicated in the heightened inflammatory\nresponse in TBI IKK2-CAGFAP mice. Is this specific to microglia or astrocytes?\nPerhaps RNAseq data can be examined to address this open question? Also, it would\nbe useful to analyze cleaved caspase-1, IL-1b, and IL-18 in this sample to provide a\nmore robust analysis of NLRP3 inflammasome activation in the model.\nResponse: Following the Reviewer’s suggestion, examination of the RNAseq data\nrevealed a significant upregulation of NLRP3 inflammasome components in\nastrocytes, whereas no major differences could be observed in CD11b+ cells. This\nsuggests that changes in inflammasome component expression is rather restricted to\nastrocytes (Figure 9j).\n9) There is a need for more concrete evidence to support deficits in wound healing in TBI\nIKK2-CAGFAP mice. Protein level and in situ analyses (imaging) of ECM and wound healing\npathways are needed to support the transcriptomics data in Figure 8.\nResponse: We thank the reviewer for the comment and added more data to support\nthe deficits in wound healing. For this, we performed deeper analyses of the lesion\narea with additional quantitative measures including deposition of NG2/CSPG4,\nreactivity of NG2 glia, expression and thickness of GFAP (Figure 4) as well as\nexpression of fibronectin (Supplementary Fig. 5). On gene expression levels, we\nadded the expression kinetics of several ECM/scar-associated factors until day 30\npost TBI (Figure 5a).\n10) A much deeper analysis of osteopontin (OPN) is needed, and a rescue experiment\nusing OPN in TBI IKK2-CAGFAP mice or inhibition of LCN2 should be considered to\nnail the mechanisms that impair wound healing following in TBI due to astrocytic NFkB\nactivation (related to figure 9).\nResponse: We thank the reviewer for the comment. However, we think that such\nrescue experiments are beyond what is feasible for this revision, considering the long\ntimeframe for such rescue experiments (e.g., OPN application or combination of\nLCN2-/- with GoF mice, obtaining the relevant licenses to adhere to animal welfare\nand legal regulations) .\nWe included some new data on Osteopontin and Lcn2 in the LoF model\n(Supplementary Fig. 11)\n11) The title needs to be tempered based on existing data, and ‘wound regeneration’\nfollowing TBI needs to be more conclusively demonstrated with empirical evidence.\nResponse: We agree with the reviewer and rephrased title to “NF-kB activation in\nastrocytes critically affects wound healing after traumatic brain injury”."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I thank the authors for addressing my point-by-point concerns.\nI just have one final query. The way the glial scar is quantified in new Figure 4, measuring GFAP thickness around the wound core is clearly challenging. It would not be clear where to measure the core from and which 'edge' of the 'scar' to end at. These are not nice concentric spheres and at day 3 the scar edge is almost impossible to define. The fact that this was done in a blinded manner is critical and the process would be extremely variable.\nHow then in Fig 4c can 'GFAP expression thickness' around the core by exactly the same measurement across three time points, with zero variability in the IKK-CA group? The lack of any variation between animals seems unlikely.", "reviewer_3": "NCOMMS-24-50636A: In the revised manuscript the authors addressed several of the points raised in the prior review. New data are provided using flow cytometry of various immune population in the injured brain in the genetic models, but there are concerns about the robustness of the data based on unusual gating strategies and use of % in data presentation. The revised manuscript is also very complex and is challenging to follow due to the inclusion of so much new additional data. Simplification of the major results/findings to increase focus and access for the reader is recommended.\nBelow are some comments about revision 1.\n1. For flow cytometry studies the live/dead staining and gating strategy used for cellular populations should be included in supplemental figures for transparency and rigor.\n2. Supplemental figure 4. The annotation of the statistical comparisons in the NNS and Catwalk data are confusing (some use colour, others do not…). It would be advisable to make the data more accessible to readers by improving the statistical comparisons in this figure.\n3. The placement of gates in new flow cytometry studies when comparing groups of mice is problematic (Fig 8a). The same gate needs to be used in each group to investigate phenotypic changes in immune populations due to genotype.\n4. The absolute numbers for immune populations in the injured brain should be presented. Are these similar to %s shown in Figure 9c-e? It is surprising that so many peripherally derived monocytes/neutrophils are being recruited so late after TBI at 30dpi. It would be informative to see the absolute numbers across the genetic models to get a clear picture of this dynamic response.\n5. The studies remain highly descriptive. The mechanistic insight about wound healing are touched upon in the final figure but left open-ended. The discussion focuses on osteopontin, but this mechanisms was not examined experimentally. This is a missed opportunity to provide true mechanistic insight on role of astrocytic NfKB role in wound healing following TBI.\n6. The addition of large amounts of new data in the revision means that the manuscript is extremely dense and hard to navigate for readers. This leads to a lack of focus. The authors are encouraged to simplify things and focus on critical take home messages in the results."}, "author_response": "Response:\nWe thank the reviewer for the very valuable comment. We agree that measuring the GFAP\nthickness around the injury is quite challenging and, as the reviewer correctly noted, the scar\nedge cannot be clearly defined at day 3. For this reason, we did not quantify the GFAP scar\nat 3 dpi and restricted our analysis to the 7- and 15-day time points after TBI.\nThe reviewer´s point regarding the precise location of scar quantification is well taken. To\naddress this, we also quantified the scar by calculating the ratio of the injury size to the total\narea covered by intense GFAP signal, which defines the GFAP scar (the yellow dotted outline\nin the Figure 4). As shown in Figure 4f, this ratio was significantly lower for the IKK2-CAGFAP\ngroup compared to the control group, further supporting our previous conclusion that GFAP\nscar formation is impaired in the IKK2-CAGFAP model.\nRegarding the reviewers´ concern about the apparent lack of variability in GFAP expression\nthickness in the IKK2-CAGFAP group, we apologize for the misunderstanding. Under wildtype\nconditions following TBI, GFAP expression is largely restricted to reactive astrocytes within\nthe grey matter of the cerebral cortex near the injury site. In contrast, IKK2-CAGFAP animals,\nwhich exhibit constitutive activated IKK2/NF-B signaling in astrocytes, display widespread\nGFAP expression that extends through the entire cortex, even under sham conditions.\nTherefore, we arbitrarily defined “GFAP expression thickness” as the maximal measurable\nextend of GFAP expression from the injury core and laterally across the ipsilateral hemisphere.\nBecause GFAP+ astrocytes in the IKK2-CAGFAP group cover the entire grey matter, this metric\nnaturally shows no variability between individual animals. In control mice however, GFAP\nexpression is confined to the grey matter surrounding the injury site and exhibits a well-defined\nboundary. Thus, the “GFAP expression thickness” in the control group varied across animals\nand time points. Only at 7dpi does the GFAP expression extend across the entire ipsilateral\nhemisphere, resulting in values comparable to the maximum (orange arrows in Figure 4).\n\nResponse:\nWe thank the reviewer for the very helpful comments and for highlighting that the revised\nmanuscript had become overly complex due to the extensive addition of new additional data.\nIn response, we have redesigned and rewritten the entire Results section, focusing on the\ncore messages of the study as recommended. The main findings are now presented in\n8primary figures, supplemented by 15 additional supplementary figures.\n1. We agree with the reviewer that the live/dead staining and gating strategy used for cellular\npopulations should be clearly presented. We have now consolidated this information -\npreviously scattered across multiple figures- into a single, dedicated Supplementary Figure\n11.\n2. We thank the reviewer for this suggestion. We have reformatted the statistical comparisons\nfor the NSS and Catwalk data in a uniform manner to ensure consistent and clearer\npresentation. Detailed descriptions of the statistical comparisons are now provided in the\nfigure legends. Furthermore, NSS data have been moved into the main figure (now Figure\n4b), while the Catwalk data are retained in Supplementary Figure 4.\n3. We appreciate the reviewer´s attention to this critical issue. All flow cytometry gates have\nbeen evaluated and re-adjusted across experimental groups to ensure valid comparisons of\nimmune cell phenotypes between the different genotypes (see Figure 7).\n4. We fully agree that absolute cell counts for immune populations in the injured brain are\nessential to get a clear picture of the dynamic post-traumatic immune response. To strengthen\nthe manuscript, we conducted additional flow cytometry experiments and performed\ncomprehensive cell count analyses across genotypes and time points post TBI.\nThese data now clearly show that the total infiltrating cell numbers are already significantly\nincreased in IKK2-CAGFAP mice even in the sham situation and continue to increase over time\n(until day 30) depending on the specific immune population, as seen particularly for lymphoid\ncells (see Figure 7j/l/n).\n5. We thank the reviewer for emphasizing the importance of rescue experiments to further\ntranslate our findings in pre-clinical therapeutical approaches in future studies. We have\nalready embarked on this extremely challenging endeavor and initiated preliminary work in\nthis direction. However, this is beyond what is feasible for the second revision period\nconsidering the long timeframe and extensive preparatory steps required to conduct\nmeaningful rescue experiments including securing new animal licenses and performing pilot\nstudies to optimize application techniques and dose-response relationships (e.g., intranasal\nrecombinant OPN application with or LCN2 neutralizing antibodies).\n6. We agree with the reviewer that the previous revision was extremely dense and difficult to\nnavigate for readers. We have therefore carefully and thoroughly reorganized the Results\nsection: all principal findings are presented across eight main figures, while the supporting and\ncontrol data, much of which directly addresses the reviewers' comments, are placed in 15\nsupplementary figures. We have also carefully revised the schematic summary of our findings\nto make the overall narrative clearer and more accessible to the reader (supplementary figure\n16)."}, {"round": 3, "reviewer_comments": {"reviewer_2": "the authors have addressed my comments", "reviewer_3": "I thank the authors for carefully addressing R1 comments and improving the clarity of the manuscript. I am happy to approve this study for publication."}, "author_response": ""}]

Decision letter