(2024) 21:198
Leng et al. Journal of Neuroinflammation
https://doi.org/10.1186/s12974-024-03165-w

Journal of Neuroinflammation

Open Access

RESEARCH

mTOR activation induces endolysosomal
remodeling and nonclassical secretion of IL‑32
via exosomes in inflammatory reactive
astrocytes
Kun Leng1,2,3*†, Brendan Rooney1†, Frank McCarthy4, Wenlong Xia5, Indigo V. L. Rose1,6,10, Sophie Bax4,
Marcus Chin7,8, Saeed Fathi3, Kari A. Herrington9, Manuel Leonetti4, Aimee Kao7, Stephen P. J. Fancy5,
Joshua E. Elias4 and Martin Kampmann1,10,11*

Abstract
Astrocytes respond and contribute to neuroinflammation by adopting inflammatory reactive states. Although recent
efforts have characterized the gene expression signatures associated with these reactive states, the cell biology
underlying inflammatory reactive astrocyte phenotypes remains under-explored. Here, we used CRISPR-based screening in human iPSC-derived astrocytes to identify mTOR activation a driver of cytokine-induced endolysosomal system
remodeling, manifesting as alkalinization of endolysosomal compartments, decreased autophagic flux, and increased
exocytosis of certain endolysosomal cargos. Through endolysosomal proteomics, we identified and focusedon one such cargo–IL-32, a disease-associated pro-inflammatory cytokine not present in rodents, whose secretionmechanism is not well understood. We found that IL-32 was partially secreted in extracellular vesicles likely to be
exosomes. Furthermore, we found that IL-32 was involved in the polarization of inflammatory reactive astrocyte states
and was upregulated in astrocytes in multiple sclerosis lesions. We believe that our results advance our understandingof cell biological pathways underlying inflammatory reactive astrocyte phenotypes and identify potential therapeutictargets.
Keywords Astrocytes, Inflammatory reactive astrocytes, Neuroinflammation, mTOR, Endolysosomal system, IL-32,
Extracellular vesicles†

Kun Leng and Brendan Rooney have contributed equally to this work.

*Correspondence:
Kun Leng
kun.leng@ucsf.edu
Martin Kampmannmartin.kampmann@ucsf.edu
1
Institute for Neurodegenerative Diseases, University of California, San
Francisco, San Francisco, CA, USA
2
Biomedical Sciences Graduate Program, University of California, San
Francisco, San Francisco, CA, USA
3
Medical Scientist Training Program, University of California, San
Francisco, San Francisco, CA, USA
4
Chan Zuckerberg Biohub, San Francisco, CA, USA
5
Departments of Neurology and Pediatrics, School of Medicine,
University of California, San Francisco, CA, USA

6
Neuroscience Graduate Program, University of California, San Francisco,
San Francisco, CA, USA
7
Memory and Aging Center, Department of Neurology, Universityof California, San Francisco, San Francisco, CA, USA
8
Small Molecule Discovery Center, Department of Pharmaceutical
Chemistry, University of California, San Francisco, San Francisco, CA, USA
9Center for Advanced Microscopy, University of California, San Francisco,
San Francisco, CA, USA
10
Weill Institute for Neurosciences, University of California, San Francisco,
San Francisco, CA, USA
11
Department of Biochemistry and Biophysics, University of California,
San Francisco, San Francisco, CA, USA© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Leng et al. Journal of Neuroinflammation(2024) 21:198

Introduction
Astrocytes maintain homeostasis of the central nervous
system in myriad ways, for example through phagocytosis of synapses [1, 2] or exocytosis of ATP or glutamate[3–5]. In the context of central nervous system injury or
disease, astrocytes respond to and amplify inflammatory
signaling cascades [6, 7], adopting inflammatory reactive
astrocyte states characterized by distinct gene expression
and cytokine signatures [8–10]. Although recent efforts
have elucidated these signatures, the cell biological pathways underlying inflammatory reactive astrocyte phenotypes remain under-explored.
The endolysosomal system encompasses a diverse poolof intracellular vesicles of varying luminal pH that mediate degradative (e.g. lysosomes, phagosomes, autophagosomes) as well as exocytic functions (e.g. recycling
endosomes, multivesicular bodies) [11, 12]. Althougha large body of work exists on the cross-regulation of
autophagy and inflammatory responses in immune cells[13], much less is known about how this occurs in brain
cell types, especially astrocytes.
Here, using human induced pluripotent stem cell(hiPSC)-derived astrocytes, we found that the inflammatory cytokines IL-1α + TNF + C1q in combination (hereafter abbreviated as “ITC”), which have been widely used
to induce inflammatory astrocyte reactivity in vitro [14],caused mTOR activation and mTOR-dependent remodeling of the endolysosomal system, which was associatedwith increased exocytosis of certain endolysosomal cargos. Through endolysosomal proteomics, we identified
and focused on one such cargo, the pro-inflammatory
cytokine IL-32. Although cerebrospinal fluid levels of
IL-32 are elevated in neuroinflammatory diseases such as
multiple sclerosis [15], the secretion mechanism of IL-32
is not well understood, as it lacks a classical signal peptide [16, 17]. Furthermore, it is unclear what cell types in
the central nervous system produce IL-32 under neuroinflammatory conditions. We found that IL-32 was upregulated in astrocytes in multiple sclerosis lesions, was likely
secreted in part via exosomes, and was involved in thepolarization of inflammatory reactive astrocyte states.
Results
To identify the cellular pathways responsible for the lossof homeostatic functions that accompanies inflammatory
astrocyte reactivity [14], we reanalyzed RNA-sequencing data from multiple hiPSC-derived astrocyte models[18–22] (Table S1, Additional file 3) treated with ITC
or similar treatments (Fig. 1a), focusing on downregulated genes. We found that downregulated genes after
ITC treatment were enriched in genes encoding proteinsassociated with the endolysosomal system (Fig. 1b, c,

Page 2 of 15

Supplementary Fig. 1), suggesting a potential link to thereported phenotype of decreased phagocytosis.
To confirm if ITC indeed perturbed endolysosomal
system function, we performed downstream experiments
using the iAstrocyte model that we previously developed (WTC11 genotype) [9]. We found that ITC reduced
LysoTracker staining without appreciably changing total
endolysosomal mass as measured by the abundance of
LAMP1 or LAMP2 (Fig. 1d–i, Additional file 1), which
tend to be enriched in lysosomes but can also mark other
endolysosomal compartments [23]. Since a decrease in
LysoTracker staining can reflect a decrease in either total
endolysosomal mass or acidity, we deduced from theabove results that ITC caused endolysosomal alkalinization. Indeed, using a genetically encoded endolysosomal pH reporter (FIRE-pHLy) [24], we found that ITC
decreased the acidity of L
­ AMP1+ endolysosomal compartments (Fig. 1j). We also observed a buildup of L
­ C3+
puncta on immunostaining after ITC (Fig. 1d, e), suggestive of perturbed autophagic flux. Using both a genetically encoded reporter [25] (Fig. 1k) and LC3-II western
blot [26] (Fig. 1l, m, Additional file 1), we found that
ITC decreased autophagic flux as well, consistent with
the importance of acidic lysosomes for degradation of
autophagic substrates [27].
To gain a more detailed understanding of how endolysosomal function is perturbed in inflammatory reactive
astrocytes, we performed mass spectrometry-based
proteomic characterization of ­L AMP1+ endolysosomal
compartments in ITC-treated vs control iAstrocytes(Fig. 2a, see Methods, Additional file 8), using the total
cell lysate as a reference. We found that v-ATPase subunits and degradative enzymes were less abundant in
endolysosomal compartments in ITC-treated iAstrocytes(Fig. 2b, c), consistent with transcript-level downregulation in the RNA-seq data (Fig. 1b). Furthermore, CST3, apotent inhibitor of lysosomal proteases [28], accumulated
in endolysosomal compartments upon ITC treatment(Table S2, Additional file 4). We also detected endolysosomal accumulation of proteins involved in vesicular exocytosis (e.g. RAB27A [29], SNAP25 [3, 5]) together with
inflammatory mediators (e.g. IL-32 [16, 17], CCL2 [30]),
suggesting that ITC may cause rerouting of endolysosomal trafficking to facilitate nonclassical secretion of
inflammatory factors.
To explore this hypothesis, we developed a flow-cytometric assay to quantify in live unpermeabilized iAstrocytes the exposure of the LAMP1 luminal domain on
the plasma membrane (see Methods, Additional file 8),
which is known to increase in degranulating immune
cells [31] and may be useful as a marker of the exocytic
activity of certain endolysosomal pathways [32]. With
this approach, we found that ITC-treated iAstrocytes

Leng et al. Journal of Neuroinflammation(2024) 21:198

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Fig. 1 Endolysosomal function is perturbed in inflammatory reactive astrocytes. a Schematic of modeling inflammatory astrocyte reactivity
in vitro. b Heatmap of changes in the expression of genes encoding lysosome-localized proteins in different hiPSC-derived astrocyte models
treated with ITC or similar treatments vs vehicle control. c Top GO Cellular Component terms enriched among the top 1000 downregulated
genes in iAstrocytes treated with ITC; endolysosomal system-related terms are highlighted by asterisks. d Representative images of LysoTracker
staining in live iAstrocytes or immunostaining of LC3 or LAMP1 in fixed and permeabilized iAstrocytes; scale bar = 75 μm. e Quantificationof imaging experiments shown in d (n = 3 wells per condition). f, g Representative immunoblot bands against LAMP1 (f) or LAMP2 (g). h, i
Quantification of immunoblot experiments shown in f, g (n = 3 wells per condition). j Measurement of the acidity of ­LAMP1+ endolysosomal
compartments using FIRE-pHLy via flow cytometry (see Methods, Additional file 8). k Measurement of autophagic flux with the LC3ΔG-RFP/LC3-GFP
fluorescent reporter from Kaizuka et al. [25]. l Immunoblot against LC3 demonstrating LC3-I and LC3-II bands. m Quantification of autophagic flux
from the LC3-II bands in l; error bars reflect the 95% confidence interval associated with the standard error of the mean; individual data points
not shown because the quantities of interest are differences between means, with no biologically meaningful pairing of individual data points
across conditions. P values where shown were calculated using two-sided Student’s t test

Leng et al. Journal of Neuroinflammation(2024) 21:198

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Fig. 2 Perturbation of endolysosomal function is accompanied by remodeling of the endolysosomal proteome. a Schematic of endolysosomal
proteomics workflow (n = 3 wells per condition). b Volcano plot of endolysosomal proteomic data. c Scatterplot comparing l­og2-foldchange of endolysosomal vs total cell lysate protein abundance in ITC-treated iAstrocytes compared to vehicle-treated iAstrocytes(IP: immunoprecipitation). d Representative histograms of cell-surface LAMP1 staining intensity in vehicle- or ITC-treated iAstrocytes compared
to isotype control staining intensity measured by flow cytometry. e Median fluorescence intensity (MFI) of cell-surface LAMP1 measured by flow
cytometry (n = 3 wells per condition); P value by two-sided Student’s t test. f Cell-surface LAMP1 or LysoTracker staining in iAstrocytes treatedwith increasing doses of bafilomycin A1 (n = 3 wells per condition; error bars reflect the standard error of the mean)

had ~ 1.8 times the amount of cell-surface LAMP1 relative to vehicle-treated controls, despite equivalent levels of total LAMP1 (Fig. 2d, e). We were able to directly
observe putative endolysosomal exocytic events from
­L AMP1+ vesicles via total internal reflection fluorescence microscopy of iAstrocytes transduced with a
LAMP1-mCherry fusion construct and loaded with
LysoTracker Green, where the green fluorescence from
LysoTracker was lost from LAMP1-mCherry+ vesicles

trafficked to the plasma membrane (Additional file 2).
Furthermore, the abundance of cell-surface LAMP1
demonstrated a positive dose–response relationshipwith bafilomycin A1 (Fig. 2f ), an inhibitor of v-ATPase
activity and autophagosome-lysosome fusion [33] thatis known to increase endolysosomal exocytosis [34–36]. Compared to cell-surface LAMP1, LysoTracker
staining demonstrated a negative dose–response
relationship (Fig. 2f ), likely reflecting alkalinization

Leng et al. Journal of Neuroinflammation(2024) 21:198

of endolysosomal compartments due to v-ATPase
inhibition.
To identify factors downstream of ITC that mediate theperturbations to endolysosomal function we observed
above, we proceeded to perform targeted CRISPR-based
inhibition (CRISPRi) screens against the “druggable

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genome” [37] in iAstrocytes treated with vehicle or
ITC (see Methods, Additional file 8) using cell-surface
LAMP1 or LysoTracker staining as proxies of endolysosomal system function (Fig. 3a). Consistent with ourobservations from the bafilomycin A1 titration curve,
the phenotype scores (labeled as “gene score”, Methods,

Fig. 3 Multi-phenotypic CRISPR-based screening identifies mTOR as a central regulator of endolysosomal system function. a Schematicof CRISPR-based screening workflow. b Pearson correlation of gene scores (see Methods, Additional file 8) of hits from the LAMP1 vs LysoTracker
screens (n = 2 biological replicate screens per condition). c Enrichment analysis against MSigDB Hallmark Pathways terms of the top 20 hits
from each screen; terms pertaining to mTOR are highlighted with stars. d Heatmap of gene scores of the hits overlapping with the highlighted
MSigDB terms in c. e, f Median fluorescence intensity (MFI) of phospho-S6 (e) or phospho-4E-BP1 (f) staining in ITC- vs. vehicle-treatediAstrocytes measured by flow cytometry. g Representative immunoblot bands corresponding to mTOR, phospho-S6, total S6, phospho-ULK1,
and phospho-AKT1 in ITC- vs vehicle-treated iAstrocytes in the presence of different mTOR inhibitors. h Quantification of immunoblotting
experiments (n = 6 wells per condition for phospho-S6/S6, n = 3 wells per condition for phospho-ULK1, n = 6 wells per condition for phospho-AKT1/
pan-AKT; P values from two-sided Student’s t test). k, l Cell-surface LAMP1 (k) or LysoTracker (l) MFI in ITC-vs. vehicle-treated iAstrocytes
in the presence of mTOR inhibitors measured by flow cytometry (n = 6 wells for DMSO treated, n = 3 wells for all other conditions; P values calculated
only for ITC-treated conditions by linear regression with adjustment for multiple testing by Holm’s method, shown only if significant)Leng et al. Journal of Neuroinflammation(2024) 21:198

Additional file 8) of hits from the cell-surface LAMP1
screens were on average inversely correlated with thosefrom the LysoTracker screens (Fig. 3b, d). Notably,
MTOR was a top hit in both screens (Fig. 3d), and we also
found a strong enrichment for genes associated with themTOR pathway in the top hits from both screens (Fig. 3c,d). These results corroborate in hiPSC-derived astrocytes
the rich literature on the regulation of autophagy and lysosome function by mTOR [38, 39], much of which was
based on experiments in transformed human cell lines,
yeast, or animal models.
To validate our screening results, we first assessed
whether ITC increased the activity of mTORC1 ormTORC2, the two protein complexes formed by mTOR
with distinct regulatory subunits and downstream
signaling pathways [40]. We found that ITC increased
mTORC1 activity as measured by increased levelsof Ser235/236-phosphoryated S6 ribosomal protein(hereon referred to as phospho-S6), Thr37/46-phosphorylated 4E-BP1, and Ser757-phosphorylated ULK1[38, 41] (Fig. 3e–i). ITC also increased mTORC2 activity as measured by Ser473-phosphorylated AKT1 [42](Fig. 3j). To see if there was evidence of mTOR activation in inflammatory reactive astrocytes more broadly
across different contexts, models, and species, we
extracted differentially expressed genes in astrocytes
from a variety of published rodent and human transcriptomic datasets and found broad enrichment for
mTOR pathway-associated genes among upregulated
genes (Supplementary Fig. 2), including in a recently
published dataset demonstrating a role for astrocyte
mTOR activation in experimental autoimmune encephalitis (EAE) [43], a mouse model of multiple sclerosis.
Next, we tested the effect of mTOR inhibitors on cellsurface LAMP1 and LysoTracker staining. Consistent
with the phenotype scores observed for MTOR knockdown in our CRISPRi screens, both rapamycin and
Rapalink-1 [44] decreased cell-surface LAMP1 levels
and increased LysoTracker staining (Fig. 3k, l), regardless of ITC treatment, although the effect of rapamycin
on LysoTracker staining was not statistically significant.
Given our factorial experimental design, we also analyzed our data with two-way ANOVA (see Methods,
Additional file 8). Rapamycin and Rapalink-1 decreased
both baseline cell-surface LAMP1 (statistically significant main effect terms) as well as reversing the ITCinduced increase in cell-surface LAMP1 (statisticallysignificant interaction terms) (Table S4, Additional
file 6, tab Fig. 3k). For LysoTracker staining, Rapalink-1
increased both baseline LysoTracker staining as well as
exacerbating the ITC-induced decrease in LysoTracker
staining, but the effects of rapamycin were not statistically significant (Table S4, Additional file 6, tab Fig. 3l).

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Both drugs significantly decreased phospho-S6 levels atbaseline as well as abrogating the ITC-induced increase(Supplementary Fig. 3a; Table S4, Additional file 6, tab
SupFig3a), demonstrating target engagement.
Focusing on mTOR as a central regulator of endolysosomal system function, we next explored how modulating mTORC1 or mTORC2 activity affected the
exocytic activity of endolysosomal pathways. First, we
tested whether the changes in cell-surface LAMP1and LysoTracker staining caused by ITC depended on
mTORC1 vs. mTORC2. We found that knockdown of
MTOR decreased cell-surface LAMP1 and increased
LysoTracker staining in ITC-treated astrocytes, which
was phenocopied to a larger degree by knockdown of
RPTOR (which encodes a subunit unique mTORC1) [45]than by knockdown of RICTOR (which encodes a subunit
unique to mTORC2) [45] (Fig. 4a), consistent with known
mTORC1-dependent mechanisms regulating autophagy
and lysosome function [38, 39]. We verified that weachieved robust knockdown of mTOR by both directly
measuring mTOR protein levels as well as downstream
phospho-S6 levels (Supplementary Fig. 3b, c). A robust
decrease in downstream phospho-S6 levels with RPTOR
knockdown also verified Raptor protein depletion.
As we did previously with mTOR inhibitors, we also
analyzed the mTORC1/2 subunit knockdown data by
two-way ANOVA. Whereas the interaction between
MTOR knockdown and ITC-treatment was statisticallysignificant for both cell-surface LAMP1 and LysoTracker
staining, the interactions between ITC treatment and
RPTOR or RICTOR knockdown were not statisticallysignificant for either cell-surface LAMP1 or LysoTracker
staining (Table S4, Additional file 6, tabs Fig. 4a, b). This
discrepancy suggests that knockdown of RPTOR or RICTOR may not cleanly inhibit mTORC1 or mTORC2 activity respectively without causing second-order changes.
Indeed, we saw that RICTOR knockdown caused anincrease in phospho-S6 levels and mTOR protein levels(Supplementary Fig. 3b, c), demonstrating that blocking
mTORC2 activity can cause a compensatory increase
in mTORC1 activity. Thus, phenotypes associated with
RPTOR or RICTOR knockdown need to be interpretedwith caution, as they likely do not reflect the effect of perturbing only mTORC1 or mTORC2 activity, respectively.
In addition to decreasing mTORC1 activity with
RPTOR knockdown, we also tested the effect of increasing mTORC1 activity through knockdown of TSC1, an
upstream inhibitor of mTORC1 [41]. We found that
TSC1 knockdown increased cell-surface LAMP1 anddecreased LysoTracker staining (Supplementary Fig. 4a,b), as one would predict. However, similar to what we
observed with RPTOR knockdown, the interaction term
between TSC1 knockdown and ITC-treatment was notLeng et al. Journal of Neuroinflammation(2024) 21:198

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Fig. 4 Cytokine-induced remodeling or pharmacological disruption of the endolyosomal system results in increased exocytosis of endolysosomal
contents. a–d Cell-surface LAMP1 or LysoTracker median fluorescence intensity (MFI) measured by flow cytometry (a), extracellular CTSB
concentration measured by electrochemiluminescence-based immunoassay (b), extracellular IL-32 concentration measured by ELISA (c),or abundance of extracellular mito-EVs measured by flow cytometry (d) in ITC- vs. vehicle-treated iAstrocytes transduced with non-targeting control(NTC) sgRNAs or sgRNAs targeting genes encoding common (MTOR) or unique mTORC1 (RPTOR) vs mTORC2 (RICTOR) subunits, with or without
co-treatment with bafilomycin A1. P values were calculated by linear regression with correction for multiple testing using Holm’s method, shown
only when significant

statistically significant (Table S4, Additional file 6, tabs
SupFig4a, b).
Next, we assessed how mTORC1 vs. mTORC2 controlled the exocytic activity of endolysosomal pathways
by measuring the extracellular levels of three different
endolysosomal cargos (see Methods, Additional file 8):cathepsin B (CTSB), a lysosome-enriched protease
known to be released extracellularly by astrocytes [46];IL-32, which we identified as being enriched in endolysosomal compartments from our proteomics dataset,
consistent with prior observations [47]; and mitochondria-containing extracellular vesicles (mito-EVs) (seeMethods, Additional file 8 and Supplementary Fig. 5),
which are known to be released by astrocytes and arethought to be derived from mitophagy [48, 49]. We foundthat ITC increased extracellular levels of CTSB, IL-32, as
well as mito-EVs, all of which were dramatically boosted
by addition of bafilomycin A1 (Fig. 4b–d). Furthermore, MTOR knockdown decreased the exocytosis of
CTSB, IL-32, and mito-EVs, although for IL-32 we only
observed a statistically significant effect in the presenceof bafilomycin A1 (Fig. 4b–d). RPTOR knockdown phenocopied MTOR knockdown to a larger degree than
RICTOR knockdown for CTSB and mito-EVs, whereas
for IL-32 RICTOR knockdown had a stronger effect than
RPTOR knockdown (Fig. 4b–d). This trend was also evident by examining the interaction terms for MTOR vs
RICTOR vs RPTOR knockdown with ITC treatment: for
CTSB and mito-EVs, the interaction between ITC treatment and MTOR or RPTOR knockdown, but not RICTOR knockdown, was statistically significant (in theabsence of bafilomycin A1); whereas for IL-32, the interaction ITC treatment and RICTOR knockdown, but not
MTOR or RPTOR knockdown, was statistically significant (in the presence of bafilomycin A1) (Table S4, Additional file 6, tabs Fig. 4b–d). On the other hand, TSC1
knockdown increased the exocytosis of mito-EVs and
CTSB (Supplementary Fig. 4c, d), as expected, with statistically significant interaction terms as well (Table S4,Additional file 6, tabs SupFig4c-d). Overall, the stronger
dependence on mTORC1 for the exocytosis of CTSB
and mito-EVs compared to IL-32 suggests that IL-32

Leng et al. Journal of Neuroinflammation(2024) 21:198

exocytosis may occur via a different endolysosomal pathway compared to CTSB and mito-EVs.
Given prior work which suggested that extracellular
IL-32 is membrane-associated and possibly a component of extracellular vesicles [47, 50, 51], we proceeded
to further characterize the secretion mechanism of IL-32
by isolating extracellular vesicles (EVs) from iAstrocyte
conditioned media via differential ultracentrifugation(see Methods, Additional file 8), with or without ITC
treatment. By western blotting, we verified that our EV
preparation contained high levels of consensus EV markers such as CD63, CD81, Hsc70, and caveolin-1 [52] andwas of acceptable purity, containing undetectable levelsof apo-lipoproteins such as ApoA-I (Fig. 5a, Additional
file 1), in accordance with the MISEV guidelines [52]. On
nanoparticle tracking analysis (see Methods, Additional
file 8), we saw that the size distribution of EVs from our
preparations centered around a diameter of 100 nm, as
expected [52] (Fig. 5b). Both western blotting and nanoparticle tracking analysis suggested an increase in theconcentration of EVs with ITC treatment (Fig. 5a, b, Supplementary Fig. 6).
Having validated our EV preparation, we proceeded toblot for IL-32 and found that IL-32 was present in EVs
isolated from ITC-treated but not vehicle-treated iAstrocytes (Fig. 5c). To see if extracellular IL-32 was exclusively EV-associated, we analyzed the EV pellet andsupernatant by western blotting and ELISA. On western
blot, IL-32 was detectable only in the EV pellet, as wereEV markers (Supplementary Fig. 7a). However, by ELISA,
we saw that extracellular IL-32 partitioned roughly
equally between the EV pellet and supernatant (Supplementary Fig. 7b). In comparison, CTSB exclusively partitioned into the supernatant (Supplementary Fig. 7c). The
discrepancy between the western blot and ELISA data for
IL-32 could potentially be explained by the fact that thewestern blot and ELISA antibodies are polyclonal antibodies likely raised against different immunogens. Perhaps the ELISA antibodies detect an IL-32 isoform notdetected by the western blot antibody.
Since EVs can be derived from direct budding of theplasma membrane or multivesicular body exocytosis(which would then be referred to as exosomes) [52], we
performed immunostaining of ITC-treated iAstrocytes
and visualized the subcellular localization of IL-32 with
confocal microscopy. We found that IL-32 co-localized
strongly with CD63 (Fig. 5d, Supplementary Fig. 8),
which is also a marker of multivesicular bodies [53], suggesting that extracellular IL-32 is derived from multivesicular body exocytosis. We proceeded to further test thishypothesis by observing the effect of bafilomycin A1 or
knockdown of genes encoding proteins involved in multivesicular body exocytosis on extracellular IL-32 levels.

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In agreement with prior reports which demonstrated
increased exosome release with bafilomycin A1 [34, 36,54], we found that treatment with bafilomycin A1 dramatically increased extracellular IL-32 levels (Fig. 5e).
Furthermore, knockdown of RAB27A/B decreased extracellular IL-32 levels (Fig. 5e), consistent with the knownrole of RAB27A/B in exosome secretion [55]. Lastly,
corroborating a prior report which identified a role for
PI4KIIIβ in exosome biogenesis [54], we found that inhibition of PI4KIIIβ (using PI4KIIIβ inhibitor 3 [56]) but
not PI4KIIα (using PI-273 [57]) decreased extracellular
IL-32 levels in the presence of bafilomycin A1 (Fig. 5f ).
Having explored the secretion mechanism of IL-32,
we subsequently characterized its function in inflammatory astrocyte reactivity. Our prior work established
two distinct polarizations of inflammatory reactive
astrocytes after ITC treatment—a V­ CAM1+ interferon/
TNF-responsive polarization associated with CXCL10
secretion vs. a ­VCAM1−/C3+ IL-1/IL-6-responsive
polarization associated with GM-CSF secretion [9]. Afterverifying that knockdown of IL32 resulted in robust
depletion of IL-32 at the protein level (Supplementary Fig. 9a, b), we found that IL32 knockdown subtly
decreased the proportion of V
­ CAM1+/C3− astrocytes
and noticeably decreased CXCL10 secretion (Fig. 6a,b), suggesting that IL-32 promotes the interferon/TNFresponsive polarization. Also, IL-32 induction by ITC
was greater in V
­CAM1+/C3+ interferon-responsive
astrocytes compared to IL-1/IL-6-rresponsive V­ CAM1−/+C3 astrocytes (Supplementary Fig. 9c–e). Furthermore,
we found that IFN-β increased the upregulation of IL-32
by ITC (Supplementary Fig. 9f ), consistent with priorwork demonstrating a role of IL-32 in antiviral responses[58–60].
Given our finding that extracellular IL-32 is associatedwith EVs, we wondered if free extracellular IL-32 could
influence inflammatory reactive astrocyte states. We
found that treatment with recombinant IL-32β or IL-32γ
at 200 ng/mL did not appreciably alter the proportion of­VCAM1+/C3− astrocytes, regardless of IL32 knockdown(Supplementary Fig. 9f ). As the concentration of IL-32β
and IL-32γ we used here is well above the range where
immune cells could be robustly activated by recombinant
IL-32 [61], our data suggests that the effect of IL-32 on
inflammatory reactive astrocytes states may be mediatedintracellularly, either through uptake of IL-32-containing
EVs or cell-autonomously through IL-32 that has notbeen secreted.
Next, given that IL-32 levels have been reported tobe elevated in the cerebrospinal fluid of patients withmultiple sclerosis and neuro-Behcet’s disease [15], we
wanted to see if IL-32 was upregulated in astrocytes
under neuroinflammatory conditions. Although the

Leng et al. Journal of Neuroinflammation(2024) 21:198

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Fig. 5 Extracellular IL-32 co-fractionates with extracellular vesicles likely derived from multivesicular body exocytosis. a Immunoblots
against consensus extracellular vesicle (EV) markers (CD63, CD81, Hsc70, Caveolin-1) or common contaminants (e.g. ApoA-I) in EVs isolated
from iAstrocyte conditioned media or total cell lysate after vehicle vs. ITC treatment. b EV size distribution measured by nanoparticle tracking
analysis. c Immunoblot against IL-32 in EVs isolated from iAstrocyte conditioned media or total cell lysate. d Representative images of dual
immunostaining against LAMP1 together with IL-32 or CD63 together with IL-32; scale bar = 60 μm. e, f Extracellular IL-32 concentration measured
by ELISA in conditioned media from ITC- vs. vehicle-treated iAstrocytes transduced with non-targeting (NTC) sgRNAs or sgRNAs targeting genes
encoding proteins involved in multivesicular body exocytosis (e), or treated with small molecules known to inhibit (PI4KIIIβ inhibitor 3) or not inhibit(PI-273) exosome biogenesis (f). P values were calculated by linear regression with correction for multiple testing by Holm’s method, shown
only when significant

Leng et al. Journal of Neuroinflammation(2024) 21:198

fact that IL-32 does not have an ortholog in rodents
precluded us from analyzing the vast amount of published transcriptomic data on mouse models of neuroinflammation, we found that IL32 transcript levels
were indeed upregulated in astrocytes in various typesof multiple sclerosis lesions in humans (Fig. 6c) byreanalyzing the comprehensive single-nucleus RNAseq dataset from Macnair et al. [62] (121 subjects
total) at the pseudobulk level [63] (see Methods, Additional file 8). There was also evidence of IL32 upregulation in oligodendrocytes, microglia, and endothelial
cells, but only in white matter lesions (Supplementary
Fig. 10). Interestingly, astrocytes were the only cell
type with evidence of IL32 upregulation in gray matter lesions (Fig. 6c). To examine whether IL-32 could
be found in other neuroinflammatory conditions, westained for IL-32 and cell type markers in post-mortem
brain tissue from pediatric patients diagnosed withhypoxic-ischemic encephalopathy, a condition involving significant neuroinflammation [63] where we had
previously identified upregulation of inflammatory
reactive astrocyte markers [9]. We found that IL-32
preferentially colocalized with ­GFAP+ astrocytes andto a much lesser degree with OLIG1 + oligodendrocytes(Fig. 6d, e). IL-32 was not detected in NeuN + neurons(Fig. 6e), consistent with our observations from themultiple sclerosis snRNA-seq data.
Finally, to ensure the overall robustness of our results,
we validated key findings in iAstrocytes derived froman independent hiPSC line of different sex (162D).
We confirmed that after ITC treatment, 162D iAstrocytes upregulated cell-surface LAMP1 (Supplementary
Fig. 11a), accumulated LC3 and p62 puncta (Supplementary Fig. 11b, c), upregulated mTORC1 activity as measured by phospho-S6 staining (Supplementary Fig. 11d),
upregulated IL-32 (Supplementary Fig. 11e), and secreted
a greater number of mito-EVs (Supplementary Fig. 11 h).
Furthermore, we also directly characterized lysosome
function by assaying intracellular CTSB activity inboth 162D iAstrocytes and WTC11 iAstrocytes; ITC

Page 10 of 15

treatment decreased intracellular CTSB activity in both
162D and WTC11 iAstrocytes (Supplementary Fig. 11 g).

Discussion
Our results establish mTOR activation as a key feature of inflammatory astrocyte reactivity induced by
IL-1α + TNF + C1q (ITC), driving endolysosomal remodeling manifesting as alkalinization of ­L AMP1+ compartments and reduced autophagic flux (Fig. 6e). While
we have focused on mTOR-dependent endolysosomal
remodeling driven by acute ITC treatment, other cellular
processes may also contribute to endolysosomal remodeling. For example, cellular senescence is associatedwith a profound remodeling of lysosome function andcontent [64], which in fact may partially occur throughmTORC1 hyperactivation [65]. Although acute treatment
with inflammatory cytokines is unlikely to induce cellular senescence, we nevertheless found overlap of senescence-associated genesets with genes upregulated by
ITC (Supplementary Fig. 2b). We speculate that perhaps
chronic inflammatory activation of astrocytes could leadto cellular senescence which would further contribute toendolysosomal remodeling.
Connecting our results here with the broader literatureon phenotypes associated with inflammatory astrocyte
reactivity, we suspect that mTOR-induced endolysosomal remodeling may account for the loss of phagocytic
activity observed in inflammatory reactive astrocytes[14]. Although we did not measure phagocytic activity
here, we observed in our previously published CRISPRi
screens on inflammatory reactivity that MTOR knockdown rescued the phagocytic deficit induced by ITC [9].
Here, we found that mTOR-dependent endolysosomal
remodeling also resulted in the increased exocytosis ofcertain endolysosomal cargos (Fig. 6e), with increased
cell-surface LAMP1 likely acting as a non-specificmarker of endolysosomal exocytic activity. Interestingly,­L AMP1+ astrocytes have been shown to modify diseaseprogression in EAE [66]. Furthermore, all three of theendolysosomal cargos we have characterized—CTSB,(See figure on next page.)
Fig. 6 IL-32 regulates the polarization of inflammatory reactive astrocytes and is upregulated in astrocytes in neuroinflammatory conditions. a,b Proportion of IL-1/IL-6-responsive (VCAM1-/C3 +) or TNF/interferon-responsive (VCAM1 + /C3-, VCAM1 + /C3 +) inflammatory reactive astrocyte
polarizations (a) or their associated cytokines (b) in ITC- vs. vehicle-treated iAstrocytes transduced with non-targeting (NTC) sgRNAs or sgRNAs
targeting IL32. c Log-scaled IL32 expression in astrocytes found in normal tissue vs. multiple-sclerosis lesions derived from pseudobulk analysis
of snRNA-seq data from Macnair et al.; n = 15 for healthy control gray matter, n = 15 for normal-appearing gray matter, n = 15 for gray matterlesion, n = 22 for healthy control white matter, n = 18 for normal-appearing white matter, n = 17 for active lesion, n = 27 for chronic active lesion,n = 13 for chronic inactive lesion, n = 8 for relapsing lesion, n = 23 for not specified. d Representative immunostaining of IL-32 and GFAP in whitematter brain tissue from patients with hypoxic-ischemic encephalopathy (HIE); scale bar 50 μm. e Percent GFAP + , OLIG1 + , or NeuN + cells
among IL-32 + cells in HIE brain tissue (n = 3 patients); P values calculated via beta regression. f, Schematic of ITC-induced, mTOR-dependent
endolysosomal remodeling and associated exocytic activity. P values were calculated using the Mann–Whitney U test in a, c, and e, and using
the two-sided Student’s t test in b

Leng et al. Journal of Neuroinflammation

Fig. 6 (See legend on previous page.)(2024) 21:198

Page 11 of 15

Leng et al. Journal of Neuroinflammation(2024) 21:198

IL-32, and mito-EVs—have been reported to be involvedin neuroinflammatory conditions. CTSB levels are elevated in the CSF as well as brain parenchyma of patientswith Alzheimer’s disease [67–70], and knockout of Ctsb
has been shown to ameliorate the neuropathology andbehavioral deficits in mouse models of Alzheimer’s disease [71–73]. Mito-EVs [74] have been shown to mediate
the transfer of mitochondria from astrocytes to neurons
after experimentally induced stroke in mice [48]. Lastly,
IL-32 levels are elevated in the cerebrospinal fluid ofpatients with multiple sclerosis or neuro-Behcet’s disease [15], and a polymorphism in the IL-32 promoter has
been associated with increased risk of multiple sclerosis
in two independent studies [75, 76].
Given that IL-32 does not have an ortholog in rodents
and that its secretion mechanism is still incompletely
characterized, we focused on elucidating IL-32 secretion, taking advantage of our in vitro hiPSC-derived
astrocyte platform. We found that after ITC treatment,
intracellular IL-32 colocalized with multivesicular bodies, and that extracellular IL-32 co-fractionated with
EVs. Whether extracellular IL-32 is exclusively associated with EVs remains to be seen, as we obtained conflicting data by western blotting vs. ELISA. Given thatthe western blot and ELISA antibodies are polyclonal
antibodies likely raised against different immunogens, it
is possible that the ELISA antibodies recognize a nonEV-associated IL-32 isoform not detected by the western
blot antibody. Alternatively, if extracellular IL-32 consists
predominantly of one isoform, it may be cleaved in a way
such that the western blot antibody recognizes only the
EV-associated fragment whereas the ELISA antibodies
recognize both the EV-associated and free fragments.
Further biochemical experiments will be necessary to
elucidate whether IL-32 isoforms may be differentiallyassociated with EVs or if extracellular cleavage of IL-32
occurs.
With respect to the secretion mechanism of extracellular IL-32, we found that knockdown of genes encoding
proteins involved in multivesicular body exocytosis suchas RAB27A/B decreased extracellular levels of IL-32, as
did pharmacological inhibition of exosome biogenesis.
Overall, our results corroborate previous reports demonstrating that a portion of extracellular IL-32 is vesicleassociated [50, 51], and we establish exosomes as thelikely candidate. With respect to extracellular IL-32 notcontained within EVs, it is possible that IL-32 may beloaded directly into multivesicular body lumens and subsequently exocytosed, or that intracellular IL-32 may bereleased directly through plasma membrane leakage indying or dead cells [77].
As for the role of IL-32 in neuroinflammation, we
found that knockdown of IL32 affected the polarization

Page 12 of 15

of inflammatory reactive states induced by ITC [9],decreasing the abundance of the interferon/TNFresponsive state and its associated cytokine CXCL10. A
limitation of knocking down IL32 with DNA-targeting
CRISPRi [78] is that we could not distinguish the contribution of the many splice isoforms of IL-32, which havebeen reported to have distinct activities [79]; future work
could elucidate this using RNA interference or RNA-targeting CRISPR-based systems [80].
Regarding how IL-32 acts upon cells, it is an open
question whether extracellular IL-32 signals through cellsurface receptors or exerts its effects intracellularly, forexample, after EV-mediated uptake or cell-autonomously
when it is not secreted. Depending on the cell type andbiological context, there is evidence for both extracellularand intracellular activity [16]. In our hands, free extracellular recombinant IL-32 at concentrations capable ofactivating immune cells [61] did not appreciably influence inflammatory reactive astrocyte states, suggestingthat our IL-32-associated phenotypes may be mediatedintracellularly.
In addition to its effects on the polarization of inflammatory reactive astrocyte states, we also found that IL-32
was induced by IFN-β (an old disease-modifying treatment for multiple sclerosis), and that IL32 transcriptlevels were upregulated in astrocytes in various multiple
sclerosis lesions. Considering the human genetics data
demonstrating the importance of IL-32 to the pathogenesis of multiple sclerosis, studying how astrocyte IL-32
contributes to multiple sclerosis would be a worthwhile
future research direction. Lastly, we found preferential
colocalization of IL-32 with astrocytes in a different neuroinflammatory condition—hypoxic-ischemic encephalopathy (HIE), suggesting that IL-32 may play a role in
HIE as well.
In conclusion, we believe that our results highlightmTOR-dependent endolysosomal remodeling as animportant and previously underappreciated aspect ofinflammatory astrocyte reactivity which can be targeted
therapeutically. We also clarified the secretion mechanism and functional role of an important disease-associated cytokine, IL-32, in astrocytes, a cell type in which
IL-32 has rarely been studied [81]. Since the receptor for
IL-32 is still unknown [16], we believe that our results
establish a strong foundation for future studies focusedon the how IL-32 mediates its biological effects and contributes to neuroinflammation.
Abbreviations
CRISPR	Clustered regularly interspaced short palindromic repeats
CRISPRi	CRISPR-based gene expression inhibition
hiPSC	Human induced pluripotent stem cell
ITC	IL-1α + TNF + C1q
ELISA	Enzyme-linked immunosorbent assay
EV	Extracellular vesicle

Leng et al. Journal of Neuroinflammation(2024) 21:198

Page 13 of 15

EAE	Experimental autoimmune encephalitis

Declarations

Supplementary Information

Ethics approval and consent to participate
Not applicable.

The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12974-​024-​03165-w.
Additional file 1. Raw western blot images and associated metadata.
Additional file 2. TIRF microscopy movie of LysoTracker Green-loaded
iAstrocytes expressing LAMP1-mCherry.
Additional file 3. Table S1: Gene-level log-scaled fold-change and p-value
information derived from RNA-seq of multiple hiPSC-derived astrocyte
models treated with ITC or similar treatments.
Additional file 4. Table S2: Endolysosomal-IP and total cell lysate proteomics data.
Additional file 5. Table S3: Metadata for studies included in gene set overlap analyses shown in Supplementary Figures 1-2.

Consent for publication
Not applicable.
Competing interests
A.W.K is a member of the Scientific Advisory Board for Nine Square Therapeutics, Inc. J.E. is an advisor to Seer, Inc. M. K. has filed a patent application related
to CRISPRi and CRISPRa screening (PCT/US15/40449), is a co-scientific founderof Montara Therapeutics and serves on the Scientific Advisory Boards of Alector, Engine Biosciences, Casma Therapeutics, and Cajal Neuroscience, and is anadvisor to Modulo Bio and Recursion Therapeutics. None of the other authors
declare competing interests.
Received: 27 March 2024 Accepted: 3 July 2024

Additional file 6. Table S4: Two-way ANOVA analysis results for experimentswith factorial design.
Additional file 7: Supplementary Figures.
Additional file 8: Methods.
Acknowledgements
We thank Greg Mohl, Avi Samelson, Olivia Teter, Biswa Ramani, Brandon
Desousa, Emmy Li, Vukasin Jovanovic, Zuzana Krejciova, Nawei Sun, and Carlo
Condello for contributions to preliminary studies or helpful discussions. We
thank members of the Kampmann lab for discussions and feedback on the
manuscript. We thank the staff at the UCSF Center for Advanced Light Microscopy and the UCSF Parnassus Flow Cytometry CoLab (RRID:SCR_018206) for
their assistance. A portion of the imaging data presented in this manuscript
were acquired on microscopes at the UCSF Center for Advanced Light Microscopy and the Innovation Core at the Weill Institute, a part of the UCSF Center
for Advanced Light Microscopy.
Author contributions
K.L., B.R., and M.K. conceptualized and led the overall project, and wrote
the manuscript with input from all co-authors. K.L. and B.R. performed theexperiments and data analysis unless stated otherwise. In consultation with
J.E., F.M aided with sample preparation for whole-cell and lysosome-specificproteomics experiments, and performed mass spectrometry and preliminary
computational analysis. I.V.L.R. performed sample prep for the cell-surface
LAMP1 CRISPRi screens. K.A.H. guided TIRF experiments. M.L. and S.B aided
with design and sample preparation for whole-cell and lysosome-specificproteomics experiments, respectively. M.Y.C. and A.W.K provided FIRE-pHLy
reagents and guidance on lysosome pH experiments. S.F conducted LAMP2
immunofluorescence experiments and data analysis. All authors reviewed themanuscript.
Funding
This research was supported by National Institutes of Health (NIH) grant F30
AG066418 to K.L.; California Institute for Regenerative Medicine grant EDUC412812 and NIH grant T32 NS115706 to I.V.L.R.; Chan Zuckerberg Initiative Ben
Barres Early Career Acceleration Awards and NIH/NIA R01 AG062359 to M.K.;
NIH/NIND grants (R01NS097551, P01NS083513 and R21NS119954) to S. Fancy;
NIH grants R01AG057342 and R56AG057342 to A.K. and M.C. S.Fancy is a
Harry Weaver Neuroscience Scholar of the National Multiple Sclerosis Society.
J.E.E., M.L., F.M, and S.B. were supported by the Chan Zuckerberg Biohub. The
funders had no role in study design, data collection and analysis, decision to
publish or preparation of the manuscript.
Availability of data and materials
The source data and code used to analyze the data presented in this study will
be shared upon request. The source data for all western blots shown in thisstudy can be found in Additional file 1.

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