Journal of Neuroinflammation

Zhang et al. Journal of Neuroinflammation(2024) 21:177
https://doi.org/10.1186/s12974-024-03170-z

Open Access

RESEARCH

Microglial AKAP8L: a key mediator in diabetesassociated cognitive impairment via
autophagy inhibition and neuroinflammation
triggering
Wen-yuan Zhang1,2, Qian-qian Wei1,2, Tao Zhang3,4, Chang-shui Wang5, Jing Chen6,7, Jian-hua Wang3,4, Xin Xie3,4 andPei Jiang3,4*
Abstract
Background Diabetes-associated cognitive impairment (DACI) poses a significant challenge to the self-managementof diabetes, markedly elevating the risk of adverse complications. A burgeoning body of evidence implicatesmicroglia as a central player in the pathogenesis of DACI.
Methods We utilized proteomics to identify potential biomarkers in high glucose (HG)-treated microglia, followed bygene knockdown techniques for mechanistic validation in vitro and in vivo.
Results Our proteomic analysis identified a significant upregulation of AKAP8L in HG-treated microglia, withconcurrent dysregulation of autophagy and inflammation markers, making AKAP8L a novel biomarker of interest.
Notably, the accumulation of AKAP8L was specific to HG-treated microglia, with no observed changes in co-cultured
astrocytes or neurons, a pattern that was mirrored in streptozotocin (STZ)-induced diabetic mice. Further studies
through co-immunoprecipitation and proximity ligation assay indicated that the elevated AKAP8L in HG-treatedmicroglial cells interacts with the mTORC1. In the STZ mouse model, we demonstrated that both AKAP8L knockdown
and rapamycin treatment significantly enhanced cognitive function, as evidenced by improved performance in the
Morris water maze, and reduced microglial activation. Moreover, these interventions effectively suppressed mTORC1
signaling, normalized autophagic flux, mitigated neuroinflammation, and decreased pyroptosis.
Conclusions Our findings highlight the critical role of AKAP8L in the development of DACI. By interacting with
mTORC1, AKAP8L appears to obstruct autophagic processes and initiate a cascade of neuroinflammatory responses.
The identification of AKAP8L as a key mediator in DACI opens up new avenues for potential therapeutic interventions.
Keywords AKAP8L, mTOR, Diabetes-associated cognitive impairment, Microglia, Autophagy, Neuroinflammation*Correspondence:
Pei Jiang
jiangpeicsu@sina.com
1
Department of Pharmacy, Zhongshan City People’s Hospital,
Zhongshan 528403, China
2
School of Pharmaceutical Sciences, Zunyi Medical University,
Zunyi 510006, China
3
Translational Pharmaceutical Laboratory, Jining First People ’ s Hospital,
Shandong First Medical University, Jining 272000, China

4

Institute of Translational Pharmacy, Jining Medical Research Academy,
Jining 272000, China
5
Department of Neurosurgery, Affiliated Hospital of Jining Medical
University, Jining 272000, China
6
Neurobiology Key Laboratory, Jining Medical University, Jining
272067, China
7
Division of Biomedical Sciences, Warwick Medical School, University of
Warwick, Coventry CV4 7AL, UK© 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 the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Introduction
Diabetes and its associated complications are significant
contributors to the global health burden [1]. Amongthese, diabetes-associated cognitive impairment (DACI)
has become a prominent concern [2]. Clinical researchsuggests that individuals with diabetes face a 1.73fold higher risk of developing dementia and a 2.27-foldincreased risk of experiencing cognitive impairment [3].
Moreover, DACI poses a challenge to the self-management of diabetes in patients, thereby exacerbating the
progression of the disease and increasing the burden of
diabetes care. The pathophysiology of DACI is complex
and includes factors such as insulin resistance, inflammation, lipid abnormalities, oxidative stress, mitochondrial
dysfunction, and autophagy [4]. Despite these insights,
the specific mechanisms underlying DACI remain to be
fully understood.
A growing body of evidence indicates that microglia
play a pivotal role in the development of DACI [5]. Ina healthy state, microglia are essential for maintaining
brain homeostasis by phagocytically clearing damaged
neurons and aggregated misfolded proteins [6]. However,under pathological conditions, such as those present indiabetes, microglia undergo a transition from a protective surveillance state to an overactivated state, which
can lead to synaptic damage, neuronal apoptosis, and the
suppression of neurogenesis [7]. Exposure of microglia
to high glucose (HG) for 24 h triggers a dose-dependent
cytotoxicity through increased apoptosis [8]. In animal models of diabetes, there is a notable correlation
between cognitive impairment and increased activationof microglia, as indicated by the upregulation of Ionized calcium binding adapter molecule 1 (Iba1), a marker
of microglial activation, within the brain [9, 10]. This iscomplemented by clinical studies that corroborate thefindings from basic research [11, 12], thereby reinforcing
the notion that microglial activation is a critical factor in
the pathogenesis of DACI.
Proteomics has become an indispensable tool in diabetes research, providing valuable insights into the intricate protein networks that can become disrupted dueto the disease. This technology has led to the discovery
of novel biomarkers such as Growth differentiation factor-15/Macrophage inhibitory cytokine-1 (GDF15/MIC1), interleukin (IL)-18Ra, and Cysteine rich with EGF
like domains 1 (CRELD1), which are indicative of diseaseprogression in diabetes [13]. Furthermore, proteomics
has been instrumental in investigating the complications
associated with diabetes. For instance, the involvement ofastrocyte N-Myc downstream regulated gene 2 (NDRG2)
in the amelioration of DACI has been confirmed throughproteomic analysis. NDRG2 has been shown to modulate astrocytic-neuronal interactions and restore synaptic function in diabetic mice by regulating the Nuclear

Page 2 of 19

factor kappa-B/Complement C3/ Complement C3a
Receptor (NF-κB/C3/C3aR) signaling pathway [14]. In
the context of vascular dementia in diabetic patients,
urine proteomic analysis has validated certain protein
markers (including Haptoglobin (HP), Serpin domaincontaining protein (SERPIND), ATP Synthase Peripheral
Stalk-Membrane Subunit B (ATP5PB), Vanin 2 (VNN2),Complement C6 (C6), among others) using the receiver
operating characteristic (ROC) curve method [15].
In this study, we have pioneeringly explored the proteomic landscape within microglia exposed to high glucose conditions. We have successfully identified A-kinaseanchor protein 8-like (AKAP8L) as a novel and significant
biomarker. Furthermore, by examining the Alzheimer’s
Disease (AD) postmortem database, we noted a consistent upregulation of AKAP8L expression in AD patients[16]. Moving forward, our objective is to rigorously validate the functional contributions of AKAP8L and to elucidate the pathways in which it is involved, utilizing bothin vivo and in vitro models. Our work is poised to yield
innovative biomarker that could pave the way for thedevelopment of potential therapeutic strategies targeting
DACI.

Materials and methods
Cells culture and treatment

Primary microglia were cultured from the brain of 1-dayold mice. Mouse brains were harvested, the meninges
were removed, and the brain pieces were dissected into
small pieces in Ca2+ and Mg2+-free HBSS (Hank’s Balanced Salt Solution). The tissue pieces were then incubated at 37 °C for 15 min in 0.125% trypsin solution,with gentle mechanical disruption to facilitate enzymatic
digestion. Cells were cultured in Dulbecco’s Modified
Eagle’s Medium (DMEM) supplemented with 10% fetal
bovine serum, 2 mM L-glutamine, 100 U/mL penicillin, and 0.1 mg/mL streptomycin at 37 °C with 5% CO2
for 10–14 days. Following this, the mixed cells in the
media were shaken at 200 rpm for 4 h at 37 °C to suspend the loosely attached microglia. The media was centrifuged, and the microglia formed a pellet. In parallel,
BV2 murine microglial cells, procured from Procell Life
Science & Technology Co., Ltd. (Wuhan, China), were
cultured under stringent conditions. These cultures wereconducted in a fully humidified atmosphere with 5%
CO2 at a temperature of 37 °C, utilizing a DMEM/F-12
medium enriched with 10% Fetal Bovine Serum (FBS).
Brain mixed cultures were prepared according to themethod outlined by previous literature, with slight adaptations [17]. To begin, brains were harvested from oneday-old mouse pups and enzymatically digested for 1 h at
37 ℃ in a 5% CO2 environment using 20 U/mL papain.
Following the enzymatic treatment, the papain wasremoved, and the tissue was gently rinsed in DMEM.

Zhang et al. Journal of Neuroinflammation(2024) 21:177

The tissue was then mechanically dissociated using finely
polished glass serological pipettes until all visible aggregates were dispersed. The dissociated cells were seeded
onto multiwell chambers coated with poly-L-lysine
and incubated in DMEM supplemented with 10% Fetal
Bovine Serum (FBS), 1% glutamine, and 1% penicillinstreptomycin. The cultures were maintained in a humidified atmosphere with 5% CO2 at 37 ℃, with the medium
being refreshed by replacing half of it with fresh medium
every three days.
The experimental design segregated the cells into Control and HG groups. The Control group was maintained
under normal glucose conditions at a concentration of
5.5 mM. Conversely, the HG group was challenged witha HG (33 mM; Solarbio, Beijing, China) environment
for a duration of 48 h [18]. Further categorization of
the BV2 cells yielded four distinct experimental groups:
Control, HG, HG + si-AKAP8L, and HG + RAPA. The
HG + si-AKAP8L group was pre-treated with AKAP8L
small interfering RNA (siRNA) for 24 h, followed byexposure to high glucose (33 mM) for an additional 48 h.
The siRNAs specific to AKAP8L were synthesized from
GenePharma (Shanghai, China) and transfected into BV2cells. Cells were seeded in plates one day before transfection to reach about 70% confluency on the day oftransfection. The transfection procedure was performed
using Lipofectamine 3000 (Invitrogen) according to the
manufacturer’s instructions. The HG + RAPA group was
initially treated with 100 nM rapamycin for 24 h, after
which they were exposed to high glucose (33 mM) for
an additional 48 h, following a rapamycin dosage previously reported in the literature [19]. Rapamycin was dissolved in dimethyl sulfoxide (DMSO), and control groups
were exposed to an equivalent concentration of DMSO(< 0.2%) to account for any solvent effects.
Animals and drug treatment

Eight-week-old male C57BL/6 mice were obtained from
Jinan Pengyue Laboratory Animal Breeding Co., Ltd.
(Jinan, China). The animals were acclimated to standard
laboratory conditions (22 ± 1 °C, 55 ± 5% humidity, anda 12-hour light-dark cycle) and had free access to foodand water. All procedures were approved by the Animal
Ethics Committee of Zhongshan City People’s Hospital,
adhering to the ethical standards outlined in the Guide
for the Care and Use of Laboratory Animals (Chinese
Council).
The animal study consisted of two episodes. In the first
episode, the mice were randomly assigned to the Control
group and the Streptozotocin (STZ; Yisheng Biotechnology Co., LTD Shanghai, China) group (n = 6/group), withthe aim of investigating whether AKAP8L is upregulated
in hippocampal microglia. In the second episode, toinvestigate whether inhibition of AKAP8L or mTORC1

Page 3 of 19

with sh-AKAP8L or rapamycin (RAPA; MCE Shanghai,
China) could improve DACI, the mice were randomly
assigned to the following four groups: the Control group,
the STZ group, the STZ + sh-AKAP8L group, and the
STZ + RAPA group (n = 12/group).
In the STZ-induced diabetic model, C57BL/6J mice
were administered an intraperitoneal injection of STZ
at a dosage of 50 mg/kg once daily for five consecutive
days, with the STZ being dissolved in a 0.01 M sodium
citrate buffer at a pH of 4.4. The Control group received
an equivalent volume of citrate buffer. Body weight andfasting blood glucose levels were assessed from a tail veinblood sample using an OneTouch® Ultra blood glucose
meter. Mice with blood glucose levels above 11.1 mmol/L
were designated as the diabetes model group for subsequent experiments.
In preliminary experiments, we observed that AKAP8L
knockdown, induced by adeno-associated virus (AAV),
had no impact on the body weight and blood glucose levels of mice, as illustrated in Supplementary Fig. 1. In the
STZ + sh-AKAP8L group, the selected shRNA sequenceand scrambled control were cloned into the pAAV vector under control of U6 promoter (Shandong ViGene
Biosciences). To perform the stereotaxic injection, mice
were anesthetized with isoflurane and fixed to a stereotaxic apparatus. Subsequently, 1 µL of AAV9-U6-sh-NC(for the Control, STZ, and STZ + RAPA groups) andAAV9-U6-sh-AKAP8L (for the STZ + sh-AKAP8L group)
were slowly injected into each side of the hippocampus(Bregma, 2.3 mm; Lateral, 1.8 mm; Vertical, 2.0 mm) of
the anesthetized mice. The injection was carried out
using a 10-µL Hamilton injector fitted with a 30-gauge
beveled needle at a rate of 0.25 µL/minute. Upon completing the injection, the needle was retained in position
for 5 min and then gently withdrawn to prevent viralreflux. As referenced in literature [20], rapamycin was
administered via intraperitoneal injection at a dosage of
2 mg/kg every other day for a period of two weeks. For
preparation, rapamycin was initially dissolved in DMSO
and subsequently diluted with a specialized solution
comprising 5% DMSO, 30% PEG400, 5% Tween 80, and60% saline to facilitate in vivo application. Control groups
were administered the same vehicle devoid of rapamycin.
After anesthesia, half of the mice in each group underwent transcardiac perfusion with cold 0.9% saline containing heparin, followed by 4% paraformaldehyde forbrain fixation. The brains were then paraffin-embedded
and sectioned at a thickness of 4 μm for staining. The
timeline for sh-AKAP8L and rapamycin treatments in
STZ mice is depicted in Supplementary Fig. 2.
Proteomic analysis

Primary microglial cells from both Control and HG
groups were subjected to a comprehensive proteomicZhang et al. Journal of Neuroinflammation(2024) 21:177

analysis. Proteins were extracted and underwent a
meticulous quality assessment. Subsequently, they were
enzymatically digested and labeled with TMT reagent(ThermoFisher), which involves the addition of acetonitrile and hydroxylamine. The labeled proteins were thenanalyzed using liquid chromatography-tandem mass
spectrometry (LC-MS/MS). The resulting mass spectrometry data were cross-referenced with major protein databases, including Uniprot, NR, GO, KEGG, andString, to obtain the subcellular localization and annotation information for each identified protein. To identify
differentially expressed proteins between the groups,
we established stringent criteria based on Fold Change(FC) and statistical significance. Proteins with FC ≥ 1.5 orFC ≤ 0.66 and p < 0.05, were selected as differential proteins. This systematic approach ensures a robust and reliable identification of proteins that may play a significant
role in the underlying biological processes.
Immunofluorescence assay

Immunofluorescence staining was performed on frozen hippocampal sections or cells embedded in paraffin.
The samples were washed three times with PBS and thenincubated overnight at 4 °C in a humidified atmospherewith primary antibodies. The following primary antibodies were used: anti-IL-1 beta (1:100, Abcam, Shanghai,
China), anti-p62 (1:100, Abcam, Shanghai, China), antiAKAP8L (1:100, Santa Cruz, Shanghai, China), anti-βIIITubulin (1:500, Servicebio, Wuhan, China), anti-GFAP(1:500, Servicebio, Wuhan, China), anti-Iba-1 (1:500,
Servicebio, Wuhan, China), anti-GSDMD (1:500, Abcam,
Shanghai, China), anti-NLRP3 (1:200, Abcam, Shanghai,
China), LC3 (1:200, Proteintech, Wuhan, China), NeuN
(1:200, Abcam, Shanghai, China) and DAPI (Servicebio,
Wuhan, China). After the primary antibody incubation,
the sections were incubated with a mixture of secondary
antibodies conjugated to Alexa-488 (green, Invitrogen,
Shanghai, China), Alexa-594 (red, Invitrogen, Shanghai,
China), and Alexa-647 (red, Invitrogen, Shanghai, China)
for 2 h at room temperature in the dark. The secondary antibodies included donkey anti-goat, anti-rabbit,
and anti-mouse, which were pre-absorbed to minimize

Page 4 of 19

cross-reactivity. The sections were then mounted using
a medium containing 50% glycerol. Examination was
performed on five random fields per section under afluorescence microscope (Olympus, Tokyo, Japan) by
an observer blinded to the experimental groups. For the
NeuN staining, three sections per mouse were analyzed.
Cells that stained positively for NeuN (NeuN+) were considered to represent living neurons, as referenced in theliterature [21].
Quantitative real-time PCR (RT- qPCR)

To assess the mRNA expression levels of IL-1β, tumor
necrosis factor (TNF)-α, Cluster of differentiation 86(CD86), IL-6, Inducible nitric oxide synthase (iNOS),
IL-4, Arginase-1 (Arg1), and Transforming growth
factor-β (TGF-β), total RNA was extracted from the samples using Trizol reagent (Invitrogen, Shanghai, China).
Subsequently, complementary DNA (cDNA) was synthesized utilizing a cDNA library construction kit (Tiangen,
Beijing, China). The sequences of the primers used aredetailed in Table 1. RT-qPCR was performed on a BioRad CFX96™ detection system using SYBR PCR MasterMix (Tiangen, Beijing, China). The thermal conditions
were established as such: a temperature of 95 ℃ for
15 min, and finally, 40 cycles of amplification at a temperature of 95 ℃ for 10 s, and a temperature of 60 ℃ for
30 s. β-actin was used as an internal standard to normalize the relative expression levels. The data for IL-1β, IL-6,
CD86, TNF-α, iNOS, IL4, Arg1, and TGF-β are presented
as the mean Z-scores from three independent experiments, calculated based on the Ct values and the standard curve method.
Co-immunoprecipitation (Co-IP) assay and Western
blotting

The Co-immunoprecipitation (Co-IP) assay was performed using a Co-IP kit (Absin Bioscienc, Shanghai,
China) according to the manufacturer’s instructions.
The BV2 cells were transfected with the FLAG-tagged
AKAP8L and HA-tagged Raptor plasmids. All steps weredone on ice or at 4 °C. Cells were cold-washed once with
1-fold PBS and then lysed with 0.3% CHAPS. Cell debris

Table 1 Primer sequences used for the qPCR analysis
Gene
TNF-α
IL-1β
IL-6
CD86
iNOS
IL-4
Arg-1
TGF-β
β-actinSense Primer (5’-3’)​G​G​T​G​C​C ​T​A​T​G​T​C ​T​C​A​G​C​C ​T​C
​G​T​G​T​C ​T ​T ​T​C​C​C​G​T​G​G​A​C​C ​T ​T​T​A​C​C​A​C ​T ​T​C​A​C​A​A​G​T​C​G​G​A​G​G​A​T​G​G​A​C​C​C​C​A​G​A​T​G​C​A​C​C​A​G​A​G​A​C​A​G​G​G​A​A​G​T​C ​T​G​A​A​G​C​A​T​C​A​T​C​G​G​C​A​T ​T ​T ​T​G​A​A​C​G​A​G​A​G​T ​T​G​G​A​A​G​C​A​T​C ​T​C ​T​G​G​C​C​C​C​G​C​A​A​C​A​A​C​G​C​C​A​T​C ​T​A​T​G​C​A​T ​T​G​C ​T​G​A​C​A​G​G​A​T​G​C​A​G​A

Antisense Primer (5’-3’)​G​C​C​A​T​A​G​A​A​C ​T​G​A​T​G​A​G​A​G​G​T​C​A​T​C ​T​C​G​G​A​G​C​C ​T​G​T​A​G​T​G​C ​T​G​C​A​A​G​T​G​C​A​T​C​A​T​C​G​T ​T​G
​G​T​C ​T​C​C​A​C​G​G​A​A​A​C​A​G​C​A​C​C​A​G​C​A​G​T​A​G​T ​T​G​C ​T​C​C ​T​C ​T​A​C​C ​T ​T​G​G​A​A​G​C​C​C ​T​A​C​A​G​A​C​A​T​C​A​C​C ​T ​T​G​C​C​A​A​T​C​C​C​C​A​G​A​G​C​C​C ​T​G​T​A​T ​T​C​C​G​T​C ​T​C​C ​T​T​G​C ​T​G​G​A​A​G​G​T​G​G​A​C​A​G​T​G​A

Zhang et al. Journal of Neuroinflammation(2024) 21:177

was pelleted by centrifugation at 15,000 rpm for 10 min.
75 µL of the lysate was taken for whole cell lysate, andthe remaining lysate was used for immunoprecipitation.
Proteins were eluted from the beads and separated bysodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE).
For Western blotting, the hippocampal tissues or cultured cells were homogenized, and the total proteins
were extracted using RIPA lysis buffer. Protein concentrations were determined using the BCA testing kit.
Each sample of 50 µg proteins was separated by electrophoresis on a 12% SDS-PAGE gel. The proteins weretransferred to polyvinylidene fluoride (PVDF) membranes and blocked in 10% non-fat milk for 1 h at room
temperature. Subsequently, membranes were incubatedovernight at 4 °C with the following primary antibodies: anti-LC3 (1:500, Proteintech, Wuhan, China), antip62 (1:500, Proteintech, Wuhan, China), anti-Beclin1(1:500, CST, Shanghai, China), anti-AKAP8L (1:1000,
Santa Cruz, Shanghai, China), anti-p-mTOR (Ser2448)(1:1000, Cell Signaling Technology, Shanghai, China),
anti-mTOR (1:1000, Cell Signaling Technology, Shanghai,
China), anti-p-p70S6K (1:1000, Cell Signaling Technology, Shanghai, China), anti-p70S6K (1:1000, Cell Signaling Technology, Shanghai, China), anti-p-ULK1 (1:1000,
Cell Signaling Technology, Shanghai, China), anti-ULK1(1:1000, Cell Signaling Technology, Shanghai, China),
anti-NLRP3 antibody (1:1000, Cell Signaling Technology, Shanghai, China), anti-ASC (1:1000, Cell Signaling
Technology, Shanghai, China), anti-Caspase-1 (1:1000,
Cell Signaling Technology, Shanghai, China), antiTXNIP antibody (1:1000, Proteintech, Wuhan, China),
anti-N-terminal of GSDMD (1:1000, Abcam, Shanghai, China), anti-GSDMD (1:1000, Abcam, Shanghai,
China), anti-IL-1β antibody (1:1000, Abcam, Shanghai,
China), and β-actin (81115-1-RR, 1:2000, Proteintech,
Wuhan, China). The blots were then washed with Trisbuffered saline-Tween-20 (TBST) for three times, andincubated with secondary antibodies. Finally, the signals
were detected with an ECL kit (Thermo Fisher, Shanghai,
China). The band intensities were analyzed using Image J
software and normalized to the β-actin.
Measurement of Autophagy Flux

The measurement of autophagy flux is referred to previous literature [22]. The BV2 cells and primary microglia
were seeded at a density of 1 × 105 cells/well and cultured
overnight, after which cells were transfected with mRFPGFP-LC3 adenovirus (Hanbio, Shanghai, China) for 24 haccording to the manufacturer’s protocol. Cells were
fixed with 4% paraformaldehyde and the digital images ofLC3-positive puncta were captured by TCS SP8 confocal
laser scanning microscope. The number of LC3-positive

Page 5 of 19

puncta per cell within each experimental group wasquantified using ImageJ software.
Proximity ligation assay

Proximity Ligation Assay (PLA) was executed utilizing the Duolink PLA kit from Sigma, following the provided protocol [23]. Cells were cytospun onto glass slides,
fixed with 4% paraformaldehyde (PFA), and permeabilized with 0.2% Triton X-100 for 5 min. After blocking with Duolink blocking buffer for 30 min, the cells
were incubated with primary antibodies against Raptor
and AKAP8L overnight at 4℃. PLA probes were mixed
with Duolink antibody diluent and applied to the slides,
followed by ligation and amplification steps to detect
protein complexes. Slides were washed, mounted with
Duolink in situ mounting medium containing DAPI to
stain nuclei, and visualized under an Olympus fluorescence microscope (Olympus, Tokyo, JPN). This method
allowed for the specific detection of protein interactions
within the cellular environment.
Enzyme-linked immunosorbent assay (ELISA)

IL-18 levels in the culture media were determined using
an ELISA kit according to the manufacturer’s protocol(R&D Systems, Cat. No. DY7625-05). Plates were read at
a wavelength of 450 nm using a TECAN Genios reader(TECAN, Durham, NC, USA).
Transmission electron microscope

In accordance with a previously published method [24],the cells were fixed with a 2.5% glutaraldehyde solution,
washed with PBS three times for 5 min each at room
temperature, treated with 1% osmic acid for 1 h, washedagain with PBS, and dehydrated through a graded seriesof propanol. Finally, the cells were embedded in an epoxy
resin after being saturated with a 2% uranyl acetate solution. Autophagy and pyroptosis in the different treatmentgroups were observed in the different treatment groups
using transmission electron microscopy (HITACHI,
HT7700, Tokyo, Japan).
Dye uptake

Cells were incubated with YO-PRO-1 iodide at a concentration of 0.2 mM for 15 min. Subsequently, the cells
were counterstained with Hoechst 33,342, and images
were acquired using an inverted microscope (Olympus,
Tokyo, Japan). This technique facilitated the assessmentof cellular integrity and viability through the visualizationof dye penetration [25].
Sholl analysis

Sholl analysis was conducted in accordance with theestablished methodology from a previous study [26].
Confocal images of Iba-1-stained brain sections wereZhang et al. Journal of Neuroinflammation(2024) 21:177

Page 6 of 19

obtained using a confocal microscope and imported into
FIJI software. The images were thresholded to binary,
including the complete processes of microglia. The surrounding processes of other cells were erased using theeraser tool to isolate individual microglia. The longest
process was drawn using the line segment tool. The
Sholl analysis function was used, defining the first shellat 10 micrometers from the soma center (to exclude thesoma from the analysis), with each subsequent shell at 5
micrometers. The software automatically calculates thenumber of intersections with each concentric shell, considering a linear profile.

Statistical analysis

Morris water maze test

To explore changes in protein expression and underlying mechanisms in DACI, we conducted a proteomics
analysis of microglia exposed to HG. This analysis identified 126 proteins that were upregulated and 93 that were
downregulated in microglia following HG treatment. The
raw data for these differentially expressed proteins arepresented in Supplementary Table 1. Notably, AKAP8L
was among the most significantly upregulated proteins,
along with an increase in proteins related to autophagy
and inflammation (Fig. 1A and B). Additionally, KEGG
pathway enrichment analysis highlighted the involvement of these proteins in pathways such as cytokinecytokine receptor interactions, autophagy, NOD-likesignaling cascades, mitophagy, and apoptosis (Fig. 1C).
Further validation of the autophagy and inflammation
pathways was performed using immunofluorescencestaining, western blot, and rt-qPCR. As expected, themRNA levels of IL-1β and TNF-α, as well as the relative
fluorescence intensity specifically for IL-1β, were markedly increased in microglia exposed to HG conditions(Fig. 1D and E, and 1F). The protein level of p62 was also
assessed by western blot, revealing a marked upregulation in HG-induced microglia (Fig. 1G). Autophagic
flux was monitored using LC3 tandem fluorescencelabeling (mRFP-GFP-LC3), which showed a decrease in
LC3 puncta numbers per cells in HG-treated microglia (Fig. 1H and I). These results indicate that HG exposure disrupts autophagic flux in microglia. Intriguingly,
our immunolocalization analysis showed that HG treatment enhanced the co-localization of p62 and AKAP8L
in microglia, implying that AKAP8L may play a role inregulating autophagy (Fig. 1J and K).

The learning and memory capabilities of the mice wereassessed using the Morris Water Maze Test (MWM).
The MWM consisted of a circular tank, subdivided into
four equally sized imaginary quadrants, with a hidden
platform submerged 1 cm below the water surface at thecenter of one quadrant. The navigation trials were conducted over the first 5 days. In each trial, the mouse wasplaced in the tank facing the wall from one of four different starting points and given 60 s to locate the hidden platform. The time taken to reach the platform wasrecorded as the escape latency. If the mouse failed to findthe platform within the allotted time, it was gently guided
to the platform and allowed to remain for 10 s; in suchcases, the escape latency was recorded as 60 s. Following
a 24-hour period, the escape platform was removed, andthe mice were placed in the water maze from the opposite
quadrant to conduct the spatial probe test. During thistest, the duration the mice spent in the former platform’s
quadrant and their swimming velocity were meticulously
documented. Visutrack 3.0 (Xinruan, Shanghai, China)
was used in behavioral test analysis. These behavioral
tests provide a comprehensive assessment of the mice’s
cognitive abilities, particularly in relation to learning andmemory functions.
Staining

Following the meticulous guidelines of the HE Staining kit (C1015, Beyotime, Beijing, China), the paraffinembedded sections were stained with hematoxylin tolabel nuclei and eosin to label cytoplasm. HE-positive
cells in the hippocampus were counted in five random
fields per section using a light microscope by an observer
who was blinded to the experimental groups, ensuring unbiased quantification. For Nissl staining, the procedure was strictly followed as per the manufacturer’s
instructions (Nissl staining kit #G1430, Solarbio, Beijing,
China). The total number of Nissl-positive neurons in the
penumbra was counted in five different fields of view per
section by an observer unaware of the treatment groups,
using light microscopy.

The statistical analysis was carried out using SPSS (IBMCorp., Armonk, NY, USA, Version 29.0), and results arepresented as means ± standard deviation. Statistical analysis was conducted using unpaired Student’s t-tests andone-way and two-way analyses of variance (ANOVA). In
order to compare the groups following ANOVA, a post
hoc Tukey test was conducted. A P-value of less than 0.05
was considered statistically significant.

Results
Proteomics revealed AKAP8L, a novel marker, in
HG-induced microglia

AKAP8L accumulation presented only in microglia, but notin astrocytes and neurons in HG-treated neural cells or in
the hippocampus of STZ mice

The upregulation of AKAP8L identified in the proteomic
analysis was further validated using Western blot andimmunofluorescence (Fig. 2A and B). To elucidate theexpression profile of AKAP8L in response to co-cultureand HG stimulation, we performed co-staining with

Zhang et al. Journal of Neuroinflammation

Fig. 1 (See legend on next page.)(2024) 21:177

Page 7 of 19

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 8 of 19(See figure on previous page.)
Fig. 1 Proteomic analysis identified AKAP8L as a novel biomarker in high glucose-induced microglia. (a) Schematic overview of the proteomic analysis
workflow in high glucose-induced microglia. (b) Volcano plot depicting differentially expressed proteins. (c) KEGG pathway enrichment analysis of differentially expressed proteins. (d) Representative immunofluorescence images of IL-1β with statistical quantification. Scale bar: 20 am. Relative mRNA
expression of TNF-α (e) and IL-1β (f). (g) Representative Western blots and statistical analysis of P62 protein levels. Representative confocal microscopy
images (h) and statistical graphs (i) of mRFP-GFP-LC3 puncta. Scale bar: 5 μm. (j) Representative immunofluorescence colocalization images and statisticalgraphs of p62 and AKAP8L. Scale bar: 10 μm. Data are Mean ± SD (n = 3–5). P-values are from unpaired Student’s t-tests. *p < 0.05 and **p < 0.01 comparedto the Control groupspecific markers: Iba-1 for microglia, GFAP for matureastrocytes, and Tubulin for neurons. Notably, AKAP8L
was found to accumulate specifically in HG-treatedmicroglia and was absent in co-cultured astrocytes andneurons (Fig. 2C and D). Consistent with these findings,
similar results were observed in the hippocampal tissueof STZ-induced diabetic mice (Fig. 2E-H). Furthermore,
STZ-induced diabetic mice exhibited cognitive impairment, as well as autophagy disorders and inflammation
within the hippocampal region, as detailed in Supplementary Figs. 3 and 4.
HG enhanced the interaction between AKAP8L andmTORC1 in primary microglia and BV2 cells

Recent literature has indicated that AKAP8L interacts
with mTORC1 to regulate anabolic metabolism in renal
epithelial cells [27]. However, it is not yet clear whether
the same interaction occurs in microglia and whether
HG enhances the interaction. To assess this interaction, we overexpressed HA-tagged Raptor and FLAGtagged AKAP8L in BV2 cells. Co-immunoprecipitationof HA-tagged Raptor confirmed its interaction with
FLAG-tagged AKAP8L under normal glucose conditions(Fig. 3A). Conversely, a reverse immunoprecipitation of
FLAG-tagged AKAP8L also co-immunoprecipitated HAtagged Raptor (Fig. 3B). Furthermore, under HG stimulation, we observed an increase in the interaction between
endogenous AKAP8L and Raptor (Fig. 3C). Proximity Ligation Assay results further substantiated that HG
stimulation enhanced the interaction between AKAP8L
and mTORC1 in both primary microglia and BV2 cells(Fig. 3D and E).
Si-AKAP8L and rapamycin mitigated HG-induced
autophagy disorders in microglia

Si-AKAP8L markedly decreased the protein expression
levels of AKAP8L, and our observations confirmed that
rapamycin did not influence AKAP8L protein expression, as illustrated in Supplementary Fig. 5. Subsequently,
we explored the effects of AKAP8L knockdown on
mTORC1-mediated autophagy pathways. Compared tothe control group, HG treatment significantly elevatedthe expression ratios of p-mTOR/mTOR, p-p70S6K/
p70S6K, and p-ULK1/ULK1 in microglia (Fig. 3F-I).
Concurrently, there was a notable decrease in Beclin-1
levels and the LC3-II/LC3-I ratio, alongside an increasein p62 levels (Fig. 3J-L), suggesting impaired autophagy

under HG conditions. Moreover, the expression patternsof these proteins were reversed with si-AKAP8L andrapamycin treatment in HG-induced microglia. Consistent with these findings, mRFP-GFP-LC3 immunostaining revealed that si-AKAP8L and rapamycin significantly
increased LC3 puncta formation per cell, and transmission electron microscopy also detected a restoration of
autophagosome and autolysosome numbers comparedto the HG group (Fig. 3M-P). Collectively, these results
imply that AKAP8L knockdown and rapamycin can ameliorate autophagy impairment by inhibiting mTORC1 in
HG-induced microglia.
Si-AKAP8L and rapamycin reduced the activation of the
NLRP3 inflammasome and pyroptosis in microglia induced
by HG

Impaired autophagy is known to exacerbate inflammation. As anticipated, HG-induced microglia showed significant upregulation of NLRP3, ASC, Caspase-1, TXNIP,GSDMD-N, and IL-1β protein levels compared to thecontrol group, with si-AKAP8L and rapamycin treatment leading to a reduction in these protein expressions(Fig. 4A). Immunofluorescence and ELISA assays further
confirmed that HG-induced microglia had elevated levels of NLRP3, GSDMD, and IL-18, which were significantly reversed by si-AKAP8L and rapamycin treatment(Fig. 4B-E). Additionally, HG-induced microglia exhibited signs of fragmented cell membranes and increased
pyroptosis, as detected by Yo-Pro-1 staining and transmission electron microscopy; these abnormalities were
notably rescued by si-AKAP8L and rapamycin treatment (Fig. 4G, H). These findings collectively suggest thatknockdown of AKAP8L and treatment with rapamycin
mitigate NLRP3 inflammasome activation and pyroptosis
in HG-induced microglia via the mTORC1 pathway.
Sh-AKAP8L and rapamycin alleviated autophagy disorders
in the hippocampus of STZ mice by inhibiting mTORC1

To assess the status of autophagy within the hippocampus of STZ mice, we conducted immunofluorescenceco-staining with the microglial marker Iba-1 and autophagy-associated proteins LC3 and p62. The obtained
images revealed a decrease in the number of LC3 puncta
per Iba-1-positive cell and an increase in the number ofp62 puncta per Iba-1-positive cell in the hippocampus of
STZ-treated mice when compared to controls (Fig. 5BE). Western blotting further confirmed these findings,

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 9 of 19

Fig. 2 AKAP8L accumulates in microglia, absent in astrocytes and neurons in HG-treated cells and STZ mice hippocampus. (a) Representative Western
blots and statistical analysis of AKAP8L protein levels. (b) Representative immunofluorescence images of AKAP8L with quantification. Scale bar: 20 μm.
Colocalization of AKAP8L with microglial marker Iba-1, astrocytic marker GFAP, and neuronal marker βIII tubulin in primary neural co-cultures (c), with corresponding statistical analysis (d). Scale bar: 10 μm. (e-f) Colocalization of Iba-1 with AKAP8L in the hippocampus of mice, with quantification. Scale bar:
10 μm. (g-h) Colocalization of GFAP and βIII tubulin with AKAP8L in the hippocampus of mice, with quantification. Scale bar: 10 μm. Data are Mean ± SD
(n = 5–6). P-values are from unpaired Student’s t-tests. *p < 0.05 and **p < 0.01 compared to the Control grouprevealing elevated phosphorylation levels of mTOR,
p70S6K, and ULK1 in the hippocampus of STZ-treatedmice (Fig. 5F). Additionally, there was a notable decreasein the ratio of LC3-II to LC3-I and Beclin-1, along withan increase in p62 levels (Fig. 5G). Collectively, theseresults indicate that mTORC1 activation in microglia
impairs autophagy in STZ-induced diabetic mice. Notably, AKAP8L knockdown and rapamycin treatment effectively reversed these autophagic deficits.

Zhang et al. Journal of Neuroinflammation

Fig. 3 (See legend on next page.)(2024) 21:177

Page 10 of 19

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 11 of 19(See figure on previous page.)
Fig. 3 HG enhanced AKAP8L and mTORC1 interaction, and both si-AKAP8L and rapamycin mitigate HG-induced autophagy disorders in microglia. Coimmunoprecipitation of FLAG-tagged AKAP8L and HA-tagged Raptor (a), and vice versa (b), as well as the interaction between endogenous AKAP8L and
Raptor (c). Proximity ligation assay reveals increased AKAP8L-Raptor interactions in BV2 cells (d) and primary microglia (e). Scale bar: 20 μm. (f) Representative blots of autophagy-related proteins. Statistical graphs of protein expression for p-mTOR/mTOR (g), p-p70S6K/p70S6K (h), p-ULK1/ULK1 (i), Beclin-1(j), p62 (k), and LC3 II/LC3 I (l). (m) Immunofluorescence staining of microglial cells transfected with mRFP-GFP-LC3 adenovirus, showing punctate LC3
distribution. Scale bar: 10 μm. (n) Electron microscopy images of microglia reveal HG-induced autophagy alterations. Scale bar: 500 nm. (o) LC3 puncta
numbers per cell. (p) Quantification of autophagosome (GFP + RFP + yellow puncta) and autolysosome (red puncta) numbers in microglia. Data are
Mean ± SD (n = 5). P-values are determined using unpaired Student’s t-tests for two-group comparisons and one-way ANOVA with Tukey’s post hoc test
for multiple comparisons. **p < 0.01 compared to the Control group. #p < 0.05 and ##p < 0.01 compared to the STZ group

Sh-AKAP8L and rapamycin reduced the activation of the
NLRP3 inflammasome and pyroptosis in the hippocampusof STZ mice

The hippocampus of STZ mice exhibited a significant upregulation of NLRP3, ASC, Caspase-1, TXNIP,GSDMD-N, and IL-1β (Fig. 6A, B). However, treatment
with sh-AKAP8L and rapamycin diminished the expression of these proteins in the hippocampal tissue of
STZ mice. Employing Z-score transformation, we confirmed that the expression of pro-inflammatory markers, including TNF-α, IL-1β, IL-6, CD86, and iNOS, waselevated in the hippocampus of STZ mice compared tocontrols (Fig. 6C). Concurrently, the expression of antiinflammatory markers such as IL-4, Arg-1, and TGF-β
was decreased in the hippocampus of STZ mice. These
results highlight a significant inflammatory responsein the hippocampus of STZ mice, which was markedly
reduced with sh-AKAP8L and rapamycin treatment.
GSDMD and the microglial marker Iba-1 colocalization was observed using immunofluorescence to assess
pyroptotic status in microglia of STZ-induced mice. The
visual data showed an increase in microglial pyroptosis in
STZ-induced mice, which was partially attenuated by shAKAP8L and rapamycin treatment (Fig. 6D). Additionally, the ramified morphology of microglia, indicative of
microglial inflammation, was assessed using Sholl analysis (Fig. 6E-G). Microglia in the control group exhibited ahighly branched, resting state morphology, whereas thosein STZ mice adopted an amoeboid shape and showed
reduced endpoints per cell. Notably, sh-AKAP8L andrapamycin treatment restored the ramified morphology and increased endpoints per cell in microglia of
STZ mice. In summary, these findings suggest that shAKAP8L and rapamycin can alleviate neuroinflammationand pyroptosis in the microglia of STZ mice.
Sh-AKAP8L and rapamycin protected against neuronaldamage and cognitive impairment in STZ mice

To evaluate the impact of AKAP8L knockdown on cognitive function in STZ-induced diabetic mice, we employed
the Morris water maze test for assessing spatial learning and memory (Fig. 7A-E). Compared to control mice,
STZ mice exhibited significantly prolonged escape latencies, reduced time in the target quadrant, and fewer platform crossings. However, mice in the sh-AKAP8L + STZ

and RAPA + STZ groups displayed shorter escape latencies, increased time in the target quadrant, and more
platform crossings compared to those in the STZ groupalone. These findings suggest that while cognitive andmemory functions are impaired in STZ mice, they can besignificantly improved with sh-AKAP8L knockdown andrapamycin treatment.
Furthermore, to assess neuronal integrity in the hippocampus—a region pivotal for cognition and memory—weperformed NeuN, HE, and Nissl staining on hippocampal
tissue samples from STZ mice (Fig. 7F-H). Microscopic
analysis revealed a marked reduction in the percentage ofintact cells and viable neurons in the hippocampal CA1
region of the STZ group when compared to the control
group. This neuronal degeneration was ameliorated by
sh-AKAP8L knockdown and rapamycin treatment. Comparable findings were observed in both the CA3 region
and the DG region of the hippocampus (Supplementary Fig. 6). Collectively, these results indicate that STZ
induces hippocampal and neuronal damage, which canbe attenuated by sh-AKAP8L knockdown and rapamycin
treatment.

Discussion
In the present study, proteomic analysis revealed amarked upregulation of AKAP8L in HG-treated microglia, accompanied by aberrant expression of autophagy
and inflammation markers. Notably, the accumulation of
AKAP8L was specific to HG-treated microglia, with noobserved changes in co-cultured astrocytes or neurons,a pattern that was mirrored in STZ-induced diabetic
mice. Further studies through co-immunoprecipitation
and PLA indicated that the elevated AKAP8L in HGtreated microglial cells interacts with the mTORC1.
In the STZ mouse model, we demonstrated that both
AKAP8L knockdown and rapamycin treatment significantly enhanced cognitive function, as evidenced by
improved performance in the Morris water maze, andreduced microglial activation. Moreover, these interventions effectively suppressed mTORC1 signaling, normalized autophagic flux, mitigated neuroinflammation, anddecreased pyroptosis. The involvement of AKAP8L in thepotential pathogenesis of DACI is illustrated in Fig. 8.
Proteomics stands as a cornerstone in diabetes research, offering unparalleled insights into theZhang et al. Journal of Neuroinflammation(2024) 21:177

Page 12 of 19

Fig. 4 Si-AKAP8L and rapamycin alleviated high glucose-induced activation of NLRP3 inflammasomes and pyroptosis in microglia. (a) Representative
blots and statistical analysis of NLRP3, ASC, Caspase-1 p20, TXNIP, GSDMD-N, and IL-1β in response to HG treatment. Representative immunofluorescence
images (b) and quantification of NLRP3 (c) and GSDMD (d). Scale bar: 20 μm. (e) ELISA quantification of IL-18 levels. Representative images (g) and statistical graphs (f) of microglia stained with YO-PRO-1 for the analysis of pyroptotic cells. Scale bar: 50 μm. (h) Transmission electron microscopy images ofpyroptotic cells, with a primary image at 2 μm and an inset at 1 μm scale. Data are Mean ± SD (n = 5). P-values are from one-way ANOVA with Tukey post
hoc multiple comparisons. **p < 0.01 compared to the Control group. ##p < 0.01 compared to the STZ group

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 13 of 19

Fig. 5 Sh-AKAP8L and rapamycin alleviated autophagy disorders in the hippocampus of STZ mice by inhibiting mTORC1. (a) Representative blots of
p-mTOR, mTOR, p-p70S6K, p70S6K, p-ULK1, ULK1, Beclin-1, p62, LC3-II, LC3-I, and β-actin. Representative immunofluorescence images (b) and statisticalgraphs (c) of Iba-1 and LC3 colocalization. Scale bar: 20 μm. Representative immunofluorescence images (d) and statistical graphs (e) of Iba-1 and p62
colocalization. Scale bar: 20 μm. (f) Statistical graphs of protein expression for phosphorylation levels of mTOR, p70S6K, and ULK1. (g) Statistical graphs ofprotein expression for LC3-II/LC3-I, p62, and Beclin-1. Data are Mean ± SD (n = 6). P-values are from one-way ANOVA with Tukey post hoc multiple comparisons. **p < 0.01 compared to the Control group. #p < 0.05 and ##p < 0.01 compared to the STZ groupmolecular mechanisms underlying the disease. However, there have been no reports on proteomic studiesof microglial cells treated with HG to date. The current
proteomic analysis, through KEGG pathway enrichment
analysis, has revealed disrupted pathways that include
the cytokine-cytokine receptor interaction, autophagy,
NOD-like receptor signaling cascade, mitophagy, andapoptosis, among others. The findings, in concordancewith existing literature [28, 29], underscore the pivotal
roles of autophagy and inflammation in the pathogenesis of DACI. Additionally, disruptions in autophagy andinflammation have been observed in the Alzheimer’s

disease (AD) brain, occurring well in advance of any
observable clinical symptoms of the condition [30]. The
volcano plot further revealed that the expression of proteins such as AKAP8L, TNF, TXNIP, SQSTM1, and IL1
was significantly upregulated, with AKAP8L being identified as a novel marker for the first time.
AKAP8L, a member of the A-kinase anchor protein(AKAP) family renowned for its role in localizing protein kinase A (PKA) to distinct subcellular sites [31], has
emerged as a focal point in research exploring its contributions to tumor development and a spectrum of biological functions. These include the regulation of RNA

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 14 of 19

Fig. 6 sh-AKAP8L and rapamycin mitigated NLRP3 inflammasome and pyroptosis in STZ mice via inhibiting mTORC1 activation. Representative blots(a) and statistical graphs (b) of NLRP3, ASC, Caspase-1, TXNIP, GSDMD-N, and IL-1β. (c) The gene expression of TNF-α, IL-1β, IL-6, CD86, iNOS, IL-4, Arg-1,
and TGF-β using Z-score transformation. (d) Representative immunofluorescence images of GSDMD, Iba-1 and DAPI. Scale bar: 20 μm. (e) Representative example binary images of microglia in the hippocampus of mice. (f) Statistical graphs of Sholl analysis results showing the number of intersections
per radius around the microglial soma. (g) Quantification of microglial process endpoints per cell. Data are Mean ± SD (n = 6). P-values are from one-way
ANOVA with Tukey post hoc multiple comparisons. **p < 0.01 compared to the Control group. ##p < 0.01 compared to the STZ groupsplicing, histone phosphorylation, and the identificationof prognostic indicators in cancer [32, 33]. In our current
research, we have identified AKAP8L as a pivotal factor in the pathogenesis of DACI. Prior research has also

documented significant increases in AKAP8L levels in
the plasma of patients afflicted with diabetic liver disease,
underscoring the substantial involvement of AKAP8L
in the complications associated with diabetes [34].

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 15 of 19

Fig. 7 Sh-AKAP8L and rapamycin protected against neuronal damage and cognitive impairment in STZ mice. (a) Swimming trajectory images of theCON, STZ, STZ + sh-AKAP8L, and STZ + RAPA groups in the MWM test. (b) Escape latency during the training session. (c) Average speed of each group. (d)Statistical graphs of the time spent in the target quadrant. (e) Number of crossings over the original platform location. (f) Images and statistical graphsof NeuN immunofluorescence staining in the hippocampal CA1 region, representing the rate of viable neurons. (g) Images and statistical graphs of H&E
staining in the hippocampal CA1 region, representing the rate of intact cells. (h) Images and statistical graphs of Nissl staining in the hippocampal CA1
region, representing the rate of intact neurons. Scale bar: 20 μm. Data are Mean ± SD (n = 6 or 12). P-values are from one-way or two-way ANOVA with
Tukey post hoc multiple comparisons. **p < 0.01 compared to the Control group. ##p < 0.01 compared to the STZ group

Remarkably, the aggregation of AKAP8L was uniquelyobserved in microglia subjected to HG conditions, withno detectable alterations in co-cultured astrocytes andneurons, a phenomenon consistent with patterns witnessed in STZ-diabetic murine models. The findings
indicate that microglia could play a pivotal role in initiating brain injury associated with diabetes. Activation ofthese cells leads to an escalation of neuronal apoptosis in
the hippocampal region of STZ-diabetic mice, thereby
aggravating the neurodegenerative cascade [35]. Focusing
on the hippocampus, which mainly comprises the CA1,
CA3, and DG — all crucial for cognitive function [36] —we reveal a significant reduction in viable neurons in the
STZ-affected hippocampal regions compared to controls.
These neuronal damages were found to be attenuated by

sh-AKAP8L knockdown and rapamycin treatment, presenting potential therapeutic strategies for DACI.
Recent literature has revealed that AKAP8L can interact with mTORC1 to regulate anabolic metabolism inrenal epithelial cells [27]. However, it is not yet clear
whether the same interaction occurs in microglia. Ourfindings indicate that HG conditions specifically enhance
the interaction between AKAP8L and mTORC1 inmicroglial cells, which may be crucial for the development of DACI. mTORC1 represents a pivotal target formodulating the metabolic profile and functionality of
microglia, and may be a promising avenue for enhancing cognitive abilities [37, 38]. The mTORC1 inhibitor rapamycin is known to mitigate the advancement ofneurodegenerative disorders by bolstering autophagy,

Zhang et al. Journal of Neuroinflammation(2024) 21:177

Page 16 of 19

Fig. 8 The mechanism diagram of AKAP8L in the potential pathogenesis of DACI

alleviating chronic inflammation, diminishing β-amyloid(Aβ) accumulation, and curbing the hyperphosphorylation of tau protein [39]. Nevertheless, rapamycin’s adverse
side effects, such as immune suppression, oral ulcers, andhyperglycemia, are linked to the inhibition of mTORC2[40, 41]. Therefore, the selective targeting of mTORC1
through specific drugs is of considerable importance [42].
AKAP8L specifically engages with the mTORC1 subunit
Raptor. Our findings demonstrate that AKAP8L inhibition replicates the effects of rapamycin, encompassingheightened autophagy, subdued neuroinflammation,
and amelioration of cognitive function in STZ-diabetic
rodents. Consequently, AKAP8L may emerge as a novel
target for the selective inhibition of mTORC1, offering a
potential therapeutic strategy for DACI.
Autophagy, a lysosomal-dependent cellular process ofcatabolism, is crucial for the clearance of protein aggregates and damaged organelles, thus maintaining cellular

homeostasis. In microglia, impaired autophagy is recognized as a significant contributor to cognitive decline [30,43]. Studies have shown that cognitive deficits are exacerbated in STZ-induced diabetic mice when autophagy
is inhibited by 3-methyladenine (3-MA) [44]. Metforminhas been shown to enhance cognitive capabilities by stimulating hippocampal autophagy [45, 46]. The mTORC1
plays a pivotal role in regulating autophagy throughits influence on the kinase ULK1 [47]. In our investigation, we found that the suppression of mTOR and thesubsequent promotion of autophagy by ULK1, achieved
either through sh-AKAP8L or rapamycin, significantly
improved cognitive function in STZ-induced diabetic
mice. Deficiency in autophagy within microglia is known
to exacerbate inflammation, which is another critical factor contributing to neurodegenerative processes and cognitive decline [48–50]. The NLRP3 inflammasome plays aprominent role in neuroinflammation across a spectrum

Zhang et al. Journal of Neuroinflammation(2024) 21:177

of neurological disorders [51]. Furthermore, NLRP3 isdirectly associated with the process of pyroptosis, a formof programmed cell death. In line with previous findings[28], our research revealed that HG conditions stimulate
pyroptosis by increasing NLRP3 expression. Additionally, we observed that the inhibition of mTORC1 using
si-AKAP8L and rapamycin effectively reduced the activation of the NLRP3 inflammasome, the production of
inflammatory mediators, and the incidence of pyroptosis.

Conclusions
In summary, proteomic analysis has uncovered a significant upregulation of AKAP8L in microglia exposed
to HG conditions, which was associated with alteredexpression patterns of autophagy and inflammationrelated proteins. Importantly, AKAP8L accumulation
was exclusive to HG-treated microglia, with no changes
detected in co-cultured astrocytes or neurons. The
research further revealed that microglia subjected to HG
environments enhance the interaction between AKAP8L
and mTORC1. Moreover, the study demonstrated thatinterventions targeting mTORC1 by AKAP8L knockdown and rapamycin could mitigate cognitive deficits in
STZ-induced diabetic mice by normalizing autophagy
and reducing neuroinflammation. These findings suggestthat the modulation of AKAP8L could represent a promising therapeutic approach for addressing DACI.
Abbreviations
AKAP	A-kinase anchor protein
AKAP8L	A-kinase anchor protein 8-like
ANOVA	Analyses of varianceArg1	Arginase-1ASC	Apoptosis-associated speck-like protein containing a CARD
ATP5PB	ATP Synthase Peripheral Stalk-Membrane Subunit B
Aβ	β-amyloid
C3	Complement C3
C3aR	Complement C3a Receptor
C6	Complement C6
CD86	Cluster of differentiation 86
CRELD1	Cysteine rich with EGF like domains 1
DACI	Diabetes-associated cognitive impairment
GDF-15	Growth differentiation factor-15
GFAP	Glial fibrillary acidic protein
GO	Gene Ontology
GSDMD	Gasdermin DHE	Hematoxylin-eosin
HG	High glucose
HP	Haptoglobin
Iba-1	Ionized calcium-binding adapter molecule 1
IL	Interleukin
iNOS	Inducible nitric oxide synthase
KEGG	Kyoto Encyclopedia of Genes and Genomes
LC3	Light Chain 3
MIC-1	Macrophage inhibitory cytokine-1
mTORC1	Mechanistic target of rapamycin complex 1
mTORC2	Mechanistic target of rapamycin complex 2
MWM	Morris Water Maze test
NDRG2	N-Myc downstream regulated gene 2
NF-κB	Nuclear factor kappa-B
NLRP3	NOD-like receptor thermal protein domain associatedprotein 3
NR	Non-Redundant Protein Database

Page 17 of 19

p62/SQSTM1	Sequestosome-1
p70S6K	The 70 kDa ribosomal protein S6 kinase
PKA	Protein kinase A
RAPA	Rapamycin
ROC	Receiver Operating Characteristic
SERPIND	Serpin domain-containing protein
siRNA	small interfering RNA
STZ	Streptozotocin
TGF-β	Transforming growth factor-β
TNF-α	Tumor necrosis factor-α
TXNIP	Thioredoxin interacting protein
ULK1	Unc-51 like autophagy activating kinase 1
VNN2	Vanin 23-MA	3-methyladenine

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12974-024-03170-z.
Supplementary Material 1
Supplementary Material 2
Supplementary Material 3
Acknowledgements
We appreciate Lei Wang’s technical assistance in this study.
Author contributions
Conceived and designed the experiments (WYZ, PJ). Performed theexperiments: (WYZ, QQW, TZ, CSW, JC). Contributed reagents/materials/
analysis tools: (WYZ, QQW, JHW, XX, PJ). Wrote the paper: (WYZ, PJ, QQW). All
authors read and approved the final manuscript.
Funding
The study was supported by the National Natural Science Foundation ofChina (81903828, 82272253, 81602846) and the Key R&D Program of Jining(2023YXNS003).
Data availability
No datasets were generated or analysed during the current study.

Declarations
Ethics approval and consent to participate
All animal experiments were performed following the Guide for Care andUse of Laboratory Animals (Chinese Council), with the approval of the Ethics
Committee of the Zhongshan City People’s Hospital.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 24 April 2024 / Accepted: 9 July 2024

References
1. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C,
Basit A, Chan JCN, Mbanya JC, et al. IDF Diabetes Atlas: Global, regional andcountry-level diabetes prevalence estimates for 2021 and projections for2045. Diabetes Res Clin Pract. 2022;183:109119.
2. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88–98.

Zhang et al. Journal of Neuroinflammation

3.
4.
5.
6.
7.
8.
9.

10.
11.

12.
13.

14.
15.

16.

17.

18.
19.
20.

21.
22.

23.

(2024) 21:177

Srikanth V, Sinclair AJ, Hill-Briggs F, Moran C, Biessels GJ. Type 2 diabetes andcognitive dysfunction-towards effective management of both comorbidities.
Lancet Diabetes Endocrinol. 2020;8:535–45.
Luo A, Xie Z, Wang Y, Wang X, Li S, Yan J, Zhan G, Zhou Z, Zhao Y, Li S. Type2 diabetes mellitus-associated cognitive dysfunction: advances in potential
mechanisms and therapies. Neurosci Biobehav Rev. 2022;137:104642.
Vargas-Soria M, Garcia-Alloza M, Corraliza-Gomez M. Effects of diabetes on
microglial physiology: a systematic review of in vitro, preclinical and clinicalstudies. J Neuroinflammation. 2023;20:57.
Yang S, Qin C, Hu ZW, Zhou LQ, Yu HH, Chen M, Bosco DB, Wang W, Wu LJ,
Tian DS. Microglia reprogram metabolic profiles for phenotype and functionchanges in central nervous system. Neurobiol Dis. 2021;152:105290.
Hickman SE, Allison EK, El Khoury J. Microglial dysfunction and defective
beta-amyloid clearance pathways in aging Alzheimer’s disease mice. J Neurosci. 2008;28:8354–60.
Wang LQ, Zhou HJ. LncRNA MALAT1 promotes high glucose-induced
inflammatory response of microglial cells via provoking MyD88/IRAK1/TRAF6
signaling. Sci Rep. 2018;8:8346.
Ramos-Rodriguez JJ, Infante-Garcia C, Galindo-Gonzalez L, Garcia-Molina Y,
Lechuga-Sancho A, Garcia-Alloza M. Increased spontaneous central bleeding
and cognition impairment in APP/PS1 mice with poorly controlled diabetes
Mellitus. Mol Neurobiol. 2016;53:2685–97.
Wang Y, Wang S, Xin Y, Zhang J, Wang S, Yang Z, Liu C. Hydrogen sulfide alleviates the anxiety-like and depressive-like behaviors of type 1 diabetic mice via
inhibiting inflammation and ferroptosis. Life Sci. 2021;278:119551.
Tanaka M, Yamakage H, Masuda S, Inoue T, Ohue-Kitano R, Araki R, Matoba
Y, Saito M, Nagaoka T, Yonezawa K, et al. Serum soluble TREM2 is a potential
novel biomarker of cognitive impairment in Japanese non-obese patientswith diabetes. Diabetes Metab. 2019;45:86–9.
Baufeld C, Osterloh A, Prokop S, Miller KR, Heppner FLJAN. High-fat dietinduced brain region-specific phenotypic spectrum of CNS resident microglia. 2016, 132:361–75.
Slieker RC, Donnelly LA, Akalestou E, Lopez-Noriega L, Melhem R, Gunes A,
Abou Azar F, Efanov A, Georgiadou E, Muniangi-Muhitu H, et al. Identificationof biomarkers for glycaemic deterioration in type 2 diabetes. Nat Commun.
2023;14:2533.
Jiang T, Li Y, He S, Huang N, Du M, Zhai Q, Pu K, Wu M, Yan C, Ma Z, Wang Q.
Reprogramming astrocytic NDRG2/NF-kappaB/C3 signaling restores thediabetes-associated cognitive dysfunction. EBioMedicine. 2023;93:104653.
Chen R, Yi Y, Xiao W, Zhong B, Shu Y, Zhang L, Zeng Y. Label-free Liquid
Chromatography-Mass Spectrometry Proteomic analysis of urinary identification in Diabetic Vascular Dementia in a Han Chinese Population. Front Aging
Neurosci. 2021;13:619945.
Xu M, Zhang DF, Luo R, Wu Y, Zhou H, Kong LL, Bi R, Yao YG. A systematic integrated analysis of brain expression profiles reveals YAP1 and other prioritized
hub genes as important upstream regulators in Alzheimer’s disease. Alzheimers Dement. 2018;14:215–29.
Rosciszewski G, Cadena V, Murta V, Lukin J, Villarreal A, Roger T, Ramos AJ. Tolllike receptor 4 (TLR4) and triggering receptor expressed on myeloid Cells-2(TREM-2) activation balance astrocyte polarization into a Proinflammatory
phenotype. Mol Neurobiol. 2018;55:3875–88.
Che H, Li H, Li Y, Wang YQ, Yang ZY, Wang RL, Wang LH. Melatonin exerts
neuroprotective effects by inhibiting neuronal pyroptosis and autophagy in
STZ-induced diabetic mice. Faseb j. 2020;34:14042–54.
Tang RH, Qi RQ, Liu HY. Interleukin-4 affects microglial autophagic flux. Neural
Regen Res. 2019;14:1594–602.
Ma J, Ye W, Yang Y, Wu T, Wang Y, Li J, Pei R, He M, Zhang L, Zhou J. Theinteraction between autophagy and the epithelial-mesenchymal transition
mediated by NICD/ULK1 is involved in the formation of diabetic cataracts.
Mol Med. 2022;28:116.
Liu M, Xu Z, Wang L, Zhang L, Liu Y, Cao J, Fu Q, Liu Y, Li H, Lou J, et al. Cottonseed oil alleviates ischemic stroke injury by inhibiting the inflammatory
activation of microglia and astrocyte. J Neuroinflammation. 2020;17:270.
Weng Z, Xu C, Zhang X, Pang L, Xu J, Liu Q, Zhang L, Xu S, Gu A. Autophagy
mediates perfluorooctanoic acid-induced lipid metabolism disorderand NLRP3 inflammasome activation in hepatocytes. Environ Pollut.
2020;267:115655.
Eke I, Makinde AY, Aryankalayil MJ, Sandfort V, Palayoor ST, Rath BH,
Liotta L, Pierobon M, Petricoin EF, Brown MF, et al. Exploiting RadiationInduced Signaling to increase the susceptibility of resistant Cancer cells totargeted drugs: AKT and mTOR inhibitors as an Example. Mol Cancer Ther.
2018;17:355–67.

Page 18 of 19

24. Ren C, Chen J, Che Q, Jia Q, Lu H, Qi X, Zhang X, Shu Q. IL-37 alleviates
TNF-α-induced pyroptosis of rheumatoid arthritis fibroblast-like synoviocytes by inhibiting the NF-κB/GSDMD signaling pathway. Immunobiology.
2023;228:152382.
25. Han X, Xu T, Fang Q, Zhang H, Yue L, Hu G, Sun L. Quercetin hinders microglial
activation to alleviate neurotoxicity via the interplay between NLRP3 inflammasome and mitophagy. Redox Biol. 2021;44:102010.
26. Stephen TL, Cacciottolo M, Balu D, Morgan TE, LaDu MJ, Finch CE, Pike
CJ. APOE genotype and sex affect microglial interactions with plaques in
Alzheimer’s disease mice. Acta Neuropathol Commun. 2019;7:82.
27. Melick CH, Meng D, Jewell JL. A-kinase anchoring protein 8L interacts with
mTORC1 and promotes cell growth. J Biol Chem. 2020;295:8096–105.
28. Huang L, You J, Yao Y, Xie M. High glucose induces pyroptosis of retinal
microglia through NLPR3 inflammasome signaling. Arq Bras Oftalmol.
2021;84:67–73.
29. Cui Y, Yang M, Wang Y, Ren J, Lin P, Cui C, Song J, He Q, Hu H, Wang K, Sun
Y. Melatonin prevents diabetes-associated cognitive dysfunction frommicroglia-mediated neuroinflammation by activating autophagy via TLR4/
Akt/mTOR pathway. Faseb j. 2021;35:e21485.
30. Eshraghi M, Adlimoghaddam A, Mahmoodzadeh A, Sharifzad F, YasavoliSharahi H, Lorzadeh S, Albensi BC, Ghavami S. Alzheimer’s Disease Pathogenesis: Role of Autophagy and Mitophagy Focusing in Microglia. Int J Mol Sci
2021, 22.
31. Zhang R, Liu L, Wang F, Zhao W, Liu K, Yu H, Zhao S, Xu B, Zhang X, Chai J, Hao
J. AKAP8L enhances the stemness and chemoresistance of gastric cancer
cells by stabilizing SCD1 mRNA. Cell Death Dis. 2022;13:1041.
32. Zhou L, Mei J, Cao R, Liu X, Fu B, Ma M, Gong B, Luo L, Liu Y, Zhu Q, Meng X.
Integrative analysis identifies AKAP8L as an immunological and prognostic
biomarker of pan-cancer. Aging. 2023;15:8851–72.
33. Luo QY, Di T, Qiu MZ, Xia ZF, Du Y, Lin RD, Yang LQ, Sun YT, Yang DJ, Sun J,
Zhang L. High AKAP8L expression predicts poor prognosis in esophageal
squamous cell carcinoma. Cancer Cell Int. 2022;22:90.
34. Chen JY, Chou HC, Chen YH, Chan HL. High glucose-induced proteome
alterations in hepatocytes and its possible relevance to diabetic liver disease.
J Nutr Biochem. 2013;24:1889–910.
35. Yun JH, Lee DH, Jeong HS, Kim HS, Ye SK, Cho CH. STAT3 activation in microglia exacerbates hippocampal neuronal apoptosis in diabetic brains. J Cell
Physiol. 2021;236:7058–70.
36. Aery Jones EA, Rao A, Zilberter M, Djukic B, Bant JS, Gillespie AK, Koutsodendris N, Nelson M, Yoon SY, Huang K, et al. Dentate gyrus and CA3 GABAergic
interneurons bidirectionally modulate signatures of internal and external
drive to CA1. Cell Rep. 2021;37:110159.
37. Hu Y, Mai W, Chen L, Cao K, Zhang B, Zhang Z, Liu Y, Lou H, Duan S, Gao Z.
mTOR-mediated metabolic reprogramming shapes distinct microglia functions in response to lipopolysaccharide and ATP. Glia. 2020;68:1031–45.
38. Carosi JM, Sargeant TJ. Rapamycin and Alzheimer disease: a hypothesis forthe effective use of rapamycin for treatment of neurodegenerative disease.
Autophagy. 2023;19:2386–90.
39. Hou SJ, Zhang SX, Li Y, Xu SY. Rapamycin responds to Alzheimer’s Disease: apotential translational therapy. Clin Interv Aging. 2023;18:1629–39.
40. Kaeberlein TL, Green AS, Haddad G, Hudson J, Isman A, Nyquist A, Rosen
BS, Suh Y, Zalzala S, Zhang X, et al. Evaluation of off-label rapamycin use topromote healthspan in 333 adults. Geroscience. 2023;45:2757–68.
41. Mannick JB, Lamming DW. Targeting the biology of aging with mTOR inhibitors. Nat Aging. 2023;3:642–60.
42. Schreiber KH, Arriola Apelo SI, Yu D, Brinkman JA, Velarde MC, Syed FA, Liao
CY, Baar EL, Carbajal KA, Sherman DS, et al. A novel rapamycin analog ishighly selective for mTORC1 in vivo. Nat Commun. 2019;10:3194.
43. Cheng J, Liao Y, Dong Y, Hu H, Yang N, Kong X, Li S, Li X, Guo J, Qin L, etal. Microglial autophagy defect causes parkinson disease-like symptoms by accelerating inflammasome activation in mice. Autophagy.
2020;16:2193–205.
44. Li Z, Hao S, Yin H, Gao J, Yang Z. Autophagy ameliorates cognitive impairment
through activation of PVT1 and apoptosis in diabetes mice. Behav Brain Res.
2016;305:265–77.
45. Kodali M, Attaluri S, Madhu LN, Shuai B, Upadhya R, Gonzalez JJ, Rao X, Shetty
AK. Metformin treatment in late middle age improves cognitive function withalleviation of microglial activation and enhancement of autophagy in the
hippocampus. Aging Cell. 2021;20:e13277.
46. Hsu CC, Wahlqvist ML, Lee MS, Tsai HN. Incidence of dementia is increased
in type 2 diabetes and reduced by the use of sulfonylureas and metformin. J
Alzheimers Dis. 2011;24:485–93.

Zhang et al. Journal of Neuroinflammation(2024) 21:177

47. Zhang D, Pan J, Xiang X, Liu Y, Dong G, Livingston MJ, Chen JK, Yin XM, Dong
Z. Protein kinase cdelta suppresses Autophagy to induce kidney cell apoptosis in Cisplatin Nephrotoxicity. J Am Soc Nephrol. 2017;28:1131–44.
48. Su P, Zhang J, Wang D, Zhao F, Cao Z, Aschner M, Luo W. The role ofautophagy in modulation of neuroinflammation in microglia. Neuroscience.
2016;319:155–67.
49. Cho MH, Cho K, Kang HJ, Jeon EY, Kim HS, Kwon HJ, Kim HM, Kim DH, Yoon
SY. Autophagy in microglia degrades extracellular β-amyloid fibrils andregulates the NLRP3 inflammasome. Autophagy. 2014;10:1761–75.
50. Dang R, Yang M, Cui C, Wang C, Zhang W, Geng C, Han W, Jiang P. Activationof angiotensin-converting enzyme 2/angiotensin (1–7)/mas receptor axis
triggers autophagy and suppresses microglia proinflammatory polarization
via forkhead box class O1 signaling. Aging Cell. 2021;20:e13480.

Page 19 of 19

51. Zhang WY, Guo YJ, Han WX, Yang MQ, Wen LP, Wang KY, Jiang P. Curcumin
relieves depressive-like behaviors via inhibition of the NLRP3 inflammasomeand kynurenine pathway in rats suffering from chronic unpredictable mild
stress. Int Immunopharmacol. 2019;67:138–44.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.